economics of innovation literature review

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1 Innovation: A Guide to the Literature

Jan Fagerberg, Professor, Centre for Technology, Innovation and Culture (TIK), University of Oslo.

• Published: 02 September 2009
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Innovation is not a new phenomenon. Arguably, it is as old as mankind itself. There seems to be something inherently “human” about the tendency to think about new and better ways of doing things and to try them out in practice. In spite of its obvious importance, innovation has not always received the scholarly attention it deserves. For instance, students of long-run economic change used to focus on factors such as capital accumulation or the working of markets, rather than on innovation. This is now changing. Research on the role of innovation in economic and social change has proliferated in recent years, particularly within the social sciences, and with a bent towards cross-disciplinarity. In fact, as illustrated in this article, in recent years the number of social-science publications focusing on innovation has increased much faster than the total number of such publications.

1.1 Introduction 1

Innovation is not a new phenomenon. Arguably, it is as old as mankind itself. There seems to be something inherently “human” about the tendency to think about new and better ways of doing things and to try them out in practice. Without it, the world in which we live would look very, very different. Try for a moment to think of a world without airplanes, automobiles, telecommunications, and refrigerators, just to mention a few of the more important innovations from the not-too-distant past. Or—from an even longer perspective—where would we be without such fundamental innovations as agriculture, the wheel, the alphabet, or printing?

In spite of its obvious importance, innovation has not always received the scholarly attention it deserves. For instance, students of long-run economic change used to focus on factors such as capital accumulation or the working of markets, rather than on innovation. This is now changing. Research on the role of innovation in economic and social change has proliferated in recent years, particularly within the social sciences, and with a bent towards cross-disciplinarity. In fact, as illustrated in Figure 1.1 , in recent years the number of social-science publications focusing on innovation has increased much faster than the total number of such publications. As a result, our knowledge about innovation processes, their determinants and social and economic impact has been greatly enhanced.

Scholarly Articles with “Innovation” in the title, 1955–2004 (per 10,000 social science articles)

When innovation studies started to emerge as a separate field of research in the 1960s, it did so mostly outside the existing disciplines and the most prestigious universities. An important event in this process was the formation in 1965 of the Science Policy Research Unit (SPRU) at the University of Sussex (see Box 1.1 ). The name of the center illustrates the tendency for innovation studies to develop unde other (at the time more acceptable?) terms, such as, for instance, “science studies” or “science policy studies.” But as we shall see in the following, one of the main lessons from the research that came to be carried out is that science is only one among several ingredients in successful innovation. As a consequence of these findings, not only the focus of research in this area but also the notions used to characterize it changed. During the late twentieth/early twenty-first century, a number of new research centers and departments have been founded, focusing on the role of innovation in economic and social change. Many of these have a cross-disciplinary orientation, illustrating the need for innovation to be studied from different perspectives. Several journals and professional associations have also been founded.

SPRU—Science Policy Research Unit—at the University of Sussex, UK was founded in 1965 with Christopher Freeman as its first director. From the beginning, it had a crossdisciplinary research staff consisting of researchers with backgrounds in subjects as diverse as economics, sociology, psychology, and engineering. SPRU developed its own cross-disciplinary Master and Ph.D. programs and carried out externally funded research, much of which came to focus on the role of innovation in economic and social change. It attracted a large number of young scholars from other countries who came to train and work here.

The research initiated at SPRU led to a large number of projects, conferences, and publications. Research Policy , which came to be the central academic journal in the field, was established in 1972, with Freeman as the first editor (he was later succeeded by Keith Pavitt, also from SPRU). Freeman's influential book, The Economics of Industrial Innovation , was published two years later, in 1974, and has since been revised twice. In 1982, the book, Unemployment and Technical Innovation , written by Freeman, Clark, and Soete, appeared, introducing a systems approach to the role of innovation in long-term economic and social change. Freeman later followed this up with an analysis of the national innovation system in Japan (Freeman 1987 ). He was also instrumental in setting up the large, collaborative IFIAS project which in 1988 resulted in the very influential book, Technical Change and Economic Theory , edited by Dosi, Freeman, Nelson, Silverberg, and Soete (both Dosi and Soete were SPRU Ph.D. graduates).

In many ways, SPRU came to serve as a role model for the many centers/institutes within Europe and Asia that were established, mostly from the mid-1980s onwards, combining cross-disciplinary graduate and Ph.D. teaching with extensive externally funded research. Most of these, as SPRU itself, were located in relatively newly formed (so-called “red-brick”) universities, which arguably showed a greater receptivity to new social needs, initiatives, and ideas than the more inert, well-established academic “leaders,” or at other types of institutions such as business or engineering schools. SPRU graduates were in many cases instrumental in spreading research and teaching on innovation to their own countries, particularly in Europe.

The leaning towards cross-disciplinarity that characterizes much scholarly work in this area reflects the fact that no single discipline deals with all aspects of innovation. Hence, to get a comprehensive overview, it is necessary to combine insights from several disciplines. Traditionally, for instance, economics has dealt primarily with the allocation of resources to innovation (in competition with other ends) and its economic effects, while the innovation process itself has been more or less treated as a “black box.” What happens within this “box” has been left to scholars from other disciplines. A lot of what happens obviously has to do with learning, a central topic in cognitive science. Such learning occurs in organized settings (e.g. groups, teams, firms, and networks), the working of which is studied within disciplines such as sociology, organizational science, management, and business studies. Moreover, as economic geographers point out, learning processes tend to be linked to specific contexts or locations. The way innovation is organized and its localization also undergo important changes through time, as underscored by the work within the field of economic history. There is also, as historians of technology have pointed out, a specific technological dimension to this; the way innovation is organized, as well as its economic and social effects, depends critically on the specific nature of the technology in question.

Two decades ago, it was still possible for a hard-working student to get a fairly good overview of the scholarly work on innovation by devoting a few years of intensive study to the subject. Not any more. Today, the literature on innovation is so large and diverse that even keeping up-to-date with one specific field of research is very challenging. The purpose of this volume is to provide the reader with a guide to this rapidly expanding literature. We do this under the following broad headings:

Innovation in the Making

The Systemic Nature of Innovation

How Innovation Differs

Innovation and Performance.

Part One focuses on the process through which innovations occur and the actors that take part: individuals, firms, organizations, and networks. As we will discuss in more detail below, innovation is by its very nature a systemic phenomenon, since it results from continuing interaction between different actors and organizations. Part Two outlines the systems perspective on innovation studies and discusses the roles of institutions, organizations, and actors in this process at the national and regional level. Part Three explores the diversity in the manner in which such systems work over time and across different sectors or industries. Finally, Part Four examines the broader social and economic consequences of innovation and the associated policy issues. The remainder of this chapter sets the stage for the discussion that follows by giving a broad overview of some of the central topics in innovation studies (including conceptual issues).

1.2 What is Innovation?

An important distinction is normally made between invention and innovation. 2 Invention is the first occurrence of an idea for a new product or process, while innovation is the first attempt to carry it out into practice. Sometimes, invention and innovation are closely linked, to the extent that it is hard to distinguish one from another (biotechnology for instance). In many cases, however, there is a considerable time lag between the two. In fact, a lag of several decades or more is not uncommon (Rogers 1995 ). Such lags reflect the different requirements for working out ideas and implementing them. While inventions may be carried out anywhere, for example in universities, innovations occur mostly in firms, though they may also occur in other types of organizations, such as public hospitals. To be able to turn an invention into an innovation, a firm normally needs to combine several different types of knowledge, capabilities, skills, and resources. For instance, the firm may require production knowledge, skills and facilities, market knowledge, a well-functioning distribution system, sufficient financial resources, and so on. It follows that the role of the innovator, 3 i.e. the person or organizational unit responsible for combining the factors necessary (what the innovation theorist Joseph Schumpeter (see Box 1.2 ) called the “entrepreneur”), may be quite different from that of the inventor. Indeed, history is replete with cases in which the inventor of major technological advances fails to reap the profits from his breakthroughs.

Long lags between invention and innovation may have to do with the fact that, in many cases, some or all of the conditions for commercialization may be lacking. There may not be a sufficient need (yet!) or it may be impossible to produce and/or market because some vital inputs or complementary factors are not (yet!) available. Thus, although Leonardo da Vinci is reported to have had some quite advanced ideas for a flying machine, these were impossible to carry out in practice due to a lack of adequate materials, production skills, and—above all—a power source. In fact, the realization of these ideas had to wait for the invention and subsequent commercialization (and improvement) of the internal combustion engine. 4 Hence, as this example shows, many inventions require complementary inventions and innovations to succeed at the innovation stage.

Another complicating factor is that invention and innovation is a continuous process. For instance, the car, as we know it today, is radically improved compared to the first commercial models, due to the incorporation of a very large number of different inventions/innovations. In fact, the first versions of virtually all significant innovations, from the steam engine to the airplane, were crude, unreliable versions of the devices that eventually diffused widely. Kline and Rosenberg ( 1986 ), in an influential paper, point out:

it is a serious mistake to treat an innovation as if it were a well-defined, homogenous thing that could be identified as entering the economy at a precise date—or becoming available at a precise point in time…. The fact is that most important innovations go through drastic changes in their lifetimes—changes that may, and often do, totally transform their economic significance. The subsequent improvements in an invention after its first introduction maybe vastly more important, economically, than the initial availability of the invention in its original form. (Kline and Rosenberg 1986 : 283)

Thus, what we think of as a single innovation is often the result of a lengthy process involving many interrelated innovations. This is one of the reasons why many students of technology and innovation find it natural to apply a systems perspective rather than to focus exclusively on individual inventions/innovations.

Joseph Schumpeter (1883–1950) was one of the most original social scientists of the twentieth century. He grew up in Vienna around the turn of the century, where he studied law and economics. For most of his life he worked as an academic, but he also tried his luck as politician, serving briefly as finance minister in the first post-World War I (socialist) government, and as a banker (without much success). He became professor at the University of Bonn in 1925 and later at Harvard University in the USA (1932), where he stayed until his death. He published several books and papers in German early on, among these the Theory of Economic Development , published in 1911 and in a revised edition in English in 1934. Among his most well-known later works are Business Cycles in two volumes (from 1939), Capitalism, Socialism and Democracy (1943), and the posthumously published History of Economic Analysis (1954).

Very early he developed an original approach, focusing on the role of innovation in economic and social change. It was not sufficient, Schumpeter argued, to study the economy through static lenses, focusing on the distribution of given resources across different ends. Economic development, in his view, had to be seen as a process of qualitative change, driven by innovation, taking place in historical time. As examples of innovation he mentioned new products, new methods of production, new sources of supply, the exploitation of new markets, and new ways to organize business. He defined innovation as “new combinations” of existing resources. This combinatory activity he labeled “the entrepreneurial function” (to be fulfilled by “entrepreneurs”), to which he attached much importance. One main reason for the important role played by entrepreneurs for successful innovation was the prevalence of inertia, or “resistance to new ways” as he phrased it, at all levels of society that entrepreneurs had to fight in order to succeed in their aims. In his early work, which is sometimes called “Schumpeter Mark I,” Schumpeter focused mostly on individual entrepreneurs. But in later works he also emphasized the importance of innovation in large firms (so-called “Schumpeter Mark II”), and pointed to historically oriented, qualitative research (case studies) as the way forward for research in this area.

In his analysis of innovation diffusion, Schumpeter emphasized the tendency for innovations to “cluster” in certain industries and time periods (and the derived effects on growth) and the possible contribution of such “clustering” to the formation of business cycles and “long waves” in the world economy (Schumpeter 1939 ). The latter suggestion has been a constant source of controversy ever since. No less controversial, and perhaps even better known, is his inspired discussion of the institutional changes under capitalism (and its possible endogenous transformation into “socialism”) in the book Capitalism, Socialism and Democracy (1943).

Sources : Swedberg 1991 ; Shionoya 1997 ; Fagerberg 2003 .

Innovations may also be classified according to “type.” Schumpeter (see Box 1.2 ) distinguished between five different types: new products, new methods of production, new sources of supply, the exploitation of new markets, and new ways to organize business. However, in economics, most of the focus has been on the two first of these. Schmookler ( 1966 ), for instance, in his classic work on “Invention and Economic Growth,” argued that the distinction between “product technology” and “production technology” was “critical” for our understanding of this phenomenon (ibid. 166). He defined the former type as knowledge about how to create or improve products, and the latter as knowledge about how to produce them. Similarly, the terms “product innovation” and “process innovation” have been used to characterize the occurrence of new or improved goods and services, and improvements in the ways to produce these good and services, respectively. 5 The argument for focusing particularly on the distinction between product and process innovation often rests on the assumption that their economic and social impact may differ. For instance, while the introduction of new products is commonly assumed to have a clear, positive effect on growth of income and employment, it has been argued that process innovation, due to its cost-cutting nature, may have a more ambiguous effect (Edquist et al. 2001 ; Pianta in this volume). However, while clearly distinguishable at the level of the individual firm or industry, such differences tend to become blurred at the level of the overall economy, because the product of one firm (or industry) may end up as being used to produce goods or services in another. 6

The focus on product and process innovations, while useful for the analysis of some issues, should not lead us ignore other important aspects of innovation. For instance, during the first half of the twentieth century, many of the innovations that made it possible for the United States to “forge ahead” of other capitalist economies were of the organizational kind, involving entirely new ways to organize production and distribution (see Bruland and Mowery in this volume, while Lam provides an overview of organizational innovation). Edquist et al. ( 2001 ) have suggested dividing the category of process innovation into “technological process innovations” and “organizational process innovations,” the former related to new types of machinery, and the latter to new ways to organize work. However, organizational innovations are not limited to new ways to organize the process of production within a given firm. Organizational innovation, in the sense used by Schumpeter, 7 also includes arrangements across firms such as the reorganization of entire industries. Moreover, as exemplified by the case of the USA in the first half of the previous century, many of the most important organizational innovations have occurred in distribution, with great consequences for a whole range of industries (Chandler 1990 ).

Another approach, also based on Schumpeter's work, has been to classify innovations according to how radical they are compared to current technology (Freeman and Soete 1997 ). From this perspective, continuous improvements of the type referred to above are often characterized as “incremental” or “marginal” innovations, 8 as opposed to “radical” innovations (such as the introduction of a totally new type of machinery) or “technological revolutions” (consisting of a cluster of innovations that together may have a very far-reaching impact). Schumpeter focused in particular on the latter two categories, which he believed to be of greater importance. It is a widely held view, however, that the cumulative impact of incremental innovations is just as great (if not greater), and that to ignore these leads to a biased view of long run economic and social change (Lundvall et al. 1992 ). Moreover, the realization of the economic benefits from “radical” innovations in most cases (including those of the airplane and the automobile, discussed earlier) requires a series of incremental improvements. Arguably, the bulk of economic benefits come from incremental innovations and improvements.

There is also the question of how to take different contexts into account. If A for the first time introduces a particular innovation in one context, while B later introduces the same innovation in another, would we characterize both as innovators? This is a matter of convention. A widely used practice, based on Schumpeter's work, is to reserve the term innovator for A and characterize B as an imitator. But one might argue that, following Schumpeter's own definition, it would be equally consistent to call B an innovator as well, since B is introducing the innovation for the first time in a new context. This is, for instance, the position taken by Hobday ( 2000 ) in a discussion of innovation in the so-called “newly industrializing countries” in Asia. 9 One might object, though, that there is a qualitative difference between ( a ) commercializing something for the first time and ( b ) copying it and introducing it in a different context. The latter arguably includes a larger dose of imitative behavior (imitation), or what is sometimes called “technology transfer.” This does not exclude the possibility that imitation may lead to new innovation(s). In fact, as pointed out by Kline and Rosenberg ( 1986 , see Box 1.3 ), many economically significant innovations occur while a product or process is diffusing (see also Hall in this volume). Introducing something in a new context often implies considerable adaptation (and, hence, incremental innovation) and, as history has shown, organizational changes (or innovations) that may significantly increase productivity and competitiveness (see Godinho and Fagerberg in this volume). 10

Sometimes it easier to characterize a complex phenomenon by clearly pointing out what it is NOT. Stephen Kline and Nathan Rosenberg did exactly this when they, in an influential paper from 1986, used the concept “the linear model” to characterize a widespread but in their view erroneous interpretation of innovation.

Basically, “the linear model” is based on the assumption that innovation is applied science. It is “linear” because there is a well-defined set of stages that innovations are assumed to go through. Research (science) comes first, then development, and finally production and marketing. Since research comes first, it is easy to think of this as the critical element. Hence, this perspective, which is often associated with Vannevar Bush's programmatic statements on the organization of the US research systems (Bush 1945 ), is well suited to defend the interests of researchers and scientists and the organizations in which they work.

The problems with this model, Kline and Rosenberg point out, are twofold. First, it generalizes a chain of causation that only holds for a minority of innovations. Although some important innovations stem from scientific breakthroughs, this is not true most of the time. Firms normally innovate because they believe there is a commercial need for it, and they commonly start by reviewing and combining existing knowledge. It is only if this does not work, they argue, that firms consider investing in research (science). In fact, in many settings, the experience of users, not science, is deemed to be the most important source of innovation (von Hippel 1988 ; Lundvall 1988 ). Second, “the linear model” ignores the many feedbacks and loops that occur between the different “stages” of the process. Shortcomings and failures that occur at various stages may lead to a reconsideration of earlier steps, and this may eventually lead to totally new innovations.

1.3 Innovation in the Making

Leaving definitions aside, the fundamental question for innovation research is of course to explain how innovations occur. One of the reasons innovation was ignored in mainstream social science for so long was that this was seen as impossible to do. The best one could do, it was commonly assumed, was to look at innovation as a random phenomenon (or “manna from heaven,” as some scholars used to phrase it). Schumpeter, in his early works, was one of the first to object to this practice. His own account of these processes emphasized three main aspects. The first was the fundamental uncertainty inherent in all innovation projects; the second was the need to move quickly before somebody else did (and reap the potential economic reward). In practice, Schumpeter argued, these two aspects meant that the standard behavioral rules, e.g., surveying all information, assessing it, and finding the “optimal” choice, would not work. Other, quicker ways had to be found. This in his view involved leadership and vision, two qualities he associated with entrepreneurship. The third aspect of the innovation process was the prevalence of “resistance to new ways”—or inertia—at all levels of society, which threatened to destroy all novel initiatives, and forced entrepreneurs to fight hard to succeed in their projects. Or as he put it: “In the breast of one who wishes to do something new, the forces of habit raise up and bear witness against the embryonic project” (Schumpeter 1934 : 86). Such inertia, in Schumpeter's view, was to some extent endogenous, since it reflected the embedded character of existing knowledge and habit, which, though “energy-saving,” tended to bias decision-making against new ways of doing things.

Hence, in Schumpeter's early work (sometimes called “Schumpeter Mark I”) innovation is the outcome of continuous struggle in historical time between individual entrepreneurs , advocating novel solutions to particular problems, and social inertia , with the latter seen as (partly) endogenous. This may, to some extent, have been an adequate interpretation of events in Europe around the turn of the nineteenth century. But during the first decades of the twentieth century, it became clear to observers that innovations increasingly involve teamwork and take place within larger organizations (see Bruland and Mowery (Ch. 13 ), Lam (Ch. 5 ), and Lazonick (Ch. 2 ) in this volume). In later work, Schumpeter acknowledged this and emphasized the need for systematic study of “cooperative” entrepreneurship in big firms (so-called “Schumpeter Mark II”). However, he did not analyze the phenomenon in much detail (although he strongly advised others to). 11

Systematic theoretical and empirical work on innovation-projects in firms (and the management of such projects) was slow to evolve, but during the last decades a quite substantial literature has emerged (see chapters by Pavitt and Lam in this volume). In general, research in this area coincides with Schumpeter's emphasis on uncertainty (Nelson and Winter 1982 ; Nonaka and Takeuchi 1995 ; Van de Ven et al. 1999 ). In particular, for potentially rewarding innovations, it is argued, one may simply not know what are the most relevant sources or the best options to pursue (still less how great the chance is of success). 12 It has also been emphasized that innovative firms need to consider the potential problems that “path dependency” may create (Arthur 1994 ). For instance, if a firm selects a specific innovation path very early, it may (if it is lucky) enjoy “first mover” advantages. But it also risks being “locked in” to this specific path through various self-reinforcing effects. If in the end it turns out that there actually existed a superior path, which some other firm equipped with more patience (or luck) happened to find, the early mover may be in big trouble because then, it is argued, it may simply be too costly or too late to switch paths. It has been suggested, therefore, that in the early phase of an innovation project, before sufficient knowledge of the alternatives is generated, the best strategy may simply be to avoid being “stuck” to a particular path, and remain open to different (and competing) ideas/solutions. At the level of the firm, this requires a “pluralistic leadership” that allows for a variety of competing perspectives (Van de Ven et al. 1999 ), in contrast to the homogenous, unitary leader style that, in the management literature, is sometimes considered as the most advantageous. 13

“Openness” to new ideas and solutions? is considered essential for innovation projects, especially in the early phases. The principal reason for this has to do with a fundamental characteristic of innovation: that every new innovation consists of a new combination of existing ideas, capabilities, skills, resources, etc. It follows logically from this that the greater the variety of these factors within a given system, the greater the scope for them to be combined in different ways, producing new innovations which will be both more complex and more sophisticated. This evolutionary logic has been used to explain why, in ancient times, the inhabitants of the large Eurasian landmass came to be more innovative, and technologically sophisticated, than small, isolated populations elsewhere around the globe (Diamond 1998 ). Applied mechanically on a population of firms, this logic might perhaps be taken to imply that large firms should be expected to be more innovative than small firms. 14 However, modern firms are not closed systems comparable to isolated populations of ancient times. Firms have learnt, by necessity, to monitor closely each other's steps, and search widely for new ideas, inputs, and sources of inspiration. The more firms on average are able to learn from interacting with external sources, the greater the pressure on others to follow suit. This greatly enhances the innovativeness of both individual firms and the economic systems to which they belong (regions or countries, for instance). Arguably, this is of particular importance for smaller firms, which have to compensate for small internal resources by being good at interacting with the outside world. However, the growing complexity of the knowledge bases necessary for innovation means that even large firms increasingly depend on external sources in their innovative activity (Granstrand, Patel, and Pavitt, 1997 ; and in this volume: Pavitt; Powell and Grodal; Narula and Zanfei).

Hence, cultivating the capacity for absorbing (outside) knowledge, so-called “absorptive capacity” (Cohen and Levinthal 1990 ), is a must for innovative firms, large or small. It is, however, something that firms often find very challenging; the “not invented here” syndrome is a well-known feature in firms of all sizes. This arguably reflects the cumulative and embedded character of firm-specific knowledge. In most cases, firms develop their knowledge of how to do things incrementally. Such knowledge, then, consists of “routines” that are reproduced through practice (“organizational memory”: Nelson and Winter 1982 ). Over time, the organizational structure of the firm and its knowledge base typically co-evolve into a set-up that is beneficial for the day-to-day operations of the firm. It has been argued, however, that such a set-up, while facilitating the daily internal communication/interaction of the firm, may in fact constrain the firm's capacity for absorbing new knowledge created elsewhere, especially if the new external knowledge significantly challenges the existing set-up/knowledge of the firm (so-called “competence destroying technical change”: Tushman and Anderson 1986 ). In fact, such problems may occur even for innovations that are created internally. Xerox, for instance, developed both the PC and the mouse, but failed to exploit commercially these innovations, primarily because they did not seem to be of much value to the firm's existing photo-copier business (Rogers 1995 ).

Thus organizing for innovation is a delicate task. Research in this area has, among other things, pointed to the need for innovative firms to allow groups of people within the organization sufficient freedom in experimenting with new solutions (Van deVen 1999 ), and establishing patterns of interaction within the firm that allow it to mobilize its entire knowledge base when confronting new challenges (Nonaka and Takeuchi 1995 ; Lam, Ch. 5 in this volume). Such organizing does not stop at the gate of the firm, but extends to relations with external partners. Ties to partners with whom communication is frequent are often called “strong ties,” while those that are more occasional are denoted as “weak ties” (Granovetter 1973 ; see Powell and Grodal, Ch. 3 in this volume). Partners linked together with strong ties, either directly, or indirectly via a common partner, may self-organize into (relatively stable) networks. Such networks may be very useful for managing and maintaining openness. But just as firms can display symptoms of path-dependency, the same can happen to established networks, as the participants converge to a common perception of reality (so-called “group-think”). Innovative firms therefore often find it useful to also cultivate so-called “weak ties” in order to maintain a capacity for changing its orientation (should it prove necessary).

1.4 The Systemic Nature of Innovation

As is evident from the preceding discussion, a central finding in the literature is that, in most cases, innovation activities in firms depend heavily on external sources. One recent study sums it up well: “Popular folklore notwithstanding, the innovation journey is a collective achievement that requires key roles from numerous entrepreneurs in both the public and private sectors” (Van de Ven et al. 1999 : 149). In that particular study, the term “social system for innovation development” was used to characterize this “collective achievement.” However, this is just one among several examples from the last decades of how system concepts are applied to the analysis of the relationship between innovation activities in firms and the wider framework in which these activities are embedded (see Edquist, Ch. 7 in this volume).

One main approach has been to delineate systems on the basis of technological, industrial, or sectoral characteristics (Freeman et al. 1982 ; Hughes 1983 ; Carlsson and Stankiewicz 1991 ; Malerba, Ch. 14 in this volume) but, to a varying degree, to include other relevant factors such as, for instance, institutions (laws, regulations, rules, habits, etc.), the political process, the public research infrastructure (universities, research institutes, support from public sources, etc.), financial institutions, skills (labor force), and so on. To explore the technological dynamics of innovation, its various phases, and how this influences and is influenced by the wider social, institutional, and economic frameworks has been the main focus of this type of analysis. Another important approach in the innovation-systems literature has focused on the spatial level, and used national or regional borders to distinguish between different systems. For example, Lundvall ( 1992 ) and Nelson et al. ( 1993 ) have used the term “national system of innovation” to characterize the systemic interdependencies within a given country (see Edquist in this volume), while Braczyk et al. ( 1997 ) similarly have offered the notion of “regional innovation systems” (see Asheim and Gertler, Ch. 11 in this volume). Since the spatial systems are delineated on the basis of political and administrative borders, such factors naturally tend to play an important role in analyses based on this approach, which has proven to be influential among policy makers in this area, especially in Europe (see Lundvall and Borrás, Ch. 22 in this volume). (Part II of this volume analyzes some of the constituent elements of such systems in more detail. 15 )

What are the implications of applying a system perspective to the study of innovation? Systems are—as networks—a set of activities (or actors) that are interlinked, and this leads naturally to a focus on the working of the linkages of the system. 16 Is the potential for communication and interaction through existing linkages sufficiently exploited? Are there potential linkages within the system that might profitably be established? Such questions apply of course to networks as well as systems. However, in the normal usage of the term, a system will typically have more “structure” than a network, and be of a more enduring character. The structure of a system will facilitate certain patterns of interaction and outcomes (and constrain others), and in this sense there is a parallel to the role of “inertia” in firms. A dynamic system also has feedbacks, which may serve to reinforce—or weaken—the existing structure/functioning of the system, leading to “lock in “(a stable configuration), or a change in orientation, or—eventually—the dissolution of the system. Hence, systems may—just as firms—be locked into a specific path of development that supports certain types of activities and constrains others. This may be seen as an advantage, as it pushes the participating firms and other actors in the system in a direction that is deemed to be beneficial. But it may also be a disadvantage, if the configuration of the system leads firms to ignore potentially fruitful avenues of exploration. The character of such processes will be affected by the extent to which the system exchanges impulses with its environment. The more open a system is for impulses from outside, the less the chance of being “locked out” from promising new paths of development that emerge outside the system. It is, therefore, important for “system managers”—such as policy makers—to keep an eye on the openness of the system, to avoid the possibility of innovation activities becoming unduly constrained by self-reinforcing path-dependency.

Another important feature of systems that has come into focus is the strong complementarities that commonly exist between the components of a system. If, in a dynamic system, one critical, complementary component is lacking, or fails to progress or develop, this may block or slow down the growth of the entire system. This is, as pointed out earlier, one of the main reasons why there is often a very considerable time lag between invention and innovation. Economic historians have commonly used concepts such as “reverse salients” and “bottlenecks” to characterize such phenomena (Hughes 1983 ; Rosenberg 1982 ). However, such constraints need not be of a purely technical character (such as, for instance, the failure to invent a decent battery, which has severely constrained the diffusion of electric cars for more than century), but may have to do with lack of proper infrastructure, finance, skills, etc. Some of the most important innovations of this century, such as electricity and automobiles (Mowery and Rosenberg 1998 ), were dependent on very extensive infrastructural investments (wiring and roads/distribution-systems for fuel, respectively). Moreover, to fulfil the potential of the new innovation, such investments often need to be accompanied by radical changes in the organization of production and distribution (and, more generally, attitudes: see Perez 1983 , 1985 ; Freeman and Louçâ 2001 ). There are important lessons here for firms and policy makers. Firms may need to take into account the wider social and economic implications of an innovation project. The more radical an innovation is, the greater the possibility that it may require extensive infrastructural investments and/or organizational and social change to succeed. If so, the firm needs to think through the way in which it may join up with other agents of change in the private or public sector. Policy makers, for their part, need to consider what different levels of government can do to prevent “bottlenecks” to occur at the system level in areas such as skills, the research infrastructure, and the broader economic infrastructure.

1.5 How Innovation Differs

One of the striking facts about innovation is its variability over time and space. It seems, as Schumpeter (see Box 1.2 ) pointed out, to “cluster,” not only in certain sectors but also in certain areas and time periods. Over time the centers of innovation have shifted from one sector, region, and country to another. For instance, for a long period the worldwide center of innovation was in the UK, and the productivity and income of its population increased relative to its neighboring countries, so that by the mid-nineteenth century its productivity (and income) level was 50 per cent higher than elsewhere; at about the beginning of the twentieth century the center of innovation, at least for the modern chemical and electrical technologies of the day, shifted to Germany; and now, for a long time, the worldwide center of innovation has been in the USA, which during most of the twentieth century enjoyed the highest productivity and living standards in the world. As explained by Bruland and Mowery in this volume, the rise of the US to world technological leadership was associated with the growth of new industries, based on the exploitation of economies of scale and scope (Chandler 1962 , 1990 ) and mass production and distribution.

How is this dynamic to be explained? Schumpeter, extending an earlier line of argument dating back to Karl Marx, 17 held technological competition (competition through innovation) to be the driving force of economic development. If one firm in a given industry or sector successfully introduces an important innovation, the argument goes, it will be amply rewarded by a higher rate of profit. This functions as a signal to other firms (the imitators), which, if entry conditions allow, will “swarm” the industry or sector with the hope of sharing the benefits (with the result that the initial innovator's first mover advantages may be quickly eroded). This “swarming” of imitators implies that the growth of the sector or industry in which the innovation occurs will be quite high for a while. Sooner or later, however, the effects on growth (created by an innovation) will be depleted and growth will slow down.

To this essentially Marxian story Schumpeter added an important modification. Imitators, he argued, are much more likely to succeed in their aims if they improve on the original innovation, i.e., become innovators themselves. This is all the more natural, he continued, because one (important) innovation tends to facilitate (induce) other innovations in the same or related fields. In this way, innovation– diffusion becomes a creative process—in which one important innovation sets the stage for a whole series of subsequent innovations—and not the passive, adaptive process often assumed in much diffusion research (see Hall in this volume). The systemic interdependencies between the initial and induced innovations also imply that innovations (and growth) “tend to concentrate in certain sectors and their surroundings” or “clusters” (Schumpeter 1939 : 100–1). Schumpeter, as is well known, looked at this dynamic as a possible explanatory factor behind business cycles of various lengths (Freeman and Louçâ 2001 ).

This simple scheme has been remarkably successful in inspiring applications in different areas. For instance, there is a large amount of research that has adapted the Marx–Schumpeter model of technological competition to the study of industrial growth, international trade, and competitiveness, 18 although sometimes, it must be said, without acknowledging the source for these ideas. An early and very influential contribution was the so-called “product-life-cycle theory” suggested by Vernon ( 1966 ), in which industrial growth following an important product innovation was seen as composed of stages, characterized by changing conditions of and location of production. 19 Basically what was assumed was that the ability to do product innovation mattered most at the early stage, in which there were many different and competing versions of the product on the market. However, with time, the product was assumed to standardize, and this was assumed to be accompanied by a greater emphasis on process innovation, scale economics, and cost-competition. It was argued that these changes in competitive conditions might initiate transfer of the technology from the innovator country (high income) to countries with large markets and/or low costs. Such transfers might also be associated with international capital flows in the form of so-called foreign direct investments (FDIs), and the theory has therefore also become known as a framework for explaining such flows (see Narula and Zanfei in this volume).

The “product-life-cycle theory,” attractive as it was in its simplicity, was not always corroborated by subsequent research. While it got some of the general conjectures (borrowed from Schumpeter) right, the rigorous scheme it added, with well-defined stages, standardization, and changing competitive requirements, was shown to fit only a minority of industries (Walker 1979 ; Cohen 1995 ). Although good data are hard to come by, what emerges from empirical research is a much more complex picture, 20 with considerable differences across industrial sectors in the way this dynamic is shaped. As exemplified by the taxonomy suggested by Pavitt (see Box 1.4 ), exploration of such differences (“industrial dynamics”) has evolved into one of the main areas of research within innovation studies (see in this volume: Ch. 14 by Malerba; Ch. 15 by Von Tunzelmann and Acha; Ch. 16 by Miles). Inspired, to a large extent, by the seminal work by Nelson and Winter (see Box 1.5 ), research in this area has explored the manner in which industries and sectors differ in terms of their internal dynamics (or “technological regimes”: see Malerba and Orsenigo 1997 ), focusing, in particular, on the differences across sectors in knowledge bases, actors, networks, and institutions (so called “sectoral systems”: see Malerba, Ch. 14 in this volume). An important result from this research is that, since the factors that influence innovation differ across industries, policy makers have to take such differences into account when designing policies. The same policy (and policy instruments) will not work equally well everywhere.

The degree of technological sophistication, or innovativeness, of an industry or sector is something that attracts a lot of interest, and there have been several attempts to develop ways of classifying industries or sectors according to such criteria. The most widely used in common parlance is probably the distinction between “high-tech,” “medium-tech,” and “low-tech,” although it is not always clear exactly what is meant by this. Often it is equated with high, medium, and low R&D intensity in production (or value added), either directly (in the industry itself) or including R&D embodied in machinery and other inputs. Based on this, industries such as aerospace, computers, semiconductors, telecommunications, pharmaceuticals, and instruments are commonly classified as “high-tech,” while “medium-tech” typically include electrical and non-electrical machinery, transport equipment, and parts of the chemical industries. The remaining, “low-tech,” low R&D category, then, comprises industries such as textiles, clothing, leather products, furniture, paper products, food, and so on (Fagerberg 1997 ; see Smith in this volume for an extended discussion).

However, while organized R&D activity is an important source of innovation in contemporary capitalism, it is not the only one. A focus on R&D alone might lead one to ignore or overlook innovation activities based on other sources, such as skilled personnel (engineers, for instance), learning by doing, using, interacting, and so forth. This led Pavitt ( 1984 ) to develop a taxonomy or classification scheme which took these other factors into account. Based a very extensive data-set on innovation in the UK (see Smith in this volume), he identified two (“high-tech”) sectors in the economy, both serving the rest of the economy with technology, but very different in terms of how innovations were created. One, which he labeled “science-based,” was characterized by a lot of organized R&D and strong links to science, while another—so-called “specialized suppliers” (of machinery, instruments, and so on)—was based on capabilities in engineering, and frequent interaction with users. He also identified a scale-intensive sector (transport equipment, for instance), also relatively innovative, but with fewer repercussions for other sectors. Finally, he found a number of industries that, although not necessarily non-innovative in every respect, received most of their technology from other sectors.

An important result of Pavitt's analysis was the finding that the factors leading to successful innovation differ greatly across industries/sectors. This obviously called into question technology or innovation polices that only focused on one mechanism, such as, for instance, subsidies to R&D.

The book An Evolutionary Theory of Economic Change (1982) by Richard Nelson and Sidney Winter is one of the most important contributions to the study of innovation and long run economic and social change. Nelson and Winter share the Schumpeterian focus on “capitalism as an engine of change.” However, building on earlier work by Herbert Simon and others (so-called “procedural” or “bounded” rationality), Nelson and Winter introduce a more elaborate theoretical perspective on how firms behave. In Nelson and Winter's models, firms' actions are guided by routines, which are reproduced through practice, as parts of the firms' “organizational memory.” Routines typically differ across firms. For instance, some firms may be more inclined towards innovation, while others may prefer the less demanding (but also less rewarding) imitative route. If a routine leads to an unsatisfactory outcome, a firm may use its resources to search for a new one, which—if it satisfies the criteria set by the firm—will eventually be adopted (so-called “satisficing” behavior).

Hence, instead of following the common practice in much economic theorizing of extrapolating the characteristics of a “representative agent” to an entire population (so-called “typological thinking”), Nelson and Winter take into account the social and economic consequences of interaction within populations of heterogeneous actors (socalled “population thinking”). They also emphasize the role of chance (the stochastic element) in determining the outcome of the interaction. In the book, these outcomes are explored through simulations, which allow the authors to study the consequences of varying the value of key parameters (to reflect different assumptions on technological progress, firm behavior, etc.). They distinguish between an “innovation regime,” in which the technological frontier is assumed to progress independently of firms' own activities (the “science based” regime), and another in which technological progress is more endogenous and depends on what the firms themselves do (the “cumulative” regime). They also vary the ease/difficulty of innovation and imitation.

Nelson and Winter's work has been an important source of inspiration for subsequent work on “knowledge-based firms,” “technological regimes,” and “industrial dynamics,” and evolutionary economics more generally, to mention some important topics.

Sources : Nelson and Winter 1982 ; Andersen 1994 ; Fagerberg 2003 .

1.6 Innovation and Economic Performance

The Marx–Schumpeter model was not intended as a model of industrial dynamics; its primary purpose was to explain long run economic change, what Schumpeter called “development.” The core of the argument was (1) that technological competition is the major form of competition under capitalism (and firms not responding to these demands fail), and (2) that innovations, e.g. “new combinations” of existing knowledge and resources, open up possibilities for new business opportunities and future innovations, and in this way set the stage for continuing change. This perspective, while convincing, had little influence on the economics discipline at the time of its publication, perhaps because it did not lend itself easily to formal, mathematical modeling of the type that had become popular in that field. More recently, however, economists (Romer 1990 ), drawing on new tools for mathematical modeling of economic phenomena, have attempted to introduce some of the above ideas into formal growth models (so-called “new growth theory” or “endogenous growth theory”). 21

In developing this perspective, Schumpeter ( 1939 ) was, as noted, particularly concerned with the tendency of innovations to “cluster” in certain contexts, and the resulting structural changes in production, organization, demand, etc. Although these ideas were not well received by the economic community at the time, the big slump in economic activity worldwide during the 1970s led to renewed attention, and several contributions emerged viewing long run economic and social change from this perspective. Both Mensch ( 1979 ) and Perez ( 1983 , 1985 ), to take just two examples, argued that major technological changes, such as, for instance, the ICT revolution today, or electricity a century ago, require extensive organizational and institutional change to run their course. Such change, however, is difficult because of the continuing influence of existing organizational and institutional patterns. They saw this inertia as a major growth-impeding factor in periods of rapid technological change, possibly explaining some of the variation of growth over time (e.g. booms and slumps) in capitalist economies. While the latter proposition remains controversial, the relationship between technological, organizational, and institutional change continues to be an important research issue (Freeman and Louçã 2001 ), with important implications both for the analysis of the diffusion of new technologies (see Hall in this volume) and the policy discourse (see Lundvall and Borras in this volume).

Although neither Marx nor Schumpeter applied their dynamic perspective to the analysis of cross-national differences in growth performance, from the early 1960s onwards several contributions emerged that explore the potential of this perspective for explaining differences in cross-country growth. In what came to be a very influential contribution, Posner ( 1961 ) explained the difference in economic growth between two countries, at different levels of economic and technological development, as resulting from two sources: innovation, which enhanced the difference, and imitation, which tended to reduce it. This set the stage for a long series of contributions, often labeled “technology gap” or “north–south” models (or approaches), focusing on explaining such differences in economic growth across countries at different levels of development (see Fagerberg 1994 , 1996 for details). As for the lessons, one of the theoretical contributors in this area summed it up well when he concluded that: “Like Alice and the Red Queen, the developed region has to keep running to stay in the same place” (Krugman 1979 : 262).

A weakness of much of this work was that it was based on a very stylized representation of the global distribution of innovation, in which innovation was assumed to be concentrated in the developed world, mainly in the USA. In fact, as argued by Fagerberg and Godinho in this volume, the successful catch-up in technology and income is normally not based only on imitation, but also involves innovation to a significant extent. Arguably, this is also what one should expect from the Schumpeterian perspective, in which innovation is assumed to be a pervasive phenomenon. Fagerberg ( 1987 , 1988 ) identified three factors affecting differential growth rates across countries: innovation, imitation, and other efforts related to the commercial exploitation of technology. The analysis suggested that superior innovative activity was the prime factor behind the huge difference in performance between Asian and Latin American NIC countries in the 1970s and early 1980s. Fagerberg and Verspagen ( 2002 ) likewise found that the continuing rapid growth of the Asian NICs relative to other country groupings in the decade that followed was primarily caused by the rapid growth in the innovative performance of this region. Moreover, it has been shown (Fagerberg 1987 ; Fagerberg and Verspagen 2002 ) that, while imitation has become more demanding over time (and hence more difficult and/or costly to undertake), innovation has gradually become a more powerful factor in explaining differences across countries in economic growth.

1.7 What do we Know about Innovation? And what do we Need to Learn more about?

Arguably, we have a good understanding of the role played by innovation in long run economic and social change, and many of its consequences:

The function of innovation is to introduce novelty (variety) into the economic sphere. Should the stream of novelty (innovation) dry up, the economy will settle into a “stationary state” with little or no growth (Metcalfe 1998 ). Hence, innovation is crucial for long-term economic growth.

Innovation tends to cluster in certain industries/sectors, which consequently grow more rapidly, implying structural changes in production and demand and, eventually, organizational and institutional change. The capacity to undertake the latter is important for the ability to create and to benefit from innovation.

Innovation is a powerful explanatory factor behind differences in performance between firms, regions, and countries. Firms that succeed in innovation prosper, at the expense of their less able competitors. Innovative countries and regions have higher productivity and income than the less innovative ones. Countries or regions that wish to catch up with the innovation leaders face the challenge of increasing their own innovation activity (and “absorptive capacity”) towards leader levels (see Godinho and Fagerberg in this volume).

Because of these desirable consequences, policy makers and business leaders alike are concerned with ways in which to foster innovation. Nevertheless, in spite of the large amount of research in this area during the past fifty years, we know much less about why and how innovation occurs than what it leads to. Although it is by now well established that innovation is an organizational phenomenon, most theorizing about innovation has traditionally looked at it from an individualistic perspective, as exemplified by Schumpeter's “psychological” theory of entrepreneurial behavior (Fagerberg 2003 ). Similarly, most work on cognition and knowledge focuses on individuals, not organizations. An important exception was, of course, Nelson and Winter ( 1982 ), whose focus on “organizational memory” and its links to practice paved the way for much subsequent work in this area. 22 But our understanding of how knowledge—and innovation—operates at the organizational level remains fragmentary and further conceptual and applied research is needed.

A central finding in the innovation literature is that a firm does not innovate in isolation, but depends on extensive interaction with its environment. Various concepts have been introduced to enhance our understanding of this phenomenon, most of them including the terms “system” or (somewhat less ambitious) “network.” Some of these, such as the concept of a “national system of innovation,” have become popular among policy makers, who have been constrained in their ability to act by lack of a sufficiently developed framework for the design and evaluation of policy. Still, it is a long way from pointing to the systemic character of innovation processes (at different levels of analysis), to having an approach that is sufficiently developed to allow for systematic analysis and assessment of policy issues. Arguably, to be really helpful in that regard, these system approaches are in need of substantial elaboration and refinement (see the chapter by Edquist in this volume).

One obstacle to improving our understanding is that innovation has been studied by different communities of researchers with different backgrounds, and the failure of these communities to communicate more effectively with one another has impeded progress in this field. One consequence of these communication difficulties has been a certain degree of “fuzziness” with respect to basic concepts, which can only be improved by bringing these different communities together in a constructive dialogue, and the present volume should be seen as a contribution towards this aim. Different, and to some extent competing, perspectives should not always be seen as a problem: many social phenomena are too complex to be analyzed properly from a single disciplinary perspective. Arguably, innovation is a prime example of this.

Iwish to thank my fellow editors and contributors for helpful comments and suggestions. Thanks also to Ovar Andreas Johansson for assistance in the research, Sandro Mendonça for his many creative inputs (which I unfortunately have not have been able to follow to the extent that he deserves), and Louise Earl for good advice. The responsibility for remaining errors and omissions is mine.

A consistent use of the terms invention and innovation might be to reserve these for the first time occurrence of the idea/concept and commercialization, respectively. In practice it may not always be so simple. For instance, people may very well conceive the same idea independently of one another. Historically, there are many examples of this; writing, for instance, was clearly invented several times (and in different cultural settings) throughout history (Diamond 1998 ). Arguably, this phenomenon may have been reduced in importance over time, as communication around the globe has progressed.

In the sociological literature on diffusion (i.e. spread of innovations), it is common to characterize any adopter of a new technology, product, or service an innovator. This then leads to a distinction between different types of innovators, depending on how quick they are in adopting the innovation, and a discussion of which factors might possibly explain such differences (Rogers 1995 ). While this use of the terminology may be a useful one in the chosen context, it clearly differs from the one adopted elsewhere. It might be preferable to use terms such as “imitator” or “adopter” for such cases.

Similarly for automobiles: while the idea of a power-driven vehicle had been around for a long time, and several early attempts to commercialize cars driven by steam, electricity, and other sources had been made, it was the incorporation of an internal combustion engine driven by low-cost, easily available petrol that made the product a real hit in the market (Mowery and Rosenberg 1998 ).

A somewhat similar distinction has been suggested by Henderson and Clark ( 1990 ). They distinguish between the components (or modules) of a product or service and theway these components are combined, e.g. the product “design” or “architecture.” A change only in the former is dubbed “modular innovation,” change only in the latter “architectural innovation.” They argue that these two types of innovation rely on different types of knowledge (and, hence, create different challenges for the firm).

In fact, many economists go so far as to argue that the savings in costs, following a process innovation in a single firm or industry, by necessity will generate additional income and demand in the economy at large, which will “compensate” for any initial negative effects of a process innovation on overall employment. For a rebuttal, see Edquist 2001 and Pianta, Ch. 21 in this volume.

Schumpeter 1934 : 66.

In the sociological literature on innovation, the term “reinvention” is often used to characterize improvements that occur to a product or service, while it is spreading in a population of adopters (Rogers 1995 ).

In the Community Innovation Survey (CIS) firms are asked to qualify novelty with respect to the context (new to the firm, industry or the world at large). See Smith in this volume for more information about these surveys.

Kim and Nelson ( 2000 a ) suggest the term “active imitation” for producers who, by imitating already existing products, modify and improve them.

For instance, in one of his last papers, he pointed out: “To let the murder out and startmy final thesis, what is really required is a large collection of industrial and locational monographs all drawn up according to the same plan and giving proper attention on the one hand to the incessant historical change in production and consumption functions and on the other hand to the quality and behaviour of leading personnel” (Schumpeter 1949/1989 : 328).

Even in cases where the project ultimately is successful in aims, entrepreneurs face the challenge of convincing the leadership of the firm to launch it commercially (which may be much more costly than developing it). This may fail if the leadership of the firm has doubts about its commercial viability. It may be very difficult for management to foresee the economic potential of a project, even if it is “technically” successful. Remember, for instance, IBM director Thomas Watson's dictum in 1948 that “there is a world market for about five computers” (Tidd et al. 1997 : 60)!

“A unified homogenous leadership structure is effective for routine trial-and-error learning by making convergent, incremental improvements in relatively stable and unambiguous situations. However, this kind of learning is a conservative process that maintains and converges organizational routines and relationships towards the existing strategic vision … although such learning is viewed as wisdom in stable environments, it produces inflexibility and competence traps in changing worlds” (Van de Ven et al. 1999 : 117).

It would also imply that large countries should be expected to be more innovative than smaller ones, consistent with, for instance, the prediction of so-called “new growth” theory (Romer 1990 ). See Verspagen in this volume.

See, in particular, Ch. 10 by Granstrand (intellectual property rights), Ch. 8 by Mowery and Sampat (universities and public research infrastructure), and Ch. 9 by O'Sullivan (finance).

This is essentially what was suggested by Porter ( 1990 ).

See Fagerberg 2002 , 2003 for a discussion of this “Marx–Schumpeter” model.

See Fagerberg ( 1996 ), Wakelin ( 1997 ), and Cantwell, Ch. 20 in this volume for overviews of some of this literature.

For a more recent analysis in this spirit, with a lot of empirical case-studies, see Utterback ( 1994 ).

Available econometric evidence suggests that innovation, measured in various ways (see Smith in this volume), matters in many industries, not only those which could be classified as being in the early stage of the product-cycle (Soete 1987 ; Fagerberg 1995 ).

For an overview, see Aghion and Howitt ( 1998 ). See also the discussion in Fagerberg ( 2002 , 2003 ), and Ch. 18 by Verspagen in this volume.

For a discussion of the role of different types of knowledge in economics, including the organizational dimension, see Cowan et al. ( 2000 ) and Ancori et al. ( 2000 ).

Aghion , P., and Howitt , P. ( 1998 ), Endogenous Growth Theory , Cambridge, Mass.: MIT Press.

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• Published: 06 March 2024

Innovation dynamics within the entrepreneurial ecosystem: a content analysis-based literature review

• Rishi Kant Kumar   ORCID: orcid.org/0000-0001-5681-0203 1 ,
• Srinivas Subbarao Pasumarti 2 ,
• Ronnie Joshe Figueiredo 3 ,
• Rana Singh 1 ,
• Sachi Rana 4 ,
• Kumod Kumar 1 &
• Prashant Kumar   ORCID: orcid.org/0000-0001-9160-5811 5  

Humanities and Social Sciences Communications volume  11 , Article number:  366 ( 2024 ) Cite this article

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• Business and management

Entrepreneurial ecosystems (EEs) delineate concepts from varied streams of literature originating from multiple stakeholders and are diagnosed by different levels of analysis. Taking up a sample of 392 articles, this study examines how innovation fosters the emergence of self-operative and self-corrective entrepreneurial ecosystems in the wake of automatic market disruptions. It also finds that measures lending vitality and sustainability to economic systems across the world through a mediating role played by governments, along with synergies exhibited by academia and “visionpreneurs” at large, give rise to aspiring entrepreneurs. The study also aligns past practices with trending technologies to enrich job markets and strengthen entrepreneurial networks through spillover and speciation. The research offers valuable insights into entrepreneurial ecosystems’ practical policy implications and self-regulating mechanisms, and it suggests that governments overseeing these entrepreneurial ecosystems should identify and nurture the existing strengths within them. Additionally, entrepreneurial ecosystems can benefit from government support through subsidies and incentives to encourage growth. In collaboration with university research, specialized incubation centers can play a pivotal role in creating new infrastructures that foster current and future entrepreneurial development.

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Introduction

Innovation provides a gateway to products/services in varied market dynamism by transcending time horizons. Innovations work on the back and call of automatic disruptions that happen in markets through the mediating role of governments, institutions, and academicians, leading to “self-operative” and “self-corrective ecosystems.” Most of the time, innovative processes are self-corrective and operate without much effort. As innovations in products keep evolving, they rekindle customers’ interest and increase the prospects of products for better sales and a long-life cycle (for example, entrepreneurs may offer new features or new looks to older products). To undertake this sort of initiative, commercial freedoms must be guaranteed, which can be used to create, deploy, and protect intangible assets (Teece, 2007 ; Sprinkle, 2003 ). Thus, innovations together with entrepreneurial networks or ecosystems provide dynamic capabilities to the economy by imparting continuity. In that process, entrepreneurs, through their better learning skills and novel methods, create opportunities in changing markets (Garnsey and Leong, 2008 ; Garnsey et al., 2008 ; Kantarelis, 2009 ; Levinson, 2010 ; Biggs et al., 2010 ), as markets are always fueled by disruptions in entrepreneurial ventures, and old products must be replaced by newer ones.

Further, synergy between entrepreneurial ecosystems and research plays a pivotal role in fostering disruptive innovations within contemporary markets. This collaboration, exemplified by the establishment of “spin-off companies” from academic research, is instrumental in guiding aspiring talent and cultivating growth in local economies. However, despite this symbiosis, a notable gap exists in knowledge spillovers between universities and their surrounding entrepreneurial and innovation ecosystems. To address this, collaborative and interactive research is recommended, as proposed by Mehta et al. ( 2016 ). Such initiatives not only facilitate self-operative and self-corrective entrepreneurial ecosystems but also contribute to knowledge spillovers that fuel product development and speciation. The interconnected processes of institutionalizing methods, policy entrepreneurship, and knowledge spillovers underscore the intricate relationship between academia, institutional research, and market dynamics, emphasizing the need for cohesive strategies to bridge existing gaps and maximizing the impact of disruptive tendencies in entrepreneurship. This mechanism can receive a boost with the assistance of sustainable innovation of society through “social entrepreneurship education (SEE) programs” (Kim et al., 2020 ), which can be designed and operated to cultivate social entrepreneurial abilities and contribute to the development of innovation hubs for entrepreneurial ecosystems (EEs). For example, a study by Igwe et al. ( 2020 ) focused on frugal innovations and informal entrepreneurship, which could lead to the creation of fresh, innovative tendencies in informal sectors of different nations.

So, looking forward, the relevance for the development of entrepreneurial networks (Teece, 2007 ), where innovation can accentuate the need for the intersection of researchers, entrepreneurship, and regional economic development while holding entrepreneurship as a key mechanism. Although there has been much innovative research done in recent years using a systematic literature review approach, it was observed that existing literature typically lacked the required comprehensive theoretical foundations; more work can contribute to the development of suitable theoretical methodologies for practical results in economic development. For example, past literature is focused on intervention of innovation with digital entrepreneurship (Satalkina and Steiner, 2020 ), social entrepreneurship (Fauzi et al., 2022 ). In a similar way, Montes-Martínez and Ramírez-Montoya ( 2022 ) oriented their research towards finding the relationship between educational and social entrepreneurship innovations using a systematic mapping technique and suggested a potential research gap in this area by collating the number of articles published and geographical contributions. Further, the literature also talked about sustainable entrepreneurship (Thananusak, 2019 ), technological innovation and entrepreneurship in management science (Shane and Ulrich, 2004 ), or the role of open innovation in entrepreneurship (Portuguez-Castro, 2023 ).

Conversely, most of these studies deliberated on the genesis, development, and operation of innovative entrepreneurial ecosystems and subsidiary literature contributing to their existence and growth, then those for laying down foundations for newer tendencies the world is witnessing and vying to enable and sustain them during the times of “Contaminant Economic Trends” (abrupt economic disruptions due to the advent of some natural, environmental, or manmade phenomenon such as COVID-19). It is essential to combine and progress research in several important areas to fill the current gaps in the literature on innovation and entrepreneurship. First, a thorough investigation of information effects is necessary for the present connection between innovation and entrepreneurial ecosystems, especially through subsidiaries businesses. Mehta et al. ( 2016 ) support collaborative research, but more research is required to understand the mechanisms and obstacles preventing knowledge transfer from institutions to entrepreneurial ecosystems. This research aims to examine the following research questions:

What are the key thematic progressions in innovation research within the field of entrepreneurial ecosystems?

What conceptual models can be recommended based on the existing literature to guide and inform future research endeavors at the intersection of innovation and entrepreneurial ecosystems?

To examine the research questions, we applied the text mining approach of the content analysis method on the articles collected on the keywords related to innovation and entrepreneurship for a selected period. This study also aims to fill this gap by designing a model of EE offering multidimensional insights into recent developments in the field of entrepreneurial ecosystems. This study contributes theoretically by synthesizing insights from a systematic literature review to construct a comprehensive model elucidating the intricate dynamics influencing entrepreneurial ecosystems. Identified decisive components—namely, “Evolutionary Theories,” “Governmental Assistance,” “Global Outreach of Academic Innovations,” “Open and Distributed Models of Innovation,” “Entrepreneurial Learning Experience,” and “Social Entrepreneurship”—provide a nuanced understanding of factors shaping enhanced entrepreneurial landscapes. The structured model unveils the synergies underpinning ecosystem development across diverse nations and economies amid economic uncertainties. Moreover, the study posits that government policies, such as subsidized infrastructural support, play a pivotal role in fostering entrepreneurial growth, thereby contributing novel perspectives to the scholarly discourse on entrepreneurial ecosystem evolution.

From this point forward, the paper progresses as follows: Section “Theoretical background and analysis” explains the meaning of innovation and its place in entrepreneurship development and entrepreneurial ecosystem networks; Section “Methodology” reviews prior literature on innovation in the entrepreneurship context; Section “Results” discusses the methodology adopted for the present study and delves into the methods of data collection and analysis for present research; Section “Discussion” discusses the results and analysis done in the present study; Section “Implications, Limitations, and Future Trends” delineates the theoretical implications of the present research and proposes a conceptual model for better innovation in entrepreneurship; and Section “Conclusion” takes up the conclusion part of the study.

Theoretical background and analysis

Past research has mainly focused on developing entrepreneurial ecosystems and their genesis. They hardly focused on what is mainly lacking in the growth process of these ecosystems and why academic knowledge fully fails to translate into entrepreneurial achievements. Moreover, past studies have explored and delineated the extant ecosystems with their peculiarities without looking deep down into the self-operative and self-corrective mechanisms of entrepreneurial ecosystems, which have their own strengths that make them resilient to economic turbulences. The present study highlights this mechanism and forwards a model that explains the process of enhanced ecosystems.

What is innovation?

As per the Schumpeterian view, the practical implementation of ideas for developing new goods and services is innovation (Mehmood et al., 2019 ). ISO TC 279, in the standard of ISO 56000:2020, states that innovation is “a new or changed entity realizing or redistributing value” (ISO, 2020). Definitions of innovations focus on newness, improvement, and the spread of ideas or technologies, products, processes, services, technologies, and artworks (Lijster, 2018 ). Business models that are brought forward by innovators to the market, governments (Bhasin, 2012 ), and society are certain modes through which innovation takes place.

Innovation and entrepreneurship

The advancement of entrepreneurial innovation has necessitated an increased demand for policy interventions that encourage and complement entrepreneurial ecosystems. These interventions are crucial for managing and containing emerging disruptions by introducing effective strategies. The goal is to harness these disruptions for the development of newer and improved entrepreneurial ecosystems, ultimately bringing greater benefits to entrepreneurial ventures. By employing business strategies in indigenous markets, entrepreneurs can carve out niches to meet existing demands and expand into international markets (Sprinkle, 2003 ).

This approach not only enhances enterprise performance in an open economy but also stimulates rapid innovation and disperses dynamic capacities across enterprises, entities, and institutions. According to Teece ( 2007 ), it establishes micro-foundations for entrepreneurial ecosystems, contributing to the formation of innovative networks that support emerging industries (Garnsey and Leong, 2008 ). Additionally, it generates conceptual dimensions by developing complementarities that assist in the adoption of compatible applications (Garnsey et al., 2008 ).

For instance, recent literature on entrepreneurial practices during the ongoing COVID-19 pandemic and post-pandemic business activities catalyzed by the digital revolution highlights the acceleratory role of digitization in expanding the business world. This digital transformation has led to the development of novel social innovations, transforming entrepreneurial practices and liberating the workforce from being “cabin cooped in individuals” to “flexible timers.” These social disruptors have also prompted the exploration of groundbreaking approaches for assessing nuances that emphasize sustainable entrepreneurial ecosystems. Lastly, we present the core concepts related to these domains in Table 1 .

Methodology

Many researchers have applied different methodologies for literature review, such as theory-based review (Debellis et al., 2021 ); framework-based systematic review (Rosado-Serrano et al., 2018 ); theme-based structured review (Pansari and Kumar, 2017 ); techno-commercial literature review (Chatterjee et al., 2018 ; Kumar et al., 2020 ); and literature review based on text mining (Kumar et al., 2019 ). As for article selection, researchers indicate selecting a database such as Scopus or Web of Science (Kumar et al., 2023 ; Donthu et al., 2021 ), with which researchers get a better grasp of a specific domain of research (Alvesson & Sandberg, 2020 ) and set the stage for future research (Elsbach & Knippenberg, 2020 ). By looking at our research questions, we have employed content analysis with a text mining approach in this study, which presents thematic analysis and helps present contextual analysis.

Database preparation

The present study seeks to explore the themes underlying the domain of innovation in entrepreneurial ecosystems. Considering the methodology followed by Akter and Wamba ( 2016 ), we searched keywords such as “business entrepreneurship,” “entrepreneurial ecosystem,” and “entrepreneurial networks” on Scopus in the abstract, title, and keywords fields to search relevant documents. There were 2136 articles matching the keywords in January 2023; following this, a search for “innovation” yielded 772 documents. The final filter was performed to select articles and reviews only, which left us with a batch of 392 documents belonging to different subject areas like business management (34.6%), followed by Social Sciences (17.0%), Economics (14.4%), Engineering (7.7%), Environmental Sciences (6.4%), Computer Sciences (3.1%), Decision Sciences (3.1%), Energy (2.7%), Psychology (1.9%), Biochemistry (1.7%), and others (7.4%). All 392 articles’ abstracts were subjected to content analysis (text mining) after selecting the timeframes outlining the extracted themes to showcase the changes in the research.

Different approaches exist for selecting time duration: while Leone et al. ( 2012 ) proposed three years, Kumar et al. ( 2019 ) suggested five years for getting ideal time durations. In this study, the initial timeframe covered research for 13 years (2003–15) as in these years there were very few publications. Afterward, two sets of two-year durations of 2016–17 and 2018–19 were included, followed by three sets of single-year durations (2020, 2021, and 2022). We initially categorized articles by year but found that there were relatively few articles published in the earlier years, with a significant increase after 2010. Consequently, selecting either a 3-year or 5-year timeframe would have resulted in sample size variation by including the number of articles in each timeframe. To address this, we segmented the articles into eight periods, each containing over 40 articles in each timeframe. The year selection was done to reduce the redundancy found during the content analysis of the abstracts.

Analysis method

Looking toward our first research question of key thematic progressions in the selected domain, we applied the content analysis method to the abstract of 392 articles. In the content analysis approach, text mining (Kumar et al., 2019 , Tiwary et al., 2021 ) is a natural language processing (NLP) technique used to explore valuable insights and uncover relationships from unstructured text data. Text mining provides various benefits due to its feature of processing and analyzing large volumes of data quickly, which allows researchers to find trends and patterns effectively, which could be difficult using human approaches. Furthermore, text mining makes it possible to generate useful numerical indices that support the quantification and methodical examination of word clusters, thereby improving the accuracy and effectiveness of content analysis techniques. Text mining is being used in academic research to speed up the analytical process and improve the quality and scope of insights obtained from unstructured textual material (Karami et al., 2020 ; Gurcan and Cagiltay, 2023 ). We applied text mining to capture the themes that emerged from the articles and to create meaningful numeric indices to analyze word clusters (Feldman & Sanger, 2007 ). As for text mining, we used the widely accepted bibliometric tool “VOSViewer” (Van Eck and Waltman, 2010 ) to analyze the abstract by creating a term co-occurrence map.

Following our RQ1 of exploring maturity and themes of innovations in entrepreneurial ecosystems, we first analyzed all the articles published annually as per maturity and research exploration. We present the results from each year group below separately:

Theme that emerged during the year 2003–2015

Conceptual visualization.

During this period, the focus was on exploring themes that were categorized under specific clusters (see Fig. 1 ), “business ecosystem, capability, customer, development, ecosystem service, entrepreneur, Europe, firm, goal, innovation ecosystem, new venture, opportunity, resource, student, success.” These word clusters indicate entrepreneurial symphony , especially capturing nurturing success in the business ecosystem . Further, a cluster containing words like “adoption, case study, culture, ecosystem, emergence, knowledge, phenomenon, small firm, society, strategy, transformation, value” indicates its connection with Cultural Catalysts , unveiling small firm transformation through ecosystem adoption . The third theme under these years contains words like “entrepreneurial innovation, entrepreneurship framework, government, innovation, issue, policy, region, Silicon Valley, university,” indicating its connection with Elevate by Innovation by crafting a robust entrepreneurship framework for regional growth and navigating government policies . The last theme under these years contains words such as “business, case, company, consumer, convergence, enterprises, factor, growth, medium, product, technology” grouping theme under TechConverge Enterprises , which navigates business growth through consumer-centric mediums and product innovation .

Theme of study during the years 2003–2015.

Together, these four themes delve into the complex worlds of innovation, company culture, and entrepreneurship. The focus on cultural catalysts and technological convergence offers a comprehensive knowledge of entrepreneurial alterations, geographic expansion strategies, and the complex aspects influencing global business performance, even while the European and regional views offer specialized insights. For example, Sprinkle ( 2003 ) drew attention to concurrent policy restrictions on commercial and entrepreneurial freedoms that inhibit bioscience advancement. Teece ( 2007 ) explored the globally dispersed sources of invention, innovation, and dynamic manufacturing capabilities to create a self-operative and self-corrective entrepreneurial network based on creative destruction, commercialization, and transformation of product technologies. Le and Tarafdar ( 2009 ) underscored the importance of interactive collaboration and value co-creation in the era of commerce and the Web 2.0 version, as took place on Facebook, Google, and Myspace.

Theoretical aspects

During this period, entrepreneurial success became synonymous with innovation research, primarily stemming from university research efforts. This led to creative destruction, fostering the commercialization, speciation, and transformation of existing products and strategies. Companies sought value co-creation, supported by government policies and academic advancements. Teece ( 2007 ) emphasized the importance of dynamic capabilities, in which firms deploy tangible assets for business through innovative networks. Governmental R&D played a pivotal role in shaping these networks, aligning research with policies. The collaborative nature of business models, as highlighted by Garnsey and Leong ( 2008 ), facilitated speciation, branching, and technological advancement, contributing to “techno-organizational speciation spin-offs” and niche creation for transformative innovations (Kantarelis, 2009 ). However, this perspective is challenged by evolving policies and practices leading to urbanization, expanding markets, and technological speciation across different geographic areas, negatively impacting rural vitality (Nybakk et al., 2009 ).

Proposition: University-driven efforts, collaborative business models, and government policies combined to drive the intersection of innovation research and entrepreneurial success, which resulted in commercialization and transformation. In addition, changing policies and practices have affected rural vitality through urbanization, market expansion, and technological evolution .

Theme emerged during the year 2016–2017

The emergence of clusters (see Fig. 2 ) during the timeframe of 2016–2017 majorly saw research surrounding themes of innovative interactions through entrepreneurial university dynamics community-driven economies (e.g., community, demand, design, economy, entrepreneurial university, government, growth), entrepreneurial evolution by nurturing sustainable innovation and open innovation economy (e.g., entrepreneurship, evolution, innovation, open, innovation), TechHub Nexus by maximizing R&D efficiency, fostering creative development and focusing commercialization capability (e.g., capability, commercialization, creative, economy) and urban prowess through innovative business models by crafting a dynamic entrepreneurial ecosystem (e.g., dynamic, ecosystem, business model, regional). Many articles address important aspects of contemporary enterprise, innovation, and regional development. These topics highlight the delicate interplay between academics, technology, and policy, offering nuanced viewpoints critical for supporting innovation, sustainable development, and entrepreneurial growth in a variety of situations.

Theme of study during the year 2016–2017.

Most prominent themes, which were accentuated through the creation of academic entrepreneurship for the creation of maker spaces and creative economy which could forward and contribute towards regional innovations through the “University’s Economic Development Mission” that was instrumental in building up the prospects for “transforming economy” leading to “regional development,” which gave rise to “new ventures development” and created platforms for novel entrepreneurship. Herein, the university ecosystem examines individual intermediaries and facilitates “Student Spin-off Industries” (Hayter, 2016 ). For example, the Bayh-Dole Act in the United States takes up ownership of students’ inventions funded by the government. Consequential, novel themes and new ventures in the entrepreneurial ecosystem emerged (Soundarajan et al., 2016 ) because of emerging models of entrepreneurial universities for transforming the economy in pursuit of regional development through “University Business Cooperations (UBCs)” (Guerrero et al., 2016 ) to tackle the disruptor dilemma by showing the entrepreneurs the profitable path providing platforms for the overall development of regional innovation systems.

Proposition: Academic entrepreneurship facilitated by initiatives like maker spaces and the creative economy may foster regional innovation and new ventures driven by the university’s economic development mission and exemplified by entities such as student spin-off industries .

Theme emerged during the years 2018–2019

The course of this timeframe saw themes associated with (see Fig. 3 ) “startups,” “network,” “innovation policy,” “service innovation,” “social entrepreneurship,” and “academic,” among others. These cluster themes drew on the concepts “Innovation Driven Gazelle Enterprises (IDEs),” “prototype equipment facilities,” “translational research by local universities,” “platformization,” “Knowledge-Intensive Entrepreneurship (KIE),” “KIE Concentration,” “innovative milieus,” “voluntary horizontal knowledge spillovers,” and “Silicon Valley.”

Theme of study during the years 2018–2019.

The most prominent of all themes were “startups” and “networks,” fueling regional entrepreneurship and leading to radically innovative products and services (de Vasconcelos Gomes et al., 2018 ). The cross-connection of entrepreneurial factors and networks in academic and industrial circles is key to transmitting knowledge bases (Qian, 2018 ), leading to the growth of startups. Furthermore, the government’s innovation policies lead to the development of “services innovation” and “social entrepreneurship” through the supportive programs of entrepreneurial development that are further boosted by strong networks created by startups advancement in any regional or national entrepreneurial ecosystem. However, it is still unknown how knowledge networks (Miller et al., 2018 ) influence entrepreneurship processes through supportive environments fostering innovative startups (Spigel and Harrison, 2018 ).

Proposition: The symbiotic relationship between startups, knowledge networks, and government innovation policies may be pivotal in driving regional entrepreneurship, particularly in the development of services innovation and social entrepreneurship, yet the specific influence of knowledge networks on entrepreneurial processes within supportive environments remains unclear and requires further exploration .

Theme that emerged during the year 2020

The themes that originated during this timeframe (see Fig. 4 ) were associated with “academic entrepreneurship,” “social entrepreneurship,” “urban-rural divide,” “disruptive innovation,” and “tourism,” the origination of which was based on tagged-in factors such as “innovation hubs for Entrepreneurial Ecosystems (EEs),” “informal entrepreneurship,” “frugal innovation,” “utility-maximization,” “business incubators,” “innovation transition,” etc.

Theme of study during the year 2020.

“Academic” and “social” were the most prominent themes that emerged during this timeframe, encompassing “academic entrepreneurship,” “social entrepreneurship,” “urban–rural divide,” and “disruptive innovation.” The theme emphasized that academic and social are the two most basic and crucial benchmarks for any economy to have the presence of entrepreneurial ecosystems. They are the only factors that give rise to social entrepreneurship that use social issues as the basis for developing new entrepreneurial ideas to establish social enterprises. This is not only blurring the urban–rural divide but is also using this divide to determine, locate, and pick new opportunities and turn them into successful social entrepreneurship model firms, giving rise to informal and frugal innovations that are leading to utility maximization in resource-scarce ecosystems. This even helps in attaining sustainable innovation, which is the only way for nations to balance industrial growth and the sustainability of resources. For example, Kim et al. ( 2020 ) discussed the role of social entrepreneurship programs in developing sustainable innovation through balanced industrial growth and opined for internal and external connectivity through innovations and sustainable informal entrepreneurship (Igwe et al. 2020 ).

Proposition:The intertwining of academic and social themes within entrepreneurial ecosystems may serve as a foundational driver for social entrepreneurship, blurring the urban–rural divide and fostering sustainable innovations that balance industrial growth with resource sustainability .

Theme that emerged during the year 2021

During this timeframe (see Fig. 5 ), the research focused on “policy implication,” “frugal innovation,” “research,” “innovative behavior,” “intermediary,” “open innovation,” “empirical evidence,” “agent,” “community,” and “social entrepreneurship,” driving on concepts such as “digitization,” “digital platform,” “digital entrepreneurial ecosystems,” “COVID-19”, “pandemic” and “women entrepreneurship,” “circular entrepreneurship,” “sociology,” “emergent entrepreneurship,” “phenomenological inquiry,” “nascent,” “knowledge-intensive,” “returnee entrepreneurial firms,” “Entrepreneurial Discovery Theory,” and “artistic place-making,” among others, which were recurrently referred to by authors in their research works. Furthermore, these themes were spawned from the factors and concepts related to “moderate innovation ecosystems,” “digital platform ecosystems,” “innovation leaders,” “culture entrepreneurship,” “interacting predictors,” etc.

Theme of study during the year 2021.

Out of all themes, the most important themes that emerged were policy implication, frugal innovation (Frugal innovations encompass affordable new products, methods, and designs developed for or emerging from the underserved lower segment of the mass market, often referred to as the ‘bottom of the pyramid), and “innovative behavior,” which were heavily drawn from “digital” associated with terms such as “digitization,” “COVID-19”, “pandemic” etc., and “women entrepreneurship,” “women entrepreneurs,” “women economic empowerment,” “job losses,” and “COVID-19 impact”. These themes essentially and visibly emanated from the term COVID-19, which has been the most effective disruption witnessed in several centuries, sending shock waves and necessitating ‘totally out of the box,’ yet basic and indigenous thought processes and helping the creation of innovations outposts (Decreton et al., 2021 ). The COVID-19 crisis prompted impactful frugal innovations, particularly among jobless women, fostering widespread women’s entrepreneurship amid the digital revolution (Cullen & De Angelis, 2021 ). Digitalization facilitated startups as effective innovation brokers, connecting ecosystems, and promoting synergies. The “Waste Not” strategy contributed to resource-efficient production, circular entrepreneurship, and social purpose organizations. This global shift towards novel economic empowerment models, including priority action roadmaps for women, emerged in response to the pandemic’s impact, creating innovative approaches and strategies (Cullen & De Angelis, 2021 ).

Proposition: The unprecedented disruption caused by COVID-19 has catalyzed transformative innovations, particularly in frugal entrepreneurship driven by jobless individuals, notably women, harnessing digital revolution and waste reduction strategies, thereby fostering women’s entrepreneurship, circular economies, and social purpose organizations on a global scale .

Theme that emerged during the year 2022

The clusters that were accentuated in this timeframe (see Fig. 6 ) were: “biomedical entrepreneurship,” “sustainability,” “translational research,” “demand,” “databases,” “social innovator,” etc. among others, which had their origination from themes such as “digital entrepreneurship,” “digital entrepreneurial ecosystems,” “smart cities,” “circular business models,” “incremental innovation,” “Schumpeterian Entrepreneurship,” “social innovations’ systems,” “Isenberg’s Entrepreneurial Ecosystem Model” (international reference guide for collecting and using data on innovation), “Financial Technology (FinTech) Innovation,” “investment advisory sector,” “trans-disciplinary research,” and “cross cutting themes,” which got frequently referred to by authors in their articles.

Theme of study during the year 2022.

This time period saw the emergence of many “incremental innovations” adding to and revitalizing the existing ones in the wake of COVID-19 (Henrekson et al., 2022 ). To this end, every nation was endeavoring to get hold of resources and diverting them towards translational research, comprising academic entrepreneurial innovations and social innovations (Audretsch et al., 2022 ), culminating in biomedical research and entrepreneurship. Biomedical entrepreneurship was in its heyday as it was the most important aspect related to the major disruptor COVID-19 at the time. As a result, there was a mushrooming of startups catering to biomedical resources to fulfill the demand that was extant in almost all the markets of the world. In addition, the most prominent entrepreneurial success was witnessed in “digital entrepreneurial enterprises,” which rose quickly due to the widespread digitization of almost all of the world’s economies in the wake of COVID-19. This trend of enterprises surpassed all records of success and they skipped decades in their growth journey.

Proposition: The aftermath of COVID-19 witnessed a global pursuit of resources for translational research encompassing social innovations, fueling a surge in biomedical entrepreneurship and the rapid success of digital enterprises due to widespread digitization surpassing conventional growth timelines .

Starting with the first research question, which aimed to organize the thematic progress of innovation research in entrepreneurship, we applied a text-mining approach of content analysis on the six identified year groups. The results highlight that in recent years digitization and frugal innovations have acted as catalysts for novel business models, termed “Abrupt Circumstantial Business Handling Practices (ACBHP)”. These practices spurred by the COVID-19 pandemic include customized products, increased home deliveries, pop-up shops, and ventures, breaking traditional business norms. This led to the emergence of a “Minimalistic Business Model of Manufacturing” (MBMM), where businesses adapted with minimal resources based on market needs during the pandemic. Such disruptions created uncertainties but also introduced new entrepreneurial ecosystem dynamics. In light of this, we present the findings as follows:

Insight 1: Speciation of innovations and technologies

Innovations, technologies, and strategies are the major drivers of economic growth and development. “Speciation” is one such force and mechanism underlying the business thought process, policy formulation, and practices. It enables the factors and actors facilitating entrepreneurship and entrepreneurial ecosystems to perform business initiation and expansion (Ganzaroli et al., 2014 ), thereby giving rise to newer research factors concerning policy formulations, dynamic capabilities lying latent, and innovative networks. Speciation largely leads to the branching and advancing of technologies (Kantarelis, 2009 ), as was found in the case of the USA, wherein speciation drew attention to the concurrent policy restrictions on commercial and entrepreneurial freedoms. Thereafter, it was witnessed in the most recent case of disruptions during the COVID-19 pandemic, wherein “digitization” was the main source used by almost every new technology as mainstream, and several speciation methods, products, and strategies emanated from that. This magnetized the innovative network and the think pools to leverage assets and strategies at hand and bring out the necessary synergies, leading to required entrepreneurial and policy frameworks to assist in entrepreneurial advancements.

Insight 2: Global outreach of academic knowledge and innovations

The global outreach of entrepreneurship facilitates rapid innovation, leading to knowledge dispersion, inventions, and enhancement of manufacturing capabilities. Moreover, it helps in “nascent opportunity generation” and innovation networks for inventions, leading to augmentation and advancement of technologies. For example, Guerrero et al. ( 2016 ) delineated the soaring need for research in business and economy and further discussed the issue of individual growth and restriction on scientific and commercial freedoms. Collaborative and interactive research has further been facilitated by innovative value co-creation (Mehta et al. 2016 ), along with the extension of new management processes for the extension of processes beyond existing ecosystems. However, at the same time, it poses a concern for damage and serious harm like mishandling, and misuse of dangerous innovative products, which is why it is necessary to foresee and assist scientific and commercial freedoms (Hayter, 2016 ) with precautions that should be taken to prevent scientific inventions and innovations from harming society in general (Roundy, 2016 ).

Insight 3: Government assistance generating synergies for growth

Government assistance by funding innovations leads to better academic research and innovation-centric activities that generate synergies, impacting and enhancing innovative business ecosystems (Harper-Anderson, 2018 ). Even in developing countries, governments have come forward with schemes for payment for ecosystem services (PES), as done in Costa Rica, for biodiversity protection and conservation endeavors (Fischer et al., 2018 ). The heterogeneity among ventures is largely facilitated by knowledge spillovers and dispersion at the global level (Autio et al., 2018 ), corporate research development (Eckhardt et al., 2018 ), and the regional economic development policy agenda of the nations (Crammond et al., 2018 ), which takes up corporate research to bring about regional-level multidimensional economic systems. To further this process, the traditional “Triple Helix Innovation Model,” focusing on the university-industry-government relationship, and the “Quadruple Helix Innovation Systems” can be used to bring about the required synergies (Mirvis and Googins, 2018 ) and ensure success in business ecosystems based on collaboration and competition (Hu, Yu & Chia, 2018 ; Carayannis et al., 2018 ).

Insight 4: Regional transformation and platformization

Regional transformation through open and distributed models of innovation facilitates the pursuit of entrepreneurship. Regional transformation can be hailed as the “basic innovation driver,” disgorging newer approaches toward entrepreneurship (Igwe et al., 2020 ) and helping policymakers and practitioners (Guerrero et al., 2020 ). Moreover, regional transformation together with platformization creates a typology of different ecosystem structures, thereby shaping high-growth entrepreneurship. Furthermore, they help in exploring the dynamics of entrepreneurial ecosystems for rural and urban areas (Huggins and Thompson, 2020 ). To this end, many regions are following the “educate, deregulate, and finance” approach to entrepreneurship, as happened in the case of “Financial and Institutional Reforms for Entrepreneurial Society” in Europe (Lyons et al., 2020 ). Another example is the “Innovation Hub Organizations” in African cities, which have become “Fixtures” (Švarc et al., 2020 ). However, regional transformation is not possible without a proper policy (Jia & Desa, 2022 ) that works on key components and factors influencing entrepreneurial processes (Halbinger, 2020 ).

Insight 5: Management of collective risk for radically innovative products

The management of collective risk by social entrepreneurial ecosystems helps in strengthening institutional environmental and bridges uncertainties to radically innovative products (Khurana and Dutta, 2021 ). Investigating innovation drivers in the informal sector may scrutinize the impact of “complementors” within business owners’ strategies, navigating formal and informal rules (Gifford et al., 2021 ). Further, regional economic ecosystems, influenced by human behavior, culture, and environment, require the measurement and development of skills. Tools like “Entrepreneurship Skill-Building Framework (ESBF)” and “Readiness Inventory for Successful Entrepreneurship (RISE),” based on “communimetrics: theory of measurement,” are crucial (Nthubu, 2021 ). The European Smart Specialization Strategy (S3) reflects the latest entrepreneurial ecosystem developments (Khatami et al., 2022 ).

In addition, addressing systematic inequities involves social innovations and financial models like “blended financing” and “public-private partnerships” (PPP) (Volkmann et al., 2021 ). Other factors include affordable business models for resource settings (Guerrero et al., 2021 ), knowledge economy expansion (Plata et al. 2021 ), and new evaluative approaches to local entrepreneurial ecosystems (Liu et al., 2021 ). Innovation strategies by companies like Apple and Uber, financial technology ecosystem development (Canh et al., 2021 ), growth-oriented entrepreneurship in the African business environment (McDaniel et al., 2021 ), and risk mitigation through public-private ownership (Moraes et al. 2023 contribute to assessing and enhancing the global entrepreneurial climate, including the US (Schaeffer, Guerrero & Fischer, 2021 ).

Insight 6: Discovering latent entrepreneurship for emergent entrepreneurship

Empirical studies underscore the crucial role of entrepreneurial learning and experience in unlocking latent resources and hidden capabilities within social and economic ecosystems. A prime example is the transformative impact observed in the US drone industry (Henrekson et al., 2022 ). Innovative ecosystems, particularly those with a knowledge-intensive focus, foster emergent entrepreneurship, notably when returnee entrepreneurs contribute to local firms, enhancing innovation performance in their home countries (Bakry et al., 2022 ). The “discovery theory” further illuminates how digital applications stimulate entrepreneurial alertness, especially in diverse innovation ecosystems, such as the influence of creative industries on social entrepreneurship (Ho and Yoon, 2022 ). The success of new ventures hinges on navigating multifaceted components within entrepreneurship ecosystems (EE) and the broader business environment (Johnson et al., 2022 ).

To overcome these challenges, entrepreneurs strategically establish complex ecosystems, temporarily gaining monopolistic advantages by eliminating competition during the development phase (Raposo et al., 2022 ). Various factors shape entrepreneurial sustainable innovations (ESIs),” with distinct emphasis on policy, finance, human capital, support, and culture within entrepreneurial ecosystems (Berman et al., 2021 ). While creating new businesses is essential, the establishment of institutions supporting entrepreneurial growth is equally vital. Although “Schumpeterian entrepreneurs play a role, the limitations of “top-down policies” in fostering thriving ecosystems for Schumpeterian entrepreneurship are evident (Henrekson et al., 2022 ). Social entrepreneurship, guided by local actors and social innovators with insights into emerging needs, can lead to profit-oriented innovations (Audretsch et al., 2022 ; Bakry et al., 2022 ). Implementing these strategies demands entrepreneurial ecosystems equipped with tools that address the complex and dynamic aspects of development (Johnson et al., 2022 ; Schmutzler et al., 2022 ).

Model for enhanced entrepreneurial ecosystems

The systematic literature review conducted for the present study has yielded insights that can be utilized to enhance entrepreneurial ecosystems. These insights have been integrated into a model explaining the relationship between various decisive components crucial for achieving improved entrepreneurial ecosystems. The key insights of the model are outlined below.

First, the attainment of enhanced entrepreneurial ecosystems is influenced by several factors that interact and synergize, ultimately resulting in the creation of new ecosystems or the enhancement of existing ones. “Evolutionary Theories,” “Governmental Assistance,” “Global Outreach of Academic Innovations,” “Open and Distributed Models of Innovation,” “Entrepreneurial Learning Experience,” and “Social Entrepreneurship” are identified as decisive components in this research. Alongside underlying factors, these components promote and contribute to the enhancement of entrepreneurial ecosystems.

Figure 7 illustrates that entrepreneurial ecosystems develop unique synergies in all nations and economies in response to different types of economic disturbances arising from individual and collective uncertainties. Although there is a pattern and path with the highest probability of yielding better network creation and rapid development of entrepreneurial ecosystems, it is generally guided by the path of economic turmoil or uncertainty they face. Additionally, government policies play a significant role in influencing the creation, operation, and pace of the progress of entrepreneurial ecosystems. For instance, in countries such as South Korea, where entrepreneurs are provided with free or subsidized space for their ventures, there is a notable boost in entrepreneurial growth, leading to the creation of a higher-quality entrepreneurial ecosystem with better services and growth prospects.

Model for Enhanced Entrepreneurial Ecosystems (bidirectional arrow represents interaction between those factors; unidirectional arrow represents research related to innovation across different domains).

Government assistance and support are crucial components that contribute to the development of entrepreneurial ecosystems. Evolutionary theories from different fields serve as a repository of past initiatives that have proven successful, guiding and enlightening the thought processes of entrepreneurs. These theories often emerge as corrective responses to individual and collective uncertainties or as attempts to rectify anomalies in different ecosystems. Furthermore, government assistance, when integrated into academic research programs, fosters the creation of heterogeneous, innovative models that can be emulated by others. Support for research projects aids in the development of entrepreneurial ecosystem models aligned with market trends and economic turbulence, providing a foundation for theories and fostering entrepreneurial growth.

In addition, the global outreach of academic innovations plays a crucial role in disseminating these innovative models. Through concerted paths, it leads to the development of newer technologies and products. The open and distributed models involved in this process facilitate knowledge spillovers, permeating and transforming the urban and rural economies of nations. Subsequently, this transformative process initiates knowledge spillovers and the diffusion of technology across nations, ushering in uncharted methodologies for addressing challenges and seizing opportunities. This dynamic gives rise to creative industries, fostering a culture of continuous learning and adaptation essential for achieving business sustainability. The enrichment of entrepreneurial learning and experience is evident across diverse nations. Ultimately, this interconnected synergy propels actors and agents of change toward assuming collective responsibilities and championing the cause of social entrepreneurship for greater good and universal growth. The diverse trajectories of entrepreneurial growth invariably encompass these interconnected elements and sequential steps, underscoring the complexity and interdependence inherent in entrepreneurial growth.

Implications, limitations, and future trends

The following section provides implications and limitations.

Theoretical implications

The study underscores crucial theoretical implications, emphasizing that innovation not only introduces novel attributes to business culture but also gives rise to ecosystems capable of developing self-operative and self-corrective mechanisms in response to market disruptions. It asserts that innovation and entrepreneurial ecosystems play pivotal roles in implementing sustainable measures to invigorate global economic systems. An examination of the specified period reveals noteworthy themes that significantly contribute to existing knowledge in business and entrepreneurship. The onset of the pandemic triggered a transformative shift in entrepreneurial ecosystems, leading to “venture mushrooming” driven by dynamic factors (Castellani et al., 2022 ). The disruption prompted a strategic response from entrepreneurial think tanks, showcasing their adept management of unprecedented challenges and highlighting the resilience and adaptability of entrepreneurial ecosystems (Ramezani and Camarinha-Matos, 2020 ). Moreover, the disruptions unveiled opportunities and novel resources, particularly in the digital realm, fostering niche entrepreneurial ecosystems driven by individuals, especially women, responding to COVID-19-related challenges (Cullen & De Angelis, 2021 ). The evolution of these ventures highlighted the self-operative and self-corrective nature of entrepreneurial ecosystems, offering insights into the evolving dynamics of the business environment.

Given the unified global markets and increasing trade transactions, entrepreneurial innovations emerge as essential tools to counter challenges to the global economy. To establish effective progressive and corrective mechanisms for market disruptions, there is a pressing need for innovative speciation that addresses specific market needs and customer bases. Global outreach of innovations is crucial for swift knowledge dissemination, and governments should develop collaborative assistance mechanisms to foster growth. Regional transformation and platformization are equally vital for cultivating novel entrepreneurial tendencies among youth. Creating a catalytic environment requires managers to take initiative in dealing with collective uncertainties, fostering the creation of radically innovative products. Finally, to facilitate the process of creating entrepreneurial ecosystems, emphasis should be placed on recognizing emerging entrepreneurial tendencies at regional, national, and international levels through timely support—technical, economic, and moral—to budding entrepreneurs and “visionpreneurs”.

Practical implications

The study underscores critical policy implications by highlighting the role of entrepreneurial ecosystems in fostering and empowering aspiring entrepreneurs. However, it acknowledges the challenges posed by unprecedented changes, which may prove difficult to address. These situations, whether rooted in knowledge banks or not, often present formidable obstacles that cannot be easily overcome with existing skill sets. The study emphasizes the need for emergent entrepreneurs to draw on their previous exposures, urging them to boldly anticipate and explore future trends, particularly as technologies and skill sets evolve with increasingly shorter product life cycles.

Furthermore, the study advocates for close collaboration between governments and entrepreneurial faculties to mitigate negative economic downturns. Given the interconnected and inseparable nature of international trade indices, this research stresses the importance of collective action to prevent potential cascading effects that could lead to significant economic damage in a short period. The research contributes practical policy implications by proposing a model for entrepreneurial ecosystems with self-operative and self-corrective mechanisms. It suggests that governments support the strengths inherent in their ecosystems, providing subsidies, incentives for growth, and specialized incubation center facilities. These facilities, collaboratively developed with university research outcomes, aim to build new infrastructures for entrepreneurial development, ensuring both present and future entrepreneurial growth.

Limitations

This study presents a comprehensive review of collected papers utilizing text mining and content analysis to delve into the dynamics of entrepreneurial ecosystems. However, it acknowledges certain limitations that could impact the breadth and clarity of perspectives. The review focused exclusively on papers matching specific keywords like “Innovation,” “Business Entrepreneurship,” “Entrepreneurial Ecosystem,” and “Entrepreneurial Networks.” Notably, the exclusion of other keywords such as “academic entrepreneurship,” “spillover effects,” and “speciation” might have yielded different insights not covered in this analysis.

Additionally, the choice of the Scopus database as the sole source for article extraction poses another limitation, as utilizing different databases could have resulted in a diverse set of research articles, potentially altering the domain and theme structures. The study’s methodology is also recognized as a limiting factor, as alternative approaches could have produced varied results. Furthermore, the consideration of a nearly two-decade timeframe raises concerns about the relevance of earlier reviews in the rapidly evolving landscape of entrepreneurial needs and trends.

Despite these limitations, the paper makes a noteworthy contribution by providing a general outline and direction for the development of enhanced entrepreneurial ecosystems. It acknowledges the lack of first-hand exposure to entrepreneurial ecosystems, which could have enriched the output. Nevertheless, the study’s significant contribution lies in its comprehensive analysis of entrepreneurial ecosystems and their interplay, aiming for greater output generation, improved growth for the collective good, and the overall welfare of economies, beyond mere economic gains.

In conclusion, while innovations and entrepreneurial ecosystems have been extensively explored in research, a collaborative effort between governments and the intelligentsia is essential to reshape policies. Addressing the identified gap in the literature, the research emphasizes that entrepreneurial ecosystems are not confined to traditional business circles but have evolved through the ingenuity of individuals facing job losses or career shifts. Therefore, this paper aims to provide the thematic improvement that happens in literature and based on that, present the enhanced entrepreneurial ecosystems. This study’s result indicates the necessity of global outreach for swift knowledge dissemination and emphasizes collaborative efforts between governments and entrepreneurial entities to foster growth. Regional transformation and platformization are identified as pivotal in nurturing novel entrepreneurial tendencies, particularly among youth.

This study elucidates critical theoretical implications, highlighting the transformative power of innovation in shaping not only novel attributes within business culture but also the creation of adaptive entrepreneurial ecosystems. The study underscores the need for proactive policymaking and infrastructure support to empower these ecosystems to navigate the evolving landscape. The collaboration between governments and the intelligentsia is highlighted as crucial for ensuring that entrepreneurial ventures thrive and contribute significantly to the broader economic context. This integrated approach aligns policy measures with the dynamic needs of entrepreneurial ecosystems, fostering resilience, adaptability, and sustained success in the face of emerging global challenges. In essence, this research not only contributes to the existing knowledge but also fills a crucial gap by shedding light on the dynamic nature of entrepreneurial ecosystems in the face of unprecedented challenges, providing valuable insights for future research and practical applications.

Data availability

All data generated or analyzed during this study are included in this published article.

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Rishi Kant Kumar, Rana Singh & Kumod Kumar

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Rishi and Srinivas designed the study, performed data analysis, and wrote the manuscript. Ronnie contributed to data collection, interpretation of results. Rana and Sachi contributed to the interpretation of the data and critical revision of the manuscript. Kumod assisted in data acquisition and manuscript preparation. Prashant provided critical feedback, improvised network diagram and model, and revised the manuscript for intellectual content. All authors have read and approved the final version of the manuscript. All contributed authors have been listed in this article.

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Kumar, R.K., Pasumarti, S.S., Figueiredo, R.J. et al. Innovation dynamics within the entrepreneurial ecosystem: a content analysis-based literature review. Humanit Soc Sci Commun 11 , 366 (2024). https://doi.org/10.1057/s41599-024-02817-9

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Innovation and international business: A systematic literature review

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Introduction

Innovation and international business are essential to achieve competitive advantages in currently unforeseen business environments. Today's company seeks innovation in its country of origin and abroad in order to compete globally. Thus, incorporating this concept into international companies' strategies is a main issue nowadays.

The aim of this systematic study is to improve the current knowledge on the relationship between innovation and international business, as well as identifying innovation tendencies for corporations to acknowledge the opportunities and challenges of this area's development in the international business context.

Methodology

Despite the abundance of innovation and international business reviews, joint reviews of both of them cannot be found. This study is the first to combine the scholarly research on both topics with the systematic literature review of academic literature of 28 years, following the PRISMA guidelines and flowchart. A search was carried out in Web of Science database; 847 initial documents were obtained and, after reviewing multiple documents according to the inclusion/exclusion criteria, the results for this research work were reduced to 236 articles.

The results of this research provide an overview of the knowledge structure of innovation and international business. As the main contribution, the results highlight four themes of investigation within a comprehensive and multidimensional framework: Innovative activities of multinational corporations, Global value chains, Innovation in emerging economies, and Cross-border knowledge. With an international perspective, insights from how to face innovation development in the international business context are presented.

Conclusions

There is a strong relationship between innovation and international business. These four research trends highlight the strategic importance of innovation in the international business field. Finally, the most interesting paths for future research are identified, targeting opportunities for improvement in both areas. This systematic literature review is expected to make significant contributions to both theory and practice in the field of innovation and international business.

1. Introduction

Innovation, knowledge and technology are relevant concepts in the international business field [ 1 ] and both areas are essential to achieve competitive advantages in current business environments [ 2 ]. Cantwell [ 3 ] adds that innovation and internationalization processes have been increasingly interlinked as key drivers of development since the first industrial revolution, all the way to today's information age.

Innovation is the cornerstone of growth and sustains organizations to counter marketplace fluctuations and prepares them for long-term growth [ 4 ]. Business model innovation, although it is very difficult to achieve [ 5 ], can itself be a pathway to competitive advantage if the model is sufficiently differentiated and hard to replicate for incumbents and new entrants alike [ 6 ]. New models of innovation have encouraged many innovative firms to change the way they search for new ideas, adopting open search strategies that involve the use of a wide range of external actors and sources to help them achieve innovation [ 7 ].

The field of international business studies came to prominence in the 1980s and early 1990s with the growth of multinational corporations [ 8 ]. In this framework, two theoretical models are extremely relevant; on the one hand, gradual internationalization or Uppsala model, according to which internationalization is seen as an incremental process that begins in foreign markets, being in closer proximity to the domestic market in terms of physical distance [ 9 , 10 ] and, on the other hand, born global firms, that is to say, companies that internationalise at or near their founding, on an average period of three years of founding, generating at least twenty five percent of their total sales from foreign countries [ 11 ]. There is an extensive literature about this issue [ [12] , [13] , [14] ].

The systematic literature review on the topic of innovation has been a frequent research method over the last ten years. Different topics have been raised and discussed: organizational innovation [ 15 ], innovation capability in SMEs [ 16 ], or digital innovation in knowledge management systems [ 17 ] among others. In the same way, systematic literature review about international business is copious, holding various topics of investigation: knowledge flows in multinational corporations [ 18 ], business systems theory [ 19 ], social network [ 20 ], or culture [ 21 , 22 ].

Despite the abundance of innovation and international business reviews, joint reviews of both of them cannot be found, such review being needed since the emerging phenomena that are changing international business' frontiers require a change, as well, in the threshold of international business’ innovation [ 23 ]. From an economic point of view, Melitz [ 24 ] shows how high exposure to trade will induce only the most productive companies to enter the international markets; in addition, internationalization has a more positive effect on innovation in high productivity companies [ 25 ]. Likewise, the number of international relations increases the capacity to innovate [ 26 ] and exposure to foreign competition is associated with greater firm innovation [ 27 ]. Companies thus grow either through innovation or through internationalization (also through a mixed strategy), with a combination of internationalization and innovation being the most advisable option when domestic markets are limited [ 28 ].

International innovativeness is a significant dimension of international business competence [ 29 ], as a nuanced understanding of innovation and international business necessitates a multidisciplinary approach to reveal their multifaceted aspects [ 30 ]. Having this in mind, the aim of this study is to improve the current knowledge on the relationship between innovation and international business, that is to say, to identify trends for companies to know better the opportunities and challenges of innovation development in the international business context. Thus, the main innovative contribution of this systematic approach is the proposal of four themes of investigation within a comprehensive and multidimensional framework: Innovative activities of multinational corporations, Global value chains, Innovation in emerging economies, and Cross-border knowledge. This systematic review is motivated by the knowledge gap found in this issue since future research could fill that knowledge gap in the international business field through the development of new theoretical frameworks that draw on various disciplines [ 31 ]. On this wise, international business literature tends to develop a plurality of approaches that makes difficult to find single recurring or dominant forms [ 32 ].

Considering the ideas presented above and creating an intersection between both the literature of innovation and that of international business, this research provides several contributions to both areas. This is done with a methodologically systematic review of academic literature on both topics. A systematic review has been increasingly adopted in the management literature and is guided by a review question that defines the topics used for the database [ 33 ]. Following a previous theoretical study, two research questions were defined.

What themes about innovation and international business have been studied jointly to date?

Which themes about both areas require further research?

The rest of the paper is structured as follows: the section “Research method” presents the methodology and both the data search and selection used for the study; meanwhile, the section “Research results” presents the main results derived from that research, being categorized in those four themes of investigation posed earlier; the section “Discussion” also presents “Limitations” and “Recommendations for future research”, as further studies are needed to provide greater insight about this new approach; finally, the section “Conclusions” has also been included.

2. Research method

This paper presents the results of a systematic review of innovation in international business. Knowledge production within the field of business research is accelerating while at the same time remaining fragmented and interdisciplinary. This makes difficult to assess the global evidence in a specific area of business research, being the reason why literature review as a research method is more important than ever [ 34 ]. An advantage of the systematic review methodology is the generalizability of the results by allowing the accumulated knowledge in the field to be systematically synthesized and analysed [ 35 ]. A systematic review should have a list of specific steps to assure that important studies regarding the topic are acquired without any bias. Overall, following Tranfield et al. [ 36 ], (1) Identification for the need for a review, (2) Selecting a sample of potentially relevant works and the pertinent literature, (3) Data synthesis, (4) Reporting the results and recommendations. The cited steps are all followed in this study. Specifically, the preferred reporting items for systematic reviews and meta-analysis, commonly called PRISMA [ 37 ], were followed in this study.

In this analysis, a systematic search was carried out in Web of Science of Clarivate Analytics, including documents published from January 01, 1993 to December 31, 2020. Birkle et al. [ 38 ] claim that Web of Science is the world's oldest, most used and reliable database of research publications (around 34000 journals today). The query used in this systematic literature review is as it follows: TS = (“Innovation” AND “International Business”). Consequently, TS = Topic; and the search terms “innovation” and “international business” were combined with the Boolean Operator “AND”. Therefore, literature search was based on two simultaneous topics, “innovation” and “international business” (as stated in the title of the publication, the abstract, the author keywords and/or the keywords plus). Each document has been published between 1993 and 2020, since the first contribution was published in 1993. A total of 847 related documents were found. Thus, it can be affirmed that the importance and size of the literature on innovation and international business is more relevant.

Different criteria for inclusion and exclusion are considered in this study. The process is described through the PRISMA flowchart ( Fig. 1 ). Excluding meetings, books, review articles, editorial materials, and others, the reviewing process generated 630 articles. Only articles published in Business Economics have been considered due to its prominent research area regarding the topics of this paper; bearing this in mind, the number of the articles was reduced to 291. Each article is written in English language following Tenzer et al. [ 39 ], stating that 75% of articles in the social sciences are written in this language; therefore, the number of articles was delimited to 264. At last, each article was read in its entirety and various articles were excluded based on full-text or abstract due to their irrelevant nature to the research; thereby, the final sample has been finally reduced to 236 articles. Other criteria, as open access or funding agencies, have not been considered in this research work.

PRISMA flowchart of the systematic literature review.

One by one, data of each article were extracted, transferred, and sorted into Microsoft Excel spreadsheet for further analysis. Therefore, a wide Excel database was created with specific information for each article: journal, title, authors' keywords, keywords plus, year of publication, and author information (number, name, affiliation, and country). Specifically, the author keywords and the keywords plus have been studied. In total, 3064 keywords (authors’ keywords and keywords plus) were counted, analysed and categorized; this analysis has allowed to obtain four themes of investigation. Subsequently, in Web of Science, these research trends have been confirmed with the latest studies published from January 01, 2021 to November 03, 2022.

3. Research results

3.1. general results.

Fig. 2 shows the number of articles on innovation and international business published each year over time. 236 articles were published during the study period 1993–2020, with no consistent trend of the number of articles published, that is to say, with some ups and downs. 80% of the total articles reviewed (189) were published during the last ten years (2011–2020). The largest number of articles was published during 2020 (43 articles), followed by 2018 (31 articles), 2015 and 2019 (both with 21 articles), and finally 2017 (15 articles). There was a lack of publications during the following years: 1994, 1995, 1998, and 2001. Qualitative, quantitative and mixed methods are included in these articles (for instance, case study, literature review, bibliometric study, or regression analysis, among others).

Number of articles published each year.

The first article was published in the Strategic Management Journal by Hagedoorn [ 40 ]; this article presents interfirm strategic alliances in the international business area and their relationship with innovative efforts. The second article was published in the Journal of International Business Studies by Buckley and Casson [ 41 ], regarding international joint ventures in terms of the accelerating pace of technological innovation.

Number of authors per article is presented in Table 1 . The number of authors ranged from 1 to 9, with a predominance of two (35.59%) and three (30.93%) authors per article, that is to say, over 65% of the articles published. Besides, the mean of authors per article is considered to be 2.44.

Number of authors per article.

Following the previously mentioned line of thought, Table 2 shows the most relevant journals for research topic. The major journals included in this study are Journal of International Business Studies (48 articles; 20.34%), International Business Review (32 articles; 13.56%), International Marketing Review (14 articles; 5.93%), Management International Review (12 articles; 5.08%), Journal of International Management (8 articles; 3.39%), and Multinational Business Review (8 articles; 3.39%). These are believed to be the top six academic journals and represent more than a half (52%) of the total scientific production.

Main authors.

Principal journals.

Eight journals have published at least three articles (Thunderbird International Business Review, Journal of World Business, Entrepreneurial Business and Economics Review, Organization Studies, Industry and Innovation, Competitiveness Review: An International Business Journal, Journal of Business Research, and Management and Organization Review). Eleven journals have published two articles, and finally fifty-seven journals have published only one article. Table 2 also indicates that this study is related to other topics (for instance, strategy, human resources, or entrepreneurship, among others). 1

Fig. 3 represents the most productive universities. It is important to mention that in this study the first university of each author has been selected, rejecting other additional institutions according to the author's institutional information. Thus, the first institution is the University of Reading with a total of eight articles published, followed by the University of Manchester, University of Leeds, and Uppsala University (each one with six articles); after them, the Duke University, the Rutgers University, and the Temple University (each one with five articles). Twenty-nine universities are included in 113 articles.

Most productive universities.

Finally, Fig. 4 shows the distribution of the research topic according to the countries of origin of the authors. Overall, the United States of America (frequency in articles = 80; 33.90%), England (52; 22.03%), China (24; 10.17%), Australia (21; 8.90%), Canada (13; 5.51%), and Italy (12; 5.08%) are the most prominent countries. Twenty-three countries were included in the sample. A country is considered part of this figure if it appears in at least three articles.

Most prominent countries.

3.2. Thematic analysis

The current state of the literature is characterized by its complexity and fragmentation. As previously reported, 3064 keywords (authors’ keywords and keywords plus) were counted, analysed and categorized. Some of the most prominent keywords, with minimum occurrences of ten, are showed in Table 4 ; the semantic difference between singular and plural forms is not considered. Obviously, “innovation” and “international business” are the most relevant concepts among the ones displayed. These two keywords together, along with the other five keywords, influence four categories. Subsequently, “multinational corporation” and “knowledge” are the most common keywords in this research work.

Themes of investigation identified.

Thus, this section synthesizes the results of this study. Four themes of investigation about the relationship between innovation and international business are suggested: Innovative activities of multinational corporations, Global value chains, Innovation in emerging economies, and Cross-border knowledge. This thematic analysis allows to answer the RQ1 posed earlier, that is to say: “What themes about innovation and international business have been studied jointly to date?” Consequently, it provides the occasion to have a more complete understanding of the research topic within a comprehensive and multidimensional framework.

3.2.1. Innovative activities of multinational corporations

A classic topic in international business research is the multinational corporation. The widest theme of investigation studies the relationship between innovation and the multinational corporation because, as Venaik et al. [ 42 ] assert, the international business literature has placed ever-greater emphasis on the role that learning and innovation play in determining multinational corporation performance.

It is common to conceive multinational corporations as a set of geographically disseminated subsidiaries that are combinations of heterogeneous technology competencies and product market responsibilities [ 43 ] or, in previous years, as firms that control and manage production establishments located in at least two countries [ 44 ]. Competitive success hinges on a multinational corporation's ability to use effectively available knowledge, and to combine it with knowledge from other locations [ 45 ]. Therefore, by interacting with locations multinational corporations have the possibility to organize their activities for balancing the exploitation of their current knowledge base and that of the new knowledge bases [ 46 ].

Knowledge that is complex to measure [ 47 ] has been recognized as critical for subsidiaries' power [ 48 ] and its transfer is driven by subunits’ motivation: subunits whose activities are mostly complementary have a natural motivation to collaborate and to ensure that the transferred knowledge is adopted, while subunits with surrogate activity relationships are less motivated [ 49 ]; consequently Miller et al. [ 50 ] assert that the use of distant knowledge contributes to innovation.

The location choice for R&D subsidiaries has been a topic of interest for researchers [ 51 ] since national subsidiaries carry out different tasks in the distinct processes of creation and innovation in multinational corporations [ 52 , 53 ]. Moreover, according to Frost et al. [ 54 ], the formation of centres of excellence in foreign subsidiaries of multinational corporations is shaped by the conditions of the subsidiary's local environment, the fundamental role played by parent firm investment as well as the role of internal and external organizations in the development of subsidiary capabilities. As Castellani et al. [ 55 ] argue, multinational corporations have organizational and technical competencies that enable them to transfer knowledge within their internal networks at a relatively low cost, so that geographic distance has a relatively low impact on international R&D investments. Nevertheless, the within-country cultural distance between subsidiaries influences headquarters to transfer projects between those same subsidiaries [ 56 ].

Therefore, the degree to which the business model links to local idiosyncrasies, local knowledge, or local innovation, impacts on the business model-related specific advantages [ 57 ]. In this context, Cantwell [ 58 ] claims that changes in the environment for international business activities have facilitated more open networked formations. Such cooperation will often take place within the field of the multinational corporation orchestrated innovation network which may include open innovation activity [ 59 ]. Prashantham and Birkinshaw [ 60 ] add that multinational corporations can cooperate with small and medium sized-enterprises as a specific type of host country business stakeholder, although the extent to that cooperation being relatively weak or not is a result of the compatibility between the intents of these disparate sets of firms.

3.2.2. Global value chains

In recent years, firms have been increasingly implementing strategies to take advantage of the comparative advantages of locations. This results in a wider geographic dispersion of firms' activities, with direct implications for creative industries’ global value [ 61 ]. Buckley [ 62 ] proposes the concept of global factory as a structure through which multinational corporations integrate their global strategies through a combination of innovation, distribution and production of goods and services. The increasing international fragmentation of economic activity gives rise to the global value chain research stream, a conceptual approach that deals with managing disaggregated and geographically dispersed value chains of multinational corporations (see, for instance, Refs. [ [63] , [64] , [65] , [66] ]).

The global value chain concept recognizes that such value-creating chains were not restricted solely to commodities but could also be extended across manufacturing and indeed to services [ 67 ]. Large multinational corporations operate in global innovation systems that are highly complex and so interdependent that the sources of new knowledge creation are hard to pinpoint [ 68 ].

Traditionally, the cluster approach emphasizes horizontal links between firms and local organizations [ 69 ] and from a network perspective, the cluster is a mechanism for the share of knowledge and learning [ 70 ] and also to innovate [ 71 ]. Nevertheless, the global value chain is strongly related with the analysis of clusters in the globalization era [ [72] , [73] , [74] , [75] ] because the need to integrate the global industry and local cluster levels is a basic one [ 64 ]. As Carloni [ 76 ] develops, clusters are supporters and accelerators of internationalization processes. Moreover, the existence of strong local innovation systems tends to be a prerequisite to guarantee sustained learning through global value chain participation [ 77 ].

Overall, participation in the global value chain is positively related to the innovation result of a country, which suggests that the international fragmentation of production may be a channel that allows international technology transfer from developed to developing countries [ 78 ]. Integration in the global value chain is perceived as a fundamental way for companies in developing countries to access knowledge to innovate [ 79 ] and to access larger markets and new technologies too [ 80 ].

3.2.3. Innovation in emerging economies

Emerging economies are low-income, rapid-growth countries using economic liberalization as their primary engine of growth [ 81 ]. Since the end of the 20th century, emerging economies, or what is the same, emerging markets, constitute the major growth opportunity in the world economic order and their potential has created a shift in multinational corporations [ 82 ] seeking to do business in emerging economies with manifold stakeholders benefited [ 83 ], although characteristics of internationalization of emerging market multinational enterprises investment simultaneously have positive and negative development consequences in their home countries [ 84 ]. In any case, multinational corporation subsidiaries and local institutions can help emerging market stakeholders, as suppliers [ 85 ].

Innovation is an important driver of economic growth in emerging economies [ 86 ]. In this context, for instance, innovation has helped develop solutions for consumers at the bottom of the pyramid [ 87 ] and middle-class consumers [ 88 ]. Emerging economies have certain characteristics, such as immature capital markets, lack of resources for innovation, and poor legal framework to protect property rights [ 89 ], that make the innovation process different from developed countries, despite having recently begun to innovate at a rapid rate, regarding the challenges they face [ 90 ]. Moreover, developing country difficulties can foster innovation capabilities and international competition [ 91 ] since in emerging markets a company gains advantage by learning to improvise with scarce resources and, in the process, to become more innovative than its competition [ 92 ]. Summing up, as Anand et al. [ 93 ] claim, innovation to and from emerging economies is a systemic outcome of an entire innovation milieu and both firms and countries are heterogeneous, following each one an idiosyncratic path in its evolution.

3.2.4. Cross-border knowledge

International business and cross-border flows of trade and investment significantly impact on the economic growth, employment and innovation potential of countries [ 64 ]. In addition, the rapid reshaping of the global economic order requires fundamental changes in international business; as a result, innovation networks will require novel reconfigurations [ 94 , 95 ]. Thus, Cantwell [ 3 ] remembers that there has been an increasing awareness of the importance of absorptive capacity on the part of firms [ 96 ]. In this same line, Contreras et al. [ 97 ] show that companies should have an organizational climate that allows them to acquire and transform knowledge in order to increase their innovativeness and be more competitive in a globalized world. Having this in mind, firms and locations co-evolve with one another, and it is possible to appreciate the rise of knowledge connectivity in innovation systems, that is a new underlying reality of the international business field [ 98 ]. The dynamics of place, space and organization continually generate new domains within which knowledge is leveraged in unique ways [ 99 ]. Knowledge circulates through two types of networks; on the one hand, organization-based linkages, or in other words, pipelines [ 100 ] and, on the other, personal relationships [ 73 ]. Accordingly, digital platforms and ecosystems are a major venue for innovation and have considerable implications for international business [ 101 , 102 ].

Multinational enterprises and subnational governments have increased their level of cooperative activity and create the basis for sustainable economic growth [ 103 ]. Discontinuities between nation-states and spatial heterogeneity within national boundaries are a relevant part of international business with different reasons, such as the historical role of national borders, the magnitude of national governments in international trade, the importance of national institutions in the formulation of business strategy and the decision making and the availability of data [ 104 ]. Hence, it is necessary to refer to the concept of global cities, or in other words, the centres of political power, corporate decision-making, knowledge generation and the exchange and movements of human capital and ideas [ 105 ]. Goerzen et al. [ 106 ] argue that distinctive characteristics of global cities (global interconnectedness, cosmopolitanism, and abundance of advanced production services) help multinational corporations to overcome the costs of doing business abroad. Nevertheless, global cities are not always necessarily the key locations for future multinational investments since knowledge and technology as well as the connection with the capabilities and company goals are crucial [ 107 ]. In any case, as Van Burg et al. [ 108 ] remember, organizational actors’ decisions about interorganizational knowledge transfer might change over time because unforeseen events can prompt actors to quite radically reframe future developments as opportunities or threats.

4. Discussion

This paper performs a systematic literature review of the relationship between innovation and international business. Particularly, using a qualitative/interpretative methodology, evolutionary trends have been recognized and are described in detail. 236 articles were published during the depicted study period 1993–2020. The largest number of articles was published during 2020, highlighting the topicality of the subject. Moreover, to enrich this section, the most recent works are included. The results of this research provide an overview of the knowledge structure of innovation and international business. The results highlight four themes of investigation within a comprehensive and multidimensional framework: Innovative activities of multinational corporations, Global value chains, Innovation in emerging economies, and Cross-border knowledge.

About the first theme, this systematic literature review shows that the multinational corporation is the most important kind of firm for innovation development in the international scenery. Thus, the location choice for its subsidiaries and its local environment is basic for the innovations' development [ 51 , 52 ] and also for the geographies of innovation [ 109 ]. This agrees with a very current line of research, which shows that the growing tendency of local technological innovation of multinational corporations, together with the increasing relevance of subsidiaries, are promoting subsidiaries' engagement in conducting innovation activities [ 110 ]. In the same way, managers’ characteristics, such as prior multinational corporations work experience and industry experience, affect subsidiary innovation [ 111 ].

As previously reported, in the field of international business two models are basic; however, although the Uppsala model keeps capturing the interest of scholars and is still one of the most cited frameworks in this area [ 112 ], it is confirmed that the born global firms’ phenomenon is very present in current literature. As Hennart et al. [ 113 ] remember, born global firms make large foreign sales at birth or shortly afterwards since they own valuable resources (for instance, advanced technologies and a high international orientation), and specific internationalization strategies (such as networks). Given the nature of this study, it should be specified that innovation plays a relevant role in the creation of born global firms [ 114 , 115 ]; moreover, born global firms contend with environmental dynamism in global markets, compelling these companies to enhance their innovation capabilities [ 116 ].

The second theme reveals a new conceptual approach, the global value chains as referred in the relationship between multinational corporations and global innovation systems [ 64 ] in view of the fact that nowadays international lead firms integrate their geographically dispersed partners, specialized suppliers, and customers in these global value chains or global production networks [ 117 ]. This new conceptual approach is confirmed due to the necessity of the adoption of a more holistic view of global value chains since this action will provide a clearer picture of how the organization and outcomes of innovative activities have evolved in this specific context [ 118 ].

About the third theme, this study supports the forecasts that indicate that emerging economies will have more economic power in the upcoming years. Therefore, this stated theme emphasizes, in the context of innovation, the opportunities for multinational corporations, local institutions, and stakeholders [ 85 ]. In any case, legal systems in emerging countries must be strengthened by harder competition laws that encourage the kind of competition that is based on innovation [ 119 ]; moreover, emerging market multinational enterprises have consolidated their global presence recently, challenging international business’ theories [ 120 ]; consequently, strengths and weaknesses are more pronounced when firms face competitors from emerging markets [ 121 ].

Finally, the fourth theme is Cross-border knowledge, which, in the internationalization context, impacts on the potential innovation of the countries [ 64 ], requiring relevant changes in their innovation networks [ 95 ]. In this sense, interaction and communication among their different intra-organizational networks facilitate multinational corporation knowledge transfer [ 122 ], whereas born global firms exploit different types of knowledge and networks to develop international opportunities [ 123 ]. Consequently, Freixanet and Churakova [ 124 ] point out to a reduction in the transaction costs as companies gain internationalization knowledge. Hence, internationalized companies devote substantial efforts to deploying and maintaining digital platforms, which plays an increasingly important role in today's digitally connected world [ 125 ].

4.1. Limitations

This study is supported by official data from 236 articles located in the Web of Science database and it has followed a rigorous research methodology. However, the scope of this kind of study always tends to involve some limitations. Firstly, other databases (Scopus or Google Scholar, for instance) have been excluded from the search. Secondly, while keywords (as innovation and international business) have been selected to cover the chosen areas as completely as possible, it could still be possible that important contributions were missed. Furthermore, certain sources of information have been not targeted (meetings, review articles, books, and editorial materials, among others). It must also be taken into consideration that the only considered area of research has been the Business Economics area (other ones, such as Computer Science or Engineering were not considered relevant enough due to the topic of the current study). In order to finish, only English language publications have been included in this analysis (thus, other languages like Spanish, Korean, or Russian were eliminated). Overall, as Greenhalgh et al. [ 126 ] defend, the literature is complex and the approach is somewhat unconventional, leading to other researchers to inevitably identify a different set of primary sources; being this an inherent characteristic of any systematic review.

4.2. Recommendations for future research

This study has strong implications for researchers. Bearing in mind the results of this investigation, it is recommended to expand the scope of this specific study to related innovation and international business topics targeting opportunities for improvement in both areas. There are multiple themes that require further research. Thus, future studies should deepen into the relationship between those specific topics, although this subject could not be an easy one. As previously stated, to enrich this section, the most recent works have been included. The implications of this study allow to answer the RQ2 question posed earlier, that is to say: “Which themes about both areas require further research?”

For instance, the global value chain requires to be fully studied in order to address the participation of multinational corporations in an understandable way holding a comparative environment between countries [ 127 ] since, as Buciuni and Pisano [ 128 ] confirm, there are a plurality of global value chain structures and a variety of innovation models within the global value chain. Similarly, the international business literature has given little attention to the comparison between the performance of advanced economy multinational corporations and the emerging market multinational corporations acting in international markets [ 129 ]. In the same way, in recent years open innovation has become a basic paradigm regarding the specific literature about innovation. However, throughout this research only one article [ 130 ] discusses the relationship between this topic and multinational corporations. In addition, a lack of specificity has been observed in the current literature regarding the role of social innovation in multinational corporation since only one recent article of this study [ 131 ] suggests to ask why and how this companies engage in social innovations. Likewise, although corporate social responsibility is a relevant factor in multinational corporation's competitiveness, only one depicted article [ 132 ] studies the development of corporate social responsibility in international business.

Furthermore, the impact of the Covid-19 pandemic on international business has barely been discussed; leading to only one article in this study addressing this question [ 133 ]. The Covid-19 pandemic is an external shock that has disrupted the foundations of our everyday life [ 134 ], not only by changing the structure of the world economy, but also by leading to lasting impacts on the international business strategies of multinational corporations [ 135 ]. This pandemic encourages multinational firms to diversify their supply chains in order to retain innovation opportunities [ 136 ] due to the uncertainty of some innovations during that time [ 137 ]. Hence, it is necessary to shed light on the long-term impacts of the Covid-19 pandemic on international business, although the true effects on multinational corporations and global value chains can only be judged over time [ 138 ]. In any case, analysing the impact of this virus in the new context of the internationalized companies becomes a necessity.

Future studies could offer more evidence about different topics in the internationalization area (for instance, innovation network adaptation, economic performance, social media, stakeholders, and corruption). Also, the importance of human resource management in the international business context should be reviewed in the near future [ 139 ]. In the same way, the majority of the studies have examined outward firm internationalization; however the phenomenon of inbound internationalization is limited and, as Bianchi and Stoian [ 140 ] add, innovation drives that inbound internationalization.

On this basis, international business scholars may contribute to addressing these knowledge gaps through research and lecturing. Therefore, this study identifies a set of analysis challenges that can be used as a research agenda for the international business research community. To conclude the present section, final remarks for practitioners are advised since the results of this work could also be useful to CEOs and managers of multinational corporations, and overall, international entrepreneurs; particularly those working in the innovation field (management, processes, or networks, among others). From a managerial viewpoint, these practitioners have encountered difficulties to align their different strategies many times. Thus, these results provide guidance to practitioners that adjust their innovation strategies along with their international business strategies in a complex competitive environment. Likewise, this paper allows a better comprehension of the dynamic reality and identifies challenges that can be used and employed by decision-makers when dealing with the unforeseen internationalization process.

5. Conclusions

Nowadays, companies seek innovation in their countries of origin and abroad in order to compete globally. Therefore, innovation is a key factor for entering into international markets. This systematic literature review shows that there is indeed a strong relationship between innovation and international business; four themes of investigation within a comprehensive and multidimensional framework are found: Innovative activities of multinational corporations, Global value chains, Innovation in emerging economies, and Cross-border knowledge. These four research trends highlight the strategic importance of innovation in international business. Nevertheless, even when the number of articles addressing such topics is growing, this research work underlines that there is still a great opportunity for studying the relationship between those concepts. Thus, incorporating innovation into internationalized companies’ strategies is a main issue in current times, even more considering the changing and challenging world that we live in.

Author contribution statement

All authors listed have significantly contributed to the development and the writing of this article.

Funding statement

This research did not receive any specific grant from funding agencies in the public, private, or not-for-profit sectors. The Open access publishing of the article was funded by University of Malaga.

Data availability statement

Declaration of interest’s statement.

The authors declare no competing interests.

1 The 236 articles were written by 575 authors. Table 3 shows the most relevant authors to the research topic. Authors such as Knight, Gary A.; Buckley, Peter J.; Cantwell, John; Coviello, Nicole; Kim, Daekwan; Kolk, Ans; Lewin, Arie Y.; Luo, Yadong; Massini, Silvia; Mudambi, Ram; Peeters, Carine; and Tippmann, Esther, have each one three or more publications during the 1993–2020 period. Furthermore, thirty-nine authors contributed to at least two articles each one.

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Original research article, impact of industrial policy on urban green innovation: empirical evidence of china’s national high-tech zones based on double machine learning.

• College of Economics and Management, Taiyuan University of Technology, Taiyuan, China

Effective industrial policies need to be implemented, particularly aligning with environmental protection goals to drive the high-quality growth of China’s economy in the new era. Setting up national high-tech zones falls under the purview of both regional and industrial policies. Using panel data from 163 prefecture-level cities in China from 2007 to 2019, this paper empirically analyzes the impact of national high-tech zones on the level of urban green innovation and its underlying mechanisms. It utilizes the national high-tech zones as a quasi-natural experiment and employs a double machine learning model. The study findings reveal that the policy for national high-tech zones greatly enhances urban green innovation. This conclusion remains consistent even after adjusting the measurement method, empirical samples, and controlling for other policy interferences. The findings from the heterogeneity analysis reveal that the impact of the national high-tech zone policy on green innovation exhibits significant regional heterogeneity, with a particularly significant effect in the central and western regions. Among cities, there is a notable push for green innovation levels in second-tier, third-tier, and fourth-tier cities. The moderating effect results indicate that, at the current stage of development, transportation infrastructure primarily exerts a negative moderating effect on how the national high-tech zone policy impacts the level of urban green innovation. This research provides robust empirical evidence for informing the optimization of the industrial policy of China and the establishment of a future ecological civilization system.

1 Introduction

The Chinese economy currently focuses on high-quality development rather than quick growth. The traditional demographic and resource advantages gradually diminish, making the earlier crude development model reliant on excessive resource input and consumption unsustainable. Simultaneously, resource impoverishment, environmental pollution, and carbon emissions are growing more severe ( Wang F. et al., 2022 ). Consequently, pursuing a mutually beneficial equilibrium between the economy and the environment has emerged as a critical concern in China’s economic growth. Green innovation, the integration of innovation with sustainability development ideas, is progressively gaining significance within the framework of reshaping China’s economic development strategy and addressing the challenges associated with resource and environmental limitations. In light of the present circumstances, and with the objectives outlined in the “3060 Plan” for carbon peak and carbon neutral, the pursuit of a green and innovative development trajectory, emphasizing heightened innovation alongside environ-mental preservation, has emerged as a pivotal concern within the context of China’s contemporary economic progress.

Industrial policy is pivotal in government intervention within market-driven resource allocation and correcting structural disparities. The government orchestrates this initiative to bolster industrial expansion and operational effectiveness. In contrast to Western industrial policies, those in China are predominantly crafted within the administrative framework and promulgated through administrative regulations. Over an extended period, numerous industrial policies have been devised in response to regional disparities in industrial development. These policies aim to identify new growth opportunities in diverse regions, focusing on optimizing and upgrading industrial structures. These strategies have been implemented at various administrative levels, from the central government to local authorities ( Sun and Sun, 2015 ). As a distinctive regional economic policy in China, the national high-tech zone represents one of the foremost supportive measures a city can acquire at the national level. Its crucial role involves facilitating the dissemination and advancement of regional economic growth. Over more than three decades, it has evolved into the primary platform through which China executes its strategy of concentrating on high-tech industries and fostering development driven by innovation. Concurrently, the national high-tech zone, operating as a geographically focused policy customized for a specific region ( Cao, 2019 ), enhances the precision of policy support for the industries under its purview, covering a more limited range of municipalities, counties, and regions. Contrasting with conventional regional industrial policies, the industry-focused policy within national high-tech zones prioritizes comprehensive resource allocation advice and economic foundations to maximize synergy and promote the long-term sustainable growth of the regional economy, and this represents a significant paradigm shift in location-based policies within the framework of carrying out the new development idea. Its inception embodies a combination of central authorization, high-level strategic planning, local grassroots decision-making, and innovative system development. In recent years, driven by the objective of dual carbon, national high-tech have proactively promoted environmentally friendly innovation. Nevertheless, given the proliferation of new industrial policies and the escalating complexity of the policy framework, has the setting up of national high-tech zones genuinely elevated the level of urban green innovation in contrast to conventional regional industrial policies? What are the underlying mechanisms? Simultaneously, concerning the variations among different cities, have the industrial policy tools within the national high-tech zones been employed judiciously and adaptable? What are the concrete practical outcomes? Investigating these matters has emerged as a significant subject requiring resolution by government, industry and academia.

2 Literature review and research hypothesis

2.1 literature review.

When considering industrial policy, the setting up national high-tech zones embodies the intersection of regional and industrial policies. Domestic and international academic research concerning setting up national high-tech zones primarily centers on economic activities and innovation. Notably, the economic impact of national high-tech zones encompasses a wide range of factors, including their influence on total factor productivity ( Tan and Zhang, 2018 ; Wang and Liu, 2023 ), foreign trade ( Alder et al., 2016 ), industrial structure upgrades ( Yuan and Zhu, 2018 ), and economic growth ( Liu and Zhao, 2015 ; Huang and Fernández-Maldonado, 2016 ; Wang Z. et al., 2022 ). Regarding innovation, numerous researchers have confirmed the positive effects of national high-tech zones on company innovation ( Vásquez-Urriago et al., 2014 ; Díez-Vial and Fernández-Olmos, 2017 ; Wang and Xu, 2020 ); Nevertheless, a few scholars have disagreed on this matter ( Hong et al., 2016 ; Sosnovskikh, 2017 ). In general, the consensus among scholars is that setting up high-tech national zones fosters regional innovation significantly. This consensus is supported by various aspects of innovation, including innovation efficiency ( Park and Lee, 2004 ; Chandrashekar and Bala Subrahmanya, 2017 ), agglomeration effect ( De Beule and Van Beveren, 2012 ), innovation capability ( Yang and Guo, 2020 ), among other relevant dimensions. The existing literature predominantly delves into the correlation between the setting up of national high-tech zones, innovation, and economic significance. However, the rise of digital economic developments, notably industrial digitization, has accentuated the limitations of the traditional innovation paradigm. These shortcomings, such as the inadequate exploration of the social importance and sustainability of innovation, have become apparent in recent years. As the primary driver of sustainable development, green innovation represents a potent avenue for achieving economic benefits and environmental value ( Weber et al., 2014 ). Its distinctiveness from other innovation forms lies in its potential to facilitate the transformation of development modes, reshape economic structures, and address pollution prevention and control challenges. However, in the context of green innovation, based on the double-difference approach, Wang et al. (2020) has pointed out that national high-tech zones enhance the effectiveness of urban green innovation, but this is only significant in the eastern region.

Furthermore, scholars have also explored the mechanisms underlying the innovation effects of national high-tech. For example, Cattapan et al. (2012) focused on science parks in Italy. They found that green innovation represents a potent avenue for achieving economic benefits as the primary driver of sustainable development, and environmental value technology transfer services positively influence product innovation. Albahari et al. (2017) confirmed that higher education institutions’ involvement in advancing corporate innovation within technology and science parks has a beneficial moderating effect. Using the moderating effect of spatial agglomeration as a basis, Li WH. et al. (2022) found that industrial agglomeration has a significantly unfavorable moderating influence on the effectiveness of performance transformation in national high-tech zones. Multiple studies have examined the national high-tech zone industrial policy’s regulatory framework and urban innovation. However, in the age of rapidly expanding new infrastructure, infrastructure construction is concentrated on information technologies like blockchain, big data, cloud computing, artificial intelligence, and the Internet; Further research is needed to explore whether traditional infrastructure, particularly transportation infrastructure, can promote urban green innovation. Transportation infrastructure has consistently been vital in fostering economic expansion, integrating regional resources, and facilitating coordinated development ( Behrens et al., 2007 ; Zhang et al., 2018 ; Pokharel et al., 2021 ). Therefore, it is necessary to investigate whether transportation infrastructure can continue encouraging innovative urban green practices in the digital economy.

In summary, the existing literature has extensively examined the influence of national high-tech zones on economic growth and innovation from various levels and perspectives, establishing a solid foundation and offering valuable research insights for this study. Nonetheless, previous studies frequently overlooked the impact of national high-tech zones on urban green innovation levels, and a subsequent series of work in this paper aims to address this issue. Further exploration and expansion are needed to understand the industrial policy framework’s strategy for relating national high-tech zones to urban green innovation. Furthermore, there is a need for further improvement and refinement of the research model and methodology. Based on these, this paper aims to discuss the industrial policy effects of national high-tech zones from the perspective of urban green innovation to enrich and expand the existing research.

In contrast to earlier research, the marginal contribution of this paper is organized into three dimensions: 1) Most scholars have primarily focused on the effects of national high-tech zones on economic activity and innovation, with less emphasis on green innovation and rare studies according to the level of green innovation perspective. The study on national high-tech zones as an industrial policy that has already been done is enhanced by this work. 2) Regarding the research methodology, the Double Machine Learning (DML) approach is used to evaluate the policy effects of national high-tech zones, leveraging the advantages of machine learning algorithms for high-dimensional and non-parametric prediction. This approach circumvents the problems of model setting bias and the “curse of dimensionality” encountered in traditional econometric models ( Chernozhukov et al., 2018 ), enhancing the credibility of the research findings. 3) By introducing transportation infrastructure as a moderator variable, this study investigates the underlying mechanism of national high-tech zones on urban green innovation, offering suggestions for maximizing the influence of these zones on policy.

2.2 Theoretical analysis and hypotheses

2.2.1 national high-tech zones’ industrial policies and urban green innovation.

As one of the ways to land industrial policies at the national level, national high-tech zones serve as effective driving forces for enhancing China’s ability to innovate regionally and its contribution to economic growth ( Xu et al., 2022 ). Green innovation is a novel form of innovation activity that harmoniously balances the competing goals of environmental preservation and technological advancement, facilitating the superior expansion of the economy by alleviating the strain on resources and the environment ( Li, 2015 ). National high-tech zones mainly impact urban green innovation through three main aspects. Firstly, based on innovation compensation effects, national high-tech zones, established based on the government’s strategic planning, receive special treatment in areas such as land, taxation, financing, credit, and more, serving as pioneering special zones and experimental fields established by the government to promote high-quality regional development. When the government offers R&D subsidies to enterprises engaged in green innovation activities within the zones, enterprises are inclined to respond positively to the government’s policy support and enhance their level of green innovation as a means of seeking external legitimacy ( Fang et al., 2021 ), thereby contributing to the advancement of urban green innovation. Secondly, based on the industrial restructuring effect, strict regulation of businesses with high emissions, high energy consumption, and high pollution levels is another aspect of implementing the national high-tech zone program. Consequently, businesses with significant emissions and energy consumption are required to optimize their industrial structure to access various benefits within the park, resulting in the gradual transformation and upgrading of high-energy-consumption industries towards green practices, thereby further contributing to regional green innovation. Based on Porter’s hypothesis, the green and low-carbon requirements of the park policy increase the production costs for polluting industries, prompting polluting enterprises to upgrade their existing technology and adopt green innovation practices. Lastly, based on the theory of industrial agglomeration, the national high-tech zones’ industrial policy facilitates the concentration of innovative talents to a certain extent, resulting in intensified competition in the green innovation market. Increased competition fosters the sharing of knowledge, technology, and talent, stimulating a market environment where the survival of the fittest prevails ( Melitz and Ottaviano, 2008 ). These increase the effectiveness of urban green innovation, helping to propel urban green innovation forward. Furthermore, the infrastructure development within the national high-tech zones establishes a favorable physical environment for enterprises to engage in creative endeavors. Also, it enables the influx of high-quality innovation capital from foreign sources, complementing the inherent characteristics of national high-tech zones that attract such capital and concentrate green innovation resources, ultimately resulting in both environmental and economic benefits. Based on the above analysis, Hypothesis 1 is proposed:

Hypothesis 1. Implementing industrial policies in national high-tech zones enhances levels of urban green innovation.

2.2.2 Heterogeneity analysis

Given the variations in economic foundations, industrial statuses, and population distributions across different regions, development strategies in different regions are also influenced by these variations ( Chen and Zheng, 2008 ). Theoretically, when using administrative boundaries or geographic locations as benchmarks, the impact of national high-tech zone industrial policy on urban green innovation should be achieved through strategies like aligning with the region’s existing industrial structure. Compared to the western and central regions, the eastern region exhibits more incredible innovation and dynamism due to advantages such as a developed economy, good infrastructure, advanced management concepts, and technologies, combined with a relatively high initial level of green innovation factor endowment. Considering the diminishing marginal effect principle of green innovation, the industrial policy implementation in national high-tech zones favors an “icing on the cake” approach in the eastern region, contrasting with a “send carbon in the snow” approach in the central and western regions. In other words, the economic benefits of national high-tech zones for promoting urban green innovation may need to be more robust than their impact on the central and western regions. Literature confirms that establishing national high-tech zones yields a more beneficial technology agglomeration effect in the less developed central and western regions ( Liu and Zhao, 2015 ), leading to a more substantial impact on enhancing the level of urban green innovation.

Moreover, local governments consider economic development, industrial structure, and infrastructure levels when establishing national high-tech zones. These factors serve as the foundation for regional classification to address variations in regional quality and to compensate for gaps in theoretical research on the link between national high-tech zone industrial policy implementation and urban green innovation. Consequently, the execution of industrial policies in national high-tech zones relies on other vital factors influencing urban green innovation. Significant variations exist in economic development and infrastructure levels among cities of different grades ( Luo and Wang, 2023 ). Generally, cities with higher rankings exhibit strong economic growth and infrastructure, contrasting those with lower rankings. Consequently, the effect of establishing a national high-tech zone on green innovation may vary across different city grades. Thus, considering the disparities across city rankings, we delve deeper into identifying the underlying reasons for regional diversity in the green innovation outcomes of industrial policies implemented in national high-tech zones based on city grades. Based on the above analysis, Hypothesis 2 is proposed:

Hypothesis 2. There is regional heterogeneity and city-level heterogeneity in the impact of national high-tech zone policies on the level of urban green innovation.

2.2.3 The moderating effect of transportation infrastructure

Implementing industrial policies and facilitating the flow of innovation factors are closely intertwined with the role of transport infrastructure as carriers and linkages. Generally, enhanced transportation infrastructure facilitates the absorption of local factors and improves resource allocation efficiency, thereby influencing the spatial redistribution of production factors like labor, resources, and technology across cities. Enhanced transportation infrastructure fosters the development of more robust and advanced innovation networks ( Fritsch and Slavtchev, 2011 ). Banister and Berechman (2001) highlighted that transportation infrastructure exhibits network properties that are fundamental to its agglomeration or diffusion effects. From this perspective, robust infrastructure impacts various economic activities, including interregional labor mobility, factor agglomeration, and knowledge exchange among firms, thereby expediting the spillover effects of green technological innovations ( Yu et al., 2013 ). In turn, this could positively moderate the influence of national hi-tech zone policies on green innovation. On the other hand, while transportation infrastructure facilitates the growth of national high-tech zone policies, it also brings negative impacts, including high pollution, emissions, and ecological landscape fragmentation. Improving transportation infrastructure can also lead to the “relative congestion effect” in national high-tech zones. This phenomenon, observed in specific regions, refers to the excessive concentration of similar enterprises across different links of the same industrial chain, which exacerbates the competition for innovation resources among enterprises, making it challenging for enterprises in the region to allocate their limited innovation resources to technological research and development activities ( Li et al., 2015 ). As a result, there needs to be a higher green innovation level. Therefore, the impact of transportation infrastructure in the current stage of development will be more complex. When the level of transport infrastructure is moderate, adequate transport infrastructure supports the promotion of urban green innovation through national high-tech zone policies. However, the impact of transport infrastructure regulation may be harmful. Based on the above analysis, Hypothesis 3 is proposed:

Hypothesis 3. Transportation infrastructure moderates the relationship between national high-tech zones and levels of urban green invention.

3 Research design

3.1 model setting.

This research explores the impact of industrial policies of national high-tech zones on the level of urban green innovation. Many related studies utilize traditional causal inference models to assess the impact of these policies. However, these models have several limitations in their application. For instance, the commonly used double-difference model in the parallel trend test has stringent requirements for the sample data. Although the synthetic control approach can create a virtual control group that meets parallel trends’ needs, it is limited to addressing the ‘one-to-many’ problem and requires excluding groups with extreme values. The selection of matching variables in propensity score matching is subjective, among other limitations ( Zhang and Li, 2023 ). To address the limitations of conventional causal inference models, scholars have started to explore applying machine learning to infer causality ( Chernozhukov et al., 2018 ; Knittel and Stolper, 2021 ). Machine learning algorithms excel at an impartial assessment of the effect on the intended target variable for making accurate predictions.

In contrast to traditional machine learning algorithms, the formal proposal of DML was made in 2018 ( Chernozhukov et al., 2018 ). This approach offers a more robust approach to causal inference by mitigating bias through the incorporation of residual modeling. Currently, some scholars utilize DML to assess causality in economic phenomena. For instance, Hull and Grodecka-Messi (2022) examined the effects of local taxation, crime, education, and public services on migration using DML in the context of Swedish cities between 2010 and 2016. These existing research findings serve as valuable references for this study. Compared to traditional causal inference models, DML offers distinct advantages in variable selection and model estimation ( Zhang and Li, 2023 ). However, in promoting urban green innovation in China, there is a high probability of non-linear relationships between variables, and the traditional linear regression model may lead to bias and errors. Moreover, the double machine learning model can effectively avoid problems such as setting bias. Based on this, the present study employs a DML model to evaluate the policy implications of establishing a national high-tech zone.

3.1.1 Double machine learning framework

Prior to applying the DML algorithm, this paper refers to the practice of Chernozhukov et al. (2018) to construct a partially linear DML model, as depicted in Eq. 1 below:

where i represents the city, t represents the year, and l n G I i t represents the explained variable, which in this paper is the green innovation level of the city. Z o n e i t represents the disposition variable, which in this case is a national high-tech zone’s policy variable. It takes a value of 1 after the implementation of the pilot and 0 otherwise. θ 0 is the disposal factor that is the focus of this paper. X i t represents the set of high-dimensional control variables. Machine learning algorithms are utilized to estimate the specific form of g ^ X i t , whereas U i t , which has a conditional mean of 0, stands for the error term. n represents the sample size. Direct estimation of Eq. 1 provides an estimate for the coefficient of dispositions.

We can further explore the estimation bias by combining Eqs 1 , 2 as depicted in Eq. ( 3 ) below:

where a = 1 n ∑ i ∈ I , t ∈ T   Z o n e i t 2 − 1 1 n ∑ i ∈ I , t ∈ T   Z o n e i t U i t , by a normal distribution having 0 as the mean, b = 1 n ∑ i ∈ I , t ∈ T   Z o n e i t 2 − 1 1 n ∑ i ∈ I , t ∈ T   Z o n e i t g X i t − g ^ X i t . It is important to note that DML utilizes machine learning and a regularization algorithm to estimate a specific functional form g ^ X i t . The introduction of “canonical bias” is inevitable as it prevents the estimates from having excessive variance while maintaining their unbiasedness. Specifically, the convergence of g ^ X i t to g X i t , n −φg > n −1/2 , as n tends to infinity, b also tends to infinity, θ ^ 0 is difficult to converge to θ 0 . To expedite convergence and ensure unbiasedness of the disposal coefficient estimates with small samples, an auxiliary regression is constructed as follows:

where m X i t represents the disposition variable’s regression function on the high-dimensional control variable, this function also requires estimation using a machine learning algorithm in the specific form of m ^ X i t . Additionally, V i t represents the error term with a 0 conditional mean.

3.1.2 The test of the mediating effect within the DML framework

This study investigates how the national high-tech zone industrial policy influences the urban green innovation. It incorporates moderating variables within the DML framework, drawing on the testing procedure outlined by Jiang (2022) , and integrates it with the practice of He et al. (2022) , as outlined below:

Equation 5 is based on Eq. 1 with the addition of variables l n t r a i t and Z o n e i t * l n t r a i t .Where l n t r a i t represents the moderating variable, which in this paper is the transportation infrastructure. Z o n e i t * l n t r a i t represents the interaction term of the moderating variable and the disposition variable. The variables l n t r a i t and Z o n e i t are added to the high-dimensional control variables X i t , and the rest of the variables in Eq. 5 are identical to Eq. 1 . θ 1 represents the disposal factor to focus on.

3.2 Variable selection

3.2.1 dependent variable: level of urban green innovation (lngi).

Nowadays, many academics use indicators like the number of applications for patents or authorizations to assess the degree of urban innovation. To be more precise, the quantity of patent applications is a measure of technological innovation effort, while the number of patents authorized undergoes strict auditing and can provide a more direct reflection of the achievements and capacity of scientific and technological innovation. Thus, this paper refers to the studies of Zhou and Shen (2020) and Li X. et al. (2022) to utilize the count of authorized green invention patents in each prefecture-level city to indicate the level of green innovation. For the empirical study, the count of authorized green patents plus 1 is transformed using logarithm.

3.2.2 Disposal variable: dummy variables for national high-tech zones (Zone)

The national high-tech zone dummy variable’s value correlates with the city in which it is located and the list of national high-tech zones released by China’s Ministry of Science and Technology. If a national high-tech zone was established in the city by 2017, the value is set to 1 for the year the high-tech zone is established and subsequent years. Otherwise, it is set to 0.

3.2.3 Moderating variable: transportation infrastructure (lntra)

Previous studies have shown that China’s highway freight transport comprises 75% of the total freight transport ( Li and Tang, 2015 ). Highway transportation infrastructure has a significant influence on the evolution of the Chinese economy. The development and improvement of highway infrastructure are crucial for modern transportation. This paper uses the research methods of Wu (2019) and uses the roadway mileage (measured in kilometers) to population as a measure of the quality of the transportation system.

3.2.4 Control variables

(1) Foreign direct investment (lnfdi): There is general agreement among academics that foreign direct investment (FDI) significantly influences urban green innovation, as FDI provides expertise in management, human resources, and cutting-edge industrial technology ( Luo et al., 2021 ). Thus, it is necessary to consider and control the level of FDI. This paper uses the ratio of foreign investment to the local GDP in a million yuan.

(2) Financial development level (lnfd): Innovation in science and technology is greatly aided by finance. For the green innovation-driven strategy to advance, it is imperative that funding for science and technology innovation be strengthened. The amount of capital raised for innovation is strongly impacted by the state of urban financial development ( Zhou and Du, 2021 ). Thus, this paper uses the loan balance to GDP ratio as an indicator.

(3) Human capital (lnhum): Highly skilled human capital is essential for cities to drive green innovation. Generally, highly qualified human capital significantly boosts green innovation ( Ansaris et al., 2016 ). Therefore, a measure was employed: the proportion of people in the city who had completed their bachelor’s degree or above.

(4) Industrial structure (lnind): Generally, the secondary industry in China is the primary source of pollution, and there is a significant impact of industrial structure on green innovation ( Qiu et al., 2023 ). The metric used in this paper is the secondary industry-to-GDP ratio for the area.

(5) Regional economic development level (lnagdp): A region’s level of economic growth is indicative of the material foundation for urban green innovation and in-fluences the growth of green innovation in the region ( Bo et al., 2020 ). This research uses the annual gross domestic product per capita as a measurement.

3.3 Data source

By 2017, China had developed 157 national high-tech zones in total. In conjunction with the study’s objectives, this study performs sample adjustments and a screening process. The study’s sample period spans from 2007 to 2019. 57 national high-tech zones that were created prior to 2000 are omitted to lessen the impact on the test results of towns having high-tech zones founded before 2007. Due to the limitations of high-tech areas in cities at the county level in promoting urban green innovation, 8 high-tech zones located in county-level cities are excluded. And 4 high-tech zones with missing severe data are excluded. Among the list of established national high-tech zones, 88 high-tech zones are distributed across 83 prefecture-level cities due to multiple districts within a single city. As a result, 83 cities are selected as the experimental group for this study. Additionally, a control group of 80 cities was selected from among those that did not have high-tech zones by the end of 2019, resulting in a final sample size of 163 cities. This paper collects green patent data for each city from the China Green Patent Statistical Report published by the State Intellectual Property Office. The author compiled the list of national high-tech zones and the starting year of their establishment on the official government website. In addition, the remaining data in this paper primarily originated from the China Urban Statistical Yearbook (2007–2019), the EPS database, and the official websites of the respective city’s Bureau of Statistics. Missing values were addressed through linear interpolation. To address heteroskedasticity in the model, the study logarithmically transforms the variables, excluding the disposal variable. Table 1 shows the descriptive analysis of the variables.

Table 1 . Descriptive analysis.

4 Empirical analysis

4.1 national high-tech zones’ policy effects on urban green innovation.

This study utilizes the DML model to estimate the impact of industrial policies implemented in national high-tech zones at the level of urban green innovation. Following the approach of Zhang and Li (2023) , the sample is split in a ratio of 1:4, and the random forest algorithm is used to perform predictions and combine Eq. ( 1 ) with Eq. ( 4 ) for the regression. Table 2 presents the results with and without controlling for time and city effects. The results indicate that the treatment effect sizes for these four columns are 0.376, 0.293, 0.396, and 0.268, correspondingly, each of which was significant at a 1% level. Thus, Hypothesis 1 is supported.

Table 2 . Benchmark regression results.

4.2 Robustness tests

4.2.1 eliminate the influence of extreme values.

To reduce the impact of extreme values on the estimation outcomes, all variables on the benchmark regression, excluding the disposal variable, undergo a shrinkage process based on the upper and lower 1% and 5% quantiles. Values lower than the lowest and higher than the highest quantile are replaced accordingly. Regression analyses are conducted. Table 3 demonstrates that removing outliers did not substantially alter the findings of this study.

Table 3 . Extreme values removal results.

4.2.2 Considering province-time interaction fixed effects

Since provinces are critical administrative units in the governance system of the Chinese government, cities within the same province often share similarities in policy environment and location characteristics. Therefore, to account for the influence of temporal changes across different provinces, this study incorporates province-time interaction fixed effects based on the benchmark regression. Table 4 presents the individual regression results. Based on the regression results, after accounting for the correlation between different city characteristics within the same province, national high-tech zone policies continue to significantly influence urban green innovation, even at the 1% level.

Table 4 . The addition of province and time fixed effects interaction terms.

4.2.3 Excluding other policy disturbances

When analyzing how national high-tech zones affect strategy for urban green innovation, it is susceptible to the influence of concurrent policies. This study accounts for other comparable policies during the same period to ensure an accurate estimation of the policy effect. Since 2007, national high-tech zone policies have been successively implemented, including the development of “smart cities.” Therefore, this study incorporates a policy dummy variable for “smart cities” in the benchmark regression. The specific regression findings are shown in Table 5 . After controlling for the impact of concurrent policies, the importance of national high-tech zones’ policy impact remains consistent.

Table 5 . Results of removing the impact of parallel policies.

4.2.4 Resetting the DML model

To mitigate the potential bias introduced by the settings in the DML model on the conclusions, the purpose of this study is to assess the conclusions’ robustness using the following methods. First, the sample split ratio of the DML model is adjusted from 1:4 to 1:2 to examine the potential impact of the sample split ratio on the conclusions of this study. Second, the machine learning algorithm is substituted, replacing the random forest algorithm, which has been utilized as a prediction algorithm, with lasso regression, gradient boosting, and neural networks to investigate the potential influence of prediction algorithms on the conclusions of this study. Third, regarding benchmark regression, additional linear models were constructed and analyzed using DML, which involves subjective decisions regarding model form selection. Therefore, DML was employed to construct more comprehensive interactive models, aiming to assess the influence of model settings on the conclusions of this study. The main and auxiliary regressions utilized for the analysis were modified as follows:

Combining Eqs ( 7 ), ( 8 ) for the regression, the interactive model yielded estimated coefficients for the disposition effect:

The results of Eq. ( 9 ) are shown in column (5) of Table 6 . And all the regression results obtained from the modified DML model are presented in Table 6 .

Table 6 . Results of resetting the DML model.

The findings indicate that the sample split ratio in the DML model, the prediction algorithm used, or the model estimation approach does not impact the conclusion that the national high-tech zone policy raises urban areas’ level of green innovation. These factors only modify the magnitude of the policy effect to some degree.

4.3 Heterogeneity analysis

4.3.1 regional heterogeneity.

The sample cities were further divided into the east, central, and west regions based on the three major economic subregions to examine regional variations in national high-tech zone policies ' effects on urban green innovation, with the results presented in Table 7 . National high-tech zone policies do not statistically significantly affect urban green innovation in the eastern region. However, they have a considerable beneficial influence in the central and western areas. The lack of statistical significance may be explained by the possibility that the setting up of national high-tech zones in the eastern region will provide obstacles to the growth of urban green innovation, such as resource strain and environmental pollution. Given the central and western regions’ relatively underdeveloped economic status and industrial structure, coupled with the preceding theoretical analysis, establishing national high-tech zones is a crucial catalyst, significantly boosting urban green innovation levels. Furthermore, the central government emphasizes that setting high-tech national zones should consider regional resource endowments and local conditions, implementing tailored policies. The central and western regions possess unique geographic locations and natural conditions that make them well-suited for developing solar energy, wind energy, and other forms of green energy. Compared to the central region, the national high-tech zone initiative has a more pronounced impact on promoting urban green innovation in the western region. While further optimization is needed for the western region’s urban innovation environment, the policy on national high-tech zones has a more substantial incentive effect in this region due to its more significant development potential, positive transformation of industrial structure, and increased policy support from the state, including the development strategy for the western region.

Table 7 . Heterogeneity test results for different regions.

4.3.2 Urban hierarchical heterogeneity

The New Tier 1 Cities Institute’s ‘2020 City Business Charm Ranking’ is the basis for this study, with the sample cities categorized into Tier 1 (New Tier 1), Tier 2, Tier 3, Tier 4, and Tier 5. Table 8 presents the regression findings for each of the groups.

Table 8 . Heterogeneity test results for different classes of cities.

The results in Table 8 reveal significant heterogeneity at the city level regarding national high-tech zones’ effects on urban green innovation, confirming Hypothesis 2 . In particular, the coefficients for the first-tier cities are not statistically significant due to the small sample size, and the same applies to the fifth-tier cities. This could be attributed to the relatively weak economy and infrastructure development issues in the fifth-tier cities. Additionally, due to their limited level of development, the fifth-tier cities may have a relatively homogeneous industrial structure, with a dominance of traditional industries or agriculture and a need for a more diversified industrial layout. National high-tech zones have not greatly aided the development of green innovation in these cities. In contrast, national high-tech zone policies in second-tier, third-tier, and fourth-tier cities have a noteworthy favorable impact on green innovation, indicating their favorable influence on enhancing green innovation in these cities. Despite the lower level of economic development in fourth-tier cities compared to second-tier and third-tier cities, the fourth-tier cities’ national high-tech zones have the most pronounced impact on promoting green innovation. This could be attributed to the ongoing transformation of industries in fourth-tier cities, which are still in the technology diffusion and imitation stage, allowing these cities’ national high-tech zones to maintain a high marginal effect. Thus, Hypothesis 2 is supported.

5 Further analysis

According to the empirical findings, setting high-tech national zones significantly raises the bar for urban green innovation. Therefore, it is essential to understand the underlying factors and mechanisms that contribute to the positive correlation. This paper constructs a moderating effect test model using Eqs 5 , 6 and provides a detailed discussion by introducing transportation infrastructure as a moderating variable.

The empirical finding of the moderating impact of transportation infrastructure is shown in Table 9 . The dichotomous interaction term Zone*lntra is significantly negative at the 5% level, suggesting that the impact of national high-tech zone policies on the level of urban green innovation is negatively moderated by transportation infrastructure. This result deviates from the general expectation, but it aligns with the complexity of the role played by transportation infrastructure in the context of modern economic development, as discussed in the previous theoretical analysis. This could be attributed to the insufficient green innovation benefits generated by the policy on national high-tech zones at the current stage, which fails to compensate for the adverse effects of excessive resource consumption and environmental pollution caused by the construction of the zone. Furthermore, transportation infrastructure can lead to an excessive concentration of similar enterprises in the high-tech zones. This excessive concentration creates a relative crowding effect, intensifying competition among enterprises. It diminishes their inclination to engage in green innovation collaboration and investment and hinders their effective implementation of technological research and development activities. Moreover, the excessive clustering of similar enterprises implies a need for more diversity in green innovation activities among businesses located in national high-tech zones. This results in duplicated green innovation outputs and hinders the advancement of green innovation. Thus, Hypothesis 3 is supported.

Table 9 . Empirical results of moderating effects.

6 Conclusion and policy recommendations

6.1 conclusion.

Based on panel data from 163 prefecture-level cities in China from 2007 to 2019, the net effect of setting national high-tech zones on urban green innovation was analyzed using the double machine learning model. The results found that: firstly, the national high-tech zone policy significantly raises the degree of local green innovation, and these results remain robust even after accounting for various factors that could affect the estimation results. Secondly, in the central and western regions, the level of urban green innovation is positively impacted by the national high-tech zone policy; However, this impact is less significant in the eastern region. In the western region compared to the central region, the national high-tech zone initiative has a stronger impact on increasing the level of urban green innovation. Across different city levels, compared to second-tier and third-tier cities, the high-tech zone policy has a more substantial impact on increasing the level of green innovation in fourth-tier cities. Thirdly, based on the moderating effect mechanism test, the construction of transportation infrastructure weakens the promotional effect of national high-tech zones on urban green innovation.

6.2 Policy recommendations

In order that national high-tech zones can better promote China’s high-quality development, this paper proposes the following policy recommendations:

(1) Urban green innovation in China depends on accelerating the setting up of national high-tech zones and creating an atmosphere that supports innovation. Establishing national high-tech zones as testbeds for high-quality development and green innovation has significantly elevated urban green innovation. Thus, cities can efficiently foster urban green innovation by supporting the development of national high-tech zones. Cities that have already established national high-tech zones should further encourage enterprises within these zones to increase their investment in research and development. They should also proceed to foster the leadership of national high-tech zones for urban green innovation, assuming the role of pilot cities as models and leaders. Additionally, it is essential to establish mechanisms for cooperation and synergy between the pilot cities and their neighboring cities to promote collective green development in the region.

(2) Expanding the pilot program and implementing tailored policies based on local conditions are essential. Industrial policies about national high-tech zones have differing effects on urban green innovation. Regions should leverage their comparative advantages, consider urban development’s commonalities and unique aspects, and foster a stable and sustainable green innovation ecosystem. The western and central regions should prioritize constructing and enhancing new infrastructure and bolster support for the high-tech green industry. The western region should seize the opportunity presented by national policies that prioritize support, quicken the rate of environmental innovation, and progressively bridge the gap with the eastern and central regions in various aspects. Furthermore, second-tier, third-tier, and fourth-tier cities should enhance the advantages of national high-tech zone policies, further maintaining the high standard of green innovation and keeping green innovation at an elevated level. Regions facing challenges in green innovation, particularly fifth-tier cities, should learn from the development experiences of advanced regions with national high-tech zones to compensate for their deficiencies in green innovation.

(3) Highlighting the importance of transportation regulation and enhancing collaboration in green innovation is crucial. Firstly, transportation infrastructure should be maximized to strengthen coordination and cooperation among regions, facilitate the smooth movement of innovative talents across regions, and facilitate the rational sharing of innovative resources, collectively enhancing green innovation. Additionally, attention ought to be given to the industrial clustering effect of parks to prevent the wastage of resources and inefficiencies resulting from the excessive clustering of similar industries. Efforts should be focused on effectively harnessing the latent potential of crucial transportation infrastructure areas as long-term drivers of development, promptly mitigating the negative impact of transportation infrastructure construction, and gradually achieving the synergistic promotion of the setting up of national high-tech zones and the raising of urban levels of green innovation, among other overarching objectives.

6.3 Limitations and future research

Our study has some limitations because the research in this paper is conducted in the institutional context of China. For example, not all countries are suitable for implementing similar industrial policies to develop the economy while focusing on environmental protection. However, we recognize that this study is interesting and relevant, and it encourages us to focus more intensely on environmental protection from an industrial policy perspective. Moreover, this paper exhibits certain limitations in the research process. Firstly, the urban green innovation measurement index was developed using the quantity of green patent authorizations. Future studies could focus on green innovation processes, such as the quality of green patents granted. Secondly, the paper employs machine learning techniques for causal inference. Subsequent investigations could delve further into the potential applications of machine learning algorithms in environmental sciences to maximize the benefits of innovative research methodologies.

Data availability statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

Author contributions

WC: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing–review and editing. YJ: Conceptualization, Data curation, Formal Analysis, Investigation, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing–original draft, Writing–review and editing. BT: Investigation, Project administration, Writing–review and editing.

The authors declare that financial support was received for the research, authorship, and/or publication of this article. This research was supported by the Youth Fund for Humanities and Social Science research of Ministry of Education (20YJC790004).

Acknowledgments

The authors are grateful to the editors and the reviewers for their insightful comments.

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The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: national high-tech zone, industrial policy, green innovation, heterogeneity analysis, moderating effect, double machine learning

Citation: Cao W, Jia Y and Tan B (2024) Impact of industrial policy on urban green innovation: empirical evidence of China’s national high-tech zones based on double machine learning. Front. Environ. Sci. 12:1369433. doi: 10.3389/fenvs.2024.1369433

Received: 12 January 2024; Accepted: 15 March 2024; Published: 04 April 2024.

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Publics’ views on ethical challenges of artificial intelligence: a scoping review

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• Helena Machado   ORCID: orcid.org/0000-0001-8554-7619 1 ,
• Susana Silva   ORCID: orcid.org/0000-0002-1335-8648 2 &
• Laura Neiva   ORCID: orcid.org/0000-0002-1954-7597 3  

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This scoping review examines the research landscape about publics’ views on the ethical challenges of AI. To elucidate how the concerns voiced by the publics are translated within the research domain, this study scrutinizes 64 publications sourced from PubMed ® and Web of Science™. The central inquiry revolves around discerning the motivations, stakeholders, and ethical quandaries that emerge in research on this topic. The analysis reveals that innovation and legitimation stand out as the primary impetuses for engaging the public in deliberations concerning the ethical dilemmas associated with AI technologies. Supplementary motives are rooted in educational endeavors, democratization initiatives, and inspirational pursuits, whereas politicization emerges as a comparatively infrequent incentive. The study participants predominantly comprise the general public and professional groups, followed by AI system developers, industry and business managers, students, scholars, consumers, and policymakers. The ethical dimensions most commonly explored in the literature encompass human agency and oversight, followed by issues centered on privacy and data governance. Conversely, topics related to diversity, nondiscrimination, fairness, societal and environmental well-being, technical robustness, safety, transparency, and accountability receive comparatively less attention. This paper delineates the concrete operationalization of calls for public involvement in AI governance within the research sphere. It underscores the intricate interplay between ethical concerns, public involvement, and societal structures, including political and economic agendas, which serve to bolster technical proficiency and affirm the legitimacy of AI development in accordance with the institutional norms that underlie responsible research practices.

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1 Introduction

Current advances in the research, development, and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics that is accompanied by calls for AI technology to be democratically accountable and trustworthy from the publics’ Footnote 1 perspective [ 1 , 2 , 3 , 4 , 5 ]. Consequently, several ethics guidelines for AI have been released in recent years. As of early 2020, there were 167 AI ethics guidelines documents around the world [ 6 ]. Organizations such as the European Commission (EC), the Organization for Economic Co-operation and Development (OECD), and the United Nations Educational, Scientific and Cultural Organization (UNESCO) recognize that public participation is crucial for ensuring the responsible development and deployment of AI technologies, Footnote 2 emphasizing the importance of inclusivity, transparency, and democratic processes to effectively address the societal implications of AI [ 11 , 12 ]. These efforts were publicly announced as aiming to create a common understanding of ethical AI development and foster responsible practices that address societal concerns while maximizing AI’s potential benefits [ 13 , 14 ]. The concept of human-centric AI has emerged as a key principle in many of these regulatory initiatives, with the purposes of ensuring that human values are incorporated into the design of algorithms, that humans do not lose control over automated systems, and that AI is used in the service of humanity and the common good to improve human welfare and human rights [ 15 ]. Using the same rationale, the opacity and rapid diffusion of AI have prompted debate about how such technologies ought to be governed and which actors and values should be involved in shaping governance regimes [ 1 , 2 ].

While industry and business have traditionally tended to be seen as having no or little incentive to engage with ethics or in dialogue, AI leaders currently sponsor AI ethics [ 6 , 16 , 17 ]. However, some concerns call for ethics, public participation, and human-centric approaches in areas such as AI with high economic and political importance to be used within an instrumental rationale by the AI industry. A growing corpus of critical literature has conceived the development of AI ethics as efforts to reduce ethics to another form of industrial capital or to coopt and capture researchers as part of efforts to control public narratives [ 12 , 18 ]. According to some authors, one of the reasons why ethics is so appealing to many AI companies is to calm critical voices from the publics; therefore, AI ethics is seen as a way of gaining or restoring trust, credibility and support, as well as legitimation, while criticized practices are calmed down to maintain the agenda of industry and science [ 12 , 17 , 19 , 20 ].

Critical approaches also point out that despite regulatory initiatives explicitly invoking the need to incorporate human values into AI systems, they have the main objective of setting rules and standards to enable AI-based products and services to circulate in markets [ 20 , 21 , 22 ] and might serve to avoid or delay binding regulation [ 12 , 23 ]. Other critical studies argue that AI ethics fails to mitigate the racial, social, and environmental damage of AI technologies in any meaningful sense [ 24 ] and excludes alternative ethical practices [ 25 , 26 ]. As explained by Su [ 13 ], in a paper that considers the promise and perils of international human rights in AI governance, while human rights can serve as an authoritative source for holding AI developers accountable, its application to AI governance in practice shows a lack of effectiveness, an inability to effect structural change, and the problem of cooptation.

In a value analysis of AI national strategies, Wilson [ 5 ] concludes that the publics are primarily cast as recipients of AI’s abstract benefits, users of AI-driven services and products, a workforce in need of training and upskilling, or an important element for thriving democratic society that unlocks AI's potential. According to the author, when AI strategies articulate a governance role for the publics, it is more like an afterthought or rhetorical gesture than a clear commitment to putting “society-in-the-loop” into AI design and implementation [ 5 , pp. 7–8]. Another study of how public participation is framed in AI policy documents [ 4 ] shows that high expectations are assigned to public participation as a solution to address concerns about the concentration of power, increases in inequality, lack of diversity, and bias. However, in practice, this framing thus far gives little consideration to some of the challenges well known for researchers and practitioners of public participation with science and technology, such as the difficulty of achieving consensus among diverse societal views, the high resource requirements for public participation exercises, and the risks of capture by vested interests [ 4 , pp. 170–171]. These studies consistently reveal a noteworthy pattern: while references to public participation in AI governance are prevalent in the majority of AI national strategies, they tend to remain abstract and are often overshadowed by other roles, values, and policy concerns.

Some authors thus contended that the increasing demand to involve multiple stakeholders in AI governance, including the publics, signifies a discernible transformation within the sphere of science and technology policy. This transformation frequently embraces the framework of “responsible innovation”, Footnote 3 which emphasizes alignment with societal imperatives, responsiveness to evolving ethical, social, and environmental considerations, and the participation of the publics as well as traditionally defined stakeholders [ 3 , 28 ]. When investigating how the conception and promotion of public participation in European science and technology policies have evolved, Macq, Tancoine, and Strasser [ 29 ] distinguish between “participation in decision-making” (pertaining to science policy decisions or decisions on research topics) and “participation in knowledge and innovation-making”. They find that “while public participation had initially been conceived and promoted as a way to build legitimacy of research policy decisions by involving publics into decision-making processes, it is now also promoted as a way to produce better or more knowledge and innovation by involving publics into knowledge and innovation-making processes, and thus building legitimacy for science and technology as a whole” [ 29 , p. 508]. Although this shift in science and technology research policies has been noted, there exists a noticeable void in the literature in regard to understanding how concrete research practices incorporate public perspectives and embrace multistakeholder approaches, inclusion, and dialogue.

While several studies have delved into the framing of the publics’ role within AI governance in several instances (from Big Tech initiatives to hiring ethics teams and guidelines issued from multiple institutions to governments’ national policies related to AI development), discussing the underlying motivations driving the publics’ participation and the ethical considerations resulting from such involvement, there remains a notable scarcity of knowledge concerning how publicly voiced concerns are concretely translated into research efforts [ 30 , pp. 3–4, 31 , p. 8, 6]. To address this crucial gap, our scoping review endeavors to analyse the research landscape about the publics’ views on the ethical challenges of AI. Our primary objective is to uncover the motivations behind involving the publics in research initiatives, identify the segments of the publics that are considered in these studies, and illuminate the ethical concerns that warrant specific attention. Through this scoping review, we aim to enhance the understanding of the political and social backdrop within which debates and prior commitments regarding values and conditions for publics’ participation in matters related to science and technology are formulated and expressed [ 29 , 32 , 33 ] and which specific normative social commitments are projected and performed by institutional science [ 34 , p. 108, [ 35 , p. 856].

We followed the guidance for descriptive systematic scoping reviews by Levac et al. [ 36 ], based on the methodological framework developed by Arksey and O’Malley [ 37 ]. The steps of the review are listed below:

2.1 Stage 1: identifying the research question

The central question guiding this scoping review is the following: What motivations, publics and ethical issues emerge in research addressing the publics’ views on the ethical challenges of AI? We ask:

What motivations for engaging the publics with AI technologies are articulated?

Who are the publics invited?

Which ethical issues concerning AI technologies are perceived as needing the participation of the publics?

2.2 Stage 2: identifying relevant studies

A search of the publications on PubMed® and Web of Science™ was conducted on 19 May 2023, with no restriction set for language or time of publication, using the following search expression: (“AI” OR “artificial intelligence”) AND (“public” OR “citizen”) AND “ethics”. The search was followed by backwards reference tracking, examining the references of the selected publications based on full-text assessment.

2.3 Stage 3: study selection

The inclusion criteria allowed only empirical, peer-reviewed, original full-length studies written in English to explore publics’ views on the ethical challenges of AI as their main outcome. The exclusion criteria disallowed studies focusing on media discourses and texts. The titles of 1612 records were retrieved. After the removal of duplicates, 1485 records were examined. Two authors (HM and SS) independently screened all the papers retrieved initially, based on the title and abstract, and afterward, based on the full text. This was crosschecked and discussed in both phases, and perfect agreement was achieved.

The screening process is summarized in Fig.  1 . Based on title and abstract assessments, 1265 records were excluded because they were neither original full-length peer-reviewed empirical studies nor focused on the publics’ views on the ethical challenges of AI. Of the 220 fully read papers, 54 met the inclusion criteria. After backwards reference tracking, 10 papers were included, and the final review was composed of 64 papers.

Flowchart showing the search results and screening process for the scoping review of publics’ views on ethical challenges of AI

2.4 Stage 4: charting the data

A standardized data extraction sheet was initially developed by two authors (HM and SS) and completed by two coders (SS and LN), including both quantitative and qualitative data (Supplemental Table “Data Extraction”). We used MS Excel to chart the data from the studies.

The two coders independently charted the first 10 records, with any disagreements or uncertainties in abstractions being discussed and resolved by consensus. The forms were further refined and finalized upon consensus before completing the data charting process. Each of the remaining records was charted by one coder. Two meetings were held to ensure consistency in data charting and to verify accuracy. The first author (HM) reviewed the results.

Descriptive data for the characterization of studies included information about the authors and publication year, the country where the study was developed, study aims, type of research (quantitative, qualitative, or other), assessment of the publics’ views, and sample. The types of research participants recruited as publics were coded into 11 categories: developers of AI systems; managers from industry and business; representatives of governance bodies; policymakers; academics and researchers; students; professional groups; general public; local communities; patients/consumers; and other (specify).

Data on the main motivations for researching the publics’ views on the ethical challenges of AI were also gathered. Authors’ accounts of their motivations were synthesized into eight categories according to the coding framework proposed by Weingart and colleagues [ 33 ] concerning public engagement with science and technology-related issues: education (to inform and educate the public about AI, improving public access to scientific knowledge); innovation (to promote innovation, the publics are considered to be a valuable source of knowledge and are called upon to contribute to knowledge production, bridge building and including knowledge outside ‘formal’ ethics); legitimation (to promote public trust in and acceptance of AI, as well as of policies supporting AI); inspiration (to inspire and raise interest in AI, to secure a STEM-educated labor force); politicization (to address past political injustices and historical exclusion); democratization (to empower citizens to participate competently in society and/or to participate in AI); other (specify); and not clearly evident.

Based on the content analysis technique [ 38 ], ethical issues perceived as needing the participation of the publics were identified through quotations stated in the studies. These were then summarized in seven key ethical principles, according to the proposal outlined by the EC's Ethics Guidelines for Trustworthy AI [ 39 ]: human agency and oversight; technical robustness and safety; privacy and data governance; transparency; diversity, nondiscrimination and fairness; societal and environmental well-being; and accountability.

2.5 Stage 5: collating, summarizing, and reporting the results

The main characteristics of the 64 studies included can be found in Table  1 . Studies were grouped by type of research and ordered by the year of publication. The findings regarding the publics invited to participate are presented in Fig.  2 . The main motivations for engaging the publics with AI technologies and the ethical issues perceived as needing the participation of the publics are summarized in Tables  2 and 3 , respectively. The results are presented below in a narrative format, with complimentary tables and figures to provide a visual representation of key findings.

Publics invited to engage with issues framed as ethical challenges of AI

There are some methodological limitations in this scoping review that should be taken into account when interpreting the results. The use of only two search engines may preclude the inclusion of relevant studies, although supplemented by scanning the reference list of eligible studies. An in-depth analysis of the topics explored within each of the seven key ethical principles outlined by the EC's Ethics Guidelines for Trustworthy AI was not conducted. This assessment would lead to a detailed understanding of the publics’ views on ethical challenges of AI.

3.1 Study characteristics

Most of the studies were in recent years, with 35 of the 64 studies being published in 2022 and 2023. Journals were listed either on the databases related to Science Citation Index Expanded (n = 25) or the Social Science Citation Index (n = 23), with fewer journals indexed in the Emerging Sources Citation Index (n = 7) and the Arts and Humanities Citation Index (n = 2). Works covered a wide range of fields, including health and medicine (services, policy, medical informatics, medical ethics, public and environmental health); education; business, management and public administration; computer science; information sciences; engineering; robotics; communication; psychology; political science; and transportation. Beyond the general assessment of publics’ attitudes toward, preferences for, and expectations and concerns about AI, the publics’ views on ethical challenges of AI technologies have been studied mainly concerning healthcare and public services and less frequently regarding autonomous vehicles (AV), education, robotic technologies, and smart homes. Most of the studies (n = 47) were funded by research agencies, with 7 papers reporting conflicts of interest.

Quantitative research approaches have assessed the publics’ views on the ethical challenges of AI mainly through online or web-based surveys and experimental platforms, relying on Delphi studies, moral judgment studies, hypothetical vignettes, and choice-based/comparative conjoint surveys. The 25 qualitative studies collected data mainly by semistructured or in-depth interviews. Analysis of publicly available material reporting on AI-use cases, focus groups, a post hoc self-assessment, World Café, participatory research, and practice-based design research were used once or twice. Multi or mixed-methods studies relied on surveys with open-ended and closed questions, frequently combined with focus groups, in-depth interviews, literature reviews, expert opinions, examinations of relevant curriculum examples, tests, and reflexive writings.

The studies were performed (where stated) in a wide variety of countries, including the USA and Australia. More than half of the studies (n = 38) were conducted in a single country. Almost all studies used nonprobability sampling techniques. In quantitative studies, sample sizes varied from 2.3 M internet users in an online experimental platform study [ 40 ] to 20 participants in a Delphi study [ 41 ]. In qualitative studies, the samples varied from 123 participants in 21 focus groups [ 42 ] to six expert interviews [ 43 ]. In multi or mixed-methods studies, samples varied from 2036 participants [ 44 ] to 21 participants [ 45 ].

3.2 Motivations for engaging the publics

The qualitative synthesis of the motivations for researching the publics’ views on the ethical challenges of AI is presented in Table  2 and ordered by the number of studies referencing them in the scoping review. More than half of the studies (n = 37) addressed a single motivation. Innovation (n = 33) and legitimation (n = 29) proved to have the highest relevance as motivations for engaging the publics in the ethical challenges of AI technologies, as articulated in 15 studies. Additional motivations are rooted in education (n = 13), democratization (n = 11), and inspiration (n = 9). Politicization was mentioned in five studies. Although they were not authors’ motivations, few studies were found to have educational [ 46 , 47 ], democratization [ 48 , 49 ], and legitimation or inspirations effects [ 50 ].

To consider the publics as a valuable source of knowledge that can add real value to innovation processes in both the private and public sectors was the most frequent motivation mentioned in the literature. The call for public participation is rooted in the aspiration to add knowledge outside “formal” ethics at three interrelated levels. First, at a societal level, by asking what kind of AI we want as a society based on novel experiments on public policy preferences [ 51 ] and on the study of public perceptions, values, and concerns regarding AI design, development, and implementation in domains such as health care [ 46 , 52 , 53 , 54 , 55 ], public and social services [ 49 , 56 , 57 , 58 ], AV [ 59 , 60 ] and journalism [ 61 ]. Second, at a practical level, the literature provides insights into the perceived usefulness of AI applications [ 62 , 63 ] and choices between boosting developers’ voluntary adoption of ethical standards or imposing ethical standards via regulation and oversight [ 64 ], as well as suggesting specific guidance for the development and use of AI systems [ 65 , 66 , 67 ]. Finally, at a theoretical level, literature expands the social-technical perspective [ 68 ] and motivated-reasoning theory [ 69 ].

Legitimation was also a frequent motivation for engaging the publics. It was underpinned by the need for public trust in and social licences for implementing AI technologies. To ensure the long-term social acceptability of AI as a trustworthy technology [ 70 , 71 ] was perceived as essential to support its use and to justify its implementation. In one study [ 72 ], the authors developed an AI ethics scale to quantify how AI research is accepted in society and which area of ethical, legal, and social issues (ELSI) people are most concerned with. Public trust in and acceptance of AI is claimed by social institutions such as governments, private sectors, industry bodies, and the science community, behaving in a trustworthy manner, respecting public concerns, aligning with societal values, and involving members of the publics in decision-making and public policy [ 46 , 48 , 73 , 74 , 75 ], as well as in the responsible design and integration of AI technologies [ 52 , 76 , 77 ].

Education, democratization, and inspiration had a more modest presence as motivations to explore the publics’ views on the ethical challenges of AI. Considering the emergence of new roles and tasks related to AI, the literature has pointed to the public need to ensure the safe use of AI technologies by incorporating ethics and career futures into the education, preparation, and training of both middle school and university students and the current and future health workforce. Improvements in education and guidance for developers and older adults were also noticed. The views of the publics on what needs to be learned or how this learning may be supported or assessed were perceived as crucial. In one study [ 78 ], the authors developed strategies that promote learning related to AI through collaborative media production, connecting computational thinking to civic issues and creative expression. In another study [ 79 ], real-world scenarios were successfully used as a novel approach to teaching AI ethics. Rhim et al. [ 76 ] provided AV moral behavior design guidelines for policymakers, developers, and the publics by reducing the abstractness of AV morality.

Studies motivated by democratization promoted broader public participation in AI, aiming to empower citizens both to express their understandings, apprehensions, and concerns about AI [ 43 , 78 , 80 , 81 ] and to address ethical issues in AI as critical consumers, (potential future) developers of AI technologies or would-be participants in codesign processes [ 40 , 43 , 45 , 78 , 82 , 83 ]. Understanding the publics’ views on the ethical challenges of AI is expected to influence companies and policymakers [ 40 ]. In one study [ 45 ], the authors explored how a digital app might support citizens’ engagement in AI governance by informing them, raising public awareness, measuring publics’ attitudes and supporting collective decision-making.

Inspiration revolved around three main motivations: to raise public interest in AI [ 46 , 48 ]; to guide future empirical and design studies [ 79 ]; and to promote developers’ moral awareness through close collaboration between all those involved in the implementation, use, and design of AI technologies [ 46 , 61 , 78 , 84 , 85 ].

Politicization was the less frequent motivation reported in the literature for engaging the publics. Recognizing the need for mitigation of social biases [ 86 ], public participation to address historically marginalized populations [ 78 , 87 ], and promoting social equity [ 79 ] were the highlighted motives.

3.3 The invited publics

Study participants were mostly the general public and professional groups, followed by developers of AI systems, managers from industry and business, students, academics and researchers, patients/consumers, and policymakers (Fig.  2 ). The views of local communities and representatives of governance bodies were rarely assessed.

Representative samples of the general public were used in five papers related to studies conducted in the USA [ 88 ], Denmark [ 73 ], Germany [ 48 ], and Austria [ 49 , 63 ]. The remaining random or purposive samples from the general public comprised mainly adults and current and potential users of AI products and services, with few studies involving informal caregivers or family members of patients (n = 3), older people (n = 2), and university staff (n = 2).

Samples of professional groups included mainly healthcare professionals (19 out of 24 studies). Educators, law enforcement, media practitioners, and GLAM professionals (galleries, libraries, archives, and museums) were invited once.

3.4 Ethical issues

The ethical issues concerning AI technologies perceived as needing the participation of the publics are depicted in Table  3 . They were mapped by measuring the number of studies referencing them in the scoping review. Human agency and oversight (n = 55) was the most frequent ethical aspect that was studied in the literature, followed by those centered on privacy and data governance (n = 43). Diversity, nondiscrimination and fairness (n = 39), societal and environmental well-being (n = 39), technical robustness and safety (n = 38), transparency (n = 35), and accountability (n = 31) were less frequently discussed.

The concerns regarding human agency and oversight were the replacement of human beings by AI technologies and deskilling [ 47 , 55 , 67 , 74 , 75 , 89 , 90 ]; the loss of autonomy, critical thinking, and innovative capacities [ 50 , 58 , 61 , 77 , 78 , 83 , 85 , 90 ]; the erosion of human judgment and oversight [ 41 , 70 , 91 ]; and the potential for (over)dependence on technology and “oversimplified” decisions [ 90 ] due to the lack of publics’ expertise in judging and controlling AI technologies [ 68 ]. Beyond these ethical challenges, the following contributions of AI systems to empower human beings were noted: more fruitful and empathetic social relationships [ 47 , 68 , 90 ]; enhancing human capabilities and quality of life [ 68 , 70 , 74 , 83 , 92 ]; improving efficiency and productivity at work [ 50 , 53 , 62 , 65 , 83 ] by reducing errors [ 77 ], relieving the burden of professionals and/or increasing accuracy in decisions [ 47 , 55 , 90 ]; and facilitating and expanding access to safe and fair healthcare [ 42 , 53 , 54 ] through earlier diagnosis, increased screening and monitoring, and personalized prescriptions [ 47 , 90 ]. To foster human rights, allowing people to make informed decisions, the last say was up to the person themselves [ 42 , 43 , 46 , 55 , 64 , 67 , 73 , 76 ]. People should determine where and when to use automated functions and which functions to use [ 44 , 54 ], developing “job sharing” arrangements with machines and humans complementing and enriching each other [ 56 , 65 , 90 ]. The literature highlights the need to build AI systems that are under human control [ 48 , 70 ] whether to confirm or to correct the AI system’s outputs and recommendations [ 66 , 90 ]. Proper oversight mechanisms were seen as crucial to ensure accuracy and completeness, with divergent views about who should be involved in public participation approaches [ 86 , 87 ].

Data sharing and/or data misuse were considered the major roadblocks regarding privacy and data governance, with some studies pointing out distrust of participants related to commercial interests in health data [ 55 , 90 , 93 , 94 , 95 ] and concerns regarding risks of information getting into the hands of hackers, banks, employers, insurance companies, or governments [ 66 ]. As data are the backbone of AI, secure methods of data storage and protection are understood as needing to be provided from the input to the output data. Recognizing that in contemporary societies, people are aware of the consequences of smartphone use resulting in the minimization of privacy concerns [ 93 ], some studies have focused on the impacts of data breaches and loss of privacy and confidentiality [ 43 , 45 , 46 , 60 , 62 , 80 ] in relation to health-sensitive personal data [ 46 , 93 ], potentially affecting more vulnerable populations, such as senior citizens and mentally ill patients [ 82 , 90 ] as well as those at young ages [ 50 ], and when journalistic organizations collect user data to provide personalized news suggestions [ 61 ]. The need to find a balance between widening access to data and ensuring confidentiality and respect for privacy [ 53 ] was often expressed in three interrelated terms: first, the ability of data subjects to be fully informed about how data will be used and given the option of providing informed consent [ 46 , 58 , 78 ] and controlling personal information about oneself [ 57 ]; second, the need for regulation [ 52 , 65 , 87 ], with one study reporting that AI developers complain about the complexity, slowness, and obstacles created by regulation [ 64 ]; and last, the testing and certification of AI-enabled products and services [ 71 ]. The study by De Graaf et al. [ 91 ] discussed the robots’ right to store and process the data they collect, while Jenkins and Draper [ 42 ] explored less intrusive ways in which the robot could use information to report back to carers about the patient’s adherence to healthcare.

Studies discussing diversity, nondiscrimination, and fairness have pointed to the development of AI systems that reflect and reify social inequalities [ 45 , 78 ] through nonrepresentative datasets [ 55 , 58 , 96 , 97 ] and algorithmic bias [ 41 , 45 , 85 , 98 ] that might benefit some more than others. This could have multiple negative consequences for different groups based on ethnicity, disease, physical disability, age, gender, culture, or socioeconomic status [ 43 , 55 , 58 , 78 , 82 , 87 ], from the dissemination of hate speech [ 79 ] to the exacerbation of discrimination, which negatively impacts peace and harmony within society [ 58 ]. As there were cross-country differences and issue variations in the publics’ views of discriminatory bias [ 51 , 72 , 73 ], fostering diversity, inclusiveness, and cultural plurality [ 61 ] was perceived as crucial to ensure the transferability/effectiveness of AI systems in all social groups [ 60 , 94 ]. Diversity, nondiscrimination, and fairness were also discussed as a means to help reduce health inequalities [ 41 , 67 , 90 ], to compensate for human preconceptions about certain individuals [ 66 ], and to promote equitable distribution of benefits and burdens [ 57 , 71 , 80 , 93 ], namely, supporting access by all to the same updated and high-quality AI systems [ 50 ]. In one study [ 83 ], students provided constructive solutions to build an unbiased AI system, such as using a dataset that includes a diverse dataset engaging people of different ages, genders, ethnicities, and cultures. In another study [ 86 ], participants recommended diverse approaches to mitigate algorithmic bias, from open disclosure of limitations to consumer and patient engagement, representation of marginalized groups, incorporation of equity considerations into sampling methods and legal recourse, and identification of a wide range of stakeholders who may be responsible for addressing AI bias: developers, healthcare workers, manufacturers and vendors, policymakers and regulators, AI researchers and consumers.

Impacts on employment and social relationships were considered two major ethical challenges regarding societal and environmental well-being. The literature has discussed tensions between job creation [ 51 ] and job displacement [ 42 , 90 ], efficiency [ 90 ], and deskilling [ 57 ]. The concerns regarding future social relationships were the loss of empathy, humanity, and/or sensitivity [ 52 , 66 , 90 , 99 ]; isolation and fewer social connections [ 42 , 47 , 90 ]; laziness [ 50 , 83 ]; anxious counterreactions [ 83 , 99 ]; communication problems [ 90 ]; technology dependence [ 60 ]; plagiarism and cheating in education [ 50 ]; and becoming too emotionally attached to a robot [ 65 ]. To overcome social unawareness [ 56 ] and lack of acceptance [ 65 ] due to financial costs [ 56 , 90 ], ecological burden [ 45 ], fear of the unknown [ 65 , 83 ] and/or moral issues [ 44 , 59 , 100 ], AI systems need to provide public benefit sharing [ 55 ], consider discrepancies between public discourse about AI and the utility of the tools in real-world settings and practices [ 53 ], conform to the best standards of sustainability and address climate change and environmental justice [ 60 , 71 ]. Successful strategies in promoting the acceptability of robots across contexts included an approachable and friendly looking as possible, but not too human-like [ 49 , 65 ], and working with, rather than in competition, with humans [ 42 ].

The publics were invited to participate in the following ethical issues related to technical robustness and safety: usability, reliability, liability, and quality assurance checks of AI tools [ 44 , 45 , 55 , 62 , 99 ]; validity of big data analytic tools [ 87 ]; the degree to which an AI system can perform tasks without errors or mistakes [ 50 , 57 , 66 , 84 , 90 , 93 ]; and needed resources to perform appropriate (cyber)security [ 62 , 101 ]. Other studies approached the need to consider both material and normative concerns of AI applications [ 51 ], namely, assuring that AI systems are developed responsibly with proper consideration of risks [ 71 ] and sufficient proof of benefits [ 96 ]. One study [ 64 ] highlighted that AI developers tend to be reluctant to recognize safety issues, bias, errors, and failures, and when they do so, they do so in a selective manner and in their terms by adopting positive-sounding professional jargon as AI robustness.

Some studies recognized the need for greater transparency that reduces the mystery and opaqueness of AI systems [ 71 , 82 , 101 ] and opens its “black box” [ 64 , 71 , 98 ]. Clear insights about “what AI is/is not” and “how AI technology works” (definition, applications, implications, consequences, risks, limitations, weaknesses, threats, rewards, strengths, opportunities) were considered as needed to debunk the myth about AI as an independent entity [ 53 ] and for providing sufficient information and understandable explanations of “what’s happening” to society and individuals [ 43 , 48 , 72 , 73 , 78 , 102 ]. Other studies considered that people, when using AI tools, should be made fully aware that these AI devices are capturing and using their data [ 46 ] and how data are collected [ 58 ] and used [ 41 , 46 , 93 ]. Other transparency issues reported in the literature included the need for more information about the composition of data training sets [ 55 ], how algorithms work [ 51 , 55 , 84 , 94 , 97 ], how AI makes a decision [ 57 ] and the motivations for that decision [ 98 ]. Transparency requirements were also addressed as needing the involvement of multiple stakeholders: one study reported that transparency requirements should be seen as a mediator of debate between experts, citizens, communities, and stakeholders [ 87 ] and cannot be reduced to a product feature, avoiding experiences where people feel overwhelmed by explanations [ 98 ] or “too much information” [ 66 ].

Accountability was perceived by the publics as an important ethical issue [ 48 ], while developers expressed mixed attitudes, from moral disengagement to a sense of responsibility and moral conflict and uncertainty [ 85 ]. The literature has revealed public skepticism regarding accountability mechanisms [ 93 ] and criticism about the shift of responsibility away from tech industries that develop and own AI technologies [ 53 , 68 ], as it opens space for users to assume their own individual responsibility [ 78 ]. This was the case in studies that explored accountability concerns regarding the assignment of fault and responsibility for car accidents using self-driving technology [ 60 , 76 , 77 , 88 ]. Other studies considered that more attention is needed to scrutinize each application across the AI life cycle [ 41 , 71 , 94 ], to explainability of AI algorithms that provide to the publics the cause of AI outcomes [ 58 ], and to regulations that assign clear responsibility concerning litigation and liability [ 52 , 89 , 101 , 103 ].

4 Discussion

Within the realm of research studies encompassed in the scoping review, the contemporary impetus for engaging the publics in ethical considerations related to AI predominantly revolves around two key motivations: innovation and legitimation. This might be explained by the current emphasis on responsible innovation, which values the publics’ participation in knowledge and innovation-making [ 29 ] within a prioritization of the instrumental role of science for innovation and economic return [ 33 ]. Considering the publics as a valuable source of knowledge that should be called upon to contribute to knowledge innovation production is underpinned by the desire for legitimacy, specifically centered around securing the publics’ endorsement of scientific and technological advancements [ 33 , 104 ]. Approaching the publics’ views on the ethical challenges of AI can also be used as a form of risk prevention to reduce conflict and close vital debates in contention areas [ 5 , 34 , 105 ].

A second aspect that stood out in this finding is a shift in the motivations frequently reported as central for engaging the publics with AI technologies. Previous studies analysing AI national policies and international guidelines addressing AI governance [ 3 , 4 , 5 ] and a study analysing science communication journals [ 33 ] highlighted education, inspiration and democratization as the most prominent motivations. Our scoping review did not yield similar findings, which might signal a departure, in science policy related to public participation, from the past emphasis on education associated with the deficit model of public understanding of science and democratization of the model of public engagement with science [ 106 , 107 ].

The underlying motives for the publics’ engagement raise the question of the kinds of publics it addresses, i.e., who are the publics that are supposed to be recruited as research participants [ 32 ]. Our findings show a prevalence of the general public followed by professional groups and developers of AI systems. The wider presence of the general public indicates not only what Hagendijk and Irwin [ 32 , p. 167] describe as a fashionable tendency in policy circles since the late 1990s, and especially in Europe, focused on engaging 'the public' in scientific and technological change but also the avoidance of the issues of democratic representation [ 12 , 18 ]. Additionally, the unspecificity of the “public” does not stipulate any particular action [ 24 ] that allows for securing legitimacy for and protecting the interests of a wide range of stakeholders [ 19 , 108 ] while bringing the risk of silencing the voices of the very publics with whom engagement is sought [ 33 ]. The focus on approaching the publics’ views on the ethical challenges of AI through the general public also demonstrates how seeking to “lay” people’s opinions may be driven by a desire to promote public trust and acceptance of AI developments, showing how science negotiates challenges and reinstates its authority [ 109 ].

While this strategy is based on nonscientific audiences or individuals who are not associated with any scientific discipline or area of inquiry as part of their professional activities, the converse strategy—i.e., involving professional groups and AI developers—is also noticeable in our findings. This suggests that technocratic expert-dominated approaches coexist with a call for more inclusive multistakeholder approaches [ 3 ]. This coexistence is reinforced by the normative principles of the “responsible innovation” framework, in particular the prescription that innovation should include the publics as well as traditionally defined stakeholders [ 3 , 110 ], whose input has become so commonplace that seeking the input of laypeople on emerging technologies is sometimes described as a “standard procedure” [ 111 , p. 153].

In the body of literature included in the scoping review, human agency and oversight emerged as the predominant ethical dimension under investigation. This finding underscores the pervasive significance attributed to human centricity, which is progressively integrated into public discourses concerning AI, innovation initiatives, and market-driven endeavours [ 15 , 112 ]. In our perspective, the importance given to human-centric AI is emblematic of the “techno-regulatory imaginary” suggested by Rommetveit and van Dijk [ 35 ] in their study about privacy engineering applied in the European Union’s General Data Protection Regulation. This term encapsulates the evolving collective vision and conceptualization of the role of technology in regulatory and oversight contexts. At least two aspects stand out in the techno-regulatory imaginary, as they are meant to embed technoscience in societally acceptable ways. First, it reinstates pivotal demarcations between humans and nonhumans while concurrently producing intensified blurring between these two realms. Second, the potential resolutions offered relate to embedding fundamental rights within the structural underpinnings of technological architectures [ 35 ].

Following human agency and oversight, the most frequent ethical issue discussed in the studies contained in our scoping review was privacy and data governance. Our findings evidence additional central aspects of the “techno-regulatory imaginary” in the sense that instead of the traditional regulatory sites, modes of protecting privacy and data are increasingly located within more privatized and business-oriented institutions [ 6 , 35 ] and crafted according to a human-centric view of rights. The focus on secure ways of data storage and protection as in need to be provided from the input to the output data, the testing and certification of AI-enabled products and services, the risks of data breaches, and calls for finding a balance between widening access to data and ensuring confidentiality and respect for privacy, exhibited by many studies in this scoping review, portray an increasing framing of privacy and data protection within technological and standardization sites. This tendency shows how forms of expertise for privacy and data protection are shifting away from traditional regulatory and legal professionals towards privacy engineers and risk assessors in information security and software development. Another salient element to highlight pertains to the distribution of responsibility for privacy and data governance [ 6 , 113 ] within the realm of AI development through engagement with external stakeholders, including users, governmental bodies, and regulatory authorities. It extends from an emphasis on issues derived from data sharing and data misuse to facilitating individuals to exercise control over their data and privacy preferences and to advocating for regulatory frameworks that do not impede the pace of innovation. This distribution of responsibility shared among the contributions and expectations of different actors is usually convoked when the operationalization of ethics principles conflicts with AI deployment [ 6 ]. In this sense, privacy and data governance are reconstituted as a “normative transversal” [ 113 , p. 20], both of which work to stabilize or close controversies, while their operationalization does not modify any underlying operations in AI development.

Diversity, nondiscrimination and fairness, societal and environmental well-being, technical robustness and safety, transparency, and accountability were the ethical issues less frequently discussed in the studies included in this scoping review. In contrast, ethical issues of technical robustness and safety, transparency, and accountability “are those for which technical fixes can be or have already been developed” and “implemented in terms of technical solutions” [ 12 , p. 103]. The recognition of issues related to technical robustness and safety expresses explicit admissions of expert ignorance, error, or lack of control, which opens space for politics of “optimization of algorithms” [ 114 , p. 17] while reinforcing “strategic ignorance” [ 114 , p. 89]. In the words of the sociologist Linsey McGoey, strategic ignorance refers to “any actions which mobilize, manufacture or exploit unknowns in a wider environment to avoid liability for earlier actions” [ 115 , p. 3].

According to the analysis of Jobin et al. [ 11 ] of the global landscape of existing ethics guidelines for AI, transparency comprising efforts to increase explainability, interpretability, or other acts of communication and disclosure is the most prevalent principle in the current literature. Transparency gains high relevance in ethics guidelines because this principle has become a pro-ethical condition “enabling or impairing other ethical practices or principles” [Turilli and Floridi 2009, [ 11 ], p. 14]. Our findings highlight transparency as a crucial ethical concern for explainability and disclosure. However, as emphasized by Ananny and Crawford [ 116 , p. 973], there are serious limitations to the transparency ideal in making black boxes visible (i.e., disclosing and explaining algorithms), since “being able to see a system is sometimes equated with being able to know how it works and governs it—a pattern that recurs in recent work about transparency and computational systems”. The emphasis on transparency mirrors Aradau and Blanke’s [ 114 ] observation that Big Tech firms are creating their version of transparency. They are prompting discussions about their data usage, whether it is for “explaining algorithms” or addressing bias and discrimination openly.

The framing of ethical issues related to accountability, as elucidated by the studies within this scoping review, manifests as a commitment to ethical conduct and the transparent allocation of responsibility and legal obligations in instances where the publics encounters algorithmic deficiencies, glitches, or other imperfections. Within this framework, accountability becomes intricately intertwined with the notion of distributed responsibility, as expounded upon in our examination of how the literature addresses challenges in privacy and data governance. Simultaneously, it converges with our discussion on optimizing algorithms concerning ethical concerns on technical robustness and safety by which AI systems are portrayed as fallible yet eternally evolving towards optimization. As astutely observed by Aradau and Blanke [ 114 , p. 171], “forms of accountability through error enact algorithmic systems as fallible but ultimately correctable and therefore always desirable. Errors become temporary malfunctions, while the future of algorithms is that of indefinite optimization”.

5 Conclusion

This scoping review of how publics' views on ethical challenges of AI are framed, articulated, and concretely operationalized in the research sector shows that ethical issues and publics formation are closely entangled with symbolic and social orders, including political and economic agendas and visions. While Steinhoff [ 6 ] highlights the subordinated nature of AI ethics within an innovation network, drawing on insights from diverse sources beyond Big Tech, we assert that this network is dynamically evolving towards greater hybridity and boundary fusion. In this regard, we extend Steinhoff's argument by emphasizing the imperative for a more nuanced understanding of how this network operates within diverse contexts. Specifically, within the research sector, it operates through a convergence of boundaries, engaging human and nonhuman entities and various disciplines and stakeholders. Concurrently, the advocacy for diversity and inclusivity, along with the acknowledgement of errors and flaws, serves to bolster technical expertise and reaffirm the establishment of order and legitimacy in alignment with the institutional norms underpinning responsible research practices.

Our analysis underscores the growing importance of involving the publics in AI knowledge creation and innovation, both to secure public endorsement and as a tool for risk prevention and conflict mitigation. We observe two distinct approaches: one engaging nonscientific audiences and the other involving professional groups and AI developers, emphasizing the need for inclusivity while safeguarding expert knowledge. Human-centred approaches are gaining prominence, emphasizing the distinction and blending of human and nonhuman entities and embedding fundamental rights in technological systems. Privacy and data governance emerge as the second most prevalent ethical concern, shifting expertise away from traditional regulatory experts to privacy engineers and risk assessors. The distribution of responsibility for privacy and data governance is a recurring theme, especially in cases of ethical conflicts with AI deployment. However, there is a notable imbalance in attention, with less focus on diversity, nondiscrimination, fairness, societal, and environmental well-being, compared to human-centric AI, privacy, and data governance being managed through technical fixes. Last, acknowledging technical robustness and safety, transparency, and accountability as foundational ethics principles reveals an openness to expert limitations, allowing room for the politics of algorithm optimization, framing AI systems as correctable and perpetually evolving.

Data availability

This manuscript has data included as electronic supplementary material. The dataset constructed by the authors, resulting from a search of publications on PubMed ® and Web of Science™, analysed in the current study, is not publicly available. But it can be available from the corresponding author on reasonable request.

In this article, we will employ the term "publics" rather than the singular "public" to delineate our viewpoint concerning public participation in AI. Our option is meant to acknowledge that there are no uniform, monolithic viewpoints or interests. From our perspective, the term "publics" allows for a more nuanced understanding of the various groups, communities, and individuals who may have different attitudes, beliefs, and concerns regarding AI. This choice may differ from the terminology employed in the referenced literature.

The following examples are particularly illustrative of the multiplicity of organizations emphasizing the need for public participation in AI. The OECD Recommendations of the Council on AI specifically emphasizes the importance of empowering stakeholders considering essential their engagement to adoption of trustworthy [ 7 , p. 6]. The UNESCO Recommendation on the Ethics of AI emphasizes that public awareness and understanding of AI technologies should be promoted (recommendation 44) and it encourages governments and other stakeholders to involve the publics in AI decision-making processes (recommendation 47) [ 8 , p. 23]. The European Union (EU) White Paper on AI [ 9 , p. 259] outlines the EU’s approach to AI, including the need for public consultation and engagement. The Ethics Guidelines for Trustworthy AI [ 10 , pp. 19, 239], developed by the High-Level Expert Group on AI (HLEG) appointed by the EC, emphasize the importance of public participation and consultation in the design, development, and deployment of AI systems.

“Responsible Innovation” (RI) and “Responsible Research and Innovation” (RRI) have emerged in parallel and are often used interchangeably, but they are not the same thing [ 27 , 28 ]. RRI is a policy-driven discourse that emerged from the EC in the early 2010s, while RI emerged largely from academic roots. For this paper, we will not consider the distinctive features of each discourse, but instead focus on the common features they share.

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Acknowledgements

The authors would like to express their gratitude to Rafaela Granja (CECS, University of Minho) for her insightful support in an early stage of preparation of this manuscript, and to the AIDA research netwrok for the inspiring debates.

Open access funding provided by FCT|FCCN (b-on). Helena Machado and Susana Silva did not receive funding to assist in the preparation of this work. Laura Neiva received funding from FCT—Fundação para a Ciência e a Tecnologia, I.P., under a PhD Research Studentships (ref.2020.04764.BD), and under the project UIDB/00736/2020 (base funding) and UIDP/00736/2020 (programmatic funding).

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Department of Sociology, Institute for Social Sciences, University of Minho, Braga, Portugal

Helena Machado

Department of Sociology and Centre for Research in Anthropology (CRIA), Institute for Social Sciences, University of Minho, Braga, Portugal

Susana Silva

Institute for Social Sciences, Communication and Society Research Centre (CECS), University of Minho, Braga, Portugal

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by HM, SS, and LN. The first draft of the manuscript was written by HM and SS. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Helena Machado .

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Machado, H., Silva, S. & Neiva, L. Publics’ views on ethical challenges of artificial intelligence: a scoping review. AI Ethics (2023). https://doi.org/10.1007/s43681-023-00387-1

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Received : 08 October 2023

Accepted : 16 November 2023

Published : 19 December 2023

DOI : https://doi.org/10.1007/s43681-023-00387-1

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