The performance consequences of ambidexterity in strategic alliance formations : Empirical investigation and computational theorizing

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

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Detail(s)

Original languageEnglish
Pages (from-to)1645-1658
Journal / PublicationManagement Science
Volume53
Issue number10
Publication statusPublished - Oct 2007

Abstract

Although alliance studies have generally favored an ambidextrous approach between exploration and exploitation, they tend to overlook a firm's characteristics, its industry constraints, or the dynamic network in which the firm is embedded. This study examines the ambidexterity hypothesis and its boundary conditions with a unique research method. We not only analyze empirical data from five U.S. industries spanning eight years, but also expand theoretical insights to the network level by building a computer simulation model. Both our empirical and simulation results reveal the contingencies of the ambidexterity hypothesis in alliance formation. Our findings show that although an ambidextrous formation of alliances benefits large firms, a focused formation of either exploratory or exploitative alliances benefits small firms. In an uncertain environment an ambidextrous formation enhances firm performance but so does a focused formation in a stable environment. Finally, the simulation model demonstrates that a firm's centrality and structural hole positions in network relations can moderate the relationships between alliance formation choices and firm performance, and that the ambidexterity hypothesis may be limited to the earlier stage of the network. Our study provides critical evidence into the viability of adopting a dynamic network perspective in understanding the ambidexterity hypothesis and advancing strategic alliance research beyond static and dyadic levels. © 2007 INFORMS.

Research Area(s)

  • Alliance formation, Ambidexterity hypothesis, Computer simulation, Empirical analysis, Social networks