Loading…

Purposeful empiricism: How stochastic modeling informs industrial marketing research

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-tradit...

Full description

Saved in:
Bibliographic Details
Published in:Industrial marketing management 2013-04, Vol.42 (3), p.421-432
Main Authors: McCabe, James, Stern, Philip, Dacko, Scott G.
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c522t-2b1f48b73263102786327c11962f042230f4e6caf736d23af3235a3ac3bc1283
cites cdi_FETCH-LOGICAL-c522t-2b1f48b73263102786327c11962f042230f4e6caf736d23af3235a3ac3bc1283
container_end_page 432
container_issue 3
container_start_page 421
container_title Industrial marketing management
container_volume 42
creator McCabe, James
Stern, Philip
Dacko, Scott G.
description 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. ► Large scale regularities that emerge from aggregated individual behavior can help develop integrated theory. ► Stochastic modeling is used to describe and interpret emergent large scale patterns in two distinct industrial networks. ► The impact of market making on interdependence and connectedness is analyzed through the lens of the stochastic model. ► Deviations from our model predictions pinpoint opportunities for further investigation.
doi_str_mv 10.1016/j.indmarman.2013.02.011
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1418121307</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0019850113000308</els_id><sourcerecordid>1418121307</sourcerecordid><originalsourceid>FETCH-LOGICAL-c522t-2b1f48b73263102786327c11962f042230f4e6caf736d23af3235a3ac3bc1283</originalsourceid><addsrcrecordid>eNqFULtOxDAQtBBIHI9vIBINTcLuOpcEOoR4SUhQXG_5nDX4SOLDTkD8PT4doqBhmyl2ZnZnhDhBKBCwOl8Vbmh7HXo9FAQoC6ACEHfEDJta5gQV7YoZAF7kzRxwXxzEuII0EsqZWDxPYe0j26nLuF-74IyL_WV27z-zOHrzquPoTNb7ljs3vGRusD70MWE7xTE43WXp9huPm2XgyDqY1yOxZ3UX-fgHD8Xi9mZxfZ8_Pt09XF895mZONOa0RFs2y1pSJRGobipJtUG8qMhCSSTBllwZbWtZtSS1lSTnWmojlwapkYfibGu7Dv594jiq3kXDXacH9lNUWGKDhBLqRD39Q135KQzpOYVyTlAjNBvDessywccY2Kp1cCnel0JQm7LVSv2WrTZlKyCVyk7Kq62SU9wPx0FF43gw3LrAZlStd_96fANzb4v4</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1352071088</pqid></control><display><type>article</type><title>Purposeful empiricism: How stochastic modeling informs industrial marketing research</title><source>International Bibliography of the Social Sciences (IBSS)</source><source>ScienceDirect Journals</source><creator>McCabe, James ; Stern, Philip ; Dacko, Scott G.</creator><creatorcontrib>McCabe, James ; Stern, Philip ; Dacko, Scott G.</creatorcontrib><description>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. ► Large scale regularities that emerge from aggregated individual behavior can help develop integrated theory. ► Stochastic modeling is used to describe and interpret emergent large scale patterns in two distinct industrial networks. ► The impact of market making on interdependence and connectedness is analyzed through the lens of the stochastic model. ► Deviations from our model predictions pinpoint opportunities for further investigation.</description><identifier>ISSN: 0019-8501</identifier><identifier>EISSN: 1873-2062</identifier><identifier>DOI: 10.1016/j.indmarman.2013.02.011</identifier><identifier>CODEN: IMMADX</identifier><language>eng</language><publisher>New York: Elsevier Inc</publisher><subject>Business networks ; Collaborative purchasing ; Dirichlet ; Dirichlet problem ; Industrial market ; Industrial markets ; Integration theory ; Management theory ; Market structure ; Marketing ; Marketing management ; Organizational behaviour ; Purposeful empiricism ; Stochastic modeling ; Stochastic models ; Studies ; Vendor supplier relations</subject><ispartof>Industrial marketing management, 2013-04, Vol.42 (3), p.421-432</ispartof><rights>2013 Elsevier Inc.</rights><rights>Copyright Elsevier Sequoia S.A. Apr 2013</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c522t-2b1f48b73263102786327c11962f042230f4e6caf736d23af3235a3ac3bc1283</citedby><cites>FETCH-LOGICAL-c522t-2b1f48b73263102786327c11962f042230f4e6caf736d23af3235a3ac3bc1283</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,778,782,27911,27912,33210,33211</link.rule.ids></links><search><creatorcontrib>McCabe, James</creatorcontrib><creatorcontrib>Stern, Philip</creatorcontrib><creatorcontrib>Dacko, Scott G.</creatorcontrib><title>Purposeful empiricism: How stochastic modeling informs industrial marketing research</title><title>Industrial marketing management</title><description>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. ► Large scale regularities that emerge from aggregated individual behavior can help develop integrated theory. ► Stochastic modeling is used to describe and interpret emergent large scale patterns in two distinct industrial networks. ► The impact of market making on interdependence and connectedness is analyzed through the lens of the stochastic model. ► Deviations from our model predictions pinpoint opportunities for further investigation.</description><subject>Business networks</subject><subject>Collaborative purchasing</subject><subject>Dirichlet</subject><subject>Dirichlet problem</subject><subject>Industrial market</subject><subject>Industrial markets</subject><subject>Integration theory</subject><subject>Management theory</subject><subject>Market structure</subject><subject>Marketing</subject><subject>Marketing management</subject><subject>Organizational behaviour</subject><subject>Purposeful empiricism</subject><subject>Stochastic modeling</subject><subject>Stochastic models</subject><subject>Studies</subject><subject>Vendor supplier relations</subject><issn>0019-8501</issn><issn>1873-2062</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>8BJ</sourceid><recordid>eNqFULtOxDAQtBBIHI9vIBINTcLuOpcEOoR4SUhQXG_5nDX4SOLDTkD8PT4doqBhmyl2ZnZnhDhBKBCwOl8Vbmh7HXo9FAQoC6ACEHfEDJta5gQV7YoZAF7kzRxwXxzEuII0EsqZWDxPYe0j26nLuF-74IyL_WV27z-zOHrzquPoTNb7ljs3vGRusD70MWE7xTE43WXp9huPm2XgyDqY1yOxZ3UX-fgHD8Xi9mZxfZ8_Pt09XF895mZONOa0RFs2y1pSJRGobipJtUG8qMhCSSTBllwZbWtZtSS1lSTnWmojlwapkYfibGu7Dv594jiq3kXDXacH9lNUWGKDhBLqRD39Q135KQzpOYVyTlAjNBvDessywccY2Kp1cCnel0JQm7LVSv2WrTZlKyCVyk7Kq62SU9wPx0FF43gw3LrAZlStd_96fANzb4v4</recordid><startdate>20130401</startdate><enddate>20130401</enddate><creator>McCabe, James</creator><creator>Stern, Philip</creator><creator>Dacko, Scott G.</creator><general>Elsevier Inc</general><general>Elsevier Sequoia S.A</general><scope>6I.</scope><scope>AAFTH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8BJ</scope><scope>FQK</scope><scope>JBE</scope></search><sort><creationdate>20130401</creationdate><title>Purposeful empiricism: How stochastic modeling informs industrial marketing research</title><author>McCabe, James ; Stern, Philip ; Dacko, Scott G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c522t-2b1f48b73263102786327c11962f042230f4e6caf736d23af3235a3ac3bc1283</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Business networks</topic><topic>Collaborative purchasing</topic><topic>Dirichlet</topic><topic>Dirichlet problem</topic><topic>Industrial market</topic><topic>Industrial markets</topic><topic>Integration theory</topic><topic>Management theory</topic><topic>Market structure</topic><topic>Marketing</topic><topic>Marketing management</topic><topic>Organizational behaviour</topic><topic>Purposeful empiricism</topic><topic>Stochastic modeling</topic><topic>Stochastic models</topic><topic>Studies</topic><topic>Vendor supplier relations</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>McCabe, James</creatorcontrib><creatorcontrib>Stern, Philip</creatorcontrib><creatorcontrib>Dacko, Scott G.</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>CrossRef</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><jtitle>Industrial marketing management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>McCabe, James</au><au>Stern, Philip</au><au>Dacko, Scott G.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Purposeful empiricism: How stochastic modeling informs industrial marketing research</atitle><jtitle>Industrial marketing management</jtitle><date>2013-04-01</date><risdate>2013</risdate><volume>42</volume><issue>3</issue><spage>421</spage><epage>432</epage><pages>421-432</pages><issn>0019-8501</issn><eissn>1873-2062</eissn><coden>IMMADX</coden><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. ► Large scale regularities that emerge from aggregated individual behavior can help develop integrated theory. ► Stochastic modeling is used to describe and interpret emergent large scale patterns in two distinct industrial networks. ► The impact of market making on interdependence and connectedness is analyzed through the lens of the stochastic model. ► Deviations from our model predictions pinpoint opportunities for further investigation.</abstract><cop>New York</cop><pub>Elsevier Inc</pub><doi>10.1016/j.indmarman.2013.02.011</doi><tpages>12</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0019-8501
ispartof Industrial marketing management, 2013-04, Vol.42 (3), p.421-432
issn 0019-8501
1873-2062
language eng
recordid cdi_proquest_miscellaneous_1418121307
source International Bibliography of the Social Sciences (IBSS); ScienceDirect Journals
subjects Business networks
Collaborative purchasing
Dirichlet
Dirichlet problem
Industrial market
Industrial markets
Integration theory
Management theory
Market structure
Marketing
Marketing management
Organizational behaviour
Purposeful empiricism
Stochastic modeling
Stochastic models
Studies
Vendor supplier relations
title Purposeful empiricism: How stochastic modeling informs industrial marketing research
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-15T14%3A17%3A07IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Purposeful%20empiricism:%20How%20stochastic%20modeling%20informs%20industrial%20marketing%20research&rft.jtitle=Industrial%20marketing%20management&rft.au=McCabe,%20James&rft.date=2013-04-01&rft.volume=42&rft.issue=3&rft.spage=421&rft.epage=432&rft.pages=421-432&rft.issn=0019-8501&rft.eissn=1873-2062&rft.coden=IMMADX&rft_id=info:doi/10.1016/j.indmarman.2013.02.011&rft_dat=%3Cproquest_cross%3E1418121307%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c522t-2b1f48b73263102786327c11962f042230f4e6caf736d23af3235a3ac3bc1283%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1352071088&rft_id=info:pmid/&rfr_iscdi=true