Abstract
We develop a theoretical Bayesian learning model to examine how a firm's learning horizon, defined as the maximum distance in a network of alliances across which the firm learns from other firms, conditions its optimal number of direct alliance partners under technological uncertainty. We compare theoretical optima for a `close' learning horizon, where a firm learns only from direct alliance partners, and a `distant' learning horizon, where a firm learns both from direct and indirect alliance partners. Our theory implies that in high tech industries, a distant learning horizon allows a firm to substitute indirect for direct partners, while in low tech industries indirect partners complement direct partners. Moreover, in high tech industries, optimal alliance formation is less sensitive to changes in structural model parameters when a firm's learning horizon is distant rather than close. Our contribution lies in offering a formal theory of the role of indirect partners in optimal alliance portfolio design that generates normative propositions amenable to future empirical refutation.
Highlights
Scholars have long noted that technological uncertainty, defined as the difficulty of accurately predicting the future state of the technological environment, motivates firms to enter into alliances with other firms (Auster 1992; Eisenhardt and Schoonhoven 1996; Hagedoorn 2002; Mody 1993; Rosenkopf and Schilling 2007; Steensma et al 2000)
We develop a theoretical Bayesian learning model to examine how a firm’s learning horizon, defined as the maximum distance in a network of alliances across which the firm learns from other firms, conditions its optimal number of direct alliance partners under technological uncertainty
Following a call to begin to consider the role of indirect partners in optimal alliance portfolio design (Lavie 2006, p. 651), we complement the study of optimal alliance formation under technological uncertainty with a systematic theory of how learning from indirect partners shapes a firm’s optimal alliance portfolio size
Summary
Scholars have long noted that technological uncertainty, defined as the difficulty of accurately predicting the future state of the technological environment, motivates firms to enter into alliances with other firms (Auster 1992; Eisenhardt and Schoonhoven 1996; Hagedoorn 2002; Mody 1993; Rosenkopf and Schilling 2007; Steensma et al 2000). A Bayesian learning framework allows us to model the effects of multiple parameters relevant to our research question in a tractable way This is important because factors such as perceived technological uncertainty, the cost of unresolved uncertainty, the viability of interfirm learning, the cost of alliances, and awareness of indirect partners can vary greatly across firms and industries (e.g., Hagedoorn 2002; Harrigan 1985; Rosenkopf and Schilling 2007; Sutcliffe and Huber 1998), while all may individually as well as jointly shape the consequences of learning from indirect partners in perhaps unanticipated ways. These examples foreshadow that the learning potential afforded by firms’ respective sets of direct and indirect partners may vary independently It follows that the theory of optimal alliance formation must explicitly account for heterogeneity in the extent to which distinct sets of indirect partners allow for learning and uncertainty reduction.
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