Piotr Gaczek, R. Pozharliev, G. Leszczyński, Marek Zielinski
{"title":"在普通医疗保健中克服消费者对人工智能的抵制","authors":"Piotr Gaczek, R. Pozharliev, G. Leszczyński, Marek Zielinski","doi":"10.1177/10949968221151061","DOIUrl":null,"url":null,"abstract":"Artificial intelligence (AI) in medicine offers a unique opportunity to improve the global health system. However, consumers remain skeptical about AI's ability to accurately assess their medical condition. The five studies here provide insights into consumers’ reluctance to use AI-produced health care recommendations. Consumers are less willing to follow a medical recommendation from AI (vs. from a human) when the medical diagnosis provides health results that are good (i.e., symptoms do not require medical care) versus bad (i.e., symptoms are worrisome and may require urgent care) (Study 1a). The effect is mediated by consumers’ perception of diagnosis trustworthiness (Study 1b) and enhanced by consumers’ health anxiety score (Study 2). Providing social proof (e.g., number of satisfied customers recommending the service) reduces the negative effect of health anxiety on consumers’ trust in the medical diagnosis and increases their willingness to follow the AI's recommendations (Study 3a). The findings provide insights into the psychological drivers of acceptance of automated health care and suggest possible actions to overcome consumers’ reluctance to follow AI medical recommendations.","PeriodicalId":48260,"journal":{"name":"Journal of Interactive Marketing","volume":"58 1","pages":"321 - 338"},"PeriodicalIF":6.8000,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Overcoming Consumer Resistance to AI in General Health Care\",\"authors\":\"Piotr Gaczek, R. Pozharliev, G. Leszczyński, Marek Zielinski\",\"doi\":\"10.1177/10949968221151061\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Artificial intelligence (AI) in medicine offers a unique opportunity to improve the global health system. However, consumers remain skeptical about AI's ability to accurately assess their medical condition. The five studies here provide insights into consumers’ reluctance to use AI-produced health care recommendations. Consumers are less willing to follow a medical recommendation from AI (vs. from a human) when the medical diagnosis provides health results that are good (i.e., symptoms do not require medical care) versus bad (i.e., symptoms are worrisome and may require urgent care) (Study 1a). The effect is mediated by consumers’ perception of diagnosis trustworthiness (Study 1b) and enhanced by consumers’ health anxiety score (Study 2). Providing social proof (e.g., number of satisfied customers recommending the service) reduces the negative effect of health anxiety on consumers’ trust in the medical diagnosis and increases their willingness to follow the AI's recommendations (Study 3a). The findings provide insights into the psychological drivers of acceptance of automated health care and suggest possible actions to overcome consumers’ reluctance to follow AI medical recommendations.\",\"PeriodicalId\":48260,\"journal\":{\"name\":\"Journal of Interactive Marketing\",\"volume\":\"58 1\",\"pages\":\"321 - 338\"},\"PeriodicalIF\":6.8000,\"publicationDate\":\"2023-02-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Interactive Marketing\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1177/10949968221151061\",\"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 Interactive Marketing","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1177/10949968221151061","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
Overcoming Consumer Resistance to AI in General Health Care
Artificial intelligence (AI) in medicine offers a unique opportunity to improve the global health system. However, consumers remain skeptical about AI's ability to accurately assess their medical condition. The five studies here provide insights into consumers’ reluctance to use AI-produced health care recommendations. Consumers are less willing to follow a medical recommendation from AI (vs. from a human) when the medical diagnosis provides health results that are good (i.e., symptoms do not require medical care) versus bad (i.e., symptoms are worrisome and may require urgent care) (Study 1a). The effect is mediated by consumers’ perception of diagnosis trustworthiness (Study 1b) and enhanced by consumers’ health anxiety score (Study 2). Providing social proof (e.g., number of satisfied customers recommending the service) reduces the negative effect of health anxiety on consumers’ trust in the medical diagnosis and increases their willingness to follow the AI's recommendations (Study 3a). The findings provide insights into the psychological drivers of acceptance of automated health care and suggest possible actions to overcome consumers’ reluctance to follow AI medical recommendations.
期刊介绍:
The Journal of Interactive Marketing aims to explore and discuss issues in the dynamic field of interactive marketing, encompassing both online and offline topics related to analyzing, targeting, and serving individual customers. The journal seeks to publish innovative, high-quality research that presents original results, methodologies, theories, and applications in interactive marketing. Manuscripts should address current or emerging managerial challenges and have the potential to influence both practice and theory in the field. The journal welcomes conceptually rigorous approaches of any type and does not favor or exclude specific methodologies.