{"title":"Artificial intelligence or human: when and why consumers prefer AI recommendations","authors":"Fei Jin, Xiaodan Zhang","doi":"10.1108/itp-01-2023-0022","DOIUrl":null,"url":null,"abstract":"Purpose Artificial intelligence (AI) is revolutionizing product recommendations, but little is known about consumer acceptance of AI recommendations. This study examines how to improve consumers' acceptance of AI recommendations from the perspective of product type (material vs experiential). Design/methodology/approach Four studies, including a field experiment and three online experiments, tested how consumers' preference for AI-based (vs human) recommendations differs between material and experiential product purchases. Findings Results show that people perceive AI recommendations as more competent than human recommendations for material products, whereas they believe human recommendations are more competent than AI recommendations for experiential products. Therefore, people are more (less) likely to choose AI recommendations when buying material (vs experiential) products. However, this effect is eliminated when is used as an assistant to rather than a replacement for a human recommendation. Originality/value This study is the first to focus on how products' material and experiential attributes influence people's attitudes toward AI recommendations. The authors also identify under what circumstances resistance to algorithmic advice is attenuated. These findings contribute to the research on the psychology of artificial intelligence and on human–technology interaction by investigating how experiential and material attributes influence preference for or resistance to AI recommenders.","PeriodicalId":47740,"journal":{"name":"Information Technology & People","volume":"168 1","pages":"0"},"PeriodicalIF":4.9000,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Technology & People","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/itp-01-2023-0022","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose Artificial intelligence (AI) is revolutionizing product recommendations, but little is known about consumer acceptance of AI recommendations. This study examines how to improve consumers' acceptance of AI recommendations from the perspective of product type (material vs experiential). Design/methodology/approach Four studies, including a field experiment and three online experiments, tested how consumers' preference for AI-based (vs human) recommendations differs between material and experiential product purchases. Findings Results show that people perceive AI recommendations as more competent than human recommendations for material products, whereas they believe human recommendations are more competent than AI recommendations for experiential products. Therefore, people are more (less) likely to choose AI recommendations when buying material (vs experiential) products. However, this effect is eliminated when is used as an assistant to rather than a replacement for a human recommendation. Originality/value This study is the first to focus on how products' material and experiential attributes influence people's attitudes toward AI recommendations. The authors also identify under what circumstances resistance to algorithmic advice is attenuated. These findings contribute to the research on the psychology of artificial intelligence and on human–technology interaction by investigating how experiential and material attributes influence preference for or resistance to AI recommenders.
期刊介绍:
Information Technology & People publishes work that is dedicated to understanding the implications of information technology as a tool, resource and format for people in their daily work in organizations. Impact on performance is part of this, since it is essential to the well being of employees and organizations alike. Contributions to the journal include case studies, comparative theory, and quantitative research, as well as inquiries into systems development methods and practice.