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

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

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• Swen Nadkarni 1 &
• Reinhard Prügl 1  

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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.

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Digital Transformation: A Literature Review and Guidelines for Future Research

An Introduction to Digital Transformation

Digital Transformation Framework: A Bibliometric Approach

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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 .

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Data structure for the technology-centric dimension of technological disruption

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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

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Going digital: how technology use may influence human brains and behavior


Camino a la digitalización: influencia de la tecnología en el cerebro y el comportamiento humano, passage au tout numérique : influence de la technologie sur le cerveau et le comportement humains, margret r. hoehe.

Author affiliations: Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany

Florence Thibaut

University Hospital Cochin - site Tarnier; University of Paris; INSERM U1266, Institute of Psychiatry and Neuroscience, Paris, France

The digital revolution has changed, and continues to change, our world and our lives. Currently, major aspects of our lives have moved online due to the coronavirus pandemic, and social distancing has necessitated virtual togetherness. In a synopsis of 10 articles we present ample evidence that the use of digital technology may influence human brains and behavior in both negative and positive ways. For instance, brain imaging techniques show concrete morphological alterations in early childhood and during adolescence that are associated with intensive digital media use. Technology use apparently affects brain functions, for example visual perception, language, and cognition. Extensive studies could not confirm common concerns that excessive screen time is linked to mental health problems, or the deterioration of well-being. Nevertheless, it is important to use digital technology consciously, creatively, and sensibly to improve personal and professional relationships. Digital technology has great potential for mental health assessment and treatment, and the improvement of personal mental performance.


La revolución digital ha cambiado y continúa cambiando nuestro mundo y nuestras vidas. Actualmente, los principales aspectos de nuestras vidas han migrado hacia el funcionamiento “online” debido a la pandemia del coronavirus, y el distanciamiento social ha requerido de cercanías virtuales. En una sinopsis de 10 artículos, se presenta una amplia evidencia de que el empleo de la tecnología digital puede influir en el cerebro y en el comportamiento humano de manera negativa y positiva. Por ejemplo, las técnicas de imágenes cerebrales muestran alteraciones morfológicas concretas en la primera infancia y durante la adolescencia, las cuales están asociadas con el empleo intenso de medios digitales. En apariencia, la utilización de la tecnología afecta las funciones cerebrales, como la percepción visual, el lenguaje y la cognición. Numerosos estudios no pudieron confirmar las preocupaciones comunes en cuanto a que el tiempo excesivo de pantalla esté relacionado con problemas de salud mental o el deterioro del bienestar. Sin embargo, es importante emplear la tecnología digital de manera consciente, creativa y sensata para mejorar las relaciones personales y profesionales. La tecnología digital tiene un gran potencial para la evaluación y el tratamiento de la salud mental, y el aumento del rendimiento mental personal.

La révolution numérique a modifié et continue à modifier notre monde et nos vies. La pandémie actuelle due au coronavirus a fait basculer en ligne de nombreux pans de notre existence et la distanciation sociale a imposé la virtualité des rassemblements. Les données des dix articles présentés ici attestent de l’influence de la technologie numérique sur les cerveaux et les comportements, de manière positive et négative. Par exemple,l’imagerie cérébrale montre des altérations morphologiques concrètes apparaissant tôt dans l’enfance et pendant l’adolescence lors d’une pratique intensive des media numériques. Cela concernerait certaines fonctions cérébrales comme la perception visuelle, le langage et la cognition. Des études approfondies n’ont pas confirmé les inquiétudes courantes quant aux répercussions d’un temps excessif passé devant un écran en termes de santé mentale ou de qualité de vie. Il est néanmoins important de privilégier une utilisation consciente, créative et raisonnable des technologies numériques afin d’améliorer les relations personnelles et professionnelles. Ces technologies ont un grand potentiel dans l’évaluation et le traitement de la santé mentale ainsi que dans l’amélioration des performances mentales personnelles.

The “Digital Revolution”: remaking the world


Within a few decades, digital technology has transformed our lives. At any time, we can access almost unlimited amounts of information just as we can produce, process, and store colossal amounts of data. We can constantly interact, and connect, with each other by use of digital devices and social media. Coping with the daily demands of life as well as pursuing pleasure in recreational activities appears inconceivable without the use of smartphones, tablets, computers, and access to Internet platforms. Presently, over 4.57 billion people, 59% of the world population, use the Internet according to recent estimates (December 31 st , 2019), ranging between 39% (Africa) and 95% (North America). 1 People are spending an enormous, “insane” amount of time online, according to the latest Digital 2019 report compiled by Ofcom 2 : on average 6 hours and 42 minutes (06:42) each day (between 03:45 in Japan and 10:02 in the Philippines), half of that on mobile devices, on average equating to more than 100 days per year for every Internet user. According to a landmark report on the impact of the “decade of the smartphone,” 3 the average person in the UK spends 24 hours a week online, with 20% of all adults spending as much as 40 hours, and those aged 16 to 24 on average 34.3 hours a week. Britons are checking their smartphones on average every 12 minutes. In the US, teen screen time averages over 7 hours a day, excluding time for homework. Digital technology has become ubiquitous and entwined with our modern lives. As Richard Hodson in the Nature Outlook on “Digital Revolution,” 2018, concluded, “an explosion in information technology is remaking the world, leaving few aspects of society untouched. In the space of 50 years, the digital world has grown to become crucial to the functioning of society.” 4 This period of societal transformation has been considered “the most recent long wave of humanity’s socio-economic evolution”. As a “meta-paradigm of societal modernization based on technological change” induced by the transformation of information, it supersedes earlier periods of technological revolution based on the transformation of material and energy, respectively, spanning over 2 million years altogether (Hilbert, p 189 in this issue). 


In particular, the excessive use of digital technology during adolescence has given rise to grave concerns that this technology is harmful and damages the (developing) brain or may even cause mental health problems. Public concern culminated in Jean Twenge’s 2017 article “Have Phones Destroyed a Generation?,” 5 which linked the rise in suicide, depression, and anxiety among teens after 2012 to the appearance of smartphones. All-too-familiar pictures: parents and children, or couples, or friends, at the table, staring at their phones, texting; colleagues staring at screens, busy with emails; individuals, heads down, hooked on their phones, blind to their surroundings, wherever they are. Individuals interacting with their devices, not with each other. “The flight from conversation,” which may erode (close) human relationships and with them the capacity for empathy, introspection, creativity, and productivity - ultimately, the social fabric of our communities. Sherry Turkle, who has studied the relationship of humans with technology for decades, has articulated these concerns in Alone Together and Reclaiming Conversation . 6 , 7 Thus, “life offline” has become a consideration and advice to limit screen time and practice digital minimalism has become popular. 8 The concerns about screen time and efforts to keep us from staring at our devices and detox our digital lives came to a sudden end with the COVID-19 coronavirus pandemic. 9 Almost overnight, nearly our entire personal, professional, educational, cultural, and political activities were moved online. The dictum of social distancing necessitated virtual togetherness.


Changing human brains and behavior?


The use of digital technology has changed, and continues to change, our lives. How could this affect human brains and behavior, in both negative and positive ways? Apparently, the ability of the human brain to adapt to any changes plays a key role in generating structural and/or functional changes induced by the usage of digital devices. The most direct evidence for an effect of frequent smart phone use on the brain is provided by the demonstration of changes in cortical activity (Korte, p 101 in this issue). Touching the screen repetitively – the average American user touches it 2176 times a day 10 – induces an increase of the cortical potentials allotted to the tactile receptors on the fingertips, leading to an enlargement, ie, reorganization of the motor and sensory cortex. It remains to be determined whether this reshaping of cortical sensory representation occurs at the expense of other motor coordination skills. Processes of neuroplasticity are particularly active in the developing brain, especially during stages of dynamic brain growth in early childhood. For instance, as demonstrated by functional magnetic resonance imaging (fMRI), extensive childhood experience with the game “Pokémon” influences the organization of the visual cortex, with distinct effects on the perception of visual objects even decades later. Furthermore, as shown by diffusion tensor MRI, early extensive screen-based media use is significantly associated with lower microstructural integrity of brain white matter tracts supporting language and literacy skills in preschoolers. 11 Also, adolescence is a time of significant development, with the brain areas involved in emotional and social behavior undergoing marked changes. Social media use can have a profound effect; eg, the size of an adolescent’s online social network was closely linked to brain anatomy alterations as demonstrated by structural MRI. The impact of digital technology use, both negative and positive, on these and many more brain-related phenomena has been elaborated in the review by Korte, who provides a comprehensive overview of the field. 


The most direct approach to assess the effect of excessive digital media use on (adolescent) brains presently appears to be the analysis of the neurobiological mechanisms underlying Internet and Gaming Disorder (IGD) (Weinstein and Lejoyeux, p 113 in this issue). The authors thoroughly survey existing brain imaging studies, summarizing the effects of IGD on the resting state, the brain’s gray matter volume and white matter density, cortical thickness, functional connectivity, and brain activations, especially in regions related to reward and decision making, and neurotransmitter systems. Taken together, individuals with IGD share many typical neurobiological alterations with other forms of addiction, but also show unique patterns of activation specifically in brain regions which are associated with cognitive, motor, and sensory function. The effects of the Internet on cognition have been comprehensively elaborated by Firth et al. 12 Examining psychological, psychiatric, and neuroimaging data, they provide evidence for both acute and sustained alterations in specific areas of cognition, which may reflect structural and functional changes in the brain. These affect: (i) attentional capacities, which are divided between multiple online sources at the loss of sustained concentration on a single task; (ii) memory processes - permanently accessible online information can change the ways in which we retrieve, store, recall and even value knowledge; and (iii) social cognition; the prospects for social interactions and the contexts within which social relationships can happen have dramatically changed. A complementary contribution rounding up these reviews is provided by Small et al (p 179 in this issue). Among the possible harmful “brain health consequences,” these investigators emphasize attention problems and their potential link to symptoms of attention deficit-hyperactivity disorder (ADHD); furthermore the (paradoxical) association of excessive social media use with the perception of social isolation, observable at any age; the impaired emotional and social intelligence, poorer cognitive/language and brain development, and disrupted sleep. A substantial part of this review is devoted to the positive effects benefiting brain health in adults and the elderly, which are referred to below. Independent of ongoing research on the negative and positive implications of digital technology use, there remains a common feeling that there is something about the whole phenomenon that is just not “natural.” “We did not evolve to be staring at a screen for most of our waking hours. We evolved to be interacting with each other face-to-face, using our senses of smell and touch and taste – not just sight and sound… it cannot be healthy to stray so far from the activities for which nature has shaped our brains and our bodies.” Giedd (p 127 in this issue) challenges this notion in his fascinating review on “The natural allure of digital media,” putting the intensive digital media use during adolescence into a grand evolutionary perspective. He argues that the “desire for digital media is in fact exquisitely aligned with the biology of the teen brain and our evolutionary heritage,” with three features of adolescence being particularly relevant to this issue: (i) hunger for human connectedness; (ii) appetite for adventure; and (iii) desire for information.


Screen time: boon or bane?


As with any major innovation that has a profound impact on our lives, finding useful information and orientation means discerning scientific evidence from media narratives. Thus, synthesizing data from recent narrative reviews and meta-analyses including more than 50 studies, Odgers and Jensen (p 143 in this issue) could not confirm a strong linkage between the quantity of adolescents’ digital technology engagement and mental health problems. “There doesn’t seem to be an evidence base that would explain the level of panic and consternation around these issues” said Odgers, in the New York Times. 13 The authors point to significant limitations and foundational flaws in the existing knowledge base related to this topic; for instance, the nearly sole reliance on screen time metrics; the disregard of individual differences; the circumstance that almost none of the study designs allowed causal inference. On the other hand, a highly robust finding across multiple studies was that offline vulnerabilities (such as risks present in low-income families, communities, etc) tend to mirror and shape online risks. The observed social and digital divides are presently being magnified through the coronavirus crisis and most likely to increase in the future, further amplifying the existing inequalities in education, mental health, and prospects for youth. The authors strongly advocate the need and opportunities to leverage digital technology to support youth in an increasingly digital, unequal society in an uncertain age; see their suggestions for parents, clinicians, educators, designers and adolescents in Box 1 . Similarly, performing an in depth overview of the existing literature, Dienlin and Johannes (p 135 in this issue) could not substantiate the common concerns that digital technology use has a negative impact on young (and adult) peoples’ mental well-being. Their findings imply that the general effects are in the negative spectrum but very small – potentially too small to matter. Importantly, different types of use have different effects: thus, procrastination and passive use were related to more negative effects, and social and active use to more positive effects. Thus, “screen time” has different effects for different people. Digital technology use tends to exert short-term effects on well-being rather than long-lasting effects on life satisfaction. “The dose makes the poison”: both low and excessive use are related to decreased well-being, while moderate use increases well-being. With a strong sense for clear explanation, the authors introduce the concepts, terms, and definitions underlying this complex field, a most valuable primer to educate the interested reader, while also addressing the methodological shortcomings that contribute to the overall controversial experimental evidence. 


Thus, against common concerns, digital technology as such does not affect mental health or deteriorate well-being. Its use can have both negative and positive consequences. Technology simply does not “happen” to people. Individuals can shape the experiences they have with technologies and the results of those experiences. Thus, it is important to shift the focus towards an active, conscious use of this technology, with the intention to improve our lives and meaningfully connect with each other. This has become, more than ever, important now: “There is increased urgency, due to coronavirus, to use technology in ways that strengthen our relationships. Much of the world has been working, educating, and socializing online for months, and many important activities will remain virtual for the foreseeable future. This period of physical distancing has shed light on what we need from technology and each other… “ Morris (p 151 in this issue) introduces her article addressing the enhancement of relationships through technology in the most timely manner with a preface on “Connecting during COVID-19 and beyond.” In this synopsis, she sums up five directions to “build on as we connect during and after the pandemic.” Furthermore, in her review, she examines how technology can be shaped in positive ways by parents, caregivers, romantic partners, and clinicians and illustrates with real life examples creative and sensible ways to adapt technology to personal and relational goals (see also ref 14 ). Highlighting the importance of context, motivation, and the nuances of use, this review encourages people to understand how technologies can be optimally used to improve personal and clinical relationships. 


Digital tools in diagnosis and therapy


The use of digital tools for practical clinical applications and improvement of mental health conditions is gaining increasing acceptance, especially due to smartphone accessibility. This could fill at least in part the treatment gap and lack of access to specialized (psychotherapeutic) care, particularly in developing countries. Even in countries with well-developed health care systems, only a minority of patients receives treatment in line with the recommendations provided by evidence-based treatment guidelines. Thus, as elaborated in a thorough, comprehensive review by Hegerl and Oehler (p 161 in this issue), web-based interventions, especially in the case of Major Depression (MD), a highly prevalent and severe disorder, promise to be a method that provides resource-efficient and widespread access to psychotherapeutic support. The authors provide detailed information on available tools for digital intervention and their core principles; these are mostly based on principles of cognitive behavioral therapy, but also include elements of other psychotherapeutic approaches. As evident from meta-analyses summarizing studies that use face-to-face psychotherapy as a comparator, digital interventions can have equivalent antidepressant efficacy. Importantly, web-based interventions are most efficient when accompanied by adequate professional guidance and, if well designed, can be successfully integrated into routine care. The authors also address carefully the risks and limitations as well as unwanted effects of available digital interventions. Another powerful digital technology is gaining importance as a clinical tool in mental health research and practice, virtual reality (VR). According to Valmaggia and collaborators (p 169 in this issue), “At any time or place, individuals can be transported into immersive and interactive virtual worlds that are in full control of the researcher or clinician. This capability is central to recent interest in how VR might be harnessed in both treatment and assessment of mental health conditions.” To date, VR exposure treatments have proven effective across a range of disorders including schizophrenia, anxiety, and panic disorders. In their review, the authors summarize comprehensively the advantages of using VR as a clinical assessment tool, which could “radically transform the landscape of assessment in mental health.” Thus, VR may overcome many of the limitations concerning the diagnosis of psychological phenomena through its ability to generate highly controlled environments, that is, real-world experiences. In addition to increasing ecological validity, VR enhances personalization, that is, VR experiences can be tailored to match individual needs, abilities, or preferences. Furthermore, VR enhances an individual’s engagement with the test or assessment. Additional advantages include the capture of real-time, automated data in real-world contexts. In sum, the authors have thoroughly addressed the opportunities and challenges of VR in any relevant aspect. Finally, to complement the applications of digital technology to improve mental health, Small et al (p 179 in this issue) provide, in the second part of their review, rich information about specific programs, videogames, and other online tools, particularly for the aging brain. These may provide mental exercises that activate neural circuitry, improve cognitive functioning, reduce anxiety, increase restful sleep, and offer many other brain health benefits.


Emerging key messages


Several key messages emerge from these reviews, which cover a substantial amount of studies: first of all, scientific evidence does not support the common concerns that excessive use of digital technology causes mental health problems and a deterioration of well-being. There is increasing consensus that the methodological foundation is weak in many studies, in part explaining the controversial results and small effect sizes obtained to date. Above all, it appears absurd to collapse, as was common practice, the highly complex interaction between “machine and man” into a uniform quantitative screen time measure. Research, public policies, and interventions need to focus on the user , and not the extent of usage of technology. Who spends time and in what form with the digital devices is what is important. This leads us to what should be the main subject of interest, but has mostly — conceptually and factually — been disregarded: the human “individual” with its motivation, intentions, goals, needs, predispositions, familial, educational and social background, and support systems, or lack thereof. Needless to say, this calls for the consideration of individual differences in all aspects of research and application. Thus, digital technology is not intrinsically good or bad: it depends on the uses it is being put to by the user, and it can be utilized by individuals in both negative and positive ways. Now, more than ever, during and post coronavirus times, it is important that technology is taken advantage of to improve communication and enhance personal, professional, and societal relationships, guaranteeing equal opportunities for access and development for all.

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A smarter way to streamline drug discovery

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The use of AI to streamline drug discovery is exploding. Researchers are deploying machine-learning models to help them identify molecules, among billions of options, that might have the properties they are seeking to develop new medicines.

But there are so many variables to consider — from the price of materials to the risk of something going wrong — that even when scientists use AI, weighing the costs of synthesizing the best candidates is no easy task.

The myriad challenges involved in identifying the best and most cost-efficient molecules to test is one reason new medicines take so long to develop, as well as a key driver of high prescription drug prices.

To help scientists make cost-aware choices, MIT researchers developed an algorithmic framework to automatically identify optimal molecular candidates, which minimizes synthetic cost while maximizing the likelihood candidates have desired properties. The algorithm also identifies the materials and experimental steps needed to synthesize these molecules.

Their quantitative framework, known as Synthesis Planning and Rewards-based Route Optimization Workflow (SPARROW), considers the costs of synthesizing a batch of molecules at once, since multiple candidates can often be derived from some of the same chemical compounds.

Moreover, this unified approach captures key information on molecular design, property prediction, and synthesis planning from online repositories and widely used AI tools.

Beyond helping pharmaceutical companies discover new drugs more efficiently, SPARROW could be used in applications like the invention of new agrichemicals or the discovery of specialized materials for organic electronics.

“The selection of compounds is very much an art at the moment — and at times it is a very successful art. But because we have all these other models and predictive tools that give us information on how molecules might perform and how they might be synthesized, we can and should be using that information to guide the decisions we make,” says Connor Coley, the Class of 1957 Career Development Assistant Professor in the MIT departments of Chemical Engineering and Electrical Engineering and Computer Science, and senior author of a paper on SPARROW.

Coley is joined on the paper by lead author Jenna Fromer SM ’24. The research appears today in Nature Computational Science .

Complex cost considerations

In a sense, whether a scientist should synthesize and test a certain molecule boils down to a question of the synthetic cost versus the value of the experiment. However, determining cost or value are tough problems on their own.

For instance, an experiment might require expensive materials or it could have a high risk of failure. On the value side, one might consider how useful it would be to know the properties of this molecule or whether those predictions carry a high level of uncertainty.

At the same time, pharmaceutical companies increasingly use batch synthesis to improve efficiency. Instead of testing molecules one at a time, they use combinations of chemical building blocks to test multiple candidates at once. However, this means the chemical reactions must all require the same experimental conditions. This makes estimating cost and value even more challenging.

SPARROW tackles this challenge by considering the shared intermediary compounds involved in synthesizing molecules and incorporating that information into its cost-versus-value function.

“When you think about this optimization game of designing a batch of molecules, the cost of adding on a new structure depends on the molecules you have already chosen,” Coley says.

The framework also considers things like the costs of starting materials, the number of reactions that are involved in each synthetic route, and the likelihood those reactions will be successful on the first try.

To utilize SPARROW, a scientist provides a set of molecular compounds they are thinking of testing and a definition of the properties they are hoping to find.

From there, SPARROW collects information on the molecules and their synthetic pathways and then weighs the value of each one against the cost of synthesizing a batch of candidates. It automatically selects the best subset of candidates that meet the user’s criteria and finds the most cost-effective synthetic routes for those compounds.

“It does all this optimization in one step, so it can really capture all of these competing objectives simultaneously,” Fromer says.

A versatile framework

SPARROW is unique because it can incorporate molecular structures that have been hand-designed by humans, those that exist in virtual catalogs, or never-before-seen molecules that have been invented by generative AI models.

“We have all these different sources of ideas. Part of the appeal of SPARROW is that you can take all these ideas and put them on a level playing field,” Coley adds.

The researchers evaluated SPARROW by applying it in three case studies. The case studies, based on real-world problems faced by chemists, were designed to test SPARROW’s ability to find cost-efficient synthesis plans while working with a wide range of input molecules.

They found that SPARROW effectively captured the marginal costs of batch synthesis and identified common experimental steps and intermediate chemicals. In addition, it could scale up to handle hundreds of potential molecular candidates.

“In the machine-learning-for-chemistry community, there are so many models that work well for retrosynthesis or molecular property prediction, for example, but how do we actually use them? Our framework aims to bring out the value of this prior work. By creating SPARROW, hopefully we can guide other researchers to think about compound downselection using their own cost and utility functions,” Fromer says.

In the future, the researchers want to incorporate additional complexity into SPARROW. For instance, they’d like to enable the algorithm to consider that the value of testing one compound may not always be constant. They also want to include more elements of parallel chemistry in its cost-versus-value function.

“The work by Fromer and Coley better aligns algorithmic decision making to the practical realities of chemical synthesis. When existing computational design algorithms are used, the work of determining how to best synthesize the set of designs is left to the medicinal chemist, resulting in less optimal choices and extra work for the medicinal chemist,” says Patrick Riley, senior vice president of artificial intelligence at Relay Therapeutics, who was not involved with this research. “This paper shows a principled path to include consideration of joint synthesis, which I expect to result in higher quality and more accepted algorithmic designs.”

“Identifying which compounds to synthesize in a way that carefully balances time, cost, and the potential for making progress toward goals while providing useful new information is one of the most challenging tasks for drug discovery teams. The SPARROW approach from Fromer and Coley does this in an effective and automated way, providing a useful tool for human medicinal chemistry teams and taking important steps toward fully autonomous approaches to drug discovery,” adds John Chodera, a computational chemist at Memorial Sloan Kettering Cancer Center, who was not involved with this work.

This research was supported, in part, by the DARPA Accelerated Molecular Discovery Program, the Office of Naval Research, and the National Science Foundation.

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• Computer science and technology
• Artificial intelligence
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• Pharmaceuticals
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What the Arrival of A.I. Phones and Computers Means for Our Data

Apple, Microsoft and Google need more access to our data as they promote new phones and personal computers that are powered by artificial intelligence. Should we trust them?

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By Brian X. Chen

Brian X. Chen is the author of Tech Fix , a weekly column about the societal implications of the tech we use.

Apple, Microsoft and Google are heralding a new era of what they describe as artificially intelligent smartphones and computers. The devices, they say, will automate tasks like editing photos and wishing a friend a happy birthday.

But to make that work, these companies need something from you: more data.

In this new paradigm, your Windows computer will take a screenshot of everything you do every few seconds. An iPhone will stitch together information across many apps you use. And an Android phone can listen to a call in real time to alert you to a scam.

Is this information you are willing to share?

This change has significant implications for our privacy. To provide the new bespoke services, the companies and their devices need more persistent, intimate access to our data than before. In the past, the way we used apps and pulled up files and photos on phones and computers was relatively siloed. A.I. needs an overview to connect the dots between what we do across apps, websites and communications, security experts say.

“Do I feel safe giving this information to this company?” Cliff Steinhauer, a director at the National Cybersecurity Alliance, a nonprofit focusing on cybersecurity, said about the companies’ A.I. strategies.

All of this is happening because OpenAI’s ChatGPT upended the tech industry nearly two years ago. Apple, Google, Microsoft and others have since overhauled their product strategies, investing billions in new services under the umbrella term of A.I. They are convinced this new type of computing interface — one that is constantly studying what you are doing to offer assistance — will become indispensable.

The biggest potential security risk with this change stems from a subtle shift happening in the way our new devices work, experts say. Because A.I. can automate complex actions — like scrubbing unwanted objects from a photo — it sometimes requires more computational power than our phones can handle. That means more of our personal data may have to leave our phones to be dealt with elsewhere.

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Introducing Apple’s On-Device and Server Foundation Models

At the 2024 Worldwide Developers Conference , we introduced Apple Intelligence, a personal intelligence system integrated deeply into iOS 18, iPadOS 18, and macOS Sequoia.

Apple Intelligence is comprised of multiple highly-capable generative models that are specialized for our users’ everyday tasks, and can adapt on the fly for their current activity. The foundation models built into Apple Intelligence have been fine-tuned for user experiences such as writing and refining text, prioritizing and summarizing notifications, creating playful images for conversations with family and friends, and taking in-app actions to simplify interactions across apps.

In the following overview, we will detail how two of these models — a ~3 billion parameter on-device language model, and a larger server-based language model available with Private Cloud Compute and running on Apple silicon servers — have been built and adapted to perform specialized tasks efficiently, accurately, and responsibly. These two foundation models are part of a larger family of generative models created by Apple to support users and developers; this includes a coding model to build intelligence into Xcode, as well as a diffusion model to help users express themselves visually, for example, in the Messages app. We look forward to sharing more information soon on this broader set of models.

Our Focus on Responsible AI Development

Apple Intelligence is designed with our core values at every step and built on a foundation of groundbreaking privacy innovations.

Additionally, we have created a set of Responsible AI principles to guide how we develop AI tools, as well as the models that underpin them:

• Empower users with intelligent tools : We identify areas where AI can be used responsibly to create tools for addressing specific user needs. We respect how our users choose to use these tools to accomplish their goals.
• Represent our users : We build deeply personal products with the goal of representing users around the globe authentically. We work continuously to avoid perpetuating stereotypes and systemic biases across our AI tools and models.
• Design with care : We take precautions at every stage of our process, including design, model training, feature development, and quality evaluation to identify how our AI tools may be misused or lead to potential harm. We will continuously and proactively improve our AI tools with the help of user feedback.
• Protect privacy : We protect our users' privacy with powerful on-device processing and groundbreaking infrastructure like Private Cloud Compute. We do not use our users' private personal data or user interactions when training our foundation models.

These principles are reflected throughout the architecture that enables Apple Intelligence, connects features and tools with specialized models, and scans inputs and outputs to provide each feature with the information needed to function responsibly.

In the remainder of this overview, we provide details on decisions such as: how we develop models that are highly capable, fast, and power-efficient; how we approach training these models; how our adapters are fine-tuned for specific user needs; and how we evaluate model performance for both helpfulness and unintended harm.

Pre-Training

Our foundation models are trained on Apple's AXLearn framework , an open-source project we released in 2023. It builds on top of JAX and XLA, and allows us to train the models with high efficiency and scalability on various training hardware and cloud platforms, including TPUs and both cloud and on-premise GPUs. We used a combination of data parallelism, tensor parallelism, sequence parallelism, and Fully Sharded Data Parallel (FSDP) to scale training along multiple dimensions such as data, model, and sequence length.

We train our foundation models on licensed data, including data selected to enhance specific features, as well as publicly available data collected by our web-crawler, AppleBot. Web publishers have the option to opt out of the use of their web content for Apple Intelligence training with a data usage control.

We never use our users’ private personal data or user interactions when training our foundation models, and we apply filters to remove personally identifiable information like social security and credit card numbers that are publicly available on the Internet. We also filter profanity and other low-quality content to prevent its inclusion in the training corpus. In addition to filtering, we perform data extraction, deduplication, and the application of a model-based classifier to identify high quality documents.

Post-Training

We find that data quality is essential to model success, so we utilize a hybrid data strategy in our training pipeline, incorporating both human-annotated and synthetic data, and conduct thorough data curation and filtering procedures. We have developed two novel algorithms in post-training: (1) a rejection sampling fine-tuning algorithm with teacher committee, and (2) a reinforcement learning from human feedback (RLHF) algorithm with mirror descent policy optimization and a leave-one-out advantage estimator. We find that these two algorithms lead to significant improvement in the model’s instruction-following quality.

Optimization

In addition to ensuring our generative models are highly capable, we have used a range of innovative techniques to optimize them on-device and on our private cloud for speed and efficiency. We have applied an extensive set of optimizations for both first token and extended token inference performance.

Both the on-device and server models use grouped-query-attention. We use shared input and output vocab embedding tables to reduce memory requirements and inference cost. These shared embedding tensors are mapped without duplications. The on-device model uses a vocab size of 49K, while the server model uses a vocab size of 100K, which includes additional language and technical tokens.

For on-device inference, we use low-bit palletization, a critical optimization technique that achieves the necessary memory, power, and performance requirements. To maintain model quality, we developed a new framework using LoRA adapters that incorporates a mixed 2-bit and 4-bit configuration strategy — averaging 3.5 bits-per-weight — to achieve the same accuracy as the uncompressed models.

Additionally, we use an interactive model latency and power analysis tool, Talaria , to better guide the bit rate selection for each operation. We also utilize activation quantization and embedding quantization, and have developed an approach to enable efficient Key-Value (KV) cache update on our neural engines.

With this set of optimizations, on iPhone 15 Pro we are able to reach time-to-first-token latency of about 0.6 millisecond per prompt token, and a generation rate of 30 tokens per second. Notably, this performance is attained before employing token speculation techniques, from which we see further enhancement on the token generation rate.

Model Adaptation

Our foundation models are fine-tuned for users’ everyday activities, and can dynamically specialize themselves on-the-fly for the task at hand. We utilize adapters, small neural network modules that can be plugged into various layers of the pre-trained model, to fine-tune our models for specific tasks. For our models we adapt the attention matrices, the attention projection matrix, and the fully connected layers in the point-wise feedforward networks for a suitable set of the decoding layers of the transformer architecture.

By fine-tuning only the adapter layers, the original parameters of the base pre-trained model remain unchanged, preserving the general knowledge of the model while tailoring the adapter layers to support specific tasks.

We represent the values of the adapter parameters using 16 bits, and for the ~3 billion parameter on-device model, the parameters for a rank 16 adapter typically require 10s of megabytes. The adapter models can be dynamically loaded, temporarily cached in memory, and swapped — giving our foundation model the ability to specialize itself on the fly for the task at hand while efficiently managing memory and guaranteeing the operating system's responsiveness.

To facilitate the training of the adapters, we created an efficient infrastructure that allows us to rapidly retrain, test, and deploy adapters when either the base model or the training data gets updated. The adapter parameters are initialized using the accuracy-recovery adapter introduced in the Optimization section.

Performance and Evaluation

Our focus is on delivering generative models that can enable users to communicate, work, express themselves, and get things done across their Apple products. When benchmarking our models, we focus on human evaluation as we find that these results are highly correlated to user experience in our products. We conducted performance evaluations on both feature-specific adapters and the foundation models.

To illustrate our approach, we look at how we evaluated our adapter for summarization. As product requirements for summaries of emails and notifications differ in subtle but important ways, we fine-tune accuracy-recovery low-rank (LoRA) adapters on top of the palletized model to meet these specific requirements. Our training data is based on synthetic summaries generated from bigger server models, filtered by a rejection sampling strategy that keeps only the high quality summaries.

To evaluate the product-specific summarization, we use a set of 750 responses carefully sampled for each use case. These evaluation datasets emphasize a diverse set of inputs that our product features are likely to face in production, and include a stratified mixture of single and stacked documents of varying content types and lengths. As product features, it was important to evaluate performance against datasets that are representative of real use cases. We find that our models with adapters generate better summaries than a comparable model.

As part of responsible development, we identified and evaluated specific risks inherent to summarization. For example, summaries occasionally remove important nuance or other details in ways that are undesirable. However, we found that the summarization adapter did not amplify sensitive content in over 99% of targeted adversarial examples. We continue to adversarially probe to identify unknown harms and expand our evaluations to help guide further improvements.

In addition to evaluating feature specific performance powered by foundation models and adapters, we evaluate both the on-device and server-based models’ general capabilities. We utilize a comprehensive evaluation set of real-world prompts to test the general model capabilities. These prompts are diverse across different difficulty levels and cover major categories such as brainstorming, classification, closed question answering, coding, extraction, mathematical reasoning, open question answering, rewriting, safety, summarization, and writing.

We compare our models with both open-source models (Phi-3, Gemma, Mistral, DBRX) and commercial models of comparable size (GPT-3.5-Turbo, GPT-4-Turbo) 1 . We find that our models are preferred by human graders over most comparable competitor models. On this benchmark, our on-device model, with ~3B parameters, outperforms larger models including Phi-3-mini, Mistral-7B, and Gemma-7B. Our server model compares favorably to DBRX-Instruct, Mixtral-8x22B, and GPT-3.5-Turbo while being highly efficient.

We use a set of diverse adversarial prompts to test the model performance on harmful content, sensitive topics, and factuality. We measure the violation rates of each model as evaluated by human graders on this evaluation set, with a lower number being desirable. Both the on-device and server models are robust when faced with adversarial prompts, achieving violation rates lower than open-source and commercial models.

Our models are preferred by human graders as safe and helpful over competitor models for these prompts. However, considering the broad capabilities of large language models, we understand the limitation of our safety benchmark. We are actively conducting both manual and automatic red-teaming with internal and external teams to continue evaluating our models' safety.

To further evaluate our models, we use the Instruction-Following Eval (IFEval) benchmark to compare their instruction-following capabilities with models of comparable size. The results suggest that both our on-device and server model follow detailed instructions better than the open-source and commercial models of comparable size.

We evaluate our models’ writing ability on our internal summarization and composition benchmarks, consisting of a variety of writing instructions. These results do not refer to our feature-specific adapter for summarization (seen in Figure 3 ), nor do we have an adapter focused on composition.

The Apple foundation models and adapters introduced at WWDC24 underlie Apple Intelligence, the new personal intelligence system that is integrated deeply into iPhone, iPad, and Mac, and enables powerful capabilities across language, images, actions, and personal context. Our models have been created with the purpose of helping users do everyday activities across their Apple products, and developed responsibly at every stage and guided by Apple’s core values. We look forward to sharing more information soon on our broader family of generative models, including language, diffusion, and coding models.

[1] We compared against the following model versions: gpt-3.5-turbo-0125, gpt-4-0125-preview, Phi-3-mini-4k-instruct, Mistral-7B-Instruct-v0.2, Mixtral-8x22B-Instruct-v0.1, Gemma-1.1-2B, and Gemma-1.1-7B. The open-source and Apple models are evaluated in bfloat16 precision.

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Apple Natural Language Understanding Workshop 2023

Earlier this year, Apple hosted the Natural Language Understanding workshop. This two-day hybrid event brought together Apple and members of the academic research community for talks and discussions on the state of the art in natural language understanding.

In this post, we share highlights from workshop discussions and recordings of select workshop talks.

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China has become a scientific superpower

From plant biology to superconductor physics the country is at the cutting edge.

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I n the atrium of a research building at the Chinese Academy of Sciences ( CAS ) in Beijing is a wall of patents. Around five metres wide and two storeys high, the wall displays 192 certificates, positioned in neat rows and tastefully lit from behind. At ground level, behind a velvet rope, an array of glass jars contain the innovations that the patents protect: seeds.

CAS —the world’s largest research organisation—and institutions around China produce a huge amount of research into the biology of food crops. In the past few years Chinese scientists have discovered a gene that, when removed, boosts the length and weight of wheat grains, another that improves the ability of crops like sorghum and millet to grow in salty soils and one that can increase the yield of maize by around 10%. In autumn last year, farmers in Guizhou completed the second harvest of genetically modified giant rice that was developed by scientists at CAS .

The Chinese Communist Party ( CCP ) has made agricultural research—which it sees as key to ensuring the country’s food security —a priority for scientists. Over the past decade the quality and the quantity of crop research that China produces has grown immensely, and now the country is widely regarded as a leader in the field. According to an editor of a prestigious European plant-sciences journal, there are some months when half of the submissions can come from China.

A journey of a thousand miles

The rise of plant-science research is not unique in China. In 2019 The Economist surveyed the research landscape in the country and asked whether China could one day become a scientific superpower. Today, that question has been unequivocally answered: “yes”. Chinese scientists recently gained the edge in two closely watched measures of high-quality science, and the country’s growth in top-notch research shows no sign of slowing. The old science world order, dominated by America, Europe and Japan, is coming to an end.

One way to measure the quality of a country’s scientific research is to tally the number of high-impact papers produced each year—that is, publications that are cited most often by other scientists in their own, later work. In 2003 America produced 20 times more of these high-impact papers than China, according to data from Clarivate, a science analytics company (see chart 1). By 2013 America produced about four times the number of top papers and, in the most recent release of data, which examines papers from 2022, China had surpassed both America and the entire European Union ( EU ).

Metrics based on citations can be gamed, of course. Scientists can, and do, find ways to boost the number of times their paper is mentioned in other studies, and a recent working paper, by Qui Shumin, Claudia Steinwender and Pierre Azoulay, three economists, argues that Chinese researchers cite their compatriots far more than Western researchers do theirs. But China now leads the world on other benchmarks that are less prone to being gamed. It tops the Nature Index, created by the publisher of the same name, which counts the contributions to articles that appear in a set of prestigious journals. To be selected for publication, papers must be approved by a panel of peer reviewers who assess the study’s quality, novelty and potential for impact. When the index was first launched, in 2014, China came second, but its contribution to eligible papers was less than a third of America’s. By 2023 China had reached the top spot.

According to the Leiden Ranking of the volume of scientific research output, there are now six Chinese universities or institutions in the world top ten, and seven according to the Nature Index. They may not be household names in the West yet, but get used to hearing about Shanghai Jiao Tong, Zhejiang and Peking (Beida) Universities in the same breath as Cambridge, Harvard and ETH Zurich. “Tsinghua is now the number one science and technology university in the world,” says Simon Marginson, a professor of higher education at Oxford University. “That’s amazing. They’ve done that in a generation.”

Today China leads the world in the physical sciences, chemistry and Earth and environmental sciences, according to both the Nature Index and citation measures (see chart 2). But America and Europe still have substantial leads in both general biology and medical sciences. “Engineering is the ultimate Chinese discipline in the modern period,” says Professor Marginson, “I think that’s partly about military technology and partly because that’s what you need to develop a nation.”

Applied research is a Chinese strength. The country dominates publications on perovskite solar panels, for example, which offer the possibility of being far more efficient than conventional silicon cells at converting sunlight into electricity. Chinese chemists have developed a new way to extract hydrogen from seawater using a specialised membrane to separate out pure water, which can then be split by electrolysis. In May 2023 it was announced that the scientists, in collaboration with a state-owned Chinese energy company, had developed a pilot floating hydrogen farm off the country’s south-eastern coast.

China also now produces more patents than any other country, although many are for incremental tweaks to designs, as opposed to truly original inventions. New developments tend to spread and be adopted more slowly in China than in the West. But its strong industrial base, combined with cheap energy, means that it can quickly spin up large-scale production of physical innovations like materials. “That’s where China really has an advantage on Western countries,” says Jonathan Bean, CEO of Materials Nexus, a British firm that uses AI to discover new materials.

The country is also signalling its scientific prowess in more conspicuous ways. Earlier this month, China’s Chang’e-6 robotic spacecraft touched down in a gigantic crater on the far side of the Moon, scooped up some samples of rock, planted a Chinese flag and set off back towards Earth. If it successfully returns to Earth at the end of the month, it will be the first mission to bring back samples from this hard-to-reach side of the Moon.

First, sharpen your tools

The reshaping of Chinese science has been achieved by focusing on three areas: money, equipment and people. In real terms, China’s spending on research and development ( R & D ) has grown 16-fold since 2000. According to the most recent data from the OECD , from 2021, China still lagged behind America on overall R & D spending, dishing out $668bn, compared with $806bn for America at purchasing-power parity. But in terms of spending by universities and government institutions only, China has nudged ahead. In these places America still spends around 50% more on basic research, accounting for costs, but China is splashing the cash on applied research and experimental development (see chart 3).

Money is meticulously directed into strategic areas. In 2006 the CCP published its vision for how science should develop over the next 15 years. Blueprints for science have since been included in the CCP ’s five-year development plans. The current plan, published in 2021, aims to boost research in quantum technologies, AI , semiconductors, neuroscience, genetics and biotechnology, regenerative medicine, and exploration of “frontier areas” like deep space, deep oceans and Earth’s poles.

Creating world-class universities and government institutions has also been a part of China’s scientific development plan. Initiatives like “Project 211”, the “985 programme” and the “China Nine League” gave money to selected labs to develop their research capabilities. Universities paid staff bonuses—estimated at an average of $44,000 each, and up to a whopping $165,000—if they published in high-impact international journals.

Building the workforce has been a priority. Between 2000 and 2019, more than 6m Chinese students left the country to study abroad, according to China’s education ministry. In recent years they have flooded back, bringing their newly acquired skills and knowledge with them. Data from the OECD suggest that, since the late 2000s, more scientists have been returning to the country than leaving. China now employs more researchers than both America and the entire EU .

Many of China’s returning scientists, often referred to as “sea turtles” (a play on the Chinese homonym haigui , meaning “to return from abroad”) have been drawn home by incentives. One such programme launched in 2010, the “Youth Thousand Talents”, offered researchers under 40 one-off bonuses of up to 500,000 yuan (equivalent to roughly $150,000 at purchasing-power parity) and grants of up to 3m yuan to get labs up and running back home. And it worked. A study published in Science last year found that the scheme brought back high-calibre young researchers—they were, on average, in the most productive 15% of their peers (although the real superstar class tended to turn down offers). Within a few years, thanks to access to more resources and academic manpower, these returnees were lead scientists on 2.5 times more papers than equivalent researchers who had remained in America.

As well as pull, there has been a degree of push. Chinese scientists working abroad have been subject to increased suspicion in recent years. In 2018 America launched the China Initiative, a largely unsuccessful attempt to root out Chinese spies from industry and academia. There have also been reports of students being deported because of their association with China’s “military-civilian fusion strategy”. A recent survey of current and former Chinese students studying in America found that the share who had experienced racial abuse or discrimination was rising.

The availability of scientists in China means that, for example in quantum computing, some of the country’s academic labs are more like commercial labs in the West, in terms of scale. “They have research teams of 20, 30, even 40 people working on the same experiments, and they make really good progress,” says Christian Andersen, a quantum researcher at Delft University. In 2023 researchers working in China broke the record for the number of quantum bits, or qubits, entangled inside a quantum computer.

China has also splurged on scientific kit. In 2019, when The Economist last surveyed the state of the country’s scientific research, it already had an enviable inventory of flashy hardware including supercomputers, the world’s largest filled-aperture radio telescope and an underground dark-matter detector. The list has only grown since then. The country is now home to the world’s most sensitive ultra-high-energy cosmic-ray detector (which has recently been used to test aspects of Albert Einstein’s special theory of relativity), the world’s strongest steady-state magnetic field (which can probe the properties of materials) and soon will have one of the world’s most sensitive neutrino detectors (which will be used to work out which type of these fundamental subatomic particles has the highest mass). Europe and America have plenty of cool kit of their own, but China is rapidly adding hardware.

Individual labs in China’s top institutions are also well equipped. Niko McCarty, a journalist and former researcher at the Massachusetts Institute of Technology who was recently given a tour of synthetic biology labs in China, was struck by how, in academic institutions, “the machines are just more impressive and more expansive” than in America. At the Advanced Biofoundry at the Shenzhen Institute of Advanced Technology, which the country hopes will be the centre of China’s answer to Silicon Valley, Mr McCarty described an “amazing building with four floors of robots”. As Chinese universities fill with state-of-the-art equipment and elite researchers, and salaries become increasingly competitive, Western institutions look less appealing to young and ambitious Chinese scientists. “Students in China don’t think about America as some “scientific Mecca” in the same way their advisers might have done,” said Mr McCarty.

Take AI , for example. In 2019 just 34% of Chinese students working in the field stayed in the country for graduate school or work. By 2022 that number was 58%, according to data from the AI talent tracker by MacroPolo, an American think-tank (in America the figure for 2022 was around 98%). China now contributes to around 40% of the world’s research papers on AI , compared with around 10% for America and 15% for the EU and Britain combined. One of the most highly cited research papers of all time, demonstrating how deep neural networks could be trained on image recognition, was written by AI researchers working in China, albeit for Microsoft, an American company. “China’s AI research is world-class,” said Zachary Arnold, an AI analyst at the Georgetown Centre for Emerging Security and Technology. “In areas like computer vision and robotics, they have a significant lead in research publications.”

Growth in the quality and quantity of Chinese science looks unlikely to stop anytime soon. Spending on science and technology research is still increasing—the government has announced a 10% increase in funding in 2024. And the country is training an enormous number of young scientists. In 2020 Chinese universities awarded 1.4m engineering degrees, seven times more than America did. China has now educated, at undergraduate level, 2.5 times more of the top-tier AI researchers than America has. And by 2025, Chinese universities are expected to produce nearly twice as many P h D graduates in science and technology as America.

To see further, ascend another floor

Although China is producing more top-tier work, it still produces a vast amount of lower-quality science too. On average, papers from China tend to have lower impact, as measured by citations, than those from America, Britain or the EU . And while the chosen few universities have advanced, mid-level universities have been left behind. China’s second-tier institutions still produce work that is of relatively poor quality compared with their equivalents in Europe or America. “While China has fantastic quality at the top level, it’s on a weak base,” explains Caroline Wagner, professor of science policy at Ohio State University.

When it comes to basic, curiosity-driven research (rather than applied) China is still playing catch-up—the country publishes far fewer papers than America in the two most prestigious science journals, Nature and Science . This may partly explain why China seems to punch below its weight in the discovery of completely new technologies. Basic research is particularly scant within Chinese companies, creating a gap between the scientists making discoveries and the industries that could end up using them. “For more original innovation, that might be a minus,” says Xu Xixiang, chief scientist at LONG i Green Energy Technology, a Chinese solar company.

Incentives to publish papers have created a market for fake scientific publications. A study published earlier this year in the journal Research Ethics , featured anonymous interviews from Chinese academics, one of whom said he had “no choice but to commit [research] misconduct”, to keep up with pressures to publish and retain his job. “Citation cartels” have emerged, where groups of researchers band together to write low-quality papers that cite each other’s work in an effort to drive up their metrics. In 2020 China’s science agencies announced that such cash-for-publication schemes should end and, in 2021, the country announced a nationwide review of research misconduct. That has led to improvements—the rate at which Chinese researchers cite themselves, for example, is falling, according to research published in 2023. And China’s middle-ranking universities are slowly catching up with their Western equivalents, too.

The areas where America and Europe still hold the lead are, therefore, unlikely to be safe for long. Biological and health sciences rely more heavily on deep subject-specific knowledge and have historically been harder for China to “bring back and accelerate”, says Tim Dafforn, a professor of biotechnology at University of Birmingham and former adviser to Britain’s department for business. But China’s profile is growing in these fields. Although America currently produces roughly four times more highly influential papers in clinical medicine, in many areas China is producing the most papers that cite this core research, a sign of developing interest that presages future expansion. “On the biology side, China is growing remarkably quickly,” says Jonathan Adams, chief scientist at the Institute for Scientific Information at Clarivate. “Its ability to switch focus into a new area is quite remarkable.”

The rise of Chinese science is a double-edged sword for Western governments. China’s science system is inextricably linked with its state and armed forces—many Chinese universities have labs explicitly working on defence and several have been accused of engaging in espionage or cyber-attacks. China has also been accused of intellectual-property theft and increasingly stringent regulations have made it more difficult for international collaborators to take data out of the country; notoriously, in 2019, the country cut off access to American-funded work on coronaviruses at the Wuhan Institute of Virology. There are also cases of Chinese researchers failing to adhere to the ethical standards expected by Western scientists.

Despite the concerns, Chinese collaborations are common for Western researchers. Roughly a third of papers on telecommunications by American authors involve Chinese collaborators. In imaging science, remote sensing, applied chemistry and geological engineering, the figures are between 25% and 30%. In Europe the numbers are lower, around 10%, but still significant. These partnerships are beneficial for both countries. China tends to collaborate more in areas where it is already strong like materials and physics. A preprint study, released last year, found that for AI research, having a co-author from America or China was equally beneficial to authors from the other country, conferring on average 75% more citations.

Several notable successes have come from working together, too. During the covid-19 pandemic a joint venture between Oxford University’s Engineering Department and the Oxford Suzhou Centre for Advanced Research developed a rapid covid test that was used across British airports. In 2015 researchers at University of Cardiff and South China Agricultural University identified a gene that made bacteria resistant to the antibiotic colistin. Following this, China, the biggest consumer of the drug, banned its use in animal feed, and levels of colistin resistance in both animals and humans declined.

In America and Europe, political pressure is limiting collaborations with China. In March, America’s Science and Technology Agreement with China, which states that scientists from both countries can collaborate on topics of mutual benefit, was quietly renewed for a further six months. Although Beijing appears keen to renew the 45-year-old agreement, many Republicans fear that collaboration with China is helping the country achieve its national-security goals. In Europe, with the exception of environmental and climate projects, Chinese universities have been effectively barred from accessing funding through the Horizon programme, a huge European research initiative.

There are also concerns among scientists that China is turning inwards. The country has explicit aims to become self-reliant in many areas of science and technology and also shift away from international publications as a way of measuring research output. Many researchers cannot talk to the press—finding sources in China for this story was challenging. One Chinese plant scientist, who asked to remain anonymous, said that she had to seek permission a year in advance to attend overseas conferences. “It’s contradictory—on the one hand, they set restrictions so that scientists don’t have freedoms like being able to go abroad to communicate with their colleagues. But on the other hand, they don’t want China to fall behind.”

Live until old, learn until old

The overwhelming opinion of scientists in China and the West is that collaboration must continue or, better, increase. And there is room to do more. Though China’s science output has grown dramatically, the share that is conducted with international collaborators has remained stable at around 20%—Western scientists tend to have far more international collaborations. Western researchers could pay more attention to the newest science from China, too. Data from a study published last year in Nature Human Behaviour showed that, for work of equivalent quality, Chinese scientists cite Western papers far more than vice versa. Western scientists rarely visit, work or study in China, depriving them of opportunities to learn from Chinese colleagues in the way Chinese scientists have done so well in the West.

Closing the door to Chinese students and researchers wishing to come to Western labs would also be disastrous for Western science. Chinese researchers form the backbone of many departments in top American and European universities. In 2022 more of the top-tier AI researchers working in America hailed from China than from America. The West’s model of science currently depends on a huge number of students, often from overseas, to carry out most day-to-day research.

There is little to suggest that the Chinese scientific behemoth will not continue growing stronger. China’s ailing economy may eventually force the CCP to slow spending on research, and if the country were to become completely cut off from the Western science community its research would suffer. But neither of these looks imminent. In 2019 we also asked if research could flourish in an authoritarian system. Perhaps over time its limits will become clear. But for now, and at least for the hard sciences, the answer is that it can thrive. “I think it’d be very unwise to call limits on the Chinese miracle,” says Prof Marginson. “Because it has had no limits up until now.” ■

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This article appeared in the Science & technology section of the print edition under the headline “Soaring dragons”

From the June 15th 2024 edition

Discover stories from this section and more in the list of contents

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The Evolution of Technology in the Classroom

Technology has always been at the forefront of human education. From the days of carving figures on rock walls to today, when most students are equipped with several portable technological devices at any given time, technology continues to push educational capabilities to new levels. In looking at where educational methods and tools have come from to where they are going in the future, technology’s importance in the classroom is evident now more than ever.

A History of Classroom Technology: The Primitive Classroom

In the Colonial years, wooden paddles with printed lessons, called Horn-Books, were used to assist students in learning verses. Over 200 years later, in 1870, technology advanced to include the Magic Lantern, a primitive version of a slide projector that projected images printed on glass plates. By the time World War I ended, around 8,000 lantern slides were circulating through the Chicago public school system. By the time the Chalkboard came around in 1890, followed by the pencil in 1900, it was clear that students were hungry for more advanced educational tools.

• Radio in the 1920s sparked an entirely new wave of learning; on-air classes began popping up for any student within listening range.
• Next came the overhead projector in 1930, followed by the ballpoint pen in 1940 and headphones in 1950.
• Videotapes arrived on the scene in 1951, creating a new and exciting method of instruction.
• The Skinner Teaching Machine produced a combined system of teaching and testing, providing reinforcement for correct answers so that the student can move on to the next lesson.
• The photocopier (1959) and handheld calculator (1972) entered the classrooms next, allowing for mass production of material on the fly and quick mathematical calculations.
• The Scantron system of testing, introduced by Michael Sokolski n 1972, allowed educators to grade tests more quickly and efficiently.

The pre-computer years were formative in the choices made for computers in the years following. Immediate response-type systems (video, calculator, Scantron) had become necessary, and quick production of teaching materials, using the photocopier, had become a standard. The U.S. Department of Education reports that high school enrollment was only 10% in 1900, but by 1992 had expanded to 95%. The number of students in college in 1930 was around 1 million, but by 2012 had grown to a record 21.6 million. Teachers needed new methods of instruction and testing, and students were looking for new ways to communicate, study, and learn.

The Entrance and Significance of Personal Computers

Although the first computers were developed in the ‘30s, everyday-use computers were introduced in the ‘80s. The first portable computer, in 1981, weighed 24 pounds and cost $1,795. When IBM introduced its first personal computer in 1981, the educational world knew that it was on the verge of greatness. Time magazine named The Computer its “ Man of the Year ” in 1982, and aptly so: the foundation of immediate learning capabilities had been laid. Time declared, “it is the end result of a technological revolution that has been in the making for four decades and is now, quite literally, hitting home.”

• Toshiba released its first mass-market consumer laptop in 1985 (the T1100), and Apple’s infamous Mac (which later evolved into the Powerbook) was available starting in 1984.
• In 1990, The World Wide Web was given life when a British researcher developed Hyper Text Markup Language, or HTML, and when the National Science Foundation (NSF) removed restrictions on the commercial use of the Internet in 1993, the world exploded into a frenzy of newfound research and communication methods.
• The first Personal Digital Assistants (PDAs) were released by Apple Computer Inc. in 1993, and with that, computers were a part of every day, if not every moment. By 2009, 97% of classrooms had one or more computers , and 93% of classroom computers had Internet access. For every 5 students, there was one computer. Instructors stated that 40% of students used computers often in their educational methods, in addition to interactive whiteboards and digital cameras. College students nowadays are rarely without some form of computer technology: 83% own a laptop, and over 50% have a Smartphone.

The Future of Technology in the Classroom

It seems like years since MySpace, first introduced in 2003, Facebook (2004) and Twitter (2007) have changed both the communication and business worlds. Instant connectivity has branched out from merely a tool of personal communication, to a platform for educational instruction and outreach. Social media is now being recognized as an accepted form of instruction in some instances, and groups such as Scholastic Teachers provide excellent support and tips for instructors. Many instructors use social media to communicate directly with their students, or to form forum-style groups for students to communicate with each other, and the method seems to be proving valuable in providing one-on-one attention to student’s questions and concerns.

With the classroom having already evolved into a hotbed of technological advances, what can the future possibly hold that could further educational proficiencies even more?

• Biometrics, a technology that recognizes people based on certain physical or behavioral traits, is on the technological horizon. The science will be used to recognize the physical and emotional disposition of students in the classroom, altering course material to tailor to each individual’s needs based on biometric signals.
• A second up-and-coming technology is Augmented Reality (AR) glasses , rumored to be on Google’s release list, and this technology could be a whole new world for education. AR Glasses (or even contact lenses) will layer data on top of what we naturally see, to allow for a real-world learning experience. For example, a student wearing AR Glasses could potentially sit at his desk and have a conversation with Thomas Edison about invention. It was Edison, after all, who said that “Books will soon be obsolete in schools. Scholars will soon be instructed through the eye.”
• Multi-touch surfaces are commonly used through equipment such as the iPhone, but the technology could become more relevant to education through entirely multi-touch surfaces, such as desks or workstations. This could allow students to collaborate with other students, even those around the world, and videos and other virtual tools could be streamed directly to the surface.

Educators and the Evolution of Technology in the Classroom

With the evolution of technology, educational capabilities are growing and changing every day. The Internet is a vast electronic library of information, and both research and instruction can be achieved through a click of the mouse. With these advances come new responsibilities to the instructor and therefore increase the value of a Master of Science in Education in Learning Design and Technology. As technology advances, an educator’s abilities will grow by leaps and bounds, and without the knowledge of these changes and capabilities, an instructor has a good chance of being left behind.

A career in education requires hard work and dedication, but, for the diligent educator, can prove very rewarding. For those who are serious about success in the education field, staying well-informed of current and changing technologies is imperative. As the world of technology evolves, the learning environment, both on-campus and online, will equally progress, and the need for teachers who are educated in technology and design will continue to grow.

Learn more about the online MSEd in Learning Design and Technology at Purdue University today and help redefine the way in which individuals learn. Call (877) 497-5851 to speak with an admissions advisor or to request more information.

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Home > Topics > The newest research paper of Associate Professor Takashi SHIRAI's group has been highlighted as the FRONT COVER of the latest issue of Nanoscale Advances

The newest research paper of Associate Professor Takashi SHIRAI's group has been highlighted as the FRONT COVER of the latest issue of Nanoscale Advances

Category：News｜Publishing : June 20, 2024

The newest research paper of Associate Professor Takashi SHIRAI's group has been honored and highlighted as the FRONT COVER of the latest issue of Nanoscale Advances , a peer-reviewed scientific journal that published by Royal Society of Chemistry. The paper entitled with「 Role of polyvinylpyrrolidone in the polyol synthesis of platinum nanoparticles 」 reported the influence of surface passivation agent on metal particles formation mechanism in polyol process. The paper was authored by Assistant Professor Yunzi XIN （ Department of Engineering (Life Science and Applied Chemistry) ）, Mr. TakuNAGATA （ Previous master student: Department of Life Science and Applied Chemistry ）, Assistant Professor Kunihiko KATO （ Department of Engineering (Life Science and Applied Chemistry) ）, Assistant Professor Yuping XU （ Department of Engineering (Life Science and Applied Chemistry) ）, Associate Professor Takashi SHIRAI （ Department of Engineering (Life Science and Applied Chemistry) ） .

Metal nanoparticles (NPs) have attracted global interest in various applications, such as energy conversion, air and water purification, sensing, and medicine, owing to their extraordinary chemical and physical properties. Metal nanoparticles with altered size and morphology have been numerously prepared via different synthetic approaches. In particular, the synthesis of metal nanoparticles via liquid-phase polyol reaction has attracted worldwide attention as one of the most facile approaches. Since the multivalent alcohols utilized in polyol reaction possess high polarity and boiling point, various the metal salts precursors can be dissolved and then reduced into metal nanoparticles at a relatively lower temperature even under the boiling point. During polyol synthesis of metal nanoparticles, the surface passivation agent (also known as capping agent, surfactant) plays an important role in preventing the aggregation and agglomeration of formed particles、 as well as size and morphology controlling. Polyvinylpyrrolidone (PVP), is one of the most popular surface passivation agents that utilized in polyol system, due to its non-toxicity and non-ionic nature. In present work, platinum (Pt)nanoparticles were synthesized under altered PVP/Pt-ion molar ratio. The hydrodynamic size of synthesized Pt nanoparticles in different solvents, the crystal structure, solid-phase size and morphology, as well as the surface chemical state and thermal stability of passivated PVP, the reduction behavior and dynamic of Pt-ion in synthesis stage were elucidated systemically, during where the role of PVP in Pt nanoparticle formation and mechanism in size/surface structure controlling were also clarified in detail.

SHIRAI Laboratory Website

Nanoscale Advances

Title: Role of polyvinylpyrrolidone in the polyol synthesis of platinum nanoparticles Authors:Yunzi Xin, Taku Nagata, Kunihiko Kato, Yuping Xu, and Takashi Shirai * 　　　　 *Corresponding author Article information: DOI: doi.org/10.1039/D4NA00118D 　

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