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Type: Journal article
Title: A dyadic segmentation approach to business partnerships
Author: Aurifeille, J.
Medlin, C.
Citation: European Journal of Economic and Social Systems (EJESS), 2001; 15(2):3-16
Publisher: EDP Sciences
Issue Date: 2001
ISSN: 1292-8895
Statement of
Jacques-Marie Aurifeille and Christopher John Medlin
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 considered 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 independent. As a step to understanding, how partnership influences firms' performance, the dyadic (i.e. two partners') case is studied. First, some theoretical issues concerning the degrees of individual and contributive interest in a dyadic population 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 segmentation strategies are discussed according to their capacity to reflect the modes of partnership and a dyadic clusterwise regression method, based on a genetic algorithm, is presented. Finally, the method is illustrated empirically using actual data of business partners in the software market.
Keywords: Business partnership; relationships; segmentation; dyads; genetic algorithm
Rights: © EDP Sciences 2001
RMID: 0020021425
DOI: 10.1051/ejess:2001112
Appears in Collections:Business School publications

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