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The effect of medical artificial intelligence innovation locus on consumer adoption of new products

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  • Huang, Xiaozhi
  • Wu, Xitong
  • Cao, Xin
  • Wu, Jifei

Abstract

The purpose of this study is to explore the effect of innovation locus on consumers' adoption of artificial intelligence products in healthcare. Across four experiments, we demonstrate that consumers are more likely to accept medical artificial intelligence for peripheral products than for core products. This effect is mediated by perceived risk and moderated by a tight-loose culture. Specifically, perceived risk significantly mediates the effect of innovation loci on consumers' adoption of medical artificial intelligence. Loose and tight culture moderates the direct and mediating effects. These findings are important theoretical contributions to the artificial intelligence and healthcare literature. We also provide some practical implications to promote the future development of medical artificial intelligence products and to improve consumers' acceptance of medical artificial intelligence.

Suggested Citation

  • Huang, Xiaozhi & Wu, Xitong & Cao, Xin & Wu, Jifei, 2023. "The effect of medical artificial intelligence innovation locus on consumer adoption of new products," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
  • Handle: RePEc:eee:tefoso:v:197:y:2023:i:c:s0040162523005875
    DOI: 10.1016/j.techfore.2023.122902
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    as
    1. Chiara Longoni & Andrea Bonezzi & Carey K Morewedge, 2019. "Resistance to Medical Artificial Intelligence," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 46(4), pages 629-650.
    2. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Correction: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 546(7660), pages 686-686, June.
    3. Gatignon, Hubert & Robertson, Thomas S, 1985. "A Propositional Inventory for New Diffusion Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 11(4), pages 849-867, March.
    4. Victoria A. Shaffer & C. Adam Probst & Edgar C. Merkle & Hal R. Arkes & Mitchell A. Medow, 2013. "Why Do Patients Derogate Physicians Who Use a Computer-Based Diagnostic Support System?," Medical Decision Making, , vol. 33(1), pages 108-118, January.
    5. J Jeffrey Inman & Margaret C Campbell & Amna Kirmani & Linda L Price, 2018. "Our Vision for the Journal of Consumer Research: It’s All about the Consumer," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 44(5), pages 955-959.
    6. Hubert Gatignon & Michael L. Tushman & Wendy Smith & Philip Anderson, 2002. "A Structural Approach to Assessing Innovation: Construct Development of Innovation Locus, Type, and Characteristics," Management Science, INFORMS, vol. 48(9), pages 1103-1122, September.
    7. Simona Botti & Kristina Orfali & Sheena S. Iyengar, 2009. "Tragic Choices: Autonomy and Emotional Responses to Medical Decisions," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 36(3), pages 337-352.
    8. DonHee Lee & Seong No Yoon, 2021. "Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges," IJERPH, MDPI, vol. 18(1), pages 1-18, January.
    9. Sancy A. Leachman & Glenn Merlino, 2017. "The final frontier in cancer diagnosis," Nature, Nature, vol. 542(7639), pages 36-38, February.
    10. Roehrich, Gilles, 2004. "Consumer innovativeness: Concepts and measurements," Journal of Business Research, Elsevier, vol. 57(6), pages 671-677, June.
    11. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
    12. Purva Grover & Arpan Kumar Kar & Yogesh K. Dwivedi, 2022. "Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions," Annals of Operations Research, Springer, vol. 308(1), pages 177-213, January.
    13. Romain Cadario & Chiara Longoni & Carey K. Morewedge, 2021. "Understanding, explaining, and utilizing medical artificial intelligence," Nature Human Behaviour, Nature, vol. 5(12), pages 1636-1642, December.
    14. Wang, Liwen & Jin, Jason Lu & Zhou, Kevin Zheng & Li, Caroline Bingxin & Yin, Eden, 2020. "Does customer participation hurt new product development performance? Customer role, product newness, and conflict," Journal of Business Research, Elsevier, vol. 109(C), pages 246-259.
    15. Tanvi Gupta & Henrik Hagtvedt & Margaret C Campbell & Chris Janiszewski, 2021. "Safe Together, Vulnerable Apart: How Interstitial Space in Text Logos Impacts Brand Attitudes in Tight versus Loose Cultures," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 48(3), pages 474-491.
    16. Dowling, Grahame R & Staelin, Richard, 1994. "A Model of Perceived Risk and Intended Risk-Handling Activity," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 21(1), pages 119-134, June.
    17. Logg, Jennifer M. & Minson, Julia A. & Moore, Don A., 2019. "Algorithm appreciation: People prefer algorithmic to human judgment," Organizational Behavior and Human Decision Processes, Elsevier, vol. 151(C), pages 90-103.
    18. Kwok Leung & Michael W Morris, 2015. "Values, schemas, and norms in the culture–behavior nexus: A situated dynamics framework," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 46(9), pages 1028-1050, December.
    19. Ulf Böckenholt & Elke U. Weber, 1992. "Use of formal Methods in Medical Decision Making," Medical Decision Making, , vol. 12(4), pages 298-306, December.
    20. Du, Shuili & Xie, Chunyan, 2021. "Paradoxes of artificial intelligence in consumer markets: Ethical challenges and opportunities," Journal of Business Research, Elsevier, vol. 129(C), pages 961-974.
    21. Sridhar Balasubramanian & Prabhudev Konana & Nirup M. Menon, 2003. "Customer Satisfaction in Virtual Environments: A Study of Online Investing," Management Science, INFORMS, vol. 49(7), pages 871-889, July.
    22. Sheehan, Ben & Jin, Hyun Seung & Gottlieb, Udo, 2020. "Customer service chatbots: Anthropomorphism and adoption," Journal of Business Research, Elsevier, vol. 115(C), pages 14-24.
    23. Robert J. Meyer & Shenghui Zhao & Jin K. Han, 2008. "Biases in Valuation vs. Usage of Innovative Product Features," Marketing Science, INFORMS, vol. 27(6), pages 1083-1096, 11-12.
    24. Forsythe, Sandra M. & Shi, Bo, 2003. "Consumer patronage and risk perceptions in Internet shopping," Journal of Business Research, Elsevier, vol. 56(11), pages 867-875, November.
    25. Millan, Elena & Reynolds, Jonathan, 2014. "Self-construals, symbolic and hedonic preferences, and actual purchase behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 21(4), pages 550-560.
    26. Hirschman, Elizabeth C, 1980. "Innovativeness, Novelty Seeking, and Consumer Creativity," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 7(3), pages 283-295, December.
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