Factors affecting consumers’ intention to use smart home products in Ho Chi Minh City
Published: August 26, 2026
doi:10.62831/nckh.2026.3552.v1
Abstract
Drawing on the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology, this study examines six determinants of consumers’ intention to use smart home products: perceived usefulness, perceived cost, perceived ease of use, perceived reliability, social influence, and perceived safety. The findings show that perceived reliability has the strongest positive effect on intention, followed by perceived ease of use, perceived usefulness, and perceived safety. Perceived cost has a negative effect, whereas social influence is not statistically significant. The results provide practical implications for smart home providers, particularly the need to improve product reliability, simplify user experience, increase perceived usefulness and safety, and reduce cost-related barriers to encourage consumer adoption in Ho Chi Minh City.
Keywords
smart home productsintention to useperceived reliabilityperceived usefulnessperceived costHo Chi Minh City
References
1.
Hoàng Trọng và Chu Nguyễn Mộng Ngọc. (2008). Phân tích dữ liệu nghiên cứu với SPSS. NXB Hồng Đức.
2.
Văn Hùng Trọng, Võ Thị Thanh Thảo, Nguyễn Thị Kiều Trang, Vũ Thị Quỳnh Anh (2020). Nghiên cứu về các yếu tố ảnh hưởng đến ý định sử dụng thiết bị nhà thông minh tại Đà Nẵng. Kỷ yếu Hội thảo Khoa học Quốc gia CITA 2020 “Công nghệ thông tin và ứng dụng trong các lĩnh vực”, tr. 300-306.
3.
Ajzen, I., & Fishbein, M. (1991). Understanding attitudes and predicting social behavior. Prentice Hall.
4.
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
Basarir-Ozel, B., Turker, H. B., & Nasir, V. A. (2022). Identifying the key drivers and barriers of smart home adoption: A thematic analysis from the business perspective. Sustainability, 14(15), 9053. https://doi.org/10.3390/su14159053.
Chung, B. Y., Skibniewski, M. J., Lucas, H. C., Jr., & Kwak, Y. H. (2008). Analyzing enterprise resource planning system implementation success factors in the engineering-construction industry. Journal of Computing in Civil Engineering, 22(6), 373-382. https://doi.org/10.1061/(ASCE)0887-3801(2008)22:6(373).
Comrey, A. L., & Lee, H. B. (1992). A first course in factor analysis. Lawrence Erlbaum Associates.
8.
Cristobal, E., Flavian, C., & Guinaliu, M. (2007). Perceived e-service quality: Measurement validation and effects on consumer satisfaction and web site loyalty. Managing Service Quality: An International Journal. https://doi.org/10.1108/09604520710744326.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340.
10.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
11.
Gao, L., & Bai, X. (2014). A unified perspective on the factors influencing consumer acceptance of Internet of Things technology. Asia Pacific Journal of Marketing and Logistics, 26(2), 211-231.
12.
Gould, J. D., & Lewis, C. (1985). Designing for usability: Key principles and what designers think. Communications of the ACM, 28(3), 300-311.
13.
Gu, W., Bao, P., Hao, W., & Kim, J. (2019). Empirical examination of intention to continue to use smart home services. Sustainability, 11(19), 5213.
14.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis.
15.
Han, H., Hsu, L. T., & Sheu, C. (2010). Application of the theory of planned behavior to green hotel choice: Testing the effect of environmental friendly activities. Tourism Management, 31(3), 325-334.
16.
Javadi, M. H. M., Dolatabadi, H. R., Nourbakhsh, M., Poursaeedi, A., & Asadollahi, A. R. (2012). An analysis of factors affecting online shopping behavior of consumers. International Journal of Marketing Studies, 4(5), 81. http://dx.doi.org/10.5539/ijms.v4n5p81.
Ji, W., & Chan, E. H. (2019). Critical factors influencing the adoption of smart home energy technology in China: A Guangdong province case study. Energies, 12(21), 4180. https://doi.org/10.3390/en12214180.
Kalakota, R., & Whinston, A. B. (1997). Electronic commerce: A manager’s guide. Addison-Wesley Professional.
19.
Kaneko, M., Arima, K., Murakami, T., Isshiki, M., & Sugimura, H. (2017). Design and implementation of interactive control system for smart houses. In 2017 IEEE International Conference on Consumer Electronics (ICCE).
20.
Lee, C., Zappaterra, L., Choi, K., & Choi, H.-A. (2014). Securing smart home: Technologies, security challenges, and security requirements. In 2014 IEEE Conference on Communications and Network Security.
21.
Li, M., Gu, W., Chen, W., He, Y., Wu, Y., & Zhang, Y. (2018). Smart home: Architecture, technologies and systems. Procedia Computer Science, 131, 393-400.
22.
Lu, J., Yu, C.-S., & Liu, C. (2005). Facilitating conditions, wireless trust and adoption intention. Journal of Computer Information Systems, 46(1), 17-24.
23.
Meyer, J. P., Becker, T. E., & Van Dick, R. (2006). Social identities and commitments at work: Toward an integrative model. Journal of Organizational Behavior, 27(5), 665-683.
24.
Nikou, S. (2019). Factors driving the adoption of smart home technology: An empirical assessment. Telematics and Informatics, 45, 101283.
25.
Notra, S., Siddiqi, M., Gharakheili, H. H., Sivaraman, V., & Boreli, R. (2014). An experimental study of security and privacy risks with emerging household appliances. In 2014 IEEE Conference on Communications and Network Security.
26.
Park, E., Cho, Y., Han, J., & Kwon, S. J. (2017). Comprehensive approaches to user acceptance of Internet of Things in a smart home environment. IEEE Internet of Things Journal, 4(6), 2342-2350.
27.
Park, E., Kim, H., & Ohm, J. Y. (2015). Understanding driver adoption of car navigation systems using the extended technology acceptance model. Behaviour & Information Technology, 34(7), 741-751.
28.
Park, E., Kim, S., Kim, Y., & Kwon, S. J. (2018). Smart home services as the next mainstream of the ICT industry: Determinants of the adoption of smart home services. Universal Access in the Information Society, 17(1), 175-190.
29.
Pliatsikas, P., & Economides, A. A. (2022). Factors influencing intention of Greek consumers to use smart home technology. Applied System Innovation, 5(1), 26.
Stojkoska, B. L. R., & Trivodaliev, K. V. (2017). A review of Internet of Things for smart home: Challenges and solutions. Journal of Cleaner Production, 140, 1454-1464.
32.
Tabachnick, B. G., & Fidell, L. S. (1996). Using multivariate statistics. HarperCollins.
33.
Vanus, J., Smolon, M., Martinek, R., Koziorek, J., Zidek, J., & Bilik, P. (2015). Testing of the voice communication in smart home care. Human-Centric Computing and Information Sciences, 5(1), 1-22.
34.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
35.
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, 157-178.
36.
Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85-102.
37.
Wu, J.-H., & Wang, S.-C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information & Management, 42(5), 719-729.
38.
Yoshigoe, K., Dai, W., Abramson, M., & Jacobs, A. (2015, December). Overcoming invasion of privacy in smart home environment with synthetic packet injection. In 2015 TRON Symposium (TROnSHOW) (pp. 1-7). IEEE.
