Search

Extant research suggests that firms rationally evaluate external and/or internal contingencies when deciding how to reconfigure their alliance portfolios. We advance a behavioral perspective which assumes that managers are boundedly rational and thus rely on behavioral heuristics when making alliance portfolio reconfiguration decisions. In panel data on U.S.‐listed biotechnology firms, we find that below‐aspiration performance motivates a firm to form alliances with novel partners within the resource scope of its existing alliance portfolio. This effect is weakened by equity ties with existing partners and strengthened by firm‐specific uncertainty. Conversely, above‐aspiration performance leads to new alliances with existing partners but outside the resource scope of the firm's existing alliance portfolio. Finally, as organizational slack increases, a firm forms alliances with novel partners focusing on new‐to‐the‐portfolio resources.

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. © 2015, The Author(s).

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. © 2015, The Author(s).