business model of research and development

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• What Is R&D?
• Understanding R&D
• Types of R&D
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• R&D vs. Applied Research
• Who Spends the Most?

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Research and Development (R&D) Definition, Types, and Importance

Investopedia / Ellen Lindner

What Is Research and Development (R&D)?

The term research and development (R&D) is used to describe a series of activities that companies undertake to innovate and introduce new products and services. R&D is often the first stage in the development process. Companies require knowledge, talent, and investment in order to further their R&D needs and goals. The purpose of research and development is generally to take new products and services to market and add to the company's bottom line .

Key Takeaways

• Research and development represents the activities companies undertake to innovate and introduce new products and services or to improve their existing offerings.
• R&D allows a company to stay ahead of its competition by catering to new wants or needs in the market.
• Companies in different sectors and industries conduct R&D—pharmaceuticals, semiconductors, and technology companies generally spend the most.
• R&D is often a broad approach to exploratory advancement, while applied research is more geared towards researching a more narrow scope.
• The accounting for treatment for R&D costs can materially impact a company's income statement and balance sheet.

Understanding Research and Development (R&D)

The concept of research and development is widely linked to innovation both in the corporate and government sectors. R&D allows a company to stay ahead of its competition. Without an R&D program, a company may not survive on its own and may have to rely on other ways to innovate such as engaging in mergers and acquisitions (M&A) or partnerships. Through R&D, companies can design new products and improve their existing offerings.

R&D is distinct from most operational activities performed by a corporation. The research and/or development is typically not performed with the expectation of immediate profit. Instead, it is expected to contribute to the long-term profitability of a company. R&D may often allow companies to secure intellectual property, including patents , copyrights, and trademarks as discoveries are made and products created.

Companies that set up and employ departments dedicated entirely to R&D commit substantial capital to the effort. They must estimate the risk-adjusted return on their R&D expenditures, which inevitably involves risk of capital. That's because there is no immediate payoff, and the return on investment (ROI) is uncertain. As more money is invested in R&D, the level of capital risk increases. Other companies may choose to outsource their R&D for a variety of reasons including size and cost.

Companies across all sectors and industries undergo R&D activities. Corporations experience growth through these improvements and the development of new goods and services. Pharmaceuticals, semiconductors , and software/technology companies tend to spend the most on R&D. In Europe, R&D is known as research and technical or technological development.

Many small and mid-sized businesses may choose to outsource their R&D efforts because they don't have the right staff in-house to meet their needs.

Types of R&D

There are several different types of R&D that exist in the corporate world and within government. The type used depends entirely on the entity undertaking it and the results can differ.

Basic Research

There are business incubators and accelerators, where corporations invest in startups and provide funding assistance and guidance to entrepreneurs in the hope that innovations will result that they can use to their benefit.

M&As and partnerships are also forms of R&D as companies join forces to take advantage of other companies' institutional knowledge and talent.

Applied Research

One R&D model is a department staffed primarily by engineers who develop new products —a task that typically involves extensive research. There is no specific goal or application in mind with this model. Instead, the research is done for the sake of research.

Development Research

This model involves a department composed of industrial scientists or researchers, all of who are tasked with applied research in technical, scientific, or industrial fields. This model facilitates the development of future products or the improvement of current products and/or operating procedures.

$42.7 billion of research and development costs later, Amazon was granted 2,244 new patents in 2020. Their patents included advancements in artificial intelligence, machine learning, and cloud computing.

Advantages and Disadvantages of R&D

There are several key benefits to research and development. It facilitates innovation, allowing companies to improve existing products and services or by letting them develop new ones to bring to the market.

Because R&D also is a key component of innovation, it requires a greater degree of skill from employees who take part. This allows companies to expand their talent pool, which often comes with special skill sets.

The advantages go beyond corporations. Consumers stand to benefit from R&D because it gives them better, high-quality products and services as well as a wider range of options. Corporations can, therefore, rely on consumers to remain loyal to their brands. It also helps drive productivity and economic growth.

Disadvantages

One of the major drawbacks to R&D is the cost. First, there is the financial expense as it requires a significant investment of cash upfront. This can include setting up a separate R&D department, hiring talent, and product and service testing, among others.

Innovation doesn't happen overnight so there is also a time factor to consider. This means that it takes a lot of time to bring products and services to market from conception to production to delivery.

Because it does take time to go from concept to product, companies stand the risk of being at the mercy of changing market trends . So what they thought may be a great seller at one time may reach the market too late and not fly off the shelves once it's ready.

Facilitates innovation

Improved or new products and services

Expands knowledge and talent pool

Increased consumer choice and brand loyalty

Economic driver

Financial investment

Shifting market trends

R&D Accounting

R&D may be beneficial to a company's bottom line, but it is considered an expense . After all, companies spend substantial amounts on research and trying to develop new products and services. As such, these expenses are often reported for accounting purposes on the income statement and do not carry long-term value.

There are certain situations where R&D costs are capitalized and reported on the balance sheet. Some examples include but are not limited to:

• Materials, fixed assets, or other assets have alternative future uses with an estimable value and useful life.
• Software that can be converted or applied elsewhere in the company to have a useful life beyond a specific single R&D project.
• Indirect costs or overhead expenses allocated between projects.
• R&D purchased from a third party that is accompanied by intangible value. That intangible asset may be recorded as a separate balance sheet asset.

R&D Considerations

Before taking on the task of research and development, it's important for companies and governments to consider some of the key factors associated with it. Some of the most notable considerations are:

• Objectives and Outcome: One of the most important factors to consider is the intended goals of the R&D project. Is it to innovate and fill a need for certain products that aren't being sold? Or is it to make improvements on existing ones? Whatever the reason, it's always important to note that there should be some flexibility as things can change over time.
• Timing: R&D requires a lot of time. This involves reviewing the market to see where there may be a lack of certain products and services or finding ways to improve on those that are already on the shelves.
• Cost: R&D costs a great deal of money, especially when it comes to the upfront costs. And there may be higher costs associated with the conception and production of new products rather than updating existing ones.
• Risks: As with any venture, R&D does come with risks. R&D doesn't come with any guarantees, no matter the time and money that goes into it. This means that companies and governments may sacrifice their ROI if the end product isn't successful.

Research and Development vs. Applied Research

Basic research is aimed at a fuller, more complete understanding of the fundamental aspects of a concept or phenomenon. This understanding is generally the first step in R&D. These activities provide a basis of information without directed applications toward products, policies, or operational processes .

Applied research entails the activities used to gain knowledge with a specific goal in mind. The activities may be to determine and develop new products, policies, or operational processes. While basic research is time-consuming, applied research is painstaking and more costly because of its detailed and complex nature.

Who Spends the Most on R&D?

Companies spend billions of dollars on R&D to produce the newest, most sought-after products. According to public company filings, these companies incurred the highest research and development spending in 2020:

• Amazon: $42.7 billion
• Alphabet.: $27.6 billion
• Huawei: $22.0 billion
• Microsoft: $19.3 billion
• Apple: $18.8 billion
• Samsung: $18.8 billion
• Facebook: $18.5 billion

What Types of Activities Can Be Found in Research and Development?

Research and development activities focus on the innovation of new products or services in a company. Among the primary purposes of R&D activities is for a company to remain competitive as it produces products that advance and elevate its current product line. Since R&D typically operates on a longer-term horizon, its activities are not anticipated to generate immediate returns. However, in time, R&D projects may lead to patents, trademarks, or breakthrough discoveries with lasting benefits to the company. 

What Is an Example of Research and Development?

Alphabet allocated over $16 billion annually to R&D in 2018. Under its R&D arm X, the moonshot factory, it has developed Waymo self-driving cars. Meanwhile, Amazon has spent even more on R&D projects, with key developments in cloud computing and its cashier-less store Amazon Go. At the same time, R&D can take the approach of a merger & acquisition, where a company will leverage the talent and intel of another company to create a competitive edge. The same can be said with company investment in accelerators and incubators, whose developments it could later leverage.

Why Is Research and Development Important?

Given the rapid rate of technological advancement, R&D is important for companies to stay competitive. Specifically, R&D allows companies to create products that are difficult for their competitors to replicate. Meanwhile, R&D efforts can lead to improved productivity that helps increase margins, further creating an edge in outpacing competitors. From a broader perspective, R&D can allow a company to stay ahead of the curve, anticipating customer demands or trends.

There are many things companies can do in order to advance in their industries and the overall market. Research and development is just one way they can set themselves apart from their competition. It opens up the potential for innovation and increasing sales. But it does come with some drawbacks—the most obvious being the financial cost and the time it takes to innovate.

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Start » strategy, what is research and development .

Research and development provides businesses with the information they need to successfully bring their products or services to market.

In any industry, even the most revolutionary products and services are rarely fully conceptualized on day 1. Most often, success in the market stems from extensive, effective research and development (R&D). This is especially true for small businesses, which contribute a significantly higher percentage of sales to R&D work than larger businesses.

Here’s everything you need to know about R&D and why it’s well worth the investment.

What is research and development?

R&D refers to the various activities businesses conduct to prepare new products or services for the marketplace. Businesses of all sizes and sectors can partake in R&D activities, though the amount of investment can vary. For example, technology and health care companies tend to have higher R&D expenses , as do enterprises with larger budgets.

Typically the first step in the development process, R&D is not expected to yield immediate profits. Rather, it focuses on innovation and setting up a company for long-term profitability. During this process, businesses may secure patents, copyrights, and other intellectual property associated with their products and services.

At larger companies, R&D activities are often handled in-house by a designated R&D department. However, some smaller companies may opt to outsource R&D to a third-party research firm, a specialist, or an educational institution.

[Read more: 7 Ways to Find Small Business Grant Opportunities ]

Types of research and development

R&D activities typically fall into one of three main categories:

• Basic research: Basic research, sometimes called fundamental research, aims to provide theoretical insight into specific problems or phenomena. For example, a company looking to develop a new toy for children might conduct basic research into child play development.
• Applied research: This type of research is practical and conducted with a specific goal in mind, most often discovering new solutions for existing problems. The children’s toy company from the previous example might conduct applied research into developing a toy that facilitates play development in a new or improved way.
• Development research: In development research, researchers focus exclusively on applied research to develop new products and improve existing ones. For example, a team of development researchers may test the hypothetical company’s new toy or implement feedback obtained from customers.

Small businesses have limited resources. They don’t have that endless budget that the Fortune 500 company has, which means the small business will have to get creative to conduct worthwhile research and development.

Becca Hoeft, CEO and Founder of Morris Hoeft Group

Why invest in research and development?

While R&D can require a significant investment, it also yields several advantages. Below are four specific areas where your business can benefit by conducting R&D.

New products

R&D supports businesses in developing new offerings or improving existing ones based on market demand. By conducting research and applying your findings to your final product, companies are more likely to develop something that meets customers’ needs and performs well in the marketplace.

R&D can help businesses understand their place in the market as well as identify inefficiencies in their workflows. Insights from R&D activities can illuminate ways to improve operations as well as where to most effectively allocate resources, increasing overall efficiency.

Cost reductions

While developing a well-researched product or service that performs well is likely to maximize profit, R&D aimed at improving internal processes and technologies can reduce the cost of bringing products and services to market.

Businesses that invest in R&D may be eligible for specific tax incentives. For one, the federal R&D tax credit offers a dollar-for-dollar reduction in tax liability for businesses that partake in various research-based activities. Eligible companies can apply for this credit by submitting Form 6765 with their business taxes.

[Read more: How to Seek Funding for Your Invention ]

Overcoming the challenges of small business R&D

According to Becca Hoeft, CEO and Founder of Morris Hoeft Group , small businesses may face numerous challenges related to R&D that their larger counterparts might not experience.

“Small businesses have limited resources,” said Hoeft. “They don’t have that endless budget that the Fortune 500 company has, which means the small business will have to get creative to conduct worthwhile research and development.”

While R&D funding is available through various government grants, university programs, and research institutions, Hoeft noted that it may take some time and strategic planning to obtain it. She recommended that small business owners start talking publicly about what kind of research they are doing and what they need to conduct it.

“Don’t hide under a rock and expect money to magically appear,” Hoeft told CO—. “Get on a stage at a relevant conference [or] start a blog series about your idea.”

Keep in mind that once you start sharing your ideas and what you want to research, “it’s out there in the universe,” said Hoeft. Therefore, protecting your intellectual property before you begin and during the research process is extremely important.

“Ensure your trademarks, patents, and copyrights are in place to protect you and your small business,” Hoeft added.

[Read more: How to Qualify for and Claim the R&D Tax Credit ]

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New business models for research and development with affordability requirements are needed to achieve fair pricing of medicines

Read our achieving fair pricing of medicines collection.

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• Peer review
• Fatima Suleman , professor 1 2 ,
• Marcus Low , postgraduate student 2 ,
• Suerie Moon , director of research 3 ,
• Steven G Morgan , professor 4
• 1 Prince Claus Chair of Development and Equity, Affordable (Bio) Therapeutics for Public Health, Utrecht University, Netherlands
• 2 Discipline of Pharmaceutical Sciences, School of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
• 3 Global Health Centre, Graduate Institute of International and Development Studies, Geneva, Switzerland
• 4 School of Population in Public Health, University of British Columbia, Vancouver, Canada
• Correspondence to F Suleman sulemanf{at}ukzn.ac.za

For research and development to systematically deliver fairly priced medicines, new approaches to financing and organisation are needed, and affordability must be integrated into push, pull, and pooling mechanisms, say Fatima Suleman and colleagues

The health of populations depends, in part, on the development and appropriate use of new drugs, diagnostics, vaccines, and other biological medicines (broadly referred to as medicines). 1 Realising the social value of pharmaceutical innovation, however, is difficult. Policies must promote investment in research and development in areas of significant unmet health need while also ensuring access to resulting innovations. 2

Pharmaceutical R&D relies heavily on the monopoly pricing enabled by patents or other forms of market exclusivity. This threatens the goals of innovation and access and can result in “unfair” prices. 2 A fair price for medicines is one that is affordable for health systems and patients while providing sufficient market incentive for industry to invest. 3

Concerns about the high and rising prices of new medicines 4 have prompted increased interest in the possibility that changing the way in which R&D is financed and organised might result in fairer prices for innovative medicines. In particular, “delinkage,” where the financing of R&D is decoupled from the price of medicines by removing market exclusivity as a driving incentive, has attracted growing attention as an alternative business model for pharmaceutical R&D. This idea was recently endorsed in the political declaration of the United Nations high level meeting on tuberculosis. 5

The range of policy tools that can facilitate fair pricing falls into three broad categories: (1) “push” mechanisms, which typically provide grants for research projects in advance; (2) “pull” mechanisms, which provide rewards for research accomplishments at various stages of the drug development process; and (3) “pooling” mechanisms, which facilitate access to knowledge to advance scientific progress, thereby shortening timelines and reducing development costs. Below we provide an overview of these mechanisms and argue that without adequately enforced affordability requirements they may not lead to fair pricing.

Push mechanisms

Push mechanisms offer direct funding for various stages of drug R&D projects in advance, usually in the form of grants. These payments can incentivise (push) research by product developers when there is an unmet need but limited commercial potential or a high risk of failure. 4 6

Conditions tied to R&D grants can include requirements that product developers price the resulting medicines affordably. A patented technology may be transferred to a body other than the grant recipient—for example, an academic institution may grant licences to a private company. In such cases, funders can require the grant recipient to include affordability guarantees in any such agreements.

By subsidising the costs of R&D, grant funding reduces the need for developers to recoup investments through higher prices. 7 For neglected diseases or other recognised areas of market failure, public or philanthropic funding accounts for all or nearly all the direct costs of R&D. 8 In these cases, charging the lowest sustainable prices for medicines is a reasonable expectation.

Traditionally, product development at a later stage has been financed by the pharmaceutical industry responding to pull incentives, with no pricing conditions attached. An exception, however, is neglected diseases, for which public or philanthropic grants have financed late stage product development. These often come with affordability requirements. Such grants are increasingly being considered for new antibiotics, medicines needed for disease outbreaks, and some paediatric formulations.

Early stage research is largely funded through public grants. Early stage grants from the US National Institutes for Health (NIH), the world’s single largest funder of biomedical research, give the funder the right to “march in” and take control of intellectual property if medicines are not made available on “reasonable terms.” The NIH, however, has never made use of this right, despite repeated petitions asking it to do so in response to high medicine prices. 9

There is some evidence that push mechanisms can steer investments, reduce barriers to entry by small and medium sized enterprises, and absorb early stage risks of failure. 10 A disadvantage of push mechanisms is the incentive for developers to oversell investments in their particular projects. 8 11 Push mechanisms may also create a tension between the desire to steer investments and giving developers insufficient flexibility to be efficient and innovative in their research. Without adequate enforceable affordability requirements push incentives will not lead to fair prices.

Pull mechanisms

Pull mechanisms deliver rewards after a research and development objective or milestone is reached. These rewards may include incentives such as tax breaks, cash prizes, patents or data exclusivity, or advance market commitments where procurers commit to buy a certain amount of medicines. In contrast to push mechanisms, pull incentives based on outcome only compensate successful achievement of milestones or end products meeting specific criteria.

Pull mechanisms can contribute to fair pricing if the rewards are designed to do so, but this has not generally been the case. To date, most pull mechanisms have aimed at promoting innovation but not affordability. For example, the US priority review voucher programme provides a tradeable voucher as a reward for priority Food and Drug Administration review of a potentially lucrative medicine for a neglected or outbreak prone disease. The programme, however, does not require the voucher recipient to set affordable prices or to supply the relevant medicine to the market. 12

In addition, not all pull mechanisms are compatible with achieving affordability goals. Monopolies, whether based on patents or data exclusivity, enable a company to price the product at relatively high levels for a certain period. Furthermore, policy makers have sometimes even declined to include affordability requirements in pull mechanisms, as in the case of the priority review voucher. Some of these mechanisms have been criticised for “socialising the risks and privatising the profits” 13 of the drug development process.

Nevertheless, it is possible to craft some pull incentives to promote affordability. For example, large scale prizes, such as the antibiotics prize fund proposed in the US, 14 would reward inventors of new medicines; in exchange, the inventor would relinquish their patent monopoly and allow new medicines to be sold close to the cost of production.

Because developers bear the development costs in advance, pull mechanisms provide a greater incentive to maximise efficiency and innovation than push mechanisms. 9 One disadvantage is that the financial risk and uncertainty inherent in pull mechanisms may deter participation. This is particularly true for smaller companies that may lack the resources to finance lengthy R&D processes. Other challenges include determining the size of the incentive needed to motivate developers while remaining cost effective, and defining drug characteristics linked to the pull incentive that are neither too specific nor too general. Finally, an effective outcome based pull system relies on a funder that will credibly commit to long term funding guarantees. 15

Pooling mechanisms

Information sharing through the pooling of data or intellectual property can expedite innovation by removing the barriers to R&D created by secrecy, patents, and data exclusivity, and by minimising wasteful duplication of effort. By doing so, pooling can lower the cost of innovation and thereby enable more affordable pricing. For example, the Medicines Patent Pool, established in 2010 with support from Unitaid, pools patents relating to medicines for HIV/AIDS, hepatitis C, and tuberculosis. This accelerates the development of fixed dose combinations and facilitates testing of multiple drugs together to develop regimens rather than individual molecules.

Data pooling is being promoted through open source innovation initiatives, in which interested stakeholders place knowledge, data, and technology in the public domain. A number of open initiatives are in operation, including the Indian Open Source Drug Discovery initiative, the Librassay initiative, and the WIPO Re:Search consortium. All these allow scientists to share information and access intellectual property to search for new treatments.

The central idea behind these initiatives is that open collaboration and exchange of information will both expedite, and lower the cost of, the development of desired innovations, leading to more affordable prices. It should be noted, however, that specific enforceable conditions requiring fair pricing are needed to ensure that lower costs do indeed result in lower prices, rather than just producing wider profit margins.

Strategic use of push, pull, and knowledge pooling mechanisms can build affordability into the R&D process. Existing push mechanisms generally function well and enjoy wide support, but more must be done to ensure that medicines resulting from such push funding are fairly priced. This includes ensuring strong pricing and access provisions in funding agreements, and better enforcement of such provisions that already exist.

Interest in pull mechanisms or those that combine push and pull has risen in recent years. According to a recent mapping exercise, at least 49 alternative R&D funding initiatives are in operation, and 32 are being planned. 4 However, many alternative models remain underused and insufficiently tested by governments and other research funders. These include prize funds, advance purchase agreements, patent buy-outs, innovative taxes, conditional licences, and pricing guarantees.

Implementing alternative R&D models requires new sources of financing, particularly when the use of high prices and market exclusivities as drivers of R&D investments are deliberately limited. Because alternative R&D business models have largely been employed in areas of market failure, the financing has come from public and philanthropic sources. For example, most of the funding for research into neglected disease (if the NIH is excluded) comes from the two largest philanthropic investors—namely, the Bill and Melinda Gates Foundation and the Wellcome Trust. Together they contributed $660m (£520m; €580m) in 2014. 16

A wide variety of both state and non-state actors also contribute significantly to alternative funding mechanisms for dealing with neglected diseases, antimicrobial resistance, and diseases with epidemic potential. However, compared with the estimated US$240bn spent on biomedical R&D annually, investment in alternative business models is a drop in the ocean, probably <1% of total investment. Efforts to build affordable prices into the R&D process itself remain the exception to the rule. 17 18

Thus an important question is whether existing examples can be replicated or scaled up. This would ensure that R&D activities systematically result in affordably priced medicines for a broader set of diseases and public health challenges, beyond the handful of areas recognised as market failures. An intriguing example has been provided by the Drugs for Neglected Diseases project to develop an affordable hepatitis C drug. The first clinical trial results were highly promising. If successful, the medicine could be sold for less than $300 per treatment course, compared with $12 500 to $100 000 for hepatitis C drugs in the same class developed through traditional models. 19

A key barrier to replication of such efforts and to testing of alternative innovation models is a lack of funding. Some WHO member states have supported the creation of a fund housed at the Special Programme for Research and Training in Tropical Diseases, hosted at WHO. They propose a voluntary financing model based on the principles of delinkage, the use of open knowledge innovation, and open licensing for access. 20 21 None of these proposals has attracted major financial support, underlining the general difficulty in generating funding for such initiatives. Significant sums have been mobilised, however, to deal with R&D for antimicrobial resistance and epidemic threats, suggesting that it is feasible. 16

It is also notable that political will is not always present. In WHO and UN processes, some influential countries have not supported promotion of alternative R&D models that may challenge the dominant market exclusivity based system.

Consensus for alternatives to the status quo is growing, 22 and calls for reform are becoming more insistent. 2 Health systems have never been so financially challenged, partly because the demands on them have never been so great as many drive towards universal health coverage. Meanwhile, the demographic and epidemiological transformation of global populations continues rapidly, with a seemingly inexorable increase in non-communicable disease and the looming threat of generalised antimicrobial resistance. 23

The potential of alternative models to facilitate more efficient R&D and lower prices is now widely recognised. It is now up to states and other funders of research to insist upon affordability requirements in all R&D funding, to enforce them, and to increase investment in alternative models.

Key messages

Governments and other research funders remain slow to invest in alternative research and development models, though the need is well recognised

Governments and other research funders should insist on binding affordability requirements as a condition of all research and development funding to ensure fair pricing of medicines

Governments and other research funders should invest in models that delink the cost of research and development from the cost of production, and invest in research that measures the efficiency of such alternative models

Contributors and sources:FS drafted the article and finalised it with contributions from ML, SM, and SGM, who provided input and critical feedback for important intellectual content. FS is the guarantor. This manuscript is based on a narrative review of the literature and the authors’ experience and expertise in working in pharmaceutical policy, pricing, and reimbursement in different settings worldwide.

Competing interests: We have read and understood BMJ policy on declaration of interests and have no relevant interests to declare.

Provenance and peer review: Commissioned; externally peer reviewed.

This article is part of a series proposed by WHO and commissioned by The BMJ . The BMJ retained full editorial control over external peer review, editing, and publication of these articles. Open access fees are funded by WHO.

This is an Open Access article distributed under the terms of the Creative Commons Attribution IGO License (https://creativecommons.org/licenses/by-nc/3.0/igo/), which permits use, distribution, and reproduction for non-commercial purposes in any medium, provided the original work is properly cited.

• United Nations Secretary-General’s High-level Panel on Access to Medicines
• ↵ World Health Organization. Essential medicines and health products. 2017. https://www.who.int/medicines/access/fair_pricing/en/
• Kiddell-Monroe R ,
• Greenberg A ,
• ↵ United Nations. Political declaration of the United Nations high-level meeting on tuberculosis. 2018. https://www.un.org/pga/72/wp-content/uploads/sites/51/2018/09/Co-facilitators-Revised-text-Political-Declaraion-on-the-Fight-against-Tuberculosis.pdf
• ↵ Novartis. Novartis expands partnership with Medicines for Malaria Venture to develop next-generation antimalarial treatment. 2016. https://www.iol.co.za/business-report/companies/novartis-expands-malaria-research-2034875
• ↵ GSK. First African country introduces GSK’s pneumococcal vaccine through innovative financing mechanism. 2011. https://www.gsk.com/en-gb/media/press-releases/first-african-country-introduces-gsk-s-pneumococcal-vaccine-through-innovative-financing-mechanism/
• ↵ Policy Cures Research. Neglected disease research and development: reflecting on a decade of global investment. G-FINDER Report, 2017. https://www.policycuresresearch.org/g-finder-2017/
• Treasure CL ,
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• ↵ Mossialos E, Morel CM, Edwards S, Berenson J, Gemmill-Toyama M, Brogan D. Policies and incentives for promoting innovation in antibiotic research. European Observatory on Health Systems and Policies, 2010. http://www.euro.who.int/__data/assets/pdf_file/0011/120143/E94241.pdf
• ↵ Vouching for access . Nat Med 2016 ; 22 : 693 . doi: 10.1038/nm.4151   pmid: 27387878 OpenUrl CrossRef PubMed
• ↵ S.771—Improving Access To Affordable Prescription Drugs Act. 115th Congress, 2017-18. https://www.congress.gov/bill/115th-congress/senate-bill/771 .
• Renwick MJ ,
• Brogan DM ,
• Mossialos E
• ↵ Policy Cures. Neglected disease research and development: the Ebola effect. G-FINDER Report, 2015. http://www.policycures.org/downloads/Y8%20GFINDER%20full%20report%20web.pdf
• ↵ Decision of the 67th World Health Assembly, WHA67.15. In: Sixty-seventh World Health Assembly, Geneva, 19-24 May 2014. World Health Organization, 2014. http://apps.who.int/gb/ebwha/pdf_files/WHA67-REC1/A67_2014_REC1-en.pdf
• Balasegaram M ,
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Business model innovation: a review and research agenda

New England Journal of Entrepreneurship

ISSN : 2574-8904

Article publication date: 16 October 2019

Issue publication date: 13 November 2019

The aim of this paper is to review and synthesise the recent advancements in the business model literature and explore how firms approach business model innovation.

Design/methodology/approach

A systematic review of business model innovation literature was carried out by analysing 219 papers published between 2010 and 2016.

Evidence reviewed suggests that rather than taking either an evolutionary process of continuous revision, adaptation and fine-tuning of the existing business model or a revolutionary process of replacing the existing business model, firms can explore alternative business models through experimentation, open and disruptive innovations. It was also found that changing business models encompasses modifying a single element, altering multiple elements simultaneously and/or changing the interactions between elements in four areas of innovation: value proposition, operational value, human capital and financial value.

Research limitations/implications

Although this review highlights the different avenues to business model innovation, the mechanisms by which firms can change their business models and the external factors associated with such change remain unexplored.

Practical implications

The business model innovation framework can be used by practitioners as a “navigation map” to determine where and how to change their existing business models.

Originality/value

Because conflicting approaches exist in the literature on how firms change their business models, the review synthesises these approaches and provides a clear guidance as to the ways through which business model innovation can be undertaken.

• Business model
• Value proposition
• Value creation
• Value capture

Ramdani, B. , Binsaif, A. and Boukrami, E. (2019), "Business model innovation: a review and research agenda", New England Journal of Entrepreneurship , Vol. 22 No. 2, pp. 89-108. https://doi.org/10.1108/NEJE-06-2019-0030

Emerald Publishing Limited

Copyright © 2019, Boumediene Ramdani, Ahmed Binsaif and Elias Boukrami

Published in New England Journal of Entrepreneurship . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Firms pursue business model innovation by exploring new ways to define value proposition, create and capture value for customers, suppliers and partners ( Gambardella and McGahan, 2010 ; Teece, 2010 ; Bock et al. , 2012 ; Casadesus-Masanell and Zhu, 2013 ). An extensive body of the literature asserts that innovation in business models is of vital importance to firm survival, business performance and as a source of competitive advantage ( Demil and Lecocq, 2010 ; Chesbrough, 2010 ; Amit and Zott, 2012 ; Baden-Fuller and Haefliger, 2013 ; Casadesus-Masanell and Zhu, 2013 ). It is starting to attract a growing attention, given the increasing opportunities for new business models enabled by changing customer expectations, technological advances and deregulation ( Casadesus-Masanell and Llanes, 2011 ; Casadesus-Masanell and Zhu, 2013 ). This is evident from the recent scholarly outputs ( Figure 1 ). Thus, it is essential to comprehend this literature and uncover where alternative business models can be explored.

Conflicting approaches exist in the literature on how firms change their business models. One approach suggests that alternative business models can be explored through an evolutionary process of incremental changes to business model elements (e.g. Demil and Lecocq, 2010 ; Dunford et al. , 2010 ; Amit and Zott, 2012 ; Landau et al. , 2016 ; Velu, 2016 ). The other approach, mainly practice-oriented, advocates that innovative business models can be developed through a revolutionary process by replacing existing business models (e.g. Bock et al. , 2012 ; Iansiti and Lakhani, 2014 ). The fragmentation of prior research is due to the variety of disciplinary and theoretical foundations through which business model innovation is examined. Scholars have drawn on perspectives from entrepreneurship (e.g. George and Bock, 2011 ), information systems (e.g. Al-debei and Avison, 2010 ), innovation management (e.g. Dmitriev et al. , 2014 ), marketing (e.g. Sorescu et al. , 2011 ) and strategy (e.g. Demil and Lecocq, 2010 ). Also, this fragmentation is deepened by focusing on different types of business models in different industries. Studies have explored different types of business models such as digital business models (e.g. Weill and Woerner, 2013 ), service business models (e.g. Kastalli et al. , 2013 ), social business models (e.g. Hlady-Rispal and Servantie, 2016 ) and sustainability-driven business models ( Esslinger, 2011 ). Besides, studies have examined different industries such as airline ( Lange et al. , 2015 ), manufacturing ( Landau et al. , 2016 ), newspaper ( Karimi and Walter, 2016 ), retail ( Brea-Solís et al. , 2015 ) and telemedicine ( Peters et al. , 2015 ).

Since the first comprehensive review of business model literature was carried out by Zott et al. (2011) , several reviews were published recently (as highlighted in Table I ). Our review builds on and extends the extant literature in at least three ways. First, unlike previous reviews that mainly focused on the general construct of “Business Model” ( George and Bock, 2011 ; Zott et al. , 2011 ; Wirtz et al. , 2016 ), our review focuses on uncovering how firms change their existing business model(s) by including terms that reflect business model innovation, namely, value proposition, value creation and value capture. Second, previous reviews do not provide a clear answer as to how firms change their business models. Our review aims to provide a clear guidance on how firms carry out business model innovation by synthesising the different perspectives existing in the literature. Third, compared to recent reviews on business model innovation ( Schneider and Spieth, 2013 ; Spieth et al. , 2014 ), which have touched lightly on some innovation aspects such as streams and motivations of business model innovation research, our review will uncover the innovation areas where alternative business models can be explored. Taking Teece’s (2010) suggestion, “A helpful analytic approach for management is likely to involve systematic deconstruction/unpacking of existing business models, and an evaluation of each element with an idea toward refinement or replacement” (p. 188), this paper aims to develop a theoretical framework of business model innovation.

Our review first explains the scope and the process of the literature review. This is followed by a synthesis of the findings of the review into a theoretical framework of business model innovation. Finally, avenues for future research will be discussed in relation to the approaches, degree and mechanisms of business model innovation.

2. Scope and method of the literature review

Given the diverse body of business models literature, a systematic literature review was carried out to minimise research bias ( Transfield et al. , 2003 ). Compared to the previous business model literature, our review criteria are summarised in Table I . The journal papers considered were published between January 2010 and December 2016. As highlighted in Figure 1 , most contributions in this field have been issued within this period since previous developments in the literature were comprehensively reviewed up to the end of 2009 ( Zott et al. , 2011 ). Using four databases (EBSCO Business Complete, ABI/INFORM, JSTOR and ScienceDirect), we searched peer-reviewed papers with terms such as business model(s), innovation value proposition, value creation and value capture appearing in the title, abstract or subject terms. As a result, 8,642 peer-reviewed papers were obtained.

Studies were included in our review if they specifically address business models and were top-rated according to The UK Association of Business Schools list ( ABS, 2010 ). This rating has been used not only because it takes into account the journal “Impact Factor” as a measure for journal quality, but also uses in conjunction other measures making it one of the most comprehensive journal ratings. By applying these criteria, 1,682 entries were retrieved from 122 journals. By excluding duplications, 831 papers were identified. As Harvard Business Review is not listed among the peer-reviewed journals in any of the chosen databases and was included in the ABS list, we used the earlier criteria and found 112 additional entries. The reviewed papers and their subject fields are highlighted in Table II . Since the focus of this paper is on business model innovation, we selected studies that discuss value proposition, value creation and value capture as sub-themes. This is not only because the definition of business model innovation mentioned earlier spans all three sub-themes, but also because all three sub-themes have been included in recent studies (e.g. Landau et al. , 2016 ; Velu and Jacob, 2014 ). To confirm whether the papers addressed business model innovation, we examined the main body of the papers to ensure they were properly coded and classified. At the end of the process, 219 papers were included in this review. Table III lists the source of our sample.

The authors reviewed the 219 papers using a protocol that included areas of innovation (i.e. components, elements, and activities), theoretical perspectives and key findings. In order to identify the main themes of business model innovation research, all papers were coded in relation to our research focus as to where alternative business models can be explored (i.e. value proposition, value creation and value capture). Coding was cross checked among the authors on a random sample suggesting high accuracy between them. Having compared and discussed the results, the authors were able to identify the main themes.

3. Prior conceptualisations of business model innovation

Some scholars have articulated the need to build the business model innovation on a more solid theoretical ground ( Sosna et al. , 2010 ; George and Bock, 2011 ). Although many studies are not explicitly theory-based, some studies partially used well-established theories such as the resource-based view (e.g. Al-Debei and Avison, 2010 ) and transaction cost economics (e.g. DaSilva and Trkman, 2014 ) to conceptualise business model innovation. Other theories such as activity systems perspective, dynamic capabilities and practice theory have been used to help answer the question of how firms change their existing business models.

Using the activity systems perspective, Zott and Amit (2010) demonstrated how innovative business models can be developed through the design themes that describe the source of value creation (novelty, lock-in, complementarities and efficiency) and design elements that describe the architecture (content, structure and governance). This work, however, overlooks value capture which limits the explanation of the advocated system’s view (holistic). Moreover, Chatterjee (2013) used this perspective to reveal that firms can design innovative business models that translate value capture logic to core objectives, which can be delivered through the activity system.

Dynamic capability perspective frames business model innovation as an initial experiment followed by continuous revision, adaptation and fine-tuning based on trial-and-error learning ( Sosna et al. , 2010 ). Using this perspective, Demil and Lecocq (2010) showed that “dynamic consistency” is a capability that allows firms to sustain their performance while innovating their business models through voluntary and emergent changes. Also, Mezger (2014) conceptualised business model innovation as a distinct dynamic capability. He argued that this capability is the firm’s capacity to sense opportunities, seize them through the development of valuable and unique business models, and accordingly reconfigure the firms’ competences and resources. Using aspects of practice theory, Mason and Spring (2011) looked at business model innovation in the recorded sound industry and found that it can be achieved through various combinations of managerial practices.

Static and transformational approaches have been used to depict business models ( Demil and Lecocq, 2010 ). The former refers to viewing business models as constituting core elements that influence business performance at a particular point in time. This approach offers a snapshot of the business model elements and how they are assembled, which can help in understanding and communicating a business model (e.g. Eyring et al. , 2011 ; Mason and Spring, 2011 ; Yunus et al. , 2010). The latter, however, focuses on innovation and how to address the changes in business models over time (e.g. Sinfield et al. , 2012 ; Girotra and Netessine, 2014 ; Landau et al. , 2016 ). Some researchers have identified the core elements of business models ex ante (e.g. Demil and Lecocq, 2010 ; Wu et al. , 2010 ; Huarng, 2013 ; Dmitriev et al. , 2014 ), while others argued that considering a priori elements can be restrictive (e.g. Casadesus-Masanell and Ricart, 2010 ). Unsurprisingly, some researchers found a middle ground where elements are loosely defined allowing flexibility in depicting business models (e.g. Zott and Amit, 2010 ; Sinfield et al. , 2012 ; Kiron et al. , 2013 ).

Prior to 2010, conceptual frameworks focused on the business model concept in general (e.g. Chesbrough and Rosenbloom, 2002 ; Osterwalder et al. , 2005 ; Shafer et al. , 2005 ) apart from Johnson et al. ’s (2008 ), which is one of the early contributions to business model innovation. To determine whether a change in existing business model is necessary, Johnson et al. (2008) suggested three steps: “Identify an important unmet job a target customer needs done; blueprint a model that can accomplish that job profitably for a price the customer is willing to pay; and carefully implement and evolve the model by testing essential assumptions and adjusting as you learn” ( Eyring et al. , 2011 , p. 90). Although several frameworks have been developed since then, our understanding of business model innovation is still limited due to the static nature of the majority of these frameworks. Some representations ignore the elements and/or activities where alternative business models can be explored (e.g. Sinfield et al. , 2012 ; Chatterjee, 2013 ; Huarng, 2013 ; Morris et al. , 2013 ; Dmitriev et al. , 2014 ; Girotra and Netessine, 2014 ). Other frameworks ignore value proposition (e.g. Zott and Amit, 2010 ), ignore value creation (e.g. Dmitriev et al. , 2014 ; Michel, 2014 ) and/or ignore value capture (e.g. Mason and Spring, 2011 ; Sorescu et al. , 2011 ; Storbacka, 2011 ). Some conceptualisations do not identify who is responsible for the innovation (e.g. Casadesus-Masanell and Ricart, 2010 ; Sinfield et al. , 2012 ; Chatterjee, 2013 ; Kiron et al. , 2013 ). Synthesising the different contributions into a theoretical framework of business model innovation will enable a better understanding of how firms undertake business model innovation.

4. Business model innovation framework

Our framework ( Figure 2 ) integrates all the elements where alternative business models can be explored. This framework does not claim that the listed elements are definitive for high-performing business models, but is an attempt to outline the elements associated with business model innovation. This framework builds on the previous work of Johnson et al. (2008) and Zott and Amit (2010) by signifying the elements associated with business model innovation. Unlike previous frameworks that mainly consider the constituting elements of business models, this framework focuses on areas of innovation where alternative business models can be explored. Moreover, this is not a static view of the constituting elements of a business model, but rather a view enabling firms to explore alternative business models by continually refining these elements. Arrows in the framework indicate the continuous interaction of business model elements. This framework consists of 4 areas of innovation and 16 elements (more details are shown in Table IV ). Each will be discussed below.

4.1 Value proposition

The first area of innovation refers to elements associated with answering the “Why” questions. While most of the previously established models in the literature include at least one of the value proposition elements (e.g. Brea-Solís et al. , 2015 ; Christensen et al. , 2016 ), other frameworks included two elements (e.g. Dahan et al. , 2010 ; Cortimiglia et al. , 2016 ) and three elements (e.g. Eyring et al. , 2011 ; Sinfield et al. , 2012 ). These elements include rethinking what a company sells, exploring new customer needs, acquiring target customers and determining whether the benefits offered are perceived by customers. Modern organisations are highly concerned with innovation relating to value proposition in order to attract and retain a large portion of their customer base ( Al-Debei and Avison, 2010 ). Developing new business models usually starts with articulating a new customer value proposition ( Eyring et al. , 2011 ). According to Sinfield et al. (2012) , firms are encouraged to explore various alternatives of core offering in more depth by examining type of offering (product or service), its features (custom or off-the-shelf), offered benefits (tangible or intangible), brand (generic or branded) and lifetime of the offering (consumable or durable).

In order to exploit the “middle market” in emerging economies, Eyring et al. (2011) suggested that companies need to design new business models that aim to meet unsatisfied needs and evolve these models by continually testing assumptions and making adjustments. To uncover unmet needs, Eyring et al. (2011) suggested answering four questions: what are customers doing with the offering? What alternative offerings consumers buy? What jobs consumers are satisfying poorly? and what consumers are trying to accomplish with existing offerings? Furthermore, Baden-Fuller and Haefliger (2013) made a distinction between customers and users in two-sided platforms, where users search for products online, and customers (firms) place ads to attract users. They also made a distinction between “pre-designed (scale) based offerings” and “project based offerings”. While the former focuses on “one-size-fits-all”, the latter focuses on specific client solving specific problem.

Established firms entering emerging markets should identify unmet needs “the job to be done” rather than extending their geographical base for existing offerings ( Eyring et al. , 2011 ). Because customers in these markets cannot afford the cheapest of the high-end offerings, firms with innovative business models that meet these customers’ needs affordably will have opportunities for growth ( Eyring et al. , 2011 ). Moreover, secondary business model innovation has been advocated by Wu et al. (2010) as a way for latecomer firms to create and capture value from disruptive technologies in emerging markets. This can be achieved through tailoring the original business model to fit price-sensitive mass customers by articulating a value proposition that is attractive for local customers.

4.2 Operational value

The second area of innovation focuses on elements associated with answering the “What” questions. Many of the established frameworks included either one element (e.g. Sinfield et al. , 2012 ; Taran et al. , 2015 ), two elements (e.g. Mason and Spring, 2011 ; Dmitriev et al. , 2014 ). However, very few included three or more elements (e.g. Mehrizi and Lashkarbolouki, 2016 ; Cortimiglia et al. , 2016 ). These elements include configuring key assets and sequencing activities to deliver the value proposition, exposing the various means by which a company reaches out to customers, and establishing links with key partners and suppliers. Focusing on value creation, Zott and Amit (2010) argued that business model innovation can be achieved through reorganising activities to reduce transaction costs. However, Al-Debei and Avison (2010) argued that innovation relating to this dimension can be achieved through resource configuration, which demonstrates a firm’s ability to integrate various assets in a way that delivers its value proposition. Cavalcante et al. (2011) proposed four ways to change business models: business model creation, extension, revision and termination by creating or adding new processes, and changing or terminating existing processes.

Western firms have had difficulty competing in emerging markets due to importing their existing business models with unchanged operating model ( Eyring et al. , 2011 ). Alternative business models can be uncovered when firms explore the different roles they might play in the industry value chain ( Sinfield et al. , 2012 ). Al-Debei and Avison (2010) suggested achieving this through answering questions such as: what is the position of our firm in the value system? and what mode of collaboration (open or close) would we choose to reach out in a business network? Dahan et al. (2010) found cross-sector partnerships as a way to co-create new multi-organisational business models. They argued that multinational enterprises (MNEs) can collaborate with nongovernmental organisations (NGOs) to create products/or services that neither can create on their own. Collaboration allows access to resources that firms would otherwise need to solely develop or purchase ( Yunus et al. , 2010 ). According to Wu et al. (2010) , secondary business model innovation can be achieved when latecomer firms fully utilise strategic partners’ complementary assets to overcome their latecomer disadvantages and build a unique value network specific to emerging economies context.

4.3 Human capital

The third area of innovation refers to elements associated with answering the “Who” questions. Most of the established frameworks in this field tend to focus less on human capital and include one element at most (e.g. Wu et al. , 2010 ; Kohler, 2015 ). However, our framework highlights four elements, which include experimenting with new ways of doing business, tapping into the skills and competencies needed for the new business model through motivating and involving individuals in the innovation process. According to Belenzon and Schankerman (2015) , “the ability to tap into a pool of talent is strongly related to the specific business model chosen by managers” (p. 795). They claimed that managers can strategically influence individuals’ contributions and their impact on project performance.

Organisational learning can be maximised though continuous experimentation and making changes when actions result in failure ( Yunus et al. , 2010 ). Challenging and questioning the existing rules and assumptions and imagining new ways of doing business will help develop new business models. Another essential element of business model design is governance, which refers to who performs the activities ( Zott and Amit, 2010 ). According to Sorescu et al. (2011) , innovation in retail business models can occur as a result of changes in the level of participation by actors engaged in performing the activities. An essential element of retailing governance is the incentive structure or the mechanisms that motivate those involved in carrying out their roles to meet customer demands ( Sorescu et al. , 2011 ). For example, discount retailers tend to establish different compensation and incentive policies ( Brea-Solís et al. , 2015 ). Revising the incentive system can have a major impact on new ventures’ performance by aligning organisational goals at each stage of growth ( Roberge, 2015 ). Zott and Amit (2010) argued that alternative business models can be explored through adopting innovative governance or changing one or more parties that perform any activities. Sinfield et al. (2012) suggested that business model innovation only requires time from a small team over a short period of time to move a company beyond incremental improvements and generate new opportunities for growth. This is supported by Michel’s (2014) finding that cross-functional teams were able to quickly achieve business model innovation in workshops through deriving new ways to capture value.

4.4 Financial value

The final area of innovation focuses on elements associated with answering the “How” questions. Previously developed frameworks tend to prioritise this area of innovation by three elements (e.g. Eyring et al. , 2011 ; Huang et al. , 2013 ), and in one instance four elements (e.g. Yunus et al. , 2010 ). These elements include activities linked with how to capture value through revenue streams, changing the price-setting mechanisms, and assessing the financial viability and profitability of a business. According to Demil and Lecocq (2010) , changes in cost and/or revenue structures are the consequences of both continuous and radical changes. They also argued that costs relate to different activities run by organisations to acquire, integrate, combine or develop resources. Michel (2014) suggested that alternative business models can be explored through: changing the price-setting mechanism, changing the payer, and changing the price carrier. Different innovation forms are associated with each of these categories.

Business model innovation can be achieved through exploring new ways to generate cash flows ( Sorescu et al. , 2011 ), where the organisation has to consider (and potentially change) when the money is collected: prior to the sale, at the point of sale, or after the sale ( Baden-Fuller and Haefliger, 2013 ). Furthermore, Demil and Lecocq (2010) suggested that changes in business models affect margins. This is apparent in the retail business models, which generate more profit through business model innovation compared to other types of innovation ( Sorescu et al. , 2011 ).

5. Ways to change business models

From reviewing the recent developments in the business model literature, alternative business models can be explored through modifying a single business model element, altering multiple elements simultaneously and/or changing the interactions between elements of a business model.

Changing one of the business model elements (i.e. content, structure or governance) is enough to achieve business model innovation ( Amit and Zott, 2012 ). This means that firms can have a new activity system by performing only one new activity. However, Amit and Zott (2012) clearly outlined a systemic view of business models which entails a holistic change. This is evident from Demil and Lecocq’s (2010) work suggesting that the study of business model innovation should not focus on isolated activities since changing a core element will not only impact other elements but also the interactions between these elements.

Another way to change business models is through altering multiple business model elements simultaneously. Kiron et al. (2013) found that companies combining target customers with value chain innovations and changing one or two other elements of their business models tend to profit from their sustainability activities. They also found that firms changing three to four elements of their business models tend to profit more from their sustainability activities compared to those changing only one element. Moreover, Dahan et al. (2010) found that a new business model was developed as a result of MNEs and NGOs collaboration by redefining value proposition, target customers, governance of activities and distribution channels. Companies can explore multiple combinations by listing different business model options they could undertake (desirable, discussable and unthinkable) and evaluate new combinations that would not have been considered otherwise ( Sinfield et al. , 2012 ).

Changing business models is argued to be demanding as it requires a systemic and holistic view ( Amit and Zott, 2012 ) by considering the relationships between core business model elements ( Demil and Lecocq, 2010 ). As mentioned earlier, changing one element will not only impact other elements but also the interactions between these elements. A firm’s resources and competencies, value proposition and organisational system are continuously interacting and this will in turn impact business performance either positively or negatively ( Demil and Lecocq, 2010 ). According to Zott and Amit (2010) , innovative business models can be developed through linking activities in a novel way that generates more value. They argued that alternative business models can be explored by configuring business model design elements (e.g. governance) and connecting them to distinct themes (e.g. novelty). Supporting this, Eyring et al. (2011) suggested that core business model elements need to be integrated in order to create and capture value ( Eyring et al. , 2011 ).

6. Discussion and future research directions

From the above synthesis of the recent development in the literature, several gaps remain unfilled. To advance the literature, possible future research directions will be discussed in relation to approaches, degrees and mechanisms of business model innovation.

6.1 Approaches of business model innovation

Experimentation, open innovation and disruption have been advocated as approaches to business model innovation. Experimentation has been emphasised as a way to exploit opportunities and develop alternative business models before committing additional investments ( McGrath, 2010 ). Several approaches have been developed to assist in business model experimentation including mapping approach, discovery-driven planning and trail-and-error learning ( Chesbrough, 2010 ; McGrath, 2010 ; Sosna et al. , 2010 ; Andries and Debackere, 2013 ). Little is known about the effectiveness of these approaches. It will be worth investigating which elements of the business model innovation framework are more susceptible to experimentation and which elements should be held unchanged. Although business model innovation tends to be characterised with failure ( Christensen et al. , 2016 ), not much has been established on failing business models. It is interesting to explore how firms determine a failing business model and what organisational processes exist (if any) to evaluate and discard these failed business models. Empirical studies could examine which elements of business model innovation framework are associated with failing business models.

Another way to develop alternative business models is through open innovation. Although different categories of open business models have been identified by researchers (e.g. Frankenberger et al. , 2014 ; Taran et al. , 2015 ; Kortmann and Piller, 2016 ), their effectiveness is yet to be established. Further research is needed to examine when can a firm open and/or close element(s) of the business model innovation framework. Future studies could also examine the characteristics of open and/or close business models.

In responding to disruptive business models, how companies extend their existing business model, introduce additional business model(s) and/or replace their existing business model altogether remains underexplored. Future research is needed to unravel the strategies deployed by firms to extend their existing business models as a response to disruptive business models. In introducing additional business models, Markides (2013) suggested that a company will be presented with several options to manage the two businesses at the same time: create a completely separate business unit, integrate the two business models from the beginning or integrate the second business model after a certain period of time. Finding the balance between separation and integration is of vital importance. Further research could identify which of these choices are most common among successful firms introducing additional business models, how is the balance between integration and separation achieved, and which choice(s) prove more profitable. Moreover, very little is known on how firms replace their existing business model. Longitudinal studies could provide insights into how a firm adopts an alternative model and discard the old business model over time. It may also be worth examining the factors associated with the adoption of business model innovation as a response to disruptive business models. Moreover, new developments in digital technologies such as blockchain, Internet of Things and artificial intelligence are disrupting existing business models and providing firms with alternative avenues to create new business models. Thus far, very little is known on digital business models, the nature of their disruption, and how firms create digital business models and make them disruptive. Future research is needed to fill these important gaps in our knowledge.

6.2 Degrees of business model innovation

Business models can be developed through varying degrees of innovation from an evolutionary process of continuous fine-tuning to a revolutionary process of replacing existing business models. Recent research shows that survival of firms is dependent on the degree of their business model innovation ( Velu, 2015, 2016 ). This review classifies these degrees of innovation into modifying a single element, altering multiple elements simultaneously and/or changing the interactions between elements of the business model innovation framework.

In changing a single element, further research is needed to examine which business model element(s) is (are) associated with business model innovation. It is not clear whether firms intentionally make changes to a single element when carrying out business model innovation or stumble at it when experimenting with new ways of doing things. It may also be worth investigating the entry (or starting) points in the innovation process. There is no consensus in the literature on which element do companies start with when carrying out their business model innovation. While some studies suggest starting with the value proposition ( Eyring et al. , 2011 ; Landau et al. , 2016 ), others suggest starting the innovation process with identifying risks in the value chain ( Girotra and Netessine, 2011 ). Dmitriev et al. (2014) suggested two entry points, namely, value proposition and target customers. In commercialising innovations, the former refers to technology-push innovation while the latter refers to market-pull innovation. Also, it is not clear whether the entry point is the same as the single element associated with changing the business model. Further research can explore the different paths to business model innovation by identifying the entry point and subsequent changes needed to achieve business model innovation.

There is little guidance in the literature on how firms change multiple business model elements simultaneously. Landau et al. (2016) claimed that firms entering emerging markets tend to focus on adjusting specific business model components. It is unclear which elements need configuring, combining and/or integrating to achieve a company’s value proposition. Furthermore, the question of which elements can be “bought” on the market or internally “implemented” and their interplay remains unanswered ( DaSilva and Trkman, 2014 ). Casadesus-Masanell and Ricart (2010) argued that “[…] there is (as yet) no agreement as to the distinctive features of superior business models” (p. 196). Further research is needed to explore these distinctive elements of high-performing business models.

In changing the interactions between business model elements, further research is needed to explore how these elements are linked and what interactions’ changes are necessary to achieve business model innovation. Moreover, the question of how firms sequence these elements remains poorly understood. Future research can explore the synergies created over time between these elements. According to Dmitriev et al. (2014) , we need to improve our understanding of the connective mechanisms and dynamics involved in business model development. More work is needed to explore the different modalities of interdependencies among these elements and empirically testing such interdependencies and their effect on business performance ( Sorescu et al. , 2011 ).

It is surprising that the link between business model innovation and organisational performance has rarely been examined. Changing business models has been found to negatively influence business performance even if it is temporary ( McNamara et al. , 2013 ; Visnjic et al. , 2016 ). Contrary to this, evidence show that modifying business models is positively associated with organisational performance ( Cucculelli and Bettinelli, 2015 ). Empirical research is needed to operationalise the various degrees of innovation in business models and examine their link to organisational performance. Longitudinal studies can also be used to explore this association since it may be the case that business model innovation has a negative influence on performance in the short run and that may change subsequently. Moreover, it is not clear whether high-performing firms change their business models or innovation in business models is a result from superior performance ( Sorescu et al. , 2011 ). Further studies are needed to determine the direction of causality. Another link that is worth exploring is business model innovation and social value, which has only been explored in a few studies looking at social business models (e.g. Yunus et al. , 2010 ; Wilson and Post, 2013 ). Further research is needed to examine this link and possibly examine both financial and non-financial business performance.

6.3 Mechanisms of business model innovation

Although we know more about how firms define value proposition, create and capture value ( Landau et al. , 2016 ; Velu and Jacob, 2014 ), what remains as a blind spot is the mechanism of business model innovation. This is due to the fact that much of the literature seems to focus on value creation. To better understand the various mechanisms of business model innovation, future studies must integrate value proposition, value creation and value capture elements. Empirical studies could use the business model innovation framework to examine the various mechanisms of business model innovation. Also, the literature lacks the integration of internal and external perspectives of business model innovation. Very few studies look at the external drivers of business model innovation and the associated internal changes. The external drivers are referred to as “emerging changes”, which are usually beyond manager’s control ( Demil and Lecocq, 2010 ). Inconclusive findings exist as to how firms develop innovative business models in response to changes in the external environment. Future studies could examine the external factors associated with the changes in the business model innovation framework. Active and reactive responses need to be explored not only to understand the external influences, but also what business model changes are necessary for such responses. A better understanding of the mechanisms of business model innovation can be achieved by not only exploring the external drivers, but also linking them to specific internal changes. Although earlier contributions linking studies to established theories such as the resource-based view, transaction cost economics, activity systems perspective, dynamic capabilities and practice theory have proven to be vital in advancing the literature, developing a theory that elaborates on the antecedents, consequences and different facets of business model innovation is still needed ( Sorescu et al. , 2011 ). Theory can be advanced by depicting the mechanisms of business model innovation through the integration of both internal and external perspectives. Also, we call for more empirical work to uncover these mechanisms and provide managers with the necessary insights to carry out business model innovation.

7. Conclusions

The aim of this review was to explore how firms approach business model innovation. The current literature suggests that business model innovation approaches can either be evolutionary or revolutionary. However, the evidence reviewed points to a more complex picture beyond the simple binary approach, in that, firms can explore alternative business models through experimentation, open and disruptive innovations. Moreover, the evidence highlights further complexity to these approaches as we find that they are in fact a spectrum of various degrees of innovation ranging from modifying a single element, altering multiple elements simultaneously, to changing the interactions between elements of the business model innovation framework. This framework was developed as a navigation map for managers and researchers interested in how to change existing business models. It highlights the key areas of innovation, namely, value proposition, operational value, human capital and financial value. Researchers interested in this area can explore and examine the different paths firms can undertake to change their business models. Although this review pinpoints the different avenues for firm to undertake business model innovation, the mechanisms by which firms can change their business models and the external factors associated with such change remain underexplored.

The evolution of business model literature (pre-2000 to 2016)

Business model innovation framework

Previous reviews of business model literature

Reviewed papers and their subject fields

Source of our sample

Business model innovation areas and elements

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Further reading

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Open Access

Peer-reviewed

Research Article

The impact of R&D effort on business model innovation: Evaluating chain mediation through collaboration breadth and depth

Contributed equally to this work with: Shuting Chen, Dengke Yu

Roles Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft

Affiliation School of Public Policy and Administration, Nanchang University, Nanchang, China

Roles Conceptualization, Funding acquisition, Project administration, Supervision, Validation, Visualization, Writing – review & editing

* E-mail: [email protected]

• Shuting Chen, 

• Published: June 5, 2023
• https://doi.org/10.1371/journal.pone.0286715
• Reader Comments

Drawing on a novel theoretical framework, we explored the impact of research and development (R&D) effort on business model innovation via external collaboration breadth and collaboration depth in sequence. We empirically analyzed a sample of 94 Chinese innovative enterprises by applying hierarchical regression analysis and chain mediation analysis. The results indicate that R&D effort positively influences business model innovation. The influencing mechanism is that R&D effort positively affects external collaboration breadth, which in turn positively stimulates external collaboration depth, and ultimately benefits the implementation of business model innovation. Therefore, the breadth and depth of external collaboration play a chain-mediating role. The study develops a new framework for understanding the relationship between R&D effort, external collaboration, and business model innovation. It combines enterprises’ internal behavior (R&D) and external behavior (collaboration) to establish an inside-out mechanism for predicting business model innovation. It enriches the theory of business model innovation. It also provides insights for managers and governments to optimize policies in innovation-driven development.

Citation: Chen S, Yu D (2023) The impact of R&D effort on business model innovation: Evaluating chain mediation through collaboration breadth and depth. PLoS ONE 18(6): e0286715. https://doi.org/10.1371/journal.pone.0286715

Editor: Xingwei Li, Sichuan Agricultural University, CHINA

Received: July 17, 2022; Accepted: May 19, 2023; Published: June 5, 2023

Copyright: © 2023 Chen, Yu. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting information files.

Funding: The author Dengke Yu was funded by the National Natural Science Foundation of China (Grants No. 71962021). The website of the funder is https://www.nsfc.gov.cn/ . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1 Introduction

With the advancement of the information economy and new technologies, such as big data, cloud technology, artificial intelligence and the internet of things [ 1 ], firms are facing unanticipated disruptive competition and new challenges. In today’s rapidly changing social and economic environment, firms are urged to change their processes, practices and operations, adopt particular business models or implement business model innovation to capitalize on emerging business opportunities and maintain development [ 2 ]. Recently, business model innovation has received an increasing amount of attention among practitioners and academics [ 3 , 4 ]. In practice, many business model innovations are emerging and significantly changing the lifestyles of people and the business rules of most industries [ 5 ]. For example, Alibaba and Amazon, world-famous e-commerce giants, have achieved successes from their unique business model innovations, which break out the traditional offline shopping mode, enable people to shop without leaving home and influence others to follow. In academia, studies have shown that firms with faster growing operating margins often attach twice as much importance to business model innovation than their inefficient competitors [ 6 ]. Obviously, business model innovation, allowing firms to create novelty or improve efficiency that goes beyond product, process and technology innovation [ 7 ], is recognized as a source of sustainable competitive advantage and competitiveness [ 8 ].

Business model innovation is defined as “designed, nontrivial changes to the key elements of a firm’s business model and/or the architecture linking the elements” [ 9 ]. Although there is growing interest in business model innovation, previous studies have been relatively static and descriptive in nature. The focus of existing research mainly includes the drawing of a blueprint for the coherence between business model elements [ 10 ], the development of case studies involving business model innovation [ 11 – 13 ] and the exploration of the relationship between business model innovation and firm performance [ 14 , 15 ]. Recently, more researchers have been devoted to exploring the antecedents of business model innovation. For instance, Yi, Chen and Li (2022) identified stakeholder ties and organizational learning as enablers for business model innovation [ 16 ]. Xu, He, Morrison, De Domenici and Wang (2022) found that entrepreneurial networks and effectuation are driving factors of business model innovation [ 17 ]. Despite these achievements, much more needs to be done to explore the field.

It is important to recognize the enabling factors through which firms can better generate new business models and maintain their competitiveness. Recent research has pointed out that R&D effort, as an internal factor, can create VRIN (valuable, rare, inimitable, and non-substitutable) knowledge and augment existing knowledge stock for firms, providing a crucial driving force of enterprise innovation [ 18 ]. However, thus far, prior studies have focused on exploring the impact of R&D effort on firm performance [ 19 , 20 ] or technological innovation ability [ 21 ], but few have investigated its role in business model innovation. It is a gap. We therefore tried to fill this gap from an endogenous perspective. Considering the extroverted and open characteristics of business model innovation, we also deemed external links and open collaboration as important enablers, since external collaborations can help firms to break down traditional boundaries and allow useful external knowledge and information to enter into and flow across an organization [ 22 – 24 ]. In the field of external collaboration, its breadth and depth have been repeatedly employed to address research questions [ 25 , 26 ]. Extant studies have shown that both are beneficial to enterprise innovation [ 19 , 27 ]. Thus, we proposed that the two are also important antecedents of business model innovation.

According to the abovementioned analysis, R&D effort, collaboration breadth and depth may be relevant to business model innovation. However, they should not work independently. They are interrelated with each other in a convoluted manner. At present, the affecting mechanism between them is still unclear. Therefore, exploring the relationship between R&D effort, collaboration breadth, collaboration depth and business model innovation is necessary. We then tried to conduct an empirical analysis based on the sample data of Chinese top-ranking innovative enterprises to explore the inside-out mechanism of business model innovation driven by R&D effort. We drew on absorptive capacity (AC) theory to develop our framework [ 28 , 29 ], in which collaboration breadth is regarded as potential absorptive capacity (PAC) and collaboration depth is regarded as realized absorptive capacity (RAC). On the basis of a literature review, our assumed logic can be described as follows. The investment and activities of R&D could promote PAC, and then the accumulation and transfer of PAC would strengthen RAC, which ultimately plays an important role in the change of an enterprise’s business model. Following such logic, we constructed an R&D-PAC-RAC-Innovation framework and built a chain mediation model to test the impact of R&D effort on business model innovation. We therefore addressed the following research questions:

• RQ1. Does R&D effort positively affect business model innovation?
• RQ2. Does collaboration breadth positively affect collaboration depth?
• RQ3. How does R&D effort indirectly affect business model innovation via the mediators of collaboration breadth and depth?

In doing so, our present study makes three important theoretical contributions to the literature. First, this study explores the relationship between R&D effort and business model innovation, which extends the research on the antecedents of business model innovation and enriches the research on the outcomes of R&D effort. Second, it opens the black box of the relationship between collaboration breadth and depth, thereby contributing to the literature on external collaboration. Finally, based on the R&D-PAC-RAC-Innovation framework, it proposes two chain mediators of collaboration breadth and collaboration depth to clarify the internal influencing mechanism of R&D effort on business model innovation to strengthen the understanding of business model innovation under the background of firms’ R&D effort.

The remainder of this paper is structured as follows. Section 2 presents our theories and hypotheses. Section 3 describes the methodology, including the introduction of sample, data collection, measures and statistical techniques, following which Section 4 outlines the results of empirical analysis. Finally, Section 5 discusses theoretical implications, practical implications, limitations and future research and presents a short conclusion.

2 Theories and hypotheses

2.1 theories.

Currently, the business environment, which is characterized by fierce competition, fast technological change and strong market turbulence, compels most firms to innovate their business models to improve their adaptive capability [ 30 ]. Business model innovation is defined as a new-to-the-firm change in at least one out of three business model dimensions: (a) a firm’s value creation, (b) a firm’s value proposition, and (c) a firm’s value capture [ 31 ]. Value creation addresses how and by what means firms create new value along the value chain using their resources and capabilities embedded in intra- and inter-organizational processes [ 32 ]. Value proposition defines the range, nature and features of the offered products and services and the conditions at which these are provided [ 33 ]. Value capture answers the question of how value proposition is converted into profits in a sustainable way [ 34 ].

For firms operating in extremely turbulent environments, their sustainability relies heavily on the core competence and dynamic capability driven by continuous innovation. As the foundation of different kinds of innovations, R&D effort, including investments in R&D human resources, R&D spending and R&D equipment, plays an important role in the sustainable growth of firms [ 35 ]. The implementation of business model innovation should be carried out on the basis of R&D effort without exception since the creative thinking and absorptive capability that are essential to business model innovation need to be gradually built and improved through the training process of continuous R&D activities.

According to open innovation theory, collaboration with external organizations is crucial to a firm’s business model innovation, which is characterized by open principles and resource integration [ 23 , 36 , 37 ]. External collaboration is access that enables firms to acquire non-redundant knowledge and capabilities residing outside their organizational and technological boundaries [ 38 , 39 ]. In prior literature, the construct was classified by two dimensions, i.e., breadth and depth [ 25 ]. Collaboration breadth is defined as the number of external sources that firms rely upon in their innovative activities, ranging from narrow to broad collaboration as external partners increase. In contrast, collaboration depth is recognized as the extent to which firms draw deeply from different external sources, ranging from surface to deep collaboration as collaborative interactions intensify [ 19 , 25 , 40 ]. The two reflect different kinds of capabilities of firms in the function of external knowledge absorption and resource integration.

2.2 R&D effort and business model innovation

The tacit nature of technological knowledge and the risks related to the loss of technological competitiveness require internal efforts in R&D activities [ 19 ]. With the advance of the knowledge-based economy, R&D effort becomes more essential for enterprises to carry out business model innovation, which is reflected in three aspects. First, the high attention on R&D promotes firms to develop new technologies and products, in turn stimulating business model innovation. However, the economic value of a technology or product remains latent until it is commercialized in some way via a business model [ 37 ]. Firms should change their existing business models to better capture the value within the new technologies or products. Next, R&D effort improves the development of technical human capital and is thus beneficial to changing the existing business model. Resource-based theory emphasizes the important role of R&D personnel knowledge and capabilities in the development of firms’ core competitiveness and sustainable competitive advantage [ 41 , 42 ]. R&D human capital supports and participates in the implementation of business model innovation. Additionally, R&D effort increases opportunities for firms to embrace new things, providing a solid foundation for business model innovation [ 24 ]. To achieve R&D goals, firms need to keep track of the changes in customer demand, obtain cutting-edge knowledge, and communicate and cooperate with external organizations. These actions help firms to catch on the status quo and keep up with the trend of the external environment to find the optimal direction for business model innovation.

Several studies have provided empirical evidence for the positive impact of R&D effort on business model innovation. For example, Zouaghi et al. (2018) suggested that R&D intensity and R&D human capital are recognized as vital driving factors for innovations new to the market place and the firm [ 19 ]. Leung and Sharma (2021) revealed that R&D effort plays an important role in a firm’s sales, profitability and value. [ 20 ]. Therefore, we proposed the following hypothesis:

• H1: R&D effort has a positive impact on business model innovation.

2.3 R&D effort and collaboration breadth

We argued that R&D effort stimulates the expansion of a firm’s external collaboration breadth due to three effects. First, the high attention on R&D leads a firm to devote itself to knowledge learning and collaborative innovation; thus, a firm’s effort on R&D would increase the invested financial resources that are essential to external collaboration. According to the corporate financial system, a part of R&D expenditure can be used for the construction of external collaboration networks and strategic alliances, as well as specific activities such as communication, learning and training, which greatly raise the collaboration breadth of firms. Collaborative R&D networks need a lot of money to support them [ 43 , 44 ]. Park, Chen and Gallagher (2002) empirically demonstrated that firms with rich financial resources are more likely to increase external collaborations [ 45 ]. In practice, we also observed that large and medium enterprises with abundant R&D expenditure tend to carry out more collaborative innovation activities with universities, research institutes and leading customers. Second, firms making more R&D efforts explore more new technological and market opportunities, which stimulates them to network more collaborators to make joint investments and develop potential value. Because R&D effort generally focuses on projects far from the market and with technological challenges rather than typical execution [ 46 ], the resources needed to take the discovered opportunities may not reside within firms, but are scattered across diverse organizations. Hence, firms that successfully exploit the opportunities need to cooperate with various partners to access and integrate heterogeneous resources [ 47 ], which are supported by and back stimulate the construction of external collaboration networks. Finally, R&D effort, through enlarging the knowledge base, helps firms expand their external collaborations. On the basis of R&D effort, firms can obtain new experience, knowledge and information [ 21 ], which enhance their capabilities to identify, assimilate and apply external knowledge and then improve the possibility of cooperation with a variety of external organizations. In other words, R&D effort enriches a firm’s knowledge stock and diversity, and the latter increases the competence and attractiveness of the firm in the development of collaborations [ 28 , 48 ].

Some studies can support our argument. Cerulli, Gabriele and Potì (2016), for example, showed that a company’s R&D input can increase the likelihood of external collaboration with various types of partners [ 49 ]. Chapman, Lucena and Afcha (2018) provided strong evidence for the significant positive impact of R&D subsidies on firm external collaboration breadth based on data analysis of Spanish firms [ 48 ]. Therefore, we advanced the following hypothesis:

• H2: R&D effort has a positive impact on collaboration breadth.

2.4 Collaboration breadth and depth

By reviewing the extant literature, it can be clearly seen that collaboration breadth and collaboration depth are generally regarded as two parallel concepts [ 22 ] and little research has explored the internal relationship between them. In view of the research omission, further examination of this issue is required.

Based on this attention-based view, a firm can be considered as a system that structurally distributes attention [ 50 – 52 ], where attention refers to the capability to process different information sources and simultaneously extract the information that is useful for certain tasks [ 53 ]. The view acknowledges that managerial attention is a firm’s most valuable resource. Decision-makers in firms are therefore supposed to “concentrate their energy, effort and mindfulness deeply on a limited number of issues and tasks” [ 50 ]. Following this view, we proposed that as external collaboration breadth increases, decision-makers in firms are exposed to increasingly more external information so that they clearly know their current situation covering what firms currently lack and need in the future. This knowledge can enable them to focus on the selection and development of the most valuable collaborations according to firms’ status quo [ 50 ]. In addition, with the expansion of collaboration breadth, the marginal value of superficial collaboration would be diminishing and decision-makers may find it more difficult to deal with the booming knowledge [ 26 ]. The pressure of information overload attributed to broad collaboration would compel decision-makers to narrow their attention to strategic alliances with VRIN characteristics. Based on the above analysis, we proposed that as the breadth of collaboration increases, the depth would also subsequently increase. Therefore, we proposed the following hypothesis:

• H3: Collaboration breadth has a positive impact on collaboration depth.

2.5 Collaboration depth and business model innovation

Some prior research has confirmed that external collaboration even plays an irreplaceable role in facilitating a firm’s internal innovation process [ 25 , 54 ]. In this study we wanted to further prove that deep collaboration with external organizations can effectively boost business model innovation. First, deep collaboration provides enterprises with access to external heterogeneous information and knowledge [ 22 ], which lays a foundation for the integration of resources and capabilities required by the process of business model innovation. Knowledge absorption and integration help enterprises generate new thoughts and ways to develop new business models with novelty and efficiency [ 55 ]. Second, deep collaboration experience cultivates trust between the enterprise and its partners [ 22 ]. Trust serves would serve as a crucial coordination mechanism that improves the accurate understanding of newly acquired external information, guarantees technological transfer without resistance, and achieves timely knowledge sharing and substantive cooperation [ 56 ], thereby contributing to the effective implementation of business model innovation.

Collaboration depth is generally divided into three dimensions, i.e., vertical, horizontal and competitor collaboration [ 41 , 57 ], which have different benefits for business model innovation. The strategic alliance with suppliers and customers enables enterprises to gain updated information from market and industry, pool complementary resources and improve learning routines [ 58 ]. Clearly, these actions can assist enterprises in developing efficiency-oriented business models [ 59 ]. Moreover, the close connection with lead users would make enterprises rapidly know the changing demand for new products with novel functions, further helping enterprises to improve the novelty of their business models [ 41 ]. In many knowledge-extensive industries, collaboration with competitors is generalized since any one enterprise cannot undertake the huge cost of technological innovation and the development of a novel business model. Deep collaboration with competitors plays the roles of risk sharing, information and resource sharing, and joint monopolization in business model innovation [ 24 ]. Finally, deep collaborations with governments, universities, consultants and others enable firms to efficiently obtain comprehensive and heterogeneous knowledge required by market development [ 19 ]. Such collaborations could also enhance the public’s identity with firms and improve their brand images, which promote the success of new business models. Following the above analysis, we proposed the third hypothesis:

• H4: Collaboration depth has a positive impact on business model innovation.

2.6 The chain-mediating effect

Martínez-Sánchez, Vicente-Oliva and Pérez-Pérez (2020) proposed a new theoretical framework along with the R&D-AC-Innovation link [ 60 ]. According to their statement, absorptive capacity (AC) was the key mediator between R&D and innovation. In other studies, as an essential ability to identify, assimilate, transform, and exploit external knowledge, AC was recognized as an important component of corporate competence [ 61 , 62 ]. When firms make efforts in R&D, the absorption of external knowledge can facilitate innovative activities and enhance innovation capability and efficiency. In a previous body of knowledge, AC was often divided into PAC and RAC [ 29 ]. Among them, PAC refers to the capability to acquire and assimilate external knowledge, whereas RAC refers to the capability to transform and exploit external knowledge [ 29 ]. They are indispensable in a firm’s process of exogenous growth [ 63 ]. However, there is a progressive relationship between the two in logic, i.e., PAC forms the premise of RAC [ 64 ]. We therefore expanded the R&D-AC-Innovation link and tried to develop an R&D-PAC-RAC-Innovation framework for our study.

In prior literature, external collaboration was summarized as a type of absorptive capacity. Broad collaboration means the high possibility of enterprises to access external knowledge but not always the effective exploitation of knowledge. Hence, it is exactly a kind of PAC. In contrast, collaboration depth reflects the degree of an enterprise’s effective utilization of external knowledge. It is therefore a kind of RAC. As the framework of R&D-PAC-RAC-Innovation revealed, firms making efforts on R&D would obtain financial support, discover new opportunities and lay a solid foundation of resources and capability to collaborate with various external organizations [ 48 ] and then gradually focus their limited attention on part of external organizations that can bring them benefits and opportunities to better realize their targets involving performance, innovativeness and sustainability [ 26 , 50 ]. That is, collaboration breadth triggers collaboration depth, and the latter could enhance the firm’s capability of business model innovation by building trust, obtaining knowledge and developing value nets [ 1 , 22 ]. In summary, we naturally proposed the following hypothesis:

• H5: Collaboration breadth and depth play a chain-mediating role in the relationship between R&D effort and business model innovation.

Fig 1 shows the conceptual research model of the study.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0286715.g001

3 Methodology

Strategy&, part of the PwC network, released the 2018 Global Innovation 1000 study, which analyses the world’s top 1000 listed corporations with the highest R&D expenditures, and which account for 40% of the total global R&D expenditure. The Global Innovation 1000 study shows that R&D expenditures increase in every region of the world, but most notably in China, where they rise 34.4 percent over the previous year.

We collected the data of Chinese enterprises ranked in Global Innovation 1000 for three reasons. First, these enterprises are in line with the innovation-driven development strategy advocated by the Chinese national government. To some extent, this means that they are of great significance to the development of emerging countries. Second, most scholars focus on the technological innovation of such innovative enterprises but always neglect their excellent performance in business model innovation. Third, compared with small and medium enterprises, they generally have sufficient resources and strong capabilities to support their frequent collaborations. Moreover, their disclosed information is open and transparent, making it convenient for data collection.

To ensure the completeness and reliability of our data analysis, the following criteria were followed in the process of sample selection. First, we selected 175 Chinese companies in the 2018 Global Innovation 1000 study. Second, considering the consistency of the data structure, we excluded 76 companies that were listed in Hong Kong, Taiwan and the United States. Third, we excluded 5 companies with missing information. Finally, 94 Chinese companies listed on the Shenzhen and Shanghai Stock Exchanges (A share) were selected.

Table 1 displays the characteristics of our sample. The industries represented are information technology (26.6%), industrials (28.7%), materials (17.0%), consumer discretionary (21.3%), healthcare (4.3%) and energy (2.1%). The companies comprise 60.6% state-owned firms and 39.4% non-state-owned firms. Among them, 17.0% engaged less than 10000 employees, 42.6% between 10001 and 30000, 19.1% between 30001 and 50000, and 21.3% more than 50000. In addition, the sample aged 6–10 years accounts for 7.4%, 11–15 years for 12.8%, 16–20 years for 40.4%, 21–25 years for 31.9% and more than 25 years for 7.4%.

https://doi.org/10.1371/journal.pone.0286715.t001

3.2 Data collection

The data collection proceeded in two stages. In the first stage, we built composite scales for external collaboration and novelty-centered business model innovation, and we identified and measured the relevant items on the basis of a content analysis of company information (for details, please see Appendices 1–3 in S1 File ). In the second stage, the data of the rest of the variables, including R&D effort, efficiency-centered business model innovation and control variables, were collected from the China Stock Market & Accounting Research Database (CSMAR Database).

In recent years, the panel method has been increasingly used [ 65 , 66 ]. In this study, we set up a panel composed of 3 members, including 1 professor and 2 doctoral students. First, the professor carefully selected the panelists from his total team members by requiring them to submit an abbreviated test survey on a randomly chosen sample company to display their understanding of external collaboration and novelty-centered business model innovation. After the selection, 2 doctoral members jointly read the information, announcements and documents of the sample companies, became familiar with the details of the external collaboration and novelty-centered business model innovation of all sample companies, and then developed the measurement scale based on the consensus. Next, the professor trained them as expert raters in data collection and analysis. The raters were provided with written guidelines on the proper way to address survey items. Data sources included annual reports, social responsibility reports, investment analysts’ reports, company news, websites and other announcements of those companies from 2016 to 2020. The process took every rater approximately six months from October 2020 to April 2021. The lack of readily available data about external collaboration and novelty-centered business model innovation obliged us to collect primary data and construct a unique dataset. Finally, we evaluated the consistency by conducting a pairwise comparison of the two raters’ scores, yielding a Pearson correlation coefficient of 0.929 (p<0.01). For the items with discrepant scores, two raters discussed with each other and reached a consensus under the guidance of the professor. All initial differences were resolved, so the final consistency was 100%.

The data of other variables were drawn from the CSMAR Database, which is a research-oriented database in the economic and financial field compiled by Shenzhen CSMAR Data Technology Co., Ltd. The database reflects the financial conditions of China and follows the professional standards of authoritative databases such as CRSP, COMPUSTAT, TAQ and THOMSON. The data of these quantitative variables came from 2018. To control the influence of extreme values, the data collected from CSMAR have been winsorized.

3.3 Measures

3.3.1 r&d effort..

R&D effort has been extensively used in innovation research as an input variance [ 5 , 19 ]. In this study, we measured it by a firm’s R&D expenditure as a proportion of the firm’s operating income [ 60 ].

3.3.2 Collaboration breadth.

Following Laursen and Salter (2006, 2014) [ 25 , 67 ] and Dong and Netten (2017) [ 26 ], we constructed collaboration breadth as the combination of nine sources of external knowledge for business model innovation: 1) supplier, 2) customer, 3) competitor, 4) government, 5) university and research institution, 6) consultancy firm, 7) venture capital investment firm, 8) trade fair and exhibition, and 9) others. For each source, we used a three-point scale to indicate the scope of collaboration (1 = no or low degree, 2 = medium degree, 3 = high degree) (see Appendix 1 in S1 File for details). On the basis of the initial score, we further coded each source as a binary variable, where 1 represents that the collaboration width of a source is medium or high (2 and 3) and 0 represents that the source is not used or its collaboration width is low (1). Finally, the variable values of the nine sources were added up to measure the total level of collaboration breadth. Obviously, the value interval of the construct is [0, 9]. It was valued as 0 when all knowledge sources were not used or had low collaboration breadth, and valued as 9 when all sources had medium or high collaboration breadth.

3.3.3 Collaboration depth.

Following Laursen and Salter (2006) [ 25 ] and Dong and Netten (2017) [ 26 ], we defined collaboration depth as the intensity of collaboration with each source of external knowledge. A three-point scale was used to indicate the intensity of collaboration (1 = no or low degree, 2 = medium degree, 3 = high degree) (see Appendix 2 in S1 File for details). Similar to collaboration breadth, each source was further coded as a binary variable, where 1 represents that the certain external knowledge source is used to a high degree (3) and 0 reflects that it is not used, or only to a low or medium degree (1 and 2). The nine dummies were added up so that each of our sample companies could obtain the score of the depth variable, ranging from 0 to 9, where 0 indicates no intense use of any external knowledge source, and 9 indicates intense usage of all 9 sources.

3.3.4 Business model innovation.

We measured business model innovation from two dimensions, i.e., novelty-centered and efficiency-centered ones. Their scores were evaluated by the following different methods. Business model innovation was comprehensively measured by the mean value of their scores after data preprocessing for novelty-centered and efficiency-centered ones into the [0,1] interval.

3 . 3 . 4 . 1 Novelty-centered business model innovation . We independently developed a new scale of novelty-centered business model innovation. Four items, reflecting the new R&D system, new manufacturing platform, new sales model and new customer service system, were used to measure it. Considering the difficulty of the detailed measurement in an objective way, we deemed the use of perceptual measures obtained by our raters [ 65 , 66 ]. The items were quantified on a five-point scale (see Appendix 3 in S1 File for details). After coding, their scores were aggregated and averaged for the final score.

3 . 3 . 4 . 2 Efficiency-centered business model innovation . We measured efficiency-centered business model innovation from the dimensions of value creation, value proposition and value capture in our study [ 31 ] (see Appendix 4 in S1 File for details). First, capital utilization ability and debt paying ability are the key elements in the process of value creation, so we selected the current ratio, equity-to-debt ratio and debt coverage ratio to measure value creation. Second, operating capacity is an essential factor in the value proposition process, which involves the turnover of goods, capital and assets. Thus, we used inventory turnover, accounts receivable turnover and total assets turnover to evaluate the value proposition. Third, it is very important for a firm to have profitability and growth ability in the process of value capture. Therefore, its measurement was composed of three financial indicators, i.e., net profit growth rate, operating income growth rate and operating profit ratio.

The entropy method is a widely used objective weighting method. It determines the weight of indicators by the variational degree of the dataset [ 68 ]. The TOPSIS method, based on the rule that the chosen alternative should have the longest distance from the negative ideal solution and the shortest distance from the positive ideal solution, is also a widely used evaluation method [ 69 ]. The negative ideal solution always maximizes the cost criteria and minimizes the benefit criteria, while the positive ideal solution is the opposite [ 70 ]. In this study, we combined the two methods and built an entropy-based TOPSIS model [ 71 ] to comprehensively assess efficiency-centered business model innovation.

3.3.5 Control variables.

To account for the effects of extraneous variables, we included firm ownership, firm age, firm size, firm location, industry, financial leverage and operating leverage as alternative explanations for business model innovation.

In China, firm ownership is an important factor that influences a firm’s strategy, operation and performance [ 22 ]. We measured it by a dummy variable that controls for potential variations between state-owned enterprises (coded as 1) and private-owned, foreign-owned or other types of enterprises (coded as 0).

Firm age also plays a role in a firm’s propensity to adopt business model innovation as it affects the flexibility of strategy and the willingness of knowledge absorption [ 72 ]. It was measured by the number of years since the firm was officially established.

Some scholars have argued that firm size matters for innovation, because large firms tend to have more resources required by innovative projects [ 73 ]. However, other scholars insisted that larger firms are more prone to organizational inertia, thereby hindering change processes [ 74 ]. To control the possible effect, firm size measured by the natural logarithm of employee scale was set as a control variable.

Firm location might influence firm innovation as different locations provide various resources and opportunities for firms. It was measured by a dummy variable [ 75 ]. We measured it as 1 when a firm is in first-tier cities, including Beijing, Shanghai, Guangzhou and Shenzhen; otherwise, it was coded as 0.

Industry may influence a firm’s cognition and its needs for business model innovation [ 5 ]. It was measured by a dummy variable. We coded it as 1 when the firm belongs to industries including materials, consumer discretionary, healthcare and energy; otherwise, it was coded as 0.

Financial leverage reflects the degree of financial risk of a firm [ 76 ]. It was measured by a dummy variable. When the financial leverage of a firm is higher than the mean value of the financial leverage of all sample firms, the value was coded as 1; otherwise, it was coded as 0.

Operating leverage reflects the degree of operating risk of a firm [ 77 ]. The method to measure it is similar to that of financial leverage.

3.4 Statistical techniques

Following Hox (1994) [ 78 ], we tested the direct effects by hierarchical regression analysis, using SPSS 24 software. Following Hayes (2018) [ 79 ], we tested the chain-mediating effects by a bias-corrected bootstrapping procedure, using PROCESS v. 3.3.

4 Empirical results

4.1 descriptive statistics and correlations.

Table 2 shows the mean, standard deviation, and correlation coefficient of the variables. We found a positive and significant relationship between (a) R&D effort and collaboration breadth (r = 0 . 458 , p <0 . 01) , (b) collaboration breadth and collaboration depth (r = 0 . 579 , p < 0 . 01) , and (c) collaboration depth and business model innovation (r = 0 . 552 , p < 0 . 01) . We also found that (a) R&D effort is positively related to collaboration depth (r = 0 . 504 , p < 0 . 01) , (b) R&D effort is positively associated with business model innovation (r = 0 . 554 , p < 0 . 01) , and (c) collaboration breadth is positively related to business model innovation (r = 0 . 380 , p < 0 . 01) . Those findings provided preliminary evidence for the hypotheses proposed in our study.

https://doi.org/10.1371/journal.pone.0286715.t002

Additionally, the highest correlation value among all variables is 0.579, far below the threshold value of 0.75 [ 80 ], suggesting no serious multicollinearity problem within the dataset. As shown in Table 3 , it is confirmed again by the variance inflation factors (VIFs) of all variables, well below ten, the recommended threshold value [ 81 ].

https://doi.org/10.1371/journal.pone.0286715.t003

4.2 Hypotheses tests

The hypotheses advanced in the study were tested through hierarchical regression analysis using SPSS 24 software. The results are shown in Table 3 . The result of Model 1 shows that R&D effort positively influences business model innovation (β = 0 . 542 , p < 0 . 01) , thus supporting Hypothesis 1. The result of Model 2 indicates that R&D effort positively affects collaboration breadth (β = 0 . 622 , p < 0 . 01) , thereby supporting Hypothesis 2. According to Model 3, there is a significantly positive relationship between collaboration breadth and collaboration depth (β = 0 . 476 , p < 0 . 01) , thus supporting Hypothesis 3. Moreover, Model 4 shows a positive effect of collaboration depth on business model innovation (β = 0 . 295 , p < 0 . 01) , thereby supporting Hypothesis 4. The above results reveal the possibility of an indirect effect of R&D effort on business model innovation through the mediating role of collaboration breadth and depth in sequence. The path coefficients of the whole conceptual model are presented in Fig 2 .

**p < 0.05, ***p < 0.01 (two-tailed tests).

https://doi.org/10.1371/journal.pone.0286715.g002

Table 4 shows the results of the chain mediation model tested by the bias-corrected bootstrapping procedure. There are three indirect effects between R&D effort and business model innovation. In detail, (a) the indirect effect via collaboration breadth is insignificant (estimate = 0 . 022 , 95% CI = [-0 . 104 , 0 . 147]) , (b) the indirect effect via collaboration depth is significant (estimate = 0 . 088 , 95% CI = [0 . 012 , 0 . 237]) , and (c) the chain-mediating effect of collaboration breadth and depth is significant (estimate = 0 . 087 , 95% CI = [0 . 025 , 0 . 167]) . Hence, Hypothesis 5 is also supported.

https://doi.org/10.1371/journal.pone.0286715.t004

4.3 Robustness tests

We analyzed the robustness of the above findings in two different ways. The first was to change the sample size, i.e., randomly selecting a subsample (N = 70). The second way was to replace a variable, i.e., using the percentage of highly skilled R&D workers to measure R&D effort. The results of robustness tests are presented in Table 5 , showing that they are consistent with the initial findings. Therefore, we concluded that our findings are robust.

https://doi.org/10.1371/journal.pone.0286715.t005

5 Discussion

5.1 theoretical implications.

The theoretical contributions of the present study are threefold. First, this study examines the relationship between R&D effort and business model innovation. In fact, to our knowledge, we offered the first empirical evidence of how business model innovation is carried out under firms’ effort in R&D. The literature review that is performed, shows that neither the relationship nor the influencing mechanism of R&D effort on business model innovation has been previously discussed. We concluded that R&D effort is conductive to business model innovation, thereby extending the research on the antecedents of business model innovation. The antecedents of business model innovation have been explored in many aspects, such as organizational search [ 82 ], big data analytics capabilities [ 83 ] and strategic orientation [ 84 ]; however, few researchers have paid attention to the role of firms’ R&D effort. Similarly, our results also enrich the research on the outcomes of R&D effort. Most of the outcome variables in existing research on R&D effort have mainly focused on the performance of total innovation activities [ 19 , 85 , 86 ], while innovative behavior has rarely been involved.

Second, it contributes to the literature on external collaboration. According to past studies, collaboration breadth and depth are generally discussed as a pair of parallel constructs. For instance, setting them as two different strategies, Zhang , Yuan and Zhang (2022) explored their impacts on growth of new technology-based firms [ 87 ]. Similarly, Jang, Ko, Chung and Woo (2023) investigated their effects on product and process innovation [ 88 ]. In contrast, we argued that the two are neither orthogonal nor mutually exclusive. They can affect and complement each other. Considering that little prior work has explored their relationship, we demonstrated from the perspective of attention theory that the expansion of collaboration breadth is conducive to the improvement of collaboration depth. These findings open the black box of the relationship between collaboration breadth and depth, which enriches the open innovation field.

Finally, this study reveals the inside-out influencing mechanism of R&D effort on business model innovation from the perspective of absorptive capacity, addressing the necessity of taking into account the positive role of external collaboration. In most previous studies, both R&D effort and external collaboration have been positioned as direct predictors or moderators of firm innovation and other outcomes. Little attention has been given to the potential mediating mechanism. To cover the gap, we took both collaboration breadth and collaboration depth as mediators to explore the internal influencing mechanism of R&D effort on business model innovation. Following the R&D-PAC-RAC-Innovation link, the results of the present study demonstrate the chain-mediating effect of collaboration breadth and collaboration depth on the relationship between R&D effort and business model innovation. Our findings may help researchers to deepen their understanding of the internal mechanism through which R&D effort affects business model innovation; moreover, they provide a new perspective to investigate how business model innovation is triggered in the context of R&D dominant culture, and how to effectively combine the business strategy and technological strategy in a firm.

5.2 Practical implications

This study has important practical implications for managers. First, to achieve business model innovation, enterprises are suggested to put much effort into R&D. R&D effort can provide internal resource bases for business model innovation. Therefore, top managers should take some specific measures from the R&D perspective, such as increasing R&D expenditure, hiring enough R&D talent, building excellent R&D teams and developing a perfect R&D strategic system, to satisfy the requirements of business model innovation.

Second, enterprises are also recommended to promote many collaborative activities with external organizations. Through external collaboration, firms need to obtain advanced knowledge, gain updated information and learn new technologies and business thoughts, the combination of which provides new inspiration for enterprises to carry out business model innovation. To better promote collaborative communications, enterprises should cultivate an open and collaborative culture, develop online cooperative platforms, build cooperative alliances and so on.

Finally, firms are advised to follow the R&D-PAC-RAC-Innovation path to achieve the goal of business model innovation. That is, firms may follow three steps to make efforts. First, they should devote themselves to R&D activities to lay the foundation of external collaboration. Second, on the basis of technological advantages, they should build collaborative networks and develop their collaborators. Third, on the basis of broad collaborations, they need to construct strategic and deep collaborations for effective actions about resource integration, knowledge sharing, and win-win business. Our study reminds managers to gradually shift their attention from numerous collaborative relationships to several deep collaborations with VRIN characteristics. Through R&D-based collaborations, new business models can be effectively developed.

This study also has practical implications for governments. To some extent, our study could lead Chinese central and local governments to make better decisions about firms’ sustainable growth. On the one hand, governments are suggested to set some policies to stimulate firm innovation, such as allocating R&D subsidies, increasing benefits for introducing innovative talents and creating a great innovative business environment. On the other hand, governments are also encouraged to take measures to promote firm collaboration with external organizations, such as establishing cooperative funds, strengthening the construction of cooperation platforms and improving the tax incentives of collaborative projects.

5.3 Limitations and future research

Our research has some limitations. First, our database was collected at one point in time, but the process of business model innovation is really a longitudinal process [ 10 ]. The cross-sectional data used in this study can only reflect the correlations between the considered variables, but cannot infer their causal relationships. The collection of longitudinal data is recommended in the future. Second, the fact that our study focuses on firms in China limits the generalizability of our results. Future research should therefore conduct cross-country analyses to raise the external validity and robustness of our conclusions. Third, the self-developed measurement of business model innovation may be flawed and inadequate, so it still needs more tests and improvements. Future research can adopt questionnaire surveys or interviews with executives to capture facets of business model innovation. Fourth, although the sample size meets the requirement of regression analysis and mediating effect test for parameter estimation, it is still insufficient compared with China’s large population and numerous companies. Hence, future research needs to expand the sample size. Finally, although our study has controlled for some environmental factors, they do not cover all the possible contextual differences capable of affecting the relationships examined in our conceptual model. Thus, the opportunity for future research should be given to the development of more control variables such as competitive intensity and firm hierarchy [ 5 ].

Regardless of the limitations described above, our study brings out some possible future research avenues. For example, it may be interesting to further investigate the role of different types of collaboration depth (e.g., vertical, horizontal and competitor collaboration depth) as mediating variables in the relationship between R&D effort and business model innovation. Likewise, it is also stimulating to develop the study by introducing some possible moderating variables from environmental (e.g., market dynamism, technological turbulence and competitive intensity), organizational (e.g., organizational openness, internal resources and capabilities) and individual (e.g., cognition and personality of top management) perspectives. In addition, a good idea is to explore the interaction of internal and external enablers.

5.4 Conclusion

Based on the logic of R&D-PAC-RAC-Innovation, our study explores for the first time how R&D effort stimulates business model innovation via the chain-mediating mechanism of external collaboration breadth and depth from the perspective of absorptive capacity. Our results support the following proposed hypotheses: (1) R&D effort positively affects business model innovation; (2) R&D effort positively influences collaboration breadth; (3) collaboration breadth positively stimulates collaboration depth; (4) collaboration depth positively affects business model innovation; and (5) collaboration breadth and depth play a chain-mediating role in the relationship between R&D effort and business model innovation. The results of the present study provide important insights for business model innovation research. Although previous studies have investigated the antecedents of business model innovation, to date a discussion of the inside-out mechanism of R&D-driven business model innovation is lacking. Our study has developed a novel theory to explain the complex influencing mechanism between R&D effort and business model innovation.

Supporting information

https://doi.org/10.1371/journal.pone.0286715.s001

https://doi.org/10.1371/journal.pone.0286715.s002

Acknowledgments

The authors gratefully acknowledge the helpful comments and suggestions of the editor and the reviewers, which have improved the presentation.

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The impact of R&D effort on business model innovation: Evaluating chain mediation through collaboration breadth and depth

Shuting chen.

School of Public Policy and Administration, Nanchang University, Nanchang, China

Associated Data

All relevant data are within the paper and its Supporting information files.

Drawing on a novel theoretical framework, we explored the impact of research and development (R&D) effort on business model innovation via external collaboration breadth and collaboration depth in sequence. We empirically analyzed a sample of 94 Chinese innovative enterprises by applying hierarchical regression analysis and chain mediation analysis. The results indicate that R&D effort positively influences business model innovation. The influencing mechanism is that R&D effort positively affects external collaboration breadth, which in turn positively stimulates external collaboration depth, and ultimately benefits the implementation of business model innovation. Therefore, the breadth and depth of external collaboration play a chain-mediating role. The study develops a new framework for understanding the relationship between R&D effort, external collaboration, and business model innovation. It combines enterprises’ internal behavior (R&D) and external behavior (collaboration) to establish an inside-out mechanism for predicting business model innovation. It enriches the theory of business model innovation. It also provides insights for managers and governments to optimize policies in innovation-driven development.

1 Introduction

With the advancement of the information economy and new technologies, such as big data, cloud technology, artificial intelligence and the internet of things [ 1 ], firms are facing unanticipated disruptive competition and new challenges. In today’s rapidly changing social and economic environment, firms are urged to change their processes, practices and operations, adopt particular business models or implement business model innovation to capitalize on emerging business opportunities and maintain development [ 2 ]. Recently, business model innovation has received an increasing amount of attention among practitioners and academics [ 3 , 4 ]. In practice, many business model innovations are emerging and significantly changing the lifestyles of people and the business rules of most industries [ 5 ]. For example, Alibaba and Amazon, world-famous e-commerce giants, have achieved successes from their unique business model innovations, which break out the traditional offline shopping mode, enable people to shop without leaving home and influence others to follow. In academia, studies have shown that firms with faster growing operating margins often attach twice as much importance to business model innovation than their inefficient competitors [ 6 ]. Obviously, business model innovation, allowing firms to create novelty or improve efficiency that goes beyond product, process and technology innovation [ 7 ], is recognized as a source of sustainable competitive advantage and competitiveness [ 8 ].

Business model innovation is defined as “designed, nontrivial changes to the key elements of a firm’s business model and/or the architecture linking the elements” [ 9 ]. Although there is growing interest in business model innovation, previous studies have been relatively static and descriptive in nature. The focus of existing research mainly includes the drawing of a blueprint for the coherence between business model elements [ 10 ], the development of case studies involving business model innovation [ 11 – 13 ] and the exploration of the relationship between business model innovation and firm performance [ 14 , 15 ]. Recently, more researchers have been devoted to exploring the antecedents of business model innovation. For instance, Yi, Chen and Li (2022) identified stakeholder ties and organizational learning as enablers for business model innovation [ 16 ]. Xu, He, Morrison, De Domenici and Wang (2022) found that entrepreneurial networks and effectuation are driving factors of business model innovation [ 17 ]. Despite these achievements, much more needs to be done to explore the field.

It is important to recognize the enabling factors through which firms can better generate new business models and maintain their competitiveness. Recent research has pointed out that R&D effort, as an internal factor, can create VRIN (valuable, rare, inimitable, and non-substitutable) knowledge and augment existing knowledge stock for firms, providing a crucial driving force of enterprise innovation [ 18 ]. However, thus far, prior studies have focused on exploring the impact of R&D effort on firm performance [ 19 , 20 ] or technological innovation ability [ 21 ], but few have investigated its role in business model innovation. It is a gap. We therefore tried to fill this gap from an endogenous perspective. Considering the extroverted and open characteristics of business model innovation, we also deemed external links and open collaboration as important enablers, since external collaborations can help firms to break down traditional boundaries and allow useful external knowledge and information to enter into and flow across an organization [ 22 – 24 ]. In the field of external collaboration, its breadth and depth have been repeatedly employed to address research questions [ 25 , 26 ]. Extant studies have shown that both are beneficial to enterprise innovation [ 19 , 27 ]. Thus, we proposed that the two are also important antecedents of business model innovation.

According to the abovementioned analysis, R&D effort, collaboration breadth and depth may be relevant to business model innovation. However, they should not work independently. They are interrelated with each other in a convoluted manner. At present, the affecting mechanism between them is still unclear. Therefore, exploring the relationship between R&D effort, collaboration breadth, collaboration depth and business model innovation is necessary. We then tried to conduct an empirical analysis based on the sample data of Chinese top-ranking innovative enterprises to explore the inside-out mechanism of business model innovation driven by R&D effort. We drew on absorptive capacity (AC) theory to develop our framework [ 28 , 29 ], in which collaboration breadth is regarded as potential absorptive capacity (PAC) and collaboration depth is regarded as realized absorptive capacity (RAC). On the basis of a literature review, our assumed logic can be described as follows. The investment and activities of R&D could promote PAC, and then the accumulation and transfer of PAC would strengthen RAC, which ultimately plays an important role in the change of an enterprise’s business model. Following such logic, we constructed an R&D-PAC-RAC-Innovation framework and built a chain mediation model to test the impact of R&D effort on business model innovation. We therefore addressed the following research questions:

• RQ1. Does R&D effort positively affect business model innovation?
• RQ2. Does collaboration breadth positively affect collaboration depth?
• RQ3. How does R&D effort indirectly affect business model innovation via the mediators of collaboration breadth and depth?

In doing so, our present study makes three important theoretical contributions to the literature. First, this study explores the relationship between R&D effort and business model innovation, which extends the research on the antecedents of business model innovation and enriches the research on the outcomes of R&D effort. Second, it opens the black box of the relationship between collaboration breadth and depth, thereby contributing to the literature on external collaboration. Finally, based on the R&D-PAC-RAC-Innovation framework, it proposes two chain mediators of collaboration breadth and collaboration depth to clarify the internal influencing mechanism of R&D effort on business model innovation to strengthen the understanding of business model innovation under the background of firms’ R&D effort.

The remainder of this paper is structured as follows. Section 2 presents our theories and hypotheses. Section 3 describes the methodology, including the introduction of sample, data collection, measures and statistical techniques, following which Section 4 outlines the results of empirical analysis. Finally, Section 5 discusses theoretical implications, practical implications, limitations and future research and presents a short conclusion.

2 Theories and hypotheses

2.1 theories.

Currently, the business environment, which is characterized by fierce competition, fast technological change and strong market turbulence, compels most firms to innovate their business models to improve their adaptive capability [ 30 ]. Business model innovation is defined as a new-to-the-firm change in at least one out of three business model dimensions: (a) a firm’s value creation, (b) a firm’s value proposition, and (c) a firm’s value capture [ 31 ]. Value creation addresses how and by what means firms create new value along the value chain using their resources and capabilities embedded in intra- and inter-organizational processes [ 32 ]. Value proposition defines the range, nature and features of the offered products and services and the conditions at which these are provided [ 33 ]. Value capture answers the question of how value proposition is converted into profits in a sustainable way [ 34 ].

For firms operating in extremely turbulent environments, their sustainability relies heavily on the core competence and dynamic capability driven by continuous innovation. As the foundation of different kinds of innovations, R&D effort, including investments in R&D human resources, R&D spending and R&D equipment, plays an important role in the sustainable growth of firms [ 35 ]. The implementation of business model innovation should be carried out on the basis of R&D effort without exception since the creative thinking and absorptive capability that are essential to business model innovation need to be gradually built and improved through the training process of continuous R&D activities.

According to open innovation theory, collaboration with external organizations is crucial to a firm’s business model innovation, which is characterized by open principles and resource integration [ 23 , 36 , 37 ]. External collaboration is access that enables firms to acquire non-redundant knowledge and capabilities residing outside their organizational and technological boundaries [ 38 , 39 ]. In prior literature, the construct was classified by two dimensions, i.e., breadth and depth [ 25 ]. Collaboration breadth is defined as the number of external sources that firms rely upon in their innovative activities, ranging from narrow to broad collaboration as external partners increase. In contrast, collaboration depth is recognized as the extent to which firms draw deeply from different external sources, ranging from surface to deep collaboration as collaborative interactions intensify [ 19 , 25 , 40 ]. The two reflect different kinds of capabilities of firms in the function of external knowledge absorption and resource integration.

2.2 R&D effort and business model innovation

The tacit nature of technological knowledge and the risks related to the loss of technological competitiveness require internal efforts in R&D activities [ 19 ]. With the advance of the knowledge-based economy, R&D effort becomes more essential for enterprises to carry out business model innovation, which is reflected in three aspects. First, the high attention on R&D promotes firms to develop new technologies and products, in turn stimulating business model innovation. However, the economic value of a technology or product remains latent until it is commercialized in some way via a business model [ 37 ]. Firms should change their existing business models to better capture the value within the new technologies or products. Next, R&D effort improves the development of technical human capital and is thus beneficial to changing the existing business model. Resource-based theory emphasizes the important role of R&D personnel knowledge and capabilities in the development of firms’ core competitiveness and sustainable competitive advantage [ 41 , 42 ]. R&D human capital supports and participates in the implementation of business model innovation. Additionally, R&D effort increases opportunities for firms to embrace new things, providing a solid foundation for business model innovation [ 24 ]. To achieve R&D goals, firms need to keep track of the changes in customer demand, obtain cutting-edge knowledge, and communicate and cooperate with external organizations. These actions help firms to catch on the status quo and keep up with the trend of the external environment to find the optimal direction for business model innovation.

Several studies have provided empirical evidence for the positive impact of R&D effort on business model innovation. For example, Zouaghi et al. (2018) suggested that R&D intensity and R&D human capital are recognized as vital driving factors for innovations new to the market place and the firm [ 19 ]. Leung and Sharma (2021) revealed that R&D effort plays an important role in a firm’s sales, profitability and value. [ 20 ]. Therefore, we proposed the following hypothesis:

• H1: R&D effort has a positive impact on business model innovation.

2.3 R&D effort and collaboration breadth

We argued that R&D effort stimulates the expansion of a firm’s external collaboration breadth due to three effects. First, the high attention on R&D leads a firm to devote itself to knowledge learning and collaborative innovation; thus, a firm’s effort on R&D would increase the invested financial resources that are essential to external collaboration. According to the corporate financial system, a part of R&D expenditure can be used for the construction of external collaboration networks and strategic alliances, as well as specific activities such as communication, learning and training, which greatly raise the collaboration breadth of firms. Collaborative R&D networks need a lot of money to support them [ 43 , 44 ]. Park, Chen and Gallagher (2002) empirically demonstrated that firms with rich financial resources are more likely to increase external collaborations [ 45 ]. In practice, we also observed that large and medium enterprises with abundant R&D expenditure tend to carry out more collaborative innovation activities with universities, research institutes and leading customers. Second, firms making more R&D efforts explore more new technological and market opportunities, which stimulates them to network more collaborators to make joint investments and develop potential value. Because R&D effort generally focuses on projects far from the market and with technological challenges rather than typical execution [ 46 ], the resources needed to take the discovered opportunities may not reside within firms, but are scattered across diverse organizations. Hence, firms that successfully exploit the opportunities need to cooperate with various partners to access and integrate heterogeneous resources [ 47 ], which are supported by and back stimulate the construction of external collaboration networks. Finally, R&D effort, through enlarging the knowledge base, helps firms expand their external collaborations. On the basis of R&D effort, firms can obtain new experience, knowledge and information [ 21 ], which enhance their capabilities to identify, assimilate and apply external knowledge and then improve the possibility of cooperation with a variety of external organizations. In other words, R&D effort enriches a firm’s knowledge stock and diversity, and the latter increases the competence and attractiveness of the firm in the development of collaborations [ 28 , 48 ].

Some studies can support our argument. Cerulli, Gabriele and Potì (2016), for example, showed that a company’s R&D input can increase the likelihood of external collaboration with various types of partners [ 49 ]. Chapman, Lucena and Afcha (2018) provided strong evidence for the significant positive impact of R&D subsidies on firm external collaboration breadth based on data analysis of Spanish firms [ 48 ]. Therefore, we advanced the following hypothesis:

• H2: R&D effort has a positive impact on collaboration breadth.

2.4 Collaboration breadth and depth

By reviewing the extant literature, it can be clearly seen that collaboration breadth and collaboration depth are generally regarded as two parallel concepts [ 22 ] and little research has explored the internal relationship between them. In view of the research omission, further examination of this issue is required.

Based on this attention-based view, a firm can be considered as a system that structurally distributes attention [ 50 – 52 ], where attention refers to the capability to process different information sources and simultaneously extract the information that is useful for certain tasks [ 53 ]. The view acknowledges that managerial attention is a firm’s most valuable resource. Decision-makers in firms are therefore supposed to “concentrate their energy, effort and mindfulness deeply on a limited number of issues and tasks” [ 50 ]. Following this view, we proposed that as external collaboration breadth increases, decision-makers in firms are exposed to increasingly more external information so that they clearly know their current situation covering what firms currently lack and need in the future. This knowledge can enable them to focus on the selection and development of the most valuable collaborations according to firms’ status quo [ 50 ]. In addition, with the expansion of collaboration breadth, the marginal value of superficial collaboration would be diminishing and decision-makers may find it more difficult to deal with the booming knowledge [ 26 ]. The pressure of information overload attributed to broad collaboration would compel decision-makers to narrow their attention to strategic alliances with VRIN characteristics. Based on the above analysis, we proposed that as the breadth of collaboration increases, the depth would also subsequently increase. Therefore, we proposed the following hypothesis:

• H3: Collaboration breadth has a positive impact on collaboration depth.

2.5 Collaboration depth and business model innovation

Some prior research has confirmed that external collaboration even plays an irreplaceable role in facilitating a firm’s internal innovation process [ 25 , 54 ]. In this study we wanted to further prove that deep collaboration with external organizations can effectively boost business model innovation. First, deep collaboration provides enterprises with access to external heterogeneous information and knowledge [ 22 ], which lays a foundation for the integration of resources and capabilities required by the process of business model innovation. Knowledge absorption and integration help enterprises generate new thoughts and ways to develop new business models with novelty and efficiency [ 55 ]. Second, deep collaboration experience cultivates trust between the enterprise and its partners [ 22 ]. Trust serves would serve as a crucial coordination mechanism that improves the accurate understanding of newly acquired external information, guarantees technological transfer without resistance, and achieves timely knowledge sharing and substantive cooperation [ 56 ], thereby contributing to the effective implementation of business model innovation.

Collaboration depth is generally divided into three dimensions, i.e., vertical, horizontal and competitor collaboration [ 41 , 57 ], which have different benefits for business model innovation. The strategic alliance with suppliers and customers enables enterprises to gain updated information from market and industry, pool complementary resources and improve learning routines [ 58 ]. Clearly, these actions can assist enterprises in developing efficiency-oriented business models [ 59 ]. Moreover, the close connection with lead users would make enterprises rapidly know the changing demand for new products with novel functions, further helping enterprises to improve the novelty of their business models [ 41 ]. In many knowledge-extensive industries, collaboration with competitors is generalized since any one enterprise cannot undertake the huge cost of technological innovation and the development of a novel business model. Deep collaboration with competitors plays the roles of risk sharing, information and resource sharing, and joint monopolization in business model innovation [ 24 ]. Finally, deep collaborations with governments, universities, consultants and others enable firms to efficiently obtain comprehensive and heterogeneous knowledge required by market development [ 19 ]. Such collaborations could also enhance the public’s identity with firms and improve their brand images, which promote the success of new business models. Following the above analysis, we proposed the third hypothesis:

• H4: Collaboration depth has a positive impact on business model innovation.

2.6 The chain-mediating effect

Martínez-Sánchez, Vicente-Oliva and Pérez-Pérez (2020) proposed a new theoretical framework along with the R&D-AC-Innovation link [ 60 ]. According to their statement, absorptive capacity (AC) was the key mediator between R&D and innovation. In other studies, as an essential ability to identify, assimilate, transform, and exploit external knowledge, AC was recognized as an important component of corporate competence [ 61 , 62 ]. When firms make efforts in R&D, the absorption of external knowledge can facilitate innovative activities and enhance innovation capability and efficiency. In a previous body of knowledge, AC was often divided into PAC and RAC [ 29 ]. Among them, PAC refers to the capability to acquire and assimilate external knowledge, whereas RAC refers to the capability to transform and exploit external knowledge [ 29 ]. They are indispensable in a firm’s process of exogenous growth [ 63 ]. However, there is a progressive relationship between the two in logic, i.e., PAC forms the premise of RAC [ 64 ]. We therefore expanded the R&D-AC-Innovation link and tried to develop an R&D-PAC-RAC-Innovation framework for our study.

In prior literature, external collaboration was summarized as a type of absorptive capacity. Broad collaboration means the high possibility of enterprises to access external knowledge but not always the effective exploitation of knowledge. Hence, it is exactly a kind of PAC. In contrast, collaboration depth reflects the degree of an enterprise’s effective utilization of external knowledge. It is therefore a kind of RAC. As the framework of R&D-PAC-RAC-Innovation revealed, firms making efforts on R&D would obtain financial support, discover new opportunities and lay a solid foundation of resources and capability to collaborate with various external organizations [ 48 ] and then gradually focus their limited attention on part of external organizations that can bring them benefits and opportunities to better realize their targets involving performance, innovativeness and sustainability [ 26 , 50 ]. That is, collaboration breadth triggers collaboration depth, and the latter could enhance the firm’s capability of business model innovation by building trust, obtaining knowledge and developing value nets [ 1 , 22 ]. In summary, we naturally proposed the following hypothesis:

• H5: Collaboration breadth and depth play a chain-mediating role in the relationship between R&D effort and business model innovation.

Fig 1 shows the conceptual research model of the study.

3 Methodology

Strategy&, part of the PwC network, released the 2018 Global Innovation 1000 study, which analyses the world’s top 1000 listed corporations with the highest R&D expenditures, and which account for 40% of the total global R&D expenditure. The Global Innovation 1000 study shows that R&D expenditures increase in every region of the world, but most notably in China, where they rise 34.4 percent over the previous year.

We collected the data of Chinese enterprises ranked in Global Innovation 1000 for three reasons. First, these enterprises are in line with the innovation-driven development strategy advocated by the Chinese national government. To some extent, this means that they are of great significance to the development of emerging countries. Second, most scholars focus on the technological innovation of such innovative enterprises but always neglect their excellent performance in business model innovation. Third, compared with small and medium enterprises, they generally have sufficient resources and strong capabilities to support their frequent collaborations. Moreover, their disclosed information is open and transparent, making it convenient for data collection.

To ensure the completeness and reliability of our data analysis, the following criteria were followed in the process of sample selection. First, we selected 175 Chinese companies in the 2018 Global Innovation 1000 study. Second, considering the consistency of the data structure, we excluded 76 companies that were listed in Hong Kong, Taiwan and the United States. Third, we excluded 5 companies with missing information. Finally, 94 Chinese companies listed on the Shenzhen and Shanghai Stock Exchanges (A share) were selected.

Table 1 displays the characteristics of our sample. The industries represented are information technology (26.6%), industrials (28.7%), materials (17.0%), consumer discretionary (21.3%), healthcare (4.3%) and energy (2.1%). The companies comprise 60.6% state-owned firms and 39.4% non-state-owned firms. Among them, 17.0% engaged less than 10000 employees, 42.6% between 10001 and 30000, 19.1% between 30001 and 50000, and 21.3% more than 50000. In addition, the sample aged 6–10 years accounts for 7.4%, 11–15 years for 12.8%, 16–20 years for 40.4%, 21–25 years for 31.9% and more than 25 years for 7.4%.

3.2 Data collection

The data collection proceeded in two stages. In the first stage, we built composite scales for external collaboration and novelty-centered business model innovation, and we identified and measured the relevant items on the basis of a content analysis of company information (for details, please see Appendices 1–3 in S1 File ). In the second stage, the data of the rest of the variables, including R&D effort, efficiency-centered business model innovation and control variables, were collected from the China Stock Market & Accounting Research Database (CSMAR Database).

In recent years, the panel method has been increasingly used [ 65 , 66 ]. In this study, we set up a panel composed of 3 members, including 1 professor and 2 doctoral students. First, the professor carefully selected the panelists from his total team members by requiring them to submit an abbreviated test survey on a randomly chosen sample company to display their understanding of external collaboration and novelty-centered business model innovation. After the selection, 2 doctoral members jointly read the information, announcements and documents of the sample companies, became familiar with the details of the external collaboration and novelty-centered business model innovation of all sample companies, and then developed the measurement scale based on the consensus. Next, the professor trained them as expert raters in data collection and analysis. The raters were provided with written guidelines on the proper way to address survey items. Data sources included annual reports, social responsibility reports, investment analysts’ reports, company news, websites and other announcements of those companies from 2016 to 2020. The process took every rater approximately six months from October 2020 to April 2021. The lack of readily available data about external collaboration and novelty-centered business model innovation obliged us to collect primary data and construct a unique dataset. Finally, we evaluated the consistency by conducting a pairwise comparison of the two raters’ scores, yielding a Pearson correlation coefficient of 0.929 (p<0.01). For the items with discrepant scores, two raters discussed with each other and reached a consensus under the guidance of the professor. All initial differences were resolved, so the final consistency was 100%.

The data of other variables were drawn from the CSMAR Database, which is a research-oriented database in the economic and financial field compiled by Shenzhen CSMAR Data Technology Co., Ltd. The database reflects the financial conditions of China and follows the professional standards of authoritative databases such as CRSP, COMPUSTAT, TAQ and THOMSON. The data of these quantitative variables came from 2018. To control the influence of extreme values, the data collected from CSMAR have been winsorized.

3.3 Measures

3.3.1 r&d effort.

R&D effort has been extensively used in innovation research as an input variance [ 5 , 19 ]. In this study, we measured it by a firm’s R&D expenditure as a proportion of the firm’s operating income [ 60 ].

3.3.2 Collaboration breadth

Following Laursen and Salter (2006, 2014) [ 25 , 67 ] and Dong and Netten (2017) [ 26 ], we constructed collaboration breadth as the combination of nine sources of external knowledge for business model innovation: 1) supplier, 2) customer, 3) competitor, 4) government, 5) university and research institution, 6) consultancy firm, 7) venture capital investment firm, 8) trade fair and exhibition, and 9) others. For each source, we used a three-point scale to indicate the scope of collaboration (1 = no or low degree, 2 = medium degree, 3 = high degree) (see Appendix 1 in S1 File for details). On the basis of the initial score, we further coded each source as a binary variable, where 1 represents that the collaboration width of a source is medium or high (2 and 3) and 0 represents that the source is not used or its collaboration width is low (1). Finally, the variable values of the nine sources were added up to measure the total level of collaboration breadth. Obviously, the value interval of the construct is [0, 9]. It was valued as 0 when all knowledge sources were not used or had low collaboration breadth, and valued as 9 when all sources had medium or high collaboration breadth.

3.3.3 Collaboration depth

Following Laursen and Salter (2006) [ 25 ] and Dong and Netten (2017) [ 26 ], we defined collaboration depth as the intensity of collaboration with each source of external knowledge. A three-point scale was used to indicate the intensity of collaboration (1 = no or low degree, 2 = medium degree, 3 = high degree) (see Appendix 2 in S1 File for details). Similar to collaboration breadth, each source was further coded as a binary variable, where 1 represents that the certain external knowledge source is used to a high degree (3) and 0 reflects that it is not used, or only to a low or medium degree (1 and 2). The nine dummies were added up so that each of our sample companies could obtain the score of the depth variable, ranging from 0 to 9, where 0 indicates no intense use of any external knowledge source, and 9 indicates intense usage of all 9 sources.

3.3.4 Business model innovation

We measured business model innovation from two dimensions, i.e., novelty-centered and efficiency-centered ones. Their scores were evaluated by the following different methods. Business model innovation was comprehensively measured by the mean value of their scores after data preprocessing for novelty-centered and efficiency-centered ones into the [0,1] interval.

3 . 3 . 4 . 1 Novelty-centered business model innovation . We independently developed a new scale of novelty-centered business model innovation. Four items, reflecting the new R&D system, new manufacturing platform, new sales model and new customer service system, were used to measure it. Considering the difficulty of the detailed measurement in an objective way, we deemed the use of perceptual measures obtained by our raters [ 65 , 66 ]. The items were quantified on a five-point scale (see Appendix 3 in S1 File for details). After coding, their scores were aggregated and averaged for the final score.

3 . 3 . 4 . 2 Efficiency-centered business model innovation . We measured efficiency-centered business model innovation from the dimensions of value creation, value proposition and value capture in our study [ 31 ] (see Appendix 4 in S1 File for details). First, capital utilization ability and debt paying ability are the key elements in the process of value creation, so we selected the current ratio, equity-to-debt ratio and debt coverage ratio to measure value creation. Second, operating capacity is an essential factor in the value proposition process, which involves the turnover of goods, capital and assets. Thus, we used inventory turnover, accounts receivable turnover and total assets turnover to evaluate the value proposition. Third, it is very important for a firm to have profitability and growth ability in the process of value capture. Therefore, its measurement was composed of three financial indicators, i.e., net profit growth rate, operating income growth rate and operating profit ratio.

The entropy method is a widely used objective weighting method. It determines the weight of indicators by the variational degree of the dataset [ 68 ]. The TOPSIS method, based on the rule that the chosen alternative should have the longest distance from the negative ideal solution and the shortest distance from the positive ideal solution, is also a widely used evaluation method [ 69 ]. The negative ideal solution always maximizes the cost criteria and minimizes the benefit criteria, while the positive ideal solution is the opposite [ 70 ]. In this study, we combined the two methods and built an entropy-based TOPSIS model [ 71 ] to comprehensively assess efficiency-centered business model innovation.

3.3.5 Control variables

To account for the effects of extraneous variables, we included firm ownership, firm age, firm size, firm location, industry, financial leverage and operating leverage as alternative explanations for business model innovation.

In China, firm ownership is an important factor that influences a firm’s strategy, operation and performance [ 22 ]. We measured it by a dummy variable that controls for potential variations between state-owned enterprises (coded as 1) and private-owned, foreign-owned or other types of enterprises (coded as 0).

Firm age also plays a role in a firm’s propensity to adopt business model innovation as it affects the flexibility of strategy and the willingness of knowledge absorption [ 72 ]. It was measured by the number of years since the firm was officially established.

Some scholars have argued that firm size matters for innovation, because large firms tend to have more resources required by innovative projects [ 73 ]. However, other scholars insisted that larger firms are more prone to organizational inertia, thereby hindering change processes [ 74 ]. To control the possible effect, firm size measured by the natural logarithm of employee scale was set as a control variable.

Firm location might influence firm innovation as different locations provide various resources and opportunities for firms. It was measured by a dummy variable [ 75 ]. We measured it as 1 when a firm is in first-tier cities, including Beijing, Shanghai, Guangzhou and Shenzhen; otherwise, it was coded as 0.

Industry may influence a firm’s cognition and its needs for business model innovation [ 5 ]. It was measured by a dummy variable. We coded it as 1 when the firm belongs to industries including materials, consumer discretionary, healthcare and energy; otherwise, it was coded as 0.

Financial leverage reflects the degree of financial risk of a firm [ 76 ]. It was measured by a dummy variable. When the financial leverage of a firm is higher than the mean value of the financial leverage of all sample firms, the value was coded as 1; otherwise, it was coded as 0.

Operating leverage reflects the degree of operating risk of a firm [ 77 ]. The method to measure it is similar to that of financial leverage.

3.4 Statistical techniques

Following Hox (1994) [ 78 ], we tested the direct effects by hierarchical regression analysis, using SPSS 24 software. Following Hayes (2018) [ 79 ], we tested the chain-mediating effects by a bias-corrected bootstrapping procedure, using PROCESS v. 3.3.

4 Empirical results

4.1 descriptive statistics and correlations.

Table 2 shows the mean, standard deviation, and correlation coefficient of the variables. We found a positive and significant relationship between (a) R&D effort and collaboration breadth (r = 0 . 458 , p <0 . 01) , (b) collaboration breadth and collaboration depth (r = 0 . 579 , p < 0 . 01) , and (c) collaboration depth and business model innovation (r = 0 . 552 , p < 0 . 01) . We also found that (a) R&D effort is positively related to collaboration depth (r = 0 . 504 , p < 0 . 01) , (b) R&D effort is positively associated with business model innovation (r = 0 . 554 , p < 0 . 01) , and (c) collaboration breadth is positively related to business model innovation (r = 0 . 380 , p < 0 . 01) . Those findings provided preliminary evidence for the hypotheses proposed in our study.

*p < 0.1;

**p < 0.05;

***p < 0.01 (two-tailed tests).

Additionally, the highest correlation value among all variables is 0.579, far below the threshold value of 0.75 [ 80 ], suggesting no serious multicollinearity problem within the dataset. As shown in Table 3 , it is confirmed again by the variance inflation factors (VIFs) of all variables, well below ten, the recommended threshold value [ 81 ].

4.2 Hypotheses tests

The hypotheses advanced in the study were tested through hierarchical regression analysis using SPSS 24 software. The results are shown in Table 3 . The result of Model 1 shows that R&D effort positively influences business model innovation (β = 0 . 542 , p < 0 . 01) , thus supporting Hypothesis 1. The result of Model 2 indicates that R&D effort positively affects collaboration breadth (β = 0 . 622 , p < 0 . 01) , thereby supporting Hypothesis 2. According to Model 3, there is a significantly positive relationship between collaboration breadth and collaboration depth (β = 0 . 476 , p < 0 . 01) , thus supporting Hypothesis 3. Moreover, Model 4 shows a positive effect of collaboration depth on business model innovation (β = 0 . 295 , p < 0 . 01) , thereby supporting Hypothesis 4. The above results reveal the possibility of an indirect effect of R&D effort on business model innovation through the mediating role of collaboration breadth and depth in sequence. The path coefficients of the whole conceptual model are presented in Fig 2 .

**p < 0.05, ***p < 0.01 (two-tailed tests).

Table 4 shows the results of the chain mediation model tested by the bias-corrected bootstrapping procedure. There are three indirect effects between R&D effort and business model innovation. In detail, (a) the indirect effect via collaboration breadth is insignificant (estimate = 0 . 022 , 95% CI = [-0 . 104 , 0 . 147]) , (b) the indirect effect via collaboration depth is significant (estimate = 0 . 088 , 95% CI = [0 . 012 , 0 . 237]) , and (c) the chain-mediating effect of collaboration breadth and depth is significant (estimate = 0 . 087 , 95% CI = [0 . 025 , 0 . 167]) . Hence, Hypothesis 5 is also supported.

Notes: RDE = R&D Effort; CB = collaboration Breadth; CD = Collaboration Depth; BMI = Business Model Innovation.

4.3 Robustness tests

We analyzed the robustness of the above findings in two different ways. The first was to change the sample size, i.e., randomly selecting a subsample (N = 70). The second way was to replace a variable, i.e., using the percentage of highly skilled R&D workers to measure R&D effort. The results of robustness tests are presented in Table 5 , showing that they are consistent with the initial findings. Therefore, we concluded that our findings are robust.

5 Discussion

5.1 theoretical implications.

The theoretical contributions of the present study are threefold. First, this study examines the relationship between R&D effort and business model innovation. In fact, to our knowledge, we offered the first empirical evidence of how business model innovation is carried out under firms’ effort in R&D. The literature review that is performed, shows that neither the relationship nor the influencing mechanism of R&D effort on business model innovation has been previously discussed. We concluded that R&D effort is conductive to business model innovation, thereby extending the research on the antecedents of business model innovation. The antecedents of business model innovation have been explored in many aspects, such as organizational search [ 82 ], big data analytics capabilities [ 83 ] and strategic orientation [ 84 ]; however, few researchers have paid attention to the role of firms’ R&D effort. Similarly, our results also enrich the research on the outcomes of R&D effort. Most of the outcome variables in existing research on R&D effort have mainly focused on the performance of total innovation activities [ 19 , 85 , 86 ], while innovative behavior has rarely been involved.

Second, it contributes to the literature on external collaboration. According to past studies, collaboration breadth and depth are generally discussed as a pair of parallel constructs. For instance, setting them as two different strategies, Zhang , Yuan and Zhang (2022) explored their impacts on growth of new technology-based firms [ 87 ]. Similarly, Jang, Ko, Chung and Woo (2023) investigated their effects on product and process innovation [ 88 ]. In contrast, we argued that the two are neither orthogonal nor mutually exclusive. They can affect and complement each other. Considering that little prior work has explored their relationship, we demonstrated from the perspective of attention theory that the expansion of collaboration breadth is conducive to the improvement of collaboration depth. These findings open the black box of the relationship between collaboration breadth and depth, which enriches the open innovation field.

Finally, this study reveals the inside-out influencing mechanism of R&D effort on business model innovation from the perspective of absorptive capacity, addressing the necessity of taking into account the positive role of external collaboration. In most previous studies, both R&D effort and external collaboration have been positioned as direct predictors or moderators of firm innovation and other outcomes. Little attention has been given to the potential mediating mechanism. To cover the gap, we took both collaboration breadth and collaboration depth as mediators to explore the internal influencing mechanism of R&D effort on business model innovation. Following the R&D-PAC-RAC-Innovation link, the results of the present study demonstrate the chain-mediating effect of collaboration breadth and collaboration depth on the relationship between R&D effort and business model innovation. Our findings may help researchers to deepen their understanding of the internal mechanism through which R&D effort affects business model innovation; moreover, they provide a new perspective to investigate how business model innovation is triggered in the context of R&D dominant culture, and how to effectively combine the business strategy and technological strategy in a firm.

5.2 Practical implications

This study has important practical implications for managers. First, to achieve business model innovation, enterprises are suggested to put much effort into R&D. R&D effort can provide internal resource bases for business model innovation. Therefore, top managers should take some specific measures from the R&D perspective, such as increasing R&D expenditure, hiring enough R&D talent, building excellent R&D teams and developing a perfect R&D strategic system, to satisfy the requirements of business model innovation.

Second, enterprises are also recommended to promote many collaborative activities with external organizations. Through external collaboration, firms need to obtain advanced knowledge, gain updated information and learn new technologies and business thoughts, the combination of which provides new inspiration for enterprises to carry out business model innovation. To better promote collaborative communications, enterprises should cultivate an open and collaborative culture, develop online cooperative platforms, build cooperative alliances and so on.

Finally, firms are advised to follow the R&D-PAC-RAC-Innovation path to achieve the goal of business model innovation. That is, firms may follow three steps to make efforts. First, they should devote themselves to R&D activities to lay the foundation of external collaboration. Second, on the basis of technological advantages, they should build collaborative networks and develop their collaborators. Third, on the basis of broad collaborations, they need to construct strategic and deep collaborations for effective actions about resource integration, knowledge sharing, and win-win business. Our study reminds managers to gradually shift their attention from numerous collaborative relationships to several deep collaborations with VRIN characteristics. Through R&D-based collaborations, new business models can be effectively developed.

This study also has practical implications for governments. To some extent, our study could lead Chinese central and local governments to make better decisions about firms’ sustainable growth. On the one hand, governments are suggested to set some policies to stimulate firm innovation, such as allocating R&D subsidies, increasing benefits for introducing innovative talents and creating a great innovative business environment. On the other hand, governments are also encouraged to take measures to promote firm collaboration with external organizations, such as establishing cooperative funds, strengthening the construction of cooperation platforms and improving the tax incentives of collaborative projects.

5.3 Limitations and future research

Our research has some limitations. First, our database was collected at one point in time, but the process of business model innovation is really a longitudinal process [ 10 ]. The cross-sectional data used in this study can only reflect the correlations between the considered variables, but cannot infer their causal relationships. The collection of longitudinal data is recommended in the future. Second, the fact that our study focuses on firms in China limits the generalizability of our results. Future research should therefore conduct cross-country analyses to raise the external validity and robustness of our conclusions. Third, the self-developed measurement of business model innovation may be flawed and inadequate, so it still needs more tests and improvements. Future research can adopt questionnaire surveys or interviews with executives to capture facets of business model innovation. Fourth, although the sample size meets the requirement of regression analysis and mediating effect test for parameter estimation, it is still insufficient compared with China’s large population and numerous companies. Hence, future research needs to expand the sample size. Finally, although our study has controlled for some environmental factors, they do not cover all the possible contextual differences capable of affecting the relationships examined in our conceptual model. Thus, the opportunity for future research should be given to the development of more control variables such as competitive intensity and firm hierarchy [ 5 ].

Regardless of the limitations described above, our study brings out some possible future research avenues. For example, it may be interesting to further investigate the role of different types of collaboration depth (e.g., vertical, horizontal and competitor collaboration depth) as mediating variables in the relationship between R&D effort and business model innovation. Likewise, it is also stimulating to develop the study by introducing some possible moderating variables from environmental (e.g., market dynamism, technological turbulence and competitive intensity), organizational (e.g., organizational openness, internal resources and capabilities) and individual (e.g., cognition and personality of top management) perspectives. In addition, a good idea is to explore the interaction of internal and external enablers.

5.4 Conclusion

Based on the logic of R&D-PAC-RAC-Innovation, our study explores for the first time how R&D effort stimulates business model innovation via the chain-mediating mechanism of external collaboration breadth and depth from the perspective of absorptive capacity. Our results support the following proposed hypotheses: (1) R&D effort positively affects business model innovation; (2) R&D effort positively influences collaboration breadth; (3) collaboration breadth positively stimulates collaboration depth; (4) collaboration depth positively affects business model innovation; and (5) collaboration breadth and depth play a chain-mediating role in the relationship between R&D effort and business model innovation. The results of the present study provide important insights for business model innovation research. Although previous studies have investigated the antecedents of business model innovation, to date a discussion of the inside-out mechanism of R&D-driven business model innovation is lacking. Our study has developed a novel theory to explain the complex influencing mechanism between R&D effort and business model innovation.

Supporting information

Acknowledgments.

The authors gratefully acknowledge the helpful comments and suggestions of the editor and the reviewers, which have improved the presentation.

Funding Statement

The author Dengke Yu was funded by the National Natural Science Foundation of China (Grants No. 71962021). The website of the funder is https://www.nsfc.gov.cn/ . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability

Psychological safety and the critical role of leadership development

When employees feel comfortable asking for help, sharing suggestions informally, or challenging the status quo without fear of negative social consequences, organizations are more likely to innovate quickly , unlock the benefits of diversity , and adapt well to change —all capabilities that have only grown in importance during the COVID-19 crisis. 1 Jonathan Emmett, Gunnar Schrah, Matt Schrimper, and Alexandra Wood, “ COVID-19 and the employee experience: How leaders can seize the moment ,” June 2020, McKinsey.com; Tera Allas, David Chinn, Pal Erik Sjatil, and Whitney Zimmerman, “ Well-being in Europe: Addressing the high cost of COVID-19 on life satisfaction ,” June 2020, McKinsey.com. Yet a McKinsey Global Survey conducted during the pandemic confirms that only a handful of business leaders often demonstrate the positive behaviors that can instill this climate, termed psychological safety , in their workforce. 2 The online survey was in the field from May 14–29, 2020, and garnered responses from 1,574 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, we analyzed the results of 1,223 participants who said they were a member of a team that they did not lead, where a team is defined as two or more people who work together to achieve a common goal. CEOs were included in the findings if they said that a) their organization had a board of directors and b) they were not the board’s chair, so that they could think of their board when asked questions about their team.

As considerable prior research shows, psychological safety is a precursor to adaptive, innovative performance—which is needed in today’s rapidly changing environment—at the individual, team, and organization levels. 3 Amy C. Edmondson, The Fearless Organization: Creating Psychological Safety in the Workplace for Learning, Innovation, and Growth, first edition, Hoboken, NJ: John Wiley & Sons, November 2018; Shirley A. Ashauer and Therese Macan, “How can leaders foster team learning? Effects of leader-assigned mastery and performance goals and psychological safety,” Journal of Psychology, November–December 2013, Volume 147, Number 6, pp. 541–61, tandfonline.com; Anne Boon et al., “Team learning beliefs and behaviours in response teams,” European Journal of Training and Development, May 2013, Volume 37, Number 4, pp. 357–79, emerald.com; Daphna Brueller and Abraham Carmeli, “Linking capacities of high-quality relationships to team learning and performance in service organizations,” Human Resource Management, July–August 2011, Volume 50, Number 4, pp. 455–77, wileyonlinelibrary.com; M. Lance Frazier et al., “Psychological safety: A meta-analytic review and extension,” Personnel Psychology, February 2017, Volume 70, Number 1, pp. 113–65, onlinelibrary.wiley.com; Nikos Bozionelos and Konstantinos C. Kostopoulos, “Team exploratory and exploitative learning: Psychological safety, task conflict, and team performance,” Group & Organization Management, June 2011, Volume 36, Number 3, pp. 385–415, journals.sagepub.com; Rosario Ortega et al., “The emotional impact of bullying and cyberbullying on victims: A European cross-national study,” Aggressive Behavior, September–October 2012, Volume 38, Issue 5, pp. 342–56, onlinelibrary.wiley.com; Corinne Post, “Deep-level team composition and innovation: The mediating roles of psychological safety and cooperative learning,” Group & Organizational Management, October 2012, Volume 37, Number 5, pp. 555–88, journals.sagepub.com; Charles Duhigg, “What Google learned from its quest to build the perfect team,” New York Times, February 25, 2016, nytimes.com. Amy Edmondson’s 1999 research previously found—and our survey findings confirm—that higher psychological safety predicts a higher degree of boundary-spanning behavior, which is accessing and coordinating with those outside of an individual’s team to accomplish goals. For example, successfully creating a “ network of teams ”—an agile organizational structure that empowers teams to tackle problems quickly by operating outside of bureaucratic or siloed structures—requires a strong degree of psychological safety.

Fortunately, our newest research suggests how organizations can foster psychological safety. Doing so depends on leaders at all levels learning and demonstrating specific leadership behaviors that help their employees thrive. Investing in and scaling up leadership-development programs  can equip leaders to embody these behaviors and consequently cultivate psychological safety across the organization.

A recipe for leadership that promotes psychological safety

Leaders can build psychological safety by creating the right climate, mindsets, and behaviors within their teams. In our experience, those who do this best act as catalysts, empowering and enabling other leaders on the team—even those with no formal authority—to help cultivate psychological safety by role modeling and reinforcing the behaviors they expect from the rest of the team.

Our research finds that a positive team climate—in which team members value one another’s contributions, care about one another’s well-being, and have input into how the team carries out its work—is the most important driver of a team’s psychological safety. 4 Past research by Frazier et al. (2017) found three categories to be the main drivers of psychological safety: positive leader relations, work-design characteristics, and a positive team climate. We conducted multiple regression with relative-importance analysis to understand which category matters most, and our results show that a positive team climate has a significantly stronger direct effect on psychological safety than the other two. Based on these results, we tested a structural-equation model (SEM) in which the frequency with which team leaders displayed four leadership behaviors predicted psychological safety both directly and indirectly via positive team climate. Exploratory analyses were conducted to determine whether the effect of the leadership behaviors affected psychological safety at different levels of team climate. By setting the tone for the team climate through their own actions, team leaders have the strongest influence on a team’s psychological safety. Moreover, creating a positive team climate can pay additional dividends during a time of disruption. Our research finds that a positive team climate has a stronger effect on psychological safety in teams that experienced a greater degree of change in working remotely than in those that experienced less change during the COVID-19 pandemic. Yet just 43 percent of all respondents report a positive climate within their team.

Positive team climate is the most important driver of psychological safety and most likely to occur when leaders demonstrate supportive, consultative behaviors, then begin to challenge their teams.

During the pandemic, we have seen an accelerated shift away from the traditional command-and-control leadership style known as authoritative leadership, one of the four well-established styles of leadership behavior we examined to understand which ones encourage a positive team climate and psychological safety . The survey finds that team leaders’ authoritative-leadership behaviors are detrimental to psychological safety, while consultative- and supportive-leadership behaviors promote psychological safety.

The results also suggest that leaders can further enhance psychological safety by ensuring a positive team climate (Exhibit 1). Both consultative and supportive leadership help create a positive team climate, though to varying degrees and through different types of behaviors.

With consultative leadership, which has a direct and indirect effect on psychological safety, leaders consult their team members, solicit input, and consider the team’s views on issues that affect them. 5 The standardized regression coefficient between consultative leadership and psychological safety was 0.54. The survey measured consultative-leadership behaviors by asking respondents how frequently their team leaders demonstrate the following behaviors: ask the opinions of others before making important decisions, give team members the autonomy to make their own decisions, and try to achieve team consensus on decisions. Supportive leadership has an indirect but still significant effect on psychological safety by helping to create a positive team climate; it involves leaders demonstrating concern and support for team members not only as employees but also as individuals. 6 The survey measured supportive leadership behaviors by asking respondents how frequently their team leaders demonstrate the following behaviors: create a sense of teamwork and mutual support within the team, and demonstrate concern for the welfare of team members. These behaviors also can encourage team members to support one another.

Another set of leadership behaviors can sometimes strengthen psychological safety—but only when a positive team climate is in place. This set of behaviors, known as challenging leadership, encourages employees to do more than they initially think they can. A challenging leader asks team members to reexamine assumptions about their work and how it can be performed in order to exceed expectations and fulfill their potential. Challenging leadership has previously been linked with employees expressing creativity, feeling empowered to make work-related changes, and seeking to learn and improve. 7 Giles Hirst, Helen Shipton, and Qin Zhou, “Context matters: Combined influence of participation and intellectual stimulation on the promotion focus–employee creative relationship,” Journal of Organizational Behavior, October 2012, Volume 33, Number 7, pp. 894–909, onlinelibrary.wiley.com; Le Cong Thuan, “Motivating follower creativity by offering intellectual stimulation,” International Journal of Organizational Analysis, December 2019, Volume 28, Number 4, pp. 817–29, emerald.com; Jie Li et al., “Not all transformational leadership behaviors are equal: The impact of followers’ identification with leader and modernity on taking charge,” Journal of Leadership and Organizational Studies, August 2017, Volume 24, Number 3, pp. 318–34, journals.sagepub.com; Susana Llorens-Gumbau, Marisa Salanova Soria, and Israel Sánchez-Cardona, “Leadership intellectual stimulation and team learning: The mediating role of team positive affect,” Universitas Psychologica, March 2018, Volume 17, Number 1, pp. 1–16, revistas.javeriana.edu.co. However, the survey findings show that the highest likelihood of psychological safety occurs when a team leader first creates a positive team climate, through frequent supportive and consultative actions, and then challenges their team; without a foundation of positive climate, challenging behaviors have no significant effect. And employees’ experiences look very different depending on how their leaders behave, according to Amy Edmondson, the Novartis Professor of Leadership and Management at Harvard Business School (interactive).

What’s more, the survey results show that a climate conducive to psychological safety starts at the very top of an organization. We sought to understand the effects of senior-leader behavior on employees’ sense of safety and found that senior leaders can help create a culture of inclusiveness that promotes positive leadership behaviors throughout an organization by role-modeling these behaviors themselves. Team leaders are more likely to exhibit supportive, consultative, and challenging leadership if senior leaders demonstrate inclusiveness—for example, by seeking out opinions that might differ from their own and by treating others with respect.

The importance of developing leaders at all levels

Our findings show that investing in leadership development across an organization—for all leadership positions—is an effective method for cultivating the combination of leadership behaviors that enhance psychological safety. Employees who report that their organizations invest substantially in leadership development are more likely to also report that their team leaders frequently demonstrate consultative, supportive, and challenging leadership behaviors. They also are 64 percent more likely to rate senior leaders as more inclusive (Exhibit 2). 8 We measured investing in leadership development by asking about agreement with the following statements: “my organization places a great deal of importance on developing its leaders,” and “my organization devotes significant resources to developing its leaders.” However, the results suggest that the effectiveness of these programs varies depending upon the skills they address.

Reorient the skills developed in leadership programs

Organizations often attempt to cover many topics in their leadership-development programs . But our findings suggest that focusing on a handful of specific skills and behaviors in these learning programs can improve the likelihood of positive leadership behaviors that foster psychological safety and, ultimately, of strong team performance. Some of the most commonly taught skills at respondents’ organizations—such as open-dialogue skills, which allow leaders to explore disagreements and talk through tension in a team—are among the ones most associated with positive leadership behaviors. However, several relatively untapped skill areas also yield beneficial results (Exhibit 3).

Two of the less-commonly addressed skills in formal programs are predictive of positive leadership. Training in sponsorship—that is, enabling others’ success ahead of one’s own—supports both consultative- and challenging-leadership behaviors, yet just 26 percent of respondents say their organizations include the skill in development programs. And development of situational humility, which 36 percent of respondents say their organizations address, teaches leaders how to develop a personal-growth mindset and curiosity. Addressing this skill is predictive of leaders displaying consultative behaviors.

Development at the top is equally important

According to the data, fostering psychological safety at scale begins with companies’ most senior leaders developing and embodying the leadership behaviors they want to see across the organization. Many of the same skills that promote positive team-leader behaviors can also be developed among senior leaders to promote inclusiveness. For example, open-dialogue skills and development of social relationships within teams are also important skill sets for senior leaders.

In addition, several skills are more important at the very top of the organization. Situational and cultural awareness, or understanding how beliefs can be developed based on selective observations and the norms in different cultures, are both linked with senior leaders’ inclusiveness.

Looking ahead

Given the quickening pace of change and disruption and the need for creative, adaptive responses from teams at every level, psychological safety is more important than ever. The organizations that develop the leadership skills and positive work environment that help create psychological safety can reap many benefits, from improved innovation, experimentation, and agility to better overall organizational health and performance. 9 We define organizational health as an organization’s ability to align on a clear vision, strategy, and culture; to execute with excellence; and to renew the organization’s focus over time by responding to market trends.

As clear as this call to action may be, “How do we develop psychological safety?” and, more specifically, “Where do we start?” remain the most common questions we are asked. These survey findings show that there is no time to waste in creating and investing in leadership development at scale to help enhance psychological safety. Organizations can start doing so in the following ways:

• Go beyond one-off training programs and deploy an at-scale system of leadership development. Human behaviors aren’t easily shifted overnight. Yet too often we see companies try to do so by using targeted training programs alone. Shifting leadership behaviors within a complex system at the individual, team, and enterprise levels begins with defining a clear strategy aligned to the organization’s overall aspiration and a comprehensive set of capabilities that are required to achieve it. It’s critical to develop a taxonomy of skills (having an open dialogue, for example) that not only supports the realization of the organization’s overall identity but also fosters learning and growth and applies directly to people’s day-to-day work. Practically speaking, while the delivery of learning may be sequenced as a series of trainings—and rapidly codified and scaled for all leaders across a cohort or function of the organization—those trainings will be even more effective when combined with other building blocks of a broader learning system, such as behavioral reinforcements. While learning experiences look much different now than before the COVID-19 pandemic , digital learning provides large companies with more opportunities to break down silos and create new connections across an organization through learning.
• Invest in leadership-development experiences that are emotional, sensory, and create aha moments. Learning experiences that are immersive and engaging are remembered more clearly and for a longer time. Yet a common pitfall of learning programs is an outsize focus on the content—even though it is usually not a lack of knowledge that holds leaders back from realizing their full potential. Therefore, it’s critical that learning programs prompt leaders to engage with and shift their underlying beliefs, assumptions, and emotions to bring about lasting mindset changes. This requires a learning environment that is both conducive to the often vulnerable process of learning and also expertly designed. Companies can begin with facilitated experiences that push learners toward personal introspection through targeted reflection questions and small, intimate breakout conversations. These environments can help leaders achieve increased self-awareness, spark the desire for further growth, and, with the help of reflection and feedback, drive collective growth and performance.
• Build mechanisms to make development a part of leaders’ day-to-day work. Formal learning and skill development serve as springboards in the context of real work; the most successful learning journeys account for the rich learning that happens in day-to-day work and interactions. The use of learning nudges (that is, daily, targeted reminders for individuals) can help learners overcome obstacles and move from retention to application of their knowledge. In parallel, the organization’s most senior leaders need to be the first adopters of putting real work at the core of their development, which requires senior leaders to role model—publicly—their own processes of learning. In this context, the concept of role models has evolved; rather than role models serving as examples of the finished product, they become examples of the work in progress, high on self-belief but low on perfect answers. These examples become strong signals for leaders across the organization that it is safe to be practicing, failing, and developing on the job.

The contributors to the development and analysis of this survey include Aaron De Smet , a senior partner in McKinsey’s New Jersey office; Kim Rubenstein, a research-science specialist in the New York office; Gunnar Schrah, a director of research science in the Denver office; Mike Vierow, an associate partner in the Brisbane office; and Amy Edmondson , the Novartis Professor of Leadership and Management at Harvard Business School.

This article was edited by Heather Hanselman, an associate editor in the Atlanta office.

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The best AI image generators to try right now

If you've ever searched Google high and low to find an image you needed to no avail, artificial intelligence (AI) may be able to help. 

With AI image generators, you can type in a prompt as detailed or vague as you'd like to fit an array of purposes and have the image you were thinking of instantly pop up on your screen. These tools can help with branding, social media content creation, and making invitations, flyers, business cards, and more.

Also: ChatGPT no longer requires a login, but you might want one anyway. Here's why

Even if you have no professional use for AI, don't worry -- the process is so fun that anyone can (and should) try it out.

OpenAI's DALL-E 2 made a huge splash because of its advanced capabilities as the first mainstream AI image generator. However, since its initial launch, there have been many developments. Other companies have released models that rival DALL-E 2, and OpenAI even released a more advanced model known as DALL-E 3 , discontinuing its predecessor. 

To help you discover which models are the best for different tasks, I put the image generators to the test by giving each tool the same prompt: "Two Yorkies sitting on a beach that is covered in snow". I also included screenshots to help you decide which is best. 

Also: DALL-E adds new ways to edit and create AI-generated images. Learn how to use it

While I found the best overall AI image generator is Image Creator from Microsoft Designer , due to its free-of-charge, high-quality results, other AI image generators perform better for specific needs. For the full roundup of the best AI image generators, keep reading. 

The best AI image generators of 2024

Image creator from microsoft designer (formerly bing image creator), best ai image generator overall.

• Powered by DALL-E 3
• Convenient to access
• Need a Microsoft account
• In preview stage

Image Creator from Microsoft Designer is powered by DALL-E 3, OpenAI's most advanced image-generating model. As a result, it produces the same quality results as DALL-E while remaining free to use as opposed to the $20 per month fee to use DALL-E. 

All you need to do to access the image generator is visit the Image Creator website and sign in with a Microsoft account. 

Another major perk about this AI generator is that you can also access it in the same place where you can access Microsoft's AI chatbot, Copilot (formerly Bing Chat) . 

This capability means that in addition to visiting Image Creator on its standalone site, you can ask it to generate images for you in Copilot. To render an image, all you have to do is conversationally ask Copilot to draw you any image you'd like. 

Also:   How to use Image Creator from Microsoft Designer (formerly Bing Image Creator)

This feature is so convenient because you can satisfy all your image-generating and AI-chatting needs in the same place for free. This combination facilitates tasks that could benefit from image and text generation, such as party planning, as you can ask the chatbot to generate themes for your party and then ask it to create images that follow the theme.

Image Creator from Microsoft Designer f eatures:  Powered by:  DALL-E 3 |  Access via:  Copilot, browser, mobile |  Output:  4 images per prompt |  P rice:  Free 

DALL-E 3 by OpenAI

Best ai image generator if you want to experience the inspiration.

• Not copyrighted
• Accurate depictions
• Confusing credits

OpenAI, the AI research company behind ChatGPT, launched DALL-E 2 in November 2022. The tool quickly became the most popular AI image generator on the market. However, after launching its most advanced image generator, DALL-E 3, OpenAI discontinued DALL-E 2. 

DALL-E 3 is even more capable than the original model, but this ability comes at a cost. To access DALL-E 3 you must be a ChatGPT Plus subscriber, and the membership costs $20 per month per user. You can access DALL-E 3 via ChatGPT or the ChatGPT app.

Using DALL-E 3 is very intuitive. Type in whatever prompt you'd like, specifying as much detail as necessary to bring your vision to life, and then DALL-E 3 will generate four images from your prompt. As you can see in the image at the top of the article, the renditions are high quality and very realistic.

OpenAI even recently added new ways to edit an image generated by the chatbot, including easy conversational text prompts and the ability to click on parts of the image you want to edit. 

Like with Copilot, you can chat and render your images on the same platform, making it convenient to work on projects that depend on image and text generation. If you don't want to shell out the money,  Image Creator by Designer  is a great alternative since it's free, uses DALL-E 3, and can be accessed via Copilot.

DALL-E 3 features: Powered by:  DALL-E 3 by OpenAI |  Access via:  ChatGPT website and app |  Output:  4 images per credit |  Price:  ChatGPT Plus subscription, $20 per month

ImageFX by Google

The best ai image generator for beginners.

• Easy-to-use
• High-quality results
• Expressive chips
• Need a Google account
• Strict guardrails can be limiting

Google's ImageFX was a dark horse, entering the AI image generator space much later than its competition, over a year after DALL-E 2 launched. However, the generator's performance seems to have been worth the wait. The image generator can produce high-quality, realistic outputs, even objects that are difficult to render, such as hands. 

Also: I just tried Google's ImageFX AI image generator, and I'm shocked at how good it is

The tool boasts a unique feature, expressive chips, that make it easier to refine your prompts or generate new ones via dropdowns, which highlight parts of your prompt and suggest different word changes to modify your output.

ImageFX also includes suggestions for the style you'd like your image rendered in, such as photorealistic, 35mm film, minimal, sketch, handmade, and more. This combination of features makes ImageFX the perfect for beginners who want to experiment. 

ImageFX from Google: Powered by:  Imagen 2  | Access via:  Website |  Output:  4 images |  Price:  free 

DreamStudio by Stability AI

Best ai image generator for customization.

• Accepts specific instruction
• Open source
• More entries for customization
• Paid credits
• Need to create an account

Stability AI created the massively popular, open-sourced, text-to-image generator, Stable Diffusion. Users can download the tool and use it at no cost. However, using this tool typically requires technical skill. 

Also :  How to use Stable Diffusion AI to create amazing images

To make the technology readily accessible to everyone (regardless of skill level), Stability AI created DreamStudio, which incorporates Stable Diffusion in a UI that is easy to understand and use. 

One of the standouts of the platform is that it includes many different entries for customization, including a "negative prompt" where you can delineate the specifics of what you'd like to avoid in the final image. You can also easily change the image ratio -- that's a key feature, as most AI image generators automatically deliver 1:1. 

DreamStudio features: Powered by:  SDXL 1.0 by Stability AI  | Access via:  Website |  Output:  1 image per 2 credits |  Price:  $1 per 100 credits |  Credits:  25 free credits when you open an account; buy purchase once you run out

Dream by WOMBO

Best ai image generator for your phone.

• Remix your own images
• Multiple templates
• One image per prompt
• Subscription cost for full access

This app took the first-place spot for the best overall app in Google Play's 2022 awards , and it has five stars on Apple's App Store with 141.6K ratings. With the app, you can create art and images with the simple input of a quick prompt. 

An added plus is this AI image generator allows you to pick different design styles such as realistic, expressionist, comic, abstract, fanatical, ink, and more. 

Also :  How to use Dream by WOMBO to generate artwork in any style

In addition to the app, the tool has a free desktop mobile version that is simple to use. If you want to take your use of the app to the next level, you can pay $90 per year or $10 per month.

Dream by WOMBO f eatures: Powered by:  WOMBO AI's machine-learning algorithm |  Access via:  Mobile and desktop versions |  Output:  1 image with a free version, 4 with a paid plan |  Price:  Free limited access

Best no-frills AI image generator

• Unlimited access
• Simple to use
• Longer wait
• Inconsistent images

Despite originally being named DALL-E mini, this AI image generator is NOT affiliated with OpenAI or DALL-E 2. Rather, it is an open-source alternative. However, the name DALL-E 2 mini is somewhat fitting as the tool does everything DALL-E 2 does, just with less precise renditions. 

Also :  How to use Craiyon AI (formerly known as DALL-E mini)

Unlike DALL-E 2, the outputs from Craiyon lack quality and take longer to render (approximately a minute). However, because you have unlimited prompts, you can continue to tweak the prompt until you get your exact vision. The site is also simple to use, making it perfect for someone wanting to experiment with AI image generators. It also generates six images, more than any other chatbot listed. 

Craiyon f eatures: Powered by:  Their model |  Access via :  Craiyon website  |  Output:  6 images per prompt |  Price:  Free, unlimited prompts 

Best AI image generator for highest quality photos

• Very high-quality outputs
• Discord community
• Monthly cost
• Confusing to set up

I often play around with AI image generators because they make it fun and easy to create digital artwork. Despite all my experiences with different AI generators, nothing could have prepared me for Midjourney -- in the best way. 

The output of the image was so crystal clear that I had a hard time believing it wasn't an actual picture that someone took of my prompt. This software is so good that it has produced award-winning art .

However, I think Midjourney isn't user-friendly and it confuses me. If you also need extra direction, check out our step-by-step how-to here: How to use Midjourney to generate amazing images and art .

Another problem with the tool is that you may not access it for free. When I tried to render images, I got this error message: "Due to extreme demand, we can't provide a free trial right now. Please subscribe to create images with Midjourney."

To show you the quality of renditions, I've included a close-up below from a previous time I tested the generator. The prompt was: "A baby Yorkie sitting on a comfy couch in front of the NYC skyline." 

Midjourney f eatures: Powered by:  Midjourney; utilizes Discord |  Access via:  Discord |  Output:  4 images per prompt |  Price:  Starts at $10/month

Adobe Firefly

Best ai image generator if you have a reference photo.

• Structure and Style Reference
• Commercial-safe
• Longer lag than other generators
• More specific prompts required

Adobe has been a leader in developing creative tools for creative and working professionals for decades. As a result, it's no surprise that its image generator is impressive. Accessing the generator is easy. Just visit the website and type the prompt of the image you'd like generated. 

Also: This new AI tool from Adobe makes generating the images you need even simpler

As you can see above, the images rendered of the Yorkies are high-quality, realistic, and detailed. Additionally, the biggest standout features of this chatbot are its Structure Reference and Style Reference features. 

Structure Reference lets users input an image they want the AI model to use as a template. The model then uses this structure to create a new image with the same layout and composition. Style Reference uses an image as a reference to generate a new image in the same style. 

These features are useful if you have an image you'd like the new, generated image to resemble, for example, a quick sketch you drew or even a business logo or style you'd like to keep consistent. 

Another perk is that Adobe Firefly was trained on Adobe Stock images, openly licensed content, and public domain content, making all the images generated safe for commercial use and addressing the ethics issue of image generators. 

Adobe Firefly f eatures:  Powered by:  Firefly Image 2 |  Access via:  Website |  Output:  4 images per prompt |  P rice:  Free 

Generative AI by Getty Images

Best ai image generator for businesses.

• Commercially safe
• Contributor compensation program
• Personalized stock photos
• Not clear about pricing
• Not individual-friendly

One of the biggest issues with AI image generators is that they typically train their generators on content from the entirety of the internet, which means the generators use aspects of creators' art without compensation. This approach also puts businesses that use generators at risk of copyright infringement. 

Generative AI by Getty Images tackles that issue by generating images with content solely from Getty Images' vast creative library with full indemnification for commercial use. The generated images will have Getty Images' standard royalty-free license, assuring customers that their content is fair to use without fearing legal repercussions.

Another pro is that contributors whose content was used to train the models will be compensated for their inclusion in the training set. This is a great solution for businesses that want stock photos that match their creative vision but do not want to deal with copyright-related issues. 

ZDNET's Tiernan Ray went hands-on with the AI image generator. Although the tool did not generate the most vivid images, especially compared to DALL-E, it did create accurate, reliable, and useable stock images. 

Generative AI by Getty Images f eatures:  Powered by:  NVIDIA Picasso |  Access via:  Website |  Output:  4 images per prompt |  P rice:  Paid (price undisclosed, have to contact the team)

What is the best AI image generator?

Image Creator from Microsoft Designer is the best overall AI image generator. Like DALL-E 3, Image Creator from Microsoft Designer combines accuracy, speed, and cost-effectiveness, and can generate high-quality images in seconds. However, unlike DALL-E 3, this Microsoft version is entirely free.

Whether you want to generate images of animals, objects, or even abstract concepts, Image Creator from Microsoft Designer can produce accurate depictions that meet your expectations. It is highly efficient, user-friendly, and cost-effective.

Note: Prices and features are subject to change.

Which is the right AI image generator for you?

Although I crowned Image Creator from Microsoft Designer the best AI image generator overall, other AI image generators perform better for specific needs. For example, suppose you are a professional using AI image generation for your business. In that case, you may need a tool like Generative AI by Getty Images which renders images safe for commercial use. 

On the other hand, if you want to play with AI art generating for entertainment purposes, Craiyon might be the best option because it's free, unlimited, and easy to use. 

How did I choose these AI image generators?

To find the best AI image generators, I tested each generator listed and compared their performance. The factors that went into testing performance included UI/UX, image results, cost, speed, and availability. Each AI image generator had different strengths and weaknesses, making each one the ideal fit for individuals as listed next to my picks. 

What is an AI image generator?

An AI image generator is software that uses AI to create images from user text inputs, usually within seconds. The images vary in style depending on the capabilities of the software, but can typically render an image in any style you want, including 3D, 2D, cinematic, modern, Renaissance, and more. 

How do AI image generators work?

Like any other AI model, AI image generators work on learned data they are trained with. Typically, these models are trained on billions of images, which they analyze for characteristics. These insights are then used by the models to create new images.

Are there ethical implications with AI image generators?

AI image generators are trained on billions of images found throughout the internet. These images are often artworks that belong to specific artists, which are then reimagined and repurposed by AI to generate your image. Although the output is not the same image, the new image has elements of the artist's original work not credited to them. 

Are there DALL-E 3 alternatives worth considering?

Contrary to what you might think, there are many AI image generators other than DALL-E 3. Some tools produce even better results than OpenAI's software. If you want to try something different, check out one of our alternatives above or the three additional options below. 

Nightcafe is a multi-purpose AI image generator. The tool is worth trying because it allows users to create unique and original artwork using different inputs and styles, including abstract, impressionism, expressionism, and more.

Canva is a versatile and powerful AI image generator that offers a wide range of options within its design platform. It allows users to create professional-looking designs for different marketing channels, including social media posts, ads, flyers, brochures, and more. 

Artificial Intelligence

The best ai chatbots: chatgpt isn't the only one worth trying, google and mit launch a free generative ai course for teachers, dall-e adds new ways to edit and create ai-generated images. learn how to use it.