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date: 07 March 2021

Open Innovationfree

  • Jennifer KuanJennifer KuanCollege of Business, California State University Monterey Bay

Summary

Open Innovation, published in 2003, was a ground-breaking work by Henry Chesbrough that placed technology and innovation at the center of attention for managers of large firms. The term open innovation refers to the ways in which firms can generate and commercialize innovation by engaging outside entities. The ideas have attracted the notice of scholars, spawning annual world conferences and a large literature in technology and innovation management (including numerous journal special issues) that documents diverse examples of innovations and the often novel business models needed to make the most of those innovations. The role of business models in open innovation is the focus of Open Business Models, Chesbrough’s 2006 follow-up to Open Innovation. Managers have likewise flocked to Chesbrough’s approach, as the hundreds of thousands of hits from an online search using the term open innovation can attest. Surveys show that the majority of large firms were engaging in open innovation practices in 2017, compared to only 20% in 2003 when Open Innovation was published.

Introduction

The term open innovation refers to the ways that firms can generate and commercialize innovations by engaging outside entities. Henry Chesbrough’s book of the same name has spawned an active literature since its publication in 2003, attracting thousands of attendees to annual world conferences. A bibliometric study that analyzed over 1,200 articles retrieved using the keyword open innovation documented the growing interest, especially in the fields of technology and innovation management (TIM) and research and development (R&D; Lopez & de Carvalho, 2018). Perhaps more importantly, these ideas have made their way into practice, where they have had significant managerial and policy impact. The 400 million hits from a search engine query on open innovation (Chesbrough, 2017) is but one measure of managerial interest. Surveys of large firms in the United States, Canada, and Europe showed that while almost 80% of firms now utilize open innovation practices, only 20% did in 2003 when Open Innovation was published (Brunswicker & Chesbrough, 2018; Chesbrough & Brunswicker, 2014). The recognition that open innovation is important for healthy, competitive firms has also reached the highest levels of policymaking, with the European Commission’s €77 billion Horizon 2020 program to promote the Three Opens: open science, open innovation, and open-to-the-world global standards (Bogers, Chesbrough, & Moedas, 2018).

The first section of this article aims to provide a straightforward summary of the major ideas and refinements of open innovation. Discussion of the historical context in which Chesbrough was writing follows because other scholars were also trying to analyze and understand the same phenomena that motivated Open Innovation. Thus, open innovation is placed in a broader intellectual milieu. This lays the groundwork for those unfamiliar with the literature to access the intricacies of interrelated literature that cannot be accessed by doing a keyword search. Somewhat like the parable of the blind men and an elephant, different scholars offered different takes on the same large shifts in industry and innovation to which Chesbrough was responding. This intellectual history helps to explain the directions that scholars have taken since Chesbrough’s book, some (but not all) of which came into conversation with Open Innovation. Several excellent literature reviews that focus on open innovation (via keyword search) but do not provide the 360° overview are also presented here. Finally, gaps and opportunities for the literature in future research are suggested.

Open Innovation: The New Imperative for Creating and Profiting from Technology

In his 2003 book, Open Innovation, Chesbrough made the argument that times have changed. The way that firms had been creating and commercializing new technology for over a century would no longer suffice. Under the “closed innovation” model, firms relied on their own research and resources to produce new or improved products and services. For example, large industrial labs, such as AT&T’s famed Bell Labs, produced research for their parent companies. And much of the reliance on big, internal labs was born of necessity because university and government research were relatively nascent (Chesbrough, 2003, p. 26).

However, the success of a spate of start-ups that challenged the supremacy of dominant incumbents called into question the wisdom of continuing to rely solely on internal research. For example, Cisco Systems “consistently managed to keep up with Lucent, and occasionally got to market ahead of it . . . despite its lack of anything like the deep research capabilities of Bell Labs” (Chesbrough, 2003, p. xviii). Microsoft and Intel had bested IBM in the personal computer market, and Nokia had overtaken Motorola and Siemens in cell phones (Chesbrough, 2003, p. xviii). The success of these new firms required new thinking and a departure from prevailing ideas about “keeping your competitors out” that Michael Porter espoused in his highly influential book, Competitive Advantage, in 1985 (Chesbrough, 2017; Chesbrough & Appleyard, 2007). Thus, almost two decades later, amid a backdrop of change in the patterns of success and failure, Chesbrough asked, “What’s going on and what should firms do about it?” His ideas about open innovation found an eager audience among scholars and firms.

What’s Going On?

Chesbrough identified four underlying factors that, together, eroded the need to do only in-house research and changed the calculation for how to commercialize new technology. (In fact, he cited two factors—and two consequences of those factors—for a total of four items.) First, public funding of university and government research increased the quantity and quality of outside researchers. Second, the availability of venture capital, which helps start-ups commercialize new technology, was growing dramatically by the 1980s. Venture capital provided a second lease on life for technologies rejected by existing firms—a start-up could commercialize the technology instead.

These two “erosion factors” combined to create a third erosion factor: start-ups moved quickly, thanks to the availability of trained researchers and venture capital, shortening the time an incumbent firm could delay investing in a technology. If an incumbent waited too long, a start-up might win. Fourth, supplier firms, which could also access the talent and financing of the first two erosion factors, became more technologically capable, further eroding the need to rely solely on in-house R&D (Chesbrough, 2006a, pp. 34–40).

What Should Firms Do About It?

Given these changes, firms should interact with the outside world to profit from technology. Hence, “open innovation.” Table 1 reproduces Chesbrough’s Table I-1 (Chesbrough, 2003, p. xxvi) and defines key principles of open innovation by comparing it to closed innovation.

Table 1. Contrasting Principles of Closed and Open Innovation

Closed Innovation Principles

Open Innovation Principles

The smart people in our field work for us.

Not all the smart people work for us. We need to work with smart people inside and outside our company.

To profit from R&D, we must discover it, develop it, and ship it ourselves.

External R&D can create significant value; internal R&D is needed to claim some portion of that value.

If we discover it ourselves, we will get it to market first.

We don’t have to originate the research to profit from it.

The company that gets an innovation to market first will win.

Building a better business model is better than getting to market first.

If we create the most and the best ideas in the industry, we will win.

If we make the best use of internal and external ideas, we will win.

We should control our intellectual property (IP), so that our competitors don’t profit from our ideas.

We should profit from others’ use of our IP, and we should buy others’ IP whenever it advances our own business model.

Source: Chesbrough (2003).

The first three rows of Table 1 capture the primary gist of open innovation as it has been embraced in practice and in the literature. Firms must import at least some of their knowledge and ideas from outside. That this is a nontrivial problem requiring effort and organization is expressed in rows four and five. The ability to make use of outside information is pervasive, affecting the firm even to its core business model.

The last row refers to how innovations can profitably leave the innovating firm. Chesbrough focuses his opening chapter on this phenomenon, using Xerox as a case study. Xerox profited by creating new start-ups to commercialize some of its technologies, “spinning out” a remarkable number of successes. Of the two dozen firms listed in Chesbrough’s Table 1-1 (pp. 6–7), nine had successful initial public offerings (IPOs) while only two filed for Chapter 11 bankruptcy. Most spin-outs had Xerox’s blessing and allowed Xerox to retain a small ownership stake. The distinction between spin-out activity and the acquisition of innovation from outside the firm is referred to as “outbound open innovation” and “inbound open innovation” (Dahlander & Gann, 2010) or “inside-out” and “outside-in” flows of innovation (Bogers et al., 2018; Chesbrough, 2017).

Illustrative Examples Highlight the Central Role of Business Models

The latter chapters of Chesbrough’s book use cases to illustrate options that firms have in engaging with outsiders on innovation. A central point of the cases is the importance of business models in unlocking the value of innovation. For example, while Xerox was quite masterful in spinning out technologies that start-ups were better positioned to exploit, this was largely because a new business model was needed. Cognitive blinders play a role, too, because Xerox’s managers failed to see a technology’s potential. IBM provides examples of how a firm can institute changes for open innovation. In the inbound direction, IBM began working closely with customers to develop new products and services, while in the outbound direction it successfully licensed its technology. Intel, the youngster of the firms discussed, was “born open.” Thus, its processes and structures were designed to be alert to innovations developing outside its walls, to fund them, and to acquire them. While Intel operated several of its own labs, its main focus was on manufacturing. Therefore, Intel’s labs relied on universities for scientific research and had routines in place to fund university research, down to specific graduate students.

These cases illustrate why business models are core to open innovation. Business models vary in their ability to commercialize an innovation, and when that innovation is novel, the best business model may also be novel. Chesbrough and Rosenbloom (2002) quoted Xerox’s Chief Scientist, John Seely Brown, who directed the Palo Alto Research Center, explaining the problem in 1998, “Not everything we start ends up fitting with our businesses later on. Many of the ideas we work on here involve a paradigm shift in order to deliver value.” Written at a time when the very concept of “business model” was still relatively new, Open Innovation built on Chesbrough and Rosenbloom’s (2002) exploration of how existing business models can cognitively constrain innovative firms when estimating the value of their innovation because the business model determines the value of an innovation. Chesbrough’s follow-up book, Open Business Models: How to Thrive in the New Innovation Landscape, focused on the need for firms to rethink their business models and provided guidance on how firms could bring about change internally (Chesbrough, 2006a). And a third volume, Open Services Innovation, addressed the growing importance of services, both as complements to innovations and as the subject of open innovation in their own right (Chesbrough, 2011).

Critiques and Opportunities for Research

Open Innovation placed innovation at the center of management and strategy decision-making for a popular and scholarly audience. At least two shortcomings of the book were raised in a book review by Helfat (2006). The first is a lack of clear definition of openness, an oversight that was eventually corrected several years later in a collection of essays on open innovation. Chesbrough’s definition showed why a term like open innovation is so much more accessible than formal definitions: “Open innovation is the use of purposive inflows and outflows of knowledge to accelerate internal innovation and expand the markets for external use of innovation” (Chesbrough, 2006b, p. 1). This definition expanded further to include nonpecuniary motivations for open innovation proposed by Dahlander and Gann (2010) and to acknowledge the importance of the business model. Hence the new definition of open innovation is “a distributed innovation process based on purposively managed knowledge flows across organizational boundaries, using pecuniary and nonpecuniary mechanisms in line with the organization’s business model” (Chesbrough & Bogers, 2014).

While the lack, in 2003, of a clear definition might seem like academic nitpicking, the lack of precision contributes to Helfat’s second observation, that Chesbrough was unclear about when to use the variations of openness. This is a practical problem for managers—when to be open and how—and scholars acknowledge the need for “boundary conditions” (Alexy, Frederiksen, & Hutter, 2020). To address this issue, Brunswicker and Chesbrough (2018) surveyed large firms about successful and unsuccessful open innovation projects. And Appleyard and Chesbrough (2017) looked at products that change from open to closed or closed to open to identify factors that affect the decision to open or close innovation. Shifts in demand, the need for quality control, growth in market adoption, and greater in-house technical capabilities can all play a role in examples like Google closing off the Android operating system.

Thus, rather than view these critiques solely as shortcomings, scholars can take them in the spirit in which they were articulated, as opportunities for more research on an increasingly important topic. Perhaps a third area of opportunity that is growing in interest is connecting open innovation to strategy. The central tenets of Open Innovation are essentially strategic, arguing that firms can choose how to innovate and how to commercialize their innovations. However, the engineering literature has embraced these ideas more extensively than the strategy literature. An explicit connection to the strategy literature by Chesbrough and Appleyard (2007) was followed up a decade later by Appleyard and Chesbrough (2017), who analyzed the open-to-closed and closed-to-open decisions as strategic. But additional theoretical analysis of strategic concepts of value creation and value capture were provided by Chesbrough, Lettl, and Ritter (2018) with the goal of encouraging future research on open innovation strategies.

Complementary, Contemporaneous Approaches—Different Parts of the Elephant

Open Innovation was Chesbrough’s approach to making sense of stunning technological and business changes in the economy. But he was not alone in noticing and responding to these tectonic shifts. Applying the blind men and the elephant metaphor, other researchers tackled different sections of the elephant, plumbing the “erosion factors” that Chesbrough identified. Thus, whereas Open Innovation illustrated through examples how a firm can best exploit its own innovative activities by working with outsiders, the other literature developed theory to understand the driving forces that make open innovation a necessity.

Written at around the time Chesbrough was developing Open Innovation, the other literature did not reference Chesbrough per se. But over time, based on citations in research papers, some came into conversation with the open innovation literature, while others did not. Notably, work on innovation outsourcing has mainly had its biggest impact on outsourcing more generally, rather than in the innovation context. Similarly, literature on technology transfer and literature on venture capital also remained in their respective fields of policy and finance. By contrast, entrepreneurship and markets for technology came much more into conversation with open innovation because of a shared focus on innovation.

However, the contribution of other literature to the understanding of erosion factors complements the strategic questions addressed in Open Innovation and provides a more complete picture of the open innovation system. Table 2 presents the erosion factors and a list of papers in the respective literature.

Table 2. Contemporaneous Literature on Erosion Factors

Erosion Factor

Topic

Contemporaneous Papers

#1

Public funding of university-based research

Technology transfer

Mowery, Nelson, Sampat, and Ziedonis (2001) on the Bayh-Dole Act; DiGregorio and Shane (2003) and Colyvas et al. (2002) on universities’ role; Feldman et al. (2002) on universities and contracting.

#2

Availability of funding for technology start-ups

Venture capital

Gompers and Lerner (2001) on the importance of venture capital; Von Burg and Kenney (2000) and Kenney and Florida (2000) on the venture capital business model; Gans, Hsu, and Stern (2002) on the nonfinancial role of venture capitalists.

#3

Start-ups provide new avenues for technology commercialization

Entrepreneurship and markets for technology

Nerkar and Shane (2003) on start-ups’ commercializing university-based research; Anton and Yao (1995) on spin-outs; Gans and Stern (2000) and Gans, Hsu, and Stern (2002) on technology licensing; Arora, Fosfuri, and Gambardella (2001) on markets for intellectual property.

#4

Suppliers provide sophisticated technology

Innovation outsourcing

Poppo and Zenger (1998) on the theory of the firm; Mayer and Argyres (2004) on contracting for innovation; Mowery, Oxley, and Silverman (1996) and Eisenhardt and Schoonhoven (1996) on alliances for knowledge transfer.

Technology Transfer—Erosion Factor #1

One of the key erosion factors that led to the phenomena detailed in Open Innovation was the rise of research performed in universities and government labs. Chesbrough cited two paths by which this source of outside research reaches a large firm. First, large incumbent firms, such as Xerox or IBM, can and should access this new source of knowledge directly. Second, start-ups and suppliers, which might be one and the same, can also access this new knowledge and then compete with, or sell components to, the large incumbent. But both paths allow the focal firm to benefit from innovation generated by the new source of outside innovation.

A similar interest in these dual paths was examined in a large literature from the perspective of the university and, to a lesser extent, government lab. This literature tried to measure and understand the movement of research from the lab to the market, which is referred to as “technology transfer” or “tech transfer” (i.e., the transfer of knowledge from universities to firms). A policy interest motivated this research topic, which asked whether publicly funded research is economically valuable. Thus, much of the research used the Bayh-Dole Act of 1980 as a starting point (Mowery, Nelson, Sampat, & Ziedonis, 2001). The Bayh–Dole Patent and Trademark Amendments Act of 1980 allowed recipients of federal research funds to patent and to license (and thus profit from) their inventions. And while studies like that by Mowery, Nelson, Sampat, and Ziedonis (2001) suggested that Bayh-Dole had little incentive effect on researchers, the act signified a serious interest in research and innovation by top policymakers.

Dual Paths of Tech Transfer: Incumbent Firms and Start-Ups

One measure of the interest in university tech transfer was the organization of at least two special issues in prestigious journals on the topic, contemporaneous to Open Innovation. The first, in Management Science, co-edited by David Mowery and Scott Shane in 2002, was a deliberately interdisciplinary approach to the multifaceted issues of tech transfer. In their introduction, Mowery and Shane (2002) observed, “Despite the growth in both formal and informal entrepreneurial activities involving university inventions over the past 20 years, little scholarly research has explored this topic. Recently, scholars from several different academic disciplines and universities have begun to systematically study and document commercial technology transfer and university entrepreneurship” (p. v.).

Consistent with the dual paths of technology transfer, some researchers examined technology transfer to large incumbents, including Cohen, Nelson, and Walsh (2002) who used the famous Carnegie-Mellon survey of industrial firms to study how publicly funded research affected big-firm R&D projects. They found that outside research suggests new projects and helps to finish existing projects, but only in certain industries, pharmaceuticals, and manufacturing. Thursby and Thursby (2002) worried that the new incentives to commercialize faculty research pressured scholars to shift their research, a concern raised by Henderson, Jaffe, and Trajtenberg’s (1998) study of the generality of university-based patents. They instead found that an increase in licensing was due to “an increased willingness of faculty and administrators to license and increased business reliance on external R&D.” Other studies focused on tech transfer to start-ups and found that personal connections to venture capital (Shane & Stuart, 2002) and to firms (Zucker, Darby, & Armstrong, 2002) helped start-ups survive and thrive.

The University’s Role in Tech Transfer

Finally, an additional perspective in this collection of studies is that of the university itself. A study (not in the special issue) asked the motivating question, “Why do some universities generate more start-ups than others?” (DiGregorio & Shane, 2003). University policies play a part, in addition to exogenous factors like the availability of venture capital and whether researchers at the university do commercially oriented work. Colyvas et al. (2002) presented case studies from Columbia University and Stanford University, focusing on commercially valuable research areas, such as pharmaceuticals and information technology. They found that financial incentives were not driving the research directions taken by faculty.

Might processes and structures within the university play a role? Mowery, Sampat, and Ziedonis (2002) measured a convergence of patent quality at universities with technology transfer offices but were unable to isolate a mechanism by which universities “learn” over time or from each other. One possibility, suggested by Feldman, Feller, Bercovitz, and Burton (2002), is organizational. They studied a specific “innovation” developed by university tech transfer offices, the equity investment in lieu of a license fee for a patent. Universities take equity in a firm instead of a licensing fee. This helps cash-strapped start-ups, but also aligns incentives between the university and the inventor. The use of this mechanism varies substantially across schools, possibly as a function of the office’s incentives (as a profit center or not) and experience with the ineffectiveness of traditional arms-length licensing.

These conclusions are consistent with those of a similar, qualitative study by Siegel, Waldman, and Link (2003) in their special issue of the International Journal of Industrial Organization in 2003. This collection of studies focused on the economics of technology transfer, and thus, a different set of topics (Link, Scott, & Siegel, 2003), including geographic clustering resulting from spillovers from universities, university incentives, policy and effects of science parks, and firm strategy. Studies by Nerkar and Shane (2003) and Panagopoulos (2003) addressed when it makes sense to engage with universities as a source of innovation—again, one of the key questions that Chesbrough left open.

Venture Capital, Erosion Factor #2

Chesbrough’s second, but perhaps most important, erosion factor is venture capital. Contemporaneous with Open Innovation was a growing interest in venture capital. Because venture capital deals with financial matters, much of the early research appeared in finance journals and, to some extent, remained fairly detached from the open innovation literature. Nevertheless, venture capital remained an essential part of the backdrop of the other, nonfinancial literature. For example, Gans, Hsu, and Stern (2002) inferred from their empirical results that “venture capitalists play a nonfinancial role in the strategy of start-up firms.”

The economic importance of venture capital attracted the notice of finance scholars. By 2000, 20% of publicly traded firms started out as start-ups funded by venture capital (Gompers & Lerner, 2001). And by financing firms in information technology, venture capital brought to market dozens of household names in computers, software, and communications—and changed the way Americans live and do business (Von Burg & Kenney, 2000). The literature sets out to document the venture capital business model and to explain the business model using financial logic. For example, the use of shares that could be converted from preferred to common (i.e., convertible securities) serves several purposes, including preventing entrepreneurs from misrepresenting the start-up’s prospects (Cornelli & Yosha, 2003), acting as an exit policy (Bascha & Walz, 2001), and facilitating early-stage financing (Bergloff, 1994; Trester, 1998).

Contracts, another documented aspect of venture investing practice, have also been examined from a financial perspective. Kaplan and Stromberg (2003) studied a sample of contracts from a Chicago venture capitalist and argued that the contracts were concerned primarily with the principal–agent and hold-up problems between venture capital and entrepreneur rather than a risk-allocation problem. They cited contract provisions that helped to align the entrepreneur’s incentives with the venture capitalist’s, such as antidilution protection, and conditions under which venture capitalists relinquish control (automatic conversion of preferred shares to common shares). Other incentive provisions include vesting and noncompete clauses for entrepreneurs. Entrepreneurs, who are typically resource-constrained, are at a disadvantage in terms of bargaining position, and so accede to venture capitalists’ demands for control rights and vesting of stock ownership (Hellman, 1998).

More descriptive nonfinancial literature also outlined this unique business model. Kenney and Florida (2000) cited a common rule of thumb for venture capital investments: “For every ten investments, three are complete losses; another three or four neither succeed nor fail; another two or three return three or more times the initial investment; and one or perhaps two investments return more than ten times the initial investment.” Venture capitalists are compensated at the end of the fund’s term, usually 10 years, by splitting returns with their investors (venture capitalists typically get 20% “carried interest”). But venture capitalists also make managerial contributions. Sahlman (1990) estimated that venture capitalists spend half their time monitoring their start-ups, which can involve active management, especially when they are lead investors (Gorman & Sahlman, 1989). This may explain why geographic proximity is important to venture capitalists (Lerner, 1995).

Statistical evidence of venture capitalists’ managerial contributions has been very difficult to disentangle from their ability to select promising start-ups. That is, if a venture capitalist were expert at selecting winning start-ups, they would have a good investment record even if they never made managerial contributions. One study from this period, by Baum and Silverman (2004), used Canadian data.

Entrepreneurship and Markets for Technology—Erosion Factor #3

Nerkar and Shane (2003), who examined start-ups that were created to commercialize university-based research, took the opposing perspective from Chesbrough’s vantage point of the large incumbent. The two perspectives are thus complementary, but because Chesbrough’s focal firm is large, the small start-up is a background factor, albeit an important one. By contrast, when the perspective is that of the start-up, incumbents loom large. Thus, Anton and Yao (1995) considered many of the same issues as Chesbrough but from the perspective of the inventor working in a large firm, and their inspiration was similar to Chesbrough’s. They observed that in 1989, 100 of the 500 fastest growing privately held companies (the Inc. 500) were based on ideas that founders had while working at a prior firm.

A series of papers by Gans and Stern expanded on this idea. They first considered the small firm’s options to license their invention to an incumbent or to be acquired by the incumbent (Gans & Stern, 2000). They also considered how the existence of start-ups and the possibility of technology licensing changes the incumbent firm’s R&D calculus. In an empirical follow-up, Gans, Hsu, and Stern (2002) tested their theory on a sample of start-ups. Here, they found that where venture capital and formal intellectual property rights are available, start-ups can sell their intellectual property to an incumbent (which the authors term a “cooperative” strategy). A third paper tackled head-on the question of whether a start-up should “cooperate” with an incumbent, as is common in the pharmaceutical industry, or whether it should grow to compete with an incumbent in the product market, as occurs in the computing industry (Gans & Stern, 2003). Again, intellectual property rights are a key part of the market for ideas that are central to this make-or-sell decision.

Indeed, the market for technology is a profound change to industrial organization, one which Arora and Gambardella (1994) traced back to a growing reliance on science as the basis for inventions. The result is the market for ideas that underpins Open Innovation and its contemporaries: Arora, Fosfuri, and Gambardella’s influential book, Markets for Technology (2001), addressed many of the same elements as Open Innovation but differed in terms of emphasis. For Arora and colleagues (2001), formal intellectual property rights, and the possibility of licensing new technology and inventions, were the primary focus, while for Chesbrough, formal intellectual property rights played a background role. Both viewed the mobility of workers with specialized technical knowledge as key, but Arora et al. treated the subject first. Chesbrough instead focused on the question of corporate strategy, the decision to make or buy innovation from the market, which Arora et al. discussed in a later chapter of their book. However, the importance of markets for technology is evidenced by the tech transfer literature as well as the outsourcing literature.

Innovation Outsourcing—Erosion Factor #4

Open Innovation exhorted firms to bring innovations and novel ideas into the firm from outside. Economic theories of outsourcing, applied to innovative inputs, therefore address this question. Several studies took various approaches to this question and have become influential, although largely because they informed the general question of outsourcing, rather than innovation.

Theory of the Firm

Poppo and Zenger (1998), like Chesbrough, were motivated by shifts in the business environment, but they focused on “apparent trends in recent decades toward disintegration, downsizing, and refocusing” (p. 875). They surveyed “top computer executives” who contract for information services, which include data entry, data center operations, network design, network operations, end-user support, training, application development, and maintenance (p. 863). This industry, the information services industry, was particularly interesting because “information services as an industry and internal activity were changing rapidly” (p. 874). Thus, Poppo and Zenger (1998) were drawn to the same phenomenon as Chesbrough, and for the same reasons.

However, their interest was to probe theories of the firm to assess what and how much a firm should access from outside. Thus, they targeted one of the key questions Chesbrough omitted. Poppo and Zenger (1998) devised a clever test of three competing theories of outsourcing: the knowledge-based theory of the firm (Grant, 1996; Kogut & Zander, 1992; Monteverde, 1995; Moran & Ghoshal, 1996), transaction cost economics (Williamson, 1991a, 1991b), and a theory of contracting costs (Barzel, 1982; Demsetz, 1988). In the knowledge-based view of the firm, firm-specific knowledge and routines within a firm are a source of efficiency. By contrast, transaction cost economics views firm-specific investments as difficult to outsource because supplier firms will be wary of being cheated: customers can underpay suppliers after they make the investment because the investments have little value on the open market. A contracting costs view of outsourcing predicts that hard-to-measure goods and services are unlikely to be outsourced.

Ultimately, Poppo and Zenger (1998) found support for transaction cost economics and for the contracting costs predictions, but not for the knowledge-based theory. One explanation is that in a setting of technological change, routines are less valuable and may even hinder performance, because “unique language, while efficient, may quickly become the wrong language” (p. 872). An important contribution of Poppo and Zenger (1998) was their use of theory to address practical questions of when and where to be “open.”

Contracting for Innovation

A variation on the theme of outsourcing innovation, also set in the computer industry, is a study by Mayer and Argyres (2004). They offered a longitudinal case study of a computer manufacturer working with a software developer. This setting, like Poppo and Zenger’s (1998), was consistent with Chesbrough’s focal examples. Mayer and Argyres’s (2004) data comprised a series of 11 contracts over the course of an almost decade-long relationship, supplemented by interviews with participants. This allowed an analysis of learning, adaptation, and change over time. There are, of course, limits to what can be inferred from a single case study; however, this particular case goes to the heart of Chesbrough’s fourth erosion factor, the innovative capabilities of supplier firms—in this case, software companies. Mayer and Argyres (2004) studied this problem, which is at the contract level of analysis, by applying transaction cost economics to understand how two partners can change and learn over the course of a relationship.

Alliances for Knowledge Transfer

Two studies that frame the make-or-buy or outsourcing question as a partnership rather than a buyer–supplier relationship are a study by Mowery, Oxley, and Silverman (1996), which was awarded the Strategic Management Journal’s Best Paper prize in 2017, and a study by Eisenhardt and Schoonhoven (1996). Like Poppo and Zenger (1998), Mowery and colleagues (1996) sought to disentangle theories for why firms partner with other firms to innovate. Here, too, they observed the same trends that Chesbrough addressed, namely that “the rate of formation of alliances has increased significantly over the last two decades and the motives for their establishment have shifted, as alliances have become widespread in technology-intensive industries (e.g., semiconductors, computers, software, commercial aircraft) in which they were of little or no importance prior to 1975” (p. 78).

For Mowery et al. (1996), the key question was whether firms partner with outsiders in order to acquire new capabilities, as suggested by a resource-based view of the firm (Barney, 1986; Penrose, 1959; Wernerfelt, 1984) and dynamic capabilities theory (Teece & Pisano, 1994; Teece, Pisano, & Shuen, 1997), or whether they partner in order to avoid having to bring specialized knowledge or capabilities into the firm (Grant & Baden-Fuller, 1995; Nakamura, Shaver, & Yeung, 1996). Mowery and colleagues (1996) assumed that if a firm cites its partner’s patent, the firm’s knowledge overlaps with its partner’s. Using patent citations as a measure of knowledge overlap, they examined whether alliances increase or decrease knowledge overlap. They found evidence of both types of alliances. In addition, they found evidence of organizational issues that Chesbrough discussed, including the role of equity investment in an alliance (Kogut, 1988) and the role of in-house research in helping a firm use outside research—“absorptive capacity” (Cohen & Levinthal, 1990).

In their study, Eisenhardt and Schoonhoven (1996) argued for a resource-based theory of alliances and against a transaction cost explanation, especially under uncertainty. Thus, they focused on industries undergoing rapid technological change, the same setting as Chesbrough’s. In such turbulent environments, firms are more likely to form alliances, not less likely, as predicted by transaction cost economics (Hennart, 1988, 1991; Pisano & Teece, 1989; Shan, 1990; Williamson, 1991b). Eisenhardt and Schoonhoven’s prediction of greater alliance formation was consistent with a resource-based view of the firm (Peteraf, 1993; Wernerfelt, 1984) in which firms use their resources strategically to survive. Using data from the semiconductor industry, they showed that start-ups in the emergent stage of the industry used “social resources,” i.e., top managers’ prior industry connections, to form advantageous alliances with large incumbents and/or customers. Eisenhardt and Schoonhoven introduced the role of managers’ personal connections as an important resource for start-ups, which was an undercurrent in Chesbrough’s work, as well. For example, Chesbrough described spin-outs from Xerox, which were born with social connections to a big incumbent firm. Eisenhardt and Schoonhoven probed this issue, taking the point of view of the start-up, by studying a setting in which some start-ups are born as spin-outs and others are not, to provide further insight on start-up survival.

Note that these studies of innovation outsourcing, unlike Chesbrough’s, were all concerned with the general theory of outsourcing and alliances. Thus, they contribute important insights on the boundary conditions of openness, as well as the rationale, trade-offs, and processes. However, while all dealt explicitly with innovative activity, in which innovation is acknowledged to be a core element of the study, only Chesbrough elevated the term innovation to the title of the study. The other studies are interpreted as applying widely to outsourcing and alliance settings generally, even though they were inspired by the same innovation-centered shift in the economy. Thus, Poppo and Zenger followed up their 1998 study with a 2002 study, using the same data, on formal and relational contracting (Poppo & Zenger, 2002). The latter study won the Strategic Management Journal’s Best Paper prize in 2019. Similarly, Mayer and Argyres followed up their study of software development with studies, using the same data set, of contracting provisions and contract design (Argyres, Bercovitz, & Mayer, 2007; Mayer & Bercovitz, 2008).

Growth of the Open Innovation Literature

Several reviews of the open innovation literature have appeared since Chesbrough first published his ideas. They provide valuable perspectives on the directions that researchers have taken. First, a literature that directly references Chesbrough has delved into questions of strategy, management, and process. That is, how do firms do what Chesbrough recommended? Literature reviews classify this research in a variety of ways, including by level of analysis and by type of open innovation. Second, the contemporaneous literature that did not reference Open Innovation has likewise grown, especially literature on make-or-buy (or vertical integration) questions related to “knowledge inputs.” Third, there has been a relative neglect of outbound open innovation, aside from a few studies.

To some extent, these trends relate to shortcomings of the original ideas. Helfat (2006) mentioned a lack of clear definition of open innovation, including a clear definition of innovation, which has led to some confusion among researchers (West & Bogers, 2014).

Extensive Growth of Research on Open Innovation—Reviews

Several excellent literature reviews take differing bibliometric approaches (i.e., counting and classifying scholarly papers) to the open innovation literature.

Drawing Clearer Distinction Between Inbound and Outbound Open Innovation

Dahlender and Gann (2010) classified open innovation along two dimensions, inbound vs. outbound and pecuniary vs. nonpecuniary. By pecuniary, the authors meant immediate financial gain as through a transaction, noting that some interactions, such as patent licensing, involve an explicit financial component while others do not. More importantly, this classification brings to the fore the distinction between inbound and outbound open innovation. Chesbrough viewed both types of open innovation as aspects of the same phenomenon, but follow-on research on outbound open innovation, despite the concept’s pride of place as Chesbrough’s first chapter, has lagged compared with work on inbound open innovation.

Phases of the Innovation Process

West and Bogers (2014) organized the literature according to the process of inbound open innovation. They identified four phases: obtaining outside innovation, integrating the innovation with internal resources, commercializing innovation, and interactions with external innovators. Each of the four phases was subdivided further, resulting in an extensive taxonomy that runs three levels deep. For example, the “obtaining” phase includes such categories as “searching,” “filtering,” and “acquiring.”

Levels of Analysis

A collaboration by two dozen open innovation scholars (Bogers et al., 2017) reviewed the literature as it spans different levels of analysis, including individuals, groups, firms, networks, communities, regions, and beyond. One goal of such an undertaking was to identify new research opportunities. For example, a proposed research category, “open innovation behavior and cognition,” could cross multiple levels of analysis, from individual-level cognition to group performance to corporate culture.

Text Analysis of Research Topics

A bibliometric study by Lopes and Carvalho (2018) documented the continued growth in the number of studies published on open innovation. Using text analysis, the study found the distinction between inbound and outbound open innovation observed by Dahlender and Gann (2010) and classified other topics, including efforts to measure innovative performance and financial performance. Another set of topics studied firms’ ability to be open, which involves capabilities and absorptive capacity, and found greater difficulty for small to medium-sized firms. There has been, however, scant attention paid to another of Helfat’s (2006) critiques: boundary conditions. When should firms be open and when should they be closed?

How to Implement Open Innovation

Scholars trying to understand how firms actually implement open innovation address the boundary condition. Taking up Chesbrough’s argument that openness is beneficial to firms, the questions for management and strategy researchers are: How and When? A series of papers by Laursen and Salter (2004, 2006, 2014) served as guideposts because the authors operationalized openness. Using a national survey of British manufacturing firms, Laursen and Salter were able to measure firms’ use of 16 outside sources of information, including suppliers, customers, competitors, and universities, and to judge how important each source was “as a source of knowledge or information in innovation activities of firms” (Laursen & Salter, 2004, p. 1208). Performance is also gauged in terms of how successful a firm has been in commercializing an innovation. And “appropriability strategy” can be measured as the intensity of a firm’s use of six different appropriability mechanisms: patents, design registrations, trademarks, secrecy, lead time, and complexity.

A survey of American manufacturers took a similar approach to estimating the share of firms using “external sources of invention” (Arora, Cohen, & Walsh, 2016). And surveys designed specifically to understand open innovation allow for greater precision in understanding firms’ internal processes and project-level successes and failures (Brunswicker & Chesbrough, 2018; Chesbrough & Brunswicker, 2014).

Openness as a Practice

Laursen and Salter (2004) were initially interested in understanding whether start-ups are more likely to access universities for innovation than incumbent firms. Thus, they connected the technology transfer literature with the entrepreneurship literature. They found low levels of utilization of universities overall, even among start-ups, but they noted that their sample contained manufacturing firms, not the sort of high-tech firms, like biotech or nanotech firms, that might make more use of universities. Incidentally, they found that the firms that do make use of universities also access other outside sources of information for innovation. That is, firms involved in technology transfer are also more “open” to other, non-university, sources.

This generalized practice of openness to outside sources is suggested by Chesbrough’s terminology, open business model (Chesbrough, 2007). In a time of industry upheaval, large incumbents like Procter & Gamble, Qualcomm, and Genzyme can reduce the cost of innovating by being more open. Thus, an open business model is one that utilizes other firms to contribute to innovation. Chesbrough pointed out that outbound open innovation can also help the bottom line as a lucrative source of profits.

With regard to start-ups, additional empirical research is consistent with Laursen and Salter (2004) in challenging the stereotype that start-ups mainly commercialize existing technology. A study of 3D printer start-ups by Greul, West, and Bock (2017) found differences in both inbound and outbound open innovation based on capabilities and strategy. Start-ups with technical capabilities founded by “intentional” entrepreneurs pursued a less open approach to innovation than start-ups founded by less-resourced “accidental” entrepreneurs.

Openness as Strategy

Chesbrough’s (2003) analysis of case studies was clearly motivated by the notion that openness is a strategic choice. Chesbrough and Appleyard (2007) began to engage with the language and literature of strategy, considering how open source and communities are an open innovation strategy that can advance the field of strategy.

But a key issue in strategy is performance, and Laursen and Salter (2006) provided the data to empirically study the relative outcomes of openness decisions. Their large-scale study of innovative performance built on their earlier observation that “open” firms often access multiple sources. Using the British survey data, they operationalized openness, measuring the “breadth” of a firm’s openness as the number of different parties it sourced information from, and the “depth” of its openness as the importance of that source to the firm’s innovative efforts. They then evaluated the classic strategic question of whether openness affects performance. They found that openness improves commercialization performance but only to a point, after which performance declines—that is, they found an inverse-U relationship. Openness has its costs and firms can be too open or, in their interpretation, can “over-search” (Katila, 2002).

In addition to the number of outside information sources, the type of partner is also important to open innovation outcomes. Du, Leten, and Vanhaverbeke (2014) studied hundreds of R&D projects at a single large firm. Data on the market performance of each project made it possible to assess the strategic choice of partner type and practice. Dividing partners into science-based and market-based categories, they found that formal project management improved market-based partnership outcomes but reduced science-based partnerships.

Another important construct from strategy is value creation and value capture, to which Chesbrough’s focus on business models made important contributions. Chesbrough et al. (2018) proposed a 2 × 2 of open innovation modes that compared their strengths in value creation or value capture and whether the value of the innovation arises through its use by the innovator (value-in-use) or via its use by customers (value-in-exchange). In addition, platform business models are a different way for firms to engage the outside world (Chesbrough, 2017). Platforms are firms that service two or more distinct customer sets that benefit from each other. For example, Apple serves (at least) two sets of customers, smartphone buyers and app developers, with its platform, the iPhone and app store. As the platform firm occupying the space between customer sets, Apple interacts with outside firms to promote innovation. A conceptual study by Nambisan, Siegel, and Kenney (2018) placed open innovation in the context of a platform firm and argued that the constellation of outside sources must be strategically chosen. Studies like this extend open innovation to situations that are increasingly important economically and that may have consequences for policymakers.

Managing Relationships in Open Innovation

Laursen and Salter’s early empirical studies brought detail to the idea that openness is a strategy involving routines throughout a firm. A firm’s daily interactions with the outside world, whether suppliers, customers, or competitors, are the basis of openness. In their 2014 study, Laursen and Salter further connected internal routines and processes to openness. Specifically, they studied appropriability strategy, operationalizing it as the number and importance of appropriability mechanisms. The reasoning was that outside information is less strategically valuable if other firms can access it, too. So, a firm that can aggressively protect that information may benefit more from it. This is similar to the work of Greul et al. (2017), in that both studies examined internal firm capabilities as a factor in innovative openness. The authors found support for their idea that appropriability strategy and open innovation strategy are connected—again, with an inverted-U relationship—as stronger appropriability allows firms to utilize more outside sources, but only to a point.

While Laursen and Salter (2014) considered a set of arms-length appropriability mechanisms, other studies took a closer look at the relationships between a firm and its sources, considering not only what kinds of control a firm can exert but also how much it should exert. Zobel and Hagedoorn (2020) brought together the strategic problem of whether or not to engage outside firms with the management problem of how to interact with the outside firms. They argued for the use of relational contracting rather than the use of more formal contracts. In some respects, this may better capture the idea of pecuniary and nonpecuniary activity proposed by Dahlender and Gann (2010). Indeed, the idea of using soft power even when a firm has superior bargaining power or control rights was further echoed by Gambardella and Panico (2014).

Growth of the Literature on Technology Outsourcing

The literature contemporaneous to Open Innovation, on the make-or-buy problem for innovation, has continued to grow alongside the literature on open innovation. A robust conversation, too extensive to review here, has applied theories of the firm to address the questions of when and how to access outside innovation. Thus, this parallel literature has much to offer the open innovation literature, and it often focuses more on the details of the technology, firms, and the industry environment than the open innovation literature.

For example, Macher’s (2006) study of semiconductor firms found that closed innovation is more effective when technology is more uncertain. In a study of the same industry, Kapoor (2013) similarly found that closed innovation is more effective for systemic innovation.

Characteristics of firms also affect the success of innovation outsourcing. For example, technological overlap between two firms affects the ability to make use of outside innovations (Sears & Hoetker, 2014). Rost (2011) explained this in terms of network theory, which Grigoriou and Rothaermel (2017) studied empirically using patent data. Moreover, an extensive literature on technology alliances studied the ability to cooperate and learn (see, for example, Hoang & Rothaermel, 2010).

The wider value chain and ecosystem also come under scrutiny, as innovative performance depends on the innovative performance of upstream firms, downstream firms, and on the stage of the technology life cycle (Adner & Kapoor, 2010). Ganco, Kapoor, and Lee (2020) combined these concerns, integrating technological interactions with the wider ecosystem of upstream and downstream firms.

Future Research Opportunities and Conclusion

The continued interest in Open Innovation bodes well for the expansion of topics yet to be fully explored, and West and Bogers (2017) presented one rather extensive wish list. Direction, inbound and outbound, has been described, but a better understanding, especially of outbound innovation, is needed. While the case of Xerox’s many successful spin-outs is featured in Chapter 1 of Open Innovation>, outbound open innovation has been relatively understudied. As Bogers et al. (2017) pointed out, “In contrast to the outside-in branch, this portion of the model is less explored and hence less well understood, both in academic research and also in industry practice.” But Chesbrough remained convinced that firms can and should use outbound or inside-out innovation to unlock the value of innovations that others are better suited to commercialize. “Outside-in flows seem to be the primary area of activity, both in academic research and in corporate practice. But there’s a second pathway that a lot of people tend to overlook—inside-out flows—and there’s value to be had there as well” (Chesbrough, 2017). Indeed, surveys are enabling researchers to assess firms’ strategic use of inbound and outbound knowledge flows as well as the different mechanisms and practices they employ for each (Brunswicker & Chesbrough, 2018).

Also, while Chesbrough focused on large firms, small firms, governments, and nonprofits need to be further studied. Open innovation as an interaction between two firms has been studied, but networks should be studied more. Crowdsourcing is an interesting form of open innovation, but individuals play an important role beyond this phenomenon. In addition, measuring performance, including failures, could contribute to the understanding of open innovation.

One weakness of the open innovation literature is its engagement with theory, hence the parallel literature on alliances, vertical integration, and ecosystems that has arisen to address related questions. West and Bogers (2017) also called for greater engagement with theoretical work, including absorptive capacity, user innovation, business model theory, and theories of the firm, especially the resource-based view of the firm, but also transaction cost economics and the knowledge-based view of the firm.

Some of these wish-list topics may be related and thus collapsed. Take, for example, inbound open innovation, the better-understood form of open innovation. The counterpart to inbound open innovation is outbound open innovation. That is, the innovation going into a firm is coming from some other source as outbound innovation. This source might be a university, a customer (or user), a supplier, etc. Accordingly, outbound open innovation could be studied from the perspective of the counterpart firm, rather than the focal firm, as Chesbrough did. Such an approach would provide structure for studying the incentives of government or university sources, customers, and small to medium-sized supplier firms. Customers can also be studied as users, for which an extensive literature on lead users exists (Shah & Tripsas, 2007; von Hippel, 1986), and as communities (Chesbrough & Appleyard, 2007).

Another theoretically motivated direction was offered by Felin and Zenger (2014), who applied a problem-solving framework to the great variety of open innovation forms. They took innovation as a “problem” to be solved by a firm. Like all problems, each innovation problem has certain characteristics that match better with some governance forms than others. Innovation governance includes such diverse forms as crowdsourcing, alliances, markets, closed innovation, etc. The problem-solving approach takes an expansive view of the many diverse forms of open innovation and compares them in a discriminating way.

An open question that West and Bogers (2017) did not raise is that of open innovation among rivals. The alliances literature sometimes deals with partners in the same industry, such as automotive firms that enter into joint ventures, but the open innovation literature has omitted such cooperative phenomena. This is despite their importance in innovative settings, such as technology standard-setting, research consortia, and more. This “horizontal” open innovation would bring new phenomena and governance forms into the open innovation literature and could inform analyses and comparisons like done by Felin and Zenger (2014). Are “communities” actually horizontally related groups of peers? If so, communities might be analytically distinct from customer/user sources of innovation, since customers are vertically “downstream” from the focal firm.

A final issue connects strategy with policy. Knott (2017) raised the question of innovation as an engine of economic growth and the disturbing decline in R&D productivity and GDP (p. 2). She cited an increase in “scientific labor” of 2.5 times over the past 40 years, a period of, at best, stagnant GDP growth and attributed the finding to a 65% decline in R&D productivity by her calculation. Knott (2017) developed a measure called the Research Quotient (RQ) to assess the efficiency of a firm’s R&D efforts. In some cases, this involves such open innovation strategies as acquisitions, but at other times it may be more efficient to patent in-house. For example, publicly available data show that a small firm, Trimble Navigation, changed R&D strategy over its lifetime from in-house patenting to the use of acquisitions as the sole means of acquiring intellectual property. The firm’s RQ increased over the in-house patenting period and declined steadily over the acquisition-only period. This contrasts with AT&T’s experience over the same period, in which in-house R&D generated steady and rapid growth and high RQ. Knott did not mean to imply with these examples that open innovation is counterproductive. Rather, she proposed a way to assess innovation strategy at the firm level in order to guide firms away from the wrong strategy and toward the right strategy. And while the “right strategy” is firm-specific, she nevertheless made progress on the question of how and when to use open innovation, with implications for economic growth.

To conclude, open innovation as explained by Chesbrough (2003) has been a compelling topic for researchers and practitioners. Its success as a set of ideas can be measured bibliographically in the number of studies scholars have done and in the concept’s use in the popular press. But from the start, critiques pointed out shortcomings in terms of theoretical rigor; that is, clear definitions (including definition of innovation) and boundary conditions (i.e., when various varieties of open innovation should be used). These critiques are less criticism than articulation of areas for future research, and scholars who reference Open Innovation (and those who do not) have made progress on these implementation questions. Significant opportunities remain for applying further theoretical rigor, in part by working within the open innovation literature and in part by connecting to a strategy and management literature that tackles these same themes.

Discussion of the Literature: Primary Sources

Chesbrough excelled at identifying interesting cases that illustrate complex concepts. His description and explanation made the material accessible and memorable to a broad audience. Therefore, the relevant primary sources for understanding open innovation are the three books on the topic, Open Innovation (Chesbrough, 2003), Open Business Models (Chesbrough, 2006a), and Open Services Innovation (Chesbrough, 2011).

Further Reading

  • Alexy, O., Frederiksen, L., & Hutter, K. (2020). Quo Vadis, open and user innovation theory? Suggestions for more theory-building. Innovation, 22(2), 97–104.
  • Huizingh, E. K. R. E. (2011). Open innovation: State of the art and future perspectives. Technovation, 31, 2–9.
  • Tucci, C. L., Chesbrough, H., Piller, F., & West, J. (2016). When do firms undertake open, collaborative activities? Introduction to the special section on open innovation and open business models. Research on open business models from the first World Open Innovation Conference. Industrial and Corporate Change, 25(2), 283–288.

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