Essec\Faculty\Model\Profile {#2188
#_id: "B00767865"
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"bid" => "B00767865"
"academId" => "1992"
"slug" => "shamsollahi-ali"
"fullName" => "Ali SHAMSOLLAHI"
"lastName" => "SHAMSOLLAHI"
"firstName" => "Ali"
"title" => array:2 [
"fr" => "Professeur assistant"
"en" => "Assistant Professor"
]
"email" => "shamsollahi@essec.edu"
"status" => "ACTIF"
"campus" => "Campus de Cergy"
"departments" => []
"phone" => "+33 (0) 1 34 43 37 23"
"sites" => []
"facNumber" => "1992"
"externalCvUrl" => "https://faculty.essec.edu/en/cv/shamsollahi-ali/pdf"
"googleScholarUrl" => "https://scholar.google.com/citations?user=WRTQVHwAAAAJ"
"facOrcId" => "https://orcid.org/0000-0002-5156-3116"
"career" => array:3 [
0 => Essec\Faculty\Model\CareerItem {#2195
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"startDate" => "2019-09-01"
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"label" => array:2 [
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"en" => "Assistant Professor of Marketing"
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"fr" => "ESSEC Business School"
"en" => "ESSEC Business School"
]
"country" => array:2 [
"fr" => "France"
"en" => "France"
]
]
+lang: "en"
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}
1 => Essec\Faculty\Model\CareerItem {#2196
#_index: null
#_id: null
#_source: array:7 [
"startDate" => "2010-03-11"
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"type" => array:2 [
"en" => "Professional appointments"
"fr" => "Positions professionnelles"
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"label" => array:2 [
"fr" => "PDG Adjoint (Recherche et Conseil)"
"en" => "Deputy CEO (Research and Consultancy)"
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"institution" => array:2 [
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"en" => "Dotic Group"
]
"country" => array:2 [
"fr" => "Iran"
"en" => "Iran"
]
]
+lang: "en"
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}
2 => Essec\Faculty\Model\CareerItem {#2197
#_index: null
#_id: null
#_source: array:7 [
"startDate" => "2005-10-01"
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"isInternalPosition" => true
"type" => array:2 [
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]
"label" => array:2 [
"fr" => "Directeur Ventes & Marketing"
"en" => "Sales & Marketing Manager"
]
"institution" => array:2 [
"fr" => "Behsazan Jonoob Eng"
"en" => "Behsazan Jonoob Eng"
]
"country" => array:2 [
"fr" => "Iran"
"en" => "Iran"
]
]
+lang: "en"
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}
]
"diplomes" => array:2 [
0 => Essec\Faculty\Model\Diplome {#2190
#_index: null
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#_source: array:6 [
"diplome" => "DIPLOMA"
"type" => array:2 [
"fr" => "Diplômes"
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"year" => "2018"
"label" => array:2 [
"en" => "Ph.D. in Marketing"
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]
"institution" => array:2 [
"fr" => "University of Melbourne"
"en" => "University of Melbourne"
]
"country" => array:2 [
"fr" => "Australie"
"en" => "Australia"
]
]
+lang: "en"
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}
1 => Essec\Faculty\Model\Diplome {#2192
#_index: null
#_id: null
#_source: array:6 [
"diplome" => "DIPLOMA"
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"year" => "2010"
"label" => array:2 [
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"fr" => "Islamic Azad University, Science and Research Branch"
"en" => "Islamic Azad University, Science and Research Branch"
]
"country" => array:2 [
"fr" => "Iran"
"en" => "Iran"
]
]
+lang: "en"
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}
]
"bio" => array:2 [
"fr" => "<p>Ali Shamsollahi is an assistant professor of Marketing at ESSEC Business School. His research interests revolve around interorganizational relationships—in particular relationship repair, evolution and dynamics—and marketing channels. His research has been published in the Journal of Marketing, Journal of the Academy of Marketing Science and Organizational Research Methods. Additionally, Ali has held different managerial roles in the industry, with the last one being the deputy CEO in a large consultancy firm in his hometown.</p>\n"
"en" => "<p>Ali Shamsollahi is an assistant professor of Marketing at ESSEC Business School. His research interests revolve around interorganizational relationships—in particular relationship repair, evolution and dynamics—and marketing channels. His research has been published in the Journal of Marketing, Journal of the Academy of Marketing Science and Organizational Research Methods. Additionally, Ali has held different managerial roles in the industry, with the last one being the deputy CEO in a large consultancy firm in his hometown.</p>\n"
]
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0 => Essec\Faculty\Model\Distinction {#2198
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}
1 => Essec\Faculty\Model\Distinction {#2199
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}
2 => Essec\Faculty\Model\Distinction {#2200
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3 => Essec\Faculty\Model\Distinction {#2201
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4 => Essec\Faculty\Model\Distinction {#2202
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5 => Essec\Faculty\Model\Distinction {#2203
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6 => Essec\Faculty\Model\Distinction {#2204
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7 => Essec\Faculty\Model\Distinction {#2205
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0 => Essec\Faculty\Model\TeachingItem {#2191
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1 => Essec\Faculty\Model\TeachingItem {#2194
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0 => Essec\Faculty\Model\ExtraActivity {#2189
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1 => Essec\Faculty\Model\ExtraActivity {#2193
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2 => Essec\Faculty\Model\ExtraActivity {#2187
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"theses" => []
"indexedAt" => "2024-05-08T22:21:22.000Z"
"contributions" => array:8 [
0 => Essec\Faculty\Model\Contribution {#2207
#_index: "academ_contributions"
#_id: "12710"
#_source: array:18 [
"id" => "12710"
"slug" => "when-the-honeymoon-is-over-a-theory-of-relationship-liabilities-and-evolutionary-processes"
"yearMonth" => "2022-06"
"year" => "2022"
"title" => "When the Honeymoon is Over: A Theory of Relationship Liabilities and Evolutionary Processes"
"description" => "CHMIELEWSKI-RAIMONDO, D., SHAMSOLLAHI, A., BELL, S. et HEIDE, J. (2022). When the Honeymoon is Over: A Theory of Relationship Liabilities and Evolutionary Processes. <i>Journal of Marketing</i>, 86(6), pp. 32-49."
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"name" => "SHAMSOLLAHI Ali"
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2 => array:1 [
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3 => array:1 [
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"ouvrage" => ""
"keywords" => array:7 [
0 => "Inter-firm relationships"
1 => "evolution"
2 => "liability of newness"
3 => "liability of adolescence,"
4 => "relationship management"
5 => "learning"
6 => "decay"
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"updatedAt" => "2023-03-15 16:47:43"
"publicationUrl" => "https://journals.sagepub.com/doi/pdf/10.1177/00222429211062247"
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"abstract" => array:2 [
"fr" => "We draw on the sociological theories of the “liability of newness” and the “liability of adolescence” to generate new insights into relationship evolution. First, we show how a new relationship during its “honeymoon” exhibits a unique constellation of two conditions, namely information asymmetry and forbearance. Next, we explain how a relationship evolves along two processes that involve passive learning and decay, respectively. In themselves, these processes will move a relationship towards a long-term “transactional” state and possibly termination, but the processes can also be actively shaped using governance mechanisms of various kinds. The latter, however, requires a nuanced account of types of governance mechanisms and the particular conditions they are intended to induce. We consider how the general mechanisms of (1) incentives and (2) information sharing can be deployed in standardized or customized fashions, respectively. Next, we suggest how different manifestation of governance mechanisms impact a relationship’s underlying evolutionary processes and evolved relationship states. In general, our framework represents a new perspective on relationship evolution; one that involves the purposeful management of initial conditions and their related evolutionary processes."
"en" => "We draw on the sociological theories of the “liability of newness” and the “liability of adolescence” to generate new insights into relationship evolution. First, we show how a new relationship during its “honeymoon” exhibits a unique constellation of two conditions, namely information asymmetry and forbearance. Next, we explain how a relationship evolves along two processes that involve passive learning and decay, respectively. In themselves, these processes will move a relationship towards a long-term “transactional” state and possibly termination, but the processes can also be actively shaped using governance mechanisms of various kinds. The latter, however, requires a nuanced account of types of governance mechanisms and the particular conditions they are intended to induce. We consider how the general mechanisms of (1) incentives and (2) information sharing can be deployed in standardized or customized fashions, respectively. Next, we suggest how different manifestation of governance mechanisms impact a relationship’s underlying evolutionary processes and evolved relationship states. In general, our framework represents a new perspective on relationship evolution; one that involves the purposeful management of initial conditions and their related evolutionary processes."
]
"authors_fields" => array:2 [
"fr" => "Marketing"
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1 => Essec\Faculty\Model\Contribution {#2209
#_index: "academ_contributions"
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"slug" => "a-dynamic-perspective-of-relationship-repair"
"yearMonth" => "2016-02"
"year" => "2016"
"title" => "A Dynamic Perspective of Relationship Repair"
"description" => "SHAMSOLLAHI, A., BELL, S. et CHMIELEWSKI-RAIMONDO, D. (2016). A Dynamic Perspective of Relationship Repair. Dans: <i>American Marketing Association Winter Academic Conference</i>. American Marketing Association (AMA), pp. B-26-B-27."
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2 => Essec\Faculty\Model\Contribution {#2211
#_index: "academ_contributions"
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"slug" => "interorganizational-relationships-dynamics-a-systematic-review"
"yearMonth" => "2018-08"
"year" => "2018"
"title" => "Interorganizational Relationships Dynamics: A Systematic Review"
"description" => "SHAMSOLLAHI, A., BELL, S. et CHMIELEWSKI-RAIMONDO, D. (2018). Interorganizational Relationships Dynamics: A Systematic Review. Dans: <i>American Marketing Association Summer Academic Conference</i>. Boston: American Marketing Association (AMA), pp. BR-34-BR-35."
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"updatedAt" => "2021-09-24 10:33:27"
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3 => Essec\Faculty\Model\Contribution {#2208
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"slug" => "long-run-effects-in-dynamic-systems-new-tools-for-cross-lagged-panel-models"
"yearMonth" => "2022-07"
"year" => "2022"
"title" => "Long-Run Effects in Dynamic Systems: New Tools for Cross-Lagged Panel Models"
"description" => "SHAMSOLLAHI, A., ZYPHUR, M.J. et OZKOK, O. (2022). Long-Run Effects in Dynamic Systems: New Tools for Cross-Lagged Panel Models. <i>Organizational Research Methods</i>, 25(3), pp. 435-458."
"authors" => array:3 [
0 => array:3 [
"name" => "SHAMSOLLAHI Ali"
"bid" => "B00767865"
"slug" => "shamsollahi-ali"
]
1 => array:1 [
"name" => "ZYPHUR Michael J."
]
2 => array:1 [
"name" => "OZKOK Ozlem"
]
]
"ouvrage" => ""
"keywords" => array:7 [
0 => "longitudinal theory"
1 => "longitudinal modeling"
2 => "causal inference"
3 => "long-run effects"
4 => "cross-lagged panel model"
5 => " human resources management"
6 => "organizational performance"
]
"updatedAt" => "2023-09-22 16:44:01"
"publicationUrl" => "https://journals.sagepub.com/doi/10.1177/1094428121993228"
"publicationInfo" => array:3 [
"pages" => "435-458"
"volume" => "25"
"number" => "3"
]
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"abstract" => array:2 [
"fr" => "Cross-lagged panel models (CLPMs) are common, but their applications often focus on “short-run” effects among temporally proximal observations. This addresses questions about how dynamic systems may immediately respond to interventions, but fails to show how systems evolve over longer timeframes. We explore three types of “long-run” effects in dynamic systems that extend recent work on “impulse responses,” which reflect potential long-run effects of one-time interventions. Going beyond these, we first treat evaluations of system (in)stability by testing for “permanent effects,” which are important because in unstable systems even a one-time intervention may have enduring effects. Second, we explore classic econometric long-run effects that show how dynamic systems may respond to interventions that are sustained over time. Third, we treat “accumulated responses” to model how systems may respond to repeated interventions over time. We illustrate tests of each long-run effect in a simulated dataset and we provide all materials online including user-friendly R code that automates estimating, testing, reporting, and plotting all effects (see https://doi.org/10.26188/13506861). We conclude by emphasizing the value of aligning specific longitudinal hypotheses with quantitative methods."
"en" => "Cross-lagged panel models (CLPMs) are common, but their applications often focus on “short-run” effects among temporally proximal observations. This addresses questions about how dynamic systems may immediately respond to interventions, but fails to show how systems evolve over longer timeframes. We explore three types of “long-run” effects in dynamic systems that extend recent work on “impulse responses,” which reflect potential long-run effects of one-time interventions. Going beyond these, we first treat evaluations of system (in)stability by testing for “permanent effects,” which are important because in unstable systems even a one-time intervention may have enduring effects. Second, we explore classic econometric long-run effects that show how dynamic systems may respond to interventions that are sustained over time. Third, we treat “accumulated responses” to model how systems may respond to repeated interventions over time. We illustrate tests of each long-run effect in a simulated dataset and we provide all materials online including user-friendly R code that automates estimating, testing, reporting, and plotting all effects (see https://doi.org/10.26188/13506861). We conclude by emphasizing the value of aligning specific longitudinal hypotheses with quantitative methods."
]
"authors_fields" => array:2 [
"fr" => "Marketing"
"en" => "Marketing"
]
"indexedAt" => "2024-05-08T22:21:45.000Z"
]
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}
4 => Essec\Faculty\Model\Contribution {#2212
#_index: "academ_contributions"
#_id: "12697"
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"id" => "12697"
"slug" => "buyer-supplier-relationship-dynamics"
"yearMonth" => "2021-01"
"year" => "2021"
"title" => "Buyer-supplier Relationship Dynamics"
"description" => "SHAMSOLLAHI, A. (2021). Buyer-supplier Relationship Dynamics. <i>ESSEC Knowledge</i>."
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"updatedAt" => "2021-11-02 13:54:59"
"publicationUrl" => "https://knowledge.essec.edu/en/economy-finance/buyer-supplier-relationship-dynamics.html"
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"pages" => ""
"volume" => ""
"number" => ""
]
"type" => array:2 [
"fr" => "Articles ou vidéos de vulgarisation"
"en" => "Press article, video or other popular media"
]
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"fr" => "Presse"
"en" => "Press"
]
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"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => ""
"en" => ""
]
"authors_fields" => array:2 [
"fr" => "Marketing"
"en" => "Marketing"
]
"indexedAt" => "2024-05-08T22:21:45.000Z"
]
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}
5 => Essec\Faculty\Model\Contribution {#2206
#_index: "academ_contributions"
#_id: "10728"
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"id" => "10728"
"slug" => "from-data-to-causes-i-building-a-general-cross-lagged-panel-model-gclm"
"yearMonth" => "2020-05"
"year" => "2020"
"title" => "From Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM)"
"description" => "ZYPHUR, M.J., ALLISON, P.D., TAY, L., VOELKLE, M.C., PREACHER, K., ZHANG, Z. ... DIENER, E. (2020). From Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM). <i>Organizational Research Methods</i>, 23(4), pp. 651-687."
"authors" => array:11 [
0 => array:3 [
"name" => "SHAMSOLLAHI Ali"
"bid" => "B00767865"
"slug" => "shamsollahi-ali"
]
1 => array:1 [
"name" => "ZYPHUR M. J."
]
2 => array:1 [
"name" => "ALLISON P. D."
]
3 => array:1 [
"name" => "TAY L."
]
4 => array:1 [
"name" => "VOELKLE M. C."
]
5 => array:1 [
"name" => "PREACHER Kristopher"
]
6 => array:1 [
"name" => "ZHANG Zhen"
]
7 => array:1 [
"name" => "HAMAKER Ellen"
]
8 => array:1 [
"name" => "PIERIDES Dean"
]
9 => array:1 [
"name" => "KOVAL Peter"
]
10 => array:1 [
"name" => "DIENER Ed"
]
]
"ouvrage" => ""
"keywords" => array:1 [
0 => "panel data model, cross-lagged panel model, causal inference, Granger causality, structural equation model, vector autoregressive VAR model, autoregression, moving average, ARMA, VARMA, panel VAR"
]
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => "https://journals.sagepub.com/doi/full/10.1177/1094428119847278"
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"pages" => "651-687"
"volume" => "23"
"number" => "4"
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"en" => "Journal articles"
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"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "This is the first paper in a series of two that synthesizes, compares, and extends methods for causal inference with longitudinal panel data in a structural equation modeling (SEM) framework. Starting with a cross-lagged approach, this paper builds a general cross-lagged panel model (GCLM) with parameters to account for stable factors while increasing the range of dynamic processes that can be modeled. We illustrate the GCLM by examining the relationship between national income and subjective well-being (SWB), showing how to examine hypotheses about short-run (via Granger-Sims tests) versus long-run effects (via impulse responses). When controlling for stable factors, we find no short-run or long-run effects among these variables, showing national SWB to be relatively stable, whereas income is less so. Our second paper addresses the differences between the GCLM and other methods. Online Supplementary Materials offer an Excel file automating GCLM input for Mplus (with an example also for Lavaan in R) and analyses using additional data sets and all program input/output. We also offer an introductory GCLM presentation at https://youtu.be/tHnnaRNPbXs. We conclude with a discussion of issues surrounding causal inference."
"en" => "This is the first paper in a series of two that synthesizes, compares, and extends methods for causal inference with longitudinal panel data in a structural equation modeling (SEM) framework. Starting with a cross-lagged approach, this paper builds a general cross-lagged panel model (GCLM) with parameters to account for stable factors while increasing the range of dynamic processes that can be modeled. We illustrate the GCLM by examining the relationship between national income and subjective well-being (SWB), showing how to examine hypotheses about short-run (via Granger-Sims tests) versus long-run effects (via impulse responses). When controlling for stable factors, we find no short-run or long-run effects among these variables, showing national SWB to be relatively stable, whereas income is less so. Our second paper addresses the differences between the GCLM and other methods. Online Supplementary Materials offer an Excel file automating GCLM input for Mplus (with an example also for Lavaan in R) and analyses using additional data sets and all program input/output. We also offer an introductory GCLM presentation at https://youtu.be/tHnnaRNPbXs. We conclude with a discussion of issues surrounding causal inference."
]
"authors_fields" => array:2 [
"fr" => "Marketing"
"en" => "Marketing"
]
"indexedAt" => "2024-05-08T22:21:45.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 7.453745
+"parent": null
}
6 => Essec\Faculty\Model\Contribution {#2210
#_index: "academ_contributions"
#_id: "10729"
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"id" => "10729"
"slug" => "from-data-to-causes-ii-comparing-approaches-to-panel-data-analysis"
"yearMonth" => "2020-05"
"year" => "2020"
"title" => "From Data to Causes II: Comparing Approaches to Panel Data Analysis"
"description" => "ZYPHUR, M.J., VOELKLE, M.C., TAY, L., ALLISON, P.D., PREACHER, K., ZHANG, Z. ... DIENER, E. (2020). From Data to Causes II: Comparing Approaches to Panel Data Analysis. <i>Organizational Research Methods</i>, 23(4), pp. 688-716."
"authors" => array:11 [
0 => array:3 [
"name" => "SHAMSOLLAHI Ali"
"bid" => "B00767865"
"slug" => "shamsollahi-ali"
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1 => array:1 [
"name" => "ZYPHUR M. J."
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2 => array:1 [
"name" => "VOELKLE M. C."
]
3 => array:1 [
"name" => "TAY L."
]
4 => array:1 [
"name" => "ALLISON P. D."
]
5 => array:1 [
"name" => "PREACHER Kristopher"
]
6 => array:1 [
"name" => "ZHANG Zhen"
]
7 => array:1 [
"name" => "HAMAKER Ellen"
]
8 => array:1 [
"name" => "PIERIDES Dean"
]
9 => array:1 [
"name" => "KOVAL Peter"
]
10 => array:1 [
"name" => "DIENER Ed"
]
]
"ouvrage" => ""
"keywords" => array:9 [
0 => "panel data model"
1 => "causal inference"
2 => "cross-lagged model"
3 => "Granger causality"
4 => "structural equation model"
5 => "multilevel model"
6 => "latent curve model"
7 => "latent growth model"
8 => "Arellano-Bond methods"
]
"updatedAt" => "2023-01-05 13:32:40"
"publicationUrl" => "https://journals.sagepub.com/doi/full/10.1177/1094428119847280"
"publicationInfo" => array:3 [
"pages" => "688-716"
"volume" => "23"
"number" => "4"
]
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]
"abstract" => array:2 [
"fr" => "This article compares a general cross-lagged model (GCLM) to other panel data methods based on their coherence with a causal logic and pragmatic concerns regarding modeled dynamics and hypothesis testing. We examine three “static” models that do not incorporate temporal dynamics: random- and fixed-effects models that estimate contemporaneous relationships; and latent curve models. We then describe “dynamic” models that incorporate temporal dynamics in the form of lagged effects: cross-lagged models estimated in a structural equation model (SEM) or multilevel model (MLM) framework; Arellano-Bond dynamic panel data methods; and autoregressive latent trajectory models. We describe the implications of overlooking temporal dynamics in static models and show how even popular cross-lagged models fail to control for stable factors over time. We also show that Arellano-Bond and autoregressive latent trajectory models have various shortcomings. By contrasting these approaches, we clarify the benefits and drawbacks of common methods for modeling panel data, including the GCLM approach we propose. We conclude with a discussion of issues regarding causal inference, including difficulties in separating different types of time-invariant and time-varying effects over time."
"en" => "This article compares a general cross-lagged model (GCLM) to other panel data methods based on their coherence with a causal logic and pragmatic concerns regarding modeled dynamics and hypothesis testing. We examine three “static” models that do not incorporate temporal dynamics: random- and fixed-effects models that estimate contemporaneous relationships; and latent curve models. We then describe “dynamic” models that incorporate temporal dynamics in the form of lagged effects: cross-lagged models estimated in a structural equation model (SEM) or multilevel model (MLM) framework; Arellano-Bond dynamic panel data methods; and autoregressive latent trajectory models. We describe the implications of overlooking temporal dynamics in static models and show how even popular cross-lagged models fail to control for stable factors over time. We also show that Arellano-Bond and autoregressive latent trajectory models have various shortcomings. By contrasting these approaches, we clarify the benefits and drawbacks of common methods for modeling panel data, including the GCLM approach we propose. We conclude with a discussion of issues regarding causal inference, including difficulties in separating different types of time-invariant and time-varying effects over time."
]
"authors_fields" => array:2 [
"fr" => "Marketing"
"en" => "Marketing"
]
"indexedAt" => "2024-05-08T22:21:45.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 7.453745
+"parent": null
}
7 => Essec\Faculty\Model\Contribution {#2213
#_index: "academ_contributions"
#_id: "11146"
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"id" => "11146"
"slug" => "buyer-supplier-relationship-dynamics-a-systematic-review"
"yearMonth" => "2021-03"
"year" => "2021"
"title" => "Buyer-Supplier Relationship Dynamics: A Systematic Review"
"description" => "SHAMSOLLAHI, A., CHMIELEWSKI-RAIMONDO, D., BELL, S. et KACHOUIE, R. (2021). Buyer-Supplier Relationship Dynamics: A Systematic Review. <i>Journal of the Academy of Marketing Science</i>, 49, pp. 418–436."
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"name" => "SHAMSOLLAHI Ali"
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1 => array:1 [
"name" => "CHMIELEWSKI-RAIMONDO Danielle"
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2 => array:1 [
"name" => "BELL Simon"
]
3 => array:1 [
"name" => "KACHOUIE Reza"
]
]
"ouvrage" => ""
"keywords" => array:1 [
0 => "Relationship dynamics, Evolution, Life cycle, Relationship development, Trajectories, Buyer-supplier relationship"
]
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => "https://link.springer.com/article/10.1007/s11747-020-00743-1"
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"fr" => "Marketing scholars have long acknowledged that buyer–supplier relationships (BSRs) evolve over time. Nevertheless, truly dynamic considerations tend to be confined to the “future research” sections of papers. Performing dynamic BSR research is difficult, not only because of the requirements of data collection and analysis, but also due to the somewhat fragmented understanding of the available studies on BSR dynamics and how an overarching understanding of their findings can refine static relationship models. We conduct a systematic literature review to organize the available research on BSR dynamics. The review process reveals four overarching themes: (1) relationship continuity, (2) relationship learning, (3) relationship stages and trajectories, and (4) relationship fluctuations. We discuss each theme, describe how the themes can be applied as a dynamic lens to research questions involving BSRs, and outline research directions that might stimulate further work on relationship dynamics."
"en" => "Marketing scholars have long acknowledged that buyer–supplier relationships (BSRs) evolve over time. Nevertheless, truly dynamic considerations tend to be confined to the “future research” sections of papers. Performing dynamic BSR research is difficult, not only because of the requirements of data collection and analysis, but also due to the somewhat fragmented understanding of the available studies on BSR dynamics and how an overarching understanding of their findings can refine static relationship models. We conduct a systematic literature review to organize the available research on BSR dynamics. The review process reveals four overarching themes: (1) relationship continuity, (2) relationship learning, (3) relationship stages and trajectories, and (4) relationship fluctuations. We discuss each theme, describe how the themes can be applied as a dynamic lens to research questions involving BSRs, and outline research directions that might stimulate further work on relationship dynamics."
]
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