Essec\Faculty\Model\Profile {#2233
#_id: "B00791036"
#_source: array:40 [
"bid" => "B00791036"
"academId" => "30310"
"slug" => "sepehri-amir"
"fullName" => "Amir SEPEHRI"
"lastName" => "SEPEHRI"
"firstName" => "Amir"
"title" => array:2 [
"fr" => "Professeur assistant"
"en" => "Assistant Professor"
]
"email" => "amir.sepehri@essec.edu"
"status" => "ACTIF"
"campus" => "Campus de Cergy"
"departments" => []
"phone" => null
"sites" => []
"facNumber" => "30310"
"externalCvUrl" => "https://faculty.essec.edu/cv/sepehri-amir/pdf"
"googleScholarUrl" => null
"facOrcId" => "https://orcid.org/0000-0002-3228-3127"
"career" => array:1 [
0 => Essec\Faculty\Model\CareerItem {#2237
#_index: null
#_id: null
#_source: array:7 [
"startDate" => "2021-09-01"
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"isInternalPosition" => true
"type" => array:2 [
"fr" => "Positions académiques principales"
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]
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]
"institution" => array:2 [
"fr" => "ESSEC Business School"
"en" => "ESSEC Business School"
]
"country" => array:2 [
"fr" => "France"
"en" => "France"
]
]
+lang: "fr"
+"parent": Essec\Faculty\Model\Profile {#2233}
}
]
"diplomes" => array:1 [
0 => Essec\Faculty\Model\Diplome {#2235
#_index: null
#_id: null
#_source: array:6 [
"diplome" => "DIPLOMA"
"type" => array:2 [
"fr" => "Diplômes"
"en" => "Diplomas"
]
"year" => "2021"
"label" => array:2 [
"en" => "Doctor of Philosophy, Other, Marketing"
"fr" => "Doctor of Philosophy, Autre, Marketing"
]
"institution" => array:2 [
"fr" => "Ivey Business School"
"en" => "Ivey Business School"
]
"country" => array:2 [
"fr" => "Canada"
"en" => "Canada"
]
]
+lang: "fr"
+"parent": Essec\Faculty\Model\Profile {#2233}
}
]
"bio" => array:2 [
"fr" => null
"en" => null
]
"department" => array:2 [
"fr" => "Marketing"
"en" => "Marketing"
]
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"en" => null
]
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]
"researchFields" => array:2 [
"fr" => "Prise de décision financière - Traitement de l'information des consommateurs sur les plateformes en ligne (p. ex. marketing numérique) - Analyse de texte - Analyse de données non structurées - Comportement du consommateur sur les plateformes en ligne"
"en" => "Financial decision-making - Processing consumer information on online platforms (e.g. digital marketing) - Text analysis - Analysis of unstructured data - Consumer behaviour on online platforms"
]
"teachingFields" => array:2 [
"fr" => "Marketing et analyses des données"
"en" => "Marketing and Data Analytics"
]
"distinctions" => []
"teaching" => []
"otherActivities" => []
"theses" => []
"indexedAt" => "2024-11-21T09:21:24.000Z"
"contributions" => array:5 [
0 => Essec\Faculty\Model\Contribution {#2238
#_index: "academ_contributions"
#_id: "15171"
#_source: array:18 [
"id" => "15171"
"slug" => "ideas-worth-spreading-when-how-and-for-whom-information-load-hurts-online-talks-popularity"
"yearMonth" => "2024-09"
"year" => "2024"
"title" => "Ideas Worth Spreading? When, How, and for Whom Information Load Hurts Online Talks’ Popularity"
"description" => "SEPEHRI, A., DUCLOS, R. et HAGHIGHI, N. (2024). Ideas Worth Spreading? When, How, and for Whom Information Load Hurts Online Talks’ Popularity. <i>Journal of Personality and Social Psychology</i>, Forthcoming."
"authors" => array:3 [
0 => array:3 [
"name" => "SEPEHRI Amir"
"bid" => "B00791036"
"slug" => "sepehri-amir"
]
1 => array:1 [
"name" => "DUCLOS Rod"
]
2 => array:1 [
"name" => "HAGHIGHI N."
]
]
"ouvrage" => ""
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"updatedAt" => "2024-09-23 16:17:40"
"publicationUrl" => null
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"pages" => ""
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"fr" => ""
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"fr" => "Marketing"
"en" => "Marketing"
]
"indexedAt" => "2024-11-21T09:21:53.000Z"
]
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}
1 => Essec\Faculty\Model\Contribution {#2236
#_index: "academ_contributions"
#_id: "12699"
#_source: array:18 [
"id" => "12699"
"slug" => "the-location-of-maximum-emotion-in-deceptive-and-truthful-texts"
"yearMonth" => "2021-08"
"year" => "2021"
"title" => "The Location of Maximum Emotion in Deceptive and Truthful Texts"
"description" => "SEPEHRI, A., MARKOWITZ, D.M. et DUCLOS, R. (2021). The Location of Maximum Emotion in Deceptive and Truthful Texts. <i>Social Psychological and Personality Science</i>, 12(6), pp. 996-1004."
"authors" => array:3 [
0 => array:3 [
"name" => "SEPEHRI Amir"
"bid" => "B00791036"
"slug" => "sepehri-amir"
]
1 => array:1 [
"name" => "MARKOWITZ David M."
]
2 => array:1 [
"name" => "DUCLOS Rod"
]
]
"ouvrage" => ""
"keywords" => array:5 [
0 => "language"
1 => "emotion"
2 => "persuasion"
3 => "research methods"
4 => "communication"
]
"updatedAt" => "2023-01-27 01:00:41"
"publicationUrl" => "https://journals.sagepub.com/doi/full/10.1177/1948550620949730"
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"pages" => "996-1004"
"volume" => "12"
"number" => "6"
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"fr" => "Revue scientifique"
"en" => "Scientific journal"
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"fr" => null
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"abstract" => array:2 [
"fr" => "Meta-analytic evidence suggests that verbal patterns of emotion betray deceit, but it is presently unclear whether the location of maximum emotion in lies and truths matters to reveal deception. We contribute to the deception literature by offering analyses at the sentence level to locate where emotion is most pronounced in deceptive versus truthful texts. Using two public data sets—news articles (Study 1) and hotel reviews (Study 2)—we found that maximum emotion occurs toward the beginning of deceptive texts while maximum emotion appears later for truthful texts. In addition to demonstrating the effect across diverse settings, we used two different measurements for emotion and separated the results by valence, replicating the maximum emotion effect each time. The predictive nature of maximum affect ranged from 54% to 56% across data sets, a rate consistent with most deception studies using 50-50 lie–truth base rates. Implications for future research and deception theory are discussed."
"en" => "Meta-analytic evidence suggests that verbal patterns of emotion betray deceit, but it is presently unclear whether the location of maximum emotion in lies and truths matters to reveal deception. We contribute to the deception literature by offering analyses at the sentence level to locate where emotion is most pronounced in deceptive versus truthful texts. Using two public data sets—news articles (Study 1) and hotel reviews (Study 2)—we found that maximum emotion occurs toward the beginning of deceptive texts while maximum emotion appears later for truthful texts. In addition to demonstrating the effect across diverse settings, we used two different measurements for emotion and separated the results by valence, replicating the maximum emotion effect each time. The predictive nature of maximum affect ranged from 54% to 56% across data sets, a rate consistent with most deception studies using 50-50 lie–truth base rates. Implications for future research and deception theory are discussed."
]
"authors_fields" => array:2 [
"fr" => "Marketing"
"en" => "Marketing"
]
"indexedAt" => "2024-11-21T09:21:53.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 7.519394
+"parent": null
}
2 => Essec\Faculty\Model\Contribution {#2240
#_index: "academ_contributions"
#_id: "12700"
#_source: array:18 [
"id" => "12700"
"slug" => "the-power-of-indirect-appeals-in-peer%e2%80%90to%e2%80%90peer-fundraising-why-s-he-can-raise-more-money-for-me-than-i-can-for-myself"
"yearMonth" => "2021-07"
"year" => "2021"
"title" => "The Power of Indirect Appeals in Peer‐to‐Peer Fundraising: Why “S/He” Can Raise More Money for Me Than “I” Can For Myself"
"description" => "SEPEHRI, A., DUCLOS, R., KRISTOFFERSON, K., VINOO, P. et ELAHI, H. (2021). The Power of Indirect Appeals in Peer‐to‐Peer Fundraising: Why “S/He” Can Raise More Money for Me Than “I” Can For Myself. <i>Journal of Consumer Psychology</i>, 31(3), pp. 612-620."
"authors" => array:5 [
0 => array:3 [
"name" => "SEPEHRI Amir"
"bid" => "B00791036"
"slug" => "sepehri-amir"
]
1 => array:1 [
"name" => "DUCLOS Rod"
]
2 => array:1 [
"name" => "KRISTOFFERSON Kirk"
]
3 => array:1 [
"name" => "VINOO Poornima"
]
4 => array:1 [
"name" => "ELAHI Hamid"
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2023-01-27 01:00:41"
"publicationUrl" => "https://myscp.onlinelibrary.wiley.com/doi/abs/10.1002/jcpy.1232#"
"publicationInfo" => array:3 [
"pages" => "612-620"
"volume" => "31"
"number" => "3"
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"abstract" => array:2 [
"fr" => "The proliferation of peer-to-peer fundraising platforms (e.g., GoFundMe, Rally, Fundly) poses conceptual and substantive challenges for behavior scientists and fundraisers. This article explores how fundraisers should craft their appeals to maximize their chance of success. Four field- and laboratory-studies find that direct appeals (i.e., narratives written in the first person by the intended recipient) raise less money than otherwise-identical indirect appeals (i.e., narratives written in the third person, seemingly by a third party on behalf of the intended recipient). The cause? Prospective donors ascribe lesser (greater) credibility to direct (indirect) appeals, which in turn curtails (increases) their giving. Since the narrative voice (direct vs. indirect) in which appeals are crafted is often discretionary (i.e., adjustable), our findings offer prescriptive guidelines for fundraisers."
"en" => "The proliferation of peer-to-peer fundraising platforms (e.g., GoFundMe, Rally, Fundly) poses conceptual and substantive challenges for behavior scientists and fundraisers. This article explores how fundraisers should craft their appeals to maximize their chance of success. Four field- and laboratory-studies find that direct appeals (i.e., narratives written in the first person by the intended recipient) raise less money than otherwise-identical indirect appeals (i.e., narratives written in the third person, seemingly by a third party on behalf of the intended recipient). The cause? Prospective donors ascribe lesser (greater) credibility to direct (indirect) appeals, which in turn curtails (increases) their giving. Since the narrative voice (direct vs. indirect) in which appeals are crafted is often discretionary (i.e., adjustable), our findings offer prescriptive guidelines for fundraisers."
]
"authors_fields" => array:2 [
"fr" => "Marketing"
"en" => "Marketing"
]
"indexedAt" => "2024-11-21T09:21:53.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 7.519394
+"parent": null
}
3 => Essec\Faculty\Model\Contribution {#2232
#_index: "academ_contributions"
#_id: "12701"
#_source: array:18 [
"id" => "12701"
"slug" => "too-much-of-a-good-thing-the-unforeseen-cost-of-tags-in-online-retailing"
"yearMonth" => "2022-06"
"year" => "2022"
"title" => "Too Much of a Good Thing? The Unforeseen Cost of Tags in Online Retailing"
"description" => "SEPEHRI, A., DUCLOS, R. et HAGHIGHI, N. (2022). Too Much of a Good Thing? The Unforeseen Cost of Tags in Online Retailing. <i>International Journal of Research in Marketing</i>, 39(2), pp. 336-348."
"authors" => array:3 [
0 => array:3 [
"name" => "SEPEHRI Amir"
"bid" => "B00791036"
"slug" => "sepehri-amir"
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1 => array:1 [
"name" => "DUCLOS Rod"
]
2 => array:1 [
"name" => "HAGHIGHI Nasir"
]
]
"ouvrage" => ""
"keywords" => array:3 [
0 => "Search-Engine Optimization (SEO)"
1 => "Categorization"
2 => "Online reviews"
]
"updatedAt" => "2023-05-24 16:26:03"
"publicationUrl" => "https://www.sciencedirect.com/science/article/pii/S0167811621000859#!"
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"volume" => "39"
"number" => "2"
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"abstract" => array:2 [
"fr" => "The advent of internet shopping brings countless options for consumers. In response, online retailers aim to facilitate search by attaching descriptive terms to their inventory (e.g., product genres or categories). Called “tagging,” such a practice has been shown to increase product visibility/exposure in an otherwise limitless sea of options. Across five distinct platforms covering a gamut of consumer domains (i.e., food recipes, mobile games, books, videos, and journal publications), we find that the benefits of tagging for product visibility/exposure come with unexpected costs. Indeed, though assigning more tags to a product does indeed boost its visibility/exposure online, doing so also hurts said product’s perceived quality. This effect emerges regardless of proxies used to quantify (i) visibility/exposure or (ii) product liking. We draw on categorization and learning research to explain these results. While multi-tagging increases the likelihood of a product to appear in consumers’ search, it also increases the likelihood of a mismatch between what consumers really sought and what said product can actually deliver."
"en" => "The advent of internet shopping brings countless options for consumers. In response, online retailers aim to facilitate search by attaching descriptive terms to their inventory (e.g., product genres or categories). Called “tagging,” such a practice has been shown to increase product visibility/exposure in an otherwise limitless sea of options. Across five distinct platforms covering a gamut of consumer domains (i.e., food recipes, mobile games, books, videos, and journal publications), we find that the benefits of tagging for product visibility/exposure come with unexpected costs. Indeed, though assigning more tags to a product does indeed boost its visibility/exposure online, doing so also hurts said product’s perceived quality. This effect emerges regardless of proxies used to quantify (i) visibility/exposure or (ii) product liking. We draw on categorization and learning research to explain these results. While multi-tagging increases the likelihood of a product to appear in consumers’ search, it also increases the likelihood of a mismatch between what consumers really sought and what said product can actually deliver."
]
"authors_fields" => array:2 [
"fr" => "Marketing"
"en" => "Marketing"
]
"indexedAt" => "2024-11-21T09:21:53.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 7.519394
+"parent": null
}
4 => Essec\Faculty\Model\Contribution {#2241
#_index: "academ_contributions"
#_id: "14109"
#_source: array:18 [
"id" => "14109"
"slug" => "passivepy-a-tool-to-automatically-identify-passive-voice-in-big-text-data"
"yearMonth" => "2023-10"
"year" => "2023"
"title" => "PassivePy: A tool to automatically identify passive voice in big text data"
"description" => "SEPEHRI, A., MIRSHAFIEE, M.S. et MARKOWITZ, D.M. (2023). PassivePy: A tool to automatically identify passive voice in big text data. <i>Journal of Consumer Psychology</i>, 33(4), pp. 714-727."
"authors" => array:3 [
0 => array:3 [
"name" => "SEPEHRI Amir"
"bid" => "B00791036"
"slug" => "sepehri-amir"
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1 => array:1 [
"name" => "MIRSHAFIEE Mitra Sadat"
]
2 => array:1 [
"name" => "MARKOWITZ David M."
]
]
"ouvrage" => ""
"keywords" => array:3 [
0 => "grammatical voice"
1 => "passive voice"
2 => "automated text analysis"
]
"updatedAt" => "2024-10-31 13:51:19"
"publicationUrl" => "https://doi.org/10.1002/jcpy.1377"
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"pages" => "714-727"
"volume" => "33"
"number" => "4"
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"fr" => null
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"abstract" => array:2 [
"fr" => "The academic study of grammatical voice (e.g., active and passive voice) has a long history in the social sciences. It has been examined in relation to psychological distance, attribution, credibility, and deception. Most evaluations of passive voice are experimental or small-scale field studies, however, and perhaps one reason for its lack of adoption is the difficulty associated with obtaining valid, reliable, and replicable results through automated means. We introduce an automated tool to identify passive voice from large-scale text data, PassivePy, a Python package (readymade website: https://passivepy.streamlit.app/). This package achieves 98% agreement with human coded data for grammatical voice as revealed in two large validation studies. In this paper, we discuss how PassivePy works, and present preliminary empirical evidence of how passive voice connects to various behavioral outcomes across three contexts relevant to consumer psychology: product complaints, online reviews, and charitable giving. Future research can build on this work and further explore the potential relevance of passive voice to consumer psychology and beyond."
"en" => "The academic study of grammatical voice (e.g., active and passive voice) has a long history in the social sciences. It has been examined in relation to psychological distance, attribution, credibility, and deception. Most evaluations of passive voice are experimental or small-scale field studies, however, and perhaps one reason for its lack of adoption is the difficulty associated with obtaining valid, reliable, and replicable results through automated means. We introduce an automated tool to identify passive voice from large-scale text data, PassivePy, a Python package (readymade website: https://passivepy.streamlit.app/). This package achieves 98% agreement with human coded data for grammatical voice as revealed in two large validation studies. In this paper, we discuss how PassivePy works, and present preliminary empirical evidence of how passive voice connects to various behavioral outcomes across three contexts relevant to consumer psychology: product complaints, online reviews, and charitable giving. Future research can build on this work and further explore the potential relevance of passive voice to consumer psychology and beyond."
]
"authors_fields" => array:2 [
"fr" => "Marketing"
"en" => "Marketing"
]
"indexedAt" => "2024-11-21T09:21:53.000Z"
]
+lang: "fr"
+"_type": "_doc"
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+"parent": null
}
]
"avatar" => "https://faculty.essec.edu/wp-content/uploads/avatars/B00791036.jpg"
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"docTitle" => "Amir SEPEHRI"
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"docDescription" => "Département: Marketing<br>Campus de Cergy"
"docType" => "cv"
"docPreview" => "<img src="https://faculty.essec.edu/wp-content/uploads/avatars/B00791036.jpg"><span><span>Amir SEPEHRI</span><span>B00791036</span></span>"
"academ_cv_info" => ""
]
#_index: "academ_cv"
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}
