Debarun Chakraborty , Arpan Kumar Kar , Smruti Patre , Shivam Gupta
{"title":"通过生成式人工智能聊天机器人提高在线杂货购物的信任度","authors":"Debarun Chakraborty , Arpan Kumar Kar , Smruti Patre , Shivam Gupta","doi":"10.1016/j.jbusres.2024.114737","DOIUrl":null,"url":null,"abstract":"<div><p>Generative Artificial Intelligence (GAI) is witnessing a lot of adoption across industries, but literature is yet to fully document the nuances of these applications. We develop a comprehensive framework for understanding the factors that affect trust in online grocery shopping (OGS) using GAI chatbots. Our exploratory study was conducted via interviews, which helped to build our model. We integrate the Elaboration Likelihood Model (ELM) and Status Quo Bias (SQB) theory to develop the Unified Framework for Trust on Technology Platforms. In our confirmatory study, by analyzing 372 responses from users, using structural equation modelling (SEM), we initially validate our path model. Subsequently, we used fuzzy set qualitative comparative analysis (fsQCA) to check the causal combinations to explain different trust levels. Apart from perceived regret avoidance, all of the other factors had a significant effect on attitude and trust. Perceived anthropomorphism moderated the associations between interaction quality, credibility, threat, and attitude.</p></div>","PeriodicalId":15123,"journal":{"name":"Journal of Business Research","volume":null,"pages":null},"PeriodicalIF":10.5000,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0148296324002418/pdfft?md5=77312a69411a30898fc5c76dea5934fc&pid=1-s2.0-S0148296324002418-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Enhancing trust in online grocery shopping through generative AI chatbots\",\"authors\":\"Debarun Chakraborty , Arpan Kumar Kar , Smruti Patre , Shivam Gupta\",\"doi\":\"10.1016/j.jbusres.2024.114737\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Generative Artificial Intelligence (GAI) is witnessing a lot of adoption across industries, but literature is yet to fully document the nuances of these applications. We develop a comprehensive framework for understanding the factors that affect trust in online grocery shopping (OGS) using GAI chatbots. Our exploratory study was conducted via interviews, which helped to build our model. We integrate the Elaboration Likelihood Model (ELM) and Status Quo Bias (SQB) theory to develop the Unified Framework for Trust on Technology Platforms. In our confirmatory study, by analyzing 372 responses from users, using structural equation modelling (SEM), we initially validate our path model. Subsequently, we used fuzzy set qualitative comparative analysis (fsQCA) to check the causal combinations to explain different trust levels. Apart from perceived regret avoidance, all of the other factors had a significant effect on attitude and trust. Perceived anthropomorphism moderated the associations between interaction quality, credibility, threat, and attitude.</p></div>\",\"PeriodicalId\":15123,\"journal\":{\"name\":\"Journal of Business Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":10.5000,\"publicationDate\":\"2024-05-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0148296324002418/pdfft?md5=77312a69411a30898fc5c76dea5934fc&pid=1-s2.0-S0148296324002418-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Business Research\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0148296324002418\",\"RegionNum\":1,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Business Research","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0148296324002418","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
摘要
生成式人工智能(GAI)在各行各业得到广泛应用,但文献尚未充分记录这些应用的细微差别。我们利用 GAI 聊天机器人开发了一个综合框架,用于了解影响在线杂货购物(OGS)信任度的因素。我们的探索性研究是通过访谈进行的,这有助于建立我们的模型。我们整合了阐释可能性模型(ELM)和现状偏差(SQB)理论,建立了技术平台信任统一框架。在确认性研究中,我们使用结构方程模型(SEM)分析了 372 份用户回复,初步验证了我们的路径模型。随后,我们使用模糊集定性比较分析(fsQCA)来检验解释不同信任水平的因果组合。除了感知到的避免后悔之外,其他因素都对态度和信任有显著影响。感知拟人化调节了互动质量、可信度、威胁和态度之间的关联。
Enhancing trust in online grocery shopping through generative AI chatbots
Generative Artificial Intelligence (GAI) is witnessing a lot of adoption across industries, but literature is yet to fully document the nuances of these applications. We develop a comprehensive framework for understanding the factors that affect trust in online grocery shopping (OGS) using GAI chatbots. Our exploratory study was conducted via interviews, which helped to build our model. We integrate the Elaboration Likelihood Model (ELM) and Status Quo Bias (SQB) theory to develop the Unified Framework for Trust on Technology Platforms. In our confirmatory study, by analyzing 372 responses from users, using structural equation modelling (SEM), we initially validate our path model. Subsequently, we used fuzzy set qualitative comparative analysis (fsQCA) to check the causal combinations to explain different trust levels. Apart from perceived regret avoidance, all of the other factors had a significant effect on attitude and trust. Perceived anthropomorphism moderated the associations between interaction quality, credibility, threat, and attitude.
期刊介绍:
The Journal of Business Research aims to publish research that is rigorous, relevant, and potentially impactful. It examines a wide variety of business decision contexts, processes, and activities, developing insights that are meaningful for theory, practice, and/or society at large. The research is intended to generate meaningful debates in academia and practice, that are thought provoking and have the potential to make a difference to conceptual thinking and/or practice. The Journal is published for a broad range of stakeholders, including scholars, researchers, executives, and policy makers. It aids the application of its research to practical situations and theoretical findings to the reality of the business world as well as to society. The Journal is abstracted and indexed in several databases, including Social Sciences Citation Index, ANBAR, Current Contents, Management Contents, Management Literature in Brief, PsycINFO, Information Service, RePEc, Academic Journal Guide, ABI/Inform, INSPEC, etc.