Knowledge and incremental innovation in M&As:
the role of geographical proximity
Name: Giulia Bertoldero Student number: 11439742
Date of submission: January 26, 2018
MSc. in Business Administration – International Management Track University of Amsterdam
Academic year 2017/2018 First supervisor: Dr. Lori DiVito
2 Statement of originality
This document is written by Giulia Bertoldero who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
3 Table of contents
Abstract ... 4
1. Introduction ... 5
2. Literature Review ... 8
2.1 Knowledge transfer in M&As ... 8
2.2 M&As and Innovation Performance ... 10
2.3 Geographical proximity ... 14
3. Theoretical Framework ... 18
3.1 The effect of knowledge relatedness on incremental innovation performance ... 18
3.2 The moderating effect of geographical proximity ... 20
4. Methodology ... 23
4.1 Research design ... 23
4.2 Data frame ... 23
4.3 Variables ... 24
5. Results ... 31
5.1 Descriptive statistics and correlation ... 31
5.2 Assumptions checking ... 33
5.3 Hypotheses testing ... 35
6. Discussion ... 39
6.1 Implications for theory and research ... 39
6.2 Managerial implication ... 44
6.3 Implications for policy makers ... 44
6.4 Limitations and suggestions for future research ... 45
7. Conclusions ... 47
8. Acknowledgments ... 49
9. References ... 50
4 Abstract
Despite the vast research on M&As and subsequent innovation performance, little is known about whether the knowledge relatedness between acquirer and target leads to post-M&A incremental innovation performance, and also whether geographical proximity can have a role in this relationship. In this study it is hypothesized that knowledge unrelatedness negatively influences incremental innovation performance, while knowledge relatedness yields the opposite result. It is also hypothesized that geographical proximity positively moderates the main relationship between the degree of knowledge relatedness and innovative performance. Geographical proximity has been measured the distance between each party’s headquarters and subsidiaries, resulting in two moderators. In order to test the hypotheses, a sample of 102 M&As has been selected and two hierarchical multiple regressions were performed, one for each moderator. The empirical results show that there is no significance to be found among the aforementioned relationships. Nevertheless, a novel aspect in this research is the use of a new proxy for incremental innovation performance and finally in depth-insights on incremental innovation performance in the M&A context.
5 1. Introduction
Android, Titan Aerospace, Nest Labs, Siri, are just a few examples of famous operating systems and companies whose ownership fell on the respectively legendary hands of Google and Apple (Jo, Park, & Kang, 2016) and are none other than clear examples of technological mergers and acquisitions (M&As). Firms like the two just mentioned giants pursue technological M&As to boost their R&D potential by absorbing the knowledge of the acquired firm and generating innovation, which could not have been pursued by applying only their own resources (Ahuja & Katila, 2001).
A great deal of empirical research has been conducted on M&As and their ability to create innovation. Nonetheless, research points out that so far insufficient attention has been given to technological M&As (Jo et al., 2016) and shows it has been lacking study that would investigate the role of geographical distance in the M&As domain (Shi, Lee, & Whinston, 2015), and of study that include incremental innovation in this context, as research on this type of innovation seems to be scarce as well (Forés & Camisón, 2016; Martens, Matthyssens, & Vandenbempt, 2012). Also, the literature leaves us with shortcomings on the similarity of technology possessed by acquirer and target firm, enhancing incremental innovation performance. (Agarwal & Helfat, 2009; Makri, Hitt, & Lane, 2010; Sheng & Chien, 2016). Another aspect that has been neglected in the literature has been a lack of diversity in measuring spatial distance, commonly based on the geographical co-ordinates of the companies’ headquarters (Böckerman & Lehto, 2006; Cai, Tian, & Xia, 2016; Ellwanger & Boschma, 2015; Ensign et al., 2013; Shi et al., 2015; Wee et al., 2016). Thus, a research gap is found on understanding the significance of geographical proximity in the technological M&As innovation context. To address this research gap, the present study will seek to answer the following question: Does the relatedness of knowledge, between the acquirer and target firm
6 in a M&A, lead to subsequent incremental innovation performance if moderated by geographical proximity?
This study focuses on the moderating effect of geographical proximity affecting the innovation performance originating from M&As. I use a quantitative approach to analyze and establish the relationships I argue in my paper. In order to examine these relationships, literature has shown the effect of knowledge relatedness between acquirer and target firm on post-M&A innovation performance (Cloodt, Hagedoorn, & Van Kranenburg, 2006; Han, Jo, & Kang, 2016; Makri et al., 2010; Sears & Hoetker, 2014) and specifically there is also evidence of flows of incremental performance (Agarwal & Helfat, 2009; Makri et al., 2010; Sheng & Chien, 2016).
Nevertheless, research has also stressed the importance of geographical proximity in the M&A context and its positive relation to innovative performance (Boschma, 2005; Cai, Tian, & Xia, 2016; Shi et al., 2015; Wee et al., 2016). Based on the reviewed literature, despite mixed results, I argue that the relationship between relatedness of knowledge and incremental innovative performance will be positive. I also expect geographical proximity to positively moderate the main relationship studied.
My study aims to contribute to the literature in the following ways: firstly, I strive to add to the innovation literature by explaining the relation between the relatedness of knowledge experienced during the acquisition process and the post-acquisition incremental innovation performance, which is area in the M&A context that is currently underdeveloped; secondly, I intend to provide further clarity on the role played by geographical proximity as moderator to the relationship between knowledge relatedness and innovation performance; thirdly, I also attempt to provide relevant managerial implications, by showing the importance of geographical proximity for acquirers striving for incremental innovation performance.
7 In the next sections, I will present the literature review containing the most relevant literature to the topics explored in my research. The theoretical framework will follow, presenting a conceptual model, which provides the theoretical base for answering my research question.Next, in the methodology section, the research design is explained. Then, results of the study are presented. In the final chapters, the discussion and conclusions are presented, comprising limitations and implications for managers and policy makers.
8 2. Literature Review
In the following section, I will present the theoretical framework. Firstly, I will briefly consider the characteristics of knowledge transferability and I will investigate how it takes place in M&As. Secondly, I will focus on the innovation performance as an outcome of M&As. Thirdly, I will proceed by describing the role of geographical proximity affecting the relationship between M&As and innovation performance. Finally, I will conclude with the research gap and research question.
2.1 Knowledge transfer in M&As
For the last couple of decades, domestic and cross-border M&As growth has intensified in numerous countries and industries (Frésard, Hege, & Phillips, 2017). In light of the expanding literature on the reasons why firms expand their boundaries internationally and the locations in which they acquire assets, the popularity of M&As persists as a solution that enables the combination of firms to efficiently transfer knowledge (Ahammad et al., 2016). Literature upholds the facilitative role of M&As in penetrating new markets, obtaining connections to a distribution channel, acquiring market power (Ahuja & Katila, 2001), but most importantly accessing external knowledge and engaging in co-development of new combined resources (Hsiao et al., 2017).
Evidence in fact suggests that certain strategic actions, such as M&As, come forth as more advisable than others in effectively redeploying firm-specific capabilities learned from another firm (Hamel, 1991; Peng, 2001). It is inferred to be essential for firms to possess the ability to acquire and transfer information in order to operate in an ever-changing global environment (Easterby-Smith, Lyles, & Tsang, 2008) and especially owning proprietary knowledge supposedly guarantees a firm withunique assetsleading to a competitive advantage
9 (Barney, 1991; Dunning, 2000; Grant, 1996; Meschi & Metais, 2006; Kogut & Zander, 1992; Peng, 2001; Peng et al., 2009; Rugman et al., 2011; Szulanski, 1996).
Technological M&As have been studied extensively from a resource-based view (RBV) of the firm, as the M&As’ goal is to boost the competitive position of one or both actors by transferring the respective knowledge and capabilities from one party to the other (Qi Ai & Hui Tan, 2017). Literature conveys that knowledge is vital to a firm’s success and the means to
develop new intangible, knowledge-based resources and obtain exceptional performance (if these core competences are idiosyncratic and non-substitutable) (Ahammad et al., 2016).
The peculiarity of core resources can be also argued from a Transaction Cost Economics (TCE) perspective; TCE theorists would discuss that the acquisition choice is the appropriate control mode to be used when dealing with high asset specificity (Brouthers, Brouthers, & Werner, 2003; Erramilli & Rao, 1993; Makino & Neupert, 2000) arising from the tradeoff of achieving the “perceived benefits of sharing risks and capital outlays on the one hand”, or bearing the “costs of a loss of control associated with a reduced (or no) ownership on the other” (Dunning, 2015, p.467).
An acquisition of another firm can in fact be examined as a union of knowledge that not only is likely to enlarge the acquirer’s knowledge base, but can also “potentially increase its innovation output by providing economies of scale and scope in research and by enhancing the acquirer’s potential for inventive recombination” (Ahuja & Katila, 2001, p. 199). The possession of knowledge therefore vitally inheres in an organization’s success, and many M&As are driven by the will to obtain or leverage new knowledge (Spoor & Chu, 2017).
Despite knowledge being a highly valuable resource of competitive advantage, acquiring this input through M&As remains still quite challenging to undertake. As the research on this area has shown (Ahammad et al., 2016; Vaara et al., 2014) acquisitions are the evidence of a case that implies benefits as well as disadvantages, which will be considered in the following
10 chapter, focusing on the knowledge potential in the M&A context and on its determinants for company performance (Foss & Pedersen, 2004).
2.2 M&As and Innovation Performance
Through the years, M&As have become more and more a valuable economic activity but, despite the remarkable amount of M&As, they do not always create the expected value for the candidate firms (Cai et al., 2016). Facilitating knowledge learning and providing the ability to redeploy the absorbed resources in the market seem not to be enough for M&As to always succeed (Gubbi et al., 2010). These inadequate achievements may be due to the acquiring firm’s inability to combine and profit from the target firm’s capabilities and/or due to the little amount and value of the transferred capabilities (Sears & Hoetker, 2014).
Notwithstanding the flourishing and rich literature offering theoretical support on the role of M&As and their outcomes, innovation literature has received only scant attention (Sammarra & Biggiero, 2008), presenting mixed results when it comes to M&As and their success. Contrarily to authors believing that M&As represent an efficient and valuable way to deploy resources, some argue against this view contending that M&As do not contribute to improvements in performance, but rather destroy value (Kiymaz & Baker, 2008).
M&As have become popular strategy leading to expand a firm’s knowledge base, acquire larger scale and scope benefits, but not all M&As are perfect conductors of innovation. Non-technological M&As for instance, do not lead to innovation (Cloodt et al., 2006). As Ahuja and Katila (2001) definition suggests, technological M&As are such if the acquired firm had at least one patent granted in the five-year period preceding the M&A (Ahuja & Katila, 2001).
By benefitting the acquirer with new collected technological inputs, as studies demonstrate, technological M&A have the ability to achieve post-deal innovation (Jo et al., 2016). Thus, firms engaging in such transactions acquire new knowledge that, if successfully
11 merged with the existing one, can promote the creation of change and innovation. (Ahuja & Katila, 2001; King et al., 2004).
Technological M&As, however, can also result in disrupted routines within the organization, especially those routines closely related to the innovation sphere, the technological subsystem of the firm, producing adverse effects on the innovative performance of the acquiring firm ( Ahuja & Katila, 2001; Cloodt et al., 2006).
Several studies have investigated on the outcomes deriving from the different characteristics of knowledge of the acquired and acquiring firms and their effect on innovation (Ahuja & Katila, 2001; Cloodt et al., 2006; Datta & Roumani, 2015). The role of the knowledge base size is suggested to have an effect on innovation performance (Desyllas & Hughes, 2010), as well as the breadth and depth of the acquiring firm’s knowledge base. Not only is the acquirer’s knowledge critical, but also the size and uniqueness of the target firm’s knowledge affects innovation performance of an acquisition (Datta & Roumani, 2015).
More scholars claim that one of the major factors fostering innovation is the integration and combination of an array of heterogeneous competences (Ahuja & Katila, 2001; Sammarra & Biggiero, 2008). Relatively assorted knowledge coming from the acquirer and the target firm is shown to enrich the acquiring firm’s knowledge base, leading to learning possibilities and mostly promoting post-M&As successful innovative performance (Ahuja & Katila, 2001; Kogut & Zander, 1992). Ahuja and Katila (2001) define the knowledge base as the “distinct elements of knowledge with which the firm has revealed a relationship” (Ahuja & Katila, 2001, p. 202).
Scholars suggest that in order to create more innovation, it is assumed to be beneficial for the acquirer to earn a large amount of new knowledge from the target firm, ergo when the knowledge overlap -knowledge base common to both parties- is low (Sears & Hoetker, 2014). In discordance with the aforementioned literature, other studies suggest that the diversity of
12 knowledge, despite being valuable, may actually obstruct the knowledge transfer, making it difficult for the acquiring party to absorb all the information, negatively influencing the development of innovation (Jo et al., 2016).
Further literature shows the various consequences of overlapped and non-overlapped knowledge in the M&A context, utilizing post-acquisition patenting as a measure for innovation performance (Ahuja & Katila, 2001; Cloodt et al., 2006; Han et al., 2016; Makri et al., 2010; Sears & Hoetker, 2014). Recent research suggest that innovation performance is directly associated with the number of patents a firm has produced (Ahuja, 2000; Owen-Smith & Powell, 2004) “determined by measuring the difference in the number of US patent applications which resulted in granted patents before and after the M&A for the acquiring firm” (Jo et al., 2016, p. 62). It has been shown that in order to facilitate learning and promote innovation as a post-M&A outcome, firms should combine moderately related knowledge bases, refraining from too unrelated or too closely related ones (Cloodt et al., 2006).
According to Makri et al. (2010) in order to assess the knowledge overlap between acquirer and target firm, one can measure the technology similarity by calculating the number of patents applied for by the acquirer and the target in the same patent class, and then multiplying the result by the total number of patents of acquiring firm in all common classes divided by its total patents (Miozzo, DiVito, & Desyllas, 2016).
Further, elucidating findings suggest that knowledge transfer in M&As between firms with related knowledge bases is positively related to incremental innovation1 performance, defined by Forés & Camisón, (2016) as “the refinement and reinforcement of existing products, processes, technologies, organizational structure and methods” (Forés & Camisón, 2016, p.
1 Incremental innovations are minor improvements or simple adjustments in current technology (Dewar & Dutton, 1986)
13 834)
.
While affinities in knowledge facilitate incremental renewal, complementarities would, on the contrary, promote disrupted strategic transformations to the firm (Agarwal & Helfat, 2009; Makri et al., 2010).This study focuses on incremental innovation because it has been shown that performance levels result higher when adopting incremental innovations, as they are perceived to be less risky and whose success can be easily foreseeable on the marketplace, by virtue of their main characteristic of being technologically alike to already existing products or services (Oerlemans, Knoben, & Pretorius, 2013).
Overall, the literature gives us mixed results in terms of knowledge relatedness promoting incremental innovation. Recent studies show that deploying valuable knowledge as well as collecting new knowledge to guarantee permanence of information within the firm stimulates knowledge exploitation and the sustenance of incremental innovation (Cepeda-Carrion, Cegarra-Navarro, & Jimenez-Jimenez, 2012). Contrarily, according to Sheng and Chien (2016), similar knowledge and the ability to improve internal capabilities consistently with the firm’s existing knowledge base enhances the firms’ capacity to achieve incremental innovation (Sheng & Chien, 2016).
As incremental innovations represent some degree of exploitation of established knowledge, a reason for firms to engage in M&As is related to the easiness of exploiting the existing technologies and opportunities in the market. It is for such reason that carrying out minor developments could lead to multiple benefits, as combined efforts and resources provide the ability to leverage at best a firm’s internal capabilities (Duane Ireland & Webb, 2007).
In order to better exploit the possessed capabilities, it is crucial for an M&A parties to be collaborative in boosting the high value stemming out of the transaction. For this reason, acquirer and target locations are believed to play a substantial role for nontransferable information, that can frequently be accessed when spatially proximate (Cai et al., 2015).
14 M&A scholars, in fact, advance that geographic proximity has an effect on organizational learning (Ensign, Chreim, Persaud, & Lin, 2013). Despite the existence of various dimensions of proximity (geographic, cognitive and organizational) acquirer-target learning may be positively boosted by geographical proximity, which can foster high levels of knowledge transfer and hence possibly helping create innovation (Boschma, 2005; Shi et al., 2015).
In the next chapter, I will elucidate the main reasoning behind the influence of geographical proximity for the propagation of knowledge.
2.3 Geographical proximity
Geographic or spatial proximity is defined as “the closeness of financial transactions, physical locations” (Shi et al., 2015, p. 6) and, generally, it has been studied using the geographical coordinates of the firms’ headquarters, which made the transactions ‘proximate’ if the respective locations were situated within a 100 km radius from each other (Ellwanger & Boschma, 2015; Wee et al., 2016). The geographic location of a firm represents an important function in the M&A context because acquisitions implicate a large number of soft-information2 transfers (Cai et al., 2016). However, while codifiable information can be easily transferred because easy to communicate, in the case of tacit knowledge and soft-information, transferability is more difficult (Grant, 1996).
In the following section, I will present what the literature has provided on its moderating effect on M&As and their innovation performance.
Geographical proximity, M&As and innovation
In the M&As context, spatial closeness increases the probability of acquisition between two countries. In quite a few industries firms have shown preferences for spatially close acquisition
15 target firms, demonstrating how geographical proximity is positively related to M&A likelihood; this is supported by research showing that transfer of information -within M&As arena - is indeed subject to geographical distance (Shi et al., 2015).
Some scholars advance issues regarding the drawbacks of proximity, which have been ofttimes neglected (Boschma, 2005). Likewise, others stress the not always beneficial role of geographical proximity, asserting that it may just occasionally be an incentive for innovative performance (Cassi & Plunket, 2015). Interactive learning can in fact be hampered by the problem of lock-in, disabling a firm to pursue innovation which instead remains inward looking not acquiring enough experience to implement new knowledge (Boschma, 2005).
Nevertheless, literature on the topic explains that if firms are ‘close’ in various ways (geographically or cognitively), they would incur less information asymmetry, thus achieving higher value (Wee et al., 2016). Spatial closeness between firms increases the likelihood for them to have higher levels of interaction, which is of central importance to the transfer of tacit knowledge. Despite the recent advances in technology for better communication, actual physical proximity is still very relevant, implying that greater spatial distance may indeed deter knowledge transfer (Ensign et al., 2013). Thus, the greater the sources of knowledge in one area, the more considerable the possible benefits for each firm located there.
As Breschi and Lissoni (2001b) advance from a study on high-tech clusters, innovational opportunities are believed to be a locational factor and, from a learning perspective, knowledge externalities are proved to be bounded in space, making firms agglomerating in a specific location more likely to absorb knowledge than firms located elsewhere (Breschi & Lissoni, 2001b).
Nevertheless, the role of geographical proximity cannot be sufficient to advance knowledge transferability without cognitive proximity (Boschma, 2005), which is an equivalent for the aforementioned knowledge relatedness (Cloodt et al., 2006; Han et al., 2016;
16 Makri et al., 2010; Sears & Hoetker, 2014). Geographical proximity combined with some level of cognitive proximity is sufficient for interactive learning to take place, enhancing the knowledge transfer and the creation of innovation (Boschma, 2005; Mattes, 2012).
Other studies deepen the topic. Those activities related to the creation and use of knowledge sources for the enhancement of innovation foster especially incremental innovation (rather than radical), through the utilization of knowledge in the refinement of products consistently with the established organization’s processes and routines (Agarwal & Helfat, 2009; Sheng & Chien, 2016). Economic geography research findings also suggest that since technological change is characterized by a dynamic knowledge learning, geographically clustered firms will tend to have favorable innovative circumstances, as knowledge learning inputs are more likely to be offered to proximate firms (Breschi & Lissoni, 2001). Despite the challenges that learning entails, heavily relying on proximity enables firms to effectively transfer knowledge, especially in the case of incremental innovation, which greatly implies tacit knowledge, the most important element in knowledge bases for the development of new solutions (Mattes, 2012).
Therefore, in the knowledge exchange process, overlapped knowledge between acquirer and target firm would consequently produce innovation performance and may be positively moderated by the presence of geographical proximity between the two firms.
In conclusion, from what we know on M&As, their ability to innovate and the moderating effect of geographical proximity, there are still many question that still need to be addressed and areas that need further research. The literature on the topic has been lacking of a study that would investigate the role of spatial distance in the M&As domain (Shi et al., 2015) and of a study that includes incremental innovation in this context, as research on this type of innovation seems to be scarce (Forés & Camisón, 2016; Martens et al., 2012).
17 The following chapter will provide the theoretical framework, where I will explain in depth the relationships between the variables that constitute my research.
18 3. Theoretical Framework
Based on the literature reviewed, I argue that the relatedness of knowledge in M&As positively affects subsequent incremental innovation performance. I also propose that this relationship is moderated by geographical proximity. The following part of my paper moves on to describe in greater detail the definitions of the constructs in my research questions. Here I provide a motivation for my speculations and present the expected relationships among constructs by using a set of hypotheses.
3.1 The effect of knowledge relatedness on incremental innovation performance
In the previous sections we understood that an acquiring firm possessing similar knowledge to the target firm can be innovatively profitable (Ahuja & Katila, 2001) but the literature also suggests the positive effect of acquisition ofassorted external knowledge, as the new acquired information could also contribute to post-M&A innovative performance (Cloodt et al., 2006). In line with Ahuja & Katila (2011) the integration of the two knowledge bases results facilitated if they present similar elements and cognitive structures (Ahuja & Katila, 2001), as the ability to use the combined and similar resourced is enhanced (Kogut and Zander,1992; Grant, 1996) and the learning process becomes more intelligible (Cohen & Levinthal, 1990). Moreover, significant existing knowledge can lead to a better assimilation and comprehension of new technology leading to the creation of new ideas and the development of new products (Tsai, 2001). Nevertheless, too similar knowledge bases contribute to little subsequent innovation performance (Ahuja & Katila, 2001).
Attention should be also drawn on the opposite situation, where there is unrelatedness of knowledge. Dissimilarity and lack of coherence in terms of existing nontransferable information among the firms can create conflicts because of the dissonant knowledge structures, and ultimately limit creation of new knowledge (Cepeda-Carrion et al., 2012).
19 It should be noted that both acquisitions that are associated to high levels of relatedness or high levels of unrelatedness of knowledge, show inferior innovation outputs (Ahuja & Katila, 2001). Thus, we could assert that generally the degree of relatedness has to be moderate, in order for the acquisition to be beneficial to the acquiring firm providing the most significant positive impact on the acquiring firm’s subsequent innovation output (Ahuja & Katila, 2001).
That being said for positive subsequent innovation output, we can observe some peculiarities on the degree of relatedness of knowledge if we take into consideration specifically incremental innovation (Makri et al., 2010).
Some scholars make a distinction between incremental and radical innovations3 on the basis of the “degree of novel technological process content embodied in the innovation”, hence the degree of new information represented by the innovation (Dewar & Dutton, 1986, p. 1423). While on one hand explorative learning, or else an increase in invention productivity through patents in new technology areas most likely results from the union of two sets of different knowledge, on the other hand an exploitative learning is enhanced by the combination technology coming from similar areas (Makri et al., 2010).
Consequently, to produce incremental innovation, rather than radical, and therefore exploit an already existing innovation, the acquirer is encouraged to merge its existing knowledge to a target firm that possesses a similar knowledge base. These intents aim at working persistently on an already known invention, rather than seeking for radically new one. Moreover, in the case of patents, ‘backward’ citations4 are an indicator of the extent of reliance on previous technology (Jaffe & de Rassenfosse, 2017), so we can presume that the more
3 See note 1
4 Backward citations are those citations that appear in a patent (Jaffe et al. 2017). Backward citations
are the element which will help me recognize an incremental innovation, since it represents and improvement and the development of an already existing innovation.
20 similar the knowledge bases between acquirer and target firm, the easier their understanding and therefore their operational application leading to incremental innovation (Cohen and Levinthal, 1990; Makri et al., 2010).
Taking into consideration the scrutinized literature, I speculate that an acquiring firm that possesses a knowledge base closely related to the target firm’s knowledge base, will lead to subsequent incremental innovation performance, while an acquiring firm that possesses a knowledge base closely unrelated to the target firm’s knowledge base will not lead to subsequent incremental innovation performance. I therefore hypothesize:
H1: Relatedness of knowledge bases between acquirer and target firm is positively related to post-M&A incremental innovation performance.
H2: Unrelatedness of knowledge bases between acquirer and target firm is negatively related to post-M&A incremental innovation performance.
3.2 The moderating effect of geographical proximity
In technological M&As when it comes to tacit knowledge transfer, its transmission requires certain mechanisms -such as face-to-face contact- that demand some degree of physical proximity. It is made evident that flows of technology diminish with geographical distance, thus the capability of firm to absorb knowledge is also weaker if its source is located far away (Böckerman & Lehto, 2006). Despite the great deal of ICT5, the literature is consistent on stressing that barriers due to geographical distance are hard to suppress (Ensign et al. 2013). For example, an acquirer has more difficulties in evaluating the value of a target firm from a remote location, which is misleading the estimation of the optimal candidate for an
21 innovatively successful M&A (Böckerman & Letho, 2006). Generally, proximate firms are believed to exchange more knowledge (especially tacit) because of the easily accessible interactions (Weterings, 2006) that allow them to “mutually discover information-based synergies” (Cai et al., p. 689) and more easily communicate soft information (Ensign et al. 2013). As a matter of fact, easier interactions occur because proximate firms tend to share a common language -relatedness of knowledge bases between acquirer and target firm- which fosters innovation efforts (Breschi & Lissoni, 2001a). Since information spillovers tend to arise where the flow of knowledge is geographically localized (Audretsch & Feldman, 1996),
innovation activities are in fact localized where the information-sharing is greater. Geographical proximity therefore intensifies the tendency for close firms to perform innovatively (Balland, Boschma, & Frenken, 2015).
As geographic proximity has generally been studied using the geographical coordinates of the firms’ headquarters (Ellwanger & Boschma, 2015; Wee et al., 2016) I expect the distance between the two respective headquarters to be relevant in influencing H1 and H2. I also infer geographic distance to play an important role relative to the location of a firm’s subsidiaries in certain area, such a technology clusters for instance. The physical location, being the headquarters’ or the subsidiaries’ site, it is assumed to advance transfer of knowledge and sustain innovation (Ensign et al., 2013).
I therefore presume that closeness means more knowledge transferring, so less information asymmetry, hence higher likelihood that geographical proximity would foster interactive learning and therefore creation of innovation from common knowledge sharing. Hence, I hypothesize:
H3: The geographical proximity between the acquirer’s headquarters and the target firm’s headquarters positively influences the relationship between the relatedness of
22 Related
HQ Acquirer – HQ Target
HQ Target – Subsidiary Acquirer
knowledge bases of the acquirer and target firm and the post-M&A incremental innovation performance.
H4: The geographical proximity between the target’s headquarters and the acquirer’s subsidiary in the target location positively influences the relationship between the relatedness of knowledge bases of the acquirer and target firm and the post-M&A incremental innovation performance.
The model
Unrelated
H1+ / H2-
H3+ / H4 + KNOWLEDGE BETWEEN
ACQUIRER AND TARGET
GEOGRAPHICAL PROXIMITY
INCREMENTAL INNOVATION PERFORMANCE
23 4. Methodology
In this section I firstly present the research design, then I provide an overview of the data frame and finally I proceed explaining every single variable studied and its use.
4.1 Research design
In order to investigate the relationships I argue in my paper, I used a quantitative approach. This method is the most suitable for the aim of my research, which is examining the significance of geographical proximity in the technological M&As innovation context (Sekaran & Bougie, 2016). The study represents a deductive approach, as I have developed my hypotheses based on existing theory, and afterwards I have designed a study to test the formulated hypotheses (Wilson, 2014). The rationale behind my research design lies on the belief that my hypothesis testing will confirm the theory and offer insights on understanding the existing relationships among the variables. To conduct my study, I collected secondary data from already existing databases.
4.2 Data frame
The population observed consists of 418 completed M&A deals by 734 firms, whereof 323 acquirers and 411 targets selected on the basis of the NAICS 2017 codes (Table 1). Since the deals I focus on regard only technological M&As, I used the NAICS codes to select those firms that appear to be most directly involved in the high-tech sector. The information was gathered from the United States Census Bureau (USCB) which has adopted and replaced -under the auspices of the Office of Management and Budget (OMB)- the Standard Industrial Classification (SIC) system in 1997. I utilized the NAICS code by virtue of its specificity: the 6 digits of the NAICS, compared to the 4 digits of the SIC codes, allowed me to analyze with major precision the technology similarity between the acquirer and the target firm.
24 Furthermore, the time period selected refers to the all the high-tech deals completed between 01/02/1997 and 28/09/2017. This time frame allowed me to calculate the amount of incremental innovation post-deal. During the data collection process, the sample got restricted to 102 deals because, despite being all firms in the high-tech sector, many of these did not present any record of patented innovations.
I extracted the data from the ZEPHYR databases by Bureau van Dijk rather than the commonly used Thomson ONE in the interest of the reliability of my data. As others scholars have managed to retrieve information on M&As using the same method (Ellwanger & Boschma, 2015; Ritala & Hurmelinna-Laukkanen, 2013), through ZEPHYR I am provided with information on the type of deal, the location of the company, the number and specific details about the subsidiaries. Through Bureau van Dijk I was also able to retrieve data on patents using the ORBIS database, through which I could obtain valuable information on the patent classes and the backwards citations for each patent.
Moreover, I study not only cross-border M&As, but also domestic deals, by virtue of the companies’ spatial location. In order to have an unbiased evaluation of the role of geographical proximity, limiting the scope to the European Union and the North America Free Trade Area could offer me a more precise insight on the size of the geographical proximity’s impact.
4.3 Variables Dependent variables
My dependent variable is incremental innovation performance. Incremental innovation refers to the innovation involving only small changes and refinement in technology (Arnold, Fang, & Palmatier, 2011; Forés & Camisón, 2016). However, scholars find it difficult to interpret and agree on the model to determine incremental inventions and their processes.
25 According to past research, innovation performance is directly associated with the number of patents a firm has produced (Ahuja, 2000; Owen-Smith & Powell, 2004). In this case nevertheless, considering my focus on the incrementality aspect of innovations, a common way to measure incremental innovation it is by taking into consideration the citations that appear in a patent, indicating the previous innovations that the current patent is built upon (Jaffe et al., 2017). In order to gather information on patent classes I utilized the ORBIS database that provides a direct connection to the European Patent Office (EPO) website. The access to this source enabled me to come in possession not only of the entire original document, but also the list of the cited documents in the patent, with the respective detailed information. I used backwards citations because, as Jaffe et al. (2017) suggest, backwards citations are indicative of the development efforts that occurred in the document to which they are linked with (Hall, Jaffe, & Trajtenberg, 2001).
As my intent is discovering the incrementality of the innovative effort coming from an M&A, backwards citations are the cue to detect the amount of developments on prior, already existing, technologies, innovation procedures or products (Garcia & Calantone, 2002).
There is, to my knowledge, not one established method to estimate incremental innovation, which is why I utilized a proxy for it: originality, as utilized by (Trajtenberg, Henderson, & Jaffe, 1997). According to Trajtenberg et al. (1997) and more recently Hall et al. (2001), to measure ‘originality’ I utilized the Herfindahl index on technological classes of cited patents, where k represents the index of the classes, and N represents the number of diverse classes to which the cited patents correspond to:
6
26 The value within the brackets explains the percentage of backwards citations belonging to each patent, over the totality of patents in a firms’ patent portfolio.
This backward-looking measure ranges from 0 to 1, and it estimates the width of technology the patent was built upon: “if a patent cites previous patents that belong to a narrow set of technologies then originality score will be low, whereas cited patents in a wide range of fields would render a high score” (Hall et al, 2001, p. 21).
More precisely, as Trajtenberg, et al (1997) explain, this notion of ‘originality’ represents how basic a patent is. A basic patent is here defined as one that provides a base for subsequent innovation. Being incremental innovations processes a series of cumulative actions of inventions build on antecedent ones that will ultimately function as a base for future developments, I can assert that this measure provides enough support to determine the level of incrementality in the innovative efforts of the firms in this sample. The more extensive the technological roots of the patented inventions of the considered acquirers, the more the patent can be considered a base for new subsequent innovations (Trajtenberg, et al., 1997).
Nevertheless, my dependent variable is ultimately incremental innovation performance. In order to measure the performance of the acquiring patents in the sample, I filtered the patents according to the years the preceded the deal and the ones that followed it and measured the change in originality.
Independent variable
My independent variable is the relatedness of knowledge between acquirer and target firm, which is an equivalent for technological similarity. In prior studies, it has been measured comparing the patent classes present in the patent portfolios of the acquiring and acquired firms (Miozzo et al., 2016, Jo et al 2016). The information on patents will be again gained utilizing the EPO website, through which, using the Bureau Van Dijk ID number of each company, I
27 could retrieve a detailed report on the company comprising information on Intellectual Property. The access to the EPO website guarantees the visualization of the complete patent, where I could find its international patent classification, a two-digit and two-letter number.
The similarity or dissimilarity between the patents classes provided me with enough information to determine whether the acquirer and target firm possess a certain degree of relatedness of knowledge. In the interest of the accuracy of my study, used the same calculation process as Miozzo et al. (2016). The technology relatedness between the two firms was measured by calculating the number overlap of all patent classes divided by the total number of patents of acquirer and target together, which has then been multiplied by the total acquirer patents in common classes over the total number of acquirer patents (Miozzo et al., 2016).
Moderator
Geographic or spatial proximity is defined as “the closeness of financial transactions, physical locations” (Shi et al., 2015, pag 6), and it has been commonly studied using the geographical coordinates of the firms’ headquarters, which made the transactions ‘proximate’ if the respective locations were situated within a 100km radius from each other (Ellwanger & Boschma, 2015; Wee et al., 2016). Considering the boundaries of the sampled companies involved in both domestic and cross-border deals, I have chosen a larger value for the radius. I have classified those headquarters that lie at respectively 500 km or less far from each other as ‘proximate’ and defined with a (0), while headquarters at more than 500 km away from each other have been defined as ‘distant’ and therefore as (1).
Furthermore, the distance between an acquirer’s subsidiary and the target firm headquarters followed a slightly different classification. If an acquiring company’s subsidiary was located in the target company’s country I have defined it as ‘proximate’ and will be noted with a (0), otherwise if the acquirer does not possess a subsidiary in the target’s country, the
28 two have be defined as ‘distant’ and therefore described with a (1). The data on the subsidiaries has also been gathered through ORBIS, which provides detailed information on the ownership details of each examined company.
Control variables Industry relatedness
An acquirer that buys a target that operates in a similar business may have a better understanding of the target’s business and this may affect acquisition integration and performance (Desyllas & Hughes, 2010). Therefore, one of my control variables is industry relatedness and I have calculated it by using a dummy variable. Acquisitions with the acquirer and the target firm possessing their primary industry activity in the same 6 digits NAICS 2017 code have been defined with a (0) and (1) otherwise.
Deal Value
According to Gupta and Misra (2007), deal size is related to the value creation motivations of the management and to its value-maximizing goals (Gupta & Misra, 2007). Moreover, prior studies confirmed that in technological M&As, the deal has an impact on subsequent innovation (Jo et al., 2016). For the mentioned reasons, I controlled for the value of the deal, a continuous variable measured in euros.
Acquirer number of patents pre-deal
The last control variable refers to the number of patents of the acquiring firm up to the deal date. Studies show that the amount of patents pre-deal represent the existing stock of knowledge of a firm (Puranam & Srikanth, 2007) and the quantity of its innovation output (Zhao, 2009).
29 4.4 Data analysis
In the data analysis process, I began by importing my data into SPSS and then I performed the necessary actions to fulfil the paramount requirements to proceed with my analyses.
Firstly, I computed the needed transformations to obtain normally distributed variables. I started with knowledge relatedness, which initially did not fit the assumption of normality, tested with the Kolmogorov-Smirnov Test (p < 0.05 leading me to reject the null hypothesis that my sample was normally distributed). According to Field (2009) when the Sig. value of the Kolmogorov-Smirnov Test or Shapiro-Wilk Test is greater than 0.05, the data is normal. I then computed a squared root transformation, and the percentage on the SPSS knowledge relatedness D(102) = .073, p < .200, which is significantly normal (Field, 2009).
Secondly, the incremental innovation performance variable was checked. This was also not normally distributed and presented quite a few outliers, so I standardized my variable to spot univariate outliers. According to Field (2009) a z-score of 3.29 or larger constitutes an outlier, so I calculated the z-score and spotted three very extreme values.
I then winsorized these three outliers since their values were not errors due to contamination in the distribution or typography errors or measurement errors, their value is just in the nature of the data (Field, 2009; Tian, 2011). I also winsorized by reason of the sample size (winsorizing less than 5% of my data would not have affected my p value, which would have still been accurate) (Field, 2009). I then performed a logarithm with base 10 transformation to normalize the distribution. I was able to remove the extreme influential points but could still spot some homogeneously distributed outliers at the end of the tails of the distribution. Nevertheless, both from a visual inspection and the descriptive statistics, a graphical demonstration of a bell-shaped curve and the levels of kurtosis and skewness between <|1| allowed me to infer that the sampling distribution was normal (Field, 2009).
30 Thirdly I checked the deal value variable for normality. Initially the variable did not meet the assumptions of normality, so I transformed it using a logarithm with base 10. Nonetheless, even after the transformation the distribution, the kurtosis was slightly above 1 (the empirical criteria for establishing normality by inspecting kurtosis and skewness is to accept values between -1 and +1) I converted that kurtosis value to the z- score by dividing it by its standard error. The resulting value was 2.86, and being above 2.58 means that it is significant at p < .01, giving me enough proof to confirm normality (Field, 2009). However, even after the transformation I could still see two small outliers at the end of both tails of the distribution, so I standardized my original variable to spot univariate outliers. According to the aforementioned method, I spotted just one univariate outlier out of the three I could see from the box plot. Once again, I performed a winsorization of this extreme value and then normalized the new variable with a logarithm with base 10. The Kolmogorov-Smirnov test confirmed the normality assumption.
Finally, I checked the last continuous variable for normality, number of acquirer patents pre-deal, which became normally distributed after a logarithm with base 10 transformation, reinforced by The Kolmogorov-Smirnov Test.
In the following chapter I describe how I ran bivariate correlations and then checked the assumptions to perform two hierarchical regressions in order to test my hypotheses.
31 5. Results
This section outlines the results of the different analyses I have run. Firstly, I present the descriptive statistics and the correlation matrix. Secondly, I list and describe how I met the underlying assumptions for the regression analysis. Finally, I explain my model and present the outcomes of the hypothesis testing.
5.1 Descriptive statistics and correlation
After having satisfied the assumptions of normality for each variable, I conducted a bivariate correlation analysis using the Pearson’s correlation. The correlation matrix (see Table 2) shows that there is a significant relationship between knowledge relatedness and the number of acquiring patents pre-deal, r = -2.11, which is significant at p < .05. This could mean that a poor knowledge relatedness among the two firms, corresponds to a high number of patents held by the acquirer up to the deal date. Furthermore, there is a very significant relationship between incremental innovation performance and industry relatedness, r = -.273, p < .01. This might indicate that a large incremental innovation performance of the acquirer would imply a poor industry relatedness between the two firms. Finally, geographical proximity between the acquirer subsidiary in the target firm country has significant negative correlation with the industry relatedness among the two, r = -.219, p < .05 and significant negative correlation with the deal value, r = -.238, p < .05. This might denote that the closer the firms the smaller is the deal value and their industry relatedness. From this first analysis we can already see that industry relatedness could be a very important valuable to control for.
However, there is almost no correlation between knowledge relatedness and incremental innovation performance (r =.141), and also no correlation between incremental innovation performance and the two moderators (respectively r = .026 for HQ proximity and r = -.075 for subsidiary proximity).
32 Table 2. Correlation Matrix
Variables M SD 1. 2. 3. 4. 5. 6. 7.
1. Knowledge relatedness .4608 .27585
2. Incremental innovation performance .2742 .14847 .141
3. HQ proximity .52 .502 -.093 .026
4. Subsidiary proximity .30 .462 .152 -.075 .038
5. Industry relatedness .42 .496 -.145 -.273** -.133 -.219*
6. Deal value 4.1768 .52721 .036 .068 .178 -.238* -.004
7. Acquirer patents pre-deal 2.3117 1.32337 .211* .126 .108 -.194 -.051 .017
Note. N=102.
*. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).
33 5.2 Assumptions checking
Before proceeding with a multiple regression analysis, I first checked if all the assumption were satisfied. Firstly, normality was previously checked and significant outliers have already been taken care of. The data has also been checked for linearity. Secondly, the predictors were inspected for multicollinearity. The Variance Inflation factor (VIF) indicates whether there is a strong linear relationship among two variables (see Table 3). In this case all VIF values are lower than 10, the average VIF is not substantially greater than 1 and finally there’s is no tolerance below 0.1 or 0.2. Therefore, I can safely assert that there is no collinearity within my data (Field, 2009). Thirdly, independence of residuals was checked. I performed the Durbin-Watson test, which showed a value of 2, meaning that the residuals are uncorrelated. Fourthly, I checked for sample size: according to Cohen (1977), with sample size of 102 I can confidently maintain a power level of .80 (Cohen, 1977). Finally, the variables were checked for homoscedasticity, or else said equal variance of residuals. After having inspected the scatter plots, I can infer that the equal variance assumption is met.
34 Table 3. Collinearity statistics
Note. Dependent Variable: Incremental innovation performance
Variables Tolerance VIF
Knowledge relatedness .910 1.099
HQ proximity .923 1.083
Subsidiary proximity .840 1.190
Industry relatedness .909 1.100
Deal value .898 1.114
35 5.3 Hypotheses testing
Two hierarchical multiple regressions were performed to predict incremental innovation performance of acquiring firms subsequent to an M&A based on the knowledge relatedness between the acquirer and the target firm and their geographical proximity.
In order to make accurate predictions, here is the regression equation of the model: Y = a + b1X1 + b2X2 + b3X3 + b4X4 + b5X5 + b6 X1X5 + ɛ
where Y represents the outcome variable, incremental innovation performance, b represents the coefficient to each predictor, respectively X1 the independent variable, knowledge relatedness, X2 the first control variable, deal value , X3 the second control variable, number of acquirer patents pre-deal, X4 the third control variable, industry relatedness, X5 either of the two moderators, HQ proximity and subsidiary proximity, and finally X1X5 the interaction term, which is the multiplication between the independent variable and either of the moderators that will determine the combined effect on the outcome variable.
I began by investigating the first two hypotheses, to see whether the knowledge relatedness predicted incremental innovation performance, after controlling the latter for industry relatedness, deal value, and the number of acquirer patents up to the deal date.
Firstly, the control variables were entered in the model. A significant regression was found (F (3,98) = 3.284, p < .024), with an R2 of .091. In the second model the predictors of knowledge relatedness and geographical proximity between HQs were added. In this second model of our regression equation (F (5,96) = 2.330, p < .048) R2 increases of 1.7% in the variation explained by the addition of the two predictors (R2 = .108). Nevertheless, the F change associated to this increase is not statistically significant (p < .407). In the third and final model the interaction term was added. The final regression equation (F (6,95) = 2.330, p < .038), with an R2 of .120, shows that not only the individual predictors are not statistically significant, but also that the interaction term is also not significant with the incremental innovation
36 performance intensity (see Table 4). Moreover, this third model has a better fit than the second one, but it’s also not statistically significant (p <.142). Hence, hypothesis 1, 2 are rejected, as knowledge relatedness has no effect on incremental innovation performance. Also, hypothesis 3 is rejected, as geographical proximity between headquarters is not significant and therefore does not moderate the relationship between knowledge relatedness and incremental innovation performance. The only significant predictor in all models is industry relatedness, a dummy control variable. The figures show that incremental innovation performance decreases of 8% more if the two firms are not in the same industry (p < .010, t-test significant -2.614, in Model 3).
The second hierarchical multiple regression was performed to test hypothesis 4. As previously done for the first multiple regression, once again in the first model the control variables were entered first. A significant regression equation was found (F (3,98) = 3.284, p < .024), with an R2 of .091, similarly to Model 1 in the previous hierarchical regression. In the second model, the regression equation (F (5,96) = 2.655, p < .027), with R2 of .121, included knowledge relatedness and the other predictor of geographical proximity: Subsidiary proximity. However, the F change of 1.648 associated to this 3% increase in predictive capacity of the second model is not statistically significant (p < .198). In the final model the interaction term was added. In this last regression equation (F (6,95) = 2.368, p < .036), with an R2 of .130, we can once again see none of the predictors are significant except for the control variable industry relatedness, which is significant at p <.006 in the last model. Subsidiary geographical proximity does not moderate the relationship between knowledge relatedness and incremental innovation performance, hence hypothesis 4 is also rejected (Table-5).
37 Table 4. Summary of First Hierarchical Regression Analysis for Variables Predicting Incremental Innovation performance with HQ proximity as moderation
Model 1 Model 2 Model 3
Variable B SE B p B SE B p B SE B p
Incremental innovation performance
.203 .117 .087 .165 .121 .174 .195 .122 .112
Deal Value .018 .027 .503 .018 .028 .520 .019 .027 .480
Acquirer patents pre-deal .012 .011 .251 .016 .011 .156 .018 .011 .119
Industry relatedness -.080 .029 .007 -.075 .030 .013 -.077 .029 .010 Knowledge relatedness .070 .054 .197 -.010 .076 .898 HQ proximity -.006 .030 .828 -.079 .057 .170 Knowledge relatedness x HQ proximity .155 .104 .142 R2 . 091 3.284 . 108 0.908 .128 2.189 F for change in R2 Note. N=102
38 Table 5. Summary of Second Hierarchical Regression Analysis for Variables Predicting Incremental Innovation performance with subsidiary proximity moderation
Model 1 Model 2 Model 3
Variable B SE B p B SE B p B SE B p
Incremental innovation performance
.203 .117 .087 .221 .128 .088 .216 .128 .094
Deal Value .018 .027 .503 .008 .028 .769 .007 .028 .814
Acquirer patents pre-deal .012 .011 .251 .013 .011 .244 .011 .011 .344
Industry relatedness -.080 .029 .007 -.082 .030 .007 -.083 .030 .006 Knowledge relatedness .078 .054 .150 .118 .068 .085 Subsidiary proximity -.041 .033 .224 .009 .061 .887 Knowledge relatedness x Subsidiary proximity -.105 .109 .335 R2 . 091 3.284 .121 2655 .130 2.368 F for change in R2 Note. N=102
39 6. Discussion
The results of this study do not support my theoretical predictions. Although prior studies have yielded clear results on the relationship between knowledge relatedness and innovation performance within the M&A context, this research fails to confirm equivalent results (Ahuja & Katila, 2001; Cepeda-Carrion et al., 2012; Cloodt et al., 2006; Kogut and Zander,1992; Grant, 1996; Makri et al., 2010; Stellner, 2015). The relatedness of knowledge between acquirer and target firm results not to have any effect on the post-M&A incremental innovation performance. Moreover, despite prior findings on the positive impact of geographical proximity on innovation efforts, this study shows no moderating effect produced by the location on the main relationship between knowledge relatedness and incremental innovation performance (Breschi & Lissoni, 2001). Nevertheless, there appears to be an important negative influence of industry relatedness on incremental innovation performance.
Overall, the results have implications for theory and practice, which will be discussed hereafter.
6.1 Implications for theory and research
Knowledge relatedness & incremental innovation performance
Little scrutiny has been given to incremental innovation performance in the M&A framework (Garcia & Calantone, 2002). The plan of the research introduced herein aimed at extending the knowledge on this field, giving more insights on the relationship between M&A knowledge relatedness and incremental innovation performance, and highlighting a novel technique to measure the incremental nature of innovations, as the difficulties in this field are renowned (Gittelman, 2008; Hall et al., 2001; Kuznets, 1962).
Suggested assumptions of innovation scholars advocate the positive role of knowledge similarities between firms engaging in M&As transactions associated to incremental renewal (Stellner, 2015), hence the choice of focusing on incremental innovation rather than radical. In
40 fact, as acquisitions tend to pursue continuous learning to gain new technological inputs that make a successful performance possible (Ahuja & Katila, 2001; King et al., 2004; Jo et al., 2016), incremental innovations have been shown to be more pronounced than radical ones in the field (Sheng & Chien, 2016). The incrementality aspect of the acquisition of new knowledge,through the use of the latter in the creation of products and routines compatible to already existing ones in the firm, has been observed especially in the high-tech sector, where firms are more prone to look for new technologies yet using their existing ones to promote incremental innovation (Sheng & Chien, 2016). Hence, the exclusive presence in this research of firms belonging to the high-tech sector, as non-technological M&As are known not to lead to innovation outputs (Cloodt et al., 2006).
Basing my research of the studies of Ahuja and Katila (2001) and Cloodt et al. (2006), both focusing on technological deals and resulting both with the conclusion of an existing curvilinear relationship between knowledge relatedness and post-deal innovation performance, I assumed there to be a similar empirical result to my analyses (Ahuja and Katila, 2001; Cloodt et al., 2006). Additionally, despite having carefully selected what Sears & Hoetker, (2014) define as technological acquisitions7, the M&As in question might have not been purely technological deals but as Makri et al. (2010) argue, could have been of a different nature, as the acquirer was motivated by simply augmenting its market power and scale economies, and not entirely driven by a transfer of knowledge motif (Makri et al., 2010; Sears & Hoetker, 2014). Nevertheless, this is supported by literature suggesting that market-stimulated innovation is likely to be incremental (Ettlie, Bridges, & O’Keefe, 1984).
7 Sears & Hoetker (2014) define technological acquisitions as deals where both parties are operating in the high-tech sector.
41 Some studies are also critical on the managerial decision of choosing M&As instead of strategic alliances in the interest of the integration of technology (Johan Hagedoorn & Duysters, 1999; Stuart, 2000). It has in fact been argued that alternative modes other than exclusively M&As contribute to the acquisition of innovative capabilities, as a more flexible form of integration of knowledge could lead to subsequent technological developments. In spite of the role of M&As as superior choice when aiming at securing new innovation sources, favoring their most valuable use to produce an innovation (Blonigen & Taylor, 2000), scholars assert that in the high-technology sector, however, learning is more appropriately associated to more flexible modes, rather than ownership (Hagedoorn & Duysters, 1999).
Another important aspect to be considered is the degree of novelty that characterizes incremental innovations. This type of innovation, though challenging to capture and carrying less risk and costs than radical ones, involves little degree of novelty (Souto, 2015). In accordance with my unpredicted results, several innovation studies assert that, although being appropriate in the high-tech sector, patents alone are not sufficient as a performance indicator (Gittelman, 2008; John Hagedoorn & Cloodt, 2003; Zhao, 2009) and there are multiple dimensions to be considered when examining any innovative process, especially if incremental (Bommer & Jalajas, 2002). Although the literature demonstrates a positive relationship between the amount of backwards citations and knowledge acquired through M&As (Duguet & MacGarvie, 2005), patents as a proxy for incremental innovation could in fact have produced inaccurate results because especially improvement of processes or products, not as novel as products new to the industry or to the world, are frequently not patented (Coad & Rao, 2008; Kleinknecht, Montfort, & Brouwer, 2002).
Moreover, single citations might just represent an individual technical feature of the patent, and therefore not express the totality of the innovation per se and, according to Arundel
42 and Kabla (1998), a single patent not always coincides with an invention, or less probably it does to one innovation (Arundel & Kabla, 1998).
In conclusion, my results show that there is no relation between subsequent incremental innovation performance of an acquiring firm and the knowledge relatedness to its target despite prior research suggesting that patents can capture the true value of innovations (Hall et al., 2001; Jaffe et al., 2017, Trajtenberg, et al., 1997) and that a certain degree of knowledge relatedness would produce post-M&A innovation performance (Ahuja & Katila, 2001, Cloodt et al., 2006). It might be for the aforementioned reasons that hypothesis 1 and 2 failed to be confirmed.
Geographical proximity
The results of this research highlight precedent criticism on the role of geographical proximity in the innovation context (Boschma, 2005), and also its role on the transfer of knowledge is called into question.
In light of studies on the effective role of geographical proximity, I used radius distance to determine the closeness between the acquirer and target headquarters, and also determined the proximity by taking into consideration the presence of subsidiaries, as innovations tend to concentrate in areas rich in dynamism, benefiting innovative processes and generally the economic environment (Torre, 2008).The presence of innovations itself can be explained by the co-location of firms who carry out innovative activities, especially in the high-technology industries and clusters where collaborative efforts arise from the synergies among the innovating local firms (Porter, 2000; Torre, 2008). In the case of patented knowledge particularly, spillover effects created by innovative companies are characterized by spatial proximity (Breschi & Lissoni, 2001b).
