Network Synergy

Abstract

Acquisitions can dramatically reshape interorganizational networks by combining previously separate nodes and allowing the acquirer to inherit the target’s ties, potentially creating network synergy. Network synergy is the extent to which combining an acquirer’s and a target’s networks through node collapse results in a more favorable structural position for the combined firm as the acquirer gains control of the target’s existing ties. We hypothesize that the likelihood of selecting a target increases when the expected network synergy is greater. Using data from the biotechnology industry (1995–2007), we find support for this hypothesis by showing that acquirers prefer targets that generate greater expected increases in network status and greater expected access to structural holes—even when we control for other known sources of synergies. We further show that these effects are driven by complementary combinations of the acquirer’s and target’s networks that go beyond the attractiveness of the target’s network per se. By integrating the networks and acquisitions literatures, we introduce a novel source of synergies, provide evidence of a previously unexplored mechanism of network change, and show how firms can exert agency in the process of network change.

How should we understand the digital economy in Asia? Critical assessment and research agenda

“…The platform economy needs new organizational forms that meet its development requirements though. Based on the principle of network synergy ( Hernandez and Shaver, 2018 ), a new type of organization that cuts across companies and markets – the digital platform – will become the foundation of the future business economy ( Torrance and Staeritz, 2019 ). This will cause the work environments of a majority of people to undergo drastic changes.…”

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

“…Alternatively, acquirers may resort to their networks or external advisors for advice. After identification, acquirers typically begin compiling information about the potential target to preliminarily assess the quality of the target and its fit with the acquirer along several dimensions (e.g., Datta, 1991;Hernandez & Shaver, 2019;Shelton, 1988).…”

“…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).…”

“…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).…”

Contracting Beyond the Market

“…To examine the impact of the proposed drivers of CBAs-use rights, negative externalities, and a community's mobilization capacity-on firms' choices of communities with which to sign CBAs, we estimated a discrete choice model (McFadden 1974). Also known as the conditional logit, this model uses variation in attributes across potential alternatives (i.e., communities) within the choice set of the chooser (i.e., firm) to estimate the effects of those attributes (see Elfenbein and Zenger (2013) for an application to supplier choice and Hernandez and Shaver (2019) for an application to acquisition choice). The probability that firm i signs a CBA with community c from a choice set s of alternatives is given by Pr ( y i c) e βx ic / ∑ c∈s e βx ic , where x ic is the vector of dyad-level and communitylevel attributes that the firm observes about each community.…”