A Two-Sided Matching Approach for Partner Selection and Assessing Complementarities in Partners’ Attributes in Inter-Firm Alliances

Abstract

Strategic alliances are undertaken to create value through complementarities of resources and capabilities of the partner firms. We develop a matching framework to study strategic alliances, taking a market perspective that explicitly incorporates key features of transactions in strategic alliances: two sided decision making in voluntary collaboration; quest for complementarities between indivisible and heterogeneous partner attributes; and competition on each side for partners on the other side. We assess the relative performance of matching models and binary choice models when estimating parameters within simulations based on a known functional relationship. Within the context of research alliances in the bio-pharmaceutical industry, we hypothesize and find support using the matching model framework for complementarity in partner size, and in upstream research capabilities.

Keywords: alliances, two-sided matching, maximum score estimator, bio-pharmaceutical industry, complementarity