Concealing or revealing? Alternative paths to profiting from innovation

European Management Journal xxx (2018) 1e10

Contents lists available at ScienceDirect

European Management Journal journal homepage: www.elsevier.com/locate/emj

Concealing or revealing? Alternative paths to profiting from innovation €m Niklas L. Hallberg*, Anna Brattstro Department of Business Administration, School of Economics and Management, Lund University, PO Box 7080, SE-220 07, Lund, Sweden

a r t i c l e i n f o

a b s t r a c t

Article history: Received 20 April 2017 Received in revised form 28 March 2018 Accepted 17 April 2018 Available online xxx

According to the resource-based view, knowledge concealing is fundamental for firms’ ability to prevent competitor imitation and profit from innovation. Open innovation scholars have recently challenged this idea, proposing instead that knowledge revealing can foster industry-wide collaboration and enhance the value of innovation. Reconciling these two perspectives requires a more detailed understanding about the tradeoffs between concealing and revealing. In this paper, we therefore develop a model that outlines the effects of knowledge revealing on the (i) value of innovation, (ii) the price of products stemming from the innovation, (iii) the comparative cost of innovation, as well as the relevant moderators of these effects. Our analysis shows that knowledge revealing may have a positive effect on profits when there are strong indirect network effects; when firms are protected from imitation by causal ambiguity, complementary assets, and intellectual property; and when the innovating firm faces high technological uncertainty. Implications for the resource-based view and research on open innovation are outlined. © 2018 Elsevier Ltd. All rights reserved.

Keywords: Selective revealing Open innovation Resource-based view Value appropriation Value creation

1. Introduction According to the resource-based view (RBV), firms profit from uniqueness and therefore have a strong incentive to conceal knowledge about valuable innovations. Through knowledge concealing, firms prevent competitor imitation, secure the appropriation of rents to scarce resources, and may ultimately sustain competitive advantage (Barney, 1991; Peteraf & Barney, 2003; Teece, 1986). Recently, this view of innovation has been challenged as the concept of “selective revealing” has gained the attraction of both scholars and practitioners (e.g., Alexy, George, & Salter, 2013; Alexy & Reitzig, 2013; Harhoff, Henkel, & Von Hippel, € berl, & Alexy, 2014). Knowledge revealing refers 2003; Henkel, Scho to the practice of creating outbound knowledge flows, where the focal firm “consciously select internally developed knowledge and make it accessible to outside actors, often for free and without contractual requirements” (Alexy et al., 2013, p. 271). Concrete examples include the use of open source code, standardization (SDOs), the use of shared/open innovation spaces, web portals for posting technical problems/solutions, and opening up and

* Corresponding author. E-mail address: [email protected] (N.L. Hallberg).

publically disclosing the content of patents. The core argument is that selective revealing may influence the behavior of external actors in a firm’s ecosystem by pushing these actors along a similar technological trajectory as the revealing firm. This form of “induced isomorphism” will in turn allow knowledge to flow more freely between different firms and thus make the focal firm better equipped to learn from its environment.1 This may involve an upstream firm that publicly discloses some part of its R&D to increase downstream demand for complementary products or when a firm engages in crowd-sourcing by publicly disclosing a certain problem and inviting external actors to participate in finding a solution. The concealing and the revealing models of innovation reach seemingly contradictive conclusions. While the concealing model emphasizes the importance of securing rents by preventing imitation, the revealing model emphasizes the importance of enhancing value creation by promoting knowledge dissemination. However, no model makes explicit the conditions under which the benefits of one approach outweighs the other. In other words, we lack an integrative framework that outlines the advantages and disadvantages of concealing and revealing under different

1 Induced isomorphism is defined as “deliberate strategic action to induce other actors to become more similar to the focal firm, particularly with respect to the production of knowledge” (Alexy et al., 2013, p. 272).

https://doi.org/10.1016/j.emj.2018.04.003 0263-2373/© 2018 Elsevier Ltd. All rights reserved.

€ m, A., Concealing or revealing? Alternative paths to profiting from innovation, Please cite this article in press as: Hallberg, N. L., & Brattstro European Management Journal (2018), https://doi.org/10.1016/j.emj.2018.04.003

2

€m / European Management Journal xxx (2018) 1e10 N.L. Hallberg, A. Brattstro

conditions. This is a gap, which is consistently emphasized in the open innovation literature (Cassiman & Valentini, 2015; Dahlander & Gann, 2010; Laursen & Salter, 2014; Salter, Wal, Criscuolo, & Alexy, 2015). We aim to address this gap in the intersection of research on competitive advantage (Barney, 1991; Teece, 1986) and innovation (Alexy et al., 2013). We do so by developing a model that describes the effects of the level of knowledge revealing on the value (V) of innovation, the price (P) of products stemming from the innovation, and the comparative cost (C) of developing and commercializing the innovation, as well as the relevant moderators of these relationships. As its core contribution, our model serves as an integrative analytical framework that allows detailing the relative effects of concealing and revealing on firm profit. The concealing model of innovation, which we argue derives directly from the RBV, is based on the assumption that a focal firm gains competitive advantage by limiting the degree to which knowledge leaves the firm and by preventing competitors from imitating its unique knowledge (e.g., Ahuja, Lampert, & Novelli, 2013; Barney, 1991; Cohen & Levinthal, 1990; Teece, 1986). Firms with higher innovation and learning over time develop unique resources. This allows them to differentiate products and production processes in a way that allows them to appropriate more value than competitors (Peteraf & Barney, 2003). The main insight from this view of innovation is that unique product market strategies, such as the launch of a new innovative product, must always be based on a corresponding unique factor market position, that is, the possession of unique resources that are not imitated by competitors (Barney, 1986). Hence, to earn profits, firms need to protect the uniqueness of its resources from competitor imitation (Barney, 1991). For example, Teece’s (1986) seminal contribution to the innovation literature is typical of this model.2 In terms of the model developed in this paper, the basic proposition derived from the concealing model is that low levels of knowledge revealing are preferable because it allows the innovating firm to shield itself from competition that may erode price and the possibility of appropriating value (Peteraf & Barney, 2003). The revealing model, however, is based on the opposite notion that high levels of knowledge revealing may promote a more efficient use of knowledge within an industry or ecosystem and thereby increase the value of a focal innovation in terms of the number of good ideas that are generated (Laursen & Salter, 2006; West & Bogers, 2014). A key idea underlying the revealing model is the view of innovation as a cumulative and collective process so that knowledge revealed at t1, which is passed on and modified by other firms, may eventually return to the focal firm at t2 (Nelson & Winter 1982). Through such knowledge spillover across firms, the revealing of knowledge at t1 improves the focal firm’s innovativeness at t2 by providing a technological know-how that this firm would not otherwise possess (Alexy et al., 2013). According to this model, firms’ incentive to engage in knowledge revealing increase when search costs are high, when partners are initially unwilling to collaborate, when there are benefits from content and structural compatibility, when there is a substitute threat from a competing ecosystem, when resources are modular, and when the revealing firm has strong capabilities for absorbing external knowledge (Alexy et al., 2013). In this paper, we seek to untangle the firm-specific economic impact of knowledge revealing. In doing this, we answer recent calls for precise and prescriptive propositions about the strategic

2 Teece (1986) explains the appropriation of gains from innovation based on appropriability regimes (e.g., patents, trade secrets, tacitness of knowledge) and complementary assets (e.g., specialization, transaction costs).

dimensions of firm’s open innovation decisions (Cassiman & Valentini, 2015; Dahlander & Gann, 2010; Laursen & Salter, 2014; Salter et al., 2015). We make our primary theoretical contribution to the RBV by pointing to certain conditions under which the absence of a certain isolating mechanism, namely secrecy, may have a positive impact on profits (Posen, Lee, & Yi, 2013). This in turn implies that the basic premise of the RBV that firms should prevent knowledge from leaving the firm does not hold under certain identifiable conditions, such as when there are strong indirect network effects; when firms are protected from imitation by causal ambiguity, complementary assets, and intellectual property; and when the innovating firm faces high technological uncertainty. Our secondary theoretical contribution is to the open innovation literature on selective revealing, which we argue shows a one-sided focus on value creation while disregarding questions related to imitation, price, and competitive advantage. Explicitly stating the conditions under which outbound openness leads to profits significantly increases the managerial relevance of the open innovation literature. Our model, which is further developed through a series of propositions in the next section, is summarized in Fig. 1. 2. How do firms profit from revealing knowledge? We focus on the revealing (or concealing) of innovative knowledge components, i.e., potentially value-creating, novel pieces of knowledge that can either be problem-related (e.g., Intel’s open calls for research around specific challenges) or solution-related (e.g., IBM decision to make patented software public) (Alexy et al., 2013; von Hippel, 1988). The actor and innovator in our model is a firm (or the relevant decision-makers within the firm) that aims to develop and commercialize an innovation through products and/or services. 2.1. Why reveal knowledge? In industries where complementary assets are spread over a large number of incumbents, firms need to access assets and knowledge residing outside their own boundaries to develop products and services that are valued by customers. The standard way of accomplishing this is through integration (e.g., acquisitions) into the network of assets that are complementary to the firm’s own assets (Williamson, 1985). Another option is hybrid contractual arrangements (e.g., long-term contracts, strategic alliances, joint ventures) with external actors in possession of the complementary assets (Doz & Hamel, 1998; Dyer & Singh, 1998). However, integration and formalized contractual collaborations are not always a feasible way to gain access to the knowledge of external actors. For example, there may be strategic reasons why an external actor will refuse to engage in formal collaborations. Economic contract theory has also identified a host of contractual hazards that make inter-organizational contractual collaboration difficult, such as the ex-ante search/matching costs of identifying viable partners (Barzel, 1997), that the pay-offs from a collaboration may be subject to strong uncertainty (Hallberg, 2015), and the ex post contractual hazards related to opportunism that typically follow relationship-specific investment in collaborations € m & Richtne r, 2014). (Brattstro Under the above conditions, selectively revealing knowledge may be an option for firms seeking to strengthen the link between their own technology and external assets in order to promote innovation. Knowledge revealing may promote interorganizational learning and future inbound knowledge flows (Alexy et al., 2013). In addition, revealing enables other firms in the environment to develop similar or complementary technologies that give rise to indirect network effects that may have strong financial impact, independently of learning effects highlighted in the open

€ m, A., Concealing or revealing? Alternative paths to profiting from innovation, Please cite this article in press as: Hallberg, N. L., & Brattstro European Management Journal (2018), https://doi.org/10.1016/j.emj.2018.04.003

€m / European Management Journal xxx (2018) 1e10 N.L. Hallberg, A. Brattstro

Indirect network effects

Competitive intensity

Isolating mechanisms

3

Technological uncertainty

Value (V) Buyer surplus (V-P, buyer’s value appropriation)

Knowledge revealing

Price (P)

Cost (C)

Value Creation (V-C)

Supplier surplus/profit (P-C, supplier’s value appropriation)

Fig. 1. Effect of knowledge revealing on value, price, and cost.

innovation literature. In the following, we use the Value-Price-Cost framework (VPC framework) to extend the learning- and incentivebased model of knowledge revealing by more directly analyzing the causal mechanisms and conditions that affect the relationship between knowledge revealing and profitability. 2.2. The Value-Price-Cost framework Our analysis builds on the VPC framework (Hoopes, Madsen, & Walker, 2003; Tirole, 1988). In this framework, firms profit from innovation by increasing the price of the products or services stemming from the innovation and/or by reducing the cost of innovation. An increase in price may result either directly from differentiation and uniqueness relative competition or indirectly from raising the value of the innovation, which gives rise to larger surplus to be divided between buyer and supplier (Hallberg, 2017a). Value (V) is defined as the usefulness of a product or service as subjectively perceived by the end consumer, whereas cost (C) refers to the total amount of resources consumed in developing, producing, and delivering a product or service. We may then define an innovating firm’s value creation as the difference between value and cost of innovation (V-C).3 Value appropriation, or profit, is in this context viewed from the perspective of the innovator/supplier and refers to the difference between price and the cost to the innovator (P-C) (Hallberg, 2018). It follows from these definitions that an innovation strategy can have adverse effects on the innovator’s value creation and value appropriation. For example, an innovation strategy that increases the value of products derived from an innovation, but which at the same time reduces the uniqueness and differentiation relative to competition, thus putting a downward pressure on price, may undermine value appropriation and the innovator’s profit.

3 The term ”value” (V) is sometimes used interchangeable with the terms “perceived benefit” and “customer benefit” (Hallberg, 2017b). It should also be noted that value (V) is not equivalent to value creation (VC). For an activity to create value (VC > 0), value (V) must exceed the resources consumed (C) in that activity.

Conversely, an innovation strategy that is solely focused on maximizing uniqueness and differentiation may over time lower the value of innovations. The strongest effect ultimately depends on situational conditions. In the next section, we identify three sets of such situational conditions. These conditions, we argue, affect the relationship between knowledge revealing and (i) the innovation value (indirect network effects), (ii) price of products stemming from innovation (competitive intensity, causal ambiguity, complementary assets, intellectual property), and (iii) comparative cost of innovation (technological uncertainty).

2.3. Effect on innovation value A key proposition of the open innovation literature is that the more that is known or revealed about the technology underlying an innovation, the more valuable will this innovation be to consumers. This effect may partly be attributed not only to learning effects but also to technological conditions referred to as network effects (McIntyre & Subramaniam, 2009). Industries may be subject to many different types of network effects depending on the size of the network (Katz & Shapiro, 1985), the technology on which the industry is based (Katz & Shapiro, 1985; Shankar & Bayus, 2003), €m & Bachmann, 2018; and the quality of network ties (Brattstro Dyer & Singh, 1998). The literature separates between three types of network effects, which may be present independently of each other. Direct network effects occur when the value consumers derive is dependent on the number of other consumers using the product (Katz & Shapiro, 1985). For example, users of phones or other social platforms gain additional utility as the number of other users within the same network increases (because there are more people to call/interact with). Indirect network effects, however, occur when the value of products is not so much dependent on the number of consumers of the focal product but on the number of different complementary products available on the same network or platform. For example, in the gaming console industry (PlayStation, Xbox) the value derived from using a particular console is directly dependent on the

€ m, A., Concealing or revealing? Alternative paths to profiting from innovation, Please cite this article in press as: Hallberg, N. L., & Brattstro European Management Journal (2018), https://doi.org/10.1016/j.emj.2018.04.003

4

€m / European Management Journal xxx (2018) 1e10 N.L. Hallberg, A. Brattstro

number of games available on the platform. Finally, as a special form of indirect network effects, the literature distinguishes the so called double-sided network effects for cases when network effects reach across different market populations so that actors in one market chose products based on what products are consumed in a different market (Gallaugher & Wang, 2002; Parker & Van Alstyne, 2005; Rochet & Tirole, 2002). For example, the value of investing in an electrical vehicle increases if there is an established network of charging posts available, and vice versa. Hence, consumption in the market of charging stations is positively related to consumption in the market of electric cars (Eisenmann, Parker, & Van Alstyne, 2006). Making a distinction between direct and indirect network effects is important for two reasons. First, many markets that are characterized by direct network effects do not exhibit strong indirect network effects, and vice versa. Second, as will be elaborated on in the next section, direct and indirect network effects have different implications for competitive intensity and price competition. Indirect network effects provide incentives for building broad alliances and sharing knowledge within the industry in order to facilitate a greater network of complementary assets. Direct network effect on the other hand may have the opposite effect of promoting increased competitive intensity, in effect, making knowledge revealing a risky strategy. 2.3.1. Direct network effects In markets where consumers place higher value on a product if other consumers also use it, knowledge revealing may increase user adoption and sales in a self-reinforcing way by reducing consumer uncertainty and switching costs related to the uncertain adoption of the product by other consumers (Katz & Shapiro, 1994). Buyers will be more willing to adopt a new technology if they understand the technology involved, the range of consumer segments that are likely to adopt it, and the type of benefits offered by the new technology. One example of this is the information campaigns about electric cars (and charging posts), where companies such as Tesla have been contributing by opening up patents and disclosing information about the underlying technology and how this new technology may benefit certain consumer groups. However, when direct network effects are strong, firms also compete vigorously to set dominant designs and standards that support their own technological positions (Farrell & Saloner, 1985). Hence, in industries where a dominant technological standard has not yet emerged and there are competing technological platforms, firms typically have to tailor their openness decisions considering both potential advantages in terms of getting other actors onboard the platform and the potential disadvantage of an elevated competitive intensity due to the lock-in and winner-takes-all rules of the market. Facilitating or encouraging direct network effect by revealing knowledge may thus be a double-edged sword. 2.3.2. Indirect network effects The value of most innovations is dependent on the availability of complementary assets and products within the industry. Complementary assets for a given innovation are subject to technological uncertainty and take a significant time do develop. Therefore, their development by other firms build on early signals about what complementary assets and products to develop and what

4 It is important to note that indirect network effect and the development of complementary assets are very different from consumer-side network effect in this regard. Consumers rarely have to make significant technological investments that are subject to uncertainty and are therefore much less in need of the type of early signals from innovators.

technological platforms to build on.4 These signals will be more timely, stronger, and accurate if more is known about other related innovations. Hence, knowledge revealing can increase the value of a focal firm’s innovation by stimulating other firms to develop more and better complementary assets. For example, new processors in mobiles may only realize their full value if there is software available that uses the capacity of the processors. Such complementary software is in turn more likely to be available if potential software developers are aware early on of the technological characteristics, strengths, and weaknesses of the processors being developed. The importance of complementary assets for profiting from innovation was acknowledged early on in the development of the RBV. For example, Teece’s (1986) seminal framework highlights appropriability regimes (technological conditions that enable secrecy and legal protection), dominant design paradigm (e.g., emergence of standards), and complementary assets (cospecialized, specialized/unilateral dependence). A key insight from this framework is that when appropriability regimes are weak and the innovation is dependent on an important complementary asset, the innovating firm may need to take control over that complementary asset (e.g., integration) in order to appropriate the value created by the innovation. Hence, the complementarity between innovations and other assets has typically been linked to questions of vertical integration, where the innovator is expected to guard against the potential bargaining power and potential hold-up of complementors (Teece, 1986; Williamson, 1985). However, in addition to being a mechanism by which innovators secure the appropriation of rents from their innovation, complementarity may also in a more direct way affect what strategies firms choose to influence external assets in the industry (Stremersch, Tellis, Franses, & Binken, 2007). We argue that the technologically driven potential for realizing indirect network effects determines the extent to which knowledge revealing has a positive impact on the value of an innovation. Consider for example the launch of the Open Handset Alliance in 2007 based around the mobile operating system Android. The dominant actor within the network, Google, chose an open strategy concerning how knowledge and influence over the operating system would be shared among members of the network. Google’s choice to selectively reveal knowledge about the Android operating system and to a large extent rely on open standards is arguably explained in terms of an ambition to exploit indirect network effects. By getting trusted application/software developers and hardware manufacturers to align their technology with the system, Google hoped to increase the value consumers experience from using Android units as new and complementary applications and better hardware become available on the network. Hence, indirect network effects between software and hardware suppliers may take off because of this positive reinforcing spiral of increasing utility when different elements of the product offer and the underlying assets are directly cospecialized to each other. As illustrated by the above example, there is an important case to be made for the economic impact of indirect network effects on the decision to reveal knowledge that is neither captured by preexisting research highlighting learning and reciprocal knowledge flows nor the literature on direct network effects. We thus argue that the presence of indirect network effects constitutes an important moderator of the relationship between knowledge revealing and the creation of value. We therefore propose the following: Proposition 1. The greater the indirect network effects in the industry, the stronger is the positive relationship between the firm’s knowledge revealing and the value of its innovations to consumers.

€ m, A., Concealing or revealing? Alternative paths to profiting from innovation, Please cite this article in press as: Hallberg, N. L., & Brattstro European Management Journal (2018), https://doi.org/10.1016/j.emj.2018.04.003

€m / European Management Journal xxx (2018) 1e10 N.L. Hallberg, A. Brattstro

2.4. Effect on price A key proposition of the RBV is that the more that is known or revealed about the technology underlying an innovation, the more likely it is that competitors will imitate the technology. When, in turn, imitation occurs, the supply of similar or substitute products increases, which puts a downward pressure on prices (Hallberg, 2017a). The uniqueness of knowledge is thus a fundamental source of profits for firms, which the firms that reveal knowledge risk losing. Specifically, revealing knowledge reduces casual ambiguity for competitors, where casual ambiguity represents an inability to interpret or make sense of the “nature of the causal connections between actions and results” (Lippman & Rumelt, 1982, p. 418). It also potentially reduces the first-mover advantage, which would otherwise have been gained from accumulating unique, cumulative, and path-dependent knowledge (Ahuja et al., 2013). This makes it less likely that the firm will appropriate the value stemming from the innovation by reducing the possibility of offering differentiated products that may be sold at a higher price. Yet, the magnitude of such effects differs across industries and firms. In the following, we outline two sets of conditions that we argue moderate the relationship between knowledge revealing and the price of products stemming from a focal innovation: competitive intensity and isolating mechanisms. 2.4.1. Competitive intensity Industries may differ significantly in the intensity of competition. The basic Proposition is that more concentrated industries exhibit less competitive intensity (Pepall, Richards, & Norman, 1999). However, competitive intensity may under certain circumstances be fierce even in concentrated industries depending on how the underlying technology constrains production capacity adjustments. When supply is flexible (Bertrand competition), the competitive intensity is typically higher than when firms have to pre-commit to a certain production quantity (Cournot competition). One interpretation of the difference between these two competitive conditions is that firms’ incentives to engage in pricecutting are greater when there is free unsold capacity (e.g., airline pricing during downturn). The presence of direct network effects also provides incentives for firms to engage in price competition in order to increase market share and installed base (Arthur, 1996; Eismann, 2006; McIntyre & Subramaniam, 2009). When direct network effects are strong, firms seek to establish a large installed base of consumers, which in turn increase the value of the product to each consumer and thus overall sales. Actively competing on price in order to achieve such sales growth is of course tempting to firms operating in these markets. Hence, knowledge revealing in industries characterized by high competitive intensity (due to low concentration, free capacity, low differentiation, and direct network effects) may lead to increased price competition. This is because the revealed knowledge is likely to be used against the revealing firm by competing firms to gain market share and increase their installed base of consumers. The reason for this is the incentive structure facing firms in markets with high competitive intensity: The potential gain from engaging in value creating collaborative behavior in terms of reciprocal knowledge sharing (inbound knowledge flows) and the development of complementary assets (indirect network effects) are simply outdone by the commercial advantage that competitors may gain by using the revealed knowledge for competitive purposes. For example, competitors may use the revealed knowledge in order to develop platform envelopment strategies by offering the revealing firms key product functionalities as part of a multiplatform bundle (Eismann, Parker, Van Alstyne, 2006, 2011), they may use the knowledge to time product releases or adapt the sequence in which

5

different product versions are released (Suarez, Grodal, & Gotsopoulos, 2015) or create price bundles that are aimed at reducing the value of the revealing firm’s product offer (Brandenburger & Stuart, 1996; Nagle & Holden, 2002). Hence, when competitive intensity is high, it is more likely that firms will experience negative consequences from knowledge revealing in terms of aggressive actions from competitors. On the basis of this, we propose that competitive intensity moderates the negative relationship between knowledge revealing and price. More formally, Proposition 2a. The stronger the competitive intensity in the industry, the stronger is the negative relationship between the firm’s knowledge revealing and the price of its products.

2.4.2. Causal ambiguity The presence of isolating mechanisms determines industrylevel imitation risk (Rumelt & Lamb, 1984). isolating mechanisms include things such as causal ambiguity (Lippman & Rumelt, 1982), co-specialized and complementary assets (Teece, 1986), and intellectual property protection (Cohen, Nelson, & Walsh, 2000). For example, industries may exhibit different degrees of causal ambiguity concerning how generally known is the link between a particular action and performance (Lippman & Rumelt, 1982). Such differences in causal ambiguity across industries are usually due to the nature of technology where some technologies are easy to forecast whereas other technologies are subject to greater uncertainty. Different industries may also exhibit different degrees of time compression diseconomics in terms of the time required for the imitation of an innovation (Dierickx & Cool, 1989). Generally, in industries where isolating mechanisms are weak, new technologies are quickly diffused across industry incumbents (Teece, Pisano, & Shuen, 1997). Such industries also exhibit a high imitation risk in the sense that competitors quickly imitate new technological advancements that have been developed or adopted by other firms. Consider, for example, the global maritime shipping industry where studies show that new innovations diffuse unexpectedly slow because firms, due to the specific conditions in this industry, have trouble observing the outcomes associated with new shipping technologies (Greve, 2009). In such industries, firms may be at less risk of being imitated even if some elements of the technology, or the specific problems that the technology is supposed to solve, are revealed. Furthermore, if firms reveal important knowledge about innovations in an industry characterized by low causal ambiguity, they may severely decrease their chances of appropriating value. This is because they make it easier for competitors to imitate the firm-specific resources that are the source of competitive advantage (e.g., biotechnology industry, Dodgson, Gann, & Salter, 2008). Hence, we argue that the level of causal ambiguity in the industry will moderate the relationship between knowledge revealing and price: Proposition 2b. The greater the causal ambiguity in an industry, the weaker is the negative relationship between the firm’s knowledge revealing and the price of its products.

2.4.3. Complementary assets The prevalence of complementary assets (when factors yield superior value in combination) constitutes a second form of isolating mechanism that affects the risk of being imitated (Rumelt & Lamb, 1984). Almost all innovations require complementary capabilities and assets besides the innovation in order to be successfully commercialized (Teece, 1986). Such complementary capabilities and assets may include distribution networks,

€ m, A., Concealing or revealing? Alternative paths to profiting from innovation, Please cite this article in press as: Hallberg, N. L., & Brattstro European Management Journal (2018), https://doi.org/10.1016/j.emj.2018.04.003

6

€m / European Management Journal xxx (2018) 1e10 N.L. Hallberg, A. Brattstro

manufacturing equipment and know-how, and sales or service infrastructure. These complementary assets can be accessed by the innovating firm through market contracting, collaborations/joint ventures, and acquisitions/integration. The comparative decision of what governance and ownership structure to employ for a certain transaction is normally assumed to be determined by the specific contractual hazards that the innovating firm exposes itself to (Williamson, 1985). The challenge for firms is to balance the comparative efficiency of markets (integration is costly because of bureaucracy/agency costs and lack of scale economies) against the hold-up risk that arise when firms use markets to contract for complementary and cospecialized assets. The dominating normative implication in the innovation literature is that the innovator should acquire the complementary assets that are cospecialized with the innovation in order to avoid hold-up and the appropriation of quasi-rents (Teece, 1986).5 However, there are also other implications of innovating firms’ ownership of complementary assets. For example, firms may employ the ownership of immobile complementary assets as an isolating mechanism to deter imitation (because this makes the innovation comparatively less valuable for other firms). When controlling complementary asset, firms can consequently reveal knowledge without suffering from a downward pressure on price. For example, consider an R&D-intensive firm in the medical technology industry that is based on a single innovation in a specific knowledge area (e.g., EMI’s CT scanner: Teece, 1986). Concealing knowledge related to the innovation could be crucial for preventing imitation and enabling a successful commercialization of the innovation. However, if the innovation is closely tied to a non-fungible complementary asset, such as clinical activity in a proximate academic hospital, imitation risk is likely to be less of a problem for the R&D firm because potential imitators are unable to replicate the full benefits of the combination of the innovation and the proximate hospital. Hence, firms with strong complementary assets are therefore more likely to profit from revealing knowledge because they are less vulnerable to imitation. More formally, Proposition 2c. The greater the firm’s control of complementary assets, the weaker is the negative relationship between the firm’s knowledge revealing and the price of its products.

2.4.4. Intellectual property A third form of isolating mechanism is intellectual property protection. This refers to legally granted rights to prevent others from using, selling, or adopting an innovation (Cohen et al., 2000). Prior studies have found that intellectual properties, such as patents, in many cases are ineffective and that firms to a large extent rely on other appropriation mechanisms, such as secrecy, lead times, and complementary capabilities. Yet, there remain industries where intellectual property is an effective guard against competitor imitation (Cohen et al., 2000). One prominent example of an industry where intellectual property provides effective protection against imitation is in the industry for drugs, where research show that patents still provide holders with monopoly rents, significant revenues through licensing, and possibilities of creating “patenting fences” that may impede competitive innovation (Cohen et al., 2000). In industries where intellectual property provides protection, we see a potentially overlooked role of patents for the use of

5 This implication builds on the assumption that complementary assets are also immobile in the sense that they are non-fungible and not subject to competition. In cases where this assumption does not hold, the innovating firm need not integrate into the asset because it is protected against hold-up by competition (Jacobides et al., 2006).

knowledge revealing. For example, consider a firm setup around a single new innovative drug. If the firm has the drug in question properly protected by intellectual property, it may be less restrictive in how it reveals knowledge about the drug itself and its production process. This, in turn, may enable other actors in the industry to improve medical treatments and process technology, which may ultimately lead to a more valuable customer offer. Hence, intellectual property constitutes an isolating mechanism that, when effective, may prohibit imitation and thus increase the payoff from knowledge revealing by reducing the negative effect on price. More formally, we propose that Proposition 2d. The stronger the intellectual property protection a firm enjoys, the weaker is the negative relationship between the firm’s knowledge revealing and the price of its products.

2.5. Effect on the cost of innovation Two conditions need to be met for the creation of innovation: (1) knowledge components are sufficiently heterogeneous to add novelty when combined and (2) these knowledge components can be combined in a cumulative way into useful output (Kogut & Zander, 1992). However, the more heterogeneous the knowledge components and the weaker the ties between actors, the more challenging is the combinative effort (Laursen & Salter, 2004). Moreover, the more distant from the focal firm’s existing knowledge, resource base, and capabilities, the more uncertain and ambiguous is the outcome (March 1991). Hence, innovation activity that relies on utilizing and potentially modifying heterogeneous knowledge that is dispersed across external actors may be very costly to the innovating firm. However, external knowledge may still be crucial for innovation despite its associated costs. Under lower and moderate levels of uncertainty, the typical ways in which innovating firms access and modify external knowledge are by acquisitions, strategic alliances, joint ventures, and contractual relationships (Doz & Hamel, 1998; Dyer & Singh, 1998).6 However, high levels of uncertainty concerning the type of knowledge that will be acquired and the location of this knowledge may make these alternatives costly to the innovating firm (Felin & Zenger, 2014). Simply put, the innovating firm may neither know what to search for nor where to search for it. In comparison to other modes of external knowledge acquisition, knowledge revealing may therefore be a cost-efficient alternative that allows the firm to cast a wide net in its search for new knowledge. The benefit of this strategy is thus that it does not require that the innovating firm is able to in advance specify the type of knowledge it is searching for and the specific location of this knowledge. Despite the comparative cost advantages of knowledge revealing under high uncertainty, it should be noted that revealing is a costly innovation practice. This type of open search is typically dispersed and unfocused, which leads to coordination, transaction, and cognitive costs (Cassiman & Valentini, 2015) that may be avoided by engaging in a more narrow search activity. For example, knowledge revealing requires that the innovating firm is able to

6 Uncertainty range from situations where decision-makers are incapable of assigning outcome probabilities, to situations subject to true uncertainty where both possible outcomes and their associated probability are unknown by the decision-maker (Knight, 1916; Milliken, 1987; Thompson, 1967). Eight factors have been identified to create uncertainty in innovation processes: technological uncertainty, market uncertainty, institutional uncertainty, political uncertainty, legitimacy uncertainty, managerial uncertainty, timing uncertainty, and consequence uncertainty (Jalonen, 2012). In this paper, we focus primarily on technological uncertainty.

€ m, A., Concealing or revealing? Alternative paths to profiting from innovation, Please cite this article in press as: Hallberg, N. L., & Brattstro European Management Journal (2018), https://doi.org/10.1016/j.emj.2018.04.003

€m / European Management Journal xxx (2018) 1e10 N.L. Hallberg, A. Brattstro

decontextualize its knowledge so that it can be picked up and incorporated by externals. It also requires that the firm has sufficient capacity to absorb knowledge in subsequent stages (Cohen & Levinthal, 1990). Recent empirical work has shown that this is a big challenge for many firms. Dahlander and Piezunka (2014), for instance, found that most firms do not succeed in incorporating external ideas e even when they explicitly asked for them. In a similar vein, Foss, Laursen, and Pedersen (2011) showed that firms struggle with incorporating customer knowledge and emphasize the need of adapting internal practices for this purpose. Previous research has shown that selective revealing may be an effective strategy when partnering uncertainty is high (Alexy et al., 2013). By revealing knowledge, firms signal that they are looking for collaboration of a particular type and thus reduce information asymmetries concerning themselves and the desired attributes of a collaborator. The costs associated with this type of knowledge revealing are likely in the range from low to medium because both the transmission and absorption of this type of matching information are relatively straightforward. Perhaps more importantly then, knowledge revealing may also be an effective strategy when technological uncertainty is high (“lack of knowledge of the details of new technology“, Jalonen, 2012: 24) because by revealing knowledge, firms signal not only their interest in potential partnerships but also their chosen technological trajectory and how they estimate the probability for the occurrence of different technological scenarios. Feedback from external actors and the possibility of influencing the actions of these external actors then enable coordination around future mutually beneficial technological platforms. We argue that the comparative cost of knowledge revealing, relative to other more structured approaches of accessing and modifying external knowledge (e.g., acquisitions, alliances), is primarily contingent on the level of technological uncertainty facing the firm. In fact, relative to other forms of uncertainty (e.g., partnering uncertainty), we find that the level of complexity associated with coordination around future technological platforms makes technological uncertainty a primary factor influencing the viability of knowledge revealing. High levels of technological uncertainty may even force innovating firms to engage in more broad and unfocused search behavior (Felin & Zenger, 2014). Hence, although costly to the firm, knowledge revealing constitutes a comparatively cost-efficient alternative for innovating firms faced with high levels of technological uncertainty because as uncertainty increases, the cost of accessing and modifying external knowledge through acquisitions, and alliances increase more rapidly than the cost of knowledge revealing. We therefore propose Proposition 3. The higher the technological uncertainty facing a firm, the less costly is knowledge revealing as a means to acquire and modify external knowledge, compared to other means of external knowledge acquisition. 2.6. Concluding discussion: the relationship between innovation value and profit The above analysis identifies important tradeoffs between concealing and revealing. On the one hand, we have argued that knowledge revealing may increase the value of innovation to consumers by fostering a more collaborative environment in which firms learn from each other and over time develop greater networks of complementary assets. At the same time, we have also showed that knowledge revealing may induce price competition, thus having a negative impact on profit by lowering the price of a focal firm’s product. Finally, we showed that knowledge revealing is a costly strategy, which may be most relevant when technological

7

uncertainty is high. Our analysis asserts that the primary benefit of knowledge revealing to innovators is that this strategy under certain conditions increases the value of innovation. For the individual innovating firm, there are two primary mechanisms through which increased value translates into profits: industry demand and product differentiation. 2.6.1. Industry demand and volume When the value of products in an industry increases as a result of more efficient use of knowledge, the industry demand curve shifts outwards, and the total output of the industry increases. Naturally, a higher total volume implies that the market as a whole will be more valuable, and ceteris paribus, that the potential for any firm in the industry to earn profits will increase. Basically, by expanding “the size of the pie,” even fixed proportions or slices of this “pie” will grow larger. Hence, firms may gain from revealing knowledge by improving their sales numbers despite keeping prices and market shares unchanged. In fact, it might even be that the value of expanding the market will be so great that the innovator’s profits increase despite a lower market share. 2.6.2. Product differentiation and price premiums A second mechanism by which knowledge revealing may raise profits is by allowing the innovator to differentiate itself from competition based on a product that offers greater consumer surplus than competing products (Peteraf & Barney, 2003). The basic problem for firms using knowledge revealing as part of a differentiation strategy is that knowledge revealing per definition involves making key innovative knowledge available to competitors who may be expected to use this knowledge in order to improve their product offer, in affect, reducing any potential differentiation advantage that the revealing firm may accomplish in its innovation process. A key to maintaining product differentiation while engaging in knowledge revealing is thus to make sure that the positive effects of knowledge revealing on value are asymmetrical across firms € m, (Gnyawali & Madhaven, 2001; McKelvie, Wiklund, & Brattstro 2018). Or more specifically, to make sure that the value enhancing new product attributes that result from a more open and efficient use of knowledge within the industry is appropriated by the innovator and not its competitors. Accomplishing this requires that the innovator carefully considers what behavior knowledge revealing will induce in other firms and what type of knowledge is revealed. First, as discussed in previous sections, incentives and competitor behavior are affected by the level of competitive intensity and the strength of isolating mechanisms. Second, a key mechanism by which knowledge revealing may be consistent with, or even improve, product differentiation is the extent to which the innovator tailors what knowledge is revealed so that it only benefits producers of complementary assets and products, while not revealing knowledge that makes it easier for competitors to imitate valuable product features or the unique characteristics of the underlying technology. The key challenge is thus to reveal knowledge that gets complementors onboard the focal technological platform, while at the same time keeping direct competitors out. Arguably, this involves selectively revealing knowledge related to compatibility problems in the own technology while not revealing knowledge directly related to solutions to specific customer problems. 3. Implications We contribute to the RBV by clarifying the key conditions under which the uniqueness of resources and the presence of isolating

€ m, A., Concealing or revealing? Alternative paths to profiting from innovation, Please cite this article in press as: Hallberg, N. L., & Brattstro European Management Journal (2018), https://doi.org/10.1016/j.emj.2018.04.003

€m / European Management Journal xxx (2018) 1e10 N.L. Hallberg, A. Brattstro

8

mechanisms, such as secrecy, can be a burden for firms. Specifically, we identify conditions at the industry and firm levels that may affect the financial impact of concealing and revealing knowledge and outline the mechanisms that underlie these effects. Several scholars have also called for more analysis and theory building on the tradeoffs and contingencies associated with open innovation (Cassiman & Valentini, 2015; Dahlander & Gann, 2010; Laursen & Salter, 2014; Salter et al., 2015). We have taken on this challenge by offering a framework for the analysis of the conditions under which the concealing and revealing of knowledge are viable paths to profiting from innovation. We argue that knowledge revealing has a general positive effect on the creation of value that is the strongest in industries that are subject to indirect network effects. However, the revealing of knowledge may also have a negative effect on profitability to the extent that it induces imitation, less differentiation, and downward price pressure. We expect this negative effect to be particularly significant in industries with high competitive intensity and weak isolating mechanisms. In addition, we also consider how the comparative costs of developing new innovations are affected by the technological uncertainty facing the firm. Our insights complement previous contributions on the benefits of selective revealing (e.g., Alexy, et al., 2013; Henkel et al., 2014) that emphasize the positive effects on value creation but say little or nothing about the potential adverse effects on value appropriation. Hence, rather than suggesting that firms are more likely to reveal knowledge when they have little access to knowledge themselves (c.f. Henkel et al., 2014), we argue that successful knowledge revealing depends on specific industry and firm factors and how these factors play out against tradeoffs between value creation and value appropriation. 3.1. Managerial implications Even though there may be a strong case to be made for open innovation and revealing under certain conditions, there are also potential pitfalls associated with this innovation strategy when it is applied in an uncritical manner without a proper analysis of the relevant business environment. In fact, as patents have become harder to uphold, secrecy has emerged as a vital mechanism for securing profits (Cohen et al., 2000), preventing or delaying competitive imitation of innovative knowledge and may in many industries be crucial for firm survival. Hence, an important implication of our arguments is that managers should carefully weigh the benefits of knowledge revealing in terms of how this might expand the size of the “value pie” that industry incumbents will share (value creation) against the expected slice of this pie that their firm is likely to capture for themselves in terms of profits (value appropriation). What makes this tradeoff difficult for managers is that value creation and value appropriation typically occur at different points in time and that the two change in strength over time. Hence, a comparative evaluation of the payoffs associated with concealing or revealing typically results in a complex calculation where the time value of money, risk profiles, and relevant opportunity costs come into play. For example, consider the following scenarios: 3.1.1. Scenario 1: the long-run loss from imitation Many of the short-term benefits from knowledge revealing, such as access to external knowledge and gaining access to new partners, may be outweighed in the long run if competitors imitate the revealed knowledge. For example, knowledge revealing may enable competitors to not only enter the focal product market but also challenge the innovating firm in other areas, such as in relationships with suppliers and complementors. The problematic aspect of this scenario is that imitators may in many ways be better

commercially prepared than the innovator, both in terms of appropriation strategy and in terms of having had time to carefully evaluate and cherry-pick the most desirable position with the focal network of firms. Hence, revealing knowledge may only be a viable strategy under specific conditions that provide a significant boost in value creation and guarantee the innovating firm some minimum level of value appropriation in later periods when imitators have played out their strategies. Managerial decisions about the commercialization of innovation based solely on research that highlight the conditions under which openness and knowledge revealing are beneficial for innovation outcomes (e.g., Alexy et al., 2013) therefore run the risk of failing to appreciate the comparative side of the problem and the likely long-run effects on profits. 3.1.2. Scenario 2: the long-run loss from lack of indirect network effects A different scenario following the decision to instead conceal knowledge about an innovation is that the firm is successful in keeping vital knowledge about the innovation secret, thus realizing the initial commercial advantages of relative competition in terms of product differentiation and potential price premiums, while at later stages facing problems related to lack of complementors in their network and insufficient number of suppliers with capabilities of developing new and improved generations of the products stemming from the initial innovation. Hence, as highlighted by this scenario, short-term commercial benefits (and price premiums) from preventing imitation by concealing knowledge may be offset by long-term negative effects related to the insufficient use of indirect network effects and low levels of inbound knowledge flows. Failing to keep competitive parity in later periods in terms of realizing indirect network effects may in knowledge-intensive industries prove to be disastrous for the innovating firm even if it has enjoyed early commercial success. 3.2. Future research and limitations The propositions outlined in this paper present some exciting new empirical and theoretical research opportunities. First, one research opportunity is to empirically study the direct effects of knowledge revealing on firm performance by using survey data. Henkel et al. (2014) provided a promising start, examining reasons for why firms in the software development industry engage in selective revealing. They captured revealing practices by surveying (i) the extent to which companies use selective revealing and (ii) what share of their software they disclose. Future research could investigate the effects of these practices on firm performance, where relevant performance variables include innovation output, firm €m, & Wennberg, 2017; profit, and firm growth (McKelvie, Brattstro McKelvie et al., 2018). Another option is to directly test the predictions of the model suggested in this paper. This involves the use of survey-based measures of knowledge revealing to identify the effect on customer value, price, and the cost of innovation. A particularly useful approach would be the use of longitudinal surveys. This design would be useful for separating the potential negative short-term effects of revealing on price from positive and more long-term effects on customer value. Naturally, a key challenge in this research concerns the development of measures of knowledge revealing. There are several options available apart from surveys. For example, studies of knowledge revealing could build on data from standardization organizations (e.g., ISO, 3GPP) on how active firms are in submitting new open proposals and change requests in the development of technical standards related to their core product. Another option is to focus on firms’ decision to open up and disclosing the content of patents as a measure of knowledge revealing (e.g., Tesla’s decision to open up some of its patents).

€ m, A., Concealing or revealing? Alternative paths to profiting from innovation, Please cite this article in press as: Hallberg, N. L., & Brattstro European Management Journal (2018), https://doi.org/10.1016/j.emj.2018.04.003

€m / European Management Journal xxx (2018) 1e10 N.L. Hallberg, A. Brattstro

Second, given the complexity of studying the payoffs associated with revealing knowledge in innovation, we believe that rich, retrospective case studies of concealing, revealing, and industry dynamics will constitute an important means of uncovering how effects on value, price, and costs play out over time. This approach has proven useful for theorizing knowledge management practices in the past (e.g., Brusoni, Prencipe, & Pavitt, 2001). A relevant future question for this type of studies is how competition and knowledge dynamics in an industry interact over time given certain knowledge sharing strategies? For example, exactly how does the decision to reveal knowledge leads to short-term competitive risks and how does initial potentially harmful competitive dynamics change into beneficial long-term effects on industry knowledge dynamics when firms engage in knowledge revealing? An interesting and highprofile case that illustrates many of the tradeoffs discussed in this paper is the different knowledge sharing strategies of Apple and Google (Android) in the telecom industry. As predicted by our model, Apple’s concealing strategy has in fact led to higher profits (through premium prices), whereas Google’s (Android’s) knowledge revealing strategy has led to widespread consumer adoption and higher sales. Third, there are also interesting opportunities for future research on the relationship between knowledge sharing strategies and the revealing firm’s organizational capabilities. While one might, as we do in this paper, view the performance impact of knowledge revealing as primarily determined by industry- and firm-level factors, another option is to study the choice between these strategies as a result of firm-specific knowledge. This research would answer the call to focus more on the microlevel origins of strategy (Hallberg & Felin, 2017). Some firms may simply be more capable of successfully concealing knowledge, while other firms are better equipped to reveal knowledge. Further, there might be important individual-level factors affecting the choice of strategy that are not captured by more macro-oriented models. This line of research constitutes a potentially fruitful opportunity for crossfertilizing insights from the capability-based view and innovation studies and could be productively enhanced by the use of in-depth, comparative case studies. Finally, we have not fully explored the possibility of combining concealing and revealing in a portfolio of innovations or products. Naturally this opportunity is dependent on the modularity of different knowledge components and the possibility of rearranging these in different ways within a firm. Given the different time spans and temporal order by which the two models generate revenues and cost, it might be that there are benefits from implementing a mixed strategy where different knowledge components are concealed and revealed in order to minimize risks related to cash-flow at different points in time. Future studies on this form of mixed knowledge sharing strategies would benefit from a multitude of methods including both survey-based data, data on standards and patents, and case studies. 3.2.1. Limitations of this work In this paper, we have primarily focused on untangling the financial impact of openness decisions. Given this focus, it is natural to take on a firm-level perspective (because this is the relevant financial unit) and to view the firm as part of a larger system (industry) where certain external conditions moderate the relationship between strategy and outcome. However, there are of course also other important levels of analysis that affect innovation strategy. One such level of analysis that we did not include in our model is the project level. As pointed earlier, openness decisions may vary across different innovation projects within the same firm depending on the specific technological character of the individual project.

9

4. Conclusion In this paper, we reconcile the concealing and revealing models of innovation by proposing specific conditions under which each strategy is more likely to lead to superior profitability for the innovating firm. We show important tradeoffs between value creation and value appropriation that may occur at different points in time. In addition, our analysis sets out a novel framework for organizing previous innovation research and relating it to the RBV by identifying contingencies and clearly separating the effects of concealing and revealing on value, price, and cost.

References Ahuja, G., Lampert, C., & Novelli, E. (2013). The second face of Appropriability: Generative appropriability and its determinants. Academy of Management Review, 38(2), 248e269. Alexy, O., George, G., & Salter, A. J. (2013). Cui bono? the selective revealing of knowledge and its implications for innovative activity. Academy of Management Review, 38(2), 270e291. Alexy, O., & Reitzig, M. (2013). Privateecollective innovation, competition, and firms’ counterintuitive appropriation strategies. Research Policy, 42(4), 895e913. Arthur, W. B. (1996 (July-August)). Increasing returns and the new world of business. Harvard Business Review, 100e109. Barney, J. B. (1986). Types of competition and the theory of Strategy: Toward an integrative framework. Academy of Management Review, 11(4), 791e800. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99e120. Barzel, Y. (1997). Economic analysis of property rights. Cambridge University Press. Brandenburger, A. M., & Stuart, H. W. (1996). Value-based business strategy. Journal of Economics and Management Strategy, 5, 5e24. € m, A., & Bachmann, R. (2018). Cooperation and coordination: The role of Brattstro trust in inter-organizational relationships. In A.-M. Nienaber, R. Searle, & S. Sitkin (Eds.), The routledge companion to trust (pp. 129e142). London: Routledge. € m, A., & Richtne r, A. (2014). Good cop - bad cop: Trust, control and the lure Brattstro of integration. Journal of Product Innovation Management, 31(3), 584e598. Brusoni, S., Prencipe, A., & Pavitt, K. (2001). Knowledge specialization, organization coupling, and the boundries of the firm: Why do firms know more than they make? Administrative Science Quarterly, 46(4), 597e621. Cassiman, B., & Valentini, G. (2015). Open innovation: Are inbound and outbound knowledge flows really complementary? Strategic Management Journal, 37(6), 1034e1046. Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128e152. Cohen, W. M., Nelson, R. R., & Walsh, J. P. (2000). Protecting their intellectual Assets: Appropriability conditions and why us manufacturing firms patent (or not). National Bureau of Economic Research. Dahlander, L., & Gann, D. M. (2010). How open is innovation? Research Policy, 39(6), 699e709. Dahlander, L., & Piezunka, H. (2014). Open to Suggestions: How organizations elicit suggestions through proactive and reactive attention. Research Policy, 43, 812e827. Dierickx, I., & Cool, K. (1989). Asset stock accumulation and sustainability of competitive advantage. Management Science, 35(12), 1504e1511. Dodgson, M., Gann, D. M., & Salter, A. (2008). The management of technological Innovation: Strategy and practice. Oxford University Press on Demand. Doz, Y., & Hamel, G. (1998). Alliance Advantage: The art of creating value through partnering. Boston: Harvard Business School Press. Dyer, J. H., & Singh, H. (1998). The relational view: Cooperative strategy and sources of interorganizational competitive advantage. Academy of Management Review, 23(4), 660e679. Eisenmann, T., Parker, G., & Van Alstyne, M. W. (2006). Strategies for two-sided markets. Harvard Business Review, 84(10), 92. Eisenmann, T., Parker, G., & Van Alstyne, M. W. (2011). Platform envelopment. Strategic Management Journal, 32, 1270e1285. Eismann, T. R. (2006). Internet companies’ growth strategies: Determinants of investment intensity and long-term performance. Strategic Management Journal, 27, 1183e1204. Farrell, J., & Saloner, G. (1985). Standardization, compatibility, and innovation. The RAND Journal of Economics, 70e83. Felin, T., & Zenger, T. R. (2014). Closed or open Innovation? Problem solving and the governance choice. Research Policy, 43(5), 914e925. Foss, N. J., Laursen, K., & Pedersen, T. (2011). Linking customer interaction and Innovation: The mediating role of new organizational practices. Organization Science, 22(4), 980e999. Gallaugher, J. M., & Wang, Y. (2002). Understanding network effects in software markets: Evidence from web server pricing. MIS Quarterly, 26(4), 303e327. Gnyawali, D. R., & Madhaven, R. (2001). Cooperative networks and competitive dynamics: A structural embeddedness perspective. Academy of Management

€ m, A., Concealing or revealing? Alternative paths to profiting from innovation, Please cite this article in press as: Hallberg, N. L., & Brattstro European Management Journal (2018), https://doi.org/10.1016/j.emj.2018.04.003

10

€m / European Management Journal xxx (2018) 1e10 N.L. Hallberg, A. Brattstro

Review, 26(3), 431e445. Greve, R. H. (2009). The diffusion of competitive advantage. Strategic Management Journal, 30, 1e23. Hallberg, N. L. (2015). Uncertainty, judgment, and the theory of the firm. Journal of Institutional Economics, 11, 623e650. Hallberg, N. L. (2017a). The micro-foundations of pricing strategy in industrial markets: A case study in the european packaging industry. Journal of Business Research, 76, 179e188. Hallberg, N. L. (2017b). What is value and how is it managed? Journal of Creating Value, 3, 173e183. Hallberg, N. L. (2018). Managing value appropriation in buyer-supplier Relationships: The role of commercial decision resources. European Management Journal, 36, 125e134. Hallberg, N. L., & Felin, T. (2017). Untangling infinite regress and the origin of capability. Journal of Management Inquiry. Available at: https://doi.org/10.1177/ 1056492617736633. Harhoff, D., Henkel, J., & Von Hippel, E. (2003). Profiting from voluntary information Spillovers: How users benefit by freely revealing their innovations. Research Policy, 32(10), 1753e1769. € berl, S., & Alexy, O. (2014). The emergence of openness: How and Henkel, J., Scho why firms adopt selective revealing in open innovation. Research Policy, 43(5), 879e890. von Hippel, E. (1988). The sources of innovation. New York: Oxford University Press. Hoopes, D. G., Madsen, T. L., & Walker, G. (2003). Guest editors’ introduction to the special issue: Why is there a resource-based view? Toward a theory of competitive heterogeneity. Strategic Management Journal, 24(10), 889e902. Jalonen, H. (2012). The uncertainty of innovation: A systematic review of the literature. Journal of Management Research, 4(1), 1e47. Katz, M. L., & Shapiro, C. (1985). Network externalities, competition, and compatibility. The American Economic Review, 75(3), 424e440. Katz, M. L., & Shapiro, C. (1994). Systems competition and network effects. Journal of Economic Perspectives, 8(2), 93e115. Knight, F. H. (1916). Risk, uncertainty and profit. New Yord: Houghton Mifflin. Kogut, B., & Zander, U. (1992). Knowledge of the firm, combinative capabilities and the replication of technology. Organization Science, 3(3), 383e397. Laursen, K., & Salter, A. (2004). Searching high and Low: What types of firms use universities as a source of innovation? Research Policy, 33(8), 1201e1215. Laursen, K., & Salter, A. (2006). Open for innovation: The role of openness in explaining innovation performance among U.K. Manufacturing firms. Strategic Management Journal, 27, 131e150. Laursen, K., & Salter, A. J. (2014). The paradox of openness: Appropriability, external search and collaboration. Research Policy, 43(5), 867e878. Lippman, S. A., & Rumelt, R. P. (1982). Uncertain Imitability: An analysis of interfirm differences in efficiency under competition. The Bell Journal of Economics, 418e438. March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71e87. McIntyre, D. P., & Subramaniam, M. (2009). Strategy in network industries: A review

and research agenda. Journal of Management, 35(6), 1494e1517. €m, A., & Wennberg, K. (2017). How young firms achieve McKelvie, A., Brattstro growth: Reconciling the roles of growth motivation and innovative activities. Small Business Economics, 49(2), 273e293. €m, A. (2018). Externally acquired or internally McKelvie, A., Wiklund, J., & Brattstro generated? Knowledge development and perceived environmental dynamism in new venture innovation. Entrepreneurship: Theory and Practice, 42(1), 24e46. Milliken, F. J. (1987). Three types of perceived uncertainty about the Environment: State, effect, and response uncertainty. Academy of Management Review, 12(1), 133e143. Nagle, T., & Holden, R. (2002). The strategy and tactics of pricingda guide to profitable decision-making (3rd ed.). New Jersey: Prentice Hall. Nelson, R. R., & Winter, S. G. (1982). An evolutionary theory of economic change. Cambridge: Belknap Press/Harvard University Press. Parker, G. G., & Van Alstyne, M. W. (2005). Two-sided network effects: A theory of information product design. Management Science, 51(10), 1494e1504. Pepall, L., Richards, D. J., & Norman, G. (1999). Industrial Organization: Contemporary theory and practice. New York: South-Western Collage Publishing. Peteraf, M. A., & Barney, J. B. (2003). Unraveling the resource-based tangle. Managerial and Decision Economics, 24(4), 309e323. Posen, H. E., Lee, J., & Yi, S. (2013). The power of imperfect imitation. Strategic Management Journal, 34(2), 149e164. Rochet, J.-C., & Tirole, J. (2002). Cooperation among competitors: Some economics of payment card associations. The RAND Journal of Economics, 549e570. Rumelt, R. P., & Lamb, R. (1984). Competitive strategic management. Toward a Strategic Theory of the Firm, 556e570. Salter, A., Wal, A. L., Criscuolo, P., & Alexy, O. (2015). Open for ideation: Individuallevel openness and idea generation in R&D. Journal of Product Innovation Management, 32(4), 488e504. Shankar, V., & Bayus, B. L. (2003). Network effects and competition: An empirical analysis of the home video game industry. Strategic Management Journal, 24(4), 375e384. Stremersch, S., Tellis, G. J., Franses, P. H., & Binken, J. L. G. (2007). Indirect network effects in new product growth. Journal of Marketing, 71(3), 52e74. Suarez, F. F., Grodal, S., & Gotsopoulos, A. (2015). Perfect timing? Dominant category, dominant design, and the window of opportunity for firm entry. Strategic Management Journal, 36(3), 437e448. Teece, D. J. (1986). Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy. Research Policy, 15(6), 285e305. Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509e533. Thompson, J. D. (1967). Organizations in action. New York: McGraw Hill. Tirole, J. (1988). The theory of industrial organization. MIT press. West, J., & Bogers, M. (2014). Leveraging external sources of innovation: A review of research on open innovation. Journal of Product Innovation Management, 31(4), 814e831. Williamson, O. E. (1985). Economic institutions of capitalism. New York: Free Press.

€ m, A., Concealing or revealing? Alternative paths to profiting from innovation, Please cite this article in press as: Hallberg, N. L., & Brattstro European Management Journal (2018), https://doi.org/10.1016/j.emj.2018.04.003