The effects of technology readiness, risks, and benefits on smart home technology adoption: extending the Theory of Planned Behavior model

References

• Accenture. (2014). The Internet of things: The future of consumer adoption. Accenture Interactive – Point of View Series. https://www.accenture.com/t20150624T211456__w__/us-en/_acnmedia/Accenture/Conversion-Assets/DotCom/Documents/Global/PDF/Technology_9/Accenture-Internet-Things.pdf
   Google Scholar
• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
   Web of Science ®Google Scholar
• Arfi, W. B., Nasr, I. B., Khvatova, T., & Zaied, Y. B. (2021). Understanding acceptance of eHealthcare by IoT natives and IoT immigrants: An integrated model of UTAUT, perceived risk, and financial cost. Technological Forecasting and Social Change, 163, 120437. https://doi.org/10.1016/j.techfore.2020.120437
   Web of Science ®Google Scholar
• Argus Insights. (2015). Consumer demand for connected home products slows dramatically in first half of 2015 and continues rapid drop off. Argus Insights. https://www.argusinsights.com/connected-home-release/
   Google Scholar
• Balta-Ozkan, N., Davidson, R., Bicket, M., & Whitmarsh, L. (2013). Social barriers to the adoption of smart homes. Energy Policy, 63, 363–374. https://doi.org/10.1016/j.enpol.2013.08.043
   Web of Science ®Google Scholar
• Brous, P., Janssen, M., & Herder, P. (2020). The dual effects of the Internet of Things (IoT): A systematic review of the benefits and risks of IoT adoption by organizations. International Journal of Information Management, 51, 101952. https://doi.org/10.1016/j.ijinfomgt.2019.05.008
   Web of Science ®Google Scholar
• Census and Statistics Department. (2021). Table 1B: Population by sex and age. Demographic Statistics Section. https://www.censtatd.gov.hk/en/web_table.html?id=1B#
   Google Scholar
• Chatterjee, S. (2021). Antecedence of attitude towards IoT usage: A proposed unified model for IT Professionals and its validation. International Journal of Human Capital and Information Technology Professionals, 12(2), 13–34. https://doi.org/10.4018/IJHCITP.2021040102
   Web of Science ®Google Scholar
• Chen, S. C., & Li, S. H. (2010). Consumer adoption of e-service: Integrating technology readiness with the theory of planned behavior. African Journal of Business Management, 4(16), 3556–3563.
   Web of Science ®Google Scholar
• Chen, S. C., Liu, M. L., & Lin, C. P. (2013). Integrating technology readiness into the expectation–confirmation model: An empirical study of mobile services. Cyberpsychology, Behavior and Social Networking, 16(8), 604–612. https://doi.org/10.1089/cyber.2012.0606
   PubMed Web of Science ®Google Scholar
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
   Web of Science ®Google Scholar
• Dynatrace. (2018). IoT consumer confidence report: Challenges for enterprise cloud monitoring on the horizon. https://assets.dynatrace.com/en/docs/report/2824-iot-consumer-confidence-report-dynatrace.pdf
   Google Scholar
• Eclipse Foundation. (2020). The eclipse foundation releases IoT commercial adoption survey results. https://www.eclipse.org/org/press-release/20200310-iot-commercial-adoption-survey-2019.php
   Google Scholar
• Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819(03)00111-3
   Web of Science ®Google Scholar
• Fishbein, M., & Ajzen, I. (2011). Predicting and changing behavior: The reasoned action approach. Taylor & Francis.
   Google Scholar
• Fong, F. (2018). Hong Kong needs earlier 5G adoption to promote IoT development. Hong Kong Economic Journal. https://www.ejinsight.com/eji/article/id/1864376/20180612-hong-kong-needs-earlier-5g-adoption-to-promote-iot-development
   Google Scholar
• Gøthesen, S., Haddara, M., & Kumar, K. N. (2023). Empowering homes with intelligence: An investigation of smart home technology adoption and usage. Internet of Things, 24, 100944. https://doi.org/10.1016/j.iot.2023.100944
   Google Scholar
• Hong, A., Nam, C., & Kim, S. (2020). What will be the possible barriers to consumers’ adoption of smart home services? Telecommunications Policy, 44(2), 101867. https://doi.org/10.1016/j.telpol.2019.101867
   Web of Science ®Google Scholar
• Hsia, H. J. (2015). Mass communications research methods: A step-by-step approach. Routledge.
   Google Scholar
• Hubert, M., Blut, M., Brock, C., Zhang, R. W., Koch, V., & Ried, R. (2019). The influence of acceptance and adoption drivers on smart home usage. European Journal of Marketing, 53(6), 1073–1098. https://doi.org/10.1108/EJM-12-2016-0794
   Web of Science ®Google Scholar
• Kahneman, D., & Tversky, A. (2013). Prospect theory: An analysis of decision under risk. In Handbook of the fundamentals of financial decision making: Part I (pp. 99–127). World Scientific Singapore.
   Google Scholar
• Kang, Y., & Kim, S. (2009). Understanding user resistance to participation in multihop communications. Journal of Computer-Mediated Communication, 14(2), 328–351. https://doi.org/10.1111/j.1083-6101.2009.01443.x
   Web of Science ®Google Scholar
• Karahoca, A., Karahoca, D., & Aksöz, M. (2018). Examining intention to adopt to internet of things in healthcare technology products. Kybernetes, 47(4), 742–770. https://doi.org/10.1108/K-02-2017-0045
   Web of Science ®Google Scholar
• Kim, H. W., & Kankanhalli, A. (2009). Investigating user resistance to information systems implementation: A status quo bias perspective. MIS Quarterly, 33(3), 567–582. https://doi.org/10.2307/20650309
   Web of Science ®Google Scholar
• Kim, Y., Park, Y., & Choi, J. (2017). A study on the adoption of IoT smart home service: Using value-based adoption model. Total Quality Management & Business Excellence, 28(9–10), 1149–1165. https://doi.org/10.1080/14783363.2017.1310708
   Web of Science ®Google Scholar
• Leng, G. S., Lada, S., Muhammad, M. Z., Ibrahim, A. A. H. A., & Amboala, T. (2011). An exploration of social networking sites (SNS) adoption in Malaysia using technology acceptance model (TAM), theory of planned behavior (TPB) and intrinsic motivation. Journal of Internet Banking and Commerce, 16(2), 1.
   Google Scholar
• Leung, L., & Chen, C. (2019). E-health/m-health adoption and lifestyle improvements: Exploring the roles of technology readiness, the expectation-confirmation model, and health-related information activities. Telecommunications Policy, 43(6), 563–575. https://doi.org/10.1016/j.telpol.2019.01.005
   Web of Science ®Google Scholar
• Leung, L., & Wei, R. (1998). Exploring factors influencing the adoption of interactive TV in Hong Kong: Implications for advertising. Asian Journal of Communication, 8(2), 124–147.
   Google Scholar
• Lu, H. P., Hsu, C. L., & Hsu, H. Y. (2005). An empirical study of the effect of perceived risk upon intention to use online applications. Information Management & Computer Security, 13(2), 106–120. https://doi.org/10.1108/09685220510589299
   Google Scholar
• Marakhimov, A., & Joo, J. (2017). Consumer adaptation and infusion of wearable devices for healthcare. Computers in Human Behavior, 76, 135–148. https://doi.org/10.1016/j.chb.2017.07.016
   Web of Science ®Google Scholar
• Miller, M. (2015). The Internet of things: How smart TVs, smart cars, smart homes, and smart cities are changing the world. Pearson Education.
   Google Scholar
• Mital, M., Chang, V., Choudhary, P., Papa, A., & Pani, A. K. (2017). Adoption of Internet of things in India: A test of competing models using a structured equation modeling approach. Technological Forecasting and Social Change, 136, 339–346. https://doi.org/10.1016/j.techfore.2017.03.001
   Web of Science ®Google Scholar
• Office of the Communications Authority. (2021). Key communications statistics. Office of the Communications Authority – Key Communications Statistics. https://www.ofca.gov.hk/en/news_info/data_statistics/key_stat/index.html
   Google Scholar
• Oracle. (2021). What Is the Internet of Things (IoT)? Internet of Things. https://www.oracle.com/hk/internet-of-things/what-is-iot/
   Google Scholar
• Padyab, A., Habibipour, A., Rizk, A., & Ståhlbröst, A. (2020). Adoption barriers of IoT in large scale pilots. Information, 11(1), 23. https://doi.org/10.3390/info11010023
   Google Scholar
• Pal, D., Arpnikanondt, C., Funilkul, S., & Chutimaskul, W. (2020). The adoption analysis of voice-based smart IoT products. IEEE Internet of Things Journal, 7(11), 10852–10867. https://doi.org/10.1109/JIOT.2020.2991791
   Web of Science ®Google Scholar
• Pal, D., Funilkul, S., Charoenkitkarn, N., & Kanthamanon, P. (2018). Internet-of-things and smart homes for elderly healthcare: An end user perspective. IEEE Access. 6, 10483–10496. https://doi.org/10.1109/ACCESS.2018.2808472
   Web of Science ®Google Scholar
• Parasuraman, A. (2000). Technology Readiness Index (TRI) a multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320. https://doi.org/10.1177/109467050024001
   Google Scholar
• Patel, K. K., & Patel, S. M. (2016). Internet of things-IOT: Definition, characteristics, architecture, enabling technologies, application & future challenges. International Journal of Engineering Science and Computing, 6(5), 6122–6131.
   Google Scholar
• Patil, K. (2016). Retail adoption of Internet of Things: Applying TAM model [Paper presentation]. 2016 International Conference on Computing, Analytics and Security Trends (CAST), 404–409. https://doi.org/10.1109/CAST.2016.7915003
   Google Scholar
• Pavlou, P. A., Liang, H., & Xue, Y. (2007). Understanding and mitigating uncertainty in online exchange relationships: A principal-agent perspective. MIS Quarterly, 31(1), 105–136. https://doi.org/10.2307/25148783
   Web of Science ®Google Scholar
• Pillai, R., & Sivathanu, B. (2020). Adoption of Internet of things (IoT) in the agriculture industry deploying the BRT framework. Benchmarking: An International Journal, 27(4), 1341–1368. https://doi.org/10.1108/BIJ-08-2019-0361
   Web of Science ®Google Scholar
• Ram, S., & Sheth, J. N. (1989). Consumer resistance to innovations: The marketing problem and its solutions. Journal of Consumer Marketing, 6(2), 5–14. https://doi.org/10.1108/EUM0000000002542
   Google Scholar
• Ramadan, Z. B., Farah, M. F., & Mrad, M. (2017). An adapted TPB approach to consumers’ acceptance of service-delivery drones. Technology Analysis & Strategic Management, 29(7), 817–828. https://doi.org/10.1080/09537325.2016.1242720
   Web of Science ®Google Scholar
• Sivathanu, B. (2018). Adoption of Internet of things (IOT) based wearables for healthcare of older adults – A behavioural reasoning theory (BRT) approach. Journal of Enabling Technologies, 12(4), 169–185. https://doi.org/10.1108/JET-12-2017-0048
   Web of Science ®Google Scholar
• Sovacool, B. K., & Del Rio, D. D. F. (2020). Smart home technologies in Europe: A critical review of concepts, benefits, risks and policies. Renewable and Sustainable Energy Reviews, 120, 109663. https://doi.org/10.1016/j.rser.2019.109663
   Web of Science ®Google Scholar
• Statista. (2023). Smart home – Hong Kong. https://www.statista.com/outlook/dmo/smart-home/hong-kong
   Google Scholar
• Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176.
   Web of Science ®Google Scholar
• The Chief Executive’s 2020 Policy Address. (2020). Striving ahead with renewed perseverance. https://www.policyaddress.gov.hk/2020/eng/pdf/PA2020.pdf
   Google Scholar
• United Nations. (2021). Technology and Innovation Report 2021: Catching technological waves – Innovation with equity. Geneva. https://unctad.org/system/files/official-document/tir2020_en.pdf
   Google Scholar
• Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36 (1), 157–178. https://doi.org/10.2307/41410412
   Web of Science ®Google Scholar
• Wangyal, S., Dechen, T., Tanimoto, S., Sato, H., & Kanai, A. (2020). A preliminary study of multi-viewpoint risk assessment of IoT. Bulletin of Networking, Computing, Systems, and Software, 9(1), 40–42.
   Google Scholar
• Wilson, C., Hargreaves, T., & Hauxwell-Baldwin, R. (2017). Benefits and risks of smart home technologies. Energy Policy, 103, 72–83. https://doi.org/10.1016/j.enpol.2016.12.047
   Web of Science ®Google Scholar
• Yang, Y., Liu, Y., Li, H., & Yu, B. (2015). Understanding perceived risks in mobile payment acceptance. Industrial Management & Data Systems, 117(1), 68–89. https://doi.org/10.1108/IMDS-01-2016-0017
   Google Scholar
• Yen, H. R. (2005). An attribute-based model of quality satisfaction for Internet self-service technology. Service Industries Journal, 25(5), 641–659. https://doi.org/10.1080/02642060500100833
   Web of Science ®Google Scholar
• Yousif, M., Hewage, C., & Nawaf, L. (2021). IoT technologies during and beyond COVID-19: A comprehensive review. Future Internet, 13(5), 105. https://doi.org/10.3390/fi13050105
   Web of Science ®Google Scholar