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Purposeful empiricism: how stochastic modeling informs industrial marketing research

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McCabe, James, Stern, Philip and Dacko, Scott G. (2013) Purposeful empiricism: how stochastic modeling informs industrial marketing research. Industrial Marketing Management, Volume 42 (Number 3). pp. 421-432. doi:10.1016/j.indmarman.2013.02.011 ISSN 0019-8501.

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Official URL: http://dx.doi.org/10.1016/j.indmarman.2013.02.011

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Abstract

It is increasingly recognized that progress can be made in the development of integrated theory for understanding, explaining and better predicting key aspects of buyer–seller relationships and industrial networks by drawing upon non-traditional research perspectives and domains. One such non-traditional research perspective is stochastic modeling which has shown that large scale regularities emerge from the individual interactions between idiosyncratic actors. When these macroscopic patterns repeat across a wide range of firms, industries and business types this commonality suggests directions for further research which we pursue through a differentiated replication of the Dirichlet stochastic model. We demonstrate predictable behavioral patterns of purchase and loyalty in two distinct industrial markets for components used in critical surgical procedures. This differentiated replication supports the argument for the use of stochastic modeling techniques in industrial marketing management, not only as a management tool but also as a lens to inform and focus research towards integrated theories of the evolution of market structure and network relationships.

Item Type: Journal Article
Subjects: H Social Sciences > HB Economic Theory
H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
H Social Sciences > HF Commerce
Divisions: Faculty of Social Sciences > Warwick Business School > Marketing Group
Faculty of Social Sciences > Warwick Business School
Library of Congress Subject Headings (LCSH): Stochastic models, Industries, Surgical instruments and apparatus, Marketing -- Management -- Data processing, Marketing -- Mathematical models, Business mathematics, Consumer behavior -- Mathematical models
Journal or Publication Title: Industrial Marketing Management
Publisher: Elsevier Inc.
ISSN: 0019-8501
Official Date: April 2013
Dates:
DateEvent
April 2013Published
Volume: Volume 42
Number: Number 3
Page Range: pp. 421-432
DOI: 10.1016/j.indmarman.2013.02.011
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 24 December 2015
Date of first compliant Open Access: 24 December 2015

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