University of Groningen Faculty of Economics & Business
Department of Innovation Management & Strategy
MASTER THESIS
Master in Business Administration – Strategic Management Innovation
Acquisitions and the Indirect Resources of Corporate
Partnerships:
The Impact of Acquisitions of Targets with alliances on
Post-Acquisition Innovation Performance
Channiël Joël
S2811367
c.joel@student.rug.nl
First Supervisor:
A. A. Oleksiak
Second Supervisor:
Dr. F. Noseleit
Word count:
12.551
ABSTRACT
This paper explores the relationship between acquisitions and firm innovative performance. Whereas the majority of current literature is limited to research that explores the direct relationship between these phenomena, this study also sheds light on the indirect relationship of corporate partnerships of the target firm. We hypothesize that acquisitions have a negative influence on innovation performance. Moreover, relying on the relational view and transaction costs economics, we hypothesize that this negative relationship is more pronounced with a high share of acquisitions of target firms engaged in strategic alliances. Working with a panel dataset of 36 firms in the biopharmaceutical industry comprising data for a time window of 11 years, we find a positive relationship between acquisitions and innovation performance. Moreover, we confirm a recent stream of literature that emphasizes the characteristics of target firms to have an influence whether acquisitions will positively effect innovation. We find that this positive effect is more pronounced with a high share of acquisitions with acquired alliance. Our study therefore highlights the differences in nature between acquisitions with and without acquired alliances, and suggests that target firm’s characteristics are important to consider in the acquisition literature and management.
TABLE OF CONTENTS
INTRODUCTION ... 4
LITERATURE REVIEW ... 6
ACQUISITIONS AND INNOVATION ... 6
CORPORATE PARTNERSHIPS IN ACQUISITIONS ... 9
METHODOLOGY ... 13 RESEARCH SETTING ... 13 DATA COLLECTION ... 14 SAMPLE ... 14 MEASURES ... 15 Dependent variable ... 15 Independent variable ... 15 Control variables ... 15 ANALYSIS ... 17 RESULTS ... 18 DESCRIPTIVE STATISTICS ... 18 REGRESSION RESULTS ... 20 DISCUSSION ... 22 RESEARCH IMPLICATIONS ... 22 MANAGERIAL IMPLICATIONS ... 25 LIMITATIONS ... 25 CONCLUSION ... 26 REFERENCES ... 28
APPENDIX A: REGRESSION COMPARISON ... 37
INTRODUCTION
Innovation, one of the most relevant drivers of economic growth, has become increasingly important for firms to achieve and maintain a competitive advantage (Cassiman, Colombo, Garrone and Veugelers, 2005). The academic field has identified a shift from closed innovation models to open innovation models, as firms try to attain knowledge outside their own boundaries (West, Vanhverbeke, and Chesbrough, 2006; Hagedoorn and Schakenraad, 1994). While establishing strategic alliances is one strategy to gain access to valuable resources, the last two decades have witnessed an increase in firms engaging into technological acquisitions (Hitt, Harrison and Ireland, 2001b).
Literature defines technological acquisitions as “acquisitions that provide technological inputs to the acquiring firm (Ahuja and Katila, 2001, p 199). Firms conduct these acquisitions to explore and commercialize new technological opportunities (Puranam, Singh and Zollo, 2006). Acquisitions are a relatively fast strategy to appropriate resources compared to internal development or external collaboration, and research has presented a consensus that acquisitions deeply impact innovation performance (Hitt et al., 2001b; Cassiman et al., 2005; Bena and Li, 2014). Yet, despite the recognized impact of acquisitions on both innovation input and output, both in the short-term and long-term, the findings of prior studies are far from conclusive. Studies propose different results as they show positive-, negative-, but also non-significant influence of acquisitions on innovation performance (Aalbers and McCarthy, 2016).
acquirer (Aalbers and McCarthy, 2016). However, the more indirect resources of corporate partnerships have remained unexplored.
The aim of this study is to refine our current understanding of acquisition implications by addressing this literature gap. In order to do so, we first explore the relation between acquisitions and innovation. Subsequently, we will examine this effect on a sub-sample with firms that pursued acquisitions, and measure if this effect will be more pronounced with a high share of acquisitions with acquired alliances compared to a low share of acquisitions with acquired alliances. Following prior literature, acquired alliances are the alliances of the target firm to which the focal firm gains access through acquisition (Oleksiak, and de Faria, 2016). Understanding both the impact of not pursuing acquisition, and the impact of the integration and management of acquisitions with or without acquired alliances provides novel evidence to the academic field determining whether not pursuing acquisition is more efficient, or which acquisition characteristic contributes to the efficiency of this process.
To test our hypotheses, we conducted a fixed-effect regression on panel data comprising 36 firms operational in the biopharmaceutical industry between 1996-2006. The findings provide significant evidence for the opposite hypothesized effect, implying the positive influence acquisitions have on innovation performance, as well as a more pronounced positive effect when the share of acquisitions with acquired alliances is high. Our main theoretical implication is that we emphasize and complement a more recent stream of research that has focused on the characteristics of acquisitions targets as they function as determining factors whether acquisitions will turn out beneficial or not. From here our main managerial implication is that we stress the importance of the consideration of the target firm’s current strategic alliances before engaging into acquisitions, in order to optimize the efficiency of this process to the acquiring firm’s needs.
LITERATURE REVIEW
In this section, we first discuss the existing literature on the relationship between acquisitions and innovation performance. Next, depending on prior work on this phenomenon, the baseline hypothesis will be formulated. Relying on the relational view and transaction costs economics, a decomposition of the baseline hypothesis will follow where the relation on innovation performance will be measured depending on the share of acquisitions with acquired alliances.
Acquisitions and Innovation
Research stresses that the understanding of the influence of acquisitions on innovation performance is important from several perspectives (Ahuja and Katila, 2001). From the organizational learning and innovation perspective, research on this impact is important to help us understand the way firms absorb and utilize external knowledge and resources. Scholars writing in the technical change field argue that a firm’s innovation performance is the outcome of the growth of its knowledge base (Henderson and Cockburn, 1996). Besides increasing its knowledge base through knowledge enhancing investments, firms can also expand their knowledge by acquiring external knowledge bases (Huber, 1991). Yet, compared to present studies aimed at the relationship between knowledge enhancing investments and innovation performance, there is a relatively small number of studies aiming at the impact of acquisitions in increasing a firm’s knowledge base. From the perspective of the resource-based view, conducting acquisitions is an important strategy to pursue the redeployment of resources into more productive uses (Anand and Singh, 1997). Overall, as acquisitions entail the recombination of firm-specific assets from the focal firm and the target firm, the understanding of this impact on innovation provides evidence on the efficiency of this recombination process.
knowledge base grows with the size of the acquired target firm’s knowledge base. From the absorptive capacity view (Cohen and Levinthal, 1990), the increase of a firm’s knowledge base and technological capability enhances its capacity to recognize, assimilate and recombine external knowledge. Hence, an acquisition does not only provide the focal firm with access to internal knowledge, but it will also provide the target firm’s understanding of external knowledge (Ahuja and Katila, 2001). Scholars depending on the economic view of the firm, posit that a positive effect of acquisitions on innovation is based on the provided opportunities to exploit economies of scale and scope (Cassiman et al., 2005). These opportunities arise as the merger of knowledge bases allows the focal firm to reduce the duplication of research investments or to provide a larger research base to bear the costs (Ahuja and Katila, 2001). Overall, research states that acquisitions entail great potential to positively influence innovation as an expansion in accessible resources and capabilities enables firms to continually renovate their knowledge stock (Jansen, van den Bosch and Volberda, 2005).
to delay, matters such as product championing may be harmed, and can lead to less invested energy allocated towards daily operations related to the technological capabilities of the firm (Pritchett, 1985; Hitt, Hoskisson, Johnson and Moesel, 1996). This may lead to a disruption of the established routines from both the focal firm and the target firm, resulting in a decrease in productivity (Haspeslagh and Jamison, 1991).
Moreover, research shows that the transformation and exploitation of newly acquired knowledge is associated with a firm’s socialization capabilities (Jansen et al., 2005). These capabilities strengthen the common norms and values of communication by creating broadly and tacitly understood codes for appropriate actions (Henderson and Cockburn, 1994). The density of social linkages contributes to the development of trust and collaboration and fosters the commonality of knowledge (Rowley, Behrens and Krackhardt, 2000). The encouragement of communication will promote an effective knowledge transfer process (Galunic and Rodan, 1998), thereby allowing firms to efficiently transform and exploit new knowledge. Research shows that dense social linkages decrease the possibility of conflicts related to objectives or implementation (Rindfleisch and Moorman, 2001). However, acquisitions encompass adjustments to social contexts, as target firms need to be incorporated into the focal firm’s operations. Operations will cease for a limited time, as contracts have to be renegotiated. This can even lead to the leaving of key employees that may include top scientists, or employees that stimulate innovation (Ernst and Vitt, 2000). Moreover, disruption in a firm’s social context negatively influences the density of its social linkages, as trust and inter-firm collaboration skills need to be redeveloped, which requires significant resources and time.
H1: The relationship between acquisitions and firm innovation performance is negative.
Corporate Partnerships in Acquisitions
Target firms may differ in for example target size (Lee and Kim, 2016), knowledge base (Prahbu et al., 2005), or geographical distance (Aalbers and McCarthy, 2016), which all may have their influence on the relation between acquisitions and innovation performance. Yet still unexplored, acquisitions may also differ in in terms of presence of corporate partnerships of the target firm. Prior literature refers to these partnerships as alliances, which can be defined as “any voluntarily initiated cooperative agreement between firms that involves exchange, sharing, or co-development, and it can include contributions by partners of capital, technology, or firm-specific assets” (Gulati and Singh, 1998, p. 781). These agreements cover joint ventures, research and developments(R&D) or production agreements, marketing or distribution agreements, or technology transfers (Lahiri and Narayanan, 2013). Firms may engage in partnerships for various incentives; to combine complementary resources (Eisenhardt and Schoonhoven, 1996), to spread costs and risk of projects that comprise high expenses and uncertainty (Hagedoorn, 1993), or to gain access to resources, technological know-how or capabilities (Powell, Koput and Smith-Doerr, 1996). As engagement in alliances has become a popular strategy for firms to enhance their internal operations (Das and Teng, 2003; Kale and Singh, 2009), it is often the case that an acquisition entails acquired alliances. As previously described, acquired alliances are the target firm’s alliances to which the acquiring firm gains access through acquisition (Oleksiak, and de Faria, 2016)
The relational view emphasizes the effect between the context of alliances and creating and transferring knowledge (Dyer and Singh, 1998). Following prior research, relational capital is needed to attain knowledge from collaboration partners, as it serves as a facilitator between the effect of trust and opportunism, and the perceived performance (Liu, Ghauri and Sinkovics, 2010; Lado, Dant and Tekleab, 2008). Relational capital is defined as “the level of mutual trust, respect, and friendship that arises out of close interaction at the individual level between alliance partners” (Kale, Singh and Perlmutter, 2000, p. 218). Research recognizes interaction and trust as crucial prerequisites for the creation of relational capital (Liu et al., 2010). The level of interaction between collaboration partners is dependent on the strength of their ties. Partners that have built strong ties through the development of trust over time will therefore show a high degree of interplay in both quality and time (Zaheer, Gözübüyük and Milanov, 2010). The second prerequisite for the creation of relational capital is inter-firm trust, as it encourages communication and knowledge transfer (Faems, Janssens, Madhok and van Looy, 2008), and establishes long-term alliances (Sarkar, Echambadi, Cavusgil and Aulakh, 2001). Yet, as ties with acquired alliances are weak and trust is still underdeveloped, the acquisition of a target with corporate partners requires significant managerial resources and time in order to create relational capital, to increase the probability of knowledge recombination.
interaction with partners; so as to maintain an effective inter-firm collaboration, and the implementation of processes to respond on partners’ actions (White and Lui, 2005).
Focusing on the alliance portfolio management perspective, the consummation of acquisitions with acquired alliances will lead to an instant expansion of new alliance partners in the focal firm’s alliance portfolio. Research has highlighted the selection of an appropriate alliance partner as a difficult, but critical decision for an alliance’s success (Hitt, Tyler, Hardee and Park, 1995; Koot, 1988). The selection of partners reflects a broad range of factors, which are derived from the focal firm’s requirements (Dacin, Hitt and Levitas, 1997). Firms aim to corporate with partners that own resources that can be leveraged in order to create synergy, or with partners from whom they can learn skills and capabilities that strengthen their own competences in order to develop their competitive advantage (Hitt et al., 2000). The partner’s behavior will be predicted to balance potential difficulties during the agreement against the potential benefits, and firms will only engage in the alliance if these benefits outweigh the anticipated problems (Polidoro, Ahuja and Mitchell, 2011). Moreover, when screening for partners related to technology collaborations, firms should select partners whose strategic objectives can be converged, while their competitive objectives deviate (Wu, Shih and Chan, 2009). As alliances may provide competitive advantage (Dyer and Singh, 1998), research directly links firm innovation performance to the success of a focal firm’s alliances (Dyer and Nobeoka, 2000). Yet, acquired alliances are not selected, but simply get inherited. Despite that these agreements can be evaluated, examined, and renegotiated to a certain extent, acquired alliances are likely to be exposed to hazards regarding to misfit of strategy, complementarity, or even conflicts. Acquired alliances therefore have a low probability to become successful.
Overall, acquisitions of target firms with acquired alliances show great potential to positively influence firm performance. However, research indicates that the realization of the benefits of acquisitions is hard because of its managerial burdens. As previously discussed, the degree of required managerial and organizational resources related to the development of trust, collaboration skills, and coordination that needs to be allocated towards the acquisition, is significantly higher in comparison to acquisitions of target firms without acquired alliances. Moreover, the absence of the process of partner selection and negotiations results in higher probability of alliances being exposed to misfit in terms of strategy or complementarity. Firms that pursue acquisitions without acquired alliances are able to reap the benefits of an increase in their knowledge base more effectively as their focus can be immediately directed to the target company. We therefore hypothesize that the negative effect of acquisitions on innovation performance is at its strongest when the focal firm contains a higher share of acquisitions with acquired alliances.
H2: The negative effect of acquisition is more pronounced when the focal firm contains a high share of acquisitions with acquired alliances compared to a low share of acquisitions with acquired alliances
The overall conceptual model, as shown in figure 1, presents the baseline effect from acquisitions on innovation performance as outlined in hypothesis 1. Subsequently, it presents the decomposition of the baseline effect where the effect on innovation performance will be measured depending on the share of acquisitions with acquired alliances.
Methodology
As suggested by van Aken, Berends and van der Bij (2012), a theory testing approach is in place if the current literature streams are already elaborated and not scattered, yet there is still a literature gap in the theoretical explanations. To date, studies have substantially examined the implications of target firms’ characteristics. Yet, the influence of indirect resources of corporate partnerships on innovation performance is, to the best of our knowledge, not yet explored. We will therefore conduct a theory testing approach.
Research Setting
We conducted the empirical tests on a panel data set that has been specifically build for this study. The dataset comprises alliance, acquisition, and patenting information of firms operative in the biopharmaceutical industry. A longitudinal approach or panel data analysis is used, as it yields major advantages compared to conventional cross-sectional or time series data sets. By providing multiple observations of each individual firm in the sample, the analysis allows more accurate measurements of the proposed effects on innovation performance (Hsiao, 2014). We focused on the largest firms in the biopharmaceutical industries, based on their revenues in 2005. This list was provided by the department of Innovation Management & Strategy at the University of Groningen.
Data Collection
As aforementioned, the focal firms included in the dataset are based on a fixed list with the largest firms in 2005. We included all acquisitions that were announced between January 1996 and December 2006 in order to ensure inclusion of enough transactions. Alliance and acquisition information was extracted from the LexisNexis database. This database provides a major advantage over the Securities Data Company (SDC) database, as the latter often does not contain information on the ending dates of the alliances (Schilling, 2009). All announcements were obtained by using algorithms containing the focal firms’ names in combination with collaboration terms within a certain number of words. The information that was retrieved concerned start- and ending dates of alliances, the type of alliances, alliance partners’ characteristics as origin, SIC, and NAICS codes, acquisitions, and detailed information regarding these transactions. Subsequently, through each firm’s acquisition data, the alliance data of the target firms was obtained in order to measure which acquisitions entailed acquired alliances. Codes that were still missing were manually searched through SEC fillings or other Internet sources. To obtain data regarding patents, we used the Bureau van Dijk’s Orbis database. This database contains data derived from the United States Patent and Trademarks Office (USPTO) and provides numbers of patents per year for each focal firm. Regarding the control variables, data was retrieved from multiple sources. We retrieved data regarding the variables R&D expenditures and total assets from SEC filings for North American firms and annual reports for non-North American firms. Data related to alliance portfolio size and industry diversity were obtained through the LexisNexis database. The final step in the data collection was to merge all the data into one dataset. The program STATA14 has served as a tool to test our hypotheses.
Sample
Measures
Dependent variable
Innovation performance. Literature proposes that patents have a direct relationship with innovation (Griliches, 1990). We counted the number of issued patents per year within the specific time period, to measure innovation performance. The count for each firm was based on a four-year window and includes a one-year time lag (Sampson, 2007). Patent data has therefore been collected for the time period from 1996 to 2010 to assure complete values for the year 2006. To give an example, to measure the effect of an acquisition pursued in 2005, we constructed the number of issued patents from 2006 – 2009. We accounted the one-year lag for the time needed for the focal firm and the target firm to transfer knowledge and to establish an efficient inter-firm collaboration, before innovation outputs can occur and patents can be issued (Hausman, Hall and Griliches, 1984).
Independent variable
Acquisitions. Acquisition activity is our independent variable. For the first hypothesis, in line with previous research (Hitt, Hoskisson, Ireland and Harrison, 1991), we measured per specific year whether a firm conducted acquisitions or not. Firms that were engaged in acquisitions have been coded with 0, and firms that were not engaged in acquisitions have been coded with 1. To allow differentiation effects between one or multiple acquisitions pursued within one year, we controlled for the yearly number of conducted acquisitions.
For the second hypothesis, we created the variable share acquisitions with acquired alliances, since we hypothesized that the negative effect of acquisitions on firm performance is more pronounced with a high share of acquisitions with acquired alliances. The value of this variable was calculated as a fraction of the count of acquisitions that entailed acquired alliances on the total number of pursued acquisitions. The variable could therefore only take a positive value between 0 and 1, and was measured per specific year. For example, if a firm pursued 5 acquisitions, of which 4 entailed alliances, the variable takes a value of 0.8. To allow differentiation between two possible zero values (where no acquisitions were pursued or no acquired alliances were at stake), we controlled for the number of acquisitions executed. Control variables
Firm age – The first control variable is firm age and refers to the time in years a firm is already operative. Older companies tend to possess more knowledge compared to younger firms, which can be allocated across innovations (Gittelman and Kogut, 2003). In line with previous research (Anderson and Eshima, 2013), the natural logarithm of firm age is applied to normalize the distribution (see appendix B: table B1 and table B2).
Industry diversity – Research indicates that a moderate level of alliance portfolio diversity positively effects innovation performance, and low and high levels of diversity negatively influence innovation (Jiang, Tao and Santoro, 2010). To construct this variable, we used the method as done by prior research (Jiang et al., 2010), using the 4-digit SIC codes to compare the relatedness between the focal firm and its alliance partners. This control variable comprises five categories. First, firms that have the same SIC codes will be coded with a 4 (e.g. both SIC codes are 2834). Firms that have the same first three digits are marked with a 3 (e.g. 2834 and 2835). Firms that have the same first two digits are marked with a 2. Firms that only share the first digit in the code are marked with a 1, and if firms share none of the digits, the alliance is coded with a 0. The control variable ranges from 0 to 4 (integers), where 0 indicates no relatedness and 4 indicates a high relatedness. Based on these categories we computed the Blau Index of Variability (Wuyts and Dutta, 2014) to aggregate the industry diversity index to firm-level. To calculate the index of firm i’s alliance portfolio, the sum of squared portions of each category j’s is subtracted from one,
where i holds for the focal firm, N holds for the possible categories, j holds for the category and…
N/(N-1), For this study, the IQC = 1.25 and we applied this to the BIV values by multiplication. By applying the IQV correction, values will be better for interpretation as we created positive decimal scores varying from 0 to 1, independent of the number of categories (Agresti and Agresti, 1978).
Alliance portfolio size – Research indicates that a firm’s alliance portfolio size has a positive impact on firm performance (Baum, Calabrese and Silverman, 2000). Accordingly, the effects of alliance portfolio size will therefore be controlled (Lavie and Miller, 2008)
R&D Intensity – Research emphasize the significant positive influence of R&D intensity on firm innovation performance (Lahiri and Narayanan, 2013). High investments in R&D enhance the focal firm’s ability to capture value from the acquired firm’s resources. To control for this possibility, we calculated the R&D intensity, by collecting data regarding R&D expenditures, transformed them to US dollars when needed, and divided them by the total assets of the focal firm (Lavie and Miller, 2008; Lee, Lee and Pennings, 2001)
Analysis
conduct the linear regression analysis (xtreg command in STATA) to verify if these non-significant outcomes hold.
However, the assumptions of this measurement will be violated, as the dependent variable is a count variable. The assumption holds for the testing of dependent variables that are continuous. In order to reduce these violations by making the linear regression analysis more appropriate for our panel dataset, the dependent variable has been transformed logarithmically. Nonetheless, as linear regression also allows negative values to take place, the logarithmic transformation still holds limitations as the logarithmic values can only consist out of positive values. Since the linear regression resulted in similar results, but provided significance regarding both hypotheses, we selected these outcomes as our main results. However, due to the previously described violations, the interpretation of the regression results should be taken with caution. For the full comparison of the different regression results, we refer to appendix B.
To test the hypotheses, the Hausman test has been conducted to see whether a fixed effect regression or a random-effect regression will be more appropriate (Greene, 2008). The Hausman test indicated that the estimations from the random-effect regression did not significantly differ than the fixed-effect regression, indicating that the random-effects regression is favored above a fixed-effect regression. However, the random-effects model estimates that the variation across the individual firms is distributed independently of the regressors (Torres-Reyna 2007). The fixed-effect model on the other hand, assumes that there is unobserved heterogeneity across individual firms, preventing that results are biased as a result of unobserved heterogeneity (Noseleit and de Faria, 2013). Therefore we conducted the fixed-effect regression.
Results
The following section presents a discussion of the results of the statistical analyses. First, the results of the descriptive statistics are discussed. This will be followed by a discussion about the outcomes of regressions for hypotheses testing.
Descriptive Statistics
statistics of the subsample for the second hypothesis. The Pearson correlation test (table 1 and table 2) and the Variance Inflection Factors (VIF) (see appendix B: table B3 and table B4) test have been conducted for both hypotheses in order to test multicollinearity. The overviews in table 1 and table 2 show that the threshold value of 0.70 is not exceeded, as the highest coefficients in the tables hold a value of 0.51 and 0.55 (Grewal, Cote and Baumgartner, 2004). Moreover, the mean of the VIF values holds for hypothesis 1 the value 1.46, and for hypothesis 2 the value 1.43. The highest VIF value for hypothesis 1 is 1.69, and the highest VIF value for hypothesis 2 is 1.90. As none of these values exceeds the critical multicollinearity value of 10.00, these tests support that multicollinearity can be ruled out (Robinson and Schumacker, 2009).
Table 1: Descriptive statistics and correlations for hypothesis 1
Variable Mean S.D. 1 2 3 4 5 6 7
Patents (log) 2.83 1.57 1.00
Acquisition 0.12 0.33 0.08 1.00
Firm age (log) 2.60 0.85 0.07 0.16 1.00
Alliance Portfolio Size 9.75 7.52 0.33 0.14 0.27 1.00
R&D intensity (focal firm) 0.21 0.15 0.18 -0.13 -0.34 -0.18 1.00
Industry Diversity 0.50 0.29 0.40 0.06 0.16 0.51 0.04 1.00
Acquisition count 0.74 1.28 -0.11 0.47 0.39 0.29 -0.24 0.16 1.00
Table 2: Descriptive statistics and correlations for hypothesis 2
Variable Mean S.D. 1 2 3 4 5 6 7
Patents (log) 2.74 1.55 1.00
Share of acquired alliances 0.35 0.45 0.36 1.00
Firm age (log) 2.62 0.97 0.18 0.06 1.00
Alliance Portfolio Size 9.48 8.25 030 0.33 0.28 1.00
R&D intensity (focal firm) 0.19 0.15 -0.04 -0.04 -0.39 -0.23 1.00
Industry Diversity 0.51 0.29 0.31 0.38 0.20 0.55 -0.14 1.00
Acquisition count 1.16 1.49 0.26 0.20 0.47 0.40 -0.26 0.21 1.00
skewed to the left, indicating that the majority of firms in the sample only obtained a small number (or even zero) patents. This is complemented by prior research done on patents (Schoenmakers and Duysters, 2010).
Figure 1: distribution of the dependent variable patents measured in a four-year window
Furthermore, regarding the main sample, the statistics of the control variables show that the average alliance portfolio consisted out of 9.7 partners, where the minimum numbers of partners was 0, and the maximum number of partners was 37. The mean of the industry diversity in the sample holds a moderate score of 0.50. Following research done by Jiang et al., (2010), the firms in our sample should experience a positive influence from their industry diversity on innovation performance. With regard to the second hypothesis, the mean of 0.35 indicates that firms in general had a low share of acquisitions with acquired alliances.
Regression Results
relation on innovation (β = 0.221, p ≤ 0.10). Therefore hypothesis 1 is rejected. For the second hypothesis, we predicted that the negative effect of acquisitions on firm innovation performance is more pronounced when the acquisition target is engaged in strategic alliances. As previously discussed, model 3 and model 4 were conducted on the sub-sample that only contained firms that pursued acquisitions. Model 3 reflects the baseline model that includes all the control variables. The results of model 3 show a significant relationship between innovation and firm age (β = 0.561, p ≤ 0.01). In model 4 the share of targets with acquisitions is added to measure the second hypothesis. Besides the significance of the control variable firm age (β = 0.433, p ≤ 0.05), a significant relationship is found between the share of acquisitions that entailed acquired alliances and innovation performance (β = 0.379, p ≤ 0.10). As this effect was not predicted, hypothesis 2 is not supported. As we described in the analysis section, the results should be taken with great care due to the described limitations. However the comparison with other regressions (see appendix A) shows significance for the second hypothesis for both negative binomial regression analyses. This indicates that the interpretation of model 4 can occur with more confidence.
Table 3: Panel data fixed-effects regression results a,b,
Variable Model 1 Model 2 Model 3 Model 4
Acquisition 0.221*
(.116) Share of acquisitions with
acquired alliances
Discussion
In the final section, a discussion about the research- and managerial implications will be presented. Subsequently, main limitations of this study will be identified.
Research Implications
Overall our empirical results contribute some valuable insights into the dynamics and implications of acquisitions management. Whereas current literature has primarily concentrated on the direct implications for firm performance, the empirical results of this study emphasize the necessity to increase the attention towards the indirect resources of acquisitions, namely the target firm’s characteristics. These factors in acquisition management have a great role in explaining and determining whether acquisitions will contribute to innovation performance. A limited number of recent studies have given attention to the characteristics of acquisition targets (Ernst and Vitt, 2000; Hitt et al., 1990; Prabhu et al., 2005). Yet, the implications of the more indirect resources of corporate partnerships have remained unexamined.
capabilities contributing to the efficiency of the recombination process of internal and external resources, which allows firms to effectively transform and exploit newly acquired knowledge into their operations (Kogut and Zander, 1992; Zahra and George, 2002).
Our second finding identifies that the positive relation between acquisitions and innovation performance is more pronounced with a high share of acquisitions with acquired alliances than with a low share of acquisitions with acquired alliances. We hereby highlight the relevancy of the phenomenon of acquired alliances. Our study emphasizes the differences in nature between acquisitions without acquired alliances and acquisitions with acquired alliances, as the integration of the latter entails the engagement into alliances with partners that are not self-initiated. We therefore encourage scholars to consider the implications of acquired alliances when examining acquisition and alliance portfolio management. As the result was the opposite effect we proposed, we invite future research to further explore the indirect resources of corporate partnerships. Important insights may be found when focusing on the type of alliances target firms possess. Hagedoorn and Schakenraad (2004) already showed that R&D collaborations positively moderate innovation performance. Other research suggests that collaborations based on exploitation result in higher short-term innovation performance, compared to collaborations based on exploration activities (Yamakawa, Yang and Lin, 2011). Depending on these findings, it might be expected that the type (i.e. R&D, marketing, manufacturing, distribution, supply), and the focus (exploration or exploitation) of the acquired alliances, influence the implications of acquisitions.
In a similar way, future research may also find valuable insights when focusing on the alliance management of these acquired alliances on a dyadic level. While a great part of our second hypothesis was developed on arguments relying on the relational view, prior research stresses the effectiveness of formal alliance governance mechanisms in coordinating and overseeing entire alliances. These studies argue that the adoption of a protected formal governance structure will decrease the probability that partners will behave opportunistically, and thereby contribute to the effectiveness and efficiency of the collaboration (Barringer and Harrison, 2000; Draulans, and Volberda, 2003). Building upon these findings, it can be expected that the type of mechanism the acquiring firm adopts to oversee the acquired alliances, might be relevant for the acquisition outcome.
may have developed alliance capabilities. Following Heimeriks and Duysters (2007), alliance experience can be defined as “the lessons learned, as well as the know-how generated through a firm’s former alliances” (p. 29). Firms with alliance experience may therefore possess knowledge how to manage their external links with success. In a similar way, other studies suggest that this success is more dependent on the internal organization of a firm. Firms with alliance experience are more likely to develop capabilities that are focused on the sharing of relevant knowledge (Schilke and Goerzen, 2010). It can therefore be expected that the internal operations of target firms with acquired alliances are more aimed towards incoming and outgoing knowledge transfer. Future research, exploring the moderating influence of the target firm’s alliance management capabilities on the relationship between acquisitions with or without acquired alliances and firm innovation performance may therefore be relevant.
Regarding our control variable alliance portfolio size, the regression results show a significant positive effect. However, this effect is not significant in our sub-sample. While we expected a positive effect, other studies have found an inverted U-shape relation between alliance portfolio size and innovation performance (Rothaermel and Deeds, 2006). Depending on these findings, the significant increase in alliance portfolio size as a result of the integration of acquired alliances, could have caused a decreasing effect on innovation performance. Future research that would also consider the number of acquired alliance might therefore be relevant. In a similar way, our descriptive statistics show that the firms in both our samples have a moderate level of industry diversity in their alliance portfolio. Following research done by Jiang et al., (2010), a moderate level of industry diversity will have a positive effect on innovation performance. Yet, the regression results do not show a significant relation between industry diversity and innovation performance in our sub-sample. Future research exploring the moderating effect of acquisitions on industry diversity might therefore provide interesting insights
Managerial Implications
The empirical findings of this study can be translated into interesting implications and guidelines that may provide support to the management of acquisitions with and without acquired alliances. Although prior literature emphasizes that the challenges related to the realization of the potential benefits of acquisitions are thresholds for firm that cannot be overcome, our results show that acquisitions have a positive effect on innovation performance. This indicates that managers should consider acquisitions when searching for strategies to complement their internal operations. With regard to the second hypothesis, despite research on the phenomenon of acquired alliances is still immature, our findings contribute to the development of a greater understanding. While prior research indicates that the integration of acquired alliances comes with great difficulties and therefore negatively influence innovation performance, our results show the opposite. As the characteristics of the acquired alliances could have contributed to our findings, we emphasize the importance for firms to consider the individual characteristics of each acquired alliance partner. Overall, we suggest that engagement in acquisitions should occur with great caution. Organizational mechanisms, and combinative- and management capabilities must be in place in order to encounter the difficulties acquisitions entail. Additionally, attention must be allocated towards the creation of social linkages with the target firm, as it contributes to the development of trust and collaborations skills. Moreover, the current strategic alliances of the target firm should be considered and analyzed, in order to optimize the efficiency of this recombination process to the focal firm’s needs.
Limitations
a possible loss of observations could have occurred when foreign firms only patented in their home countries. In addition, although research suggests patents as an appropriate mean for innovation performance (Hagedorn and Cloodt, 2003), a deficiency in this measurement lies in the fact that not all firms use patents as a mean to protect their innovations. Conversely, patenting on out-dated innovations occurs as firms try to protect their current products, making the degree to what extent patents reflect innovation less reliable (Puranam, Singh and Chaudhuri 2009). Lastly, the usage of weighted patents strongly influences the reliability of the patent data and thus the overall model (Sampson, 2007). Research therefore emphasizes the importance of the filtration of patent citations (Albrecht, Bosma, van Dinter, Ernst van Ginkel, and Versloot-Spoelstra, 2010). However, due to time constrictions, and the fact that this kind of data was difficult to obtain from the Orbis Database, we did no apply weighted patents in this study. Future research could rely on weighted patents in their analysis, thereby increasing the reliability and confidence of their findings. Lastly, as mentioned in the analysis section, the interpretation of the results of this study should be taken with caution, due to the described limitations in the statistical model.
CONCLUSION
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APPENDIX A: REGRESSION COMPARISON
As shown in table A1 and table A2, we compared the results of four different regressions. We conducted two linear regression analyses; the xtreg for the non-transformed dependent variable, and the xtreg_log for the logarithmic transformed dependent variable. We also conducted the negative binomial regressions using the xtnbreg and nbreg commands. Despite its limitations described in the analysis section, the xtnbreg analysis delivers results with a relatively high accuracy, yet as research suggest, the interpretation of these results should occur with caution (Alison and Waterman, 2002). As the xtnbreg regression only functions as a comparison, its outcomes can be valuable, as it may reinforce the results of the linear regression with logarithmic transformation. The same holds for the results of the nbreg analysis, as it is the analysis that should normally be used looking at the characteristics of our panel dataset. The outcomes can increase the degree of reliability of the results of the linear regression analysis.
As presented in table A1 and table A2, the two negative binomial regressions do not show similar results with regards to the control variables. The nbreg command in general holds significance for three controls (firm age, R&D intensity and acquisition count) compared to only significance for one control variable (alliance portfolio size) for the xtnbreg command. The commands share that they both show non-significance for the first hypothesis, and show significance for the second hypothesis (β = 0.548, p ≤ 0.01; β = 0.741, p ≤ 0.01).
The two linear regression results do not present similar results with regards to the control variables as they contain differences in coefficients and significance levels. Model 2 stands out, as the xtreg_log shows a significant effect on innovation (β = 0.221, p ≤ 0.01), where as the xtreg analysis does not show significance. With regards to the measurement of hypothesis 2 in model 4, xtreg_log shows a positive effect on innovation with a significance level of 0.10 %, whereas the xtreg does not show a positive effect on innovation.
Table A1: Regression comparison for model 1 and 2 a,b,c
Table A2: Regression comparison for model 3 and 4 a,b,c
a Significance: *p ≤ 0.1; ** p ≤ 0.05; *** p ≤ 0.01 b Standard errors are reported in parentheses
c The F-test values belong the linear regressions. The X2 value belongs to the xtnbreg model. The xntbreg does provide R2 values
Variable Model 1 Model 2
nbreg xtnbreg xtreg Xtreg_log Nbreg xtnbreg xtreg Xtreg_log
Acquisition 0.025 0.059 0.482 0.221*
(.098) (.099) (7.705) (.116)
Share of acquisitions with acquired alliances
Firm age (log) 1.065*** 0.104 -3.302 0.597*** 1.064*** 0.109 -3.233 0.622*** (.206) (.092) (.7.626) (.120) (.206) (.093) (7.705) (.120)
Alliance portfolio size 0.014 0.013* 0.990** 0.015* 0.014 0.013* 0.993** 0.017** (.009) (.007) (.488) (.008) (.009) (.007) (.491) (.008) R&D intensity 0.395** 0.324 16.275 0.293 0.403** 0.324 16.331 0.316 (.201) (.235) (18.212) (.282) (.198) (.235) (18.264) (.281) Industry diversity 0.160 0.236 1.040 0.375* 0.162 0.237 1.090 0.399* (.185) (.207) (13.538) (.220) (.185) (.207) (13.583) (.219) Acquisition count 0.118** 0.035 8.309** 0.001 0.111* 0.020 8.200 -0.050 (.054) (.047) (3.578) (.058) (.065) (.053) (3.925) (.064) Constant -3.303*** 1.131*** 33.484 0.985*** -3.302*** 1.115*** 33.243 0.889*** (.650) (.262) (20.627) (.322) (.649) (.264) (.20.964) (.324) Number of observations 312 312 312 288 312 312 312 288 Number of firms 36 36 36 36 36 36 36 36
F-test/X2/log pseudo likelihood -1088.2759 -961.7995 4.53*** 12.14*** -1088.238 -961.6221 3.76*** 10.83***
R2 within 0.0771 0.1972 0.0722 0.2090
Prob>chi2 0.0000 0.0001 0.0000 0.0002
R2 overal/pseudo R2 0.2572 0.0593 0.0418 0.2572 0.0673 0.0447
Variable Model 3 Model 4
nbreg xtnbreg xtreg Xtreg_log nbreg xtnbreg xtreg Xtreg_log
Acquisition
Share of acquisitions with acquired alliances
0.548*** 0.741*** 10.460 0.379* (.203) (.152) (6.423) (.207)
Firm age (log) 0.845*** -0.148 8.710 0.561*** 0.721** -0.289** 5.132 0.433** (.305) (.121) (6.901) (.208) (.308) (.121) (7.207) (.217)
Alliance portfolio size 0.011 0.002 0.587 0.002 0.003 -0.003 0.401 -0.003 (.011) (.010) (.370) (.013) (.010) (.010) (.385) (.013) R&D intensity 0.442 0.441 36.275* -0.012 0.547* 0.637 39.521** 0.142 (.366) (.456) (18.624) (.560) (.360) (.438) (18.629) (.561) Industry diversity 0.044 0.281 -3.211 0.317 0.057 0.253 -2.956 0.276 (.285) (.320) (10.813) (.351) (.262) (.323) (10.755) (.349) Acquisition count 0.133 0.170*** 8.614*** 0.061 0.097 0.123* 8.19*** 0.039 (.0847 (.061) (2.643) (.084) (.089) (.065) (2.641) (.084) Constant -2.678*** 1.356*** -1.136 1.273** -2.121** 1.578*** 5.642 1.501** (.862) (.380) (19.032) (.567) (.907) (.393) (19.381) (.576) Number of observations 177 177 177 155 177 177 177 155 Number of firms 22 22 22 22 22 22 22 22
F-test/X2/log pseudo likelihood -630.0452 -547.5937 10.57*** 4.38*** -624.5713 -535.9288 9.35*** 4.28***
R2 within 0.2605 0.1460 0.2734 0.1680
Prob>chi2 0.0000 0.0039 0.0000 0.0000
APPENDIX B: HISTOGRAM AND TABLES
Table B1: Skewed distribution of firm age
Table B3: Variance Inflection Factors (VIF) results for hypothesis 1
Variable VIF 1/VIF
Patents (log) 1.42 0.70
Share of acquired alliances 1.39 0.72
Firm age (log) 1.27 0.79
Alliance Portfolio Size 1.63 0.61
R&D intensity (focal firm) 1.30 0.77
Industry Diversity 1.49 0.67
Acquisition count 1.69 0.59
Mean 1.46
Table B4: Variance Inflection Factors (VIF) results for hypothesis 2
Variable VIF 1/VIF
Patents (log) 1.21 0.83
Share of acquired alliances 1.22 0.82
Firm age (log) 1.35 0.74
Alliance Portfolio Size 1.90 0.53
R&D intensity (focal firm) 1.22 0.82
Industry Diversity 1.53 0.65
Acquisition count 1.56 0.64
