Pub Date : 2022-11-10DOI: 10.1177/00222437221141052
A. Martinovici, R. Pieters, Tülin Erdem
Trajectories of attention capture the accumulation of brand utility during complex decision-making tasks. Thus, attention trajectories, as reflected in eye movements, predict the final brand choice of 85% of consumers before they implement it. Even when observing eye movements in only the first quarter of the decision process, attention already predicts brand choice much better (45%) than chance levels (20%). This superior prediction performance is due to a “double attention lift” for the chosen brand: The chosen brand receives progressively more attention toward the moment of choice, and more of this attention is devoted to integrating information about the brand rather than to comparing it with other options. In contrast, the currently owned brand grabs attention early in the task, and its attention gain persists for brand-loyal consumers and shifts for brand-switching consumers. A new attention and choice model used in tandem with the Bayesian K-fold cross-validation methodology on eye-tracking data from 325 representative consumers uncovered these attention trajectory effects. The findings contribute to closing important knowledge gaps in the attention and choice literature and have implications for marketing research and managerial practice.
{"title":"Attention Trajectories Capture Utility Accumulation and Predict Brand Choice","authors":"A. Martinovici, R. Pieters, Tülin Erdem","doi":"10.1177/00222437221141052","DOIUrl":"https://doi.org/10.1177/00222437221141052","url":null,"abstract":"Trajectories of attention capture the accumulation of brand utility during complex decision-making tasks. Thus, attention trajectories, as reflected in eye movements, predict the final brand choice of 85% of consumers before they implement it. Even when observing eye movements in only the first quarter of the decision process, attention already predicts brand choice much better (45%) than chance levels (20%). This superior prediction performance is due to a “double attention lift” for the chosen brand: The chosen brand receives progressively more attention toward the moment of choice, and more of this attention is devoted to integrating information about the brand rather than to comparing it with other options. In contrast, the currently owned brand grabs attention early in the task, and its attention gain persists for brand-loyal consumers and shifts for brand-switching consumers. A new attention and choice model used in tandem with the Bayesian K-fold cross-validation methodology on eye-tracking data from 325 representative consumers uncovered these attention trajectory effects. The findings contribute to closing important knowledge gaps in the attention and choice literature and have implications for marketing research and managerial practice.","PeriodicalId":48465,"journal":{"name":"Journal of Marketing Research","volume":"60 1","pages":"625 - 645"},"PeriodicalIF":6.1,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48116221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-05DOI: 10.1177/00222437221140028
Stacie F. Waites, Adam Farmer, Jonathan Hasford, R. Welden
Nonprofit organizations often position their charitable efforts as fulfilling the immediate needs of those who are disadvantaged (termed “immediate aid appeals”). This study explores an alternative positioning strategy focused on the use of autonomous aid appeals, which promote the use of donated funds to facilitate the eventual self-sufficiency of those in need. Seven studies show that people are more likely to donate to a charity that uses autonomous aid appeals than immediate aid appeals. The authors generalize this effect to various contexts and examine it with actual donation behavior. They find that managerially relevant boundary conditions support a serial mediation model first through perceptions of impact and then by feelings of hope for the recipient's future. To support the proposed framework, they conduct mediation analyses and two process-by-moderation studies. The findings have practical implications for charities and their promotional messaging.
{"title":"Teach a Man to Fish: The Use of Autonomous Aid in Eliciting Donations","authors":"Stacie F. Waites, Adam Farmer, Jonathan Hasford, R. Welden","doi":"10.1177/00222437221140028","DOIUrl":"https://doi.org/10.1177/00222437221140028","url":null,"abstract":"Nonprofit organizations often position their charitable efforts as fulfilling the immediate needs of those who are disadvantaged (termed “immediate aid appeals”). This study explores an alternative positioning strategy focused on the use of autonomous aid appeals, which promote the use of donated funds to facilitate the eventual self-sufficiency of those in need. Seven studies show that people are more likely to donate to a charity that uses autonomous aid appeals than immediate aid appeals. The authors generalize this effect to various contexts and examine it with actual donation behavior. They find that managerially relevant boundary conditions support a serial mediation model first through perceptions of impact and then by feelings of hope for the recipient's future. To support the proposed framework, they conduct mediation analyses and two process-by-moderation studies. The findings have practical implications for charities and their promotional messaging.","PeriodicalId":48465,"journal":{"name":"Journal of Marketing Research","volume":"60 1","pages":"950 - 967"},"PeriodicalIF":6.1,"publicationDate":"2022-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45818210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-05DOI: 10.1177/00222437221140052
W. Choi, Kinshuk Jerath, M. Sarvary
Advertising to a consumer provides potentially useful information to the consumer and moves them along the purchase journey, and tracking the consumer's online activities enables an advertiser to infer the consumer's purchase journey state and target repeat ads accordingly. However, many consumers dislike being tracked, and, furthermore, repeat advertising may lead to ad wearout. The authors develop a model with consumers, an advertiser, and an ad network to investigate, under the preceding considerations, the impact of regulations that endow consumers with the choice to opt in to or out of online tracking. The authors find that, if ad effectiveness is intermediate, opting in to tracking decreases ad repetition; otherwise, opting in increases ad repetition. To make an opt-in decision, a consumer weighs the cost of ad wearout from repeat ads against the benefit of obtaining potentially relevant product information from them, and the consumer opts in to tracking if either ad effectiveness is intermediate or sensitivity to ad wearout is low. This opt-in pattern creates counterintuitive implications; for instance, higher ad effectiveness, even though it implies higher ad valuation for the advertiser, may reduce repeat ads and the ad network's profit. Under regulation that requires consumer consent for tracking, the results shed light on when and why consumers give such consent, and provide useful insights for practitioners and policy makers.
{"title":"Consumer Privacy Choices and (Un)Targeted Advertising Along the Purchase Journey","authors":"W. Choi, Kinshuk Jerath, M. Sarvary","doi":"10.1177/00222437221140052","DOIUrl":"https://doi.org/10.1177/00222437221140052","url":null,"abstract":"Advertising to a consumer provides potentially useful information to the consumer and moves them along the purchase journey, and tracking the consumer's online activities enables an advertiser to infer the consumer's purchase journey state and target repeat ads accordingly. However, many consumers dislike being tracked, and, furthermore, repeat advertising may lead to ad wearout. The authors develop a model with consumers, an advertiser, and an ad network to investigate, under the preceding considerations, the impact of regulations that endow consumers with the choice to opt in to or out of online tracking. The authors find that, if ad effectiveness is intermediate, opting in to tracking decreases ad repetition; otherwise, opting in increases ad repetition. To make an opt-in decision, a consumer weighs the cost of ad wearout from repeat ads against the benefit of obtaining potentially relevant product information from them, and the consumer opts in to tracking if either ad effectiveness is intermediate or sensitivity to ad wearout is low. This opt-in pattern creates counterintuitive implications; for instance, higher ad effectiveness, even though it implies higher ad valuation for the advertiser, may reduce repeat ads and the ad network's profit. Under regulation that requires consumer consent for tracking, the results shed light on when and why consumers give such consent, and provide useful insights for practitioners and policy makers.","PeriodicalId":48465,"journal":{"name":"Journal of Marketing Research","volume":"60 1","pages":"889 - 907"},"PeriodicalIF":6.1,"publicationDate":"2022-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41773374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-23DOI: 10.1177/00222437221137533
Kathleen T. Li, Garrett P. Sonnier
Causal inference using quasi-experimental data is of great interest to marketers. The factor model approach to estimate treatment effects accommodates a large number of control units and can easily handle a large number of treatment units while flexibly allowing for cases where the treatment is outside the range of the control units. However, the factor model method lacks formal inference theory, instead relying on bootstrap or permutation procedures with strong assumptions. Specifically, the extant Xu (2017) bootstrap procedure requires that the treatment and control error variances are equal. In this research the authors establish that when this assumption is violated, the bootstrap procedure results in biased coverage intervals. The authors develop a formal inference theory for the factor model approach to estimate the average treatment effects on the treated. The approach enables formal quantification of uncertainty through hypothesis testing and confidence intervals. The inference method is applicable to both stationary and nonstationary data. More importantly, the inference theory accommodates treatment and control unit outcomes with different distributions, which includes different error variances as a special case. The authors show the performance of the inference theory with simulated data. Finally, they apply the method to empirically quantify the uncertainty in the effect of legalizing recreational marijuana on the beer market and the sales effect of a digitally native online brand opening a physical showroom.
{"title":"Statistical Inference for the Factor Model Approach to Estimate Causal Effects in Quasi-Experimental Settings","authors":"Kathleen T. Li, Garrett P. Sonnier","doi":"10.1177/00222437221137533","DOIUrl":"https://doi.org/10.1177/00222437221137533","url":null,"abstract":"Causal inference using quasi-experimental data is of great interest to marketers. The factor model approach to estimate treatment effects accommodates a large number of control units and can easily handle a large number of treatment units while flexibly allowing for cases where the treatment is outside the range of the control units. However, the factor model method lacks formal inference theory, instead relying on bootstrap or permutation procedures with strong assumptions. Specifically, the extant Xu (2017) bootstrap procedure requires that the treatment and control error variances are equal. In this research the authors establish that when this assumption is violated, the bootstrap procedure results in biased coverage intervals. The authors develop a formal inference theory for the factor model approach to estimate the average treatment effects on the treated. The approach enables formal quantification of uncertainty through hypothesis testing and confidence intervals. The inference method is applicable to both stationary and nonstationary data. More importantly, the inference theory accommodates treatment and control unit outcomes with different distributions, which includes different error variances as a special case. The authors show the performance of the inference theory with simulated data. Finally, they apply the method to empirically quantify the uncertainty in the effect of legalizing recreational marijuana on the beer market and the sales effect of a digitally native online brand opening a physical showroom.","PeriodicalId":48465,"journal":{"name":"Journal of Marketing Research","volume":"60 1","pages":"449 - 472"},"PeriodicalIF":6.1,"publicationDate":"2022-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46419532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-19DOI: 10.1177/00222437221110139
Chiara Longoni, Luca Cian, Ellie J. Kyung
Artificial intelligence (AI) is pervading the government and transforming how public services are provided to consumers across policy areas spanning allocation of government benefits, law enforceme...
{"title":"Algorithmic Transference: People Overgeneralize Failures of AI in the Government","authors":"Chiara Longoni, Luca Cian, Ellie J. Kyung","doi":"10.1177/00222437221110139","DOIUrl":"https://doi.org/10.1177/00222437221110139","url":null,"abstract":"Artificial intelligence (AI) is pervading the government and transforming how public services are provided to consumers across policy areas spanning allocation of government benefits, law enforceme...","PeriodicalId":48465,"journal":{"name":"Journal of Marketing Research","volume":"22 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138508559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-18DOI: 10.1177/00222437221136491
Yitian Liang, Zhongqiang Huang, Lei Su
Parenting motivation, the inspiration and drive to take care of one's children, is a powerful instinct for facilitating human reproduction. In a set of hypotheses, the authors address how, why, and among whom parenting motivation affects a pervasive decision-making tendency, namely, variety seeking. Six studies, including a large-scale panel data study and five online and lab studies, show that, when shopping, parenting motivation spurs feelings of time crunch that result in less variety seeking among consumers. The effect is diminished when time-saving parenting support exists (which reduces feelings of time crunch in parenting), when consumers are led to believe that they have sufficient time available for shopping, and when they do not have much loyalty to any brand offered in the choice set and thus cannot save time by simply choosing the top-of-mind product option. The current research thus contributes to the growing literature on how parenting motivation affects consumer decision making. In addition, it augments the literature on variety seeking by identifying an important factor that can influence it.
{"title":"Too Time-Crunched to Seek Variety: The Influence of Parenting Motivation on Consumer Variety Seeking","authors":"Yitian Liang, Zhongqiang Huang, Lei Su","doi":"10.1177/00222437221136491","DOIUrl":"https://doi.org/10.1177/00222437221136491","url":null,"abstract":"Parenting motivation, the inspiration and drive to take care of one's children, is a powerful instinct for facilitating human reproduction. In a set of hypotheses, the authors address how, why, and among whom parenting motivation affects a pervasive decision-making tendency, namely, variety seeking. Six studies, including a large-scale panel data study and five online and lab studies, show that, when shopping, parenting motivation spurs feelings of time crunch that result in less variety seeking among consumers. The effect is diminished when time-saving parenting support exists (which reduces feelings of time crunch in parenting), when consumers are led to believe that they have sufficient time available for shopping, and when they do not have much loyalty to any brand offered in the choice set and thus cannot save time by simply choosing the top-of-mind product option. The current research thus contributes to the growing literature on how parenting motivation affects consumer decision making. In addition, it augments the literature on variety seeking by identifying an important factor that can influence it.","PeriodicalId":48465,"journal":{"name":"Journal of Marketing Research","volume":"60 1","pages":"812 - 833"},"PeriodicalIF":6.1,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43510294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-13DOI: 10.1177/00222437221134802
Matthew D. Rocklage, Sharlene He, Derek D. Rucker, L. Nordgren
Sentiment analysis has fundamentally changed marketers’ ability to assess consumer opinion. Indeed, the measurement of attitudes via natural language has influenced how marketing is practiced on a day-to-day basis. Yet recent findings suggest that sentiment analysis's current emphasis on measuring valence (i.e., positivity or negativity) can produce incomplete, inaccurate, and even misleading insights. Conceptually, the current work challenges sentiment analysis to move beyond valence. The authors identify the certainty or confidence of consumers’ sentiment as a particularly potent facet to assess. Empirically, they develop a new computational measure of certainty in language—the Certainty Lexicon—and validate its use with sentiment analysis. To construct and validate this measure, the authors use text from 11.6 million people who generated billions of words, millions of online reviews, and hundreds of thousands of entries in an online prediction market. Across social media data sets, in-lab experiments, and online reviews, the authors find that the Certainty Lexicon is more comprehensive, generalizable, and accurate in its measurement compared with other tools. The authors also demonstrate the value of measuring sentiment certainty for marketers: certainty predicted the real-world success of commercials where traditional sentiment analysis did not. The Certainty Lexicon is available at www.CertaintyLexicon.com.
{"title":"Beyond Sentiment: The Value and Measurement of Consumer Certainty in Language","authors":"Matthew D. Rocklage, Sharlene He, Derek D. Rucker, L. Nordgren","doi":"10.1177/00222437221134802","DOIUrl":"https://doi.org/10.1177/00222437221134802","url":null,"abstract":"Sentiment analysis has fundamentally changed marketers’ ability to assess consumer opinion. Indeed, the measurement of attitudes via natural language has influenced how marketing is practiced on a day-to-day basis. Yet recent findings suggest that sentiment analysis's current emphasis on measuring valence (i.e., positivity or negativity) can produce incomplete, inaccurate, and even misleading insights. Conceptually, the current work challenges sentiment analysis to move beyond valence. The authors identify the certainty or confidence of consumers’ sentiment as a particularly potent facet to assess. Empirically, they develop a new computational measure of certainty in language—the Certainty Lexicon—and validate its use with sentiment analysis. To construct and validate this measure, the authors use text from 11.6 million people who generated billions of words, millions of online reviews, and hundreds of thousands of entries in an online prediction market. Across social media data sets, in-lab experiments, and online reviews, the authors find that the Certainty Lexicon is more comprehensive, generalizable, and accurate in its measurement compared with other tools. The authors also demonstrate the value of measuring sentiment certainty for marketers: certainty predicted the real-world success of commercials where traditional sentiment analysis did not. The Certainty Lexicon is available at www.CertaintyLexicon.com.","PeriodicalId":48465,"journal":{"name":"Journal of Marketing Research","volume":"60 1","pages":"870 - 888"},"PeriodicalIF":6.1,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44849649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-07DOI: 10.1177/00222437221134115
Hannah H. Chang, A. Mukherjee, Amitava Chattopadhyay
The authors posit that in an initial exposure to a broadcast video, hearing different voices narrate (in succession) a persuasive message encourages consumers’ attention and processing of the message, thereby facilitating persuasion; this is referred to as the voice numerosity effect. Across four studies (plus validation and replication studies)—including two large-scale, real-world data sets (with more than 11,000 crowdfunding videos and over 3.6 million customer transactions, and more than 1,600 video ads) and two controlled experiments (with over 1,800 participants)—the results provide support for the hypothesized effect. The effect (1) has consequential, economic implications in a real-world marketplace, (2) is more pronounced when the message is easier to comprehend, (3) is more pronounced when consumers have the capacity to process the ad message, and (4) is mediated by the favorability of consumers’ cognitive responses. The authors demonstrate the use of machine learning, text mining, and natural language processing to process and analyze unstructured (multimedia) data. Theoretical and marketing implications are discussed.
{"title":"More Voices Persuade: The Attentional Benefits of Voice Numerosity","authors":"Hannah H. Chang, A. Mukherjee, Amitava Chattopadhyay","doi":"10.1177/00222437221134115","DOIUrl":"https://doi.org/10.1177/00222437221134115","url":null,"abstract":"The authors posit that in an initial exposure to a broadcast video, hearing different voices narrate (in succession) a persuasive message encourages consumers’ attention and processing of the message, thereby facilitating persuasion; this is referred to as the voice numerosity effect. Across four studies (plus validation and replication studies)—including two large-scale, real-world data sets (with more than 11,000 crowdfunding videos and over 3.6 million customer transactions, and more than 1,600 video ads) and two controlled experiments (with over 1,800 participants)—the results provide support for the hypothesized effect. The effect (1) has consequential, economic implications in a real-world marketplace, (2) is more pronounced when the message is easier to comprehend, (3) is more pronounced when consumers have the capacity to process the ad message, and (4) is mediated by the favorability of consumers’ cognitive responses. The authors demonstrate the use of machine learning, text mining, and natural language processing to process and analyze unstructured (multimedia) data. Theoretical and marketing implications are discussed.","PeriodicalId":48465,"journal":{"name":"Journal of Marketing Research","volume":"60 1","pages":"687 - 706"},"PeriodicalIF":6.1,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43901153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-07DOI: 10.1177/00222437221134237
Jia Liu, Ziwei Cong
Recommender systems on online platforms are often accused of polarizing user attention and consumption. The authors examine this phenomenon using a quasi-experiment conducted by Zhihu, the largest online knowledge-sharing platform (or Q&A community) in China. Zhihu originally used a content-based filtering algorithm, which recommends content to users on the basis of the topics to which each user has subscribed. After more than a year, Zhihu moved to a social filtering algorithm, which recommends content with which users’ social connections are already engaged. The authors find that this algorithm change increased the creation of social ties by approximately 15% but decreased question subscriptions by 20% and answer contributions by 23%. The authors show that users’ increased social interests mainly involved following popular users, leading to a greater concentration of social interests on the platform. However, users’ topical interests became less concentrated, as popular topics received significantly fewer subscribers than unpopular topics. The authors explain these findings by exploring the underlying mechanism. They show that compared with content-based filtering algorithms, social filtering algorithms are more likely to expose general users to content consumed by their followees, who are more interested in niche topics than general users are.
{"title":"The Daily Me Versus the Daily Others: How Do Recommendation Algorithms Change User Interests? Evidence from a Knowledge-Sharing Platform","authors":"Jia Liu, Ziwei Cong","doi":"10.1177/00222437221134237","DOIUrl":"https://doi.org/10.1177/00222437221134237","url":null,"abstract":"Recommender systems on online platforms are often accused of polarizing user attention and consumption. The authors examine this phenomenon using a quasi-experiment conducted by Zhihu, the largest online knowledge-sharing platform (or Q&A community) in China. Zhihu originally used a content-based filtering algorithm, which recommends content to users on the basis of the topics to which each user has subscribed. After more than a year, Zhihu moved to a social filtering algorithm, which recommends content with which users’ social connections are already engaged. The authors find that this algorithm change increased the creation of social ties by approximately 15% but decreased question subscriptions by 20% and answer contributions by 23%. The authors show that users’ increased social interests mainly involved following popular users, leading to a greater concentration of social interests on the platform. However, users’ topical interests became less concentrated, as popular topics received significantly fewer subscribers than unpopular topics. The authors explain these findings by exploring the underlying mechanism. They show that compared with content-based filtering algorithms, social filtering algorithms are more likely to expose general users to content consumed by their followees, who are more interested in niche topics than general users are.","PeriodicalId":48465,"journal":{"name":"Journal of Marketing Research","volume":"60 1","pages":"767 - 791"},"PeriodicalIF":6.1,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41686660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-21DOI: 10.1177/00222437221130722
Min Kim, Jie Zhang
The authors develop an attribute-based mixed-membership model of consumers’ preference for stockkeeping units in store assortments. The model represents the underlying “topics of interest” that drive shopping behaviors as probability distributions over product attributes. It overcomes several limitations of latent Dirichlet allocation topic models and is particularly useful for making preference predictions in large and frequently changing store assortments. The authors apply the proposed model to investigate topics driving browsing and purchase activities in an online deal marketplace of fashion products and explore how preference structures evolve over time. They find commonalities and differences in the topics that drive the browsing and purchase stages of online shopping processes. In general, browsing covers a broader range of product attributes than purchases. Consumers tend to browse products of premium positioning and/or deep discounts in the deal marketplace, but when purchasing, they tend to gravitate toward lower-tiered products at their original prices and modest depths of discounts. The authors illustrate how insights from the proposed model can be utilized to profile consumers based on their price preferences and to improve personalized product recommendations. They show that the model's performance is particularly strong in predicting preferences for new products that are not in the existing assortment.
{"title":"Discovering Online Shopping Preference Structures in Large and Frequently Changing Store Assortments","authors":"Min Kim, Jie Zhang","doi":"10.1177/00222437221130722","DOIUrl":"https://doi.org/10.1177/00222437221130722","url":null,"abstract":"The authors develop an attribute-based mixed-membership model of consumers’ preference for stockkeeping units in store assortments. The model represents the underlying “topics of interest” that drive shopping behaviors as probability distributions over product attributes. It overcomes several limitations of latent Dirichlet allocation topic models and is particularly useful for making preference predictions in large and frequently changing store assortments. The authors apply the proposed model to investigate topics driving browsing and purchase activities in an online deal marketplace of fashion products and explore how preference structures evolve over time. They find commonalities and differences in the topics that drive the browsing and purchase stages of online shopping processes. In general, browsing covers a broader range of product attributes than purchases. Consumers tend to browse products of premium positioning and/or deep discounts in the deal marketplace, but when purchasing, they tend to gravitate toward lower-tiered products at their original prices and modest depths of discounts. The authors illustrate how insights from the proposed model can be utilized to profile consumers based on their price preferences and to improve personalized product recommendations. They show that the model's performance is particularly strong in predicting preferences for new products that are not in the existing assortment.","PeriodicalId":48465,"journal":{"name":"Journal of Marketing Research","volume":"60 1","pages":"665 - 686"},"PeriodicalIF":6.1,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48998739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：