A dyadic segmentation approach to business partnerships

Abstract

In business science, the studied objects are often groups of partners rather than independent firms. Extending classical segmentation to these polyads raises conceptual problems, principally: defining what should be consid- ered as common or specific at the partners' and at the segment levels. The general approaches consist either in merging partners characteristics and performances into a single macro-object, thus loosing their specific contributions to each partner's performance, or in modelling partners' performance as if their models were inde- pendent. As a step to understanding, how partnership influences firms' perform- ance, the dyadic (i.e. two partners') case is studied. First, some theoretical issues concerning the degrees of individual and contributive interest in a dyadic popula- tion are discussed. Next, partnership's conceptualisation is based upon two models for each firm: a self-model that reflects how the firm's characteristics explain its own performance, and a contributive-model model that reflects how these characteristics influence the partner's performance. This allows definition of three relationship modes: merging, teaming and sharing. Subsequently, dyad segmenta- tion strategies are discussed according to their capacity to reflect the modes of part- nership and a dyadic clusterwise regression method, based on a genetic algorithm, is presented. Finally, the method is illustrated empirically using actual data of busi- ness partners in the software market.