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A new health care system enabled by machine intelligence: Elderly people's trust or losing self control

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  • Shareef, Mahmud Akhter
  • Kumar, Vinod
  • Dwivedi, Yogesh K.
  • Kumar, Uma
  • Akram, Muhammad Shakaib
  • Raman, Ramakrishnan

Abstract

An autonomous homecare system can ensure independence for elderly people and increase cooperation, social interaction, and adaptation. Widespread diffusion and inclusion of information and communication technology (ICT) in modern equipment has advanced the concept of machine autonomy. This study seeks to understand the effect of trust required for elderly people to accept autonomous homecare systems instead of human support. It attempts to reveal how trust and personal characteristics can increase intent to adopt an autonomous system. In this regard, different trust models and literature on human psychology to adopt ICT driven system are explored and analyzed to develop a parsimonious trust disposition model for autonomous system. The study was conducted among elderly and disabled people in retirement homes and rehabilitation centers of different major cities of Ontario, Canada through random sampling by employing both experiment and survey. In the first phase, a structured and designed experiment was conducted in three retirement homes which included 159 elderly people and in two rehabilitation centers which included 20 disabled people. Then a survey-based empirical study was conducted among the same people with the structured questionnaire to answer the questions based on their perceptions of both receiving service from human beings and viewing the video about an autonomous system governed by ambient intelligence. This study reveals that elderly people can be motivated to develop trust in this less-familiar system if they both believe they can operate it and find a sense of belongingness and feelings of social interactivity from this seemingly living machine system. The findings of this study provide clear direction for academics and practitioners. This research indicates that elderly people must have control on using any system in the absence of human support while maintaining a more solitary life in a retirement home. However, the system must be easy to learn and operate. It should also be designed in such a way that can create and impart social feelings.

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  • Shareef, Mahmud Akhter & Kumar, Vinod & Dwivedi, Yogesh K. & Kumar, Uma & Akram, Muhammad Shakaib & Raman, Ramakrishnan, 2021. "A new health care system enabled by machine intelligence: Elderly people's trust or losing self control," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    • Handle: RePEc:eee:tefoso:v:162:y:2021:i:c:s0040162520311604
    DOI: 10.1016/j.techfore.2020.120334
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    References listed on IDEAS

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    10. Olatunji A. Shobande & Lawrence Ogbeifun & Simplice A. Asongu, 2022. "Globalisation, technology and global health," Working Papers of the African Governance and Development Institute. 22/070, African Governance and Development Institute..
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    14. Suen, Hung-Yue & Hung, Kuo-En, 2024. "Revealing the influence of AI and its interfaces on job candidates' honest and deceptive impression management in asynchronous video interviews," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    15. Balakrishnan, Janarthanan & Abed, Salma S. & Jones, Paul, 2022. "The role of meta-UTAUT factors, perceived anthropomorphism, perceived intelligence, and social self-efficacy in chatbot-based services?," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    16. Văidean, Viorela Ligia & Achim, Monica Violeta, 2022. "When more is less: Do information and communication technologies (ICTs) improve health outcomes? An empirical investigation in a non-linear framework," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    17. Badghish, Saeed & Shaik, Aqueeb Sohail & Sahore, Nidhi & Srivastava, Shalini & Masood, Ayesha, 2024. "Can transactional use of AI-controlled voice assistants for service delivery pickup pace in the near future? A social learning theory (SLT) perspective," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    18. Xing, Fei & Peng, Guochao & Zhang, Bingqian & Li, Shuyang & Liang, Xinting, 2021. "Socio-technical barriers affecting large-scale deployment of AI-enabled wearable medical devices among the ageing population in China," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    19. Alina Betlej & Jan Gondek & Natalia Gondek, 2023. "Ageing and Keeping Pace with Technology: A Grounded Theory Study on Blind Adults’ Experiences of Adapting to New Technologies," IJERPH, MDPI, vol. 20(3), pages 1-19, January.
    20. Rijswijk, Kelly & de Vries, Jasper R. & Klerkx, Laurens & Turner, James A., 2023. "The enabling and constraining connections between trust and digitalisation in incumbent value chains," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    21. Richet, Jean-Loup, 2022. "How cybercriminal communities grow and change: An investigation of ad-fraud communities," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    22. Liu, Xiaohui & He, Xiaoyu & Wang, Mengmeng & Shen, Huizhang, 2022. "What influences patients' continuance intention to use AI-powered service robots at hospitals? The role of individual characteristics," Technology in Society, Elsevier, vol. 70(C).

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