Yun-Peng Yuan , Li Liu , Garry Wei-Han Tan , Keng-Boon Ooi
{"title":"消费者对算法的认知和对供应商的信任是否会影响人工智能应用的结构保证?","authors":"Yun-Peng Yuan , Li Liu , Garry Wei-Han Tan , Keng-Boon Ooi","doi":"10.1016/j.tele.2024.102188","DOIUrl":null,"url":null,"abstract":"<div><div>This study aims to understand how perceptions of algorithms and trusting beliefs in service providers facilitate consumers’ perceived structural assurance of using commercial AI applications. The present study adopts a combined approach of partial least squares-structural equation modeling and fuzzy-set qualitative comparative analysis (PLS-SEM-fsQCA) to understand the linear and combined effects of the studied factors on perceived structural assurance with 297 effective responses. The PLS-SEM findings revealed that algorithmic perceptions (i.e., Fairness, Accountability, and Transparency) and trusting beliefs (i.e., Benevolence, Competence, and Integrity) were positively associated with Perceived Structural Assurance. The fsQCA findings indicate four configural solutions of causal conditions that explain Perceived Structural Assurance, and each solution reflects a particular type of consumers who have unique considerations when assessing commercial AI’s structural assurance. This study adds to consumer behavior studies by introducing consumers’ perceptions of algorithms and trusting beliefs in evaluating their structural assurances in commercial AI applications from linear and complexity perspectives.</div></div>","PeriodicalId":48257,"journal":{"name":"Telematics and Informatics","volume":"94 ","pages":"Article 102188"},"PeriodicalIF":7.6000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Do consumers’ perceptions of algorithms and trusting beliefs in providers affect perceived structural assurances of AI-powered applications?\",\"authors\":\"Yun-Peng Yuan , Li Liu , Garry Wei-Han Tan , Keng-Boon Ooi\",\"doi\":\"10.1016/j.tele.2024.102188\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This study aims to understand how perceptions of algorithms and trusting beliefs in service providers facilitate consumers’ perceived structural assurance of using commercial AI applications. The present study adopts a combined approach of partial least squares-structural equation modeling and fuzzy-set qualitative comparative analysis (PLS-SEM-fsQCA) to understand the linear and combined effects of the studied factors on perceived structural assurance with 297 effective responses. The PLS-SEM findings revealed that algorithmic perceptions (i.e., Fairness, Accountability, and Transparency) and trusting beliefs (i.e., Benevolence, Competence, and Integrity) were positively associated with Perceived Structural Assurance. The fsQCA findings indicate four configural solutions of causal conditions that explain Perceived Structural Assurance, and each solution reflects a particular type of consumers who have unique considerations when assessing commercial AI’s structural assurance. This study adds to consumer behavior studies by introducing consumers’ perceptions of algorithms and trusting beliefs in evaluating their structural assurances in commercial AI applications from linear and complexity perspectives.</div></div>\",\"PeriodicalId\":48257,\"journal\":{\"name\":\"Telematics and Informatics\",\"volume\":\"94 \",\"pages\":\"Article 102188\"},\"PeriodicalIF\":7.6000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Telematics and Informatics\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0736585324000923\",\"RegionNum\":2,\"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":"Telematics and Informatics","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0736585324000923","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
Do consumers’ perceptions of algorithms and trusting beliefs in providers affect perceived structural assurances of AI-powered applications?
This study aims to understand how perceptions of algorithms and trusting beliefs in service providers facilitate consumers’ perceived structural assurance of using commercial AI applications. The present study adopts a combined approach of partial least squares-structural equation modeling and fuzzy-set qualitative comparative analysis (PLS-SEM-fsQCA) to understand the linear and combined effects of the studied factors on perceived structural assurance with 297 effective responses. The PLS-SEM findings revealed that algorithmic perceptions (i.e., Fairness, Accountability, and Transparency) and trusting beliefs (i.e., Benevolence, Competence, and Integrity) were positively associated with Perceived Structural Assurance. The fsQCA findings indicate four configural solutions of causal conditions that explain Perceived Structural Assurance, and each solution reflects a particular type of consumers who have unique considerations when assessing commercial AI’s structural assurance. This study adds to consumer behavior studies by introducing consumers’ perceptions of algorithms and trusting beliefs in evaluating their structural assurances in commercial AI applications from linear and complexity perspectives.
期刊介绍:
Telematics and Informatics is an interdisciplinary journal that publishes cutting-edge theoretical and methodological research exploring the social, economic, geographic, political, and cultural impacts of digital technologies. It covers various application areas, such as smart cities, sensors, information fusion, digital society, IoT, cyber-physical technologies, privacy, knowledge management, distributed work, emergency response, mobile communications, health informatics, social media's psychosocial effects, ICT for sustainable development, blockchain, e-commerce, and e-government.