Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/1185
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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, 2001; 15(2):3-16 |
Publisher: | EDP Sciences |
Issue Date: | 2001 |
ISSN: | 1292-8895 1292-8909 |
Statement of Responsibility: | 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 |
DOI: | 10.1051/ejess:2001112 |
Published version: | http://dx.doi.org/10.1051/ejess:2001112 |
Appears in Collections: | Aurora harvest 2 Business School publications |
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hdl_1185.pdf | Published version | 125.89 kB | Adobe PDF | View/Open |
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