Inventor knowledge recombination behaviors in a pharmaceutical merger: The role of intra-firm networks

Micki Eisenman*, Srikanth Paruchuri

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

We study firms’ abilities to increase the generative appropriability of their knowledge by studying the knowledge recombination patterns of inventors in the context of a merger between two equally sized pharmaceutical firms. Specifically, we study inventors’ choices to recombine knowledge originating in the firm with which they merge. We hypothesize that mergers focus inventors’ attention to units of knowledge originating in the other firm and that therefore, inventors will choose to recombine more of this knowledge, which exists in their intra-firm network, following a merger. We also hypothesize that inventors vary in terms of their recombination choices following a merger. We explore these differences by linking inventors’ network positions with their abilities and motivations to recombine knowledge originating in the other firm. Specifically, we hypothesize an inverted-U shaped relationship between centrality and knowledge recombination from the other firm and a linear relationship between brokerage and knowledge recombination from the other firm. We test our hypotheses using patent data from the merger between Bristol-Myers and Squibb and find support for our hypotheses. The paper contributes to knowledge recombination research by exploring changes in knowledge recombination dynamics following a merger and by understanding how mergers affect firms’ generative research trajectories. Practically, we suggest that managers should identify and nurture certain types of inventors following a merger to be able to better leverage the knowledge bases of merging firms.

Original languageEnglish
Pages (from-to)189-201
Number of pages13
JournalLong Range Planning
Volume52
Issue number2
DOIs
StatePublished - Apr 2019

Bibliographical note

Publisher Copyright:
© 2018 Elsevier Ltd

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