Millions of users on the Internet have fallen into phishing website traps. Detection tools are designed to warn users against such attacks, but often fail to achieve this purpose. One crucial reason behind this is that users rarely have a chance to interact and build a relationship with a detection tool that stealthily runs at the backend. A warning message on a rarely seen interface from such a tool hardly inspires users’ trust in its authenticity and accuracy. In this study, we propose an ontology-based intelligent interface personalization (OBIIP) design for the warning interfaces of phishing website detection tools. We first constructed an ontology of warning interface elements (OWIE), which is a comprehensive knowledgebase for warning interface design. We then used OWIE in the design and creation of an OBIIP prototype and assessed it in a laboratory experiment and an online experiment. The results show the significant value of OBIIP in improving users’ performance in terms of self-protection against website phishing attacks and building a stronger relationship with the detection tool in terms of trust in and use of the tool.
{"title":"Ontology-Based Intelligent Interface Personalization for Protection Against Phishing Attacks","authors":"Fatemeh Mariam Zahedi, Yan Chen, Huimin Zhao","doi":"10.1287/isre.2021.0065","DOIUrl":"https://doi.org/10.1287/isre.2021.0065","url":null,"abstract":"Millions of users on the Internet have fallen into phishing website traps. Detection tools are designed to warn users against such attacks, but often fail to achieve this purpose. One crucial reason behind this is that users rarely have a chance to interact and build a relationship with a detection tool that stealthily runs at the backend. A warning message on a rarely seen interface from such a tool hardly inspires users’ trust in its authenticity and accuracy. In this study, we propose an ontology-based intelligent interface personalization (OBIIP) design for the warning interfaces of phishing website detection tools. We first constructed an ontology of warning interface elements (OWIE), which is a comprehensive knowledgebase for warning interface design. We then used OWIE in the design and creation of an OBIIP prototype and assessed it in a laboratory experiment and an online experiment. The results show the significant value of OBIIP in improving users’ performance in terms of self-protection against website phishing attacks and building a stronger relationship with the detection tool in terms of trust in and use of the tool.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136209200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chuang Tang, Shaobo (Kevin) Li, Yi Ding, Ram D. Gopal, Guanglei Zhang
The coronavirus disease 2019 (COVID-19) pandemic has seen a rise in racial discrimination against Asian communities, notably the Chinese population. Despite growing research on various aspects of the pandemic, there is a notable gap in understanding its behavioral impact regarding racial discrimination. This study delves into the manifestations of COVID-19-related racial discrimination and antidiscrimination efforts on online platforms using large-scale data sets from Yelp.com and SafeGraph. We specifically examined how the pandemic affected Chinese restaurants compared with non-Chinese ones at different pandemic phases. Our findings are significant; the pandemic triggered an immediate surge in racial discrimination, resulting in a substantial decrease in customers visiting Chinese restaurants. Importantly, we applied advanced text mining and machine learning techniques to analyze user behavior, consistently revealing that increased discrimination prompted users to take antidiscrimination actions on online platforms. This research highlights a tangible form of racial discrimination through reduced patronage of Chinese restaurants and underscores the capacity of consumers to combat discrimination on online platforms. It calls for targeted policy interventions to address and prevent racial discrimination, particularly in the context of public health crises.
{"title":"Racial Discrimination and Anti-discrimination: The COVID-19 Pandemic’s Impact on Chinese Restaurants in North America","authors":"Chuang Tang, Shaobo (Kevin) Li, Yi Ding, Ram D. Gopal, Guanglei Zhang","doi":"10.1287/isre.2021.0568","DOIUrl":"https://doi.org/10.1287/isre.2021.0568","url":null,"abstract":"The coronavirus disease 2019 (COVID-19) pandemic has seen a rise in racial discrimination against Asian communities, notably the Chinese population. Despite growing research on various aspects of the pandemic, there is a notable gap in understanding its behavioral impact regarding racial discrimination. This study delves into the manifestations of COVID-19-related racial discrimination and antidiscrimination efforts on online platforms using large-scale data sets from Yelp.com and SafeGraph. We specifically examined how the pandemic affected Chinese restaurants compared with non-Chinese ones at different pandemic phases. Our findings are significant; the pandemic triggered an immediate surge in racial discrimination, resulting in a substantial decrease in customers visiting Chinese restaurants. Importantly, we applied advanced text mining and machine learning techniques to analyze user behavior, consistently revealing that increased discrimination prompted users to take antidiscrimination actions on online platforms. This research highlights a tangible form of racial discrimination through reduced patronage of Chinese restaurants and underscores the capacity of consumers to combat discrimination on online platforms. It calls for targeted policy interventions to address and prevent racial discrimination, particularly in the context of public health crises.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"199 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136210855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Review spammers can harm the trustworthy environment of online platforms by purposefully posting unauthentic ratings and comments for products or online merchants, with the aim of gaining improper benefits. Though a vast majority of methods have been proposed to resolve the spammer detection problem, several challenges such as collusion recognition, label scarcity and biased distributions, etc., are still persistent and call for further investigation. Building on the prevalent collusive spamming behaviors and the network homophily theory, we introduce a reviewer network to account for the explicit co-review relations, and then propose a semi-supervised probabilistic collaborative learning model to capture both reviewers' individual behavioral features and the reviewer network. Our model features in integrating partial labels propagation with a pseudo-labeling strategy and the feature-based learning for reviewer network modelling, which is proved theoretically to be a weighted logistic regression on a network-related synthetic data set. The rich parameters that characterize the importance of network information, the strength of network homophily, and the value of unlabeled data, make our model more transparent. The empirical evaluations on two distinctive real-life data sets have demonstrated the effectiveness of our model and the value of unlabeled data learning, in which the reviewer network after proper trimming shows strong homophily effect and plays a vital role. In particular, the proposed model shows robustness against label scarcity and biased label distribution.
{"title":"Are Neighbors Alike? A Semi-supervised Probabilistic Collaborative Learning Model for Online Review Spammers Detection","authors":"Zhiang Wu, Guannan Liu, Junjie Wu, Yong Tan","doi":"10.1287/isre.2022.0047","DOIUrl":"https://doi.org/10.1287/isre.2022.0047","url":null,"abstract":"Review spammers can harm the trustworthy environment of online platforms by purposefully posting unauthentic ratings and comments for products or online merchants, with the aim of gaining improper benefits. Though a vast majority of methods have been proposed to resolve the spammer detection problem, several challenges such as collusion recognition, label scarcity and biased distributions, etc., are still persistent and call for further investigation. Building on the prevalent collusive spamming behaviors and the network homophily theory, we introduce a reviewer network to account for the explicit co-review relations, and then propose a semi-supervised probabilistic collaborative learning model to capture both reviewers' individual behavioral features and the reviewer network. Our model features in integrating partial labels propagation with a pseudo-labeling strategy and the feature-based learning for reviewer network modelling, which is proved theoretically to be a weighted logistic regression on a network-related synthetic data set. The rich parameters that characterize the importance of network information, the strength of network homophily, and the value of unlabeled data, make our model more transparent. The empirical evaluations on two distinctive real-life data sets have demonstrated the effectiveness of our model and the value of unlabeled data learning, in which the reviewer network after proper trimming shows strong homophily effect and plays a vital role. In particular, the proposed model shows robustness against label scarcity and biased label distribution.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136295236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Privacy is one of the most pressing concerns in the continuously evolving landscape of information technology. Despite decades of vigorous and multifaceted exploration in the interdisciplinary field of information privacy, a consensual or unifying theory remains elusive. Moreover, the complexities of issues surrounding privacy are frequently labeled as “too big to understand” in the public press. At this critical juncture, it is beneficial to delve deeper into the foundational assumptions that privacy scholars have about privacy phenomena. In this commentary, we offer a fresh perspective by drawing on Dreyfus’ influential exegesis of the Heideggerian onto-epistemological framework to reflect on these assumptions. The perspective we offer yields three integrative recommendations for future privacy research to open to new research directions. We illustrate how these new directions could not only grow future privacy research but also facilitate the design of more effective privacy-protection measures in practice.
{"title":"An Onto-Epistemological Analysis of Information Privacy Research","authors":"Heng Xu, Nan Zhang","doi":"10.1287/isre.2021.0633","DOIUrl":"https://doi.org/10.1287/isre.2021.0633","url":null,"abstract":"Privacy is one of the most pressing concerns in the continuously evolving landscape of information technology. Despite decades of vigorous and multifaceted exploration in the interdisciplinary field of information privacy, a consensual or unifying theory remains elusive. Moreover, the complexities of issues surrounding privacy are frequently labeled as “too big to understand” in the public press. At this critical juncture, it is beneficial to delve deeper into the foundational assumptions that privacy scholars have about privacy phenomena. In this commentary, we offer a fresh perspective by drawing on Dreyfus’ influential exegesis of the Heideggerian onto-epistemological framework to reflect on these assumptions. The perspective we offer yields three integrative recommendations for future privacy research to open to new research directions. We illustrate how these new directions could not only grow future privacy research but also facilitate the design of more effective privacy-protection measures in practice.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134975863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we study the consumers’ variety-seeking behavior in recommender system applications and propose a comprehensive framework to measure such behavior based on past consumption records. The effectiveness of the proposed framework is validated through user questionnaire studies conducted at Alibaba, where our constructed variety-seeking measures match well with consumers’ self-reported levels of their variety-seeking behaviors. We subsequently present a recommendation framework that combines the identified variety-seeking levels with unexpected recommender systems in the data mining literature to address consumers’ heterogenous desire for product variety, in which we provide more unexpected product recommendations to variety-seeking consumers and vice versa. Through off-line experiments on three different recommendation scenarios and a large-scale online controlled experiment at a major video-streaming platform, we demonstrate that those models following our recommendation framework significantly increase various business performance metrics and generate tangible economic impact for the company. Our findings lead to important managerial implications to better understand consumers’ variety-seeking behaviors and design recommender systems. As a result, the best performing model in our proposed frameworks is deployed by the company to serve all consumers on the video-streaming platform.
{"title":"When Variety Seeking Meets Unexpectedness: Incorporating Variety-Seeking Behaviors into Design of Unexpected Recommender Systems","authors":"Pan Li, Alexander Tuzhilin","doi":"10.1287/isre.2021.0053","DOIUrl":"https://doi.org/10.1287/isre.2021.0053","url":null,"abstract":"In this paper, we study the consumers’ variety-seeking behavior in recommender system applications and propose a comprehensive framework to measure such behavior based on past consumption records. The effectiveness of the proposed framework is validated through user questionnaire studies conducted at Alibaba, where our constructed variety-seeking measures match well with consumers’ self-reported levels of their variety-seeking behaviors. We subsequently present a recommendation framework that combines the identified variety-seeking levels with unexpected recommender systems in the data mining literature to address consumers’ heterogenous desire for product variety, in which we provide more unexpected product recommendations to variety-seeking consumers and vice versa. Through off-line experiments on three different recommendation scenarios and a large-scale online controlled experiment at a major video-streaming platform, we demonstrate that those models following our recommendation framework significantly increase various business performance metrics and generate tangible economic impact for the company. Our findings lead to important managerial implications to better understand consumers’ variety-seeking behaviors and design recommender systems. As a result, the best performing model in our proposed frameworks is deployed by the company to serve all consumers on the video-streaming platform.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135591478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qiang Wei, Yao Mu, Xunhua Guo, Weijie Jiang, Guoqing Chen
Recommender systems are widely used by platforms/merchants to find the products that are likely to interest consumers. However, existing dynamic methods still face challenges with regard to diverse behaviors, variability in interest shifts, and the identification of psychological dynamics. Premised on the marketing funnel perspective to analyze consumer shopping journeys, this study proposes a novel and effective machine learning approach for product recommendation, namely, multi-stage dynamic Bayesian network (MS-DBN), which models the generative processes of consumers’ interactive behaviors with products in light of their stage transitions and interest shifts. In this way, consumers’ stage-interest-behavior dynamics can be learnt, especially the variability in interest shifts. This provides managerial implications for practice. MS-DBN demonstrates significant performance advantage with general applicability by extracting the generalizable regularity during shopping journeys, which compensates the diversity and sparsity frequently observed in consumer behaviors. In addition, aided by the identification strategies integrated into the learning process, the latent variables in the model can be detected such that consumers’ invisible psychological stages and interests in products can be identified from their observed behaviors, shedding light on the targeted marketing of platforms/merchants and thus enriching the practical value of the approach.
{"title":"Dynamic Bayesian Network–Based Product Recommendation Considering Consumers’ Multistage Shopping Journeys: A Marketing Funnel Perspective","authors":"Qiang Wei, Yao Mu, Xunhua Guo, Weijie Jiang, Guoqing Chen","doi":"10.1287/isre.2020.0277","DOIUrl":"https://doi.org/10.1287/isre.2020.0277","url":null,"abstract":"Recommender systems are widely used by platforms/merchants to find the products that are likely to interest consumers. However, existing dynamic methods still face challenges with regard to diverse behaviors, variability in interest shifts, and the identification of psychological dynamics. Premised on the marketing funnel perspective to analyze consumer shopping journeys, this study proposes a novel and effective machine learning approach for product recommendation, namely, multi-stage dynamic Bayesian network (MS-DBN), which models the generative processes of consumers’ interactive behaviors with products in light of their stage transitions and interest shifts. In this way, consumers’ stage-interest-behavior dynamics can be learnt, especially the variability in interest shifts. This provides managerial implications for practice. MS-DBN demonstrates significant performance advantage with general applicability by extracting the generalizable regularity during shopping journeys, which compensates the diversity and sparsity frequently observed in consumer behaviors. In addition, aided by the identification strategies integrated into the learning process, the latent variables in the model can be detected such that consumers’ invisible psychological stages and interests in products can be identified from their observed behaviors, shedding light on the targeted marketing of platforms/merchants and thus enriching the practical value of the approach.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135696117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Coopetition has been a common practice, especially among emerging markets. The coopetition relationship between a ride-sharing platform and a car-rental firm is distinct in that they operate under two different business models. Although the platform controls both its demand and supply by setting rider prices and driver wages, the car-rental firm operates under the traditional model with a fixed supply and cost structure. Both the platform and car-rental firm compete for riders seeking transportation. If the two cooperate, a driver is allowed to rent from the rental firm and drive for the platform; otherwise, only those owning personal vehicles are allowed to drive for the platform. We show that such supply-side cooperation intensifies demand-side price competition and decreases total revenue. Therefore, coopetition is mutually beneficial only when it leads to a significant decrease in supply costs. We find that the two firms are likely to form a coopetition relationship when the total rider market size is not high, the degree of rider substitutability between the two firms is low, and the platform has a significant market-size advantage over the rental firm. Coopetition between the platform and the rental firm benefits riders and hurts drivers, but it benefits society overall.
{"title":"When Sharing Economy Meets Traditional Business: Coopetition Between Ride-Sharing Platforms and Car-Rental Firms","authors":"Chenglong Zhang, Jianqing Chen, Srinivasan Raghunathan","doi":"10.1287/isre.2022.0011","DOIUrl":"https://doi.org/10.1287/isre.2022.0011","url":null,"abstract":"Coopetition has been a common practice, especially among emerging markets. The coopetition relationship between a ride-sharing platform and a car-rental firm is distinct in that they operate under two different business models. Although the platform controls both its demand and supply by setting rider prices and driver wages, the car-rental firm operates under the traditional model with a fixed supply and cost structure. Both the platform and car-rental firm compete for riders seeking transportation. If the two cooperate, a driver is allowed to rent from the rental firm and drive for the platform; otherwise, only those owning personal vehicles are allowed to drive for the platform. We show that such supply-side cooperation intensifies demand-side price competition and decreases total revenue. Therefore, coopetition is mutually beneficial only when it leads to a significant decrease in supply costs. We find that the two firms are likely to form a coopetition relationship when the total rider market size is not high, the degree of rider substitutability between the two firms is low, and the platform has a significant market-size advantage over the rental firm. Coopetition between the platform and the rental firm benefits riders and hurts drivers, but it benefits society overall.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135696514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Swift and unexpected shifts of financial regulations can have profound implications for the general population. This is evidenced by China’s abrupt transition in its stance on P2P lending in 2018. Initially embracing these platforms, the abrupt regulatory pivot to widespread shutdowns. Our empirical research, drawing upon credit application data, demonstrates how this indiscriminate approach hindered economic development opportunities for a significant portion of borrowers, particularly the underprivileged. As a remedy, we advocate for the implementation of AI-driven regulatory frameworks. Rather than a monolithic approach to all borrowers, AI helps distinguish between real financial risks and those that can be managed. This nuanced strategy safeguards individuals’ economic progression, while efficiently mitigating financial hazards. For policymakers and industry stakeholders, our findings underscore the importance of contemplating the broader ramifications of regulatory decisions and harnessing innovative methodologies, such as AI, to strike an optimal balance.
{"title":"Consequences of China’s 2018 Online Lending Regulation and the Promise of PolicyTech","authors":"Yidi Liu, Xin Li, Zhiqiang (Eric) Zheng","doi":"10.1287/isre.2021.0580","DOIUrl":"https://doi.org/10.1287/isre.2021.0580","url":null,"abstract":"Swift and unexpected shifts of financial regulations can have profound implications for the general population. This is evidenced by China’s abrupt transition in its stance on P2P lending in 2018. Initially embracing these platforms, the abrupt regulatory pivot to widespread shutdowns. Our empirical research, drawing upon credit application data, demonstrates how this indiscriminate approach hindered economic development opportunities for a significant portion of borrowers, particularly the underprivileged. As a remedy, we advocate for the implementation of AI-driven regulatory frameworks. Rather than a monolithic approach to all borrowers, AI helps distinguish between real financial risks and those that can be managed. This nuanced strategy safeguards individuals’ economic progression, while efficiently mitigating financial hazards. For policymakers and industry stakeholders, our findings underscore the importance of contemplating the broader ramifications of regulatory decisions and harnessing innovative methodologies, such as AI, to strike an optimal balance.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135244447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Firms increasingly harness data that are created as by-products of information systems usage to evaluate and manage employees. However, such “trace data” can be a double-edged sword. The data can provide a whole new visibility into work practices but also, make work less transparent if the employees start to change their behavior to shape the data. We study this dilemma in the context of knowledge work that has traditionally eluded behavioral measurement. We show that when knowledge workers become aware of data collection and have an interest in how their work may be represented by the data, they start to actively perform the data. We identify different patterns by which employees shape work practices to produce trace data. The changes affect not only the actions and data of the focal employee but also, the actions and data of their colleagues and subordinates. Therefore, to fully realize the potential of trace data, managers may need to get involved in designing the data and to set a trace data policy that states how the data will be used in the organization.
{"title":"The Performative Production of Trace Data in Knowledge Work","authors":"Aleksi Aaltonen, Marta Stelmaszak","doi":"10.1287/isre.2019.0357","DOIUrl":"https://doi.org/10.1287/isre.2019.0357","url":null,"abstract":"Firms increasingly harness data that are created as by-products of information systems usage to evaluate and manage employees. However, such “trace data” can be a double-edged sword. The data can provide a whole new visibility into work practices but also, make work less transparent if the employees start to change their behavior to shape the data. We study this dilemma in the context of knowledge work that has traditionally eluded behavioral measurement. We show that when knowledge workers become aware of data collection and have an interest in how their work may be represented by the data, they start to actively perform the data. We identify different patterns by which employees shape work practices to produce trace data. The changes affect not only the actions and data of the focal employee but also, the actions and data of their colleagues and subordinates. Therefore, to fully realize the potential of trace data, managers may need to get involved in designing the data and to set a trace data policy that states how the data will be used in the organization.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136308390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Review-in-review (RIR) is a feature that allows viewers to generate positive or negative evaluations for primary quality evaluations of a product (e.g., ratings and reviews). This study reveals that it can cause social desirability bias in primary ratings: Reviewers who desire social recognition are driven to adjust their ratings (about 7.4% likelihood) to elicit more helpful responses and avoid unhelpful ones. This bias can be shown as distorted conformity to the prior rating distribution or extremity, depending on the RIR types. The model identifies how bias magnitude correlates with users’ social characteristics, thereby identifying vulnerable individuals. Platforms can incentivize less vulnerable users and remind susceptible ones to decrease the bias and can supplement rating conditional on the identified vulnerability extent (e.g., the distribution by the “independent” raters) to mitigate the bias’s impact on rating viewers. The simulation analysis compares the bias under different counterfactual RIR system designs, finding a composite RIR system (e.g., helpful and unhelpful RIRs) partially neutralizes the bias, obviating the need to remove all RIR features. The model further adapts to evaluate underexplored RIRs forms and can provide a “de-biased” metric while preserving individual ratings.
{"title":"Does Help Help? An Empirical Analysis of Social Desirability Bias in Ratings","authors":"Jinyang Zheng, Yong Tan, Guopeng Yin, Jianing Ding","doi":"10.1287/isre.2020.0406","DOIUrl":"https://doi.org/10.1287/isre.2020.0406","url":null,"abstract":"Review-in-review (RIR) is a feature that allows viewers to generate positive or negative evaluations for primary quality evaluations of a product (e.g., ratings and reviews). This study reveals that it can cause social desirability bias in primary ratings: Reviewers who desire social recognition are driven to adjust their ratings (about 7.4% likelihood) to elicit more helpful responses and avoid unhelpful ones. This bias can be shown as distorted conformity to the prior rating distribution or extremity, depending on the RIR types. The model identifies how bias magnitude correlates with users’ social characteristics, thereby identifying vulnerable individuals. Platforms can incentivize less vulnerable users and remind susceptible ones to decrease the bias and can supplement rating conditional on the identified vulnerability extent (e.g., the distribution by the “independent” raters) to mitigate the bias’s impact on rating viewers. The simulation analysis compares the bias under different counterfactual RIR system designs, finding a composite RIR system (e.g., helpful and unhelpful RIRs) partially neutralizes the bias, obviating the need to remove all RIR features. The model further adapts to evaluate underexplored RIRs forms and can provide a “de-biased” metric while preserving individual ratings.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136263991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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