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Dynamic capabilities in the public sector: The case of the UK’s Government Digital Service

This study by the UCL Institute for Innovation and Public Purpose explores the concept of dynamic capabilities in the public sector using the UK’s Government Digital Service (GDS) as a case study.

8 January 2021

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UCL Institute for Innovation and Public Purpose (IIPP) Working Paper Series: IIPP WP 2021/01

• Rainer Kattel | Professor of Innovation and Public Governance, Deputy Director, UCL Institute for Innovation and Public Purpose
• Ville Takala | Honorary Research Fellow, UCL Institute of Innovation and Public Purpose

Kattel, R. and Takala, V. (2021). Dynamic capabilities in the public sector: The case of the UK’s Government Digital Service. UCL Institute for Innovation and Public Purpose, Working Paper Series (IIPP WP 2021/01). Available at: https://www.ucl.ac.uk/bartlett/public-purpose/wp2021-01

This study explores the concept of dynamic capabilities in the public sector. Using the UK’s Government Digital Service (GDS) as a case study, we demonstrate how such capabilities form and how they evolve over time. Drawing on expert interviews with former and current employees, we argue that GDS’s success was based on introducing new ways of working and providing value in government. Through successfully professionalising such new skills and notions of value across government, GDS eventually undermined its own dynamic capabilities. Drawing on our findings, we show that dynamic capabilities are systemic resources and abilities to question existing routines and capacities. We conclude by arguing that dynamic capabilities need periodic renewal and nurturing as they are constantly being absorbed into existing routines.

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Dynamic Capability as a Theory of Competitive Advantage: Contributions and Scope Conditions

Warwick Business School, University of Warwick, Coventry, England

Saïd Business School, University of Oxford

• Published: 07 March 2016
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Dynamic capability is a theory of competitive advantage in rapidly changing environments. We reconcile this explanation with previous theories of competitive advantage, showing how it informs and complements explanations based on market positions, firm resources, and Schumpeterian creative destruction. We examine the scope conditions of dynamic capability; that is, when the theory has more and less explanatory power. We find that dynamic capability has greatest explanatory power when a partially foreseeable technological change is on the verge of transforming market competition; and less explanatory power when dynamic capabilities are not undervalued or scarce; when change is unforeseeable; when change is easily foreseeable; when the effect size of new capabilities is small; in industries subject to repeated technological shifts; and in markets that reward short bursts of extraordinary performance over long-term persistence. We discuss these scope conditions and show how dynamic capability combines with prior theories to explain competitive advantage in different industry contexts.

1 Introduction

The theory of dynamic capability explains why firms succeed or fail in market competition. Teece (2007) wrote: “The ambition of the dynamic capabilities framework is nothing less than to explain the sources of enterprise-level competitive advantage over time, and provide guidance to managers for avoiding the zero profit condition that results when homogeneous firms compete in perfectly competitive markets” (2007: 1320). This is consistent with the formulation in Teece et al. (1997) : “The fundamental question of strategic management is how firms achieve and sustain competitive advantage. We confront this question here by developing the dynamic capabilities approach” (1997: 509).

In proposing a theory of dynamic capability Teece et al. (1997) argued that existing theories failed to address the conditions of twenty-first-century competition; that is, they could not explain competitive advantage when competitive forces and resource-based advantages were subject to rapid obsolescence. To compete in conditions of rapid innovation and global competition, firms cannot rely on traditional sources of advantage such as industry structures and strategic positions (scale economies, vertical integration, product differentiation); baseline capabilities in product development, manufacturing, or marketing; or the efficiencies of learned routines and standard operating procedures. Only by building a super-capability for change itself—the capacity to sense, seize, and shape new market opportunities—could firms thrive in the market volatility and technological dynamism so prevalent in twenty-first-century global competition.

This paper examines dynamic capability as a theory of competitive advantage. Market volatility and technological dynamism are not unique to dynamic capability theory, but dynamic capability theorists brought new claims, and a new emphasis, that did not exist in previous theories. Hence, we examine the theory’s contributions to strategic management theory and research, and we explore its scope conditions; that is, the limits of its contributions and the conditions under which dynamic capability or other theories may provide the best explanation of competitive advantage.

2 Dynamic Capability and Competitive Advantage

By the mid-1990s, the concept of competitive advantage was well established in strategic management, and several theories provided explanations of superior returns in market competition. Porter’s theories attributed competitive advantage to protected market positions in structurally attractive industries or segments ( Porter, 1980 ), or to cost or differentiation advantages supported by activities in a value chain or activity system ( Porter, 1985 , 1996 ). The resource-based view attributed competitive advantage to resources and capabilities protected from imitation by cost, scarcity, or causal ambiguity ( Penrose, 1959 ; Wernerfelt, 1984 ; Barney, 1986 ). Evolutionary views attributed competitive advantage to selection processes, learned routines, and innovative capabilities in the face of Schumpeterian creative destruction ( Nelson and Winter, 1982 ; Dosi and Nelson, 1994 ). These theories generated productive debates on firm performance—for example, on whether success derives from industry conditions or firm-specific resources, from innovation or imitation, from market positions or internal routines, from strategic flexibility or long-term commitment. Regarded collectively, these theories offered a broad palate of ideas and stimulated productive empirical research on competitive advantage.

At the same time, these theories emerged from different assumptions and intellectual traditions and did not offer a cohesive or consistent view of sustained competitive advantage. Many questions remained unanswered. How do resources create competitive positions, or derive from them? Under what conditions do competitive positions, resources, or selection processes dominate? How can positions and resources create advantages under creative destruction? Can globalization and new technologies change the nature of competitive advantage? Despite the range of theories of competitive advantage prior to dynamic capability theory, there were few bridges between the theories and no cohesive or consensus view of competitive advantage.

A few scholars recognized this problem and took steps to bridge the gap—for example some of Porter’s frameworks (value chain, activity systems) can be interpreted as efforts to bridge industrial economics with resource-based thinking. Other theorists linked industrial economics with evolutionary theories of the firm. For example, the first issue of Industrial and Corporate Change featured this statement from the editors:

The firm is very poorly understood with respect to both structure and behaviour … Hence, the need for a journal to help stimulate and accommodate research on the business enterprise, particularly as it relates to issues of change. We are also especially interested in industrial structure, by which is meant, the relationships which exist among firms and between industry and other institutions including governments. ( Dosi et al., 1992 : vii)

In this context the paper by Teece et al. (1997) can be seen as an attempt to build new bridges between the resource-based view and evolutionary theories of the firm (see Figure 1 ). Many scholars recognized the need to bridge the resource-based and evolutionary theories, and ideas such as “temporary advantage” and “hypercompetition” had emerged by the mid-1990s ( D’Aveni, 1994 ). Empirical data suggested that market volatility was on the rise, and industry leadership in profit rates and shareholder returns was becoming less persistent. But none of the existing theories explained how firms could sustain resource-based advantages when such advantages were inherently destabilized by global competition and technological innovation. This was the contribution of dynamic capability theory, which stepped into the breach to reconcile the resource-based view with evolutionary theories of competitive advantage.

Theories of competitive advantage

The text of Teece et al. (1997) shows how concerned the authors were to link resources and capabilities with theories of innovation and creative destruction. The authors defined dynamic capabilities (hereinafter DCs) as “the firm’s ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments” (1997: 516). The authors argued that Porter’s theories and the resource-based view provided “analyses of firm-level strategies for sustaining and safeguarding extant competitive advantage,” but “performed less well with respect to assisting in the understanding of how and why certain firms build competitive advantage in regimes of rapid change.” Hence, strategic management needed a theory to explain “the strategic problem facing an innovating firm in a world of Schumpeterian competition” (1997: 515).

The original paper by Teece et al. (1997) did not provide a complete or concise theory of competitive advantage. Rather, it provided a general landscape of definitions and concepts, drawn largely from the existing vocabulary of evolutionary economics (routines, learning, innovation, path dependence) and the resource-based view (capabilities, core competences). These concepts laid the foundation for future developments. Teece (2007) , for example, defined four categories of DCs: sensing opportunities and threats; shaping the evolution of markets and innovations; seizing market and technological opportunities; and managing market and technological threats. These capabilities, supported by mechanisms such as design skills for creating new business models, and cognitive and creative skills for sensing new opportunities (see Hodgkinson and Healey, 2011 ), began to fill in the behavioral and cognitive micro-foundations of a more complete theory of dynamic capability. Merging these ideas with the original 1997 constructs, Teece argued that routines and learning processes are “a subset of the processes that support sensing, seizing and managing threats; together, they might be thought of as asset ‘orchestration’ processes” ( Teece, 2007 : 1341).

Teece’s versions of dynamic capability made a crucial distinction between baseline capabilities (such as market knowledge and manufacturing expertise) and DCs (such as the ability to transform markets and learn new manufacturing technologies). Baseline or “zero-level” capabilities ( Winter, 2003 ) can drive firm success during periods of stability, but higher-level DCs enable the firm to foresee industry trends, anticipate market opportunities, adopt or create new technologies, and transform the organization in periods of rapid change. As in the resource-based view, firms have heterogeneous resource portfolios, and some firms will possess stronger DCs than others. However, Teece argued that the resource-based view, by focusing on gaining and protecting advantages in baseline capabilities, was “inherently static” (2007: 1344). Even in relatively stable industries, “the wide diffusion of knowledge with respect to such functions means that much can be outsourced or implemented inside any enterprise with relative facility” ( Teece, 2007 : 1345). Hence, “Absent a broader overarching set of dynamic capabilities, a firm that is merely competent in operations will fail” (2007: 1346). In unstable markets, when baseline capabilities tend rapidly to obsolescence, success requires higher-level “orchestration” processes for sensing and seizing new opportunities.

The original Teece et al. paper spawned a large number of commentaries and extensions, which continue to the present day (e.g., Helfat et al, 2007 ; Teece, 2009 , 2014 ). Some of the commentaries took a fairly critical tone: for example, Winter (2003) suggested that the theory was full of “mystery and confusion” (2003: 994); and Williamson (1999) argued that the definition of core competence in Teece et al. (1997) was “underdeveloped” and “very nearly circular,” and that dynamic capability contained “no apparatus by which to advise firms on when and how to reconfigure their core competences” ( Williamson, 1999 : 1093) Hence, “the argument relies on ex post rationalization: show me a success story and I will show you (uncover) a core competence” (1999: 1093).

Eisenhardt and Martin (2000) criticized Teece et al. (2007) for dismissing best practice and operational expertise, since competitive advantage in dynamic environments often stems from superior routines and processes in fundamental areas such as product development, strategic decision making, and strategic alliancing. Rather than being valuable, rare, inimitable, and non-substitutable, the most effective capabilities are “valuable, somewhat rare, equifinal, substitutable, and fungible” ( Eisenhardt and Martin, 2000 : 1111). As Peteraf et al. (2013) have shown, the Teece et al. and Eisenhardt-Martin frameworks represent two fundamentally different strands of dynamic capability theory drawn from separate domains of knowledge.

We believe that the primary contribution of dynamic capability theory to strategic management theory and practice derives from its claim that the landscape of global business competition has fundamentally changed, and that the new competitive environment requires new ways of thinking about competitive advantage. Disagreements persist across various frameworks of dynamic capability, but all of them bring the empirical fact of increasing competitive dynamism to bear on the limitations of existing theories of competitive advantage. In Teece’s words, “open regimes of free trade and investment, global dispersion in the sources of new knowledge, and the multi-invention or systemic character of this innovation have ‘upped the ante’ for modern management” (2007: 1346). By insisting that strategic management adapt itself to contemporary market conditions, dynamic capability has brought new vigor to strategic management theory and improved its relevance to management practice.

3 Empirical Evidence

The theory of dynamic capability does not claim to invalidate previous theories of competitive advantage or to explain competitive advantage universally. As mentioned, the theory arose specifically in response to competitive conditions that emerged at the end of the twentieth century. Thus, we believe it is important to examine the scope of its applicability to a broader range of contexts. Does dynamic capability theory explain competitive advantage in stable industries? Does it apply when innovation is costly and time-consuming? Does it apply in conditions of extreme technological risk? Can firms over-invest in DCs? To begin our analysis of these questions, we examine empirical evidence on the effectiveness of DCs.

No one doubts that innovation is a good thing, and it is certain that a firm possessing the kinds of “orchestration” capabilities discussed by Teece (2007) would have advantages over firms that did not possess them. So the vital question is not whether DCs matter, but whether they always matter—and if so, how much they matter and over what period of time .

Any theory based on competitive capabilities must confront the fact that competitive performance is never caused by capabilities alone. Aside from other causes of competitive success (reputation, endowments, position, influence), there is always an element of chance or randomness in competition ( Powell, 2003 ; Denrell, 2004 ). If chance is important, capabilities may not explain much of the outcome, even if some competitors are far more capable than others. Because randomness matters, and because it varies across domains, capabilities matter more in some domains than others—for example, more in tennis than in fishing, and more in chess than in roulette or Monopoly.

Randomness can determine competitive outcomes even if capabilities are essential to success. This happens when players have similar training or are drawn from similar pools of talent, or when there is a natural threshold of ability that competitors are asymptotically approaching. For example, rowing is a game of skill but rowers at Oxford and Cambridge are drawn from the same pool of talent, and the outcome of 160 years of the Oxford vs. Cambridge boat races is indistinguishable from a random walk. Chess is a game of skill but games between international grandmasters often result in a draw, and tournaments at the highest level have been decided on whether a player slept poorly the night before or was distracted at a crucial moment.

If DCs lie at the core of enterprise success, what should we expect to observe empirically? At an aggregate level, we should observe profit and growth persistence in industrial markets, and persistence should be driven by firm-level differences in DCs. At a micro level, we should find that profitable firms are those that identify and profitably invest in new market opportunities, and we should observe persistence in DCs over time—that is, firms that identify and profitably invest in new market opportunities should maintain these capabilities over time. Of course, the degree of persistence depends partly on the capabilities themselves—for example, whether they operate at the level of the CEO, top management team, or functional areas. But whatever their source, if they count as competitive advantages we should observe persistence at the level of the firm.

In the paragraphs below, we review the empirical evidence on profit persistence and on whether the capacity to invest in new market opportunities is sustainable. Overall, we find that the empirical evidence is mixed and not generally consistent with a large effect size for firm-level dynamic capability.

3.1 Profitability

There is abundant evidence that profit rates persist to some degree ( Cubbin and Geroski, 1987 ; Jacobsen, 1988 ), although they regress to the mean and relatively few firms have high profitability for long periods of time ( Waring, 1996 ; Henderson et al., 2012 ). While firm differences explain a large share of profit variation ( Rumelt, 1991 ; McGahan and Porter, 2002 ), evidence suggests that industry factors may have more influence on the persistence of firm-specific advantages ( McGahan and Porter, 1999 , 2003 ).

There is little evidence that successful firms consistently sense and seize new market opportunities. Guided by the profit data, a more plausible theory is that successful firms capture a new market opportunity once, either by ability or chance, and then exploit this opportunity until industry conditions change. Persistence is scarce, and nothing in the profit data suggests that firms display significant mastery over the process of strategic change. Indeed, as much as half of profit variance cannot be explained by attributes of firms or industries ( McGahan and Porter, 2002 ), and much of the variation is due to temporary events that have no plausible connection to DCs. Overall, the evidence on persistence suggests that no single firm-specific explanation, including dynamic capability, accounts for profit persistence.

3.2 Entrepreneurial Performance

Empirical studies show a degree of persistence in entrepreneurial success—that is, entrepreneurs who succeed in one venture are more likely to succeed in the next ( Gompers et al., 2010 ; Parker, 2013 ). The effect does not last long, however. According to Parker (2013 : 662), “these positive effects are nearly completely exhausted by the end of the next spell.” Gompers et al. (2010) found that companies with a previously successful entrepreneur have a predicted success rate of 30.7 percent, whereas those with entrepreneurs who failed in prior ventures have a 21.3 percent success rate, and companies with first-time entrepreneurs have a 17.1 percent chance of success.

Is entrepreneurial persistence due to DCs? Gompers et al. (2010) showed that entrepreneurs who succeeded in the past had access to better resources and more favorable conditions in the future. They also found that entrepreneurs who entered an industry at a favorable time were more likely to do so in the future. This last piece of evidence is consistent with a theory of dynamic capability, suggesting that DCs account to some extent for repeat success among serial entrepreneurs.

3.3 Firm Growth

It is plausible to suggest that firms with DCs have better growth prospects than firms that do not have them. A firm with DCs will anticipate and exploit changes in technology or market demand, whereas a firm without them will miss growth opportunities or stagnate in a declining industry. Overall, a theory of dynamic capability suggests persistence in growth rates—that is, firms with high (low) growth rates in the past should have high (low) growth rates in the future.

The empirical evidence does not generally support this thesis ( Marsili, 2001 ; Geroski, 2005 ; Davidsson, 2006 ; Coad, 2009 ). The correlation between growth rates in consecutive periods is not zero but is very small and sometimes negative ( Bottazzi et al., 2002 ; Coad, 2007 ). Growth variance explained by firm-specific attributes is surprisingly small. Geroski (2005) summed up the evidence as follows:

The overwhelming impression that one gets from this research is that corporate growth rates are extremely hard to predict. The R-squareds in these regressions are always extremely low, growth differences between firms in any given year are swamped by variations over time in the growth rates of individual firms, and correlations between pairs of firms over time are very small, even when they operate in the same industry. (2005: 129)

Similarly, Coad (2009) concluded: “Without doubt, the main result that emerges from our survey of empirical work into firm growth is that the stochastic element is predominant” (2009: 96).

This evidence is hard to reconcile with a theory of dynamic capability. Helfat et al. discuss this anomaly in their book on DCs (2007: Chap. 7). They agree that the absence of growth persistence is inconsistent with dynamic capability. They argue, however, that the evidence suffers from econometric limitations—for example, the statistical tests assume normality despite fat-tailed distributions of growth rates, and the slopes in regressions are assumed equal for all firms. Helfat et al. also argued that growth persistence should only be expected in high-growth industries with a focus on innovation, not in stable or mature industries. As an example, the authors cited Bottazzi et al. (2001) , who addressed these and other econometric issues in a study of the Italian pharmaceutical industry. This study gave evidence of significant positive autocorrelation in growth rates, from which Helfat et al. concluded that there is “substantial evidence of growth persistence, especially when the data are disaggregated to allow for variation between firms and time periods” (2001: 113).

The findings of Bottazzi et al. (2001) contradict the hypothesis of zero autocorrelation in growth rates in an innovative market. Indeed, studies often contradict this null hypothesis. However, the effect sizes remain surprisingly small. For example, Bottazzi et al. found an autocorrelation of 0.3 for a time-lag of one year and 0.1 for a time-lag of two years (2001: 1171). Thus, last year’s growth rate explains 9 percent of this year’s growth rate and 1 percent of next year’s growth rate. Other studies have reached similar conclusions: for example, Coad (2007) found autocorrelation in growth rates of about 0.15 (see Coad, 2007 : 79, Fig. 4), some of which may have been due to inflation and accounting dependencies between years ( Chan et al., 2003 ).

Overall, the evidence on growth persistence offers minimal support for a theory of dynamic capability. There is some evidence of persistence, but the effect sizes in the growth data, as in the profit data, do not suggest that DCs play a major role in the creation and persistence of competitive advantage.

In fairness to the theory of dynamic capability, it is possible that some firms sense and seize new market opportunities, but that these advantages do not manifest themselves in statistical data on profitability or growth rates. For example, an innovative firm might invest heavily in R&D, or make a strategic choice to focus on a single market rather than expanding its asset base, and either of these choices might suppress profit or growth rates ( Helfat et al., 2007 ). Moreover, some DCs may sit at the corporate level and exhibit relatively small effects in business units; for example, corporate capabilities in asset orchestration ( Bardolet et al., 2013 ) or capital allocation ( Bardolet et al., 2011 ) may have effects dispersed over many business units and long periods of time. Finally, we note that a lack of persistence in aggregate data does not prove that no firms possess DCs; if only a small number of firms gain advantages from DCs, or if the best firms respond to volatility by changing industries, the effects may not be captured by data at the industry level. We turn, therefore, to other forms of evidence for DCs, in the areas of innovation, acquisition performance, and forecasting.

3.4 Innovation Persistence

Firms that sense and seize new market opportunities may be more likely than other firms to develop valuable innovations. Empirical studies provide qualified support for this expectation, although much of it is based on patent data and shares the limitations of these data. Across all firms, there is little or no persistence in innovation (measured as persistence in patenting). There is a small subset of firms, however, that does produce larger numbers of patents ( Geroski et al., 1997 ; Malerba et al., 1997 ; Cefis and Orsenigo, 2001 ; Cefis, 2003 ).

Geroski et al. (1997) argued that the evidence did not support a theory of dynamic capability, or any idea that innovation derives from persistence in firm-specific capabilities. According to the authors, “Our observations sit uncomfortably alongside this kind of theorizing. Although some firms do innovate persistently, only a very few do and they do not enjoy very long innovation spells” (1997: 45). They argued that innovative capabilities, even for the most capable firms, were subject to “sharply diminishing returns” (1997: 45).

Even when patent persistence exists, it may not be due to DCs. For example, firms with a history of investment in R&D may have cost advantages in innovation relative to firms that have not invested in the past ( Sutton, 1991 ). In recent survey research, Ganter and Hecker (2013) concluded that incentive mechanisms were more important than firm-specific capabilities in explaining innovation persistence. Overall, the data suggest a degree of innovation persistence, but do not show that persistence is driven by DCs.

3.5 Acquisition Performance

Acquisitions provide a means for capable firms to identify and capture new opportunities. Although studies show that acquisitions do not generally benefit the acquiring firm ( Barney, 1988 ; Andrade et al., 2001 ), this does not necessarily contradict a theory of dynamic capability. It is not necessary that firms benefit from acquisitions generally but only that the most capable firms do so. On this point the evidence suggests that some firms do, in fact, benefit financially from acquisitions when they bring unique resources and capabilities to the acquired firm ( Capron and Pistre, 2002 ).

Do these firms benefit from acquisitions due to superior dynamic capability? Corporate-level DCs—for example, in opportunity identification, negotiation, capital allocation, or the avoidance of acquisition biases—could account for the benefits of acquisitions. However, it remains unclear whether any business entity actually controls these capabilities. For example, Jaffe et al. (2013) estimated the role of the current CEO in acquisition performance, and found that acquisition performance only persisted when acquisitions occurred under the same CEO. The findings do not undermine the importance of DCs, but suggest that DCs in merger and acquisition may rest with individuals rather than organizations.

3.6 Innovation by Incumbent Firms

If high-performing firms possess DCs, we might expect successful incumbents to be among the most innovative firms. The evidence does not generally support this conclusion. Dominant firms often fail to sense or seize new market opportunities ( Tushman and Anderson, 1986 ; Henderson and Clark, 1990 ; Christensen, 1997 ). In some cases, market leaders do not perceive technological or market shifts ( Christensen, 1997 ). In others, they perceive opportunities and try to seize them, but respond with inferior technologies ( Tripsas, 1997 ). Patent studies show that incumbent firms continue to patent at a high rate as they age, but their patents become less significant over time, being less cited by other firms ( Sorensen and Stuart, 2000 ). It is possible that successful firms like Intel and Microsoft once had DCs but now succeed for other reasons, such as scale and market power. However, there is little evidence that DCs for innovation are directly linked to current success.

3.7 Forecasting

Is it plausible that a firm could consistently forecast market trends better than its competitors? Empirical studies of forecasting accuracy show that predicting important business outcomes is hard. Forecasts of market demand and product success are generally inaccurate, with an average absolute percentage error close to 50 percent ( Fildes et al., 2009 ). For fast-moving consumer goods like movies and music, the most successful methods have an absolute percentage error of 70 percent ( Lee et al., 2003 ). Errors in forecasts of macroeconomic quantities average about 20 percent, despite significant autocorrelation ( Armstrong and Collopy, 1992 ; Denrell and Fang, 2010 ).

Notwithstanding the poor empirical record of forecasts, people believe they can make accurate forecasts. Studies of managers, venture capitalists, and entrepreneurs show that they systematically overestimate the accuracy and precision of their forecasts ( Zacharakis and Shepherd, 2001 ; Cassar, 2009 ; Cassar and Craig, 2009 ). Overall, it has not been shown that people have the capacity to produce consistently superior forecasts in highly volatile business environments.

3.8 Summary of Empirical Evidence

Although some research findings can be interpreted as supporting dynamic capability theory, the evidence as a whole does not make a strong case for dynamic capability as a general theory of competitive advantage. When found to exist, observations of dynamic capability are apt to be singular and extremely rare, suggesting that the theory applies, at best, to very few firms. Of course, scarcity does not make DCs less valuable but more valuable for firms that happen to possess them. But the scarcity of dynamic capability raises doubts about how much profit variability is really explained by the theory, how it can be tested empirically, and its scope of application as a theory of competitive advantage.

Why is the empirical evidence so equivocal? There are at least six possible explanations. First, empirical propositions linking dynamic capability with firm performance may not have been fairly and rigorously tested. Second, dynamic capability may apply to so few firms or industries that it explains little or no variation in firm performance. Third, dynamic capability may exist in many contexts, but with performance effects that are too small to detect statistically. Fourth, the prediction of market volatility and technological shifts may be subject to substantial noise; if even the most informed predictions go wrong, then randomness may swamp the effects of DCs. Fifth, the best technologies and capabilities do not always win; for example, if consumers base their buying decisions on the choices of previous customers ( Salganik et al., 2006 ), an early mover can dominate the market without superior capability ( Arthur, 1989 ). And sixth, dynamic capability may require a confluence of complementary assets so unusual that it is unlikely to actually occur—or if it does occur, unlikely to survive the fragility of a complex system of interconnected activities ( Simonton, 1999 , 2003 ; Shane, 2000 ). As Geroski (2005) pointed out, “When competencies are strategically complementary, the failure of one causes the whole group to fail” (2005: 137).

In sum, the empirical evidence on dynamic capability is equivocal, and there are many reasons why this could be the case. To take the analysis further, we now examine whether theoretical arguments support the claim that investments in dynamic capability are likely to be both undervalued and sustainable.

4 Theoretical Considerations

Our reading of the evidence suggests that dynamic capability is at best only weakly associated with sustained profitability. This does not imply that dynamic capability is a bad theory or that firms should not invest in DCs; indeed, it is not clear that the empirical evidence unequivocally supports any theory of competitive advantage. Furthermore, even if the evidence showed that DCs were essential to sustained profitability, it does not follow that firms could earn superior returns by investing in them. This depends on whether DCs are undervalued in the market and whether sustained profitability is itself achievable or advantageous. To take the analysis further, we examine whether theoretical arguments support the claim that investments in dynamic capability are likely to be both undervalued and sustainable.

4.1 Dynamic Capabilities and Market Valuation

The returns to investing in dynamic capability, like any other investment, depend to some degree on whether the capability is undervalued in a competitive market ( Barney, 1986 ). If the capability is accurately priced, firms cannot profit by acquiring it—and if many firms realize its usefulness, no firm can profit by acquiring the capability through acquisition, executive hiring, asset purchases, or other means.

A similar argument applies to capabilities that must be cultivated internally. A firm that knows the value of DCs will spend time and resources to obtain them—and if many firms succeed, no firm will achieve a competitive advantage. If the capabilities are protected by patents, scarcity, causal ambiguity, or other barriers to imitation, rivals will invest in competing technologies, forcing the firm to increase its own investments and increasing the probability of breakthrough innovations by rivals ( Tirole, 1988 ).

Are DCs undervalued? Valuing the capabilities that produce innovation is not an exact science, and no one knows their true value. This implies that some DCs may be undervalued due to randomness and fallible judgment. On the other hand, it does not imply that DCs are systematically undervalued, and indeed they may be systematically overvalued. For example, strategy consultancies and other institutions responsible for disseminating ideas in strategic management—business schools, business media, practitioner journals, professional associations—produce a steady stream of rhetoric on market leadership, technological innovation, global awareness, and strategic change as generalized solutions to strategy problems. These institutions contrast dynamic strategies with traits said to be associated with strategic failure, such as inertia, imitation, and operational thinking. Every senior executive has been exposed to the rhetoric of strategic change, and few are likely to undervalue innovation or its capabilities.

Agency-based arguments suggest that CEOs might place a higher value on DCs than shareholders. A CEO with a track record of guiding a firm through technological change would be highly valued in the market—more highly than a CEO who managed a profitable firm in a stable industry. Many firms in turbulent industries seek a CEO with experience in managing change, and these firms offer more lucrative compensation packages than firms not threatened by disruptive technologies. Hence, CEOs who believe that DCs enable firms to succeed in turbulent times may over-invest in these capabilities to improve their career prospects.

On the other hand, some of the specific processes described by Teece et al. (1997) and Teece (2007) may be undervalued. Dynamic capability appears to be costly and time-consuming to produce, and its benefits manifest themselves over long periods of time. If DCs are abstract higher-order capabilities such as the ability to sense new opportunities, then their existence and value may be more obvious to some firms than others. Valuation must start with the baseline capabilities on which DCs operate, such as product research and marketing. To value DCs correctly, the firm must link these baseline capabilities to higher-order capabilities, and then link all the components to their ultimate payoffs. This is a non-trivial exercise in credit assignment, and subject to large judgmental errors ( Denrell et al., 2004 ). Moreover, the delay between investments in DCs and their payoffs impedes learning ( Rahmandad, 2008 ), so a firm that invests in DCs may abandon them before realizing their true value. Through such processes, firms could underestimate the value of DCs.

4.2 Dynamic Capabilities and Sustainability

In truly volatile industries, it is not obvious that sustained competitive advantage is the best way to capitalize on strategic innovation. Indeed, some firms might be better off avoiding sustained competitive advantage—that is, making their money fast and getting out. For any positive discount rate, current profits are more valuable than future profits, and any firm should prefer to make its money fast. This is not always possible, and in most industries it takes time to achieve high sales and profitability. But this is less true in volatile markets than in traditional manufacturing industries, and longevity can be costly. Pursuing new market opportunities is riskier in volatile markets than in other contexts, and the longer a firm tries to compete this way the more likely it is to fail, and the more opportunities it gives to competitors to replicate or surpass its advantages. 1 Moreover, markets are not always as volatile as they appear; exploration strategies tend to sacrifice strategic focus and commitment ( Del Sol and Ghemawat, 1998 ), which are more effective if the industry turns out to be less volatile than expected ( Hannan and Freeman, 1977 ).

The problem of discounting applies not only to dynamic capability, but to any theory of sustained competitive advantage. However, it is especially germane to dynamic capability theory. The conditions described by Teece (2007) —“open regimes of free trade and investment, global dispersion in the sources of new knowledge, and the multi-invention or systemic character of this innovation” (2007: 1346)—are those in which accurate forecasts and well-judged investments are least likely to occur over long periods of time. As Eisenhardt and Martin (2000) pointed out, high-velocity markets and sustainable competitive advantage are in mutual tension, and dynamic capability theory encounters “a boundary condition in high-velocity markets where the duration of competitive advantage is inherently unpredictable, time is central to strategy, and dynamic capabilities are themselves unstable” (2000: 1118).

In sum, DCs can only produce sustained competitive advantage if they are undervalued and unobtainable by competitors and sustainable over long periods of time. However, the conditions for which DCs were developed—technological innovation and global competition in fast-changing industries—are only moderately conducive to undervaluation, and not conducive to the sustainability of advantages. Hence, it is important to specify the conditions in which dynamic capability can explain competitive advantage, and in which firms should make strategic investments in DCs. In Section 5 we consider the boundaries and scope conditions of dynamic capability as a theory of competitive advantage.

5 Scope Conditions for Dynamic Capability

As a theory of firm performance, dynamic capability tries to explain sustained competitive advantage in conditions of rapid technological innovation and free global flows of trade and information. The previous sections highlight some of the problems associated with this kind of explanation, both as a descriptive theory of performance and a normative prescription for strategists. These considerations suggest a number of limitations and scope conditions for the applicability of the theory of dynamic capability. We summarize these scope conditions in Table 1 , and discuss them separately below.

5.1 Rate of Industry Change

As a theory of competitive advantage, dynamic capability is least applicable when the rate of industry change is low. While this scope condition is fairly obvious—the theory being designed to explain performance in volatile industries—it is perhaps less obvious that investing in DCs is ill-advised if the industry is stable. Investing in irrelevant capabilities is costly and disruptive for the firm, diverting resources from the true drivers of competitive advantage and misaligning the firm with its environment. Empirical evidence suggests that industry stability is important as an explanation of sustained profitability ( McGahan and Porter, 1999 , 2003 ), but this advantage is negated if the firm neglects its environment. Indeed, even moderate to fast industry change should not always induce a firm to invest in DCs. Rather than adjusting to a moving target, many firms would be better off waiting for the dust to settle, realigning later in a more “quantum” or “revolutionary” fashion ( Miller and Friesen, 1984 ; Tushman and Romanelli, 1994 ).

5.2 Decisiveness of Dynamic Capabilities

Of the many factors that determine success and failure in market competition—industry forces, market power, financial capital, material assets, intangible assets, luck—firm-specific capabilities are a relatively small subset, and DCs are a smaller subset. How important are DCs for managing change? Even in a volatile industry, market power and financial resources may prove more decisive than the capacity to sense new market opportunities; and baseline capabilities such as cost efficiency and brand management may prove more important than DCs. This could happen, for example, if many firms recognized the need for change but firms varied substantially in the kinds of “x-efficiencies” that tend to survive all forms of industry change, such as those related to marketing and operational effectiveness ( Leibenstein, 1966 ; Powell, 2004 ; Teece, 2014 ). Hence, the theory of dynamic capability has the greatest explanatory power when non-capabilities and baseline capabilities are either unimportant or relatively equal among firms, and when industry change erodes the value of the existing bases of competitive success.

5.3 Survivability of Industry Change

Many firms have survived periods of industry volatility, even without DCs. For example, Apple survived the dominance of personal computers in the 1990s, when Steve Jobs said that his strategy was to “wait for the next big thing” ( Rumelt, 2011 : 14). If firms can hibernate during volatile times, they need not invest heavily in the capacity to manage change. Of course, hibernation carries its own risks, and the feasibility of hibernation depends on the firm’s other assets and baseline capabilities, its ownership structure, and on the nature and duration of industry change. If severe volatility persists for long periods, or if the firm’s strategic assets cannot be otherwise deployed, then industry change will threaten the firm’s survivability.

5.4 Continuity of Change

Proponents of dynamic capability theory argue that the theory is most applicable when an industry is subject to continuous volatility, or to successive waves of change. But this assumes that the same DCs drive firm success over long periods of time, and through successive waves of change. If industry conditions four years from now require different DCs than industry conditions two years from now, then survival does not require DCs but “dynamic dynamic capabilities.” Moreover, if investments in dynamic capability only yield payoffs for short periods of time, then hibernation may prove a more effective strategy. For example, anticipating a radical change in customer preferences—say, a preference for mobile devices—may require innovations in both marketing and manufacturing, and it is not obvious that any generalized dynamic capability underpins the ability to manage both kinds of change.

5.5 Predictability of Change

High-velocity industries are defined by rapid change and high uncertainty (lack of predictability). In these conditions, is it plausible that firms with DCs will make better ex ante predictions than other firms? Some firms will make better predictions than others, but people often impute “strategic vision” ex post to executives who get lucky or make good guesses ( Bertrand and Mullainathan, 2001 ). Accurate predictions alone do not prove the existence or effectiveness of DCs.

Predicting the future of volatile industries is hard for everyone, and there is no evidence that successful firms possess a superior underlying capability to predict uncertain events. Indeed, firms with inferior forecasting ability on average—that is, wider variability of estimates—are more likely to predict extreme events ( Tetlock, 2005 ; Denrell and Fang, 2010 ). Even if a firm does possess DCs—say, in researching and anticipating consumer demand—it may fail to predict a rival’s technological breakthrough or a sudden consumer fad. In general, planned investments in DCs are least effective when the timing and nature of industry change are most unpredictable.

We also note that DCs cannot produce competitive advantage when change is highly predictable. Any firm can perform an easy task, and predicting the future is only valuable and scarce if the task is difficult ( Schoemaker, 1990 ). This is true irrespective of the rate of industry change. In a volatile industry, a dynamic capability for sensing or shaping change is only valuable if the changes are difficult to predict. If all firms can predict volatility with equal accuracy, rivals will tend to respond similarly and the inputs to DCs will be correctly priced in the market.

Taken together, these arguments imply that DCs have the greatest explanatory power when industry change is moderately predictable; that is, when change is not easy to predict but predictable enough to give a very insightful firm an advantage over rivals, yet not so unpredictable that all firms’ forecasts are swamped by random noise. Regardless of the rate of change, extreme predictability or unpredictability reduces the power of DCs to produce or explain variation in profitability.

5.6 Awareness of Change

DCs are more likely to confer performance advantages to firms whose competitors are unaware of DCs or how they contribute to firm performance. Are firms aware of DCs? As noted earlier, strategy consultancies, business schools, and other institutions have drawn attention to the impacts of globalization and technological innovation, and the literature on DCs has itself contributed to the publicity. The awareness of firms depends also on whether the industry has experienced strategic change in the past. If DCs enable firms to weather industry volatility, firms competing in industries with a history of volatility are more likely to understand their value and seek to acquire them. This is linked to the question of predictability of industry change: when industry change is predictable, whatever the reason for the predictability (availability of information, common experience, historical precedents), DCs are less likely to confer sustained competitive advantage.

The scope conditions of dynamic capability are defined by many factors, but the above considerations suggest that two of these factors are of primary importance: the rate of industry change and the predictability of industry change. These factors are represented graphically in Figure 2 , on vertical and horizontal axes. As shown in Figure 2 , competitive advantage in relatively slow-moving and predictable industries is best explained by conventional theories of competitive advantage, such as competitive positioning and resource-based theories. These industries offer little premium on responsiveness or prediction accuracy and competitive advantage can accrue to firms with superior market power or baseline resources and capabilities.

The scope of dynamic capability theory

At the other extreme, competitive advantage in the fastest changing and least predictable industries is best explained by evolutionary or selection-based theories of competitive advantage. These industries offer a premium on responsiveness, but the lack of predictability means that success is driven more by environmental selection processes than by executive insight or deliberate cultivation of DCs ( Alchian, 1950 ). Depending on circumstances, such industries may reward an early-moving entrepreneur or an incumbent firm that hibernates for a time and then emerges as the eventual winner. The outcome is hard to predict both in theory and practice, making it difficult to link strategies ex ante with their performance consequences.

The theory of dynamic capability has its greatest applicability in the relatively narrow midrange of markets characterized by intermediate combinations of industry change and predictability of change. Dynamic capability theory offers no new insights on competitive advantage in easily predictable or entirely unpredictable markets, or when industry change is perpetually slow. However, it is entirely credible to claim that DCs contribute to long-term performance in regimes of moderate change and predictability. In these markets, firms may differ in many important respects: in their awareness of industry trends, experience in dealing with industry change, expertise in relevant technologies, and insights into the existence and usefulness of DCs. More importantly, these differences could plausibly impact firm performance, since the combinations of moderate change and predictability in Figure 2 would reward predictive accuracy and the cultivation of new capabilities for addressing industry change.

Of course, these conclusions are subject to all the caveats already discussed. If a rival firm with market power can wait out the change and imitate later as a low-cost second mover, then DCs may not confer long-term advantage. If an industry experiences successive waves of change for which the latest DCs are ill-adapted, then DCs will not be enough to survive in the long run. If a volatile industry becomes more stable over time, as when industries pass through their life cycles, the basis of competitive advantage will return to baseline capabilities such as customer relationships and production efficiency, and the maintenance of DCs may become costly to the firm. If the capability resides in a CEO or top management team, then it is susceptible to changes in personnel. Hence, although dynamic capability is a plausible explanation of firm success in the midrange industries in Figure 2 , it is neither a necessary nor sufficient condition for sustained competitive advantage.

On the other hand, these arguments suggest that DCs hold a legitimate place in the landscape of strategic management theories. Indeed, it could be argued that the theory has its greatest explanatory power at the most crucial moment in the evolution of a firm. Looking again at Figure 2 , many industries and firms pass through a life cycle that corresponds roughly to a move from the upper right to the lower left corner of the figure—that is, from instability to stability. Hence, the entrepreneurial context is fraught with selection pressures, and the mature period is characterized by competitive advantages in equilibrium. As we have seen in recent industry evolutions—for example, in web-based industries like online education and online messaging—the decisive period for many firms is what happens in between; namely, the fast-paced, imperfectly predictable stage between entrepreneurship and industry maturity. It is here that firms tend to define themselves for the long run, with success hinging on the capacity to foresee the path to industry maturity while developing capabilities to shape the future and capture new opportunities as they arise. It is in these environments, and in this crucial phase of industry evolution, that DCs provide the best explanation of firm survival and sustained competitive advantage.

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The situation is not unlike a person who devises a new way of making money in a casino. Should the person monetize the trick in a long series of small bets or in one large bet? Small bets are less risky for any individual bet, but the longer the series lasts the higher the probability of exposure. On the whole, one large bet is more likely to succeed.

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Driving change in higher education: the role of dynamic capabilities in strengthening universities’ third mission

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• Published: 17 January 2024

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• Maribel Guerrero   ORCID: orcid.org/0000-0001-7387-1999 1 , 2 &
• Matthias Menter   ORCID: orcid.org/0000-0001-6981-6778 3  

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Universities play a crucial role in social, economic, and technological development. Over the last decades, higher education systems have experimented with multiple transformations due to social demands, socioeconomic paradigms, and external shakeouts. Even though teaching and research are still the core functions of universities, other activities are emerging within/beyond the universities’ scope and boundaries to configure the “third mission.” Despite the increasing importance of universities’ third mission, little is known about the role of dynamic capabilities underpinning the configuration of the third mission across higher education systems. Using a unique longitudinal dataset that captures the German higher education landscape from 2000 to 2016, we investigate the effect of dynamic teaching/research capabilities for achieving the third university mission (knowledge transfer and technology commercialization). Our results reveal tensions between complementary and substitution effects when pursuing universities’ three missions (teaching, research, and knowledge transfer and technology commercialization), requiring university managers’ and policymakers’ strategic decisions. We provide implications for university managers and the university community as well as policymakers during the re-configuration process of becoming more entrepreneurial and innovative, highlighting the relevance of effectively managing universities’ dynamic capabilities.

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Universities have undergone significant transformations in recent decades, responding to societal demands, economic shifts, and external pressures. The third mission of universities thereby serves as a driving force and encompasses endeavors that go beyond traditional academic functions, such as knowledge transfer and technology commercialization. Despite its increasing importance, little is known about the underlying mechanisms that lead to third mission outcomes. To shed light on this crucial topic, this paper delves into the impact of dynamic teaching and research capabilities on achieving the third mission’s goals. Our findings reveal goal conflicts that universities face in balancing their three missions, requiring university managers and policymakers to make strategic decisions to navigate these tensions effectively. As universities aim to become more entrepreneurial and innovative, effectively managing dynamic capabilities and making strategic decisions becomes paramount during reconfiguration processes, enabling universities to unlock their full potential for economic, technological, and societal impacts.

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1 Introduction

Over the last decades, worldwide higher education systems have been exposed to multiple transformations derived from “stakeholder pressures,” such as the emergence of new economic and technological paradigms (Audretsch, 2014 ), big societal challenges (Menter, 2023 ; Pinheiro et al., 2017 ), the United Nations’ sustainable development goals (Guerrero & Lira, 2023 ), economic crises (Lehmann et al., 2018 ), and pandemics (Guerrero & Pugh, 2022 ; Siegel & Guerrero, 2021 ). In these transformation processes, even though teaching and research are still considered the core functions of universities, other activities have impregnated entrepreneurial/innovative orientations within/beyond universities’ scope to configure the “third mission” (Compagnucci & Spigarelli, 2020 ).

Extant empirical research has evidenced that each higher education system has adopted specific university transformation pathways conditioned on organizational patterns, policymakers’ strategies, and contextual conditions (Audretsch, 2014 ; Cunningham et al., 2022 ; Guerrero & Urbano, 2012 ; Guerrero et al., 2015 ; O’Shea et al., 2007 ). This explains why the accumulated literature has evidenced diverse domains (managerial, entrepreneurial, innovative, and social engagement) and operational measures (new lifelong learning models, university community spin-offs/start-ups, knowledge transfer, technology commercialization, and social engagement) applied to the third university mission in each higher education system (Berghaeuser & Hoelscher, 2020 ; Compagnucci & Spigarelli, 2020 ; Guerrero et al., 2023 ).

Universities require organizational-level dynamic capabilities to navigate through these organizational transformation processes and ensure long-term survival (Leih & Teece, 2016 ). O’Reilly et al. ( 2019 ) show that dynamic capabilities are especially important for knowledge transfer activities, hence for the realization of the third university mission. These findings are confirmed by Stolze and Sailer (2022) who find that dynamic capabilities positively affect third mission advancement. Despite calls from scholars to develop dynamic capabilities to enable transformation processes in higher education institutions (Guerrero et al., 2021 ; Yuan et al., 2018 ), little is still known about the role of dynamic capabilities underpinning the configuration of the third mission across higher education systems. Inspired by this academic gap, this paper theorizes about the dynamic capabilities configuring the third university mission related to knowledge transfer and technology commercialization. More concretely, we pay attention to the effect of ordinary and dynamic teaching/research capabilities on achieving the third mission in the German higher education system. Whereas ordinary capabilities refer to existing skills and routines, dynamic capabilities refer to the ability to adapt, innovate, and reconfigure these capabilities to respond to changing circumstances and seize new opportunities (Schriber & Löwstedt, 2020 ). We assume that pre-existing ordinary teaching/research capabilities combined with emergent dynamic teaching/research capabilities positively contribute to the configuration of the third university mission, considering potential substitution effects. With a unique longitudinal dataset that captures the German higher education landscape from 2000 to 2016, we test this assumption using zero-inflated negative binomial regressions. Our results reveal the importance of managing dynamic teaching/research capabilities to configure the third university mission in Germany.

Our study offers both theoretical and practical contributions. First, we extend the discussion about the role and impact of dynamic capabilities in relation to universities’ third mission, enabling universities to be flexible and adaptive to change and highlighting the need for the strategic management of universities (Navarro & Gallardo, 2003 ). Second, we theorize about the (complementary or substitution) effects of ordinary and dynamic capabilities in the configuration of third mission outcomes (Guerrero et al., 2021 ; Heaton et al., 2020 , 2023 ) by proposing a tested conceptual framework and evidencing the rivalry in the allocation of resources. Third, our study provides strategic insights for university managers and the university community as well as policymakers that could be useful during the re-configuration or rejuvenation processes for becoming more entrepreneurial, as there are tensions between complementary and substitution effects when pursuing universities’ three missions (teaching, research, and knowledge transfer and technology commercialization), requiring strategic decisions by university managers and policymakers (Heaton et al., 2019 ; Teece, 2023 ).

The remainder of our paper is structured as follows. The second section describes the theoretical framework by outlining the evolution of the third university mission and the contribution of dynamic capabilities to the third university mission (e.g., knowledge transfer and technology commercialization). Section  3 explains the adopted methodological approach. Section  4 shows the results, followed by Sect.  5 that discusses the contributions, implications, limitations, and future avenues of research. A final section concludes.

2 Theory development

2.1 the evolution of the third university mission.

Several authors have called the “first academic revolution” when the university integrated research along with teaching as a core activity in the late nineteenth and early twentieth century, as well as the “second academic revolution” when the university impregnated the innovative and entrepreneurial orientation in the twenty-first century (Etzkowitz et al., 2000 ; Klofsten et al., 2019 ; Philpott et al., 2011 ). Behind each revolution, universities have experimented with multiple internal pressures (restricted sources of funding, growing/reducing numbers of students) and external pressures (increasing social demands, higher educational reforms, new socioeconomic paradigms, financial/economic crises, and pandemics) (Audretsch, 2014 ; Clark, 1998 ; Guerrero & Pugh, 2022 ; Guerrero & Urbano, 2012 ; Laredo, 2007 ; Menter, 2023 ). Consequently, these internal and external pressures have importantly shifted the university’s primary focus on performing teaching and research by adding a third mission perceived as a “contribution to society” in a broad sense (Compagnucci & Spigarelli, 2020 ).

Understanding the third university mission demands contextualizing university adaptation, response, or transformation in the function of certain events. In this respect, Audretsch ( 2014 ) explains multiple historical facts/events that have influenced the introduction of an innovative and entrepreneurial orientation within North American universities. In this vein, North American universities legitimized the third mission, understood as the contribution to economic and social well-being, derived from university outcomes related to knowledge generation, technological inventions, and commercialization via spin-offs, and intellectual property mechanisms like patents and licenses (Audretsch, 2014 ; O’Shea et al., 2008 ). In this context, directly or indirectly, the legislation reinforced the legitimization of the third university mission (e.g., the Bayh-Dole Act) as well as the emergence of the phenomenon of “academic entrepreneurship” within universities (Dabić et al., 2022 ; Grimaldi et al., 2011 ; Lockett et al., 2005 ; Siegel & Wright, 2015 ). It was unsurprising that adaptative transformation legislative patterns were implemented worldwide, aiming to foster the socioeconomic contribution of universities via educational, technological, innovative, and entrepreneurial outcomes (Cunningham et al., 2019 , 2021 ; Gores & Link, 2021 ).

In the UK higher education context, for example, the official higher education statistics offices have legitimized the third university mission by requesting specific information about university spin-offs, research contracts, grants, intellectual property, patents, licenses, and other qualitative metrics (Guerrero et al., 2015 ). Undoubtedly, the UK university’s third mission contributions to educational and regional growth have been influenced by the implementation of the 2014 UK’s Research Excellence Framework, which is focused on distributing public funds according to the university impacts (Audretsch et al., 2022 ). Similarly, the German higher education system has dramatically changed over the last two to three decades as a result of multiple federal/state programs (e.g., Innovative Hochschule, Real-World Laboratories, German Excellence Initiative) aiming to foster an innovative “third university mission” (Berghaeuser & Hoelscher, 2020 ; Graf & Menter, 2022 ). In the German context, given the public interventions, the third university mission has been understood as (a) knowledge transfer and technology commercialization (patents, research collaborations, consulting), (b) further education (advanced professional programs, short-term certificate studies), and (c) social engagement (community service, civic engagement, social entrepreneurship) (Henke et al., 2016a , 2016b ; Pasternack et al., 2015 ). Indeed, a recent study has shown that German universities’ statements, representing the university management view, have effectively impregnated knowledge transfer and technology commercialization orientation (Berghaeuser & Hoelscher, 2020 ).

Based on these arguments, at the contextual level, the understanding and metrics of the third university mission depend on the particularities of each higher education system. At the organizational level, little is known about how university leaders in each particular higher educational system have internally defined, visualized, communicated, implemented, and operationalized the meaning of the third mission—where innovative and entrepreneurial orientations are not merely the creation of spin-offs or knowledge transfer and technology commercialization mechanisms but rather an attitude or behavior in the daily academic life for all members within the academic community (Klofsten et al., 2019 ). For instance, among the university community members, tensions arise (Philpott et al., 2011 ), as well as ambiguities in their roles (Lam, 2010 ) due the internal capacity restrictions, impeding the realization of entrepreneurial and innovative objectives. Based on these arguments, we assume the (complementary/substitutive) role of organizational-level dynamic capabilities in the primary university activities (teaching and research) as critical levers in the configuration of the third university mission (Guerrero et al., 2021 ; Heaton et al., 2019 ; O’Reilly et al., 2019 ).

2.2 The role of dynamic capabilities in the configuration of the third university mission

The concept of ordinary and dynamic capabilities is well established, and researchers have largely used these concepts to explain diverging performance paths across organizations (Teece, 2007 ). While ordinary capabilities are understood as organizational abilities (or prerequisites) to perform efficiently (do things right) well-delineated technical tasks through a core focus on operations, administration, and governance (Teece, 2014 ), dynamic capabilities are understood as the organizational ability to integrate, build, and reconfigure internal and external capabilities to address changing business environments (Teece et al., 2016 : 8). In this view, dynamic capabilities represent the ability of managers to conceive new combinations of pre-existing organizational routines and entrepreneurial management to pursue sustaining competitiveness (Teece, 2023 : 122), as well as to address rapidly changing environments (Helfat et al., 2007 ; Teece et al., 1997 ). According to Teece ( 2007 ), dynamic capabilities can be categorized into sensing (identification and assessment of an opportunity), seizing (mobilization of resources to address an opportunity and to capture value from doing so), and transforming (continued renewal), with a core focus on effectiveness (doing the right things).

In higher education, researchers have recognized that both ordinary and dynamic capabilities enable universities to fulfill the third mission by adopting an entrepreneurial and innovative paradigm (O’Reilly et al., 2019 ). For example, Navarro and Gallardo ( 2003 ) documented the university’s strategic change by configuring the third mission to respond to the greater social demands. Then, Yuan et al. ( 2018 ) evidenced how universities significantly enhance third mission outcomes (e.g., knowledge transfer and technology commercialization) by orchestrating university assets and impregnating entrepreneurial/innovative behaviors within the university community. Recently, Schriber and Löwstedt ( 2020 ) have shown the role of ordinary and dynamic capabilities in responding to dynamically changing environments. A common pattern in these studies has been how dynamic capabilities are represented by the university managers’ abilities to redirect resources (skilled personnel, facilities, equipment, and processes) and core activities (teaching and research) toward a sense of opportunities, prioritize the investment, and transform them to keep it resilient and aligned with the ecosystem and stakeholders (Heaton et al., 2020 ). However, adopting dynamic capabilities to understand universities’ third mission configuration requires a systemic-level approach by considering internal interdependencies to determine the most critical (Heaton et al., 2019 ). According to Heaton et al. ( 2019 ), teaching-research-commercialization interdependency poses a considerable challenge to university managers, who must decide whether and how to manage it, and the extent to which it can be managed. Therefore, we need to understand how ordinary and dynamic capabilities in teaching and research affect third mission outcomes in a systemic way (Heaton et al., 2019 , 2020 , 2023 ), particularly whether ordinary and dynamic teaching/research capabilities may complement or substitute each other (see examples in Table  1 ).

2.3 Hypotheses development

Regarding teaching capabilities , universities with an innovative and entrepreneurial orientation are characterized by high-quality teaching outcomes (Guerrero & Urbano, 2012 ) and by sustainable opportunities in implementing new teaching business models (Guerrero et al., 2021 ). Implicitly, to pursue a sustained income and performance, university managers efficiently allocate the available resources (ordinary capabilities) to achieve the traditional students’ demand for university educational programs, as well as to achieve the high-quality academic standards required by the labor market (Heaton et al., 2019 ) and higher education agencies (Audretsch et al., 2022 ). Directly or indirectly, the efficient achievement of traditional educational programs endows the university community (students, managers, and staff) with certain dynamic capabilities enabling them to identify new teaching opportunities, behave entrepreneurially, and contribute to the third university mission (Compagnucci & Spigarelli, 2020 ; Heinonen & Hytti, 2010 ). For example, due to external pressures (e.g., technological and digital advances), well-recognized university faculty have identified new educational opportunities based on the student’s needs (e.g., short-term certifications, specializations or specific competencies) and have reconfigured new educational offers by proposing innovative academic programs with multiple flexible modalities in-person, online, and hybrid (Guerrero et al., 2021 ). Given the emergence of new market segmentations, university managers have re-evaluated and seized resources to expand the offer by taking advantage of rapid technical/digital teaching–learning advances, such as massive open online courses (MOOCs), digital campuses connected via devices and virtual reality, and telepresence education using artificial intelligence (Dillenbourg, 2008 ; Heaton et al., 2019 ). It explains why MOOCs have been considered “the most significant technological advance in the pedagogic part of higher education in a millennium” and why university managers have sensed/seized these opportunities (Teece, 2018 : 98). The most successful MOOCs or digital campuses have directly or indirectly enhanced knowledge transfer and technology commercialization via new higher education business models and digital educational platforms (Audretsch & Belitski, 2021 ). Consequently, in the most successful cases, university managers have invested resources in exploiting opportunities and ensuring sustained performance (Guerrero et al., 2021 ). In this assumption, universities’ ordinary teaching capabilities (high-quality educational programs) and dynamic capabilities (new digital educational certifications) have supported the third university mission, especially the most innovative educational trends, by providing the most updated knowledge/skills critical for developing entrepreneurial innovations that would be transferred and commercialized. Based on these arguments, we propose the following hypotheses:

H1a: Ordinary teaching capabilities positively contribute to the configuration of the third university mission.

H1b: Dynamic teaching capabilities positively contribute to the configuration of the third university mission

Regarding research capabilities , universities with an innovative and entrepreneurial orientation are characterized by high-quality research outcomes, as well as sustainable research and development outcomes (Guerrero et al., 2015 ). University managers effectively cover the research standards required by allocating the resources to researchers to achieve the university’s evaluations and higher education agencies (Etzkowitz, 2003 ). Research activities constitute a prerequisite for knowledge transfer and technology commercialization (Compagnucci & Spigarelli, 2020 ). While a signal regarding ordinary research capabilities is knowledge dissemination via publications (Cunningham & Menter, 2021 ; Graf & Menter, 2022 ), more disruptive research outcomes are strongly related to knowledge spillover effects from the publications. In this view, the research citations represent the proxy of an advanced representation of dynamic research capabilities that facilitate the emergence of new collaborative projects among multiple scientists from local/international research centers, labs, or worldwide universities (Romero et al., 2021 ). For example, due to societal and stakeholder pressures, well-recognized university researchers have identified new research scholarly impact opportunities considering innovative solutions to societal challenges (e.g., climate, equality, and sustainability) and external crisis (e.g., financial, natural disasters, and pandemics) (Audretsch et al., 2022 ; Guerrero & Pugh, 2022 ). In this context, university managers should prioritize and seize resources in those research activities that represent sustainable competitive advantage (Heaton et al., 2020 ), a priority for the university stakeholders (Siegel & Guerrero, 2021 ), as well as a substantial contribution to socioeconomic development (Audretsch et al., 2022 ). In this assumption, universities’ ordinary research capabilities (publications) and dynamic research capabilities (dissemination) support knowledge transfer and technology commercialization, especially the most innovative research, by providing the most updated knowledge and human talent critical for developing sustained research impacts. Based on these arguments, we propose the following hypotheses:

H2a: Ordinary research capabilities positively contribute to the configuration of the third university mission.

H2b: Dynamic research capabilities positively contribute to the configuration of the third university mission

Regarding mixed teaching-research capabilities , the allocation of resources and capabilities depends on the orientation of each organization as well as its position within the ecosystem (Belitski & Heron, 2017 ). The first general assumption is a complementing effect of universities’ ordinary and dynamic capabilities in the third mission outcomes (Yuan et al., 2018 ). Teaching-research interdependency may enrich the quality of teaching, the number of publications, and social engagement (Compagnucci & Spigarelli, 2020 ; Heaton et al., 2020 ), for example, the development of a specific granted project with the participation of different stakeholders where experimented faculty and skilled students are actively involved in developing entrepreneurial/innovative solutions to specific societal problems or priorities (Guerrero & Pugh, 2022 ). In this way, university managers will simultaneously allocate existing resources or seize new ones to ensure the project’s success and ensure the university’s sustained performance (Heaton et al., 2020 ). A second general assumption is a rivalry in allocating resources and capabilities between teaching and research activities (Guerrero & Urbano, 2012 ). Teaching-research interdependency may detract from the amount/quality of teaching done by faculty engaged in research, consequently, those involved in knowledge transfer and technology commercialization activities (Heaton et al., 2019 ). For example, faculty (inventors and researchers) will be more incentivized to invest time in publications or inventions instead of teaching. As resources are scarce, university managers must make strategic decisions about their allocation. University managers could redefine faculty categories/numbers according to their profiles/experiences and sense resources to prioritize better-performance projects or profitable new business models. It represents an “organization face trade-offs in choosing between alternative capability development” (Wang & Ahmed, 2007 : 41). Marzocchi et al. ( 2019 ) reinforce these findings, emphasizing different pathways induced by the underlying allocation and deployment of resources and capabilities. In this assumption, we recognize that a rivalry in allocating ordinary/dynamic teaching-research capabilities will negatively affect the configuration of universities’ third mission. It explains the evolution of an innovative and entrepreneurial orientation that allows capturing value-added from the primary university activities (teaching and research). Based on these arguments, we propose the following hypothesis:

H3: A substitution effect between ordinary/dynamic teaching-research capabilities negatively contributes to the configuration of the third university mission

Figure  1 shows the proposed theoretical model investigating the direct effect of ordinary and dynamic teaching capabilities (hypotheses 1a and 1b) and ordinary and dynamic research capabilities (hypotheses 2a and 2b) on the third university mission (knowledge transfer and technology commercialization), as well as the mixed effect of both ordinary and dynamic capabilities in the domains of teaching and research (hypothesis 3).

Theoretical model

3 Methodology

3.1 data collection.

Our empirical analyses are based on a unique longitudinal dataset of 1478 observations that captures the German higher education system landscape integrated by 90 universities within a timeframe from 2000 to 2016. To build this dataset, we combined secondary sources of information such as the OECD REGPAT and the 2019 HAN databases and the Scopus database as well as university websites. Further information was retrieved from the German Statistics Office.

3.2 Variables

Table 2 shows the variables included in our analyses.

Our dependent variable, the third university mission , linked with technology and knowledge commercialization, is measured by the number of university patents from each German university. Previous empirical studies have used the number of patents granted by universities as an appropriate proxy to capture knowledge transfer and technology commercialization as an outcome of the third university mission (Laredo, 2007 ). Given the drivers and particularities of the German higher education system, knowledge transfer and technology commercialization represent a central part of the self-description of the third mission of German universities (Berghaeuser & Hoelscher, 2020 ; Graf & Menter, 2022 ; Henke et al., 2016a , 2016b ; Pasternack et al., 2015 ).

Four independent variables were used to capture the impact of ordinary and dynamic capabilities in the domains of teaching and research on entrepreneurial outcomes. First, ordinary teaching capabilities are operationalized by the number of students per professor and university. According to Heaton et al. ( 2019 ), university managers effectively allocate resources to achieving ordinary or routine activities. In this view, an efficient metric for capturing the allocation of resources in traditional teaching models is the number of students per university faculty. Second, dynamic teaching capabilities are measured by the number of MOOCs per university. According to Teece ( 2018 ), MOOCs represent a dynamic capability derived from the university managers’ ability to sense and seize new opportunities given the contemporary educational trends and massive students’ needs. In this view, the number of MOOCs per university represents the university distinction between identifying a sustained contribution and the achievement of the third university mission (Guerrero et al., 2021 ; Menter, 2022 ). Third, ordinary research capabilities are operationalized by the number of publications per professor. Likewise teaching, university managers are focused on effectively allocating resources for research to achieve the required standard by higher education agencies (Audretsch et al., 2022 ). Therefore, the number of publications per university researcher is the most appropriate measure to capture a successful allocation of resources and capture the university research outcomes (Menter et al., 2018 ). Fourth, dynamic research capabilities are measured by the number of highly cited publications per university. This metric evidenced the scholarly impact of the university’s research on how others disseminate the knowledge produced by university researchers (Audretsch et al., 2022 ).

Our control variables are based on previous studies. We include four control variables Footnote 1 : (a) gender diversity measured by the share of female university research fellows compared to all university research fellows (Menter, 2022 ), (b) industry orientation measured by the amount of university third-party funds from industry per professor and per university (in 1000 €) (O’Reilly et al., 2019 ), (c) public funding measured as a dummy variable indicating whether a university received public funding from the German Excellence Initiative or not Footnote 2 (Menter et al., 2018 ), and (d) size measured by the number of students per university Footnote 3 (Guerrero et al., 2021 ) .

3.3 Statistical model

Given the nature of our dependent variable (count variable with excessive zeros; 868 out of 1717 observations of our dependent variable assume the value zero), we use zero-inflated negative binomial regressions Footnote 4 to test our proposed model (see Ghazal & Zulkhibri, 2015 ; Ghio et al., 2019 ; Siegel & Wessner, 2012 ). We thereby employ robust standard errors. We further include year and region dummies. Besides investigating the direct linear effect of ordinary and dynamic teaching and research capabilities (see M1 to M2), we are interested in the interaction of the respective ordinary and dynamic capabilities, particularly whether ordinary and dynamic teaching and research capabilities are complements or substitutes. M3 to M7 thus test our full model with all control variables and interaction terms.

As a robustness test, we use a logistic panel regression approach, converting our dependent count variable (number of patents) into a dummy variable (the third university mission identified as knowledge transfer and technology commercialization). We again employ robust standard errors and insert the same control variables. Besides investigating the direct linear effect of ordinary and dynamic teaching and research capabilities (see M8 to M9), we are interested in the interaction of the respective ordinary and dynamic capabilities, particularly whether ordinary and dynamic teaching and research capabilities are complements or substitutes. M10 to M14 thus test our full model with all control variables and interaction terms.

4.1 Contextualization

We observe large differences in the German higher education landscape regarding descriptive statistics, with some universities being very innovative across all three university missions (see Table  3 ). In contrast, other universities rather lag behind, as indicated by the value of zero in dynamic teaching capabilities (first university mission), ordinary and dynamic research capabilities (second university mission), and knowledge transfer and technology commercialization (third university mission). Also, the ordinary teaching and research capabilities differ significantly, with some universities focusing on teaching without pronounced research activities. However, not only do activities devoted to teaching, research, knowledge transfer, and technology commercialization within universities vary, but also, the general profile of German universities differs significantly, with some universities having a strong focus on natural sciences and others having a core focus on social sciences. As a result, also the industry orientation varies significantly.

The correlation matrix reveals further insights into the relationship between all three university missions. High bivariate correlations can be found between ordinary and dynamic research capabilities and knowledge transfer and technology commercialization activities ( r  = 0.58 | r  = 0.51). In contrast, the bivariate correlations between ordinary and dynamic teaching capabilities and knowledge transfer and technology commercialization activities are low ( r  = 0.12 | r  = 0.10). Further, a strong industry orientation seems to be strongly related to ordinary research capabilities ( r  = 0.55). University size also shows high bivariate correlations with ordinary and dynamic research capabilities ( r  = 0.62 | r  = 0.53) and knowledge transfer and technology commercialization activities ( r  = 0.66).

4.2 The direct effect of ordinary and dynamic teaching/research capabilities

Table 4 shows the statistical analysis results to test our proposed hypotheses.

Regarding teaching capabilities, our results show that the third mission of universities is not, per se, positively influenced by German universities’ ordinary and dynamic teaching capabilities. Whereas ordinary teaching capabilities show negative and statistically significant coefficients ( β 1  =  − 0.022; p  < 0.01 | β 2  =  − 0.011; p  < 0.01 | β 6  =  − 0.010; p  < 0.01), dynamic teaching capabilities reveal positive and significant coefficients ( β 5  = 0.972; p  < 0.01 | β 7  = 0.675; p  < 0.01). An implicit explanation is that teaching is present in developing innovative and entrepreneurial capabilities equipping the university community (students, faculty, and staff) with capabilities enabling them to sense/seize new opportunities (Guerrero et al., 2021 ; Heinonen & Hytti, 2010 ). However, the effective contribution of these capabilities will depend on the audience and its entrepreneurial expectations that may not be fully captured in our proxies. Based on our results, we need to reject hypothesis 1a yet can confirm hypothesis 1b.

Regarding research capabilities, our results show that German universities’ ordinary and dynamic research capabilities have a positive impact on the development of the third mission of universities. Both ordinary research capabilities ( β 1  = 0.207; p  < 0.01 | β 6  = 0.114; p  < 0.01) as well as dynamic research capabilities ( β 1  = 0.014; p  < 0.01 | β 7  = 0.017; p  < 0.01) show positive and significant coefficients. Previous empirical studies have found that universities with more advanced ordinary and dynamic research capabilities perform better in knowledge transfer and technology commercialization (Berghaeuser & Hoelscher, 2020 ; Graf & Menter, 2022 ; O’Reilly et al., 2019 ). A plausible explanation is that ordinary and dynamic research capabilities result from the universities’ ability to sense opportunities, seize opportunities, and transform research capabilities to meet the demands of knowledge transfer and technology commercialization (Heaton et al., 2019 , 2020 ). Based on our results, we find support for hypotheses 2a and 2b.

4.3 The mixed effect of ordinary and dynamic teaching/research capabilities

Besides the (indicatively) positive linear impact of ordinary and dynamic teaching/research capabilities, the interaction effect among ordinary and dynamic teaching and research capabilities ( β 7  =  − 0.009; p  < 0.01 | β 7  =  − 0.002; p  < 0.01) shows a negative and statistically significant coefficient, indicating a potential substitutive impact of ordinary and dynamic capabilities in the domains of teaching and research. A plausible explanation for the substitution effect is that innovativeness in education (by offering MOOCs) and in research (by engaging in high-impact research) might consume internal capacities and resources in knowledge transfer and technology commercializing (by patenting research). The same holds for all other combinations of teaching and research ordinary and dynamic capabilities, having a combined negative yet not statistically significant effect on third mission outcomes (knowledge transfer and technology commercialization). Again, engagement in the domain of (innovative) teaching and research might consume internal capacities in knowledge transfer and technology commercialization (e.g., by patenting research). Whereas university size and especially a focus on natural sciences seems to be further beneficial for the third mission outcomes of universities ( β 7  = 0.000; p  < 0.01), universities’ industry orientation appears to be negatively associated with the third university mission of knowledge transfer and technology commercialization ( β 7  =  − 0.005; p  < 0.01). Hence, strong university-industry collaborations seem to offer fewer incentives for universities to transfer or commercialize new knowledge or technologies. Our results are robust and confirmed by our alternative logistic regression approach (see Table  5 ).

Universities thus need to make strategic decisions on how to invest their capacities and resources and which paths to pursue, i.e., innovativeness in the first mission (teaching) vs. innovativeness in the second mission (research) vs. innovativeness in the third mission (knowledge transfer and technology commercialization). These potential tensions might be further triggered by the different types of knowledge generated through the different types of innovative behavior. Whereas, for example, MOOCs (as an output of innovative teaching) represent an international entrepreneurial orientation of education to provide “update” capsules of knowledge to people in a flexible way across the globe, patents (as an output of the third mission) create very specific knowledge that is devoted to a rather limited group of individuals (Guerrero et al., 2021 ). Based on our results, we find support for hypothesis 3.

5 Discussion

5.1 theoretical and practical contributions.

Previous studies have highlighted that the strategic view of universities demands more academic debate (Audretsch & Belitski, 2022 ; Klofsten et al., 2019 ), especially nowadays, considering several externalities and exogenous effects (Kawamorita et al., 2020 ; Siegel & Guerrero, 2021 ). Our study contributes to these timely academic and policymaker conversations. First, we extend the literature on dynamic capabilities by differentiating between ordinary and dynamic capabilities in the higher education context. We show that both ordinary and dynamic capabilities in the domains of teaching and research affect universities’ third mission, highlighting the need for the strategic management of universities. We thus provide relevant insights into how ordinary and dynamic capabilities (internal determinants) have been strongly related to German universities’ third mission pathways over the last two decades (Graf & Menter, 2022 ). Especially innovative educational trends and the most innovative research support knowledge transfer and technology commercialization by providing the most updated knowledge for developing entrepreneurial innovations. Second, we extend the conversation about theorizing the (complementary or substitution) effect of ordinary and dynamic capabilities in the configuration of the third mission outcomes (Guerrero et al., 2021 ; Heaton et al., 2020 , 2023 ) by proposing a tested conceptual framework and evidencing the rivalry in the allocation of resources. This study exposes the contribution and rivalry among dynamic teaching and research capabilities in configuring the third mission of universities (Guerrero et al., 2021 ; Romero et al., 2021 ). In this vein, this study also extends the academic discussion about the little attention paid to teaching capabilities in developing the third university mission (Guerrero & Urbano, 2012 ; Heinonen & Hytti, 2010 ). Third, our study provides strategic insights for university managers and the university community as well as policymakers that could be useful during the re-configuration or rejuvenation processes for becoming more entrepreneurial, as there are tensions when pursuing universities’ three missions (teaching, research, knowledge transfer, and technology commercialization), requiring strategic decisions by university managers and policymakers (Heaton et al., 2019 ; Teece, 2023 ). The development of the third university mission depends on ordinary and dynamic capabilities, which must be leveraged and managed. Hence, strategic decision-making about allocating and deploying resources and capabilities is required (Heaton et al., 2019 , 2020 ).

5.2 Implications

Several implications emerge from our study. For university managers , universities should adopt an entrepreneurial orientation to transform old routines into new ones in knowledge-based dynamic environments (Teece, 2018 , 2023 ). In this vein, university managers should transform universities’ activities and shape (entrepreneurial) ecosystems through sui generis strategic acts that neither stem from routines nor give rise to new routines (Belitski & Heron, 2017 ; Heaton et al., 2019 ). This study provides insights into the relevance of dynamic capabilities and the rivalry in using ordinary and dynamic teaching/research capabilities, calling for effective management of resources to accomplish university missions. For the university community , the results provide some insights into the supportive role of teaching and research in developing entrepreneurial behaviors in accomplishing the German universities’ third mission in terms of knowledge transfer and technology commercialization (Guerrero et al., 2021 ; Heinonen & Hytti, 2010 ; O’Reilly et al., 2019 ). However, the effectiveness in developing dynamic capabilities will depend not only on university strategies but also on potential university entrepreneurs’ objectives, expectations, and needs. A good combination of educational programs and new knowledge-creation scenarios could generate significant outcomes for potential entrepreneurs and the university. For policymakers , this study provides insights into the relevance of engaging in teaching-research activities and the collaboration between universities and industries to generate value added in the region via knowledge transfer and technology commercialization. Hence, policy initiatives need to consider the scarce set of resources of universities/scientists (Audretsch et al., 2022 ; Mankins et al., 2014 ), as the simultaneous development of diverging ordinary and dynamic capabilities does not seem to be possible. A learning lesson from this study is the consideration of a long-term perspective of the higher education landscape that allows understanding universities’ pathways to rethink the present/future strategies of universities.

5.3 Limitations and research agenda

This study has several limitations. The first limitation is associated with the proxy used to measure the university mission outcomes. Even though recent studies in the German context have recognized the impregnation of knowledge transfer and technology commercialization as the third university mission (Berghaeuser & Hoelscher, 2020 ), given the dataset definition, we did not include measures like spin-offs, start-ups, or contract revenues. Likewise, the proxies related to ordinary and dynamic teaching and research capabilities could be improved and refined. A natural extension of our study could be collecting data from surveys or retrospective case studies that allow measuring the objective/subjective particularities behind each university mission outcome to be captured. The second limitation is associated with our missing regional-industrial focus. We should have explored the regional context that is crucial for capturing the effect on the configuration of regional ordinary and dynamic capabilities. Therefore, future researchers should consider the regional dimension and the dual relationships between universities and regions; hence, universities’ entrepreneurial and innovative ecosystems should be embedded ((Belitski & Heron, 2017 ; Heaton et al., 2019 ; Schaeffer et al., 2021 ). The third limitation is associated with the definition/measurement of rivalry effects of ordinary and dynamic capabilities on the third mission. A natural extension should be improving the theoretical approach for a better understanding of the rivalry (e.g., adopting asymmetries of information or agency theory approaches), as well as enhancing the testing by capturing in-depth longitudinal information about the university allocation strategy.

6 Conclusions

The objective of this paper was to theorize about the role of dynamic capabilities configuring the third university mission related to knowledge transfer and technology commercialization. Based on a unique longitudinal sample of German universities, this study provides empirical evidence about the tensions in using dynamic teaching and research capabilities to achieve the third university mission (knowledge transfer and research commercialization) in the German context. It highlights the relevance of effectively managing universities’ ordinary and dynamic capabilities. In our role as social science researchers and university members, we would like to stimulate scholars from different social science fields to rethink more broadly the opportunities for making an impact with our research focus on developing universities’ dynamic capabilities and begin doing so more often. We believe it is the perfect time to “make a difference” and “support the strategic entrepreneurial transformation of our workplaces” through our research, teaching, and interaction with multiple socioeconomic agents. Hence, we call for more strategic thinking and decision-making, enabling the adoption of an innovative and entrepreneurial paradigm and opening up new pathways for universities’ third mission.

Data Availability

Not applicable

Change history

14 february 2024.

A Correction to this paper has been published: https://doi.org/10.1007/s11187-024-00902-6

We specifically focus on natural sciences, as associated disciplines are more likely to engage in formal knowledge transfer and technology commercialization activities, hence commercializing newly created knowledge e.g., via patenting (see Abreu & Grinevich, 2013 ).

See Menter et al. ( 2018 ) for a detailed description of the scope and aim of the German Excellence Initiative, a public policy initiative which aimed at promoting cutting-edge research at universities: “The Excellence Initiative has sparked a pioneering spirit at universities, along with new ideas and diverse new forms of cooperation between universities and non-university research institutions” (DFG, 2013 : 13).

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Guerrero, M., Menter, M. Driving change in higher education: the role of dynamic capabilities in strengthening universities’ third mission. Small Bus Econ (2024). https://doi.org/10.1007/s11187-024-00869-4

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Accepted : 31 December 2023

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DOI : https://doi.org/10.1007/s11187-024-00869-4

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• Third mission of universities
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