Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/1185
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Aurifeille, J. | - |
dc.contributor.author | Medlin, C. | - |
dc.date.issued | 2001 | - |
dc.identifier.citation | European Journal of Economic and Social Systems, 2001; 15(2):3-16 | - |
dc.identifier.issn | 1292-8895 | - |
dc.identifier.issn | 1292-8909 | - |
dc.identifier.uri | http://hdl.handle.net/2440/1185 | - |
dc.description.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. | - |
dc.description.statementofresponsibility | Jacques-Marie Aurifeille and Christopher John Medlin | - |
dc.language.iso | en | - |
dc.publisher | EDP Sciences | - |
dc.rights | © EDP Sciences 2001 | - |
dc.source.uri | http://dx.doi.org/10.1051/ejess:2001112 | - |
dc.subject | Business partnership | - |
dc.subject | relationships | - |
dc.subject | segmentation | - |
dc.subject | dyads | - |
dc.subject | genetic algorithm | - |
dc.title | A dyadic segmentation approach to business partnerships | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1051/ejess:2001112 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Medlin, C. [0000-0003-0567-2538] | - |
Appears in Collections: | Aurora harvest 2 Business School publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
hdl_1185.pdf | Published version | 125.89 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.