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What Is a Value Chain Analysis? 3 Steps

• 03 Dec 2020

Successful businesses create value with each transaction —for their customers in the form of satisfaction and for themselves and their shareholders in the form of profit. Companies that generate greater value with each sale are better positioned to profit than those that produce less value.

To evaluate how much value your company is creating, it’s critical to understand its value chain. Below is an overview of what a value chain is, why it’s important to understand, and steps you can take to conduct one and help your company create and retain more value from its sales.

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Understanding the Value Chain

The term value chain refers to the various business activities and processes involved in creating a product or performing a service. A value chain can consist of multiple stages of a product or service’s lifecycle, including research and development, sales, and everything in between. The concept was conceived by Harvard Business School Professor Michael Porter in his book The Competitive Advantage: Creating and Sustaining Superior Performance .

Taking stock of the processes that comprise your company’s value chain can help you gain insight into what goes into each of its transactions. By maximizing the value created at each point in the chain, your company can be better positioned to share more value with customers while capturing a greater share for itself. Similarly, knowing how your firm creates value can enable you to develop a greater understanding of its competitive advantage .

Components of a Value Chain

According to Porter’s definition, all of the activities that make up a firm's value chain can be split into two categories that contribute to its margin: primary activities and support activities.

Primary activities are those that go directly into the creation of a product or the execution of a service, including:

• Inbound logistics : Activities related to receiving, warehousing, and inventory management of source materials and components
• Operations : Activities related to turning raw materials and components into a finished product
• Outbound logistics : Activities related to distribution, including packaging, sorting, and shipping
• Marketing and sales : Activities related to the marketing and sale of a product or service, including promotion, advertising, and pricing strategy
• After-sales services : Activities that take place after a sale has been finalized, including installation, training, quality assurance, repair, and customer service

Secondary activities help primary activities become more efficient—effectively creating a competitive advantage—and are broken down into:

• Procurement : Activities related to the sourcing of raw materials, components, equipment, and services
• Technological development : Activities related to research and development, including product design, market research , and process development
• Human resources management : Activities related to the recruitment, hiring, training, development, retention, and compensation of employees
• Infrastructure : Activities related to the company’s overhead and management, including financing and planning

What Is Value Chain Analysis?

Value chain analysis is a means of evaluating each of the activities in a company’s value chain to understand where opportunities for improvement lie.

Conducting a value chain analysis prompts you to consider how each step adds or subtracts value from your final product or service. This, in turn, can help you realize some form of competitive advantage, such as:

• Cost reduction , by making each activity in the value chain more efficient and, therefore, less expensive
• Product differentiation , by investing more time and resources into activities like research and development, design, or marketing that can help your product stand out

Typically, increasing the performance of one of the four secondary activities can benefit at least one of the primary activities.

Why Is Value Chain Analysis Important?

Value chain analysis is essential for businesses to understand the sequence of activities required to deliver a product or service.

In addition to optimizing budgets and establishing competitive advantage, businesses can also use value chain analysis for:

• Supply chain management: Value chain analysis provides insights into how each component of the supply chain adds value to the final product or service, which can lead to better supplier coordination and logistics management.
• Strategic decision-making: Because the value chain provides an overview of the company's operations and interactions with external stakeholders, you can use it to help inform strategic decisions regarding partnerships, outsourcing, product development, and market expansion strategies.
• Improving customer satisfaction: Insights gained from conducting a value chain analysis can be used to enhance the quality of your product and customer service, which can lead to higher customer satisfaction and loyalty.
• Innovation and development: The value chain can also highlight opportunities for innovation like improving existing processes or identifying new ways in which your product can provide value to the consumer.
• Environmental and social impact: Additionally, value chain analysis can be used to assess a company's operations' environmental and social impacts , which can help businesses adopt more sustainable practices and reduce their environmental footprint.

How to Conduct a Value Chain Analysis

1. Identify Value Chain Activities

The first step in conducting a value chain analysis is to understand all of the primary and secondary activities that go into your product or service’s creation. If your company sells multiple products or services, it’s important to perform this process for each one.

2. Determine Activities' Values and Costs

Once the primary and secondary activities have been identified, the next step is to determine the value that each business activity adds to the process, along with the costs involved.

When thinking about the value created by activities, ask yourself: How does each increase the end user’s satisfaction or enjoyment? How does it create value for my firm? For example, does constructing the product out of certain materials make it more durable or luxurious for the user? Does including a certain feature make it more likely your firm will benefit from network effects and increased business?

Similarly, it’s important to understand the costs associated with each step in the process. Depending on your situation, you may find that lowering expenses is an easy way to improve the value each transaction provides.

3. Identify Competitive Advantage Opportunities

Once you’ve compiled your value chain and understand the cost and value associated with each step, you can analyze it through the lens of whatever competitive advantage you’re trying to achieve.

For example, if your primary goal is to reduce your firm’s costs, you should evaluate each piece of your value chain through the lens of reducing expenses. Which steps could be more efficient? Are there any that don’t create significant value and could be outsourced or eliminated to substantially reduce costs?

Similarly, if your primary goal is to achieve product differentiation, which parts of your value chain offer the best opportunity to realize that goal? Would the value created justify the investment of additional resources?

Using value chain analysis, you can uncover several opportunities for your firm, which can prove difficult to prioritize. It’s typically best to begin with improvements that take the least effort but offer the greatest return on investment .

One Piece of the Puzzle

Value chain analysis can be a highly effective means of understanding and contextualizing your business’s processes, but it’s just one tool at your disposal. There's a host of other frameworks and concepts that can help you evaluate organizational performance, craft winning strategies, and be more effective in your role.

Ready to learn additional frameworks that can enable you to make smarter business decisions? Explore our eight-week course Economics for Managers and other online Strategy courses , and find out more about how to develop effective pricing strategies.

This post was updated on May 16, 2024. It was originally published on December 3, 2020.

About the Author

The Official Journal of the Pan-Pacific Association of Input-Output Studies (PAPAIOS)

• Open access
• Published: 02 August 2021

An extended approach to value chain analysis

• Klemen Knez   ORCID: orcid.org/0000-0003-4772-7074 1 ,
• Andreja Jaklič 1 &
• Metka Stare 1  

Journal of Economic Structures volume  10 , Article number:  13 ( 2021 ) Cite this article

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Metrics details

In the article, we propose a comprehensive methodology of value chain analysis in the international input–output framework that introduces a new measure of value chain participation and an extended typology of value chains, with the novel inclusion of domestic value chain to address the extent of fragmentation of purely domestic production. This allows for the simultaneous analysis of both global and domestic production fragmentation, the complex patterns of their evolution and their impact on economic development. The main contribution of the proposed methodology is conceptual: it permits the measurement of all value chain paths that pass through each country-sector from production to final consumption, whether the path includes downstream linkages, upstream linkages or their combination. Empirical application of this methodology shows the importance of including domestic fragmentation in value chain analysis: The fragmentation of both global and domestic levels of production has a significant positive correlation with economic growth. This implies that the effects of global production fragmentation must be analysed together with the changing structure of the fragmentation of domestic production to obtain the whole picture, one that might provide important information for policymaking and industrial policy.

1 Introduction

In recent decades, the growing complexity of the division of labour has been reflected in the fact that ever more production is occurring within value chains, both at home and abroad. Theoretical and empirical approaches to the analysis of value chains have advanced rapidly, yet are very eclectic and heterogeneous. The earliest definitions of commodity chains Footnote 1 date back to the world-systems Footnote 2 theory: “What we mean by such chains is the following: take an ultimate consumable item and trace back the set of inputs that culminated in this item— the prior transformations, the raw materials, the transportation mechanisms, the labour input into each of the material processes, the food inputs into the labour. This linked set of processes we call a commodity chain (Hopkins and Wallerstein 1977 )”. In the 1990s, the research programme of global commodity chains was first systematically outlined by Gereffi’s seminal contribution (Gereffi 1994 ) that defined three interlocking dimensions of the research: the input–output dimension, the spatial dimension, and the question of commodity chain governance Footnote 3 . This research period was characterised by moving away from a historical and macroeconomic perspective towards a special focus on industrial chains and the inter-firm cooperation perspective, with numerous case studies on value chains. The global value chain framework emerged early in the new century with the express aim of unifying the previous heterogeneous research (Gereffi 1999 ; Gereffi et al. 2001 ). On one hand, the global value chain approach increased the focus on the enterprise level and merged with the literature from international business and management Footnote 4 , while also drawing from the new institutional transaction cost approach Footnote 5 . On the other hand, the creation of international input–output tables Footnote 6 led to a revival of the aggregated macroeconomic approach to global value chains, albeit with a different focus than the world-systems approach. Footnote 7

In this article, we present a new methodology for measuring different value chain participation rates in the international input–output framework. Compared to the most widely used measurement of value chain participation introduced by Wang et al. ( 2017 ), we make two fundamental conceptual enhancements.

First, our methodology creates a single and consistent measurement of value chain participation on the country-sector level, as opposed to the two (upstream and downstream) participation rates that feature in Wang’s methodology. The argumentation and logic used to derive a single value chain participation share on the country-sector level is very similar to the approach of Arto et al. ( 2019 ), which combines the source- and sink-based approaches to export decomposition. The idea is that decomposition based on final demand (sink-based decomposition) is independent of the decomposition of downstream value added (source-based) and thus both can be linearly combined to grasp both the information regarding the source of value added as well as the path to final demand simultaneously. Methodologies of export decomposition have recently seen significant improvements (Arto et al. 2019 ; Borin and Mancini 2019 ; Miroudot and Ye 2021 ). However, the value chain participation rate methodologies either still chiefly rely on the value-added export matrix to describe the value flows between any two country-sectors in the economy (Johnson and Noguera 2012 ) and result in separate upstream and downstream participation rate measures or combine a sink- and a source-based measure in merely one-sided, forward-looking measures. Our approach to value chain decomposition no longer uses the value-added export matrix and instead breaks down the asymmetric value chain stemming both downstream and upstream from each country-sector concerned simultaneously . Creating a single consistent variable on the country-sector level that measures the overall level of participation in value chains enables the empirical testing of many research theses that were previously either limited to the aggregate level or had to be articulated separately in terms of measuring the impacts of upstream and downstream value chain integration.

Second, our methodology allows extensions of the value chain typology that are not possible with Wang’s approach to the decomposition of production activities or with export decompositions. We introduce a novel measure of the domestic value chain participation rate to measure the share of production which represents the extent of the fragmentation of domestic production. In place of a single and undifferentiated domestic component, we distinguish domestic production, which is fragmented (involving measurable cooperation among domestic firms), and domestic production, which is not fragmented (consisting of producing direct value for consumption without the cooperation of domestic firms). This makes our concept of the domestic value chain a completely new and different concept compared to Wang’s domestic component, which does not distinguish the two and combines both categories within a single undifferentiated concept. While Wang’s share of the domestic component is only a simple residual—a negation of the share of the fragmentation of global production and the global Ricardian trade share that does not provide information about the nature of the domestic economy, our novel methodology allows us to measure the extent of fragmentation of domestic production in addition to the usual study of the fragmentation of international production.

We aim to use our approach to provide methodological tools that facilitate exploration of the complex interrelationship of global and domestic value chains and their evolution over time. We believe this will add to understanding of the diverse patterns of the structural integration of various countries/sectors and the different effects of such patterns on economic development. While this is primarily a methodological contribution, we shall use elementary empirical data to try to show the possible link between the level of fragmentation of global and domestic production and overall economic growth.

The article is structured as follows: In Sect. 2 , we review the existing value chain indicators and address their shortcomings. In Sect. 3 , we present our methodology. In Sect. 3.1 , we present a new conceptualisation of value chain in the international I–O framework and define our object of disaggregation. A new value chain typology is presented in Sect. 3.2 where we also derive participation shares. In Sect. 4 , we present an example of empirical application and some basic empirical results of the new methodology to show the insights into economic structures that can be gained by using the new value chain measures and which links exist between value chain integration patterns and overall economic growth. Finally, we discuss the contributions of the paper, its limitations and possibilities for further research.

2 Background

The most recent macroeconomic analyses of global value chains rely on the international input–output methodology. As international I–O data are essentially an integrated standard accounting data set harmonised on the sectoral level, information is lacking on the typology of value chain governance. This means the international I–O database cannot be the sole source for the study of production networks, which theoretically differ from purely open trade transactions by including at least some level of hierarchy, and which investigate the local embedding of production linkages (Buckley  2009 ; Henderson et al. 2002 ; Hess and Coe 2006 ; Hortaçsu and Syverson 2009 ). However, the general framework of global value chains can function without such distinctions and this makes the international I–O data set one of its most important sources of information. The key benefit of applying the I–O methodology in global value chain analysis is that aggregated information about the structure of value chains can be obtained, as opposed to isolated firm-specific case studies that can provide a more detailed understanding of different aspects of a given value chain. Thus, of the three dimensions of commodity chain research noted by Gereffi ( 1994 ), both the I–O aspect and the spatial dimension, can be considered in the international I–O approach, while the governance aspect cannot. Various aggregated and sectoral global value chain indicators, indices and measures have been proposed, all derived from the international I–O framework. GVC indicators may be roughly divided into measures of length Footnote 8 and participation rates, which we will discuss briefly.

Early I–O measures of the GVC structure were simple upstream and downstream indicators that corresponded to the measure of distance to final demand (upstream) and the Leontief measure of backward linkage (downstream) and were often referred to as the length of a value chain (Ahmad et al. 2017). Fally ( 2011 ) and Antràs et al. ( 2012 ) defined the downstream indicator to “reflect how many plants (stages) are involved in production one after the other” up to the point observed and the upstream indicator to “measure how many plants this product will pass through (e.g. by assembly with other products) before it reaches final demand (Fally 2011 , 10)”. Fally ( 2011 ) defined them as the number of vertical stages weighted by the value added of each stage, with the distance between each stage set to 1. Footnote 9 Since then, the average vertical distance has been the basic measure of the length of the value chain in the international I–O framework. Miller and Temurshoev ( 2015 ) further specified the existing measures by presenting upstream and downstream indicators in a matrix formulation using Ghosh’s forward and Leontief’s backward coefficient matrices (Ghosh 1958 ; Leontief 1936 ). These upstream and downstream measures are simple measures of the upstream and downstream length of value chains measured by the average vertical distance. Within this framework, further improvements were introduced by Muradov ( 2016 ), who focused on separating the domestic from the global production component while calculating the length of value chains.

The existing dominant conceptualisation of GVC participation measures is largely based on the work of Johnson and Noguera ( 2012 ), who produced a value-added export matrix that captures information on value flows in the economy between any two points (country-sectors) in the economy. This provides the basis for the disaggregation of value on the country-sector level, depending on whether the value was produced domestically for domestic consumption or involved cross-border transactions for either final or productive consumption (Koopman et al. 2014 ; Los et al. 2015 ; Wang et al. 2017 ). Since the value-added export matrix tells us about the source and destination of value added and covers all possible paths between any two country-sectors in the economy, there are two indicators of the share of GVC participation—the upstream and downstream share. The conception of the upstream participation share of participation starts from the value added of individual industries (country-sectors), disaggregating all possible paths leading to the realisation of their value, while the conception of the downstream share of participation starts with final consumption, disaggregating all possible paths of the downstream production linkages. Within this framework, disaggregation is defined on the domestic part, the “Ricardian trade” in finished goods, the simple GVC and the complex GVC, which is currently the most widely used accounting framework for GVC participation and thus far has been used by the best-known research on GVC carried out jointly by the WTO, the WB group, the OECD, IDE-JETRO, RCGVC-UIBE and the China Development Research Foundation (GVC Development Reports). Further improvements and clarifications of the framework were made by Borin and Mancini ( 2019 ), who derive their own measure of GVC-related bilateral trade flows by decomposing export to that attributable to traditional trade and GVC trade. Their indicator is composed of source-based backward and sink-based forward parts of their export decomposition, which can be calculated in a bilateral, country and world setting.

The development of I–O participation share measures of value chains, which are the primary interest of this article, evolved simultaneously with the development of methodologies of decomposing trade in value added (Johnson and Noguera 2012 ) as well as value added in trade (Arto et al. 2019 ; Borin and Mancini 2019 ; Miroudot and Ye 2021 ). However, despite similarities and some conceptual and formal mathematical overlapping, the fields of value chain participation share measures and value added in trade are driven by quite distinct research questions and research interests. On one hand, principal interest in decomposing exports is the correct evaluation of cross-border flows (properly removing double counting), assessing trade policy impacts and conducting overall impact analysis, either in a bilateral setting or with a focus on a specific country. On the other hand, value chain participation measures attempt to grasp the structure of an economy, sectoral and country interdependencies and the specific embeddedness of each production unit in different value chain structures, both at home and abroad. Value chain participation share measures usually correspond to a share of production, which statistically satisfies certain a priori criteria, such as “at least two cross-border transactions” or “at least one cross-border production sharing transaction”. The reviewed literature has contributed to better understanding of value chains and their I–O applied research, but still suffers two shortcomings that we try to address and improve with our approach.

The first main shortcoming of all current value chain participation share indicators is the lack of a single uniform measure for different value chain participation rates on the country-sector level. First, the value chain decomposition of Wang et al. ( 2017 ) results in downstream and upstream value chain participation rates, which provide two different types of information at the country-sector level. This is relevant for some types of analysis that deal with the relationship between upstream and downstream participation in GVCs, but there is a variety of situations where a common measure of GVC participation, defined uniformly on the country-sector level, is required either as the main object of the analysis or as a supplementary or control variable. Footnote 10 Second, GVC measures based on the decomposition of exports, even though they overcome the sink- and source-based decomposition in one unifying framework of export decomposition (Arto et al. 2019 ; Borin and Mancini 2019 ), are conceptually unable to offer a consistent solution to the question of a single country-sector value chain participation measure. That is because the criteria for export decomposition (separating domestic value added from foreign value added and the removal of double counting) do not correspond with the general criteria for different value chains on the country-sector level (the share of production with a certain number of cross-border transactions). Although export can be decomposed both with regard to the origin of the value added as well as the final demand, the very fact that the object of decomposition is export means it has a one-sided, forward orientation since export decomposition cannot address the fragmentation of production of a country-sector that has little or no exports (but can still form part of the fragmentation of a global value chain downstream). In this sense, the attempt by Borin and Mancini ( 2019 ) to provide a GVC measure of bilateral trade by decomposing exports cannot identify the share of production of a given country-sector which satisfies the criterion of a certain number of cross-border transactions, but only examines its forward part and is hence conceptually similar to Wang’s forward GVC measure. Our attempt to solve this issue demands the decomposition of the gross output (total output) of each country-sector to simultaneously account for both downstream and upstream value chain linkages.

The second major shortcoming of existing value chain indicators is the lack of a measure of domestic value chain fragmentation. The decomposition put forward by Wang et al. ( 2017 ) includes a broadly defined “domestic component”, which covers all of the value that does not comply with the GVC and Ricardian trade criteria. One of the major contributions of this article is to conceptually further divide this broad domestic component into a first part which comprises domestic production fragmentation (involving production sharing between at least two domestic firms) and the second part which does not. This yields new information regarding the share of production not involved in the fragmentation of global production, but is part of the fragmentation of domestic production and enables research into the role of domestic production fragmentation, which was impossible with the existing conceptualisations. As a result of the present disaggregation of participation shares into the “domestic component” and the GVC participation rates (and the Ricardian trade share) consisting of a simple duality that in its construction sums to 1, the share of the domestic component is never used in regressions (due to collinearity) and never even examined as a theoretical concept. It is simply a residual, a share that does not interest researchers given that all the information they disaggregate is included in their GVC participation rates. The existing approaches are used by researchers to focus exclusively on the international dimension of the fragmentation of production, neglecting the potential held by the international I–O methodology that allows analysis of domestic production fragmentation. Our approach is breaks ground in this area as it proposes a new concept of domestic fragmentation able to be measured on its own and according to its own definition and that is not collinear with the sum of the GVC participation rate.

Our methodological approach starts with the formal criteria, which is common for most of the GVC literature where value chains are defined according to certain transaction criteria (number of cross-border production-sharing transactions or similar). It is important to note that any such criteria are arbitrary and potential multiplicity of such criteria and hence value chain typologies can coexist and offer researchers some leeway in their empirical applications. Footnote 11 With a view to creating a uniform value chain measure on the country-sector level, we use the total output of each country-sector as the starting point of our disaggregation. Decomposing total output (as opposed to export or total value added) enables us to simultaneously grasp both the downstream and upstream value chain paths as well as the structure of the economy that is entirely domestic. Our decomposition begins with a set of the presented value chain tree matrices ( \(\tau _i\) ) which describe all of the value chain paths, from any country-sector of primary origin to any country-sector of production for final consumption that passes through (include a production stage of) a single particular country-sector. The logic of our approach is very similar to that of Arto et al. ( 2019 ) for combining the sink- and source-based decomposition of exports: because the decomposition of paths to final demand is independent of the decomposition of downstream value added, these decompositions can be linearly combined to capture both types of information in a single decomposition along two different dimensions at the same time. The big distinction with this approach is that object of decomposition is different—in our case, it is the total output (gross output) of each country-sector. Our choice of the object of decomposition is a prerequisite for properly capturing downstream linkages and, more importantly, properly accounting for the domestic structure of the economy. This formulation is the first attempt to capture information concerning the asymmetric value chain tree, which is a specific feature of each individual country-sector (Fig. 1 ). The proposed value chain tree matrices are unique in that they allow us to simultaneously capture the structure of the downstream and upstream value chain paths and to define value chain participation rates as a single measure for each country-sector. The crucial point of the proposed methodology is to enable the disaggregation of value chains based solely on the structure of value chain paths—taking into account whether these paths include only domestic production fragmentation, international production fragmentation or no production fragmentation at all. This allows us to introduce the concept of domestic value chain fragmentation that simply cannot be created within the existing framework of 2 separate participation indices. This multiplies the research opportunities offered by the value chain methodology based on the international input–output structure by permitting general analysis of the fragmentation of both domestic and global production and their interdependence along with any mutual effects of their development.

Applying this methodology, we show that increasing fragmentation of global production in recent decades has been a general trend for most countries (with a backlash in later years), but different institutional arrangements and structural economic positions led to various types of global economic integration, bringing diverse effects for domestic fragmentation. With our methodology, we shall empirically demonstrate that in many countries with high growth and ever stronger global integration domestic fragmentation also increased. However, one can find cases where domestic fragmentation stagnated or even declined whereas fragmentation of the global value chain increased. The different types of integration in global value chains are the outcome of several structural and institutional developments. Footnote 12 On one hand, the simultaneous increase in domestic and global fragmentation might only be a consequence of the growing complexity and division of labour. Yet, on the other hand, the simultaneous rise in global fragmentation and drastic decline in domestic integration might be due to the fracturing of domestic vertically integrated companies, parts of which are integrated into global value chains as subsidiaries, or due to the gradual replacement of domestic suppliers by globally traded inputs, which may increase following a foreign takeover or privatisation. The wide range of possibilities mean that every production unit may hold a different structural position within global production as a whole, and different structural positions may imply varying levels of dependence, which can be a factor of economic performance, especially during a crisis (Horvath and Grabowski 1999 ).

3.1 The value chain tree

3.1.1 conceptualisation.

We understand a value chain as a series of stages in the production of a product or service for the end user, where each stage adds value and the total value of the end product is the sum of the value added in each stage. For a value chain to exist, there must be at least two separate production stages. The existing GVC framework is analytically and empirically based on the idea that value is created in the production process and added to the value already present in the intermediate goods being used. The old value (value of intermediaries) is only transferred to the new product, while the newly created value is added linearly to the transferred value. The same idea also lies behind the elimination of double counting in standard gross trade statistics and exploration of the hidden underlying trade in value added, which provides insight into the international structure of trade (Arto et al. 2019 ; Johnson and Noguera 2012 ; Miroudot and Ye 2021 ). We make the same basic assumptions for value chain analysis.

We examine the structure of the economy from the perspective of a small unit Footnote 13 (country-sector) and capture its structural position within domestic and international production by measuring the degree of integration into domestic or global value chains. Each production unit is located within the production structure with a number of production-sharing transactions. On one side, the conditions of production are linked to the inputs produced by other firms in downstream linkages and, on the other, the final consumption of its product may only be reached after a series of upstream linkages in which its output is used as an input by other firms.

Accordingly, if one concentrates on a specific unit (country-sector) and aims to capture the upstream and downstream value chain linkages simultaneously , the value chain can be viewed as a tree, in contrast to the snake or spider analogy (see Fig. 1 ). Footnote 14 In the general case, the product is partly consumed immediately after production but also partly sent on to further stages of production and from each of these upstream stages it is further decomposed in the same way (etc., ad infinitum), spreading out like twigs and leaves until it ends completely in final consumption. Similarly, the primary value-creating activity can be represented by the structure of the roots, whereby value is only partially created in each stage since it requires pre-existing intermediates, which in turn are further decomposed in the same way ad infinitum.

Value chain tree. Source: own conceptualisation and design. Arrows represent production-sharing transactions—buying and selling of intermediate products for production. Orange colour denotes production that does not involve any production sharing, while any combination of red or orange paths denotes domestic production fragmentation. Any value chain path which includes a cross-country production-sharing transaction (a black arrow) is part of a global value chain from the perspective of the particular unit in focus. The paths of value creating and value realisation in a general case continue to branch ad infinitum (three levels are chosen only for demonstration purposes)

To conceptualise and measure the value chain structure of each specific smallest unit of analysis (country-sector), we introduce the value chain path concept. From the perspective of a firm, a value chain path is a series of transactions between firms that lead from a value-adding process to final demand. While currently no data exist that would account for every transaction between all firms Footnote 15 , firm transactions still represent a basis for any I–O sectoral aggregation, which can help us detect tangible differences in the value chain path structure in different country-sectors. While it is impossible with the given limits of accounting data to follow a certain value chain path of each specific product of each specific firm, it is nevertheless possible to analyse the average sectoral structure of value chain paths subject to whether the aggregated transactions between firms (and to the final consumer) are domestic or global. Our use of the signifier “transactions between firms” and “production-sharing transactions” thus does not refer to individual transactions, but instead refers to the information captured by the aggregated sectoral international I–O data regarding the average structure of value chain transactions. Since we do not focus on following transactions for an individual product but distinguish domestic from cross-border transactions between production units, aggregated I–O data are a sufficient starting point. While the accounting rules require transactions between firms in the same sector and the same country to be formally accounted (represented in aggregated form by the purely diagonal elements of the international Leontief coefficient matrix), the same goes for transactions between domestic firms from different sectors (represented in aggregated form by the block diagonal elements of the international Leontief coefficient matrix with purely diagonal elements 0). In this aggregated setting, one can differentiate between domestic and cross-border transactions (quantitatively in terms of shares), which gives the basis for decomposing different value chain paths based on the criterion of the number of cross-border or domestic production-sharing transactions. As shown in Fig. 1 , the value chain path can be decomposed with respect to two dimensions: Origin (where the value was primarily created) and the final stage of production (where the end product for consumption is finished).

Our goal of deriving a single value chain participation share measure on the country-sector level requires the derivation of an object able to track the value passing through a specific country-sector in focus along all possible paths from its origin to its end use. In this way, we decompose the value that forms part of the production process of a given country-sector along all its paths, which not only include the downstream paths leading to the country-sector under study and the upstream paths leading from it to final consumption, but also, and above all, the paths that combine upstream and downstream linkages and pass through that country-sector. In general, any value share can originate in any country-sector, and the same value share can also reach final consumption as a product of any country-sector. Compared to the approach of Johnson and Noguera, we add a third dimension Footnote 16 —the midpoint—the siphon through which the value from any origin to any final stage flows (Fig. 1 ), by combining decompositions based on value added and the final demand value chain path. This approach relies on similar reasoning as that of decomposing exports based on both value added and final demand (Arto et al. 2019 ).

The value chain tree of each country-sector is defined as the structure of the value chain paths, where this country-sector is the siphon via which the value chain paths pass. We show that each unit of analysis (country-sector) has a unique value chain structure that represents its structural position in the economy. Its output can be decomposed along every possible path within its value chain tree—i.e. along every value chain path that has its primary origin in any country-sector, passes through downstream linkages to the production stage of the country-sector which defines the value chain tree (the siphon), and ends in final consumption through upstream linkages as the final product of any country-sector.

Understanding the structure of value chains by empirically measuring all such paths of each country-sector (the smallest unit of analysis) is already an end in itself and can help with further understanding of the economy and its changing structure in terms of global integration, its specific regional and sectoral forms, and the complex interactions between domestic and global production fragmentation.

3.1.2 Derivation

The object of disaggregation is a country-sector’s total output. Each country-sector’s total output is disaggregated along both downstream and upstream linkages that are unique to its specific value chain structure. Downstream disaggregation represents all possible value chain paths from the origin of production and upstream disaggregation all possible paths to satisfy the final demand, both with respect to the unique value chain tree of each country-sector. In this way, we disaggregate the same object—the total output of each country-sector—simultaneously along its downstream and upstream paths.

In contrast to approaches based on the matrix of value-added exports (Johnson and Noguera 2012 ; Wang et al. 2017 ) to cover all value-added flows between any two country-sectors in an economy, we propose a new object—a set of matrices that describe the value chain structure of each country-sector separately, covering all value chain paths from each primary origin to each final stage via the output of a single specific country-sector (Fig. 1 ). In this conceptualisation, each country-sector has a corresponding value chain tree described by the value chain tree matrix—while the value chain structure of the economy as a whole is described by the set of such matrices.

We derive our disaggregation within the static international Leontief demand-driven model. C , F and x are the main accounting datasets representing the intermediate consumption matrix, final consumption matrix and total output vector. The Leontief coefficient matrix is usually derived as \(A=C{\hat{x}} ^{-1}\) . The variables with hat are vectors transformed into diagonal matrices, \({\hat{f}}\) represents a diagonal matrix of final demand and \({\hat{v}}_C\) a diagonal matrix of value-added coefficients. Footnote 17 The usual pairs of indices characterising the country and sector of origin ( s , i ) and the final destination ( d , j ) are replaced by a single index for each country-sector for more transparent notation. Since we are no longer working in the \(n\times n\) dimensional space, but in the \(n \times n \times n\) dimensional space, we would need 3 pairs of indices, 1 pair for the country-sector of origin, 1 pair for the final stage and also 1 pair for the country-sector, which is the siphon through which all possible value chain paths characterise its specific value chain structure. Instead, we are working with only 3 indices, one for the country-sector of origin ( k ), one for the final stage country-sector ( j ) and one to characterise the country-sector value chain tree—the country-sector representing the siphon through which the value chain paths pass ( i ). Footnote 18

We start with the upstream part, by using standard Leontief’s derivation:

Definition 1

Upstream output decomposition W :

\(W={\hat{x}}^{-1}(I-A)^{-1}{\hat{f}}.\)

The matrix W represents the upstream output decomposition along all upstream value chain paths. Its element \(w_{ij}\) represents the share of the total output of country-sector i that reaches final consumption as the end product of country-sector j , along all possible upstream production fragmentation paths in the economy. The i th row of W represents the disaggregation of the total output of the i th country-sector into output shares according to its final production stages that account for all direct and indirect paths of the upstream value transfers leading to the full realisation of total output (by being used directly or indirectly by other country-sectors as intermediate productive consumption). Each i th row of W may thus be characterised as a discrete probability distribution. On one hand, the upstream output shares of each country-sector i add up consistently to 1: \(\sum _{j=1}^n w_{ij}=1\) \(\forall i\) . On the other hand, there is a clear economic interpretation of the probability distribution: \(w_{ij}\) represents the probability that a randomly selected part of the total output of the i th country-sector will eventually be consumed as the final product of country-sector j , along any upstream value chain path.

For the downstream part, we begin with identity:

Definition 2

Downstream output decomposition Z :

\(Z= \hat{v_C} (I-A)^{-1}.\)

The matrix Z represents the downstream output decomposition along all downstream value chain paths. Its element \(z_{ki}\) represents the share of the total output of country-sector i that is primarily created in country-sector k , along any possible downstream production fragmentation path in the economy. The i th column of Z represents the disaggregation of the total output of the i th country-sector into output shares, which represent all direct and indirect paths of the downstream value transfer from each country-sector that has contributed to the production of its output (through the direct or indirect production of intermediate productive consumption used by i ). Each i th column of Z may thus be characterised as a discrete probability distribution. On one hand, the downstream output shares of the individual country-sectors i add up consistently to 1: \(\sum _{k=1}^n z_{ki}=1\) \(\forall i\) . On the other hand, there is a clear economic interpretation of the probability distribution: \(z_{ki}\) represents the probability that a randomly selected part of the total output of the i th country-sector was produced by country-sector k , along any downstream value chain path.

The two matrices presented, W and Z , may appear as two sides of the same coin—similar to forward and backward decomposition, which has largely been exhausted in the international input–output literature. However, if we focus on a single country-sector ( i ), the i th column of Z and the i th row of W represent two probability distributions that take the transfers in the value chain into account, which result in two completely different and independent types of information. The i th column of Z contains information on the downstream structure of the value chain of the respective i th country-sector and the i th row of W contains information on the upstream structure of the value chain of the respective i th country-sector. For a given i th country-sector, the two probability distributions are asymmetrical. Most importantly, both probability distributions relate to the same object of investigation—the total output of country-sector i .

Using the total output of each country-sector seems to be the only way to disaggregate the same object into its upstream and downstream value chains. The object of decomposition of the upstream part (which is decomposed based on the paths to final demand) of a certain country-sector can be either its total output or total value added (even its export). However, the same is not possible for the downstream part (which is decomposed according to the origins of its value-added). The object of decomposition of the downstream part of a certain country-sector can only be its total output, which also makes up the totality of value-added shares along the whole downstream value chain. Footnote 19 In other words, the country-sector’s total output is an object that has both an upstream and a downstream path, while total value added and total export represent only that part of the output which has an upstream path, even if this upstream path is disaggregated by value-added origin. Using the total output share as the basis for disaggregating to the individual country-sector level is therefore a legitimate choice. This mainly explains why we derived the W matrix in terms of shares of total output ( 3.4 , 3.5 ) and not, as is usual, in terms of shares of value added—to make it perfectly clear that both upstream and downstream disaggregation have the same object—the total output of i , which includes both the value added of country-sector i and the total value added of the other country-sectors ( k ) downstream. The same object (total output) is then distributed along the upstream value chain paths (as determined by the i th row of W ) until it reaches final consumption along an upstream value chain path.

All input–output analyses assume the homogeneity of the smallest classification object (country-sector in our case). The level of detail of the data corresponds to the level of detail of the sector (and country) classification and within a country-sector there is no further information and quite strict homogeneity assumptions apply. We use the assumption of the homogeneity of production of each country-sector to combine the two probability distributions.

\(z_{ki}\) represents the share of the total output of the i th country-sector, which was primarily produced by country-sector k . Due to the homogeneity of the total output of the i th country-sector, the \(w_{ij}\) represents not only the probability that a random part of the total output of the i th country-sector reaches final consumption as a product of j , but also the probability that a random part of any share of the output of the i th country-sector reaches final consumption as a product of j . Since \(z_{ki}\) is a share of the i th country-sector’s total output, its upstream decomposition is clearly and uniquely defined by the i th row of w .

The product \(w_{ij}z_{ki}\) thus simply represents the probability that a certain part of the total output of the i th country-sector is primarily produced in k and reaches final consumption as the product of j along any value chain path (upstream, downstream or a combination) passing through i . In other words, it represents the share of the total output of i that was produced by k and reached final consumption as a product of j . A simple multiplication of probabilities requires that the two events—a random portion of the total output of i produced by k and a random portion of the total output of i completed for consumption by j —are statistically independent. First, if certain parts of the total output of a particular country-sector were to behave differently from certain other parts of the same output, this would violate the homogeneity assumption, which is the basic assumption of the input–output structure and methodology. Second, at the level of economic theory it is relatively easy to argue about the statistical independence of the structure of upstream and downstream value chains: Nothing about the downstream structure of production in the i th country-sector implies anything about its upstream structure and vice versa . Both are calculated independently and provide completely different information: the downstream decomposition gives information about the inputs produced by other country-sectors used directly or indirectly in the production process of the i th country-sector, and the upstream decomposition gives information about how the product of the i th country-sector is consumed either directly or as part of the final product of other country-sectors.

Two separate vectors which disaggregate the value chain paths of the downstream ( i th column of Z ) and upstream value chain ( i th row of W ) thus span an entire matrix of total output shares that capture the value chain tree structure of the i th country-sector. We combine them with the direct product that defines the matrix of the value chain tree for each country-sector ( i ) by multiplying each element of \(Z\vec {e_i}\) (the i th column of Z ) by each element of \(\vec {e_i}^{T}W\) (the i th row of W ).

Definition 3

Value chain tree matrix

\(\tau _i=Z\vec {e_i} \otimes \vec {e_i}^{T}W\) ; \(\tau _i\in \mathbb{R}^{n\times n}\) , where \(\vec {e_i} \in \mathbb{R}^n\) represents the standard orthonormal basis of \(\mathbb{R}^n.\)

This defines each element of the value chain tree matrix \(t_{ijk} \in \tau _i\) as \(t_{ijk}=w_{ij}z_{ki}\) . Each element of the value chain tree matrix \(\tau _i\) thus represents a share of the total output of country-sector i , which is primarily produced in country-sector k and consumed as an end product of country-sector j , along any upstream and downstream value chain path.

The main point of our derivation is not the expressed final value distribution of the total output of each country-sector along any of its upstream and downstream value chain paths, but the expression of the total output distribution (of the respective country-sector) along any value chain path, be it a downstream value chain path, an upstream value chain path or any combination of both paths at the same time.

The structure of the value chain tree matrices allows us to focus our disaggregation on the composition of the value chain paths covered by the two global Leontief inverses in the equation, the first representing all downstream parts of the value chain paths and the second representing all upstream parts of the value chain paths.

A single value chain path is determined by a series of concrete transactions between companies: It is a unique path from primary value creation (value created in production, not transferred from intermediate products) to value realisation (final consumption, not productive consumption of intermediate products), which passes through the production stage of the i th country-sector. The total output of i is not only disaggregated along all possible paths leading from any country-sector of origin via country-sector i to any country-sector of final stage production (as determined by \(\tau _i\) ), but is also disaggregated in much finer detail, along all the unique value chain paths that pass through i . That a concrete value chain path only forms part of the value chain tree matrix can easily be recognised if both inverses in \(\tau _i\) are replaced by an infinite series ( \((I-A)^{-1}=I+A+A^2+\cdots\) ). Such disaggregation then results in an infinite number of value chain paths, and the total output of the i th country-sector is distributed over all of these paths.

A certain value chain path share of the total output of i is determined by the Leontief technical coefficients \(a_{ij}\in A\) . For example, take a value chain path consisting of value primarily produced in country-sector \(CS_1\) Footnote 20 , then used as an intermediate in \(CS_2\) , which in turn is used as an intermediate in i (the country-sector whose value chain is broken down), and then sent as an intermediate to \(CS_3\) , which is then sent as an intermediate to \(CS_4\) , where it is finished and sold for consumption. This value chain path has an origin ( \(CS_1\) ), a midpoint ( i ) and a final destination of production ( \(CS_4\) ), as well as a concrete path with a length of 5 (5 country-sectors contribute to production from origin to final consumption). The share of the total output of the i th country-sector that may be attributed to this specific path is:

A specific unique value chain path of the i th country-sector’s value chain tree, that has its origin in k and final stage in j , can be written as:

Such a path has a downstream length of d and an upstream length of \(u-1-d\) and the path is determined by a unique set of production-sharing transactions from the origin to the final stage (from origin \(j=CS_0\) , to \(CS_1\) , to \(CS_2\) , ..., to \(i=CS_{d}\) , and further to \(CS_{d+1}\) , \(CS_{d+2}\) , ..., to \(k=CS_u\) ). Leontief technical coefficients \(a_{CS_{p-1}CS_{p}}\) determine each production-sharing transaction. The summation along the total output shares of i attributed to all such unique value chain paths, taking into account all permutations of possible transaction sequences and also all possible lengths (all possible length combinations of downstream and upstream lengths) as well as all possible origins and final stage destinations, results in a unit:

Our conceptualisation allows us to define decomposition criteria applicable to each value chain path of the value chain tree of the i th country-sector. Based on this property, we will decompose the value chain structure of each country-sector separately in the following section.

3.2 The value chain typology

3.2.1 definitions.

The framework of the international I–O analysis allows the separate analysis of final transactions to consumers and transactions between companies. Based on this characteristic, we propose a typology of value chains based solely on the structure of linkages between enterprises, while adding a further decomposition with regard to different possible transactions to reach the final consumption post festum . Footnote 21 Each matrix \(\tau _i\) expressed by equation 3.13 represents the desegmentation of the total product of country-sector i along different downstream and upstream paths. When we refer to a value chain, we refer to the specific share of value (share of output) that corresponds to a particular value chain path. Path Footnote 22 of each value share generally includes any combination of domestic and cross-border production-sharing transactions, which can take place both downstream and upstream relative to the respective country-sector. Our criteria for the value chain typology thus refer to each specific value share corresponding to a single path within a value chain tree specific to each country-sector.

Definition 4

• Domestic value chain

Domestic value chain (DVC) is a value that involves at least 1 domestic production-sharing transaction and involves only domestic production-sharing transactions along its path.

Definition 5

• Global value chain

Global value chain (GVC) is a value that involves at least 1 cross-border production-sharing transaction along its path. We further distinguish two types of global value chains: simple and complex.

Definition 5.1

Simple global value chain

Simple global value chain (SGVC) is a value that involves exactly 1 cross-border production-sharing transaction anywhere along its path.

Definition 5.2

Complex global value chain

Complex global value chain (CGVC) is a value that involves more than 1 cross-border production-sharing transaction along its path.

Definition 6

No value chain

No value chain (NVC) is a value that does not involve any production-sharing transactions and has no value chain path within production.

A few brief comments are appropriate on our definitions and their interpretation. No material product or service belongs to a single classification of value chain, and no enterprise can be considered part of a single type of value chain. The output of each enterprise belongs to a variety of value chain paths. In general, one part of the output comprises many cross-border transactions, another part only domestic transactions, and yet another part their relatively complex interrelationship. Each product (or country-sector in our case) can be assigned different shares of the value chain paths. These shares are objects that provide information about the structure of the economy. For example, virtually no enterprise could be classified exclusively as part of a no value chain, but some enterprises that provide services (e.g. domestic services) have a relatively high share of output that has no value chain path, especially in services, where salaries account for almost all of the enterprise’s expenditure and where their product directly satisfies final demand. On one hand, enterprises that specialise in intermediate goods are always part of a value chain, whether domestic or global. On the other hand, even modern industries such as food-processing and pharmaceuticals, also have a certain (usually small) share of value added that is not part of any value chain (no value chain share), corresponding to the share of domestic value added in these industries that is also directly consumed (part of output that has no value chain path). The value chain shares and their changes are the object that provide information about the structure of the economy, whether on the sector or country level. As the economy develops, the division of labour also increases, which corresponds to the growing fragmentation of production, in particular international production fragmentation, and a decrease in shares where there is limited or no value chain fragmentation. Compared to the existing typology of value chains, this revised typology allows for analysis of the relationship between global and domestic fragmentation, which might prove especially relevant for the policies of developing countries.

3.2.2 The decomposition of paths

Our value chain typology is established according to criteria along the entire value chain. For this reason, we disaggregate the value chain tree matrices \(\tau _i\) in terms of criteria for different types of value chain paths. Our decomposition consists of the decomposition of two Leontief inverses, which may be interpreted as the decomposition of the downstream part and upstream part of each value chain path, as defined by equation 3.11 : \(\tau _i=\hat{v_C}(I-A)^{-1}\vec {e_i} \otimes \vec {e_i}^{T}{\hat{x}}^{-1}(I-A)^{-1}{\hat{f}}\) . The decomposition is constructed based on of the criteria of the number of cross-border and domestic production-sharing transactions that are consistent with the revised value chain typology.

First, we investigate the decomposition of only a single Leontief inverse (interpreted symmetrically with respect to our criteria in the upstream and downstream value chain) and only then do we analyse the decomposition of all value chain paths characterised by the two Leontief inverses. The international I–O data have a specific block matrix structure in which the block diagonal elements represent domestic production-sharing transactions and the block off-diagonal elements represent international production-sharing transactions ( \(A_D\) denotes domestic—block diagonal—and \(A_{ CB }\) cross-border—block-off diagonal—part of A ), which allows us to decompose the Leontief inverse in the following way:

I obviously represents that part of the output which contains no production-sharing transactions —no value chain linkages. In the upstream part, it represents the share of total output that directly satisfies final demand (i.e. no upstream value chain), while in the downstream part it represents the direct value added of the country-sector whose production is being decomposed (i.e. no downstream value chain).

\(A_D(I-A_D)^{-1}=A_D+A_D^2+A_D^3+ \dots\) represents that part of output which contains at least 1 domestic production-sharing transaction and contains only domestic production-sharing transactions .

\((I-A_D)^{-1}A_{CB}(I-A_D)^{-1}\) represents that part of the output which contains at least 1 production-sharing transaction and contains exactly one cross-border production-sharing transaction somewhere along its value chain path. This can be demonstrated by paraphrasing the part as all possible combinations of a single cross-border transaction among any possible set of domestic production-sharing transactions that occur before or after the single cross-border production-sharing transaction:

\((I-A)^{-1}-(I-A_D)^{-1}-(I-A_D)^{-1}A_{CB}(I-A_D)^{-1}\) represents that part of the output which contains at least two or more production-sharing transactions , of which at least two are cross-border production-sharing transactions . This logically follows from the fact that parts (1), (2) and (3) cover the total output that contains less than two cross-border transactions, and that the full Leontief inverse covers the total output.

3.2.3 Value chain tree matrix decomposition

We proceed by disaggregating all of the value chain paths as they are structured in the value chain tree matrices. Using the decomposition of the Leontief inverse that we disaggregated in the previous subsection and inserting it into Eq. 3.11 , we obtain 16 components ( \(4 \times 4\) product) for each matrix \(\tau _i\) . Footnote 23 This disaggregation along both the upstream and downstream paths is the basis for deriving value chain shares that correspond to our typology. We decompose each \(\tau _i\) matrix describing all possible value chain paths of the output of the i th country-sector into a matrix consisting of domestic value chain paths only, a matrix containing all possible global value chain paths (as well as simple and complex global value chain paths separately), and a matrix consisting only of the value that has no value chain path.

Definition 7

Domestic value chain tree \(\tau _{i}^{DVC}\)

The domestic value chain tree represents all value chain paths of the output of each country-sector which, according to Definition 4 , are part of the domestic value chains. In Fig. 1 , the domestic value chain paths are marked in red. Domestic value chain paths are defined as all paths that contain at least one red-coloured linkage (representing transactions between domestic enterprises) and include only red-coloured linkages and orange paths (representing the value creation or realisation in the respective country-sector in focus). The first part ( \(\hat{v_C}A_D(I-A_D)^{-1}\vec {e_i} \otimes \vec {e_i}^{T}{\hat{x}}^{-1}{\hat{f}}\) ) covers the downstream domestic value added (downstream domestic path), which ends as the i th country-sector final stage (no upstream path), the second part ( \(\hat{v_C}\vec {e_i} \otimes \vec {e_i}^{T}{\hat{x}}^{-1}A_D(I-A_D)^{-1}{\hat{f}}\) ) covers the value added of the i th country-sector (no downstream path) that is transferred via the upstream domestic value chain (upstream domestic path), and the third part ( \(\hat{v_C}A_D(I-A_D)^{-1}\vec {e_i} \otimes \vec {e_i}^{T}{\hat{x}}^{-1}A_D(I-A_D)^{-1}{\hat{f}}\) ) comprises the downstream domestic value added that is used as an intermediate product in the production of i and then used as an intermediary further in the upstream domestic value chain until it reaches final demand (both downstream and upstream domestic paths). All three cases meet the definition of a domestic value chain.

Definition 8

Global value chain tree \(\tau _{i}^{GVC}\)

The global value chain tree represents all paths of the output of the individual country-sector, which form part of global value chains according to Definition 5 . In Fig. 1 , the global value chain paths are represented by all paths containing at least one black-coloured linkage (representing cross-border transactions between enterprises). Global value chain paths can contain any number of red (domestic) and orange (no value chain) linkages provided there is at least one black (cross-border) linkage along their path. The first element ( \(\hat{v_C}(I-A_D)^{-1}\vec {e_i} \otimes \vec {e_i}^{T}{\hat{x}}^{-1}\big [(I-A)^{-1}-(I-A_D)^{-1}\big ]{\hat{f}}\) ) covers the downstream domestic and no value chain paths, which have global upstream linkages (simple or complex), the second element ( \(\hat{v_C}\big [(I-A)^{-1}-(I-A_D)^{-1}\big ]\vec {e_i} \otimes \vec {e_i}^{T}{\hat{x}}^{-1}(I-A_D)^{-1}{\hat{f}}\) ) covers downstream global linkages (simple or complex), which have a upstream domestic or no value chain path and the third element ( \(\hat{v_C}\big [(I-A)^{-1}-(I-A_D)^{-1}\big ]\vec {e_i} \otimes \vec {e_i}^{T}{\hat{x}}^{-1}\big [(I-A)^{-1}-(I-A_D)^{-1}\big ]{\hat{f}}\) ) covers the value that has global paths both upstream and downstream. All of these cases correspond to our definition of a global value chain.

Definition 8.1

Simple global value chain tree \(\tau _{i}^{SGVC}\)

The simple global value chain tree represents all paths of the output of each country-sector that are part of simple global value chains as defined by 5.1 The first element ( \(\hat{v_C}(I-A_D)^{-1}\vec {e_i} \otimes \vec {e_i}^{T}{\hat{x}}^{-1}(I-A_D)^{-1}A_{ CB }(I-A_D)^{-1}{\hat{f}}\) ) covers a downstream domestic and no value chain path that has simple global upstream linkages and the second element ( \(\hat{v_C}(I-A_D)^{-1}A_{ CB }(I-A_D)^{-1}\vec {e_i} \otimes \vec {e_i}^{T}{\hat{x}}^{-1}(I-A_D)^{-1}{\hat{f}}\) ) covers downstream simple global linkages that have an upstream domestic or no value chain path. These are the only cases that fit our definition of a simple global value chain. A value chain path covering both downstream and upstream simple global linkages already has more than 1 cross-border transaction and is hence part of a complex global value chain.

Definition 8.2

Complex global value chain tree \(\tau _{i}^{CGVC}\)

The complex global value chain tree represents all paths of the output of individual country-sectors that form part of complex global value chains as defined in 5.2 The first element ( \(\hat{v_C}(I-A_D)^{-1}\vec {e_i} \otimes \vec {e_i}^{T}{\hat{x}}^{-1}\big [(I-A)^{-1}-(I-A_D)^{-1}-(I-A_D)^{-1}A_{ CB }(I-A_D)^{-1}\big ]{\hat{f}}\) ) covers the downstream domestic and no value chain path, having complex global upstream linkages, the second element ( \(\hat{v_C}\big [(I-A)^{-1}-(I-A_D)^{-1}-(I-A_D)^{-1}A_{ CB }(I-A_D)^{-1}\big ]\vec {e_i} \otimes \vec {e_i}^{T}{\hat{x}}^{-1}(I-A_D)^{-1}{\hat{f}}\) ) comprises downstream complex global linkages, which have an upstream domestic or no value chain path, and the third element ( \(\hat{v_C}\big [(I-A)^{-1}-(I-A_D)^{-1}\big ]\vec {e_i} \otimes \vec {e_i}^{T}{\hat{x}}^{-1}\big [(I-A)^{-1}-(I-A_D)^{-1}\big ]{\hat{f}}\) ) represents combinations of global downstream and upstream paths (simple-simple, simple-complex, complex-simple, complex-complex). All of these elements meet our definition of a complex global value chain because the value in all cases crosses borders for production at least twice.

Definition 9

No value chain tree \(\tau _{i}^{NVC}\)

A no value chain tree represents that part of the output of each country-sector which is not part of a value chain according to Definition 6 . In Fig. 1 , a no value chain path is represented by the orange colour only (any other linkage represents a value chain path). Solely the share of value added produced in the respective country-sector in focus (no downstream stages) and also completed for final consumption (no upstream stages) in the same production phase satisfies this criterion. Since the I–O method distinguishes between a product used as an intermediate product within the same sector Footnote 24 and the product manufactured for final consumption, the use of this definition as no value chain does not depend on the level of detail of I–O data disaggregation. The cyclical effect of the production of intermediate goods within the same country-sector is already included in the domestic value chain tree and, after taking into account all of the defined value chain paths (domestic, simple and complex global value chain paths), a value share remains without a value chain path and with a simple representation as the value added of the country-sector which is also directly consumed. This represents a value that has no path in terms of transactions that represent the fragmentation of production.

This concludes the value chain tree decomposition, which can be written as:

3.2.4 The value chain participation rates

In Sect. 3.1 , we showed that a set of value chain tree matrices \(\tau _i\) represents all possible value chain paths of the output of each country-sector and that the summation along all shares of total output assigned to all such unique value chain paths yields a unity for each value chain tree (Eq. 3.14 ). Namely, we presented a unique disaggregation of the output of each country-sector along all of its value chain paths. In the same way, the summation along the two disaggregating dimensions of our decomposed set of matrices (global, domestic and no value chain tree matrices) captures the overall share of the total output of each country-sector i that meets the criteria by which the value chain paths were decomposed by including either only domestic value chain paths, only global value chain paths, or only values that have no value chain paths at all. In other words, the summation of the disaggregated value chain matrices along any origin and end stage represents the share of output of each country-sector that has either a domestic, a global or a no value chain.

Definition 10

Domestic value chain share DVCs

\(DVCs \in \mathrm{I\!R}^n\) ; \(DVCs_i = \sum _{j=1}^n \sum _{k=1}^n t_{ijk}^{DVC}\) ; \(DVCs= \begin{bmatrix} {\mathbf {1}}^T \tau _{1}^{DVC} {\mathbf {1}} \\ {\mathbf {1}}^T \tau _{2}^{DVC} {\mathbf {1}} \\ \vdots \\ {\mathbf {1}}^T \tau _{n}^{DVC} {\mathbf {1}} \end{bmatrix}.\)

Domestic value chain share represents the share of each country-sector’s output that has a domestic value chain path.

Definition 11

Global value chain share GVCs

\(GVCs \in \mathrm{I\!R}^n\) ; \(GVCs_i = \sum _{j=1}^n \sum _{k=1}^n t_{ijk}^{GVC}\) ; \(GVCs= \begin{bmatrix} {\mathbf {1}}^T \tau _{1}^{GVC} {\mathbf {1}} \\ {\mathbf {1}}^T \tau _{2}^{GVC} {\mathbf {1}} \\ \vdots \\ {\mathbf {1}}^T \tau _{n}^{GVC} {\mathbf {1}} \end{bmatrix}.\)

Global value chain share represents the share of each country-sector’s output that has a global value chain path.

Definition 11.1

Simple global value chain share SGVCs

\(SGVCs \in \mathrm{I\!R}^n\) ; \(SGVCs_i = \sum _{j=1}^n \sum _{k=1}^n t_{ijk}^{SGVC}\) ; \(SGVCs= \begin{bmatrix} {\mathbf {1}}^T \tau _{1}^{SGVC} {\mathbf {1}} \\ {\mathbf {1}}^T \tau _{2}^{SGVC} {\mathbf {1}} \\ \vdots \\ {\mathbf {1}}^T \tau _{n}^{SGVC} {\mathbf {1}} \end{bmatrix}.\)

Simple global value chain share represents the share of each country-sector’s output that has a simple global value chain path.

Definition 11.2

Complex global value chain share CGVCs

\(CGVCs \in \mathrm{I\!R}^n\) ; \(CGVCs_i = \sum _{j=1}^n \sum _{k=1}^n t_{ijk}^{CGVC}\) ; \(CGVCs= \begin{bmatrix} {\mathbf {1}}^T \tau _{1}^{CGVC} {\mathbf {1}} \\ {\mathbf {1}}^T \tau _{2}^{CGVC} {\mathbf {1}} \\ \vdots \\ {\mathbf {1}}^T \tau _{n}^{CGVC} {\mathbf {1}} \end{bmatrix}.\)

Complex global value chain share represents the share of each country-sector’s output that has a complex global value chain path.

Definition 12

No value chain share NVCs

\(NVCs \in \mathrm{I\!R}^n\) ; \(NVCs_i = \sum _{j=1}^n \sum _{k=1}^n t_{ijk}^{NVC}\) ; \(NVCs= \begin{bmatrix} {\mathbf {1}}^T \tau _{1}^{NVC} {\mathbf {1}} \\ {\mathbf {1}}^T \tau _{2}^{NVC} {\mathbf {1}} \\ \vdots \\ {\mathbf {1}}^T \tau _{n}^{NVC} {\mathbf {1}} \end{bmatrix}.\)

A no value chain share represents the share of each country-sector’s output that has a no value chain path.

With this, we conclude our disaggregation of each country-sector’s total output with respect to its specific value chain integration based on production-sharing linkages. We can summarise our decomposition in the simple vector form:

3.2.5 Decomposition of the transaction to the final consumer

Since all value chain paths within production are covered and decomposed, we still have one last transaction to the consumer to complete the value chain path from production to consumption. We can decompose the final transaction to the consumer upon the criterion of whether it is a transaction to domestic consumers or a cross-border transaction (export of the final product for consumption). Domestic consumption here refers to the country-sector in which the last stage of production took place and not the country-sector whose value chain we are analysing. Each country-sector has a unique value chain and a specific structure of value chain paths. The completion of each value chain path by a transaction to the consumer can be achieved by an additional cross-border transaction of exporting the final product or consumption in the country where the product was finalised. Such a further decomposition of the value chain paths allows a more detailed analysis of the value chains.

The I–O data include information on the transaction to final consumers within matrix F , which can be decomposed into its cross-border and domestic flows to final consumers ( \(F=F_{CB} + F_{D}\) ) due to its block vector structure. We construct a matrix of all cross-border final consumption flows and a matrix of all domestic consumption flows:

Every value chain path within production can thus be further decomposed with an additional criterion of a transaction to final consumers. Each set of disaggregated value chain matrices, defined by Eqs. 3.16 and 3.17 , can be separated on two matrices, one covering all of the production paths that end in domestic final consumption (no export - \(\tau _i^{NE}\) ) and the other all of the production value chain paths that end with exporting for final consumption ( \(\tau _i^{E}\) ).

Due to their simple additive properties of operation, all of the decomposed value chain tree matrices are similarly decomposed to ones with exporting or with no exporting as the final transaction.

The value shares that are part of each value chain path are thus further decomposed, as explained in Sect. 3.2.4 . The final decomposition of the output is thus a decomposition along each value chain, as defined by criteria that simultaneously take account of transactions related to the production fragmentation (different value chains) and the final transaction to the consumer. A share of value that has either a domestic, global or no value chain has as its final transaction to the consumer either an export or a no export transaction, which provides a detailed decomposition of the participation shares that can be used to construct different composite indices suitable for different research questions.

4 Results and discussion

The proposed measures broaden the scope for empirical application and static analysis of international production and trade. The contribution of our approach entails the simultaneous insight into domestic and global value chains, which allows the study of their interaction and structural changes in economies. All elements of the new typology may vary over time, from country to country and sector to sector and are relevant research topics. The derived participation shares are also simple fragmentation measures, and each smallest unit of analysis (country-sector) is represented by a single measure (scalar share) that covers the extent of overall value chain fragmentation, as opposed to separate downstream and upstream indicators.

Due to the limitations of the paper and its chiefly methodological focus, we present only some very basic empirical results. First, we show the global averages of value chain participation rates based on WIOD 2016 data and the global average participation rates for the manufacturing and service sectors separately (Figs. 2 , 3 and 4 ). Using our methodological approach, we observe that the global average GVC share of world output consistently exceeds 20%, reached almost 24% at its peak before the global recession, and then stagnated slightly below this level until 2014 (Fig. 2 ). This suggests that the most recent estimates of GVCs’ share of production between 10 and 15% (Dollar 2017 , p. 2; Li et al. 2019 , p. 12) may be undervalued. As expected, the manufacturing sector is globally integrated to an above-average extent, with the share in the global value chain rising from 35 to over 40% before the crisis and then stagnating around this level after a brief recovery. The share of the complex global value chain shows the highest relative growth, while the average increase in global value chain integration exceeds the decline in domestic value chain integration. Interestingly, the decline in global integration in times of crisis had almost no impact on that part of the economy without value chain fragmentation, while domestic fragmentation increased almost in proportion to the decline in global integration. Hence, the crisis did not lead to a general decline in the fragmentation of production, but only to a decrease in its global character. For services, in contrast, less than 15% of total output has a global value chain path, although services show some increase in global integration, mainly due to decreasing domestic integration (which may be attributed to the globalisation of business services), while that part of the economy without a value chain appears relatively stable. For this reason, vulnerability to external financial shocks was much less pronounced in services during the crisis.

Source: WIOD, 2016; own calculations.

World average participation rates

Source: WIOD, 2016; own calculations

World average of manufacturing.

Source: WIOD 2016; own calculations

World average of services.

China manufacturing participation rates.

New EU countries manufacturing.

USA manufacturing participation rates. creditSource: WIOD 2016; own calculations

As the data for the world average conceal large differences between countries, we also show the value chain participation shares of manufacturing for China, the USA and the average of the economically most integrated new EU members—3 Baltic and 4 Visegrad countries (Figs. 5 , 6 and 7 ), which reveal structural differences and diverse patterns of development in global and domestic integration. China has on average a high share of domestic production integration (around 65%) and is one of the few economies where the share of domestic integration grew by almost 10 percentage points between 2004 and 2014. In the United States, the picture is reversed, while the already lower average share of domestic integration is steadily shrinking. A completely different pattern is seen in the Baltic and Visegrad European countries, which became EU members in the new millennium. On average, these countries’ integration into global value chains in the manufacturing industries rose from an already high 53 to 69% during the observed period. At the same time, there was a huge relative decline in the already below-average share of domestic fragmentation from 32 to 18%. Interestingly, almost all of the growth in the global value chain share in Central and Eastern EU countries was due to the increase in complex global value chain linkages, while simple global value chain linkages remain relatively stable.

Finally, we use the fact that we have created uniform participation rates by performing a simple between-effects regression to test the relationship between the level of domestic and global fragmentation and economic growth measured by GDP per capita. Since short-term productivity fluctuations can hardly be explained by an economic structure expressed in value chain shares, we use a cross-sectional approach to test the long-term effects of different levels of domestic or global fragmentation on economic growth. Our observations relate to the 43 countries included in the WIOD 2016 data, and the variables are their average annual growth, the average DVC and GVC shares, with the average logarithm of GDP per capita as a control for convergence, the average logarithm of the annual population as a control for the size of the country, and the EU control dummy for potential EU specifics. The main regression equation with between effects is derived in the usual way out of a general panel data model:

\(y_{it}=\alpha _i + logGDP_{it}\beta _1+DVC_{it}\beta _2+GVC_{it}\beta _3+\epsilon _{it},\)

\(\overline{y_i}=\alpha +\overline{logGDP_i}\beta _1+\overline{DVC_i}\beta _2+\overline{GVC_i}\beta _3+ (\alpha _i-\alpha +\overline{\epsilon _{i}}).\)

To ensure a consistent estimator, \(\alpha _i\) must be random effects. \(y_{it}\) is yearly growth of GDP per capita, \(logGDP_{it}\) is a logarithm of GDP per capita, while \(DVC_{it}\) and \(GVC_{it}\) represent shares of domestic and global value chains as calculated by the proposed methodology. The number of countries is 43 and number of time units is 15 (from 2000-2014), making a total of 645 observations in the panel.

The regression results are shown in Table 1 . The logarithm of GDP per capita is a significant variable and negatively related to growth. The result simply reflects the fact that higher GDP implies less potential for higher growth rates, as implied by the convergence literature. Taking this into account, both the DVC share and the GVC share are highly significant variables that have a positive effect on growth rates. Therefore, both domestic and global integration can have a significant impact on economic growth. The same result applies after the introduction of additional controls on country size and EU specifics. Due to the principally methodological orientation of the article, we refrain from a detailed interpretation of the regression results. Yet, it should be noted that it is difficult to separate cause and effect while applying econometric analyses—a country in recession for external reasons could experience a decline in global and domestic production fragmentation due to those same external reasons. In any case, there is a correlation between economic growth and the degree of production fragmentation, whether it is domestic or global. A country that experiences an overall decrease in production fragmentation (domestic fragmentation declines faster than global increases), regardless of an increase in global production integration, might experience a negative impact on economic growth compared to similarly developed countries, in line with our findings. Footnote 25 An increase only in participation in global value chains therefore does not necessarily enhance the growth due to various forms of integration Footnote 26 with different effects on domestic integration, which is also an important factor in determining economic growth. Further studies are needed to examine the relationship between domestic and global fragmentation and diverse patterns of structural integration that could also help in assessing the impact of unpredictable circumstances (e.g. COVID 19) on individual countries, regions or sectors.

5 Conclusion

We have proposed a new methodology for measuring the participation shares of different types of value chains in the international input–output framework. We addressed the lack of a consistent unitary measure of value chain integration on the country-sector level by proposing a new concept of the value chain tree for each country-sector, covering all value chain paths from value creation (downstream linkages) through a single country-sector to final consumption (upstream linkages) simultaneously. By capturing the structure of all value chains in a series of value chain tree matrices, we add a new mathematical object that serves as a basis for deriving the proposed new indicator of value chain participation, which we contribute to the existing collection of indicators.

This methodology allows us to introduce an extended typology of value chains by distinguishing and disaggregating all production activity into the following types: no value chain, domestic value chain, and global value chain—further differentiated into simple and complex global value chains. The most important new conceptual subdivision in the extended typology relates to the subdivision of the existing ’domestic component’ into a no value chain and a domestic value chain. This subdivision, which is only possible with the proposed methodology, provides a better representation of domestic production interdependencies and permits comparative analyses of the simultaneous development of domestic and foreign production interdependencies, thereby enabling aggregated analyses of domestic and global production fragmentation and its interrelated development as influenced by outsourcing or offshoring. Another big change introduced by the new typology is its fundamental production-related character: all distinctions between different types of value chains are made only with regard to (potential) production fragmentation, with a separate examination of the transaction to the final consumer—which may or may not be cross-border. This affirms the concept of value chain as related primarily to the fragmentation of production, while the post festum differentiation is also derived based on the last transaction to the final consumer.

The proposed methodology and typology of value chains provides researchers with new opportunities to conduct future research on different levels of disaggregation, be it comparative geographical analysis (e.g. comparing the evolution of value chain measures between two countries or between groups of countries) or observing the evolution of value chains in different sectoral disaggregations. The preliminary illustration of the new methodology, which attempts to link both domestic and global production fragmentation with long-term growth rates, shows a positive correlation between both global and domestic production fragmentation with economic growth. This result may indicate that it is the general complexity of the division of labour, reflected in the general fragmentation of production, that is chiefly correlated with growth, irrespective of its global or domestic nature. Accordingly, the proposed measure and the new typology of value chains, in particular the novel conceptualisation of domestic value chain fragmentation, could bring to light important information that has been concealed in the existing typology, which conceptualises the domestic component only as a negation of the global value chain and thus did not allow research with explicit questions concerning domestic integration. The complex development of globalisation in recent decades and the shifts of late towards the localisation and regionalisation of economic integration caused by political, economic and external factors make this new approach increasingly relevant. The proposed measure, particularly in conjunction with data from other sources, could further deepen the theoretical discussion and empirical investigations.

In conclusion, we believe that our new methodological approach and the new extended typology of value chains associated with it provide fertile grounds for obtaining deeper insights into different types of value chains as well as a broader set of tools of use for various extensions of research.

Availability of data and materials

The datasets analysed during the current study are available at http://www.wiod.org .

The term global commodity chain is a predecessor of global value chain.

Embracing a historical and macroeconomic approach to the analysis of the global division of labour, the world-systems approach examines the unequal patterns of exchange along global commodity chains as well as different structural patterns of the international integration of the core, periphery and semi-periphery (Arrighi and Drangel 1986 ).

Governance was conceived as either consumer-driven (apparel sector) or producer-driven (automotive sector). This approach was further extended by Ponte and Sturgeon ( 2013 ).

Porter’s (1985) concept of the intra-firm value chain is often used to discuss the specialisation of enterprises, and core competencies and business literature on multinational enterprises overlap with the global value chain framework.

Which was used to extend the producer-driven and consumer-driven governance typology of commodity chain research to a more general typology of value chain linkages, from transactions in a completely free market to a strict hierarchy (Gereffi et al. 2005 ).

In international economics, use of the input–output methodology grew in importance as researchers of various international incentives integrated nationally based input–output tables into harmonised global input–output tables. The most prominent are the World Input–Output Database (Timmer et al. 2015 ), the OECD’s Trade in Value Added and the EORA (Lenzen et al. 2013 ).

While all heterogeneous approaches to value chains focus on a development issue, the recent GVC approach has been adopted by international institutions to highlight the gains from liberalisation and industrial upgrading, while the world-systems approach critically examines unequal rewards along the value chain and different structural integration patterns that may cause the perpetuation of unequal development (Gereffi 2018 ; Taglioni and Winkler 2016 ).

Relative position indices can easily be derived from length measures as simple ratios.

Using a method similar to that used to calculate the average propagation length required for the analysis of the dynamic response to shocks, defined by Dietzenbacher and Romero ( 2016 ).

It is also obvious that a simple solution, such as using the average of existing upstream and downstream indicators, cannot be justified in theory. If, for example, a given country-sector’s share in the upstream global value chain is high (close to 100%) and its share in the downstream global value chain is relatively low (close to 0%), then the average share in the value chain would be around 50%, which is misleading because the value chain as a whole is almost entirely global (using the criterion that the value crosses a border at least once). As far as value chain paths are concerned, despite the small share of downstream global value chain paths, a high share (close to 100%) of the same paths continues in the upstream global value chain such that production as a whole has a very high global share (close to 100%), while the use of the average of the upstream and downstream indicators does not correspond to the definition of the global value chain.

For example, in a forthcoming article we explore the decomposition of value chains based on the criterion of the number of domestic transactions subject to meeting the usual global value chain criterion of having at least one production-sharing cross-border transaction. In this setting, we decompose the global value chain share into a GVC with no domestic cooperation, a GVC with simple domestic cooperation, and a GVC with complex domestic cooperation, offering information on the specific pattern of the EU periphery’s integration.

For example, the concept of integrated periphery was introduced to describe a specific type of integration in the case of the Slovak and Czech car industries, characterised by their proximity to consumer markets, cheaper labour force, the absence of positive spillover effects and lack of domestic linkages (Oldřich and Vladan 2019 ; Pavlínek 2018 ).

In our derivation, which is consistent with most existing international I–O data, the country-sector is the smallest object of analysis. When we refer to our methodology and derive it, the reference to the country-sector refers to the smallest object of analysis given by the level of detail of the I–O data set. If the I–O data sets were built on a more detailed structure at the enterprise level (greatly increasing the dimension), the proposed methodology and measures would work in the same way, with the value chain still structured around the smallest possible unit—in this case the enterprise. Despite the starting point of analysis of value chain structure being the smallest units of analysis, the approach offers many different aggregation possibilities to capture the changing economic structure of production as a whole.

The vertical and horizontal fragmentation of production is often represented with metaphors of snakes (sequential value transfers from one firm to further stages in a linear sequence) and spiders (simultaneous value transfers from different firms to the same company) (Baldwin and Venables 2013 ).

Technically, that would require I–O matrices of a dimension as large as the number of all firms of all countries included in such an international I–O structure.

Formal addition of further n dimensions to the usual \(n \times n\) dimensions.

Definitions of all notations are given in Appendix A .

The simplification consists only of the notation. We retain all the complexity of the block-matrix structure of the international I–O data and remove only the large number of indices, which would make the equations much more difficult to read.

Our downstream output decomposition formally coincides with the output decomposition of the approach that integrates output decomposition with a demand-driven decomposition of exports (Arto et al. 2019 ).

\(CS_k\) represents an index for different country-sectors. \(a_{ CS _1CS_2}\) thus represents a single Leontief technical coefficient indicating that the value produced by \(CS_2\) requires a \(a_{ CS _1CS_2}\) share of \(CS_1\) input.

For example, Wang’s disaggregation into simple and complex GVCs uses the number of cross-border transactions, regardless of whether the value crossed a border for production or whether it is only an export to end users. Such a criterion mixes two conceptually different transactions, leading to unnecessary calculation complexity and the impossibility of further conceptual disaggregation. Existing definitions of the typology of value chains, like all such definitions, are constructed in a relatively arbitrary way. More important than strict adherence to the prevailing definitions is the clarity of the proposed revision and the presentation of the conceptual relationship of the new concepts with the old ones. Our proposal facilitates a more detailed decomposition that will allow researchers to construct an indicator better suited to their research questions. Since the revised typology is based on a more detailed decomposition compared to the currently prevailing typology, researchers can (by simply adding components of the revised decomposition) also replicate objects that correspond to existing studies.

Here we examine the path of production fragmentation, while the path to final consumption, which represents an additional transaction, is analysed in Sect. 3.2.5 .

Details of the disaggregation are given in Appendix B .

This is determined by the pure diagonal elements of the Leontief technical matrix A . Each \(a_{ii}\) represents the portion of the total product of the i th country-sector that requires the use of the intermediate product of the same country-sector in the production process, thereby covering cyclical transactions within a sector. These cyclical transactions are of course included in the decomposition of the domestic value chain and not the no value chain since cyclical transactions represent the fragmentation of a domestic value chain.

The Greek and Italian economies, which experienced the longest recession in the EU during the period, experienced this very pattern (general reduction of production fragmentation, chiefly a reduction of domestic production fragmentation and increased integration into global value chains).

A variety of institutional and structural economic positions brings a range of effects of global integration on the country level.

In the international I–O framework, F is usually disaggregated on the country level as well as in an additional dimension of final consumption (household, government and non-profit consumption, fixed capital formation and changes in inventories), which in our derivation is irrelevant and left out. Disaggregation by countries is relevant for enabling the separation of domestic final consumption and export.

The decimal numbers are truncated on the fourth digit.

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Acknowledgements

The authors thank the editor and all reviewers for their comments and suggestions that helped improve this article.

The authors of this article acknowledge the financial support received from the Slovenian Research Agency (research core funding No. P5-0177 and No. 52075).

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Appendix A: Notations

\(n_S\in \mathrm{I\!N}\) Number of sectors.

\(n_C\in \mathrm{I\!N}\) Number of countries.

\(n\in \mathrm{I\!N}\) ; \(n=n_S*n_C\) Number of country-sectors.

\({\mathbf {1}}\in \mathrm{I\!R}^n\) Vector of ones.

\(\vec {1}\in \mathrm{I\!R}^{n_C}\) vector of ones.

\(\vec {e_i} \in \mathrm{I\!R}^n\) ; \({e_i}_j=\delta _{ij}\) Standard orthonormal basis of \(\mathrm{I\!R}^n\) .

\(I \in \mathrm{I\!R}^{n\times n}\) Identity matrix.

\(x \in \mathrm{I\!R}^n\) Total output vector.

\({\hat{x}} \in \mathrm{I\!R}^{n\times n}\) ; \({\hat{x}}=diag(x)\) Total output matrix.

\(C \in \mathrm{I\!R}^{n\times n}\) Intermediate consumption matrix.

\(F\in \mathrm{I\!R}^{n\times n_C}\) Final consumption matrix on the country level. Footnote 27

\(f \in \mathrm{I\!R}^n\) ; \(f=F\vec {1}\) Total final consumption vector.

\({\hat{f}}\in \mathrm{I\!R}^{n\times n}\) ; \({\hat{f}}=diag(f)\) Total final consumption matrix.

\(A \in \mathrm{I\!R}^{n\times n}\) ; \(A =C{\hat{x}}^{-1}\) Leontief technical coefficient matrix.

\(G \in \mathrm{I\!R}^{n\times n}\) ; \(G ={\hat{x}}^{-1}C\) Ghosh technical coefficient matrix.

\(v \in \mathrm{I\!R}^n\) ; \(v^T= x^T-{\mathbf {1}}^TC={\mathbf {1}}({\hat{x}}-A{\hat{x}})={\mathbf {1}}^T(I-A){\hat{x}}\) Vector of total value added.

\({\hat{v}}\in \mathrm{I\!R}^{n\times n}\) ; \({\hat{v}}=diag(v)\) Total value-added matrix.

\(v_C \in \mathrm{I\!R}^n\) ; \(v_C^T= v^T{\hat{x}}^{-1}={\mathbf {1}}^T(I-A)\) Vector of value-added coefficients – value-added share in total output.

\({\hat{v}}_C\in \mathrm{I\!R}^{n\times n}\) ; \({\hat{v}}_C=diag(v_C)\) Value-added coefficients matrix.

C , A and G have a block-matrix structure \(\mathrm{I\!R}^{(n_S\times n_S)\times (n_C\times n_C) }\) , while F has a block vector structure \(\mathrm{I\!R}^{n_S \times (n_C\times n_C)}\) . Diagonal block elements with respect to countries represent domestic intermediate transfers and domestic consumption and off diagonal block elements represent transactions that cross a border either for intermediate use or final consumption.

\(C=C_{CB} + C_{D}\) \(A=A_{CB} + A_{D}\) \(G=G_{CB} + G_{D}\) \(F=F_{CB} + F_{D}\) \(f_{CB} \in \mathrm{I\!R}^n\) ; \(f_{CB}=F_{CB}\vec {1}\) Total final consumption by exporting.

\(f_{D} \in \mathrm{I\!R}^n\) ; \(f_{D}=F_{D}\vec {1}\) Total final consumption by domestic transactions.

\({\hat{f}}_{CB}\in \mathrm{I\!R}^{n\times n}\) ; \({\hat{f}}_{CB}=diag(f_{CB})\) Total final consumption by exporting matrix.

\({\hat{f}}_{D}\in \mathrm{I\!R}^{n\times n}\) ;

\({\hat{f}}_{D}=diag(f_{D})\) Total final consumption by domestic transactions matrix.

Appendix B: \(\tau _i\) decomposition

We make a demonstration of the methodology on a simple 2 sector 2 countries numerical example. Footnote 28 This simple case of international economy has following intermediate consumption matrix and final demand:

Total output is the sum of all the intermediate and final demand:

Calculation of value added coefficients and Leontief technical coefficients:

We continue with separate upstream and downstream decompositions, W and Z :

Value chain tree matrices are calculated for each country-sector in the following manner:

For each type of value chain (DVC, GVC, NVC,...) we have 4 matrices, each covering all the value chain paths of each country-sector (we have 4 in our example) that conform to our value chain criteria.

The value chain participation shares are obtained by summation of all elements of the value chain tree matrices:

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What Is Value Chain Analysis?

An example of value chain activities, how to use value chain analysis, who is michael porter, what is competitive advantage, what is a global value chain, the bottom line.

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Value Chain Analysis: What It Is and How to Use It

Charlene Rhinehart is a CPA , CFE, chair of an Illinois CPA Society committee, and has a degree in accounting and finance from DePaul University.

A value chain  represents all the activities and processes involved in creating a product or performing a service. As such, it encompasses every stage of the product's or service's lifecycle, from design to production and distribution.

In seeking higher profit margins or a competitive advantage , companies can conduct a value-chain analysis to identify each step (or link) in the chain and look for ways to improve it.

Key Takeaways

• A value chain represents every step that's involved in creating a product or delivering a service, from start to finish.
• A company's value chain can be divided into primary activities and secondary (or support) activities.
• Value chain analysis is the process of examining each of those activities in terms of what they cost, the value they deliver, and how they might be optimized in keeping with the company's competitive strategy. It also looks at how the various activities interrelate.
• Companies can compete on the basis of delivering a unique or superior product or one that is attractive primarily due to a lower price.

Michael E. Porter, a Harvard Business School professor, is credited with introducing the concept of a value chain in his 1985 book The Competitive Advantage: Creating and Sustaining Superior Performance.

"The value chain disaggregates a firm into its strategically relevant activities in order to understand the behavior of costs and the existing and potential sources of differentiation," he wrote. "A firm gains competitive advantage by performing these strategically important activities more cheaply or better than its competitors."

Value chain analysis is the process of identifying each of these activities, determining their costs and the value they deliver, and then looking for ways to optimize them in keeping with the company's overall strategy.

As the Harvard Business School puts it, this process "forces managers to consider and see each activity not just as a cost, but as a step that has to add some increment of value to the finished product or service."

Michael Porter's framework for value chain analysis groups activities into two broad categories: primary activities and secondary (or support) activities.

Consider an asset management firm . Its value chain might consist of the following kinds of activities:

Primary activities

• Investing: The investment team (portfolio managers, analysts) is tasked with making the investment decisions.
• Operations and trading:  The operations and trading teams are tasked with ensuring the investments are in line with the clients' goals and the trades are made at the best execution price.
• Marketing and sales:  Responsible for procuring additional clients for the firm.
• Service (client relationship management):  Responsible for providing all the touch points to current clients.

Secondary (or support) activities

• Technology:  To make the operation run smoothly,
• Human resources: To find and retain talent for the firm.
• Other infrastructure:  This includes the lawyers and risk managers who ensure that the firm is operating within the regulations established by the SEC.

That may be only a partial list. In Porter's words, " Everything a firm does should be captured in a primary or support activity."

In examining its value chain, a business needs to consider its value proposition , or what sets it apart from its competitors. Value chain analysis may be conducted with the goal of improving profits by creating a product or service that is so superior that customers are willing to pay more for it, or one that undercuts the competition by delivering a product or service of respectable quality at a lower price.

Improving a value chain simply for the sake of improvement should not be the end goal. Instead, a company should decide why it wants to improve its value chain in the context of its desired competitive advantage.

Two common competitive advantage strategies include low-cost provider and specialization/differentiation of product or service. 

• Low-cost provider. Here the value chain analysis will focus largely on costs and how a company can reduce those costs to give itself a competitive advantage in the marketplace.
• Specialization/differentiation.  In this strategy, the analysis will focus on the activities that create a unique product or differentiation in service, which may, in turn, allow it to charge a higher price.

Let's go back to our asset management example. If the firm wants to pursue a strategy of differentiation by delivering steady, top quartile returns on clients' investments, it will focus on the investment team, operations, and traders, along with the related support activities. If its goal is to differentiate itself through stellar service, it will focus its efforts on client relationship management.

Michael Porter is the Bishop William Lawrence University Professor at Harvard Business School and the director of the school's Institute for Strategy and Competitiveness. His 19 books include The Competitive Advantage: Creating and Sustaining Superior Performance and Competitive Strategy: Techniques for Analyzing Industries and Competitors .

Competitive advantage is what gives one company an edge over others in the marketplace. It can take the form of a comparative advantage , where the company is able to produce a good or service more efficiently than its competitors, allowing it to sell at a lower price and/or enjoy a higher profit margin; or it can be a differential advantage , where a company's product or service is perceived to differ from its competitors' in a way that is superior, allowing it to charge a higher price.

A global value chain (GVC) refers to a value chain in which the activities and processes involved in bringing a product to market occur in more than one country.

Value chain analysis can help companies identify ways to create and deliver products and services that, by virtue of their superior quality or lower cost, provide a competitive advantage in the marketplace. Conducting a value chain analysis can focus management on which activities add the most value, in keeping with the company's overall competitive strategy, and help drive future products and services. Another benefit is that the analysis can draw attention to support activities, which are sometimes overlooked in adding value.

Harvard Business School Online. " What Is a Value Chain Analysis? 3 Steps ."

Harvard Business School Institute for Strategy & Competitiveness. " The Value Chain ."

Harvard Business School, Faculty & Research. " Michael E. Porter ."

Organisation for Economic Co-operation and Development. " Global Value Chains. "

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Value chain analysis is the process of observing and evaluating each business activity involved in the creation of a finished product or service. The purpose of value chain analysis is to find areas of improvement within the value chain that will increase a company’s competitive advantage.

Harvard Business School Professor Michael Porter introduced the concept of a value chain in his 1985 book, Competitive Advantage: Creating and Sustaining Superior Performance .

He explains that value chains represent the activities a company performs to design, produce, market, deliver and support its products. These activities are narrower than traditional functions, such as marketing. He writes that a company’s value chain and the way it performs the activities within it are a “reflection of its history, its strategy, its approach to implementing its strategy and the underlying economics of the activities themselves.”

Core to his concept is the idea that value chain activities—from assembling products to training employees—create customer value and are the “basic units of competitive advantage.” 1 Therefore, maximizing the value for each activity is key to market success.

While often used interchangeably, supply chains and value chains are distinct, but interconnected, terms.

A supply chain is the network of suppliers instrumental to product creation—from the providers of raw materials to the organizations that deliver the final product to consumers. This is why supply chains are vital to the activities within a company’s value chain.

Optimized , high-performing supply chains allow value chains to function effectively, improving customer satisfaction and creating greater product value. For example, effective  supply chain management  will minimize cost, waste and time in the production cycle. On the other hand, efficient value chains that maximize value and improve a company’s competitive advantage enable supply chains that produce optimal products that meet customer needs and wants.

Learn about the processes used to manage environmental performance data and the steps required to account for greenhouse gas (GHG) emissions.

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According to Porter, the goal of value chain analysis should be improving the value chain to gain a competitive advantage—delivering the most value, which, in turn, increases profit margins. In his value chain framework, there are two main types of competitive advantage a company can pursue: cost leadership and differentiation.

Cost leadership advantage

Also called a cost advantage, this type of competitive advantage focuses on how to reduce costs by making activities in the value chain more efficient. The goal is to produce a product for a lower price than competitors, sell it at a lower price and still enjoy a higher profit margin. By doing so, companies can work to become the low-cost producers in their industry.

This goal can be accomplished through economies of scale, proprietary technology or preferred access to suppliers. It’s key to remember that products still need to be high quality and comparable to those offered by competitors. Walmart and McDonalds are examples of companies with a cost leadership competitive advantage.

Differentiation advantage

The second type of competitive advantage seeks product differentiation that is unique and so valued by customers that companies can charge a premium price. Success here hinges on the higher price exceeding the extra costs incurred while making a product stand out among competitors.

Differentiation varies and can be based on product features; the way products are sold or even how they are marketed. Starbucks and Apple are examples of companies that have gained a competitive advantage through differentiated, premium products.

Porter’s value chain model divides its activities into two broad categories: primary and support.

Primary activities are involved in the production process of a product, its sale to the customer and post-sale customer service. Primary activities are split into five generic categories:

• Inbound logistics activities are related to receiving raw materials and parts for products, such as warehousing, inventory management and supplier returns.
• Operations activities are associated with transforming raw materials and parts (inputs) into final products (outputs), such as machining, assembly, testing and facility operations.
• Outbound logistics activities center around collecting, storing and delivering products to customers, such as packaging, shipping logistics and order processing.
• Marketing and sales activities encourage and provide a way for customers to buy a product, such as advertising, promotions and pricing.
• Service activities are those that enhance or maintain product value after the sale, such as quality assurance, trainings and warranties.

Support activities, also called secondary activities, back the primary activities by making them more efficient. Support activities are split into four generic categories:

• Procurement activities are those involved in the purchasing of raw materials and parts used in the value chain, from product-specific inputs to office supplies.
• Technology development activities are those that upgrade and improve products and processes, such as research, product design and servicing procedures.
• Human resource management activities are those related to recruiting, hiring, training, developing and compensating employees.
• Firm infrastructure includes activities that are often considered overhead, such as general management, quality control and legal operations.

The importance of each category to a company’s competitive advantage varies by business type and industry. For example, inbound and outbound logistics may be more vital to a distributor than to a retailer, for whom outbound logistics may not be a significant consideration.

While there are many different templates and routes to completing a value chain analysis, these four steps tend to stay consistent:

Classify and understand your value chain activities

To make improvements to your value chain for a competitive edge, you need to gain a strong understanding of every relevant activity that goes into the creation of your product or service. This includes both primary and support activities. If your company has multiple products or services, then repeat this step until you have a clear picture of the activities for each one.

Define the value and cost drivers of each activity

Next, identify the value and cost drivers of each activity. For example, establish how each activity works to increase customer satisfaction with the product or service. Then, identify the costs involved. To identify the value of your products or services, try to understand your customers’ perception of value—such as by giving surveys.

Benchmark your value chain against your competitors’

In the game of competitive strategy, knowing how your peers are performing is critical. While competitors’ value chains are unlikely to be publicly available, you can get an idea of them through benchmarking. One way to do this is by comparing relevant processes, business models and performance metrics from the competition with your own.

Identify your opportunities to gain a competitive advantage

After you’ve identified your value chain activities, their values and their costs, you can move forward into analysis to determine where best to achieve a competitive advantage. To streamline value chain analysis, set a primary goal—such as lower costs. Then, analyze each activity with the goal of cost reduction.

While gaining a competitive advantage to increase customer value and profit margins is the overarching benefit of value chain analysis, there are plenty of other benefits that fall under that umbrella. For example, a strong understanding of each activity within your value chain makes it easier to identify opportunities for supporting environmental, social and governance (ESG) goals; increasing efficiencies; reducing waste and introducing automation.

Collect ESG data from third parties for value chain analysis within a single portal and assess ESG performance for a competitive advantage.

Achieve end-to-end visibility and control over the entire supply chain planning processes.

Apply the power of artificial intelligence (AI) and the speed of automation to improve supply chain management, resiliency and sustainability.

Learn more about the first blockchain food safety solution that's creating a more transparent and trustworthy global food supply chain.

Discover how to build your own blockchain-enabled collaboration and data-sharing ecosystem with your supply chain partners.

Find best practices for B2B Integration, managed file transfer, order management and blockchain solutions from users and experts.

Learn how supply chain optimization makes use of technology and resources to maximize efficiency and performance in a supply network.

Learn how supply chain management handles the entire production flow of a good or service—from raw components to final product delivery.

Learn how supply chain analytics can help you make sense of supply chain data—uncovering patterns and generating insights.

IBM Sterling Supply Chain Intelligence Suite is an AI-based optimization and automation solution. Designed for organizations struggling to solve supply chain disruptions through traditional transformation, the suite facilitates supply network resiliency and sustainability, increases agility and accelerates time-to-value through actionable insights, smarter workflows and intelligent automation.

1   “ Competitive Advantage: Creating and Sustaining Superior Performance ”  (link resides outside ibm.com), Michael E. Porter, NY: Free Press, 1985

Value Chain Analysis

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Analysis of value chain ; Supply chain analysis ; VCA

Definition/Description

Value chains are an essential element for sustainable development, not only in economic terms but also in the environmental, social, and cultural dimensions. Despite its interest, the analysis of value chains encounters various barriers, among which the difficulty in obtaining the necessary information stands out. Value chains can become complex and dynamic systems, with different dimensions (company, sector, industry, country, global), and diverse forms, that interact with each other. That is why, in the same way, there is no single methodology or a better methodology for the generality of the cases. There are multiple value chain analysis (VCA) methods, designed and applied mainly according to the approach from which it is studied (firm, industry, country, or global) and the information and time available. VCA in practice should use or combine different methods or types of VCA, which provide different...

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The Value Chain

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Developed by Michael Porter and used throughout the world for nearly 30 years, the value chain is a powerful tool for disaggregating a company into its strategically relevant activities in order to focus on the sources of competitive advantage, that is, the specific activities that result in higher prices or lower costs.

A company’s value chain is typically part of a larger value system that includes companies either upstream (suppliers) or downstream (distribution channels), or both. This perspective about how value is created forces managers to consider and see each activity not just as a cost, but as a step that has to add some increment of value to the finished product or service.

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3.2. Value Chain Analysis

Introduction.

Value chain analysis is a process that requires four interconnected steps: data collection and research, value chain mapping, analysis of opportunities and constraints, and vetting of findings with stakeholders and recommendations for future actions. These four steps are not necessarily sequential and can be carried out simultaneously.

The figure to the right is a simple graphic illustrating the analysis process and components. The value chain team collects data and information through secondary and primary sources by way of research and interviews. Mapping helps to organize the data, and highlights the market segments, participant/actors, their functions and linkages. The collected data is analyzed using the value chain framework to reveal constraints within the chain that prevent or limit the exploitation of end market opportunities. The resulting analysis of opportunities and constraints should be vetted with stakeholders through events such as workshops, focus groups or “reporting-out” days.

Each of these components are described in further detail below.

Prior to performing value chain analysis, some preparation is required.

Step One: Data Collection

Good value chain analysis begins with good data collection, from the initial desk research to the targeted interviews . The value chain framework—that is, the structural and dynamic factors affecting the chain—provides an effective way to organize the data, prioritize opportunities and plan interventions. To review the framework and its role in value chain analysis, click here .

The desk research consists of a rapid examination of readily available material. The aim is to familiarize the team with the industry, its market and the business environment in which it operates, as well as to identify sources for additional information. Information such as statistics on exports/imports, consumption reports, global trade figures, etc., can be obtained through the Internet, phone calls and documents from trade, commerce and industry ministries, specialized industry journals, and professional and trade association newsletters. Once the desk research is conducted, an initial value chain map can be drafted for refinement during the primary research phase.

Interviews are conducted with 1) firms and individuals from all functional levels of the chain, and 2) individuals outside the value chain such as writers, journalists or economists. In addition to providing information about the movement of product and the distribution of benefits, the interviews should inform on value chain actors’ current capacity to learn; how information is exchanged among participants; from where they learn about new production techniques, new markets and market trends; gender dynamics that affect value chain performance; and the extent of trust that exists among actors. Interviews can help to identify where chain participants see opportunities for and constraints to upgrading. Missing or inadequate provision of services necessary to move the value chain to the next level of competitiveness can be identified locally, regionally or nationally.

In addition to individual interviews, focus group discussions are a useful way to explore concepts, generate ideas, determine differences in opinion between stakeholder groups and triangulate with other data collection methods. The group may consist of 7-10 people who perform the same or a similar function in the value chain. Guided discussion better captures the social interaction and spontaneous thought processes that inform decision making, which is often lost in structured interviews. Please see the Guide to Focus Group DiscussionsGuide to Focus Group Discussions (https://www.marketlinks.org/resources/microreport-138-guide-focus-group-discussions) for more information.

Click here to see a summary of the advantages and disadvantages of four primary research tools (interviews, focus groups, surveys and observation). Other useful resources on techniques for data collection include the following:

• The SEEP Network's Technical Note, "An Inventory of BDS Market Assessment Methods for Programs Targeting Microenterprises"SEEP Network's Technical Note on market assessment methods (http://www.bdsknowledge.org/dyn/bds/docs/446/Inventory%20of%20Methods%20Final%20PDF.pdf)
• The SEEP Network's "Building a Team for BDS Market Assessment and Key Issues to Consider When Starting BDS Market Assessment"SEEP Network (https://seepnetwork.org/Blog-Post/Building-a-Team-for-Market-Assessment-and-Key-Issues-to-Consider-When-Starting-a-Market-Assessment) 
• The ILO's "Guide to Market Assessment for BDS Program Design"http://www.bdsknowledge.org/dyn/bds/docs/377/Guide%20to%20BDS%20MA%20for%20Program%20Design%20Miehlbradt.pdf

The qualitative data gathered by these methods will reveal dynamic factors of the value chain such as trends, incentives and relationships. To complement this, quantitative analysis of the chain is necessary to provide a picture of the current situation in terms of the distribution of value-added, profitability, productivity, production capacity and benchmarking against competitors. Analyzing these factors highlights inefficiencies and areas for reducing cost.

Step Two: Value Chain Mapping

Value chain mapping is the process of developing a visual depiction of the basic structure of the value chain. A value chain map illustrates the way the product flows from raw material to end markets and presents how the industry functions. It is a compressed visual diagram of the data collected at different stages of the value chain analysis and supports the narrative description of the chain.

The purpose of a visual tool in the analysis process is to develop a shared understanding among value chain stakeholders of the current situation of the industry. The mapping exercise provides an opportunity for multi-stakeholder discussions to reveal opportunities and bottlenecks to be addressed in subsequent stages of the project cycle . Maps also help to identify information gaps that require further research.

A two-phased process for developing the value chain map is recommended, as follows: a) initial basic mapping and b) adjusted mapping. Initial mapping is based on the information derived from desk research and knowledge at the outset of the analysis. The second phase includes revisions based on interviews and feedback from firms and individuals brought into the analysis process. As value chain maps are representations of a complex system, the analysis must balance the need to generalize with the desire to charge the map with details. Mapping is a dynamic process; therefore, adjustments should be made as needed.

Step Three: Analysis of Opportunities and Constraints Using the Value Chain Framework

Step three uses the value chain framework as a lens through which the gathered data is analyzed. The framework is a useful tool to identify systemic chain-level issues rather than focus on firm-level problems. While interviews give the value chain team the chance to gather information from individual firms, the value chain framework helps to organize this information in such a way that the analysis moves from a firm-level to a chain-level perspective. If the chain cannot be competitive, the success of individual firms is compromised. Therefore, taking a systemic approach is key to sustaining the competitiveness of the chain and the MSEs operating within it.

The factors affecting performance of the chain are further analyzed to characterize opportunities and constraints to competitiveness. These factors are:

• end markets
• business enabling environment
• vertical linkages
• horizontal linkages
• supporting markets
• value chain governance
• inter-firm relationships

Each plays a role in influencing value chain competitiveness. Using a table format, these factors of the value chain framework can be evaluated in terms of offering opportunities for upgrading and the constraints to taking advantage of these opportunities. Click here for further discussion on the factors and how to analyze them.

Step Four: Vetting Findings of Chain Analysis through Stakeholder Workshops

Value chain analysis helps develop a private-sector vision to reflect stakeholders’ interest in improving the efficiency and competitiveness of the chain. The fourth step, vetting findings, uses value chain analysis through a structured event (or series of events) like a workshop or reporting-out day to facilitate discussion with and among selected participants.Participatory Approaches to Value Chain Development Briefing Paper (https://www.marketlinks.org/resources/participatory-approaches-value-chain-development-briefing-paper)

The objective of these events is to bring participants together who are responsible for critical market functions, service provision, and the legal, regulatory and policy environment. The goal is to have these participants—who have an incentive to drive investments in upgrading—to develop and assist in implementing a private sector-led competitiveness strategy . To develop this strategy, the stakeholders will need to prioritize the opportunities and constraints identified during the value chain analysis. With an open format, such structured events foster buy-in to the analysis process.

Participants are selected based on the role they play in the value chain, or their responsibility for critical market functions. There should also be MSE, medium and larger firm and association representatives who, during the interview phase, exhibited an understanding of the issues related to the value chain (especially the opportunities), a strong interest in the types of questions posed during the interview, and leadership skills among peers or the community.

Vetting events can take on several forms from simple one day reporting-out sessions to more structured workshops that stretch to two or three days. The events are planned to reinforce the importance of knowing and understanding the end market. In presenting the findings of the value chain analysis, workshop leaders should stress that to remain competitive, stakeholders and other participants must continuously learn what end markets demand in terms of product specifications, quality, and other requirements.

It can be powerful to have a series of buyers present at the workshop. Where not possible, a phone call or pre-recorded video interview can be an effective means for stakeholders to see and hear directly from the buyer. Pre-recorded interviews were used successfully for the Haiti handicraft value chain vetting workshopHandmade in Haiti: The Perspectives of Global Buyers (https://www.marketlinks.org/library/handmade-haiti-perspective-global-buyers) and for the Tanzania high-value vegetables stakeholder workshop.Tanzania High-value Vegetables Value Chain Stakeholder Workshop (http://www.tzdpg.or.tz/fileadmin/_migrated/content_uploads/Vegetables_-Executive_Summary_FFV_Studies_pdf_1_.pdf)

The event should include facilitated discussions, review and adjustments of value chain map and a review of the analysis table mentioned above. For this exercise, it is recommended that the completed table be projected on a screen, and additions and modifications made during discussions inserted with the computer projecting the table. This assures a participatory process and on-the-spot adjustment witnessed by attending participants. If changes are made, the updated table can be rapidly printed and distributed to participants before they leave.

In environments characterized by a number of donor partners working with the same group of firms, burn-out and skepticism particularly among the most important change drivers is likely. In some instances, the firms most important to driving change may not attend a full-day workshop even though they may be highly committed to the upgrading process and strategy for making the industry more competitive. If time allows, the analysis team can meet with these firms in advance of the workshop to convince them of the value of the competitive planning process. If this is not possible, the analysis team should meet with these firms as soon after the workshop as possible to vet findings and secure buy-in or commitment to the industry competitiveness planning process.

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Comprehensive Guide to Value Chain Analysis with Examples by Industry

By Kate Eby | April 11, 2017 (updated January 21, 2023)

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The competitive environment for organizations of all shapes and sizes - and in all industry verticals - is more challenging than ever before. Technological advancements have enabled businesses to design and build more quickly, sell across multiple channels, react instantly to changing demands, and cut costs simply by outsourcing an activity. To achieve competitive advantage, an organization ultimately delivers more value at an equal or lower cost. Value chain analysis is the method for determining the critical path to enhance customer value while reducing costs. 

Since the mid-1980s, Michael Porter’s value chain analysis (i.e., his original five forces value chain model) has been a useful tool for numerous companies to develop and sustain breakthrough competitive advantages. Today, there is some concern that as new technology changes how a society does business and how a consumer relates to goods and services, Porter’s 30-year-old conception of value chain analysis will not remain relevant and useful.

A value chain refers to the activities that take place within a company in order to deliver a valuable product to market. The value chain system was first described in Tableau Economique , written in the 18th century by the French economist Francois Quesnay. Many experts since have expanded on this source, as well as Porter’s massively influential model, including Robert M. Grant (contemporary strategy analysis) and Wassily Leontief (the input-output model).  However, the value chain analysis pioneered and illustrated by Michael Porter in his groundbreaking book, Competitive Advantage , remains an indispensable methodology. Having evolved and adapted over the years, companies and industry specialists continue to successfully implement Porter’s value chain analysis. The practice is now also a vital part of societal global initiatives. According to Angel Gurria, OECD Secretary-General, “International trade and investment have undergone accelerated changes with the emergence of global value chains.” A global value chain involves the coordination of activities, people, and processes across geographies. For more information, see The Emergence of Global Value Chains: What Do They Mean for Business ?

In the following discussion, you’ll discover everything you need to know about value chain analysis, including generous examples by industry.

What Is Value Chain Analysis? Porter’s Concept of Value Chain Analysis

Value chain analysis focuses on analyzing the internal activities of a business in an effort to understand costs, locate the activities that add the most value, and differentiate from the competition. To develop an analysis, Porter's model outlines primary business functions as the basic areas and activities of inbound logistics, operations, outbound logistics, marketing and sales, and service. The model also identifies the discrete tasks found in the important support activities of firm infrastructure, human resources management, technology, and procurement. 

The overall goal of value chain analysis it to identify areas and activities that will benefit from change in order to improve profitability and efficiency. For more on Porter’s value chain model and a detailed description of the goals, functions, and tasks of a value chain, read The Art of Value Chain Analysis . Here you will learn about Porter’s primary and supporting value chain activities and how to apply value chain analysis to your business.

What are the Cost Drivers of Value Chain Analysis?

Cost advantage results from a reduction in costs associated with activities in a value chain. After the value chain has been defined, it’s important to associate costs to the activities and then make adjustments for efficiency. Porter’s 10 cost drivers are factors that can impact the cost of an activity. An organization can aim to control these cost drivers in order to improve efficiency, add value, and differentiate.

• Economies of scale
• Learning and spillovers
• Pattern of capacity utilization
• Interrelationships
• Integration
• Organization policies
• Institutional factors

Value chain analysis is more than a straightforward cost-to-profit model. It expands on the principles of economies of scale and capacity. There are limits to lowering costs and increasing capacity that can inhibit business growth. Value chain analysis stresses that competitive differentiation can also focus on the perceived value to the customer that justifies a product's price tag. Finding these perceived values could mean the difference between getting a consumer to spend three dollars on a cup of Starbuck's coffee rather than one dollar on a competitor's discount brand. 

Who Uses Value Chain Analysis?

The management and analysis of value chains are becoming both industry specific and increasingly global, taking into account fast-changing markets, adjustments necessitated by new technologies, delivery methods, trade and government involvement, and fast-paced and fickle consumer demands. Add to that the global value chain's emphasis on sustainability, as well as its goal to expand the economic prospects of the world's poorest nations by fostering partnerships (especially in the agri-business sector). An example of this is discussed in the December 2009 briefing paper, Upgrading Along Value Chains: Strategies for Poverty Reduction in Latin America by Jonathan Mitchell, Christopher Coles, and Jodie Keane. In both micro and macro-change management strategies, business leaders continue to successfully implement Porter’s deceptively simple value chain framework.

The reason for this continued success is that Porter's framework is, first and foremost, a general model. It is not meant to be a standalone, rigid framework that creates barriers between functions or gives equal weight to every task that brings a product or service to market. Various departments, including human resources, marketing, sales, and operations utilize value chain analysis. Similarly, a wide variety of industries such as enterprise, manufacturing, retail, service, and technology, in addition to governments and their agencies, successfully adapt the basic value chain concept, and understand that not all functions or activities need to receive the same level of scrutiny. 

For example, the Department of Defense (DOD) has a design-chain operations reference (DCOR) that cites little need to spend time or resources analyzing marketing and sales activities in their overall value chain. Although this is probably an accurate and reasonable evaluation for the DOD’s purposes, it’s one that few other enterprises are likely to echo. 

Therefore, the first order of any value chain strategy is to identify the important tasks and functions necessary to deliver your product or service. Once you identify value activities, you can then focus analysis on where you can add value and discover areas for optimization, differentiation, or cost efficiency. When you complete these aspects of analysis, you’re ready to put together a plan for changes. 

Examples of Value Chain Analysis by Industry

For now and into the near future, value chains are a useful management strategy for many different industries. However, as industries become more global, more cooperative, and more socially aware, they’ve come to perceive value chains differently based on their specific needs. Companies like FedEx see the future as a circular chain that values renewability. The World Bank, the United Nations Conference on Trade Development, and the International Crops Institute for the Semi-Arid Tropics all use global value chains to foster international cooperation to assist the world's poorest countries. 

Companies that depend on global resources are developing initiatives to support global value chains by working with governments, United Nations partners, and economic aid organizations. In fact, in December of 2015, twenty value chain experts from various organizations, including OECD, FAP, ILO, UNIDO, WFP, WTO, ACID/VOCA, and GIZ, gathered for the “Inclusive and Sustainable Value Chain Development” meeting in Vienna, Austria, to discuss inclusive and sustainable agriculture value chains. 

As one of the biggest purchasers of cocoa in the world, Nestlé has developed the "Every Woman, Every Child Initiative." To improve company value, they have committed to providing expertise, sustainable solutions, and social improvements, especially in the area of child labor. A number of companies create partnerships to provide opportunities for overseas development assistance through the development of agri-food value chains, such as those in the macadamia industry in Kenya, the sweet sorghum by-products in India, and the seed nut harvests in Uganda. These initiatives advocate a greatly expanded view of the value chain called collaborative value networks. 

Additional examples include:

• Food and Beverage: Selecting and sourcing high-quality coffee beans, developing loyalty through excellent customer service, and aggressively marketing their brand were key elements in Starbucks’ creation of a unique identity and a robust competitive edge. Rather than focusing on premium pricing, Pizza Hut outpaced the competition by offering fast delivery of a less expensive product. 
• Delivery Service: To increase market share and brand loyalty, FedEx's value chain emphasizes and invests in employee development through excellent human resources initiatives and infrastructure improvements. 
• Retail: Walmart is constantly performing value chain analysis in order to keep costs low for their customers. From regularly evaluating suppliers and integrating in-store and online shopping experiences to remaining innovative in order to differentiate, Walmart is driven by their commitment to helping people save money.

Implementing and Using Value Chain Analysis

Porter’s generic strategies — so named because they can be administered to products or services in all industries — act as a starting point, not an absolute, step-by-step guide. However, Porter’s generic model identifies three general steps in value chain analysis: the initial evaluation of tasks, the location of areas of cross-functionality, and the discovery of dynamic areas of opportunity. For extensive strategies and actionable guidance to support or begin a process of value chain management, consult Michael Porter's book, Competitive Advantage . 

Additionally, to help manage and fulfill the strategies of Porter’s model, there are numerous templates, articles, online courses, and other roadmaps available to develop goals, strategies, and methods of value chain analysis. Many present industry-specific insight, models, and assistance.   For example, approaches that focus on discovering cost advantages and disadvantages include:

• Identifying primary and supporting activities
• Rating the importance of each activity in providing value to the product or service
• Identifying the cost drivers that cause a change in the activity cost
• Identifying linkages and dependencies
• Identifying cost reduction and value improvement opportunities

Approaches with a focus on finding differentiation include:

• Identifying activities that create value for your customers
• Identifying differentiation activities that improve customer value
• Identifying the best opportunity for differentiation

Value chain analysis as a tool also concentrates on finding activity links or, as Porter called them, bridges between both the primary and secondary functions of a department, business unit, or enterprise. Although the model is clear in defining general, discrete functions, there are numerous areas of interactions and cross-functionality that can identify cost opportunities, areas of greater efficiencies, and methods to distinguish a brand. 

Factors that can influence the value you provide include finding and utilizing the right people, motivating the team, remaining relevant, incorporating technology, and listening to customer feedback.

When analyzing the value chain, it is important to include many stakeholders, and to study the entire market to find areas for competitive opportunities. It is also vital to provide clarity and information, and to define goals. There are thousands of activities varying in importance in the primary and supporting areas of the chain, and opportunities are discovered through cooperative research and analysis, brainstorming, surveying, and observation.

Advantages of Value Chain Analysis

The advantages of value chain analysis can be seen by breaking product and service activities into smaller pieces in an effort to fully understand the associated costs and areas of differentiation. With value chain analysis, you can easily identify those activities where you can quickly reduce cost, optimize effort, eliminate waste, and increase profitability.

Analyzing activities also gives insights into elements that bring greater value to the end user. Some of the resulting activities may be as simple as negotiating with suppliers on raw material cost, focusing on end-user experiences that are enhanced by new communication or customer service experiences, and identifying activities that are better served by outsourcing — those that are not a core competency, result in process improvements, or are less expensive when performed by external suppliers. It is common practice for organizations of all sizes and in all industry verticals to outsource to strategic partners.

A company may choose to design a product or service, but use an outsourced provider to build or manufacture the product. When deciding to outsource, it’s important to consider whether the end customer will have a concern with the company outsourcing the specific activity, whether outsourcing impacts delivery time, and of course, the associated costs. In addition to negotiations, creating a better experience, and finding opportunities to outsource, analysis may also advocate the need for greater or more expensive resources that increase product value, develop loyalty, or create differentiation from your competition. 

Disadvantages of Value Chain Analysis

Value chain analysis is no simple feat. Some of the difficulties involve gathering data (which can be labor and time-intensive), identifying the tasks or functions that can add perceived or real value, and developing and deploying the plan. Additionally, it is not always easy to find appropriate information in order to break your value chain down into primary and supporting activities. 

Value Chain Analysis Tips

When implementing value chain analysis, questions of how to identify the relevant tasks, pinpoint areas for cost versus benefit, and locate the best strategies can be arduous for an in-house committee or a project manager. Here are some tips to overcoming these challenges:

• Involve your team in identifying and analyzing activities. Your results will be more thorough than if you attempt to identify them on your own.
• Obtain customer feedback on your results from a trustworthy, well-known customer.
• Decide whether you are trying to reduce costs or differentiate.

Dawn Roberts , a business consultant focused on streamlining processes and making value-based improvements, provides the following advice on how to improve the efficiency of value chains: “Businesses tend to dig into efficiency matters only when market pressures force them. When organizations are experiencing high profits, inefficiencies typically soar. I work with businesses to make their value chains more efficient – freeing up employee time, reducing stressful firefighting, and increasing profitability.” 

Ms. Roberts shares the following pointers on how to make value chains more efficient:

• “Minimize any and all waiting time. Don’t look at your timeline linearly, i.e., one task at a time. Instead, identify tasks you can start simultaneously based on dependencies. This can really help optimize your schedule.
• Standardize whenever possible. Is there a repeatable task that your company duplicates in numerous projects or jobs? If so, find the most optimal approach for that task and standardize it. This will increase efficiency substantially.
• Allow for wiggle room. If you plan for too tight of a timeline, you’ll run into problems. Build in time that allows you to react to uncontrollable interruptions; that is, make a contingency plan. If you don’t need it, great. If you do, it’s better to have planned for unforeseen delays.
• Communicate your timeline to stakeholders. I recommend clearly organized Gantt charts . Communicating these with clients and managers demonstrates organization and dependability. Update your timeline with the progress you’ve made as you move forward. This will help you build trust."

Today’s Impact on Value Chain Analysis

Many companies manage both physical and virtual value chains. The differences in approaches are best illustrated by comparing the consumer experiences of utilizing the services of a brick and mortar bank and those of online banking. Customer service experiences, infrastructure and technology needs, and personnel and training are a few of the many factors to consider when analyzing and adapting these value chains.

In reimagining the basic value chain, you should also study rapid technological advances. The Consumer Goods Forum Board findings recognize that the consumer is now more involved in product development. And, as society onboards new and unknown technologies, value chains need to be continuously reengineered. Societal questions about increased urbanization, the rapid rise of online shopping, and the changing expectations of the consumer are all considerations as valuable as the cost of raw materials, warehousing, and the delivery of any product or service. As companies adapt the basic value chain to the 21st century, many look at this methodology as a journey of transformation rather than a destination. As such, value chain analysis will continue to be a relevant and useful tool to develop and maintain a sustainable, competitive advantage.

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Value Chain Analysis

Value chain analysis (VCA) is a process where a firm identifies its primary and support activities that add value to its final product and then analyzes these activities to reduce costs or increase differentiation.

Value chain represents the internal activities a firm engages in when transforming inputs into outputs.

What is Value Chain Analysis

Value chain analysis is a strategy tool used to analyze internal firm activities. Its goal is to recognize which activities are the most valuable (i.e., are the source of cost or differentiation advantage) to the firm and which ones could be improved to provide competitive advantage .

In other words, by looking into internal activities, the analysis reveals where a firm’s competitive advantages or disadvantages are. The firm that competes through differentiation advantage will try to perform its activities better than competitors would do.

If it competes through cost advantage, it will try to perform internal activities at lower costs than competitors would do. When a company is capable of producing goods at lower costs than the market price or to provide superior products, it earns profits.

M. Porter introduced the generic value chain model in 1985. Value chain represents all the internal activities a firm engages in to produce goods and services. VC is formed of primary activities that add value to the final product directly and support activities that add value indirectly.

Although primary activities add value directly to the production process, they are not necessarily more important than support activities. Nowadays, competitive advantage mainly derives from technological improvements or innovations in business models or processes. Therefore, such support activities as ‘information systems’, ‘R&D’ or ‘general management’ are usually the most important source of differentiation advantage.

On the other hand, primary activities are usually the source of cost advantage, where costs can be easily identified for each activity and properly managed.

A firm’s VC is a part of a larger industry’s VC. The more activities a company undertakes compared to the industry’s VC, the more vertically integrated it is. Below, you can find an industry’s value chain and its relation to a firm-level VC.

Using the tool

There are two different approaches on how to perform the analysis, which depend on what type of competitive advantage a company wants to create (cost or differentiation advantage). The table below lists all the steps needed to achieve cost or differentiation advantage using VCA.

Competitive advantage types

Cost advantage

To gain a cost advantage, a firm has to go through 5 analysis steps:

Step 1. Identify the firm’s primary and support activities. All the activities (from receiving and storing materials to marketing, selling and after-sales support) that are undertaken to produce goods or services have to be clearly identified and separated from each other. This requires an adequate knowledge of the company’s operations because value chain activities are not organized in the same way as the company itself. The managers who identify value chain activities have to look into how work is done to deliver customer value.

Step 2. Establish the relative importance of each activity in the total cost of the product. The total costs of producing a product or service must be broken down and assigned to each activity. Activity-based costing is used to calculate costs for each process. Activities that are the major sources of cost or done inefficiently (when benchmarked against competitors) must be addressed first.

Step 3. Identify cost drivers for each activity. Only by understanding what factors drive the costs can managers focus on improving them. Costs for labor-intensive activities will be driven by work hours, work speed, wage rate, etc. Different activities will have different cost drivers.

Step 4. Identify links between activities. Reduction of costs in one activity may lead to further cost reductions in subsequent activities. For example, fewer components in the product design may lead to fewer faulty parts and lower service costs. Therefore identifying the links between activities will lead to a better understanding of how cost improvements would affect the whole value chain. Sometimes, cost reductions in one activity lead to higher costs for other activities.

Step 5. Identify opportunities for reducing costs. When the company knows its inefficient activities and cost drivers, it can plan on how to improve them. Too high wage rates can be dealt with by increasing production speed, outsourcing jobs to low-wage countries or installing more automated processes.

Differentiation advantage

VCA is done differently when a firm competes on differentiation rather than costs. This is because the source of differentiation advantage comes from creating superior products, adding more features and satisfying varying customer needs, which results in higher cost structure.

Step 1. Identify the customers’ value-creating activities. After identifying all value chain activities, managers have to focus on those activities that contribute the most to creating customer value. For example, Apple products’ success mainly comes not from great product features (other companies have high-quality offerings, too) but from successful marketing activities.

Step 2. Evaluate the differentiation strategies for improving customer value. Managers can use the following strategies to increase product differentiation and customer value:

• Add more product features;
• Focus on customer service and responsiveness;
• Increase customization;
• Offer complementary products.

Step 3. Identify the best sustainable differentiation. Usually, superior differentiation and customer value will be the result of many interrelated activities and strategies used. The best combination of them should be used to pursue sustainable differentiation advantage.

Value Chain Analysis Example

This example is partially adopted from R. M. Grant’s book ‘Contemporary Strategy Analysis’ p.241. It illustrates the basic VCA for an automobile manufacturing company that competes on cost advantage. This analysis doesn’t include support activities that are essential to any firm’s value chain; thus the analysis itself is not complete.

• Grant, R.M. (2010). Contemporary Strategy Analysis. 7th ed. John Wiley & Sons, p. 239-241
• Wikipedia (2013). Value Chain. Available at: https://en.wikipedia.org/wiki/Value_chain
• NetMBA (2010). Value Chain.
• Value Stream Mapping (VSM)
• PEST & PESTEL Analysis
• SWOT Analysis - How to Do It Properly
• SWOT analysis of Amazon 2023

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Value Chain Analysis: Explained with Examples

A value chain analysis is a strategic management tool used to examine the series of activities within a business or organization that create, deliver, and support a product or service. Michael Porter first introduced the concept in 1985 in his book, “Competitive Advantage: Creating and Sustaining Superior Performance.” 

The value chain analysis aims to help businesses identify their strengths and weaknesses, improve efficiency, increase profitability, and gain a competitive advantage in the market.

A typical value chain analysis consists of two main components (explained with examples):

Primary activities : .

These core activities directly contribute to creating, delivering, and supporting a product or service. They include:

Inbound logistics:  Inbound logistics refers to the activities involved in receiving, storing, and managing the flow of raw materials, components, or other inputs required for the production process. 

Efficient inbound logistics ensure smooth operations, maintain inventory levels, minimize delays, and reduce overall costs. For example, let’s consider a company that manufactures smartphones. In this case, inbound logistics would involve the following:

• Sourcing: The company needs to establish relationships with suppliers and procure various components such as processors, memory chips, batteries, cameras, and display screens.
• Receiving: The company receives shipments of components from various suppliers. This process may involve inspecting the quality and quantity of the materials, verifying documentation, and ensuring compliance with relevant regulations.
• Warehousing: The company stores the components in warehouses or storage facilities. Efficient storage and organization are essential to prevent damage, minimize inventory holding costs, and ensure quick access to required materials.
• Inventory management: The company must track and manage the inventory levels of various components to avoid stockouts, overstocking, and obsolescence. This may involve using inventory management systems, implementing just-in-time (JIT) strategies, and regularly updating demand and lead times forecasts.
• Transportation: The company transports the components from storage facilities to production plants, where they are assembled into smartphones. This may involve coordinating with internal or third-party logistics providers, optimizing transportation routes, and ensuring timely delivery to avoid production delays.

In summary, inbound logistics in the smartphone manufacturing example involve sourcing and procuring components, receiving and inspecting shipments, warehousing and inventory management, and transportation of components to the production facilities. 

By optimizing these processes, the company can ensure a steady supply of materials, minimize costs, and maintain efficient production operations.

Operations:  Operations refer to the processes and activities involved in converting raw materials or inputs into finished products or services. This stage includes production, manufacturing, assembly, and quality control. 

Efficient operations are essential for meeting customer expectations, maintaining product quality, and controlling production costs. For example, let’s consider a bakery that produces bread. In this case, the operations would involve the following:

• Production planning: The bakery needs to determine the types and quantities of bread to produce based on customer demand, available resources, and production capacity. This planning stage may include forecasting, scheduling, and resource allocation.
• Preparation: The bakery begins by preparing the necessary ingredients, such as flour, water, yeast, salt, and any additional flavorings or additives. This step involves measuring and mixing the ingredients according to specific recipes and proportions.
• Dough making: The ingredients are mixed and kneaded to form a dough. The dough is then allowed to ferment or proof, which is an essential step for developing the bread’s texture and flavor.
• Shaping: After proofing, the dough is divided into portions and shaped into the desired form, such as loaves, rolls, or baguettes. The shaped dough may undergo another round of proofing to allow for further fermentation and expansion.
• Baking: The shaped dough is placed in an oven and baked at the appropriate temperature and duration according to the type of bread being produced. This step transforms the dough into a finished bread product with a desirable crust, texture, and flavor.
• Cooling and packaging: Once baked, the bread is removed from the oven, allowed to cool, and then packaged for sale or distribution. Proper cooling and packaging are essential for maintaining the quality and freshness of the bread.
• Quality control: Throughout the production process, the bakery must implement quality control measures to ensure that the bread meets established standards for taste, texture, appearance, and safety. This may involve regular inspections, testing, and monitoring of processes, equipment, and finished products.

In summary, the operations in the bakery example involve production planning, ingredient preparation, dough making and proofing, shaping, baking, cooling and packaging, and quality control. 

By optimizing these processes, the bakery can consistently produce high-quality bread products that meet customer expectations while controlling production costs and maximizing efficiency.

Outbound logistics:  Outbound logistics refers to the activities involved in storing, packaging, and distributing finished products to customers, wholesalers, or retailers. 

Efficient outbound logistics are crucial for delivering products on time, ensuring customer satisfaction, and maintaining product quality during transportation and storage. For example, let’s consider a clothing company that manufactures and sells apparel. In this case, the outbound logistics would involve the following:

• Storage: Once the clothing items are produced, they are stored in warehouses or distribution centers until they are ready to be shipped to customers or retailers. Proper storage practices are essential to prevent damage, minimize inventory holding costs, and ensure quick access to the products when needed.
• Order processing: The company receives and processes customer orders, which may involve checking inventory levels, confirming order details, and generating shipping labels and documentation.
• Packaging: The clothing items are packaged for shipment, considering factors such as product protection, packaging materials, and branding. Packaging should protect the products from damage during transportation while also being cost-effective and visually appealing.
• Transportation: The packaged products are transported to their final destinations, which may include customers, retail stores, or wholesale partners. This step involves coordinating with internal or third-party logistics providers, selecting appropriate transportation modes (e.g., truck, rail, air, or sea), and optimizing shipping routes and schedules to minimize transportation costs and delivery times.
• Tracking and delivery: The company tracks the progress of shipments and provides updates to customers or retail partners, ensuring that products arrive on time and in good condition. This may involve tracking systems, maintaining communication with logistics providers, and addressing any issues or delays during transportation.
• Returns and reverse logistics: The company manages product returns, exchanges, or recalls, which may involve processing refunds, restocking returned items, or disposing of defective or damaged products. Effective reverse logistics processes can minimize costs, recover value from returned items, and maintain customer satisfaction.

In summary, the example of outbound logistics in the clothing company involves storage, order processing, packaging, transportation, tracking and delivery, and managing returns and reverse logistics. 

By optimizing these processes, the company can ensure the timely and efficient delivery of products, maintain product quality, and enhance customer satisfaction while controlling costs.

Marketing and sales : Marketing and sales refer to the activities and strategies to promote, sell, and deliver a product or service to customers. These activities are crucial for creating brand awareness, generating demand, attracting and retaining customers, and ultimately driving revenue and growth for the business.

For example, let’s consider a software company that develops and sells project management tools. In this case, marketing and sales activities might include:

• Market research: The company researches to understand customer needs, preferences, and pain points, as well as to identify market trends, competition, and potential target segments. This information helps the company develop and refine its product offerings and marketing strategies.
• Product positioning: The company determines how to position its project management tool in the market, highlighting its unique features, benefits, and advantages over competing products. This positioning helps shape the company’s branding, messaging, and marketing communications.
• Advertising and promotion: The company creates and executes advertising campaigns to raise awareness and generate interest in its project management tool. This may involve digital marketing channels (e.g., search engine marketing, social media, email marketing), traditional media (e.g., print, radio, TV), or events and trade shows.
• Content marketing: The company produces and shares valuable content, such as blog posts, whitepapers, webinars, and case studies, to educate potential customers about project management best practices and demonstrate the benefits and features of its software.
• Sales strategy: The company develops a sales strategy, which may include identifying target customers, setting sales targets, and selecting the most effective sales channels (e.g., direct sales, resellers, or online sales). The company may also establish a sales team or work with external sales partners to reach potential customers.
• Lead generation and nurturing: The company generates leads through marketing efforts and engages with potential customers to guide them through the sales funnel. This process may involve providing product demonstrations, offering free trials, and addressing customer questions or concerns.
• Closing sales: The company’s sales team converts leads into paying customers by negotiating pricing, terms, and contracts and closing deals.
• Customer relationship management: The company maintains ongoing customer relationships, providing support, gathering feedback, and identifying opportunities for upselling or cross-selling additional products or services.

Service : Service refers to the activities focused on providing post-sale support to customers, such as maintenance, repair, customer service, technical support, and warranty management. 

Efficient and effective service is essential for maintaining customer satisfaction, building customer loyalty, and promoting positive word-of-mouth, which can lead to repeat business and new customers.

For example, let’s consider a company that sells home appliances, such as refrigerators, washing machines, and air conditioners. In this case, the service activities might include:

• Installation: The company provides installation services for its products, ensuring they are properly set up and functioning as intended. The company’s technicians or authorized service partners may offer this service.
• Customer support: The company establishes a customer support team to assist customers with questions or concerns regarding the product’s usage, features, or maintenance. Support channels may include phone, email, live chat, social media, or a self-service knowledge base.
• Technical support: The company offers technical support to help customers troubleshoot and resolve any issues or problems they may encounter while using the product. This support may be provided by a specialized team of technicians trained and knowledgeable about the specific appliances.
• Maintenance and repair: The company provides maintenance and repair services to keep the appliances in good working condition, extend their lifespan, and prevent potential issues or failures. This may involve routine maintenance, such as cleaning or replacing filters and addressing specific repair needs, such as fixing a malfunctioning component.
• Warranty management: The company manages warranty claims, providing customers coverage for certain repairs or replacements of defective parts or products within a specified period. This process may involve validating warranty eligibility, processing claims, and coordinating repairs or replacements.
• Customer feedback and product improvement: The company collects and analyzes customer feedback regarding product performance, usability, and service experience to identify areas for improvement and inform future product development or service enhancements.
• Training and education: The company may offer training and education resources for customers, such as user manuals, tutorial videos, or workshops, to help them better understand and utilize the product features and benefits.

In summary, service activities in the home appliance company include installation, customer support, technical support, maintenance and repair, warranty management, customer feedback and product improvement, and training and education. 

By providing efficient and effective service, the company can enhance customer satisfaction and loyalty, reduce the likelihood of negative reviews or complaints, and foster long-term customer relationships that contribute to business success.

Support activities : 

These activities indirectly contribute to the value-creation process by supporting primary activities. They include:

Procurement : Procurement refers to the processes and activities involved in sourcing, purchasing, and managing the goods, services, or resources required for a company’s operations. It is critical to ensure the necessary materials and resources are available while minimizing costs and risks associated with suppliers and supply chain disruptions.

For example, let’s consider a furniture manufacturing company. In this case, procurement activities might include the following:

• Supplier identification and evaluation: The company identifies potential suppliers for the raw materials (e.g., wood, metal, fabric) and other resources (e.g., hardware, tools, machinery) needed for its manufacturing process. It evaluates these suppliers based on quality, reliability, pricing, and delivery capabilities to select the most suitable partners.
• Contract negotiation: The company negotiates contracts with its chosen suppliers, establishing terms and conditions, pricing, delivery schedules, and other important aspects of the supplier relationship. The goal is to secure favorable terms and conditions that meet the company’s needs while managing costs and risks.
• Purchase order management: The company issues purchase orders (POs) to its suppliers, specifying the quantity, specifications, and delivery dates for the required materials or resources. This process may involve managing and tracking the status of POs, coordinating with suppliers to ensure timely delivery, and addressing any issues or delays that may arise.
• Quality control and inspection: The company inspects the received materials or resources to ensure they meet the established quality standards and specifications. This process may involve conducting tests, audits, or visual inspections and working with suppliers to address any quality issues or concerns.
• Inventory management: The company manages the inventory of purchased materials or resources, ensuring that they are stored properly and efficiently to minimize costs and prevent damage or obsolescence. This may involve using inventory management systems, implementing just-in-time (JIT) strategies, or conducting regular inventory audits.
• Supplier relationship management: The company maintains ongoing relationships with its suppliers, collaborating on continuous improvement initiatives, addressing issues or concerns, and evaluating supplier performance to ensure a stable and reliable supply chain.
• Risk management: The company identifies and manages risks associated with its procurement activities, such as supply chain disruptions, supplier bankruptcy, or fluctuations in commodity prices. This may involve developing contingency plans, diversifying supplier portfolios, or implementing risk mitigation strategies.

In summary, procurement activities in the furniture manufacturing company include supplier identification and evaluation, contract negotiation, purchase order management, quality control and inspection, inventory management, supplier relationship management, and risk management. 

By effectively managing procurement processes, the company can ensure a reliable and cost-effective supply of materials and resources, supporting its manufacturing operations and overall business objectives.

Technology development:  Technology development refers to the activities and processes involved in researching, designing, creating, and improving technologies, products, or services that enable a company to gain a competitive advantage, enhance its offerings, or streamline its operations. 

These activities are crucial for driving innovation, staying ahead of competitors, and meet evolving customer needs and market trends. For example, let’s consider an electric vehicle (EV) manufacturing company. In this case, technology development activities might include:

• Research and development (R&D): The company invests in R&D to explore new technologies, materials, and processes to improve its EVs’ performance, efficiency, or affordability. This may involve conducting experiments, simulations, or tests and collaborating with research institutions, partners, or suppliers.
• Product design and engineering: The company designs and engineers its EVs, incorporating technological advancements to create innovative features and systems, such as advanced battery technology, efficient electric motors, or cutting-edge charging infrastructure. This process may involve computer-aided design (CAD) tools, prototyping, and iterative design cycles.
• Software development: The company develops software systems and applications for its EVs, such as battery management systems, autonomous driving capabilities, or user-friendly infotainment systems. This may involve creating custom software, integrating third-party solutions, or collaborating with technology partners.
• Intellectual property (IP) management: The company protects and manages its IP, such as patents, trademarks, or copyrights, to maintain a competitive edge and safeguard its technological innovations. This may involve filing patents, monitoring potential IP infringements, or negotiating licensing agreements.
• Technology implementation and integration: The company integrates its developed technologies into its EVs, ensuring they function seamlessly together and meet established performance, safety, and quality standards. This may involve coordinating with suppliers, conducting tests and validations, or refining production processes.
• Continuous improvement: The company continuously monitors its EVs’ performance and customer feedback to identify areas for improvement or opportunities to incorporate new technologies. This may involve conducting market research, analyzing customer data, or implementing a culture of continuous learning and innovation.
• Technology partnerships and collaboration: The company may form strategic alliances or collaborations with other companies, research institutions, or industry experts to access new technologies, share knowledge, or jointly develop innovative solutions to enhance its EV offerings.

In summary, technology development activities in the electric vehicle manufacturing company include research and development, product design and engineering, software development, intellectual property management, technology implementation and integration, continuous improvement, and technology partnerships and collaboration. 

By effectively managing technology development processes, the company can create innovative products and services that differentiate it from competitors, meet customer needs, and drive business growth.

Human resource management:  Human resource management (HRM) refers to the activities and processes involved in managing a company’s workforce, including recruitment, training, development, performance management, compensation, and employee relations. 

Effective HRM is essential for attracting, retaining, and motivating a skilled and productive workforce that contributes to the company’s success and competitive advantage.

For example, let’s consider a software development company. In this case, HRM activities might include:

• Workforce planning: The company identifies its staffing needs, such as the number of employees, required skill sets, and organizational structure, to support its current and future business objectives. This may involve analyzing company growth projections, industry trends, or competitive landscape.
• Recruitment and selection: The company attracts and selects suitable candidates to fill job openings using various recruitment channels (e.g., job boards, social media, or referrals) and selection methods (e.g., interviews, assessments, or background checks). This process aims to identify candidates with the necessary skills, experience, and cultural fit for the organization.
• Onboarding and orientation: The company provides new employees with a comprehensive onboarding and orientation program to help them acclimate to the organization, understand their job responsibilities, and become familiar with company policies, culture, and values.
• Training and development: The company offers ongoing training and development opportunities to help employees enhance their skills, knowledge, and capabilities and to stay current with industry trends and technological advancements. This may involve providing in-house training, sponsoring external courses or certifications, or offering mentorship and coaching programs.
• Performance management: The company implements a performance management system to set performance expectations, track employee progress, and provide regular feedback and evaluations. This process aims to ensure that employees meet their performance goals and contribute to the company’s overall objectives.
• Compensation and benefits: The company designs and administers a competitive compensation and benefits package to attract, retain, and motivate employees. This may include salaries, bonuses, stock options, health insurance, retirement plans, and other perks or incentives.
• Employee relations and engagement: The company fosters a positive work environment and maintains open communication channels with its employees to address any concerns, grievances, or suggestions. This may involve conducting employee surveys, organizing team-building activities, or implementing employee recognition programs.
• Legal compliance and risk management: The company ensures compliance with labor laws, regulations, and industry standards, such as fair labor practices, health and safety requirements, or equal employment opportunity policies. This may involve maintaining accurate employee records, conducting audits, or implementing risk mitigation strategies.

In summary, HRM activities in the software development company include workforce planning, recruitment and selection, onboarding and orientation, training and development, performance management, compensation and benefits, employee relations and engagement, and legal compliance and risk management. 

By effectively managing its human resources, the company can build a skilled, motivated, and engaged workforce that contributes to its long-term success and competitive advantage.

Infrastructure:  Infrastructure, in the context of a business or organization, refers to the essential physical and organizational structures, facilities, and systems that support its operations and enable it to function effectively. This includes buildings, equipment, IT systems, communication networks, utilities, and the management and maintenance of these resources.

For example, let’s consider a retail company that operates a chain of stores. In this case, the infrastructure might include the following:

• Store locations: The company’s physical retail spaces, including the buildings, parking lots, and surrounding areas, where customers shop for products. These spaces must be designed, constructed, and maintained to provide customers and employees with a safe, comfortable, and visually appealing environment.
• Distribution centers and warehouses: The company’s facilities for storing and distributing inventory, which may include warehouses, distribution centers, or fulfillment centers. These facilities must be strategically located, well-organized, and efficiently managed to ensure that products are available and delivered on time to the retail stores.
• Transportation and logistics: The company’s transportation and logistics systems involve the movement of goods from suppliers to distribution centers and from distribution centers to retail stores. This may include trucks, trains, other transportation methods, and the management of shipping schedules, routes, and logistics providers.
• Information technology (IT) systems: The company’s IT infrastructure, including hardware, software, networks, and data centers, supports its business operations, such as inventory management, point-of-sale systems, customer relationship management, and financial reporting. These systems must be reliable, secure, and scalable to meet the company’s evolving needs.
• Communication networks: The company’s internal and external communication systems, such as phone lines, internet connections, or video conferencing tools, enable employees, suppliers, and customers to communicate and collaborate effectively.
• Utilities and services: The company’s access to essential utilities and services, such as electricity, water, heating and cooling, waste management, and security, ensure the smooth operation of its facilities and support the health and safety of employees and customers.
• Maintenance and facilities management: The company’s processes and personnel are responsible for maintaining and managing its physical infrastructure, such as building maintenance, equipment repair, landscaping, or cleaning services.

In summary, infrastructure in the retail company example includes store locations, distribution centers and warehouses, transportation and logistics, IT systems, communication networks, utilities and services, and maintenance and facilities management. 

By investing in and effectively managing its infrastructure, the company can support its day-to-day operations, provide a seamless shopping experience for customers, and maintain a competitive edge in the market.

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A Beginner’s Guide on How to Conduct Value Chain Analysis

In today’s ever-expanding business landscape, where many brands offer similar products or services, a value proposition plays a crucial role as a business statement that communicates the economic value and motivates customers to choose your brand. Unsurprisingly, many visionary businesses are committed to creating a compelling value statement that effectively encapsulates their brand’s unique selling point and highlights their distinctive qualities compared to competitors.

Clearly, businesses, products, or services that do not provide value to customers risk losing their customer base or facing significant business challenges. So, how can you measure and assess value? This is where Value Chain Analysis comes into play. In this guide for beginners, we will define value chain analysis, differentiate it from the supply chain, emphasize its importance, and outline a step-by-step approach to conducting a value chain analysis.

Table of Content

What is a value chain analysis, value chain vs. supply chain: what’s the difference, why is conducting value chain analysis important for sales teams, key components of a value chain analysis.

• Step-by-Step Guide On How To Conduct a Value Chain Analysis

Common Mistakes in a Value Chain Analysis

Value chain analysis examples.

• Tips For Putting Your Value Chain Analysis to Good Use

A value chain analysis is a strategic tool to assess and evaluate a company’s activities and processes to deliver a product or service to customers. It involves examining the entire sequence of activities, from sourcing raw materials and production to marketing, sales, and customer support.

A value chain analysis aims to understand how each activity within the organization adds value to the final product or service and contributes to the company’s overall competitive advantage. By breaking down the value chain into primary and support activities, businesses can identify opportunities for cost reduction, process improvement, and differentiation.

What is Porter’s Value Chain Framework?

Porter’s value chain framework, developed by Michael Porter, is a strategic management tool that helps analyze the activities performed within an organization to create value for its customers. It breaks down a company’s activities into primary and support activities, providing a systematic approach to understanding the internal operations and identifying opportunities for competitive advantage.

While “value chain” and “supply chain” are related, they refer to different business operations. Here’s a difference between the two terms:

Supply chain refers to the network of all the individuals, organizations, resources, activities, and technology involved in creating and selling a product. It focuses on the external network of suppliers, manufacturers, distributors, retailers, and other entities involved in the flow of materials, products, and information, from sourcing raw materials to delivering the final product to the end customer.

On the other hand, the value chain encompasses all the activities and processes within a company involved in creating, delivering, and supporting a product or service. It includes primary activities (production, marketing, and customer service) and support activities (such as procurement, technology development, and human resource management).

Also, unlike a value chain that primarily focuses on internal activities and processes within the company, focusing on how value is created within the organization, a supply chain management involves collaboration and coordination with external entities, including suppliers, manufacturers, distributors, and retailers, to ensure a seamless flow of goods and information across the entire supply chain network.

Value chain analysis helps sales teams understand the areas that are more efficient and are likely to help them generate more profit. Here are five ways value chain analysis can benefit sales teams:

1. Lower costs

Value chain analysis helps assess your organizational costs and identify the cost drivers. For example, if a subscription or a business cost does not yield the desired outcome, you could consider taking it out to create a difference in your profit margin and give you a significant cost advantage over your competitors.

2. Improved efficiency

Analyzing your value chain will help you break down your company logistics, operations, and firm infrastructure and help you identify the best ways to streamline your sales processes and enhance your business efficiency.

3. Increased profits

Value chain analysis can help sales teams get better insights into the processes and sales activities to increase profits. It helps you identify loopholes to increase customer value and cut avoidable costs that inevitably boost the profit margin.

4. Gain a Competitive advantage

Conducting a value chain analysis helps you compare your products, services, and operations with your competitors. With this data, you can determine the best way to get a competitive advantage and move ahead of them to become the market leader.

5. Get new customers

A value chain analysis helps you gain a competitive advantage over your clients, increase efficiency, and position your business better. It gives you more authority to entice your new customers and get them to choose you over your competitors.

6. Lesser Errors

Value chain analysis helps you detect loopholes or trouble spots in your production process and determine how your sales teams use your time for business efficiency. Knowing these issues enables you to minimize errors and supercharger business success.

A value chain analysis is a strategic management tool that helps identify the activities within a company’s operations that create value for its customers and contribute to its competitive advantage. It examines transforming inputs into outputs, from procuring raw materials to delivering the final product or service to customers. The critical components of a value chain analysis typically include:

1. Inbound Logistics:

This component focuses on the activities involved in sourcing, receiving, storing, and distributing raw materials or components needed for production. It includes tasks such as inventory management, transportation, and supplier relationships.

2. Operations:

This component involves the core production processes, transforming the raw materials or components into finished products or services. It encompasses manufacturing, assembly, packaging, quality control, and maintenance activities.

3. Outbound Logistics:

This component deals with the activities required to deliver the finished products or services to customers. It includes order processing, warehousing, transportation, and distribution.

4. Marketing and Sales:

This component encompasses activities related to promoting and selling the products or services to customers. It involves market research, advertising, pricing, sales channels, and customer relationship management.

5. Service:

This component focuses on the activities that support customers after selling the product or service. It includes customer support, warranty services, repairs, maintenance, and any other assistance provided to ensure customer satisfaction.

6. Procurement:

This component involves acquiring the necessary inputs to support the value chain activities, such as raw materials, supplies, and equipment. It includes supplier selection, negotiation, contracting, and procurement management tasks.

7. Technology and Infrastructure:

This component encompasses the resources, systems, and technology required to support the value chain activities. It includes physical infrastructure (e.g., buildings, machinery) and information technology infrastructure (e.g., hardware, software, networks).

8. Human Resources:

This component focuses on the people involved in the value chain activities. It includes workforce planning, recruitment, training, performance management, and employee development.

9. Firm Infrastructure:

This component refers to the overall organizational structure, management, and support functions that facilitate the value chain activities. It includes finance, accounting, legal, and strategic planning functions.

Analyzing each component of the value chain helps identify strengths and weaknesses, opportunities for improvement, and potential cost-saving measures. It enables companies to optimize their operations, enhance value creation, and gain a competitive edge in the market.

Step-by-Step Guide On How to Conduct a Value Chain Analysis

Conducting a value chain analysis involves systematically examining the activities within a company’s operations to identify sources of value creation and competitive advantage. Here is a step-by-step guide on how to conduct a value chain analysis:

Step #1: Define the Scope

Clearly define the scope of your value chain analysis. Determine whether you want to analyze the entire organization or focus on a specific product, service, or business unit.

Step #2: Identify the Value Chain Activities

Identify and list all the activities involved in creating and delivering your product or service. This typically includes inbound logistics, operations, outbound logistics, marketing and sales, service, procurement, technology and infrastructure, human resources, and firm infrastructure.

Step #3: Analyze Primary Activities

Focus on the primary activities that directly contribute to value creation. These include inbound logistics, operations, outbound logistics, marketing and sales, and service. Analyze each activity to understand how it adds value, what resources are required, and how it impacts cost and customer satisfaction.

Step #4: Analyze Support Activities

Move on to analyzing the support activities, which provide the necessary infrastructure and resources to support the primary activities. This includes procurement, technology and infrastructure, human resources, and firm infrastructure. Evaluate how these activities enable the primary activities and contribute to overall value creation.

Step #5: Identify Value Drivers

Identify the specific factors or elements that create value within each activity. For example, in marketing and sales, value drivers could be branding, advertising, or customer segmentation. In operations, value drivers could be quality control, process efficiency, or innovation.

Step #6: Assess Costs

Analyze the costs associated with each activity and value driver. Identify cost drivers that significantly impact the overall cost structure. This analysis helps identify areas where cost reduction or efficiency improvements can be achieved.

Step #7: Evaluate Competitive Advantage

Assess how each activity and value driver contributes to the company’s competitive advantage. Identify unique strengths and capabilities that differentiate your company from competitors. Consider factors such as quality, price, speed, customization, or customer service.

Step #8: Identify Opportunities and Weaknesses

Based on the analysis, identify areas of opportunity for improvement and areas of weakness that may hinder value creation or competitive advantage. Look for potential cost-saving measures, process enhancements, or innovation opportunities.

Step #9: Benchmark Competitors’

Compare your value chain activities with those of your competitors. Identify areas where competitors excel and areas where you have a competitive advantage. This analysis can provide insights into areas you need further improvement or differentiation.

Step #10: Develop an Action Plan

Based on the findings of your analysis, develop an action plan that outlines specific initiatives and strategies to enhance value creation, address weaknesses, and leverage competitive advantages. Prioritize the actions based on their potential impact and feasibility.

Step #11: Implement and Monitor

Execute the action plan and monitor the progress. Continuously evaluate and measure the impact of the implemented initiatives. Adjust the plan as needed to ensure ongoing value creation and competitiveness.

Remember that conducting a value chain analysis is an iterative process. It requires collaboration across different organizational functions and ongoing evaluation to stay aligned with market dynamics and changing customer needs.

When conducting a value chain analysis, people often make several common mistakes. These mistakes can hinder the effectiveness and accuracy of the analysis. Here are some of the common mistakes to avoid:

1. Narrow focus

One of the most common mistakes is focusing on only one part of the value chain. It’s important to analyze the entire chain, from raw materials to the end customer. Ignoring any stage can lead to missing crucial insights and opportunities for improvement.

2. Lack of data

Value chain analysis requires detailed data and information about each chain stage. Insufficient or inaccurate data can result in flawed conclusions and ineffective strategies. Ensure you can access reliable data sources and gather sufficient information to support your analysis.

3. Failure to consider external factors

Value chain analysis should consider external factors that may impact the chain, such as industry trends, market dynamics, and regulatory changes. Neglecting these external factors can lead to an incomplete understanding of the chain and its potential vulnerabilities.

4. Overlooking non-traditional activities

Sometimes, organizations overlook non-traditional activities that can significantly impact the value chain. These activities may include research and development, innovation, marketing, customer service, and information systems. Make sure to consider all relevant activities within the value chain to capture their impact accurately.

5. Ignoring interdependencies

Value chain activities are often interconnected, and changes in one activity can affect others. Failing to recognize these interdependencies can lead to suboptimal decision-making. It’s crucial to understand how changes in one stage of the chain can influence others to identify potential bottlenecks or areas of improvement.

6. Lack of stakeholder involvement

Value chain analysis should involve input from various stakeholders, including employees, suppliers, customers, and partners. Neglecting to involve these stakeholders can result in incomplete insights and biased perspectives. Engage relevant stakeholders throughout the analysis process to gather diverse viewpoints and enhance the accuracy of your findings.

7. Static analysis

Value chain analysis should not be a one-time exercise. Recognizing that the value chain is dynamic and subject to change over time is essential. Failing to review and update the analysis regularly can lead to outdated strategies and missed opportunities for improvement.

By avoiding these common mistakes, you can conduct a more comprehensive and accurate value chain analysis, leading to better strategic decision-making and enhanced competitiveness.

Value chain analysis can be applied to various industries and sectors to identify opportunities for improvement, cost reduction, and value creation. Here are a few examples of value chain analysis in different contexts:

Value Chain Analysis Example #1: Apple Inc.

Apple’s value chain analysis involves assessing various company operations activities. For example, in inbound logistics, Apple focuses on efficient supplier management to ensure the timely delivery of components for its products.

In operations, the company emphasizes quality control and innovative manufacturing processes. Outbound logistics include the distribution of products through its global retail network and online channels. Marketing and sales activities focus on creating a unique brand experience, while service activities involve customer support and repairs.

Value Chain Analysis Example #2: Amazon.com Inc.

Amazon’s value chain analysis involves understanding its extensive e-commerce operations. In inbound logistics, Amazon focuses on efficient inventory management and strategic partnerships with suppliers. Operations involve warehouse management, order fulfillment, and packaging.

Outbound logistics revolve around rapid and reliable delivery. Marketing and sales activities leverage personalized recommendations and targeted advertising. Service activities include customer support, returns management, and subscription services like Amazon Prime.

Value Chain Analysis Example #3: Tesla Inc.

Tesla’s value chain analysis centers around its electric vehicle manufacturing and energy solutions. Inbound logistics include sourcing high-quality batteries and other components. Operations involve innovative vehicle manufacturing processes and advanced technologies.

Outbound logistics focus on global distribution and efficient charging infrastructure. Marketing and sales activities involve digital marketing, direct sales, and unique customer experiences. Service activities encompass over-the-air software updates and customer support.

Value Chain Analysis Example #4: McDonald’s Corporation

Mcdonald’s value chain analysis in the fast-food industry emphasizes efficiency and consistency. In inbound logistics, the company manages the supply chain for fresh ingredients. Operations involve standardized food preparation and quality control.

Outbound logistics focus on quick service and efficient delivery. Marketing and sales activities emphasize brand promotion, targeted advertising, and menu innovation. Service activities include customer experience and support.

Value Chain Analysis Example #5: Procter & Gamble (P&G)

P&G’s value chain analysis in the consumer goods industry involves multiple product categories. In inbound logistics, P&G manages the sourcing and transportation of raw materials. Operations involve innovative manufacturing processes and quality control.

Outbound logistics focus on efficient distribution to retail channels. Marketing and sales activities revolve around brand management, advertising, and customer insights. Service activities include consumer support, product returns, and loyalty programs.

These examples illustrate how different companies apply value chain analysis to understand their operations, identify areas for improvement, and create value for their customers. It is important to note that the specific activities and strategies may vary depending on the company’s industry, competitive landscape, and strategic priorities.

Tips for Putting Your Value Chain Analysis to Good Use

Once you have conducted a value chain analysis, it is essential to effectively utilize the insights gained to drive meaningful improvements and competitive advantages. Here are some tips for putting your value chain analysis to good use:

1. Identify Key Value Drivers

Identify the activities within the value chain that significantly impact cost reduction, differentiation, or customer value. Focus your resources and efforts on these key value drivers to maximize the impact of your analysis.

2. Set Clear Objectives

Define objectives based on your findings of value chain analysis. Whether it is improving operational efficiency, reducing costs, enhancing product quality, or enhancing customer experience, having specific goals will guide your actions and initiatives.

3. Prioritize Actions

Prioritize the actions and initiatives identified in your analysis based on their potential impact and feasibility. Determine which activities require immediate attention and which can be addressed long-term. This will help you allocate resources effectively and make progress toward your objectives.

4. Develop Action Plans

Develop detailed action plans that outline the steps, responsibilities, and timelines for implementing the identified initiatives. Ensure the action plans are specific, measurable, achievable, relevant, and time-bound (SMART) to facilitate effective execution.

5. Involve Stakeholders

Engage relevant stakeholders throughout the implementation process. Seek input and collaboration from employees, suppliers, customers, and partners to gain diverse perspectives and ensure buy-in for the proposed changes. Foster a culture of continuous improvement and cross-functional collaboration.

6. Monitor Progress

Establish key performance indicators (KPIs) and monitoring mechanisms to track the progress of the initiatives. Regularly review and analyze the data to evaluate the effectiveness of the implemented actions. Adjust your strategies if necessary to stay on track and achieve your objectives.

7. Embrace Innovation and Technology

Leverage innovative technologies and solutions to enhance efficiency, streamline processes, and improve customer value within the value chain. Explore digital transformation opportunities, automation, data analytics, and other emerging trends that can drive competitive advantage.

8. Continuously Improve

Value chain analysis is an ongoing process. Continuously evaluate and update your analysis as market conditions, technologies, and customer expectations evolve. Regularly review and refine your strategies to stay ahead of the competition and adapt to changing dynamics.

By following these tips, you can effectively translate the insights from your value chain analysis into practical actions that drive positive outcomes for your organization. Remember, the ultimate goal is to leverage your research to create value, enhance competitiveness, and achieve your business objectives.

Stay Ahead of Your Game With the Right Value Chain Analysis

A well-executed value chain analysis is essential for organizations to stay ahead of the game in today’s competitive business environment. By thoroughly understanding the activities, processes, and relationships that create value for customers, companies can identify areas for improvement, cost reduction, and differentiation.

With the right value chain analysis, organizations can make informed decisions, prioritize actions, and implement targeted strategies to optimize operations, enhance customer experience, and gain a competitive edge. It is crucial to continuously monitor and adapt the value chain analysis to stay agile and responsive to changing market dynamics. By leveraging the power of value chain analysis, organizations can position themselves as industry leaders and achieve sustained success.

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Module 4: Value Chain Analysis – Research Methods

This is the second part in a workshop series about value chain analysis. Part two reviews the research methods used to conduct value chain analysis. The training took place on March 16th, 2022. This module contains a video recording of the training session along with PDF versions of the slides presented. This series was co-organized by Kasetsart University, Thailand.

OBJECTIVES:

-Understand various aspects of value chain analysis research

-Become more comfortable with the research methods used in value chain analysis through examples of work being done in South Asia and Southeast Asia

CONTRIBUTORS:

Suresh Babu (IFPRI), Kulapa Kuldilok (KU), Ben Belton (MSU), Pasakorn Thammachote (KU)

Begin Module

Nvidia Value Chain Analysis

Nvidia value chain analysis is an analytical framework that assists in identifying business activities that can create value and competitive advantage to the business. Specifically, business leaders can create competitive advantage through dividing the business into various activities and analysing each activity individually from value creation perspective.

Figure below illustrates the essence of Nvidia value chain analysis.

Value chain analysis

Primary Activities in Nvidia Value Chain Analysis

Nvidia inbound logistics.

Nvidia inbound logistics involves managing the supply chain of components and raw materials into the premises of its contract manufacturers. The multinational technology company performs quality checks of semiconductors and other spare parts using test equipment purchased from industry-leading suppliers such as Advantest America Inc.

Nvidia benefits from the economies of scale in inbound logistics activities due to the large amount of spare parts and components it purchases. Furthermore, the company has strategic relationships with its key suppliers in place and these relationships play an instrumental role in new product development.

  Nvidia Operations

Nvidia employs fabless manufacturing strategy. It employs third party companies outside of United States for all phases of the manufacturing process, including wafer fabrication, assembly, testing, and packaging. [1] Nvidia does not engage in any of these activities directly and chooses to focus the design, marketing and distribution of its products.

The company works with world-class manufacturers to produce its products. Semiconductor wafers are produced by leading companies such as Taiwan Semiconductor Manufacturing Company Limited and Samsung Electronics Co. Ltd, adapter card products and switch systems are produced by the likes of Flex Ltd., Jabil Inc., and Universal Scientific Industrial Co., Ltd. and Fabrinet manufactures Nvidia cables.

Assembly, testing and packaging of products and platforms are done by Amkor Technology, King Yuan Electronics Co., Ltd., Omni Logistics, LLC, Siliconware Precision Industries Company Ltd., Wistron Corporation and other companies within the same level. Fabless manufacturing strategy allows Nvidia to focus on its core competency, that is designing and developing innovative products.

Nvidia Outbound Logistics

Nvidia outbound logistics involves warehousing and distribution of ready goods. Potential areas for value creation in outbound logistics involve delivering to consumers directly without intermediaries, using advanced information and communication technologies, sharing distribution costs with adjacent businesses and others.

Nvidia uses third-party companies to collect ready items from the facilities of its producers to the warehouses of its distributors and re-sellers. The multinational technology company also sells to end-users through its website directly. For this sales channel Nvidia also uses third parties to procure ready products from its producer facilities to its own warehouses.

Nvidia Marketing and Sales

Marketing and sales as a primary activity involve the development and implementation of marketing strategy to increase revenues. Nvidia marketing communication messages focus on unique selling proposition of the brand. Specifically, the software and fables company attempts to associate its brand image with advanced features and capabilities and superior quality. The company also uses the channel marketing utilising its channel partners and original equipment manufacturers to pass through the marketing message to end-users.

Nvidia Service

Service primary element in value chain analysis relates to the level of customer support Nvidia offers its end-users after the sales. The company has more than 50 offices worldwide with customer service desks. Nvidia maintains a dedicated customer service portal on its website where customers can get help with their Nvidia products and services, as well as, contact sales representative about other products. Furthermore, the company has a Partner Locator feature on its website, where customers can find Nvidia partners to purchase from and subsequently these partners are expected to offer superior customer services.

Nvidia Corporation Report contains a full version of Nvidia value chain analysis. The report illustrates the application of the major analytical strategic frameworks in business studies such as SWOT, PESTEL, Porter’s Five Forces, Ansoff Matrix and McKinsey 7S Model on Nvidia . Moreover, the report contains analyses of Nvidia leadership, business strategy, organizational structure and organizational culture. The report also comprises discussions of Nvidia marketing strategy, ecosystem and addresses issues of corporate social responsibility.

[1] Annual Review 2022 , Nvidia Corporation

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Problem definition: The narrow definition of supply chain finance (SCF) as a financing scheme for accounts receivables fails to capture the knowledge creation of MSOM research scholars of the last 25 years who labored in the area under this label. A ...

Supply Chain Risk Management: An Invigorating Outlook

This article describes how as mankind has been ever-evolving, so are their needs, growing in leaps and bounds. Risk management in supply chains have become a prerequisite as it involves a series of steps like procuring, processing, and distribution, ...

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• data envelopment analysis
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T/F: In value-chain analysis, research and development is a broader concept than technology development.

The given statement " In value-chain analysis, research and development is a broader concept than technology development" is False.

In value-chain analysis, technology development is a subset of research and development (R&D). R&D encompasses all activities aimed at improving a product, process, or service, while technology development specifically refers to activities aimed at creating new or improving existing technologies.

Technology development is one aspect of R&D and is critical to many businesses, as it can lead to the creation of new products, processes, and services, and result in a competitive advantage for the company.

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Related Questions

consider the following two markets for good x. note: the markets are identical except that demand for good x in market 1 is more elastic than demand for good x in market 2. if there is an equivalent increase in supply in each market, in which market will the price change most?

The market with the more elastic demand for good x will experience the greatest change in price following an equivalent increase in supply.

This is because when demand is more elastic, a larger proportion of buyers are sensitive to changes in price , so the quantity demanded changes more significantly in response to a change in price.

This causes a greater decrease in price in order to clear the increased supply. Conversely, in market 2 with less elastic demand, the price of good x is relatively less sensitive to changes in supply , so the price change would be less significant.

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once you have gained agreement from the prospect that they will buy your product, continue selling. this will convince them they made a good buying decision. group of answer choices true false

The given statement “Once you have gained agreement from the prospect that they will buy your product, continue selling . This will convince them they made a good buying decision” is true.

This is because continuing to sell after gaining agreement from the prospect allows the seller to provide additional information about the product which may help the prospect to make a more informed decision.

This is particularly important if the buyer is uncertain about the purchase, as the seller can offer information on the product’s features and benefits, warranties, discounts, and other details that may help the buyer to feel more confident about the purchase.

Furthermore, if the seller is able to provide a successful sales presentation and a pleasant shopping experience, the buyer will likely feel satisfied and confident in their purchase, and this can lead to repeat business in the future.

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companies that try to act in a socially responsible way are likely to focus on which type of activities? (check all that apply) multiple select question. hiring only women and minorities increasing the diversity of their workforce providing safety equipment and training for their workers using recyclable packaging

Businesses that make an effort to conduct business in a socially responsible manner are probably going to concentrate on diversifying their workforce and using recyclable packaging.

The term " workforce diversity " refers to the variety of traits and traits among employees within a business or organisation. Age, gender, race, ethnicity, religion, sexual orientation, and cultural background are a few examples of these differences. A workforce that reflects the range of experiences and backgrounds found in society as a whole can help businesses better understand and cater to the diverse customer base. Additionally, it can bring a variety of viewpoints, abilities, and concepts to the workplace, which can boost innovation, creativity, and problem-solving skills. In addition to being a socially responsible strategy, embracing diversity and fostering an inclusive workplace can have a positive impact on businesses.

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using recyclable packaging

increasing the diversity of their workforce

providing safety equipment and training of their workers

Explanation:

The _________ is a phenomenon where women are selected for leadership roles that set them up to fail.

The term "glass cliff" describes how women are frequently elevated to leadership roles in specific businesses during periods of crisis or downturn and are hence predisposed to failure.

A phenomenon known as the "glass cliff" serves to confirm the preconceived notion that women are not cut out for positions of authority. The term "glass cliff" is often used to describe the difficulty women experience, but it also describes the difficulties minorities and other oppressed groups encounter when promoted to leadership positions.

The term " glass ceiling " refers to prejudiced hurdles that stop women from moving to higher positions within an organization or assuming leadership roles merely because they are women (Li and Leung, 2001). There are a few presumptions that underlie the glass ceiling phenomena.

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Given the following financial statements for ZEIN Company: ZEIN Company Balance Sheet At December 31 Assets 2017 2016 Cash EGP102,700 EGP33,400 Accounts Receivables 60,800 37,000 Inventories 126,900 102,650 Land 79,500 107,000 Plant Assets 315,000 205,000 Accumulated Depreciation (44,500) (40,000) Total Assets 640,400 445,050 Liabilities and Stockholders' Equity Accounts Payable 57,700 48,280 Accrued Expenses Payable 15,100 18,830 Bonds Payable 145,000 70,000 Common Stock 250,000 200,000 Retained Earnings 172,600 107,940 Total Liabilities and Stockholders' Equity 640,400 445,050 ZEIN Company Income Statement For the year ended, December 31, 2017 Sales EGP297,500 Cost of goods sold 99,460 Gross profit 198,040 Operating expenses 53,110 Earnings Before Interest and Tax 144,930 Gain on sale of plant assets 5,000 Income before tax expense 149,930 Tax expense (Tax rate = 24. 86%) 37,270 Net income 112,660 Additional information: 1. Operating expenses including depreciation expense of EGP30,500. 2. New plant assets costing EGP146,000 were purchased for cash during the year. 3. Land was sold at cost. 4. Plant assets costing EGP36,000 with book value of EGP10,000 were sold for EGP15,000. 5. A cash dividend of EGP48,000 was declared and paid during the year. 6. Bonds with EGP75,000 face value were issued for cash. 7. The company issued common stock for EGP50,000 for cash. Prepare the cash flow statement for ZEIN Company for 2017

The financial statement for ZEIN Company and preparing of cash flow statement is mentioned below.

Depreciation expense EGP    30,500

Gain on sale of plant assets    (5,000)

Increase in accounts receivable   (23,800)

Increase in inventories       (24,250)

Increase in accounts payable    9,420

Increase in accrued expenses payable   (3,730)

Net cash provided by operating activities EGP  95,800

Cash flows from investing activities:

Purchase of plant assets (146,000)

Proceeds from sale of land - Proceeds from sale of plant assets 15,000

Net cash used in investing activities (131,000)

Cash flows from financing activities:

Proceeds from issuance of bonds payable 75,000

Proceeds from issuance of common stock 50,000

Dividends paid (48,000

Net cash provided by financing activities 77,000

Net increase in cash 41,800

Cash at beginning of year 33,400

Cash at end of year EGP75,200

A company's working capital , or the amount of money available for operations and transactions, is determined by a cash flow analysis . That is calculated by subtracting current liabilities (liabilities due during the upcoming accounting period) from current assets (cash or near-cash assets like notes receivable).

A cash flow statement, also known as a statement of cash flows, is a financial statement in financial accounting that breaks down the analysis into operating, investing, and financing activities and shows how changes in balance sheet accounts and income affect cash and cash equivalents.

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What is a group owned insurance company that is formed to assume and spread

Risk among its members is known as a mutual insurance company .

A mutual insurance company is owned by its policyholders , who are also the beneficiaries of the insurance coverage . The company operates for the benefit of its members and is focused on spreading risk and protecting its members from financial loss.

The premiums collected from policyholders are used to pay claims and cover operating expenses, and any surplus funds are typically returned to policyholders in the form of dividends. Because mutual insurance companies are owned by their policyholders, they often have a strong commitment to customer service and a focus on meeting the needs of their members.

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Import tariffs protect domestic producers against foreign competitors. True or False

The statement "Import tariffs protect domestic producers against foreign competitors ." is true because of subsidies help foreign competitors gain export markets by lowering production costs .

Protectionism is the policy of defending domestic industries against foreign competition through tariffs, subsidies, import quotas, or other restrictions or handicaps imposed on foreign competitors' imports. Many countries have implemented protectionist policies , despite the fact that virtually all mainstream economists agree that free trade benefits the global economy in general.

Tariffs that the government imposes are the basic protectionist policies. They raise the price of imported goods, making them more expensive (and thus less appealing) than domestic goods. It can also be used to promote self-sufficiency in defense industries .

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at 7.4 percent interest, how long does it take to double your money? (do not round intermediate calculations and round your answer to 2 decimal places, e.g., 32.16.)

At 7.4 percent interest , it will take 9.71 years to double the money.

As per Time Value of Money ,

Future Value = Present Value x (1 + Rate)^Number of years

Here Future Value = 2

Present Value = 1

Rate = 7.4% = 0.074

Putting them in value, 2 = 1 x (1.074)^n = 9.71 years

In finance and economics, interest is the payment of an amount when the borrower or custodian financial institution returns the original amount to the lender or depositor in a certain percentage by the borrower or depositors. It is different from the fee that a borrower may pay a lender or a third party.

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A Classified Balance Sheet separates assets and liabilities into categories that distinguish between accounts that are identified as current from those that are identified as long-term. This statement is true/false

The statement "A Classified Balance Sheet separates assets and liabilities into categories that distinguish between accounts that are identified as current from those that are identified as long-term." is true. It is because Classified Balance Sheet  has a function to separates asset and liabilities into such categories.

In the term of accounting and financial economic, A classified balance sheet generally can be defined as a type of sheet that display asset, liability, and equity totals as its unclassified counterpart, but does so with greater detail. A classified balance sheet  has a function to classifying asset and liabilities into various categories.

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Why are early adopters important for new product marketers?

Early adopters' experience with and pending endorsement of a new product or technology is essential for evaluating whether or not the majority of the population will accept it.

Their support and word-of-mouth marketing for a new product or technology can strengthen its reputation and assist the company recruit additional customers.

Early adopters, who are not the very first consumers of a new product (they are the innovators) but who nevertheless buy extremely early in the product's existence, are vital to the diffusion of the products because they are often opinion leaders.

Early adopters are people who use new things before the majority of consumers do. They are trend-setters and risk-takers who have a big impact on whether a new product succeeds or fails. For this reason, a lot of companies try to win over early adopters.

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an interest rate is 6% per annum with annual compounding. what is the equivalent rate with continuous compounding?

The equivalent rate with continuous compounding is 6.1798% per annum. It is also important while comparing the value of two different investments.

What do you mean by equivalent rate ?

Equivalent rate is a term used to describe the relationship between two different types of interest or exchange rates . For example, if the US Dollar is at a certain exchange rate against the Euro, the equivalent rate can be used to determine what that exchange rate would be when compared to another currency, such as the British Pound. The equivalent rate is important in understanding how different currencies relate to one another and how their values may or may not fluctuate over time. It is also important when comparing the value of two different investments , as the equivalent rate can be used to determine which investment would result in a higher return.

So, The equivalent rate with continuous compounding is 6.1798% per annum.

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The process of defining a marketing problem and opportunity, systematically collecting and analyzing information, and recommending actions is called:A. market decision analysisB. statistical analysisC. marketing researchD. SWOT analysisE. concept testing

Analyzing information, and recommending actions is called marketing research. The systematic collection, recording, and analysis of qualitative and quantitative data about problems related to the marketing of goods and services is known as marketing research. The answer is OPTION C

The objective is to determine and evaluate how shifting aspects of the marketing mix affect consumer behaviour. Primary qualitative market research frequently involves interviews, which can range from in-depth discussions to straightforward questions.

When a company phones a current client to inquire about how they are enjoying a product they recently bought, that is an example of a market qualitative interview. Information that is pertinent to decision-making is provided by marketing research, which benefits marketing management.

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how did he get the money he needed? he earned it by working for the teacher after school. he earned it by trapping and selling furs. he borrowed it from the bank. he borrowed it from grace.

Option 1 is Correct. He acquired it by doing after- school work for the instructor. If they choose a School Direct ( money ) training program, students are hired as unqualified teachers while they gain experience.

This institution might occasionally be one where the student already works or has ties. While many professions can be demanding at times, teaching can be especially challenging, and around 80% of teachers report feeling stressed at work.

We've considered strategies to help you manage the stress you could experience since we recognize that teaching is a hard profession with high demands . Free of charge is the program. being able to make money while you learn, as well as getting a deal on London transportation.

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Correct Question:

How did he get the money he needed?

1. he earned it by working for the teacher after school.

2. he earned it by trapping and selling furs.

3. he borrowed it from the bank.

4. he borrowed it from grace.

A company that is highly leveraged is considered to have ________. A) high risk with high reward B) low risk with high reward C) high debt D) a liquidity crisis E) high accounts receivable

A firm that is highly leveraged is considered to have C: high debt .

A company that is highly leveraged has a high debt-to-equity ratio , meaning that it has a high level of debt relative to its equity. This means that a significant portion of the company's financing comes from borrowing, rather than from its own capital . Companies that are highly leveraged are considered to have a high level of financial risk, as their debt obligations increase the risk that they will not be able to meet their financial obligations. In the event of a financial downturn , highly leveraged companies may be more vulnerable to bankruptcy or other financial distress. On the other hand, companies with low debt levels are generally considered to have lower risk and are more stable.

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which of the following, if true, would illustrate why price indexes such as the upi might overstate inflation in the cost of going to college? check all that apply. energy drinks became increasingly popular on college campuses between 2020 and 2022 due to significant improvements in flavor, but this quality change is hard to measure. as the price of premium streaming services rose, fewer students decided to buy them, opting instead to borrow log-in information from friends and relatives. professors required each student to buy eight notebooks, regardless of the price. a new, cheaper internet option rolled out services nationwide.

Between 2010 and 2012, energy drinks' popularity on college campuses grew as a result of major flavour enhancements. The cost of calculators increased .

What exactly is the idea of popularity?

In sociology, popularity refers to how much other people enjoy or value a particular person, idea, location, thing, or other notion. Reciprocal liking, interpersonal attraction, and other related elements can all contribute to liking. Dominance, supremacy, and other related traits can contribute to social rank. Some people gain popularity by being likeable. For instance, they might be upbeat, amiable, dependable , and considerate. In other instances, persons enjoy popularity as a result of their social standing, prosperity, or attractiveness .

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lee, the owner of a financial consulting firm, plans to announce several renovations to make the company offices more accommodating to employees with visual, hearing, mobility, or cognitive impairments. which announcement made by lee is bias-free?

We are installing new accommodations for all employees with disabilities made by lee is bias free .

Any condition that makes it difficult for a person to participate in specific activities or to have equal access in a certain society is called a disability. Disability can be caused by a combination of cognitive, developmental, intellectual, mental , physical, or sensory problems.

A person can have a birth defect or a developmental disability during their lifetime. However, defects are not binary and can appear in certain characteristics depending on the individual. Historically, they have only been recognized based on a limited number of criteria. A defect can be obvious or completely undetectable . 

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partan credit bank is offering 8.3 percent compounded daily on its savings accounts. you deposit $7,500 today. a. how much will you have in the account in 5 years?

You will have in the account in 5 years is $13,115.03

Account value is calculated with the formula below:

Value = Initial investment x (1 + interest rate)^n

where, interest rate = 8.3%/365

interest rate = 0.022740%

interest rate = 0.0002274

n = number of years x 365

Initial investment = $7500

a. We have to determine how much you will have in the account in 5 years.

Account value in 5 years = 7500 x (1.0002274)^1825

where n = 5 x 365 = 1825

Account value in 5 years = 7500 x 1.514307

Account value in 5 years = 11,357.30151

Account value in 5 years is $11,357.30

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Why do economists say that scarcity is everywhere?

The reason is that, despite the fact that there are only a finite number of productive resources available to produce goods and services like jeans, human needs for goods and services are virtually unbounded. This results in the phenomenon known as scarcity , according to economists.

The reason is that while there are only so many productive resources available to make things and services, such as jeans, human wants for commodities and services are essentially limitless.

This leads to the phenomenon that economists refer to as scarcity.

It's sometimes said that the fundamental issue in economics is scarcity.

We live in a world where human needs are limitless, but there is a finite amount of land, labor, and capital available to meet those needs.

Because scarcity is ubiquitous, it applies to every person, organization, and sector of the economy .

There won't be any issues in an economy if there are sufficient or abundant resources.

Therefore, lack causes economic issues.

Therefore, the reason is that, despite the fact that there are only a finite number of productive resources available to produce goods and services like jeans, human needs for goods and services are virtually unbounded. This results in the phenomenon known as scarcity, according to economists.

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In the probabilistic model, increasing the service level will __________.

In the probabilistic model , increasing the service level will a. increase the cost of the inventory policy.

The service level in a probabilistic inventory model denotes the likelihood that the inventory on hand will be sufficient to meet the demand for a given commodity. Raising the service level necessitates a larger inventory to increase the likelihood that the demand can be met with the available supply. As a result, raising the service level will often result in an increase in the cost of the inventory policy since the higher service level necessitates the acquisition or production of more inventory.

Cost of goods, holding costs, and ordering costs are some elements that affect the cost of an inventory policy. A decision-maker can select an inventory policy that balances the costs and advantages of various service levels by taking these aspects into account. The decision on the inventory policy will be based on the particulars of the issue, including the demand pattern, the lead time, and the expenses related to maintaining and ordering inventory.

Complete Question:

a. increase the cost of the inventory policy.

b. have no impact on the cost of the inventory policy.

c. reduce the cost of the inventory policy.

d. cannot be determined.

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Impact of consumer protection act on the marketing function

The impact of the consumer protection act on the marketing function is that  Under this act, people are veritably happy because this act tells the people the right rate of the particulars, and all effects which help consumers to cover them from high request prices, fake prices of particulars, etc.

The Consumer Protection Act 68 of 2008( “ CPA ”) has introduced significant restrictions on the way suppliers are permitted to request goods and services from consumers. The CPA is designed to cover consumers before they enter into a sale and indeed if they don't eventually distribute with the supplier.

It helps people to get their rights, on the other hand, it's a big problem for the dealer because they don't get important profit due to the action of this act. It helps consumers veritably much in marketing work.

Consumer Protection Act provides Consumer Rights to help consumers from fraud or specified illegal practices. These rights insure that consumers can make better choices in the business and get help with complaints.

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which generic business-level strategy is based on the intent to lower costs so that a company can lower prices and still make a profit? group of answer choices price differentiation strategy focus differentiation strategy broad differentiation strategy focus low-cost strategy broad low-cost strategy

The broad low-cost strategy generic business-level strategy is based on intent to lower costs so that company can lower prices and still make profit.

A low-cost strategy is a business approach that aims to provide products or services at a lower cost than competitors while maintaining quality standards. The strategy aims to attract price-sensitive customers who are looking for affordable alternatives. Companies adopting a low-cost strategy may achieve cost advantages by streamlining their operations, reducing overhead costs , or finding cheaper raw materials. To succeed in this strategy, companies need to focus on operational efficiency, reduce waste, and optimize resources. The low-cost strategy can be beneficial for companies operating in price-sensitive markets or facing intense competition. However, it requires careful planning and management to ensure that cost-cutting measures do not compromise product quality or customer experience.

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how much revenue did u.s. businesses earn from this holiday in recent years?

Holiday shopping accounted for 19.5% of annual retail revenue . Up to 30% of a retailer's annual sales can come from holiday sales.

The National Retail Federation reports that holiday sales increased by 4% in 2019 after a five-year average increase of 3.5%. In 2020, holiday retail sales increased 8.3%.

Even though we celebrate a wide variety of occasions , all of them bring people together in peace and harmony. Holidays of all kinds help us forget our problems and find the peace we need to grow as individuals and as a society.

Retail's busiest time of year is during the holidays. Department stores and specialty retailers can see up to a quarter of their annual sales in November and December. To ensure that they have enough stock on hand, businesses place orders for holiday and seasonal merchandise months in advance.

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which type of growth describes a company that grows quickly at first but then grows slower in later years?

The type of growth that describes a company that grows quickly at first but then grows slower in later years is often referred to as "curved " or "curvilinear " growth.

This type of growth pattern typically follows an " S-curve " shape, where the company experiences rapid growth during an initial phase of development, followed by a period of slower growth as it reaches maturity and saturation in its market.

The S-curve growth model is commonly used to describe the life cycle of a product, a market, or a company . In the early stages of development, a new product or company may experience high growth rates as it gains market share, establishes its brand, and achieves economies of scale. However, as the market becomes more crowded and competition increases, growth rates may slow down, and the company may need to focus on improving efficiency, innovation, or differentiation to sustain its competitive advantage and continue growing.

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which of the following can aid company strategists in identifying key success factors in their industry? multiple choice global distribution capabilities of suppliers product attributes and service characteristics that buyers consider to be crucial low switching costs of buyers and suppliers accurate filling of buyer orders short delivery time capability

Product attributes and service characteristics that buyers consider to be crucial can aid company strategists in identifying key success factors in their industry. Hence, option (b) is correct.

Product attributes are the characteristics that define an item; they are both objective and subjective facts that might assist a prospective buyer in making a purchase decision. Technical requirements, design elements including colour and size, manufacturing materials, and price are only a few of them. Initially, they can be loosely classified into two groups:

Physical traits that are tangible are those that can be perceived using any sense, such as colour, form, and size. These characteristics, for instance, all apply to smartphones and help potential buyers envision how the device will appear and feel in their hands.

Intangible qualities are non-physical traits that are difficult to describe just by looking at something. These hardware specifications for smartphones include things like battery life, internal storage, and screen resolution.

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in cultures with blank , team members will expect a clear hierarchy and a single leader with decision-making power. multiple choice question.

In cultures with a great deal of power distance, team members will expect a clear hierarchy and a single leader with the power to make choices.

A Low Power Distance culture prioritizes treating everyone equally, whereas a High Power Distance culture encourages showing considerable reverence to a person in authority .

The underpinning of societal order is inequality in High Power Distance cultures.

Team members will anticipate a clear hierarchy and a single leader with authority to make decisions in cultures with large power distances.

Belgian, French, Malaysian, and Arab cultures are a few examples of high-power distance cultures.

Organizations with a lower power distance are flat.

Employees and managers are regarded as nearly equals.

The Netherlands, the UK, the USA, Germany, and the Nordic nations are a few examples of low power distance cultures.

Therefore, in cultures with a great deal of power distance, team members will expect a clear hierarchy and a single leader with the power to make choices.

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Correct question:

In cultures with _____, team members will expect a clear hierarchy and a single leader with decision-making power.

the insurance company bears the risk when using prospective experience rating. group of answer choices true false

The following statement "the insurance company bears the risk when using prospective experience rating " is True.

Insurance is a type of financial loss protection in which one party agrees to compensate the other for loss, damage , or harm. This is a sort of risk management that is primarily used to reduce the risk of an eventual or uncertain loss.

A firm that offers insurance is known as an underwriter, insurance company, insurance company, or underwriter . A policyholder is a person or organisation that buys insurance, whereas an insured is a person or organisation that is protected by the policy .

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enables an organization to optimize its product development process, from design to market, while ensuring that it complies with industry, quality, and regulatory standards?

Lifecycle Data Management enables an organization to optimize its product development process, from design to market, while ensuring that it complies with industry, quality, and regulatory standards .

What is data lifecycle management?

Data entry to data destruction are all managed through the process of data lifecycle management (DLM) . Data is divided into phases based on many criteria, and it progresses through these stages when it completes various tasks or adheres to particular conditions.

What is a data life cycle diagram?

In order to manage business data within the parameters of the business process, the data lifecycle diagram is a crucial component. It shows how data is managed from conception through disposal. As a separate entity from business operations and activities, the data is viewed as a self-contained entity.

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the best selling brand of sunscreen on target to women ages 25-40 is an example of which core driver of the information age:

An illustration of an information core driver in the information age is the sunscreen brand that sells well to women between the ages of 25 and 40.

A brand is an item, product, or idea that is publicly set with the exception of similar pieces. It makes it simple to communicate and interesting topics. The process of developing and promoting a brand's name, attributes, and personality is known as branding.

When creating your marketing plan, you must focus on these 4 major brand image. Strong brand identity, brand recognition , brand history, and perceived brand are essential for a strong brand.

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What factors are used to determine the rank of a country from the Global Manufacturing Competitiveness Index

Education, talent , High-tech infrastructure and productivity are used to determine the rank of a country from the Global Manufacturing Competitiveness Index .

Manufacturing-related activities are increasingly evolving throughout worldwide nations. Manufacturing revenues and exports are boosting economic development, prompting countries to focus more on creating sophisticated manufacturing capabilities through investments in high-tech infrastructure and education.

Nations and businesses are working hard to advance to the next technological frontier and improve their economic well-being. And, as the digital and physical worlds of manufacturing collide, innovative technologies have become even more critical to corporate and national-level competitiveness.

In fact, in most advanced nations, technology - intensive sectors dominate the global manufacturing scene and appear to offer a solid path to achieving or maintaining manufacturing competitiveness.

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which career readiness competency represents a desire to learn and improve and is desired by employers because it drive creativity and innovation?

A proactive approach to learning Companies prioritizes career preparedness skills because they encourage creativity and innovation . It shows a drive to develop and learn.

A generalization of active learning called proactive learning aims to let go of irrational presumptions and so arrive at practical applications.

Proactive learning orientation Career readiness competency is valued by companies as it fosters creativity and innovation.

It reflects a desire to learn and grow.

The fact that proactive learning develops into a habit is one of its most noteworthy advantages.

Instead of waiting for a problem to occur and then trying to fix it, employees are more likely to look for solutions on their own and get a head start on their training.

Therefore, a proactive approach to learning Companies prioritizes career preparedness skills because they encourage creativity and innovation. It shows a drive to develop and learn.

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Acuity Knowledge Partners acquires PPA Group to expand automation capabilities for commercial lenders

LONDON, 01 July, 2024, – Acuity Knowledge Partners (“Acuity”), a leading provider of high-value research, analytics and business intelligence solutions to the financial services sector, today announced the acquisition of PPA Group (“PPA”).

PPA is a leading technology-enabled service provider to financial institutions in Germany and Switzerland, providing services focused on bespoke structuring of global financial and ESG data for commercial lending and ESG analysis.

With offices in Darmstadt, Germany and Zurich, Switzerland, PPA has over two decades of experience in working with leading European banks to help them better understand and utilise complex counterparty data. PPA’s software-enabled services, developed in-house, and deep domain expertise deliver accurate, cost-effective and secure results to its growing client base

“Advanced, accurate and timely data processing to assist financial institutions engaged in commercial lending is a key growth area for Acuity. The acquisition of PPA Group is a strategic investment to support Acuity’s commitment and growing presence in supporting best practices in commercial lending. I am also delighted that we will partner with the highly experienced PPA management team to scale this business and bring its capabilities to Acuity’s global client base” said Robert King, Chief Executive Officer, Acuity Knowledge Partners. “Our combined platform will provide clients with a market-leading service, and we look forward to integrating PPA’s innovative use of AI and Machine Learning into our portfolio of solutions. The acquisition also adds direct presence in two important and major European financial markets, Germany, and Switzerland.”

Heimo Saubach, Chief Executive Officer, PPA Group, said, “We have built a best-in-class data extraction and processing business serving European lending customers. Becoming part of Acuity Knowledge Partners will allow us to expand our market share globally and provide additional value to our existing customer base. It also means we can combine our advanced understanding of AI platforms with that of Acuity to create new and market-leading solutions. Acuity and PPA share the same mission of being a strategic and trusted partner to our customers and a great place to work for our employees. We feel very comfortable becoming part of Acuity and are confident that we can add value to our common endeavours.”

4GC acted as the exclusive M&A advisor for PPA.

About Acuity Knowledge Partners

Acuity Knowledge Partners (Acuity) is a leading provider of bespoke research, analytics and technology solutions to the financial services sector, including asset managers, corporate and investment banks, private equity and venture capital firms, hedge funds and consulting firms. Its global network of over 6,000 analysts and industry experts, combined with proprietary technology, supports more than 600 financial institutions and consulting companies to operate more efficiently, unlock their human capital and transform operations. Acuity is headquartered in London and operates from 10 locations worldwide.

Acuity was established as a separate business from Moody’s Corporation in 2019, following its acquisition by Equistone Partners Europe (Equistone). In January 2023, funds advised by global private equity firm Permira acquired a majority stake in the business from Equistone, which remains invested as a minority shareholder.

For further information, please visit www.acuitykp.com .

About PPA Group

PPA Group is a leading technology partner and service provider for the digitalization and preparation of corporate data on behalf of financial service providers. More than 50 commercial banks from the D-A-CH region have outsourced the process of data preparation for internal bank decision-making processes to PPA. Around 150 experts at the locations in Darmstadt (D) and Glattbrugg (CH) process more than 300,000 documents per year. PPA combines the capabilities of artificial intelligence and trained experts in a hybrid process approach and delivers individually structured data in customer-specific processes. Compliance with the regulatory requirements for outsourcing management for banks (Bundesbank, EBA, FINMA) is confirmed annually by external audits.

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Media contact: Stuti Das Global Head of Communications and PR Acuity Knowledge Partners [email protected]

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• Artificial intelligence in healthcare

DrAfter123/DigitalVision Vectors

10 high-value use cases for predictive analytics in healthcare

Predictive analytics can support population health management, financial success, and better outcomes across the value-based care continuum..

• Editorial Staff

As healthcare organizations pursue improved care delivery and increased operational efficiency, digital transformation remains a key strategy to help achieve these goals. Many health systems’ digital transformation journey involves identifying the value of their data and capitalizing on that value through big data analytics.

Of the four types of healthcare data analytics , predictive analytics currently has some of the highest potential for value generation. This type of analytics goes beyond showing stakeholders what happened and why, allowing users to gain insight into what’s likely to happen based on historical data trends.

Being able to forecast potential future patterns has game-changing potential as healthcare organizations aim to move from reactive to proactive, but those looking to leverage predictive analytics must first define relevant use cases.

In this primer, HealthITAnalytics will outline 10 predictive analytics use cases, in alphabetical order, that health systems can pursue as part of a successful predictive analytics strategy .

1. CARE COORDINATION

Improved care coordination can bolster patient outcomes and satisfaction, and predictive analytics is one way healthcare organizations can enhance these efforts. Predictive analytics is beneficial in hospital settings, where care coordination staff are trying to prevent outcomes like patient deterioration and readmission while optimizing patient flow.

Some healthcare organizations are already beginning to see success after deploying advanced analytics to reduce hospital readmissions .

In June, a research team from New York University (NYU) Grossman School of Medicine successfully built a large language model (LLM) known as NYUTron to predict multiple outcomes, including readmissions and length of stay.

The tool, detailed in a Nature study , can accurately forecast 30-day all-cause readmission, in-hospital mortality, comorbidity index, length of stay, and insurance denials using unaltered electronic health record (EHR) data. At the time of the study’s publication, NYUTron could predict 80 percent of all-cause readmissions, a five percent improvement over existing models.

According to a December 2023 NEJM Catalyst study , predictive models deployed at Corewell Health have seen similar success, keeping 200 patients from being readmitted and resulting in a $5 million cost savings.

In a 2022 interview with HealthITAnalytics , leadership from Children’s of Alabama discussed how real-time risk prediction allows the health system to tackle patient deterioration and pursue intensive care unit (ICU) liberation.

Alongside its applications for inpatient care management, predictive analytics is particularly useful for other preventive care uses, such as disease detection.

2. EARLY DISEASE DETECTION

Effective disease management is vital to improving patient outcomes, but capturing and analyzing the necessary data only became plausible with the advent of predictive analytics.

Using predictive analytics for disease management requires healthcare organizations to pool extensive patient data — including EHRs, genomics, social determinants of health (SDOH), and other information — to identify relevant trends. These insights can then be used as a starting point to guide early disease detection and diagnosis efforts, anticipate disease progression, flag high-risk patients, and optimize treatment plans and resource allocation.

The promise of big data and predictive analytics is valuable in infectious disease monitoring.

In a February 2024 PLOS One study , researchers from the University of Virginia detailed the development of an online big data dashboard to track enteric infectious disease burden in low- and middle-income countries.

The dashboard is part of the Planetary Child Health & Enterics Observatory (Plan-EO) initiative, which aims to provide an evidence base to help geographically target child health interventions.

The dashboard will pull data from various sources to map transmission hotspots and predict outbreaks of diarrheal diseases, which public health stakeholders can use to better understand disease burden and guide decision-making.

The impacts of infectious disease are often inequitable, which may lead some to question the role that predictive analytics plays in concerns about health equity. Like any advanced data analytics approach, these tools must be used responsibly to avoid perpetuating health disparities, but when used responsibly, predictive tools can positively impact equity efforts.

3. HEALTH EQUITY

Care disparities, bias and health inequity are rampant in the United States healthcare system. Researchers and clinicians are on the front lines of efforts to ensure that patients receive equitable care, but doing so requires healthcare stakeholders to gain a deep, nuanced understanding of how factors like SDOH impact patients .

Predictive analytics can help draw a wealth of information from the large, complex data needed to guide these efforts.

The health of those in marginalized communities is disproportionately impacted by housing, care access, social isolation and loneliness , food insecurity, and other issues. Effectively capturing data on these phenomena and designing interventions to address them is challenging, but predictive analytics has already bolstered these efforts.

Recently, researchers from Cleveland Clinic and MetroHealth were awarded over $3 million from the National Institutes of Health (NIH) to develop a digital twin-based, neighborhood-focused model to reduce disparities.

The Digital Twin Neighborhoods project uses de-identified EHR data to design digital replicas of real communities served by both organizations. Experts on the project indicated that by pulling geographic, biological, and SDOH information, researchers can better understand place-based health disparities.

Models developed using these data can simulate life course outcomes in a community. Tools that accurately predict the outcomes observed within a population’s EHRs can inform health equity interventions.

In 2021, United Healthcare launched a predictive analytics-based advocacy program to help address SDOH and improve care for its members. The system uses machine learning to identify individuals who may need social services support.

These insights are incorporated into an agent dashboard that member advocates can use, alongside more traditional tools like questionnaires, to gather more information from the patient about their situation. If necessary, the advocate connects the individual with support mechanisms.

Efforts like these also demonstrate the utility of predictive analytics tools in patient and member engagement.

4. PATIENT ENGAGEMENT

Patient engagement plays a vital role in enhancing healthcare delivery. The advent of big data analytics in healthcare provides many opportunities for stakeholders to actively involve patients in their care.

Predictive analytics has shown promise in allowing health systems to proactively address barriers to patient engagement, such as appointment no-shows and medication adherence.

In a 2021 interview with HealthITAnalytics , Community Health Network leadership detailed how the health system bolsters its engagement efforts by using predictive analytics to reduce appointment no-shows and conduct post-discharge outreach.

A key aspect of this strategy is meeting patients where they are to effectively individualize their care journeys and improve their outcomes.

Appointment no-shows present a significant hurdle to achieving these aims, leading Community Health Network to implement automated, text message-based appointment reminders, with plans to deploy a two-way communication system to streamline the appointment scheduling process further.

The health system took a similar approach to post-discharge outreach, successfully deploying an automated solution during the COVID-19 pandemic.

To further enhance these systems, Community Health Network turned to predictive analytics.  By integrating a predictive algorithm into existing workflows, the health system could personalize outreach for appointment no-shows. Patients at low risk for no-shows may receive only one text message, but those at higher risk receive additional support, including outreach to determine whether unmet needs that the health system can help address are preventing them from making it to appointments.

Data analytics can also support medication adherence strategies by identifying non-adherence or predicting poor adherence.

One 2020 study published in Psychiatry Research showed that machine learning models can “accurately predict rates of medication adherence of [greater than or equal to 80 percent] across a clinical trial, adherence over the subsequent week, and adherence the subsequent day” among a large cohort of participants with a variety of conditions.

Research published in the March 2020 issue of BMJ Open Diabetes Research & Care found that a machine learning model tasked with identifying type 2 diabetes patients at high risk of medication nonadherence was accurate and sensitive, achieving good performance.

Outside the clinical sphere, predictive analytics is also useful for helping organizations like payers meet their strategic goals.

5. PAYER FORECASTING

Payers are an integral part of the US healthcare system. As payer organizations work with providers to guide members' care journeys, they generate a wealth of data that provides insights into healthcare utilization, costs, and outcomes.

Predictive analytics can help transform these data and inform efforts to improve payer forecasting . With historical data, payers can use predictive modeling to identify care management trends, forecast membership shifts, project enrollment churn, and pinpoint changes in service demand, among other uses.

In June 2023, leaders from Elevance Health discussed how the payer’s emphasis on predictive analytics is key to improving member outcomes.

Elevance utilizes a predictive algorithm to personalize member experience by addressing diabetes management and fall risk. The predictive model pulls clinical indicators like demographics, comorbidities, and A1C levels to forecast future A1C patterns and identify individuals with uncontrolled or poorly controlled diabetes.

From there, the payer can help members manage their condition through at-home lab A1C test kits and increased member and care team engagement.

The second predictive tool incorporates data points — including past diagnoses, procedures, and medications, the presence of musculoskeletal-related conditions and connective tissue disorders, analgesic or opioid drug usage, and frailty indicators — to flag women over the age of 65 at higher risk of fracture from a fall.

Elevance then conducts outreach to these individuals to recommend bone density scans and other interventions to improve outcomes.

These efforts are one example of how predictive analytics can improve the health of specific populations, but these tools can also be applied to population health more broadly.

6. POPULATION HEALTH

While much of healthcare is concerned with improving individual patients’ well-being, advancing the health of populations is extremely valuable for boosting health outcomes on a large scale. To that end, many healthcare organizations are pursuing data-driven population health management .

Predictive analytics tools can enhance these initiatives by guiding large-scale efforts in chronic disease management and population-wide care coordination.

In one 2021 American Journal of Preventive Medicine study , a research team from New York University’s School of Global Public Health and Tandon School showed that machine learning-driven models incorporating SDOH data can accurately predict cardiovascular disease burden. Further, insights from these tools can guide treatment recommendations.

The early identification of chronic disease risk is also helpful in informing preventive care interventions and flagging gaps in care .

Being closely related to population health , public health can also benefit from applying predictive analytics.

Researchers from the Center for Neighborhood Knowledge at UCLA Luskin, writing in the International Journal of Environmental Health in 2021, detailed how a predictive model successfully helped them identify which neighborhoods in Los Angeles County were at the greatest risk for COVID-19 infections.

The tool mapped the county on a neighborhood-by-neighborhood basis to evaluate residents’ vulnerability to infection using four indicators: barriers to accessing health care, socioeconomic challenges, built-environment characteristics, and preexisting medical conditions.

The model allowed stakeholders to harness existing local data to guide public health decision-making, prioritize vulnerable populations for vaccination, and prevent new COVID-19 infections.

Alongside large-scale initiatives like these, predictive modeling can also support the advancement of precision medicine.

7. PRECISION MEDICINE

The emergence of genomics and big data analytics has opened new doors in the realm of tailored health interventions. Precision and personalized medicine rely on individual patients’ data points to guide their care and improve their well-being.

From cancer to genetic conditions, predictive analytics is a crucial aspect of precision medicine.

In 2021, a meta-analysis presented at the American Society for Radiation Oncology (ASTRO) Annual Meeting showed that a genetic biomarker test could accurately predict treatment response in men with high-risk prostate cancer.

The test analyzes gene activity in prostate tumors to generate a score to represent the aggressiveness of a patient’s cancer. These insights can be used to personalize treatment plans that balance survival risk with quality of life.

Researchers from Arizona State University (ASU) revealed in a 2024 Cell Systems paper that they developed a machine learning model to predict how a patient’s immune system will respond to foreign pathogens.

The tool uses information on individualized molecular interactions to characterize how major histocompatibility complex-1 (MHC-1) proteins — key players in the body’s ability to recognize foreign cells — impact immune response.

MHC-1s exist on the cell surface and bind foreign peptides to present to the immune system for recognition and attack. These proteins also come in thousands of varieties across the human genome, making it difficult to forecast how various MHC-1s interact with a given pathogen.

The ASU research addressed this by analyzing just under 6,000 MHC-1 alleles, shedding light on how these molecules interact with peptides and revealing that individuals with a diverse range of MHC-1s were more likely to survive cancer treatment.

Using the model, providers could potentially forecast pathological outcomes for patients, bolstering treatment planning and clinical decision-making.

In addition to these successes at the microscopic level, predictive analytics is also useful on the macro level in healthcare.

8. RESOURCE ALLOCATION AND SUPPLY CHAIN

Optimization of the supply chain and resource allocation ensures that providers and patients receive the equipment, medications, and other tools that they need to support positive outcomes. Data analytics plays a massive role in this, as supply chain management and resource use rely heavily on accurately recording and tracking resources as they move from the assembly line into the clinical setting.

Predictive analytics takes this one step further by helping stakeholders anticipate and address supply chain issues before they arise while optimizing resource use.

Seattle Children's Hospital is using predictive modeling in the form of digital twins to help the health system streamline hospital operations , particularly resource allocation.

By using digital twin simulation to “clone” the hospital, stakeholders can model how certain events, strategies, or policies might impact operational efficiency. This capability was critical in the wake of COVID-19, as it allowed the health system to identify how rapidly its personal protective equipment (PPE) supplies would diminish, forecast bed capacity, and generate insights around labor resources.

Predictive analytics can also be used by distinct parts of the supply chain to help prevent shortages.

The 2022 infant formula shortage is one example of how supply chain disruptions can significantly impact health.

One potential way for parents to deal with the formula shortage was to turn to human breast milk banks, which distribute donated milk to vulnerable babies and their families. However, accomplishing this vital work requires milk banks to effectively screen donors, accept donations, process and test them to ensure they’re safe, and dispense them.

In an interview with HealthITAnalytics , stakeholders from Mothers' Milk Bank at WakeMed Health & Hospitals described how data analytics can help optimize aspects of this process.

A crucial part of ensuring that milk is available to those who need it is tracking milk waste. Milk can be wasted for various reasons, but the presence of bacteria is one of the primary causes. To address this, the milk bank began analyzing donor records to determine what factors may make a batch of milk more likely to test positive for bacillus .

The milk bank can then use the insights generated from the analysis to predict which donors may be at high risk for having bacillus in their milk, allowing milk from these individuals to be tested separately. This removes any bacillus -positive samples before the milk is pooled for processing.

Predictive analytics is also helpful in assessing and managing risks in clinical settings.

9. RISK STRATIFICATION

Patient risk scores have the potential to improve care management initiatives, as they allow providers to formulate improved prevention strategies to eliminate or reduce adverse outcomes. Risk scores are used to help understand what characteristics may make a patient more susceptible to various conditions.

From there, the scores can inform risk stratification efforts, which enables health systems to categorize patients based on whether they are low-, medium- or high-risk. These data can show how one or more factors increase a patient's risk.

Risk stratification is one of the most valuable use cases for predictive analytics because of its ability to prevent adverse outcomes.

In February 2024, leaders from Parkland Health & Hospital System (PHHS) and Parkland Center for Clinical Innovation (PCCI) in Dallas, Texas, detailed one of these high-value use cases.

Parkland’s Universal Suicide Screening Program is an initiative designed to flag patients at risk of suicide who may have flown under the health system’s radar through proactive screening of all Parkland patients aged 10 or older, regardless of the reason for the clinical encounter.

During the encounter, nursing staff ask the patient a set of standardized, validated questions to assess their suicide risk. This information is then incorporated into the EHR for risk stratification.

These data are useful for stakeholders looking to better understand patients’ stories, including factors like healthcare utilization before suicide. Coupling these insights with state mortality could help predict and prevent suicide in the future.

Risk stratification is also crucial for improving outcomes for some of the youngest, most vulnerable patients: newborns.

Parkland also runs an initiative that uses SDOH data to identify at-risk pregnant patients and enable early interventions to help reduce preterm births .

The program’s risk prediction model and text message-based patient education program have been invaluable in understanding the nuances of preterm birth risk for Parkland patients. Major risk factors like cervical length and history of spontaneous preterm delivery may not be easy to determine for some patients. Further, many preterm births appear to be associated with additional risk factors outside of these – like prenatal visit attendance.

Using these additional factors to forecast risk, Parkland has developed clinical- and population-level interventions that have resulted in a 20 percent reduction in preterm births.

These use cases, among other things, demonstrate the key role predictive analytics can play in advancing value-based care.

10. VALUE-BASED CARE SUCCESS

Value-based care incentivizes healthcare providers to improve care quality and delivery by linking reimbursement to patient outcomes. To achieve value-based care success, providers rely on a host of tools: health information exchange (HIE), data analytics, artificial intelligence (AI) and machine learning (ML), population health management solutions, and price transparency technologies.

Predictive analytics can be utilized alongside these tools to drive long-term success for healthcare organizations pursuing value-based care.

Accountable care organizations (ACOs) are significant players in the value-based care space, and predictive modeling has already helped some achieve their goals in this area.

Buena Vida y Salud ACO partnered with the Health Data Analytics Institute (HDAI) in 2023 to explore how predictive analytics could help the organization keep patients healthy at home.

At the outset of the collaboration, the ACO’s leadership team was presented with multiple potential use cases in which data analysis could help with unplanned admissions, worsening heart failure, pneumonia development, and more.

However, providers were overwhelmed when given risk-stratified patient lists for multiple use cases. Upon working with its providers, the ACO found that allowing clinicians to choose the use cases or patient cohorts they wanted to focus on was much more successful.

The approach has helped the ACO engage its providers and enhance care management efforts through predictive modeling and digital twins. These tools provide fine-grain insights into the drivers of outcomes like pneumonia-related hospitalization, which guide the development of care management interventions.

These 10 use cases are just the beginning of predictive analytics' potential to transform healthcare. As data analytics technologies like AI, ML and digital twins continue to advance, the value of predictive analytics is likely to increase exponentially.

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An extended approach to value chain analysis

Klemen knez.

Centre of International Relations, University of Ljubljana, Ljubljana, Slovenia

Andreja Jaklič

Metka stare, associated data.

The datasets analysed during the current study are available at http://www.wiod.org .

In the article, we propose a comprehensive methodology of value chain analysis in the international input–output framework that introduces a new measure of value chain participation and an extended typology of value chains, with the novel inclusion of domestic value chain to address the extent of fragmentation of purely domestic production. This allows for the simultaneous analysis of both global and domestic production fragmentation, the complex patterns of their evolution and their impact on economic development. The main contribution of the proposed methodology is conceptual: it permits the measurement of all value chain paths that pass through each country-sector from production to final consumption, whether the path includes downstream linkages, upstream linkages or their combination. Empirical application of this methodology shows the importance of including domestic fragmentation in value chain analysis: The fragmentation of both global and domestic levels of production has a significant positive correlation with economic growth. This implies that the effects of global production fragmentation must be analysed together with the changing structure of the fragmentation of domestic production to obtain the whole picture, one that might provide important information for policymaking and industrial policy.

Introduction

In recent decades, the growing complexity of the division of labour has been reflected in the fact that ever more production is occurring within value chains, both at home and abroad. Theoretical and empirical approaches to the analysis of value chains have advanced rapidly, yet are very eclectic and heterogeneous. The earliest definitions of commodity chains 1 date back to the world-systems 2 theory: “What we mean by such chains is the following: take an ultimate consumable item and trace back the set of inputs that culminated in this item— the prior transformations, the raw materials, the transportation mechanisms, the labour input into each of the material processes, the food inputs into the labour. This linked set of processes we call a commodity chain (Hopkins and Wallerstein 1977 )”. In the 1990s, the research programme of global commodity chains was first systematically outlined by Gereffi’s seminal contribution (Gereffi 1994 ) that defined three interlocking dimensions of the research: the input–output dimension, the spatial dimension, and the question of commodity chain governance 3 . This research period was characterised by moving away from a historical and macroeconomic perspective towards a special focus on industrial chains and the inter-firm cooperation perspective, with numerous case studies on value chains. The global value chain framework emerged early in the new century with the express aim of unifying the previous heterogeneous research (Gereffi 1999 ; Gereffi et al. 2001 ). On one hand, the global value chain approach increased the focus on the enterprise level and merged with the literature from international business and management 4 , while also drawing from the new institutional transaction cost approach 5 . On the other hand, the creation of international input–output tables 6 led to a revival of the aggregated macroeconomic approach to global value chains, albeit with a different focus than the world-systems approach. 7

In this article, we present a new methodology for measuring different value chain participation rates in the international input–output framework. Compared to the most widely used measurement of value chain participation introduced by Wang et al. ( 2017 ), we make two fundamental conceptual enhancements.

First, our methodology creates a single and consistent measurement of value chain participation on the country-sector level, as opposed to the two (upstream and downstream) participation rates that feature in Wang’s methodology. The argumentation and logic used to derive a single value chain participation share on the country-sector level is very similar to the approach of Arto et al. ( 2019 ), which combines the source- and sink-based approaches to export decomposition. The idea is that decomposition based on final demand (sink-based decomposition) is independent of the decomposition of downstream value added (source-based) and thus both can be linearly combined to grasp both the information regarding the source of value added as well as the path to final demand simultaneously. Methodologies of export decomposition have recently seen significant improvements (Arto et al. 2019 ; Borin and Mancini 2019 ; Miroudot and Ye 2021 ). However, the value chain participation rate methodologies either still chiefly rely on the value-added export matrix to describe the value flows between any two country-sectors in the economy (Johnson and Noguera 2012 ) and result in separate upstream and downstream participation rate measures or combine a sink- and a source-based measure in merely one-sided, forward-looking measures. Our approach to value chain decomposition no longer uses the value-added export matrix and instead breaks down the asymmetric value chain stemming both downstream and upstream from each country-sector concerned simultaneously . Creating a single consistent variable on the country-sector level that measures the overall level of participation in value chains enables the empirical testing of many research theses that were previously either limited to the aggregate level or had to be articulated separately in terms of measuring the impacts of upstream and downstream value chain integration.

Second, our methodology allows extensions of the value chain typology that are not possible with Wang’s approach to the decomposition of production activities or with export decompositions. We introduce a novel measure of the domestic value chain participation rate to measure the share of production which represents the extent of the fragmentation of domestic production. In place of a single and undifferentiated domestic component, we distinguish domestic production, which is fragmented (involving measurable cooperation among domestic firms), and domestic production, which is not fragmented (consisting of producing direct value for consumption without the cooperation of domestic firms). This makes our concept of the domestic value chain a completely new and different concept compared to Wang’s domestic component, which does not distinguish the two and combines both categories within a single undifferentiated concept. While Wang’s share of the domestic component is only a simple residual—a negation of the share of the fragmentation of global production and the global Ricardian trade share that does not provide information about the nature of the domestic economy, our novel methodology allows us to measure the extent of fragmentation of domestic production in addition to the usual study of the fragmentation of international production.

We aim to use our approach to provide methodological tools that facilitate exploration of the complex interrelationship of global and domestic value chains and their evolution over time. We believe this will add to understanding of the diverse patterns of the structural integration of various countries/sectors and the different effects of such patterns on economic development. While this is primarily a methodological contribution, we shall use elementary empirical data to try to show the possible link between the level of fragmentation of global and domestic production and overall economic growth.

The article is structured as follows: In Sect. 2 , we review the existing value chain indicators and address their shortcomings. In Sect. 3 , we present our methodology. In Sect. 3.1 , we present a new conceptualisation of value chain in the international I–O framework and define our object of disaggregation. A new value chain typology is presented in Sect. 3.2 where we also derive participation shares. In Sect. 4 , we present an example of empirical application and some basic empirical results of the new methodology to show the insights into economic structures that can be gained by using the new value chain measures and which links exist between value chain integration patterns and overall economic growth. Finally, we discuss the contributions of the paper, its limitations and possibilities for further research.

The most recent macroeconomic analyses of global value chains rely on the international input–output methodology. As international I–O data are essentially an integrated standard accounting data set harmonised on the sectoral level, information is lacking on the typology of value chain governance. This means the international I–O database cannot be the sole source for the study of production networks, which theoretically differ from purely open trade transactions by including at least some level of hierarchy, and which investigate the local embedding of production linkages (Buckley  2009 ; Henderson et al. 2002 ; Hess and Coe 2006 ; Hortaçsu and Syverson 2009 ). However, the general framework of global value chains can function without such distinctions and this makes the international I–O data set one of its most important sources of information. The key benefit of applying the I–O methodology in global value chain analysis is that aggregated information about the structure of value chains can be obtained, as opposed to isolated firm-specific case studies that can provide a more detailed understanding of different aspects of a given value chain. Thus, of the three dimensions of commodity chain research noted by Gereffi ( 1994 ), both the I–O aspect and the spatial dimension, can be considered in the international I–O approach, while the governance aspect cannot. Various aggregated and sectoral global value chain indicators, indices and measures have been proposed, all derived from the international I–O framework. GVC indicators may be roughly divided into measures of length 8 and participation rates, which we will discuss briefly.

Early I–O measures of the GVC structure were simple upstream and downstream indicators that corresponded to the measure of distance to final demand (upstream) and the Leontief measure of backward linkage (downstream) and were often referred to as the length of a value chain (Ahmad et al. 2017). Fally ( 2011 ) and Antràs et al. ( 2012 ) defined the downstream indicator to “reflect how many plants (stages) are involved in production one after the other” up to the point observed and the upstream indicator to “measure how many plants this product will pass through (e.g. by assembly with other products) before it reaches final demand (Fally 2011 , 10)”. Fally ( 2011 ) defined them as the number of vertical stages weighted by the value added of each stage, with the distance between each stage set to 1. 9 Since then, the average vertical distance has been the basic measure of the length of the value chain in the international I–O framework. Miller and Temurshoev ( 2015 ) further specified the existing measures by presenting upstream and downstream indicators in a matrix formulation using Ghosh’s forward and Leontief’s backward coefficient matrices (Ghosh 1958 ; Leontief 1936 ). These upstream and downstream measures are simple measures of the upstream and downstream length of value chains measured by the average vertical distance. Within this framework, further improvements were introduced by Muradov ( 2016 ), who focused on separating the domestic from the global production component while calculating the length of value chains.

The existing dominant conceptualisation of GVC participation measures is largely based on the work of Johnson and Noguera ( 2012 ), who produced a value-added export matrix that captures information on value flows in the economy between any two points (country-sectors) in the economy. This provides the basis for the disaggregation of value on the country-sector level, depending on whether the value was produced domestically for domestic consumption or involved cross-border transactions for either final or productive consumption (Koopman et al. 2014 ; Los et al. 2015 ; Wang et al. 2017 ). Since the value-added export matrix tells us about the source and destination of value added and covers all possible paths between any two country-sectors in the economy, there are two indicators of the share of GVC participation—the upstream and downstream share. The conception of the upstream participation share of participation starts from the value added of individual industries (country-sectors), disaggregating all possible paths leading to the realisation of their value, while the conception of the downstream share of participation starts with final consumption, disaggregating all possible paths of the downstream production linkages. Within this framework, disaggregation is defined on the domestic part, the “Ricardian trade” in finished goods, the simple GVC and the complex GVC, which is currently the most widely used accounting framework for GVC participation and thus far has been used by the best-known research on GVC carried out jointly by the WTO, the WB group, the OECD, IDE-JETRO, RCGVC-UIBE and the China Development Research Foundation (GVC Development Reports). Further improvements and clarifications of the framework were made by Borin and Mancini ( 2019 ), who derive their own measure of GVC-related bilateral trade flows by decomposing export to that attributable to traditional trade and GVC trade. Their indicator is composed of source-based backward and sink-based forward parts of their export decomposition, which can be calculated in a bilateral, country and world setting.

The development of I–O participation share measures of value chains, which are the primary interest of this article, evolved simultaneously with the development of methodologies of decomposing trade in value added (Johnson and Noguera 2012 ) as well as value added in trade (Arto et al. 2019 ; Borin and Mancini 2019 ; Miroudot and Ye 2021 ). However, despite similarities and some conceptual and formal mathematical overlapping, the fields of value chain participation share measures and value added in trade are driven by quite distinct research questions and research interests. On one hand, principal interest in decomposing exports is the correct evaluation of cross-border flows (properly removing double counting), assessing trade policy impacts and conducting overall impact analysis, either in a bilateral setting or with a focus on a specific country. On the other hand, value chain participation measures attempt to grasp the structure of an economy, sectoral and country interdependencies and the specific embeddedness of each production unit in different value chain structures, both at home and abroad. Value chain participation share measures usually correspond to a share of production, which statistically satisfies certain a priori criteria, such as “at least two cross-border transactions” or “at least one cross-border production sharing transaction”. The reviewed literature has contributed to better understanding of value chains and their I–O applied research, but still suffers two shortcomings that we try to address and improve with our approach.

The first main shortcoming of all current value chain participation share indicators is the lack of a single uniform measure for different value chain participation rates on the country-sector level. First, the value chain decomposition of Wang et al. ( 2017 ) results in downstream and upstream value chain participation rates, which provide two different types of information at the country-sector level. This is relevant for some types of analysis that deal with the relationship between upstream and downstream participation in GVCs, but there is a variety of situations where a common measure of GVC participation, defined uniformly on the country-sector level, is required either as the main object of the analysis or as a supplementary or control variable. 10 Second, GVC measures based on the decomposition of exports, even though they overcome the sink- and source-based decomposition in one unifying framework of export decomposition (Arto et al. 2019 ; Borin and Mancini 2019 ), are conceptually unable to offer a consistent solution to the question of a single country-sector value chain participation measure. That is because the criteria for export decomposition (separating domestic value added from foreign value added and the removal of double counting) do not correspond with the general criteria for different value chains on the country-sector level (the share of production with a certain number of cross-border transactions). Although export can be decomposed both with regard to the origin of the value added as well as the final demand, the very fact that the object of decomposition is export means it has a one-sided, forward orientation since export decomposition cannot address the fragmentation of production of a country-sector that has little or no exports (but can still form part of the fragmentation of a global value chain downstream). In this sense, the attempt by Borin and Mancini ( 2019 ) to provide a GVC measure of bilateral trade by decomposing exports cannot identify the share of production of a given country-sector which satisfies the criterion of a certain number of cross-border transactions, but only examines its forward part and is hence conceptually similar to Wang’s forward GVC measure. Our attempt to solve this issue demands the decomposition of the gross output (total output) of each country-sector to simultaneously account for both downstream and upstream value chain linkages.

The second major shortcoming of existing value chain indicators is the lack of a measure of domestic value chain fragmentation. The decomposition put forward by Wang et al. ( 2017 ) includes a broadly defined “domestic component”, which covers all of the value that does not comply with the GVC and Ricardian trade criteria. One of the major contributions of this article is to conceptually further divide this broad domestic component into a first part which comprises domestic production fragmentation (involving production sharing between at least two domestic firms) and the second part which does not. This yields new information regarding the share of production not involved in the fragmentation of global production, but is part of the fragmentation of domestic production and enables research into the role of domestic production fragmentation, which was impossible with the existing conceptualisations. As a result of the present disaggregation of participation shares into the “domestic component” and the GVC participation rates (and the Ricardian trade share) consisting of a simple duality that in its construction sums to 1, the share of the domestic component is never used in regressions (due to collinearity) and never even examined as a theoretical concept. It is simply a residual, a share that does not interest researchers given that all the information they disaggregate is included in their GVC participation rates. The existing approaches are used by researchers to focus exclusively on the international dimension of the fragmentation of production, neglecting the potential held by the international I–O methodology that allows analysis of domestic production fragmentation. Our approach is breaks ground in this area as it proposes a new concept of domestic fragmentation able to be measured on its own and according to its own definition and that is not collinear with the sum of the GVC participation rate.

Our methodological approach starts with the formal criteria, which is common for most of the GVC literature where value chains are defined according to certain transaction criteria (number of cross-border production-sharing transactions or similar). It is important to note that any such criteria are arbitrary and potential multiplicity of such criteria and hence value chain typologies can coexist and offer researchers some leeway in their empirical applications. 11 With a view to creating a uniform value chain measure on the country-sector level, we use the total output of each country-sector as the starting point of our disaggregation. Decomposing total output (as opposed to export or total value added) enables us to simultaneously grasp both the downstream and upstream value chain paths as well as the structure of the economy that is entirely domestic. Our decomposition begins with a set of the presented value chain tree matrices ( τ i ) which describe all of the value chain paths, from any country-sector of primary origin to any country-sector of production for final consumption that passes through (include a production stage of) a single particular country-sector. The logic of our approach is very similar to that of Arto et al. ( 2019 ) for combining the sink- and source-based decomposition of exports: because the decomposition of paths to final demand is independent of the decomposition of downstream value added, these decompositions can be linearly combined to capture both types of information in a single decomposition along two different dimensions at the same time. The big distinction with this approach is that object of decomposition is different—in our case, it is the total output (gross output) of each country-sector. Our choice of the object of decomposition is a prerequisite for properly capturing downstream linkages and, more importantly, properly accounting for the domestic structure of the economy. This formulation is the first attempt to capture information concerning the asymmetric value chain tree, which is a specific feature of each individual country-sector (Fig. ​ (Fig.1). 1 ). The proposed value chain tree matrices are unique in that they allow us to simultaneously capture the structure of the downstream and upstream value chain paths and to define value chain participation rates as a single measure for each country-sector. The crucial point of the proposed methodology is to enable the disaggregation of value chains based solely on the structure of value chain paths—taking into account whether these paths include only domestic production fragmentation, international production fragmentation or no production fragmentation at all. This allows us to introduce the concept of domestic value chain fragmentation that simply cannot be created within the existing framework of 2 separate participation indices. This multiplies the research opportunities offered by the value chain methodology based on the international input–output structure by permitting general analysis of the fragmentation of both domestic and global production and their interdependence along with any mutual effects of their development.

Value chain tree. Source: own conceptualisation and design. Arrows represent production-sharing transactions—buying and selling of intermediate products for production. Orange colour denotes production that does not involve any production sharing, while any combination of red or orange paths denotes domestic production fragmentation. Any value chain path which includes a cross-country production-sharing transaction (a black arrow) is part of a global value chain from the perspective of the particular unit in focus. The paths of value creating and value realisation in a general case continue to branch ad infinitum (three levels are chosen only for demonstration purposes)

Applying this methodology, we show that increasing fragmentation of global production in recent decades has been a general trend for most countries (with a backlash in later years), but different institutional arrangements and structural economic positions led to various types of global economic integration, bringing diverse effects for domestic fragmentation. With our methodology, we shall empirically demonstrate that in many countries with high growth and ever stronger global integration domestic fragmentation also increased. However, one can find cases where domestic fragmentation stagnated or even declined whereas fragmentation of the global value chain increased. The different types of integration in global value chains are the outcome of several structural and institutional developments. 12 On one hand, the simultaneous increase in domestic and global fragmentation might only be a consequence of the growing complexity and division of labour. Yet, on the other hand, the simultaneous rise in global fragmentation and drastic decline in domestic integration might be due to the fracturing of domestic vertically integrated companies, parts of which are integrated into global value chains as subsidiaries, or due to the gradual replacement of domestic suppliers by globally traded inputs, which may increase following a foreign takeover or privatisation. The wide range of possibilities mean that every production unit may hold a different structural position within global production as a whole, and different structural positions may imply varying levels of dependence, which can be a factor of economic performance, especially during a crisis (Horvath and Grabowski 1999 ).

The value chain tree

Conceptualisation.

We understand a value chain as a series of stages in the production of a product or service for the end user, where each stage adds value and the total value of the end product is the sum of the value added in each stage. For a value chain to exist, there must be at least two separate production stages. The existing GVC framework is analytically and empirically based on the idea that value is created in the production process and added to the value already present in the intermediate goods being used. The old value (value of intermediaries) is only transferred to the new product, while the newly created value is added linearly to the transferred value. The same idea also lies behind the elimination of double counting in standard gross trade statistics and exploration of the hidden underlying trade in value added, which provides insight into the international structure of trade (Arto et al. 2019 ; Johnson and Noguera 2012 ; Miroudot and Ye 2021 ). We make the same basic assumptions for value chain analysis.

We examine the structure of the economy from the perspective of a small unit 13 (country-sector) and capture its structural position within domestic and international production by measuring the degree of integration into domestic or global value chains. Each production unit is located within the production structure with a number of production-sharing transactions. On one side, the conditions of production are linked to the inputs produced by other firms in downstream linkages and, on the other, the final consumption of its product may only be reached after a series of upstream linkages in which its output is used as an input by other firms.

Accordingly, if one concentrates on a specific unit (country-sector) and aims to capture the upstream and downstream value chain linkages simultaneously , the value chain can be viewed as a tree, in contrast to the snake or spider analogy (see Fig. ​ Fig.1 1 ). 14 In the general case, the product is partly consumed immediately after production but also partly sent on to further stages of production and from each of these upstream stages it is further decomposed in the same way (etc., ad infinitum), spreading out like twigs and leaves until it ends completely in final consumption. Similarly, the primary value-creating activity can be represented by the structure of the roots, whereby value is only partially created in each stage since it requires pre-existing intermediates, which in turn are further decomposed in the same way ad infinitum.

To conceptualise and measure the value chain structure of each specific smallest unit of analysis (country-sector), we introduce the value chain path concept. From the perspective of a firm, a value chain path is a series of transactions between firms that lead from a value-adding process to final demand. While currently no data exist that would account for every transaction between all firms 15 , firm transactions still represent a basis for any I–O sectoral aggregation, which can help us detect tangible differences in the value chain path structure in different country-sectors. While it is impossible with the given limits of accounting data to follow a certain value chain path of each specific product of each specific firm, it is nevertheless possible to analyse the average sectoral structure of value chain paths subject to whether the aggregated transactions between firms (and to the final consumer) are domestic or global. Our use of the signifier “transactions between firms” and “production-sharing transactions” thus does not refer to individual transactions, but instead refers to the information captured by the aggregated sectoral international I–O data regarding the average structure of value chain transactions. Since we do not focus on following transactions for an individual product but distinguish domestic from cross-border transactions between production units, aggregated I–O data are a sufficient starting point. While the accounting rules require transactions between firms in the same sector and the same country to be formally accounted (represented in aggregated form by the purely diagonal elements of the international Leontief coefficient matrix), the same goes for transactions between domestic firms from different sectors (represented in aggregated form by the block diagonal elements of the international Leontief coefficient matrix with purely diagonal elements 0). In this aggregated setting, one can differentiate between domestic and cross-border transactions (quantitatively in terms of shares), which gives the basis for decomposing different value chain paths based on the criterion of the number of cross-border or domestic production-sharing transactions. As shown in Fig. ​ Fig.1, 1 , the value chain path can be decomposed with respect to two dimensions: Origin (where the value was primarily created) and the final stage of production (where the end product for consumption is finished).

Our goal of deriving a single value chain participation share measure on the country-sector level requires the derivation of an object able to track the value passing through a specific country-sector in focus along all possible paths from its origin to its end use. In this way, we decompose the value that forms part of the production process of a given country-sector along all its paths, which not only include the downstream paths leading to the country-sector under study and the upstream paths leading from it to final consumption, but also, and above all, the paths that combine upstream and downstream linkages and pass through that country-sector. In general, any value share can originate in any country-sector, and the same value share can also reach final consumption as a product of any country-sector. Compared to the approach of Johnson and Noguera, we add a third dimension 16 —the midpoint—the siphon through which the value from any origin to any final stage flows (Fig. ​ (Fig.1), 1 ), by combining decompositions based on value added and the final demand value chain path. This approach relies on similar reasoning as that of decomposing exports based on both value added and final demand (Arto et al. 2019 ).

The value chain tree of each country-sector is defined as the structure of the value chain paths, where this country-sector is the siphon via which the value chain paths pass. We show that each unit of analysis (country-sector) has a unique value chain structure that represents its structural position in the economy. Its output can be decomposed along every possible path within its value chain tree—i.e. along every value chain path that has its primary origin in any country-sector, passes through downstream linkages to the production stage of the country-sector which defines the value chain tree (the siphon), and ends in final consumption through upstream linkages as the final product of any country-sector.

Understanding the structure of value chains by empirically measuring all such paths of each country-sector (the smallest unit of analysis) is already an end in itself and can help with further understanding of the economy and its changing structure in terms of global integration, its specific regional and sectoral forms, and the complex interactions between domestic and global production fragmentation.

The object of disaggregation is a country-sector’s total output. Each country-sector’s total output is disaggregated along both downstream and upstream linkages that are unique to its specific value chain structure. Downstream disaggregation represents all possible value chain paths from the origin of production and upstream disaggregation all possible paths to satisfy the final demand, both with respect to the unique value chain tree of each country-sector. In this way, we disaggregate the same object—the total output of each country-sector—simultaneously along its downstream and upstream paths.

In contrast to approaches based on the matrix of value-added exports (Johnson and Noguera 2012 ; Wang et al. 2017 ) to cover all value-added flows between any two country-sectors in an economy, we propose a new object—a set of matrices that describe the value chain structure of each country-sector separately, covering all value chain paths from each primary origin to each final stage via the output of a single specific country-sector (Fig. ​ (Fig.1). 1 ). In this conceptualisation, each country-sector has a corresponding value chain tree described by the value chain tree matrix—while the value chain structure of the economy as a whole is described by the set of such matrices.

We derive our disaggregation within the static international Leontief demand-driven model. C , F and x are the main accounting datasets representing the intermediate consumption matrix, final consumption matrix and total output vector. The Leontief coefficient matrix is usually derived as A = C x ^ - 1 . The variables with hat are vectors transformed into diagonal matrices, f ^ represents a diagonal matrix of final demand and v ^ C a diagonal matrix of value-added coefficients. 17 The usual pairs of indices characterising the country and sector of origin ( s , i ) and the final destination ( d , j ) are replaced by a single index for each country-sector for more transparent notation. Since we are no longer working in the n × n dimensional space, but in the n × n × n dimensional space, we would need 3 pairs of indices, 1 pair for the country-sector of origin, 1 pair for the final stage and also 1 pair for the country-sector, which is the siphon through which all possible value chain paths characterise its specific value chain structure. Instead, we are working with only 3 indices, one for the country-sector of origin ( k ), one for the final stage country-sector ( j ) and one to characterise the country-sector value chain tree—the country-sector representing the siphon through which the value chain paths pass ( i ). 18

We start with the upstream part, by using standard Leontief’s derivation: x = ( I - A ) - 1 f , 3.3

Definition 1

Upstream output decomposition W :

W = x ^ - 1 ( I - A ) - 1 f ^ .

The matrix W represents the upstream output decomposition along all upstream value chain paths. Its element w ij represents the share of the total output of country-sector i that reaches final consumption as the end product of country-sector j , along all possible upstream production fragmentation paths in the economy. The i th row of W represents the disaggregation of the total output of the i th country-sector into output shares according to its final production stages that account for all direct and indirect paths of the upstream value transfers leading to the full realisation of total output (by being used directly or indirectly by other country-sectors as intermediate productive consumption). Each i th row of W may thus be characterised as a discrete probability distribution. On one hand, the upstream output shares of each country-sector i add up consistently to 1: ∑ j = 1 n w ij = 1 ∀ i . On the other hand, there is a clear economic interpretation of the probability distribution: w ij represents the probability that a randomly selected part of the total output of the i th country-sector will eventually be consumed as the final product of country-sector j , along any upstream value chain path.

For the downstream part, we begin with identity:

Definition 2

Downstream output decomposition Z :

Z = v C ^ ( I - A ) - 1 .

The matrix Z represents the downstream output decomposition along all downstream value chain paths. Its element z ki represents the share of the total output of country-sector i that is primarily created in country-sector k , along any possible downstream production fragmentation path in the economy. The i th column of Z represents the disaggregation of the total output of the i th country-sector into output shares, which represent all direct and indirect paths of the downstream value transfer from each country-sector that has contributed to the production of its output (through the direct or indirect production of intermediate productive consumption used by i ). Each i th column of Z may thus be characterised as a discrete probability distribution. On one hand, the downstream output shares of the individual country-sectors i add up consistently to 1: ∑ k = 1 n z ki = 1 ∀ i . On the other hand, there is a clear economic interpretation of the probability distribution: z ki represents the probability that a randomly selected part of the total output of the i th country-sector was produced by country-sector k , along any downstream value chain path.

The two matrices presented, W and Z , may appear as two sides of the same coin—similar to forward and backward decomposition, which has largely been exhausted in the international input–output literature. However, if we focus on a single country-sector ( i ), the i th column of Z and the i th row of W represent two probability distributions that take the transfers in the value chain into account, which result in two completely different and independent types of information. The i th column of Z contains information on the downstream structure of the value chain of the respective i th country-sector and the i th row of W contains information on the upstream structure of the value chain of the respective i th country-sector. For a given i th country-sector, the two probability distributions are asymmetrical. Most importantly, both probability distributions relate to the same object of investigation—the total output of country-sector i .

Using the total output of each country-sector seems to be the only way to disaggregate the same object into its upstream and downstream value chains. The object of decomposition of the upstream part (which is decomposed based on the paths to final demand) of a certain country-sector can be either its total output or total value added (even its export). However, the same is not possible for the downstream part (which is decomposed according to the origins of its value-added). The object of decomposition of the downstream part of a certain country-sector can only be its total output, which also makes up the totality of value-added shares along the whole downstream value chain. 19 In other words, the country-sector’s total output is an object that has both an upstream and a downstream path, while total value added and total export represent only that part of the output which has an upstream path, even if this upstream path is disaggregated by value-added origin. Using the total output share as the basis for disaggregating to the individual country-sector level is therefore a legitimate choice. This mainly explains why we derived the W matrix in terms of shares of total output ( 3.4 , 3.5 ) and not, as is usual, in terms of shares of value added—to make it perfectly clear that both upstream and downstream disaggregation have the same object—the total output of i , which includes both the value added of country-sector i and the total value added of the other country-sectors ( k ) downstream. The same object (total output) is then distributed along the upstream value chain paths (as determined by the i th row of W ) until it reaches final consumption along an upstream value chain path.

All input–output analyses assume the homogeneity of the smallest classification object (country-sector in our case). The level of detail of the data corresponds to the level of detail of the sector (and country) classification and within a country-sector there is no further information and quite strict homogeneity assumptions apply. We use the assumption of the homogeneity of production of each country-sector to combine the two probability distributions.

z ki represents the share of the total output of the i th country-sector, which was primarily produced by country-sector k . Due to the homogeneity of the total output of the i th country-sector, the w ij represents not only the probability that a random part of the total output of the i th country-sector reaches final consumption as a product of j , but also the probability that a random part of any share of the output of the i th country-sector reaches final consumption as a product of j . Since z ki is a share of the i th country-sector’s total output, its upstream decomposition is clearly and uniquely defined by the i th row of w .

The product w ij z ki thus simply represents the probability that a certain part of the total output of the i th country-sector is primarily produced in k and reaches final consumption as the product of j along any value chain path (upstream, downstream or a combination) passing through i . In other words, it represents the share of the total output of i that was produced by k and reached final consumption as a product of j . A simple multiplication of probabilities requires that the two events—a random portion of the total output of i produced by k and a random portion of the total output of i completed for consumption by j —are statistically independent. First, if certain parts of the total output of a particular country-sector were to behave differently from certain other parts of the same output, this would violate the homogeneity assumption, which is the basic assumption of the input–output structure and methodology. Second, at the level of economic theory it is relatively easy to argue about the statistical independence of the structure of upstream and downstream value chains: Nothing about the downstream structure of production in the i th country-sector implies anything about its upstream structure and vice versa . Both are calculated independently and provide completely different information: the downstream decomposition gives information about the inputs produced by other country-sectors used directly or indirectly in the production process of the i th country-sector, and the upstream decomposition gives information about how the product of the i th country-sector is consumed either directly or as part of the final product of other country-sectors.

Two separate vectors which disaggregate the value chain paths of the downstream ( i th column of Z ) and upstream value chain ( i th row of W ) thus span an entire matrix of total output shares that capture the value chain tree structure of the i th country-sector. We combine them with the direct product that defines the matrix of the value chain tree for each country-sector ( i ) by multiplying each element of Z e i → (the i th column of Z ) by each element of e i → T W (the i th row of W ).

Definition 3

Value chain tree matrix

τ i = Z e i → ⊗ e i → T W ; τ i ∈ R n × n , where e i → ∈ R n represents the standard orthonormal basis of R n .

This defines each element of the value chain tree matrix t ijk ∈ τ i as t ijk = w ij z ki . Each element of the value chain tree matrix τ i thus represents a share of the total output of country-sector i , which is primarily produced in country-sector k and consumed as an end product of country-sector j , along any upstream and downstream value chain path.

The main point of our derivation is not the expressed final value distribution of the total output of each country-sector along any of its upstream and downstream value chain paths, but the expression of the total output distribution (of the respective country-sector) along any value chain path, be it a downstream value chain path, an upstream value chain path or any combination of both paths at the same time.

The structure of the value chain tree matrices allows us to focus our disaggregation on the composition of the value chain paths covered by the two global Leontief inverses in the equation, the first representing all downstream parts of the value chain paths and the second representing all upstream parts of the value chain paths.

A single value chain path is determined by a series of concrete transactions between companies: It is a unique path from primary value creation (value created in production, not transferred from intermediate products) to value realisation (final consumption, not productive consumption of intermediate products), which passes through the production stage of the i th country-sector. The total output of i is not only disaggregated along all possible paths leading from any country-sector of origin via country-sector i to any country-sector of final stage production (as determined by τ i ), but is also disaggregated in much finer detail, along all the unique value chain paths that pass through i . That a concrete value chain path only forms part of the value chain tree matrix can easily be recognised if both inverses in τ i are replaced by an infinite series ( ( I - A ) - 1 = I + A + A 2 + ⋯ ). Such disaggregation then results in an infinite number of value chain paths, and the total output of the i th country-sector is distributed over all of these paths.

A certain value chain path share of the total output of i is determined by the Leontief technical coefficients a ij ∈ A . For example, take a value chain path consisting of value primarily produced in country-sector C S 1 20 , then used as an intermediate in C S 2 , which in turn is used as an intermediate in i (the country-sector whose value chain is broken down), and then sent as an intermediate to C S 3 , which is then sent as an intermediate to C S 4 , where it is finished and sold for consumption. This value chain path has an origin ( C S 1 ), a midpoint ( i ) and a final destination of production ( C S 4 ), as well as a concrete path with a length of 5 (5 country-sectors contribute to production from origin to final consumption). The share of the total output of the i th country-sector that may be attributed to this specific path is:

A specific unique value chain path of the i th country-sector’s value chain tree, that has its origin in k and final stage in j , can be written as:

Such a path has a downstream length of d and an upstream length of u - 1 - d and the path is determined by a unique set of production-sharing transactions from the origin to the final stage (from origin j = C S 0 , to C S 1 , to C S 2 , ..., to i = C S d , and further to C S d + 1 , C S d + 2 , ..., to k = C S u ). Leontief technical coefficients a C S p - 1 C S p determine each production-sharing transaction. The summation along the total output shares of i attributed to all such unique value chain paths, taking into account all permutations of possible transaction sequences and also all possible lengths (all possible length combinations of downstream and upstream lengths) as well as all possible origins and final stage destinations, results in a unit:

Our conceptualisation allows us to define decomposition criteria applicable to each value chain path of the value chain tree of the i th country-sector. Based on this property, we will decompose the value chain structure of each country-sector separately in the following section.

The value chain typology

Definitions.

The framework of the international I–O analysis allows the separate analysis of final transactions to consumers and transactions between companies. Based on this characteristic, we propose a typology of value chains based solely on the structure of linkages between enterprises, while adding a further decomposition with regard to different possible transactions to reach the final consumption post festum . 21 Each matrix τ i expressed by equation 3.13 represents the desegmentation of the total product of country-sector i along different downstream and upstream paths. When we refer to a value chain, we refer to the specific share of value (share of output) that corresponds to a particular value chain path. Path 22 of each value share generally includes any combination of domestic and cross-border production-sharing transactions, which can take place both downstream and upstream relative to the respective country-sector. Our criteria for the value chain typology thus refer to each specific value share corresponding to a single path within a value chain tree specific to each country-sector.

Definition 4

Domestic value chain

Domestic value chain (DVC) is a value that involves at least 1 domestic production-sharing transaction and involves only domestic production-sharing transactions along its path.

Definition 5

Global value chain

Global value chain (GVC) is a value that involves at least 1 cross-border production-sharing transaction along its path. We further distinguish two types of global value chains: simple and complex.

Definition 5.1

Simple global value chain

Simple global value chain (SGVC) is a value that involves exactly 1 cross-border production-sharing transaction anywhere along its path.

Definition 5.2

Complex global value chain

Complex global value chain (CGVC) is a value that involves more than 1 cross-border production-sharing transaction along its path.

Definition 6

No value chain

No value chain (NVC) is a value that does not involve any production-sharing transactions and has no value chain path within production.

A few brief comments are appropriate on our definitions and their interpretation. No material product or service belongs to a single classification of value chain, and no enterprise can be considered part of a single type of value chain. The output of each enterprise belongs to a variety of value chain paths. In general, one part of the output comprises many cross-border transactions, another part only domestic transactions, and yet another part their relatively complex interrelationship. Each product (or country-sector in our case) can be assigned different shares of the value chain paths. These shares are objects that provide information about the structure of the economy. For example, virtually no enterprise could be classified exclusively as part of a no value chain, but some enterprises that provide services (e.g. domestic services) have a relatively high share of output that has no value chain path, especially in services, where salaries account for almost all of the enterprise’s expenditure and where their product directly satisfies final demand. On one hand, enterprises that specialise in intermediate goods are always part of a value chain, whether domestic or global. On the other hand, even modern industries such as food-processing and pharmaceuticals, also have a certain (usually small) share of value added that is not part of any value chain (no value chain share), corresponding to the share of domestic value added in these industries that is also directly consumed (part of output that has no value chain path). The value chain shares and their changes are the object that provide information about the structure of the economy, whether on the sector or country level. As the economy develops, the division of labour also increases, which corresponds to the growing fragmentation of production, in particular international production fragmentation, and a decrease in shares where there is limited or no value chain fragmentation. Compared to the existing typology of value chains, this revised typology allows for analysis of the relationship between global and domestic fragmentation, which might prove especially relevant for the policies of developing countries.

The decomposition of paths

Our value chain typology is established according to criteria along the entire value chain. For this reason, we disaggregate the value chain tree matrices τ i in terms of criteria for different types of value chain paths. Our decomposition consists of the decomposition of two Leontief inverses, which may be interpreted as the decomposition of the downstream part and upstream part of each value chain path, as defined by equation 3.11 : τ i = v C ^ ( I - A ) - 1 e i → ⊗ e i → T x ^ - 1 ( I - A ) - 1 f ^ . The decomposition is constructed based on of the criteria of the number of cross-border and domestic production-sharing transactions that are consistent with the revised value chain typology.

First, we investigate the decomposition of only a single Leontief inverse (interpreted symmetrically with respect to our criteria in the upstream and downstream value chain) and only then do we analyse the decomposition of all value chain paths characterised by the two Leontief inverses. The international I–O data have a specific block matrix structure in which the block diagonal elements represent domestic production-sharing transactions and the block off-diagonal elements represent international production-sharing transactions ( A D denotes domestic—block diagonal—and A CB cross-border—block-off diagonal—part of A ), which allows us to decompose the Leontief inverse in the following way:

• I obviously represents that part of the output which contains no production-sharing transactions —no value chain linkages. In the upstream part, it represents the share of total output that directly satisfies final demand (i.e. no upstream value chain), while in the downstream part it represents the direct value added of the country-sector whose production is being decomposed (i.e. no downstream value chain).
• A D ( I - A D ) - 1 = A D + A D 2 + A D 3 + ⋯ represents that part of output which contains at least 1 domestic production-sharing transaction and contains only domestic production-sharing transactions .
• ( I - A D ) - 1 A CB ( I - A D ) - 1 represents that part of the output which contains at least 1 production-sharing transaction and contains exactly one cross-border production-sharing transaction somewhere along its value chain path. This can be demonstrated by paraphrasing the part as all possible combinations of a single cross-border transaction among any possible set of domestic production-sharing transactions that occur before or after the single cross-border production-sharing transaction: ( I - A D ) - 1 A CB ( I - A D ) - 1 = A CB + A CB A D + A CB A D 2 ⋯ + A D A CB + A D A CB A D + A D A CB A D 2 ⋯ + A D 2 A CB + A D 2 A CB A D + A D 2 A CB A D 2 + ⋯ + ⋮
• ( I - A ) - 1 - ( I - A D ) - 1 - ( I - A D ) - 1 A CB ( I - A D ) - 1 represents that part of the output which contains at least two or more production-sharing transactions , of which at least two are cross-border production-sharing transactions . This logically follows from the fact that parts (1), (2) and (3) cover the total output that contains less than two cross-border transactions, and that the full Leontief inverse covers the total output.

Value chain tree matrix decomposition

We proceed by disaggregating all of the value chain paths as they are structured in the value chain tree matrices. Using the decomposition of the Leontief inverse that we disaggregated in the previous subsection and inserting it into Eq. 3.11 , we obtain 16 components ( 4 × 4 product) for each matrix τ i . 23 This disaggregation along both the upstream and downstream paths is the basis for deriving value chain shares that correspond to our typology. We decompose each τ i matrix describing all possible value chain paths of the output of the i th country-sector into a matrix consisting of domestic value chain paths only, a matrix containing all possible global value chain paths (as well as simple and complex global value chain paths separately), and a matrix consisting only of the value that has no value chain path.

Definition 7

Domestic value chain tree τ i DVC

The domestic value chain tree represents all value chain paths of the output of each country-sector which, according to Definition 4 , are part of the domestic value chains. In Fig. ​ Fig.1, 1 , the domestic value chain paths are marked in red. Domestic value chain paths are defined as all paths that contain at least one red-coloured linkage (representing transactions between domestic enterprises) and include only red-coloured linkages and orange paths (representing the value creation or realisation in the respective country-sector in focus). The first part ( v C ^ A D ( I - A D ) - 1 e i → ⊗ e i → T x ^ - 1 f ^ ) covers the downstream domestic value added (downstream domestic path), which ends as the i th country-sector final stage (no upstream path), the second part ( v C ^ e i → ⊗ e i → T x ^ - 1 A D ( I - A D ) - 1 f ^ ) covers the value added of the i th country-sector (no downstream path) that is transferred via the upstream domestic value chain (upstream domestic path), and the third part ( v C ^ A D ( I - A D ) - 1 e i → ⊗ e i → T x ^ - 1 A D ( I - A D ) - 1 f ^ ) comprises the downstream domestic value added that is used as an intermediate product in the production of i and then used as an intermediary further in the upstream domestic value chain until it reaches final demand (both downstream and upstream domestic paths). All three cases meet the definition of a domestic value chain.

Definition 8

Global value chain tree τ i GVC

The global value chain tree represents all paths of the output of the individual country-sector, which form part of global value chains according to Definition 5 . In Fig. ​ Fig.1, 1 , the global value chain paths are represented by all paths containing at least one black-coloured linkage (representing cross-border transactions between enterprises). Global value chain paths can contain any number of red (domestic) and orange (no value chain) linkages provided there is at least one black (cross-border) linkage along their path. The first element ( v C ^ ( I - A D ) - 1 e i → ⊗ e i → T x ^ - 1 [ ( I - A ) - 1 - ( I - A D ) - 1 ] f ^ ) covers the downstream domestic and no value chain paths, which have global upstream linkages (simple or complex), the second element ( v C ^ [ ( I - A ) - 1 - ( I - A D ) - 1 ] e i → ⊗ e i → T x ^ - 1 ( I - A D ) - 1 f ^ ) covers downstream global linkages (simple or complex), which have a upstream domestic or no value chain path and the third element ( v C ^ [ ( I - A ) - 1 - ( I - A D ) - 1 ] e i → ⊗ e i → T x ^ - 1 [ ( I - A ) - 1 - ( I - A D ) - 1 ] f ^ ) covers the value that has global paths both upstream and downstream. All of these cases correspond to our definition of a global value chain.

Definition 8.1

Simple global value chain tree τ i SGVC

The simple global value chain tree represents all paths of the output of each country-sector that are part of simple global value chains as defined by 5.1 The first element ( v C ^ ( I - A D ) - 1 e i → ⊗ e i → T x ^ - 1 ( I - A D ) - 1 A CB ( I - A D ) - 1 f ^ ) covers a downstream domestic and no value chain path that has simple global upstream linkages and the second element ( v C ^ ( I - A D ) - 1 A CB ( I - A D ) - 1 e i → ⊗ e i → T x ^ - 1 ( I - A D ) - 1 f ^ ) covers downstream simple global linkages that have an upstream domestic or no value chain path. These are the only cases that fit our definition of a simple global value chain. A value chain path covering both downstream and upstream simple global linkages already has more than 1 cross-border transaction and is hence part of a complex global value chain.

Definition 8.2

Complex global value chain tree τ i CGVC

The complex global value chain tree represents all paths of the output of individual country-sectors that form part of complex global value chains as defined in 5.2 The first element ( v C ^ ( I - A D ) - 1 e i → ⊗ e i → T x ^ - 1 [ ( I - A ) - 1 - ( I - A D ) - 1 - ( I - A D ) - 1 A CB ( I - A D ) - 1 ] f ^ ) covers the downstream domestic and no value chain path, having complex global upstream linkages, the second element ( v C ^ [ ( I - A ) - 1 - ( I - A D ) - 1 - ( I - A D ) - 1 A CB ( I - A D ) - 1 ] e i → ⊗ e i → T x ^ - 1 ( I - A D ) - 1 f ^ ) comprises downstream complex global linkages, which have an upstream domestic or no value chain path, and the third element ( v C ^ [ ( I - A ) - 1 - ( I - A D ) - 1 ] e i → ⊗ e i → T x ^ - 1 [ ( I - A ) - 1 - ( I - A D ) - 1 ] f ^ ) represents combinations of global downstream and upstream paths (simple-simple, simple-complex, complex-simple, complex-complex). All of these elements meet our definition of a complex global value chain because the value in all cases crosses borders for production at least twice.

Definition 9

No value chain tree τ i NVC

A no value chain tree represents that part of the output of each country-sector which is not part of a value chain according to Definition 6 . In Fig. ​ Fig.1, 1 , a no value chain path is represented by the orange colour only (any other linkage represents a value chain path). Solely the share of value added produced in the respective country-sector in focus (no downstream stages) and also completed for final consumption (no upstream stages) in the same production phase satisfies this criterion. Since the I–O method distinguishes between a product used as an intermediate product within the same sector 24 and the product manufactured for final consumption, the use of this definition as no value chain does not depend on the level of detail of I–O data disaggregation. The cyclical effect of the production of intermediate goods within the same country-sector is already included in the domestic value chain tree and, after taking into account all of the defined value chain paths (domestic, simple and complex global value chain paths), a value share remains without a value chain path and with a simple representation as the value added of the country-sector which is also directly consumed. This represents a value that has no path in terms of transactions that represent the fragmentation of production.

This concludes the value chain tree decomposition, which can be written as:

The value chain participation rates

In Sect. 3.1 , we showed that a set of value chain tree matrices τ i represents all possible value chain paths of the output of each country-sector and that the summation along all shares of total output assigned to all such unique value chain paths yields a unity for each value chain tree (Eq. 3.14 ). Namely, we presented a unique disaggregation of the output of each country-sector along all of its value chain paths. In the same way, the summation along the two disaggregating dimensions of our decomposed set of matrices (global, domestic and no value chain tree matrices) captures the overall share of the total output of each country-sector i that meets the criteria by which the value chain paths were decomposed by including either only domestic value chain paths, only global value chain paths, or only values that have no value chain paths at all. In other words, the summation of the disaggregated value chain matrices along any origin and end stage represents the share of output of each country-sector that has either a domestic, a global or a no value chain.

Definition 10

Domestic value chain share DVCs

D V C s ∈ I R n ; D V C s i = ∑ j = 1 n ∑ k = 1 n t ijk DVC ; D V C s = 1 T τ 1 DVC 1 1 T τ 2 DVC 1 ⋮ 1 T τ n DVC 1 .

Domestic value chain share represents the share of each country-sector’s output that has a domestic value chain path.

Definition 11

Global value chain share GVCs

G V C s ∈ I R n ; G V C s i = ∑ j = 1 n ∑ k = 1 n t ijk GVC ; G V C s = 1 T τ 1 GVC 1 1 T τ 2 GVC 1 ⋮ 1 T τ n GVC 1 .

Global value chain share represents the share of each country-sector’s output that has a global value chain path.

Definition 11.1

Simple global value chain share SGVCs

S G V C s ∈ I R n ; S G V C s i = ∑ j = 1 n ∑ k = 1 n t ijk SGVC ; S G V C s = 1 T τ 1 SGVC 1 1 T τ 2 SGVC 1 ⋮ 1 T τ n SGVC 1 .

Simple global value chain share represents the share of each country-sector’s output that has a simple global value chain path.

Definition 11.2

Complex global value chain share CGVCs

C G V C s ∈ I R n ; C G V C s i = ∑ j = 1 n ∑ k = 1 n t ijk CGVC ; C G V C s = 1 T τ 1 CGVC 1 1 T τ 2 CGVC 1 ⋮ 1 T τ n CGVC 1 .

Complex global value chain share represents the share of each country-sector’s output that has a complex global value chain path.

Definition 12

No value chain share NVCs

N V C s ∈ I R n ; N V C s i = ∑ j = 1 n ∑ k = 1 n t ijk NVC ; N V C s = 1 T τ 1 NVC 1 1 T τ 2 NVC 1 ⋮ 1 T τ n NVC 1 .

A no value chain share represents the share of each country-sector’s output that has a no value chain path.

With this, we conclude our disaggregation of each country-sector’s total output with respect to its specific value chain integration based on production-sharing linkages. We can summarise our decomposition in the simple vector form: D V C s + G V C s + N V C s = 1 , 3.20

Decomposition of the transaction to the final consumer

Since all value chain paths within production are covered and decomposed, we still have one last transaction to the consumer to complete the value chain path from production to consumption. We can decompose the final transaction to the consumer upon the criterion of whether it is a transaction to domestic consumers or a cross-border transaction (export of the final product for consumption). Domestic consumption here refers to the country-sector in which the last stage of production took place and not the country-sector whose value chain we are analysing. Each country-sector has a unique value chain and a specific structure of value chain paths. The completion of each value chain path by a transaction to the consumer can be achieved by an additional cross-border transaction of exporting the final product or consumption in the country where the product was finalised. Such a further decomposition of the value chain paths allows a more detailed analysis of the value chains.

The I–O data include information on the transaction to final consumers within matrix F , which can be decomposed into its cross-border and domestic flows to final consumers ( F = F CB + F D ) due to its block vector structure. We construct a matrix of all cross-border final consumption flows and a matrix of all domestic consumption flows:

Every value chain path within production can thus be further decomposed with an additional criterion of a transaction to final consumers. Each set of disaggregated value chain matrices, defined by Eqs. 3.16 and 3.17 , can be separated on two matrices, one covering all of the production paths that end in domestic final consumption (no export - τ i NE ) and the other all of the production value chain paths that end with exporting for final consumption ( τ i E ).

Due to their simple additive properties of operation, all of the decomposed value chain tree matrices are similarly decomposed to ones with exporting or with no exporting as the final transaction.

The value shares that are part of each value chain path are thus further decomposed, as explained in Sect. 3.2.4 . The final decomposition of the output is thus a decomposition along each value chain, as defined by criteria that simultaneously take account of transactions related to the production fragmentation (different value chains) and the final transaction to the consumer. A share of value that has either a domestic, global or no value chain has as its final transaction to the consumer either an export or a no export transaction, which provides a detailed decomposition of the participation shares that can be used to construct different composite indices suitable for different research questions. D V C s NE + G V C s NE + N V C s NE + D V C s E + G V C s E + N V C s E = 1 3.26

Results and discussion

The proposed measures broaden the scope for empirical application and static analysis of international production and trade. The contribution of our approach entails the simultaneous insight into domestic and global value chains, which allows the study of their interaction and structural changes in economies. All elements of the new typology may vary over time, from country to country and sector to sector and are relevant research topics. The derived participation shares are also simple fragmentation measures, and each smallest unit of analysis (country-sector) is represented by a single measure (scalar share) that covers the extent of overall value chain fragmentation, as opposed to separate downstream and upstream indicators.

Due to the limitations of the paper and its chiefly methodological focus, we present only some very basic empirical results. First, we show the global averages of value chain participation rates based on WIOD 2016 data and the global average participation rates for the manufacturing and service sectors separately (Figs. ​ (Figs.2, 2 , ​ ,3 3 and ​ and4). 4 ). Using our methodological approach, we observe that the global average GVC share of world output consistently exceeds 20%, reached almost 24% at its peak before the global recession, and then stagnated slightly below this level until 2014 (Fig. ​ (Fig.2). 2 ). This suggests that the most recent estimates of GVCs’ share of production between 10 and 15% (Dollar 2017 , p. 2; Li et al. 2019 , p. 12) may be undervalued. As expected, the manufacturing sector is globally integrated to an above-average extent, with the share in the global value chain rising from 35 to over 40% before the crisis and then stagnating around this level after a brief recovery. The share of the complex global value chain shows the highest relative growth, while the average increase in global value chain integration exceeds the decline in domestic value chain integration. Interestingly, the decline in global integration in times of crisis had almost no impact on that part of the economy without value chain fragmentation, while domestic fragmentation increased almost in proportion to the decline in global integration. Hence, the crisis did not lead to a general decline in the fragmentation of production, but only to a decrease in its global character. For services, in contrast, less than 15% of total output has a global value chain path, although services show some increase in global integration, mainly due to decreasing domestic integration (which may be attributed to the globalisation of business services), while that part of the economy without a value chain appears relatively stable. For this reason, vulnerability to external financial shocks was much less pronounced in services during the crisis.

World average participation rates

Source: WIOD, 2016; own calculations.

World average of manufacturing.

Source: WIOD, 2016; own calculations

World average of services.

Source: WIOD 2016; own calculations

As the data for the world average conceal large differences between countries, we also show the value chain participation shares of manufacturing for China, the USA and the average of the economically most integrated new EU members—3 Baltic and 4 Visegrad countries (Figs. ​ (Figs.5, 5 , ​ ,6 6 and ​ and7), 7 ), which reveal structural differences and diverse patterns of development in global and domestic integration. China has on average a high share of domestic production integration (around 65%) and is one of the few economies where the share of domestic integration grew by almost 10 percentage points between 2004 and 2014. In the United States, the picture is reversed, while the already lower average share of domestic integration is steadily shrinking. A completely different pattern is seen in the Baltic and Visegrad European countries, which became EU members in the new millennium. On average, these countries’ integration into global value chains in the manufacturing industries rose from an already high 53 to 69% during the observed period. At the same time, there was a huge relative decline in the already below-average share of domestic fragmentation from 32 to 18%. Interestingly, almost all of the growth in the global value chain share in Central and Eastern EU countries was due to the increase in complex global value chain linkages, while simple global value chain linkages remain relatively stable.

China manufacturing participation rates.

New EU countries manufacturing.

USA manufacturing participation rates. creditSource: WIOD 2016; own calculations

Finally, we use the fact that we have created uniform participation rates by performing a simple between-effects regression to test the relationship between the level of domestic and global fragmentation and economic growth measured by GDP per capita. Since short-term productivity fluctuations can hardly be explained by an economic structure expressed in value chain shares, we use a cross-sectional approach to test the long-term effects of different levels of domestic or global fragmentation on economic growth. Our observations relate to the 43 countries included in the WIOD 2016 data, and the variables are their average annual growth, the average DVC and GVC shares, with the average logarithm of GDP per capita as a control for convergence, the average logarithm of the annual population as a control for the size of the country, and the EU control dummy for potential EU specifics. The main regression equation with between effects is derived in the usual way out of a general panel data model:

y it = α i + l o g G D P it β 1 + D V C it β 2 + G V C it β 3 + ϵ it ,

y i ¯ = α + l o g G D P i ¯ β 1 + D V C i ¯ β 2 + G V C i ¯ β 3 + ( α i - α + ϵ i ¯ ) .

To ensure a consistent estimator, α i must be random effects. y it is yearly growth of GDP per capita, l o g G D P it is a logarithm of GDP per capita, while D V C it and G V C it represent shares of domestic and global value chains as calculated by the proposed methodology. The number of countries is 43 and number of time units is 15 (from 2000-2014), making a total of 645 observations in the panel.

The regression results are shown in Table ​ Table1. 1 . The logarithm of GDP per capita is a significant variable and negatively related to growth. The result simply reflects the fact that higher GDP implies less potential for higher growth rates, as implied by the convergence literature. Taking this into account, both the DVC share and the GVC share are highly significant variables that have a positive effect on growth rates. Therefore, both domestic and global integration can have a significant impact on economic growth. The same result applies after the introduction of additional controls on country size and EU specifics. Due to the principally methodological orientation of the article, we refrain from a detailed interpretation of the regression results. Yet, it should be noted that it is difficult to separate cause and effect while applying econometric analyses—a country in recession for external reasons could experience a decline in global and domestic production fragmentation due to those same external reasons. In any case, there is a correlation between economic growth and the degree of production fragmentation, whether it is domestic or global. A country that experiences an overall decrease in production fragmentation (domestic fragmentation declines faster than global increases), regardless of an increase in global production integration, might experience a negative impact on economic growth compared to similarly developed countries, in line with our findings. 25 An increase only in participation in global value chains therefore does not necessarily enhance the growth due to various forms of integration 26 with different effects on domestic integration, which is also an important factor in determining economic growth. Further studies are needed to examine the relationship between domestic and global fragmentation and diverse patterns of structural integration that could also help in assessing the impact of unpredictable circumstances (e.g. COVID 19) on individual countries, regions or sectors.

Regression results

Source: WIOD, 2016; WB; own calculations

* p < 0.05 , ** p < 0.01 , *** p < 0.001

We have proposed a new methodology for measuring the participation shares of different types of value chains in the international input–output framework. We addressed the lack of a consistent unitary measure of value chain integration on the country-sector level by proposing a new concept of the value chain tree for each country-sector, covering all value chain paths from value creation (downstream linkages) through a single country-sector to final consumption (upstream linkages) simultaneously. By capturing the structure of all value chains in a series of value chain tree matrices, we add a new mathematical object that serves as a basis for deriving the proposed new indicator of value chain participation, which we contribute to the existing collection of indicators.

This methodology allows us to introduce an extended typology of value chains by distinguishing and disaggregating all production activity into the following types: no value chain, domestic value chain, and global value chain—further differentiated into simple and complex global value chains. The most important new conceptual subdivision in the extended typology relates to the subdivision of the existing ’domestic component’ into a no value chain and a domestic value chain. This subdivision, which is only possible with the proposed methodology, provides a better representation of domestic production interdependencies and permits comparative analyses of the simultaneous development of domestic and foreign production interdependencies, thereby enabling aggregated analyses of domestic and global production fragmentation and its interrelated development as influenced by outsourcing or offshoring. Another big change introduced by the new typology is its fundamental production-related character: all distinctions between different types of value chains are made only with regard to (potential) production fragmentation, with a separate examination of the transaction to the final consumer—which may or may not be cross-border. This affirms the concept of value chain as related primarily to the fragmentation of production, while the post festum differentiation is also derived based on the last transaction to the final consumer.

The proposed methodology and typology of value chains provides researchers with new opportunities to conduct future research on different levels of disaggregation, be it comparative geographical analysis (e.g. comparing the evolution of value chain measures between two countries or between groups of countries) or observing the evolution of value chains in different sectoral disaggregations. The preliminary illustration of the new methodology, which attempts to link both domestic and global production fragmentation with long-term growth rates, shows a positive correlation between both global and domestic production fragmentation with economic growth. This result may indicate that it is the general complexity of the division of labour, reflected in the general fragmentation of production, that is chiefly correlated with growth, irrespective of its global or domestic nature. Accordingly, the proposed measure and the new typology of value chains, in particular the novel conceptualisation of domestic value chain fragmentation, could bring to light important information that has been concealed in the existing typology, which conceptualises the domestic component only as a negation of the global value chain and thus did not allow research with explicit questions concerning domestic integration. The complex development of globalisation in recent decades and the shifts of late towards the localisation and regionalisation of economic integration caused by political, economic and external factors make this new approach increasingly relevant. The proposed measure, particularly in conjunction with data from other sources, could further deepen the theoretical discussion and empirical investigations.

In conclusion, we believe that our new methodological approach and the new extended typology of value chains associated with it provide fertile grounds for obtaining deeper insights into different types of value chains as well as a broader set of tools of use for various extensions of research.

Acknowledgements

The authors thank the editor and all reviewers for their comments and suggestions that helped improve this article.

Appendix A: Notations

n S ∈ I N Number of sectors.

n C ∈ I N Number of countries.

n ∈ I N ; n = n S ∗ n C Number of country-sectors.

1 ∈ I R n Vector of ones.

1 → ∈ I R n C vector of ones.

e i → ∈ I R n ; e i j = δ ij Standard orthonormal basis of I R n .

I ∈ I R n × n Identity matrix.

x ∈ I R n Total output vector.

x ^ ∈ I R n × n ; x ^ = d i a g ( x ) Total output matrix.

C ∈ I R n × n Intermediate consumption matrix.

F ∈ I R n × n C Final consumption matrix on the country level. 27

f ∈ I R n ; f = F 1 → Total final consumption vector.

f ^ ∈ I R n × n ; f ^ = d i a g ( f ) Total final consumption matrix.

A ∈ I R n × n ; A = C x ^ - 1 Leontief technical coefficient matrix.

G ∈ I R n × n ; G = x ^ - 1 C Ghosh technical coefficient matrix.

v ∈ I R n ; v T = x T - 1 T C = 1 ( x ^ - A x ^ ) = 1 T ( I - A ) x ^ Vector of total value added.

v ^ ∈ I R n × n ; v ^ = d i a g ( v ) Total value-added matrix.

v C ∈ I R n ; v C T = v T x ^ - 1 = 1 T ( I - A ) Vector of value-added coefficients – value-added share in total output.

v ^ C ∈ I R n × n ; v ^ C = d i a g ( v C ) Value-added coefficients matrix.

C , A and G have a block-matrix structure I R ( n S × n S ) × ( n C × n C ) , while F has a block vector structure I R n S × ( n C × n C ) . Diagonal block elements with respect to countries represent domestic intermediate transfers and domestic consumption and off diagonal block elements represent transactions that cross a border either for intermediate use or final consumption.

C = C CB + C D A = A CB + A D G = G CB + G D F = F CB + F D f CB ∈ I R n ; f CB = F CB 1 → Total final consumption by exporting.

f D ∈ I R n ; f D = F D 1 → Total final consumption by domestic transactions.

f ^ CB ∈ I R n × n ; f ^ CB = d i a g ( f CB ) Total final consumption by exporting matrix.

f ^ D ∈ I R n × n ;

f ^ D = d i a g ( f D ) Total final consumption by domestic transactions matrix.

Appendix B: τ i decomposition

We make a demonstration of the methodology on a simple 2 sector 2 countries numerical example. 28 This simple case of international economy has following intermediate consumption matrix and final demand:

Total output is the sum of all the intermediate and final demand:

Calculation of value added coefficients and Leontief technical coefficients:

We continue with separate upstream and downstream decompositions, W and Z :

Value chain tree matrices are calculated for each country-sector in the following manner:

For each type of value chain (DVC, GVC, NVC,...) we have 4 matrices, each covering all the value chain paths of each country-sector (we have 4 in our example) that conform to our value chain criteria.

The value chain participation shares are obtained by summation of all elements of the value chain tree matrices:

Authors' Contributions

KK contributed the methodological derivations and empirical results, all three authors contributed to the literature review, the discussion of the results and extensive proofreading.

The authors of this article acknowledge the financial support received from the Slovenian Research Agency (research core funding No. P5-0177 and No. 52075).

Availability of data and materials

Declarations.

Not applicable.

The authors declare that they have no competing interests.

1 The term global commodity chain is a predecessor of global value chain.

2 Embracing a historical and macroeconomic approach to the analysis of the global division of labour, the world-systems approach examines the unequal patterns of exchange along global commodity chains as well as different structural patterns of the international integration of the core, periphery and semi-periphery (Arrighi and Drangel 1986 ).

3 Governance was conceived as either consumer-driven (apparel sector) or producer-driven (automotive sector). This approach was further extended by Ponte and Sturgeon ( 2013 ).

4 Porter’s (1985) concept of the intra-firm value chain is often used to discuss the specialisation of enterprises, and core competencies and business literature on multinational enterprises overlap with the global value chain framework.

5 Which was used to extend the producer-driven and consumer-driven governance typology of commodity chain research to a more general typology of value chain linkages, from transactions in a completely free market to a strict hierarchy (Gereffi et al. 2005 ).

6 In international economics, use of the input–output methodology grew in importance as researchers of various international incentives integrated nationally based input–output tables into harmonised global input–output tables. The most prominent are the World Input–Output Database (Timmer et al. 2015 ), the OECD’s Trade in Value Added and the EORA (Lenzen et al. 2013 ).

7 While all heterogeneous approaches to value chains focus on a development issue, the recent GVC approach has been adopted by international institutions to highlight the gains from liberalisation and industrial upgrading, while the world-systems approach critically examines unequal rewards along the value chain and different structural integration patterns that may cause the perpetuation of unequal development (Gereffi 2018 ; Taglioni and Winkler 2016 ).

8 Relative position indices can easily be derived from length measures as simple ratios.

9 Using a method similar to that used to calculate the average propagation length required for the analysis of the dynamic response to shocks, defined by Dietzenbacher and Romero ( 2016 ).

10 It is also obvious that a simple solution, such as using the average of existing upstream and downstream indicators, cannot be justified in theory. If, for example, a given country-sector’s share in the upstream global value chain is high (close to 100%) and its share in the downstream global value chain is relatively low (close to 0%), then the average share in the value chain would be around 50%, which is misleading because the value chain as a whole is almost entirely global (using the criterion that the value crosses a border at least once). As far as value chain paths are concerned, despite the small share of downstream global value chain paths, a high share (close to 100%) of the same paths continues in the upstream global value chain such that production as a whole has a very high global share (close to 100%), while the use of the average of the upstream and downstream indicators does not correspond to the definition of the global value chain.

11 For example, in a forthcoming article we explore the decomposition of value chains based on the criterion of the number of domestic transactions subject to meeting the usual global value chain criterion of having at least one production-sharing cross-border transaction. In this setting, we decompose the global value chain share into a GVC with no domestic cooperation, a GVC with simple domestic cooperation, and a GVC with complex domestic cooperation, offering information on the specific pattern of the EU periphery’s integration.

12 For example, the concept of integrated periphery was introduced to describe a specific type of integration in the case of the Slovak and Czech car industries, characterised by their proximity to consumer markets, cheaper labour force, the absence of positive spillover effects and lack of domestic linkages (Oldřich and Vladan 2019 ; Pavlínek 2018 ).

13 In our derivation, which is consistent with most existing international I–O data, the country-sector is the smallest object of analysis. When we refer to our methodology and derive it, the reference to the country-sector refers to the smallest object of analysis given by the level of detail of the I–O data set. If the I–O data sets were built on a more detailed structure at the enterprise level (greatly increasing the dimension), the proposed methodology and measures would work in the same way, with the value chain still structured around the smallest possible unit—in this case the enterprise. Despite the starting point of analysis of value chain structure being the smallest units of analysis, the approach offers many different aggregation possibilities to capture the changing economic structure of production as a whole.

14 The vertical and horizontal fragmentation of production is often represented with metaphors of snakes (sequential value transfers from one firm to further stages in a linear sequence) and spiders (simultaneous value transfers from different firms to the same company) (Baldwin and Venables 2013 ).

15 Technically, that would require I–O matrices of a dimension as large as the number of all firms of all countries included in such an international I–O structure.

16 Formal addition of further n dimensions to the usual n × n dimensions.

17 Definitions of all notations are given in Appendix A .

18 The simplification consists only of the notation. We retain all the complexity of the block-matrix structure of the international I–O data and remove only the large number of indices, which would make the equations much more difficult to read.

19 Our downstream output decomposition formally coincides with the output decomposition of the approach that integrates output decomposition with a demand-driven decomposition of exports (Arto et al. 2019 ).

20 C S k represents an index for different country-sectors. a C S 1 C S 2 thus represents a single Leontief technical coefficient indicating that the value produced by C S 2 requires a a C S 1 C S 2 share of C S 1 input.

21 For example, Wang’s disaggregation into simple and complex GVCs uses the number of cross-border transactions, regardless of whether the value crossed a border for production or whether it is only an export to end users. Such a criterion mixes two conceptually different transactions, leading to unnecessary calculation complexity and the impossibility of further conceptual disaggregation. Existing definitions of the typology of value chains, like all such definitions, are constructed in a relatively arbitrary way. More important than strict adherence to the prevailing definitions is the clarity of the proposed revision and the presentation of the conceptual relationship of the new concepts with the old ones. Our proposal facilitates a more detailed decomposition that will allow researchers to construct an indicator better suited to their research questions. Since the revised typology is based on a more detailed decomposition compared to the currently prevailing typology, researchers can (by simply adding components of the revised decomposition) also replicate objects that correspond to existing studies.

22 Here we examine the path of production fragmentation, while the path to final consumption, which represents an additional transaction, is analysed in Sect. 3.2.5 .

23 Details of the disaggregation are given in Appendix B .

24 This is determined by the pure diagonal elements of the Leontief technical matrix A . Each a ii represents the portion of the total product of the i th country-sector that requires the use of the intermediate product of the same country-sector in the production process, thereby covering cyclical transactions within a sector. These cyclical transactions are of course included in the decomposition of the domestic value chain and not the no value chain since cyclical transactions represent the fragmentation of a domestic value chain.

25 The Greek and Italian economies, which experienced the longest recession in the EU during the period, experienced this very pattern (general reduction of production fragmentation, chiefly a reduction of domestic production fragmentation and increased integration into global value chains).

26 A variety of institutional and structural economic positions brings a range of effects of global integration on the country level.

27 In the international I–O framework, F is usually disaggregated on the country level as well as in an additional dimension of final consumption (household, government and non-profit consumption, fixed capital formation and changes in inventories), which in our derivation is irrelevant and left out. Disaggregation by countries is relevant for enabling the separation of domestic final consumption and export.

28 The decimal numbers are truncated on the fourth digit.

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ORIGINAL RESEARCH article

Spatial network structure and formation mechanism of china’s marine science and technology innovation efficiency from the perspective of innovation value chain.

• 1 School of Geographical Science, Liaoning Normal University, Dalian, China
• 2 Institute of Marine Sustainable Development, Liaoning Normal University, Dalian, China
• 3 HEKRl of Marine Economy and Coastal Economic Zone, Hebei Normal University of Science and Technology, Qinhuangdao, China

Research on the marine science and technology innovation efficiency (MSTE) from the perspective of innovation value chain is not only an inevitable requirement for in-depth exploration of Marine science and technology innovation activities, but also an important guidance for the sustainable development and optimization of marine economy. Based on the innovation value chain perspective, the marine science and technology innovation process is divided into three phases: basic innovation, applied research and development, and gainful transformation, and the chain network DEA model is used to measure the MSTE of 11 provinces and municipalities along the coast of China from 2007 to 2021; the modified gravity model and social network analysis are used to examine the spatial correlation network characteristics of the marine MSTE at different phases and their influencing factors. The results show that the spatial correlation of China marine MSTE gradually develops from a sparse and dispersed state to a close trend, and the three phases gradually show a development pattern from unicentre, polycentre and networked. There is no strict hierarchical structure in the spatial correlation network of marine MSTE, the applied research and development and revenue transformation phases are more relevant than the basic innovation phase, and the cross-regional collaborative innovation needs to be improved. The high-efficiency provinces have a strong ability to radiate the MSTE to other provinces, and can absorb a large amount of innovation resources. The spatial correlation network of MSTE development has formed four plates of two-way spillover, broker, net spillover and net benefit in all three phases, and the transmission of kinetic energy of regional MSTE development has obvious gradient characteristics. The strength of government support, marine industry structure, and marine management services are conducive to enhancing the spatial correlation of the three phases of innovation development. Through this study, we can not only grasp the overall pattern and development dynamics of China Marine science and technology innovation, but also deeply analyze the internal logic and formation mechanism of its spatial correlation network structure, so as to provide scientific basis for optimizing resource allocation and improving innovation efficiency.

1 Introduction

The development of marine science and technology innovation is the most active and revolutionary factor to lead the new direction and new issues of global ocean governance and to promote the construction of a new order of the oceans ( Wang et al., 2021a ). In the 21st century, mankind has entered a period of large-scale exploitation and utilization of the oceans. Under the leadership of the Party Central Committee and the State Council, China’s marine science and technology is accelerating the transformation to innovation-led. The 18th CPC National Congress put forward the strategy of building a “strong marine country”, which guides and promotes the accelerated development of MSTE; the 19th CPC National Congress report put forward the idea of “adhering to the integration of land and sea, and accelerating the construction of a strong marine country”, which conforms to the development trend of our country and the world; the 20th CPC National Congress The report puts forward: “promote the modernization of the national security system and capacity, and resolutely safeguard national security and social stability”, a series of major decisions and deployments of the CPC Central Committee have reflected the importance of marine science and technology innovation and development, and marine science and technology innovation is becoming more and more prominent in the national science and technology pattern ( Li et al., 2021 ). Although the national development of marine science and technology has achieved relatively rapid development, but facing scientific and technological innovation consciousness and ability is not strong, the shortage of senior skilled personnel in the marine field, the supply of marine scientific and technological achievements of poor quality and efficiency, marine scientific and technological achievements of the transformation of the institutional mechanism needs to be improved and other issues ( Chen and Ma, 2020 ). MSTE affects the speed and quality of marine economic development, the construction of marine science and technology innovation system should not only focus on the input and supply quality of innovation resources, but also pay attention to the marine science and technology outputs and results transformation, especially in the case of a relatively scarce element of innovation resources, the efficiency problem is more important. Rational allocation of marine innovation resources, improving MSTE, identifying regional differences, and increasing the level of transformation of results are of great practical significance in alleviating the imbalance and inadequacy of regional economic development.

Traditional marine science and technology research involves multiple departments and disciplines, leading to the dispersion of scientific and technological resources, duplication of research activities and disorderly competition ( Yu and Zou, 2020 ). With the rapid development and popularization of computers and transport facilities, the regional layout of China’s marine science and technology innovation field is undergoing profound changes. This change is gradually weakening the limitations of geographic space on innovation, accelerating the flow of innovative resource elements such as talents, funds, information, resources, etc., which has profoundly affected and changed the traditional way of marine science and technology innovation development ( Wang et al., 2021b ). With the continuous promotion of the regional coordinated development strategy, the marine science and technology innovation links between coastal areas have become increasingly close, and innovation cooperation and exchange have become more frequent. The spatial association of regional MSTE breaks the “influence of spatial proximity on the distribution of geographical phenomenon attributes”, and gradually forms a complex spatial association network relationship [6]. The traditional way of information and data exchange and sharing will, to some extent, inhibit the linkage development of MSTE ( Chen et al., 2023a ). Therefore, it is of great theoretical significance and practical value to accurately characterize the network structure and its evolution trend of the spatial correlation of MSTE, to reveal the status and role of each province in the network, and to analyze the influencing factors of the spatial correlation of the MSTE in order to strengthen the inter-regional scientific and technological innovation cooperation network, to promote the optimal allocation and flow of innovation resources, and to facilitate the linkage development of MSTE.

The strategic goal of building an ocean power has been incorporated into China’s national strategy, and the construction of an ocean power has risen to an unprecedented strategic height. Marine science and technology innovation is the key and key to the construction of ocean power, the rise of ocean power cannot be separated from the research and development of science and technology and the use of science and technology, to enhance the strength of MSTE, strengthen the regional synergy and linkage, and to promote the interaction and collaboration of different regions has become the world’s major coastal developed countries economic dominance and participation in the global ocean governance of the key initiatives ( Liu and Wang, 2022 ), for this reason, it is necessary to China’s coastal MSTE of spatial correlation network for systematic research. Based on the perspective of innovation value chain, this paper divides the marine science and technology innovation process into basic innovation phase, applied research and development phase, and gainful transformation phase, and applies the chain DEA model to scientifically and accurately measure the MSTE in coastal provinces and cities; at the same time, it conducts a systematic research on the spatial correlation network of the MSTE. How are the characteristics and evolution trend of the spatial correlation network of the MSTE? What are the characteristics and evolution trend of the spatial correlation network of MSTE in China? What is the position and role of each region in the spatial correlation network of MSTE? What are the factors affecting the formation of the spatial network? The study of the above questions will help to identify the problems existing in the process of China’s MSTE development in the process of building a strong marine power strategy, and provide policy references for optimizing the spatial correlation network of MSTE, constructing a cross-regional mechanism for improving MSTE, and implementing the innovation-driven strategy.

2 Literature review

2.1 innovation value chains.

Innovation value chain theory is a comprehensive theory, which not only covers the research content of technological innovation, but also combines the value chain theory, organically combines the two together, emphasizes the importance of value creation and transfer in the process of technological innovation, and highlights the value attributes that technological innovation has ( Zhang and Wang, 2021 ). Technological innovation is not only a technical process, but also a value creation process, involving the entire process from the initial idea to the final product or service, and every link in this process is closely linked to value creation and transfer; at the same time, the innovation value chain theory also emphasizes the interactions and influences between different links, as well as their impact on the final value creation ( Liu et al., 2022 ). By combining technological innovation with value chain theory, innovation value chain theory provides us with a more comprehensive and in-depth understanding of the process and mechanism of technological innovation ( Hu et al., 2022 ). In practice, the innovation value chain theory can help enterprises better manage and optimize their own value chains, and improve their productivity and profitability; at the same time, it can also help policy makers formulate scientific and effective policies to promote the sustainable development of enterprises and society ( Ganotakis and Love, 2012 ).

Currently, when many scholars use the innovation value chain theory to assess the efficiency of science and technology innovation, they focus on the division of the phases of the innovation value chain. It mainly includes two-phase and three-phase innovation value chains. In terms of the two-phase innovation value chain, Roper et al. (2008) believe that the recursive process of knowledge sourcing, transformation and utilization constitutes the innovation value chain, divide the innovation process into two phases of knowledge collection and exploitation, and emphasize the role of skills, capital investment and other resources of the firm in the value creation process. Carayannis et al. (2015) break down the innovation process in into two key phases, the first phase is knowledge production, which involves the creation and exploration of new ideas, concepts, technologies or solutions; and the second phase is knowledge transformation, which involves transforming the knowledge that has been produced into actual products, services or practices. Based on the innovation value chain perspective, Liu et al. (2020) divided the innovation process into the knowledge condensation phase and the market transformation phase, and used the network DEA-SBM model to analyze China’s inter-provincial total innovation efficiency and two-phase efficiency. In terms of three-phase innovation value chain, Hansen and Birkinshaw (2007) were the first to put forward the concept of innovation value chain, which divides the innovation process into the generation of ideas, the transformation of ideas and the dissemination of ideas, providing a new way of thinking for the study of the value of innovation. Taghizadeh et al. (2014) believe that the innovation value chain has three important phases, namely, the creation, transformation and dissemination of knowledge and ideas, and empirically analyze the total efficiency of inter-provincial innovation and the two-phase efficiency in China using the network DEA-SBM model and ideas in three important phases, and empirically explore the impact of innovation strategy of innovation value chain on technological innovation. Based on the framework of innovation value chain analysis, Liu and Kan (2011) explored the chain relationship between knowledge source, knowledge output and scientific and technological achievements transformation performance. In addition, there are also scholars put forward the concept of “open innovation”, which believes that enterprises should pay more attention to the use of external resources to carry out innovation, and classify the process of technological innovation ( Chen et al., 2024a ; Haschka and Herwartz, 2020 ).

In summary, the criteria for dividing and articulating the phases of the innovation value chain theory vary from time to time and from need to need. Scholars have proposed different division methods based on different theoretical frameworks and practical needs. However, regardless of the division method, the core idea of the innovation value chain theory is the same: to achieve the organic combination of technological innovation and business value by breaking down the process of technological innovation into a number of interconnected phases and implementing systematic analysis. This paper makes a collation of the relevant literature as shown in Table 1 .

Table 1 Summary of measurement and methodological literature on China’s innovation value Chain research.

2.2 Efficiency of marine science, technology and innovation

Enhancing the MSTE can further promote the development of marine science and technology, enhance the competitiveness of marine industry, and provide a new power source for the development of marine economy. In foreign countries, the measurement and evaluation of MSTE is mainly carried out from a micro point of view, for example, Odeck and Brathen (2012) measured the average technical efficiency of seaports using data envelopment analysis and stochastic frontier analysis, and in-depth analysis of the regional differences in efficiency and the difference between the two efficiency measurement models; Chen et al. (2013) constructed a data envelopment analysis model for the MSTE evaluation of Zhejiang Province. The improvement of MSTE can not only promote the development of marine science and technology, but also have a far-reaching impact on the marine industry. For example, Tingley et al. (2005) used DEA and SFA models to analyze the technical efficiency and influencing factors of the English Channel fishery respectively; Yang and Lou (2016) used stochastic frontier analysis (SFA) and data envelopment analysis (DEA) methods to analyze the technical efficiency of Japan’s marine fishery. Meanwhile, enhancing the MSTE can also promote the development of marine economy, improve the efficiency of resource utilization, and reduce waste and pollution. Some scholars ( Xie and Ju, 2012 ) analyze the regional differences and convergence of MSTE in China. In analyzing the influencing factors of MSTE, foreign scholars have conducted extensive research.

It can be seen that these literatures mainly adopt the traditional single-phase model to measure the MSTE. Gradually, some scholars have begun to focus on analyses based on innovation process decomposition and innovation value chain perspectives, which better reflect the actual situation of marine science and technology innovation. Li et al., 2016 constructed a complex network DEA model to measure the overall MSTE, scientific research efficiency, transformation efficiency and education efficiency in 11 coastal provinces and cities in China. Xu and Li (2018) measured the overall efficiency and sub-phase efficiency of marine innovation in 11 coastal regions of China with the help of a two-phase DEA model. In addition, Garcia-Soto et al. (2021) measured the comprehensive MSTE by dividing the marine science and technology innovation process into the innovation research and development phase and the results transformation phase under the perspective of the innovation value chain, and provided an in-depth analysis of the current situation of marine science and technology innovation in China.

In summary, the research on the MSTE has become a hot field. Through in-depth exploration of the influencing factors, measurement and evaluation methods, and promotion strategies of MSTE, we can better understand the internal mechanism of marine science and technology innovation, and provide useful insights for promoting marine science and technology innovation and industrial development in practice. In China, the measurement and evaluation of the MSTE has also begun to receive attention gradually, and data envelopment analysis models are constructed to evaluate and study the MSTE ( Table 2 ).

Table 2 Summary of the literature of marine science and technology innovation efficiency research in China.

2.3 Network of spatial correlations for innovation efficiency

Regional innovation networks mainly focus on the interaction and cooperation between different organizations, enterprises and institutions in geographic space to promote innovation and scientific and technological progress ( Cheng et al., 2023 ). Through network analysis, the spatial structure of China’s MSTE network as well as the inter-regional linkage relationship can be explored, and the spatial linkage network influencing factors can be analyzed from a relational perspective ( Xue et al., 2022 ). Scholars have begun to construct regional innovation network from the perspective of efficiency, and make preliminary analyses of the overall characteristics of the network, individual characteristics and influencing factors. For example, Sun et al. (2022) constructed a spatial correlation network based on the gravity model, and then analyzed the structural characteristics of China’s green science and technology innovation efficiency network and the network influencing factors. Yao et al. (2023) used the modified gravity model and social network analysis to explore the spatial correlation network characteristics of the efficiency of industrial green technological innovation. Zhang and Wu (2021) used the modified gravity model and social network analysis to analyze the evolution law and spatial spillover effect of green innovation network structure, and adopted the secondary distribution process to identify its driving factors. However, still lacks the exploration of the spatial correlation network of MSTE.

A large number of studies have shown that network structure and network relationships can have a significant impact on the innovation performance or innovation efficiency of participating subjects. Complex network structure and high degree of network relationship can effectively promote the flow of information and knowledge, improve the possibility of innovative subjects to obtain new ideas, new technologies and new opportunities, and thus enhance their innovative performance and efficiency ( Chen et al., 2023b ). In the field of innovation, regions with high innovation efficiency can often occupy the center of the network, obtaining a large number of innovation resources by virtue of their location advantages, and promoting the improvement of their own innovation efficiency; while regions with lower innovation efficiency are often in the periphery, with smaller network beneficiary effects, increasing the gap of inter-regional innovation efficiency, and exacerbating the imbalance of the development of innovation efficiency ( Wang et al., 2022 ). In addition, with the increasing degree of inter-regional correlation, the innovation efficiency of the region is more and more closely related to the innovation activities of the neighboring regions, indicating that the innovation development of the region is no longer an isolated process, but a process of mutual influence and mutual promotion with the surrounding environment and the development status of other regions.

2.4 Review of research

To sum up, the research on the MSTE is a hot topic in the field of marine economic research at home and abroad, but there are still the following aspects worthy of further research and exploration. At present, there are many researches on the MSTE, but most of the literature considers marine science and technology innovation as a single input-output activity and treats the process of science and technology innovation as a “black box”, which ignores the heterogeneity of different phases of marine science and technology innovation; this is not conducive to the search for the root causes of regional inefficiency in science and technology innovation. Fewer scholars have included the transformation phase of innovation results into the analysis framework, and divided the marine science and technology innovation process into the basic innovation phase, the applied research and development phase, and the gainful transformation phase, focusing on the internal structure of the marine science and technology innovation system, which can help to evaluate the innovation process in a more comprehensive way, and make clear the performance of the differences in the MSTE. ②Existing research results show that China’s MSTE has spatial spillover effects, but although the traditional spatial measurement model can reflect spatial relationships, it is unable to identify the chain of correlation relationships among regions from the complex network structure, and it is difficult to reveal the inner role and operation mechanism generated by spatial correlation. The modified gravity model and social network analysis method can study the structural data and its correlation relationship, which can overcome the limitation of the traditional “attribute data” and can effectively solve the above problems. Some studies have paid more attention to the overall structure of the spatial correlation network of MSTE and the characteristics of individual networks, and less attention to the causative factors and mechanisms of the formation of the network structure ( Tian et al., 2024 ).

3 Spatial correlation of marine science and technology innovation efficiency and network structure formation mechanism for research methodology and data sources

According to the theory of spatial linkage and interaction, and with reference to the existing research on MSTE, this paper argues that MSTE, as an important indicator for measuring the high-quality development of the marine economy, and the spatial correlation network of MSTE is an important part of the marine economic growth network ( Wu et al., 2023 ; Zhang et al., 2023 ). Existing research shows that the MSTE of China’s coastal regions affect each other, constituting the spatial correlation path of MSTE within the region, which is manifested as the link between nodes in each region; and, along with the deepening of the link between the input and output processes of marine science and technology innovation in each region, the link between nodes within the region has gradually evolved into the ponderous spatial correlation network of MSTE ( Liu et al., 2020 ; Shang et al., 2023 ) The rapid flow of population, industry and other types of spatial factors across the region, as well as the basic regulatory role of the market mechanism have become the key factors to promote the spatial network of MSTE. At the same time, in order to make up for the “market defects” and “market chaos” and other failures in the whole process of marine science and technology innovation development, the government needs to intervene and regulate the market, which is an important factor to promote the spatial interactions of the MSTE in the coastal areas. The spatial correlation and network formation mechanism of China’s MSTE are shown in Figure 1 .

Figure 1 Formation mechanism of the evolution of the spatial correlation network structure of China’s MSTE.

Firstly, due to the spatial heterogeneity of historical development foundation, geographical location, natural resource endowment and macroeconomic policy, MSTE in different regions presents an imbalance in the spatial dimension, that is, the level of development of marine science and technology innovation in the space there is a certain “potential energy difference”. The existence of “potential difference” can promote the free and orderly flow of production factors across regions, and the formation of a reasonable division of labor and interactive cooperation between regions. At the same time, MSTE between the neighboring regions, neighboring regions, there is a natural geographic connection, closer economic ties and scientific and technological collaboration, the development of efficiency is also higher ( Huang and Wang, 2020 ). Once the relevant new marine technology emerges, all regions, in order to strengthen the support of marine science and technology innovation, will compete to adopt the latest technology in the marine production process, thus promoting the spatial spillover effect of marine production technology between regions, enhancing the vitality of the regional factor flow, and continuously transmitting and radiating to the neighboring regions and other regions.

Secondly, in the light of the world’s scientific and technological frontiers and the major strategic needs of the country in building an ocean powerhouse, it is necessary to insist on the continuous improvement of MSTE, so as to promote the sustainable development of the marine economy through marine science and technology innovation. MSTE is essentially the result of the interaction of various types of fluid resource elements, such as resources, capital, labor and information, etc. Under the impetus of emerging technologies and transportation modernization, inter-regional factors of production achieve efficient and convenient flow, which leads to spatial linkage of regional MSTE, which is mainly characterized by the connecting lines or edges between various spatial nodes ( Li et al., 2023 ). Marine science and technology innovation involves marine aquaculture, seafood processing, ecological and environmental management, comprehensive use of marine resources and marine biomedicine and other aspects, different factors will continue to flow into their own conditions in good regions, and ultimately the formation of innovation breakthroughs, leading the development of the core region, in the spatial network of MSTE of the high efficiency of the region and nodes. When MSTE in the core area reaches a certain standard, it will arrive at the place of radiation acceptance through the radiation channel under the action of radiation power, cultivate the new motive power of marine emerging industry, leading to the change of MSTE in the area, and through the transfer of elements, finally build the development pattern of complementary advantages and high-quality development.

Thirdly, in the context of the high-quality development of the marine economy, the flow of elements of marine science and technology innovation still needs to follow the market adjustment mechanism. The mechanism of marine science and technology innovation is imperfect, the lack of effective marine science and technology achievements transformation mechanism, marine science and technology achievements industrialization is low, etc. The improvement of the market mechanism can effectively allocate the limited resources associated information to the different demand and supply areas, and constantly strengthen the spatial correlation relationship of regional MSTE ( Chen et al., 2022 ). Marine science and technology innovation capacity is not strong, the effective supply of scientific and technological knowledge is insufficient, and marine science and technology investment channels are single, it is urgent to make full use of market-based means to promote the industrialization of marine ecology and industrial ecology, to innovate trans-regional marine ecological resources rights and interests trading mechanism, to realize the trans-regional flow of all elements of the ocean, and to promote the coordinated development of the coastal areas with higher quality. In addition, with the construction of a unified national market, guiding the regions and the internal balance of supply and demand, price regulation and technology management and other mobile elements, constantly breaking through MSTE breakthroughs in spatial distance limitations, and gradually formed a cross-regional spatial dynamic interaction and correlation network.

Finally, if China is to achieve the transformation from a major maritime power to a maritime power, it must insist on continuous innovation in China’s marine science and technology management system, and government intervention has become an important part of the formation of the spatial correlation network for MSTE. There are unfair competition and market failure in market development, appropriate government intervention and support can guarantee fair competition, maintain market order, and realize resource integration and flow of technological elements through the complementarity of advantages between different regions. At the same time, the irrationality of marine science and technology resource allocation and input also seriously affects the stimulation of the innovation potential of the marine science and technology talent team, it is necessary to strengthen China’s marine science and technology innovation input and top-level design, to guide the policy, technology, funding, talent, management and public services and other key elements of the water input across the region, and ultimately promote the formation of a spatial correlation network of MSTE of multi-region connectivity ( Li and Cui, 2023 ).

4 Research methodology and data sources

4.1 mste measurement: chain network dea model.

Currently, stochastic frontier analysis ( Liu et al., 2018 ), data envelopment analysis ( Dai et al., 2015 ), SBM super-efficiency model ( Wang et al., 2020 ) and other methods are used to calculate MSTE. Based on this, this paper draws on the existing research on MSTE, divides the marine science and technology innovation process into three phases ( Chen et al., 2024b ). Basic innovation, applied R&D, and gainful transformation, and constructs the chain network DEA model ( Figure 2 ).

Figure 2 Three-phase chained network DEA model based on shared inputs.

Assuming that there are n decision-making units, each DMUj (j=1, 2,…, n) production system includes two subsystems that are closely connected before and after. As shown in Figure 2 , the first phase consists of having m items of initial inputs X i j (i = 1, 2,…, m) to get D items of intermediate outputs Z d j (d = 1, 2,…, D) to P items of other outputs Z * p j (p = 1, 2,…, P). The second phase consists of G-item supplementary inputs   X g j (g=1, 2,…, G) and intermediate outputs X g j (p=1, 2,…, P) to get H-item intermediate outputs Z h j (h=1, 2,…, H) to Q-item other outputs Z * q j (q=1, 2,…, Q). In the third phase the output of item S, Y r j (r=1, 2,…, S) is obtained from the supplementary inputs of item K,   X k j (k=1, 2,…, K) and intermediate outputs Z * h j (h=1, 2,…, H).

Based on the assumption of variable returns to scale, efficiency in the basic innovation phase   θ 0 1 * ( Equation 1 ); efficiency in the applied R&D phase   θ 0 2 * ( Equation 2 ); efficiency in the revenue transformation phase   θ 0 3 * ( Equation 3 ); and overall efficiency   θ 0 * ( Equation 4 ) ( Chen et al., 2024b ).

Where: μ 1 、 μ 2 、 μ 3 are unconstrained real variable reflecting the state of returns to scale of DMUj. The chain network DEA model constructed in this paper has 11 decision variables (11 provinces): three shared input variables and two intermediate output variables in the first phase; two supplementary input variables and one intermediate output variable in the second phase; and two additional input variables and three final output variables in the third phase. DEA P2.1 was applied to solve the above model to obtain the efficiency values of   θ 0 1 * 、 θ 0 2 *   、 θ 0 3 * for the first, second and third phases of the 0th decision unit, and the overall efficiency was found by geometric averaging the efficiency values of the three phases ( Tian et al., 2023 ).

4.2 Measuring the spatial correlation of marine science, technology and innovation efficiency: modifying the gravity model

The modified gravity model ( Equation 5 ) is as follows with reference to the research results of Chen et al (2023a) :

Where, F i j denotes the strength of spatial correlation of MSTE between provinces and regions i and j; K i j denotes the contribution of province i in the linkage of MSTE between provinces and regions i and j; D i 、 D j are the MSTE of provinces and regions i and j; P i 、 P j 、 G i 、 G j 、 g i 、 g j are the annual number of resident population, GDP; GDP per capita of provinces and regions i and j, respectively; and d ij is the economic distance between provinces and regions i and j.

4.3 Characteristics of spatially linked networks of MSTE: a social network analysis approach

In this paper, we use Ucinet and Gephi software to analyze the overall network characteristics, network centrality and block model of MSTE in 11 provinces and cities along the coast of China, and the formula of each indicator, analysis method and definition of each block are shown in Table 3 .

Table 3 Description of social network analysis indicators.

4.4 Influencing factors and modelling of spatial correlation of MSTE

The QAP model ( Equation 6 ) is used to explore the formation and influence mechanism of network structure. The constructed econometric model is

Where: Ω denotes the spatial network relationship matrix of the study object and Xm(m=1,2,…,n) denotes the matrix of influencing factors.

4.5 Selection of indicators and data sources

This paper draws on the evaluation system given by existing research results and constructs a MSTE evaluation index system including initial inputs, intermediate outputs, other outputs, supplementary inputs and final outputs according to the theory of the innovation value chain, and breaks down the process of marine science and technology innovation into the basic innovation phase, the applied research and development phase, and the gainful transformation phase ( Figure 3 ).

Figure 3 Marine science and technology innovation system.

Based on the existing research results, this paper selects 11 provincial-level administrative units in China ‘s coastal areas as research samples. Considering the impact of national policies and the scientificity and accessibility of data, the research period is set to 2007-2021. In addition, the data involved in the measurement of MSTE and the gravity model of this paper come from the China Marine Statistical Yearbook, China Marine Economy Statistical Bulletin, National Maritime Domain Use Management Bulletin, as well as the statistical yearbooks of each province, the website system of the National Bureau of Statistics and other data. The distance between provinces and cities is calculated by using the distance function of ArcGIS software; the capital stock of the marine economy is corrected by using the comparable data of GDP of the oceans and GDP of coastal areas to eliminate the influence of price and other factors.

5 Analysis of the spatial network structure and influencing factors of MSTE

5.1 measurement of marine sti efficiency and its spatial correlation.

In order to identify the evolution trend of the spatial linkage strength of MSTE in an all-round way, after obtaining the gravitational relationship between regions according to the modified gravitational model, we then establish the binary spatial correlation matrix between regions, and use the UCINET to draw the spatial correlation network topology map of MSTE. Due to space limitation, this paper selects the cross sections in 2007, 2015 and 2021 at the basic innovation phase, applied R&D phase, and transformation phase for comparative analysis ( Figures 4A–I ).

Figure 4 (A–I) Strength of three-phase spatial network linkages of MSTE.

The nodes in the figure represent the 11 coastal provinces in China, the lines between the nodes represent the correlation between the provinces, and the direction of the arrows represents the direction of the spillover of MSTE. Overall, the number of lines between the nodes of the three phases of innovation efficiency increases over time, and the spatial correlation gradually develops from a sparse and dispersed state to a close and balanced trend, and the spatial correlation of MSTE between different regions is increasingly strengthened. MSTE of each province not only affects the neighboring provinces, but also breaks the geographical limitation, and also forms an inseparable spatial correlation network among the relatively distant provinces. For example, in 2015, Shanghai, in the applied R&D phase, not only establishes correlations with neighboring Jiangsu and Zhejiang, but also generates spillovers of MSTE to Hainan, Hebei, Guangxi, etc. In 2007-2021, the basic innovation phase gradually transforms into a dual-core development pattern of Shanghai and Jiangsu with Shanghai as the core, and the applied R&D phase transforms into a multi-core development pattern of Shanghai, Jiangsu, Hebei and Shandong with Shanghai as the core; and the gain of the applied R&D phase transforms into a multi-core development pattern of Shanghai, Jiangsu, Hebei and Shandong. Shandong multi-core development pattern; revenue transformation phase from Shanghai as the core gradually transform Shanghai, Jiangsu, Hebei, Shandong, Tianjin, Guangdong and other networked development pattern. In addition, the network structure of MSTE is similar in each year and each phase, indicating that the spatial association network structure of provinces in China’s coastal areas is highly robust, and there is no significant change in the relative network status of each city.

5.2 Overall network characteristics of marine STI efficiency and its evolution

To further analyze the overall structural characteristics of the spatial correlation network of MSTE, the correlation degree, network density, network hierarchy, network efficiency, and nearest upper limit of the three-phase network were calculated with the help of Ucinet software ( Table 4 ).

(1) From the network correlation degree, the network correlation degree of the three phases of MSTE in China’s coastal regions from 2007 to 2021 is 1, indicating that there are more obvious spatial correlation and spillover effects in the overall network structure, the accessibility between nodes of each region is at a high level, and the spatial correlation network of MSTE is more stable, while it has a high degree of reciprocity.

(2) From the network rank degree, the network rank degree of the three phases during the study period shows a fluctuating upward trend, but all of them are maintained at a low level, with the network rank degree ranging from 0.209 to 0.336, which is much smaller than the maximum value of the rank degree1, reflecting the fact that the network of spatial correlation of the efficiency of marine scientific and technological innovations does not exist in the strict hierarchical structure, and that marine scientific and technological innovations between regions develop in a mutually influencing manner with a clear tendency of trans-regional synergistic innovations. The network rank degree of the basic innovation phase rises from 0.209 in 2007 to 0.336 in 2021, and the network rank degree of the applied R&D phase and gainful transformation phase rises from 0.209 in 2007 to 0.336 in 2021, which indicates that the correlation of MSTE is stronger in the applied R&D phase and gainful transformation phase, and that inter-region exhibits obvious This indicates that the MSTE is more relevant in the applied R&D phase and the revenue transformation phase, and the regions show obvious trends of technological synergistic innovation, with better interconnection and cooperation.

(3) From the perspective of network efficiency, the network efficiency of the three phases in the study period generally shows a fluctuating downward trend, indicating that with the increase of connectivity between nodes, the redundant channels of MSTE between provinces are increasing, which in turn leads to the improvement of network connectivity. However, the network efficiency but still maintains at a high level, between 0.6444-0.8667, indicating that the path of MSTE linkage network is relatively single and lacks diversity, and the overall network stability among provinces needs to be strengthened.

(4) In terms of network density, the network density of the three phases during the study period shows a fluctuating upward trend, with the network density increasing from 0.209 in 2007 to 0.336 in 2021, indicating that the spatial connection of the efficiency of marine science and technology innovation among the provinces is getting closer and closer, and that marine science and technology innovation is playing a more important role in the pattern of the country’s economic development and opening up to the outside world, and is safeguarding the interests of the country in terms of national sovereignty, security and development. position is more prominent, and its role in the construction of national ecological civilization is more significant. However, the overall network density value is at a low level compared with the maximum network density value1, and there is still a need to further strengthen the spatial linkage of marine S&T innovation efficiency among provinces. The recent upper limit values of the three phases of marine S&T innovation efficiency in China’s coastal areas increased significantly from 2007 to 2021, which indicates the increasing stability of its spatial network.

Table 4 Overall network characteristics of MSTE, 2007-2021.

5.3 Individual network characteristics of marine technology innovation efficiency and its evolution

The above analysis of the overall network structure only reflects the overall picture of the MSTE network, and it is not possible to explore the node centrality in depth. Therefore, this paper analyzes the individual network characteristics of the spatial network of MSTE from 2007 to 2021 through the indicators of degree centrality, proximity centrality, and intermediate centrality measured by the Ucinet software, so as to determine the status and role of each province and city in the spatial network of marine science and technology innovation. In order to determine the position and role of each province and city in the spatial network of marine science and technology innovation, as well as the differences between the three phases of the innovation value chain. Due to space limitation, only the results of 2007, 2015 and 2021 are shown ( Figure 5 ).

(1) Degree centrality. According to the measurement results of the centrality of the point degree of the basic innovation phase, compared with 2007, the change of the centrality of the degree of the MSTE of China’s coastal marine science and technology in 2021 is small, and the high value areas are mainly concentrated in the areas of Shanghai, Tianjin and Guangdong, which indicates that they are more connected with other areas, and they have an advantage in the spatial network of MSTE and are in the central position of the network. The provinces of Guangxi, Fujian and Hebei are in the lower value area, indicating that they are less connected with other regions and less influential. Meanwhile the measurement results of point-degree centrality in the applied R&D phase and the gainful transformation phase are basically the same as those of the basic innovation phase. Point in degree is the degree of influence of a node by other nodes, i.e., node generates benefit effect, and point out degree is the degree of influence of a node on other nodes, i.e., node spillover effect. The overall increase of point out degree and point in degree from 2007 to 2021 is relatively slow, indicating that the change of receiving relationship and spillover relationship among provinces changes is also small. Most of the provinces and cities at the center of the spatial correlation network of MSTE have lower out-degree and higher in-degree, indicating that the region has obvious “siphon effect” and can absorb a large amount of innovation resources, which is a significant benefit effect.

(2) Degree of proximity to the center. The value of the proximity centrality degree of most provinces and regions during the study period shows an upward trend, indicating that MSTE in all provinces and regions is more closely linked, and that with the construction of a strong marine country, the construction of marine science and technology innovation platforms has been strengthened to further enhance the capability of independent innovation in marine science and technology. Hebei, Shanghai, Guangdong and Zhejiang are close to a higher degree of centrality, ranking at a higher level, and are able to quickly establish communication channels with other provinces in the spatial association network, and act as central actors in the network, playing a good driving role. Fujian, Guangxi, Hebei and other provinces have a lower degree of proximity to the center, play a small role in driving other provinces, and are in a dominated position in the network. In the basic innovation phase, Zhejiang, Shanghai and Jiangsu provinces and cities have a higher mean value of proximity to the center, and these provinces are all located in the eastern ocean economic circle, which has a strong foundation of the marine economy, a superior business environment for the marine industry, and leads the world in marine scientific and technological innovations, attracting science and technology, capital and talents from all over the world, and dominating the global marine factor agglomeration. The opposite is true for provinces such as Guangxi, Hebei, Liaoning and Fujian, which, due to their geographic location, are hardly able to benefit from other provinces in the spatial correlation network of innovation efficiency, and likewise, they do not have an obvious influence and driving effect on other provinces. The overall level of proximity centrality in the revenue transformation phase is lower than that in the applied R&D phase, indicating that with specialized technology transfer agencies, the independent intellectual property rights innovation system and the system of distributing revenues from scientific research results can be further improved and standardized to promote and safeguard the transformation of scientific and technological innovations into the new driving force of the marine economy.

(3) Intermediate centrality. The intermediate centrality degree is a measure of the control ability of provinces and cities over innovation resources in the spatial correlation network of MSTE. During the study period, the mean value of the intermediate centrality degree of MSTE showed an upward trend, indicating that the influence of some core nodes in the network structure has increased, while the network as a whole shows obvious unbalanced characteristics. In the basic innovation phase, Shanghai, Jiangsu, Shandong and other provinces and cities have the highest degree of intermediary centrality; in the applied R&D phase, Shanghai, Jiangsu, Shandong, Hebei and Guangdong have the highest degree of intermediary centrality, and the network control ability is significantly ahead of the network, and they become the important hubs and bridges for the occurrence of innovation linkages in other regions. In the gainful transformation phase, the addition of Fujian, Tianjin and other provinces and municipalities by 2021 indicates that these provinces and municipalities control the development of marine science and technology innovation and innovation efficiency linkages in other provinces and municipalities in the spatial linkage network, and most regions hold a certain degree of discourse in the MSTE network. Guangxi and Hebei are not on the shortest path connecting any two regions in the innovation efficiency network, and they lack control over innovation resources, which may be caused by a variety of reasons, such as relative backwardness in economic development, serious loss of high-quality talents, and lack of motivation due to the lack of innovation culture.

Figure 5 (A–C) Network centrality analysis of spatial correlation of MSTE in China.

5.4 Modelling of marine technology and innovation efficiency blocks

In order to further reveal the spatial clustering and relationships and spillover paths in the spatial correlation network of MSTE, this paper adopts the CONCOR algorithm to analyze the block model of the spatial network of MSTE along the coasts of China, and sets the maximum segmentation depth to 2 and the convergence criterion to 0.2, which results in the plate division and spatial correlation relationship of the empty correlation network of the innovation efficiency of the high-tech industry ( Table 5 ).

Table 5 Block model analysis.

At the phase of basic innovation, in 2007, the “net spillover” segment was mainly Guangxi, which had more spillover relationships than receiving relationships, had a high degree of external dependence, and was in a disadvantageous position in the spatial correlation network of MSTE.2021, the number of provinces in the “net spillover” segment increased, indicating that the disadvantageous position of these transformed provinces in MSTE has increased. In 2021, the number of provinces in the “net spillover” block increases, indicating that the disadvantageous position of MSTE of these transformed provinces has increased, which is mainly due to the insufficient authority of the national marine science and technology development planning programme at this phase, and the unresolved institutional and institutional factors restricting the innovation of marine science and technology, in addition to the irrationality of the allocation of marine science and technology resources and inputs that seriously affect the stimulation of the innovation potential of the basic phase of the marine industry. In 2007-2021, the “net benefit” plate is mainly composed of Jiangsu, Shanghai and Zhejiang; the “two-way overflow” plate is mainly composed of Guangdong, Tianjin and Fujian, which mainly involves state-level and provincial-level high-tech zones, which gather innovative resources, superimpose supportive policies, and have remarkable effect on the development of industrial agglomeration and strong attraction to factors in other provinces and cities. The attraction of factors from other provinces and cities is strong. Both the applied R&D phase and the revenue transformation phase form four segments, and most of the segments are composed of several neighboring provinces, except for some segments, which indicates that geographically neighboring provinces have more associative relationships with each other. From the distribution of each plate, the distribution of the plates in the 2 phases is similar, and the net gain plate is mainly located in economically developed regions with strong marine science and technology innovation capacity; the broker plate is located in Guangxi, Hebei, Liaoning and other provinces, mainly because the region actively implements the guidance for the transformation of marine scientific and technological achievements, and fully combines the particularities and difficulties of the marine field, and makes an overall consideration and systematic deployment, and plays an important role as a link in marine science and technology innovation.

On this basis, to further demonstrate the spillover relationship among the plates of the spatial network of MSTE, a map of the interactions among the four major plates was drawn, with space limitations showing only the correlation between the plates in 2007 and 2021. According to the data in Figure 6 , in 2007, the first plate consisted of Jiangsu, Shandong and Hebei; the second plate consisted of Guangxi; the third plate consisted of Hainan, Guangdong and Tianjin; and the fourth plate consisted of Fujian, Shanghai, Liaoning and Zhejiang. The second plate has a spillover effect on the third and fourth plates in addition to its internal relations. At the same time, the first plate produces spillover effects to this fourth plate, which strengthens the spillover relationship between them and plays an intermediary role in the network. In addition, the fourth plate receives spillovers from the other three plates, which may be influenced by geographic location, economic linkages and resource allocation between the plates. According to the data in 2021, the second plate received a significantly stronger spillover effect from the third and fourth plates, which indicates that the linkages between the plates tend to be more complete. At the same time, the spillover effect of the second plate on the fourth plate is strengthened, and the spillover effect of the fourth plate on the first and second plates is significantly enhanced, which indicates that the fourth plate is beginning to show an exemplary leading role in the linkage network. This change may reflect the differences in the level of marine S&T innovation and the allocation of innovation resources among different sectors, as well as the mutual influence in interregional linkages. Overall, the spatial correlation of MSTE mostly occurs between the plates, and the linkage within the plates is relatively loose; although each plate has obvious clustering characteristics, its internal spatial relationship is not close, and the linkage between provinces within the plate needs to be enhanced; the role of each plate in the network is heterogeneous, with plate 2 and 3 being the main spillover plates, with a greater number of external spillover relationships, and plate 4 being the main beneficiary plate, and plate 1 being the broker. plate, and plate 1 is the broker plate, which plays an important intermediary role in the network.

Figure 6 (A, B) Correlation between the efficiency segments of China’s marine science and technology innovation.

5.5 Analysis of factors influencing the spatial correlation network of marine science, technology and innovation efficiency

According to the statistical results in Table 6 , it is found that the correlation coefficients of the three factors, namely the optimization of the marine industrial structure, the improvement of the marine management services and the increase in the degree of openness to the outside world, have passed the test of significance at the level of 1%, which indicates that these three factors play an important role in promoting the formation of the spatial correlation network structure of MSTE. The richness of marine resource endowment means that there are more resources for S&T innovation, thus increasing the potential of S&T innovation. Increased openness to the outside world means more opportunities for international cooperation and exchange, which promotes the development of domestic S&T innovation. However, the correlation coefficient for the size of the marine economy is negative, which may imply that as the size of the marine economy expands, the needs and requirements for S&T innovation will increase accordingly, leading to an increase in S&T innovation pressure. Although the importance of government support for marine S&T innovation is self-evident, the differences between provinces do not have a significant impact on the efficiency of marine S&T innovation.

Table 6 Analysis of results of impact factors.

In order to control the influence of multicollinearity on the regression results, this study conducted a QAP regression analysis on the model of factors influencing the spatial correlation of MSTE in China with the help of Ucinet 6.0 software. According to Table 5 , the regression coefficient of marine resource endowment passed the 1% significance test and was positive, indicating that marine resource endowment has a significant positive effect on the spatial association of MSTE. In addition, the three factors of marine industry structure, degree of openness to the outside world and scale of marine economy have a significant effect at the 5% level and their regression coefficients are positive, which indicates that these factors also have a positive effect on the spatial correlation of MSTE. The regression coefficient of freshwater aquaculture technology is significantly positive at the 1% level, indicating that the greater the difference in energy-saving technologies, the more likely the spatial correlation of MSTE occurs between provinces. In terms of marine management services, their degree of perfection provides strong support and guarantee for science and technology innovation and promotes the process of marine science and technology innovation. However, the regression coefficient of government support shows a significant negative association at a statistical significance level of 10%. This may be related to the blind investment behavior of local governments in the process of developing marine economy, resulting in the insignificant impact of government support on input-output MSTE.

6 Discussion

Based on the perspective of innovation value chain, the marine science and technology innovation system is studied, the internal structure and operation process of the system are analyzed in depth, the sharing of the initial marine innovation R&D inputs in the sub-phases of marine science and technology innovation is taken into consideration, and the characteristics of the networked flow of innovation R&D inputs in multiple paths and hierarchical levels are revealed. The marine science and technology innovation process is divided into three sub-phases: basic innovation, applied R&D, and gainful transformation, and a perfect indicator system containing initial innovation R&D inputs, intermediate scientific and technological fruit outputs, non-innovation R&D inputs, and final economic outputs is constructed, and considering that the scientific and technological fruit inputs can’t be transformed into economic outputs spontaneously, additional non-innovation R&D inputs are added. The index system constructed in this paper can better fit the input and output status of marine science and technology innovation and scientifically measure MSTE.

Through literature combing, it is known that most of the existing MSTE studies ignore the potential impact of inter-regional geospatial links, and regard the study area as a homogeneous closed system. Therefore, this paper amends the gravity model to consider the innovation capacity of marine science and technology innovation, marine economic and social situation, take the geometric mean of MSTE, GDP, and the number of resident population as the regional quality, set the distance attenuation coefficient to 1 to adapt to the study of China’s coastal region scope, and measure the strength of the three-phase MSTE among the coastal provinces. Starting from the social network perspective, quantifying the marine science and technology innovation and innovation linkages between regions to portray MSTE network, and analyzing the network evolution characteristics based on the spatial and temporal perspectives can effectively grasp the evolution trend of the development of the linkages of MSTE and facilitate the provision of a reference basis for the high-quality development of China’s marine economy.

Improvement of the traditional gravity model based on the three phases of MSTE in China’s coastal regions and the economic distance between regions can improve the science, accuracy and comprehensiveness of the quantitative measurement of marine science and technology innovation linkages; in the study of influencing factors, the use of the QAP analysis method, which is able to deal with the correlation between relational data as well as the role of driving, to explore the impact of the differences in the various types of elements of the development of MSTE on the linkage of marine science and technology innovation can better put forward the countermeasures to promote the synergistic development of regional marine science and technology innovation from the point of view of the disparity in the basis of development between regions.

This paper still has the following shortcomings. The spatial correlation network constructed in this paper can only reveal the marine science and technology innovation correlation between provinces, and it is difficult to judge the strength of the correlation, and in the future, we can explore the dynamic correlation relationship and spillover effect of MSTE at different spatial scales by refining the research scale. This paper only analyses the factors affecting the spatial correlation of MSTE, but due to the vastness of China’s sea area and the large differences in the natural, economic and social development between regions, the personality traits can be further analyzed to further analyze the mechanism affecting the regional marine innovation efficiency. This study did not simulate and predict the evolution trend of the spatial correlation network structure of MSTE, which can be investigated by scenario analysis in the future. In future research, these issues will be further explored, and the related issues of the spatial correlation network of MSTE will be investigated in greater depth.

7 Conclusions and policy implications

7.1 conclusion.

(1) The spatial correlation of MSTE has gradually developed from a sparse and scattered state to a close and balanced trend, and the spatial correlation of MSTE between different regions has been increasingly strengthened. The three stages gradually show a development pattern of single center, multi center and network. The network structure characteristics of MSTE in each stage are similar, indicating that the spatial correlation network structure of China ‘s coastal provinces has strong robustness. This provides new opportunities and challenges for the development of marine science and technology innovation in China ‘s coastal provinces. In the future, all regions need to continue to strengthen cooperation and exchanges to jointly promote the prosperity and development of marine science and technology innovation.

(2) Through the overall network characteristics, it is found that the network correlation degree of MSTE in the three stages from 2007 to 2021 is 1, and the robustness of the network is strong. The network relationship number and network density show a fluctuating growth trend, the network grade and network efficiency are declining, and the redundant channels of MSTE among provinces are increasing. There is no strict hierarchical structure in the spatial correlation network, and the spatial correlation of innovation and development between regions is increasing year by year. The correlation between applied R&D and profitability transformation stage is stronger than that of basic innovation stage, and cross-regional collaborative innovation needs to be improved. We should strengthen exchanges and cooperation among regions, share innovative resources and experience, and jointly promote the development of marine science and technology innovation.

(3) Through the characteristics of individual networks, it is found that in the basic innovation stage, Shanghai, Tianjin, Guangdong and other regions occupy the central position in the spatial correlation network, occupy the advantage in the spatial network of MSTE, and form a significant spillover effect on other regions; Guangxi, Fujian, Hebei and other regions have weak control over the spatial correlation network, and many of them are at the edge of the correlation network; the measurement results of the applied R&D stage and the profitability transformation stage are basically consistent with the basic innovation stage. In the three stages showed a clear center-edge distribution. In the future marine science and technology innovation work, we should pay more attention to the efficiency improvement of applied R&D and achievement transformation, so as to promote the coordinated development of the whole innovation system.

(4) The spatial correlation network for the development of MSTE has formed four segments, namely, two-way spillover, brokers, net spillover and net benefit, at all three phases, and the transmission of kinetic energy for the development of regional science and technology innovation has a clear gradient characteristic. In the basic innovation phase, the “net benefit” segment is mainly composed of Jiangsu, Shanghai and Zhejiang; the “two-way spillover” segment is mainly composed of Guangdong, Tianjin and Fujian, which is highly attractive to factors from other provinces and cities. Most of the plates in the applied R&D phase and gainful transformation phase are composed of several neighboring provinces, and the net gain plate is mainly located in economically developed regions with strong marine science and technology innovation capacity; the broker plate is located in Guangxi, Hebei, Liaoning and other provinces, which plays an important role in marine science and technology innovation as a link.

(5) The government’s support for the marine industry, the structural adjustment and optimization of the marine industry, and the improvement of marine management services all help to enhance the spatial relevance of the development of MSTE, which is manifested in various aspects, such as the close coordination between policies and industrial development, the optimization of resource allocation, and the improvement of market mechanisms. The positive effect of the abundance and utilization value of marine resources on the development of MSTE tends to diminish, i.e. the advantages of marine resources are gradually diminishing. The degree of marketisation and the degree of opening up to the outside world, on the other hand, have a negative effect, probably because excessive marketisation may lead to unfair distribution of resources, hindering the smooth development of innovation; while the increase in the degree of opening up to the outside world may make the domestic marine industry face fiercer competition, and may also bring about a certain impact on the local industry.

7.2 Policy implications

(1) Deepen cooperation and exchanges between regions, break geographical boundaries, and form a closer and more balanced marine science and technology innovation network. Build a marine science and technology innovation cooperation platform, strengthen information sharing, resource integration and complementary advantages, and form a closer innovation network. At the same time, it encourages cross-regional and cross-industry collaborative innovation and promotes cross-border integration of marine science and technology innovation. In particular, it is necessary to strengthen the radiation and driving role of central areas such as Shanghai, Tianjin and Guangdong, promote the sharing of innovative resources and experience through technology transfer and personnel training, and promote the coordinated development of marine science and technology innovation.

(2) According to the different characteristics of the basic innovation stage, the applied R & D stage and the profitable transformation stage, it is suggested that each region should formulate targeted innovation policies and development strategies according to its own development stage and advantageous areas. In the stage of basic innovation, we should pay attention to strengthening basic research and personnel training; in the stage of applied research and development, pay attention to technological innovation and product upgrading; in the stage of profitability transformation, pay attention to market expansion and brand building. Through the coordinated development of different stages, promote the all-round development of marine science and technology innovation.

(3) In view of the robustness of the network structure of marine science and technology innovation efficiency, it is suggested that each region should further optimize the network layout and improve the network efficiency and stability on the basis of maintaining the existing network structure. It can strengthen the construction of key nodes and improve the overall connectivity and transmission efficiency of the network. At the same time, we should pay attention to cultivating new innovation growth points, expanding network coverage, and improving the overall level of marine science and technology innovation.

(4) In view of the influence of the government ‘s support for the marine industry, the adjustment and optimization of the marine industrial structure, and the improvement of marine management services, it is suggested that the government should increase its support for marine science and technology innovation, formulate more preferential policy measures, and attract more innovative resources and talent investment. At the same time, it will promote the structural adjustment and optimization of the marine industry and enhance the added value and market competitiveness of the industry. Strengthen the improvement of marine management services and provide better protection and support for marine science and technology innovation.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.

Author contributions

XC: Conceptualization, Data curation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing. QD: Formal analysis, Funding acquisition, Investigation, Resources, Writing – review & editing. WJ: Funding acquisition, Investigation, Methodology, Project administration, Resources, Writing – original draft. JZ: Investigation, Methodology, Project administration, Resources, Software, Writing – original draft. CL: Resources, Software, Supervision, Validation, Visualization, Writing – original draft.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the funding for the key research base of humanities and Social Sciences of Universities in Hebei Province (HYZD202301).This work was supported by the National Natural Science Fund Project of China (No.42076222).

Acknowledgments

We acknowledge the use of data support from “Ministry of Ecology and Environment of the People’s republic of China, Ministry of Natural Resources of the People’s Republic of China, China marineic Information Network(http://www.nmdis.org.cn) and National Science & Technology Infrastructure(http://mds.nmdis.org.cn)”. In addition, Editage (http://www.editage.cn/) has corrected this article for English language errors. Constructive suggestions.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: innovation value chains, marine science and technology, innovation efficiency, spatially connected networks, social network analysis, formation mechanisms

Citation: Chen X, Di Q, Jia W, Zhang J and Liang C (2024) Spatial network structure and formation mechanism of China’s marine science and technology innovation efficiency from the perspective of innovation value chain. Front. Mar. Sci. 11:1430243. doi: 10.3389/fmars.2024.1430243

Received: 09 May 2024; Accepted: 18 June 2024; Published: 05 July 2024.

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*Correspondence: Xiaolong Chen, [email protected] ; Qianbin Di, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.