Sepideh Ebrahimi, Maryam Ghasemaghaei, I. Benbasat
{"title":"信任和推荐质量对采用交互式和非交互式推荐代理的影响:Meta分析","authors":"Sepideh Ebrahimi, Maryam Ghasemaghaei, I. Benbasat","doi":"10.1080/07421222.2022.2096549","DOIUrl":null,"url":null,"abstract":"ABSTRACT Research on recommendation agents (RAs) originally focused on interactive RAs, which rely on explicit methods, i.e., eliciting user-provided inputs to learn about consumers’ needs and preferences. Recently, due to the availability of large amounts of data about individuals, the focus shifted toward non-interactive RAs that use implicit methods rather than explicit ones to understand users’ needs. This paper examined the differences between interactive and non-interactive RA types in terms of how they influence the impacts of two important antecedents of RA adoption, namely recommendation quality and trust on users’ cognitive and affective attitudes and behavioral intention. To that end, we developed a set of hypotheses and tested them empirically using a meta-analytic structural equation modeling approach. Our findings provide strong support for the influence of interactivity on RA users’ attitudes and cognitions. While we found that recommendation quality exerts a strong influence on consumers’ cognitive attitudes toward interactive RAs, this influence is statistically non-significant in the context of non-interactive RAs, in which recommendation quality mainly drives consumers’ affective attitudes toward the agent. Furthermore, while we found that cognitive attitudes exert a stronger influence than affective ones on consumers’ adoption of non-interactive RAs, our results indicate that the reverse is true with interactive RAs. Given the recent rise in the popularity of non-interactive RA tools, our results carry important implications for researchers and practitioners. Specifically, this study contributes to the extensive literature on consumers’ use of RAs by providing a better understanding of the differences between interactive and non-interactive RAs. For practitioners, the findings provide guidance for designers and providers of RAs on developing and improving RAs that are more likely to be adopted by consumers.","PeriodicalId":50154,"journal":{"name":"Journal of Management Information Systems","volume":"39 1","pages":"733 - 764"},"PeriodicalIF":5.9000,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"The Impact of Trust and Recommendation Quality on Adopting Interactive and Non-Interactive Recommendation Agents: A Meta-Analysis\",\"authors\":\"Sepideh Ebrahimi, Maryam Ghasemaghaei, I. 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Our findings provide strong support for the influence of interactivity on RA users’ attitudes and cognitions. While we found that recommendation quality exerts a strong influence on consumers’ cognitive attitudes toward interactive RAs, this influence is statistically non-significant in the context of non-interactive RAs, in which recommendation quality mainly drives consumers’ affective attitudes toward the agent. Furthermore, while we found that cognitive attitudes exert a stronger influence than affective ones on consumers’ adoption of non-interactive RAs, our results indicate that the reverse is true with interactive RAs. Given the recent rise in the popularity of non-interactive RA tools, our results carry important implications for researchers and practitioners. Specifically, this study contributes to the extensive literature on consumers’ use of RAs by providing a better understanding of the differences between interactive and non-interactive RAs. 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The Impact of Trust and Recommendation Quality on Adopting Interactive and Non-Interactive Recommendation Agents: A Meta-Analysis
ABSTRACT Research on recommendation agents (RAs) originally focused on interactive RAs, which rely on explicit methods, i.e., eliciting user-provided inputs to learn about consumers’ needs and preferences. Recently, due to the availability of large amounts of data about individuals, the focus shifted toward non-interactive RAs that use implicit methods rather than explicit ones to understand users’ needs. This paper examined the differences between interactive and non-interactive RA types in terms of how they influence the impacts of two important antecedents of RA adoption, namely recommendation quality and trust on users’ cognitive and affective attitudes and behavioral intention. To that end, we developed a set of hypotheses and tested them empirically using a meta-analytic structural equation modeling approach. Our findings provide strong support for the influence of interactivity on RA users’ attitudes and cognitions. While we found that recommendation quality exerts a strong influence on consumers’ cognitive attitudes toward interactive RAs, this influence is statistically non-significant in the context of non-interactive RAs, in which recommendation quality mainly drives consumers’ affective attitudes toward the agent. Furthermore, while we found that cognitive attitudes exert a stronger influence than affective ones on consumers’ adoption of non-interactive RAs, our results indicate that the reverse is true with interactive RAs. Given the recent rise in the popularity of non-interactive RA tools, our results carry important implications for researchers and practitioners. Specifically, this study contributes to the extensive literature on consumers’ use of RAs by providing a better understanding of the differences between interactive and non-interactive RAs. For practitioners, the findings provide guidance for designers and providers of RAs on developing and improving RAs that are more likely to be adopted by consumers.
期刊介绍:
Journal of Management Information Systems is a widely recognized forum for the presentation of research that advances the practice and understanding of organizational information systems. It serves those investigating new modes of information delivery and the changing landscape of information policy making, as well as practitioners and executives managing the information resource.