TECHNOLOGICAL ACQUISITIONS AND INNOVATION: ARE EXPERIENCED ACQUIRERS PRIVILEGED?

TECHNOLOGICAL ACQUISITIONS AND INNOVATION:

ARE EXPERIENCED ACQUIRERS PRIVILEGED?

By Annelies Dussel

University of Groningen Faculty of Economics and Business

Msc. Business Administration Strategy and Innovation

2 ABSTRACT

This study examines the post-M&A innovative performance of acquiring firms in four major high-tech sectors, in an organizational learning perspective. The replication study shows that technological acquisitions have a positive effect on the innovative output of the acquiring firm. With regard to organizational learning, experienced acquirers appear to have an advantage in the post-M&A integration process during the first years after the acquisition. Repetition skills provide these firms with a more controlled integration, fastening knowledge transfer in the early period after the event. This curvilinear moderating effect is also measured by acquirers entailing experience with intra-industry acquisitions. The experience skill only shows a positive effect in the first two years after the acquisition, entailing negative effects on the long term. Knowledge management routines imply path-dependency, which in turns results in decreased flexibility to respond to changing environments. This impedes innovation activities. Finally, international acquirers appear to benefit most from repetition, with positive impacts over time for acquirers experienced with inter-industry acquisitions. Cross-border acquisitions face the high degrees of dissimilarity requiring flexible attitudes of acquiring firms. This indicates that the acquisition-derived knowledge base is most benefiting for firms when it can be applied in a flexible manner.

Keywords: Mergers and Acquisitions, Innovation, Organizational Learning, Experience, Knowledge

3 Content INTRODUCTION ... 4 Problem Definition ... 5 Conceptual Framework ... 6 Preview ... 6 LITERATURE ... 6 METHODOLOGY ... 12 RESULTS ... 15 DISCUSSION ... 17

Key Findings and Managerial Implications ... 17

Limitations and future research ... 20

CONCLUSION ... 22

APPENDICES ... 24

4 INTRODUCTION

Merger and acquisition activity plays a major role in restructuring companies and whole industries, and is proven to affect organizational performance. In M&A activity firms are exposed to new, valuable resources, together with the influx of knowledge and new ideas. Depending on the technological capabilities of the acquired firm, opportunities for organizational learning arise (Cloodt et al, 2006). Marco and Gauser (2008) argue that mainly large firms tend to acquire small, diversified firms, inclined to tap the innovative potential of entrepreneurial firms in acquiring their knowledge (Graebner, Eisenhardt & Roundy, 2010). Although this integration process is assumed to be post-M&A performance enhancing, the acquiring firm has to be able to create positive synergies with the acquired firm (Barney, 1986; Chondrakis, 2012). The capabilities of the acquiring firm to successfully absorb the gained resources are in these situations of major importance (Kallunki, Pyykkö & Laamanen, 2009).

It is argued that technological firms are more capable of integrating the dissimilar knowledge, therefore showing higher amounts of value creation (Cloodt et al, 2006; Kallunki, et al, 2009). Besides the higher success rates, it appears that M&A events are also more common to those industries, examining high-tech, established companies like Cisco Systems, Google Inc, Lily and Hewlett Packard. The high growth rate of small, high-tech firms makes them attractive to incumbent firms (Kohers & Kohers, 2000) in creating value. Acquisitions of this nature are encouraged by keeping up with the pace of technological development.

However, acquiring companies have to keep in mind that most acquisitions fail to meet pre-acquisition expectations (Zhao, 2009; Cloodt et al, 2006; McCarthy, 2011). Studies investigating the underlying causes of the failure rates have provided managers with several topics to keep in mind. Organizational learning (Ahuja & Katila, 2001; Cloodt, Hagedoorn & van Kranenburg, 2006), resource deployment (Barney, 1986; Capron, Dussauge & Mitchell, 1998) and the creation of unique synergies (Leland, 2007; Chondrakis, 2012) are popular investigated topics related to M&A events.

(Al-5 Laham, Schweizer & Amburgey, 2010). For instance, Cisco Systems acquisition activities have enabled the company to excel at the post-acquisition integration process (Heimeriks, Schijven & Gates, 2012).

Ellis, Reus, Lamont and Ranft (2011) argue that research concerning acquisition experience and post-M&A performance shows divergent results, ranging from U-shaped relationships to negative or non-significant relations. Therefore, they suggest disparate effects of specific kinds of prior acquisition experience have to be researched to create a more comprehensive understanding. Moreover, firms have to be cautious not to apply prior successful experience to dissimilar new events (Haleblian & Finkelstein, 2002). Therefore, it is relevant to these firms to examine distinctions in acquisition experience and its moderating effect on acquisition performance. As is presented later on in the study, this positive replication study is able to confirm findings of Cloodt et al (2006), proving robustness over time (Burman, Reed & Alm, 2010). Moreover, it creates relevant new insights in the role of organizational learning in relation to M&A events and post-M&A innovative performance.

Problem Definition

Empirical economic research is often prone to error, which emphasizes the added value of replication studies. The period under study in this research differs from Cloodt et al (2006): instead of M&A events between 1989-1993, a major part of the sixth merger wave is examined, covering m&A activity during 2003-2007. The geographical scope of M&A activity of the two periods does not significantly differ (Martynova & Regenboog, 2008). Both cover Europe, North America and Asia, although a little increase of M&A activity in Asia can be found (McCarthy, 2011). In terms of industry-relatedness both periods are quite similar, yet the sixth wave is characterized by higher degrees of internationalization (McCarthy, 2011). This provides a relatively solid base for replication. As mentioned earlier, this study counts for post-acquisition organizational learning effects. Because technological acquisitions require high investments and are risky compared to other acquisitions, it is of value to understand these effects and consequently what gains to expect. Therefore, the purpose of the study is to investigate the role of organizational learning in post-M&A innovative performance. This is conducted by investigating several acquisition characteristics and their relation to the post-acquisition performance. Post-acquisition integration management of firms is critical for knowledge sharing and the absorption of new technologies, which in turn influences the innovative performance of the acquiring firm. Investigating these factors will contribute to the understanding of M&A events, supporting managers in their know-how of acquisitions and knowledge creation. The research question central to this study is:

6 Conceptual Framework

A conceptual model concerning the expected relationships between the independent variables and the dependent variables of this study is provided in figure 1.

Figure 1: Conceptual model

Preview

The paper is organized as follows. The next session presents a theoretical background on organizational learning in relation to merger and acquisition activity, resulting in several hypotheses. Subsequently, a description of the dataset is shown together with a description of the empirical methodology. The results of the analysis are discussed, providing managerial implications. Limitations are drawn, together with suggestions for further research. Finally, a conclusion formulating an answer to the research question is presented.

LITERATURE

Technological acquisitions

Cloodt et al (2006) made a distinction between technological and non-technological acquisitions in their study in investigating the effects on innovative performance. Technological acquisitions are characterized by targets showing patenting activity, which indicates the technological knowledge production of these firms (Ahuja & Katila, 2001). As mentioned in the former section, M&As are often intended to assist companies in enlarging their knowledge base, and to strengthen innovative capabilities (Kallunki et al, 2009). However, a distinction between those firms and firms with other drivers for M&A activity has to be recognized. Firms can also have the strategic intent to expand to new product markets, or for instance enlarge the geographical scope of their operations. With these firms, less visible effects on innovative performance are

7 expected, because their main focus does not deal with the enrichment of their innovative capabilities (Cloodt et al, 2006; Kallunki et al, 2009). Cloodt et al therefore found a slightly negative effect of non-technological acquisitions on post-M&A innovative outcomes.

However, acquisition of external technologies is often a means by which knowledge-intensive firms increase their technical capabilities and enhance strategic renewal (Graebner, Eisenhardt & Roundy, 2010). Inclined to facilitate knowledge transfer, technological firms appear to be more capable of integrating the inflows of dissimilar knowledge than non-technological acquirers (Kallunki et al, 2009). Naturally, technological acquisitions face several unique challenges. For instance, the value of knowledge capital is difficult to measure due to its intangible nature (Bloch, 2008; Graebner et al, 2010). The integration of the combined assets can become a failure due to this uncertainty. Despite the high risk acquisitions entail, positive effects of technological acquisitions on innovation have been measured before (Kapoor & Lim, 2005; Gantumur & Stephan, 2011). Hence, the following hypothesis can be derived:

Hypothesis 1: Technological acquisitions will have a positive effect on the post-M&A innovative performance of the acquiring firm.

The organizational learning perspective

Post-M&A innovative performance of technological acquisitions is also subject to many factors. In this research, organizational learning in respect to M&A performance is investigated. A tough to replicate skill of firms, proven by former academic studies to be beneficial to M&A value creation (Porrini, 2004). In organizational learning theory, accumulated experience with an activity is associated to higher productivity by repetition of this activity (Morrison, 2008). The development of this experience is of strategic importance, because it promotes fast decision-making in returning events. Furthermore, tacit assets like experience are difficult to imitate or trade (Dierickx & Cool, 1989).

8 and knowledge. In some cases, firms use repeated acquisitions as a strategy (Peng and Fang, 2009). In acquiring new knowledge and technologies, firms can chose to acquire one firm, however also common is the acquisition of several firms over time to enlarge the knowledge base. This latter strategy results in the creation of a skill influencing the performance of acquisitions: organizational learning (Haleblian & Finkelstein, 1999; Peng & Fang, 2009).

Although “no acquisitions are ever quite the same” (Heimeriks, et al, 2012), firms possessing acquisition experience entail an advantage during the post-acquisition integration process (Teece, 1977; Al-Laham, Schweizer & Amburgey, 2010). Cohen and Levinthal (1990) state that “the ability of a firm to recognize the value of new, external information, assimilate it, and apply it to commercial ends is critical to its innovative capabilities”. In acquisition processes it is hard to value the assets of the other firm, and the integration of the combined assets can become a failure due to this uncertainty. According to Haleblian and Kim (2010) firms develop routines and standards in repeating activities, which eases repetition. This indicates that experienced firms become more accurate in judging value which fastens and eases integration. Teece (1977) furthermore states that knowledge and technology transfer is a decreasing cost activity, either financial as non-financial. As a consequence, lower knowledge transfer costs are assigned to experienced acquirers.

Al-Laham, Schweizer and Amurgery (2010) support the learning curve advantage by pointing out that prior experience with integration shows a positive effect on the post-M&A performance. Porrini (2004) complements this finding by stating that the beneficiary learning effects are primarily assigned to high-tech firms. Because learning effects relatively decrease over time, and this specific learning skill is primarily applicable in the early post-M&A stage, the subsequent hypothesis is derived:

Hypothesis 2. There is a positive, non-linear moderating effect of pre-acquisition integration experience on the relation between technological acquisitions and post-M&A innovative performance.

Relatedness and acquisition experience

9 industry. The acquisition of this complementary knowledge facilitates higher productivity and efficiency to enhance the current competitiveness of the acquirer (Buckley, Glaister, Klijn & Tan, 2009). Furthermore, Hagedoorn and Duysters (2000) found that acquisitions can contribute to increases in innovation if a strategic fit among the companies involved is noticed. According to the authors, a strategic fit yields corresponding SIC-codes, which implies similar operations. When partners of distinct industries algamate knowledge, or, in other words, enable supplementary knowledge transfer, it is shown to increase the knowledge scope of both parties (Buckley et al, 2009). The exposure to new and diverse environments challenges firms to question current operations and routines, thereby enabling them to absorb the new information and knowledge (Collins, Holcom, Certo, Hitt & Lester, 2009). Research by Makri, Hitt and Lane (2010) suggests that acquiring supplementary knowledge creates the highest incentives for post-M&A innovative performance. Similarities in knowledge contribute to renewal on an incremental level, while acquiring less familiar resources creates a higher likelihood for discontinuous strategic transformations to occur (Makri et al, 2010). This is supported by Graebner, Eisenhardt & Roundy (2010), who argue that firms which acquire technological firms investing in the same R&D resources perform less on long-term innovative activity than firms that significantly differ from their target company.

However, with regard to post-M&A integration, supplementary firm acquisitions entail higher risk. The acquiring firm is exposed to unknown resources, which jeopardizes the earlier mentioned valuation and integration of acquired assets. Haleblian and Finkelstein (2002) therefore argue that high degrees of similarity in industry and environment between the acquirer and target company facilitates more positive knowledge transfer, due to the ease of applying more appropriate behavior. As a consequence, related acquisitions have shown more positive post M&A results (Haleblian & Finkelstein, 2002).

10 Hypothesis 3. With technological acquisitions, acquirers entailing related acquisition experience show more positive results in terms of post-M&A innovative performance than inexperienced acquirer.

Cross-border acquisition experience

As mentioned in the first section, the sixth merger wave is characterized by relatively higher degrees of internationalization compared to former waves (McCarthy, 2011). Firms attempt to acquire foreign firms attracted by possible transfer of new technologies and knowledge (Harrison, Hitt, Hoskisson & Ireland, 1991; Bresman, Birkinshaw, Nobel, 1999) and the presence of more competitive product markets (Mattooa, Olarreaga & Saggic, 2004). In line with acquiring targets from dissimilar industries, cross-border acquisitions facilitate learning and enable obtaining tacit knowledge, such as expertise and capabilities. Cross-border acquisitions provide the opportunity to reach those resources without incurring the trial and error cost of starting up a new subsidiary (Ahamm & Glaister, 2011). However, despite all endeavor for this internationalization, cross-border acquisitions often result in less positive results than domestic events (Moeller & Schlingemann, 2005; Gozzi, Zou, & Ghauri, 2008; Levine & Schmukler, 2010; Ahamm & Glaister, 2011). Post-acquisition integration plays a major role in this failure. International culture differences can hinder integration efforts, resulting in sub-optimal outcomes. According to Zollo and Sing (2004), diverse levels of integration consequently imply omnifarious results. For cross-border acquisitions, a high level of integration is required to realize the added value estimated in advance (Capron, 1990). International M&A events are characterized by the complex institutional environments differences, and these circumstances affect the learning process of firms (Collins et al 2009). As a consequence firms have to learn how to maintain operations in diverse cultural settings (Vermeulen and Barkema, 2002). The earlier mentioned high integration level however entails a large disruption of current routines. This disruption is not likely to positively influence the post-M&A performance of the acquirer. Elaborating on the earlier introduced learning perspective, experienced cross-border acquirers are suggested to be able to know the right selection of targets, and to understand the complexities an integration process entails (Zou, & Ghauri, 2008). Gained experience assists firms to “see through the fog” of causal ambiguity characterizing for the complex activities of integration (Heimeriks et al, 2012). The final hypothesis summarizes the results of this section, again assuming organizational learning influence being most relevant in the early post-M&A stages.

12 METHODOLOGY

Sample and Data Collection

The data collected to conduct the replication study consists of a relatively large international sample of 2048 acquisitions announced in the period 2003-2007. The term acquisition refers to all mergers and takeovers within which one party obtains a majority of stakes. The firms included are filtered on base of Standard Industrial Classification (SIC) codes, selecting only those operating in four high-tech industries distinguished by Cloodt et al (2006). These include the pharmaceutical industry Code 283), the computers and office machinery industry (SIC-Code 357), electronics and communications (SIC-(SIC-Code 36), and finally the aerospace and defense industry (SIC-Code 372 and 376). Furthermore, only firms operating from the North of America, Europe or Japan are included. The main part of the organizations included in the sample operate in the electronics and communications industry (43.8%), followed by the pharmaceutical industry (37.5%). The remaining companies are active in the computer and office machinery industry (13.5%) or in aerospace and defense (5.2%). North American countries are well represented, with 65 per cent of all companies originating from Canada or the United States. European companies (27.8%) count for the second largest share in this international sample, the final 6.4% of acquirers originate from Japan. Appendix I provides the descriptive statistics of all variables.

The analysis distinguishes non-tech and high-tech acquisitions based on target’s patenting activities 5 years preceding the takeover. According to this criterion, 977 M&A events are classified as technological acquisitions compared to 1071 non-technological acquisitions. The firms included are collected based on SIC-codes covering the four high-tech industries described above. In total, the sample covers a 19 year period, with annual patent data of both acquirer and target between 1998 and 2011 and M&A data of the four high-tech industries between 1993 and 2007. This random sample of firms is taken from the Thompson Financials Securities Data databank, with additional information provided by Datastream. Data on patents is taken from the publicly available European and American patent databases, respectively the US patent and Trademark Office (USPTO) database and the European Patent Office (EPO) database.

Dependent Variable

13 Technological acquisitions. For 977 M&A events target companies show patenting activity 5 years preceding the M&A. These acquisitions are considered as technological acquisitions. All remaining acquisitions are consequently defined as non-technological acquisitions.

Acquisition experience. To investigate learning effects in the M&A activity, a categorization is made resulting in a quantitative variable. The companies included in the sample are characterized as experienced based on any initiated acquisition activity in the 10 years preceding the M&A event. These events also yield acquisitions with the acquirer obtaining at least a 51% of target’s shares. Based on this categorization, 1218 M&A events are initiated by experienced acquirers, compared to 830 inexperienced M&A initiators.

Related Acquisition Experience. The variable concerning related industry experience is derived from the 1218 experienced companies. Similarity between the firms is measured by the SIC-codes which represent the industry the company operates in. The extent of similarity depends on the conformity between acquirer’s and target’s primary digit of the SIC codes. Accordingly, 1102 M&A events are initiated by acquirers with any related acquisition experience, and 787 M&A events initiated by acquirers experienced with M&A events between firms operating in the exact same industry.

International Acquisition Experience. This continuous variable is derived from acquisition experience, distinguishing between experienced acquirers in terms of international M&A events en domestic events in the ten years preceding the M&A event of analysis. This criterion shows that 866 of 2048 experienced companies are experienced with acquiring foreign companies.

Control variables

To create a proper understanding of the effect of the explanatory variables on the innovative performance of firms, some control variables are added to check for possible additional effects. Industry

14 Continent

The analysis incorporates M&A activity in North America, Europe and Japan. As measured by World Intellectual Property Organization (WIPO, 2011a), patent filing activity currently still differs strongly per country. For instance, USA-based companies file relatively many patents a year compared to European or Japanese firms. These differences can be partly explained by differences in ownership structures and the applied intellectual property protection regimes (Hall & Oriani, 2006), also recognized by the European Union (Commission of the European Communities, 2007). Finally, according to McCarthy (2011), North American acquisitions in the wave of research were, in contrast to Japanese and European acquisitions, mainly focused on diversification instead of strengthening the core competences. A control variable concerning acquirer continent is included to check for these differences.

Patent heterogeneity.

Unobserved heterogeneity between samples of firms is included in the analysis, represented by Xit-1 in the regression equation. This variable is measured by the sum of the patents obtained by the acquiring firm, three years preceding the acquisition.

International vs. Domestic acquisitions.

As mentioned in the literature section, cross-border acquisitions entail higher risk of failure due to cultural differences among the incorporated firms (Moeller & Schlingemann, 2005). Overcoming these differences is required for post-acquisition integration efforts to generate positive outcomes. In imitation of Cloodt et al (2006) a variable to control for cultural distance between firms in M&A events is included. Cloodt et al (2006) found a positive impact of cultural differences on innovative performance and as Martynova and Regenboog (2008) denoted in their study, the sixth merger wave showed relatively high amounts of cross-border acquisitions, providing a good base for comparison. The sample covered by this study yields many international acquisitions, and therefore a control variable to check for differences is included.

Model Specification

The analysis uses a combination of a time series study with cross-sections, also defined as an unbalanced panel dataset model. Following Ahuja and Katila (2001) and Cloodt et al (2006) the following random effects negative binomial regression model can be designated:

15 is used to predict current values of the dependent variable , based on the current and lagged variables of the explanatory variables. The control variables affecting are captured in , and is the lagged vector of the independent variables for year j = 1–4. is the vector of regression coefficients for the control variables and the s are the vectors of regression coefficients for the jth period lagged independent variables (Cloodt et al, 2006). The sum of the regression coefficients on the distributed lags shows the total impact of an M&A event overtime. A pitfall of a repeated measures research design with count is the correlation of observations of one firm, which biases results. Failure to incorporate correlation of responses can lead to incorrect estimation of regression model parameters, particularly when such correlations are large (Cloodt et al, 2006). Therefore, the general estimation equation approach is used in this analysis. Besides accounting for correlation, the method is also flexible enough for non-Gaussian variables and best applicable to limited range dependent variables (Gardner, Mulvey & Shaw, 1995; Ballinger, 2004).

RESULTS

In order to test the four hypotheses several negative binomial regressions are conducted, as described in the former section. First of all, potential correlation between the variables under analysis is tested. All independent variables show little correlation with the dependent variable post-innovative performance. The variables acquisition experience and related acquisition experience are relatively high correlated (0.73). This correlation is, however, as expected, because the major part of experienced acquirers has targeted firms from similar industries in the past. Both variables are tested separately, therefore this correlation induces no impediment. Furthermore, the control variable pre-sample patents, which checks for heterogeneity, also correlates high with the dependent variable (0.77). This was also anticipated in advance, because tests by Ahuja and Katila (2001) and Cloodt et al (2006) generated similar results. Inclined to stick with their model the variable is retained in this analysis. The explanatory and control variables show small correlation coefficients. Hence, the correlation matrix indicates no reason for exclusion. Appendices II to V provide the resulting models of the negative binomial regressions with distributed lag analysis on the cumulative post-M&A innovative performance of the targeted and acquiring firms.

Technological Acquisitions

16 indeed demonstrate a positive relationship, with the highest positive value during the first post-M&A stages. These significant values provide strong support for the first hypothesis.

Acquisition Experience

Hypothesis 2 recognizes the positive effect of acquisition experience on the post-M&A integration capabilities of the acquiring firm. The individual coefficients, as shown in models 9-12 in Appendix III, show small, positive effects in the first years post-acquisition. The fourth post-M&A year variable indicates a negative effect on the innovative output, designating the impact to be negative overtime. This is also translated in the beta-coefficients of the explanatory variable concerning technological acquisitions. When recognizing experience as a moderating factor, technological acquisitions appear to show more positive results in the first year after the acquisition. However, in this setting the positive values decrease faster over time, demonstrating the negative value of experience over time. The effects appear to be curvilinear, supporting the non-linearity nature, thus hypothesis 2 is partly supported.

Related Acquisition Experience

The third variable, related acquisition experience, also appears to have a slightly curvilinear effect. Models 13-16 in appendix IV summarize the negative binary regression results. The first two beta values underpin a positive effect of experience with acquisitions among firms in similar industries, showing support for the hypothesis. The values of the third and fourth post-M&A year however suggest negative effects on the long term. This could support the earlier described assumption of short-term incremental innovation as a result of related acquisitions (Makri, Hitt & Lane, 2010). However, it could also imply that the advantages derived from this kind of experience are designated to the integration process, and have less value on the long term. The variable concerning related experience is of little impact, given that the inclusion of this variable shows only small moderating effects. However, as with acquisition experience in general, related acquisition experience shows small positive moderating effects in the early stages and more negative effects on the long term. Consequently, the third hypothesis is partly supported.

International Acquisition Experience

17 acquisitions provide a positive moderating effect. As expected, overall acquisition experience explains only a small part of acquisition performance variance.

Controls

Most of the findings with regard to the control variables are in line with the earlier expectations. The international versus domestic nature of the acquisition appears to be of most importance. This variable clearly demonstrates the inconvenience associated with the cultural and operational differences, which impedes integration. Consequently, cross-border acquisitions result in far more negative results on post-M&A performance than domestic acquisitions. Inter-industry differences concerning patent activity are also estimated, indicating most positive results for acquisition activity in the computer and office machinery industry, significant in all derived models. Effects for other industries were not significant in this study. Continental differences are shown to be primarily of importance for European firms in this sample. These firms overall show more negative results on post-acquisition innovative performance. Finally, the heterogeneity variable pre-sample patents indicates a stagnation in the number of patents granted in the period 2008-2011 in comparison with 2003-2007.

DISCUSSION

Key Findings and Managerial Implications

This study partly replicates the high-tech industry study of Cloodt et al (2006) in investigating the effects of acquisition on post-M&A innovative performance of firms. Next to the replication an extension that deals with organizational learning in an M&A context is added.

Technological acquisitions create opportunities for innovation

18 innovation purposes (Harrison et al, 1991; Ahuja & Katila, 2001). Hence, in line with expectations technological acquisitions provide a benefit, and entail a significant positive impact in terms of post-M&A innovative performance for high-tech firms. Thus, high-tech firms should stimulate the acquisition of technological acquisitions if they compete in fast-moving industries and innovative performance is necessary for success.

Experienced acquirers only benefit on the short term

As mentioned in the literature section, innovative performance as a result of a technological acquisition is affected by additional factors characterizing acquisition events. Integration of the acquired tacit and explicit resources asks for adequate post-M&A processes (Harrison et al, 1991). This study argues that prior experience with those processes entails positive effects for innovative performance in the early post-M&A stages due to the learning curve. Positive effects on technological acquisition performance are indeed measured in the first two years after the acquisition event. Therefore, the advantage of routines and repetition are likely to be translated in the stages of acquisition integration.

However, two years post-M&A an opposite effect is measured. From that moment, the learned integration skill leaves the experienced acquirers with a disadvantage compared to their inexperienced co-acquirers. This disadvantage might be derived from the earlier favored routines. Individuals in firms often stick to deeply rooted routines for dealing with problems or new situations. This risk-avoiding, problem-solving approach is rapidly deployed to save time and effort (Liao, Fei & Liu, 2008). However, the knowledge base with prior-experience may not be appropriate to guide the new challenges an organization faces after an M&A event (Levintal, 1995) and undermines organizational flexibility. A limited variety of integration routines results in constrained organizational responsiveness and high levels of path-dependency (Collinson & Wilson, 2006). This in turn impedes innovation activities. Hence, the long-term negative effects of prior-experience can be due knowledge inertia.

Similar results can be found for the related acquisition experience curve. Although this organizational skill also positively guides the first stages of post-M&A integration, negative effects are measured on the long term.

19 firms in evolving industries related acquisition experience can result in a barrier for innovation. This embeddedness explains the long-term negative post-M&A effects measured in this study.

International experience can enhance organizational flexibility and reduce inertia

Although international acquisition experience also induces repetition and routine development, its moderating effect on technological acquisition outcomes appears to be of positive nature. As mentioned earlier, international acquisitions lead to the exposure to unknown resources, and result in more disruptive post-M&A processes. These processes jeopardize integration and knowledge transfer (Haleblian & Finkelstein, 2002). Also, the magnitude of time required for integration is relatively large for these acquisitions, what likely leads to lower performance. However, Howard-Grenville (2005) argues the existence of flexible organizational routines. These routines, she suggests, are able to adapt to changing environments, and thus persists over time. As a consequence, highly routinized companies like Toyota, operating in turbulent environments, are still able to respond flexibly to changes (Adler, Goldoftas, & Levine, 1999). Another example can be found by IKEA. This company systematically seeks to transfer local knowledge in its internal format for replication. This way adaptation to different environments is facilitated, while still retaining core operations (Jonsson & Foss, 2011).

In this study, acquiring companies experienced with disruptive acquisition processes are likely to have developed similar skills. The acquisition of several international firms exposes the company each time to very distinctive settings and processes, which leads to continuous organizational learning. When international acquisition experience is included in the analysis, it is also of less importance whether the current acquisition is international or domestic. This further proves the additional value of relevant experience. Winter and Szulanski (2001) argue that although the “transfer and implementation of well-documented practice and standard operating procedures have high priority” in repetitive activities, there is “no requirement that companies must copy exactly”. Therefore, firms which acquired many distinguishing organizations entailing different processes, and simultaneously recognized the flexible replication practice, are likely to benefit most of this acquisition experience. Differences among the four industries or diversity of nationality of firms appear to be of less relevance to the measured innovative output. Therefore, mainly homogenous results among the industries included can be assumed. This homogeneity supports the assumption of being high-tech industries of similar nature as indicated by Cloodt et al (2006).

20 hubris-related behavior influencing future activities. Overestimating integration capabilities might affect post-M&A performance negatively. Therefore, acquiring firms have to be aware of the pitfall of overestimation due to gained experience. Routines have to be evaluated in the perspective derived from the acquisition to be sure they are suited to apply in the new situation. Open-mindedness and willingness to change among employees of the acquiring organization is of major concern. Without a working force willing to adapt to the new derived operations, technological acquisitions aimed at improving innovative output are likely to become a failure.

Limitations and future research

Although this study provides several new insights in acquisition in relation to organizational learning, it is also characterized by several limitations.

First of all, longitudal studies entail more difficulties in measuring the isolated takeover effects. During the period covered by this study organizations may apply strategic and operational decision-making influencing innovation activities of the incorporated firms (Martynova & Regenboog, 2005). Also changes in the patent application policy may have arisen. This study does not count for these organizational factors. In testing the effects of acquisition experience a case study might be a proper addition to investigate exact firm-level consequences of M&A activity and innovative performance. Case studies provide more thorough investigation, allowing for more processes and activities cooperated in analyzing results.

Furthermore, the period under study covers the main part of the sixth merger wave, except the part during 2008 (Martynova & Regenboog, 2008). Floegel, Johanning and Gebken (2005) argue that acquisitions during the later stages of merger wave however entail more negative effects on post-acquisition performance. Therefore, the results of this study might be a little biased due to the chosen period, and consequently show relatively positive results. Future studies concerning M&A activity in an organizational learning perspective could also cover non-merger acquisitions or cover the whole period to provide a more comprehensive understanding.

21 transfer can however be investigated more depth. Knowledge is a tacit, socially-embedded resource, property of employees. A trend can be seen in acquirers focusing on human capital retention in M&A events. David Lawee, vice president of corporate development at Google, stated the following: “two-thirds of Google’s recent acquisitions have been successful, based on measures such as employee retention” Ranft and Lord (2000) also emphasize that retention of personnel is likely to be of central importance during the acquisition integration for knowledge-intensive organizations, because this retention entails easier integration. Therefore, the effect human capital retention in acquisitions aimed at innovation strengthening should be taken into account in future M&A studies.

22 CONCLUSION

The purpose of this paper was to partly replicate the study by Cloodt et al (2006) in investigating innovative performance as a result of acquisition activity. Organizational learning is included as a moderating variable, investigating potential benefits carried out by experienced acquirers. Data on four high-tech industries in Europe, North-America and Japan is used to conduct this study.

The study proposes that the technological nature of an acquisition increase the likelihood of beneficial innovative output generated by the acquisition. In general, it is found that high-tech companies acquiring technological firms experienced a significant increase in innovative performance after the M&A event. Difficulties in understanding the value of the acquired resources, and in effectively integrating them with existing operations might affect results (Cohen and Levinthal, 1990; Martynova et al., 2008)., however do not efface the positive outcomes. Similarity in operations and strategic intent among technological firms enhances integration and value creation (Kallunki et al, 2009).

It is furthermore argued that integration success is prone to acquirer characteristics. In this study, the learning curve of firms initiating several acquisitions over time is assumed to be moderate acquisition effects on innovation efforts positively. Firms entailing prior-experience are likely to have developed routines and standards, which eases repetition (Haleblian and Kim, 2010). Related acquisition experience is also hypothesized as being beneficial for acquirers. It is assumed that these firms are better able to discover information asymmetries and to designate the distinction between complementary and supplementary assets. The findings support the advantage of experienced firms in the first post-M&A years, however show negative effects on the long term. These long-term effects are similar for acquirers experienced with related acquisitions. The negative results of experience can be explained by knowledge inertia. Knowledge inertia indicates firms to stick to their knowledge base with prior-experience, although this knowledge may not be appropriate to guide the new challenges the organizations face (Levintal, 1995). Therefore, firms should develop a skill in acquisition repetition which enables them to adapt to every new situation created by sequel acquisitions. This skill might be found by acquirers experienced with cross-border acquisitions.

24 APPENDICES

APPENDIX I – Descriptive Statistics

Negative Binomial Regression with Binomial Lag

Analysis Mean Std. Deviation

Post-M&A patent applications 72.934 296.164

Non-Technological (t-1) 0.523 0.499 Non-Technological (t-2) 0.523 0.499 Non-Technological (t-3) 0.523 0.499 Non-Technological (t-4) 0.523 0.499 Acquisition Experience (t-1) 6.007 13.271 Acquisition Experience (t-2) 6.004 13.271 Acquisition Experience (t-3) 6.002 13.272 Acquisition Experience (t-4) 6.000 13.272

Related Acquisition Experience (t-1) 3.298 6.541

Related Acquisition Experience (t-2) 3.296 6.542

Related Acquisition Experience (t-3) 3.295 6.542

Related Acquisition Experience (t-4) 3.294 6.543

International Acquisition Experience (t-1) 2.00 4.294

International Acquisition Experience (t-2) 2.00 4.294

International Acquisition Experience (t-3) 2.00 4.295

International Acquisition Experience (t-4) 1.99 4.295

International Acquisition (t-1) 0.632 0.482

International Acquisition (t-2) 0.632 0.482

International Acquisition (t-3) 0.631 0.482

International Acquisition (t-4) 0.630 0.482

Pharmaceuticals (1) 0.374 0.484

Computer and Office Machinery (2) 0.135 0.342

Electronics and Communications (3) 0.438 0.496

Aerospace and Defense 0.052 0.223

North-America 0.658 0.474

Japan 0.064 0.245

Europe 0.278 0.448

25 APPENDIX II – Hypothesis 1: Technological Acquisitions and Innovative Performance Model 1-4

Univariate Model

Dependent Variable Model 1 Pijt Model 2 Pijt Model 3 Pijt Model 4 Pijt Technological (t-1) 0.415*** Technological (t-2) 0.149*** Technological (t-3) 0.045** Technological (t-4) 0.013 _cons 1.108*** 1.308*** 1.380*** 1.402*** N 2048 2048 2048 2048 * p < 0.10 ** p < 0.05 *** p < 0.01 Model 5-8 Multivariate Model

Dependent Variable Model 5

Pijt Model 6 Pijt Model 7 Pijt Model 8 Pijt Technological (t-1) 0.596*** Technological (t-2) 0.147*** Technological (t-3) 0.052*** Technological (t-4) 0.019 Pharmaceuticals -0.180 0.037 0.027 0.024

Computer and Office

26 APPENDIX III – Hypothesis 2: Acquisition Experience as a Moderator

Model 9-12

Multivariate Model

Dependent Variable Model 9

Pijt Model 10 Pijt Model 11 Pijt Model 12 Pijt Technological (t-1) 0.598*** Technological (t-2) 0.151*** Technological (t-3) 0.0392** Technological (t-4) 0.020 Acquisition Experience (t-1) 0.067*** 0.068*** 0.068*** 0.068*** Acquisition Experience (t-2) 0.002*** 0.002*** 0.002*** 0.002*** Acquisition Experience (t-3) 0.001 0.000 0.000 0.000 Acquisition Experience (t-4) -0.004*** -0.004*** -0.004*** -0.004*** Pharmaceuticals -0.059 0.151 0.144 0.141

Computer and Office

Machinery 0.445*** 0.554*** 0.554*** 0.548*** Electronics and Communications 0.156 0.311** 0.302** 0.297** North-America -0.043 -0.079 -0.079 -0.077 Europe -1.014*** -1.052*** -1.045*** -1.042*** Patent Heterogeneity 0.000 0.000 0.000 0.000 International Acquisition -0.512*** -0.440*** -0.430*** -0.427*** _cons 0.943*** 1.125*** 1.169*** 1.181*** N 2048 2048 2048 2048 * p < 0.10 ** p < 0.05 *** p < 0.01

APPENDIX IV – Hypothesis 3: Related Acquisition Experience as a Moderator Models 13-16

Multivariate Model

Dependent Variable Model 13

Pijt Model 14 Pijt Model 15 Pijt Model 16 Pijt Technological (t-1) 0.640*** Technological (t-2) 0.122*** Technological (t-3) 0.048*** Technological (t-4) 0.025

Related Acquisition Experience (t-1) 0.115*** 0.115*** 0.117*** 0.117***

Related Acquisition Experience (t-2) 0.004*** .004*** 0.005*** 0.005***

Related Acquisition Experience (t-3) -0.002 -0.002 -0.002 -0.002

Related Acquisition Experience (t-4) -0.007*** -0.007*** -0.006*** -0.007***

Pharmaceuticals 0.003 0.218 0.222 0.220

Computer and Office Machinery 1.081*** 1.233*** 1.241*** 1.233***

Electronics and Communications 0.049 0.208 0.204 0.199

North-America -0.074 -0.104 -0.111 -0.109 Europe -1.154*** -1.194*** -1.192*** -1.189*** Patent Heterogeneity 0.000 0.000 0.000 0.000 International Acquisition -0.558*** -0.500*** -0.485*** -0.481*** _cons 0.966*** 1.188*** 1.222*** 1.235*** N 2047 2047 2047 2047 * p < 0.10 ** p < 0.05 *** p < 0.01

27 Models 17-20

Multivariate Model

Dependent Variable Model 17

Pijt Model 18 Pijt Model 19 Pijt Model 20 Pijt Technological (t-1) 0.663*** Technological (t-2) 0.135*** Technological (t-3) 0.055** Technological (t-4) 0.021

International Acquisition Experience (t-1) 0.143*** 0.140*** 0.142*** 0.143***

International Acquisition Experience (t-2) 0.002 0.003 0.003 0.003

International Acquisition Experience (t-3) 0.002 0.002 0.002 0.002

International Acquisition Experience (t-4) 0.003* 0.003* 0.003* 0.003*

Pharmaceuticals -0.070 0.137 0.132 0.129

Computer and Office Machinery 0.737*** 0.871*** 0.870*** 0.863***

Electronics and Communications 0.187 0.338** 0.329** 0.327**

28 REFERENCES

- Adegbesan, JA. 2009. On the origins of competitive advantage: Strategic Factor markets and heterogeneous resource complementarity. Academy of Management Review, 34(3):463‐475. - Adler, P. S., Goldoftas, B. & Levine, D.I. 1999. Flexibility versus efficiency: A case study of model changeovers in the Toyota production system. Organization Science, 10(1):43–68.

- Aham, M.F. & Gleister, K.W. 2011. Postacquisition Management and Performance of Cross-Border Acquisitions. International Studies of Management & Organization, 41(3): 59–75

- Al-Laham, A., Schweizer, L. & Amburgey, T.L. 2010. Dating before marriage? Analyzing the influence of pre-acquisition experience and target familiarity on acquisition success in the ‘‘M&A as R&D’’ type of acquisition. Scandinavian Journal of Management, 26:25-37

- Ahuja, G., Katila, R., 2001. Technological acquisitions and the innovation performance of acquiring firms: a longitudinal study. Strategic Management Journal, 22:197–220

- Ballinger, G.A. 2004. Using Generalized Estimating Equations for Longitudinal Data Analysis. Organizational Research Methods, 7(2):127-150

- Barney, J.B. 1986. Strategic Factor Markets: Expectations, Luck, and Business Strategy. Management Science, 32(10):1231-1241

- Bertrand, O. & Zuniga, P. 2006. R&D and M&A: Are Cross-Border M&A Different? An

Investigation on OECD Countries’, International Journal of Industrial Organization, 24(2): 401–23 - Bloch, C. The Market Valuation of Knowledge Assets. Economics of Innovation and New Technology, 17(3): 269-284.

- Bresman, H., Birkinshaw, J. & Nobel, R. 1999. Knowledge Transfer in International Acquisitions. Journal of International Business Studies, 30(3): 439–460.

- Bryer, L.G. & Lebson, S.J. 2003. Intellectual Property Assets in Mergers & Acquisitions. New York: John Wiley & Sons

- Burman, L.E., Reed, W.R. & Alm, J. 2010. A Call for Replication Studies. Public Finance Review, 38: 787-793

- Buckley, P.J., Glaister, K.W., Klijn, E. & Tan, H. 2009. Knowledge Accession and Knowledge Acquisition in Strategic Alliances: The Impact of Supplementary and Complementary Dimensions. British Journal of Management, 20: 598–609

- Capron, L., Dussauge, P & Mitchell, W. 1998. Resource redeployment following horizontal acquisitions in Europe and North America. Strategic Management Journal, 19(7): 631-661. - Capron, L. 1999. The Long-Term Performance of Horizontal Acquisitions. Strategic Management Journal, 20(11): 987–1018.

- Chondrakis, 2012. Unique synergies in technology acquisitions. University of Oxford, working paper, 1-46.

29 - Cohen, W.M., Levinthal, D.A. 1990. Absorptive capacity: a new perspective on learning and innovation. Administrative Science Quarterly, 35(1): 128-152

- Collins, J.D., Holcom, T.R., Certo S.T., Hitt, M.A. & Lester, R.H. Learning by doing: Cross-border mergers and acquisitions. Journal of Business Research, 62: 1329–1334

- Collinson, S. & Wilson, D.C. Inertia in Japanese Organizations: Knowledge Management Routines and Failure to Innovate. Organization Studies, 27: 1359-1387

- Commission of the European Communities, 2007. Enhancing the patent system in Europe. Available via:

http://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:52007DC0165:EN:NOT [Visited on 21-06-2012]

- Dierickx I, Cool K. 1989. Asset stock accumulation and sustainability of competitive advantage. Management Science, 35(12):1504–11

- Ellis, KM., Reus, T.H., Lamont, B.T. & Ranft, A.L. 2011. Transfer Effects in Large Acquisitions: How Size-specific Experience Matters. Academy of Management Journal, 54(6): 1261–1276 - Espedal, B. 2006. Do Organizational Routines Change as Experience Changes? Journal of Applied Behavioral Science, 42: 468-490

- Ettie, J.E. Bridges, W.P. & O’Keefe, R.D. 1984. Organization Strategy and Structural Differences for Radical Versus Incremental Innovation. Management Science, 30(6): 682-695

- Fiol, C.M. & Lyles, M.A. 1985. Organizational Learning. The Academy of Management Review, 10(4): 803-813

- Floegel, V., Johanning, L. & Gebken, T. 2005. The Dynamics Within Merger Waves: Evidence from the Industry Merger Waves of the 1990s. Working paper

- Gantumur, T. & Stephan, A. 2011. Mergers & acquisitions and innovation performance in the telecommunications equipment industry. Industrial and Corporate Change, 21(2): 277–314 - Gardner, W. Mulvey, E.P. & Shaw, E.C. 1995. Regression Analyses of Counts and Rates: Poisson, Overdispersed Poisson, and Negative Binomial Models. Psychological Bulletin, 118(3):392-404 - Gozzi, J.C., Levine, R. & Schmukler, S.L. 2010. Patterns of international capital raisings. Journal of International Economics, 80(1): 45-57

- Greabner, M.E., Eisenhardt, K.M. & Roundy, P.T. 2010. Success and Failure in Technology Acquisitions: Lessons for Buyers and Sellers. Academy of Management Perspectives, 73-92

- Haleblian, J. & Finkelstein, S. 1999. The Influence of Organizational Acquisition Experience on Acquisition Perfornnance: A Behavioral Learning Perspective. Administrative Science Quarterly, 44: 29-56

30 - Hall, B.H. & Oriani, R. Does the market value R&D investment by European firms? Evidence from a panel of manufacturing firms in France, Germany, and Italy. International Journal of Industrial Organization, 24: 971– 993

- Hagedoorn, J. & Duysters, G. 2000. The effect of mergers and acquisitions on the technological performance of companies in a high-tech environment. Working paper, University of Maastricht - Hannan, M.T. & Freeman, J. 1984. Structural Inertia and Organizational Change. American Sociological Review, 49(2): 149-164

- Harrison, J.S., Hitt, M.A., Hoskisson, R.E. & Ireland, R.D. 1991. Synergies and Post-Acquisition Performance: Differences Versus Similarities in Resource Allocations. Journal of Management, 17(1): 173–190.

- Heimeriks, K.H., Schijven, M. & Gates, S. 2012. Manifestations of Higher-Order Routines: The Underlying Mechanisms of Deliberate Learning in the Context of Post-acquisition Integration. Academy of Management Journal, 55(3): 703–726.

- Hitt, M.A., Harrison, J.S. & Ireland, R.D. 2001. Mergers and Acquisitions: A Guide to Creating Value for Stakeholders. Cary, NC: Oxford University Press

- Howard-Grenville, J.A. The Persistence of Flexible Organizational Routines: The Role of Agency and Organizational Context. Organization Science, 16(6): 618–636

- Huber, G.P. Organizational Learning: The Contributing Processes and the Literatures. Organization Science, 2(1): 88-115

- Jonsson, A. & Foss, N.J. 2011. International expansion through flexible replication: Learning from the internationalization experience of IKEA. Journal of International Business Studies, 42: 1079–1102

- Kallunki, J.P., Pyykkö, E. & Laamanen, T. 2009. Stock Market Valuation, Profitability and R&D Spending of the Firm: The Effect of Technology Mergers and Acquisitions. Journal of Business Finance & Accounting, 36(7,8): 838–862

- Kapoor, R. & Lim, K. 2005. The Impact of Acquisitions on the Innovation Performance of Inventors at Semiconductor Companies. Academy of Management Best Conference Paper

- Kim, J. & Finkelstein, S. 2009. The Effects Of Strategic And Market Complementarity On Acquisition Performance: Evidence From The U.S. Commercial Banking Industry, 1989–2001. Strategic Management Journal, 30: 617–646

- Kohers, N., & Kohers, T. 2000. The value creation potential of high-tech mergers. Financial Analyst Journal , 56(3): 40-50.

- Leland, H.E. 2007. Financial Synergies and the Optimal Scope of the Firm: Implications for Mergers, Spinoffs, and Structured Finance. Journal of Finance, 62(2):765-807

- Levinthal, D. 1995. Three Faces of Organizational Learning: Wisdom, Inertia, and Discovery. Conference Paper

31 - Marco, A.C. & Gausser, G.C. 2008. The Role of Patent Rights in Mergers: Consolidation in Plant Biotechnology. American Journal of Agricultural Economics, 90(1): 133–151

- Markides, C. & Geroski, P. 2005. Fast Second. How Smart Companies Bypass Radical Innovation to Enter and Dominate New Market. Jossey-Bass: San Francisco.

- Martynova, M. & Regenboog, L. 2005. Takeover Waves: Triggers, Performance and Motives. Discussion paper

- Martynova, M. & Regenboog, L. 2008. A century of corporate takeovers: What have we learned and where do we stand? Journal of Banking & Finance, 32:2148–2177

- Mattooa, A., Olarreaga, M. & Saggic, K. 2004. Mode of foreign entry, technology transfer, and FDI policy. Journal of Development Economics, 75 (1): 95–111

- McCarthy, K.J. The Sixth Merger Wave: Evidence of Global Significance and Local Difference. University of Groningen: Groningen

- Moeller S.B., Schlingemann, F.P. & Stulz R.M. 2004. Firm size and the gains from acquisitions. Journal of Financial Economics, 73(2): 201-228

- Moeller, S.B. & Schlingemann, F.P. 2005. Global Diversification and Bidder Gains: A Comparison Between Cross-Border and Domestic Acquisitions. Journal of Banking & Finance, 29: 533–564 - Morrison, J.B. 2008. Putting the Learning Curve in Context. Journal of Business Research, 61:1182–1190

- Porrini, P. 2004. Alliance experience and value creation in high-tech and low-tech acquisitions. Journal of High Technology Management Research, 15: 267–292

- Peng, Y.S. & Fang, C.P. 2010. Acquisition experience, board characteristics, and acquisition behavior. Journal of Business Research, 63:502–509

- Ranft, A.L. & Lord, M.D. 2000. Acquiring New Knowledge: The Role of Retaining Human Capital in Acquisitions of High-Tech Firms. The Journal of High Technology Management Research, 11(2): 295-319

- Teece, 1977. Technology Transfer by Multinational Firms: The Resource Cost of Transferring Technological Know-How. The Economic Journal, 87: 242-261

- Travlos, N.G. 1987. Corporate Takeover Bids, Methods of Payment, and Bidding Firms' Stock Returns. The Journal of Finance, 42(4): 943-963

- Wagner, M. 2011. Acquisition as a Means for External Technology Sourcing: Complementary, Substitutive or Both? Journal of Engineering and Technology Management, 28: 283–299

- Wernerfelt, B. 1984. A Resource-based View of the Firm. Strategic Management Journal, 5: 171-180

32 - WIPO, 2011a. World Intellectual Property Indicators, 2011. Resident patent filings per $billion gross domestic product (2000-2010). Available via:

http://www.wipo.int/ipstats/en/statistics/patents/ . [Visited on 07-06-2012]

- WIPO, 2011b. World Intellectual Property Indicators, 2011. Patent publications by field of technology (2000-2009) by leading countries. Available via:

http://www.wipo.int/ipstats/en/statistics/patents/ . [Visited on 07-06-2012]

- Zhao, X. 2009. Technological Innovation and Acquisitions. Management Science, 55(7):1170– 1183

- Zhou, H. & Ghauri, P.N. 2008. Learning through International Acquisitions: The Process of Knowledge Acquisition in China. Management International Review, 48(2): 207-226