Geographic overlap and acquisition pairing

Abstract

Research Summary: This study examines the role of geographic factors in explaining acquisition pairing using a novel conditional logic methodology. Drawing from information asymmetry arguments regarding acquisition decisions, we theorize that geographic overlap between the acquirer and potential targets' businesses and operations enables the acquirer to collect more information about the potential target through its multiple business operations that are geographically proximate. We also demonstrate moderating boundary conditions. In particular, we examine acquiring firm characteristics, acquiring firm size and geographic dispersion, which both weaken the relationship between geographic overlap and acquisition pairing. Likewise, we examine two dyadic distance moderators, geographic distance and product dissimilarity, both of which increase information asymmetry between the acquirer and potential targets, which increases the effect of geographic overlap in facilitating acquisition pairing. Managerial Summary: Firms pursuing acquisition activities face severe information asymmetry when evaluating potential targets. This study investigates how acquiring firms leverage geographic conditions to overcome information asymmetry and choose targets that they can better evaluate. We find that acquirers are more likely to choose targets that have subsidiaries or business operations overlapping in the same states as the acquirers themselves. This is particularly true for small acquirers, which lack resources and capabilities to seek external assistance, and acquirers that have business operations in more concentrated locations. We also find that acquiring targets with geographically overlapped business operations is especially salient when the target's headquarters is distantly located from the acquirer or when the target offers dissimilar products from the acquirer.

The invention performance implications of coopetition: How technological, geographical, and product market overlaps shape learning and competitive tension in R&D alliances

“…A key advantage of these data is that they allow us to dynamically capture the unique degree of product similarity for each pair of firms with high reliability, as the data are legally required to be accurate and updated in the current fiscal year (Shi, Zhang, & Hoskisson, 2017). Furthermore, the data impose no restrictions on firms' product market space and consider firms' corporate product diversification (Chen, Kale, & Hoskisson, 2018). Thus, they also address product and industry changes; this aspect is important, given that firms may introduce and discontinue products over time.…”

“…7 Compared to conventional industry‐based measures (e.g., SIC‐ or NAICS‐based), the product similarity measure better explains, for example, the extent to which managers mention competition in the “Management's Discussion and Analysis” section of the SEC 10‐K filings and the extent to which firms are mentioned by managers as being competitors (Hoberg & Phillips, 2016). The measure is established and used in research on strategy (e.g., Chen et al, 2018), marketing (e.g., Kashmiri, Nicol, & Hsu, 2017), innovation (e.g., Li, Qiu, & Wang, 2019), and finance (e.g., Grullon, Larkin, & Michaely, 2019).…”

“…Using the upstream vertical‐relatedness measure from Frésard, Hoberg, and Phillips (2019), we controlled for firm vertical relatedness with the R&D partner since such relatedness may reduce the partner's willingness to disclose valuable knowledge to the firm owing to the potential for backward integration. Finally, to account for firm geographical dispersion , and hence the importance or weight of the geographical overlap for a firm (Baum & Korn, 1996), we used a novel validated measure developed by Chen et al (2018) to count the number of U.S. states where a firm has business activities using data from SEC 10‐K filings.…”

The Pre-Deal Phase of Mergers and Acquisitions: A Review and Research Agenda

“…As a consequence, the concept of fit, understood as firm characteristics that either supplement or complement each other, has been central in the research focusing on target selection (Datta, 1991;Shelton, 1988). Acquirers have been found to select targets that have resources, products and R&D pipelines similar to their own (Schildt & Laamanen, 2006;Yu et al, 2016), targets with complementary capabilities (Kaul & Wu, 2016), targets with related human capital (Lee et al, 2018), targets with geographically overlapping operations (Chen et al, 2018;Ramos & Shaver, 2013), and targets that produce network synergies (Hernandez & Shaver, 2019).…”

“…Acquirers have been found to select targets that have resources, products, and R&D pipelines similar to their own (Schildt & Laamanen, 2006; Yu, Umashankar, & Rao, 2016); targets with complementary capabilities (Kaul & Wu, 2016); targets with related human capital (K. Lee, Mauer, & Xu, 2018); targets with geographically overlapping operations (Z. Chen, Kale, & Hoskisson, 2018; Ramos & Shaver, 2013); and targets that produce network synergies (Hernandez & Shaver, 2019).…”