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Merger waves, entry-timing, and cross-border acquisition completion: A frictional lens perspective ⁎
Mohammad Fuada, , Ajai S. Gaurb a b
Indian Institute of Management Lucknow, Prabandh Nagar, IIM Road, Lucknow, 226013, India Rutgers Business School – Newark and New Brunswick, 1 Washington Park, Room 1098, Newark, NJ, 07102, USA
A R T I C LE I N FO
A B S T R A C T
Keywords: Deal abandonment Entry-timing Cross-border merger and acquisition waves Emerging markets
We study the impact of deal announcement and entry-timing within a cross-border acquisition (CBA) wave on the likelihood of acquisition completion. Drawing upon the frictional lens perspective, we identify two types of frictional forces- wave-friction and partner-friction within merger waves. We follow a simulation-based methodology and identify three CBA waves for Indian acquirers between 1995 and 2015. Our findings suggest that acquisition announcement within a merger wave as compared to outside of a wave is negatively related to the likelihood of deal completion. Further, within a merger wave, we find an inverted U-shaped relationship between entry-timing and the likelihood of deal completion.
1. Introduction International expansion often happens in waves (Delios, Gaur, & Makino, 2008). Studies report that more than 50 percent of acquisitions are conducted within merger waves (McNamara, Haleblian, & Dykes, 2008). These waves are characterized by periods of heightened and intense acquisition activity (Andrade, Mitchell, & Stafford, 2001; Rhodes-Kropf & Viswanathan, 2004). Despite their popularity, crossborder acquisitions (CBAs) are inherently risky (Malhotra & Gaur, 2014) and a significant number of acquisitions fail to get completed after the initial announcement (Popli, Akbar, Kumar, & Gaur, 2016). Though a rich body of literature exists on the determinants of deal completion, including firm and country-level factors (Dikova, Rao Sahib, & Van Witteloostuijn, 2010; Zhang, Zhou, & Ebbers, 2011), the literature is largely silent on the role of merger waves on deal completion. Merger waves may have important consequences for the success or failure of a CBA. Fundamentally there are two different environments in which acquisition deals are executed - within a merger wave and outside of a wave. Both of these settings differ in terms of uncertainty and associated risks (Haleblian, Devers, McNamara, Carpenter, & Davison, 2009; Harford, 2005; Mitchell & Mulherin, 1996). In general, there is a higher level of uncertainty within waves, which amplifies the complexity of deal negotiations, leading to inaccurate assessments by managers (Duchin & Schmidt, 2013). These differences are likely to have a significant impact on the deal being conducted within a wave
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versus a non-wave environment, and consequently on the likelihood of deal completion. Scholars have argued that even the position of a deal within a wave may influence deal completion (Doan, Rao Sahib, & Van Witteloostuijn, 2016). Yet, we have a limited understanding of the relationship between the timing of the deal announcement and the likelihood of its completion. We draw upon the frictional lens perspective to develop our arguments about the effect of merger waves on deal completion (Luo & Shenkar, 2011; Popli et al., 2016; Shenkar, 2012). We argue that the merger wave context on account of its intense acquisition activity, increased uncertainty and its time-bound nature (Duchin & Schmidt, 2013), creates frictional dissonance between both the target and the acquirer. This dissonance is likely to adversely impact deal completion. Specifically, we posit two types of frictional forces that impact deal completion. First, there is wave-friction, which arises due to the deal being conducted within an uncertain, high risk and intensely competitive environment of the wave. Wave-friction is present due to the heightened industry-level uncertainty and increased volatility within waves (Duchin & Schmidt, 2013) which results in information asymmetry issues between the target and the acquirer. Second, there is partner-friction, which arises due to the differing perspectives of the acquirer and the target along the different phases of the wave. Partnerfriction is related to the timing of the deal within a wave such as initial, peak and decline phases of a wave. As the wave progresses, the acquirer and the target differ in their motives to complete the acquisition which leads to varying levels of friction across different phases of the wave.
Corresponding author. E-mail addresses:
[email protected] (M. Fuad),
[email protected] (A.S. Gaur).
https://doi.org/10.1016/j.jwb.2018.12.001 Received 5 February 2018; Received in revised form 25 November 2018; Accepted 1 December 2018 1090-9516/ © 2018 Elsevier Inc. All rights reserved.
Please cite this article as: Fuad, M., Journal of World Business, https://doi.org/10.1016/j.jwb.2018.12.001
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performance (Andonova, Rodriguez, & Sanchez, 2013; Carow, Heron, & Saxton, 2004). Scholars have also examined the interdependent nature of a firm’s strategic actions in different contexts including merger waves. For example, McNamara et al. (2008), argue that early movers within merger waves have superior information and late movers simply mimic the actions of leaders. However, such late participation may result in firms not being able to derive early-mover advantages. In line with this, McNamara et al. (2008) found a curvilinear relationship between the timing of deal announcement within a wave and acquirer’s performance. Conducting a deal within a merger wave entails high uncertainty compared to outside of the wave (Duchin & Schmidt, 2013; Xu, 2017). In uncertain environments, firms with superior resources and higher levels of slack are more likely to undertake risks and move early in a wave (Haleblian et al., 2012). Early-movers with superior information regarding targets may pre-empt their competitors in acquiring potential targets (Carow et al., 2004). Early-movers gain by cherry picking resources from a larger set of targets at lower costs (Boulding & Christen, 2008; Makadok, 1998, 2001). Thus, early-movers within a wave are more likely to experience superior post-acquisition performance (Fuad & Sinha, 2018; McNamara et al., 2008). Despite these benefits, moving early in a wave also entails several risks. First, at the inception of the wave, there is heightened uncertainty with regard to its progression as an acquisition may not be considered a legitimate action (Haleblian et al., 2012). This uncertainty reduces as the wave progresses. Second, at the start of the wave, there is a high risk of adverse selection and lack of information regarding potential targets. As a result, firms that move early in the wave may be susceptible to suboptimum decisions in deal negotiation (Xu, 2017). Thus, participating in waves and competing for the best configuration of assets may expose a firm to increased uncertainty and risks compared to conducting a deal outside the wave or in the later phase of a wave.
We incorporate these differences within and outside of a wave as well as the differing motives of both the acquirer and the target firm within a wave environment. We test our theoretical arguments in the context of Indian CBAs between 1995 and 2015. First, we identify whether a CBA deal is a part of a merger wave or not. We then study the role of merger waves and timing within a wave on the likelihood of deal completion. Our findings provide evidence that compared to non-wave deals, deals conducted within merger waves are less likely to be completed. Further, within an acquisition wave, there is an inverted U-shaped relationship between entry-timing and deal completion. Our study makes three important contributions. First, we contribute to the acquisition completion literature from a frictional lens perspective, by identifying a consistent, yet overlooked aspect of acquisitions, namely the wave context. Our finding that announcement within waves is negatively related to deal completion significantly contributes to our understanding of macroeconomic factors important for successfully completing CBA deals. Second, we theorize partner-friction within a wave by taking into account both the acquirer and the target’s perspectives. In line with prior studies, we highlight the active role played by targets in deal completion (Doan et al., 2016; Graebner & Eisenhardt, 2004). Finally, our study responds to the recent calls to conduct research on the temporal aspect of internationalization and CBAs (Popli, Akbar, Kumar, & Gaur, 2017; Shi, Sun, & Prescott, 2012). In doing so, we contribute to the literature on entry decision by addressing the questions about “when to enter?” in the context of merger waves (Zachary, Gianiodis, Payne, & Markman, 2015). 2. Theory and hypotheses 2.1. Merger waves Merger waves are short periods of heightened and intense merger activity. Prior literature has identified merger waves as one of the common features of merger activity (Andrade et al., 2001) with five major merger waves occurring in the last century (Martynova & Renneboog, 2008; Stearns & Allan, 1996). Multiple triggers of merger waves constitute exogenous shocks (Harford, 2005) and periods of high market to book ratios (Rhodes-Kropf & Viswanathan, 2004; Rhodes–Kropf, Robinson, & Viswanathan, 2005). Waves often occur because of prior mergers which highlights the interdependent nature of acquisitions within merger waves (Haleblian, McNamara, Kolev, & Dykes, 2012). Once a wave starts, firms within and outside of an industry begin responding to the changing environment by redeploying their resources, resulting in reorganization of industry assets (Harford, 2005). Accordingly, firms compete to achieve the best combination of industry assets within an acquisition wave (McNamara et al., 2008). The industrial organization model suggests that entrants may foresee the reaction of their rivals and account for the interdependence of their actions (Tirole, 1988). Such actions and the mutual interdependence may alter the market structure (Pattnaik, Lu, & Gaur, 2018) and industry competition (Conner, 1991), which in turn may impact entry decisions and early-mover advantages for firms involved in acquisitions (Gaur, Malhotra, & Zhu, 2013; Suarez & Lanzolla, 2007). Building upon the industrial organization literature, Lieberman and Montgomery (1988) argue that early-movers gain competitive advantages by controlling scarce resources, retaining technology leadership, developing network effects and increasing customers’ switching costs. However, an action by a firm, which may impact a competitor’s strategic position, would attract a response from the rival firm. As with other strategic actions, there is an interdependence among participating firms within an acquisition wave, such that an action by one firm may influence its peers and motivate its rivals to react accordingly (Chen, 1996; Haleblian et al., 2012). This has led scholars to study the impact of timing within waves on different dimensions of acquisition
2.2. Frictional lens perspective and acquisition completion During the public takeover period, multiple forces influence acquisition completion (Dikova et al., 2010). These forces include the acquirer’s escalating commitment and costs (Boeh, 2011), reputational risk (Scalera, Mukherjee, & Piscitello, 2018) and the push by investment banks (Hunter & Jagtiani, 2003) towards deal completion. Both acquirer and target firms need to make various strategic and administrative decisions and need to comply with multiple regulatory authorities (Clougherty, 2005; Muehlfeld, Rao Sahib, & Van Witteloostuijn, 2012). Since these decisions require a considerable amount of resources and time, there is an escalation of costs incurred by firms (Meyer & Altenborg, 2008). Additionally, there are costs related to the advisory and regulatory fees as investment banks push for the deal closure (Boeh, 2011; Hunter & Jagtiani, 2003). All these forces assume importance once the deal is announced, and impact the acquisition completion. According to the frictional lens perspective, “friction implies a paradigmatic shift from abstract differences to a degree of contact between two entities” (Popli et al., 2016: 406). It is the extent to which two or more entities including organizations and individuals, resist one another during their interactions (Luo & Shenkar, 2011; Shenkar, Luo, & Yeheskel, 2008). Prior studies on friction have highlighted the role of cultural and institutional differences in conducting foreign direct investment (FDI) activities including acquisition completion (Orr & Scott, 2008; Popli et al., 2016). In the context of merger waves, we argue that firms experience two types of frictional forces – wave-friction and partner-friction. We discuss these in detail below: 2.3. Wave-friction and acquisition completion We refer to the wave-friction as the resistance that firms experience when they attempt to complete a deal within a merger wave. In Fig. 1, we depict representative wave and non-wave environments. A key 2
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Fig. 1. Acquisition deals within a wave and outside of a wave.
risks of adverse selection within a wave are acute. Failing to conduct proper assessments may threaten a firm’s alignment with the changed industry structure and consequently deal closure. In sum, there is an increased level of information asymmetry and uncertainty about the industry, the value of the target firm and potential synergies between the target and the acquirer within a wave as compared to a non-wave environment. Extant literature highlights uncertainty and information asymmetry as a critical factor in deal abandonment (Dikova et al., 2010; Very & Schweiger, 2001; Zhang et al., 2011). Additionally, unlike other types of risks internal to a firm, structural changes to the industry are largely outside of a firm’s control and adversely impact the level of risks and therefore the likelihood of deal completion. Based on the above arguments, we posit that acquisitions announced within waves are less likely to be completed.
element of the frictional lens perspective is that macro-level ambient conditions surrounding an entity influence frictional forces due to the contact with the ambient environment (Luo & Shenkar, 2011). In line with this, wave-friction is the extent to which the acquirer and the wave environment resist the completion of a deal within a wave. The wave imposes environmental drag forces which increase the uncertainty and the complexity associated with waves. Increased wave-friction implies that deciphering the industry environment is difficult for the acquirer, resulting in heightened information asymmetry and costs related to deal completion. Wave-friction may lower the likelihood of deal completion due to multiple factors prevalent at the industry, firm and deal level respectively. First, wave periods are associated with higher levels of uncertainty compared to non-wave periods due to changes in the industry structure. Exogenous shocks such as changes in the input costs, innovations and deregulations alter the structure of the industries experiencing waves (Andrade et al., 2001; Harford, 2005; Mitchell & Mulherin, 1996). As the industry undergoes reorganization, there is increased market volatility within a wave compared to the non-wave environment. The volatility in the environment increases macro-level uncertainty leading to deal-level interim uncertainty (Bhagwat, Dam, & Harford, 2016). Further, market uncertainty increases the information processing requirements of firms and increases information asymmetry (Li, Boulding, & Staelin, 2010; Stevens, Makarius, & Mukherjee, 2015). Hence industry-level volatility associated with waves may lead to inaccurate assessment of the target firms and thus impact deal closure. Second, acquirers also experience a higher level of volatility during merger waves compared to the non-wave periods (Duchin & Schmidt, 2013). This is due to greater difficulties and uncertainty in target valuation within a merger wave. The quality of analysis by financial investors and consultants reduces within merger waves due to heightened acquisition activity compared to limited merger activity during nonwave periods (Clement & Tse, 2005). Khanna, Noe, and Sonti (2007) found that banks reduce the screening of initial public offerings during merger waves due to a fixed supply of specialized resources. Harford (2005) highlights that revisions to analyst forecast following acquisitions were significantly greater for the deals conducted within waves as compared to those conducted outside of waves. Thus managers may initiate acquisitions without comprehensive information regarding targets and are more prone to inaccurate assessments within a wave. Accordingly, inaccurate evaluation of targets is often associated with the reduced likelihood of deal completion (Dikova et al., 2010). Finally, despite the industry-level uncertainty, acquirers are also pressured to conduct “mergers of necessity” within a wave in contrast to “elective mergers” which are conducted outside of a wave (Duchin & Schmidt, 2013). Since industries associated with merger waves undergo structural changes and reconfiguration (Harford, 2005; Mitchell & Mulherin, 1996), firms are forced to adapt to the new industry structure and therefore conduct acquisitions borne out of necessity. Conducting “mergers of necessity” would require careful due diligence since the
Hypothesis 1. CBA deals announced within a merger wave are less likely to be completed as compared to those announced outside of the merger wave.
2.4. Partner-friction within a wave and acquisition completion In addition to the general environmental friction within a wave, frictional forces may also exist between the acquirer and the target firm. In line with Luo and Shenkar (2011), we define partner-friction as the extent to which both the acquirer and the target firms resist or act in opposition to each other over the course of the merger wave, thereby limiting the ability and/or the willingness of the involved parties to complete the deal. While there is some level of partner-friction regardless of whether a deal takes place within or outside of a wave environment, we argue that partner-friction is accentuated within waves, and varies according to the position of the deal within a merger wave. This is because both the target and the acquirer’s willingness to close the deal may differ over the wave duration. In Fig. 2, we demonstrate the motives of the acquirer and the target across different stages of a representative merger wave. Entry-timing in the context of merger waves refers to the order of entry by the focal firm compared to its industry counterparts (Zachary et al., 2015). We argue that the motivation of the acquirer and the target often differ along different stages of the wave and, thus impact deal completion (Doan et al., 2016). Accordingly, partner-friction between the target and the acquirer would be high during the initial and the decline phases, but low during the peak phase of a merger wave. We elaborate on these differing motives during the three phases below. 2.4.1. Initial phase The beginning of a merger wave is marked with a high level of uncertainty as acquisitions in the wave are not yet considered a legitimate action (Haleblian et al., 2012). The uncertainty associated with the wave and the lack of information about potential targets leads to a high level of complexity in decision making at the start of the wave. At the same time, there is a large pool of potential targets at the beginning 3
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Fig. 2. Partner-friction within a wave.
market, and the bidding process would lead to a significant increase in its price. Once the wave peak passes, the intensity of bidding falls as the number of acquirers decrease, and therefore the target may witness a lower price as compared to that at the peak of the wave. Hence both the target and the acquirer would want to complete the deal during the peak stage of the wave and as such partner-friction would be the least.
(Carow et al., 2004) and therefore, acquirers may be motivated to move early in order to pre-empt competitors, avail first-mover advantages and lock-in the potential targets (Child & Rodrigues, 2005; Lieberman & Montgomery, 1988). However, during the initial phase of a wave, the target may not be motivated to complete the deal because of valuation and price issues. As the acquisition activities begin, the price of the target at the initial stage of a wave is relatively lower than at the peak stage of the wave. As the wave progresses, multiple acquisitions are announced, and information regarding potential targets gets shared with the larger market, leading to competition for assets and collective bidding up of target prices (McNamara et al., 2008). Scholars have shown that with the announcement of more acquisitions, there is a considerable increase in the stock prices within the seller’s industry (Gaur et al., 2013; Mitchell & Mulherin, 1996). Andrade et al. (2001) showed that as acquisition activities pick up, the target firm’s shareholders gained the same return in three days that they expected to gain over a sixteen-month period. Clearly, a target would not be motivated to complete a deal in the early phase of the wave.
2.4.3. Decline phase In the decline phase of the wave, as there are fewer potential targets, acquirers may find it difficult to identify partners of their choice (Doan et al., 2016). Further, many of the critical resources may have already been acquired by early-movers and hence finding synergies and co-specialized assets may be difficult in later stages (McNamara et al., 2008). Hence acquirers may not be motivated to complete a deal in the decline phase of the wave. Additionally, target firms may not have many options to choose from, as fewer potential acquirers enter the wave in the decline phase of the wave. As the wave peak passes, the benefits of collective bidding may also be absent in the decline phase. Delaying the deal further might lead to resource obsolescence as the industry reallocation gets completed by the end of the wave. Hence in the initial and the decline phase of the wave, acquirer and target firms’ motives contradict each other thereby leading to increased partner-friction, which negatively impacts deal completion. In the peak phase of the wave, the interests of both the acquirer and the target firms align, thereby reducing partner-friction. This alignment of motives during the peak phase increases the likelihood of deal completion. Thus we argue a curvilinear relationship between the timing within a wave and the likelihood of deal completion.
2.4.2. Peak phase At the peak of the wave, the acquirer would be motivated to close the deal because of reduced uncertainty compared to the early phases of the wave. The reduced uncertainty is on account of two factors. First, there is a surge in the number of deals and acquisitions become an accepted strategic action within the industry (Haleblian et al., 2012). Second, uncertainty in deciding the acquisition premium is reduced. Managers often look outside of their firms in deciding the premium to be paid for an acquisition (Haunschild, 1994). Heightened bidding process at the wave peak leads to increased information sharing and spill over to the wider industry counterparts (McNamara et al., 2008). Hence a bidder lacking information regarding the target may follow the price points set by other competing bidders in deciding the target’s valuation. Thus we argue that compared to the initial phase of the wave, there is considerable information flow at the peak phase of the wave which reduces uncertainty and would keep the acquirer motivated to close the deal. Moreover, during the peak phase of the wave, the target would also be interested in completing the deal as its true value is known to the
Hypothesis 2. Within a merger wave, there is an inverted U-shaped relationship between entry-timing and the likelihood that the CBA deal would be completed. 2.5. Deal complexity and acquisition completion within a wave Apart from the uncertain environment of merger waves, deal-level complexity may further increase frictional resistance and negatively influence acquisition completion. A key feature of deal complexity is the size of the deal, especially during waves where large-sized and 4
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Indian CBA waves were initiated following the liberalization of the Indian economy in 1991 and financial reforms which led to an increase in outward FDI (Delios, Gaur, & Kamal, 2009). Specifically, financial policies post-liberalization were aimed at facilitating FDI by relaxing repatriations, changes in foreign approval processes and investment ceiling conditions (Popli & Sinha, 2014). These policy changes and reforms lead to heightened acquisition activities (Gaur, Kumar, & Singh, 2014; Popli, Ladkani, & Gaur, 2017). The liberalization shock led to opening up of the Indian economy, however the enactment of the Foreign Exchange Management Act (FEMA) in 1999–2000 was a key policy change. This act relaxed and facilitated laws related to outward FDI and capital transactions conducted by Indian firms. In 1999, the neutrality condition was removed and firms could repatriate the foreign earnings in the form of dividends, royalties and other payments. Earlier, firms were required to repatriate the full amount of the outward investment within a period of five years. Preceding the FEMA, in 1997, various steps were also taken by the Indian Government to encourage the information technology (IT) and software industry firms to expand abroad. All of these may have contributed towards the merger wave seen in 1999 in the business services industry. Around 2001–02, the Indian government further reformed the financial sector to enable firms to compete internationally. Specifically, funding rules for acquisitions, joint ventures or wholly owned subsidiaries were relaxed and firms could invest 100 percent of their proceeds as American depositary receipts and global depository receipts (Popli & Sinha, 2014). Relaxations in automatic approval route and increased funding was a key factor in the surge of CBAs. In 2003, the automatic route was relaxed such that Indian firms could fund up to 100 percent of their networth which was further increased to 200 percent in 2005. This triggered a sharp increase in CBAs in 2004–2005 (Gopinath, 2007). Accordingly, the automatic route policies may be a possible trigger for the second wave in 2004. Since these reforms were cross-border in nature and directed at outward FDI, their impact on domestic acquisitions may be insignificant. Hence we focus on CBAs in this study. Further, since these policies had a major impact on the outward FDI, Indian firms provide an interesting context to study CBA waves.
mega-deals are prevalent (Alexandridis, Fuller, Terhaar, & Travlos, 2013). There are multiple reasons why a large-sized deal within a wave is less likely to be completed. First, there is increased complexity associated with large target firms (Alexandridis et al., 2013; Zollo, 2009). In the case of larger firms, there are more factors to consider for proper due diligence, which requires additional time. However, the very nature of a wave means that firms have limited time, which forces the acquirers to complete the initial negotiations and make sure that they lock in the target and not let it slip to a competitor. In the process, acquirers are more likely to make sub-optimal decisions in the negotiation stage, making an inaccurate evaluation of the target and the potential synergies (Duchin & Schmidt, 2013). In contrast, smaller deals are associated with lower costs and fewer challenges compared to large deals (Doan, Rao Sahib, & Van Witteloostuijn, 2018). Hence even in high uncertainty wave environment, smaller deals may require relatively fewer resource commitments compared to larger deals. Since resource constraint is an important factor in deal abandonment, large sized deals are more likely to be abandoned as firms gather more information in the post-announcement phase. Second, large sized deals require greater intra-firm coordination (Ellis, Reus, Lamont, & Ranft, 2011). In addition, large deals also require substantial managerial involvement and interactions with the regulatory authorities (Doan et al., 2018). As industry structure undergoes a change, regulatory authorities may be concerned about the competitive environment and industry consolidation, and may conduct deeper scrutiny resulting in a delay or denial of the approval (Clougherty, 2005). In both these scenarios, the acquirer would incur additional costs and time delays in the case of large-sized deals compared to smaller deals. Finally, Duchin and Schmidt (2013) argue that firms are pressured to conduct “mergers of necessity” within merger waves compared to “elective mergers” during non-wave periods. Firms conducting largesized acquisitions may be forced to engage in a greater level of due diligence and careful planning. This is because the challenges and costs associated with large deals involve greater uncertainty and risks (Zollo, 2009). Coupled with the risks of conducting a large deal, market volatility and higher deal-level risks within waves (Bhagwat et al., 2016) would also lower the likelihood of acquisition completion. Accordingly, we propose that large-sized deals within a wave are less likely to be completed.
3.2. Data and sample We study CBAs conducted by Indian firms post the liberalization of the economy (Popli & Sinha, 2014). We obtained the data on acquisitions from the SDC Platinum database. We identified all CBAs conducted by Indian acquirers between 1995 and 2015. We excluded deals in which the acquirer was an investor group and those involving acquisitions with joint venture partners (Fuad & Sinha, 2018; Harford, 2005; Popli et al., 2016). An investor group comprises of a group of acquirers coming together to conduct an acquisition and includes investment firms and investors. Acquisitions by investor groups and firms in the financial sector have different asset structures and need to be removed (Haleblian et al., 2012). Also, acquiring a joint venture partner is different in terms of the knowledge structures of both the firms and therefore may impact deal finalization (Popli et al., 2016). Therefore, we remove such deals from our sample.
Hypothesis 3. The likelihood of a deal completion within a merger wave is lesser for larger deals as compared to smaller deals. The conceptual model depicting the hypothesized relationships is shown in Fig. 3. 3. Methods 3.1. Study context We examine our theoretical arguments on CBAs by Indian firms as this context provides a unique opportunity to investigate CBA waves.
3.2.1. Wave identification process Liberalization in India was characterized by a series of reforms which led to increased CBAs around 2000. Accordingly, in line with Harford (2005), we split the overall time duration into two periods: 1995–2001 and 2002–2012. This data split is for identification of CBA waves. Consistent with prior studies, we identified CBA waves in each of these industries in a two-stage process (Haleblian et al., 2012; Harford, 2005; McNamara et al., 2008). Stage 1: In the first stage for each time period, we manually identify the peak of the wave where the number of deals in a given time period
Fig. 3. Conceptual model of hypothesized relationships. 5
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55 30 79 53 71 67 37 107 Jan-2004 - Sep-2008 Mar-1999- Nov-2001 Jan-2004 - Nov-2009 120 215 Chemicals and Allied Products Business Services
3.3.2. Independent variables There are two key independent variables. First, we identify if a CBA is part of the wave using a dummy variable which takes the value of 1 if the acquisition is conducted in the wave, and 0 otherwise. An
28 73
Table 1 Details of wave and non-wave deals.
3.3.1. Dependent variable Our dependent variable is the deal completion status. We checked from the SDC ‘Status’ field whether the deal is completed or not. All deals with a status of ‘intended’, ‘intent withdrawn’ and ‘pending’ were categorized as not completed. We manually checked for those deals with the status of ‘unknown’ to find their current status and classified them accordingly. Only those deals with the status of ‘completed’ were assigned a value of 1, while all other deals were assigned a value of 0 (Dikova et al., 2010). Further, we randomly selected 10 percent of the deals and manually checked them for their final status. After manual checking in news articles and annual reports, we found that the status of these deals concurred with the status mentioned in the SDC database.
Industry Description
3.3. Measures
2 Digit SIC Code
Total cross-border deals between 1995- 2015
Wave Duration
Deals within wave
Deals outside the wave
Deals completed within wave
Deals completed outside wave
is the highest with a minimum of 10 deals. Next, we move forward from the peak year and identify the end of a wave as the year in which the number of deals is at least half of the peak year deals. Similarly, we move backward from the peak year and identify the start of the wave. Thus the first stage of the wave identification process results in identifying the duration of the potential industry merger waves. Stage 2: While in step 1 we are able to identify a potential wave based on the peak year, start year and end year deal numbers, in step 2 we take into account the deal volume in the non-wave years. Hence in the second stage to ensure that such acquisitions did not happen randomly or by chance, we further conducted a simulation based analysis (Haleblian et al., 2012; McNamara et al., 2008). This random distribution test indicates that identified waves are not merely due to chance. In other words, the potential wave identified in step 1 should have a higher number of deals compared to the simulated wave in step 2 (Harford, 2005). The simulated wave provides the threshold of probable occurrence which should be exceeded for a wave to be classified as a true wave (Netter, Stegemoller, & Wintoki, 2011). This test has been widely adopted in identifying merger waves (Haleblian et al., 2012; Xu, 2017). Total bids in the wave period (identified in stage 1) were taken, 1000 distributions of acquisitions over this period were generated, and the deals were randomly assigned to each year of the wave. The peak year concentrations in the potential waves in stage 1 were matched to the simulated waves’ peak years. Waves which exceeded 95th percentile were identified to have not occurred randomly and thereby classified as final waves. All waves in step 1 also passed the stage 2 test and were classified as final waves. We identified three waves, one in ‘Chemicals and Allied Products’ (SIC code 28) and two waves within ‘Business Services’ industry (SIC code 73) with a total of 335 announced CBA deals. Due to missing data points for few of our variables, the final sample size consisted of 208 deals. Table 1 depicts the characteristics of these deals for both the industries while Table 2 highlights the geographic distribution of targets. Within Chemicals and Allied Products industry, around 56 percent of the total deals were classified as wave deals whereas for Business Services 67 percent of the deals belonged to the wave. These figures are in line with earlier studies on Indian CBA waves (Fuad & Sinha, 2018; Popli & Sinha, 2014). We conducted additional checks to test that our reduced sample was representative of the initial dataset. We ran multiple chi-square analyses based on industry and country status (developed versus developing country). Further, we conducted a t-test for the timing variable in the initial and final sample sets. No significant differences were observed between the final sample and the initial data, suggesting that our sample is representative of the population and that sample selection bias is not an issue.
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firms are more likely to conduct cash transactions (Myers & Majluf, 1984). Hence we controlled for payments made in cash only or by using other modes such as stock. The variable takes a value of 1 if the payment was made in cash only and 0 otherwise. We also controlled for the target country development status and industry relatedness. Since a substantial number of acquisitions by Indian firms are conducted in developed markets, we account for the development status of the target country using an indicator variable. The variable takes a value of 1 for developed countries and 0 for developing countries (UNCTAD, 2015). Institutional distance between the target and acquirer countries, was operationalized using a Euclidean measure comprising of six governance indicators from World Bank (Contractor, Yong, & Gaur, 2016). We operationalized industry relatedness using an indicator variable with a value equal to 1 if the acquirer and target belonged to same industry and 0 otherwise. Further, we controlled for industry fixed effects at the 2 digit SIC code. Since majority of the deals were conducted in developed countries such as US and UK, we also controlled for their fixed effects in our models. All the firm-level financial data was lagged by one year and was retrieved from the PROWESS database. We tested our models by conducting logistic regression, given that the dependent variable is dichotomous in nature (Doan et al., 2018). The unit of analysis is at the deal level, however, several deals may be announced by the same firm. Thus to account for the lack of interdependence in our data due to multiple acquisitions by the same firm, we use clustered robust errors by acquirer firm (Dikova et al., 2010; Muehlfeld et al., 2012). Additionally, as a robustness check, we tested our models using probit regression. Since the results for both the regressions were similar, we report the results of logistic regression in our main models.
Table 2 Country-wise distribution of targets. Argentina Australia Bahamas Belgium Brazil Bulgaria Canada Chile China Czech Republic Denmark Egypt Fiji Finland France Gabon Germany Hong Kong Hungary Ireland Israel Italy Japan Malaysia Mexico
1 7 1 5 4 1 11 3 3 1 1 4 1 4 7 1 14 1 1 1 4 2 2 3 1
Nepal New Zealand Oman Philippines Poland Portugal Romania Senegal Singapore South Africa Spain Sweden Switzerland Thailand Netherlands United Arab Emirates United Kingdom United States Yemen Zambia
4 1 1 2 2 1 4 1 14 5 4 1 4 1 4 6 34 160 1 1
acquisition deal belongs to a wave if the announcement date for that particular deal falls within the duration of the merger wave. Our second independent variable is entry-timing for wave deals. By incorporating order of entry measure to move beyond categorical classification (Zachary et al., 2015), we operationalized the timing variable as the relative order of announcements compared to the first deal announced in an industry wave. We assign a value of 1 to the first announcement, the value of 2 to the second deal and so on. Thus smaller values indicate early-movers and higher values indicate latemovers in a wave. Additionally, as a robustness test, we operationalized timing within each industry wave in terms of the number of days (Haleblian et al., 2012). Further, we included the deal size variable to test for the interaction effect. We operationalized this variable by taking a logarithmic transformation of the deal value variable. We mean centered the squared and interaction terms.
4. Results Table 3 presents the descriptive statistics including correlations, means, and standard deviations. All variance inflation factors were below 10, and hence our results are not influenced by multicollinearity issues. Table 4 reports the results of the logistic regression. Model 1 includes all control variables while Models 2–3 report the predictor variables including the interaction effect. Models 4–5 report findings on the subsample of wave deals and include the robustness test for alternate operationalization of the entry-timing variable. Model 1 shows that firms affiliated to a business group have a higher likelihood of deal completion compared to standalone firms (β = 0.82, p < 0.10). Our finding is consistent with that of Kim and Song (2017) in which business groups lead to a flow of information and facilitate completion of announced deals. Further, institutional distance (β = 0.56, p < 0.05) influences deal completion. Hypothesis 1 predicts that CBAs announced within a merger wave are less likely to be completed than those announced outside of a wave. In Model 3, the wave deals variable is negative and significant (β= -1.26, p < 0.10), supporting Hypothesis 1. With respect to entrytiming within the wave, Hypothesis 2 predicts an inverted U-shaped relationship between entry-timing within a merger wave and the likelihood of deal completion. Our results in Model 4 suggest that as the wave progresses the likelihood of deal completion initially increases (β = 0.02, p < 0.05) and then decreases during later stages of the wave (β= -0.001, p < 0.01). Hence Hypothesis 2 is also supported. Hypothesis 3 predicts that larger deals within merger waves are less likely to be completed. We do not find the interaction term to be significant and hence Hypothesis 3 is not supported.
3.3.3. Control variables We controlled for various factors which may impact the likelihood of deal completion. Business group affiliation, a key characteristic of emerging markets (Singh & Delios, 2017; Mukherjee, Makarius, & Stevens, 2018) may affect deal completion (Kim & Song, 2017). We retrieved the business group information from PROWESS database and controlled for it by incorporating a dummy variable with a value equal to 1 for affiliate firms and 0 for standalone firms (Gaur & Kumar, 2009; Gaur et al., 2014). We manually checked the unlisted firms to ascertain whether they belonged to a business group or not. Since the prior experience of a firm may also impact deal completion (Zhang et al., 2011), we controlled for the CBA experience by the number of acquisitions conducted by a firm prior to the acquisition year as retrieved from SDC database. Additionally, we controlled for firm-level financial controls such as acquirer size, age, and prior performance and export intensity. Acquirer size is the logarithmic transformation of the firm’s sales prior to the acquisition. We measured the age of the acquirer as the number of years since the incorporation of the firm and its focal acquisition announcement. Acquirer’s prior performance referred to the return on equity prior to the focal acquisition announcement. Export intensity was measured as the ratio of exports as a percentage of sales. We also controlled for the percentage of shares sought in the deal (Fuad & Sinha, 2018). Type of payment may also impact deal completion since managers with superior informational advantages relating to target
4.1. Robustness tests We conducted multiple checks to ascertain the robustness of our findings. First, we operationalized the timing variable as the “number of days that each acquisition announcement occurred after the first 7
Journal of World Business xxx (xxxx) xxx–xxx
Model 1
Model 2
Model 3
Model 4
Model 5
Intercept
−0.78 (1.22)
0.24 (1.32)
0.17 (1.37)
−0.15 (1.67)
−1.75 (1.90)
BG affiliation
0.82* (0.43)
1.10** (0.50)
1.06** (0.52)
1.11* (0.67)
1.32* (0.71)
Prior CBA experience
−0.11 (0.07)
−0.20** (0.08)
−0.18* (0.09)
−0.26 (0.18)
−0.29 (0.18)
Deal size
0.24 (0.17)
0.32* (0.18)
0.29 (0.19)
0.42** (0.20)
0.36* (0.21)
Percentage sought
0.01 (0.01)
0.01 (0.01)
0.01 (0.01)
−0.01 (0.01)
−0.00 (0.01)
Cash payment
0.03 (0.54)
0.13 (0.54)
0.23 (0.54)
−0.14 (0.72)
0.14 (0.71)
Institutional distance
0.56** (0.27)
0.54** (0.26)
0.53** (0.26)
0.89*** (0.32)
0.86*** (0.32)
Developed country
−0.07 (0.69)
0.05 (0.69)
0.01 (0.68)
−0.51 (0.94)
−0.23 (0.88)
Industry relatedness
−0.26 (0.44)
−0.09 (0.47)
−0.13 (0.48)
−0.10 (0.53)
−0.04 (0.54)
Acquirer firm size
−0.08 (0.11)
−0.17 (0.13)
−0.15 (0.13)
−0.24 (0.20)
−0.30 (0.19)
Acquirer age
−0.00 (0.01)
−0.01 (0.01)
−0.01 (0.01)
−0.00 (0.01)
0.01 (0.02)
Acquirer prior performance
0.00
0.00
0.00
0.00
0.00
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
0.01 (0.01)
0.01 (0.01)
0.01 (0.01)
0.00 (0.01)
0.00 (0.01)
−1.35** (0.67)
−1.26* (0.72)
Entry-timing
0.02** (0.01)
2.45*** (0.91)
Entry-timing squared
−0.001*** (0.00)
−1.06 (2.47)
1.00 −0.02 −0.15* −0.21** −0.15
1.00 −0.02 0.02 0.01
1.00 −0.08 0.08
1.00 –
1.00
Table 4 Results of logistic regression.
1.00 0.50** 0.28** 0.10 −0.39** 0.02 1.00 −0.12 −0.12* 0.03 −0.03 0.35** −0.04 1.00 −0.03 −0.01 −0.18** −0.06 0.11 0.04 0.04 1.00 0.48** −0.16** −0.02 −0.17** −0.04 0.17* −0.06 −0.10 1.00 −0.05 −0.13* −0.08 0.14* 0.10 0.14* −0.16* −0.04 −0.04
9 7 6
8
10
11
12
13
14
15
M. Fuad, A.S. Gaur
Export intensity
1.00 −0.12* 0.06 0.09 0.00 0.02 −0.02 −0.11 0.20** 0.03 0.15* 1.00 0.22** −0.04 −0.07 0.10 −0.02 0.37** 0.09 0.11 0.09 0.01 0.26** 1.00 0.27** 0.03 0.02 0.01 0.06 −0.11* 0.50** 0.34** −0.02 0.18** −0.19** 0.13
Wave deals x deal size
Industry fixed effects Target country fixed effects Log pseudolikelihood Wald chi-square N
1.00 0.05 0.02 0.16** 0.06 0.03 0.10 0.05 −0.05 0.07 −0.03 0.06 0.07 −0.09 0.05
Yes Yes
Yes Yes
Yes Yes
Yes Yes
Yes Yes
−93.58 28.62** 208
−90.98 35.62*** 208
−90.58 39.37*** 208
−66.39 38.51*** 156
−67.33 38.55*** 156
acquisition in the wave” divided by the “total number of days in the wave” (Haleblian et al., 2012: 1043). This variable is a relative delay measure and ranges from a value of 0 for the first announcement to a value of 1 for the last announcement in a wave. If multiple deals were announced on the same day, then the value of the entry-timing variable would be identical for each of those deals. The results reported in Model 5 of Table 4 partially support Hypothesis 2. Second, we normalized the entry-order variable by dividing the values by the mean of the rank variable. The results for the curvilinear relationship between announcement timing and deal completion were robust to this operationalization. Third, we incorporated the listing status of the target firm and industry effects at the 3 digit SIC level. The results were similar to the above findings. Fourth, to ascertain the robustness of our findings to different methods, we conducted probit regression to study the
Two tailed tests. * p < 0.05. ** p < 0.01.
Deal completion status BG affiliation Prior CBA experience Deal size Percentage sought Cash payment Institutional distance Developed country Industry relatedness Acquirer firm size Acquirer age Acquirer prior performance Export intensity Wave deals Entry-timing 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
0.29 (0.47)
Estimates are shown with clustered errors in parentheses. Two tailed tests. * p < .10. ** p < .05. *** p < .01.
0.81 0.53 1.17 2.80 84.73 0.85 3.66 0.82 0.26 4.49 24.49 26.42 51.28 0.63 41.27
0.40 0.50 2.17 1.51 27.86 0.35 1.02 0.38 0.44 2.24 19.84 37.29 38.23 0.48 28.53
1.00 0.30** 0.15** −0.16** 0.15** −0.10 −0.01 −0.04 0.46** 0.28** 0.11 −0.16* −0.06 −0.13
4 1 S.D. Mean Variable S.No
Table 3 Means, Standard deviations and correlations.
2
3
5
Wave deals
8
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M. Fuad, A.S. Gaur
Table 5 Results of probit regression.
Table 6 Multilevel probit and logistic regressions. Model 1
Model 2
Probit regression
Logistic regression
Intercept
0.08 (0.74)
−0.21 (0.93)
Model 1
Model 2
Model 3
Model 4
BG affiliation
0.63** (0.28)
0.62* (0.35)
Intercept
0.23 (0.86)
0.06 (1.01)
0.46 (1.61)
0.21 (1.72)
Prior CBA experience
−0.11** (0.05)
−0.15 (0.10)
BG affiliation
0.62** (0.29)
0.67* (0.34)
1.06* (0.60)
1.16* (0.65)
Deal size
0.16* (0.09)
0.23** (0.11)
Prior CBA experience
−0.11*** (0.04)
−0.17* (0.09)
−0.18*** (0.07)
−0.30* (0.16)
Percentage sought
0.00 (0.00)
−0.00 (0.01)
Deal size
0.18** (0.07)
0.26*** (0.09)
0.32** (0.15)
0.46*** (0.17)
Cash payment
0.20 (0.29)
−0.04 (0.37)
Percentage sought
0.00 (0.00)
−0.00 (0.00)
0.01 (0.01)
−0.01 (0.01)
Institutional distance
0.31** (0.14)
0.53*** (0.18)
Cash payment
0.22 (0.25)
−0.04 (0.17)
0.26 (0.50)
−0.15 (0.31)
Developed country
0.04 (0.37)
−0.18 (0.51)
Institutional distance
0.15 (0.15)
0.19 (0.21)
0.29 (0.27)
0.37 (0.37)
Industry relatedness
−0.08 (0.26)
−0.04 (0.29)
Developed country
0.11 (0.29)
0.27 (0.29)
0.07 (0.80)
0.22 (0.63)
Acquirer firm size
−0.10 (0.07)
−0.14 (0.10)
Industry relatedness
−0.05 (0.38)
0.02 (0.32)
−0.07 (0.74)
0.02 (0.58)
Acquirer age
−0.00 (0.01)
0.00 (0.01)
Acquirer firm size
−0.10 (0.10)
−0.16 (0.12)
−0.16 (0.20)
−0.27 (0.23)
Acquirer prior performance
0.00 (0.00)
0.00 (0.00)
Acquirer age
−0.00 (0.01)
0.00 (0.01)
−0.01 (0.01)
0.00 (0.01)
Export intensity
0.00 (0.00)
0.00 (0.00)
Acquirer prior performance
0.00 (0.00)
0.00 (0.00)
0.00 (0.00)
0.00 (0.00)
Wave deals
−0.75* (0.38)
Export intensity
0.00 (0.00)
0.00 (0.00)
0.01 (0.00)
0.00 (0.00)
Wave deals x deal size
0.18 (0.22)
Wave deals
−0.78*** (0.29)
−1.33** (0.62)
0.19 (0.20)
0.33 (0.44)
Entry-timing
0.01** (0.01)
Wave deals x deal size
Entry-timing squared
−0.00*** (0.00) Yes Yes −66.13 43.55**** 156
Entry-timing
0.01*** (0.00)
0.02**** (0.01)
Entry-timing squared
−0.00**** (0.00)
−0.001**** (0.00)
Industry fixed effects Target country fixed effects Log pseudolikelihood Wald chi-square N
Yes Yes −90.21 43.85**** 208
Industry fixed effects Country level random effects (Variance)
Estimates are shown with clustered errors in parentheses. Two tailed tests. * p < .10. ** p < .05. *** p < .01. **** p < .001.
Log pseudolikelihood Wald chi-square N
Yes
Yes
Yes
Yes
0.03 (0.15)
0.11 (0.11)
0.04 (0.70)
0.26 (0.38)
−93.44 55.47**** 208
−71.44 184.40**** 156
−93.72 44.18**** 208
−71.47 173.26**** 156
Estimates are shown with robust errors in parentheses. Two tailed tests. * p < .10. ** p < .05. *** p < .01. **** p < .001.
hypothesized relationships. The results are reported in Table 5 and found to be qualitatively similar to the logistic regression results. Further, since the data is multilevel with information at the country and firm levels, we ran multilevel logistic and probit regression analyses to test for robustness of our findings. The results were consistent with earlier findings and are reported in Table 6. Finally, we also report the average marginal effects in Table 7. Model 1 reports the average marginal effects of the wave deals variable whereas Model 2 and Model 3 report the results of the timing variable operationalized as rank order and relative days respectively.
completion is largely underexplored. We address this research gap and respond to the call to study the temporal aspects of acquisitions (Shi et al., 2012). Drawing upon the frictional lens perspective, we studied the role of wave-friction and partner-friction on deal completion. In line with prior literature (Harford, 2005; McNamara et al., 2008), we conducted a two-step, wave simulation process and identified three merger waves between 1995–2015, all of which belonged to technology-intensive industries. We developed hypotheses and tested the impact of deal announcement within a wave and its timing on acquisition completion. Our results highlight the negative impact of wave participation on deal completion. Further, and more interestingly, we find an inverted U-shaped relationship between the timing of announcement
5. Discussion and conclusion The occurrence of acquisitions in waves is a consistent feature of the acquisition activity (Andrade et al., 2001; Haleblian et al., 2012). Though the phenomenon of clustering of acquisitions impacts firm performance, its influence on the pre-integration phase of deal 9
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close a large deal. Managers and top executives may overestimate their ability to extract benefits and synergies from the target firm and therefore may bid for large deals and close the deal (Alexandridis et al., 2013). However, such large deals are associated with poor performance. Hence the behavioral attributes of managers (Haleblian et al., 2009; Hayward & Hambrick, 1997) may be one of the possible reasons for lack of support for Hypothesis 3. Further, within a wave environment, the timing of the deal announcement had an inverted U-shaped relationship with the likelihood of deal completion. Our results depart from studies conducted on US firms where a negative linear relationship between timing and acquisition completion was observed (Doan et al., 2016). Partner-friction between target and acquirer firms plays a critical role in deal completion, especially within a wave. The differing motives of the acquirer and the target firms along the wave and the time-bound uncertain nature of the wave influence deal completion. Our results contribute to prior literature on the relational aspect of deal completion and the role of targets in completing a deal (Graebner & Eisenhardt, 2004).
Table 7 Average marginal effects (logistic regression). Model 1 **
Model 2
Model 3
BG affiliation
0.14 (0.06)
0.17 (0.08)
0.18** (0.08)
Prior CBA experience
−0.02* (0.01)
−0.04 (0.02)
−0.04* (0.02)
Deal size
0.04** (0.02)
0.06** (0.03)
0.05* (0.03)
Percentage sought
0.00 (0.00)
−0.00 (0.00)
−0.00 (0.00)
Cash payment
0.03 (0.07)
−0.01 (0.09)
0.02 (0.09)
Institutional distance
0.08** (0.04)
0.11** (0.05)
0.11** (0.05)
Developed country
−0.07 (0.11)
−0.04 (0.14)
−0.03 (0.14)
Industry relatedness
−0.02 (0.06)
−0.02 (0.06)
−0.01 (0.06)
Acquirer firm size
−0.02 (0.02)
−0.04 (0.02)
−0.04 (0.02)
Acquirer age
−0.00 (0.00)
0.00 (0.00)
0.00 (0.00)
Acquirer prior performance
0.00 (0.00)
0.00 (0.00)
0.00 (0.00)
Export intensity
0.00 (0.00)
0.00 (0.00)
0.00 (0.00)
Wave deals
−0.11* (0.06) 0.001 (0.00)
0.34** (0.13)
Yes Yes 156
Yes Yes 156
Entry-timing Industry fixed effects Target country fixed effects N
Yes Yes 208
**
5.1. Theoretical contributions *
We contribute in several ways to the scholarly understanding of merger waves and their influence on acquisition completion. First, we contribute towards the literature on deal abandonment in the context of CBAs. Multiple determinants of deal completion and abandonment have been identified in prior literature such as firm and country level factors (Dikova et al., 2010; Zhang et al., 2011). We identify additional important factors, namely, the role of merger waves and entry-timing within the wave which has been underexplored in prior literature. We highlight that not only do country-level and firm-level factors matter but also the environment of acquisition waves in which a deal is executed impacts deal completion. Second, we develop a novel frictional perspective consisting of wave-friction and partner-friction. We find support for our conceptualization of such differing forces within a wave and our findings contribute towards the frictional lens perspective (Luo & Shenkar, 2011; Shenkar, 2012) adopted in acquisition deal abandonment studies. While earlier studies using the frictional lens identified the role of country-level factors such as culture (Shenkar et al., 2008), our study extends the frictional lens perspective by identifying the influence of wave-friction and partner-friction on deal completion. Our study also contributes to environmental uncertainty literature by highlighting the link between exogenous shocks and changes to the industry structure (Harford, 2005; Mitchell & Mulherin, 1996). Finally, we advance entry-timing literature by answering the question about “when to enter?” (Lieberman & Montgomery, 2013; Zachary et al., 2015). We note that a firm is better off by conducting an acquisition in a non-wave environment where wave-friction forces may be absent, which may result in a higher likelihood of deal completion. Further, a participating firm within a wave may position its deal announcement around the peak phase of the wave where the partnerfriction is lowest. Though prior works on entry-timing have studied dichotomous variables such as early or late mover or first mover and second mover, we utilize an ordered as well as a continuous measure of timing within a wave. In doing so, our study responds to the calls to study entry-timing and its impact on strategic initiatives such as acquisitions (Haleblian et al., 2009; Shi et al., 2012; Zachary et al., 2015). Our contribution lies in highlighting that wave and non-wave deals are fundamentally different. Future CBA studies should account for this distinction rather than treating all acquisition deals as homogenous in terms of uncertainty and riskiness of the acquisition environment.
Average marginal effects are shown with standard errors in parentheses. * p < .10. ** p < .05.
within a wave and the likelihood of acquisition completion. Our findings suggest that CBAs conducted within a wave experience wave-friction, which makes it more difficult to complete wave deals as compared to the non-wave deals. Acquisition waves are associated with shifts in the industry structure resulting in an industry-wide reallocation of assets (Harford, 2005; Mitchell & Mulherin, 1996). Firms respond to the changed environment by acquiring the best combinations of assets in order to attain a strategic fit with the dynamic environment. In the wave environment, there is a spurt of acquisition activity which intensifies over time and finally declines, whereas in a non-wave environment the industry is relatively less volatile. Further, the heightened uncertainty at the industry, firm and deal level during waves adversely impacts acquisition completion within a wave. We find that wave and non-wave contexts are different, and firms participating in a wave experience heightened uncertainty while completing a deal (Duchin & Schmidt, 2013). Our results, viewed through the frictional lens, are consistent with prior literature on deal abandonment. Literature highlights that uncertainty and difficulty in decision making result from cultural and institutional distances (Dikova et al., 2010; Gaur & Lu, 2007; Luo & Shenkar, 2011). We contribute to this burgeoning wave literature by identifying the effect of the environment in which a deal is announced, specifically whether a deal is announced within a wave or a non-wave environment. We do not find support for Hypothesis 3. This may be because, despite the high uncertainty in decision making within a wave, managers may be driven by private benefits and therefore, more likely to
5.2. Managerial implications For managers, our study highlights the consequences of conducting a deal within a wave and specifically it's timing on deal completion. 10
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Future research may also explore the characteristics of merger waves and how they impact wave-friction or partner-friction within a wave. Such studies may identify the differences between waves, their characteristics and their impact on deal completion. Scholars may also study the role of timing on other dependent variables such as innovation (Shankar, Carpenter, & Krishnamurthi, 1998). Another promising area of research may be to study the role of business groups on deal completion and their performance within a merger wave (Fuad & Sinha, 2018). Further, decision making within merger waves is characterized by a high degree of uncertainty (Duchin & Schmidt, 2013). Hence understanding the decision-making process of both targets and acquirers may be conducted by interviewing managers of firms participating in waves.
International expansion through acquisitions involve considerable resources and is risky in nature (Gaur, Ma, & Ding, 2018; Malhotra & Gaur, 2014), and failure to close a deal might impact the reputation of the firm and expose it to high risks (Muehlfeld et al., 2012). Therefore, managers may conduct CBAs outside of a wave in order to avoid wavefriction. However, if an acquisition opportunity presents itself within a wave, ceteris paribus managers should ‘ride in the middle’ of the wave rather than being first movers or late entrants in order to successfully close a deal. Acquisitions completed at the peak of a wave are associated with lower performance as compared to early-movers and later entrants (McNamara et al., 2008). Hence our findings highlight an inherent trade-off between different goal measures such as completing a deal and performance implication of deal announcement within a wave (Muehlfeld et al., 2012). On the one hand, there are performance gains to be achieved by moving early in the wave (Carow et al., 2004; Fuad & Sinha, 2018). However, the risk of deal abandonment is also high. Firms announcing their deal in the middle of the wave might benefit from a higher likelihood of deal completion. However, the post-acquisition performance of such acquirers is lower (McNamara et al., 2008). In the decline phase of the wave, though deal abandonment risk is high, however, the performance of late movers is superior to that of acquirers at the peak of the wave (McNamara et al., 2008). Firms move early (Haleblian et al., 2012) or strategically wait (Andonova et al., 2013) within a wave based on informational advantages and potential benefits. These firms have a general sense of the wave direction and deal activity. Doan et al. (2016) argue that while firms may face difficulty in accurately identifying the wave peak, managers should have a sense of where the wave is moving rather than aiming to correctly identify the wave peak. Thus, the role of industry awareness and competitor actions are important considerations for firms participating in merger waves.
Acknowledgements The authors gratefully acknowledge and thank JWB Senior editor Siah Hwee Ang and three anonymous reviewers for their constructive feedback and guidance. References Alexandridis, G., Fuller, K. P., Terhaar, L., & Travlos, N. G. (2013). Deal size, acquisition premia and shareholder gains. Journal of Corporate Finance, 20, 1–13. Andonova, V., Rodriguez, Y., & Sanchez, I. D. (2013). When waiting is strategic: Evidence from Colombian acquisitions 1995–2008. Journal of Business Research, 66(10), 1736–1742. Andrade, G., Mitchell, M., & Stafford, E. (2001). New evidence and perspectives on mergers. The Journal of Economic Perspectives, 15(2), 103–120. Bhagwat, V., Dam, R., & Harford, J. (2016). The real effects of uncertainty on merger activity. The Review of Financial Studies, 29(11), 3000–3034. Boeh, K. K. (2011). Contracting costs and information asymmetry reduction in cross‐border acquisition. Journal of Management Studies, 48(3), 568–590. Boulding, W., & Christen, M. (2008). Disentangling pioneering cost advantages and disadvantages. Marketing Science, 27(4), 699–716. Carow, K., Heron, R., & Saxton, T. (2004). Do early birds get the returns? An empirical investigation of early-mover advantages in acquisitions. Strategic Management Journal, 25(6), 563–585. Chen, M. J. (1996). Competitor analysis and inter-firm rivalry: Towards a theoretical integration. The Academy of Management Review, 21(1), 100–134. Child, J., & Rodrigues, S. B. (2005). The internationalization of Chinese firms: A case for theoretical extension? Management and Organization Review, 1(3), 381–410. Clement, M. B., & Tse, S. Y. (2005). Financial analyst characteristics and herding behavior in forecasting. The Journal of Finance, 60(1), 307–341. Clougherty, J. A. (2005). Antitrust holdup source, cross‐national institutional variation, and corporate political strategy implications for domestic mergers in a global context. Strategic Management Journal, 26(8), 769–790. Conner, K. R. (1991). A historical comparison of resource-based theory and five schools of thought within industrial organization economics: do we have a new theory of the firm? Journal of Management, 17(1), 121–154. Contractor, F., Yong, Y., & Gaur, A. S. (2016). Firm-specific intangible assets and subsidiary profitability: The moderating role of distance, ownership strategy and subsidiary experience. Journal of World Business, 51(6), 950–964. Delios, A., Gaur, A. S., & Kamal, S. (2009). International acquisitions and the globalization of firms from India. In J. Chaisse, & P. Gugler (Eds.). Expansion of trade and FDI in Asia: Strategic and policy challenges. New York, NY: Routledge. Delios, A., Gaur, A. S., & Makino, S. (2008). The timing of international expansion: Information, rivalry and imitation among Japanese firms, 1980–2002. Journal of Management Studies, 45(1), 169–195. Dikova, D., Rao Sahib, P., & Van Witteloostuijn, A. (2010). Cross-border acquisition abandonment and completion: The effect of institutional differences and organizational learning in the international business service industry, 1981–2001. Journal of International Business Studies, 41(2), 223–245. Doan, T. T., Rao Sahib, P., & Van Witteloostuijn, A. (2016). The role of timing in a merger wave on overcoming challenges in the acquisition pre-merger process. Academy of Management Proceedings, 2016(1), 14545. Doan, T. T., Rao Sahib, P., & Van Witteloostuijn, A. (2018). Lessons from the flipside: How do acquirers learn from divestitures to complete acquisitions? Long Range Planning, 51(2), 252–266. https://doi.org/10.1016/j.lrp.2018.01.002. Duchin, R., & Schmidt, B. (2013). Riding the merger wave: Uncertainty, reduced monitoring, and bad acquisitions. Journal of Financial Economics, 107(1), 69–88. Ellis, K. M., Reus, T. H., Lamont, B. T., & Ranft, A. L. (2011). Transfer effects in large acquisitions: How size-specific experience matters. The Academy of Management Journal, 54(6), 1261–1276. Fuad, M., & Sinha, A. K. (2018). Entry-timing, business groups and early-mover advantage within industry merger waves in emerging markets: A study of Indian firms. Asia Pacific Journal of Management, 35(4), 919–942. https://doi.org/10.1007/ s10490-017-9531-2. Gaur, A. S., & Delios, A. (2015). International diversification of emerging market firms:
5.3. Policy implications For policymakers, our study emphasizes the importance of liberalization and policy changes on CBA waves. It should be noted that deregulation and financial reforms impact industry structure and lead to merger waves (Andrade et al., 2001; Harford, 2005), as in the case of Indian firms. Relaxation of foreign exchange management regulations and capital transactions by the Government of India not only led to country-level changes but also provided a “springboard” (Luo & Tung, 2007) to internationalization for Indian firms (Gaur & Delios, 2015; Gaur et al., 2014). In addition, regulatory authorities also need to be cognizant of the increased pressure of scrutinizing deals during merger waves (Doan et al., 2018). Heightened merger activity may impose resource constraints during merger waves which may negatively impact deal completion. Therefore, during merger waves, competition authorities may seek to build increased levels of resources and requisite processes to cater to the increased volume of deals. 5.4. Limitations and directions for future research Although our study contributes towards merger waves and its timing on deal abandonment, our paper also has few limitations. First, our study identified only technology-intensive firms as experiencing waves, specifically chemicals and allied products and business services. This limits the generalizability of our findings across different industries. Second, we focus on CBAs by Indian firms. The triggers of merger waves in the case of CBAs and domestic acquisitions often differ. Future studies could focus on comparing the acquisition behavior in domestic and CBA waves across emerging and developed markets. Third, we were unable to control for the year fixed effects due to multicollinearity issues with the entry-timing and wave deals variables as the bulk of wave deals in our sample happened between 2004 and 2009. Studies focused on other contexts that offer more variation in the temporality of waves could help overcome this limitation. 11
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