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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...
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Published in: | Industrial marketing management 2013-04, Vol.42 (3), p.421-432 |
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container_end_page | 432 |
container_issue | 3 |
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container_title | Industrial marketing management |
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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 |
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► 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. 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► 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> |
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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 |
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