Abstract
Firms increasingly look for collaborations with alliance partners in order to develop breakthrough innovation due to a struggle to develop such innovations internally. But how does the configuration of the alliance portfolio influence, i.e. geographical proximity and alliance portfolio size, the development of breakthroughs? Based on longitudinal data from the U.S. pharmaceutical industry, an alliance sample is constructed to investigate these relationships. This study contributes a two-fold, for breakthrough innovation, collaborating with proximate partners is better and an increasing alliance portfolio size has a positive effect. Therefore, the findings offer an approach in which to configure strategic alliance to achieve breakthrough innovation. 1. Introduction
2007); facilitating foreign market entry (Dacin, Oliver & Roy, 2007); easing the liability of foreignness (Zaheer, 1995); and providing economies of scale and scope (Grant & Baden-fuller, 2004).
Firms engage in strategic alliances when the benefits out the costs of collaborating (Parkhe, 1993; Kogut, 1998). The cost of collaborating arise from the search of information or in other words selecting a fit partner, pre-collaborating arrangements and monitoring the collaboration (Williamson, 1981). Moreover, the collaboration lowers economic returns, because revenues are shared with partners.
Thus, firms engage in strategic alliances expecting to reap the benefits of collaborating, yet many of these alliances fail or are dissolved prematurely. The high failure rate, together with a proliferation of strategic alliances, has led to the development of an ongoing body of literature on the phenomenon of strategic alliances (Gomes, Barnes & Mahmood, 2016).
Although considerable research has been done on the configuration of strategic alliances, e.g. the influence alliance portfolio size has on the development of innovation, two issues arise. First, researches considering the effect of alliance portfolio size on innovation mostly focuses on the implications for innovation in general, not specifying the difference antecedents of incremental and breakthrough innovation. Secondly, studies have yet to examine the effect of geographical characteristics of alliance partners on breakthrough innovation.
Why is it important to consider these alliance characteristics with regard to breakthrough innovation? Because, the development of breakthrough innovation differs from other sorts of innovations, e.g. incremental, it requires novel knowledge whereas incremental innovation is built upon the firms’ already existing knowledge base (Ahuja & Lampert, 2001). Therefore, the absence of novel knowledge increases the likelihood of generating incremental innovation which is a product of an extension of current knowledge instead of knowledge new to firm (Rosenkopf & Nerkar, 2001). Moreover, in comparison to other types of innovation, it is shown that breakthrough innovation is built on more tacit knowledge (Inkpen & Tsang, 2005).
Moreover, the transaction cost theory and resource-based view argue that strategic alliances are better suitable for the development of breakthrough innovations than e.g. arms-length contracts or licensing. The transaction cost theory principles that the best situational characteristic for alliances are high uncertainty and asset specificity (Tsang, 2000). Tacit knowledge is very firm specific as it is embedded in the organizations and individual. Moreover, the un-observability of tacit knowledge increases the uncertainty for the knowledge seeker. Adding to this, the resource-based view suggests that transferring firm specific knowledge through e.g. licensing or arm-length contracts would mean a loss of value. Due to bounded rationality, the licensee will not be able to generate the same value from the knowledge as the knowledge holder (Tsang, 2000). So strategic alliances facilitate the transfer of tacit knowledge, which is beneficial for the development of breakthrough innovation. Therefore, more and more firms pursuing breakthrough innovation engage in strategic alliances (Dong, McCarthy & Schoenmakers, 2017).
Secondly, the alliance portfolio literature shows that the positive effect of an increasing alliance portfolio size starts to diminish, caused by a lack of managerial attention (Deeds and Hill, 1996). Laursen & Salter (2006) suggest that managerial attention is a limited, yet important, resource in the assimilation of external resource. Also, an increase in the number of alliance partners is counterproductive on the efficient allocation of attention given the bounded rationality; the efficacy of managers to screen and monitor partners is likely to decrease (Deeds & Hill, 1996). Moreover, the lack of attention allocation with an increasing number of alliance partners leads to less intensity in linkages (Hoffman, 2007), for example: less time is spent per partner.
This research aims to contribute a two-fold to the literature on alliance portfolio. First, this research contributes that due to the effect on inter-firm transfer of tacit knowledge, geographical characteristics has an influence on the development of breakthrough innovations. Secondly, the study contributes that because of the importance of attention to understand external knowledge, the relationship between alliance portfolio size and breakthrough innovations will be differ from the relationship it holds with innovation in general.
2. Theoretical background
The theoretical background is directed at explaining strategic alliances and its relationship to the development of breakthrough innovations. Although strategic alliances can be used for various purposes, like marketing and manufacturing, this study considers inter-firm knowledge transfer and how its role in the development of breakthrough innovations.
Firms with a broad knowledge base are more likely to develop breakthrough innovations. Broad knowledge, or elsewise a diverse knowledge base, is accumulated through extensive exploration (Zhou & Li, 2012). Therefore, firms engage in a manifold of strategic alliances to gain benefit from a multiplicity of sources. The collective of alliances the firm engages is referred to as the alliance portfolio. The literature on alliance portfolio suggest that multiple partners provide the firm with, access to a pool of (scarce) resources, help manage or divvy uncertainties and risks and enable the recombination of contributions of separate partners (Wassmer, 2010). The following paragraphs rationalize how strategic alliances are used to facilitate inter-firm knowledge exchange and the role it has in the development of breakthrough innovations. 2.1 Strategic alliances Strategic alliances are cooperative agreements between two or more partners involving, exchange, sharing and co-development (Gulati, 1998). Other definitions add that strategic alliances are an agreement to jointly carry out a task, involving more interactions than a one-time arm’s-length contract (Rivera-Santos & Inkpen, 2009). Such cooperative agreements represents an important part of a firms’ strategy to grow, it presents the firm with an alternative to other tactics, like licensing, arm’s-length contract or acquisitions. The importance of strategic alliances for firms is underlined by a recent survey, that shows that 53% of U.S. CEOs are considering the initiation of an alliance within the next twelve months (PWC, 2017). Ideally, the benefits a firm will gain from a strategic alliance is the creation of knowledge recombination possibilities (Bos, Faems & Noseleit, 2017); divvying the risk amongst alliance partners (Wayhuni, Ghauri & Karsten, 2007); facilitating foreign market entry (Dacin, Oliver & Roy, 2007); easing the liability of foreignness (Zaheer, 1995); and providing economies of scale and scope (Grant & Baden-fuller, 2004). Firms engage in strategic alliances when the benefits out the costs of collaborating (Parkhe, 1993; Kogut, 1998). The cost of collaborating arise from the search of information or in other words selecting a fit partner, pre-collaborating arrangements and monitoring the collaboration (Williamson, 1981). Moreover, the collaborating lowers economic returns, because revenues are shared with partners. With the use of different theoretical perspectives, a body of literature has examined matters such as why strategic alliances are formed and how they are employed to learn from partners.
establishment of a long-term contract difficult. Strategic alliances offer a solution to this issue by alignment of incentives (Tsang, 2000). The shortcoming of the transaction cost perspective is that it only considers the cost of collaborating. In contrast, the resource-based view considers the benefits of collaborating as motive to engage in strategic alliances opposed to the focus on cost unlike the transaction cost theory (Tsang, 2000). According to the resource-based view, external resources, e.g. knowledge, finance or legitimacy, improve the strategic position of the firm (Eisenhardt & Schoonhoven, 1996). This theory is extended with the knowledge-based view, a stream of strategic alliance literature that considers the firms’ knowledge as the most important strategic resource. It is unlikely that all critical resources and value creation exist within the boundaries of the firms yet rather they are embedded in the network of inter-firm relationships (George et al, 2001). Dyer & Singh (1998) relate that the advantages of collaborating come from relation-specific assets, knowledge-sharing routines, complementary resources/capabilities and effective governance (Dyer & Singh, 1998: 662). Thus, no organization is an island and needs some form of interfirm relationship to advance (Parmigiani & Rivera-Santos, 2011).
From the knowledge-based perspective, considerable research has been done on how strategic alliances enable firms to learn from partners and expand internal knowledge. This is due to firms not being equipped with all the necessary resources, as external resources are accumulated through inter-organizational relationships such as strategic alliances, or internal knowledge. Knowledge represents a competitive advantage due to its inimitable characteristics of knowledge (Grant, 1996). Simonin (1999) considers strategic alliances as the most adequate medium to overcome inimitability between knowledge seeker and holder. Researches explore how knowledge is transferred (Mowery, Oxley & Silverman, 1996) and how firms internalize acquired knowledge (Cohen & Levinthal, 1990). Before considering the mechanisms of inter-firm knowledge transfer, it is important to point out that, the manner in which knowledge is transferred is influenced by the type of knowledge that is targeted. Additionally, the literature on knowledge makes a distinction between two types of knowledge: know-how and knowing about. Know-how is identified with tacit knowledge (skills, know-how, and contextual knowledge) while knowing-about is identified with explicit knowledge (Grant, 1996). “Tacitness refers to the implicit and non-codifiable accumulation of skills that results from learning by doing” (Reed & DeFillippi,1990: p. 89). In contrary to tacit knowledge, explicit knowledge, the identifier for knowing-about can be articulated and codified (Grant & Baden-fuller, 2004). Therefore, explicit knowledge is easier to transfer, as it is codified and observable whereas tacit knowledge is invisible and embedded in the organization. Tacit knowledge is also referred to as sticky knowledge as it is known to be immobile. Therefore, it requires more face-to-face and direct experience to transfer (Nonaka and Toyama, 2003).
how the existing knowledge base helps assimilates related knowledge. Anand & Khanna (2000) show that firms learn from previous alliances by exploring, or exploiting, new knowledge (Lavie & Rosenkopf, 2006). Finally, the accumulated knowledge is recombined with the firms’ internal knowledge base to further develop products or services (Gulati, 1998). Therefore, the coexistence of diverse knowledge within the boundaries firm produce the sort of learning and problem solving that yields innovation (Cohen & Levinthal, 1990).
2.2 Strategic alliances and breakthrough innovations
In the process of developing innovations, firms face challenges such as high risks and costs, perceived shorter product-life cycles and increasing competition through foreign market entry. These challenges are recognizably prominent when considering technological intensive industries (Mowery, Oxley & Silverman, 1996; Sampson, 2007). In such turbulent environments, firms should rather focus on breakthrough innovations instead of incremental (Zhou, Yim & Tse, 2005).
The degree of innovation can range from completely new to minor changes, when the innovation is new to the firm and marketplace, they are considered breakthrough innovations. Being “new” herein refers to features of the product or process, being unprecedented, creating a significant improvement from the current state or offering a fixed cost reduction (O’Connor & Rice, 2001). Other definitions of breakthrough innovations add the involvement of new technology, suggesting that breakthrough innovations change consumptions partners and create greater consumer benefit (Cheng & Chen, 2013). Firms pursue the development of breakthrough innovations, because it is associated with inimitability and superior economic returns. As a result of the newness of the innovation, outsiders to the firm that developed the breakthrough innovation will find it harder to imitate, thereby the firm will gain lead-time (Lieberman & Montgomery, 1998). During this lead-time the only the firm will receive remunerations from the innovation. In other words, the firm will accumulate Schumpeterian rents, that is a remuneration of offerings that are unique to the firm and cannot be easily imitated (Darroch, Miles & Paul, 2005). Thus, if successful, breakthrough innovations are of high value for the developing firm. Nonetheless, the newness of breakthrough innovations entails that developing such innovations come with higher levels of uncertainty and risk in comparison to other innovations (Mascitelli, 2000; Forés & Camisón, 2016). Therefore, firms find it difficult to develop breakthrough innovations internally (Gulati & Gargiulo, 1998). The difficulties of developing breakthrough innovations can possibly be overcome by engaging in strategic alliances (Ahuja, 2000).
The development of breakthrough innovation differs from other sorts of innovations, e.g. incremental, because it requires novel knowledge whereas incremental innovation is built upon the firms’ already existing knowledge base (Ahuja & Lampert, 2001). Therefore, the absence of novel knowledge increases the likelihood of generating incremental innovation which is a product of an extension of current knowledge instead of knowledge new to firm (Rosenkopf & Nerkar, 2001). Moreover, in comparison to other types of innovation, it is shown that breakthrough innovation is built on more tacit knowledge (Inkpen & Tsang, 2005).
internalization of external tacit knowledge. Fundamentally, tacit knowledge is not articulated which means that transferring it is more difficult and costlier (Sakellariou, Karantinou & Goffin, 2017). Still, strategic alliances can facilitate the explication of tacit knowledge through socialization (Nonaka and Toyama, 2003). Furthermore, the transaction cost theory and resource-based view argue that strategic alliances are better suitable than e.g. arms-length contracts or licensing. The transaction cost theory principles that the best situational characteristic for alliances are high uncertainty and asset specificity (Tsang, 2000). Tacit knowledge is very firm specific as it is embedded in the organizations and individual. Moreover, the un-observability of tacit knowledge increases the uncertainty for the knowledge seeker. Adding to this, the resource-based view suggests that transferring firm specific knowledge through e.g. licensing or arm-length contracts would mean a loss of value. Due to bounded rationality, the licensee will not be able to generate the same value from the knowledge as the knowledge holder (Tsang, 2000). So strategic alliances facilitate the transfer of tacit knowledge, which is beneficial for the development of breakthrough innovation. Therefore, more and more firms pursuing breakthrough innovation engage in strategic alliances (Dong, McCarthy & Schoenmakers, 2017).
3. Hypotheses development
The basic premise of the hypotheses is that, to foster breakthrough innovations firms need to internalize external novel (Cheng & Chen, 2014) and tacit knowledge (Mascitelli, 2000). Tacit knowledge is generally hard to transfer (Reed & DeFillippi,1990). To realize the transfer of tacit knowledge, the firm needs to establish strong linkages (Hoffman, 2007) and directly experience the targeted knowledge (Nonaka and Toyama, 2003). The geographical configuration of partners (Zaheer & Hernandez, 2011) and the number of collaborative ties (Bos, Faems & Noseleit, 2017) are known to influence the knowledge transfer and thus, innovation. 3.1 Geographical proximity In order to develop breakthrough innovation, firms are expanding the area in which they search for novel knowledge. A tactic to expand the search area is engaging in strategic alliances with geographically distant partners. Henceforth, alliances are not only formed with firms in geographical proximity but some alliances transcend borders (Wahyuni, Ghauri & Karsten, 2007). This permits the firms’ alliance partners to be geographically dispersed. The spatial distance between the firm and its partners is important to consider with regard to breakthrough innovation. This is due to the literature that demonstrates that geographical proximity holds benefits and demerits inter-firm knowledge transfer (Rosenkopf & Almeida, 2003; Boschma, 2005) and the reliance of breakthrough innovation on novel knowledge (Cheng & Chen, 2013).
is disadvantageous in seeking novel sources of knowledge, it holds benefits when it comes to interfirm knowledge transfer.
Knowledge spillovers are known to be geographically localized, thereby proximity to the knowledge source is beneficial for the knowledge seeker (Jaffe, Trajtenberg & Henderson, 1993; Baptista & Swann, 1998), thus firms in proximity of the knowledge source gain more from spillovers than distant firms. The importance of knowledge spillover makes geographical proximity to the knowledge source critical for innovative activity (Feldman, 1996). As knowledge spillovers trigger the recombination of externally found knowledge with the internal knowledge base (Zahra & George, 2002) subsequently providing new cognitive schemes leading to innovation possibilities.
New (tacit) knowledge, due to its uncodified nature, will flow more easily locally than it does over spatial distances (Baptista & Swann, 1998). The transfer of tacit knowledge differs from explicit knowledge, because it is uncodified, ambiguous interpret and less mobile, thereby making it more difficult to transfer between firms (Mascitelli, 2000; Cavusgil & Calantone, 2003; Coccia, 2007). According to Maskell & Malmberg (1999), the more tacit knowledge is the more important geographical proximity between knowledge seeker and the source becomes. “Tacit knowledge lies below the surface of conscious thought and is accumulated through a lifetime of experience, experimentation, perception, and learning by doing” (Mascitelli, 2000: p. 179).
As Nonaka and Toyoma (2003) suggest, the creation of knowledge is a process of socialization and tacit knowledge can only be internalized by direct experience (e.g. face-to-face contact). Direct experience is necessary, because the challenge is to spread something that is in the mind of individuals (Bertels. Kleinschmidt & Koen, 2011). Whenever firms are distant from each other partnering amongst employees becomes more difficult to arrange and thereby spatial distance limits this socialization process (Ambos & Ambos, 2009). Geographical proximity in turn, reduces the cost of knowledge transfer, increases the frequency of personal interaction and builds rapport between local actors thereby facilitating inter-firm knowledge transfer (Rosenkopf & Almeida, 2003). Geographical proximity therefore eases the challenge of transferring tacit knowledge, by facilitating the required direct experience and interactions necessary to understand the novel knowledge.
With the understanding that geographical proximity has both demerit and benefits, with the regard to inter-firm knowledge exchange, it is likely that it affects the probability of breakthrough innovation. Developing this type of innovation is dependent on creating novel knowledge recombination (Cheng & Chen, 2013) through the transfer of tacit knowledge (Mascitelli, 2000).
localized. Especially when it comes to tacit knowledge that is uncodified and requires direct experience to transfer. Therefore, direct experience is better facilitated when partners are in proximity of the firm (Ambos & Ambos, 2009). Thus, geographical proximity holds significant benefits with respect to breakthrough innovation, leading to the following hypothesis: H1: Geographical proximity of alliance partners is positively related to the breakthrough innovation performance of the focal firm 3.2 Alliance portfolio size Srivastava and Gnyawali (2011) propose that characteristics of the portfolio determine how firms cope with uncertainties in innovation. Alliance portfolio characteristics are often related to the configuration of the portfolio, which is discussed in different contexts e.g. portfolio diversity (e.g. Wuyts & Dutta, 2014), linkage intensity (Hoffman, 2007), network positions (Dong, McCarthy & Schoenmakers, 2017) and portfolio size (Lahiri & Narayanan, 2013). A central question in the alliance portfolio literature is whether alliance portfolio size is beneficial or not, especially in the context of innovation performance. In conclusion, the body of work on alliance portfolio shows an inverted U-shape relationship between alliance portfolio size and innovation performance of the firm (e.g. Deeds & Hill, 1996).
Even though firms benefit from an increasing number of partners, literature provides several arguments to explain why these positive returns start to diminish and even turn negative after a certain point (e.g. Bos, Faems & Noseleit, 2017).
Firstly, the contribution of all partners is not equal, and so collaborating with more partners increases the likelihood of having counterproductive partners in the alliance portfolio which results in diminishing returns (Deeds and Hill, 1996). Secondly, Rothaermel (2001) contributes that having more partners leads to inefficiencies. According to the transaction cost theory, these inefficiencies occur during managerial roles i.e. information search and monitoring partners. This indicates that the number of partners constrains the quality of partner search and monitoring. Thus, the firm may initiate a partnership with an unfit partner, or the partner will exploit the lack of monitoring and exhibit opportunistic behavior by not contributing while exploiting the firms’ knowledge. Therefore, reaching a point where the transaction cost of the collaborative relationship outweighs the contributions (Rothaermel, 2001) and the attention allocation per partner becomes less effective (Laursen & Salter, 2006; Klingebiel & Rammer, 2013). This will ultimately lead to negative returns (Deeds & Hill, 1996).
leads to less intensity in linkages (Hoffman, 2007), for example: less time is spent per partner. In the development of breakthrough innovation this is especially disadvantageous, because without sufficient understanding of external knowledge, the likelihood of creating incremental innovations rather than breakthroughs is higher (Zhou & LI, 2012). That being so, weak linkages and a lack of attention inhibit the transfer of tacit knowledge, which requires direct experience e.g. face-to-face contacts. This means understanding the external knowledge will become harder for the firm than seeking knowledge.
In consequence, a negative relationship is expected due to the effect on the attention allocation and the importance of intense collaboration to develop breakthrough innovations. Leading to the following hypothesis: H2: Alliance portfolio size is negatively related to the breakthrough innovation performance of the focal firm. 4. Methodology 4.1 Empirical setting The hypotheses were tested using data from alliances in the pharmaceutical industry (SIC: 2833-2836). This specific industry was selected for numerous reasons. Patents are of strategic importance since the pharmaceutical industry is a knowledge-intensive industry with a high inclination to patent. Therefore, the innovative output of the industry can be accurately recorded through patent data. Also, pharmaceutical companies are known to exploit strategic alliances to gain external knowledge (Dong, McCarthy & Schoenmakers, 2017). Moreover, data on the dependent variable, breakthrough innovation, as well as the control variables are provided by Dong, McCarthy & Schoenmakers (2017), whom chose the pharmaceutical industry as the empirical setting in their research. Data and sample The sample set is constructed using data provided by Dong, McCarthy & Schoenmakers (2017). The provided dataset records forward patent citations and control variables on a firm-year level that from now on will be referred to as the JPIM-dataset. In conjunction with the JPIM-dataset, an alliance sample was built using the Thomson Reuters’ Securities Data Company (SDC) Platinum database. This database records all alliances and acquisitions involving the U.S. targets since 1979 (Dong, McCarthy & Schoenmakers, 2017). The first step was to refine the SDC-dataset to alliances where at least one participant is active in the pharmaceutical industry (SIC: 2833-2836). Continuing with the exclusion of records without specification of the firms’ location because the research aims to account for geographical distance. This reduced the total count of alliances to 3,882 dyadic pharmaceutical alliances. The second step in constructing the sample was aggregating the dyadic alliances to construct a firm-year level alliance sample. The reason for this is that the data on the dependent variable is documented on firm-year level. Aggregating the dyadic alliances is done using the Committee on Uniform Security Identification Procedures (CUSIP) number, a unique 6-digit company code.
Dong et al (2017) and records 2,281 observations on breakthrough innovation performance on a firm-year level. To match the remaining alliance sample from the SDC with the JPIM-dataset, a new variable named CUSIPYEAR was created. This variable combines the CUSIP of the firm with the YEAR of the alliance. This variable is used to match the records in both datasets. Out of the 2,281 observations, geographical data was found on 1,004 observations. In conclusion, a final sample set of 1,004 observations recording the breakthrough innovation performance and average distance towards alliance partners, on a firm-year level of 368 firms was reached. 4.2 Measures Dependent variable Breakthrough innovation. As previously mentioned, the data on the dependent variable was pre-set. The dependent variable breakthrough innovation is operationalized “as the patents that receive forward citations above the 97th1 percentile of all patents within a particular class as breakthrough innovations” (Dong, McCarthy & Schoenmakers, 2017: p. 533). The authors did this following the practice of Srivastava and Gnyawali (2011) and Zheng and Yang (2015). Independent variables Geographical distance. To operationalize geographical distance the great circle distance is calculated. The great circle distance is the shortest distance between two points on a sphere following the surface of the sphere instead of a straight line. Data on the location, i.e. city, of the partnering firms in alliances is collected using the SDC-dataset. Using this data, the geographical coordinates of the locations is determined using an online tool named GPS Visualizer following the practice of McCarthy and Aalbers (2017). With this data in hand, the great circle distance is calculated in Excel by employing the Haversine-formula.
Alliance portfolio size. Following conceptualizations by e.g. Hoffman (2007) and Baum, Calabrese & Silverman (2000), the second independent variable, alliance portfolio size, is operationalized as a collective of dyadic alliances the focal firm engages in (per year) (Wassmer, 2010).
Control variables2
Cross-border alliances. Studies have demonstrated that cross-national alliances typically generate lower returns than domestic ones. Compared with domestic partners, collaboration with foreign partners requires greater investments in means of communication and transportation that supports interaction (Lavie & Miller, 2008). Thus, it is likely to influence innovative activity. Moreover, the control variable indicates whether the focal firm had a partner abroad in their alliance portfolio that year, or not.
1 For robustness check purposes, also the breakthrough innovations using the 99th, 98th, 95th, and 90th forward citation
percentiles were identified. Consistent results were obtained.
2 The control variables R&D intensity, Financial leverage, Prior performance and Firm size are operationalized and determined
R&D intensity. Firms with a higher R&D intensity are more likely to invest in innovative activities. The variable is operationalized by dividing the R&D expenditure by the total sales. Financial leverage. This is measured by long-term debt divided by total assets. The financial leverage is argued to influence the attitude of the firm regarding risk in innovative activities. Prior performance. Performance is likely to influence the search for innovation. Therefore, it is controlled for by measuring operating income before depreciation over total assets.
Firm size. This is measured by the natural logarithm of total sales as firm size is influential on the assimilation of external knowledge.
Year dummies. Finally, the fixed effect of time is controlled for by including year dummies in all analyses.
4.3 Analysis strategy
Table 1 Sample distribution Table 2 Descriptive statistics and correlations Breakthrough Innovation Obs. % <1 367 71,54% 1-5 102 19,88% 6-10 20 3,90% 11-20 10 1,95% 21-30 5 0,97% 30> 9 1,75% Total 513
Mean SD Min Max (1) (2) (3) (4) (5) (6) (7)
(1) Breakthrough innovation 1.96 6.12 0 53 1.000
(2) Geographical distance 3719.5 3160.2 0 18.611 -0.0839 1.000
(3) Alliance portfolio size 1.73 1.60 1 15 0.2241 -0.0141 1.000
(4) Cross-border participant .56 .50 0 1 -0.0470 0.5421 0.2625 1.000
(5) R&D intensity 7.26 66.43 0 1639 -0.0438 -0.0476 -0.0803 -0.0778 1.000
(6) Financial leverage .11 .18 0 2.43 0.0765 0.0814 0.0766 0.0811 -0.0693 1.000
(7) Prior performance -.18 .69 -17 .71 0.2273 0.0319 0.2580 0.1820 -0.1717 -0.0239 1.000
5. Results
Table 2 shows descriptive statistics and correlations while Table 3 displays the results of the negative binomial regression analyses. Due to the correlation between prior performance, and firm size being over 0.70, the variance inflation factor (VIF) is estimated to evaluate the threat of collinearity. The maximum VIF is 2.54, which is below the recommended threshold of 10. Model 1 tests the effects of the control variables and Model 2-4 tests the hypotheses. Regarding the control variables, Model 1 finds that Firm size is positively and significantly related to breakthrough innovation (95% CI = [0.24 0.46]). Another control variable, Cross-border participant, is also significantly related to breakthrough innovation, however negatively (95% CI = [-1.24 -0.25]).
Additionally, Model 2 tests hypothesis 1, displaying the relationship between breakthrough innovation and geographical distance. The results show a significant negative correlation between geographical distance and breakthrough innovation indicating an increase of 1000km costs 0,8 patents. Since, the control variable, Cross-border participant, is strongly correlated to the dependent variable, model 3 excludes the control variable. This yields the same results, but increases the significance level of geographical distance to 99%, thus support for hypothesis 1 is found.
Finally, Model 4 tests the second hypothesis, the relationship of alliance portfolio size and breakthrough innovation. The hypothesis predicts a negative relationship. In contrary, the results show a positive significant effect. Thus, no support for hypothesis 2 is found.
For robustness check, different percentiles (90th and 99th) are used instead of the 97th percentile
Table 3 Results negative binomial regression analysis
Model 1 Model 2 Model 3 Model 4 Model 5
Geographical distance -.000* (.000) -.000*** (.000) -.000 (.000) Alliance portfolio size .134** (.057) .127** (.057) Cross-border participant -.745*** (.251) -.519* (.28) -.827*** (.247) -.640** (.280) R&D intensity .026 (.019) .021 (.017) .026 (.019) .021 (.017) .017 (.016) Financial leverage -.620 (.983) -.408 (.984) -.328 (.990) -.507 (.962) -.338 (.966) Prior performance .067 (.676) .143 (.685) .099 (.690) .061 (.665) .006 (.672) Firm size .352*** (.055) .336*** (.056) .329*** (.056) .329*** (.054) .318*** (.054)
Year dummies Yes Yes Yes Yes Yes
Wald Chi-square 107.51 110.16 106.81 114.13 116.06
N 481 481 481 481 481
6. Discussion Theoretical
Although considerable research has been done on the configuration of strategic alliances, e.g. the influence alliance portfolio size has on the development of innovation, two issues arise. First, researches considering the effect of alliance portfolio size on innovation mostly focuses on the implications for innovation in general, not specifying the difference antecedents of incremental and breakthrough innovation. Secondly, studies have yet to examine the effect of geographical characteristics of alliance partners on breakthrough innovation.
The basic thesis of this study is that breakthrough innovation development differs from other sorts of innovations. The thesis factors that geographical characteristics and alliance portfolio size will have different effects in regards to breakthrough innovations.
Studies now show an inverted U-shape relationship between alliance portfolio size and innovation (Bos, Faems & Noseleit, 2017). The general consensus is that an increasing number of alliance partners are beneficial to a certain extent. The tipping point is reached when transaction costs outweigh the benefits of collaborating causing the efficacy of attention allocation per partner to decrease. Bounded rationality in turn, negatively affects the search of partners and monitoring. If this is the case, the firm initiating partnerships with unfit partners will result in not having a partner that contributes what is expected. Or, the partner will exhibit opportunistic behavior by not contributing while exploiting the firms’ knowledge because the monitoring of this behavior falls short. Regarding the effect of geographical characteristics on innovation, the literature focuses on the interfirm knowledge transfer, which is the basis for developing innovation in strategic alliances. Studies show an enigma that revolves around the search and transfer of knowledge. Geographical proximity reduces the cost of knowledge transfer, increases the frequency of personal interaction, builds rapport between local actors, all resulting in the facilitation of interfirm knowledge transfer (Rosenkopf & Almeida, 2003). Knowledge transfer is easier when firms are co-located because the transfer of knowledge, in some cases, demands direct contact e.g. tacit knowledge. However, geographical proximity reduces the likelihood of finding novel knowledge. Since geographic context is relatively stable (Rosenkopf & Almeida, 2003), novel knowledge is found far from the firms’ boundaries because knowledge tends to be location specific (Boschma, 2005)
outperform those with foreign partners. No support has been found for the predicted negative effect of alliance portfolio size on breakthrough innovation.
The results show a significant positive effect of alliance portfolio size on breakthrough innovation. Literature offers several explanations for the difference between the expected relationship between alliance portfolio size and breakthrough innovation. First, next to novel knowledge, breakthrough innovation draws benefits form a diverse knowledge pool (Sampson, 2007; Wuyts & Dutta, 2014). An increasing number of alliance partners can lead to an increasing diversity of the alliance portfolio (Lahiri & Narayanan, 2013). Secondly, the alliance portfolio represents a source of alliance experience (Wassmer, 2010). Literature has shown that the alliance portfolio, i.e. the number of alliances the firm engages in, is influential on the ability of the firm to harness the benefits of alliances. Firms learn from previous experiences and develop alliance management capabilities, e.g. configuration and control, to gain more benefit from future alliances (Anand & Khanna, 2000). Lastly, there is a possibility that the results are affected by the sample used in this study. Table 2 shows the maximum number of partners in the sample is 15. Bos, Faems & Noseleit (2017) find that diminishing returns with relatively large alliance portfolios occur at 26 partners or more and negative when the number of partners is over 40. Managerial
Third, the research does not control for all other dimensions of distance, like cognitive and organizational distance, and only imperfectly considers cultural distance by considering borders. Boschma (2005) shows that these strongly overlap with geographical distance and constitute predictors of innovation.
Fourth, the alliance portfolio size used in the study is limited (max. 15 partners) to definitely exclude a possible inverted U-shape. Future research should consider the effect of larger alliance portfolios on breakthrough innovations.
Finally, this study does not account for the moderating effect of alliance management capabilities. Literature indicates a moderating effect of alliance management on how firms benefit from strategic alliances. Therefore, future research should examine the moderating effect of alliance management capabilities. Moreover, these shortcomings give openings for further future research. For example, according to Mowery, Oxley & Silverman (1996) equity-based governance structures are better for the transfer of tacit knowledge. Future research could address whether equity-based alliance governance can be a substitute for geographical proximity. Furthermore, future research could focus on examining if alliance experience affects the relationship between alliance portfolio size and breakthrough innovation as firms with more experience are known to reap more benefits from collaborating.
7. Conclusion
The intent of this study is to research how strategic alliances can be best configured to harness breakthrough innovations. To improve the understanding of the effect of strategic alliances on breakthrough innovations, two alliance characteristics are operationalized i.e. geographical proximity and alliance portfolio size.
The findings show that geographical proximity benefits the development of breakthrough innovations. Furthermore, the effect is particularly strong when pertaining to domestic alliances. Surprisingly, the findings contradict a predicted negative effect of alliance portfolio size. This demonstrates a positive relationship regarding breakthrough innovation.
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Appendix A. Robustness checks
Table 4. Robustness check: dependent variable 99th percentile
Model 1 Model 2 Model 3 Model 4 Model 5
Geographical distance -.000 (.000) -.000*** (.000) -.000 (.000) Alliance portfolio size .096** (.047) .089* (.046) Cross-border participant -.687*** (.227) -.523** (.254) -.760*** (.227) -.627* (.255) R&D intensity .013 (.008) .012 (.008) 0.013 (.009) .012 (.008) .012 (.008) Financial leverage -.300 (.890) -.107 (.890) .017 (.888) -.234 (.881) -.090 (.883) Prior performance -.368 (.650) -.351 (.656) -.496 (.642) -.439 (.639) -.418 (.644) Firm size .419*** (.052) .412*** (.052) .408*** (.052) .402*** (.051) .397*** (.052)
Year dummies Yes Yes Yes Yes Yes
Wald Chi-square 137.39 139.32 135.03 142.17 143.41
N 481 481 481 481 481
*p < 0.1 **p < 0.05 ***p <0.01; The dependent variable is breakthrough innovation in the 97th percentile.
Table 5. Robustness check dependent variable 90th percentile
Model 1 Model 2 Model 3 Model 4 Model 5
Geographical distance -.000 (.000) -.000*** (.000) -.000 (.000) Alliance portfolio size .175*** (.059) .168*** (.059) Cross-border participant -.544** (.223) -.334 (.265) -.653*** (.218) -.511 (.259) R&D intensity .009 (.012) .007 (.011) 0.008 (.012) .007 (.010) .006 (.009) Financial leverage -1.19 (.976) -1.03 (.976) -1.03 (.977) -1.02 (.952) -.919 (.954) Prior performance -.349 (.608) -.219 (.610) -.197 (.617) -.495 (.602) -.402 (.605) Firm size .379*** (.051) .365*** (.051) .358*** (.051) .356*** (.049) .348*** (.050)
Year dummies Yes Yes Yes Yes Yes
Wald Chi-square 130.2 132.11 130.53 140.95 141.99
N 481 481 481 481 481
