Skip to main content Skip to main navigation menu Skip to site footer
Articles
DOI: 10.14254/1795-6889.2024.20-1.5
Submitted: 2022-12-09
Published: 2024-05-27

Using the UTAUT2 components and trust to predict consumer acceptance of smart home technology: A systematic review

University of Maribor
University of Maribor
smart home technology acceptance unified theory of acceptance and use of technology 2 trust review

Abstract

While sales of smart home technology are increasing, some are still hesitant to use such products. These differences in smart home technology acceptance could potentially be explained with the extended Unified theory of acceptance and use of technology (UTAUT2). However, the explanatory power of UTAUT2 in this context is still relatively unclear, and additional extensions, such as the inclusion of trust, have been proposed recently. In this systematic review, we address this issue by synthetizing evidence from 32 articles dealing with the relationship between the UTAUT2 components, trust, and smart home technology acceptance. Our results reveal that the UTAUT2 components and trust are all consistently correlated with behavioral intentions. In contrast, multivariate results show that only performance expectancy, hedonic motivation, and price value are consistent predictors of technology acceptance. In the discussion, we outline possible explanations for such results and highlight the limitations of our review.

Metrics

Metrics Loading ...

References

  1. Accenture. (2019). Consumer perceptions of smart home technology use worldwide as of 2019. Statista. https://www-statista-com.ezproxy.lib.ukm.si/statistics/1075518/smart-home-technology-consumer-perception-worldwide/
  2. Ahn, M., Kang, J., & Hustvedt, G. (2016). A model of sustainable household technology acceptance. International Journal of Consumer Studies, 40(1), 83–91. https://doi.org/10.1111/ijcs.12217 DOI: https://doi.org/10.1111/ijcs.12217
  3. Aldossari, M. Q., & Sidorova, A. (2018). Consumer acceptance of internet of things (IoT): Smart home context. Journal of Computer Information Systems, 1–11. https://doi.org/10.1080/08874417.2018.1543000 DOI: https://doi.org/10.1080/08874417.2018.1543000
  4. Aldrich, F. (2003). Smart homes: Past, present and future. In R Harper (Ed.), Inside the smart home (pp. 17–39). Springer. https://doi.org/10.1007/1-85233-854-7 DOI: https://doi.org/10.1007/1-85233-854-7_2
  5. Alkawsi, G. A., Ali, N., Mustafa, A. S., Baashar, Y., Alhussian, H., Alkahtani, A., Tiong, S. K., & Ekanayake, J. (2021). A hybrid SEM-neural network method for identifying acceptance factors of the smart meters in Malaysia: Challenges perspective. Alexandria Engineering Journal, 60(1), 227–240. https://doi.org/10.1016/j.aej.2020.07.002 DOI: https://doi.org/10.1016/j.aej.2020.07.002
  6. Andini, N. D., & Adiwijaya, K. (2021). What factors do affect the adoption of internet of things (smart home) in Indonesia. International Journal of Business and Technology Management, 3(2), 84–97.
  7. Baudier, P., Ammi, C., & Deboeuf-Rouchon, M. (2020). Smart home: Highly-educated students’ acceptance. Technological Forecasting and Social Change, 153, 119355. https://doi.org/10.1016/j.techfore.2018.06.043 DOI: https://doi.org/10.1016/j.techfore.2018.06.043
  8. Brauner, P., Van Heek, J., & Ziefle, M. (2017). Age, gender, and technology attitude as factors for acceptance of smart interactive textiles in home environments: Towards a smart textile technology acceptance model. In ICT4AWE 2017 - Proceedings of the 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health, (pp. 13–24). Springer Cham. DOI: https://doi.org/10.5220/0006255600130024
  9. Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS Quarterly: Management Information Systems, 29(3), 399–426. https://doi.org/10.2307/25148690 DOI: https://doi.org/10.2307/25148690
  10. Burnham, J. F. (2006). Scopus database: A review. Biomedical Digital Libraries, 3, 1. https://doi.org/10.1186/1742-5581-3-1 DOI: https://doi.org/10.1186/1742-5581-3-1
  11. Canziani, B., & MacSween, S. (2021). Consumer acceptance of voice-activated smart home devices for product information seeking and online ordering. Computers in Human Behavior, 119, 106714. https://doi.org/10.1016/j.chb.2021.106714 DOI: https://doi.org/10.1016/j.chb.2021.106714
  12. Chen, C. F., Xu, X., Adams, J., Brannon, J., Li, F., & Walzem, A. (2020). When East meets West: Understanding residents’ home energy management system adoption intention and willingness to pay in Japan and the United States. Energy Research and Social Science, 69, 101616. https://doi.org/10.1016/j.erss.2020.101616 DOI: https://doi.org/10.1016/j.erss.2020.101616
  13. Chen, C. F., Xu, X., & Arpan, L. (2017). Between the technology acceptance model and sustainable energy technology acceptance model: Investigating smart meter acceptance in the United States. Energy Research and Social Science, 25, 93–104. https://doi.org/10.1016/j.erss.2016.12.011 DOI: https://doi.org/10.1016/j.erss.2016.12.011
  14. Chen, K., & Chan, A. H. S. (2011). A review of technology acceptance by older adults. Gerontechnology, 10(1), 1–12. https://doi.org/10.4017/gt.2011.10.01.006.00 DOI: https://doi.org/10.4017/gt.2011.10.01.006.00
  15. Cimperman, M., Makovec Brenčič, M., & Trkman, P. (2016). Analyzing older users’ home telehealth services acceptance behavior-applying an Extended UTAUT model. International Journal of Medical Informatics, 90, 22–31. https://doi.org/10.1016/j.ijmedinf.2016.03.002 DOI: https://doi.org/10.1016/j.ijmedinf.2016.03.002
  16. Clutch. (2019). 33% of people plan to invest in a smart home device in the next 3 years, demonstrating the growth of IoT in the home. https://www.prnewswire.com/news-releases/33-of-people-plan-to-invest-in-a-smart-home-device-in-the-next-3-years-demonstrating-the-growth-of-iot-in-the-home-300858930.html
  17. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008 DOI: https://doi.org/10.2307/249008
  18. Dirks, K. T., & Ferrin, D. L. (2002). Trust in leadership: Meta-analytic findings and implications for research and practice. Journal of Applied Psychology, 87(4), 611–628. https://doi.org/10.1037/0021-9010.87.4.611 DOI: https://doi.org/10.1037//0021-9010.87.4.611
  19. Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of price, brand, and store information on buyers’ product evaluations. Journal of Marketing Research, 28(3), 307–319. https://doi.org/10.2307/3172866 DOI: https://doi.org/10.1177/002224379102800305
  20. Etemad-Sajadi, R., & Gomes Dos Santos, G. (2019). Senior citizens’ acceptance of connected health technologies in their homes. International Journal of Health Care Quality Assurance, 32(8), 1162–1174. https://doi.org/10.1108/IJHCQA-10-2018-0240 DOI: https://doi.org/10.1108/IJHCQA-10-2018-0240
  21. Gefen, D. (2000). The role of familiarity and trust. Omega: The International Journal of Management Science, 28(6), 725–737. https://doi.org/10.1016/S0305-0483(00)00021-9 DOI: https://doi.org/10.1016/S0305-0483(00)00021-9
  22. Große-Kreul, F. (2022). What will drive household adoption of smart energy? Insights from a consumer acceptance study in Germany. Utilities Policy, 75. https://doi.org/10.1016/j.jup.2021.101333 DOI: https://doi.org/10.1016/j.jup.2021.101333
  23. Gu, W., Bao, P., Hao, W., & Kim, J. (2019). Empirical examination of intention to continue to use smart home services. Sustainability, 11(19), 1–12. https://doi.org/10.3390/su11195213 DOI: https://doi.org/10.3390/su11195213
  24. Gumz, J., Fettermann, D. C., Sant’Anna, Â. M. O., & Tortorella, G. L. (2022). Social influence as a major factor in smart meters’ acceptance: Findings from Brazil. Results in Engineering, 15. https://doi.org/10.1016/j.rineng.2022.100510 DOI: https://doi.org/10.1016/j.rineng.2022.100510
  25. Harper, R. (2003). Inside the smart home: Ideas, possibilities and methods. In Inside the Smart Home (pp. 1–13). Springer. https://doi.org/10.1007/b97527 DOI: https://doi.org/10.1007/1-85233-854-7_1
  26. Heerink, M., Kröse, B., Evers, V., & Wielinga, B. (2010). Assessing acceptance of assistive social agent technology by older adults: The Almere model. International Journal of Social Robotics, 2(4), 361–375. https://doi.org/10.1007/s12369-010-0068-5 DOI: https://doi.org/10.1007/s12369-010-0068-5
  27. Herrero, Á., San Martín, H., & Garcia-De los Salmones, M. (2017). Explaining the adoption of social networks sites for sharing user-generated content: A revision of the UTAUT2. Computers in Human Behavior, 71, 209–217. https://doi.org/10.1016/j.chb.2017.02.007 DOI: https://doi.org/10.1016/j.chb.2017.02.007
  28. Higgins, J. P., & Green, S. (2008). Cochrane handbook for systematic reviews of interventions: Cochrane book series. John Wiley and Sons. https://doi.org/10.1002/9780470712184 DOI: https://doi.org/10.1002/9780470712184
  29. Juric, J., & Lindenmeier, J. (2019). An empirical analysis of consumer resistance to smart-lighting products. Lighting Research and Technology, 51(4), 489–512. https://doi.org/10.1177/1477153518774080 DOI: https://doi.org/10.1177/1477153518774080
  30. Kelley, T. L. (1927). Interpretation of educational measurements. World Book Inc.
  31. Kohnke, A., Cole, M. L., & Bush, R. (2014). Incorporating UTAUT predictors for understanding home care patients’ and clinicians' acceptance of healthcare telemedicine equipment. Journal of Technology Management and Innovation, 9(2), 29–41. https://doi.org/10.4067/S0718-27242014000200003 DOI: https://doi.org/10.4067/S0718-27242014000200003
  32. Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly: Management Information Systems, 31(4), 705–737. https://doi.org/10.2307/25148817 DOI: https://doi.org/10.2307/25148817
  33. Mamonov, S., & Koufaris, M. (2020). Fulfillment of higher-order psychological needs through technology: The case of smart thermostats. International Journal of Information Management, 52, 102091. https://doi.org/10.1016/j.ijinfomgt.2020.102091 DOI: https://doi.org/10.1016/j.ijinfomgt.2020.102091
  34. Marikyan, D., Papagiannidis, S., & Alamanos, E. (2019). A systematic review of the smart home literature: A user perspective. Technological Forecasting and Social Change, 138, 139–154. https://doi.org/10.1016/j.techfore.2018.08.015 DOI: https://doi.org/10.1016/j.techfore.2018.08.015
  35. Martín-Martín, A., Orduna-Malea, E., Thelwall, M., & Delgado López-Cózar, E. (2018). Google Scholar, Web of Science, and Scopus: A systematic comparison of citations in 252 subject categories. Journal of Informetrics, 12(4), 1160–1177. https://doi.org/10.1016/j.joi.2018.09.002 DOI: https://doi.org/10.1016/j.joi.2018.09.002
  36. Mashal, I., & Shuhaiber, A. (2019). What makes Jordanian residents buy smart home devices? A factorial investigation using PLS-SEM. Kybernetes, 48(8), 1681–1698. https://doi.org/10.1108/K-01-2018-0008 DOI: https://doi.org/10.1108/K-01-2018-0008
  37. Maswadi, K., Ghani, N. A., & Hamid, S. (2022). Factors influencing the elderly’s behavioural intention to use smart home technologies in Saudi Arabia. PLoS ONE, 17, 1–30. https://doi.org/10.1371/journal.pone.0272525 DOI: https://doi.org/10.1371/journal.pone.0272525
  38. Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709–734. https://doi.org/10.2307/258792 DOI: https://doi.org/10.5465/amr.1995.9508080335
  39. McCloskey, D., & Bennett, G. (2020). Examining artificial intelligence acceptance: Exploring smart speaker adoption with the unified theory of acceptance and use of technology (UTAUT2). Pennsylvania Economic Review, 27(2), 44–56.
  40. Meyer-Waarden, L., Cloarec, J., Adams, C., Aliman, D. N., & Wirth, V. (2022). Home, sweet home: How well-being shapes the adoption of artificial intelligence-powered apartments in smart cities. Systemes d’Information et Management, 26(4), 55–88. https://doi.org/10.3917/sim.214.0055 DOI: https://doi.org/10.3917/sim.214.0055
  41. Michler, O., Decker, R., & Stummer, C. (2019). To trust or not to trust smart consumer products: A literature review of trust-building factors. Management Review Quarterly, 70, 391–420. https://doi.org/10.1007/s11301-019-00171-8 DOI: https://doi.org/10.1007/s11301-019-00171-8
  42. Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., Altman, D., Antes, G., Atkins, D., Barbour, V., Barrowman, N., Berlin, J. A., Clark, J., Clarke, M., Cook, D., D’Amico, R., Deeks, J. J., Devereaux, P. J., Dickersin, K., Egger, M., Ernst, E., … Tugwell, P. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097 DOI: https://doi.org/10.1371/journal.pmed.1000097
  43. Moon, Y. J., & Hwang, Y. H. (2016). A study of effects of UTAUT-based factors on acceptance of smart health care services. Lecture Notes in Electrical Engineering, 354, 495–501. https://doi.org/10.1007/978-3-662-47895-0 DOI: https://doi.org/10.1007/978-3-662-47895-0
  44. Nikou, S. (2019). Factors driving the adoption of smart home technology: An empirical assessment. Telematics and Informatics, 45, 101283. https://doi.org/10.1016/j.tele.2019.101283 DOI: https://doi.org/10.1016/j.tele.2019.101283
  45. Oyinlola Ayodimeji, Z., Janardhanan, M., Marinelli, M., & Patel, I. (2021). Adoption of smart homes in the UK: Customers’ perspective. Lecture Notes in Mechanical Engineering, 19–30. https://doi.org/10.1007/978-981-15-9956-9_5 DOI: https://doi.org/10.1007/978-981-15-9956-9_3
  46. 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 DOI: https://doi.org/10.1109/ACCESS.2018.2808472
  47. Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134. https://doi.org/10.1080/10864415.2003.11044275 DOI: https://doi.org/10.1080/10864415.2003.11044275
  48. PricewaterhouseCoopers. (2016). Smart home technology - internet of things or indifference to things? https://pwc.blogs.com/press_room/2016/05/smart-home-technology-internet-of-things-or-indifference-to-things-pwc-survey.html
  49. Reinisch, C., Kofler, M. J., Iglesias, F., & Kastner, W. (2011). Thinkhome energy efficiency in future smart homes. Eurasip Journal on Embedded Systems, 104617. https://doi.org/10.1155/2011/104617 DOI: https://doi.org/10.1155/2011/104617
  50. Shanthana Lakshmi, S., & Gupta, D. (2021). The smart set: A study on the factors that affect the adoption of smart home technology. Machine Learning for Predictive Analysis, 443–450. DOI: https://doi.org/10.1007/978-981-15-7106-0_44
  51. Shuhaiber, A., & Mashal, I. (2019). Understanding users’ acceptance of smart homes. Technology in Society, 58, 101110. https://doi.org/10.1016/j.techsoc.2019.01.003 DOI: https://doi.org/10.1016/j.techsoc.2019.01.003
  52. Sorwar, G., Aggar, C., Penman, O., Seton, C., & Ward, A. (2022). Factors that predict the acceptance and adoption of smart home technology by seniors in Australia: a structural equation model with longitudinal data. Informatics for Health and Social Care, 1–15. https://doi.org/10.1080/17538157.2022.2069028 DOI: https://doi.org/10.1080/17538157.2022.2069028
  53. Sovacool, B. K., & Furszyfer Del Rio, D. D. (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 DOI: https://doi.org/10.1016/j.rser.2019.109663
  54. Strategy Analytics. (2019). Consumer spending on smart home systems worldwide from 2014 to 2023 (in billion U.S. dollars). Statista. https://www-statista-com.ezproxy.lib.ukm.si/statistics/693303/smart-home-consumer-spending-worldwide/
  55. Taiwo, A. A., & Downe, A. G. (2013). The theory of user acceptance and use of technology (UTAUT): A meta-analytic review of empirical findings. Journal of Theoretical and Applied Information Technology, 49(1), 48–58. https://doi.org/10.12691/ajis-3-2-3
  56. Tamilmani, K., Rana, N. P., & Dwivedi, Y. K. (2019). Use of ‘Habit’ is not a habit in understanding individual technology adoption: A review of UTAUT2 based empirical studies. In IFIP International Federation for Information Processing (Vol. 533). Springer International Publishing. https://doi.org/10.1007/978-3-030-04315-5 DOI: https://doi.org/10.1007/978-3-030-04315-5_19
  57. Tamilmani, K., Rana, N. P., & Dwivedi, Y. K. (2020). Consumer acceptance and use of information technology: A meta-analytic evaluation of UTAUT2. Information Systems Frontiers. https://doi.org/10.1007/s10796-020-10007-6 DOI: https://doi.org/10.1007/s10796-020-10007-6
  58. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems, 27(3), 425–478. https://doi.org/10.2307/30036540 DOI: https://doi.org/10.2307/30036540
  59. Venkatesh, V., Thong, J. Y. L., & 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 DOI: https://doi.org/10.2307/41410412
  60. Vimalkumar, M., Sharma, S. K., Singh, J. B., & Dwivedi, Y. K. (2021). ‘Okay google, what about my privacy?’: User’s privacy perceptions and acceptance of voice based digital assistants. Computers in Human Behavior, 120, 106763. https://doi.org/10.1016/j.chb.2021.106763 DOI: https://doi.org/10.1016/j.chb.2021.106763
  61. Wang, X., McGill, T. J., & Klobas, J. E. (2020). I want it anyway: Consumer perceptions of smart home devices. Journal of Computer Information Systems, 60(5), 437–447. https://doi.org/10.1080/08874417.2018.1528486 DOI: https://doi.org/10.1080/08874417.2018.1528486
  62. Yang, H., Yu, J., Zo, H., & Choi, M. (2016). User acceptance of wearable devices: An extended perspective of perceived value. Telematics and Informatics, 33(2), 256–269. https://doi.org/10.1016/j.tele.2015.08.007 DOI: https://doi.org/10.1016/j.tele.2015.08.007
  63. YouGov. (2018). In your opinion, what are the disadvantages of smart homes? Statista. https://www-statista-com.ezproxy.lib.ukm.si/statistics/965019/opinion-on-home-automation-disadvantages-in-italy/
  64. Zaharia, S., & Würfel, M. (2021). Voice commerce - Studying the acceptance of smart speakers. International Conference on Human Interaction and Emerging Technologies, 449–454. DOI: https://doi.org/10.1007/978-3-030-55307-4_68

How to Cite

Plohl, N., & Čuš Babič, N. (2024). Using the UTAUT2 components and trust to predict consumer acceptance of smart home technology: A systematic review. Human Technology, 20(1), 93–113. https://doi.org/10.14254/1795-6889.2024.20-1.5