Key Determinants for Users’ Intention To Use Smart Home Technology: A Case of Reseidents in Mianyang, Yibin and Wanzhou, China
DOI:
https://doi.org/10.14456/shserj.2024.62Keywords:
Hedonic Motivation, Social Influence, Price Value, Intention to Use, Smart Home TechnologyAbstract
Purpose: The objective of this research is to investigate the various determinants that affect residents' inclination to adopt smart home technology in the cities of Mianyang, Yibin, and Wanzhou, located in China. The conceptual framework encompasses factors such as perceived usefulness, perceived ease of use, personal innovativeness, trust, hedonic motivation, social influence, price value and intention to use. Research design, data, and methodology: In this study, the target population consisted of 500 smart home users residing in Mianyang, Yibin, and Wanzhou, China. To ensure the content validity and reliability of the questionnaire, Item-Objective Congruence (IOC) and a pilot test of Cronbach's Alpha were employed before distribution. Data analysis was conducted using Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) to assess the model's goodness of fit and validate the causal relationships among variables for hypothesis testing. Results: All hypotheses were found to be supported. The variables of perceived usefulness, perceived ease of use, personal innovativeness, trust, hedonic motivation, social influence, and price value were discovered to significantly influence the intention to use smart home technology. Additionally, it was observed that perceived ease of use exerted a significant impact on perceived usefulness. Conclusions: In particular, it has important theoretical guidance and practical value for improving the user experience of Chinese residents using smart homes.
References
Aldosari, M. A., Aljabaa, A. H., Al-Sehaibany, F. S., & Albarakati, S. F. (2018). Learning style preferences of dental students at a single institution in Riyadh, Saudi Arabia, evaluated using the VARK questionnaire. Advances in medical education and practice, 9, 179-186. https://doi.org/10.2147/AMEP.S157686
Al-Mamary, Y. H., & Shamsuddin, A. (2015). Adoption of Management Information Systems in Context of Yemeni Organizations: A Structural Equation Modeling Approach. Journal of Digital Information Management, 13(6), 429-444.
Andini, N. D., & Adiwijaya, K. (2021). What factors do influence the adoption of internet of things (Smart Home) in Indonesia. International Journal of Business and Technology Management, 3(2), 84-97. http://myjms.mohe.gov.my/index.php/ijbtm
Awang, Z. (2012). A Handbook on SEM Structural Equation Modelling: SEM Using AMOS Graphic (5th ed.). Universiti Teknologi Mara Kelantan.
Baudier, P., Ammi, C., & Deboeuf-Rouchon, M. (2020). Smart home: Highly-educated students' acceptance. Technological Forecasting and Social Change 153, 119355.
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246. https://doi.org/10.1037/0033-2909.107.2.238
Brezavšček, A., Šparl, P., & Žnidaršič, A. (2014). Extended technology acceptance model for SPSS acceptance among Slovenian students of social sciences. Organizacija, 47(2), 116-127. https://doi.org/10.2478/orga-2014-0009
Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing, 77(4), 511-535.
Chong, A. Y.-L., Chan, F. T., & Ooi, K.-B. (2012). Predicting consumer decisions to adopt mobile commerce: Cross country empirical examination between China and Malaysia. Decision Support Systems, 53(1), 34-43.
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
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.
Doney, P. M., & Cannon, J. P. (1997). An examination of the nature of trust in buyer-seller relationships. The Journal of Marketing, 61(2), 35-51.
El-Masri, M., & Tarhini, A. (2017). Factors influencing the adoption of e-learning systems in Qatar and USA: Extending the unified theory of acceptance and use of technology 2 (UTAUT2). Educational Technology Research and Development, 65(3), 743-763.
Fan, X., Duangekanong, S., & Xu, M. (2021). Factors Affecting College Students’ Intention to Use English U-learning in Sichuan, China. AU-GSB E-JOURNAL, 14(2), 118-129. https://doi.org/10.14456/augsbejr.2021.20
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
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. https://doi.org/10.1108/APJML-06-2013-0061
Gerhart, N., Peak, D., & Prybutok, V. (2015). Searching for new answers: The application of task-technology fit to e-textbook usage. Decision Sciences Journal of Innovative Education, 13(1), 91-111.
Greenough, J. (2016, April 23). The US Smart Home Market has been struggling- Here’s how and why the market will take off. Business Insider. http://www. businessinsider. com/the-us-smart-home-marketreport-adoption-forecasts-top-products-and-the-cost-and-fragmentation-problems-that-could-hindergrowth-2015-9
Hong, J. C., Lin, P. H., & Hsieh, P. C. (2017). The effect of consumer innovativeness on perceived value and continuance intention to use smartwatch. Computers in Human Behavior, 67, 264-272.
Hubert, M., Blut, M., Brock, C., Backhaus, C., & Eberhardt, T. (2017). Acceptance of smartphone-based mobile shopping: mobile benefits, customer characteristics, perceived risks, and the influence of application context. Psychology & Marketing, 34(2), 175-194.
Kijsanayotin, B., Pannarunothai, S., & Speedie, S. M. (2009). Factors influencing health information technology adoption in Thailand’s community health centers: Applying the UTAUT model. International Journal of Medical Informatics, 78(6), 404-416.
Kim, K. J., & Shin, D. H. (2015). An acceptance model for smart watches: Implications for the adoption of future wearable technology. Internet Research, 25(4), 527-541.
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, 28(9-10), 1-17. http://dx.doi.org/10.1080/14783363.2017.1310708
Lee, Y.-K., Park, J.-H., Chung, N., & Blakeney, A. (2012). A unified perspective on the factors influencing usage intention toward mobile financial services. Journal of Business Research, 65(1), 1590-1599.
Leeraphong, A., Papasratorn, B., & Chongsuphajaisiddhi, V. (2015, November 23-24). A Study on Factors Influencing Elderly Intention to Use Smart Home in Thailand : A Pilot Study [Paper Presentation]. The 10th International Conference on e-Business (iNCEB2015), Bangkok, Thailand.
Leong, G.-W., Ping, T.-A., & Muthuveloo, R. (2017). Antecedents of behavioral intention to adopt internet of things in the context of smart city in Malaysia. Global Business and Management Research, 9(4), 442-456.
Li, S., Tryfonas, T., & Li, H. (2016). The Internet of Things: a security point of view. Internet Research, 26(2), 337-359. https://doi.org/10.1108/IntR-07-2014-0173
Lin, C.-P., & Bhattacherjee, A. (2010). Extending technology usage models to interactive hedonic technologies: A theoretical model and empirical test. Information Systems Journal, 20(2), 163-181. https://doi.org/10.1111/j.1365-2575.2007.00265.x
Lin, H.-F. (2011). An empirical investigation of mobile banking adoption: The effect of innovation attributes and knowledge-based trust. International Journal of Information Management, 31(3), 252-260.
Nikou, S. (2019). Factors driving the adoption of SHT: An empirical assessment. Telematics and Informatics, 45, 1-10. https://doi.org/10.1016/j.tele.2019.101283
Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.
Pedroso, R., Zanetello, L., Guimaraes, L., Pettenon, M., Goncalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry, 43(3), 37-40. https://doi.org/10.1590/0101-60830000000081
Rocks-de Boer, P., Deursen, A. J. A. M., & Van Rompay, T. (2020). Internet-of-Things Skills Among the General Population: Task-Based Performance Test Using Activity Trackers. JMIR Human Factors, 7(4), e22532. https://doi.org/10.2196/22532
Sánchez-Franco, M. J., Villarejo-Ramos, Á., & Martín-Velicia, F. (2009). The moderating effect of gender on relationship quality and loyalty toward Internet service providers. Information & Management, 46(3), 196-202.
https://doi.org 10.1016/j.im.2009.02.001
Schillewaert, N., Ahearne, M. J., Frambach, R. T., & Moenaert, R. K. (2005). The adoption of information technology in the sales force. Industrial Marketing Management, 34(4), 323-336.
Sharma, S., Pradhan, K., Satya, S., & Vasudevan, P. (2005). Potentiality of earthworms for waste management and in other uses- a review. Journal of American Science, 1(1), 4-16.
Shuhaiber, A., & Mashal, I. (2019). Understanding users’ acceptance of smart home. Technology in Society, 58, 101110.
Siau, K., & Shen, Z. (2003). Building customer trust in mobile commerce. Communications of the ACM, 46(4), 91-94.
Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. In M. A. Lange (Ed.), Leading-edge psychological tests and testing research (pp. 27-50). Nova Science Publishers.
Soper, D. (2023). Calculator: A-priori Sample Size for Structural Equation Models. Daniel Soper.
https://www.danielsoper.com/statcalc/calculator.aspx?id=89
Sponselee, A., Schouten, B., Bouwhuis, D., & Willems, C. (2008). Smart Home Technology for the Elderly: Perceptions of Multidisciplinary Stakeholders. In M. Mühlhäuser, A. Ferscha, & E. Aitenbichler (Eds.), Constructing Ambient Intelligence. (pp. 314-326). Springer. https://doi.org/10.1007/978-3-540-85379-4_37
Statista Research Department. (2023, July 11). Smart home control & connectivity penetration rate by country. https:// bg.qianzhan.com/report/detail/459/190724-0905885f.html
Straub, D. W. (1989). Validating instruments in MIS research. MIS Quarterly, 13(2), 147– 169. https://doi.org/10.2307/248922
Thi-Hong-Linh, N., Minh-Hung, D., Hai-Dang, N., Dang-HongNgan, V., DuyPhuong-Trinh, V., & Hoanh-Su, L. (2018). Key determinants for users’ intention to use smarthome devices in Vietnam. Journal of Multimedia Information System, 5(4), 283-290. http://dx.doi.org/10.9717/ JMIS.2018.5.4.283
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-316.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
Venkatesh, V., Thong, J., & 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.
Woodruff, R. B. (1997). Customer value: The next source for competitive advantage. Journal of the Academy of Marketing Science, 25(2), 139-153.
Wu, J. H., & Wang, Y. M. (2006). Measuring KMS Success: A Respecification of the DeLone and McLean’s Model. Journal of Information & Management, 43(6), 728-739. http://dx.doi.org/10.1016/j.im.2006.05.002
Yang, H., Lee, H., & Zo, H. (2017). User acceptance of smart home services: an extension of the theory of planned behavior. Industrial Management & Data Systems, 117(1), 68-89, https://doi.org/10.1108/IMDS-01-2016-0017
Zaharia, S., & Würfel, M. (2021). Voice commerce - studying the acceptance of smart speakers. In T. Ahram, R. Taiar, K. Langlois & A. Choplin (Eds.), Human interaction, emerging technologies and future applications III (pp.449-454). Springer.
Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means–end model and synthesis of evidence. Journal of Marketing, 52(3), 2-22.
Zhou, T. (2012). Examining location-based services usage from the perspectives of unified theory of acceptance and use of technology and privacy risk. Journal of Electronic Commerce Research, 13(2), 135-144.
Zikmund, W. G. (2003). Business research methods (7th ed.). Thomson/ South-Western.
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