An exploration of the patterns underlying related and unrelated collaborative ventures using neural network: Empirical investigation of collaborative venture formation data spanning 1985–2001

Collaborative ventures—both equity-based partnerships as well as project-based alliances—have dominated the international business scene over the past two decades. By means of this study we investigate the patterns of related and unrelated collaborative venture formation. Using a large database of over 90,000 collaborative ventures formed during the 1985–2001 period, this study clusters collaborative ventures on the basis of the industry group and home country relatedness of the collaborating partners. Self-organizing map technique within neural network methodology is used to accomplish this objective. The clusters obtained from the self-organizing map form the basis for developing taxonomy of collaborative ventures in which the neurons underlying clusters are classified based on the country of origin and industry affiliations of the collaborating partners and the collaborative venture. The distinguishing characteristics of the clusters and the taxonomy help augment our current understanding of the formation of collaborative ventures.