Marketing analytical studies of optimal salesforce compensation policies typically rely on a set of restrictive assumptions. In this paper, a model of decentralized salesforce compensation is developed, wherein some of the classical assumptions are challenged. Response Surface Methodology is used to optimize decentralized compensation policies over a set of simulated conditions. The proposed approach is then illustrated with two empirical applications in artificial and real settings. The objective is to provide some preliminary evidence about decentralized structures and to recommend salesforce compensation policies.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.39335 |
| Date | January 1992 |
| Creators | Rouziès-Ségalla, Dominique |
| Publisher | McGill University |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Format | application/pdf |
| Coverage | Doctor of Philosophy (Faculty of Management.) |
| Rights | All items in eScholarship@McGill are protected by copyright with all rights reserved unless otherwise indicated. |
| Relation | alephsysno: 001290741, proquestno: NN74830, Theses scanned by UMI/ProQuest. |
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