research report on technology

Digital transformation: a review, synthesis and opportunities for future research

• Open access
• Published: 18 April 2020
• Volume 71 , pages 233–341, ( 2021 )

Cite this article

You have full access to this open access article

• Swen Nadkarni 1 &
• Reinhard Prügl 1  

150k Accesses

287 Citations

11 Altmetric

Explore all metrics

In the last years, scholarly attention was on a steady rise leading to a significant increase in the number of papers addressing different technological and organizational aspects of digital transformation. In this paper, we consolidate existing findings which mainly stem from the literature of information systems, map the territory by sharing important macro- and micro-level observations, and propose future research opportunities for this pervasive field. The paper systematically reviews 58 peer-reviewed studies published between 2001 and 2019, dealing with different aspects of digital transformation. Emerging from our review, we develop inductive thematic maps which identify technology and actor as the two aggregate dimensions of digital transformation. For each dimension, we derive further units of analysis (nine core themes in total) which help to disentangle the particularities of digital transformation processes and thereby emphasize the most influential and unique antecedents and consequences. In a second step, in order to assist in breaking down disciplinary silos and strengthen the management perspective, we supplement the resulting state-of-the-art of digital transformation by integrating cross-disciplinary contributions from reviewing 28 papers on technological disruption and 32 papers on corporate entrepreneurship. The review reveals that certain aspects, such as the pace of transformation, the culture and work environment, or the middle management perspective are significantly underdeveloped.

Similar content being viewed by others

Digital Transformation: A Literature Review and Guidelines for Future Research

An Introduction to Digital Transformation

Digital Transformation Framework: A Bibliometric Approach

Avoid common mistakes on your manuscript.

1 Introduction

Digital transformation, defined as transformation ‘concerned with the changes digital technologies can bring about in a company’s business model, … products or organizational structures’ (Hess et al. 2016 , p. 124), is perhaps the most pervasive managerial challenge for incumbent firms of the last and coming decades. However, digital possibilities need to come together with skilled employees and executives in order to reveal its transformative power. Thus, digital transformation needs both technology and people. In the last years, scholarly attention, particularly in the information systems (IS) literature, was on a steady rise leading to a significant increase in the number of papers addressing different technological and organizational aspects of digital transformation. In the light of this development, we are convinced it is the right time to map the territory and reflect on the current state of knowledge. Therefore, in this paper we aim at providing a descriptive, thematic analysis of the field by critically assessing where, how and by whom research on digital transformation is conducted. Based on this analysis, we identify future research opportunities.

We approach this objective in two steps. First, we adopt an inductive approach and conduct a systematic literature review (following Tranfield et al. 2003 ; Webster and Watson 2002 ) of 58 peer-reviewed papers dealing with digital transformation. By applying elements of grounded theory and content analysis (Corley and Gioia 2004 ; Gioia et al. 1994 ) we identify important core themes in the literature that are particularly pronounced and/or unique in transformations enabled by digital technologies. In a second step, in order to assist in breaking down disciplinary silos (Jones and Gatrell 2014 ) and avoiding the building of an ivory tower (Bartunek et al. 2006 ; Fuetsch and Suess-Reyes 2017 ), we supplement the pre-dominantly IS-based digital transformation literature with a broader management perspective. Accordingly, we integrate cross-disciplinary contributions from reviewing 28 papers on technological disruption and 32 papers on corporate entrepreneurship.

We find these research fields particularly suitable for informing digital transformation research for two reasons. First, by reviewing the literature on technological disruption we hope to derive implications regarding technology adoption and integration. Burdened with the legacy of old technology, bureaucratic structures and core rigidities (Leonard-Barton 1992 ), incumbents may face major challenges in this respect during their digital transformation journey. Second, we expect corporate entrepreneurship to add a more holistic perspective on firm-internal aspects during the process of transformation, such as management influence or the impact of knowledge and organizational learning.

Our findings and related contributions are threefold: First, based on a systematic and structured analysis we develop digital transformation maps which inductively categorize and describe the existing body of research. These thematic maps identify technology and actor as the two aggregate dimensions of digital transformation. Within these dimensions, we reveal nine core themes which help to disentangle the particularities of digital transformation processes and thereby emphasize the most influential and unique antecedents and consequences of this specific type of transformation. Thus, it becomes possible to identify the predominant contextual factors for which research would create the strongest leverage for a better understanding of the challenges inherent in digital transformation. Second, we contribute to the advancement of this field by elaborating opportunities for future research on digital transformation which integrate the three perspectives mentioned above. In particular, informed by corporate entrepreneurship, we find that the important middle management perspective on digital transformation has thus far been largely neglected by researchers. Also, emerging from our review we call for more studies on the various options for integrating digital transformation within organizational architectures and existing processes. Third, in reviewing the adjacent literature on technological disruption and corporate entrepreneurship, we strengthen the valuable management perspective within the primarily IS-based discussion on digital transformation. This way we avoid the reinvention of the wheel while at the same time enable the identification of cross-disciplinary research opportunities. We hope to stimulate discussion between these different but strongly related disciplines and enable mutual learning and a fruitful exchange of ideas.

2 Conceptual foundations

Technology as a major determinant of organizational form and structure has been well acknowledged by academics for a long time (Thompson and Bates 1957 ; Woodward 1965 ; Scott 1992 ). Following a significant decline of interest in this relationship until the mid-1990s (Zammuto et al. 2007 ), innovations in information technologies (IT) and the rise of pre-internet technologies have revitalized its relevance in the context of organizational transformation. Thus, the literature on IT-enabled organizational transformation, a concept which originates from the field of information systems (IS) that has caught considerable academic attention starting back in the early 1990s (Ranganathan et al. 2004 ; Besson and Rowe 2012 ), may be seen as one of the scholarly roots of digital transformation research. In his seminal book, Morton ( 1991 ) argued that companies must experience fundamental transformations for effective IT implementation. In the course of the years a shift of attention occurred from technological to managerial and organizational issues (Markus and Benjamin 1997 ; Doherty and King 2005 ). Non-technological aspects such as leadership, culture, and employee training were found to be equally important for successful IT-enabled transformation (Markus 2004 ). This is supported by Orlikowski ( 1996 ) who found empirical evidence from a 2-year case study that organizational transformation was in fact enabled by technology, but not caused by it.

Today, information technologies have become ‘one of the threads from which the fabric of organization is now woven’ (Zammuto et al. 2007 , p. 750). Digital technologies are considered a major asset for leveraging organizational transformation, given their disruptive nature and cross-organizational and systemic effects (Besson and Rowe 2012 ). In order to achieve successful digital transformation, changes must occur at various levels within the organization, including an adaptation of the core business (Karimi and Walter 2015 ), the exchange of resources and capabilities (Cha et al. 2015 ; Yeow et al. 2018 ), the reconfiguration of processes and structures (Resca et al. 2013 ), adjustments in leadership (Hansen and Sia 2015 ; Singh and Hess 2017 ), and the implementation of a vivid digital culture (Llopis et al. 2004 ). Therefore, the scope of our review revolves around digital transformation at the organizational level only (in contrast to implications at the individual level).

In this study, we conceptualize digital transformation at the intercept of the adoption of disruptive digital technologies on the one side and actor-guided organizational transformation of capabilities, structures, processes and business model components on the other side. In other words, and in line with Hess et al. ( 2016 ), we define digital transformation as organizational change triggered by digital technologies. Hence, we argue that two perspectives of digital transformation within organizations must be captured: a technology-centric and an actor-centric perspective. To exploit the technology-centric perspective we include the literature on technological disruption (e.g. Tushman and Anderson 1986 ; Anderson and Tushman 1990 ) and merge it with research on digital transformation. For the actor-centric perspective, we derive essential implications from the field of corporate entrepreneurship (Guth and Ginsberg 1990 ), which we believe may add valuable insights regarding actor-driven innovation and renewal processes within firms. In the following, we offer a brief introduction to both concepts and their relationship with digital transformation.

Rice et al. ( 1998 ) define disruptive innovations as ‘game changers’ which have the potential ‘(1) for a 5–10 times improvement in performance compared to existing products; (2) to create the basis for a 30–50% reduction in costs; or (3) to have new-to-the world performance features’ (p. 52). Similarly, Utterback ( 1994 ) emphasizes this disruptiveness at the firm and industry level and provides a similar ‘game changer’ definition in terms of ‘change that sweeps away much of a firm’s existing investment in technical skills and knowledge, designs, production technique, plant and equipment’ (p. 200). Tushman and Anderson ( 1986 ) distinguish between product and process disruptiveness. Product disruptiveness encompasses new product classes, product substitutions, or fundamental product improvements. Process disruptiveness may take the form of process substitutions or process innovations which radically improve industry-specific dimensions of merit. Christensen and Raynor ( 2003 ) introduce a further form of disruptive innovations, namely disruptive business model innovations, which represent the implementation of fundamentally different business models in an existing business.

We argue that digital technologies may reflect in all of these definitions of disruptive innovation. They may represent new-to-the-world product innovations, dislocate existing processes, and open up entirely new business models. As resumed in a recent study by Li et al. ( 2017 ), e-commerce for instance is defined as a disruptive technology (Johnson 2010 ) which involves significant changes to an organization’s culture, business processes, capabilities, and markets (Zeng et al. 2008 ; Cui and Pan 2015 ).

Corporate entrepreneurship (CE) on the other side is a multi-dimensional concept at the intersection of entrepreneurship and strategic management in existing organizations (Zahra 1996 ; Hitt et al. 2001 ; Dess et al. 2003 ). We adopt the conceptualization proposed by Guth and Ginsberg ( 1990 , p. 5), who argue that corporate entrepreneurship deals with two phenomena ‘(1) the birth of new businesses within existing organizations, i.e. internal innovation or venturing, and (2) the transformation of organizations through renewal of the key ideas on which they are built, i.e. strategic renewal.’ Particularly the aspect of strategic renewal in corporate entrepreneurship, also labelled as strategic change, revival, transformation (Schendel 1990 ), reorganization, redefinition (Zahra 1993 ), or organizational renewal (Stopford and Baden-Fuller 1994 ), provides a promising interface to digital transformation. As stated by Covin and Miles ( 1999 , p. 50), corporate entrepreneurship ‘revitalizes, reinvigorates and reinvents’—processes also required for digital transformation. Various authors have stated that corporate entrepreneurship is a vehicle to improve competitive positioning and transform corporations (Schollhammer 1982 ; Miller 1983 ; Khandwalla 1987 ; Guth and Ginsberg 1990 ; Naman and Slevin 1993 ; Lumpkin and Dess 1996 ). Considering the disruptive nature of many current digital technologies, we believe that organizations need to fundamentally renew and redefine the key ideas of their business in order to fully exploit the potential of digitization and eventually achieve successful transformation. The literature places particular attention on the role of middle managers as the locus of corporate entrepreneurship (Burgelman 1983 , Floyd and Wooldridge 1999 ). Concluding, we will review the research on corporate entrepreneurship and identify those contributions which we believe may offer valuable knowledge regarding actor-driven internal renewal and change processes in the light of digital transformation.

Our review of the literature on digital transformation, technological disruption and corporate entrepreneurship is conducted in a two-step approach. First, we review, analyze and synthesize existing articles on digital transformation. Then, in a second step we supplement these findings be simultaneously reviewing the literature stream on technological disruption and corporate entrepreneurship. We believe a separate analysis and contrasting of the research streams is appropriate for two reasons: first, it provides the reader with more clarity on the status quo of digital transformation knowledge and prevents the confusion of concepts emerging from different literature fields. Second, white spots and opportunities for future research regarding digital transformation become much more visible in such a structured approach.

3 Research methodology

A systematic review is a type of literature review that applies an explicit algorithm and a multi-stage review strategy in order to collect and critically appraise a body of research studies (Mulrow 1994 ; Pittaway et al. 2004 ; Crossan and Apaydin 2010 ). This transparent and reproducible process is ideally suited for analyzing and structuring the vast and heterogeneous literature on digital transformation. In conducting our review, we followed the guidelines of Tranfield et al. ( 2003 ) and the recommendations of Denyer and Neely ( 2004 , p. 133) Footnote 1 as well as Fisch and Block ( 2018 ) in order to ensure a high quality of the review.

The nature of our review is both scoping and descriptive (Rowe 2014 ; Paré et al. 2015 ) as we aim to provide an initial indication of the potential size and nature of the available literature as well as to summarize and map existing findings from digital transformation research. By developing opportunities for future research, our review further contributes to the advancement of this field and stimulates theory development.

For the purpose of data collection, we exclusively limit our focus on peer-reviewed academic journals as recommended by McWilliams et al. ( 2005 ). Thus, we opted to exclude work in progress, conference papers, dissertations, or books. First, based on discussion among the authors and the reading of a few highly-cited papers, we designed our search criteria using combinations of keywords containing ‘ digital* AND transform*’ , ‘ digital* AND disrupt*’ , ‘ digitalization’ , and ‘ digitization ’. Then, we manually searched each issue of each volume of the leading journals in the management Footnote 2 and IS field (AIS Basket of eight). Footnote 3 In addition, we run our search query against five different electronic databases: Business Source Premier (EBSCO) , Scopus , Science Direct , Social Sciences Citation Index (SSCI) , and Google Scholar . We used all years available and only included articles referring to business, management, or economics in order to exclude irrelevant publications. We abstained from including digital innovation in our search (the only exception in our sample is a recent literature review by Kohli and Melville ( 2019 ), in order to capture consolidated insights). Although we realize that it is a hot topic in IS research at the moment (e.g. Fichman et al. 2014 ; Nambisan et al. 2017 ; Yoo et al. 2010 , 2012 ), we aim to concentrate our focus on papers dealing with digital transformation on a broader level (firm and industry), rather than with transitions within innovation management.

Our first search query was conducted mid 2017 and yielded an initial sample of 1722 publications. This very large sample was mainly due to the broad ambiguity of the terms ‘digital’ and ‘disrupt’. Given these broad search parameters, we anticipated that only a small fraction of this very large sample would prove to be of substantive relevance to us. To select these relevant articles for our final sample, we performed a predefined and structured multi-step selection process (similar to the approach of Siebels and Knyphausen-Aufseß 2012 ; Vom Brocke et al. 2015 ) and defined specific criteria for inclusion (Templier and Paré 2015 ). The filters during our selection process included (1) scanning the titles, (2) reading abstracts, (3) removing duplicates, (4) full reading and in-depth analysis of the remaining papers, and finally (5) cross-referencing and backward searching by looking through the bibliographies of the most important articles to find additional relevant work. The initial pool was split in half between two panelists who separately performed the scanning of titles, analysis of abstracts and removal of duplicates. After these early steps, the sample could be narrowed down to 155 articles. As we arrived at step 4 “full reading and in-depth analysis of the remaining papers”, both panelists read and independently classified each of the remaining 155 studies. During this process, papers qualified for the final sample if they satisfied three requirements: (1) articles were required to have their primary focus and contribution within digital transformation research or digitally-induced organizational transformation (e.g. a vast number of papers inadequately captured the topic of digital transformation as they primarily focused on business model innovation), (2) articles needed to be based on a sound theoretical foundation and therefore not primarily practitioner oriented (such as articles that offer popular recommendations to business leaders on how to survive digital transformation), (3) papers that were not addressing digital transformation at an organizational level (e.g. the rise of home-based online businesses by entrepreneurs) were dismissed. Whenever disagreements emerged regarding the inclusion or classification of an article, we engaged in discussion and tried to resolve the issue together to make our selection rules more reliable. We updated the review in the autumn of 2018 for any articles that had appeared between then. Following this approach, 58 studies passed all five selection steps and were included in our final sample.

Within this sample, conceptual articles (27) and case studies (20) are dominant. Roughly 60% of the articles stem from the IS literature, while 40% cover a broader management perspective of digital transformation. While the reviewed papers span a time frame from 2001 to 2018, approximately eighty-percent of articles were published within the past 5 years, indicating the relative novelty of digital transformation as a research discipline. The distribution of our sample according to journals is provided in Table  4 of “ Appendix ”.

Upon the recommendation of Webster and Watson ( 2002 ), our categorization and analysis of the literature was concept-centric. First, to facilitate analysis and build a basis for our initial coding, each selected paper was reviewed to determine the following database information.

(1) Article title, (2) outlet, (3) research methodology, (4) sample, (5) region, and (6) key findings (see full database in Table  5 of “ Appendix ”). Next, we started coding our sample, adopting elements of the approach introduced by Corley and Gioia ( 2004 ). We began by identifying initial concepts in the data and grouping them into provisional categories and first order concepts (open coding). Then, we engaged in axial coding (Locke 2001 ) and searched for relationships and common patterns between and among these provisional categories, which allowed us to assemble them into second order themes. Finally, we assigned these second order themes to aggregate dimensions, representing the highest level of abstraction in our coding. In sum, reviewing and analyzing the extant literature, 194 coded insights were generated within the field of digital transformation: 61 first order concepts, nine second order themes, and two aggregate dimensions. The nine second order themes represent core themes across the papers, which finally constitute two aggregate dimensions: technology and actor. In conclusion, we define digital transformation as actor-driven organizational transformation triggered by the adoption of technology-driven digital disruptions. The result of the coding process is a high-level inductive map of the core themes in digital transformation research (Fig.  1 ).

Digital transformation high-level thematic map emerging from the analysis of the literature

The reviewed studies from our sample provide a rich body of knowledge regarding the specific contextual factors of digital transformation. This may be beneficial to both researchers and practitioners enabling a more comprehensive understanding of the peculiarities of digital transformation (in comparison to previous technology-driven transformations).

4.1 Macro-level findings

On a macro level, the central observation emerging from our review is that both technology- and actor-centric aspects take center stage within this debate. This is also reflected in various definitions of digital transformation provided in the sample. For example, Lanzolla and Anderson ( 2008 ) represent the technology-centric side and emphasize the diffusion of digital technologies as an enabler for transformation. Such digital technologies may include big data, mobile, cloud computing or search-based applications (White 2012 ). Similarly, Hess et al. ( 2016 ) note that digital transformation is ‘concerned with the changes digital technologies can bring about in a company’s business model, which result in changed products or organizational structures or in the automation of processes’ (p. 124). However, Hess et al. ( 2016 ) also highlight the role of actors (e.g. managers) in promoting transformation processes, while facing the challenge of simultaneously balancing the exploration and exploitation of resources. Leaders must have trust in the value and benefits of new IT technologies and support their implementation (Chatterjee et al. 2002 ).

In total, we find an almost even distribution of papers studying the two dimensions of technology and actor: 33% are technology-centric, 34% are actor-centric, and 33% of papers cover both technology and actor. However, within these two dimensions we observe a rather uneven distribution of articles by second order themes. On the technology-centric side, we find that understanding the implications of digital technologies on the consumer interface and market environment are highly active research streams. In comparison, understanding the pace of change in times of digital transformation and its direct impact on incumbents is so far comparably understudied. On the actor-centric side, our review reveals a very dominant focus on leadership and capabilities in a digital context, while in contrast company culture and work environment thus far received less recognition. We also find that the status-quo of digital transformation literature is rather diverse, in a sense that papers discuss topics across various categories of our thematic map and are therefore not restricted nor focused to a specific unit of analysis. The vast majority of articles is related to adjacent topics of digital transformation underpinning its nature as a diverse and broad field of research while again indicating its emerging nature.

In addition, we observe some degree of diversity in the theoretical foundations drawn upon. Different theories are applied by several authors to capture the context of digital transformation, e.g. alignment view, configuration theory, resource-based view, dynamic capabilities, organizational learning theory, network view or business process reengineering. It would be interesting to use other theoretical angles, for example from the literature on corporate entrepreneurship and technological disruption, in order to increase theoretical diversity. Such an exchange with different fields of research would broaden the scope of the field and help bridging an ivory divide . Finally, from a methodological perspective, we observe that actor-centric papers primarily use case studies while technology-centric studies at this point are pre-eminently conceptual. In general, the literature is scarce regarding quantitative empirical evidence. We see this as a strong indicator for the early stage of digital transformation research.

4.2 Micro-level findings: the technology-centric side of the equation

In the following, we present and discuss the most important findings of the second order themes within the technology-centric dimension. In Fig.  2 we provide a thematic map for this dimension and in Table  1 a brief summary including illustrative quotes.

Thematic map for technology-driven themes in digital transformation literature

4.2.1 Pace of change and time to market

In times of digital transformation, the speed of technological change is disproportionally accelerating with new digital capabilities being rolled out every year. The technological capability of applications such as the Internet of Things (IoT), big data, cloud computing, and mobile technologies significantly increases the overall pace of change. For example, entire industries, like the newspaper business, have been transformed and digitized within a very short period of time (Karimi and Walter 2015 ). Further, the cloud and online platforms have revolutionized the process and pace of turning an innovative idea into a business (Vey et al. 2017 ). Today, innovative ideas can be realized within days and companies set-up literally ‘overnight’. In this sense, in the digital world striving for a ‘first-mover advantage’ due to a ‘winner takes it all’ environment has become more important for incumbent firms (Grover and Kohli 2013 ) as they have much less time to respond to such threats and should not give away first-mover advantages too easily.

Moreover, pure digital companies like Facebook, Google or Amazon have substantially raised the overall time to market and speed of product launches (Bharadwaj et al. 2013 ). With continuous improvements in hardware, software and connectivity, these companies set the pace for a tightly timed series of product launches. Thus, firms in the hybrid world (digital and physical) are being put under enormous pressure to also accelerate their product introductions. In a digitally transformed market, the control of speed of product development and launches is increasingly transferred to an ‘ecosystem of innovation’ in the sense of a network of actors with complementary products and services (Bharadwaj et al. 2013 ).

4.2.2 Technology capability and integration

The technological capability and power of digital transformation applications, such as for example the Internet of Things (IoT), big data, cloud computing, and mobile technologies, is in terms of computing power, data storage and information distribution in many cases significantly higher than in previous technology-driven transformations. Earlier business transformations were mostly concerned about introducing internal management information systems such as enterprise resource planning (ERP) or customer relationship management (CRM). These transformations were usually limited to improvements to business processes within firm boundaries (see Ash and Burn 2003 ; Kauffman and Walden 2001 in: Li et al. 2017 ). But today, cross-boundary digital technologies such as IoT devices (Ng and Wakenshaw 2017 ), 3D printing (Rayna and Striukova 2016 ), and big data analytics (Dremel et al. 2017 ), drive transformations that go far beyond internal process optimizations as they potentially induce drastic changes to business models (Rayna and Striukova 2016 ), organizational strategy (Bharadwaj et al. 2013 ), corporate culture (El Sawy et al. 2016 ; Dremel et al. 2017 ; Sia et al. 2016 ), and entire industry structures (Kohli and Johnson 2011 ).

Further, the review confirms that the role and significance of data itself is changing profoundly and that personal data has become one of the most powerful assets in the digital era (Ng and Wakenshaw 2017 ). In fact, we believe the impact of the massive increase in quantity and quality of data generated every day (Bharadwaj et al. 2013 ) and the game changing power of big data analytics (Günther et al. 2017 ) are yet to be fully experienced and understood by society, economy and academics.

With regards to the process of dematerialization of tangible products and objects (e.g. CDs, books, machinery etc.), triggered by the transformative capabilities of digital technologies, the most notable insight is that intriguingly, in many cases the digital substitutes, for example e-books, offer superior performance and higher customer benefits than their physical counterparts (Loebbecke and Picot 2015 ). This, for example, is in contrast to the assumptions provided by Christensen ( 1997 ) more than 20 years ago, arguing that new disruptive technologies usually provide different values from mainstream technologies and are often initially inferior to mainstream technologies, therefore only serving niche markets in the beginning.

Finally, regarding technology integration, the current state of research emphasizes the importance of flexible IT (Cha et al. 2015 ), new enterprise platforms (El Sawy et al. 2016 ), and a strong and scalable operational backbone (Sebastian et al. 2017 ) as part of an agile digital infrastructure. The old paradigms of technology integration are not effective any more. However, in a second step we need to reach a more comprehensive understanding of ‘how’ and ‘where’ the integration of technology and transformation activities should be embedded within the organizational architectures of incumbent firms.

4.2.3 Consumer and other stakeholder interface

With regards to the customer interface, which is currently receiving the highest levels of attention by scholars, we conclude that there is some solid research particularly on changes in consumer behavior (Berman 2012 ; El Sawy et al. 2016 ; Ives et al. 2016 ; Lanzolla and Anderson 2008 ), consumer preferences (Vey et al. 2017 ) and consumer knowledge (Berman 2012 ; Granados and Gupta 2013 ). Firstly, our review confirms that in the new digital marketplace, consumers behave differently than before, and traditional marketing techniques may not apply anymore. Today there are myriad choices to easily gather information about products and services far before the actual purchase. For instance, customer buying decisions are increasingly influenced by online customer-to-customer interaction via platforms and social media, where users share products feedbacks, upload home video clips, or publish blog entries (Berman 2012 ). In this sense, digital technologies are also transforming firms’ customer-side operations (Setia et al. 2013 ) and customer engagement strategies (Sebastian et al. 2017 ). For example, reaching out to customers in a digital environment requires digital omnichannel marketing, including e.g. social media, mobile apps, and augmented reality (El Sawy et al. 2016 ). Secondly, we may note that digital technologies increasingly reduce the information asymmetries between sellers and buyers (Granados and Gupta 2013 ). In this sense, information ubiquity (Vey et al. 2017 ) and instant access to data via mobile technologies (Berman 2012 ) profoundly change the long-established seller–customer relationship. And thirdly, the current literature raises awareness for the emergence of multi-sided business models. While in the ‘old’ world, intermediaries were matching sellers and buyers, in the digital market place, intermediation increasingly takes place through the establishment of multi-sided digital platforms and networks (Bharadwaj et al. 2013 ; Evens 2010 ; Pagani 2013 ).

4.2.4 Distributed value creation and value capture

The review of the literature reveals that the value chain has become far more distributed in times of digital transformation—particularly value creation and value capture. Two major changes can be observed here: (1) digital technologies offer opportunities to customers to co-create products with the manufacturer, e.g. via digital platforms (El Sawy et al. 2016 ; Ng and Wakenshaw 2017 ), and (2) on an inter-firm level value is increasingly co-created and captured in a series of partnerships in a value network (Evens 2010 ). As Bharadwaj et al. ( 2013 ) argue, network effects are the key differentiator and driver of value creation and capture in a digital world. The focus of value creation is therefore shifting from value chain to value networks. For this purpose, companies like Google are experimenting with multi-sided business models. In such a multilayered business model, a company gives away certain products or services in one layer to capture value at a different layer (Bharadwaj et al. 2013 ). Google is giving away its Android operating system for free and captures value via the ability to control advertising on every phone that uses Android.

In more general terms, we may conclude that control of value in the digital world is less and less determined by R&D capabilities, competitors, or industry boundaries. Instead the buyer, not the seller, determines the dimensions of value that matter (Keen and Williams 2013 ). Therefore, businesses need to engage with their customers at every point in the process of value creation (Berman 2012 ). Also, the strong impact of digital technologies on incumbent’s value chains imply some degree of deviation from the classical and often analog core business. For example, new product-related competencies, platform capabilities or value architectures will be required. And, incumbents must prepare for new forms of monetization in the digitized marketplace.

4.2.5 Market environment and rules of competition

This is a rather broad and diverse categorization in our review, as it comprises technology-driven changes in the market environment. After consumer-centric aspects this research stream received the most attention by scholars in the review (on the technology-centric side). In sum, the current state of literature recognizes three major developments. First, digital transformation redefines, blurs and even dissolves existing industry boundaries which may lead to cross-industry competition (Sia et al. 2016 ; Weill and Woerner 2015 ). Dominant industry logics (Sabatier et al. 2012 ) apparently do not work anymore in times of digital transformation. The ‘new kid on the block can come out of the blue’ (Vey et al. 2017 , p. 23) and even individuals can become competitors as 3D Printing is expected to lead to a sharp increase in competition from SMEs and individual entrepreneurs (Rayna and Striukova 2016 ). And with the emergence of multi-sided business models also incumbents are starting to disrupt new markets (Weill and Woerner 2015 ). For instance, Google is disrupting the mobility sector with its self-driving car subsidiary Waymo, while Amazon has introduced AmazonFresh as a grocery delivery service which is seen as a potentially tough competitor to supermarkets. Second, with the emergence of digital platforms, networks, and ecosystems the market infrastructure becomes increasingly interconnected (Grover and Kohli 2013 ; Majchrzak et al. 2016 ; Markus and Loebbecke 2013 ). In a broader sense, we see a shift from controlling or participating in a linear value chain to operating in an ecosystem or network (Weill and Woerner 2015 ). As different types of innovation networks with different cognitive and social translations regarding knowledge emerge, novel properties of digital infrastructure in support of each network are required. Digital technologies therefore increase innovation network knowledge heterogeneity (Lyytinen et al. 2016 ). Third, the free flow of digital goods precipitates an erosion of property rights and higher risks of imitation (Loebbecke and Picot 2015 ).

4.3 Micro-level findings: the actor-centric side of the equation

In the following, we present and discuss the most important findings of the second order themes within the actor-centric dimension. In Fig.  3 we provide a thematic map for this dimension and in Table  2 a brief summary including illustrative quotes.

Thematic map for actor-driven themes in digital transformation literature

4.3.1 Transformative leadership

Understanding the impact of digital transformation on leadership and management behavior is a very active and prioritized research focus. In total, 23 papers in our review explore this aspect. First and foremost, research calls for a shift in the traditional view of IT strategy as being subordinate to business strategy (El Sawy et al. 2016 ). In the course of the past two decades information technologies have surpassed their subordinate role as administrative ‘back office’ assets and evolved into an essential element of corporate strategy building. Thus, incumbents should align IT and business strategies on equal terms and fuse them into ‘digital business strategy’ (Bharadwaj et al. 2013 ).

Also, emphasis is placed on the changing nature of leadership itself, caused by digital transformation. Such changes may include rapid optimization of top management decision-making processes enabled by instant access to information and expansive data sets (Mazzei and Noble 2017 ), new communication principles (Bennis 2013 ; Granados and Gupta 2013 ), or changes in leadership education (Sia et al. 2016 ). Further, there is consensus that senior management requires a new digital mindset in order to captain their company’s digital transformation journey. Therefore, incumbents should also rethink their leadership education practices. In the past, leadership programs have been primarily about leadership and communication skills. But in times of digital transformation, executives must become ‘tech visionaries’ and develop their transformative powers. For example, Sia et al. ( 2016 ) have conducted a case study on an Asian bank that uses hackathons to educate their senior managers. Media transparency and exposure are further key challenges of digitization where top managers may require some additional education. Given the ubiquity of information and the speed of online data dissemination (via mobile phones, viral effects of social media etc.), leaders today are significantly more exposed publicly than their analog predecessors. Therefore, according to Bennis ( 2013 ) leadership in the digital era needs to be learned through embracing transparency and adaptive capacity (specifically resilience as the ability to rebound from problems and crisis).

Finally, the vast extent and complexity of digital transformation leads to the emergence of an additional position at the top management level—the Chief Digital Officer (Dremel et al. 2017 ; Tumbas et al. 2017 ). Given the immense challenges of digital transformation and the claim for a new mindset and different skills, CEOs or even CIOs are conceivably not the best match (Singh and Hess 2017 ). Particularly not if they are expected to drive digital transformation in addition to their original tasks.

4.3.2 Managerial and organizational capabilities

Our analysis suggests that in order to effectively drive digital transformation additional and refined capabilities are required—both managerial and organizational (Li et al. 2017 )—in comparison to the analogue world.

At the managerial level, for one thing, a much faster strategy and implementation cycle is needed to cope with the pace of digital transformation (Daniel and Wilson 2003 ). The turbulent and ever-changing digital environment is forcing managers to make decisions and implement strategies significantly faster than they had been previously required to. In order to study managerial capabilities in the context of digital transformation, some studies have adopted the theory of dynamic capabilities (Daniel and Wilson 2003 ; Li et al. 2017 ; Yeow et al. 2018 ) as introduced by Teece et al. ( 1997 ), Teece ( 2007 , 2014 ). In particular, results indicate that dynamic capabilities may support the refinement of digital strategy and are therefore not separate from alignment, but on the contrary have the potential to enact and guide the process of aligning.

At the organizational level, one of the most intriguing challenges for incumbents will be to manage the ambidexterity of capabilities in terms of analog and digital capabilities. Firms need to incorporate ‘old’ and ‘new’ capabilities into their organizational structure in a complementary and not impeding way. In addition, capabilities in two further areas are of particular importance to many firms. First, capabilities to implement and operate in networks (Bharadwaj et al. 2013 ), platforms (Li et al. 2017 ; Sebastian et al. 2017 ), and ecosystems (El Sawy et al. 2016 ; Weill and Woerner 2015 ). Depending on contextual factors like for example their industry or business model, companies must learn to take advantage of network effects in terms of complementary capabilities while also learn how to become more of an ecosystem rather than continue managing value chains. Second, in the digital era it is essential to develop sensing capabilities, such as entrepreneurial alertness and environmental scanning (Kohli and Melville 2019 ), in order to identify new ideas and critically evaluate, design, modify and eventually deliver new business models (Berman 2012 ; Daniel and Wilson 2003 ).

4.3.3 Company culture

Digital transformation is not exclusively a technology-driven challenge but requires deep cultural change. Everyone within the organization must be prepared with an adaptive skill set and digital know-how. Two major insights can be identified within the existing literature. First, digital transformation demands a data-sharing and data-driven corporate culture (Dremel et al. 2017 ). Data as such must be recognized much more as a valuable resource and an enabler to become a digital enterprise. This will require higher operational transparency in daily-business and work-routines and a data-sharing mindset among employees. In this sense, incumbents need to develop their informatic culture to an informational culture (Llopis et al. 2004 ). In comparison to an informatic culture, an informational culture values IT as a core element of strategic and tactical decisions and clearly understands the financial and transformative potential of digital technologies. Second, digital transformation may trigger cultural conflict between younger and comparably inexperienced digital employees and older but more experienced pre-digitization employees (Kohli and Johnson 2011 ). Management is well advised to prevent that two different cultures arise within the same organization—a group of employees who understand digital technologies and those who have a long-standing track record in the traditional business but are technologically lagging behind. Facilitating a learning friendly culture (Kohli and Melville 2019 ) and publicly affirming support and trust by the executive level may effectively mitigate such a potential cultural divide.

4.3.4 Work environment

Our review reveals that digital transformation is changing the daily work environment in incumbent firms in terms of work structures (Hansen and Sia 2015 ; Loebbecke and Picot 2015 ), job roles, and workplace requirements (White 2012 ). For example, digital interconnectivity enables the emergence of flexible and networked cross-location teams across the entire geographical company map. In this context, traditional hierarchical work structures dissolve and new opportunities emerge beyond company boundaries, such as the integration of external freelancers (Loebbecke and Picot 2015 ). Also, the implementation of a digital workplace becomes inevitable. Particularly for ‘born digital’ younger employees a digitally well-equipped workplace may represent a major criterion for their choice of employer (El Sawy et al. 2016 ). According to White ( 2012 ), a digital workplace must be adaptive, compliant, imaginative, predictive, and location-independent.

However, the most notable insight in this perspective is that—in addition to a potential cultural divide—digitization may effectively lead to a growing skills gap between pre-digitization workers and recently hired digitally savvy employees (Kohli and Johnson 2011 ). In fact, while digital technologies significantly help to optimize and accelerate many work processes and thereby increase productivity, incumbents must be aware that many employees might not keep pace with this digital high-speed train and feel left behind. It is unclear how such a tradeoff is considered and how firms could handle related conflicts.

5 Avoiding an ivory tower: drawing on existing knowledge from adjacent research fields

We assume that pre-existing knowledge on corporate transformation processes in general is partly already available and may provide implications for digital transformation. Therefore, at this point in our review, we aim to stimulate a theoretical discussion by identifying potential white spots abstracted from adjacent research fields. For this purpose, we additionally reviewed 28 studies from the literature on technological disruption (to gain technology-centric input) and 32 papers from corporate entrepreneurship (to expand the actor-centric view). By this, we supplement the pre-dominantly IS-based digital transformation literature with a broader management perspective. First, by reviewing the literature on disruptive innovations we hope to derive implications regarding technology adoption and integration. Burdened with the legacy of old technology, bureaucratic structures and core rigidities (Leonard-Barton 1992 ), incumbents may face major challenges in this respect during their digital transformation journey. Second, we expect corporate entrepreneurship to add a more holistic perspective on firm-internal aspects during the process of transformation, such as management contribution or the impact of knowledge and learning.

We rigorously conducted the same review and analysis process as for our digital transformation sample. A database and concept matrix (Webster and Watson 2002 ) for the sample on technological disruption and corporate entrepreneurship are provided in Tables  6 and 7 of “ Appendix ”. The data structures, which summarize the second order themes for both the actor-centric and technology-centric dimension of these additional research fields are illustrated in Figs.  5 and 6 of “ Appendix ”. Within the main body of this article, we only draw attention toward three key implications (Fig.  4 ). In the following, we provide a brief synthesis of these implications and their grounding in the respective literature. In a second step, we transfer and apply these implications to the context of digital transformation and integrate them into an agenda for future research opportunities.

Expanding the digital transformation high-level thematic map with insights from technological disruption and corporate entrepreneurship

5.1 Insights from technological disruption

Existing knowledge from the adoption of disruptive technologies suggests that in order to successfully integrate, commercialize or develop disruptive technologies incumbents need to create organizations that are independent from but interconnected in one way or another with the mainstream business (Bower and Christensen 1995 ). The reasons for this are manifold. For example, managers are encouraged to protect disruptive technologies from the processes and incentives that are targeted to serve established customers. Rather, disruptive innovations should be placed in separate new organizations that work with future customers for this technology (Bower and Christensen 1995 ; Gans 2016 ). Further, separation potentially helps to unravel the discord between viewing disruptive innovations as a threat or an opportunity. Exempted from obligations to a parent company, separate ventures are more likely to perceive a novel technology as an opportunity (Gilbert and Bower 2002 ). And lastly, a freestanding business also enables local adaptation and increased sensitivity to changes in the environment (Hill and Rothaermel 2003 ).

5.2 Insights from corporate entrepreneurship

Our review of the corporate entrepreneurship literature identifies two major implications that have not been (adequately) considered in digital transformation research yet.

First, the literature indicates that middle management plays a crucial role in redefining a firm’s strategic context and by this driving organizational transformation. A middle management perspective has thus far been completely neglected in digital transformation research. We see this as a major gap, since the middle layers of management are ‘where the action is’ (Floyd and Wooldridge 1999 , p. 124). Top management should control the level and the rate of change and ensure that entrepreneurial activities correspond to their strategic vision (Burgelman 1983 ), but middle managers at the implementation level are the driving force and key determinant behind organizational transformation. However, on the downside, middle managers may also represent a major barrier to organizational change (Thornberry 2001 ). Typically, managers have the task to minimize risks, make sure everything is compliant to the rules and perform their functional roles. Thus, middle managers usually have the most to lose from radical changes and are therefore often the least likely to be entrepreneurial or to support transformations (Thornberry 2001 ). In order to solve middle and operational manager’s risk-awareness and unleash their entrepreneurial spirit, research suggests encouraging autonomous behavior (Shimizu 2012 ). In sum, reviewing the literature on corporate entrepreneurship raises our awareness for the impact of hierarchy and management levels on organizational transformation (Hornsby et al. 2009 ).

Second, a closer cooperation and regular exchange between incumbents and start-ups in order to accelerate entrepreneurial transformation is proposed (Engel 2011 ; Kohler 2016 ). Incumbents should recognize start-up companies as a source of external innovation and develop suitable models for collaboration (e.g. corporate accelerators). In particular, incumbents are advised to implement three common best practices from successful start-ups in order to facilitate transformation: (1) working in small omni-functional teams, (2) goal-driven rapid development instead of bureaucratic processes, and (3) field-level exploration of market potential instead of complex and tedious quantitative models (Engel 2011 ). In addition, corporate entrepreneurship underlines the importance of organizational learning as a vehicle to drive and shape cultural transformation (Dess et al. 2003 ; Floyd and Wooldridge 1999 ; Zahra 2015 ). We come to understand that learning, and in fact also knowledge management, are intimately tied to the concept of organizational transformation. A culture of learning and knowledge drives experimentation, encourages the development of an adaptive skill set, reshapes competitive positioning, and opens the minds of employees to new realities (Zahra et al. 1999 ).

6 Opportunities for future research

Based on the cross-disciplinary perspectives from reviewing the literature on digital transformation, technological disruption and corporate entrepreneurship, we propose opportunities for future research on digital transformation. Using our thematic map as a lens to view future research opportunities, we focus on the two dimensions of technology and actor. For the technology-centric dimension we expand on the structural and operational integration of digital technologies and organizational transformation initiatives as well as gaining a deeper understanding of the pace of technological transformation. For the actor-centric dimension we address three topics: we start at the leadership level by emphasizing the relevance of middle management in digital transformation, after that we refer to the potential skills gap and threat of an employee divide in incumbent organizations induced by digital technologies, and finally we move beyond organizational boundaries to turn toward the potential benefits and drawbacks of cooperating with start-ups and pure digital companies to boost transformation. For each area, we propose a set of research questions. Altogether, the agenda is organized around five guiding topics (Table  3 ).

6.1 Integration of digital transformation within organizational structures and activities in incumbent firms

Our review of the literature on digital transformation reveals a knowledge gap regarding this topic. However, we do gain some interesting cross-disciplinary insights from technological disruption at this point. In fact, as already discussed, studies on technological disruption indicate that in order to successfully integrate, commercialize or develop disruptive technologies incumbents need to create organizations that are completely independent from but interconnected in one way or another with the mainstream business (Bower and Christensen 1995 ; Gans 2016 ; Gilbert and Bower 2002 ; Hill and Rothaermel 2003 ).

Thus, the question arises as to how incumbents should incorporate their digital transformation activities. Several options and interesting questions arise in this matter that future research may investigate on:

Which forms of organizational architecture are most suitable for digital transformation? Seamless integration of digital technologies requires building an agile and scalable digital infrastructure that enables continuous scalability of new initiatives (Sia et al. 2016 ). For example, Resca et al. ( 2013 ) suggest a platform-based organization. In addition, digital transformation demands a new kind of enterprise platform integration (El Sawy et al. 2016 ). Given the high intensity of interactive digital connectivity between the outside and inside of a company, traditional enterprise platforms (like ERP) and the ‘old’ supply chain management integration paradigm are in many cases not the most suitable solution anymore. Therefore, flexible IT is a key transformation resource in the digital world (Cha et al. 2015 ). Pursuing an open innovation approach might be another alternative for incumbents.

When and why is it an advantage/disadvantage to start digital transformation in a new organization which is completely independent from traditional business, as suggested by technological disruption research? Under what circumstances and why do spill - over - effects to the parent organization happen/not happen? ? For example, Ravensburger AG , a German toy and jigsaw puzzle company, founded Ravensburger Digital GmbH as a subsidiary in 2009. The purpose of the subsidiary was to become the firm’s digital competence center. In 2017, the digital subsidiary was reincorporated in the parent organization as a digital unit with the goal to apply their digital knowledge to transform the traditional business segments. We call for more qualitative case study research devoted to this question to develop our understanding in this topic.

How, when, and why do incumbents benefit from adopting a ‘let a hundred flowers bloom’ philosophy versus taking a ‘launch, learn, pivot’ approach? In the first scenario, a company would start its digital initiatives across all divisions simultaneously and locally to encourage broad experimentation. Such an approach was adopted by AmerisourceBergen Corp. , an American drug wholesale company. The company is convinced that digital transformation is a matter of culture that needs to be established across the entire organization. For this purpose, it implemented agile project teams throughout the entire enterprise, of which each focused on different aspects. On the downside, companies following such a broad approach may risk losing focus and at some point, the various initiatives may start competing against each other. Hence, we believe it is crucial to have a big picture in mind and accordingly allocate resources and attention very thoughtfully. Alternatively, incumbents may start with a pilot transformation project in a smaller market or subsidiary. Arguably, a major advantage is the opportunity to assure that customers are happy with the transformation results and everything is working out well before starting the large roll out in other markets. And it provides incumbents time to fine-tune their initiatives. For example, American medical company Alcon premiered their initial transformation efforts in Brazil before ramping up their rollout in 27 further countries.

6.2 Pace of digital transformation

The rapid pace of technological change is perhaps the most defining characteristic of digital transformation in distinction to previous IT-enabled transformations. Yet, as this topic is only addressed by four papers in our sample it is still to be studied in more depth. For example, there is consensus among the studies that the pace of change has accelerated significantly, however the parameters that define the pace of change remain yet to be defined. Further, we are informed that some industries like the newspaper business have been digitally transformed within a very short period of time (Karimi and Walter 2015 ), while other branches are still under transformation or are yet to be converted. We posit two exemplary research questions regarding the pace of digital transformation:

What are the parameters that define the pace of change? Our review reveals that the speed of product launches (Bharadwaj et al. 2013 ) and the time it takes to turn an idea into a business (Vey et al. 2017 ) are two potential indicators, but we certainly need to obtain a more comprehensive conceptualization at this point.

Why do industries adopt to digital transformation at a different speed? For example, consider front-runner industries like the media or publishing versus late-comers such as oil and gas. In this specific case, the easiness to dematerialize and digitize the product portfolio is certainly a main reason. However, other industries are less obvious, and we would like to invite future research to investigate upon these conditions. What are the parameters that define whether an industry is more or less transformative?

6.3 The role of middle management in digital transformation

We have learned from our review of the corporate entrepreneurship literature that middle managers are the locus of organizational transformation in incumbent firms (Floyd and Wooldridge 1999 ; Hornsby et al. 2002 , 2009 ; Shimizu 2012 ). While top management controls the level and rate of change, middle managers are in charge of execution (Burgelman 1983 ). Hence, one may conclude that middle managers are the kingpin of digital transformation. Yet, there is not a single paper in our sample that covers a middle management perspective in digital transformation. We believe that this subject has been highly neglected in research to this point and deserves far more attention in future. Several topics are particularly interesting:

How and why is digital transformation affecting the role, tasks and identity of middle managers? How and why do middle managers react to these changes? Based on our review, we expect a deep change in the nature of middle management’s role and influence in a ‘digitally transformed’ company ranging from administration to leadership aspects. Middle managers require a new attitude as they move from directing and controlling stable processes and people at the middle of hierarchy to managing resources and connecting people in the middle of networks. In addition, middle managers in the digital era must step up to their role of supporting, enabling, and coaching people to use the available digital tools. They are expected to facilitate the organization.

What kind of new responsibilities and functions in middle management hierarchy are required to accelerate digital transformation? The odds are that change fatigue might grow on employees and digital transformation may start faltering. For this purpose, horizontal functions such as business-process management layers or central administration platforms may be implemented (McKinsey & Company 2017 ). They could be shared across multiple initiatives within the organization and help to accelerate transformation.

Which mindset and digital literacy do middle managers need to be the driving force behind digital transformation? How, when, and why are middle managers motivated/not motivated to drive transformation? Research on corporate entrepreneurship emphasizes that middle managers are often the least likely to support change as they are inherently risk-averse, hardly entrepreneurial and very attached to their functional routines (Thornberry 2001 ). In addition, middle managers may easily get stressed about their ‘sandwich’ position in-between senior management and the operational level. So how can we expect middle managers to be the speedboat of digital transformation? Also, incumbents need to carefully evaluate the existing digital skills and literacy of their middle managers. How comfortable do they feel with digital tools, social media, the cloud and similar trends? They may not fulfill their coaching and leadership role if they heavily struggle with technology in the first place.

How and why is digital transformation affecting the interface of the top management team (TMT) and middle managers? The relationship between the TMT and middle managers is a very special and important relationship which significantly affects both strategy formulation and the quality of implementation. Middle managers are the organizational ‘linking pins’ between top and operational level and thus heavily rely on a good exchange with their superiors. To what extent and in which ways does digital transformation affect this special leader–follower relationship? How are digital technologies changing the speed and quality of information exchange? What is the impact on the inter-personal level?

What is the impact of digital transformation on the overall importance of the middle management layer? Since the 1950s, research indicates the decline of middle managers in terms of both numbers and influence (Dopson and Stewart 1993 ; Leavitt and Whisler 1958 ; Pinsonneault and Kraemer 1997 ). The shift in emphasis from planning and controlling to speed and flexibility is severely affecting the assumedly ‘slow’ middle. Are middle managers afraid that digital technologies will replace most of their traditional tasks and functions, e.g. communicating and monitoring strategy? Will digitalization naturally empower lower level operational managers at the bottom and consequently eliminate the middle layer?

6.4 A growing skills gap and threat of an employee divide

Given the complexity and explosive pace of digital technologies, there is a threat of a growing skills gap between pre-digitization workers and recently hired digitally savvy employees (Kohli and Johnsons 2011 ). A couple of topics are particularly interesting for future research:

How, when and why are incumbents able/unable to mitigate a growing skills gap and employee divide in the face of digital transformation? Given the increased complexity of digital technologies, traditional IT trainings may not be effective anymore. In a similar vein, how could different levels of knowledge and experience residing within different employees be integrated in the context of digital transformation? Future research might examine the mechanisms required for facilitating or hindering such an integration.

How and when are incumbents able/unable to incorporate ‘old’ and ‘new’ capabilities within their organization? On the one hand firms need to develop new capabilities to continuously transform their business, while on the other hand they must leverage their existing knowledge and skills in order to maintain their existing operations. Thus, for the time of transformation incumbents need to develop multiple, often inconsistent competencies simultaneously. In this context, how do firms ensure not to lose focus while mastering the challenge of ambidexterity in times of digital transformation?

Who in the company is managing the development and transformation of skills (e.g. HR, senior leadership, IT division, functional teams, employees etc .), and how and why does that impact outcomes of digital transformation ? This question is not addressed by current research at all. However, according to a survey (Capgemini Consulting 2013) this lack of alignment with digital strategy is rather worrisome. Responsibilities for skills transformation and development in times of digitization need to be clearly defined and allocated. Empirical academic research in this direction might be helpful to understand the status-quo in incumbent firms regarding this issue.

6.5 Cooperation with startups and pure tech companies to accelerate digital transformation

Corporate entrepreneurship proposes a closer cooperation and regular exchange between incumbents and start-ups in order to accelerate entrepreneurial transformation (Engel 2011 ; Kohler 2016 ). In fact, start-ups are often perceived as the forerunners of digital transformation. They are praised for faster innovation capabilities, higher levels of agility, a culture of risk-taking, and supremely digitized processes and workflows. In contrast, incumbents have more experience, access to capital, established brand trust and a huge customer base. Hence, a cooperation between start-ups and incumbents may be beneficial for both parties. In addition, non-tech incumbents may also consider cooperating with pure digital players which are beyond their start-up phase but are important knowledge carriers in digital matters. Two topics are particularly interesting:

Assuming that successful start - ups have a good digital culture — what are the constituent pillars of such a digital culture? And how could incumbents incorporate these “best practices” and “lessons learned”?

What are the benefits of employee exchange programs with technology companies or start - ups to scale - up digital skills? For example, in early 2008 consumer goods giant Procter and Gamble and Google have been swapping two dozen employees in an effort to foster creativity, exchange thoughts on online advertisement and strengthen their mutual relationship. This program worked very well for both sides.

7 Limitations and conclusion

Our review is not without limitations. First, the specific objectives and nature of our filtering process applied during the review naturally come with a certain selection bias. For example, data collection, analysis and interpretation remain influenced by the subjective assessments of the researchers. Also, despite being the common rule within systematic literature reviews, searching exclusively in peer-reviewed academic journals might have omitted some relevant research contained in books or dissertations. However, by means of a rigorous and transparent search process, an as complete as possible review sample was collected and analyzed subsequently. Second, using a high-level thematic map for such a complex multi-dimensional phenomenon like digital transformation highlights particular connections while it potentially fails to capture others. Specifically, critics may point to the lack of analytical depth within each second order theme. However, we believe that within the limited scope of a review our broad thematic description nevertheless adds value to the advancement of this field and should rather be seen as a holistic starting point for future research to dive deeper into the characteristics of sub-themes of digital transformation. Finally, we are aware that our focus on the organizational level of digital transformation within the private sector does not fully capture the implications of digital transformation for our society, as it also occurs at various other levels, such as the individual level or public sector. As such, future researchers may apply alternative approaches to review and synthesize the existing literature on digital transformation. For example, in contrast to our inductive method to code and analyze our sample, it may also be interesting to apply a more deductive and pre-structured method, in particular when focusing on a deeper understanding of the sub-themes emerging from our analysis. Accordingly, future research could benefit from adopting a phenomenon-based research strategy as proposed by von Krogh et al. ( 2012 ).

Concluding, our paper contributes to the extant discussion by consolidating, mapping and analyze the existing research on digital transformation, sharing important macro- and microlevel observations in the literature and proposing corresponding future research directions. Emerging from our review of 58 studies, we develop a thematic map which identifies technology and actor as the two aggregate dimensions of digital transformation and that elaborates on the predominant contextual concepts (second order themes) within these dimensions. From a macrolevel perspective, we observe that the status-quo of digital transformation literature is rather diverse, in a sense that papers discuss topics across various clusters and concepts. Further, we find some degree of diversity in the theoretical foundations drawn upon as well as confirm that the existing literature in general is scarce regarding quantitative empirical evidence. Another important contribution of our paper is bringing different lenses together by integrating knowledge from related disciplinary areas outside IS management, such as technological disruption and corporate entrepreneurship. With our review, we hope to provide a comprehensive and solid foundation for the on-going discussions on digital transformation and to stimulate future research on this exciting topic.

The development of clear and precise aims and objectives; pre-planned methods; a comprehensive search of all potentially relevant articles; the use of explicit, reproducible criteria in the selection of articles; an appraisal of the quality of the research and the strength of the findings; a synthesis of individual studies using an explicit analytic framework; and a balanced, impartial and comprehensible presentation of the results.

The search included Academy of Management Journal , Administrative Science Quarterly , Entrepreneurship Theory and Practice , Journal of Management Studies , Strategic Management Journal .

The search included European Journal of Information Systems , Information Systems Journal , Information Systems Research , Journal of the Association for Information Systems , Journal of Information Technology , Journal of Management Information Systems , Journal of Strategic Information Systems , MIS Quarterly , MISQ Executive .

Ahuja G, Morris Lampert C (2001) Entrepreneurship in the large corporation: a longitudinal study of how established firms create breakthrough inventions. Strateg Manag J 22:521–543

Google Scholar  

Alos-Simo L, Verdu-Jover AJ, Gomez-Gras JM (2017) How transformational leadership facilitates e-business adoption. Ind Manag Data Syst 117:382–397

Anderson P, Tushman ML (1990) Technological discontinuities and dominant designs: a cyclical model of technological change. Adm Sci Q 35:604–633

Ash CG, Burn JM (2003) Assessing the benefits from e-business transformation through effective enterprise management. Eur J Inf Syst 12:297–308

Bartunek JM, Rynes SL, Ireland RD (2006) What makes management research interesting, and why does it matter. Acad Manag J 49:9–15

Bennis W (2013) Leadership in a digital world: embracing transparency and adaptive capacity. MIS Q 37:635–636

Bergek A, Berggren C, Magnusson T, Hobday M (2013) Technological discontinuities and the challenge for incumbent firms: destruction, disruption or creative accumulation? Res Policy 42:1210–1224

Berman SJ (2012) Digital transformation: opportunities to create new business models. Strategy Leadersh 40:16–24

Berman SJ, Davidson S, Ikeda K, Korsten PJ, Marshall A (2016) How successful firms guide innovation: insights and strategies of leading CEOs. Strategy Leadersh 44:21–28

Besson P, Rowe F (2012) Strategizing information systems-enabled organizational transformation: a transdisciplinary review and new directions. J Strateg Inf Syst 21:103–124

Bharadwaj A, El Sawy O, Pavlou P, Venkatraman N (2013) Digital business strategy: toward a next generation of insights. MIS Q 37:471–482

Birkinshaw J (1997) Entrepreneurship in multinational corporations: the characteristics of subsidiary initiatives. Strateg Manag J 18:207–229

Bower JL, Christensen CM (1995) Disruptive technologies: catching the wave. Harv Bus Rev 73:43–53

Burgelman RA (1983) Corporate entrepreneurship and strategic management: insights from a process study. Manag Sci 29:1349–1364

Cha KJ, Hwang T, Gregor S (2015) An integrative model of IT-enabled organizational transformation: a multiple case study. Manag Decis 53:1755–1770

Chatterjee D, Grewal R, Sambamurthy V (2002) Shaping up for e-commerce: institutional enablers of the organizational assimilation of web technologies. MIS Q 26:65–89

Chen J, Nadkarni S (2017) It’s about time! CEOs’ temporal dispositions, temporal leadership, and corporate entrepreneurship. Adm Sci Q 62:31–66

Christensen CM (1997) The innovator’s dilemma. When new technologies cause great firms to fail. Harvard Business School Press, Boston

Christensen CM, Raynor ME (2003) Why hard-nosed executives should care about management theory. Harv Bus Rev 81:66–75

Corbett A, Covin JG, O’Connor GC, Tucci CL (2013) Corporate entrepreneurship: state-of-the-art research and a future research agenda. J Prod Innov Manag 30:812–820

Corley KG, Gioia DA (2004) Identity ambiguity and change in the wake of a corporate spin-off. Adm Sci Q 49:173–208

Covin JG, Miles MP (1999) Corporate entrepreneurship and the pursuit of competitive advantage. Entrepreneursh Theory Pract 23:47–63

Crossan MM, Apaydin M (2010) A multi-dimensional framework of organizational innovation: a systematic review of the literature. J Manag Stud 47:1154–1191

Cui M, Pan SL (2015) Developing focal capabilities for e-commerce adoption: a resource orchestration perspective. Inf Manag 52(2):200–209

Daniel EM, Wilson HN (2003) The role of dynamic capabilities in e-business transformation. Eur J Inf Syst 12:282–296

Danneels E (2004) Disruptive technology reconsidered: a critique and research agenda. J Prod Innov Manag 21:246–258

DaSilva CM, Trkman P, Desouza K, Lindič J (2013) Disruptive technologies: a business model perspective on cloud computing. Technol Anal Strateg Manag 25:1161–1173

Denyer D, Neely A (2004) Introduction to special issue: innovation and productivity performance in the UK. Int J Manag Rev 5:131–135

Dess GG, Ireland RD, Zahra SA, Floyd SW, Janney JJ, Lane PJ (2003) Emerging issues in corporate entrepreneurship. J Manag Stud 29:351–378

Dijkman RM, Sprenkels B, Peeters T, Janssen A (2015) Business models for the Internet of Things. Int J Inf Manag 35(6):672–678

Doherty NF, King M (2005) From technical to socio-technical change: tackling the human and organizational aspects of systems development projects. Eur J Inf Syst 14:1–5

Dopson S, Stewart R (1993) Information technology, organizational restructuring and the future of middle management. New Technol Work Employ 8(1):10–20

Downes L, Nunes P (2013) Big bang disruption. Harv Bus Rev 91:44–56

Dremel C, Wulf J, Herterich MM, Waizmann JC, Brenner W (2017) How AUDI AG established big data analytics in its digital transformation. MIS Q Exec 16(2):81–100

Dushnitsky G, Lenox MJ (2005) When do incumbents learn from entrepreneurial ventures? Corporate venture capital and investing firm innovation rates. Res Policy 34(5):615–639

El Sawy OA, Malhotra A, Park Y, Pavlou PA (2010) Research commentary-seeking the configurations of digital ecodynamics: it takes three to tango. Inf Syst Res 21(4):835–848

El Sawy OA, Kræmmergaard P, Amsinck H, Vinther AL (2016) How LEGO built the foundations and enterprise capabilities for digital leadership. MIS Q Exec 15(2):141–166

Engel JS (2011) Accelerating corporate innovation: lessons from the venture capital model. Res-Technol Manag 54(3):36–43

Evens T (2010) Value networks and changing business models for the digital television industry. J Media Bus Stud 7(4):41–58

Fichman RG, Dos Santos BL, Zheng Z (2014) Digital innovation as a fundamental and powerful concept in the information systems curriculum. MIS Q 38(2):329–A15

Fisch C, Block J (2018) Six tips for your (systematic) literature review in business and management research. Manag Rev Q 68:103–106

Floyd SW, Wooldridge B (1999) Knowledge creation and social networks in corporate entrepreneurship: the renewal of organizational capability. Entrepreneursh Theory Pract 23(3):123–144

Fuetsch E, Suess-Reyes J (2017) Research on innovation in family businesses: are we building an ivory tower? J Fam Bus Manag 7(1):44–92

Gans JS (2016) Keep calm and manage disruption. MIT Sloan Manag Rev 57(3):83–90

Gawer A, Cusumano MA (2014) Industry platforms and ecosystem innovation. J Prod Inov Manag 31(3):417–433

Gerstner WC, König A, Enders A, Hambrick DC (2013) CEO narcissism, audience engagement, and organizational adoption of technological discontinuities. Adm Sci Q 58(2):257–291

Gerth AB, Peppard J (2016) The dynamics of CIO derailment: how CIOs come undone and how to avoid it. Bus Horiz 59(1):61–70

Gilbert C, Bower JL (2002) Disruptive change. When trying harder is part of the problem. Harv Bus Rev 80(5):94–101

Gioia DA, Thomas JB, Clark SM, Chittipeddi K (1994) Symbolism and strategic change in academia: the dynamics of sensemaking and influence. Org Sci 5(3):363–383

Granados N, Gupta A (2013) Transparency strategy: competing with information in a digital world. MIS Q 37(2):637–641

Grover V, Kohli R (2013) Revealing your hand: caveats in implementing digital business strategy. MIS Q 37(2):655–662

Günther WA, Mehrizi MHR, Huysman M, Feldberg F (2017) Debating big data: a literature review on realizing value from big data. J Strateg Inf Syst 26:191–209

Günzel F, Holm AB (2013) One size does not fit all—understanding the front-end and back-end of business model innovation. Int J Innov Manag 17(1):1340002-1–1340002-34

Guth WD, Ginsberg A (1990) Guest editors’ introduction: corporate entrepreneurship. Strateg Manag J 11:5–15

Habtay SR, Holmén M (2014) Incumbents’ responses to disruptive business model innovation: the moderating role of technology vs. market-driven innovation. Int J Entrep Innov Manag 18(4):289–309

Hagberg J, Sundstrom M, Egels-Zandén N (2016) The digitalization of retailing: an exploratory framework. Int J Retail Distrib Manag 44(7):694–712

Hansen R, Sia SK (2015) Hummel’s digital transformation toward omnichannel retailing: key lessons learned. MIS Q Exec 14(2):51–66

Hess T, Matt C, Benlian A, Wiesböck F (2016) Options for formulating a digital transformation strategy. MIS Q Exec 15(2):123–139

Hill CW, Rothaermel FT (2003) The performance of incumbent firms in the face of radical technological innovation. Acad Manag Rev 28(2):257–274

Hitt M, Ireland R, Camp S, Sexton D (2001) Strategic entrepreneurship: entrepreneurial strategies for wealth creation. Strateg Manag J 22:479–491

Holm AB, Günzel F, Ulhøi JP (2013) Openness in innovation and business models: lessons from the newspaper industry. Int J Technol Manag 61(3/4):324–348

Hornsby JS, Kuratko DF, Zahra SA (2002) Middle managers’ perception of the internal environment for corporate entrepreneurship: assessing a measurement scale. J Bus Vent 17(3):253–273

Hornsby JS, Kuratko DF, Shepherd DA, Bott JP (2009) Managers’ corporate entrepreneurial actions: examining perception and position. J Bus Ventur 24(3):236–247

Hu H, Huang T, Zeng Q, Zhang S (2016) The role of institutional entrepreneurship in building digital ecosystem: a case study of Red Collar Group (RCG). Int J Inf Manag 36(3):496–499

Ireland RD, Covin JG, Kuratko DF (2009) Conceptualizing corporate entrepreneurship strategy. Entrepreneursh Theory Pract 33(1):19–46

Ives B, Palese B, Rodriguez JA (2016) Enhancing customer service through the internet of things and digital data streams. MIS Q Exec 15(4):279–297

Johnson M (2010) Barriers to innovation adoption: a study of e-markets. Ind Manag Data Syst 110(2):157–174

Johnson MW, Christensen CM, Kagermann H (2008) Reinventing your business model. Harv Bus Rev 86(12):50–59

Jones O, Gatrell C (2014) Editorial: the future of writing and reviewing for IJMR. Int J Manag Rev 16(3):249–264

Karimi J, Walter Z (2015) The role of dynamic capabilities in responding to digital disruption: a factor-based study of the newspaper industry. J Manag Inf Syst 32(1):39–81

Karimi J, Walter Z (2016) Corporate entrepreneurship, disruptive business model innovation adoption, and its performance: the case of the newspaper industry. Long Range Plan 49(3):342–360

Kauffman RJ, Walden EA (2001) Economics and electronic commerce: survey and directions for research. Int J Electric Commun 5(4):5–116

Keen P, Williams R (2013) Value architectures for digital business: beyond the business model. MIS Q 37(2):643–648

Khandwalla PN (1987) Generators of pioneering-innovative management: some Indian evidence. Org Stud 8(1):39–59

Koen PA, Bertels H, Elsum IR, Orroth M, Tollett BL (2010) Breakthrough innovation dilemmas. Res Technol Manag 53(6):48–51

Kohler T (2016) Corporate accelerators: building bridges between corporations and startups. Bus Horiz 59(3):347–357

Kohli R, Johnson S (2011) Digital transformation in latecomer industries: CIO and CEO Leadership Lessons from Encana Oil and Gas (USA) Inc. MIS Q Exec 10(4):141–156

Kohli R, Melville NP (2019) Digital innovation: a review and synthesis. Inf Syst J 29(1):200–223

Kuratko DF, Covin JG, Hornsby JS (2014) Why implementing corporate innovation is so difficult. Bus Horiz 57(5):647–655

Lant TK, Mezias SJ (1990) Managing discontinuous change: a simulation study of organizational learning and entrepreneurship. Strateg Manag J 11:147–179

Lanzolla G, Anderson J (2008) Digital transformation. Bus Strateg Rev 19(2):72–76

Lavie D (2006) Capability reconfiguration: an analysis of incumbent responses to technological change. Acad Manag Rev 31(1):153–174

Leavitt HJ, Whisler TL (1958) Management in the 1980’s. In: Technology, organizations and innovation. London and New York, pp 41–48

Leonard-Barton D (1992) Core capabilities and core rigidities: a paradox in managing new product development. Strateg Manag J 13(S1):111–125

Leong C, Tan B, Xiao X, Tan FTC, Sun Y (2017) Nurturing a FinTech ecosystem: the case of a youth microloan startup in China. Int J Inf Manag 37(2):92–97

Li L, Su F, Zhang W, Mao JY (2017) Digital transformation by SME entrepreneurs: a capability perspective. Inf Sys J 28(6):1129–1157

Ling YAN, Simsek Z, Lubatkin MH, Veiga JF (2008) Transformational leadership’s role in promoting corporate entrepreneurship: examining the CEO-TMT interface. Acad Manag J 51(3):557–576

Liu DY, Chen SW, Chou TC (2011) Resource fit in digital transformation: lessons learned from the CBC Bank global e-banking project. Manag Decis 49(10):1728–1742

Llopis J, Gonzalez MR, Gasco JL (2004) Transforming the firm for the digital era: an organizational effort towards an E-culture. Hum Syst Manag 23(4):213–225

Locke K (2001) Grounded theory in management research. Sage, London

Loebbecke C, Picot A (2015) Reflections on societal and business model transformation arising from digitization and big data analytics: a research agenda. J Strateg Inf Syst 24(3):149–157

Lucas HC Jr, Goh JM (2009) Disruptive technology: how Kodak missed the digital photography revolution. J Strateg Inf Syst 18(1):46–55

Lumpkin GT, Dess GG (1996) Clarifying the entrepreneurial orientation construct and linking it to performance. Acad Manag J 2(1):135–172

Lyytinen K, Yoo Y, Boland RJ Jr (2016) Digital product innovation within four classes of innovation networks. Inf Sys J 26(1):47–75

Majchrzak A, Markus ML, Wareham J (2016) Designing for digital transformation: lessons for information systems research from the study of ICT and societal challenges. MIS Q 40(2):267–277

Marion T, Dunlap D, Friar J (2012) Instilling the entrepreneurial spirit in your RandD team: what large firms can learn from successful start-ups. IEEE Trans Eng Manag 59(2):323–337

Markus ML (2004) Technochange management: using IT to drive organizational change. J Inf Technol 19(1):4–20

Markus ML, Benjamin RI (1997) The magic bullet theory in IT-enabled transformation. Sloan Manag Rev 38:55–68

Markus ML, Loebbecke C (2013) Commoditized digital processes and business community platforms: new opportunities and challenges for digital business strategies. MIS Q 37(2):649–654

Matt C, Hess T, Benlian A (2015) Digital transformation strategies. Bus Inf Syst Eng 57(5):339–343

Mazzei MJ, Noble D (2017) Big data dreams: a framework for corporate strategy. Bus Horiz 60(3):405–414

McKinsey & Company (2017) A CEO guide for avoiding the ten traps that derail digital transformations. Digital McKinsey. https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/a-ceo-guide-for-avoiding-the-ten-traps-that-derail-digital-transformations . Accessed 28 July 2018

McWilliams A, Siegel D, Van Fleet DD (2005) Scholarly journals as producers of knowledge: theory and empirical evidence based on data envelopment analysis. Organ Res Methods 8:185–201

Meyer AD, Brooks GR, Goes JB (1990) Environmental jolts and industry revolutions: organizational responses to discontinuous change. Strateg Manag J 11:93–110

Miller D (1983) The correlates of entrepreneurship in three types of firms. Manag Sci 29:770–791

Mithas S, Tafti A, Mitchell W (2013) How a firm’s competitive environment and digital strategic posture influence digital business strategy. MIS Q 37(2):511–536

Moreau F (2013) The disruptive nature of digitization: the case of the recorded music industry. Int J Arts Manag 15(2):18–31

Morton MS (1991) The corporation of the 1990s: information technology and organizational transformation. Oxford University Press, New York

Mulrow CD (1994) Systematic reviews: rationale for systematic reviews. Br Manag J 309:597–599

Naman JL, Slevin DP (1993) Entrepreneurship and the concept of fit: a model and empirical tests. Strateg Manag J 14:137–153

Nambisan S, Lyytinen K, Majchrzak A, Song M (2017) Digital innovation management: reinventing innovation management research in a digital world. MIS Q 41(1):223–238. https://doi.org/10.25300/MISQ/2017/41:1.03

Nason RS, McKelvie A, Lumpkin GT (2015) The role of organizational size in the heterogeneous nature of corporate entrepreneurship. Small Bus Econ 45(2):279–304

Ng IC, Wakenshaw SY (2017) The Internet-of-Things: review and research directions. Int J Res Market 34(1):3–21

Obal M (2013) Why do incumbents sometimes succeed? Investigating the role of interorganizational trust on the adoption of disruptive technology. Ind Market Manag 42(6):900–908

Orlikowski WJ (1996) Improvising organizational transformation over time: a situated change perspective. Inf Syst Res 7(1):63–92

Pagani M (2013) Digital business strategy and value creation: framing the dynamic cycle of control points. MIS Q 37(2):617–632

Paré G, Trudel MC, Jaana M, Kitsiou S (2015) Synthesizing information systems knowledge: a typology of literature reviews. Inf Manag 52(2):183–199

Peltola S (2012) Can an old firm learn new tricks? A corporate entrepreneurship approach to organizational renewal. Bus Horiz 55(1):43–51

Pinsonneault A, Kraemer KL (1997) Middle management downsizing: an empirical investigation of the impact of information technology. Manag Sci 43(5):659–679

Pittaway L, Robertson M, Munir K, Denyer D, Neely A (2004) Networking and innovation: a systematic review of the evidence. Int J Manag Rev 5:137–168

Ranganathan C, Watson-Manheim MB, Keeler J (2004) Bringing professionals on board: lessons on executing IT-enabled organizational transformation. MIS Q Exec 3(3):151–160

Rayna T, Striukova L (2016) From rapid prototyping to home fabrication: how 3D printing is changing business model innovation. Technol Forecast Soc Change 102:214–224

Resca A, Za S, Spagnoletti P (2013) Digital platforms as sources for organizational and strategic transformation: a case study of the Midblue project. J Theor Appl Electron Commer Res 8(2):71–84

Rice MP, O’Connor GC, Peters LS, Morone JG (1998) Managing discontinuous innovation. Res-Technol Manag 41(3):52–58

Rigby DK, Sutherland J, Takeuchi H (2016) Embracing agile. Harv Bus Rev 94(5):40–50

Rindova VP, Kotha S (2001) Continuous “morphing”: competing through dynamic capabilities, form, and function. Acad Manag J 44(6):1263–1280

Rowe F (2014) What literature review is not: diversity, boundaries and recommendations. Eur J Inf Syst 23(3):241–255

Roy R, Sarkar MB (2016) Knowledge, firm boundaries, and innovation: mitigating the incumbent’s curse during radical technological change. Strateg Manag J 37(5):835–854

Sabatier V, Craig-Kennard A, Mangematin V (2012) When technological discontinuities and disruptive business models challenge dominant industry logics: insights from the drugs industry. Technol Forecast Soc Change 79(5):949–962

Sambamurthy V, Bharadwaj A, Grover V (2003) Shaping agility through digital options: reconceptualizing the role of information technology in contemporary firms. MIS Q 27(2):237–263

Sandström CG (2016) The non-disruptive emergence of an ecosystem for 3D Printing—insights from the hearing aid industry’s transition 1989–2008. Technol Forecast Soc Change 102:160–168

Schendel D (1990) Introduction to the special issue on corporate entrepreneurship. Strateg Manag J 11:1–3

Schollhammer H (1982) Internal corporate entrepreneurship. In: Kent CA, Sexton DL, Vesper KH (eds) Encyclopedia of entrepreneurship. Prentice Hall, Englewood Cliffs, pp 209–229

Schuchmann D, Seufert S (2015) Corporate learning in times of digital transformation: a conceptual framework and service portfolio for the learning function in banking organisations. Int J Adv Corp Learn 8(1):31–39

Scott WR (1992) Organizations rational, natural, and open systems. Prentice Hall, Englewood Cliffs

Sebastian IM, Ross JW, Beath C, Mocker M, Moloney KG, Fonstad NO (2017) How big old companies navigate digital transformation. MIS Q Exec 16(3):197–213

Setia P, Venkatesh V, Joglekar S (2013) Leveraging digital technologies: How information quality leads to localized capabilities and customer service performance. MIS Q 37(2):565–590

Seufert S, Meier C (2016) From eLearning to digital transformation: a framework and implications for LandD. Int J Adv Corp Learn 9(2):27–33

Shaughnessy H (2016) Harnessing platform-based business models to power disruptive innovation. Strategy Leadersh 44(5):6–14

Shimizu K (2012) Risks of corporate entrepreneurship: autonomy and agency issues. Org Sci 23(1):194–206

Sia SK, Soh C, Weill P (2016) How DBS bank pursued a digital business strategy. MIS Q Exec 15(2):105–121

Siebels J, Knyphausen-Aufseß D (2012) A review of theory in family business research: the implications for corporate governance. Int J Manag Rev 14:280–304

Singh A, Hess T (2017) How chief digital officers promote the digital transformation of their companies. MIS Q Exec 16(1):1–17

Stopford JM, Baden-Fuller CW (1994) Creating corporate entrepreneurship. Strateg Manag J 15(7):521–536

Svahn F, Mathiassen L, Lindgren R (2017) Embracing digital innovation in incumbent firms: how Volvo cars managed competing concerns. MIS Q 41(1):239–253

Teece DJ (2007) Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strateg Manag J 28(13):1319–1350

Teece DJ (2014) A dynamic capabilities-based entrepreneurial theory of the multinational enterprise. J Int Bus Stud 45(1):8–37

Teece DJ, Pisano G, Shuen A (1997) Dynamic capabilities and strategic management. Strateg Manag J 18(7):509–533

Templier M, Paré G (2015) A framework for guiding and evaluating literature reviews. Commun Assoc Inf Syst 37:112–137

Thompson JD, Bates FL (1957) Technology, organization, and administration. Adm Sci Q 2:325–342

Thornberry N (2001) Corporate entrepreneurship: antidote or oxymoron? Eur Manag J 19(5):526–533

Tongur S, Engwall M (2014) The business model dilemma of technology shifts. Technovation 34(9):525–535

Tranfield D, Denyer D, Smart P (2003) Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Br J Manag 14(3):207–222

Tumbas S, Berente N, vom Brocke J (2017) Three types of chief digital officers and the reasons organizations adopt the role. MIS Q Exec 16(2):121–134

Turner T, Pennington WW (2015) Organizational networks and the process of corporate entrepreneurship: how the motivation, opportunity, and ability to act affect firm knowledge, learning, and innovation. Small Bus Econ 45(2):447–463

Turró A, Urbano D, Peris-Ortiz M (2014) Culture and innovation: the moderating effect of cultural values on corporate entrepreneurship. Technol Forecast Soc Change 88:360–369

Tushman ML, Anderson P (1986) Technological discontinuities and organizational environments. Adm Sci Q 31:439–465

Urbano D, Turró A (2013) Conditioning factors for corporate entrepreneurship: an in (ex)ternal approach. Int Entrep Manag J 9(3):379–396

Utterback JM (1994) Mastering the dynamics of innovation. Harvard Business School Press, Boston

Vecchiato R (2017) Disruptive innovation, managerial cognition, and technology competition outcomes. Technol Forecast Soc Change 116:116–128

Vey K, Fandel-Meyer T, Zipp JS, Schneider C (2017) Learning and development in times of digital transformation: facilitating a culture of change and innovation. Int J Adv Corp Learn 10(1):22–32

Vom Brocke J, Simons A, Riemer K, Niehaves B, Plattfaut R, Cleven A (2015) Standing on the shoulders of giants: challenges and recommendations of literature search in information systems research. Commun Assoc Inf Syst 37(1):9

von Krogh G, Rossi-Lamastra C, Haefliger S (2012) Phenomenon-based research in management and organisation science: when is it rigorous and does it matter? Long Range Plan 45(4):277–298

von Pechmann F, Midler C, Maniak R, Charue-Duboc F (2015) Managing systemic and disruptive innovation: lessons from the Renault Zero Emission Initiative. Ind Corp Change 24(3):677–695

Webster J, Watson RT (2002) Analyzing the past to prepare for the future: writing a literature review. MIS Q 26(2)::xiii–xxiii

Weill P, Woerner SL (2015) Thriving in an increasingly digital ecosystem. MIT Sloan Manag Rev 56(4):27–34

White M (2012) Digital workplaces: vision and reality. Bus Inf Rev 29(4):205–214

Woodward J (1965) Industrial organization theory and practice. Oxford University Press, New York

Yeow A, Soh C, Hansen R (2018) Aligning with new digital strategy: a dynamic capabilities approach. J Strateg Inf Syst 27(1):43–58

Yoo Y, Henfridsson O, Lyytinen K (2010) Research commentary—the new organizing logic of digital innovation: an agenda for information systems research. Inf Syst Res 21(4):724–735

Yoo Y, Boland RJ Jr, Lyytinen K, Majchrzak A (2012) Organizing for innovation in the digitized world. Organ Sci 23(5):1398–1408

Yu D, Hang CC (2010) A reflective review of disruptive innovation theory. Int J Manag Rev 12(4):435–452

Zahra SA (1993) A conceptual model of entrepreneurship as firm behavior: a critique and extension. Entrepreneursh Theory Pract 17(4):5–21

Zahra SA (1996) Goverance, ownership, and corporate entrepreneurship: the moderating impact of industry technological opportunities. Acad Manag J 39(6):1713–1735

Zahra SA (2015) Corporate entrepreneurship as knowledge creation and conversion: the role of entrepreneurial hubs. Small Bus Econ 44(4):727–735

Zahra SA, Covin JG (1995) Contextual influences on the corporate entrepreneurship–performance relationship: a longitudinal analysis. J Bus Ventur 10(1):43–58

Zahra SA, Nielsen AP, Bogner WC (1999) Corporate entrepreneurship, knowledge, and competence development. Entrepreneursh Theory Pract 23(3):169–189

Zammuto RF, Griffith TL, Majchrzak A, Dougherty DJ, Faraj S (2007) Information technology and the changing fabric of organization. Org Sci 18(5):749–762

Zeng Q, Chen W, Huang L (2008) E-business transformation: an analysis framework based on critical organizational dimensions. Tsinghua Sci Technol 13(3):408–413

Download references

Acknowledgements

Open Access funding provided by Projekt DEAL.

Author information

Authors and affiliations.

Chair of Innovation, Technology & Entrepreneurship, Zeppelin University, Am Seemooser Horn 20, 88045, Friedrichshafen, Germany

Swen Nadkarni & Reinhard Prügl

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Swen Nadkarni .

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations

See Tables  4 , 5 , 6 and 7 and Figs.  5 and 6 .

Data structure for the technology-centric dimension of technological disruption

Data structure for the technology-centric dimension of corporate entrepreneurship

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Nadkarni, S., Prügl, R. Digital transformation: a review, synthesis and opportunities for future research. Manag Rev Q 71 , 233–341 (2021). https://doi.org/10.1007/s11301-020-00185-7

Download citation

Received : 14 July 2019

Accepted : 28 March 2020

Published : 18 April 2020

Issue Date : April 2021

DOI : https://doi.org/10.1007/s11301-020-00185-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Digital transformation
• Digital disruption
• Technological disruption
• Corporate entrepreneurship
• Literature review
• Research agenda

JEL Classification

• Find a journal
• Publish with us
• Track your research

Education Technology: An Evidence-Based Review

In recent years, there has been widespread excitement around the potential for technology to transform learning. As investments in education technology continue to grow, students, parents, and teachers face a seemingly endless array of education technologies from which to choose—from digital personalized learning platforms to educational games to online courses. Amidst the excitement, it is important to step back and understand how technology can help—or in some cases hinder—how students learn. This review paper synthesizes and discusses experimental evidence on the effectiveness of technology-based approaches in education and outlines areas for future inquiry. In particular, we examine RCTs across the following categories of education technology: (1) access to technology, (2) computer-assisted learning, (3) technology-enabled behavioral interventions in education, and (4) online learning. While this review focuses on literature from developed countries, it also draws upon extensive research from developing countries. We hope this literature review will advance the knowledge base of how technology can be used to support education, outline key areas for new experimental research, and help drive improvements to the policies, programs, and structures that contribute to successful teaching and learning.

We are extremely grateful to Caitlin Anzelone, Rekha Balu, Peter Bergman, Brad Bernatek, Ben Castleman, Luke Crowley, Angela Duckworth, Jonathan Guryan, Alex Haslam, Andrew Ho, Ben Jones, Matthew Kraft, Kory Kroft, David Laibson, Susanna Loeb, Andrew Magliozzi, Ignacio Martinez, Susan Mayer, Steve Mintz, Piotr Mitros, Lindsay Page, Amanda Pallais, John Pane, Justin Reich, Jonah Rockoff, Sylvi Rzepka, Kirby Smith, and Oscar Sweeten-Lopez for providing helpful and detailed comments as we put together this review. We also thank Rachel Glennerster for detailed support throughout the project, Jessica Mardo and Sophie Shank for edits, and to the Spencer Foundation for financial support. Any errors or omissions are our own. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

MARC RIS BibTeΧ

Download Citation Data

Mentioned in the News

More from nber.

In addition to working papers , the NBER disseminates affiliates’ latest findings through a range of free periodicals — the NBER Reporter , the NBER Digest , the Bulletin on Retirement and Disability , the Bulletin on Health , and the Bulletin on Entrepreneurship  — as well as online conference reports , video lectures , and interviews .

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• NEWS & VIEWS FORUM
• 10 February 2020

Scrutinizing the effects of digital technology on mental health

• Jonathan Haidt &

You can also search for this author in PubMed   Google Scholar

You have full access to this article via your institution.

The topic in brief

• There is an ongoing debate about whether social media and the use of digital devices are detrimental to mental health.

• Adolescents tend to be heavy users of these devices, and especially of social media.

• Rates of teenage depression began to rise around 2012, when adolescent use of social media became common (Fig. 1).

• Some evidence indicates that frequent users of social media have higher rates of depression and anxiety than do light users.

• But perhaps digital devices could provide a way of gathering data about mental health in a systematic way, and make interventions more timely.

Figure 1 | Depression on the rise. Rates of depression among teenagers in the United States have increased steadily since 2012. Rates are higher and are increasing more rapidly for girls than for boys. Some researchers think that social media is the cause of this increase, whereas others see social media as a way of tackling it. (Data taken from the US National Survey on Drug Use and Health, Table 11.2b; go.nature.com/3ayjaww )

JONATHAN HAIDT: A guilty verdict

A sudden increase in the rates of depression, anxiety and self-harm was seen in adolescents — particularly girls — in the United States and the United Kingdom around 2012 or 2013 (see go.nature.com/2up38hw ). Only one suspect was in the right place at the right time to account for this sudden change: social media. Its use by teenagers increased most quickly between 2009 and 2011, by which point two-thirds of 15–17-year-olds were using it on a daily basis 1 . Some researchers defend social media, arguing that there is only circumstantial evidence for its role in mental-health problems 2 , 3 . And, indeed, several studies 2 , 3 show that there is only a small correlation between time spent on screens and bad mental-health outcomes. However, I present three arguments against this defence.

First, the papers that report small or null effects usually focus on ‘screen time’, but it is not films or video chats with friends that damage mental health. When research papers allow us to zoom in on social media, rather than looking at screen time as a whole, the correlations with depression are larger, and they are larger still when we look specifically at girls ( go.nature.com/2u74der ). The sex difference is robust, and there are several likely causes for it. Girls use social media much more than do boys (who, in turn, spend more of their time gaming). And, for girls more than boys, social life and status tend to revolve around intimacy and inclusion versus exclusion 4 , making them more vulnerable to both the ‘fear of missing out’ and the relational aggression that social media facilitates.

Second, although correlational studies can provide only circumstantial evidence, most of the experiments published in recent years have found evidence of causation ( go.nature.com/2u74der ). In these studies, people are randomly assigned to groups that are asked to continue using social media or to reduce their use substantially. After a few weeks, people who reduce their use generally report an improvement in mood or a reduction in loneliness or symptoms of depression.

The best way forward

Third, many researchers seem to be thinking about social media as if it were sugar: safe in small to moderate quantities, and harmful only if teenagers consume large quantities. But, unlike sugar, social media does not act just on those who consume it. It has radically transformed the nature of peer relationships, family relationships and daily activities 5 . When most of the 11-year-olds in a class are on Instagram (as was the case in my son’s school), there can be pervasive effects on everyone. Children who opt out can find themselves isolated. A simple dose–response model cannot capture the full effects of social media, yet nearly all of the debate among researchers so far has been over the size of the dose–response effect. To cite just one suggestive finding of what lies beyond that model: network effects for depression and anxiety are large, and bad mental health spreads more contagiously between women than between men 6 .

In conclusion, digital media in general undoubtedly has many beneficial uses, including the treatment of mental illness. But if you focus on social media, you’ll find stronger evidence of harm, and less exculpatory evidence, especially for its millions of under-age users.

What should we do while researchers hash out the meaning of these conflicting findings? I would urge a focus on middle schools (roughly 11–13-year-olds in the United States), both for researchers and policymakers. Any US state could quickly conduct an informative experiment beginning this September: randomly assign a portion of school districts to ban smartphone access for students in middle school, while strongly encouraging parents to prevent their children from opening social-media accounts until they begin high school (at around 14). Within 2 years, we would know whether the policy reversed the otherwise steady rise of mental-health problems among middle-school students, and whether it also improved classroom dynamics (as rated by teachers) and test scores. Such system-wide and cross-school interventions would be an excellent way to study the emergent effects of social media on the social lives and mental health of today’s adolescents.

NICK ALLEN: Use digital technology to our advantage

It is appealing to condemn social media out of hand on the basis of the — generally rather poor-quality and inconsistent — evidence suggesting that its use is associated with mental-health problems 7 . But focusing only on its potential harmful effects is comparable to proposing that the only question to ask about cars is whether people can die driving them. The harmful effects might be real, but they don’t tell the full story. The task of research should be to understand what patterns of digital-device and social-media use can lead to beneficial versus harmful effects 7 , and to inform evidence-based approaches to policy, education and regulation.

Long-standing problems have hampered our efforts to improve access to, and the quality of, mental-health services and support. Digital technology has the potential to address some of these challenges. For instance, consider the challenges associated with collecting data on human behaviour. Assessment in mental-health care and research relies almost exclusively on self-reporting, but the resulting data are subjective and burdensome to collect. As a result, assessments are conducted so infrequently that they do not provide insights into the temporal dynamics of symptoms, which can be crucial for both diagnosis and treatment planning.

By contrast, mobile phones and other Internet-connected devices provide an opportunity to continuously collect objective information on behaviour in the context of people’s real lives, generating a rich data set that can provide insight into the extent and timing of mental-health needs in individuals 8 , 9 . By building apps that can track our digital exhaust (the data generated by our everyday digital lives, including our social-media use), we can gain insights into aspects of behaviour that are well-established building blocks of mental health and illness, such as mood, social communication, sleep and physical activity.

Stress and the city

These data can, in turn, be used to empower individuals, by giving them actionable insights into patterns of behaviour that might otherwise have remained unseen. For example, subtle shifts in patterns of sleep or social communication can provide early warning signs of deteriorating mental health. Data on these patterns can be used to alert people to the need for self-management before the patterns — and the associated symptoms — become more severe. Individuals can also choose to share these data with health professionals or researchers. For instance, in the Our Data Helps initiative, individuals who have experienced a suicidal crisis, or the relatives of those who have died by suicide, can donate their digital data to research into suicide risk.

Because mobile devices are ever-present in people’s lives, they offer an opportunity to provide interventions that are timely, personalized and scalable. Currently, mental-health services are mainly provided through a century-old model in which they are made available at times chosen by the mental-health practitioner, rather than at the person’s time of greatest need. But Internet-connected devices are facilitating the development of a wave of ‘just-in-time’ interventions 10 for mental-health care and support.

A compelling example of these interventions involves short-term risk for suicide 9 , 11 — for which early detection could save many lives. Most of the effective approaches to suicide prevention work by interrupting suicidal actions and supporting alternative methods of coping at the moment of greatest risk. If these moments can be detected in an individual’s digital exhaust, a wide range of intervention options become available, from providing information about coping skills and social support, to the initiation of crisis responses. So far, just-in-time approaches have been applied mainly to behaviours such as eating or substance abuse 8 . But with the development of an appropriate research base, these approaches have the potential to provide a major advance in our ability to respond to, and prevent, mental-health crises.

These advantages are particularly relevant to teenagers. Because of their extensive use of digital devices, adolescents are especially vulnerable to the devices’ risks and burdens. And, given the increases in mental-health problems in this age group, teens would also benefit most from improvements in mental-health prevention and treatment. If we use the social and data-gathering functions of Internet-connected devices in the right ways, we might achieve breakthroughs in our ability to improve mental health and well-being.

Nature 578 , 226-227 (2020)

doi: https://doi.org/10.1038/d41586-020-00296-x

Twenge, J. M., Martin, G. N. & Spitzberg, B. H. Psychol. Pop. Media Culture 8 , 329–345 (2019).

Article   Google Scholar  

Orben, A. & Przybylski, A. K. Nature Hum. Behav. 3 , 173–182 (2019).

Article   PubMed   Google Scholar  

Odgers, C. L. & Jensen, M. R. J. Child Psychol. Psychiatry https://doi.org/10.1111/jcpp.13190 (2020).

Maccoby, E. E. The Two Sexes: Growing Up Apart, Coming Together Ch. 2 (Harvard Univ. Press, 1999).

Google Scholar  

Nesi, J., Choukas-Bradley, S. & Prinstein, M. J. Clin. Child. Fam. Psychol. Rev. 21 , 267–294 (2018).

Rosenquist, J. N., Fowler, J. H. & Christakis, N. A. Molec. Psychiatry 16 , 273–281 (2011).

Orben, A. Social Psychiatry Psychiatr. Epidemiol. https://doi.org/10.1007/s00127-019-01825-4 (2020).

Mohr, D. C., Zhang, M. & Schueller, S. M. Annu. Rev. Clin. Psychol. 13 , 23–47 (2017).

Nelson, B. W. & Allen, N. B. Perspect. Psychol. Sci. 13 , 718–733 (2018).

Nahum-Shani, I. et al. Ann. Behav. Med. 52 , 446–462 (2018).

Allen, N. B., Nelson, B. W., Brent, D. & Auerbach, R. P. J. Affect. Disord. 250 , 163–169 (2019).

Download references

Reprints and permissions

Competing Interests

N.A. has an equity interest in Ksana Health, a company he co-founded and which has the sole commercial licence for certain versions of the Effortless Assessment of Risk States (EARS) mobile-phone application and some related EARS tools. This intellectual property was developed as part of his research at the University of Oregon’s Center for Digital Mental Health (CDMH).

Related Articles

See all News & Views

• Human behaviour

How a tree-hugging protest transformed Indian environmentalism

Comment 26 MAR 24

Scientists under arrest: the researchers taking action over climate change

News Feature 21 FEB 24

Gender bias is more exaggerated in online images than in text

News & Views 14 FEB 24

Interpersonal therapy can be an effective tool against the devastating effects of loneliness

Correspondence 14 MAY 24

Use fines from EU social-media act to fund research on adolescent mental health

Correspondence 09 APR 24

Circulating myeloid-derived MMP8 in stress susceptibility and depression

Article 07 FEB 24

Daniel Kahneman obituary: psychologist who revolutionized the way we think about thinking

Obituary 03 MAY 24

Pandemic lockdowns were less of a shock for people with fewer ties

Research Highlight 01 MAY 24

Rwanda 30 years on: understanding the horror of genocide

Editorial 09 APR 24

Postdoc in CRISPR Meta-Analytics and AI for Therapeutic Target Discovery and Priotisation (OT Grant)

APPLICATION CLOSING DATE: 14/06/2024 Human Technopole (HT) is a new interdisciplinary life science research institute created and supported by the...

Human Technopole

Research Associate - Metabolism

Houston, Texas (US)

Baylor College of Medicine (BCM)

Postdoc Fellowships

Train with world-renowned cancer researchers at NIH? Consider joining the Center for Cancer Research (CCR) at the National Cancer Institute

Bethesda, Maryland

NIH National Cancer Institute (NCI)

Faculty Recruitment, Westlake University School of Medicine

Faculty positions are open at four distinct ranks: Assistant Professor, Associate Professor, Full Professor, and Chair Professor.

Hangzhou, Zhejiang, China

Westlake University

PhD/master's Candidate

PhD/master's Candidate    Graduate School of Frontier Science Initiative, Kanazawa University is seeking candidates for PhD and master's students i...

Kanazawa University

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

McKinsey Technology Trends Outlook 2023

After a tumultuous 2022 for technology investment and talent, the first half of 2023 has seen a resurgence of enthusiasm about technology’s potential to catalyze progress in business and society. Generative AI deserves much of the credit for ushering in this revival, but it stands as just one of many advances on the horizon that could drive sustainable, inclusive growth and solve complex global challenges.

To help executives track the latest developments, the McKinsey Technology Council  has once again identified and interpreted the most significant technology trends unfolding today. While many trends are in the early stages of adoption and scale, executives can use this research to plan ahead by developing an understanding of potential use cases and pinpointing the critical skills needed as they hire or upskill talent to bring these opportunities to fruition.

Our analysis examines quantitative measures of interest, innovation, and investment to gauge the momentum of each trend. Recognizing the long-term nature and interdependence of these trends, we also delve into underlying technologies, uncertainties, and questions surrounding each trend. This year, we added an important new dimension for analysis—talent. We provide data on talent supply-and-demand dynamics for the roles of most relevance to each trend. (For more, please see the sidebar, “Research methodology.”)

New and notable

All of last year’s 14 trends remain on our list, though some experienced accelerating momentum and investment, while others saw a downshift. One new trend, generative AI, made a loud entrance and has already shown potential for transformative business impact.

Research methodology

To assess the development of each technology trend, our team collected data on five tangible measures of activity: search engine queries, news publications, patents, research publications, and investment. For each measure, we used a defined set of data sources to find occurrences of keywords associated with each of the 15 trends, screened those occurrences for valid mentions of activity, and indexed the resulting numbers of mentions on a 0–1 scoring scale that is relative to the trends studied. The innovation score combines the patents and research scores; the interest score combines the news and search scores. (While we recognize that an interest score can be inflated by deliberate efforts to stimulate news and search activity, we believe that each score fairly reflects the extent of discussion and debate about a given trend.) Investment measures the flows of funding from the capital markets into companies linked with the trend. Data sources for the scores include the following:

• Patents. Data on patent filings are sourced from Google Patents.
• Research. Data on research publications are sourced from the Lens (www.lens.org).
• News. Data on news publications are sourced from Factiva.
• Searches. Data on search engine queries are sourced from Google Trends.
• Investment. Data on private-market and public-market capital raises are sourced from PitchBook.
• Talent demand. Number of job postings is sourced from McKinsey’s proprietary Organizational Data Platform, which stores licensed, de-identified data on professional profiles and job postings. Data is drawn primarily from English-speaking countries.

In addition, we updated the selection and definition of trends from last year’s study to reflect the evolution of technology trends:

• The generative-AI trend was added since last year’s study.
• We adjusted the definitions of electrification and renewables (previously called future of clean energy) and climate technologies beyond electrification and renewables (previously called future of sustainable consumption).
• Data sources were updated. This year, we included only closed deals in PitchBook data, which revised downward the investment numbers for 2018–22. For future of space technologies investments, we used research from McKinsey’s Aerospace & Defense Practice.

This new entrant represents the next frontier of AI. Building upon existing technologies such as applied AI and industrializing machine learning, generative AI has high potential and applicability across most industries. Interest in the topic (as gauged by news and internet searches) increased threefold from 2021 to 2022. As we recently wrote, generative AI and other foundational models  change the AI game by taking assistive technology to a new level, reducing application development time, and bringing powerful capabilities to nontechnical users. Generative AI is poised to add as much as $4.4 trillion in economic value from a combination of specific use cases and more diffuse uses—such as assisting with email drafts—that increase productivity. Still, while generative AI can unlock significant value, firms should not underestimate the economic significance and the growth potential that underlying AI technologies and industrializing machine learning can bring to various industries.

Investment in most tech trends tightened year over year, but the potential for future growth remains high, as further indicated by the recent rebound in tech valuations. Indeed, absolute investments remained strong in 2022, at more than $1 trillion combined, indicating great faith in the value potential of these trends. Trust architectures and digital identity grew the most out of last year’s 14 trends, increasing by nearly 50 percent as security, privacy, and resilience become increasingly critical across industries. Investment in other trends—such as applied AI, advanced connectivity, and cloud and edge computing—declined, but that is likely due, at least in part, to their maturity. More mature technologies can be more sensitive to short-term budget dynamics than more nascent technologies with longer investment time horizons, such as climate and mobility technologies. Also, as some technologies become more profitable, they can often scale further with lower marginal investment. Given that these technologies have applications in most industries, we have little doubt that mainstream adoption will continue to grow.

Organizations shouldn’t focus too heavily on the trends that are garnering the most attention. By focusing on only the most hyped trends, they may miss out on the significant value potential of other technologies and hinder the chance for purposeful capability building. Instead, companies seeking longer-term growth should focus on a portfolio-oriented investment across the tech trends most important to their business. Technologies such as cloud and edge computing and the future of bioengineering have shown steady increases in innovation and continue to have expanded use cases across industries. In fact, more than 400 edge use cases across various industries have been identified, and edge computing is projected to win double-digit growth globally over the next five years. Additionally, nascent technologies, such as quantum, continue to evolve and show significant potential for value creation. Our updated analysis for 2023 shows that the four industries likely to see the earliest economic impact from quantum computing—automotive, chemicals, financial services, and life sciences—stand to potentially gain up to $1.3 trillion in value by 2035. By carefully assessing the evolving landscape and considering a balanced approach, businesses can capitalize on both established and emerging technologies to propel innovation and achieve sustainable growth.

Tech talent dynamics

We can’t overstate the importance of talent as a key source in developing a competitive edge. A lack of talent is a top issue constraining growth. There’s a wide gap between the demand for people with the skills needed to capture value from the tech trends and available talent: our survey of 3.5 million job postings in these tech trends found that many of the skills in greatest demand have less than half as many qualified practitioners per posting as the global average. Companies should be on top of the talent market, ready to respond to notable shifts and to deliver a strong value proposition to the technologists they hope to hire and retain. For instance, recent layoffs in the tech sector may present a silver lining for other industries that have struggled to win the attention of attractive candidates and retain senior tech talent. In addition, some of these technologies will accelerate the pace of workforce transformation. In the coming decade, 20 to 30 percent of the time that workers spend on the job could be transformed by automation technologies, leading to significant shifts in the skills required to be successful. And companies should continue to look at how they can adjust roles or upskill individuals to meet their tailored job requirements. Job postings in fields related to tech trends grew at a very healthy 15 percent between 2021 and 2022, even though global job postings overall decreased by 13 percent. Applied AI and next-generation software development together posted nearly one million jobs between 2018 and 2022. Next-generation software development saw the most significant growth in number of jobs (exhibit).

Image description:

Small multiples of 15 slope charts show the number of job postings in different fields related to tech trends from 2021 to 2022. Overall growth of all fields combined was about 400,000 jobs, with applied AI having the most job postings in 2022 and experiencing a 6% increase from 2021. Next-generation software development had the second-highest number of job postings in 2022 and had 29% growth from 2021. Other categories shown, from most job postings to least in 2022, are as follows: cloud and edge computing, trust architecture and digital identity, future of mobility, electrification and renewables, climate tech beyond electrification and renewables, advanced connectivity, immersive-reality technologies, industrializing machine learning, Web3, future of bioengineering, future of space technologies, generative AI, and quantum technologies.

End of image description.

This bright outlook for practitioners in most fields highlights the challenge facing employers who are struggling to find enough talent to keep up with their demands. The shortage of qualified talent has been a persistent limiting factor in the growth of many high-tech fields, including AI, quantum technologies, space technologies, and electrification and renewables. The talent crunch is particularly pronounced for trends such as cloud computing and industrializing machine learning, which are required across most industries. It’s also a major challenge in areas that employ highly specialized professionals, such as the future of mobility and quantum computing (see interactive).

Michael Chui is a McKinsey Global Institute partner in McKinsey’s Bay Area office, where Mena Issler is an associate partner, Roger Roberts  is a partner, and Lareina Yee  is a senior partner.

The authors wish to thank the following McKinsey colleagues for their contributions to this research: Bharat Bahl, Soumya Banerjee, Arjita Bhan, Tanmay Bhatnagar, Jim Boehm, Andreas Breiter, Tom Brennan, Ryan Brukardt, Kevin Buehler, Zina Cole, Santiago Comella-Dorda, Brian Constantine, Daniela Cuneo, Wendy Cyffka, Chris Daehnick, Ian De Bode, Andrea Del Miglio, Jonathan DePrizio, Ivan Dyakonov, Torgyn Erland, Robin Giesbrecht, Carlo Giovine, Liz Grennan, Ferry Grijpink, Harsh Gupta, Martin Harrysson, David Harvey, Kersten Heineke, Matt Higginson, Alharith Hussin, Tore Johnston, Philipp Kampshoff, Hamza Khan, Nayur Khan, Naomi Kim, Jesse Klempner, Kelly Kochanski, Matej Macak, Stephanie Madner, Aishwarya Mohapatra, Timo Möller, Matt Mrozek, Evan Nazareth, Peter Noteboom, Anna Orthofer, Katherine Ottenbreit, Eric Parsonnet, Mark Patel, Bruce Philp, Fabian Queder, Robin Riedel, Tanya Rodchenko, Lucy Shenton, Henning Soller, Naveen Srikakulam, Shivam Srivastava, Bhargs Srivathsan, Erika Stanzl, Brooke Stokes, Malin Strandell-Jansson, Daniel Wallance, Allen Weinberg, Olivia White, Martin Wrulich, Perez Yeptho, Matija Zesko, Felix Ziegler, and Delphine Zurkiya.

They also wish to thank the external members of the McKinsey Technology Council.

This interactive was designed, developed, and edited by McKinsey Global Publishing’s Nayomi Chibana, Victor Cuevas, Richard Johnson, Stephanie Jones, Stephen Landau, LaShon Malone, Kanika Punwani, Katie Shearer, Rick Tetzeli, Sneha Vats, and Jessica Wang.

Explore a career with us

Related articles.

McKinsey Technology Trends Outlook 2022

Value creation in the metaverse

Quantum computing funding remains strong, but talent gap raises concern

• Browse All Articles
• Newsletter Sign-Up

TechnologicalInnovation →

No results found in working knowledge.

• Were any results found in one of the other content buckets on the left?
• Try removing some search filters.
• Use different search filters.

Along with Stanford news and stories, show me:

• Student information
• Faculty/Staff information

We want to provide announcements, events, leadership messages and resources that are relevant to you. Your selection is stored in a browser cookie which you can remove at any time using “Clear all personalization” below.

Image credit: Claire Scully

New advances in technology are upending education, from the recent debut of new artificial intelligence (AI) chatbots like ChatGPT to the growing accessibility of virtual-reality tools that expand the boundaries of the classroom. For educators, at the heart of it all is the hope that every learner gets an equal chance to develop the skills they need to succeed. But that promise is not without its pitfalls.

“Technology is a game-changer for education – it offers the prospect of universal access to high-quality learning experiences, and it creates fundamentally new ways of teaching,” said Dan Schwartz, dean of Stanford Graduate School of Education (GSE), who is also a professor of educational technology at the GSE and faculty director of the Stanford Accelerator for Learning . “But there are a lot of ways we teach that aren’t great, and a big fear with AI in particular is that we just get more efficient at teaching badly. This is a moment to pay attention, to do things differently.”

For K-12 schools, this year also marks the end of the Elementary and Secondary School Emergency Relief (ESSER) funding program, which has provided pandemic recovery funds that many districts used to invest in educational software and systems. With these funds running out in September 2024, schools are trying to determine their best use of technology as they face the prospect of diminishing resources.

Here, Schwartz and other Stanford education scholars weigh in on some of the technology trends taking center stage in the classroom this year.

AI in the classroom

In 2023, the big story in technology and education was generative AI, following the introduction of ChatGPT and other chatbots that produce text seemingly written by a human in response to a question or prompt. Educators immediately worried that students would use the chatbot to cheat by trying to pass its writing off as their own. As schools move to adopt policies around students’ use of the tool, many are also beginning to explore potential opportunities – for example, to generate reading assignments or coach students during the writing process.

AI can also help automate tasks like grading and lesson planning, freeing teachers to do the human work that drew them into the profession in the first place, said Victor Lee, an associate professor at the GSE and faculty lead for the AI + Education initiative at the Stanford Accelerator for Learning. “I’m heartened to see some movement toward creating AI tools that make teachers’ lives better – not to replace them, but to give them the time to do the work that only teachers are able to do,” he said. “I hope to see more on that front.”

He also emphasized the need to teach students now to begin questioning and critiquing the development and use of AI. “AI is not going away,” said Lee, who is also director of CRAFT (Classroom-Ready Resources about AI for Teaching), which provides free resources to help teach AI literacy to high school students across subject areas. “We need to teach students how to understand and think critically about this technology.”

Immersive environments

The use of immersive technologies like augmented reality, virtual reality, and mixed reality is also expected to surge in the classroom, especially as new high-profile devices integrating these realities hit the marketplace in 2024.

The educational possibilities now go beyond putting on a headset and experiencing life in a distant location. With new technologies, students can create their own local interactive 360-degree scenarios, using just a cell phone or inexpensive camera and simple online tools.

“This is an area that’s really going to explode over the next couple of years,” said Kristen Pilner Blair, director of research for the Digital Learning initiative at the Stanford Accelerator for Learning, which runs a program exploring the use of virtual field trips to promote learning. “Students can learn about the effects of climate change, say, by virtually experiencing the impact on a particular environment. But they can also become creators, documenting and sharing immersive media that shows the effects where they live.”

Integrating AI into virtual simulations could also soon take the experience to another level, Schwartz said. “If your VR experience brings me to a redwood tree, you could have a window pop up that allows me to ask questions about the tree, and AI can deliver the answers.”

Gamification

Another trend expected to intensify this year is the gamification of learning activities, often featuring dynamic videos with interactive elements to engage and hold students’ attention.

“Gamification is a good motivator, because one key aspect is reward, which is very powerful,” said Schwartz. The downside? Rewards are specific to the activity at hand, which may not extend to learning more generally. “If I get rewarded for doing math in a space-age video game, it doesn’t mean I’m going to be motivated to do math anywhere else.”

Gamification sometimes tries to make “chocolate-covered broccoli,” Schwartz said, by adding art and rewards to make speeded response tasks involving single-answer, factual questions more fun. He hopes to see more creative play patterns that give students points for rethinking an approach or adapting their strategy, rather than only rewarding them for quickly producing a correct response.

Data-gathering and analysis

The growing use of technology in schools is producing massive amounts of data on students’ activities in the classroom and online. “We’re now able to capture moment-to-moment data, every keystroke a kid makes,” said Schwartz – data that can reveal areas of struggle and different learning opportunities, from solving a math problem to approaching a writing assignment.

But outside of research settings, he said, that type of granular data – now owned by tech companies – is more likely used to refine the design of the software than to provide teachers with actionable information.

The promise of personalized learning is being able to generate content aligned with students’ interests and skill levels, and making lessons more accessible for multilingual learners and students with disabilities. Realizing that promise requires that educators can make sense of the data that’s being collected, said Schwartz – and while advances in AI are making it easier to identify patterns and findings, the data also needs to be in a system and form educators can access and analyze for decision-making. Developing a usable infrastructure for that data, Schwartz said, is an important next step.

With the accumulation of student data comes privacy concerns: How is the data being collected? Are there regulations or guidelines around its use in decision-making? What steps are being taken to prevent unauthorized access? In 2023 K-12 schools experienced a rise in cyberattacks, underscoring the need to implement strong systems to safeguard student data.

Technology is “requiring people to check their assumptions about education,” said Schwartz, noting that AI in particular is very efficient at replicating biases and automating the way things have been done in the past, including poor models of instruction. “But it’s also opening up new possibilities for students producing material, and for being able to identify children who are not average so we can customize toward them. It’s an opportunity to think of entirely new ways of teaching – this is the path I hope to see.”

• May 17, 2024 | Tiny Titans: Revolutionary Microcapacitors Set to Supercharge Next-Gen Electronics
• May 17, 2024 | Scientists Uncover Unexpected Hidden Dangers of a Common Antibiotic
• May 17, 2024 | What if Your Earbuds Could Do Everything Your Smartphone Could but Better? New Synthetic Materials Could Make It Possible
• May 17, 2024 | NASA’s Artemis IV: Building Gateway, Humanity’s First Lunar Space Station
• May 17, 2024 | Ultrasonic Illusions: How Tiger Beetles Use Mimicry to Outsmart Bats

Technology News

Read the latest technology news on SciTechDaily, your comprehensive source for the latest breakthroughs, trends, and innovations shaping the world of technology. We bring you up-to-date insights on a wide array of topics, from cutting-edge advancements in artificial intelligence and robotics to the latest in green technologies, telecommunications, and more.

Our expertly curated content showcases the pioneering minds, revolutionary ideas, and transformative solutions that are driving the future of technology and its impact on our daily lives. Stay informed about the rapid evolution of the tech landscape, and join us as we explore the endless possibilities of the digital age.

Discover recent technology news articles on topics such as Nanotechnology ,  Artificial Intelligence , Biotechnology ,  Graphene , Green Tech , Battery Tech , Computer Tech , Engineering , and Fuel-cell Tech featuring research out of MIT , Cal Tech , Yale , Georgia Tech , Karlsruhe Tech , Vienna Tech , and Michigan Technological University . Discover the future of technology with SciTechDaily.

Technology May 17, 2024

Tiny Titans: Revolutionary Microcapacitors Set to Supercharge Next-Gen Electronics

New microcapacitors developed by scientists show record energy and power densities, paving the way for on-chip energy storage in electronic devices. Researchers are striving to…

What if Your Earbuds Could Do Everything Your Smartphone Could but Better? New Synthetic Materials Could Make It Possible

Harvard Physicists Demonstrate First Metro-Area Quantum Network in Boston

Smelting Steel Without Fossil Fuels: Solar Power Shatters the 1,000°C Barrier for Industrial Heating

MIT Engineers Create Game-Changing Lead Detection Device

How Game Theory Is Making AI Smarter

Revolutionizing Renewable Energy: Scientists Develop New Low-Cost Battery With Over 8000 Uses

The Gentle Prick: Sea Slug-Inspired Stretchable Microneedles Revolutionize Biomedical Sensors

Atomic Ballet: Scientists Make Surprising Discovery in Battery Technology

Technology May 14, 2024

New Breakthrough Paves the Way for Vision Implants That Can Restore Sight

A remarkably small implant, with electrodes the size of a single neuron that can also remain intact in the body over time – a unique…

Technology May 13, 2024

World’s Purest Silicon Paves the Way for Next-Gen Quantum Computers

A major breakthrough in quantum computing has been achieved with the development of ultra-pure silicon, setting the stage for the creation of powerful, scalable quantum…

NASA Licenses 3D-Printable High-Temperature Superalloy That Can Last 2,500x Longer

NASA’s superalloy GRX-810, designed for extreme air and spaceflight conditions, is now being licensed to four American companies, enhancing the U.S. economy through improved aerospace…

Technology May 12, 2024

Revolutionizing Photonics: Lithium Tantalate Powers Next-Gen Optical Circuits

New photonic integrated circuit technology based on lithium tantalate improves cost-efficiency and scalability, making significant advancements in optical communications and computing. Optical communications and computing…

Researchers Warn: AI Systems Have Already Learned How To Deceive Humans

Numerous artificial intelligence (AI) systems, even those designed to be helpful and truthful, have already learned how to deceive humans. In a review article recently…

Technology May 11, 2024

Stanford Scientists Develop Revolutionary AR Headset: Holographic Tech Turns Ordinary Glasses Into 3D Wonderland

Engineers at Stanford claim to have made a significant advancement in augmented reality by combining developments in display technologies, holographic imaging, and artificial intelligence. Researchers…

Revolutionary AI Device Mimics Human Brain With Few-Molecule Computing

Progress in developing compact AI devices using molecular vibrations and confirming their functionality A collaborative research team from NIMS and Tokyo University of Science has…

Technology May 10, 2024

Revolutionary Battery Tech Promises Less Charging Time, More Energy Storage

What determines the cycle life of batteries? And, more importantly, how can we extend it? An international research team led by TU Delft has discovered…

New Research Could Lead to More Efficient Televisions, Computer Screens and Lighting

New interdisciplinary research from the University of St Andrews has the potential to enhance the efficiency of televisions, computer screens, and lighting systems. Researchers at…

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Springer Nature - PMC COVID-19 Collection

Impacts of digital technologies on education and factors influencing schools' digital capacity and transformation: A literature review

Stella timotheou.

1 CYENS Center of Excellence & Cyprus University of Technology (Cyprus Interaction Lab), Cyprus, CYENS Center of Excellence & Cyprus University of Technology, Nicosia-Limassol, Cyprus

Ourania Miliou

Yiannis dimitriadis.

2 Universidad de Valladolid (UVA), Spain, Valladolid, Spain

Sara Villagrá Sobrino

Nikoleta giannoutsou, romina cachia.

3 JRC - Joint Research Centre of the European Commission, Seville, Spain

Alejandra Martínez Monés

Andri ioannou, associated data.

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Digital technologies have brought changes to the nature and scope of education and led education systems worldwide to adopt strategies and policies for ICT integration. The latter brought about issues regarding the quality of teaching and learning with ICTs, especially concerning the understanding, adaptation, and design of the education systems in accordance with current technological trends. These issues were emphasized during the recent COVID-19 pandemic that accelerated the use of digital technologies in education, generating questions regarding digitalization in schools. Specifically, many schools demonstrated a lack of experience and low digital capacity, which resulted in widening gaps, inequalities, and learning losses. Such results have engendered the need for schools to learn and build upon the experience to enhance their digital capacity and preparedness, increase their digitalization levels, and achieve a successful digital transformation. Given that the integration of digital technologies is a complex and continuous process that impacts different actors within the school ecosystem, there is a need to show how these impacts are interconnected and identify the factors that can encourage an effective and efficient change in the school environments. For this purpose, we conducted a non-systematic literature review. The results of the literature review were organized thematically based on the evidence presented about the impact of digital technology on education and the factors that affect the schools’ digital capacity and digital transformation. The findings suggest that ICT integration in schools impacts more than just students’ performance; it affects several other school-related aspects and stakeholders, too. Furthermore, various factors affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the digital transformation process. The study results shed light on how ICTs can positively contribute to the digital transformation of schools and which factors should be considered for schools to achieve effective and efficient change.

Introduction

Digital technologies have brought changes to the nature and scope of education. Versatile and disruptive technological innovations, such as smart devices, the Internet of Things (IoT), artificial intelligence (AI), augmented reality (AR) and virtual reality (VR), blockchain, and software applications have opened up new opportunities for advancing teaching and learning (Gaol & Prasolova-Førland, 2021 ; OECD, 2021 ). Hence, in recent years, education systems worldwide have increased their investment in the integration of information and communication technology (ICT) (Fernández-Gutiérrez et al., 2020 ; Lawrence & Tar, 2018 ) and prioritized their educational agendas to adapt strategies or policies around ICT integration (European Commission, 2019 ). The latter brought about issues regarding the quality of teaching and learning with ICTs (Bates, 2015 ), especially concerning the understanding, adaptation, and design of education systems in accordance with current technological trends (Balyer & Öz, 2018 ). Studies have shown that despite the investment made in the integration of technology in schools, the results have not been promising, and the intended outcomes have not yet been achieved (Delgado et al., 2015 ; Lawrence & Tar, 2018 ). These issues were exacerbated during the COVID-19 pandemic, which forced teaching across education levels to move online (Daniel, 2020 ). Online teaching accelerated the use of digital technologies generating questions regarding the process, the nature, the extent, and the effectiveness of digitalization in schools (Cachia et al., 2021 ; König et al., 2020 ). Specifically, many schools demonstrated a lack of experience and low digital capacity, which resulted in widening gaps, inequalities, and learning losses (Blaskó et al., 2021 ; Di Pietro et al, 2020 ). Such results have engendered the need for schools to learn and build upon the experience in order to enhance their digital capacity (European Commission, 2020 ) and increase their digitalization levels (Costa et al., 2021 ). Digitalization offers possibilities for fundamental improvement in schools (OECD, 2021 ; Rott & Marouane, 2018 ) and touches many aspects of a school’s development (Delcker & Ifenthaler, 2021 ) . However, it is a complex process that requires large-scale transformative changes beyond the technical aspects of technology and infrastructure (Pettersson, 2021 ). Namely, digitalization refers to “ a series of deep and coordinated culture, workforce, and technology shifts and operating models ” (Brooks & McCormack, 2020 , p. 3) that brings cultural, organizational, and operational change through the integration of digital technologies (JISC, 2020 ). A successful digital transformation requires that schools increase their digital capacity levels, establishing the necessary “ culture, policies, infrastructure as well as digital competence of students and staff to support the effective integration of technology in teaching and learning practices ” (Costa et al, 2021 , p.163).

Given that the integration of digital technologies is a complex and continuous process that impacts different actors within the school ecosystem (Eng, 2005 ), there is a need to show how the different elements of the impact are interconnected and to identify the factors that can encourage an effective and efficient change in the school environment. To address the issues outlined above, we formulated the following research questions:

a) What is the impact of digital technologies on education?

b) Which factors might affect a school’s digital capacity and transformation?

In the present investigation, we conducted a non-systematic literature review of publications pertaining to the impact of digital technologies on education and the factors that affect a school’s digital capacity and transformation. The results of the literature review were organized thematically based on the evidence presented about the impact of digital technology on education and the factors which affect the schools’ digital capacity and digital transformation.

Methodology

The non-systematic literature review presented herein covers the main theories and research published over the past 17 years on the topic. It is based on meta-analyses and review papers found in scholarly, peer-reviewed content databases and other key studies and reports related to the concepts studied (e.g., digitalization, digital capacity) from professional and international bodies (e.g., the OECD). We searched the Scopus database, which indexes various online journals in the education sector with an international scope, to collect peer-reviewed academic papers. Furthermore, we used an all-inclusive Google Scholar search to include relevant key terms or to include studies found in the reference list of the peer-reviewed papers, and other key studies and reports related to the concepts studied by professional and international bodies. Lastly, we gathered sources from the Publications Office of the European Union ( https://op.europa.eu/en/home ); namely, documents that refer to policies related to digital transformation in education.

Regarding search terms, we first searched resources on the impact of digital technologies on education by performing the following search queries: “impact” OR “effects” AND “digital technologies” AND “education”, “impact” OR “effects” AND “ICT” AND “education”. We further refined our results by adding the terms “meta-analysis” and “review” or by adjusting the search options based on the features of each database to avoid collecting individual studies that would provide limited contributions to a particular domain. We relied on meta-analyses and review studies as these consider the findings of multiple studies to offer a more comprehensive view of the research in a given area (Schuele & Justice, 2006 ). Specifically, meta-analysis studies provided quantitative evidence based on statistically verifiable results regarding the impact of educational interventions that integrate digital technologies in school classrooms (Higgins et al., 2012 ; Tolani-Brown et al., 2011 ).

However, quantitative data does not offer explanations for the challenges or difficulties experienced during ICT integration in learning and teaching (Tolani-Brown et al., 2011 ). To fill this gap, we analyzed literature reviews and gathered in-depth qualitative evidence of the benefits and implications of technology integration in schools. In the analysis presented herein, we also included policy documents and reports from professional and international bodies and governmental reports, which offered useful explanations of the key concepts of this study and provided recent evidence on digital capacity and transformation in education along with policy recommendations. The inclusion and exclusion criteria that were considered in this study are presented in Table ​ Table1 1 .

Inclusion and exclusion criteria for the selection of resources on the impact of digital technologies on education

To ensure a reliable extraction of information from each study and assist the research synthesis we selected the study characteristics of interest (impact) and constructed coding forms. First, an overview of the synthesis was provided by the principal investigator who described the processes of coding, data entry, and data management. The coders followed the same set of instructions but worked independently. To ensure a common understanding of the process between coders, a sample of ten studies was tested. The results were compared, and the discrepancies were identified and resolved. Additionally, to ensure an efficient coding process, all coders participated in group meetings to discuss additions, deletions, and modifications (Stock, 1994 ). Due to the methodological diversity of the studied documents we began to synthesize the literature review findings based on similar study designs. Specifically, most of the meta-analysis studies were grouped in one category due to the quantitative nature of the measured impact. These studies tended to refer to student achievement (Hattie et al., 2014 ). Then, we organized the themes of the qualitative studies in several impact categories. Lastly, we synthesized both review and meta-analysis data across the categories. In order to establish a collective understanding of the concept of impact, we referred to a previous impact study by Balanskat ( 2009 ) which investigated the impact of technology in primary schools. In this context, the impact had a more specific ICT-related meaning and was described as “ a significant influence or effect of ICT on the measured or perceived quality of (parts of) education ” (Balanskat, 2009 , p. 9). In the study presented herein, the main impacts are in relation to learning and learners, teaching, and teachers, as well as other key stakeholders who are directly or indirectly connected to the school unit.

The study’s results identified multiple dimensions of the impact of digital technologies on students’ knowledge, skills, and attitudes; on equality, inclusion, and social integration; on teachers’ professional and teaching practices; and on other school-related aspects and stakeholders. The data analysis indicated various factors that might affect the schools’ digital capacity and transformation, such as digital competencies, the teachers’ personal characteristics and professional development, as well as the school’s leadership and management, administration, infrastructure, etc. The impacts and factors found in the literature review are presented below.

Impacts of digital technologies on students’ knowledge, skills, attitudes, and emotions

The impact of ICT use on students’ knowledge, skills, and attitudes has been investigated early in the literature. Eng ( 2005 ) found a small positive effect between ICT use and students' learning. Specifically, the author reported that access to computer-assisted instruction (CAI) programs in simulation or tutorial modes—used to supplement rather than substitute instruction – could enhance student learning. The author reported studies showing that teachers acknowledged the benefits of ICT on pupils with special educational needs; however, the impact of ICT on students' attainment was unclear. Balanskat et al. ( 2006 ) found a statistically significant positive association between ICT use and higher student achievement in primary and secondary education. The authors also reported improvements in the performance of low-achieving pupils. The use of ICT resulted in further positive gains for students, namely increased attention, engagement, motivation, communication and process skills, teamwork, and gains related to their behaviour towards learning. Evidence from qualitative studies showed that teachers, students, and parents recognized the positive impact of ICT on students' learning regardless of their competence level (strong/weak students). Punie et al. ( 2006 ) documented studies that showed positive results of ICT-based learning for supporting low-achieving pupils and young people with complex lives outside the education system. Liao et al. ( 2007 ) reported moderate positive effects of computer application instruction (CAI, computer simulations, and web-based learning) over traditional instruction on primary school student's achievement. Similarly, Tamim et al. ( 2011 ) reported small to moderate positive effects between the use of computer technology (CAI, ICT, simulations, computer-based instruction, digital and hypermedia) and student achievement in formal face-to-face classrooms compared to classrooms that did not use technology. Jewitt et al., ( 2011 ) found that the use of learning platforms (LPs) (virtual learning environments, management information systems, communication technologies, and information- and resource-sharing technologies) in schools allowed primary and secondary students to access a wider variety of quality learning resources, engage in independent and personalized learning, and conduct self- and peer-review; LPs also provide opportunities for teacher assessment and feedback. Similar findings were reported by Fu ( 2013 ), who documented a list of benefits and opportunities of ICT use. According to the author, the use of ICTs helps students access digital information and course content effectively and efficiently, supports student-centered and self-directed learning, as well as the development of a creative learning environment where more opportunities for critical thinking skills are offered, and promotes collaborative learning in a distance-learning environment. Higgins et al. ( 2012 ) found consistent but small positive associations between the use of technology and learning outcomes of school-age learners (5–18-year-olds) in studies linking the provision and use of technology with attainment. Additionally, Chauhan ( 2017 ) reported a medium positive effect of technology on the learning effectiveness of primary school students compared to students who followed traditional learning instruction.

The rise of mobile technologies and hardware devices instigated investigations into their impact on teaching and learning. Sung et al. ( 2016 ) reported a moderate effect on students' performance from the use of mobile devices in the classroom compared to the use of desktop computers or the non-use of mobile devices. Schmid et al. ( 2014 ) reported medium–low to low positive effects of technology integration (e.g., CAI, ICTs) in the classroom on students' achievement and attitude compared to not using technology or using technology to varying degrees. Tamim et al. ( 2015 ) found a low statistically significant effect of the use of tablets and other smart devices in educational contexts on students' achievement outcomes. The authors suggested that tablets offered additional advantages to students; namely, they reported improvements in students’ notetaking, organizational and communication skills, and creativity. Zheng et al. ( 2016 ) reported a small positive effect of one-to-one laptop programs on students’ academic achievement across subject areas. Additional reported benefits included student-centered, individualized, and project-based learning enhanced learner engagement and enthusiasm. Additionally, the authors found that students using one-to-one laptop programs tended to use technology more frequently than in non-laptop classrooms, and as a result, they developed a range of skills (e.g., information skills, media skills, technology skills, organizational skills). Haßler et al. ( 2016 ) found that most interventions that included the use of tablets across the curriculum reported positive learning outcomes. However, from 23 studies, five reported no differences, and two reported a negative effect on students' learning outcomes. Similar results were indicated by Kalati and Kim ( 2022 ) who investigated the effect of touchscreen technologies on young students’ learning. Specifically, from 53 studies, 34 advocated positive effects of touchscreen devices on children’s learning, 17 obtained mixed findings and two studies reported negative effects.

More recently, approaches that refer to the impact of gamification with the use of digital technologies on teaching and learning were also explored. A review by Pan et al. ( 2022 ) that examined the role of learning games in fostering mathematics education in K-12 settings, reported that gameplay improved students’ performance. Integration of digital games in teaching was also found as a promising pedagogical practice in STEM education that could lead to increased learning gains (Martinez et al., 2022 ; Wang et al., 2022 ). However, although Talan et al. ( 2020 ) reported a medium effect of the use of educational games (both digital and non-digital) on academic achievement, the effect of non-digital games was higher.

Over the last two years, the effects of more advanced technologies on teaching and learning were also investigated. Garzón and Acevedo ( 2019 ) found that AR applications had a medium effect on students' learning outcomes compared to traditional lectures. Similarly, Garzón et al. ( 2020 ) showed that AR had a medium impact on students' learning gains. VR applications integrated into various subjects were also found to have a moderate effect on students’ learning compared to control conditions (traditional classes, e.g., lectures, textbooks, and multimedia use, e.g., images, videos, animation, CAI) (Chen et al., 2022b ). Villena-Taranilla et al. ( 2022 ) noted the moderate effect of VR technologies on students’ learning when these were applied in STEM disciplines. In the same meta-analysis, Villena-Taranilla et al. ( 2022 ) highlighted the role of immersive VR, since its effect on students’ learning was greater (at a high level) across educational levels (K-6) compared to semi-immersive and non-immersive integrations. In another meta-analysis study, the effect size of the immersive VR was small and significantly differentiated across educational levels (Coban et al., 2022 ). The impact of AI on education was investigated by Su and Yang ( 2022 ) and Su et al. ( 2022 ), who showed that this technology significantly improved students’ understanding of AI computer science and machine learning concepts.

It is worth noting that the vast majority of studies referred to learning gains in specific subjects. Specifically, several studies examined the impact of digital technologies on students’ literacy skills and reported positive effects on language learning (Balanskat et al., 2006 ; Grgurović et al., 2013 ; Friedel et al., 2013 ; Zheng et al., 2016 ; Chen et al., 2022b ; Savva et al., 2022 ). Also, several studies documented positive effects on specific language learning areas, namely foreign language learning (Kao, 2014 ), writing (Higgins et al., 2012 ; Wen & Walters, 2022 ; Zheng et al., 2016 ), as well as reading and comprehension (Cheung & Slavin, 2011 ; Liao et al., 2007 ; Schwabe et al., 2022 ). ICTs were also found to have a positive impact on students' performance in STEM (science, technology, engineering, and mathematics) disciplines (Arztmann et al., 2022 ; Bado, 2022 ; Villena-Taranilla et al., 2022 ; Wang et al., 2022 ). Specifically, a number of studies reported positive impacts on students’ achievement in mathematics (Balanskat et al., 2006 ; Hillmayr et al., 2020 ; Li & Ma, 2010 ; Pan et al., 2022 ; Ran et al., 2022 ; Verschaffel et al., 2019 ; Zheng et al., 2016 ). Furthermore, studies documented positive effects of ICTs on science learning (Balanskat et al., 2006 ; Liao et al., 2007 ; Zheng et al., 2016 ; Hillmayr et al., 2020 ; Kalemkuş & Kalemkuş, 2022 ; Lei et al., 2022a ). Çelik ( 2022 ) also noted that computer simulations can help students understand learning concepts related to science. Furthermore, some studies documented that the use of ICTs had a positive impact on students’ achievement in other subjects, such as geography, history, music, and arts (Chauhan, 2017 ; Condie & Munro, 2007 ), and design and technology (Balanskat et al., 2006 ).

More specific positive learning gains were reported in a number of skills, e.g., problem-solving skills and pattern exploration skills (Higgins et al., 2012 ), metacognitive learning outcomes (Verschaffel et al., 2019 ), literacy skills, computational thinking skills, emotion control skills, and collaborative inquiry skills (Lu et al., 2022 ; Su & Yang, 2022 ; Su et al., 2022 ). Additionally, several investigations have reported benefits from the use of ICT on students’ creativity (Fielding & Murcia, 2022 ; Liu et al., 2022 ; Quah & Ng, 2022 ). Lastly, digital technologies were also found to be beneficial for enhancing students’ lifelong learning skills (Haleem et al., 2022 ).

Apart from gaining knowledge and skills, studies also reported improvement in motivation and interest in mathematics (Higgins et. al., 2019 ; Fadda et al., 2022 ) and increased positive achievement emotions towards several subjects during interventions using educational games (Lei et al., 2022a ). Chen et al. ( 2022a ) also reported a small but positive effect of digital health approaches in bullying and cyberbullying interventions with K-12 students, demonstrating that technology-based approaches can help reduce bullying and related consequences by providing emotional support, empowerment, and change of attitude. In their meta-review study, Su et al. ( 2022 ) also documented that AI technologies effectively strengthened students’ attitudes towards learning. In another meta-analysis, Arztmann et al. ( 2022 ) reported positive effects of digital games on motivation and behaviour towards STEM subjects.

Impacts of digital technologies on equality, inclusion and social integration

Although most of the reviewed studies focused on the impact of ICTs on students’ knowledge, skills, and attitudes, reports were also made on other aspects in the school context, such as equality, inclusion, and social integration. Condie and Munro ( 2007 ) documented research interventions investigating how ICT can support pupils with additional or special educational needs. While those interventions were relatively small scale and mostly based on qualitative data, their findings indicated that the use of ICTs enabled the development of communication, participation, and self-esteem. A recent meta-analysis (Baragash et al., 2022 ) with 119 participants with different disabilities, reported a significant overall effect size of AR on their functional skills acquisition. Koh’s meta-analysis ( 2022 ) also revealed that students with intellectual and developmental disabilities improved their competence and performance when they used digital games in the lessons.

Istenic Starcic and Bagon ( 2014 ) found that the role of ICT in inclusion and the design of pedagogical and technological interventions was not sufficiently explored in educational interventions with people with special needs; however, some benefits of ICT use were found in students’ social integration. The issue of gender and technology use was mentioned in a small number of studies. Zheng et al. ( 2016 ) reported a statistically significant positive interaction between one-to-one laptop programs and gender. Specifically, the results showed that girls and boys alike benefitted from the laptop program, but the effect on girls’ achievement was smaller than that on boys’. Along the same lines, Arztmann et al. ( 2022 ) reported no difference in the impact of game-based learning between boys and girls, arguing that boys and girls equally benefited from game-based interventions in STEM domains. However, results from a systematic review by Cussó-Calabuig et al. ( 2018 ) found limited and low-quality evidence on the effects of intensive use of computers on gender differences in computer anxiety, self-efficacy, and self-confidence. Based on their view, intensive use of computers can reduce gender differences in some areas and not in others, depending on contextual and implementation factors.

Impacts of digital technologies on teachers’ professional and teaching practices

Various research studies have explored the impact of ICT on teachers’ instructional practices and student assessment. Friedel et al. ( 2013 ) found that the use of mobile devices by students enabled teachers to successfully deliver content (e.g., mobile serious games), provide scaffolding, and facilitate synchronous collaborative learning. The integration of digital games in teaching and learning activities also gave teachers the opportunity to study and apply various pedagogical practices (Bado, 2022 ). Specifically, Bado ( 2022 ) found that teachers who implemented instructional activities in three stages (pre-game, game, and post-game) maximized students’ learning outcomes and engagement. For instance, during the pre-game stage, teachers focused on lectures and gameplay training, at the game stage teachers provided scaffolding on content, addressed technical issues, and managed the classroom activities. During the post-game stage, teachers organized activities for debriefing to ensure that the gameplay had indeed enhanced students’ learning outcomes.

Furthermore, ICT can increase efficiency in lesson planning and preparation by offering possibilities for a more collaborative approach among teachers. The sharing of curriculum plans and the analysis of students’ data led to clearer target settings and improvements in reporting to parents (Balanskat et al., 2006 ).

Additionally, the use and application of digital technologies in teaching and learning were found to enhance teachers’ digital competence. Balanskat et al. ( 2006 ) documented studies that revealed that the use of digital technologies in education had a positive effect on teachers’ basic ICT skills. The greatest impact was found on teachers with enough experience in integrating ICTs in their teaching and/or who had recently participated in development courses for the pedagogical use of technologies in teaching. Punie et al. ( 2006 ) reported that the provision of fully equipped multimedia portable computers and the development of online teacher communities had positive impacts on teachers’ confidence and competence in the use of ICTs.

Moreover, online assessment via ICTs benefits instruction. In particular, online assessments support the digitalization of students’ work and related logistics, allow teachers to gather immediate feedback and readjust to new objectives, and support the improvement of the technical quality of tests by providing more accurate results. Additionally, the capabilities of ICTs (e.g., interactive media, simulations) create new potential methods of testing specific skills, such as problem-solving and problem-processing skills, meta-cognitive skills, creativity and communication skills, and the ability to work productively in groups (Punie et al., 2006 ).

Impacts of digital technologies on other school-related aspects and stakeholders

There is evidence that the effective use of ICTs and the data transmission offered by broadband connections help improve administration (Balanskat et al., 2006 ). Specifically, ICTs have been found to provide better management systems to schools that have data gathering procedures in place. Condie and Munro ( 2007 ) reported impacts from the use of ICTs in schools in the following areas: attendance monitoring, assessment records, reporting to parents, financial management, creation of repositories for learning resources, and sharing of information amongst staff. Such data can be used strategically for self-evaluation and monitoring purposes which in turn can result in school improvements. Additionally, they reported that online access to other people with similar roles helped to reduce headteachers’ isolation by offering them opportunities to share insights into the use of ICT in learning and teaching and how it could be used to support school improvement. Furthermore, ICTs provided more efficient and successful examination management procedures, namely less time-consuming reporting processes compared to paper-based examinations and smooth communications between schools and examination authorities through electronic data exchange (Punie et al., 2006 ).

Zheng et al. ( 2016 ) reported that the use of ICTs improved home-school relationships. Additionally, Escueta et al. ( 2017 ) reported several ICT programs that had improved the flow of information from the school to parents. Particularly, they documented that the use of ICTs (learning management systems, emails, dedicated websites, mobile phones) allowed for personalized and customized information exchange between schools and parents, such as attendance records, upcoming class assignments, school events, and students’ grades, which generated positive results on students’ learning outcomes and attainment. Such information exchange between schools and families prompted parents to encourage their children to put more effort into their schoolwork.

The above findings suggest that the impact of ICT integration in schools goes beyond students’ performance in school subjects. Specifically, it affects a number of school-related aspects, such as equality and social integration, professional and teaching practices, and diverse stakeholders. In Table ​ Table2, 2 , we summarize the different impacts of digital technologies on school stakeholders based on the literature review, while in Table ​ Table3 3 we organized the tools/platforms and practices/policies addressed in the meta-analyses, literature reviews, EU reports, and international bodies included in the manuscript.

The impact of digital technologies on schools’ stakeholders based on the literature review

Tools/platforms and practices/policies addressed in the meta-analyses, literature reviews, EU reports, and international bodies included in the manuscript

Additionally, based on the results of the literature review, there are many types of digital technologies with different affordances (see, for example, studies on VR vs Immersive VR), which evolve over time (e.g. starting from CAIs in 2005 to Augmented and Virtual reality 2020). Furthermore, these technologies are linked to different pedagogies and policy initiatives, which are critical factors in the study of impact. Table ​ Table3 3 summarizes the different tools and practices that have been used to examine the impact of digital technologies on education since 2005 based on the review results.

Factors that affect the integration of digital technologies

Although the analysis of the literature review demonstrated different impacts of the use of digital technology on education, several authors highlighted the importance of various factors, besides the technology itself, that affect this impact. For example, Liao et al. ( 2007 ) suggested that future studies should carefully investigate which factors contribute to positive outcomes by clarifying the exact relationship between computer applications and learning. Additionally, Haßler et al., ( 2016 ) suggested that the neutral findings regarding the impact of tablets on students learning outcomes in some of the studies included in their review should encourage educators, school leaders, and school officials to further investigate the potential of such devices in teaching and learning. Several other researchers suggested that a number of variables play a significant role in the impact of ICTs on students’ learning that could be attributed to the school context, teaching practices and professional development, the curriculum, and learners’ characteristics (Underwood, 2009 ; Tamim et al., 2011 ; Higgins et al., 2012 ; Archer et al., 2014 ; Sung et al., 2016 ; Haßler et al., 2016 ; Chauhan, 2017 ; Lee et al., 2020 ; Tang et al., 2022 ).

Digital competencies

One of the most common challenges reported in studies that utilized digital tools in the classroom was the lack of students’ skills on how to use them. Fu ( 2013 ) found that students’ lack of technical skills is a barrier to the effective use of ICT in the classroom. Tamim et al. ( 2015 ) reported that students faced challenges when using tablets and smart mobile devices, associated with the technical issues or expertise needed for their use and the distracting nature of the devices and highlighted the need for teachers’ professional development. Higgins et al. ( 2012 ) reported that skills training about the use of digital technologies is essential for learners to fully exploit the benefits of instruction.

Delgado et al. ( 2015 ), meanwhile, reported studies that showed a strong positive association between teachers’ computer skills and students’ use of computers. Teachers’ lack of ICT skills and familiarization with technologies can become a constraint to the effective use of technology in the classroom (Balanskat et al., 2006 ; Delgado et al., 2015 ).

It is worth noting that the way teachers are introduced to ICTs affects the impact of digital technologies on education. Previous studies have shown that teachers may avoid using digital technologies due to limited digital skills (Balanskat, 2006 ), or they prefer applying “safe” technologies, namely technologies that their own teachers used and with which they are familiar (Condie & Munro, 2007 ). In this regard, the provision of digital skills training and exposure to new digital tools might encourage teachers to apply various technologies in their lessons (Condie & Munro, 2007 ). Apart from digital competence, technical support in the school setting has also been shown to affect teachers’ use of technology in their classrooms (Delgado et al., 2015 ). Ferrari et al. ( 2011 ) found that while teachers’ use of ICT is high, 75% stated that they needed more institutional support and a shift in the mindset of educational actors to achieve more innovative teaching practices. The provision of support can reduce time and effort as well as cognitive constraints, which could cause limited ICT integration in the school lessons by teachers (Escueta et al., 2017 ).

Teachers’ personal characteristics, training approaches, and professional development

Teachers’ personal characteristics and professional development affect the impact of digital technologies on education. Specifically, Cheok and Wong ( 2015 ) found that teachers’ personal characteristics (e.g., anxiety, self-efficacy) are associated with their satisfaction and engagement with technology. Bingimlas ( 2009 ) reported that lack of confidence, resistance to change, and negative attitudes in using new technologies in teaching are significant determinants of teachers’ levels of engagement in ICT. The same author reported that the provision of technical support, motivation support (e.g., awards, sufficient time for planning), and training on how technologies can benefit teaching and learning can eliminate the above barriers to ICT integration. Archer et al. ( 2014 ) found that comfort levels in using technology are an important predictor of technology integration and argued that it is essential to provide teachers with appropriate training and ongoing support until they are comfortable with using ICTs in the classroom. Hillmayr et al. ( 2020 ) documented that training teachers on ICT had an important effecton students’ learning.

According to Balanskat et al. ( 2006 ), the impact of ICTs on students’ learning is highly dependent on the teachers’ capacity to efficiently exploit their application for pedagogical purposes. Results obtained from the Teaching and Learning International Survey (TALIS) (OECD, 2021 ) revealed that although schools are open to innovative practices and have the capacity to adopt them, only 39% of teachers in the European Union reported that they are well or very well prepared to use digital technologies for teaching. Li and Ma ( 2010 ) and Hardman ( 2019 ) showed that the positive effect of technology on students’ achievement depends on the pedagogical practices used by teachers. Schmid et al. ( 2014 ) reported that learning was best supported when students were engaged in active, meaningful activities with the use of technological tools that provided cognitive support. Tamim et al. ( 2015 ) compared two different pedagogical uses of tablets and found a significant moderate effect when the devices were used in a student-centered context and approach rather than within teacher-led environments. Similarly, Garzón and Acevedo ( 2019 ) and Garzón et al. ( 2020 ) reported that the positive results from the integration of AR applications could be attributed to the existence of different variables which could influence AR interventions (e.g., pedagogical approach, learning environment, and duration of the intervention). Additionally, Garzón et al. ( 2020 ) suggested that the pedagogical resources that teachers used to complement their lectures and the pedagogical approaches they applied were crucial to the effective integration of AR on students’ learning gains. Garzón and Acevedo ( 2019 ) also emphasized that the success of a technology-enhanced intervention is based on both the technology per se and its characteristics and on the pedagogical strategies teachers choose to implement. For instance, their results indicated that the collaborative learning approach had the highest impact on students’ learning gains among other approaches (e.g., inquiry-based learning, situated learning, or project-based learning). Ran et al. ( 2022 ) also found that the use of technology to design collaborative and communicative environments showed the largest moderator effects among the other approaches.

Hattie ( 2008 ) reported that the effective use of computers is associated with training teachers in using computers as a teaching and learning tool. Zheng et al. ( 2016 ) noted that in addition to the strategies teachers adopt in teaching, ongoing professional development is also vital in ensuring the success of technology implementation programs. Sung et al. ( 2016 ) found that research on the use of mobile devices to support learning tends to report that the insufficient preparation of teachers is a major obstacle in implementing effective mobile learning programs in schools. Friedel et al. ( 2013 ) found that providing training and support to teachers increased the positive impact of the interventions on students’ learning gains. Trucano ( 2005 ) argued that positive impacts occur when digital technologies are used to enhance teachers’ existing pedagogical philosophies. Higgins et al. ( 2012 ) found that the types of technologies used and how they are used could also affect students’ learning. The authors suggested that training and professional development of teachers that focuses on the effective pedagogical use of technology to support teaching and learning is an important component of successful instructional approaches (Higgins et al., 2012 ). Archer et al. ( 2014 ) found that studies that reported ICT interventions during which teachers received training and support had moderate positive effects on students’ learning outcomes, which were significantly higher than studies where little or no detail about training and support was mentioned. Fu ( 2013 ) reported that the lack of teachers’ knowledge and skills on the technical and instructional aspects of ICT use in the classroom, in-service training, pedagogy support, technical and financial support, as well as the lack of teachers’ motivation and encouragement to integrate ICT on their teaching were significant barriers to the integration of ICT in education.

School leadership and management

Management and leadership are important cornerstones in the digital transformation process (Pihir et al., 2018 ). Zheng et al. ( 2016 ) documented leadership among the factors positively affecting the successful implementation of technology integration in schools. Strong leadership, strategic planning, and systematic integration of digital technologies are prerequisites for the digital transformation of education systems (Ređep, 2021 ). Management and leadership play a significant role in formulating policies that are translated into practice and ensure that developments in ICT become embedded into the life of the school and in the experiences of staff and pupils (Condie & Munro, 2007 ). Policy support and leadership must include the provision of an overall vision for the use of digital technologies in education, guidance for students and parents, logistical support, as well as teacher training (Conrads et al., 2017 ). Unless there is a commitment throughout the school, with accountability for progress at key points, it is unlikely for ICT integration to be sustained or become part of the culture (Condie & Munro, 2007 ). To achieve this, principals need to adopt and promote a whole-institution strategy and build a strong mutual support system that enables the school’s technological maturity (European Commission, 2019 ). In this context, school culture plays an essential role in shaping the mindsets and beliefs of school actors towards successful technology integration. Condie and Munro ( 2007 ) emphasized the importance of the principal’s enthusiasm and work as a source of inspiration for the school staff and the students to cultivate a culture of innovation and establish sustainable digital change. Specifically, school leaders need to create conditions in which the school staff is empowered to experiment and take risks with technology (Elkordy & Lovinelli, 2020 ).

In order for leaders to achieve the above, it is important to develop capacities for learning and leading, advocating professional learning, and creating support systems and structures (European Commission, 2019 ). Digital technology integration in education systems can be challenging and leadership needs guidance to achieve it. Such guidance can be introduced through the adoption of new methods and techniques in strategic planning for the integration of digital technologies (Ređep, 2021 ). Even though the role of leaders is vital, the relevant training offered to them has so far been inadequate. Specifically, only a third of the education systems in Europe have put in place national strategies that explicitly refer to the training of school principals (European Commission, 2019 , p. 16).

Connectivity, infrastructure, and government and other support

The effective integration of digital technologies across levels of education presupposes the development of infrastructure, the provision of digital content, and the selection of proper resources (Voogt et al., 2013 ). Particularly, a high-quality broadband connection in the school increases the quality and quantity of educational activities. There is evidence that ICT increases and formalizes cooperative planning between teachers and cooperation with managers, which in turn has a positive impact on teaching practices (Balanskat et al., 2006 ). Additionally, ICT resources, including software and hardware, increase the likelihood of teachers integrating technology into the curriculum to enhance their teaching practices (Delgado et al., 2015 ). For example, Zheng et al. ( 2016 ) found that the use of one-on-one laptop programs resulted in positive changes in teaching and learning, which would not have been accomplished without the infrastructure and technical support provided to teachers. Delgado et al. ( 2015 ) reported that limited access to technology (insufficient computers, peripherals, and software) and lack of technical support are important barriers to ICT integration. Access to infrastructure refers not only to the availability of technology in a school but also to the provision of a proper amount and the right types of technology in locations where teachers and students can use them. Effective technical support is a central element of the whole-school strategy for ICT (Underwood, 2009 ). Bingimlas ( 2009 ) reported that lack of technical support in the classroom and whole-school resources (e.g., failing to connect to the Internet, printers not printing, malfunctioning computers, and working on old computers) are significant barriers that discourage the use of ICT by teachers. Moreover, poor quality and inadequate hardware maintenance, and unsuitable educational software may discourage teachers from using ICTs (Balanskat et al., 2006 ; Bingimlas, 2009 ).

Government support can also impact the integration of ICTs in teaching. Specifically, Balanskat et al. ( 2006 ) reported that government interventions and training programs increased teachers’ enthusiasm and positive attitudes towards ICT and led to the routine use of embedded ICT.

Lastly, another important factor affecting digital transformation is the development and quality assurance of digital learning resources. Such resources can be support textbooks and related materials or resources that focus on specific subjects or parts of the curriculum. Policies on the provision of digital learning resources are essential for schools and can be achieved through various actions. For example, some countries are financing web portals that become repositories, enabling teachers to share resources or create their own. Additionally, they may offer e-learning opportunities or other services linked to digital education. In other cases, specific agencies of projects have also been set up to develop digital resources (Eurydice, 2019 ).

Administration and digital data management

The digital transformation of schools involves organizational improvements at the level of internal workflows, communication between the different stakeholders, and potential for collaboration. Vuorikari et al. ( 2020 ) presented evidence that digital technologies supported the automation of administrative practices in schools and reduced the administration’s workload. There is evidence that digital data affects the production of knowledge about schools and has the power to transform how schooling takes place. Specifically, Sellar ( 2015 ) reported that data infrastructure in education is developing due to the demand for “ information about student outcomes, teacher quality, school performance, and adult skills, associated with policy efforts to increase human capital and productivity practices ” (p. 771). In this regard, practices, such as datafication which refers to the “ translation of information about all kinds of things and processes into quantified formats” have become essential for decision-making based on accountability reports about the school’s quality. The data could be turned into deep insights about education or training incorporating ICTs. For example, measuring students’ online engagement with the learning material and drawing meaningful conclusions can allow teachers to improve their educational interventions (Vuorikari et al., 2020 ).

Students’ socioeconomic background and family support

Research show that the active engagement of parents in the school and their support for the school’s work can make a difference to their children’s attitudes towards learning and, as a result, their achievement (Hattie, 2008 ). In recent years, digital technologies have been used for more effective communication between school and family (Escueta et al., 2017 ). The European Commission ( 2020 ) presented data from a Eurostat survey regarding the use of computers by students during the pandemic. The data showed that younger pupils needed additional support and guidance from parents and the challenges were greater for families in which parents had lower levels of education and little to no digital skills.

In this regard, the socio-economic background of the learners and their socio-cultural environment also affect educational achievements (Punie et al., 2006 ). Trucano documented that the use of computers at home positively influenced students’ confidence and resulted in more frequent use at school, compared to students who had no home access (Trucano, 2005 ). In this sense, the socio-economic background affects the access to computers at home (OECD, 2015 ) which in turn influences the experience of ICT, an important factor for school achievement (Punie et al., 2006 ; Underwood, 2009 ). Furthermore, parents from different socio-economic backgrounds may have different abilities and availability to support their children in their learning process (Di Pietro et al., 2020 ).

Schools’ socioeconomic context and emergency situations

The socio-economic context of the school is closely related to a school’s digital transformation. For example, schools in disadvantaged, rural, or deprived areas are likely to lack the digital capacity and infrastructure required to adapt to the use of digital technologies during emergency periods, such as the COVID-19 pandemic (Di Pietro et al., 2020 ). Data collected from school principals confirmed that in several countries, there is a rural/urban divide in connectivity (OECD, 2015 ).

Emergency periods also affect the digitalization of schools. The COVID-19 pandemic led to the closure of schools and forced them to seek appropriate and connective ways to keep working on the curriculum (Di Pietro et al., 2020 ). The sudden large-scale shift to distance and online teaching and learning also presented challenges around quality and equity in education, such as the risk of increased inequalities in learning, digital, and social, as well as teachers facing difficulties coping with this demanding situation (European Commission, 2020 ).

Looking at the findings of the above studies, we can conclude that the impact of digital technologies on education is influenced by various actors and touches many aspects of the school ecosystem. Figure  1 summarizes the factors affecting the digital technologies’ impact on school stakeholders based on the findings from the literature review.

Factors that affect the impact of ICTs on education

The findings revealed that the use of digital technologies in education affects a variety of actors within a school’s ecosystem. First, we observed that as technologies evolve, so does the interest of the research community to apply them to school settings. Figure  2 summarizes the trends identified in current research around the impact of digital technologies on schools’ digital capacity and transformation as found in the present study. Starting as early as 2005, when computers, simulations, and interactive boards were the most commonly applied tools in school interventions (e.g., Eng, 2005 ; Liao et al., 2007 ; Moran et al., 2008 ; Tamim et al., 2011 ), moving towards the use of learning platforms (Jewitt et al., 2011 ), then to the use of mobile devices and digital games (e.g., Tamim et al., 2015 ; Sung et al., 2016 ; Talan et al., 2020 ), as well as e-books (e.g., Savva et al., 2022 ), to the more recent advanced technologies, such as AR and VR applications (e.g., Garzón & Acevedo, 2019 ; Garzón et al., 2020 ; Kalemkuş & Kalemkuş, 2022 ), or robotics and AI (e.g., Su & Yang, 2022 ; Su et al., 2022 ). As this evolution shows, digital technologies are a concept in flux with different affordances and characteristics. Additionally, from an instructional perspective, there has been a growing interest in different modes and models of content delivery such as online, blended, and hybrid modes (e.g., Cheok & Wong, 2015 ; Kazu & Yalçin, 2022 ; Ulum, 2022 ). This is an indication that the value of technologies to support teaching and learning as well as other school-related practices is increasingly recognized by the research and school community. The impact results from the literature review indicate that ICT integration on students’ learning outcomes has effects that are small (Coban et al., 2022 ; Eng, 2005 ; Higgins et al., 2012 ; Schmid et al., 2014 ; Tamim et al., 2015 ; Zheng et al., 2016 ) to moderate (Garzón & Acevedo, 2019 ; Garzón et al., 2020 ; Liao et al., 2007 ; Sung et al., 2016 ; Talan et al., 2020 ; Wen & Walters, 2022 ). That said, a number of recent studies have reported high effect sizes (e.g., Kazu & Yalçin, 2022 ).

Current work and trends in the study of the impact of digital technologies on schools’ digital capacity

Based on these findings, several authors have suggested that the impact of technology on education depends on several variables and not on the technology per se (Tamim et al., 2011 ; Higgins et al., 2012 ; Archer et al., 2014 ; Sung et al., 2016 ; Haßler et al., 2016 ; Chauhan, 2017 ; Lee et al., 2020 ; Lei et al., 2022a ). While the impact of ICTs on student achievement has been thoroughly investigated by researchers, other aspects related to school life that are also affected by ICTs, such as equality, inclusion, and social integration have received less attention. Further analysis of the literature review has revealed a greater investment in ICT interventions to support learning and teaching in the core subjects of literacy and STEM disciplines, especially mathematics, and science. These were the most common subjects studied in the reviewed papers often drawing on national testing results, while studies that investigated other subject areas, such as social studies, were limited (Chauhan, 2017 ; Condie & Munro, 2007 ). As such, research is still lacking impact studies that focus on the effects of ICTs on a range of curriculum subjects.

The qualitative research provided additional information about the impact of digital technologies on education, documenting positive effects and giving more details about implications, recommendations, and future research directions. Specifically, the findings regarding the role of ICTs in supporting learning highlight the importance of teachers’ instructional practice and the learning context in the use of technologies and consequently their impact on instruction (Çelik, 2022 ; Schmid et al., 2014 ; Tamim et al., 2015 ). The review also provided useful insights regarding the various factors that affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the transformation process. Specifically, these factors include a) digital competencies; b) teachers’ personal characteristics and professional development; c) school leadership and management; d) connectivity, infrastructure, and government support; e) administration and data management practices; f) students’ socio-economic background and family support and g) the socioeconomic context of the school and emergency situations. It is worth noting that we observed factors that affect the integration of ICTs in education but may also be affected by it. For example, the frequent use of ICTs and the use of laptops by students for instructional purposes positively affect the development of digital competencies (Zheng et al., 2016 ) and at the same time, the digital competencies affect the use of ICTs (Fu, 2013 ; Higgins et al., 2012 ). As a result, the impact of digital technologies should be explored more as an enabler of desirable and new practices and not merely as a catalyst that improves the output of the education process i.e. namely student attainment.

Conclusions

Digital technologies offer immense potential for fundamental improvement in schools. However, investment in ICT infrastructure and professional development to improve school education are yet to provide fruitful results. Digital transformation is a complex process that requires large-scale transformative changes that presuppose digital capacity and preparedness. To achieve such changes, all actors within the school’s ecosystem need to share a common vision regarding the integration of ICTs in education and work towards achieving this goal. Our literature review, which synthesized quantitative and qualitative data from a list of meta-analyses and review studies, provided useful insights into the impact of ICTs on different school stakeholders and showed that the impact of digital technologies touches upon many different aspects of school life, which are often overlooked when the focus is on student achievement as the final output of education. Furthermore, the concept of digital technologies is a concept in flux as technologies are not only different among them calling for different uses in the educational practice but they also change through time. Additionally, we opened a forum for discussion regarding the factors that affect a school’s digital capacity and transformation. We hope that our study will inform policy, practice, and research and result in a paradigm shift towards more holistic approaches in impact and assessment studies.

Study limitations and future directions

We presented a review of the study of digital technologies' impact on education and factors influencing schools’ digital capacity and transformation. The study results were based on a non-systematic literature review grounded on the acquisition of documentation in specific databases. Future studies should investigate more databases to corroborate and enhance our results. Moreover, search queries could be enhanced with key terms that could provide additional insights about the integration of ICTs in education, such as “policies and strategies for ICT integration in education”. Also, the study drew information from meta-analyses and literature reviews to acquire evidence about the effects of ICT integration in schools. Such evidence was mostly based on the general conclusions of the studies. It is worth mentioning that, we located individual studies which showed different, such as negative or neutral results. Thus, further insights are needed about the impact of ICTs on education and the factors influencing the impact. Furthermore, the nature of the studies included in meta-analyses and reviews is different as they are based on different research methodologies and data gathering processes. For instance, in a meta-analysis, the impact among the studies investigated is measured in a particular way, depending on policy or research targets (e.g., results from national examinations, pre-/post-tests). Meanwhile, in literature reviews, qualitative studies offer additional insights and detail based on self-reports and research opinions on several different aspects and stakeholders who could affect and be affected by ICT integration. As a result, it was challenging to draw causal relationships between so many interrelating variables.

Despite the challenges mentioned above, this study envisaged examining school units as ecosystems that consist of several actors by bringing together several variables from different research epistemologies to provide an understanding of the integration of ICTs. However, the use of other tools and methodologies and models for evaluation of the impact of digital technologies on education could give more detailed data and more accurate results. For instance, self-reflection tools, like SELFIE—developed on the DigCompOrg framework- (Kampylis et al., 2015 ; Bocconi & Lightfoot, 2021 ) can help capture a school’s digital capacity and better assess the impact of ICTs on education. Furthermore, the development of a theory of change could be a good approach for documenting the impact of digital technologies on education. Specifically, theories of change are models used for the evaluation of interventions and their impact; they are developed to describe how interventions will work and give the desired outcomes (Mayne, 2015 ). Theory of change as a methodological approach has also been used by researchers to develop models for evaluation in the field of education (e.g., Aromatario et al., 2019 ; Chapman & Sammons, 2013 ; De Silva et al., 2014 ).

We also propose that future studies aim at similar investigations by applying more holistic approaches for impact assessment that can provide in-depth data about the impact of digital technologies on education. For instance, future studies could focus on different research questions about the technologies that are used during the interventions or the way the implementation takes place (e.g., What methodologies are used for documenting impact? How are experimental studies implemented? How can teachers be taken into account and trained on the technology and its functions? What are the elements of an appropriate and successful implementation? How is the whole intervention designed? On which learning theories is the technology implementation based?).

Future research could also focus on assessing the impact of digital technologies on various other subjects since there is a scarcity of research related to particular subjects, such as geography, history, arts, music, and design and technology. More research should also be done about the impact of ICTs on skills, emotions, and attitudes, and on equality, inclusion, social interaction, and special needs education. There is also a need for more research about the impact of ICTs on administration, management, digitalization, and home-school relationships. Additionally, although new forms of teaching and learning with the use of ICTs (e.g., blended, hybrid, and online learning) have initiated several investigations in mainstream classrooms, only a few studies have measured their impact on students’ learning. Additionally, our review did not document any study about the impact of flipped classrooms on K-12 education. Regarding teaching and learning approaches, it is worth noting that studies referred to STEM or STEAM did not investigate the impact of STEM/STEAM as an interdisciplinary approach to learning but only investigated the impact of ICTs on learning in each domain as a separate subject (science, technology, engineering, arts, mathematics). Hence, we propose future research to also investigate the impact of the STEM/STEAM approach on education. The impact of emerging technologies on education, such as AR, VR, robotics, and AI has also been investigated recently, but more work needs to be done.

Finally, we propose that future studies could focus on the way in which specific factors, e.g., infrastructure and government support, school leadership and management, students’ and teachers’ digital competencies, approaches teachers utilize in the teaching and learning (e.g., blended, online and hybrid learning, flipped classrooms, STEM/STEAM approach, project-based learning, inquiry-based learning), affect the impact of digital technologies on education. We hope that future studies will give detailed insights into the concept of schools’ digital transformation through further investigation of impacts and factors which influence digital capacity and transformation based on the results and the recommendations of the present study.

Acknowledgements

This project has received funding under Grant Agreement No Ref Ares (2021) 339036 7483039 as well as funding from the European Union’s Horizon 2020 Research and Innovation Program under Grant Agreement No 739578 and the Government of the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy. The UVa co-authors would like also to acknowledge funding from the European Regional Development Fund and the National Research Agency of the Spanish Ministry of Science and Innovation, under project grant PID2020-112584RB-C32.

Data availability statement

Declarations.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

• Archer K, Savage R, Sanghera-Sidhu S, Wood E, Gottardo A, Chen V. Examining the effectiveness of technology use in classrooms: A tertiary meta-analysis. Computers & Education. 2014; 78 :140–149. doi: 10.1016/j.compedu.2014.06.001. [ CrossRef ] [ Google Scholar ]
• Aromatario O, Van Hoye A, Vuillemin A, Foucaut AM, Pommier J, Cambon L. Using theory of change to develop an intervention theory for designing and evaluating behavior change SDApps for healthy eating and physical exercise: The OCAPREV theory. BMC Public Health. 2019; 19 (1):1–12. doi: 10.1186/s12889-019-7828-4. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Arztmann, M., Hornstra, L., Jeuring, J., & Kester, L. (2022). Effects of games in STEM education: A meta-analysis on the moderating role of student background characteristics. Studies in Science Education , 1-37. 10.1080/03057267.2022.2057732
• Bado N. Game-based learning pedagogy: A review of the literature. Interactive Learning Environments. 2022; 30 (5):936–948. doi: 10.1080/10494820.2019.1683587. [ CrossRef ] [ Google Scholar ]
• Balanskat, A. (2009). Study of the impact of technology in primary schools – Synthesis Report. Empirica and European Schoolnet. Retrieved 30 June 2022 from: https://erte.dge.mec.pt/sites/default/files/Recursos/Estudos/synthesis_report_steps_en.pdf
• Balanskat, A. (2006). The ICT Impact Report: A review of studies of ICT impact on schools in Europe, European Schoolnet. Retrieved 30 June 2022 from:  https://en.unesco.org/icted/content/ict-impact-report-review-studies-ict-impact-schools-europe
• Balanskat, A., Blamire, R., & Kefala, S. (2006). The ICT impact report.  European Schoolnet . Retrieved from: http://colccti.colfinder.org/sites/default/files/ict_impact_report_0.pdf
• Balyer, A., & Öz, Ö. (2018). Academicians’ views on digital transformation in education. International Online Journal of Education and Teaching (IOJET), 5 (4), 809–830. Retrieved 30 June 2022 from  http://iojet.org/index.php/IOJET/article/view/441/295
• Baragash RS, Al-Samarraie H, Moody L, Zaqout F. Augmented reality and functional skills acquisition among individuals with special needs: A meta-analysis of group design studies. Journal of Special Education Technology. 2022; 37 (1):74–81. doi: 10.1177/0162643420910413. [ CrossRef ] [ Google Scholar ]
• Bates, A. W. (2015). Teaching in a digital age: Guidelines for designing teaching and learning . Open Educational Resources Collection . 6. Retrieved 30 June 2022 from: https://irl.umsl.edu/oer/6
• Bingimlas KA. Barriers to the successful integration of ICT in teaching and learning environments: A review of the literature. Eurasia Journal of Mathematics, Science and Technology Education. 2009; 5 (3):235–245. doi: 10.12973/ejmste/75275. [ CrossRef ] [ Google Scholar ]
• Blaskó Z, Costa PD, Schnepf SV. Learning losses and educational inequalities in Europe: Mapping the potential consequences of the COVID-19 crisis. Journal of European Social Policy. 2022; 32 (4):361–375. doi: 10.1177/09589287221091687. [ CrossRef ] [ Google Scholar ]
• Bocconi S, Lightfoot M. Scaling up and integrating the selfie tool for schools' digital capacity in education and training systems: Methodology and lessons learnt. European Training Foundation. 2021 doi: 10.2816/907029,JRC123936. [ CrossRef ] [ Google Scholar ]
• Brooks, D. C., & McCormack, M. (2020). Driving Digital Transformation in Higher Education . Retrieved 30 June 2022 from: https://library.educause.edu/-/media/files/library/2020/6/dx2020.pdf?la=en&hash=28FB8C377B59AFB1855C225BBA8E3CFBB0A271DA
• Cachia, R., Chaudron, S., Di Gioia, R., Velicu, A., & Vuorikari, R. (2021). Emergency remote schooling during COVID-19, a closer look at European families. Retrieved 30 June 2022 from  https://publications.jrc.ec.europa.eu/repository/handle/JRC125787
• Çelik B. The effects of computer simulations on students’ science process skills: Literature review. Canadian Journal of Educational and Social Studies. 2022; 2 (1):16–28. doi: 10.53103/cjess.v2i1.17. [ CrossRef ] [ Google Scholar ]
• Chapman, C., & Sammons, P. (2013). School Self-Evaluation for School Improvement: What Works and Why? . CfBT Education Trust. 60 Queens Road, Reading, RG1 4BS, England.
• Chauhan S. A meta-analysis of the impact of technology on learning effectiveness of elementary students. Computers & Education. 2017; 105 :14–30. doi: 10.1016/j.compedu.2016.11.005. [ CrossRef ] [ Google Scholar ]
• Chen, Q., Chan, K. L., Guo, S., Chen, M., Lo, C. K. M., & Ip, P. (2022a). Effectiveness of digital health interventions in reducing bullying and cyberbullying: a meta-analysis. Trauma, Violence, & Abuse , 15248380221082090. 10.1177/15248380221082090 [ PubMed ]
• Chen B, Wang Y, Wang L. The effects of virtual reality-assisted language learning: A meta-analysis. Sustainability. 2022; 14 (6):3147. doi: 10.3390/su14063147. [ CrossRef ] [ Google Scholar ]
• Cheok ML, Wong SL. Predictors of e-learning satisfaction in teaching and learning for school teachers: A literature review. International Journal of Instruction. 2015; 8 (1):75–90. doi: 10.12973/iji.2015.816a. [ CrossRef ] [ Google Scholar ]
• Cheung, A. C., & Slavin, R. E. (2011). The Effectiveness of Education Technology for Enhancing Reading Achievement: A Meta-Analysis. Center for Research and reform in Education .
• Coban, M., Bolat, Y. I., & Goksu, I. (2022). The potential of immersive virtual reality to enhance learning: A meta-analysis. Educational Research Review , 100452. 10.1016/j.edurev.2022.100452
• Condie, R., & Munro, R. K. (2007). The impact of ICT in schools-a landscape review. Retrieved 30 June 2022 from: https://oei.org.ar/ibertic/evaluacion/sites/default/files/biblioteca/33_impact_ict_in_schools.pdf
• Conrads, J., Rasmussen, M., Winters, N., Geniet, A., Langer, L., (2017). Digital Education Policies in Europe and Beyond: Key Design Principles for More Effective Policies. Redecker, C., P. Kampylis, M. Bacigalupo, Y. Punie (ed.), EUR 29000 EN, Publications Office of the European Union, Luxembourg, 10.2760/462941
• Costa P, Castaño-Muñoz J, Kampylis P. Capturing schools’ digital capacity: Psychometric analyses of the SELFIE self-reflection tool. Computers & Education. 2021; 162 :104080. doi: 10.1016/j.compedu.2020.104080. [ CrossRef ] [ Google Scholar ]
• Cussó-Calabuig R, Farran XC, Bosch-Capblanch X. Effects of intensive use of computers in secondary school on gender differences in attitudes towards ICT: A systematic review. Education and Information Technologies. 2018; 23 (5):2111–2139. doi: 10.1007/s10639-018-9706-6. [ CrossRef ] [ Google Scholar ]
• Daniel SJ. Education and the COVID-19 pandemic. Prospects. 2020; 49 (1):91–96. doi: 10.1007/s11125-020-09464-3. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Delcker J, Ifenthaler D. Teachers’ perspective on school development at German vocational schools during the Covid-19 pandemic. Technology, Pedagogy and Education. 2021; 30 (1):125–139. doi: 10.1080/1475939X.2020.1857826. [ CrossRef ] [ Google Scholar ]
• Delgado, A., Wardlow, L., O’Malley, K., & McKnight, K. (2015). Educational technology: A review of the integration, resources, and effectiveness of technology in K-12 classrooms. Journal of Information Technology Education Research , 14, 397. Retrieved 30 June 2022 from  http://www.jite.org/documents/Vol14/JITEv14ResearchP397-416Delgado1829.pdf
• De Silva MJ, Breuer E, Lee L, Asher L, Chowdhary N, Lund C, Patel V. Theory of change: A theory-driven approach to enhance the Medical Research Council's framework for complex interventions. Trials. 2014; 15 (1):1–13. doi: 10.1186/1745-6215-15-267. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Di Pietro G, Biagi F, Costa P, Karpiński Z, Mazza J. The likely impact of COVID-19 on education: Reflections based on the existing literature and recent international datasets. Publications Office of the European Union; 2020. [ Google Scholar ]
• Elkordy A, Lovinelli J. Competencies, Culture, and Change: A Model for Digital Transformation in K12 Educational Contexts. In: Ifenthaler D, Hofhues S, Egloffstein M, Helbig C, editors. Digital Transformation of Learning Organizations. Springer; 2020. pp. 203–219. [ Google Scholar ]
• Eng TS. The impact of ICT on learning: A review of research. International Education Journal. 2005; 6 (5):635–650. [ Google Scholar ]
• European Commission. (2020). Digital Education Action Plan 2021 – 2027. Resetting education and training for the digital age. Retrieved 30 June 2022 from  https://ec.europa.eu/education/sites/default/files/document-library-docs/deap-communication-sept2020_en.pdf
• European Commission. (2019). 2 nd survey of schools: ICT in education. Objective 1: Benchmark progress in ICT in schools . Retrieved 30 June 2022 from: https://data.europa.eu/euodp/data/storage/f/2019-03-19T084831/FinalreportObjective1-BenchmarkprogressinICTinschools.pdf
• Eurydice. (2019). Digital Education at School in Europe , Luxembourg: Publications Office of the European Union. Retrieved 30 June 2022 from: https://eacea.ec.europa.eu/national-policies/eurydice/content/digital-education-school-europe_en
• Escueta, M., Quan, V., Nickow, A. J., & Oreopoulos, P. (2017). Education technology: An evidence-based review. Retrieved 30 June 2022 from  https://ssrn.com/abstract=3031695
• Fadda D, Pellegrini M, Vivanet G, Zandonella Callegher C. Effects of digital games on student motivation in mathematics: A meta-analysis in K-12. Journal of Computer Assisted Learning. 2022; 38 (1):304–325. doi: 10.1111/jcal.12618. [ CrossRef ] [ Google Scholar ]
• Fernández-Gutiérrez M, Gimenez G, Calero J. Is the use of ICT in education leading to higher student outcomes? Analysis from the Spanish Autonomous Communities. Computers & Education. 2020; 157 :103969. doi: 10.1016/j.compedu.2020.103969. [ CrossRef ] [ Google Scholar ]
• Ferrari, A., Cachia, R., & Punie, Y. (2011). Educational change through technology: A challenge for obligatory schooling in Europe. Lecture Notes in Computer Science , 6964 , 97–110. Retrieved 30 June 2022  https://link.springer.com/content/pdf/10.1007/978-3-642-23985-4.pdf
• Fielding, K., & Murcia, K. (2022). Research linking digital technologies to young children’s creativity: An interpretive framework and systematic review. Issues in Educational Research , 32 (1), 105–125. Retrieved 30 June 2022 from  http://www.iier.org.au/iier32/fielding-abs.html
• Friedel, H., Bos, B., Lee, K., & Smith, S. (2013). The impact of mobile handheld digital devices on student learning: A literature review with meta-analysis. In Society for Information Technology & Teacher Education International Conference (pp. 3708–3717). Association for the Advancement of Computing in Education (AACE).
• Fu JS. ICT in education: A critical literature review and its implications. International Journal of Education and Development Using Information and Communication Technology (IJEDICT) 2013; 9 (1):112–125. [ Google Scholar ]
• Gaol FL, Prasolova-Førland E. Special section editorial: The frontiers of augmented and mixed reality in all levels of education. Education and Information Technologies. 2022; 27 (1):611–623. doi: 10.1007/s10639-021-10746-2. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Garzón J, Acevedo J. Meta-analysis of the impact of Augmented Reality on students’ learning gains. Educational Research Review. 2019; 27 :244–260. doi: 10.1016/j.edurev.2019.04.001. [ CrossRef ] [ Google Scholar ]
• Garzón, J., Baldiris, S., Gutiérrez, J., & Pavón, J. (2020). How do pedagogical approaches affect the impact of augmented reality on education? A meta-analysis and research synthesis. Educational Research Review , 100334. 10.1016/j.edurev.2020.100334
• Grgurović M, Chapelle CA, Shelley MC. A meta-analysis of effectiveness studies on computer technology-supported language learning. ReCALL. 2013; 25 (2):165–198. doi: 10.1017/S0958344013000013. [ CrossRef ] [ Google Scholar ]
• Haßler B, Major L, Hennessy S. Tablet use in schools: A critical review of the evidence for learning outcomes. Journal of Computer Assisted Learning. 2016; 32 (2):139–156. doi: 10.1111/jcal.12123. [ CrossRef ] [ Google Scholar ]
• Haleem A, Javaid M, Qadri MA, Suman R. Understanding the role of digital technologies in education: A review. Sustainable Operations and Computers. 2022; 3 :275–285. doi: 10.1016/j.susoc.2022.05.004. [ CrossRef ] [ Google Scholar ]
• Hardman J. Towards a pedagogical model of teaching with ICTs for mathematics attainment in primary school: A review of studies 2008–2018. Heliyon. 2019; 5 (5):e01726. doi: 10.1016/j.heliyon.2019.e01726. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hattie J, Rogers HJ, Swaminathan H. The role of meta-analysis in educational research. In: Reid AD, Hart P, Peters MA, editors. A companion to research in education. Springer; 2014. pp. 197–207. [ Google Scholar ]
• Hattie J. Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge. 2008 doi: 10.4324/9780203887332. [ CrossRef ] [ Google Scholar ]
• Higgins S, Xiao Z, Katsipataki M. The impact of digital technology on learning: A summary for the education endowment foundation. Education Endowment Foundation and Durham University; 2012. [ Google Scholar ]
• Higgins, K., Huscroft-D’Angelo, J., & Crawford, L. (2019). Effects of technology in mathematics on achievement, motivation, and attitude: A meta-analysis. Journal of Educational Computing Research , 57(2), 283-319.
• Hillmayr D, Ziernwald L, Reinhold F, Hofer SI, Reiss KM. The potential of digital tools to enhance mathematics and science learning in secondary schools: A context-specific meta-analysis. Computers & Education. 2020; 153 (1038):97. doi: 10.1016/j.compedu.2020.103897. [ CrossRef ] [ Google Scholar ]
• Istenic Starcic A, Bagon S. ICT-supported learning for inclusion of people with special needs: Review of seven educational technology journals, 1970–2011. British Journal of Educational Technology. 2014; 45 (2):202–230. doi: 10.1111/bjet.12086. [ CrossRef ] [ Google Scholar ]
• Jewitt C, Clark W, Hadjithoma-Garstka C. The use of learning platforms to organise learning in English primary and secondary schools. Learning, Media and Technology. 2011; 36 (4):335–348. doi: 10.1080/17439884.2011.621955. [ CrossRef ] [ Google Scholar ]
• JISC. (2020). What is digital transformation?.  Retrieved 30 June 2022 from: https://www.jisc.ac.uk/guides/digital-strategy-framework-for-university-leaders/what-is-digital-transformation
• Kalati, A. T., & Kim, M. S. (2022). What is the effect of touchscreen technology on young children’s learning?: A systematic review. Education and Information Technologies , 1-19. 10.1007/s10639-021-10816-5
• Kalemkuş, J., & Kalemkuş, F. (2022). Effect of the use of augmented reality applications on academic achievement of student in science education: Meta-analysis review. Interactive Learning Environments , 1-18. 10.1080/10494820.2022.2027458
• Kao C-W. The effects of digital game-based learning task in English as a foreign language contexts: A meta-analysis. Education Journal. 2014; 42 (2):113–141. [ Google Scholar ]
• Kampylis P, Punie Y, Devine J. Promoting effective digital-age learning - a European framework for digitally competent educational organisations. JRC Technical Reports. 2015 doi: 10.2791/54070. [ CrossRef ] [ Google Scholar ]
• Kazu IY, Yalçin CK. Investigation of the effectiveness of hybrid learning on academic achievement: A meta-analysis study. International Journal of Progressive Education. 2022; 18 (1):249–265. doi: 10.29329/ijpe.2022.426.14. [ CrossRef ] [ Google Scholar ]
• Koh C. A qualitative meta-analysis on the use of serious games to support learners with intellectual and developmental disabilities: What we know, what we need to know and what we can do. International Journal of Disability, Development and Education. 2022; 69 (3):919–950. doi: 10.1080/1034912X.2020.1746245. [ CrossRef ] [ Google Scholar ]
• König J, Jäger-Biela DJ, Glutsch N. Adapting to online teaching during COVID-19 school closure: Teacher education and teacher competence effects among early career teachers in Germany. European Journal of Teacher Education. 2020; 43 (4):608–622. doi: 10.1080/02619768.2020.1809650. [ CrossRef ] [ Google Scholar ]
• Lawrence JE, Tar UA. Factors that influence teachers’ adoption and integration of ICT in teaching/learning process. Educational Media International. 2018; 55 (1):79–105. doi: 10.1080/09523987.2018.1439712. [ CrossRef ] [ Google Scholar ]
• Lee, S., Kuo, L. J., Xu, Z., & Hu, X. (2020). The effects of technology-integrated classroom instruction on K-12 English language learners’ literacy development: A meta-analysis. Computer Assisted Language Learning , 1-32. 10.1080/09588221.2020.1774612
• Lei, H., Chiu, M. M., Wang, D., Wang, C., & Xie, T. (2022a). Effects of game-based learning on students’ achievement in science: a meta-analysis. Journal of Educational Computing Research . 10.1177/07356331211064543
• Lei H, Wang C, Chiu MM, Chen S. Do educational games affect students' achievement emotions? Evidence from a meta-analysis. Journal of Computer Assisted Learning. 2022; 38 (4):946–959. doi: 10.1111/jcal.12664. [ CrossRef ] [ Google Scholar ]
• Liao YKC, Chang HW, Chen YW. Effects of computer application on elementary school student's achievement: A meta-analysis of students in Taiwan. Computers in the Schools. 2007; 24 (3–4):43–64. doi: 10.1300/J025v24n03_04. [ CrossRef ] [ Google Scholar ]
• Li Q, Ma X. A meta-analysis of the effects of computer technology on school students’ mathematics learning. Educational Psychology Review. 2010; 22 (3):215–243. doi: 10.1007/s10648-010-9125-8. [ CrossRef ] [ Google Scholar ]
• Liu, M., Pang, W., Guo, J., & Zhang, Y. (2022). A meta-analysis of the effect of multimedia technology on creative performance. Education and Information Technologies , 1-28. 10.1007/s10639-022-10981-1
• Lu Z, Chiu MM, Cui Y, Mao W, Lei H. Effects of game-based learning on students’ computational thinking: A meta-analysis. Journal of Educational Computing Research. 2022 doi: 10.1177/07356331221100740. [ CrossRef ] [ Google Scholar ]
• Martinez L, Gimenes M, Lambert E. Entertainment video games for academic learning: A systematic review. Journal of Educational Computing Research. 2022 doi: 10.1177/07356331211053848. [ CrossRef ] [ Google Scholar ]
• Mayne J. Useful theory of change models. Canadian Journal of Program Evaluation. 2015; 30 (2):119–142. doi: 10.3138/cjpe.230. [ CrossRef ] [ Google Scholar ]
• Moran J, Ferdig RE, Pearson PD, Wardrop J, Blomeyer RL., Jr Technology and reading performance in the middle-school grades: A meta-analysis with recommendations for policy and practice. Journal of Literacy Research. 2008; 40 (1):6–58. doi: 10.1080/10862960802070483. [ CrossRef ] [ Google Scholar ]
• OECD. (2015). Students, Computers and Learning: Making the Connection . PISA, OECD Publishing, Paris. Retrieved from: 10.1787/9789264239555-en
• OECD. (2021). OECD Digital Education Outlook 2021: Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots. Retrieved from: https://www.oecd-ilibrary.org/education/oecd-digital-education-outlook-2021_589b283f-en
• Pan Y, Ke F, Xu X. A systematic review of the role of learning games in fostering mathematics education in K-12 settings. Educational Research Review. 2022; 36 :100448. doi: 10.1016/j.edurev.2022.100448. [ CrossRef ] [ Google Scholar ]
• Pettersson F. Understanding digitalization and educational change in school by means of activity theory and the levels of learning concept. Education and Information Technologies. 2021; 26 (1):187–204. doi: 10.1007/s10639-020-10239-8. [ CrossRef ] [ Google Scholar ]
• Pihir, I., Tomičić-Pupek, K., & Furjan, M. T. (2018). Digital transformation insights and trends. In Central European Conference on Information and Intelligent Systems (pp. 141–149). Faculty of Organization and Informatics Varazdin. Retrieved 30 June 2022 from https://www.proquest.com/conference-papers-proceedings/digital-transformation-insights-trends/docview/2125639934/se-2
• Punie, Y., Zinnbauer, D., & Cabrera, M. (2006). A review of the impact of ICT on learning. Working Paper prepared for DG EAC. Retrieved 30 June 2022 from: http://www.eurosfaire.prd.fr/7pc/doc/1224678677_jrc47246n.pdf
• Quah CY, Ng KH. A systematic literature review on digital storytelling authoring tool in education: January 2010 to January 2020. International Journal of Human-Computer Interaction. 2022; 38 (9):851–867. doi: 10.1080/10447318.2021.1972608. [ CrossRef ] [ Google Scholar ]
• Ran H, Kim NJ, Secada WG. A meta-analysis on the effects of technology's functions and roles on students' mathematics achievement in K-12 classrooms. Journal of computer assisted learning. 2022; 38 (1):258–284. doi: 10.1111/jcal.12611. [ CrossRef ] [ Google Scholar ]
• Ređep, N. B. (2021). Comparative overview of the digital preparedness of education systems in selected CEE countries. Center for Policy Studies. CEU Democracy Institute .
• Rott, B., & Marouane, C. (2018). Digitalization in schools–organization, collaboration and communication. In Digital Marketplaces Unleashed (pp. 113–124). Springer, Berlin, Heidelberg.
• Savva M, Higgins S, Beckmann N. Meta-analysis examining the effects of electronic storybooks on language and literacy outcomes for children in grades Pre-K to grade 2. Journal of Computer Assisted Learning. 2022; 38 (2):526–564. doi: 10.1111/jcal.12623. [ CrossRef ] [ Google Scholar ]
• Schmid RF, Bernard RM, Borokhovski E, Tamim RM, Abrami PC, Surkes MA, Wade CA, Woods J. The effects of technology use in postsecondary education: A meta-analysis of classroom applications. Computers & Education. 2014; 72 :271–291. doi: 10.1016/j.compedu.2013.11.002. [ CrossRef ] [ Google Scholar ]
• Schuele CM, Justice LM. The importance of effect sizes in the interpretation of research: Primer on research: Part 3. The ASHA Leader. 2006; 11 (10):14–27. doi: 10.1044/leader.FTR4.11102006.14. [ CrossRef ] [ Google Scholar ]
• Schwabe, A., Lind, F., Kosch, L., & Boomgaarden, H. G. (2022). No negative effects of reading on screen on comprehension of narrative texts compared to print: A meta-analysis. Media Psychology , 1-18. 10.1080/15213269.2022.2070216
• Sellar S. Data infrastructure: a review of expanding accountability systems and large-scale assessments in education. Discourse: Studies in the Cultural Politics of Education. 2015; 36 (5):765–777. doi: 10.1080/01596306.2014.931117. [ CrossRef ] [ Google Scholar ]
• Stock WA. Systematic coding for research synthesis. In: Cooper H, Hedges LV, editors. The handbook of research synthesis, 236. Russel Sage; 1994. pp. 125–138. [ Google Scholar ]
• Su, J., Zhong, Y., & Ng, D. T. K. (2022). A meta-review of literature on educational approaches for teaching AI at the K-12 levels in the Asia-Pacific region. Computers and Education: Artificial Intelligence , 100065. 10.1016/j.caeai.2022.100065
• Su J, Yang W. Artificial intelligence in early childhood education: A scoping review. Computers and Education: Artificial Intelligence. 2022; 3 :100049. doi: 10.1016/j.caeai.2022.100049. [ CrossRef ] [ Google Scholar ]
• Sung YT, Chang KE, Liu TC. The effects of integrating mobile devices with teaching and learning on students' learning performance: A meta-analysis and research synthesis. Computers & Education. 2016; 94 :252–275. doi: 10.1016/j.compedu.2015.11.008. [ CrossRef ] [ Google Scholar ]
• Talan T, Doğan Y, Batdı V. Efficiency of digital and non-digital educational games: A comparative meta-analysis and a meta-thematic analysis. Journal of Research on Technology in Education. 2020; 52 (4):474–514. doi: 10.1080/15391523.2020.1743798. [ CrossRef ] [ Google Scholar ]
• Tamim, R. M., Bernard, R. M., Borokhovski, E., Abrami, P. C., & Schmid, R. F. (2011). What forty years of research says about the impact of technology on learning: A second-order meta-analysis and validation study. Review of Educational research, 81 (1), 4–28. Retrieved 30 June 2022 from 10.3102/0034654310393361
• Tamim, R. M., Borokhovski, E., Pickup, D., Bernard, R. M., & El Saadi, L. (2015). Tablets for teaching and learning: A systematic review and meta-analysis. Commonwealth of Learning. Retrieved from: http://oasis.col.org/bitstream/handle/11599/1012/2015_Tamim-et-al_Tablets-for-Teaching-and-Learning.pdf
• Tang C, Mao S, Xing Z, Naumann S. Improving student creativity through digital technology products: A literature review. Thinking Skills and Creativity. 2022; 44 :101032. doi: 10.1016/j.tsc.2022.101032. [ CrossRef ] [ Google Scholar ]
• Tolani-Brown, N., McCormac, M., & Zimmermann, R. (2011). An analysis of the research and impact of ICT in education in developing country contexts. In ICTs and sustainable solutions for the digital divide: Theory and perspectives (pp. 218–242). IGI Global.
• Trucano, M. (2005). Knowledge Maps: ICTs in Education. Washington, DC: info Dev / World Bank. Retrieved 30 June 2022 from  https://files.eric.ed.gov/fulltext/ED496513.pdf
• Ulum H. The effects of online education on academic success: A meta-analysis study. Education and Information Technologies. 2022; 27 (1):429–450. doi: 10.1007/s10639-021-10740-8. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Underwood, J. D. (2009). The impact of digital technology: A review of the evidence of the impact of digital technologies on formal education. Retrieved 30 June 2022 from: http://dera.ioe.ac.uk/id/eprint/10491
• Verschaffel, L., Depaepe, F., & Mevarech, Z. (2019). Learning Mathematics in metacognitively oriented ICT-Based learning environments: A systematic review of the literature. Education Research International , 2019 . 10.1155/2019/3402035
• Villena-Taranilla R, Tirado-Olivares S, Cózar-Gutiérrez R, González-Calero JA. Effects of virtual reality on learning outcomes in K-6 education: A meta-analysis. Educational Research Review. 2022; 35 :100434. doi: 10.1016/j.edurev.2022.100434. [ CrossRef ] [ Google Scholar ]
• Voogt J, Knezek G, Cox M, Knezek D, ten Brummelhuis A. Under which conditions does ICT have a positive effect on teaching and learning? A call to action. Journal of Computer Assisted Learning. 2013; 29 (1):4–14. doi: 10.1111/j.1365-2729.2011.00453.x. [ CrossRef ] [ Google Scholar ]
• Vuorikari, R., Punie, Y., & Cabrera, M. (2020). Emerging technologies and the teaching profession: Ethical and pedagogical considerations based on near-future scenarios  (No. JRC120183). Joint Research Centre. Retrieved 30 June 2022 from: https://publications.jrc.ec.europa.eu/repository/handle/JRC120183
• Wang LH, Chen B, Hwang GJ, Guan JQ, Wang YQ. Effects of digital game-based STEM education on students’ learning achievement: A meta-analysis. International Journal of STEM Education. 2022; 9 (1):1–13. doi: 10.1186/s40594-022-00344-0. [ CrossRef ] [ Google Scholar ]
• Wen X, Walters SM. The impact of technology on students’ writing performances in elementary classrooms: A meta-analysis. Computers and Education Open. 2022; 3 :100082. doi: 10.1016/j.caeo.2022.100082. [ CrossRef ] [ Google Scholar ]
• Zheng B, Warschauer M, Lin CH, Chang C. Learning in one-to-one laptop environments: A meta-analysis and research synthesis. Review of Educational Research. 2016; 86 (4):1052–1084. doi: 10.3102/0034654316628645. [ CrossRef ] [ Google Scholar ]

Cornell Chronicle

• Architecture & Design
• Arts & Humanities
• Business, Economics & Entrepreneurship
• Computing & Information Sciences
• Energy, Environment & Sustainability
• Food & Agriculture
• Global Reach
• Health, Nutrition & Medicine
• Law, Government & Public Policy
• Life Sciences & Veterinary Medicine
• Physical Sciences & Engineering
• Social & Behavioral Sciences
• Coronavirus
• News & Events
• Public Engagement
• New York City
• Photos of the Week
• Big Red Sports
• Freedom of Expression
• Student Life
• University Statements

Around Cornell

• All Stories
• In the News
• Expert Quotes
• Cornellians

News directly from Cornell's colleges and centers

Research: Technology is changing how companies do business

By sarah mangus-sharpe.

A new study from the Cornell SC Johnson College of Business advances understanding of the U.S. production chain evolution amidst technological progress in information technology (IT), shedding light on the complex connections between business IT investments and organizational design. Advances in IT have sparked significant changes in how companies design their production processes. In the paper " Production Chain Organization in the Digital Age: Information Technology Use and Vertical Integration in U.S. Manufacturing ," which published April 30 in Management Science, Chris Forman , the Peter and Stephanie Nolan Professor in the Dyson School of Applied Economics and Management , and his co-author delved into what these changes mean for businesses and consumers.

Forman and Kristina McElheran, assistant professor of strategic management at University of Toronto, analyzed U.S. Census Bureau data of over 5,600 manufacturing plants to see how the production chains of businesses were affected by the internet revolution. Their use of census data allowed them to look inside the relationships among production units within and between companies and how transaction flows changed after companies invested in internet-enabled technology that facilitated coordination between them. The production units of many of the companies in their study concurrently sold to internal and external customers, a mix they refer to as plural selling. They found that the reduction in communication costs enabled by the internet shifted the mix toward more sales outside of the firm, or less vertical integration.

The research highlights the importance of staying ahead of the curve in technology. Companies that embrace digital technologies now are likely to be the ones that thrive in the future. And while there are still many unanswered questions about how these changes will play out, one thing is clear: The relationship between technology and business is only going to become more and more intertwined in the future.

Read the full story on the Cornell SC Johnson College of Business news site, BusinessFeed.

Media Contact

Media relations office.

Get Cornell news delivered right to your inbox.

You might also like

Gallery Heading

• Better Business Chronicles
• Operations Technology and Information Management
• Research With Impact

Research: Technology Is Changing How Companies Do Business

In the fast-paced world of modern business, technology plays a crucial role in shaping how companies operate. One area where this impact is particularly significant is in the organization of production chains—specifically the way goods are made and distributed.

A new study from the Cornell SC Johnson College of Business advances understanding of the U.S. production chain evolution amidst technological progress in information technology (IT), shedding light on the complex connections between business IT investments and organizational design.

Advances in IT have sparked significant changes in how companies design their production processes. In the paper “ Production Chain Organization in the Digital Age: Information Technology Use and Vertical Integration in U.S. Manufacturing ,” which published April 30 in Management Science, Chris Forman, the Peter and Stephanie Nolan Professor in the Dyson School of Applied Economics and Management, and his co-author delved into what these changes mean for businesses and consumers.

In running a manufacturing plant, a key decision is how much of the production process is handled in-house and how much is outsourced to other companies. This decision, known as vertical integration, can have big implications for a business. Advances in information and communication technology, such as those brought about by the internet, shifted the network of production flows for many firms.

Forman and Kristina McElheran, assistant professor of strategic management at University of Toronto, analyzed U.S. Census Bureau data of over 5,600 manufacturing plants to see how the production chains of businesses were affected by the internet revolution. Their use of census data allowed them to look inside the relationships among production units within and between companies and how transaction flows changed after companies invested in internet-enabled technology that facilitated coordination between them. The production units of many of the companies in their study concurrently sold to internal and external customers, a mix they refer to as plural selling. They found that the reduction in communication costs enabled by the internet shifted the mix toward more sales outside of the firm, or less vertical integration.

“The internet has made it cheaper and faster for companies to communicate and share information with each other. This means they can work together more efficiently without the need for as much vertical integration,” said Forman.

While some might worry that relying on external partners could make businesses more vulnerable, the research suggests otherwise. In fact, companies that were already using a plural governance approach before the internet age seem to be the most adaptable to these changes. Production units that were capacity-constrained were also among those that made the most significant changes to transaction flows after new technology investments.

“Technology is continuing to reshape the way companies operate and are organized,” Forman said. “More recently, changes in the use of analytics in companies have been accompanied by changes in organizations, and the same is very likely ongoing with newer investments in artificial intelligence.”

The research highlights the importance of staying ahead of the curve in technology. Companies that embrace digital technologies now are likely to be the ones that thrive in the future. And while there are still many unanswered questions about how these changes will play out, one thing is clear: The relationship between technology and business is only going to become more and more intertwined in the future.

• SC Johnson College

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

• Publications
• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

Teens and Video Games Today

85% of u.s. teens say they play video games, and about four-in-ten do so daily. teens see both positive and negative sides of video games – from problem-solving and making friends to harassment and sleep loss, table of contents.

• Who plays video games?
• How often do teens play video games?
• What devices do teens play video games on?
• Social media use among gamers
• Teen views on how much they play video games and efforts to cut back
• Are teens social with others through video games?
• Do teens think video games positively or negatively impact their lives?
• Why do teens play video games?
• Bullying and violence in video games
• Appendix A: Detailed charts
• Acknowledgments
• Methodology

Pew Research Center conducted this analysis to better understand teens’ use of and experiences with video games.

The Center conducted an online survey of 1,453 U.S. teens from Sept. 26 to Oct. 23, 2023, through Ipsos. Ipsos recruited the teens via their parents, who were part of its KnowledgePanel . The KnowledgePanel is a probability-based web panel recruited primarily through national, random sampling of residential addresses. The survey was weighted to be representative of U.S. teens ages 13 to 17 who live with their parents by age, gender, race and ethnicity, household income, and other categories.

This research was reviewed and approved by an external institutional review board (IRB), Advarra, an independent committee of experts specializing in helping to protect the rights of research participants.

Here are the questions used for this analysis , along with responses, and  its methodology .

There are long-standing debates about the impact of video games on youth. Some credit them for helping young people form friendships and teaching them about teamwork and problem-solving . Others say video games expose teenagers to violent content, negatively impact their sleep and can even lead to addiction.

With this in mind, Pew Research Center surveyed 1,423 U.S. teens ages 13 to 17 about their own video game habits – from how often they play to the friends they’ve made and whether it gets in the way of them doing well in school or getting a good night’s sleep. 1

Key findings from the survey

• Video games as a part of daily teen life: 85% of U.S. teens report playing video games, and 41% say they play them at least once a day. Four-in-ten identify as a gamer.
• Gaming as a social experience: 72% of teens who play video games say that a reason why they play them is to spend time with others. And some have even made a friend online from playing them – 47% of teen video game players say they’ve done this.
• Helpful with problem-solving, less so for sleep: Over half of teens who play video games say it has helped their problem-solving skills, but 41% also say it has hurt their sleep.
• Bullying is a problem: 80% of all teens think harassment over video games is a problem for people their age. And 41% of those who play them say they’ve been called an offensive name when playing.
• Boys’ and girls’ experiences differ: Most teen boys and girls play video games, but larger shares of boys identify as gamers (62% vs. 17%) and play every day (61% vs. 22%). Boys who play them are also more likely to experience positive things from it, like making friends, and more troubling things like harassment.

Jump to read about: Who plays video games | Socializing over video games | Views about video games’ impact | Harassment and violence in video games      

Playing video games is widespread among teens. The vast majority of U.S. teens (85%) say they play them. Just 15% say they never do, according to the survey conducted Sept. 26-Oct. 23, 2023.

In addition to asking whether teens play video games, we also wanted to learn whether they consider themselves gamers. Overall, four-in-ten U.S. teens think of themselves as gamers. Just under half of teens (45%) play video games but do not think of themselves as gamers.

Nearly all boys (97%) say they play video games, compared with about three-quarters of teen girls. There is a substantial gap by gender in whether teens identify as gamers: 62% of teen boys do, compared with 17% of girls. 2

By gender and age

Younger teen girls are more likely than older girls to say they play video games: 81% of girls ages 13 to 14 compared with 67% of those ages 15 to 17. But among boys, nearly all play video games regardless of age. 

Similar shares of teens play video games across different racial and ethnic groups and among those who live in households with different annual incomes. Go to Appendix A for more detail on which teens play video games and which teens identify as gamers.

We also asked teens how often they play video games. About four-in-ten U.S. teens say they play video games daily, including 23% who do so several times a day.

Another 22% say they play several times a week, while 21% play them about once a week or less.

Teen boys are far more likely than girls to say they play video games daily (61% vs. 22%). They are also much more likely to say they play them several times a day (36% vs. 11%).

By whether someone identifies as a gamer

About seven-in-ten teens who identify as gamers (71%) say they play video games daily. This drops to 30% among those who play them but aren’t gamers.

By household income

Roughly half of teens living in households with an annual income of less than $30,000 (53%) say they play video games at least daily. This is higher than those in households with an annual income of $30,000 to $74,999 (42%) and $75,000 or more (39%).

Go to Appendix A to see more details about who plays video games and identifies as a gamer by gender, age, race and ethnicity, and household income.

Most teens play video games on a gaming console or a smartphone. When asked about five devices, most teens report playing video games on a gaming console (73%), such as PlayStation, Switch or Xbox. And 70% do so on a smartphone. Fewer – though still sizable shares – play them on each of the following:

• 49% say they play them on a desktop or laptop computer
• 33% do so on a tablet  
• 24% play them on a virtual reality (VR) headset such as Oculus, Meta Quest or PlayStation VR

Many teens play video games on multiple devices. About a quarter of teens (27%) do so on at least four of the five devices asked about, and about half (49%) play on two or three of them. Just 8% play video games on one device.

Teen boys are more likely than girls to play video games on four of the five devices asked about – all expect tablets. For instance, roughly nine-in-ten teen boys say they ever play video games on a gaming console, compared with 57% of girls. Equal shares of teen boys and girls play them on tablets.  

Teens who consider themselves gamers are more likely than those who play video games but aren’t gamers to play on a gaming console (95% vs. 78%), desktop or laptop computer (72% vs. 45%) or a virtual reality (VR) headset (39% vs. 19%). Similar shares of both groups play them on smartphones and tablets.

One way that teens engage with others about video games is through online platforms. And our survey findings show that teen gamers stand out for their use of two online platforms that are known for their gaming communities – Discord and Twitch :

• 44% of teen gamers say they use Discord, far higher than video game players who don’t identify as gamers or those who use the platform but do not play video games at all. About three-in-ten teens overall (28%) use Discord.
• 30% of teens gamers say they use Twitch. About one-in-ten other teens or fewer say the same; 17% of teens overall use the platform.

Previous Center research shows that U.S. teens use online platforms at high rates .

Teens largely say they spend the right amount of time playing video games. When asked about how much time they spend playing them, the largest share of teens (58%) say they spend the right amount of time. Far fewer feel they spend too much (14%) or too little (13%) time playing them.

Teen boys are more likely than girls to say they spend too much time playing video games (22% vs. 6%).

By race and ethnicity

Black (17%) and Hispanic (18%) teens are about twice as likely than White teens (8%) to say they spend too little time playing video games. 3

A quarter of teens who consider themselves gamers say they spend too much time playing video games, compared with 9% of those who play video games but don’t identify as gamers. Teen gamers are also less likely to think they spend too little time playing them (19% vs. 10%).

Fewer than half of teens have reduced how much they play video games. About four-in-ten (38%) say they have ever chosen to cut back on the amount of time they spend playing them. A majority (61%) report that they have not cut back at all.

This share is on par with findings about whether teenagers have cut back with their screen time – on social media or their smartphone.

Although boys are more likely to say they play video games too much, boys and girls are on par for whether they have ever cut back. About four-in-ten teen boys (39%) and girls (38%) say that they have ever cut back.

And gamers are as likely to say they have cut back as those who play video games but don’t identify as gamers (39% and 41%).

A main goal of our survey was to ask teens about their own experiences playing video games. For this section of the report, we focus on teens who say they play video games.

Socializing with others is a key part of the video game experience. Most teens who play video games do so with others, and some have developed friendships through them.

About nine-in-ten teen video game players (89%) say they play them with other people, in person or online. Far fewer (11%) play them only on their own.

Additionally, about half (47%) report that they have ever made a friend online because of a video game they both play. This equals 40% of all U.S. teens who have made a friend online because of a video game.

These experiences vary by:  

• Gender: Most teen boy and girl video game players play them with others, though it’s more common among boys (94% vs. 82%). Boys who play video games are much more likely to say they have made a friend online because of a video game (56% vs. 35%).
• Race and ethnicity: Black (55%) and Hispanic (53%) teen video game players are more likely than White teen video game players (43%) to say they have made a friend online because of them.
• Whether someone identifies as a gamer: Nearly all teen gamers report playing video games with others (98%). Fewer – though still most – of those who play video games but aren’t gamers (81%) also play them with others. And about seven-in-ten (68%) say they have made a friend online because of a video game, compared with 29% of those who play them but don’t identify as gamers.

Teens who play video games are particularly likely to say video games help their problem-solving skills. More than half of teens who play video games (56%) say this.

Additionally, more think that video games help, rather than hurt, three other parts of their lives that the survey asked about. Among teens who play video games:

• Roughly half (47%) say it has helped their friendships
• 41% say it has helped how they work with others
• 32% say it has helped their mental health

No more than 7% say playing video games has hurt any of these.

More teens who play video games say it hurts, rather than helps, their sleep. Among these teens, 41% say it has hurt how much sleep they get, while just 5% say it helps. And small shares say playing video games has impacted how well they do in school in either a positive or a negative way.

Still, many teens who play video games think playing them doesn’t have much an impact in any of these areas. For instance, at least six-in-ten teens who play video games say it has neither a positive nor a negative impact on their mental health (60%) or their school performance (72%). Fewer (41%) say this of their problem-solving skills.

Teen boys who play video games are more likely than girls to think playing them has helped their problem-solving skills, friendships and ability to work with others. For instance, 55% of teen boys who play video games say this has helped their friendships, compared with 35% of teen girls.

As for ways that it may hurt their lives, boys who play them are more likely than girls to say that it has hurt the amount of sleep they get (45% vs. 37%) and how well they do in school (21% vs. 11%). 

Teens who consider themselves gamers are more likely than those who aren’t gamers but play video games to say video games have helped their friendships (60% vs. 35%), ability to work with others (52% vs. 32%), problem-solving skills (66% vs. 47%) and mental health (41% vs. 24%).

Gamers, though, are somewhat more likely to say playing them hurt their sleep (48% vs. 36%) and how well they do in school (20% vs. 14%).

By whether teens play too much, too little or the right amount

Teens who report playing video games too much stand out for thinking video games have hurt their sleep and school performance. Two-thirds of these teens say it has hurt the amount of sleep they get, and 39% say it hurt their schoolwork. Far fewer of those who say they play the right amount (38%) or too little (32%) say it has hurt their sleep, or say it hurt their schoolwork (12% and 16%).

Teens who play video games say they largely do so to be entertained. And many also play them to be social with and interact with others. Teens who play video games were asked about four reasons why they play video games. Among those who play video games:

• Nearly all say fun or entertainment is a major or minor reason why they play video games – with a large majority (87%) saying it’s a major reason.
• Roughly three-quarters say spending time with others is a reason, and two-thirds say this of competing with others. Roughly three-in-ten say each is a major reason.
• Fewer – 50% – see learning something as a reason, with just 13% saying it’s a major reason.

While entertainment is by far the most common reason given by teens who play video games, differences emerge across groups in why they play video games.

Teens who identify as gamers are particularly likely to say each is major reason, especially when it comes to competing against others. About four-in-ten gamers (43%) say this is a major reason, compared with 13% of those who play video games but aren’t gamers.

Teen boys who play video games are more likely than girls to say competing (36% vs. 15%), spending time with others (36% vs. 27%) and entertainment (90% vs. 83%) are major reasons they play video games.

Black and Hispanic teens who play video games are more likely than White teens to say that learning new things and competing against others are major reasons they play them. For instance, 29% of Black teen video game players say learning something new is a major reason, higher than 17% of Hispanic teen video game players. Both are higher than the 7% of White teen video game players who say the same.

Teens who play video games and live in lower-income households are especially likely to say competing against others and learning new things are major reasons. For instance, four-in-ten teen video game players who live in households with an annual income of less than $30,000 say competing against others is a major reason they play. This is higher than among those in households with annual incomes of $30,000 to $74,999 (29%) and $75,000 or more (23%).

Cyberbullying can happen in many online environments, but many teens encounter this in the video game world.

Our survey finds that name-calling is a relatively common feature of video game life – especially for boys. Roughly four-in-ten teen video game players (43%) say they have been harassed or bullied while playing a video game in one of three ways: 

• 41% have been called an offensive name
• 12% have been physically threatened
• 8% have been sent unwanted sexually explicit things

Teen boys are particularly likely to say they have been called an offensive name. About half of teen boys who play video games (48%) say this has happened while playing them, compared with about a third of girls (32%). And they are somewhat more likely than girls to have been physically threatened (15% vs. 9%).

Teen gamers are more likely than those who play video games but aren’t gamers to say they been called and offensive name (53% vs. 30%), been physically threatened (17% vs. 8%) and sent unwanted sexually explicit things (10% vs. 6%).

Teens – regardless of whether they’ve had these experiences – think bullying is a problem in gaming. Eight-in-ten U.S. teens say that when it comes to video games, harassment and bullying is a problem for people their age. This includes 29% who say it is a major problem.

It’s common for teens to think harassment while playing video games is a problem, but girls are somewhat more likely than boys to say it’s a major problem (33% vs. 25%).

There have also been decades-long debates about how violent video games can influence youth behavior , if at all – such as by encouraging or desensitizing them to violence. We wanted to get a sense of how commonly violence shows up in the video games teens are playing.

Just over half of teens who play video games (56%) say at least some of the games they play contain violence. This includes 16% who say it’s in all or most of the games they play.

Teen boys who play video games are far more likely than girls to say that at least some of the games they play contain violence (69% vs. 37%).

About three-quarters of teen gamers (73%) say that at least some of the games they play contain violence, compared with 40% among video game players who aren’t gamers.   

• Throughout this report, “teens” refers to those ages 13 to 17. ↩
• Previous Center research of U.S. adults shows that men are more likely than women to identify as gamers – especially the youngest adults. ↩
• There were not enough Asian American respondents in the sample to be broken out into a separate analysis. As always, their responses are incorporated into the general population figures throughout the report. ↩

Sign up for our weekly newsletter

Fresh data delivery Saturday mornings

Sign up for The Briefing

Weekly updates on the world of news & information

• Friendships
• Online Harassment & Bullying
• Teens & Tech
• Teens & Youth

How Teens and Parents Approach Screen Time

Teens and internet, device access fact sheet, teens and social media fact sheet, teens, social media and technology 2023, what the data says about americans’ views of artificial intelligence, most popular, report materials.

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Age & Generations
• Coronavirus (COVID-19)
• Economy & Work
• Family & Relationships
• Gender & LGBTQ
• Immigration & Migration
• International Affairs
• Internet & Technology
• Methodological Research
• News Habits & Media
• Non-U.S. Governments
• Other Topics
• Politics & Policy
• Race & Ethnicity
• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

Copyright 2024 Pew Research Center

Suggestions or feedback?

MIT News | Massachusetts Institute of Technology

• Machine learning
• Social justice
• Black holes
• Classes and programs

Departments

• Aeronautics and Astronautics
• Brain and Cognitive Sciences
• Architecture
• Political Science
• Mechanical Engineering

Centers, Labs, & Programs

• Abdul Latif Jameel Poverty Action Lab (J-PAL)
• Picower Institute for Learning and Memory
• Lincoln Laboratory
• School of Architecture + Planning
• School of Engineering
• School of Humanities, Arts, and Social Sciences
• Sloan School of Management
• School of Science
• MIT Schwarzman College of Computing

Using ideas from game theory to improve the reliability of language models

Press contact :.

Previous image Next image

Imagine you and a friend are playing a game where your goal is to communicate secret messages to each other using only cryptic sentences. Your friend's job is to guess the secret message behind your sentences. Sometimes, you give clues directly, and other times, your friend has to guess the message by asking yes-or-no questions about the clues you've given. The challenge is that both of you want to make sure you're understanding each other correctly and agreeing on the secret message.

MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers have created a similar "game" to help improve how AI understands and generates text. It is known as a “consensus game” and it involves two parts of an AI system — one part tries to generate sentences (like giving clues), and the other part tries to understand and evaluate those sentences (like guessing the secret message).

The researchers discovered that by treating this interaction as a game, where both parts of the AI work together under specific rules to agree on the right message, they could significantly improve the AI's ability to give correct and coherent answers to questions. They tested this new game-like approach on a variety of tasks, such as reading comprehension, solving math problems, and carrying on conversations, and found that it helped the AI perform better across the board.

Traditionally, large language models answer one of two ways: generating answers directly from the model (generative querying) or using the model to score a set of predefined answers (discriminative querying), which can lead to differing and sometimes incompatible results. With the generative approach, "Who is the president of the United States?" might yield a straightforward answer like "Joe Biden." However, a discriminative query could incorrectly dispute this fact when evaluating the same answer, such as "Barack Obama."

So, how do we reconcile mutually incompatible scoring procedures to achieve coherent, efficient predictions? 

"Imagine a new way to help language models understand and generate text, like a game. We've developed a training-free, game-theoretic method that treats the whole process as a complex game of clues and signals, where a generator tries to send the right message to a discriminator using natural language. Instead of chess pieces, they're using words and sentences," says Athul Jacob, an MIT PhD student in electrical engineering and computer science and CSAIL affiliate. "Our way to navigate this game is finding the 'approximate equilibria,' leading to a new decoding algorithm called 'equilibrium ranking.' It's a pretty exciting demonstration of how bringing game-theoretic strategies into the mix can tackle some big challenges in making language models more reliable and consistent."

When tested across many tasks, like reading comprehension, commonsense reasoning, math problem-solving, and dialogue, the team's algorithm consistently improved how well these models performed. Using the ER algorithm with the LLaMA-7B model even outshone the results from much larger models. "Given that they are already competitive, that people have been working on it for a while, but the level of improvements we saw being able to outperform a model that's 10 times the size was a pleasant surprise," says Jacob. 

"Diplomacy," a strategic board game set in pre-World War I Europe, where players negotiate alliances, betray friends, and conquer territories without the use of dice — relying purely on skill, strategy, and interpersonal manipulation — recently had a second coming. In November 2022, computer scientists, including Jacob, developed “Cicero,” an AI agent that achieves human-level capabilities in the mixed-motive seven-player game, which requires the same aforementioned skills, but with natural language. The math behind this partially inspired the Consensus Game. 

While the history of AI agents long predates when OpenAI's software entered the chat in November 2022, it's well documented that they can still cosplay as your well-meaning, yet pathological friend. 

The consensus game system reaches equilibrium as an agreement, ensuring accuracy and fidelity to the model's original insights. To achieve this, the method iteratively adjusts the interactions between the generative and discriminative components until they reach a consensus on an answer that accurately reflects reality and aligns with their initial beliefs. This approach effectively bridges the gap between the two querying methods. 

In practice, implementing the consensus game approach to language model querying, especially for question-answering tasks, does involve significant computational challenges. For example, when using datasets like MMLU, which have thousands of questions and multiple-choice answers, the model must apply the mechanism to each query. Then, it must reach a consensus between the generative and discriminative components for every question and its possible answers. 

The system did struggle with a grade school right of passage: math word problems. It couldn't generate wrong answers, which is a critical component of understanding the process of coming up with the right one. 

“The last few years have seen really impressive progress in both strategic decision-making and language generation from AI systems, but we’re just starting to figure out how to put the two together. Equilibrium ranking is a first step in this direction, but I think there’s a lot we’ll be able to do to scale this up to more complex problems,” says Jacob.   

An avenue of future work involves enhancing the base model by integrating the outputs of the current method. This is particularly promising since it can yield more factual and consistent answers across various tasks, including factuality and open-ended generation. The potential for such a method to significantly improve the base model's performance is high, which could result in more reliable and factual outputs from ChatGPT and similar language models that people use daily. 

"Even though modern language models, such as ChatGPT and Gemini, have led to solving various tasks through chat interfaces, the statistical decoding process that generates a response from such models has remained unchanged for decades," says Google Research Scientist Ahmad Beirami, who was not involved in the work. "The proposal by the MIT researchers is an innovative game-theoretic framework for decoding from language models through solving the equilibrium of a consensus game. The significant performance gains reported in the research paper are promising, opening the door to a potential paradigm shift in language model decoding that may fuel a flurry of new applications."

Jacob wrote the paper with MIT-IBM Watson Lab researcher Yikang Shen and MIT Department of Electrical Engineering and Computer Science assistant professors Gabriele Farina and Jacob Andreas, who is also a CSAIL member. They presented their work at the International Conference on Learning Representations (ICLR) earlier this month, where it was highlighted as a "spotlight paper." The research also received a “best paper award” at the NeurIPS R0-FoMo Workshop in December 2023.

Share this news article on:

Press mentions, quanta magazine.

MIT researchers have developed a new procedure that uses game theory to improve the accuracy and consistency of large language models (LLMs), reports Steve Nadis for Quanta Magazine . “The new work, which uses games to improve AI, stands in contrast to past approaches, which measured an AI program’s success via its mastery of games,” explains Nadis. 

Previous item Next item

Related Links

• Article: "Game Theory Can Make AI More Correct and Efficient"
• Jacob Andreas
• Athul Paul Jacob
• Language & Intelligence @ MIT
• Computer Science and Artificial Intelligence Laboratory (CSAIL)
• Department of Electrical Engineering and Computer Science
• MIT-IBM Watson AI Lab

Related Topics

• Computer science and technology
• Artificial intelligence
• Human-computer interaction
• Natural language processing
• Game theory
• Electrical Engineering & Computer Science (eecs)

Related Articles

Reasoning and reliability in AI

Explained: Generative AI

Synthetic imagery sets new bar in AI training efficiency

Simulating discrimination in virtual reality

More mit news.

Janabel Xia: Algorithms, dance rhythms, and the drive to succeed

Read full story →

Jonathan Byrnes, MIT Center for Transportation and Logistics senior lecturer and visionary in supply chain management, dies at 75

Researchers develop a detector for continuously monitoring toxic gases

The beauty of biology

Navigating longevity with industry leaders at MIT AgeLab PLAN Forum

Jeong Min Park earns 2024 Schmidt Science Fellowship

• More news on MIT News homepage →

Massachusetts Institute of Technology 77 Massachusetts Avenue, Cambridge, MA, USA

• Map (opens in new window)
• Events (opens in new window)
• People (opens in new window)
• Careers (opens in new window)
• Accessibility
• Social Media Hub
• MIT on Facebook
• MIT on YouTube
• MIT on Instagram

Senators studied AI for a year. Critics call the result ‘pathetic.’

The 31-page “road map” calls for a $32 billion infusion for AI research and development and asks congressional committees to develop legislation.

For much of the last year, Senate Majority Leader Charles E. Schumer and a bipartisan group of his colleagues have been huddling with tech CEOs, civil rights leaders and top researchers to develop an “all hands on deck” plan to address the urgent threats posed by artificial intelligence.

This week, the Senate AI Gang, as the group is known, unveiled the fruits of that effort, celebrating a sprawling 31-page road map that calls for billions of new funding in AI research as the “deepest” AI legislative document to date. But consumer advocates are furious about the final product, saying that the document is far too vague about how it will protect people from AI’s harms and that the senators’ initiative is sucking up the oxygen from other efforts to aggressively regulate the technology.

“This road map leads to a dead end,” Evan Greer, the director of Fight for the Future, an advocacy group, said in a statement that called the plan “pathetic” — a criticism echoed by others.

The immediate divisions over the plan bring to the fore the challenges of regulating the swiftly evolving technology in a deeply polarized Congress during an election year. Rather than pursuing a single comprehensive bill, the AI Gang has decided to delegate the legislative work to Senate committees, which are at drastically different stages in their efforts to understand the promise and peril of AI.

GET CAUGHT UP

70 years later, 1 in 3 Black people say integration didn’t help Black students

Journalists sue Chicago Tribune owner alleging pay discrimination

Abbott grants Daniel Perry pardon in murder of Black Lives Matter protester

NFL disavows Harrison Butker’s comments, cites commitment to inclusion

6 Airbnb red flags to spot before you make a booking mistake

Schumer (D-N.Y.) expects that some AI bills could pass the Senate and potentially the House by the end of the year, but he noted that much of this work will extend into the next Congress. But the plan faces an uncertain future next year, as key lawmakers working on tech issues are scheduled to retire and the 2024 elections could reshape the leadership of Congress and the White House. The Senate Rules Committee on Wednesday advanced a trio of bipartisan bills addressing the role of AI in elections, which Schumer noted was one of the most urgent issues facing Congress amid the 2024 elections.

“We’re not going to wait on legislation that addresses every aspect of AI in society,” Schumer said. “If some areas are ready earlier than others, they should go forward.”

Other congressional committees are just beginning their work on artificial intelligence, as major tech companies are plowing forward with ever more advanced systems intended to further entrench the technology in consumers’ lives. On Monday, OpenAI announced a handful of upgrades that will make it easier for people to talk to ChatGPT, drawing comparisons with the 2013 film “Her,” which depicts a human falling in love with a digital voice assistant. On Tuesday, Google announced that it would roll out this week AI-generated answers to the top of everyone’s search results in the United States, transforming the way people access information online.

Lawmakers have repeatedly promised that they will regulate AI with greater urgency than they did with social media. For the last half decade, lawmakers have held numerous hearings and introduced a flurry of bills to address the ways social media allegedly harms children, undermines elections and imperils users’ privacy. But to date, the main social media legislation that Congress has passed is a law that could force a sale or ban of TikTok. With the new roadmap, critics say lawmakers risk repeating the same mistakes.

Tech industry groups were largely supportive of the road map. TechNet, whose members include OpenAI, Google and Meta, said in a statement that the directive “will strengthen America’s global competitiveness in AI and emerging technologies” through providing $32 billion for AI research and development, which will be distributed to the Energy Department, Commerce Department, National Science Foundation, and National Institute of Standards and Technology.

Tony Samp, the head of AI policy at the DLA Piper law firm and former founding director of the Senate AI working group, said the Senate’s process helped raise awareness about AI among lawmakers and helped prompt ideas for how to legislate. DLA Piper, including Samp, has lobbied on behalf of clients including OpenAI, according to federal disclosures.

“There are some who think you can wave a magic wand and Congress could pass comprehensive AI legislation, but that thinking ignores the political realities in the United States, as well as the real objectives of the AI Insight Forums and the road map,” Samp told The Washington Post.

Reggie Babin, a senior counsel at the law firm Akin and a former chief counsel to Schumer, told The Post that the working group succeeded in its goals. Babin has lobbied on behalf of Akin clients, including Adobe, according to federal disclosures.

“The goal of the process was to figure out how to make sure that 80 percent of stakeholders in the middle of this conversation are satisfied while preserving space for continued engagement on all fronts,” he said. “I think the working group hit that mark.”

The lawmakers gathered input for the road map in private sessions dubbed Insight Forums. Over the nine sessions, lawmakers met with executives including OpenAI CEO Sam Altman and Tesla CEO Elon Musk, as well as consumer advocates and civil rights leaders, such as Maya Wiley, the president and CEO of the Leadership Conference on Civil and Human Rights, a Washington-based group that represents a number of civil rights organizations. Liz Shuler, the president of the AFL-CIO who participated in the Insight Forums, called the road map an “important start” to passing AI legislation.

But civil society leaders were frustrated that the road map only made a cursory mention of AI bias, amid widespread concerns that the technology can replicate and exacerbate harmful stereotypes. Nik Marda, a technical lead on AI governance at Mozilla, noted on X that “bias” was mentioned in the road map as many times as “space debris.”

Rashad Robinson, the president of the civil rights group Color of Change, said the report shows Schumer “is not taking AI seriously.” Robinson called for lawmakers to move swiftly to respond to the bias that AI can pose.

Schumer told The Post in a statement that he shares the goals of the advocates.

“Leader Schumer agrees with their goals and we’re going to continue to work closely with them as legislation is written,” said Schumer spokeswoman Allison Biasotti.

Greer said the report reads like industry had outsize influence over the process, and it was written by Altman and other tech lobbyists.

“They heard from experts about the urgency of addressing AI harms and then paid lip service to that while giving industry most of what they want: money and ‘light touch’ regulatory proposals,” Greer told The Post.

The United States’ efforts to regulate AI lag far behind those of the European Union, which last year advanced a wide-ranging AI Act that sets limits on AI based on how risky regulators deem an application to be. The E.U. AI Act, for instance, bans social scoring systems similar to those used in China, and it places transparency requirements on high-risk applications of AI in medical devices or employment settings.

Many observers hoped that the road map would provide clarity on a path forward for Congress to address some of the thorniest issues in AI governance that have divided the tech industry — including the future of copyright law and the growing debate over the regulation of AI models that are open source, or freely available to the public, without the guardrails that OpenAI, Google and Microsoft place on their models. But Schumer’s report doesn’t mention open source, and it largely punts issues of intellectual property rights to government agencies, directing lawmakers to review existing and forthcoming reports from the U.S. Copyright Office and the U.S. Patent and Trademark Office on AI.

In a briefing with reporters Tuesday night, Sen. Todd Young (R-Ind.), a member of the AI Gang, said the report had “a high level of specificity” for a document of its size and scope. But senators acknowledged the report leaves key questions unanswered. The report calls for a $32 billion investment in nondefense AI research and development, but it does not specify how much funding should be directed to the military. The report also leaves key questions about how Congress should regulate consumer privacy in the age of AI to the committees.

“Where vagueness was required to come to an agreement, we embrace vagueness,” Young said.

But that strategy doesn’t work, said Suresh Venkatasubramanian, a Brown University professor who co-authored the White House’s AI Bill of Rights. He participated in the forums and felt as if lawmakers ignored the problems raised during the sessions.

“Embracing vagueness at this point is basically saying the status quo is just fine," he said.

The report “repackages” many issues around AI that have been debated on Capitol Hill for years, and its delivery may prompt some legislators to “yearn for more than just polished reiterations,” said Divyansh Kaushik, a vice president at Beacon Global Strategies, which advises companies on national security issues.

“After almost a year of hearings, briefings and forums, I think members are hungry for tangible, actionable steps and crisp legislative blueprints, something beyond the theoretical … a call for concrete, actionable strategies,” he said. “The real challenge begins now and it’s one for congressional committees: ensuring that this report does more than stir the pot, but rather sparks a sustained drive toward innovative and decisive policymaking to ensure American competitiveness on these critical technologies."

• Future Students
• Current Students
• Faculty/Staff

News and Media

• News & Media Home
• Research Stories
• School's In
• In the Media

You are here

70 years after brown v. board of education, new research shows rise in school segregation.

As the nation prepares to mark the 70th anniversary of the landmark U.S. Supreme Court ruling in Brown v. Board of Education , a new report from researchers at Stanford and USC shows that racial and economic segregation among schools has grown steadily in large school districts over the past three decades — an increase that appears to be driven in part by policies favoring school choice over integration.

Analyzing data from U.S. public schools going back to 1967, the researchers found that segregation between white and Black students has increased by 64 percent since 1988 in the 100 largest districts, and segregation by economic status has increased by about 50 percent since 1991.

The report also provides new evidence about the forces driving recent trends in school segregation, showing that the expansion of charter schools has played a major role.  

The findings were released on May 6 with the launch of the Segregation Explorer , a new interactive website from the Educational Opportunity Project at Stanford University. The website provides searchable data on racial and economic school segregation in U.S. states, counties, metropolitan areas, and school districts from 1991 to 2022. 

“School segregation levels are not at pre- Brown levels, but they are high and have been rising steadily since the late 1980s,” said Sean Reardon , the Professor of Poverty and Inequality in Education at Stanford Graduate School of Education and faculty director of the Educational Opportunity Project. “In most large districts, school segregation has increased while residential segregation and racial economic inequality have declined, and our findings indicate that policy choices – not demographic changes – are driving the increase.” 

“There’s a tendency to attribute segregation in schools to segregation in neighborhoods,” said Ann Owens , a professor of sociology and public policy at USC. “But we’re finding that the story is more complicated than that.”

Assessing the rise

In the Brown v. Board decision issued on May 17, 1954, the U.S. Supreme Court ruled that racially segregated public schools violated the Equal Protection Clause of the Fourteenth Amendment and established that “separate but equal” schools were not only inherently unequal but unconstitutional. The ruling paved the way for future decisions that led to rapid school desegregation in many school districts in the late 1960s and early 1970s.

Though segregation in most school districts is much lower than it was 60 years ago, the researchers found that over the past three decades, both racial and economic segregation in large districts increased. Much of the increase in economic segregation since 1991, measured by segregation between students eligible and ineligible for free lunch, occurred in the last 15 years.

White-Hispanic and white-Asian segregation, while lower on average than white-Black segregation, have both more than doubled in large school districts since the 1980s. 

Racial-economic segregation – specifically the difference in the proportion of free-lunch-eligible students between the average white and Black or Hispanic student’s schools – has increased by 70 percent since 1991. 

School segregation is strongly associated with achievement gaps between racial and ethnic groups, especially the rate at which achievement gaps widen during school, the researchers said.  

“Segregation appears to shape educational outcomes because it concentrates Black and Hispanic students in higher-poverty schools, which results in unequal learning opportunities,” said Reardon, who is also a senior fellow at the Stanford Institute for Economic Policy Research and a faculty affiliate of the Stanford Accelerator for Learning . 

Policies shaping recent trends 

The recent rise in school segregation appears to be the direct result of educational policy and legal decisions, the researchers said. 

Both residential segregation and racial disparities in income declined between 1990 and 2020 in most large school districts. “Had nothing else changed, that trend would have led to lower school segregation,” said Owens. 

But since 1991, roughly two-thirds of districts that were under court-ordered desegregation have been released from court oversight. Meanwhile, since 1998, the charter sector – a form of expanded school choice – has grown.

Expanding school choice could influence segregation levels in different ways: If families sought schools that were more diverse than the ones available in their neighborhood, it could reduce segregation. But the researchers found that in districts where the charter sector expanded most rapidly in the 2000s and 2010s, segregation grew the most. 

The researchers’ analysis also quantified the extent to which the release from court orders accounted for the rise in school segregation. They found that, together, the release from court oversight and the expansion of choice accounted entirely for the rise in school segregation from 2000 to 2019.

The researchers noted enrollment policies that school districts can implement to mitigate segregation, such as voluntary integration programs, socioeconomic-based student assignment policies, and school choice policies that affirmatively promote integration. 

“School segregation levels are high, troubling, and rising in large districts,” said Reardon. “These findings should sound an alarm for educators and policymakers.”

Additional collaborators on the project include Demetra Kalogrides, Thalia Tom, and Heewon Jang. This research, including the development of the Segregation Explorer data and website, was supported by the Russell Sage Foundation, the Robert Wood Johnson Foundation, and the Bill and Melinda Gates Foundation.   

More Stories

⟵ Go to all Research Stories

Get the Educator

Subscribe to our monthly newsletter.

Stanford Graduate School of Education

482 Galvez Mall Stanford, CA 94305-3096 Tel: (650) 723-2109

• Contact Admissions
• GSE Leadership
• Site Feedback
• Web Accessibility
• Career Resources
• Faculty Open Positions
• Explore Courses
• Academic Calendar
• Office of the Registrar
• Cubberley Library
• StanfordWho
• StanfordYou

Improving lives through learning

• Stanford Home
• Maps & Directions
• Search Stanford
• Emergency Info
• Terms of Use
• Non-Discrimination
• Accessibility

© Stanford University , Stanford , California 94305 .