Journal of Management Studies 61:5 July 2024 doi:10.1111/joms.12968 Understanding the Link between Post-­Acquisition Resource Reconfiguration and Technology Out-­Licensing Thomas Maximilian Kluetera , Solon Moreirab and Clinton Ofoedua a IESE Business School; bFox School of Business, Temple University We develop a novel framework to explain how the unique properties of out-­licensing enable R&D reconfiguration in the context of technology acquisitions. Out-­licensing is an attractive R&D strategy following acquisitions as it expands opportunities for resource reconfiguration to outside the organization by using external partners while at the same time allowing firms to continue to benefit from the technology, both financially and strategically. We also propose that the positive relationship between technology acquisitions and out-­licensing is weaker when firms cannot determine the full value potential of their R&D due to uncertainty or when they have high availability of short-­term financial slack resources. Using a sample of bio-­ pharmaceutical firms, the result of a 2SLS fixed-­effect regression that accounts for the potential endogeneity of technology acquisitions provides support for our theoretical framework. ABSTRACT Keywords: licensing, resource reconfiguration, slack, technology acquisitions, technological uncertainty INTRODUCTION Acquisitions are an important way for firms to access strategic technology-­based resources that can be used to enhance their R&D and increase the number and quality of innovations (Chaturvedi and Prescott, 2022). In contrast to other forms of external knowledge sourcing (e.g., alliances), acquisitions provide access to entire blocks of the target’s R&D resources and capabilities (Ahuja and Katila, 2001). Therefore, benefits Address for reprints: Thomas Klueter, IESE Business School, Barcelona 08034, Spain (tmklueter@iese.edu). All three authors contributed equally. This is an open access article under the terms of the Creative Commons Attribution-NonCommercialNoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd. T. M. Klueter et al. accruing from technology acquisitions do not solely come from the transactions, but the acquirer’s ability to ‘graft’ acquired R&D onto its own resource base, i.e., integrating and reconfiguring the targets and acquirer’s R&D resources (Colombo and Rabbiosi, 2014; Puranam et al., 2006; Zaheer et al., 2013). When examining resource reconfiguration, researchers typically discuss the internalizing of acquired resources to internally recombine them in novel ways or the externalizing and/or deleting of resources through external divestitures (Capron et al., 2001; Feldman and Sakhartov, 2022; Karim, 2006). However, in the context of technology acquisitions, there are alternative resource reconfiguration strategies. In particular, out-­ licensing allows an acquirer to externalize the recombination and investments in a set of technologies without discarding the resources from their internal R&D portfolio. While maintaining control of the technology, firms also profit from technologies they do not want to develop internally through upfront fees and save R&D costs by transferring responsibilities in developing a technology to the licensee (Conti et al., 2013; Laursen et al., 2017; Markman et al., 2008). Thus, out-­licensing has unique properties that make it an important strategy for firms to adjust their R&D following acquisitions. Yet, despite its strategic importance, the link between acquisitions and technology out-­licensing has not been examined and conceptualized. Such a lack of research connecting licensing to resource reorganization following acquisitions is even more surprising, given that licensing is considered to be one of the mostly commonly used strategies to adjust, rebalance and reorganize R&D activities (Anand and Khanna, 2000; Conti et al., 2013). We aim to fill this gap by systematically exploring the relationship between technology acquisitions and out-­licensing. Building on the literatures of resource reconfiguration (Chaturvedi and Prescott, 2022; Karim and Capron, 2016; Karim and Mitchell, 2000), technology licensing (e.g., Fosfuri, 2006; Laursen et al., 2017) and acquisitions (e.g., Colombo and Rabbiosi, 2014), we develop a novel framework to explain the ways in which the properties of out-­licensing allow R&D reconfiguration in the context of technology acquisitions. In particular, we propose that out-­licensing expands opportunities for resource reconfiguration to outside the organization by using external partners while, at the same time, allowing firms to continue benefiting from the technology, both financially and strategically. As such, we expect a baseline relationship between the number of technology acquisitions and the intensity of subsequent out-­licensing. We further explain the link between acquisitions and licensing by examining two important contingencies for their relationship by explicating how out-­licensing may not only hold advantages but may also have downsides. In particular, extant work has noted that out-­licensing can carry the inherent risk of providing strategic technologies to possible rivals (Fosfuri, 2006; Moreira et al., 2019). Moreover, out-­licensing entails leaving the onus of recombination and advancement predominantly with another firm, which can increase dependencies and coordination costs when compared to developing and enlarged R&D base in-­house (Moreira et al., 2018; Moreira et al., 2019). Considering these trade-­offs, we theorize that there are two conditions under which firms may refrain from utilizing out-­licensing as a resource reconfiguration strategy. First, when the acquired knowledge increases and is uncertain, the acquirer may find it harder, in the short term, to determine the potential of its R&D resources (Markman et al., 2009; McDermott and O’Connor, 2002). This makes it more difficult to determine what technologies to out-­licence. Moreover, such © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 2102 2103 uncertainty related to the target’s firm technologies may make firms more hesitant to out-­ license as the acquirer could fear giving possibly valuable technologies to a future competitor (Bianchi et al., 2014; Fosfuri, 2006). Second, when firms have substantial deployable resources post-­acquisition that are available in the short-­term, they are more flexible in exploring all of the innovation directions the acquired knowledge base offers internally without coordinating with another firm (Kuusela et al., 2017; Nohria and Gulati, 1996), ultimately decreasing their need to use out-­licensing following acquisitions. We test our theoretical framework examining the acquisition and out-­licensing activities of 467 bio-­pharmaceutical firms between 1985 and 2014. The underlying dataset is compiled from several sources, including ReCap partnering database, Compustat North America, Pharmaprojects and patent data from the United States Patent and Trademark Office (UPTSO). The results of a 2SLS fixed-­effect regression support the idea that acquisitions are associated with a significant subsequent increase in firms’ out-­licensing activities. At the same time, the level of uncertainty of the acquired R&D base and the short-­term availability of financial slack can attenuate this tendency. The paper makes several contributions to the literature on both acquisitions and technology licensing. To the best of our knowledge, this study is the first to anchor technology out-­licensing as a resource reconfiguration strategy in the context of technology acquisitions. Out-­licensing is significantly different from the resource reconfigurations strategies found in prior studies, including discontinuations, divestments and in-­house recombination (Capron et al., 2001; Karim, 2006). In particular, most strategies described in prior studies only separate the internalization or externalization of resources post-­acquisitions. We show that, in the R&D context, licensing allows for resource reconfiguration to outside the organization while, at the same time, allowing firms to retain residual ownership and control over a technology. Thus, firms can choose a resource reconfiguration strategy that combines the advantages of adding and moving as well deleting resources, which are core to the post-­acquisition reconfiguration process (Karim, 2006) and could be a reason that licensing is such an important R&D strategy post-­acquisition. We also reveal that heterogeneity in the characteristics of the target firm’s technological portfolio matters by showing that when firms cannot determine the full value potential of their R&D due to uncertainty, they are less likely to engage in out-­licensing, even if they have acquired large blocks of R&D (Markman et al., 2009). In a similar vein, we explore the role that short-­term available financial slack resources have in terms of the relationship between acquisitions and licensing. We illustrate how the presence of financial slack resources will ease the process of integrating R&D post-­acquisitions, making firms rely less on licensing following acquisitions. Overall, the paper conceptualizes out-­licensing as a key resource reconfiguration strategy that firms can deploy post-­acquisition. THEORETICAL FRAMEWORK Technology Acquisitions and Resource Reconfiguration The R&D resources accessed through acquisitions enlarge the acquirer’s R&D capacity and lead to important benefits such as increasing the amount and quality of its innovations (Ahuja and Katila, 2001; Karim and Kaul, 2015). Prior research has noted that © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd.. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Resource Reconfiguration and Out-­Licensing T. M. Klueter et al. benefits from technology acquisitions stem from the reconfiguration of the combined R&D resources (Colombo and Rabbiosi, 2014; Puranam et al., 2006) and that post-­ acquisition redeployment between a target and acquirer is crucial to realizing strategic advantages such as improving the acquirer’s R&D (Ahuja and Katila, 2001). It follows that firms need to adjust their R&D processes (Karim and Mitchell, 2000) in order to generate value from the acquisition. Capron et al. (2001) explain that the reshuffling of resources post-­acquisition ‘involve[s] changes that range from allowing the firm to augment existing activities to undertaking substantial transformation of routines and resources’ (p. 820). The authors note that such process can entail the use by a target or acquirer of the other business’ resources, for example, the sharing of manufacturing facilities, distribution systems or the addition and reorganization of complete business units.1 We closely follow this work and define resource reconfiguration in R&D as the ‘addition, deletion or, movement of technological resources’ following an acquisition (Capron et al., 1998; Feldman and Sakhartov, 2022; Karim, 2006:802; Karim and Mitchell, 2004). Thus, in the context of technology acquisitions, resource reconfiguration pertains to the reorganization processes that unfold with the intention to manage and adjust the expanded R&D activities (e.g., Aghasi et al., 2017; Kapoor and Lim, 2007). While adding and moving resources internally expands the variety and breadth of R&D and creates new opportunities for recombination for the acquirer, we also know that this process is highly complex and uncertain (Feldman and Sakhartov, 2022) as the acquiring firm must simultaneously reconfigure technological inputs, organizational assets and ongoing R&D projects (e.g., Kroon et al., 2022; Ranft and Lord, 2000). Moreover, a firm can only pursue a finite number of R&D initiatives internally (Galunic and Rodan, 1998). Consequently, prior work has shown that firms often have to discharge and delete resources, which is the reason that eliminating resources is another critical element of the post-­acquisition reconfiguration process (Capron, 1999; Capron et al., 2001; Karim, 2006; Vidal and Mitchell, 2015). There are two main R&D strategies firms use when reconfiguring their resources through deletion (Capron et al., 2001; Karim and Capron, 2016; Kuusela et al., 2017). One possibility is to narrow the number of R&D initiatives and discarding those projects holding little value (Arora et al., 2009). Discontinuation of R&D projects also helps unlock scarce resources in the acquiring firm (Chao et al., 2009). In addition to simply discarding R&D, firms can also divest assets, which entails the outright sales of resources to another firm (Anand and Singh, 1997; Capron et al., 2001; Chaturvedi and Prescott, 2022; Folta et al., 2016). Both strategies allow firms to free up resources in the short term by shedding internal R&D activities which are currently not needed or that are redundant (Kuusela et al., 2017). However, there are alternative strategies for resource reconfiguration following technology acquisitions that the literature has not yet considered. R&D Out-­Licensing We noted that, following technology acquisitions, resource reconfiguration has typically been conceptualized in terms of internalizing the acquired R&D with the intention of © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 2104 2105 recombining and expanding upon it or externalizing and/or deleting those resources through divestitures or discontinuations. Alternative strategies for resource reconfiguration, however, allow the acquirer to externalize the recombination and investments in a set of technologies but without fully discarding the resources from their own R&D portfolio. In particular, firms can engage in the market for technology using out-­licensing to commercialize and profit from technologies they do not want to develop internally but over which they want to maintain ownership and control (Conti et al., 2013; Markman et al., 2008). Out-­licensing relies on arm’s-­length transactions in which the technology holder sells to other firms the rights to exploit and use a technology in their own R&D initiatives in exchange for upfront fees or/and royalties (Arora et al., 2001). They most commonly involve technologies which will be used by the licensee to complement on-­going R&D efforts (Moreira et al., 2018; Moreira et al., 2020). The volume of licensing activities has grown substantially over the last decades and is currently considered to be one of the most common means of developing and commercializing new technologies (Moreira et al., 2019). Next, we systematically introduce out-­licensing as an important R&D strategy and explain how it connects to resource reconfiguration following technology acquisitions. Out-­Licensing as Resource Reconfiguration Following Technology Acquisitions As acquisition intensity increases, firms need to rely on strategies that allow them to reconfigure R&D resources. However, as mentioned earlier, acquirers may be unable to internally exploit an entire new set of technological opportunities (Colombo and Rabbiosi, 2014; Meyer and Lieb-­Dóczy, 2003; Sirmon et al., 2007) and often must relinquish control of these assets by discontinuing them or divesting. These problems are compounded as firms often engage in multiple acquisitions within a short window. In such a context, out-­licensing has several important characteristics that can support the post-­acquisition reconfiguration process. Out-­licensing involves the transfer of already existing technologies; therefore, as an element of the deal, firms can specify which technologies are being licensed (Klueter et al., 2017; Moreira et al., 2020). This is relevant as it allows the technology holder to evaluate its existing technological portfolio and current R&D initiatives with the goal of defining those that can be commercialized in upstream technology markets and those to continue to exploit fully in-­house. Relatedly, prior studies have noted the importance of out-­licensing to swiftly balance the size of R&D projects and that a growth in R&D initiatives is associated with more out-­licensing activity (e.g., Nishimura and Okada, 2014). Thus, licensing provides a flexible strategy for firms that need to adjust their R&D resources. Furthermore, out-­licensing offers direct upfront fees paid by the licensee, which improves the technology holder’s financial position in the short term and allowing the additional revenue to be channelled to the firm’s other R&D initiatives (Moreira et al., 2019). This gives firms the flexibility to deal with resource constraints from an increase in R&D opportunities post-­acquisitions (Ding and Eliashberg, 2002). It follows that out-­licensing is a resource-­ freeing strategy that helps firms reconfigure their R&D portfolio without requiring the significant resource investments necessary to keeping all technologies in-­house. © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd.. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Resource Reconfiguration and Out-­Licensing T. M. Klueter et al. Through licensing, a firm also retains the ownership of the technology; in the long run, allowing the licensor to earn returns from the licensee through royalties (Laursen et al., 2017). This increases options as firms retain the ownership of the technology despite another firm being involved in its subsequent development and commercialization (Ziedonis, 2007). This is an important characteristic as divestures and discontinuations imply realization of losses, given that these two strategies lead to outright termination of R&D initiatives. In contrast, using licensing to manage post-­acquisition resource reconfiguration does not entail the outright deletion of resources but instead allows firms to generate short-­and long-­term revenue streams based on upfront payments and royalties (Anand and Khanna, 2000). Beyond the monetary advantages, there are also strategic gains in granting a firm the right to build on licensed technologies (Choi, 2002). The technology holder not only retains ownership, but may also benefit from the advancement of the technologies through the investments of the licensee (Laursen et al., 2017). Compared to simple deletions or divestitures, which are irreversible, firms do not discard their resources and control but may participate in the technology’s future value creation. Once more, we expect such an R&D strategy to be particularly salient as the volume of R&D resources increases post-­acquisitions and the acquirer would be unable to develop all technologies internally. Overall, out-­licensing is significantly different from the previously discussed resource reconfigurations strategies, including discontinuations and divestments. Out-­licensing expands the opportunities for resource reconfiguration to outside the organization by using another firm while, at the same time, firms can continue to benefit from the technology, both financially and strategically. As such, out-­licensing combines the strategic advantages of adding and moving as well deleting resources (Karim, 2006). Considering these specific properties, we expect out-­licensing to be an important R&D strategy when firms engage in technology acquisitions. We propose: Hypothesis 1: Technology acquisitions will be associated with increasing subsequent out-­ licensing from the acquiring firm. Retaining Technologies In-­House Post-­Acquisition –­Contingencies Despite its possible advantages, involving an external firm in resource-­reconfiguration via out-­licensing also holds risks that could make firms refrain from deploying this strategy when facing an increase in R&D resources following acquisitions. We know that out-­licensing can carry the inherent risk of providing strategic technologies to possible rivals, which can lead competitive implications related to rent dissipation (Fosfuri, 2006; Moreira et al., 2019). Moreover, when out-­licensing, the responsibility of recombination and advancement of the technology predominantly lies with another firm. This can increase dependencies and coordination costs when compared to developing and enlarged R&D base in-­house (Arora et al., 2013; Moreira et al., 2019). Building on these arguments, we next consider two important contingencies that may tilt firms toward keeping technologies in-­house following an intense period of acquisitions rather than relying on out-­licensing. First, when the acquired knowledge increases and is uncertain, it is unclear how the expanded R&D base can be effectively combined, which raises the risk of licensing-­out valuable technologies and thus nurturing a future © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 2106 2107 rival (Fosfuri, 2006; Miller, 2002). Second, when firms are endowed with resources sufficient to supporting the post-­acquisition integration and recombination process, they may initially try to keep their technologies in-­house to avoid having to coordinate resource reconfiguration with another firm (Iyer and Miller, 2008). Both contingencies also connect to the literature on acquisitions. The technological uncertainty of the target’s R&D has been directly associated with the need for post-­acquisitions integration (e.g., Puranam et al., 2009). Relatedly, the availability of resources to fuel the efforts to combine acquirer’s and target’s R&D has already been shown to affect the integration and divestment approach following acquisitions (e.g., Iyer and Miller, 2008; Kuusela et al., 2017; Larsson and Finkelstein, 1999). We rely on these contingencies theoretically to explain how, in some cases, reconfiguration through out-­licensing may not transpire, even after an intense period of acquisitions. The role of technological uncertainty. Acquisitions differ with respect to the types of technological knowledge and assets they add to the acquirer’s existing R&D. In particular, the acquired technologies may have varying levels of uncertainty, which we define in terms of the extent to which the technological feasibility, as well as its innovative and commercial potential, are known ex ante (Laursen et al., 2017; Ziedonis, 2007). Enlarging the acquirer’s R&D base, paired with adding uncertain technologies, can lead to issues in using out-­licensing as a resource reconfiguration strategy as firms lack the ability to readily assess the future potential of its technology pool (Oriani and Sobrero, 2008). Technology acquisitions provide access to technologies in early stages of development that often possess a significant number of new features, which are difficult to assess in terms of their potential for future knowledge recombination (Galunic and Rodan, 1998). For such acquired technologies, it is also unclear what assets and expertise will ultimately be required to exploit them (Ahuja and Lampert, 2001; Markman et al., 2009; Vinokurova and Kapoor, 2020). Technological uncertainty likely creates difficulties for resource reconfiguration via out-­licensing as the process requires short-­term decisions as to which projects can be out-­licensed to external partners and which are more relevant to internal development (Conti et al., 2013; McDermott and O’Connor, 2002). However, when the acquired knowledge base is uncertain, firms do not fully understand how the existing and acquired technologies can be integrated or if the acquired technologies could, ultimately, replace existing internal technologies. This makes firms more careful about prematurely licensing-­out existing internal technologies as they may need additional time to determine the full potential of a new knowledge pool. It follows that when firms enlarge the R&D pool through acquisitions and those technologies come with a high level of uncertainty, it is harder to appropriately assess which technologies are likely to be needed in the future for recombination in-­house and which can be out-­licensed. In addition, out-­licensing from a highly uncertain technology pool also increases the risk that firms make technologies available for commercialization that, ultimately, turn out to be highly valuable (Fosfuri, 2006; Laursen et al., 2017). This can have detrimental consequences in terms of raising new competitors and losing a strategic advantage (Moreira et al., 2019). Therefore, when the level of uncertainty underlying the acquired © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd.. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Resource Reconfiguration and Out-­Licensing T. M. Klueter et al. technologies is too high, firms may refrain from using out-­licensing as resource reconfiguration strategy as they would also consider the associated competitive risks of providing their technologies to an external firm (Fosfuri, 2006). Finally, out-­licensing relies on the ability of firms to write contracts that document the future use of the technology along with a renumeration structure (Choi, 2002). Indeed, a well-­designed contract (for example, using the grant back clause provision) can be used to reduce the risk of a firm’s losing its technological edge and experiencing damage to its competitive position (Laursen et al., 2017). However, when there is too much uncertainty about the future value of the post-­acquisition R&D pool, it is also much harder for the technology holders to rely on contractual clauses to cover possible contingencies related to the use of that technology (Ziedonis, 2007). Therefore, uncertainty reduces a firm’s ability to anticipate all contingencies related to the future development of a technology and consequently makes codifying these contingencies much more difficult. We know from prior research that when the future development of a technology is extremely uncertain, licensing contracts can be ineffective in establishing legal remedies to the misuse or misappropriation of a technology (Choi, 2002). In those cases, it is also much more challenging to determine an appropriate remuneration structure for the out-­licensed technologies, one that requires codifying royalties or upfront payments and other contractual contingencies (Kotha et al., 2018). Based on all arguments, we suggest that when the technological uncertainty of the acquired technologies is higher, we will observe a reduction in a firm’s willingness to out-­ license technologies following acquisitions. Hypothesis 2: The positive association between acquisitions and subsequent out-­ licensing will be weaker the higher the level of uncertainty of the acquired knowledge base. The role of financial slack. A key insight from the literature on resource integration is that firms are differently endowed with available financial resources that can help support the post-­acquisition R&D process internally (Kuusela et al., 2017). In particular, firms may have financial slack resources, i.e., resources in excess of the minimum necessary, which can be allocated toward organizational tasks (Voss et al., 2008). Therefore, financial slack resources are fundamentally associated with the trial-­and-­error process and the recombination of new technologies that we expect to unfold post-­acquisition (George, 2005). Indeed, in the absence of slack, we know that firms are much more likely to avoid pursuing a large number of R&D tasks as this will also lead to higher rates of unsuccessful attempts (Nohria and Gulati, 1996). It follows that, in the presence of financial slack, firms may opt to first explore possible novel recombinations internally to better learn about the potential of the combined R&D resources post-­acquisition, and not immediately out-­license their technologies to external firms to free up resources. This effect will be greatest following a period of intense technology acquisitions as not only the potential for recombination, but also short-­term resource constraints, are high. With the availability of slack, some of the key features of out-­licensing, such as receiving upfront payments in the short run to support ongoing internal R&D projects, © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 2108 2109 also become less relevant. The presence of slack provides a ‘buffer’ from immediate resource constraints (Bourgeois III, 1981; Chen and Miller, 2007; Wiseman and Bromiley, 1996), reducing the need to cross-­finance R&D projects through the immediate proceeds from out-­licensing. Hence, the trade-­off between quickly providing a technology to an external firm through out-­licensing and keeping a technology in-­house to explore its potential likely tilts toward the latter. It follows that a higher availability of financial slack reduces the need to use out-­licensing to free up resources following a period of intense technology acquisitions, at least in the short run. In a similar vein, research has shown that financial slack may allow firms to continue operating existing businesses while reducing the number of divestments to address possible resource constraints (Kuusela et al., 2017; Shimizu, 2007). Conversely, firms without financial slack would face intense resource constraints following a period of intense acquisitions. While such firms could benefit from an exhaustive internal exploration of the innovative potential of their enlarged knowledge base, they would more likely engage in out-­licensing owing to a lack of resources. Considering all arguments, we propose: Hypothesis 3: The positive association between acquisitions and subsequent out-­licensing will be weaker the higher the level of the acquiring firm’s financial slack. METHODOLOGY Context: The Pharmaceutical Industry We test our hypotheses in the context of the biopharmaceutical industry. Several characteristics make it an ideal setting to study the relationship between technology acquisitions and out-­licensing. First, the existing competencies of firms in this industry are threatened by breakthrough technologies such as gene expression and gene sequencing, as well as new therapeutic approaches such as the use of monoclonal antibodies or stem cells (Kapoor and Klueter, 2015). These ongoing technological changes require firms in the industry to regularly use technology acquisitions as a means of quickly upgrading underlying research capabilities and gaining access to complex, highly specialized skills and knowledge (Higgins and Rodriguez, 2006; Schuhmacher et al., 2016). Furthermore, firms frequently use licensing with the goals of developing and commercializing technologies with an external partner (Laursen et al., 2017; Moreira et al., 2019; Reepmeyer et al., 2011). Indeed, the biopharmaceutical industry is considered one in which a well-­functioning market for technologies has emerged (Moreira et al., 2020). A second reason for choosing this industry is that firms are continuously investing in formal appropriability mechanisms (i.e., patents) as a strategy to capture value from their investments in innovation (Caner et al., 2018). With these investments, firms can better derive rent from their technologies while minimizing the risks that their technologies can be invented around, imitated, or commercialized royalty-­or rent-­free (Cohen and Levinthal, 1989). This is important as it allowed us to determine technology acquisitions in the first place and to derive several relevant technology-­based measures for our study using firms’ patenting activities. © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd.. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Resource Reconfiguration and Out-­Licensing T. M. Klueter et al. Finally, the biopharmaceutical industry is highly competitive in both upstream and downstream markets, as investment in R&D only leads to a few new products being launched every year. As a result, firms pay attention to their competitive position, choosing strategies such as those required to acquire and/or out-­license technologies that ensure and safeguard their competitive advantage and R&D effectiveness (Fosfuri, 2006; Moreira et al., 2020). Sampling To create our sample, we first used the Compustat database to identify publicly listed companies operating in the biopharmaceutical industry. Our focus on public firms ensured that the vital information needed to construct the key control variables, including financial data, was available. We limited the sample to firms classified under the Standard Industrial Classification (SIC) system for pharmaceutical firms: 2834 –­Pharmaceutical Preparations. Firms in the industry are not only active acquirers of technology-­based companies but are also holders of numerous patented technologies that can be licensed out to acquire monetary and strategic benefits (Arora et al., 2001). We constructed a panel data of the financial information of each firm in our sample, which covered the years 1985 to 2014. Next, we used the corresponding firm names from Compustat to link those organizations to the Recap Deloitte database, which has been utilized extensively in prior studies examining questions related to knowledge sourcing and innovation (e.g., Laursen et al., 2017). Compiled by Recombinant Capital, Recap Deloitte is a comprehensive database containing a variety of information about technology acquisitions and licensing deals, including details about the technology exchanged and transferred. Based on information reported on Recap, we were able to retrieve detailed data on acquisition and licensing deals. We also linked the United States Patent and Trademark Office (UPTSO) PatentsView database to Compustat. In order to connect these two databases, we started from the company name listed in Compustat and then manually matched its patent data using PatentsView. Whenever necessary, we used the M&A information to correct changes in patent ownership to compute our variables. This dataset gave us access to relevant information related to the technological profiles of the firms in our sample. To produce a final database, we linked the firms with data from Pharmaprojects to capture product development activities. After dropping 288 firm-­year observations due to missing financial data from Compustat, the final dataset comprised 467 unique firms and 551 technology mergers and acquisitions made between 1985 and 2014.2 We structured the dataset as a panel with 5217 firm-­year observations, i.e., each firm i appears only once in year t but multiple times throughout the panel. Overall, the sample consists of firms that were very active in technological acquisitions as well as others that conducted few or no acquisitions at all. This addresses methodological problems that arise in evaluating the impact of acquisitions on the post-­acquisition reconfiguration of acquiring firms as it includes both firms that are active in acquisitions and those that did not make acquisitions (Ahuja and Katilla, 2001). In the absence of the latter group of firms, it would be difficult to refute the argument that resource reconfiguration by acquiring firms was not shared by similar non-­acquiring firms (Fowler and Schmidt, 1988). In other words, to test the effect of acquisitions on out-­licensing, one needs a counterfactual based on firms that do not acquire. © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 2110 2111 Dependent Variable Technology out-­licensing. Using the Recap database to track their annual numbers of out-­licensing deals, this variable captures the firms’ licensing behaviours. We counted the number of licensing deals the sample firms made each year. The measure corresponds to the total count of out-­licensing deals that firm i has engaged in year t.3 We tracked out-­licensing activities longitudinally for up to 30 years for each firm. To facilitate our econometric approach (see ‘Model Choice’), we used the logarithm of Technology Out-­Licensing +1. Independent Variables To test the consistency of our estimations against prior studies, we employ two main independent variables to test the baseline effect of acquisitions on out-­licensing decisions. The first variable is based on the count of acquisitions and the second on the number of patents related to the target firms. We operationalize these variables as follows: Technology acquisitions. We used the Recap database, which tracks acquisitions under a specific ‘transaction type’. We aggregated the acquisition deals yearly to obtain the total number of acquisitions firms made each year. To ensure complete adherence to our definition of Technology Acquisitions, we restricted our count to those acquisitions of target firms that had applied for patents or that had out-­licensed technologies within the five years prior to the year of being acquired. By doing so, we excluded non-­technology acquisitions such as those aimed at expanding geographically or simply to increase manufacturing but that may add little or nothing to the technological knowledge base of the acquirer and are thus less likely to impact the acquiring firms’ subsequent R&D activities and R&D strategy (Ahuja and Katila, 2001). Size of acquired knowledge base. We counted the cumulative number of patents that the target firm had acquired within the five-­years from a focal year. After these patents were identified, we removed all duplicates to ensure that a patent is counted only once. Because firms may enter into multiple acquisitions in a given year, we aggregated the values for the target’s knowledge base at the focal acquirer firm’s i at year t. This measure follows prior work by Ahuja and Katilla (2001). To reduce skewness, the final measure is based on its logarithm +1. Technology uncertainty. A core theoretical argument of our study is that the degree of the technology uncertainty related to an acquired technology is an important contingency determining if firms following acquisitions engage in out-­licensing. We followed prior work by Ziedonis (2007) and measured technology uncertainty based on the number of backward citations found in the target firm’s patent portfolio.4 The idea is that the lower the number of backward citations of the acquired technology, the more likely the technology is at the technological frontier of the industry. For such acquired technologies, uncertainty is high as they do not build on any previous knowledge (Ahuja and Katila, 2001). Thus, fewer references to prior technologies will increase uncertainty regarding their recombination potential. For our measure, Technology uncertainty, we captured the degree of technology uncertainty by calculating the average number of © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd.. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Resource Reconfiguration and Out-­Licensing T. M. Klueter et al. backward citations of all the acquired patents from the target firm within the five-­ year period prior to the year of acquisition. We then inverted the values, to reflect an increasing technology uncertainty, by multiplying by −1. Financial slack. We followed prior work and captured financial slack through the ratio of a firm’s equity to debt each year (Bourgeois III, 1981; Iyer and Miller, 2008; Kuusela et al., 2017). This relationship between equity and debt is also commonly used as the measure for ‘potential slack’ (Bromiley, 1991), which, for example, prior studies found most salient in the context of acquisitions. Such potential slack is believed to act as a short-­term ‘buffer’ to shield the firm from resource constraints (Bourgeois III, 1981; Chen and Miller, 2007; Wiseman and Bromiley, 1996). Control Variables To ensure that the firms in our analysis were comparable in their observable characteristics, we controlled for the firm-­and technology-­level variables that might affect the rate of technology acquisitions by firms and the decision of a firm to engage in technology out-­licensing activities. We winsorized any variable that showed extreme values of skewness at the 5th and 95th percentile levels. R&D productivity. As firms’ internal R&D productivity may provide a strong incentive to engage in technology acquisitions (Higgins and Rodriguez, 2006), we added the ratio of patent stock to R&D expenditure for a focal year time t as a control in our model. Patent stock of acquiring firm. We expected that the number of patents in a firm’s portfolio prior to an acquisition might influence the willingness of the firm to engage in technology out-­licensing activities (Arora and Ceccagnoli, 2006). We controlled for this by calculating the total number of patents the acquiring firm had accumulated in a five-­ year window prior to a focal year time t. The final measure is based on its logarithm +1. Acquiring firm product pipeline. A robust product pipeline may allow firms to out-­license technologies. Moreover, more product in development may require firms to free R&D resources that can be channelled to other areas of the innovation stage (Nishimura and Okada, 2014). This variable was tracked using data from Pharma-­Projects to calculate the total number of drugs in development (i.e., in stages prior to market launch) in each year. The final measure is based on its logarithm +1. Phase III failures. We also controlled for the fact that later-­stage failures in firms’ research pipelines may have an impact on those firms’ decisions to acquire other firms with late-­ stage products (Higgins and Rodriguez, 2006) but may also affect out-­licensing choices (Hu et al., 2015). We counted the number of failures that firms experienced at the phase III stage using Pharmaprojects. Strategic alliances. A firm’s openness to seek out external knowledge was a measure of the number of collaborations in which an acquiring firm engaged during the three years prior to focal year t. R&D alliances can be defined as strategic partnerships in which the © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 2112 2113 focal firm in our sample pools-­in resources or capabilities with other firms for innovation purposes (Ghosh and Klueter, 2022). The information used to count the number of strategic partnerships in a given year was extracted from Recap. The final measure is based on its logarithm +1. Out-­licensing experience. The experience garnered from engaging in out-­licensing activities before the acquisition may shape post-­acquisition decisions to out-­license technologies. Firms that previously did so are more likely to possess the necessary knowledge to manage and appreciate the benefits of licensing out technologies (Laursen et al., 2017). The variable takes the total number of out-­licensed technologies in which a firm has engaged within three years prior to a focal year t. To reduce skewness, the final measure is based on its logarithm +1. In-­licensing experience. To address the possibility of our argued relationship being driven by open innovation, which involves an ongoing inflow or outflow of licensed technologies, we controlled for the number of in-­licensing activities of the acquiring firm within three years prior to a focal year t (Chesbrough et al., 2006). To reduce skewness, the final measure is based on its logarithm +1. Organizational myopia. Firms may have general tendencies to experiment with new knowledge. Thus, we captured the previous exploration activities of a firm as the extent to which a firm uses their earlier ideas or patents to build upon or develop new knowledge. The measure captures the backward citations of the patents of the acquiring firm accumulated in the five years prior to the focal year time t and computes a ratio of self-­citations to total citations. Industry competition. The greater the competition in the industry, the greater the reluctance of a firm to out-­license its technologies because of the fear of further depleting its potential profit downstream (Moreira et al., 2019). We captured this variable using 1 − the Herfindahl–­ Hirschman index (HHI), calculated using firm sales based on the four digits SIC code. Technological diversity. We controlled for the degree of technology diversity in the firms’ knowledge base by calculating the Herfindahl index based on technology classes for the number of patents in firm’s patent portfolio, accumulated during the five years from a focal year. The final diversity measure was calculated by subtracting this value from 1, reflecting the dispersion of patent classes across different technology domains. Backward citations. Similar to the measure related to target firms, we also included a measure capturing the number of backward citations in the acquiring firms’ knowledge base within the five-­year period to a focal year (Lee and Kim, 2019). To reduce skewness, the final measure is based on its logarithm +1. R&D intensity. The commitment to R&D activities of an acquiring firm can affect the rate of its licensing activities. High R&D intensity could be an indicator that firms possess valuable technologies that will be attractive to other firms in the industry. Therefore, we add a control using the ratio between R&D expenditures incurred by a given firm i at year t and the total sales in that same year. © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd.. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Resource Reconfiguration and Out-­Licensing T. M. Klueter et al. Firm size. Larger firms could have strong incentives to keep their technologies in-­house as they are likely in possession of large complementary assets, including marketing and sales personnel. Hence, we controlled for overall assets volume using the natural log of total assets of a firm in each year. Market growth. This variable captures the rate of growth in acquiring firms’ product markets as market performance can shape out-­licensing (Fosfuri, 2006). We computed the size of the product market based on total sales reported by firms operating in the same four-­digit SIC code. Market growth then is defined as (Market Sizet − Market Sizet_1)/Market Sizet_1. Potential licensees. We controlled for the Number of Firms present in the same industry as the focal firm, as the level of demand for technologies in the industry also shapes the possible licensing deals that can be made. To reduce skewness, the final measure is based on its logarithm. Exogenous sunk cost. Prior research (Moreira et al., 2019) has shown that sunk costs are an important determinant of a firm’s out-­licensing decisions. Therefore, we controlled for exogenous sunk costs by using the ratio of firm’s i yearly values for property, plant, and equipment to total assets, as reported in Compustat. Model Choice Given the nature of our variables and our empirical setup, we opted to test our hypotheses using a set of fixed-­effect estimators while employing robust standard error to account for potential heteroskedasticity. We used year-­and firm-­fixed effects to account for several characteristics related to the acquiring firm and the overall temporal trends in licensing rates that might lead to a biased estimation. Despite using such specifications, it is still possible that unobserved factors could simultaneously affect our explanatory variables and a firm’s out-­licensing strategy. This could lead to biased estimations and inaccurate inferences from our estimations. Therefore, to rule out such alternative explanations, we required a research design that captured exogenous changes in firms’ out-­licensing decisions. To tackle this issue and make our analysis more robust, we deployed a two-­stage least squares (2SLS) regressions approach.5 To implement the 2SLS approach, one must use at least one valid instrument that fulfils the conditions of relevance and exogeneity, i.e., the instrument has to be correlated with the endogenous variables and only affect the dependent variable through their effect on the endogenous variable. First, we built on recent studies examining acquisitions that used the changes of Statements of Financial Accounting Standard (SFAS) 141 and 142 that happened in 2001 as an instrument (Ali and Kravet, 2016; Shalev et al., 2013; Zhang and Tong, 2021). Such changes in accounting rules affected the incentives for chief executive officers (CEOs) to engage in acquisitions because it eliminated the pooling interest method of accounting (SFAS 141) and amortization of goodwill (SFAS 142), which are important factors in shaping the financing of acquisitions (Shalev et al., 2013). Despite the possible effect of the new SFAS rules on acquisitions activity, there was no indication that it would have a direct effect on firms’ technology out-­licensing activity. Additionally, we used a second instrument called organizational capital (OC), which is the ratio of selling, general and administrative expenditures6 to total assets. Organizational © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 2114 2115 capital has been defined by prior studies as the ‘knowledge used to combine human skills and physical capital into systems for producing and delivering want-­satisfying products’ (Evenson and Westphal, 1995, p. 2337). Thus, based on these studies organizational capital represents the knowledge and routines embodied in employees and companies’ capabilities. We argue that firms with high organizational capital will be less likely to acquire other firms for two reasons. First, it is well documented that acquisitions are one of the prioritized ways that firms will use to overcome internal deficiencies associated with downstream or upstream capabilities. Accordingly, firms with high organizational capital will be less likely to have little need to turn to acquisitions to overcome internal issues. Second, as firms have increasing levels of organizational capabilities, it becomes harder for them to find suitable available external targets that add value for them. Therefore, firms with high levels of organizational capital will have fewer options in terms of viable targets to acquire in the first place. This suggests that organizational capital will negatively predict the acquisition activities of a focal firm. Our empirical model of the 2SLS approach for Technology Acquisitions is as follows: Technology acquisitionsi,t = 𝛿 1 SFAS Changei,t + 𝛿 1 OCi,t + Controlsi,t + 𝛾 i + 𝛾 t + 𝜂 i,t Technology Out − licensingi,t +1 = Technology Acquisitionsi,t + Controlsi,t + 𝛾 i + 𝛾 t + 𝜀i,t We also estimate the same setup for Size of Acquired Knowledge Base using the following approach: Size of Acquired Knowledge Basei,t = 𝛿 1 SFAS Changei,t + 𝛿 1 OCi,t + Controlsi,t + 𝛾 i + 𝛾 t + 𝜂 i,t Technology Out − licensingi,t +1 = Size of Acquired Knowledge Basei,t + Controlsi,t + 𝛾 i + 𝛾 t + 𝜀i,t As described above, we first estimated our models for the variable Technology acquisitions and then Size of Acquired Knowledge Base. In both cases, we used them as dependent variables estimated with the SFAS Change, captured using a dummy variable equal to 1 for all the events of stock-­for-­stock mergers and acquisitions that occurred after 2001; the other observations are zeros, and the OC ratio, while including all controls. Accordingly, our estimation of interest deployed Technology Out-­ Licensing in which a firm i engages at year t as the dependent variable and the values of exogenous changes in Technology acquisitions and The Size of the Acquired Knowledge Base. To increase the robustness of our estimations, we included firm-­ and year-­fixed effects in both the stages of the instrument variable regressions and clustered standard errors at the firm level. RESULTS Main Findings Table I reports the descriptive statistics (standard deviations and means) and simple pairwise correlations between the dependent and independent variables in the regression analysis. In our sample, the correlations among the independent variables did © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd.. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Resource Reconfiguration and Out-­Licensing T. M. Klueter et al. not raise significant concerns regarding collinearity between our main independent variables and the controls. Furthermore, we also noted that the mean–­variance inflation factors (VIF) associated with the variables in our empirical models did not raise further concerns. In Table II, we report the estimations to test our hypotheses. Models I and II correspond to the first and second stages of our 2SLS estimations. We use the changes in SFAS and OC as instruments for predicting the technology acquisitions deals in Model I (First Stage). From the analysis, we find that there is a strong association between our instruments and the number of technology acquisition deals in which the sample firms engaged. We further tested the strength of the instrument in the first stage and found that F-­statistics was 74.68, which is greater than the required F-­ statistic of 10 in Stock and Watson (2003), suggesting that the instrument is not weak. Our overidentification test for our instrument yielded an insignificant Hansen J statistic (Hansen J-­statistic = 0.944, p-­value = 0.331), suggesting that the instruments are likely uncorrelated with the error. We observe in Model II (Second Stage) that, after accounting for endogeneity from unobserved factors, the positive relationship between Technology Acquisitions and Technology out-­licensing remains highly significant (β = 0.117, p-­value < 0.05). This result offers support for the argument in Hypothesis 1 that an increase in technology acquisition is associated with an increase in firms’ rates of technology out-­licensing. The magnitude of the effect is also substantial; based on the result, firms on average would experience a 11.7 per cent increase in out-­licensing for each additional technology acquisition in which they engaged. In addition, these results corroborate previous findings that have suggested a positive relationship between technology out-­licensing and firm R&D intensity (β = 0.003, p-­value < 0.05), and negative relationship with firm size (β = −0.016, p-­value < 0.05) and Organizational Myopia (β = −0.344, p-­value < 0.05) (Moreira et al., 2019). We also find that firms with prior in-­licensing experience (β = 0.073, p-­value < 0.001) out-­license more often (Chesbrough et al., 2006). In Models III and IV, we continue relying on 2SLS estimations to test our argument as to the moderating effect of Technology Uncertainty and Financial Slack. The results reported in Model III show that the degree of technology uncertainty of the acquired technologies post-­acquisition weakens the positive relationship between the acquiring firm’s technology acquisitions and its technology out-­licensing activities. The co-­efficient for the interaction term, Technology acquisitions and Technology uncertainty, is negative and statistically significant (β = −0.004, p-­value < 0.05). To give further credence to this result, we show the interaction effect graphically in Figure 1. The margin plot demonstrates that the increase in technology out-­licensing due to an additional technology acquisition will be reduced more for acquired knowledge with high (above the mean) technology uncertainty than for those with low technology uncertainty. All this suggests broad support for Hypothesis 2, which predicts the effect of acquisition on out-­licensing to be weaker, the greater the degree of uncertainty of the acquired technologies. Next, based on Model IV, we examine the moderating effect of financial slack. The results show that high availability of financial slack weakens the positive relationship between the acquiring firm’s technology acquisitions and its technology out-­licensing activities. The © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 2116 Technology licensing-­out Technology acquisitions Size of acquired knowledge Technology uncertainty Financial slack Productivity Patent stock of acquiring firm Acquiring firm product pipeline Phase III failure Strategic alliance Out-­licensing experience In-­licensing experience Organizational myopia Industry competition Technological diversity Backward citations (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) 1.709 0.611 0.937 0.053 0.769 0.673 0.191 0.068 1.570 1.798 0.208 0.191 −10.295 0.161 0.106 0.260 Mean 1.333 0.355 0.007 0.088 0.887 0.695 0.369 0.252 1.328 1.786 0.905 0.501 23.889 0.728 0.408 0.451 S.D 0.182 −0.017 0.000 0.129 0.358 0.690 0.409 0.172 0.397 0.318 0.005 0.014 −0.116 0.188 0.183 1.000 (1) 0.124 0.045 −0.065 0.096 0.345 0.202 0.142 0.229 0.326 0.267 −0.027 −0.030 −0.103 0.768 1.000 (2) 0.110 0.049 −0.028 0.129 0.354 0.189 0.133 0.212 0.338 0.291 −0.027 −0.032 −0.076 1.000 (3) −0.151 −0.040 −0.003 −0.078 −0.299 −0.135 −0.079 −0.146 −0.302 −0.284 0.020 0.038 1.000 (4) −0.007 −0.013 0.040 −0.012 0.007 0.036 0.027 −0.021 −0.015 −0.016 0.019 1.000 (5) 0.053 −0.076 0.039 0.041 −0.067 0.012 −0.004 −0.034 −0.038 0.048 1.000 (6) 0.598 −0.171 −0.050 0.630 0.541 0.417 0.318 0.284 0.654 1.000 (7) 0.341 −0.043 −0.055 0.369 0.654 0.533 0.343 0.396 1.000 (8) 0.114 0.009 −0.027 0.124 0.327 0.215 0.136 1.000 (9) 0.204 −0.048 −0.090 0.140 0.293 0.544 1.000 (10) (Continues) 0.288 −0.076 −0.025 0.185 0.431 1.000 (11) 2117 © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd.. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License (1) Variables Table I. Descriptive statistics and correlations coefficients (N = 5217; Firms = 467) Resource Reconfiguration and Out-­Licensing Research intensity Market growth Potential licensees Exogenous sunk cost (19) (20) (21) Backward citations (16) Firm size Technological diversity (15) (17) Industry competition (14) 0.148 5.498 0.071 4.389 2.487 1.709 0.611 0.937 0.053 0.769 5.498 0.071 0.134 0.260 0.076 2.672 5.526 1.333 0.355 0.007 0.088 0.887 S.D 0.134 0.260 0.076 2.672 5.526 S.D 0.053 0.142 −0.025 0.638 −0.098 0.205 0.044 −0.037 0.210 1.000 (12) 0.016 0.091 0.006 0.271 0.015 (1) 0.063 −0.023 −0.014 0.343 −0.030 0.304 −0.147 −0.031 1.000 (13) 0.015 0.089 −0.055 0.352 −0.056 (2) 0.256 −0.447 0.525 −0.045 0.004 −0.237 0.115 1.000 (14) 0.040 0.062 −0.029 0.354 −0.067 (3) 0.166 −0.152 0.088 0.015 −0.101 −0.606 1.000 (15) −0.061 0.023 −0.010 −0.347 0.081 (4) 0.274 −0.096 0.134 0.056 −0.140 0.073 0.003 0.047 0.130 −0.043 0.636 −0.001 (8) 0.218 0.050 −0.025 1.000 (18) −0.027 0.661 −0.054 (7) −0.189 1.000 (17) −0.012 −0.027 0.019 −0.081 0.021 (6) −0.151 0.305 0.058 1.000 (16) −0.059 −0.008 0.050 0.016 0.024 (5) 0.153 −0.350 1.000 (19) 0.050 0.071 −0.023 0.327 −0.048 (9) −0.257 1.000 (20) −0.037 0.182 −0.056 0.210 0.052 (10) 1.000 (21) −0.025 0.171 −0.025 0.320 0.040 (11) T. M. Klueter et al. © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License (18) Organizational myopia Variables (13) Exogenous sunk cost (21) In-­licensing experience Mean Potential licensees (20) (12) 0.148 Market growth (19) 4.389 Firm size 2.487 Research intensity (18) Mean (17) Variables Table I. (Continued) 2118 (0.020) (0.014) (0.033) 0.000 0.014 (0.031) (0.035) 0.068* 0.016 (0.035) (0.017) 0.024 0.052 (0.012) (0.018) 0.016 0.014 (0.012) (0.006) 0.005 (0.002) −0.012 −0.003 (0.014) (0.007) 0.002 −0.003 −0.006 (0.001) (0.002) (0.020) −0.001 (0.033) 0.062 (0.037) 0.016 (0.016) 0.016 (0.018) 0.005 (0.006) −0.003 (0.095) (0.005) (0.020) −0.002 (0.033) 0.068* (0.036) 0.024 (0.016) 0.017 (0.018) 0.003 (0.020) −0.003 (0.033) 0.062 (0.038) 0.016 (0.016) 0.017 (0.018) 0.004 (0.006) −0.003 (0.070) −0.003 −0.204* (0.002) −0.214** (0.002) (0.015) 0.009 (0.001) 0.001 (0.069) 0.071 (Continues) Model V 2SLS Second Stage −0.004* (0.014) 0.010 (0.001) 0.000 (0.057) 0.138* Model IV 2SLS Second Stage −0.004* (0.014) −0.003 (0.001) 0.001 (0.063) 0.000 0.052 (0.052) Model III 2SLS Second Stage 0.117* Model II 2SLS Second Stage 0.013*** Model I 2SLS First Stage 2119 © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd.. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Out-­licensing experience Strategic alliances Phase III failures Acquiring firm product pipeline Patent stock of acquiring firm R&D productivity Technology acquisitions × financial slack Technology acquisitions × technology uncertainty Financial slack Technology uncertainty Technology acquisitions Variables Table II. 2SLS regression model predicting out-­licensing Resource Reconfiguration and Out-­Licensing (0.086) 1.011*** −0.089 (0.071) −0.088 (0.053) −0.084 (0.270) 0.043 (1.585) (1.673) (0.288) 0.496 −0.348 (0.007) (0.005) (0.001) −0.016* 0.017** (0.001) (0.015) 0.003* −0.000 (0.010) (0.030) −0.000 0.005 (0.019) (31.074) 0.018 0.044* (33.899) (0.175) −19.387 −3.183 (0.071) −0.089 (0.270) −0.112 (1.598) 0.403 (0.007) −0.016* (0.001) 0.003* (0.015) −0.001 (0.030) 0.014 (31.162) −21.936 (0.171) −0.332 (0.014) 0.072*** Model III 2SLS Second Stage (0.071) −0.090 (0.269) −0.095 (1.588) 0.503 (0.007) −0.016* (0.001) 0.003* (0.015) 0.001 (0.030) 0.019 (31.026) −20.071 (0.175) −0.323 (0.014) 0.075*** Model IV 2SLS Second Stage (0.070) −0.089 (0.268) −0.122 (1.600) 0.409 (0.007) −0.016* (0.001) 0.003* (0.014) 0.000 (0.030) 0.015 (31.046) −22.612 (0.170) −0.311 (0.014) 0.074*** Model V 2SLS Second Stage T. M. Klueter et al. © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License SFAS (instrument) Exogenous sunk cost Potential licensees Market growth Firm size R&D intensity Backward citations Technological diversity industry competition (0.136) (0.014) −0.344* −0.152 (0.012) Organizational myopia 0.073*** 0.036** In-­licensing experience Model II 2SLS Second Stage Model I 2SLS First Stage Variables Table II. (Continued) 2120 Yes Yes 4737 Firm fixed effects Cluster at firm Observations 4737 Yes Yes Yes 4737 YES YES YES (30.475) 21.150 Model III 2SLS Second Stage 4737 YES YES YES (30.339) 19.306 Model IV 2SLS Second Stage 4737 YES YES YES (30.357) 21.830 Model V 2SLS Second Stage 2121 © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd.. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Note: Robust standard errors in parentheses; *p < 0.05; **p < 0.01; ***p < 0.001. Yes 18.618 (30.388) 2.746 Model II 2SLS Second Stage (33.163) Year fixed effects Constant −0.063** Organization capital (instrument) (0.024) Model I 2SLS First Stage Variables Table II. (Continued) Resource Reconfiguration and Out-­Licensing T. M. Klueter et al. 0 Technology Out-Licensing (Log) 2 4 6 co-­efficient for the interaction term Technology acquisitions × Financial Slack is negative and statistically significant (β = −0.214, p-­value < 0.01). We also show the interaction effect graphically in Figure 2. The margin plot demonstrates that the increase in technology out-­ licensing due to an additional technology acquisition is reduced for firms with high financial slack. These results strongly support the idea that financial slack plays an important role in a firm’s post-­acquisition integration, thereby affecting its decision as to whether to commercialize its technologies through out-­licensing or keep them in-­house for further development. Finally, we placed both interaction terms in one model (Model V), and the results remained consistent. We replicated the same empirical setup and estimations using the variable Size of the Acquired Knowledge Base. As one can see in Table III, the results are similar to those observed for the effect using the count of acquisitions. One difference is that the interaction term, Size of Acquired Knowledge Base and Technology Uncertainty, continues to predict a negative effect on our dependent variable, but the two-­tailed test becomes slightly less significant (p < 0.10) for this alternative acquisition measure. We note that this can be due to the fact that both variables, the Size of Acquired Knowledge Base and Technology Uncertainty, are based on the underlying patents associated with the target firms. This can lead to redundancy, making it harder to disentangle the individual effects for those two proxies. Still, we consider that the overall results point in the same direction as predicted in our theory. 1 2 3 4 5 Technology Acquisitions Low Technology Uncertainty 6 7 8 High Technology Uncertainty Figure 1. Graphical presentation of moderation: technology acquisitions and technology uncertainty [Colour figure can be viewed at wileyonlinelibrary.com] © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 2122 2123 Additional Analyses We ran several checks to ensure the robustness of our findings and to rule out alternative explanations. We detail them within the following sections, testing different lag structures, testing alternative model specifications, and testing alternative variable operationalization and controls. Testing different lag structures. First, we explored different time windows of the theorized effects of plus/minus two years and alternated the period chosen for our sample to ensure that our results were not spurious. Our main results have a one-­year t + 1 lag structure, and, in results available from the authors, we also provide the estimations for two additional periods t + 2 and t + 3. We find that the effect of acquisitions on out-­ licensing remains positive and statistically significant with t + 2 period. For t + 3 period, the effect, while positive, becomes weaker and non-­significant. Overall, the results are consistent with our main estimations and our theoretical framework how technology acquisitions shape out-­licensing. 1 .5 0 -.5 Technology Out-Licensing (Log) 1.5 Testing alternative model specifications. Next, we replicated our results in Table II using the GLM and Poisson count model. We performed this additional test to assess if our results were sensitive to different model specifications. The GLM estimations follow the same approach recommended by Rönkkö et al. (2022) as a strategy to avoid taking the logarithm of the dependent variable and creating potential model misspecification. 1 2 3 4 5 Technology Acquisitions Low Financial Slack 6 7 8 High Financial Slack Figure 2. Graphical presentation of moderation: technology acquisitions and financial slack [Colour figure can be viewed at wileyonlinelibrary.com] © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd.. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Resource Reconfiguration and Out-­Licensing T. M. Klueter et al. The Poisson approach follow the guidelines proposed by Lin and Wooldridge (2019) and also avoids taking the logarithm of our dependent variable, with the advantage of still accounting to endogeneity through the use of two-­stage models. These two approaches represent an important trade-­off we had to make in the paper as, on one side, we can use longitudinal estimations with within firm-­fixed effects and the possibility to correct for endogeneity. On the other side, we can estimate the GLM model accounting for the distribution of our dependent variable without using log transformations (Rönkkö et al., 2022). Overall, the results remain comparable to our main results as we continue to find a baseline effect of Acquisitions on Out-­licensing. Indeed, in Table IV Models I and III, we demonstrate that the effect of technological acquisitions on out-­licensing activities is positive and highly significant, which aligns with our prior results using the 2SLS model. We also examined the moderating effect of technology uncertainty and financial slack, finding that the results of the interaction terms in models II and IV (Table IV) still support our arguments as seen in Hypothesis 2 and 3. These findings provide assurance that our results are robust to different econometric model specifications and are not sensitive to taking the log of the dependent variable. Testing alternative variable operationalization. Next, we assess different operationalization of our key variables. To ensure that the result for the interaction term testing the argument in Hypothesis 2 and 3 is not conditional on a particular measurement for technology uncertainty and financial slack, we repeated our analysis using alternative measures.7 For technology uncertainty, using the Pharmaprojects database, we tracked the acquisitions of firms by documenting the new products in development that were acquired. We then split the analysis by firms which, compared to others in the industry, engaged in low uncertainty acquisitions (i.e., with drugs further along in development) and high uncertainty acquisitions (i.e., with drugs in development at more earlier stages). The results show that out-­licensing occurs more frequently following low uncertainty acquisitions, which syncs well with the analysis done using technology uncertainty proxied through patents. Relatedly, the uncertainty associated with the acquirer knowledge base should also be relevant to moderating the relationship between acquisitions and out-­licensing. We tested this assumption by applying a similar approach to Ziedonis (2007) and measured technology uncertainty based on the number of backward citations found in the acquirer patent portfolio. In line with our expectations, the moderating effect for the acquirer’s knowledge base uncertainty is also negative and statistically significant. We also examined the role of Absorptive Capacity on our variables of interest. Interestingly, our proxy for Absorptive Capacity does not show any significant effect in our sample. We speculate that this lack of effect is due to the fact that our sample is comprised of firms that actively invest in the development of new drugs and therapies. That means that, for this sample, most firms will all have extremely high levels of investment in R&D, making it difficult to capture this effect empirically. Next, we examined various alternative slack measures, building on the argument that an increase in a firm’s liquidity and the existing amount of unabsorbed resources is likely to result from anticipated needs to meet short-­term obligations (Cheng and Kesner, 1997). Therefore, for financial slack, we instead used the ratio of working © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 2124 0.001 (0.020) (0.028) (0.033) (0.040) 0.004 0.066* (0.035) (0.069) 0.029 0.036 (0.017) (0.023) −0.046 0.011 (0.019) (0.025) 0.052* 0.006 (0.006) (0.005) −0.020 −0.003 (0.014) (0.012) −0.003 −0.002 −0.013 (0.002) (0.005) (0.020) 0.000 (0.033) 0.061 (0.036) 0.027 (0.016) 0.010 (0.019) 0.005 (0.006) −0.003 (0.020) −0.001 (0.033) 0.066* (0.035) 0.036 (0.016) 0.015 (0.019) 0.005 (0.006) (0.020) −0.001 (0.033) 0.061 (0.036) 0.028 (0.016) 0.014 (0.019) 0.004 (0.006) −0.003 (0.070) (0.055) −0.002 −0.154* (0.002) −0.164** (0.002) (0.015) 0.009 (0.002) −0.001 (0.078) 0.084 (Continues) Model V 2SLS Second Stage −0.003 (0.014) 0.009 (0.002) −0.002 (0.060) 0.139* Model IV 2SLS Second Stage −0.003 (0.014) −0.003 (0.002) −0.001 (0.069) −0.002 0.063 (0.054) Model III 2SLS Second Stage 0.120* Model II 2SLS Second Stage 0.030*** Model I 2SLS First Stage 2125 © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd.. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Out-­licensing experience Strategic alliances Phase III failures Acquiring firm product pipeline Patent stock of acquiring firm R&D productivity Size of acquired knowledge × financial slack Size of acquired knowledge × technology uncertainty Financial slack Technology uncertainty Size of acquired knowledge base Variables Table III. 2SLS regression model predicting out-­licensing Resource Reconfiguration and Out-­Licensing (0.072) (0.144) 1.004*** −0.077 (0.078) (0.277) −0.182 −0.061 (0.441) (1.649) (2.707) −0.147 0.632 (0.007) (0.010) −1.506 −0.016* (0.001) (0.001) 0.020* 0.003* (0.015) (0.015) −0.001 0.001 (0.030) (0.031) −0.002 0.020 (31.885) (55.435) 0.031 −16.287 (0.179) −29.745 (0.295) (0.072) −0.082 (0.290) −0.157 (1.726) 0.061 (0.007) −0.017* (0.001) 0.003* (0.015) 0.001 (0.030) 0.018 (33.484) −27.108 (0.176) −0.357* (0.014) 0.069*** Model III 2SLS Second Stage (0.072) −0.074 (0.279) −0.068 (1.670) 0.672 (0.007) −0.018 (0.001) 0.003* (0.015) 0.003 (0.030) 0.021 (32.167) −15.991 (0.179) −0.371* (0.014) 0.072*** Model IV 2SLS Second Stage (0.072) −0.079 (0.291) −0.159 (1.752) 0.118 (0.007) −0.018** (0.001) 0.003* (0.014) 0.003 (0.030) 0.019 (33.807) −26.445 (0.176) −0.347* (0.014) 0.071*** Model V 2SLS Second Stage T. M. Klueter et al. © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License SFAS (Instrument) Exogenous sunk cost Potential licensees Market growth Firm size R&D intensity Backward citations Technological diversity Industry competition −0.383* (0.014) 0.176 (0.020) Organizational myopia 0.070*** 0.063** In-­licensing experience Model II 2SLS Second Stage Model I 2SLS First Stage Variables Table III. (Continued) 2126 Yes 4737 Cluster at firm Observations 4737 Yes Yes 4737 YES YES YES (32.754) 26.223 Model III 2SLS Second Stage 4737 YES YES YES (31.462) 15.353 Model IV 2SLS Second Stage 4737 YES YES YES (33.062) 25.608 Model V 2SLS Second Stage 2127 © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd.. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Note: Robust standard errors in parentheses; *p < 0.05; **p < 0.01; ***p < 0.001. Yes Firm fixed effects Yes (31.189) Yes 15.605 (54.101) Model II 2SLS Second Stage 28.564 Year fixed effects Constant −0.116* Organization capital (instrument) (0.049) Model I 2SLS First Stage Variables Table III. (Continued) Resource Reconfiguration and Out-­Licensing T. M. Klueter et al. Table IV. GLM and POISSON models predicting out-­licensing (main results) Variables Model I (GLM) Model II (GLM) Model III Second Stage (Poisson) Model IV Second Stage (Poisson) Technology acquisitions 0.121** 0.039 0.257* 0.188 (0.043) (0.067) (0.116) (0.122) 0.003 0.005** −0.001 0.001 (0.002) (0.002) (0.003) (0.003) 0.022 0.066 0.011 0.052 (0.056) (0.058) (0.059) (0.056) Technology uncertainty Financial slack Technology acquisitions × technology uncertainty −0.005** −0.005** (0.001) (0.002) Technology acquisitions × financial slack −0.464* −0.458* (0.201) (0.233) Year fixed effects Yes Yes Yes Yes Firm fixed effects Yes Yes Yes Yes Cluster at firm Yes Yes Yes Yes Observations 4737 4737 3949 3949 Robust standard errors in parentheses; *p < 0.05; **p < 0.01; ***p < 0.001. Note: Controls and instrument included but not shown in table. capital to sales as well as the current ratio (current assets divided by current liabilities); our results supporting our theoretical predictions in Hypothesis 3 remains highly comparable to those reported in our main analyses. We also tested the curvilinear effect on slack as the moderating effect may differ at very high and very low values of financial slack. The results reveal no additional improvement of the model when adding the squared term of slack as an additional moderating squared term of slack to the model. Contrasting Divestitures as Alternative R&D Reconfiguration Strategy We noted earlier that extant research has broadly discussed divestments in the context of post-­acquisitions reconfiguration (e.g., Feldman and Sakhartov, 2022; Karim and Capron, 2016; Mariotti et al., 2023). Accordingly, we wanted to contrast our out-­licensing results to those examining divestitures. We captured assets divestitures as the sale of physical or organizational assets of the acquiring firm post-­acquisition (Capron et al., 2001). Such transactions are reported in Recap, and we generated the variable Assets Divestitures decision by tracking the various divestiture related activities and aggregating them yearly to get a cumulative number of divestitures a focal firm had engaged divestitures within one year from the focal year. We find that divestitures are much rarer as licensing such that, for every divestiture, we observe around 4.5 licensing deals. In addition, licensing transactions always © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 2128 2129 involve some underlying technology (in the form of patents, software or simply technological knowledge), but divestitures can be related to non-­R&D transactions. For example, firms can divest full business units, sometimes those even unrelated to the biopharmaceutical industry (such as adhesives, chemicals or cosmetics). When excluding those non-­R&D-­related divestitures, we observe 15.4 licensing deals for each divestiture for firms in our sample, suggesting the broad use of licensing over divestitures in the industry. Once again, we used a 2SLS regression model predicting the decision of firms to engage in assets divestiture similar to the analysis for out-­licensing. In Table V, Model I shows the results, with Asset Divestitures activities as a dependent variable. The result of the coefficient of technology acquisitions in this model is positive and shows a statistically significant effect. This finding aligns with the conventional perception that firms tend to engage in the divestiture of assets following an acquisition (Capron et al., 2001). In Models II and III, we probed the relationship between assets divestiture and technology acquisitions by interacting the technology acquisition activities with technology uncertainty and financial slack. The result showed a negative effect, but that effect is only statistically significant for the interaction of financial slack and technology acquisitions. We also find different effects from controls as, for example, Phase III Failures predict divestitures but not out-­licensing. These findings, overall, provide evidence of a possible strategic difference between the two modes, assets divestiture and out-­licensing. We elaborate on these differences further in the discussion. DISCUSSION Examining resource reconfiguration following acquisitions is at the heart of understanding how firms accrue value when acquiring technology-­based firms (Karim and Capron, 2016; Karim and Mitchell, 2000). In this paper, we systematically examine out-­licensing as an important post-­acquisitions strategy for firms to engage resource reconfiguration. As a departing point, we show that, following technology acquisitions, resource reconfiguration using out-­licensing increases. At the same time, we reveal pertinent trade-­offs in using out-­licensing as a resource reconfiguration strategy post-­acquisition. Namely, the acquirer may consider future rent dissipating effects and the potential damage of giving away potentially highly valuable technology to another firm (Fosfuri, 2006). Supporting this idea, we find that, when acquirers add highly uncertain technologies, firms are more likely to keep R&D projects in-­house, owing to the difficulties of assessing the true potential of their post-­acquisition R&D resources. Moreover, we show that the availability of financial slack that can be used to support the acquisition integration process in the short term may tilt firms toward keeping technologies in-­house instead of using out-­licensing. These findings contribute to two literatures on technology acquisitions and resource reconfiguration in several ways. First, they explain why out-­licensing is a key resource-­ reconfiguration strategy following technology acquisitions. Prior studies have examined resource reconfiguration predominantly through externalizing resources by divesting or discontinuing them due to resource constraints (Capron et al., 2001; Karim, 2006). We show that out-­licensing is an important resource reconfiguration strategy as it allows the acquirer to externalize the subsequent development of a technology and save costs, while, © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd.. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Resource Reconfiguration and Out-­Licensing T. M. Klueter et al. Table V. 2SLS regression model predicting assets divestitures Variables Model I Second Stage Model II Second Stage Model III Second Stage Model IV Second Stage Technology acquisitions 0.161* 0.143 0.178* 0.160 (0.078) (0.090) (0.078) (0.090) −0.000 −0.000 −0.000 −0.000 (0.001) (0.001) (0.001) (0.001) 0.010 0.010 0.021 0.021 (0.012) (0.012) (0.013) (0.013) Technology uncertainty Financial slack Technology acquisitions × technology uncertainty −0.001 (0.002) Technology acquisitions × financial slack R&D productivity −0.001 (0.002) −0.176* −0.175* (0.074) (0.080) −0.007 −0.007 −0.007 −0.007 (0.004) (0.004) (0.004) (0.004) Patent stock of acquiring firm 0.037* 0.036* 0.035 0.035 (0.018) (0.018) (0.018) (0.018) Acquiring firm product pipeline 0.013 0.013 0.014 0.014 (0.014) (0.014) (0.014) (0.014) Phase III failures 0.073** 0.071** 0.073** 0.071* (0.028) (0.029) (0.028) (0.029) 0.013 0.010 0.010 0.007 (0.043) (0.044) (0.043) (0.045) −0.436** −0.431** −0.419** −0.414** (0.134) (0.134) (0.132) (0.133) −38.637*** −38.390*** −38.151*** −37.897*** (7.803) (7.832) (7.756) (7.787) −0.006 −0.007 −0.005 −0.007 (0.025) (0.025) (0.025) (0.025) −0.026* −0.026* −0.025* −0.025* (0.012) (0.012) (0.012) (0.012) −0.000 −0.000 −0.000 0.000 (0.001) (0.001) (0.001) (0.001) 0.002 −0.001 −0.001 0.001 (0.006) (0.006) (0.006) (0.006) 7.275* 7.176 7.177* 7.075 (3.639) (3.659) (3.642) (3.664) 0.029 0.029 0.029 0.029 (0.058) (0.058) (0.058) (0.058) Previous divestiture Organizational myopia Industry competition Technological diversity Backward citations R&D intensity Firm size Market growth Exogenous sunk cost (Continues) © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 2130 2131 Table V. (Continued) Variables Model I Second Stage Model II Second Stage Model III Second Stage Model IV Second Stage Constant 36.327*** 36.099*** 35.868*** 35.632*** (7.324) (7.350) (7.278) (7.307) Year fixed effects Yes Yes Yes Yes Firm fixed effects Yes Yes Yes Yes Cluster at firm Yes Yes Yes Yes Observations 4737 4737 4737 4737 Note: Robust standard errors in parentheses; *p < 0.05; **p < 0.01; ***p < 0.001. at the same time, the acquirer maintains residual ownership of the technology (Moreira et al., 2019; Moreira et al., 2020). Thus, resource reconfiguration through out-­licensing entails externalizing resources while simultaneously benefiting from the subsequent recombination efforts of the external licensees (Karim, 2006). This extends theories on resource reconfiguration and resource redeployment by showing the utility and unique advantages of out-­licensing in supporting this process, compared to previously discussed strategies (Capron et al., 2001). As such, insights from our study complement prior work that has documented resource reconfiguration in commercial units post-­acquisitions to choices related to technologies which have not yet reached commercial markets (Feldman and Sakhartov, 2022; Karim and Capron, 2016). We also provide important contingencies in the form of technology uncertainty and financial slack that increase the understanding of the well-­known trade-­offs firms face when out-­licensing (Fosfuri, 2006). This explains the reasons that acquirers, despite augmenting their R&D resources through acquisitions, may continue to opt to develop their technologies in-­house rather than using out-­licensing for reconfiguration (e.g., Choi, 2002; Moreira et al., 2019). These insights augment discussions in the literature as to when firms resort to technology commercialization strategies such as out-­licensing in the first place and the role of internal resources to support the R&D resource reconfiguration process (Chaturvedi and Prescott, 2022; Karim and Capron, 2016; Kuusela et al., 2017). Finally, this study systematically links two important strategic transactions: acquisitions as the in-­sourcing of technological solutions and licensing by commercializing technologies through the market for technology. Why and when firms choose to out-­ licence their technologies has long been a centre stage debate in the licensing literature (e.g., Arora and Ceccagnoli, 2006; Fosfuri, 2006; Moreira et al., 2023). We show that technology acquisitions may be an important antecedent for out-­licensing due to the need to reconfigure resources (Laursen et al., 2017). This complements studies that have examined the interplay between different in-­sourcing activities such as acquisitions following alliances (Shi et al., 2012; Shi and Prescott, 2011), as well as balancing in-­sourcing and out-­sourcing activities in open innovation (Cassiman et al., 2005). © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd.. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Resource Reconfiguration and Out-­Licensing T. M. Klueter et al. The paper also provides insights for practitioners. Given the prevalence of acquisitions in many technology-­intensive industries (Fosfuri and Giarratana, 2010) managers need to carefully assess the strategies at their disposal for resource reconfiguration. The paper notes the frequent use of out-­licensing, in particular when compared to divestitures, which makes clear that managers consider out-­licensing as an important strategy for resource reconfiguration following acquisitions. Yet, even though out-­licensing is considered one of the most pertinent technology commercialization strategies, the paper reveals important trade-­offs of managers when making such decisions. When managers are unable to assess the ultimate potential of their technologies, they may refrain from out-­licensing post-­acquisitions. In a similar vein, managers may opt to continue experimenting with the acquired R&D base in-­house if they have sufficient resources to support the knowledge integration process. Given these trade-­offs, firms may consider establishing a dedicated out-­licensing team that can handle the transfer of knowledge to interested external parties. Some of the most active firms in acquisitions, such as Merck & Co, AstraZenca, GE or IBM, have all already established dedicated out-­licensing groups. The study has limitations that provide several interesting avenues for future research. Clearly, the bio-­pharmaceutical industry is an important area in which to study in-­ sourcing in the form of technology acquisitions and out-­licensing (Nicholls-­Nixon and Woo, 2003). While we theorized on a general level about out-­licensing as resource reconfiguration strategy following acquisitions, the particularities of the biopharmaceutical industry call for further research testing our framework in other contexts. In its current form, we also focused on counting the number of acquisitions and, using a 2SLS approach, providing robust results on subsequent out-­licensing. Alternatively, researchers could analyse firms’ strategies, contingent on making acquisitions, to examine their acquired knowledge bases as well as the structure of those bases. Although we provide tests for different alternative measures, future research should take this a step further. For example, one could more carefully explore the technological distance of knowledge from target to acquiror as well as the degree to which technologies complement each other (Zaheer et al., 2013). Future studies could investigate such factors conditional on making acquisitions, building on the research which has explored their role in the context of divestitures following acquisitions (Capron et al., 2001; Karim and Capron, 2016). Finally, we did not link technology out-­licensing to specific technologies and how that activity relates to previously made acquisitions. In our context, some out-­licensing cases can be specific, for example, out-­licensing a distinctly new project in the form of a compound in development. However, in many cases, that also relates to broader technologies, which cannot be easily attributed to specific projects. We were able to gain some insights by matching in-­licensed and out-­licensed products for a subset of transactions (see section ‘Additional Analyses’). However, we encourage researchers to find better ways to more systematically link technology out-­licensing transactions to actual patents or products in development. Overall, connecting out-­licensing to resource reconfiguration can provide important insights into how firms systematically manage their post-­acquisition and open innovation activities in general. We hope our study helps spur further interest by researchers who can build on and extend our framework with the goal of shedding additional light on this important topic. © 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies and John Wiley & Sons Ltd. 14676486, 2024, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joms.12968 by Tilburg University, Wiley Online Library on [22/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 2132 2133 ACKNOWLEDGMENTS T. Klueter acknowledges the research support provided by the Mack Institute for Innovation Management at The Wharton School. NOTES [1] The work on post-­acquisition reconfiguration also includes larger conglomerates that include additional businesses to their product portfolio and engage in subsequent rebalancing. [2] Estimating our models without the variables leading to missing values provided no evidence of potential issues with sample attrition. 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