Diffusion of New Products with Heterogeneous Consumers
Does a new product spread faster among heterogeneous or homogeneous consumers? We analyze this question using the stochastic discrete Bass model in which consumers may differ in their individual external influence rates { p j } and in their individual internal influence rates { q j } . When the netw...
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Veröffentlicht in: | Mathematics of operations research 2023-02, Vol.48 (1), p.257-287 |
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description | Does a new product spread faster among heterogeneous or homogeneous consumers? We analyze this question using the stochastic discrete Bass model in which consumers may differ in their individual external influence rates
{
p
j
}
and in their individual internal influence rates
{
q
j
}
. When the network is complete and the heterogeneity is only manifested in
{
p
j
}
or only in
{
q
j
}
, it always slows down the diffusion, compared with the corresponding homogeneous network. When, however, consumers are heterogeneous in both
{
p
j
}
and
{
q
j
}
, heterogeneity slows down the diffusion in some cases but accelerates it in others. Moreover, the dominance between the heterogeneous and homogeneous adoption levels is global in time in some cases but changes with time in others. Perhaps surprisingly, global dominance between two networks is not always preserved under “additive transformations”, such as adding an identical node to both networks. When the network is not complete, the effect of heterogeneity depends also on its spatial distribution within the network. |
doi_str_mv | 10.1287/moor.2022.1261 |
format | Article |
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{
p
j
}
and in their individual internal influence rates
{
q
j
}
. When the network is complete and the heterogeneity is only manifested in
{
p
j
}
or only in
{
q
j
}
, it always slows down the diffusion, compared with the corresponding homogeneous network. When, however, consumers are heterogeneous in both
{
p
j
}
and
{
q
j
}
, heterogeneity slows down the diffusion in some cases but accelerates it in others. Moreover, the dominance between the heterogeneous and homogeneous adoption levels is global in time in some cases but changes with time in others. Perhaps surprisingly, global dominance between two networks is not always preserved under “additive transformations”, such as adding an identical node to both networks. When the network is not complete, the effect of heterogeneity depends also on its spatial distribution within the network.</description><identifier>ISSN: 0364-765X</identifier><identifier>EISSN: 1526-5471</identifier><identifier>DOI: 10.1287/moor.2022.1261</identifier><language>eng</language><publisher>Linthicum: INFORMS</publisher><subject>agent-based model ; analysis ; bass model ; Consumer behavior ; Consumers ; Diffusion ; diffusion in networks ; Diffusion rate ; discrete models ; Heterogeneity ; marketing ; Mathematical models ; Neural networks ; Operations research ; Primary: 90B15 ; Product development ; Spatial distribution ; Stochastic models</subject><ispartof>Mathematics of operations research, 2023-02, Vol.48 (1), p.257-287</ispartof><rights>Copyright Institute for Operations Research and the Management Sciences Feb 2023</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c362t-afca39c9a88c634327eb70ab83b86e42743a02fa281843966583f50c67813c383</citedby><cites>FETCH-LOGICAL-c362t-afca39c9a88c634327eb70ab83b86e42743a02fa281843966583f50c67813c383</cites><orcidid>0000-0002-2640-6661 ; 0000-0002-0173-7560</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://pubsonline.informs.org/doi/full/10.1287/moor.2022.1261$$EHTML$$P50$$Ginforms$$H</linktohtml><link.rule.ids>314,780,784,3690,27922,27923,62614</link.rule.ids></links><search><creatorcontrib>Fibich, Gadi</creatorcontrib><title>Diffusion of New Products with Heterogeneous Consumers</title><title>Mathematics of operations research</title><description>Does a new product spread faster among heterogeneous or homogeneous consumers? We analyze this question using the stochastic discrete Bass model in which consumers may differ in their individual external influence rates
{
p
j
}
and in their individual internal influence rates
{
q
j
}
. When the network is complete and the heterogeneity is only manifested in
{
p
j
}
or only in
{
q
j
}
, it always slows down the diffusion, compared with the corresponding homogeneous network. When, however, consumers are heterogeneous in both
{
p
j
}
and
{
q
j
}
, heterogeneity slows down the diffusion in some cases but accelerates it in others. Moreover, the dominance between the heterogeneous and homogeneous adoption levels is global in time in some cases but changes with time in others. Perhaps surprisingly, global dominance between two networks is not always preserved under “additive transformations”, such as adding an identical node to both networks. When the network is not complete, the effect of heterogeneity depends also on its spatial distribution within the network.</description><subject>agent-based model</subject><subject>analysis</subject><subject>bass model</subject><subject>Consumer behavior</subject><subject>Consumers</subject><subject>Diffusion</subject><subject>diffusion in networks</subject><subject>Diffusion rate</subject><subject>discrete models</subject><subject>Heterogeneity</subject><subject>marketing</subject><subject>Mathematical models</subject><subject>Neural networks</subject><subject>Operations research</subject><subject>Primary: 90B15</subject><subject>Product development</subject><subject>Spatial distribution</subject><subject>Stochastic models</subject><issn>0364-765X</issn><issn>1526-5471</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNqFkE1LxDAQhoMouK5ePRc8d01m8tWjrB8rLOpBwVvIxkS72GZNWhb_vS0VPDqXYeB534GHkHNGFwy0umxiTAugAMMp2QGZMQGyFFyxQzKjKHmppHg9Jic5byllQjE-I_K6DqHPdWyLGIoHvy-eUnzrXZeLfd19FCvf-RTffetjn4tlbHPf-JRPyVGwn9mf_e45ebm9eV6uyvXj3f3yal06lNCVNjiLlaus1k4iR1B-o6jdaNxo6TkojpZCsKCZ5lhJKTQGQZ1UmqFDjXNyMfXuUvzqfe7MNvapHV4aUBXlwyAM1GKiXIo5Jx_MLtWNTd-GUTO6MaMbM7oxo5shUEwB72Jb5z9cCwQAyuWAlBNStyGmJv9X-QMl9m9X</recordid><startdate>20230201</startdate><enddate>20230201</enddate><creator>Fibich, Gadi</creator><general>INFORMS</general><general>Institute for Operations Research and the Management Sciences</general><scope>OQ6</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>JQ2</scope><orcidid>https://orcid.org/0000-0002-2640-6661</orcidid><orcidid>https://orcid.org/0000-0002-0173-7560</orcidid></search><sort><creationdate>20230201</creationdate><title>Diffusion of New Products with Heterogeneous Consumers</title><author>Fibich, Gadi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c362t-afca39c9a88c634327eb70ab83b86e42743a02fa281843966583f50c67813c383</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>agent-based model</topic><topic>analysis</topic><topic>bass model</topic><topic>Consumer behavior</topic><topic>Consumers</topic><topic>Diffusion</topic><topic>diffusion in networks</topic><topic>Diffusion rate</topic><topic>discrete models</topic><topic>Heterogeneity</topic><topic>marketing</topic><topic>Mathematical models</topic><topic>Neural networks</topic><topic>Operations research</topic><topic>Primary: 90B15</topic><topic>Product development</topic><topic>Spatial distribution</topic><topic>Stochastic models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fibich, Gadi</creatorcontrib><collection>ECONIS</collection><collection>CrossRef</collection><collection>ProQuest Computer Science Collection</collection><jtitle>Mathematics of operations research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fibich, Gadi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Diffusion of New Products with Heterogeneous Consumers</atitle><jtitle>Mathematics of operations research</jtitle><date>2023-02-01</date><risdate>2023</risdate><volume>48</volume><issue>1</issue><spage>257</spage><epage>287</epage><pages>257-287</pages><issn>0364-765X</issn><eissn>1526-5471</eissn><abstract>Does a new product spread faster among heterogeneous or homogeneous consumers? We analyze this question using the stochastic discrete Bass model in which consumers may differ in their individual external influence rates
{
p
j
}
and in their individual internal influence rates
{
q
j
}
. When the network is complete and the heterogeneity is only manifested in
{
p
j
}
or only in
{
q
j
}
, it always slows down the diffusion, compared with the corresponding homogeneous network. When, however, consumers are heterogeneous in both
{
p
j
}
and
{
q
j
}
, heterogeneity slows down the diffusion in some cases but accelerates it in others. Moreover, the dominance between the heterogeneous and homogeneous adoption levels is global in time in some cases but changes with time in others. Perhaps surprisingly, global dominance between two networks is not always preserved under “additive transformations”, such as adding an identical node to both networks. When the network is not complete, the effect of heterogeneity depends also on its spatial distribution within the network.</abstract><cop>Linthicum</cop><pub>INFORMS</pub><doi>10.1287/moor.2022.1261</doi><tpages>31</tpages><orcidid>https://orcid.org/0000-0002-2640-6661</orcidid><orcidid>https://orcid.org/0000-0002-0173-7560</orcidid></addata></record> |
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subjects | agent-based model analysis bass model Consumer behavior Consumers Diffusion diffusion in networks Diffusion rate discrete models Heterogeneity marketing Mathematical models Neural networks Operations research Primary: 90B15 Product development Spatial distribution Stochastic models |
title | Diffusion of New Products with Heterogeneous Consumers |
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