Shih-Lun “Allen” Tseng, Heshan Sun, Radhika Santhanam, Shuya Lu, Jason B. Thatcher
Current studies show gamification, the integrating of game design elements into target systems, enhances user engagement and instrumental task outcomes. Despite its potential for improving behavioral outcomes, gamification can also lead to maladaptive behaviors, behaviors directed at misappropriating gamified systems. We conceptualized gamified system maladaptive behaviors (GSMB), which involve technology and gamified task maladaptations. We developed a model that depicts three drivers of GSMB from design elements, how they fulfill or frustrate psychological innate needs, which in turn drive GSMB, and how GSMB affect task performance. We tested how the three drivers of design elements affect GSMB in Study 1 by empirically examining users of a gamified system, Pocket Points. The results support our conceptualization of GSMB, and design issues as its antecedents. To further unpack this relationship, we then employed a within-subject experiment and a follow-up survey in Study 2. By manipulating the design issues, we found that GSMB adversely affect task performance, because these users may focus too intently on winning the game, at the expense of task performance. By assessing the fulfillment of psychological needs, our findings suggest that design in gamified systems may not uniformly fulfill the satisfaction of psychological needs and consequently triggers GSMB.
{"title":"Rethinking Gamification Failure: A Model and Investigation of Gamified System Maladaptive Behaviors","authors":"Shih-Lun “Allen” Tseng, Heshan Sun, Radhika Santhanam, Shuya Lu, Jason B. Thatcher","doi":"10.1287/isre.2021.0284","DOIUrl":"https://doi.org/10.1287/isre.2021.0284","url":null,"abstract":"Current studies show gamification, the integrating of game design elements into target systems, enhances user engagement and instrumental task outcomes. Despite its potential for improving behavioral outcomes, gamification can also lead to maladaptive behaviors, behaviors directed at misappropriating gamified systems. We conceptualized gamified system maladaptive behaviors (GSMB), which involve technology and gamified task maladaptations. We developed a model that depicts three drivers of GSMB from design elements, how they fulfill or frustrate psychological innate needs, which in turn drive GSMB, and how GSMB affect task performance. We tested how the three drivers of design elements affect GSMB in Study 1 by empirically examining users of a gamified system, Pocket Points. The results support our conceptualization of GSMB, and design issues as its antecedents. To further unpack this relationship, we then employed a within-subject experiment and a follow-up survey in Study 2. By manipulating the design issues, we found that GSMB adversely affect task performance, because these users may focus too intently on winning the game, at the expense of task performance. By assessing the fulfillment of psychological needs, our findings suggest that design in gamified systems may not uniformly fulfill the satisfaction of psychological needs and consequently triggers GSMB.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"33 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138820019","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}
Emotion artificial intelligence (AI) is shown to vary systematically in its ability to accurately identify emotions, and this variation creates potential biases. In this paper, we conduct an experiment involving three commercially available emotion AI systems and a group of human labelers tasked with identifying emotions from two image data sets. The study focuses on the alignment between facial expressions and the emotion labels assigned by both the AI and humans. Importantly, human labelers are given the AI’s scores and informed about its algorithmic fairness measures. This paper presents several key findings. First, the labelers’ scores are affected by the emotion AI scores, consistent with the anchoring effect. Second, information transparency about the AI’s fairness does not uniformly affect human labeling across different emotions. Moreover, information transparency can even increase human inconsistencies. Plus, significant inconsistencies in the scoring among different emotion AI models cast doubt on their reliability. Overall, the study highlights the limitations of individual decision making and information transparency regarding algorithmic fairness measures in addressing algorithmic fairness. These findings underscore the complexity of integrating emotion AI into practice and emphasize the need for careful policies on emotion AI.
{"title":"The Anchoring Effect, Algorithmic Fairness, and the Limits of Information Transparency for Emotion Artificial Intelligence","authors":"Lauren Rhue","doi":"10.1287/isre.2019.0493","DOIUrl":"https://doi.org/10.1287/isre.2019.0493","url":null,"abstract":"Emotion artificial intelligence (AI) is shown to vary systematically in its ability to accurately identify emotions, and this variation creates potential biases. In this paper, we conduct an experiment involving three commercially available emotion AI systems and a group of human labelers tasked with identifying emotions from two image data sets. The study focuses on the alignment between facial expressions and the emotion labels assigned by both the AI and humans. Importantly, human labelers are given the AI’s scores and informed about its algorithmic fairness measures. This paper presents several key findings. First, the labelers’ scores are affected by the emotion AI scores, consistent with the anchoring effect. Second, information transparency about the AI’s fairness does not uniformly affect human labeling across different emotions. Moreover, information transparency can even increase human inconsistencies. Plus, significant inconsistencies in the scoring among different emotion AI models cast doubt on their reliability. Overall, the study highlights the limitations of individual decision making and information transparency regarding algorithmic fairness measures in addressing algorithmic fairness. These findings underscore the complexity of integrating emotion AI into practice and emphasize the need for careful policies on emotion AI.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"70 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138820518","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}
This study investigates the role of telework adjustment in addressing gender inequality in the labor market induced by disasters, taking the COVID-19 disaster as an example. Disasters often disrupt labor markets, disproportionately impacting female workers because of traditionally greater domestic responsibilities, thus increasing gender inequality. In such a case, telework adjustment has emerged as a silver lining, granting enhanced flexibility, particularly benefiting female workers and catering to their needs. Our analysis reveals that (1) comparing workers in the same industry and holding the same occupation, we find that female workers’ telework adjustment rate is more responsive to external constraints and is 7% higher than that of male workers. (2) Telework adjustment helps reduce gender inequality in labor market outcomes via two means: (i) the higher telework adjustment rate among female workers (which reduces gender inequality by 25.48%) and (ii) the stronger marginal effect of telework adjustment on female workers (which reduces gender inequality by 31.94%). (3) Better digital infrastructure can enhance the mitigating effect of telework adjustment. Our findings offer compelling insights for policymakers and business leaders, emphasizing the strategic role of telework adjustment and digital infrastructure investments as crucial levers in promoting gender inequality during and beyond disaster scenarios.
{"title":"Can Telework Adjustment Help Reduce Disaster-Induced Gender Inequality in Job Market Outcomes?","authors":"Jingbo Hou, Chen Liang, Pei-Yu Chen, Bin Gu","doi":"10.1287/isre.2023.0241","DOIUrl":"https://doi.org/10.1287/isre.2023.0241","url":null,"abstract":"This study investigates the role of telework adjustment in addressing gender inequality in the labor market induced by disasters, taking the COVID-19 disaster as an example. Disasters often disrupt labor markets, disproportionately impacting female workers because of traditionally greater domestic responsibilities, thus increasing gender inequality. In such a case, telework adjustment has emerged as a silver lining, granting enhanced flexibility, particularly benefiting female workers and catering to their needs. Our analysis reveals that (1) comparing workers in the same industry and holding the same occupation, we find that female workers’ telework adjustment rate is more responsive to external constraints and is 7% higher than that of male workers. (2) Telework adjustment helps reduce gender inequality in labor market outcomes via two means: (i) the higher telework adjustment rate among female workers (which reduces gender inequality by 25.48%) and (ii) the stronger marginal effect of telework adjustment on female workers (which reduces gender inequality by 31.94%). (3) Better digital infrastructure can enhance the mitigating effect of telework adjustment. Our findings offer compelling insights for policymakers and business leaders, emphasizing the strategic role of telework adjustment and digital infrastructure investments as crucial levers in promoting gender inequality during and beyond disaster scenarios.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"2 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138573544","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}
Online brand communities often use social plug-in features, such as the Like button, to facilitate social interactions and engage users with the brands. However, whether and how such a community feature affects users’ purchases remain open questions. Analysis of user behavior following the adoption of the Like feature indicates a surprising downturn in purchases, with a 4.1% decrease in orders and a 25.0% reduction in expenditure. Notably, online purchases dip by 3.4% in order numbers and 21.1% in expenditure, with a slighter offline decrease. The treatment effect of the adoption is not always negative but varies over time and across users. First, the Like feature adoption has a positive effect on users’ purchases in the first two months (primarily through enhancing their community participation), and the treatment effect turns negative in subsequent months, leading to the overall negative treatment effect on purchases. Second, the negative treatment effect likely stems from unflattering social comparison and can become weaker or even positive when users accrue more Likes. However, only a small proportion of users receive sufficient Likes to be motivated to purchase more. Our results caution against potential downsides of the Like feature in online communities and provide valuable managerial implications.
{"title":"Do “Likes” in a Brand Community Always Make You Buy More?","authors":"Chen Liang, Ji Wu, Xinxin Li","doi":"10.1287/isre.2022.0008","DOIUrl":"https://doi.org/10.1287/isre.2022.0008","url":null,"abstract":"Online brand communities often use social plug-in features, such as the Like button, to facilitate social interactions and engage users with the brands. However, whether and how such a community feature affects users’ purchases remain open questions. Analysis of user behavior following the adoption of the Like feature indicates a surprising downturn in purchases, with a 4.1% decrease in orders and a 25.0% reduction in expenditure. Notably, online purchases dip by 3.4% in order numbers and 21.1% in expenditure, with a slighter offline decrease. The treatment effect of the adoption is not always negative but varies over time and across users. First, the Like feature adoption has a positive effect on users’ purchases in the first two months (primarily through enhancing their community participation), and the treatment effect turns negative in subsequent months, leading to the overall negative treatment effect on purchases. Second, the negative treatment effect likely stems from unflattering social comparison and can become weaker or even positive when users accrue more Likes. However, only a small proportion of users receive sufficient Likes to be motivated to purchase more. Our results caution against potential downsides of the Like feature in online communities and provide valuable managerial implications.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"2 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138560270","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}
Elham Shafiei Gol, Michel Avital, Mari-Klara Stein
Organizations increasingly engage with external communities for value generation through an ever-growing multitude of digital services. Absorptive capacity, or the organizational capability to identify, assimilate, and apply new knowledge for commercial ends, is a key determinant of how organizations successfully generate value from external sources of knowledge and sustain a competitive advantage. Crowdworking—a novel form of digitally mediated work—allows organizations to hire on-demand highly skilled external experts to leverage their knowledge, skills, and networks. The approach of integrating crowdworking into organizations is increasingly gaining traction among large corporations seeking to harness the knowledge in external communities for value generation. Building on an in-depth embedded case study in a large organization that relies on two established crowdwork platforms, we explore and shed light on how the organization developed its crowdworking-related absorptive capacity to generate value from external experts. The paper offers new insights into the prevailing modus operandi related to harnessing external knowledge in today’s organizations.
{"title":"Crowdworking: Nurturing Expert-Centric Absorptive Capacity","authors":"Elham Shafiei Gol, Michel Avital, Mari-Klara Stein","doi":"10.1287/isre.2020.0413","DOIUrl":"https://doi.org/10.1287/isre.2020.0413","url":null,"abstract":"Organizations increasingly engage with external communities for value generation through an ever-growing multitude of digital services. Absorptive capacity, or the organizational capability to identify, assimilate, and apply new knowledge for commercial ends, is a key determinant of how organizations successfully generate value from external sources of knowledge and sustain a competitive advantage. Crowdworking—a novel form of digitally mediated work—allows organizations to hire on-demand highly skilled external experts to leverage their knowledge, skills, and networks. The approach of integrating crowdworking into organizations is increasingly gaining traction among large corporations seeking to harness the knowledge in external communities for value generation. Building on an in-depth embedded case study in a large organization that relies on two established crowdwork platforms, we explore and shed light on how the organization developed its crowdworking-related absorptive capacity to generate value from external experts. The paper offers new insights into the prevailing modus operandi related to harnessing external knowledge in today’s organizations.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"18 2-4","pages":""},"PeriodicalIF":4.9,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138508905","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}
Online platforms often encounter the challenge of system vulnerabilities, such as bugs, which can be exploited by certain users for illicit gains. These platforms face a dilemma when devising countermeasures, particularly in deciding whether to penalize rule breakers. Different countermeasures can lead to varying economic impacts, including subsequent user engagement. In this study, based on unique field data from a prominent online gaming platform, we discovered that the occurrence of bugs has a negative effect on the online duration and consumption of observing players. Surprisingly, although the platform is responsible for the bugs, not penalizing rule breakers results in more substantial reductions in platform engagement among observing players compared with penalizing them. This effect is particularly pronounced for observers who are directly affected by rule violations. Our findings emphasize the essential role of the platform in fairly punishing rule breakers. To ensure the long-term prosperity of an online platform and the overall welfare of its participants, it is crucial for the platform to maintain high-quality system control and implement effective governance mechanisms for rule enforcement, thereby restoring justice and order to the online community.
{"title":"Platform Loophole Exploitation, Recovery Measures, and User Engagement: A Quasi-Natural Experiment in Online Gaming","authors":"Jianqing Chen, Shu He, Xue Yang","doi":"10.1287/isre.2020.0416","DOIUrl":"https://doi.org/10.1287/isre.2020.0416","url":null,"abstract":"Online platforms often encounter the challenge of system vulnerabilities, such as bugs, which can be exploited by certain users for illicit gains. These platforms face a dilemma when devising countermeasures, particularly in deciding whether to penalize rule breakers. Different countermeasures can lead to varying economic impacts, including subsequent user engagement. In this study, based on unique field data from a prominent online gaming platform, we discovered that the occurrence of bugs has a negative effect on the online duration and consumption of observing players. Surprisingly, although the platform is responsible for the bugs, not penalizing rule breakers results in more substantial reductions in platform engagement among observing players compared with penalizing them. This effect is particularly pronounced for observers who are directly affected by rule violations. Our findings emphasize the essential role of the platform in fairly punishing rule breakers. To ensure the long-term prosperity of an online platform and the overall welfare of its participants, it is crucial for the platform to maintain high-quality system control and implement effective governance mechanisms for rule enforcement, thereby restoring justice and order to the online community.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"22 3","pages":""},"PeriodicalIF":4.9,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138508903","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}
Recommender systems are ubiquitous on various online platforms and provide significant value to the users in helping them find relevant content/items to consume. After item consumption, users can often provide feedback (i.e., their preference ratings for the item) to the system. Research studies have shown that recommender systems’ predictions, observed by users, can cause biases in users’ postconsumption preference ratings. Because these ratings are typically fed back to the system as training data for future predictions, this process is likely to influence the system’s performance over time. We use a simulation approach to investigate the longitudinal impact of preference biases on the dynamics of recommender systems’ performance. Our results reveal that preference biases significantly impair recommendation performance and users’ consumption outcomes, and larger biases cause disproportionately large negative effects. Additionally, less popular and less distinctive (in terms of their content) items are more susceptible to preference biases. Furthermore, considering the substantial impact of preference biases on recommendation performance, we examine the issue of debiasing user-submitted ratings. We find that relying solely on historical rating data is unlikely to be effective in debiasing; thus, we propose/evaluate new debiasing approaches that use additional relevant information that can be collected by recommendation platforms.
{"title":"Longitudinal Impact of Preference Biases on Recommender Systems’ Performance","authors":"Meizi Zhou, Jingjing Zhang, Gediminas Adomavicius","doi":"10.1287/isre.2021.0133","DOIUrl":"https://doi.org/10.1287/isre.2021.0133","url":null,"abstract":"Recommender systems are ubiquitous on various online platforms and provide significant value to the users in helping them find relevant content/items to consume. After item consumption, users can often provide feedback (i.e., their preference ratings for the item) to the system. Research studies have shown that recommender systems’ predictions, observed by users, can cause biases in users’ postconsumption preference ratings. Because these ratings are typically fed back to the system as training data for future predictions, this process is likely to influence the system’s performance over time. We use a simulation approach to investigate the longitudinal impact of preference biases on the dynamics of recommender systems’ performance. Our results reveal that preference biases significantly impair recommendation performance and users’ consumption outcomes, and larger biases cause disproportionately large negative effects. Additionally, less popular and less distinctive (in terms of their content) items are more susceptible to preference biases. Furthermore, considering the substantial impact of preference biases on recommendation performance, we examine the issue of debiasing user-submitted ratings. We find that relying solely on historical rating data is unlikely to be effective in debiasing; thus, we propose/evaluate new debiasing approaches that use additional relevant information that can be collected by recommendation platforms.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"22 2","pages":""},"PeriodicalIF":4.9,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138508904","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}
Pub Date : 2023-11-10DOI: 10.1287/isre.2023.editorial.v34.n4
Suprateek Sarker, Edgar A. Whitley, K. Goh, Y. Hong, Magnus Mähring, Pallab Sanyal, Ning Su, Heng Xu, J. Xu, Jingjing Zhang, Huimin Zhao
{"title":"Editorial: Some Thoughts on Reviewing for Information Systems Research and Other Leading Information Systems Journals","authors":"Suprateek Sarker, Edgar A. Whitley, K. Goh, Y. Hong, Magnus Mähring, Pallab Sanyal, Ning Su, Heng Xu, J. Xu, Jingjing Zhang, Huimin Zhao","doi":"10.1287/isre.2023.editorial.v34.n4","DOIUrl":"https://doi.org/10.1287/isre.2023.editorial.v34.n4","url":null,"abstract":"","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"82 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139281227","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}
When Does Beauty Pay? A Large-Scale Image Based Appearance Analysis on Career Transitions In this study, we collect up to 15 years of career histories for over 40,000 MBA graduates from top 100 MBA programs in the United States. We find that attractive MBA graduates earn at least $2,508 more in yearly salary compared with plain-looking (unattractive) MBA graduates. The attractiveness premium is even larger for top 10 percentile attractive graduates, for those with arts undergraduate majors, and those in managerial roles, nontechnical jobs, and non-IT industries. Policymakers should note that the attractiveness bias is not much smaller in size than gender bias. It is pervasive over time (in individuals in their 30s and 40s and not just 20s) and across industries. It may need a similar focus as gender or racial bias in labor markets. Companies can craft their HR trainings and procedures guided by this finding. A study of this scale is only possible using cutting-edge machine learning and generative AI methods (instead of human subjects) for large-scale data processing.
{"title":"When Does Beauty Pay? A Large-Scale Image-Based Appearance Analysis on Career Transitions","authors":"Nikhil Malik, Param Vir Singh, Kannan Srinivasan","doi":"10.1287/isre.2021.0559","DOIUrl":"https://doi.org/10.1287/isre.2021.0559","url":null,"abstract":"When Does Beauty Pay? A Large-Scale Image Based Appearance Analysis on Career Transitions In this study, we collect up to 15 years of career histories for over 40,000 MBA graduates from top 100 MBA programs in the United States. We find that attractive MBA graduates earn at least $2,508 more in yearly salary compared with plain-looking (unattractive) MBA graduates. The attractiveness premium is even larger for top 10 percentile attractive graduates, for those with arts undergraduate majors, and those in managerial roles, nontechnical jobs, and non-IT industries. Policymakers should note that the attractiveness bias is not much smaller in size than gender bias. It is pervasive over time (in individuals in their 30s and 40s and not just 20s) and across industries. It may need a similar focus as gender or racial bias in labor markets. Companies can craft their HR trainings and procedures guided by this finding. A study of this scale is only possible using cutting-edge machine learning and generative AI methods (instead of human subjects) for large-scale data processing.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"97 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135342050","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}
Jingchuan Pu, Young Kwark, Sang Pil Han, Qiang Ye, Bin Gu
Many online platforms are now offering free samples to seasoned reviewers, hoping to get feedback. While these reviewers are given free samples to review, they also buy and review products themselves. The regular ratings for the purchased products are the majority. This brings up the question: Does receiving free products make them rate their personal purchases more positively? And if so, why? We explored two possibilities. First, uncertainty reduction mechanism: The idea that trying free samples makes buyers more confident in their purchases, leading to greater satisfaction and higher ratings for the purchased products; Second, reciprocity mechanism: The idea that reviewers might feel obliged to give better ratings as a “thank you” for the free samples or with the expectations of getting more free samples, which could introduce bias. Our research indicates that giving free samples mainly helps in reducing purchase uncertainty, making customers genuinely happier with their subsequent purchases. So, online platforms can benefit from this strategy, as it seems to uplift genuine positive reviews rather than create biased ones. However, it is still essential to monitor for any undue bias to maintain trustworthiness in reviews.
{"title":"Uncertainty Reduction vs. Reciprocity: Understanding the Effect of a Platform-Initiated Reviewer Incentive Program on Regular Ratings","authors":"Jingchuan Pu, Young Kwark, Sang Pil Han, Qiang Ye, Bin Gu","doi":"10.1287/isre.2019.0176","DOIUrl":"https://doi.org/10.1287/isre.2019.0176","url":null,"abstract":"Many online platforms are now offering free samples to seasoned reviewers, hoping to get feedback. While these reviewers are given free samples to review, they also buy and review products themselves. The regular ratings for the purchased products are the majority. This brings up the question: Does receiving free products make them rate their personal purchases more positively? And if so, why? We explored two possibilities. First, uncertainty reduction mechanism: The idea that trying free samples makes buyers more confident in their purchases, leading to greater satisfaction and higher ratings for the purchased products; Second, reciprocity mechanism: The idea that reviewers might feel obliged to give better ratings as a “thank you” for the free samples or with the expectations of getting more free samples, which could introduce bias. Our research indicates that giving free samples mainly helps in reducing purchase uncertainty, making customers genuinely happier with their subsequent purchases. So, online platforms can benefit from this strategy, as it seems to uplift genuine positive reviews rather than create biased ones. However, it is still essential to monitor for any undue bias to maintain trustworthiness in reviews.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"105 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135540211","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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