{"title":"人类是队友:人类与人工智能合作的信号可提高消费者对聊天机器人的接受度","authors":"You Li , Yi Li , Qian Chen , Yaping Chang","doi":"10.1016/j.ijinfomgt.2024.102771","DOIUrl":null,"url":null,"abstract":"<div><p>Human–artificial intelligence (AI) teaming is a service system in which AI agents work interdependently toward a common goal alongside human agents. Although many consumer services rely on chatbots working with humans, little is known about the influence of human–AI teaming on consumers’ perceptions and use of chatbots in online service encounters. Using signaling theory, the present research examines whether and how human–AI teaming (vs. independent AI) increases consumer acceptance of chatbots. Through six scenario-based studies and an interview, we found that human–AI teaming can use human capabilities to endorse the effectiveness and authenticity of AI, leading to increased chatbot acceptance. However, this effect was not observed when AI capability was clear or the human service experience was negative. We contribute to information systems research by showing the mechanism and boundary conditions underlying the effect of human–AI teaming on chatbot acceptance. We also provide practical insights for managers emphasizing how human teammates in AI–consumer conversations can increase consumer acceptance of AI.</p></div>","PeriodicalId":48422,"journal":{"name":"International Journal of Information Management","volume":"76 ","pages":"Article 102771"},"PeriodicalIF":20.1000,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Humans as teammates: The signal of human–AI teaming enhances consumer acceptance of chatbots\",\"authors\":\"You Li , Yi Li , Qian Chen , Yaping Chang\",\"doi\":\"10.1016/j.ijinfomgt.2024.102771\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Human–artificial intelligence (AI) teaming is a service system in which AI agents work interdependently toward a common goal alongside human agents. Although many consumer services rely on chatbots working with humans, little is known about the influence of human–AI teaming on consumers’ perceptions and use of chatbots in online service encounters. Using signaling theory, the present research examines whether and how human–AI teaming (vs. independent AI) increases consumer acceptance of chatbots. Through six scenario-based studies and an interview, we found that human–AI teaming can use human capabilities to endorse the effectiveness and authenticity of AI, leading to increased chatbot acceptance. However, this effect was not observed when AI capability was clear or the human service experience was negative. We contribute to information systems research by showing the mechanism and boundary conditions underlying the effect of human–AI teaming on chatbot acceptance. We also provide practical insights for managers emphasizing how human teammates in AI–consumer conversations can increase consumer acceptance of AI.</p></div>\",\"PeriodicalId\":48422,\"journal\":{\"name\":\"International Journal of Information Management\",\"volume\":\"76 \",\"pages\":\"Article 102771\"},\"PeriodicalIF\":20.1000,\"publicationDate\":\"2024-02-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Information Management\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0268401224000197\",\"RegionNum\":1,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"INFORMATION SCIENCE & LIBRARY SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Information Management","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0268401224000197","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
Humans as teammates: The signal of human–AI teaming enhances consumer acceptance of chatbots
Human–artificial intelligence (AI) teaming is a service system in which AI agents work interdependently toward a common goal alongside human agents. Although many consumer services rely on chatbots working with humans, little is known about the influence of human–AI teaming on consumers’ perceptions and use of chatbots in online service encounters. Using signaling theory, the present research examines whether and how human–AI teaming (vs. independent AI) increases consumer acceptance of chatbots. Through six scenario-based studies and an interview, we found that human–AI teaming can use human capabilities to endorse the effectiveness and authenticity of AI, leading to increased chatbot acceptance. However, this effect was not observed when AI capability was clear or the human service experience was negative. We contribute to information systems research by showing the mechanism and boundary conditions underlying the effect of human–AI teaming on chatbot acceptance. We also provide practical insights for managers emphasizing how human teammates in AI–consumer conversations can increase consumer acceptance of AI.
期刊介绍:
The International Journal of Information Management (IJIM) is a distinguished, international, and peer-reviewed journal dedicated to providing its readers with top-notch analysis and discussions within the evolving field of information management. Key features of the journal include:
Comprehensive Coverage:
IJIM keeps readers informed with major papers, reports, and reviews.
Topical Relevance:
The journal remains current and relevant through Viewpoint articles and regular features like Research Notes, Case Studies, and a Reviews section, ensuring readers are updated on contemporary issues.
Focus on Quality:
IJIM prioritizes high-quality papers that address contemporary issues in information management.