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Understanding users’ acceptance of smart homes

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  • Shuhaiber, Ahmed
  • Mashal, Ibrahim

Abstract

Smart homes allow owners to monitor and control wide range of home appliances remotely and intelligently. Despite its potential, the acceptance of smart homes by residents is still far from expectations. Thus, this study aims to investigate the factors that influence residents’ acceptance and usage of smart home. By extending the Technology Acceptance Model (TAM), this research incorporates more factors related to users such as trust, awareness, enjoyment, and perceived risks to study intention to use smart homes and investigate their impact quantitatively by using SEM-PLS approach. Results show that trust, awareness, enjoyment, and perceived risks, with perceived usefulness and perceived ease of use significantly influence attitude towards smart homes which, in turn, impact the intention to use smart homes.

Suggested Citation

  • Shuhaiber, Ahmed & Mashal, Ibrahim, 2019. "Understanding users’ acceptance of smart homes," Technology in Society, Elsevier, vol. 58(C).
    • Handle: RePEc:eee:teinso:v:58:y:2019:i:c:s0160791x18300484
    DOI: 10.1016/j.techsoc.2019.01.003
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    References listed on IDEAS

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    1. Gefen, David, 2000. "E-commerce: the role of familiarity and trust," Omega, Elsevier, vol. 28(6), pages 725-737, December.
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    Cited by:

    1. Ronaghi, Mohammad Hossein & Forouharfar, Amir, 2020. "A contextualized study of the usage of the Internet of things (IoTs) in smart farming in a typical Middle Eastern country within the context of Unified Theory of Acceptance and Use of Technology model," Technology in Society, Elsevier, vol. 63(C).
    2. Pal, Debajyoti & Zhang, Xiangmin & Siyal, Saeed, 2021. "Prohibitive factors to the acceptance of Internet of Things (IoT) technology in society: A smart-home context using a resistive modelling approach," Technology in Society, Elsevier, vol. 66(C).
    3. Gansser, Oliver Alexander & Reich, Christina Stefanie, 2021. "A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application," Technology in Society, Elsevier, vol. 65(C).
    4. Maras, Marie-Helen & Miranda, Michelle D., 2023. "Augmented body surveillance: Human microchip implantations and the omnipresent threat of function creep," Technology in Society, Elsevier, vol. 74(C).
    5. Weiss, Daniel & Nemeczek, Fabian, 2021. "A text-based monitoring tool for the legitimacy and guidance of technological innovation systems," Technology in Society, Elsevier, vol. 66(C).
    6. Kasilingam, Dharun Lingam, 2020. "Understanding the attitude and intention to use smartphone chatbots for shopping," Technology in Society, Elsevier, vol. 62(C).
    7. Ho, Manh-Tung & Le, Ngoc-Thang B. & Mantello, Peter & Ho, Manh-Toan & Ghotbi, Nader, 2023. "Understanding the acceptance of emotional artificial intelligence in Japanese healthcare system: A cross-sectional survey of clinic visitors’ attitude," Technology in Society, Elsevier, vol. 72(C).
    8. Furszyfer Del Rio, D.D., 2022. "Smart but unfriendly: Connected home products as enablers of conflict," Technology in Society, Elsevier, vol. 68(C).
    9. Lim, Joon Soo & Zhang, Jun, 2022. "Adoption of AI-driven personalization in digital news platforms: An integrative model of technology acceptance and perceived contingency," Technology in Society, Elsevier, vol. 69(C).
    10. Mashal, Ibrahim & Alsaryrah, Osama & Chung, Tein-Yaw & Yuan, Fong-Ching, 2020. "A multi-criteria analysis for an internet of things application recommendation system," Technology in Society, Elsevier, vol. 60(C).
    11. Aggarwal, Nitin & Albert, Leslie J. & Hill, Timothy R. & Rodan, Simon A., 2020. "Risk knowledge and concern as influences of purchase intention for internet of things devices," Technology in Society, Elsevier, vol. 62(C).
    12. Nilashi, Mehrbakhsh & Abumalloh, Rabab Ali & Samad, Sarminah & Alrizq, Mesfer & Alyami, Sultan & Abosaq, Hamad & Alghamdi, Abdullah & Akib, Noor Adelyna Mohammed, 2022. "Factors impacting customer purchase intention of smart home security systems: Social data analysis using machine learning techniques," Technology in Society, Elsevier, vol. 71(C).
    13. Yoon, Sungjoon & Oh, Jongchul, 2022. "A theory-based approach to the usability of augmented reality technology: A cost-benefit perspective," Technology in Society, Elsevier, vol. 68(C).
    14. Hoffmann, Stefan & Lasarov, Wassili & Reimers, Hanna, 2022. "Carbon footprint tracking apps. What drives consumers' adoption intention?," Technology in Society, Elsevier, vol. 69(C).

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