The Impact of Acquisition Program Diversity on the Long-Term Performance of Technology Acquisitions in the Pharmaceutical Industry

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European Management Journal xxx (xxxx) xxx

Available online 26 July 2023

Long-term performance of technology acquisitions: The role of acquisition program diversity, innovation, and alliance portfolio size – Evidence from the pharmaceutical industry

Lars Schweizer

^*

, Le Wang, Eva Koscher

Strategic Management, Goethe University Frankfurt, Theodor-W.-Adorno Platz 4, D-60629 Frankfurt, Germany

A R T I C L E I N F O Keywords:

Acquisition program M&A performance Innovation Alliance portfolio Pharmaceutical industry

A B S T R A C T

This study investigates whether serial acquisitions enhance shareholder value in the context of technology acquisitions. We analyze how acquisition program diversity – defined as differences between multiple targets’ firm types – affects long-term acquisition performance and examine how this relationship is contingent on strategic resources. By testing data regarding 312 technology acquisitions by 147 public pharmaceutical companies from 2002 to 2021, we find that the effect of acquisition program diversity on shareholder value follows an inverted U- shaped curve moderated by the acquirers’ innovation level and alliance portfolio size. Our findings indicate that the performance of technology acquisitions depends on the acquiring firms’ diversification degree regarding the industry value chain and internal and external resources relevant to corporate development strategies.

1. Introduction

Established incumbent firms face several challenges in their efforts to survive and sustain their competitive advantage because of the emer- gence of radical technological changes (Christensen & Bower, 1996;

Cooper & Schendel, 1976; Hill & Rothaermel, 2003; Kapoor & Klueter, 2015). Radical innovations often initiate a creative destruction process, leading to the replacement of incumbents by new entrants (Schumpeter, 1942). Therefore, many established firms invest financial and managerial resources to adapt to the prevalent environmental dynamism (Teece, Pisano, & Shuen, 1997). In academics and practice, mergers and acquisitions (M&A) are oftentimes considered as means by which established firms obtain external technologies (Ranft & Lord, 2002). As such, the incumbent firms engage in M&A activities to add to their technological capabilities, enhance their market power, and achieve strategic renewal (Agarwal & Helfat, 2009; Uhlenbruck, Hitt, & Semadeni, 2006).

In line with prior research, this type of M&A is referred to as technology acquisitions (e.g., Ahuja & Katila, 2004; Puranam, Singh, & Zollo, 2006) in the present study.

Although corporate acquisitions are a prominent growth strategy for many technology firms (e.g., Cisco, Google, SAP, Lilly), prior M&A research indicates that most acquiring firms experience unrewarding performance outcomes (Aktas, De Bodt, & Roll, 2009; Hitt et al., 2012).

Failure rates are high and reported to range between 40 and 60 percent (Christensen, Alton, Rising, & Waldeck, 2011). Accordingly, strategy and management scholars have called for pinpointing theoretical frameworks that help to explain acquisition performance (Galavotti, 2019; Haleblian, Devers, McNamara, Garpenter, & Davison, 2009; Hitt, Harrison, Ireland, & Best, 1998; Hoskisson, Johnson, & Moesel, 1994).

The most recent meta-analysis studies find mixed support for acquisition experience impacting performance (i.e., contingent on certain condi- tions) (King, Wang, Samimi, & Cortes, 2021; Langosch & Tumlinson, 2021) and mediated through different post-acquisition integration strategies (Schweizer, Wang, Koscher, & Michaelis, 2022). Findings from many previous studies investigating M&A performance have also provided diverse implications.

For instance, the acquisition performance would be positive if the most value-destructive deals were avoided (Moeller, Schlingemann, &

Stulz, 2004), whereas technology acquisitions are usually different (Bower, 2001) and have conflicting findings (e.g., Ahuja & Katila, 2001;

Cloodt, Hagedoorn, & Van Kranenburg, 2006; Jones, Lanctot, & Teegen, 2000; King, Slotegraaf, & Kesner, 2008; Makri, Hitt, & Lane, 2010;

Puranam et al., 2006; Uhlenbruck et al., 2006). Superior acquisition performance may be explained by previous acquisition experience, and scholars have investigated whether, if compared with first-time acquisitions, performance improvements may be obtained over time thanks to

* Corresponding author.

E-mail addresses: [email protected] (L. Schweizer), [email protected] (L. Wang), [email protected] (E. Koscher).

Contents lists available at ScienceDirect

European Management Journal

journal homepage: www.elsevier.com/locate/emj

https://doi.org/10.1016/j.emj.2023.07.002

Received 11 November 2021; Received in revised form 31 May 2023; Accepted 18 July 2023

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the experiential learning of relevant capabilities (Trichterborn, Knyphausen-Aufseß, & Schweizer, 2016). An emergent research stream in the M&A literature argues that firms that engage in multiple acquisitions – so-called serial acquirers – driven by a sound business logic (named as acquisition program) seem to have a better chance of success than ad hoc and one-off acquirers (Chao, 2018; Chatterjee, 2009). The underlying logic is that acquisition success depends on the acquirer’s acquisition capabilities, and acquisition capability development takes place primarily on the acquisition program level (Laamanen & Keil, 2008). A growing body of evidence suggests that cumulating acquisition experience per se does not automatically lead to better acquisition capabilities (Barkema & Schijven, 2008; Hayward, 2002; Schijven, Kolev,

& Haleblian, 2021). However, there is no consistent evidence that serial

acquirers have higher M&A capabilities and create substantial value for their shareholders (Barkema & Schijven, 2008; Chao, 2018). Laamanen and Keil (2008) suggest that the overall performance of serial acquirers may depend on the pattern of multiple acquisitions, and in a recent literature review on experience and learning in corporate acquisitions, Galavotti (2019) has outlined that qualitative features, such as experience timing/recency, codification, and target heterogeneity, play an essential role in developing acquisition capabilities and becoming a more successful acquirer and that further research in these areas is necessary. Furthermore, a recent study by Strobl, Bauer, and Degischer (2022) has shown that the context of acquisition experience is of great importance for explaining knowledge codification efforts that ultimately might lead to superior M&A capability and performance. They highlight the need for further research in this area because most previous research has treated experience type and experience management as a black box (Dao & Strobl, 2019; Ellis, Reus, Lamont, & Ranft, 2011).

Addressing this academic void, our study aims to explore how the characteristics of an acquisition program influence the technology acquirers’ shareholder value. Specifically, we ask: How do target firms’

types within an acquisition program affect the stock market performance of the acquiring firm’s technology acquisitions?

Two motivations drive this study. First, in contrast to extensive research at the level of individual acquisition, research in acquisition programs is recent and still underdeveloped (Laamanen & Keil, 2008).

Extending prior studies that have already noted the importance of heterogeneity in multiple acquisitions (e.g., Ellis et al., 2011; Haleblian &

Finkelstein, 1999; Hayward, 2002; Muehlfeld, Rao Sahib, & van Witte- loostuijn, 2012), we examine the variety of the industry value chain positions in which acquired target firms are involved. Second, previous research has mainly explored the performance implications of acquisition decisions given one specific strategic motive, for instance, undertaking cross-border acquisitions for productivity enhancement (Bertrand & Capron, 2015). In this study, we examine the performance impacts of a firm’s acquisition decision (e.g., the functional type of target firms to purchase) combining additional strategic priorities (e.g., the choice to invest resources in innovation that manifest in high levels of innovation performance) (O’Brien, 2003), as well as the decision to engage in alliance activities for orchestrating the industry value chain (Hinterhuber, 2002).

By integrating these previously mentioned perspectives associated with corporate development strategies, our study contributes to the literature in the following ways. First, acquisition performance for buyers is frequently measured in recent studies on technology-driven M&A as innovative performance rather than financial performance (Meglio & Risberg, 2011). Our analysis of the long-term stock market performance of technology acquisitions adds another dimension for a deeper understanding of technology acquisition performance. It is in line with Haleblian’s et al. (2009) suggestion that different success measures are required in M&A research. In addition, Laabs and Schiereck (2010) highlight the need to analyze the long-term post-acquisition performance. Second, the primary research question focuses on the diversity among multiple acquisition targets, thereby highlighting the role of acquisition patterns in analyzing the acquisition performance of

established technology firms. Third, this study contributes to the broader research on firms’ corporate development strategies by jointly investigating the effects of various strategic choices on acquisition performance.

We test our hypotheses using a sample of 312 acquisition an- nouncements by 147 publicly traded pharmaceutical firms between 2002 and 2021. The data come from multiple sources: (1) Capital IQ, (2) Thomson One/Refinitiv, (3) DataStream, and (4) patent data from the United States Patents and Trademarks Office. Furthermore, in line with the objective of examining the performance of technology acquisitions, we focus on acquisition deals where the main motive is to gain access to target firms’ technologies.

The remainder of this study is structured as follows. In the next section, we develop our theory and formulate hypotheses before describing the method and results. Finally, theoretical and practical conclusions and avenues for future research are derived.

2. Theoretical framework and hypotheses

This study analyzes the role of acquisition program diversity in explaining the long-term performance of technology acquisitions measured by shareholder value. The underlying rationale for our study is that the stock market performance of a technology acquisition depends on the functional diversity of an acquirer firm’s prior targets, whereby this effect is contingent on various factors that influence value creation and value capture in M&A activities. Because the short-term stock market performance based on the traditional event study methodology is subject to the validity of the market efficiency hypothesis (Zollo & Meier, 2008) and is not able to consider whether synergies really have been realized or not, this study uses the long-term stock market performance to examine the effects of various factors concerning the post-acquisition integration on M&A success as a more appropriate measure.

2.1. Experience and learning in acquisitions

Organizational learning is best described as an iterative, dynamic process (Hayward, 2002) with experience as the foundation as well as the stimulus for learning (Galavotti, 2019). Previous research has outlined that firms substantially differ in their ability to perform acquisitions (Barkema & Schijven, 2008), and this has sparked an interest in the performance of serial acquirers. The key question is whether acquirers can learn from one acquisition to another and build acquisition capabilities (e.g., for target identification/selection, due diligence, negotia- tion, integration, and retention of the target’s valuable human capital) (Arikan & McGahan, 2010) that ultimately lead to superior performance (Galavotti, 2019). Building on the argument that organizational learning results in capability building and improved firm performance, the Organizational Learning Hypothesis predicts that firms should become more successful with an increasing number of acquisitions (Ismail &

Abdallah, 2013).

Accordingly, acquisition scholars have approached the link between acquisition experience – typically operationalized by the number of previous acquisitions a firm has already conducted (Weber, zu Knyphausen-Aufseß, & Schweizer, 2018) – and acquisition performance with the intuitive expectation that this relationship should be a positive one, but the “profoundly inconsistent empirical results obtained have increasingly challenged the idea that experience automatically trans- lates into learning” (Galavotti, 2019). In the past two decades, a number of qualitative and distinctive attributes of experience have been identified as crucial points, namely experience timing (Kim, Haleblian, &

Finkelstein, 2011; Kim & Finkelstein, 2009; Meschi & M´etais, 2013), experience heterogeneity (Haleblian & Finkelstein, 1999; Hayward, 2002; Zollo, 2009), deliberate learning and experience codification (Trichterborn et al., 2016; Zollo, 2009; Zollo & Singh, 2004), and performance feedback from prior experiences (Haleblian, Kim, &

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Rajagopalan, 2006; Hutzschenreuter, Kleindienst, & Schmitt, 2014;

Ismail & Abdallah, 2013). In particular, experience heterogeneity has drawn attention as acquisition scholars started to take a closer look at the heterogeneous group of serial acquirers, introducing the distinction between serial acquirers with and without an acquisition program (Weber et al., 2018). Serial acquirers with an acquisition program conduct acquisitions that share a common business logic that determines how a firm’s acquisitions will – individually and collectively – create value for the acquirer’s shareholders (Chatterjee, 2009; Schipper &

Thompson, 1983). This usually means that the targets within an acquisition program share certain similarities, such as the industry, business model, or same stage in the industry value chain (Weber et al., 2018). On the other hand, serial acquirers without an acquisition program can be classified as sporadic acquires that conduct acquisitions not connected by a common business logic.

Building on prior research findings, this study analyses the role of acquisition program diversity in technology acquisitions, therefore advancing research on the link between acquisition experience and acquisition performance. This is especially important because research at the intersection of the two research fields performance of serial acquirers (e.g., Aktas, De Bodt, & Roll, 2011; Chatterjee, 2009; Laa- manen & Keil, 2008) and learning in acquisitions (e.g., Barkema &

Schijven, 2008; Heimeriks, Schijven, & Gates, 2012) is still in its infancy (Weber et al., 2018).

2.2. Acquisition program diversity and technology acquisition performance

We define acquisition program diversity as the functional diversity of acquiring firms’ previously acquired targets (i.e., their different industry value chain positions). High levels of diversity (i.e., a balanced acquisition program) indicate that an acquiring firm has engaged in M&A activities equally in all three categories, namely upstream, downstream, and horizontal (in relation to itself), and therefore becomes more diversified. Moderate levels reflect the situation whereby an acquiring firm has purchased different numbers of target firms in all these categories. An acquiring firm is considered to have low levels of acquisition program diversity when it has carried out acquisitions in only one or two of the three possible categories, and the number of its M&A deals in each category is considerably different. This means that most targets come from the same value chain position. Thus, the acquirer has to deal with mainly horizontal integration (even if a firm is following an acquisition program where targets are predominantly suppliers [downstream] or customers [upstream], by acquiring several targets from one industry value chain position, the acquiring firm will have to deal with horizontal integration).

In this study, we investigate how acquisition program diversity influences the performance of a firm’s technology acquisition. Although the extant management literature on the relationship between business diversification and acquisition performance focuses on industry relatedness between the acquiring and target firms (e.g., Datta & Puia, 1995;

Harrison, Hitt, Hoskisson, & Ireland, 1991; Makri et al., 2010) or – in the context of technology acquisitions – on technological relatedness (e.g., Ahuja & Katila, 2001; Sears & Hoetker, 2014), the influence of diversity among acquired target firms in terms of their industry value chain position on acquisition performance is underexplored, even though value chain questions play an essential role for many firms today, especially global ones (Pananond, Gereffi, & Pedersen, 2020). Theoretical analysis of this relationship should consider potential benefits as well as costs. In addition, because we measure long-term acquisition performance by shareholder value, we need to take into account both the shareholders’

and the firm’s perspectives.

In the context of technology acquisitions, firms with higher levels of acquisition program diversity are likely to earn better acquisition performance for the following reasons.

Better industry knowledge. Only by conducting a comprehensive

screen of the whole industry value chain, market segments in which acquisitions have the ability to create value and possible acquisition targets can be identified (BCG, 2020). Firms looking for targets in different value chain positions therefore need to possess deep knowledge of the whole industry. The search for targets is especially challenging for technology acquisitions because they often involve target firms that are quite small relative to their buyers (Graebner, Eisenhardt, & Roundy, 2010). Compared with acquirers that follow a narrow business logic for their acquisition programs, firms that pursue targets in different value chain positions tend to possess deeper and broader information about the industry environment in which they are involved.

Less asset divestiture. Acquisition success strongly depends on integration with respect to employees and processes. Acquiring comparable numbers of target firms operating in different value chain positions leads to lower costs associated with post-acquisition integration than if all past acquisitions are of the same type. In the latter case, acquiring firms are subject to larger tensions of removing redundancies generated by the increased similarity between acquired assets (Ahuja & Katila, 2001;

Makri et al., 2010). Support for this argument comes from the literature about horizontal acquisitions because serial acquirers with low diversity in their acquisition program are concentrating on a certain value chain position when selecting their targets, so they inevitably have to deal with horizontal integration. Previous research has outlined that asset divestiture – defined as the partial or complete sale of physical and organizational assets, shut down of facilities, and reduction of work forces (Capron, 1999; Capron, Mitchell, & Swaminathan, 2001) – is especially likely in the context of horizontal integrations (O’Shaugh- nessy & Flanagan, 1998). Such asset divestitures and especially work force reductions lead to stress, uncertainty, and resistance among employees (e.g., Bauer, King, & Matzler, 2016; Fried, Tiegs, Naughton, &

Ashforth, 1996; Newman & Krzystofiak, 1993). This is considered one of the most destructive elements with regard to post-acquisition performance (Schweizer & Bilsdorfer, 2016; Seo & Hill, 2005).

Higher bargaining power. The relative bargaining positions of the buying and selling firms are found to positively affect acquirer performance (Laamanen, Brauer, & Junna, 2014). Firms with larger acquisition program diversity are normally involved in various value chain steps and possess higher market power compared with other industry competitors (Porter, 1980; Schweizer, 2005b). Thus, we expect that these firms can take bargaining advantages to capture the synergistic value created by the focal technology acquisition.

Complementary resources and capabilities. A higher degree of diversity in a firm’s acquisition program provides advantages in terms of competitive advantage because diverse acquired targets can add complementary assets to the acquiring firm’s existing resource bases (Har- rison, Hitt, Hoskisson, & Ireland, 2001; Hitt, Robert, Hoskisson, & Kim, 1997). Moreover, acquisitions appear to perform better when there are complementary resources and capabilities (King et al., 2008; Makri et al., 2010). Pharma companies can benefit more from acquiring biotech firms because of complementary capabilities (especially their special research and development [R&D] capabilities) with less redun- dancy than acquiring another traditional pharma firm.

Together, these four theoretical arguments predict a positive relationship between acquisition program diversity and acquisition performance. However, other arguments predict a negative relationship between the two variables. These are as follows.

Increased demands. As firms engage in acquisition activities in more diverse value chain positions, they will be required to interact with a larger set of elements (e.g., the simultaneous use of resources across domains) (Hashai, 2015). Consequently, it will likely become more difficult for the acquiring firm to coordinate scarce resources among different acquired target firms. Therefore, the effectiveness of a man- ager’s ability to exploit previous acquisitions to benefit the focal one will decrease because of the increased demands on managerial attention and bounded rationality (Simon, 1991). Support for this argument comes from the literature about diversification, which is vast and

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well-developed (Campa & Kedia, 2002). Previous research has outlined that diversified firms trade at a discount relative to non-diversified firms in their industries (Berger & Ofek, 1995; Lang & Stulz, 1994; Servaes, 1996). This finding of a “diversification discount” seems robust to different periods and countries (Campa & Kedia, 2002).

Less experiential learning effects. As the diversity level of acquired target firms increases, learning transfer will become more difficult (Ellis et al., 2011). An acquisition program with high levels of diversity entails high costs for the implementation of the focal technology acquisition.

This is because the dissimilarity between different types of target firms may result in higher efforts for acquirers to develop and accumulate M&A capabilities, which play an important role in explaining M&A performance (Trichterborn et al., 2016). Although it may be possible to develop M&A capabilities by transferring prior acquisition experience where target firms from the same value chain have many similar characteristics (Finkelstein & Haleblian, 2002), the specific contexts of different types of acquisitions normally give rise to more difficulties in developing such capabilities (Haleblian & Finkelstein, 1999; Laamanen

& Keil, 2008; Zollo & Singh, 2004).

Bounded rationality. The last argument for a negative effect of acquisition program diversity comes from the Bounded Rationality Theory (Simon, 1957) and takes into account the shareholders’

perspective. Contrary to the efficient market hypothesis, which states that

“security prices at any point in time” fully reflect “all available information” (Fama, 1970, p. 388) and provide an unbiased estimate of the discounted value of a firm’s future cash flows, the financial market may struggle to accurately predict the value of a focal technology acquisition if relationships between past acquisition activities are highly complex and too many types of synergy arguments are embedded in a given acquisition program.

In sum, we suggest that although acquisition program diversity may positively influence technology acquisition performance at lower levels, at higher levels, this relationship turns to be negative. Put differently, we expect that the relationship between acquisition program diversity and the long-term stock market performance of technology acquisitions will follow an inverted U-shaped relationship. As outlined by Haans, Pieters, and He (2016), an inverted U-shaped relationship exists if Y first increases with X at a decreasing rate to reach a maximum, after which Y decreases at an increasing rate. In other words, it is not sufficient that the independent variable exerts both positive and negative effects to have an inverted U-shaped relationship. It is also mandatory that the positive effect is greater than the negative effect when the independent variable is low and vice versa when the independent variable is high (Cefis, Marsili, & Rigamonti, 2020). In our framework, this implies the positive effects of acquisition program diversity are dominant over the negative effects only when acquisition program diversity is low. Thus, we state the following hypothesis.

Hypothesis 1. The relationship between acquisition program diversity and the long-term stock market performance of technology acquisitions will follow an inverted U-shaped relationship.

2.3. The moderating role of acquirer innovation level

The guiding hypothesis in this study is that a balanced degree of acquisition program diversity helps to improve a firm’s ability to create and capture value for shareholders, thereby contributing to the market performance of its technology acquisitions. According to the resource- based view (Barney, 1991), the heterogeneity in firms’ performance depends on their abilities to leverage resources and capabilities that are valuable, rare, and difficult to imitate or substitute. This suggests that acquisition program diversity’s impact on the technology acquisition performance may be more or less pronounced depending on the supe- riority of acquiring firms’ resources and capabilities. Hence, firms with higher innovation levels may be better positioned to gain from larger acquisition program diversity to enhance the stock market performance

of technology acquisitions for the following reasons.

First, past research suggests that post-acquisition integration is a crucial issue for M&A success (Haspeslagh & Jemison, 1991; Jemison &

Sitkin, 1986) because the realization of the potential value of an acquisition depends on the actual process of post-acquisition implementation (Capron, Dussauge, & Mitchell, 1998; Larsson & Finkelstein, 1999). In the context of technology acquisitions, acquiring firms normally have to deal with more complex integration processes to realize the synergistic benefits identified during the pre-acquisition phase (e.g., Puranam, Singh, & Chaudhuri, 2009; Schweizer, 2005a). Firms with larger innovative resources and capabilities are typically characterized by higher levels of “absorptive capacity” (i.e., they are more capable of recognizing the value of knowledge produced elsewhere and assimi- lating and applying it) (Cohen & Levinthal, 1976, 1990). Innovative firms necessarily possess a large knowledge basis, and earlier studies based on the absorptive capacity argument found that a larger knowledge basis prior to an acquisition positively influences post-acquisition innovation (Ahuja & Katila, 2001; Cloodt et al., 2006) and integration performance (Junni & Sarala, 2013; Zaheer, Hernandez, & Banerjee, 2010). Similarly, a recent study by Cefis et al. (2020) has illustrated how the relationship between relatedness and value creation in acquisitions depends on R&D investments and acquisition experience, which reflect the acquirer’s absorptive capacity. Hence, acquiring firms with higher innovation levels are expected to benefit more from increased acquisition program diversity.

Second, as the innovation level increases, firms tend to engage in high-impact innovation and search for targets with new, emerging, and pioneering technologies (Ahuja & Lampert, 2001). Sourcing high-impact innovative resources requires greater collaboration, mutual learning, and unlearning with external partners (Sivadas & Dwyer, 2000). Assuming that industry-level resources and capabilities purchased by a firm through M&A can help enhance the performance of the focal technology acquisitions, this impact may be more significant for firms with higher innovation levels given that these firms are confronted by larger uncertainties.

Third, firms that engage in high levels of innovation are more capable of eliciting, using, and applying knowledge to solve problems (Dyer & Nobeoka, 2000). Many of them have multiple complementary learning processes and, in such firms, specific routines and procedures could be adapted to increase organizational learning capability (Zahra &

George, 2002). Acquiring diverse target firms in various value chain positions can often hamper the accumulation of acquisition capabilities through the experience-based learning mechanism. For innovative firms with higher learning capability, the disadvantages of acquisition program diversity may be mitigated.

Therefore, we suggest.

Hypothesis 2. The acquirer innovation level will positively moderate the predicted inverted U-shaped relationship between acquisition program diversity and the long-term stock market performance of technology acquisitions.

2.4. The moderating role of alliance portfolio size

A significant amount of research on external corporate development activities indicates that alternative governance structures, such as acquisitions and alliances, should be studied in comparative terms (McCann, Reuer, & Lahiri, 2015; Zollo & Reuer, 2010). Accordingly, this section will analyze how acquisitions and alliances jointly influence the focal acquisition performance.

To better cope with environmental changes, many established firms have been committed to business model innovation (Casadesus-Masa- nell & Zhu, 2013; Teece, 2010). Often, they try to participate in more steps of the whole value chain to achieve a higher total revenue potential (Schweizer, 2005b). Some of these firms prefer using alliances for their value chain orchestration activities. As research has shown, this is

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especially the case if uncertainties regarding rent appropriation are low (Hinterhuber, 2002).

In general, alliance networks enable firms to access and integrate multiple resources from different, simultaneous partners (Van Wijk &

Nadolska, 2020) and provide a variety of information and learning benefits to partnering firms (Powell, Koput, & Smith-Doerr, 1996). For instance, alliances with new biotechnology firms are one way for pharmaceutical companies to adapt to the latest biotechnology (Hill &

Rothaermel, 2003). As the number of alliances increases, firms can better learn and use diverse knowledge from partners (Deeds, Decarolis,

& Coombs, 2000; Grant, 1996). In a recent meta-analysis by Bitencourt,

de Oliveira Santini, and Ladeira (2020), alliances are investigated as a positive determinant on dynamic capabilities, which can further have a positive effect on firm performance. However, a firm’s alliance portfolio size will weaken the impact of acquisition program diversity on technology acquisition performance in the following ways.

First, as the alliance portfolio size increases, the range of external knowledge available to the focal firm becomes larger. Similarly, firms with higher levels of acquisition program diversity are connected with upstream and downstream units (Monteverde, 1995), and their enhanced knowledge base enables them to have a better understanding of the key contextual contingencies surrounding the environment (Jacobides & Winter 2005). Because firms with a larger alliance portfolio size have greater abilities to exploit market imperfections, there is a lesser likelihood that acquiring another target firm from a different value chain position will provide more bargaining power for them.

Second, not only diverse acquired target firms but also alliance partners may provide useful private information relating to acquisition opportunities (Gulati, 1999; Haunschild, 1993), allowing managers to identify current and potential strategic interdependencies between potential target and acquiring firms (Dyer & Singh, 1998). However, redundant information sources could weaken their individual beneficial effects (Haunschild & Beckman, 1998). In line with this reasoning, acquisition program diversity may not provide as much additional information as if the alliance portfolio size was relatively small.

Third, as the alliance portfolio size increases, the bureaucracy and transaction costs of simultaneously coordinating across multiple parties become higher (Rothaermel, 2001). Consequently, it is likely to become more difficult to distribute managerial attention among alliance partners (Hoang & Rothaermel, 2005). Given that managers’ attention is a limited resource to firms that undertake strategic actions, the managerial costs generated by the increased acquisition program diversity may be a more severe problem for firms with a larger alliance portfolio size.

Taken together, these arguments lead to the following hypothesis.

Hypothesis 3. The acquirer alliance portfolio size will negatively moderate the predicted inverted U-shaped relationship between acquisition program diversity and the long-term stock market performance of technology acquisitions.

To sum up, the diversity degree of acquired targets within an acquisition program is expected to have an inverted U-shaped impact on the shareholder value of a focal technology acquisition (i.e., as acquisition program diversity increases, acquisition performance first increases and subsequently decreases). Furthermore, we assume that this curvilinear effect of acquisition program diversity is contingent on the acquiring firm’s innovation level and alliance portfolio size. Although the performance effects of the acquiring firm’s alliance portfolio size and its acquisition program diversity substitute for each other, the acquiring firm’s innovation level strengthens the relationship between acquisition program diversity and the focal technology acquisition’s shareholder value. Fig. 1 summarizes the research model of this study.

3. Method

3.1. Empirical context

The hypotheses in this study were tested using data from the global pharmaceutical industry, which is an ideal context for the present empirical analyses for several reasons. First, this industry is one of the largest, most established, and knowledge-intensive sectors. It is furthermore vitally important from a business and social perspective (Yu, Umashankar, & Rao, 2015). Second, many pharmaceutical companies attempt to combine complementary assets along the knowledge value chain to complete the innovation process, including drug discov- ery and early-stage development, large-scale manufacturing, preclinical and clinical trials, regulatory management, and finally, distribution and sales (Hess & Rothaermel, 2011). Third, the importance of product innovation and the difficulty in developing new products through in-house efforts alone mean that firms in this industry are constantly looking for required resources from external sources (Sorescu, Chandy,

& Prabhu, 2007). As such, firms in this industry frequently undertake

acquisition activities. For instance, some pharmaceutical companies acquire biotechnology firms, which are normally located at the upstream pole of the pharmaceutical industry value chain (Rothaermel, 2001).

Overall, the pharmaceutical industry had more than doubled its global M&A activities from 2002 to 2019. Ahead of the COVID-19 pandemic, the global pharmaceutical M&A activity in 2019 was at an all-time high with a total deal value of more than USD 400 billion, of which almost half was attributable to the top 10 pharmaceutical M&A deals (Thompson Reuters). However, because of increased wariness for completing large M&A deals as well as the more volatile market environment for dealmaking, the global pharmaceutical deal value in 2020 significantly decreased to USD 191 billion. With the ease of lockdown measures and widespread use of COVID-19 vaccines and boosters, global

M&A activities in the pharmaceutical sector tended to recover in 2021

(USD 219 billion), but the combined value of the top 10 deals in 2021 (less than USD 53 billion) was still lower than the one reached in the

Fig. 1. Research framework.

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previous year (approximately USD 97 billion). It seems that the market volatility had restricted some companies’ deal-making capacity for larger deals. In the future, industry experts expect to see more deals being made up and down the value chains from raw components to product distribution and accelerating acquisitions of small and midsized targets providing specialty solutions. Despite external pressures and fluctuating dynamics (e.g., supply chain shortages and slowing manufacturing), corporate as well as private equity investors are still expected to continue pursuing M&As in the pharmaceutical sector.

Large pharma deals are also expected to come to pass (i.e., M&A activities involving pharmaceutical companies remain high and continue to attract new investors) (PwC Global M&A Industry Trends in Health Industries 2021). Still, the outcomes and financial consequences of pharma deals are anything but uniformly positive (Koberstein, 2000).

Restricting the empirical context to a specific industry allows for comparability across acquisitions and helps address internal validity concerns. Considering these factors, we assume that the global pharmaceutical industry is an appropriate setting to test the proposed research model.

3.2. Sample and data

The sample of acquisitions used in this study was obtained from the Thomson One/Refinitiv M&A database, a comprehensive database of financial transactions conducted by firms worldwide. We reviewed all 7173 acquisitions with information on deal values undertaken by pharmaceutical firms (Standard Industrial Classification 2843) in the database between January 2002 and December 2021, of which 2687 were acquisitions by publicly traded firms. The sample was limited to publicly traded firms because it is difficult to obtain comparable business and financial information for private firms.

Following the procedure suggested by Yu et al. (2015), we conducted individual and joint multivariate variance analysis tests on public and private samples based on four financial ratios (sales ratio, total assets ratio, current assets ratio, and liability ratio). The results of these tests indicate that the means of the two samples are equal, suggesting that the sample of public firms was not statistically different from the sample of private firms. In addition, we restricted the sample to deals that ach- ieved a controlling ownership transfer and had a transaction value between EUR 10 and 999 million, excluding acquisition deals with minority target stakes and large mega-mergers. These steps and some further screening resulted in a sample of 312 transactions undertaken by 147 publicly traded acquirers with full information needed for the

regression analysis. Some descriptive statistics of our final sample are presented in Table 1.

The data used in this study rely on analyzing secondary data collected from several different sources (e.g., Capital IQ, DataStream, Thomson Banker One/Refinitiv, United States Patents and Trademarks Office, company annual reports, and other public documents). The table in the appendix presents an overview of variables and data sources.

3.3. Dependent variables

Long-term stock market performance is the dependent variable assessed using the buy-and-hold abnormal return (BHAR) methodology (Barber & Lyon, 1997; Lyon, Barber, & Tsai, 1999; Mitchell & Stafford, 2000). This method allows for assessing abnormal returns over a longer time horizon (one year in the present study) and thereby overcomes the limitations associated with the use of narrow windows around the announcement date in traditional event studies. Following recent management studies (e.g., Basuil & Datta, 2015; Song, Zeng, & Zhou, 2021), we use this method to calculate a focal acquisition’s long-term stock market performance. Because recent studies have highlighted the limitations of short-term cumulative abnormal returns (CAR) used as a measure of shareholder wealth effects in prior acquisition research (e.

g., Dutta & Jog, 2009; Oler, Harrison, & Allen, 2008; Zollo & Meier, 2008), the BHAR approach – which was specifically developed to estimate long-term financial performance – is more appropriate for capturing value creation in the post-acquisition phase. As shown in Barber and Lyon (1997), the BHAR approach can eliminate some potential misspecification biases, such as new listing and rebalancing biases, compared with the long-run CAR approach. In addition, the BHAR method can better represent investors’ experience (Mitchell &

Stafford, 2000).

The BHARs were computed for the year following the acquisition announcement date. The computation process involved three steps: (1) generation of benchmark portfolios using the MSCI World Health Care Index and complemented by additional firms of the corresponding country pharmaceutical index that are not included in the MSCI index but similar to the acquiring firm in terms of market capitalization, book- to-market ratio, and previous performance), (2) matching a firm to its benchmark portfolio, and (3) computation of BHAR using the following formula:

BHARi,p=∏¹²

t=1

(1+Ri,t

)− ∏¹²

t=1

(1+Rp,t

)

where Ri,t is the rate of stock return of acquiring firm i in month t, and Rp,t is the rate of stock return of the benchmark portfolio.

3.4. Main independent variables

Acquisition program diversity is the main predictor, which mirrors how the industry value chain positions (upstream, downstream, and horizontal) of past target firms within an acquisition program differ. In this study, all previously acquired firms are treated as being active in an acquisition program for five years from their announcement date. This time frame has been used in several management studies to assess the influences of previously completed acquisitions (e.g., Basuil & Datta, 2015; Bergh, 2001; Stettner & Lavie, 2014). The formula is as follows:

Acquisition program diversity=1−

∑³

m=1

p²_i,m

where i represent the acquirer, and pi,m is the share of each type of target firms in the total acquisition program. We used Blau’s (1977) index of heterogeneity to operate the measure of this variable, which is the most commonly used measure in terms of variety (Harrison & Klein, 2007). A similar procedure has been widely used in many studies on alliance Table 1

Descriptive statistics of the final sample comprising 312 deals.

Ratio (%) Target by country and region

China (Mainland) 35.6

United States 34.3

EMEA 17.9

Asia/Pacific other 7.7

America other 4.5

Acquirer by country and region

China (Mainland) 34.0

United States 21.5

EMEA 28.8

Asia/Pacific other 10.9

America other 4.8

Transaction feature

Cross-border deals 40.1

Domestic deals 59.9

Target industry

Pharmaceuticals 74,4

Biotechnology 13,8

Healthcare equipment and supplies 6,1

Other consumer products and retailing 5.8

Note: Total =100 in each category.

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diversity (e.g., Duysters, Heimeriks, Lokshin, Meijer, & Sabidussi, 2012;

Lee, 2007; Powell et al., 1996). This diversity index’s minimum value of 0 occurs when all target firms belong to the same category and reaches its maximum value of 0.67 when a firm has purchased target firms in all value chain positions. Consistent with the alliance literature (e.g., Sil- verman & Baum, 2002), we coded Blau’s index as 0 if the acquiring firm did not carry out any acquisition prior to the focal deal.

Acquirer innovation level is the first moderator variable, reflecting the acquiring firm’s ability to actually create impactful innovations.

Following existing literature on the measurement of technological capabilities (e.g., Lahiri & Narayanan, 2013; Sears & Hoetker, 2014), we measured this variable as the number of granted patents filed by the acquiring firm before the focal acquisition announcement, which is weighted by the number of forward citations. Although the count of patents represents the invention quantity (Hall & Ziedonis, 2001), the extent to which a firm’s patents are subsequently cited controls for the quality differences (Makri et al., 2010).

Alliance portfolio size is the second moderator variable. It refers to the number of alliances in which the focal acquiring firm is involved.

Because the impact of an alliance on the performance measures may require some time (Stuart, 2000), we measured this variable – according to past alliance studies (e.g., Schilling & Phelps, 2007) – as the cumulative number of alliances that the acquiring firm has entered into during the three years before the focal acquisition announcement.

3.5. Control variables

In addition to the main independent variables mentioned previously, this study controlled for several factors that have been shown to influence acquisition performance in previous research (Spector & Brannick, 2011). First, we controlled for influencing factors in terms of acquiring firms’ characteristics. Acquirer size was measured as the log of the number of employees in the acquiring firm (Datta, Musteen, & Herr- mann, 2009), whereby larger firms with more resources available for the undertaking and implementation of acquisitions are expected to exhibit superior performance outcomes in acquisitions (Basuil & Datta, 2015).

Acquirer free cash flow was measured as the operating income before depreciation, which was normalized by total assets because the same cash flow has different implications for firms of different sizes (Lang, Stulz, & Walkling, 1991). As is common in the literature, free cash flow is used to control for organizational slack (e.g., Basuil & Datta, 2015), which is associated with negative acquisition outcomes because of potential agency problems (Jensen, 1986). Acquirer diversification was proxied by the number of different SIC code industries in which the acquiring firm operates (Laamanen et al., 2014). Acquirer acquisition experience was measured by the number of acquisitions carried out by the acquirer during the three years before the focal acquisition (Haleb- lian & Finkelstein, 1999). Acquirer leverage was measured using the acquirer’s debt-to-equity ratio, which reflects the acquirer’s capital structure (i.e., the relative proportion of debt/equity to finance a firm’s assets representing its financial slacks) (Campbell, Sirmon, & Schijven, 2016; Vermeulen & Barkema, 2001). Acquirer ROE indicates the acquiring firm’s previous accounting performance, which is expected to influence subsequent performance (King et al., 2008, 2021). This variable was measured by dividing the acquirer’s net income in the previous fiscal year before the announced deal by its shareholder’s equity.

Acquirer R&D was measured using the acquirer’s R&D expenditure divided by its sales (Kallunki, Pyykko, ¨ & Laamanen, 2009; King et al., 2008), which is considered a proxy for measuring an acquirer’s absorptive capacity that may be beneficial to the performance of technology acquisitions.

Second, we controlled for multiple factors regarding the deal characteristics. Acquisition value was operationalized as the natural loga- rithm of the transaction value in USD millions (Meschi & Metais, 2006), normalized by the acquirer’s market value of assets. Large deals tend to involve greater integration difficulties, thus potentially reducing

acquisition returns (Humphery-Jenner, 2014). Method of payment is a dummy variable, which controls for the impact of consideration type, taking the value of 1 if the acquisition is predominantly cash-financed and a value of 0 otherwise. Transactions predominantly paid by cash are expected to be associated with a positive market reaction (King et al., 2021). Cross-border acquisition indicates whether the acquiring and target firms are based in different countries (1) or not (0). Previous studies have argued and tested that cross-border deals may perform worse because of the integration difficulties and the information asymmetries of distance (e.g., Vaara & Tienari, 2010).

Finally, we controlled for several factors from other perspectives.

Relative size was measured as the ratio between a target firm’s total assets one year before acquisition and the acquiring firm’s total assets for the same time frame (Ellis et al., 2011). Seller distress was measured by the selling firm’s debt-to-total assets ratio: when the target firm is divested by a financially constrained seller, this variable takes the value of 1 (0 otherwise). Prior M&A research has found that firms acquiring from a financially distressed seller have a higher level of bargaining power and thus can achieve higher stock returns (Laamanen et al., 2014). Target industry M&A was measured as the total value of the acquisition deals that occurred in the target company industry in the preceding year. Because significant M&A activity in the target’s industry might increase competition for targets – thus driving up acquisition premiums (Haleblian, McNamara, Kolev, & Dykes, 2012; McNamara, Haleblian, & Dykes, 2008) – it might affect the acquisition’s financial performance (Humphery-Jenner, 2014). People deployment is a dummy variable that takes the value of 1 if top scientists of the acquired target firms are retained (Sorescu et al., 2007). Geographic distance between the acquirer and target firms may affect acquisition performance in certain contexts (Galdino, Gordon, & King, 2022). We measured this variable in this study using the log of miles between the acquirer and target headquarters according to their ZIP codes (Chakrabarti &

Mitchell, 2013, 2016) to correct for any skew. We used the approach used by prior studies (e.g., Reuer & Lahiri, 2014) to calculate this variable.

3.6. Empirical model

The purpose of this paper is to analyze how the functional diversity of acquired target firms within an acquisition program affects the long- term market performance of technology acquisitions. We used ordinary least squares (OLS) regression to test the hypotheses and estimated robust White-Huber standard errors to correct for potential hetero- skedasticity in all analyses (Hampel, Ronchetti, Rousseeuw, & Stahel, 1986). The analysis was conducted using the statistical package STATA 13.

4. Results

Table 2 provides the bivariate correlation matrix of all variables used in the analyses.

Table 3 displays the results of models predicting the long-term market performance of technology acquisitions. Model 1 displays the results of control variables only. Model 2 includes the linear direct effect of acquisition program diversity, and Model 3 adds a squared term of acquisition program diversity to the regression to test the predicted inverted U-shaped relationship. Models 4 and 5 display the results with interaction terms. Consistent with the expectations, the interaction model explains a significantly larger amount of variance than those including control variables or other independent variables alone. The mean variance inflation factors for different models range from 1.24 to 1.76, suggesting that multicollinearity is not a problem in this study. The estimated coefficients of people deployment in all model specifications are significantly positive. This result reveals that retaining strategic resources, such as top scientists, is beneficial for the stock market performance of technology acquisitions.

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Table 2 Descriptive statistics and correlations. Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 1 1 2 0.01 1 3 −0.07 0.09 1 4 0.08 −0.03 −0.18 1 5 0.02 −0.04 0.21 −0.05 1 6 −0.02 0.02 0.15 −0.12 0.19 1 7 0.06 −0.01 0.05 0.03 0.16 0.04 1 8 0.02 −0.03 0.11 −0.02 0.09 −0.01 0.13 1 9 0.05 0.04 0.02 −0.02 0.21 −0.05 0.03 0.02 1 10 0.06 0.02 0.03 −0.01 0.18 0.31 −0.04 0.01 0.02 1 11 0.01 −0.03 0.26 0.08 0.15 −0.07 0.04 −0.06 0.16 −0.06 1 12 −0.03 0.04 0.09 −0.02 0.13 −0.04 −0.01 0.02 0.12 0.08 0.07 1 13 0.12 0.05 0.06 −0.05 0.17 0.22 −0.04 0.03 −0.14 0.11 0.06 −0.09 1 14 0.05 −0.07 0.08 −0.01 0.11 −0.01 0.02 0.21 −0.08 0.07 0.06 0.27 0.18 1 15 0.03 −0.05 0.09 −0.03 0.24 0.16 0.06 0.03 0.09 −0.02 0.05 −0.01 0.16 0.05 1 16 0.04 −0.05 0.03 −0.06 0.01 −0.03 0.03 0.02 −0.04 −0.03 0.01 0.06 0.12 0.08 −0.06 1 17 −0.01 0.04 −0.02 0.03 −0.04 −0.06 −0.05 0.01 0.03 −0.02 0.03 −0.05 0.04 −0.04 −0.03 −0.05 1 18 0.09 −0.07 −0.01 0.15 0.05 0.01 −0.04 0.11 −0.02 0.03 0.02 0.13 0.06 −0.02 −0.04 0.01 −0.04 1 19 −0.01 0.02 0.04 0.03 −0.02 0.06 0.02 0.05 −0.17 0.05 −0.01 0.03 0.08 0.31 0.06 −0.02 −0.01 0.09 1 Mean 0.03 0.42 0.57 2.98 3.16 0.11 1.02 1.06 0.42 0.14 0.12 5.33 0.64 0.40 0.23 0.06 0.22 0.72 4.36 SD 0.14 0.53 1.41 1.54 0.94 0.09 0.95 1.21 0.35 0.11 0.08 1.12 0.51 0.29 0.36 0.15 0.26 0.38 2.15 Notes: 1 BHAR 1-year, 2 Acquisition program diversity, 3 Acquirer innovation level, 4 Alliance portfolio size, 5 Acquirer size, 6 Acquirer free cash flow (FCF), 7 Acquirer diversification, 8 Acquirer acquisition experience, 9 Acquirer leverage, 10 Acquirer ROE, 11 Acquirer R&D, 12 Acquisition value, 13 Method of payment, 14 Cross-border acquisition, 15 Relative size, 16 Seller distress, 17 Target industry M&A, 18 People deployment, 19 Geographic distance n =312; absolute correlations above 0.09 are significant at p <0.05; based on two tailed tests.

Table 3

Regression results on the effects of acquisition program diversity on technology acquisition performance.

Variable Model 1 Model 2 Model 3 Model 4 Model 5

Control variables Acquirer size 0.04

(0.03) 0.09

(0.04) 0.08

(0.04) 0.10

(0.06) 0.18

(0.07) Acquirer FCF − 0.34

(0.22) − 0.36

(0.28) −0.33

(0.25) −0.35

(0.23) −0.37

(0.25) Acquirer

diversification 0.51

(0.24) 0.45

(0.21) 0.58

(0.25) 0.61

(0.34) 0.58

(0.26) Acquirer

acquisition experience

0.05

(0.04) 0.07

(0.05) 0.07

(0.05) 0.08

(0.05) Acquirer

leverage 0.31

(1.42) 0.32

(1.51) 0.28

(1.15) 0.27

(1.24) 0.29

(1.41) Acquirer ROE 0.95*

(0.56) 0.97*

(0.55) 1.22*

(0.61) 1.31*

(0.62) 1.38*

(0.65) Acquirer R&D 0.06

(0.51) 0.06

(0.49) 0.08

(0.50) 0.08

(0.53) 0.08

(0.62) Acquisition

value − 0.03

(0.11) − 0.02

(0.19) −0.03

(0.21) −0.03

(0.19) −0.03

(0.18) Method of

payment 0.85*

(0.42) 0.89*

(0.45) 0.92*

(0.47) 0.91*

(0.41) 0.95*

(0.48) Cross-border

acquisition 0.14

(0.13) 0.15

(0.14) 0.15

(0.16) 0.12

(0.13) 0.14

(0.15) Relative size 0.04

(0.05) 0.06

(0.04) 0.05

(0.04) 0.06

(0.05) Seller distress 0.12*

(0.05) 0.13*

(0.06) 0.11

(0.06) 0.10

(0.05) 0.10

(0.06) Target

industry M&A

− 0.22 (0.31)

− 0.19 (0.28)

−0.21 (0.30)

−0.20 (0.30)

−0.20 (0.32) People

deployment 0.05***

(0.01) 0.05***

(0.01) 0.06***

(0.01) Geographic

distance

− 0.05 (0.04)

−0.06 (0.06)

−0.06 (0.05)

−0.05 (0.05) Moderators

Acquirer innovation level

− 0.63*

(0.31)

−0.64*

(0.32)

−0.82**

(0.32)

−0.61**

(0.27) Alliance

portfolio size 0.35**

(0.13) 0.38**

(0.15) 0.34*

(0.14) 0.35*

(0.15) Key predictors

Acquisition program diversity (APD)

− 0.78

(0.51) 1.24*

(0.62) 1.43

(0.86) 0.95

(0.49)

Acquisition program diversity squared (APD²)

−2.58**

(1.03) −2.92*

(1.46) −1.06**

(0.34)

Interactions APD x Acquirer innovation level

5.82**

(2.06)

APD²x Acquirer innovation level

−3.25***

(0.78)

APD x Alliance portfolio size

−1.13**

(0.42) APD²x

Alliance portfolio size

1.37**

(0.61)

Year dummies Yes Yes Yes Yes Yes

Acquirer country

dummies Yes Yes Yes Yes Yes

Target country

dummies Yes Yes Yes Yes Yes

N observations 312 312 312 312 312

N acquirers 147 147 147 147 147

R² 0.11 0.17 0.21 0.24 0.26