A dyadic segmentation approach to business partnerships

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dc.contributor.authorAurifeille, J.
dc.contributor.authorMedlin, C.
dc.date.issued2001
dc.description.abstractIn 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.
dc.description.statementofresponsibilityJacques-Marie Aurifeille and Christopher John Medlin
dc.identifier.citationEuropean Journal of Economic and Social Systems, 2001; 15(2):3-16
dc.identifier.doi10.1051/ejess:2001112
dc.identifier.issn1292-8895
dc.identifier.issn1292-8909
dc.identifier.orcidMedlin, C. [0000-0003-0567-2538]
dc.identifier.urihttp://hdl.handle.net/2440/1185
dc.language.isoen
dc.publisherEDP Sciences
dc.rights© EDP Sciences 2001
dc.source.urihttps://doi.org/10.1051/ejess:2001112
dc.subjectBusiness partnership
dc.subjectrelationships
dc.subjectsegmentation
dc.subjectdyads
dc.subjectgenetic algorithm
dc.titleA dyadic segmentation approach to business partnerships
dc.typeJournal article
pubs.publication-statusPublished

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