The purpose of this study is to investigate the adoption of Internet of Things (IoT)-based smart home devices and services in the context of Initial Trust formation. The data, collected from 505 technology enthusiasts residing in the most technologically advanced cities in India, were analysed using partial least squares structural equation modelling. It employs the UTAUT2 model with Initial Trust to propose and test the research framework. Results show a significant influence of effort expectancy, social influence, price value and firm reputation on behavioural intention. The ease of use of the technology challenges user acceptance, while hedonic value significantly influences technology acceptance. Firm reputation does not affect Initial Trust formation, in line with the findings of previous studies. Control variables such as age and education influenced Initial Trust formation, echoing similar studies in e-commerce and mobile payment adoption. Extending the UTAUT2 model, this study sheds light on key determinants shaping IoT-based smart home adoption, offering insights for future studies and industry practitioners.
AdapaA., NahF. F.-H., HallR. H., SiauK., & SmithS. N. (2018). Factors influencing the adoption of smart wearable devices. International Journal of Human–Computer Interaction, 34(5), 399–409.
2.
AldossariM. Q., & SidorovaA. (2020). Consumer acceptance of Internet of Things (IoT): Smart home context. Journal of Computer Information Systems, 60(6), 507–517. https://doi.org/10.1080/08874417.2018.1543000
3.
Al-QutayriM. (2010). Smart home systems. BoD–Books on Demand.
4.
AndersonJ. G., & BalasE. A. (2006). Computerization of primary care in the United States. International Journal of Healthcare Information Systems and Informatics (IJHISI), 1(3), 1–23.
5.
ArfiW. B., NasrI. B., KhvatovaT., & ZaiedY. 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.
6.
BaudierP., AmmiC., & Deboeuf-RouchonM. (2020). Smart home: Highly-educated students’ acceptance. Technological Forecasting and Social Change, 153, 119355. https://doi.org/10.1016/j.techfore.2018.06.043
7.
BrownS. A., & VenkateshV. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS Quarterly, 29(3), 399–426. https://doi.org/10.2307/25148690
8.
BunduchiR. (2005). Business relationships in internet-based electronic markets: The role of goodwill trust and transaction costs. Information Systems Journal, 15(4), 321–341. https://doi.org/10.1111/j.1365-2575.2005.00199.x
9.
ChaoualiW., YahiaI. B., & SouidenN. (2016). The interplay of counter-conformity motivation, social influence, and trust in customers’ intention to adopt Internet banking services: The case of an emerging country. Journal of Retailing and Consumer Services, 28, 209–218.
10.
ChawlaY., Kowalska-PyzalskaA., & Skowrońska-SzmerA. (2020). Perspectives of smart meters’ roll-out in India: An empirical analysis of consumers’ awareness and preferences. Energy Policy, 146, 111798. https://doi.org/10.1016/j.enpol.2020.111798
11.
ChenC., de RubensG. Z., XuX., & LiJ. (2020). Coronavirus comes home? Energy use, home energy management, and the social-psychological factors of COVID-19. Energy Research & Social Science, 68, 101688.
12.
ChildersT. L., CarrC. L., PeckJ., & CarsonS. (2001). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing, 77(4), 511–535. https://doi.org/10.1016/S0022-4359(01)00056-2
13.
CockbillS. A., MitchellV., & MayA. J. (2020). Householders as designers? Generating future energy services with United Kingdom home occupiers. Energy Research & Social Science, 69, 101615. https://doi.org/10.1016/j.erss.2020.101615
14.
DamantJ., KnappM., FreddolinoP., & LombardD. (2017). Effects of digital engagement on the quality of life of older people. Health & Social Care in the Community, 25(6), 1679–1703. https://doi.org/10.1111/hsc.12335
15.
DavisF. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results [PhD Thesis, Massachusetts Institute of Technology, Sloan School of Management]. https://dspace.mit.edu/bitstream/handle/1721.1/15192/14927137-MIT.pdf
16.
DavisF. 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
17.
DavisF. D., BagozziR. P., & WarshawP. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x
18.
DoneyP. M., & CannonJ. P. (1997). An examination of the nature of trust in buyer–seller relationships. Journal of Marketing, 61(2), 35–51.
19.
FabiV., SpigliantiniG., & CorgnatiS. P. (2017). Insights on smart home concept and occupants’ interaction with building controls. Energy Procedia, 111, 759–769. https://doi.org/10.1016/j.egypro.2017.03.238
20.
FaulF., ErdfelderE., LangA.-G., & BuchnerA. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods,39, 175–191.
21.
FutureIoT. (2022, January 14). India IoT market to reach US$9.28 billion by 2025. https://futureiot.tech/india-iot-market-to-reach-us9-28-billion-by-2025/
22.
GaoL., & WaechterK. A. (2017). Examining the role of initial trust in user adoption of mobile payment services: An empirical investigation. Information Systems Frontiers, 19(3), 525–548.
23.
GeisserS. (1974). A predictive approach to the random effect model. Biometrika, 61(1), 101–107.
24.
GiraudS., VolanschiN., & ConselC. (2020). Empowering caregivers to customizing the assistive computing support of older adults—An end-user domain-specific approach. International Journal of Human–Computer Interaction, 36(15), 1447–1459. https://doi.org/10.1080/10447318.2020.1752479
25.
GittoL. (2021). Ability and frequency of ICTs use in an older adults’ sample: Implications for developing an active aging educational strategy. International Journal of Knowledge and Learning, 14(1), 86–100. https://doi.org/10.1504/IJKL.2021.115035
26.
GranovetterM. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380.
27.
GranzerW., KastnerW., NeugschwandtnerG., & PrausF. (2006). Security in networked building automation systems. 2006 IEEE International Workshop on Factory Communication Systems, 283–292.
28.
GuZ., WeiJ., & XuF. (2016). An empirical study on factors influencing consumers’ initial trust in wearable commerce. Journal of Computer Information Systems, 56(1), 79–85.
29.
HairJ. F., BlackW. C., BabinB. J., & AndersonR. E. (2013). Multivariate data analysis always learning.
30.
HairJ. F., RingleC. M., & SarstedtM. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152.
31.
HairJ. F., SarstedtM., RingleC. M., & MenaJ. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414–433. https://doi.org/10.1007/s11747-011-0261-6
32.
HajliM. N., SimsJ., FeathermanM., & LoveP. E. (2015). Credibility of information in online communities. Journal of Strategic Marketing, 23(3), 238–253.
33.
HartwickJ., & BarkiH. (1994). Explaining the role of user participation in information system use. Management Science, 40(4), 440–465. https://doi.org/10.1287/mnsc.40.4.440
34.
HeijdenH. V. D. (2004). User acceptance of hedonic information systems. MIS Quarterly: Management Information Systems, 28(4), 695–704. https://doi.org/10.2307/25148660
35.
HenselerJ., RingleC. M., & SarstedtM. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.
36.
HenselerJ., RingleC. M., & SinkovicsR. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing. Emerald Group Publishing Limited.
37.
JahnM., JentschM., PrauseC. R., PramudiantoF., Al-AkkadA., & ReinersR. (2010). The energy aware smart home. 2010 5th International Conference on Future Information Technology, 1–8.
38.
JarvenpaaS. L., TractinskyN., & SaarinenL. (2006). Consumer trust in an internet store: A cross-cultural validation. Journal of Computer-Mediated Communication, 5(2). https://doi.org/10.1111/j.1083-6101.1999.tb00337.x
39.
KadylakT., & CottenS. R. (2020). United States older adults’ willingness to use emerging technologies. Information, Communication & Society, 23(5), 736–750.
40.
KawoosaF. (2022). Voice control is driving the usage of smart home in India. Techarc. https://techarc-my.sharepoint.com/personal/faisal_techarc_net/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Ffaisal%5Ftecharc%5Fnet%2FDocuments%2FExternal%20Downloads%2FThe%20Smart%20Homes%20of%20India%5FAmazon%5FTecharc%2Epdf&parent=%2Fpersonal%2Ffaisal%5Ftecharc%5Fnet%2FDocuments%2FExternal%20Downloads&ga=1
41.
KimS. S., & MalhotraN. K. (2005). A longitudinal model of continued IS use: An integrative view of four mechanisms underlying postadoption phenomena. Management Science, 51(5), 741–755. https://doi.org/10.1287/mnsc.1040.0326
42.
KimK. K., & PrabhakarB. (2004). Initial trust and the adoption of B2C e-commerce: The case of internet banking. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 35(2), 50–64.
43.
KimG., ShinB., & LeeH. G. (2009). Understanding dynamics between initial trust and usage intentions of mobile banking. Information Systems Journal, 19(3), 283–311. https://doi.org/10.1111/j.1365-2575.2007.00269.x
44.
KimH.-W., XuY., & KohJ. (2004). A comparison of online trust building factors between potential customers and repeat customers. Journal of the Association for Information Systems, 5(10), 392–420. https://doi.org/10.17705/1jais.00056
45.
KitkowskaA., HögbergJ., & WästlundE. (2022). Online terms and conditions: Improving user engagement, awareness, and satisfaction through UI design. CHI Conference on Human Factors in Computing Systems, 1–22.
46.
KlineR. B. (2011). Convergence of structural equation modeling and multilevel modeling. Sage Publications.
47.
KockN. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of E-Collaboration, 11(4), 1–10.
48.
LarocheM., NepomucenoM. V., & RichardM.-O. (2010). How do involvement and product knowledge affect the relationship between intangibility and perceived risk for brands and product categories? Journal of Consumer Marketing, 27(3), 197–210.
49.
LeeM. K. O., & TurbanE. (2001). A trust model for consumer internet shopping. International Journal of Electronic Commerce, 6(1), 75–91. https://doi.org/10.1080/10864415.2001.11044227
50.
LeeY., & KozarK. A. (2012). Understanding of website usability: Specifying and measuring constructs and their relationships. Decision Support Systems, 52(2), 450–463.
51.
LiX., HessT. J., & ValacichJ. S. (2008). Why do we trust new technology? A study of initial trust formation with organizational information systems. The Journal of Strategic Information Systems, 17(1), 39–71.
52.
LimayemM., HirtS. G., & CheungC. 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
53.
LohseG. L., & SpillerP. (1998). Electronic shopping. Communications of the ACM, 41(7), 81–87. https://doi.org/10.1145/278476.278491
54.
MacKenzieS. B., & PodsakoffP. M. (2012). Common method bias in marketing: Causes, mechanisms, and procedural remedies. Journal of Retailing, 88(4), 542–555. https://doi.org/10.1016/j.jretai.2012.08.001
55.
MahfuzM. A., KhanamL., & HuW. (2016). The influence of culture on m-banking technology adoption: An integrative approaches of UTAUT2 and ITM. 2016 Portland International Conference on Management of Engineering and Technology (PICMET), 824–835.
56.
MarkR. (1996). Research made simple: A handbook for social workers. Sage Publications.
57.
MartinsC., OliveiraT., & PopovičA. (2014). Understanding the internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34(1), 1–13. https://doi.org/10.1016/j.ijinfomgt.2013.06.002
58.
MayerR. C., DavisJ. H., & SchoormanF. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709–734. https://doi.org/10.5465/amr.1995.9508080335
59.
McKnightD. H., CummingsL. L., & ChervanyN. L. (1998). Initial trust formation in new organizational relationships. Academy of Management Review, 23(3), 473–490. https://doi.org/10.5465/AMR.1998.926622
60.
MehtaP., JebarajakirthyC., MaseehH. I., AnubhaA., SahaR., & DhandaK. (2022). Artificial intelligence in marketing: A meta-analytic review. Psychology & Marketing, 39(11), 2013–2038.
61.
MitalM., ChangV., ChoudharyP., PapaA., & PaniA. K. (2018). 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.
62.
NamT., & PardoT. A. (2011). Conceptualizing smart city with dimensions of technology, people, and institutions. ACM International Conference Proceeding Series, 282–291. https://doi.org/10.1145/2037556.2037602
OrC. K., & KarshB.-T. (2009). A systematic review of patient acceptance of consumer health information technology. Journal of the American Medical Informatics Association, 16(4), 550–560.
65.
PalD., FunilkulS., VanijjaV., & PapasratornB. (2018). Analyzing the elderly users’ adoption of smart-home services. IEEE Access, 6, 51238–51252. https://doi.org/10.1109/ACCESS.2018.2869599
66.
ParkJ., AmendahE., LeeY., & HyunH. (2019). M-payment service: Interplay of perceived risk, benefit, and trust in service adoption. Human Factors and Ergonomics in Manufacturing & Service Industries, 29(1), 31–43. https://doi.org/10.1002/hfm.20750
67.
PodsakoffP. M., MacKenzieS. B., LeeJ.-Y., & PodsakoffN. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.
68.
QuelchJ. A., & KleinL. R. (2007). The Internet and international marketing. In Readings in modern marketing (pp. 381–385). The Chinese University Press.
69.
RahiS., Othman MansourM. M., AlghizzawiM., & AlnaserF. M. (2019). Integration of UTAUT model in internet banking adoption context: The mediating role of performance expectancy and effort expectancy. Journal of Research in Interactive Marketing, 13(3), 411–435. https://doi.org/10.1108/JRIM-02-2018-0032
70.
RahmanMd. S., DasS., HossainG. Md. S., & TajrinT. (2021). Teenagers’ behavioural intention towards wearable technologies and intention to recommend others: An empirical study in Bangladesh. Journal of Science and Technology Policy Management, 13(1), 110–131. https://doi.org/10.1108/JSTPM-05-2020-0088
71.
Rahmani-AndebiliM., & ShenH. (2017). Price-controlled energy management of smart homes for maximizing profit of a GENCO. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(4), 697–709.
72.
SarstedtM., RingleC. M., & HairJ. F. (2021). Partial least squares structural equation modeling. In Handbook of market research (pp. 587–632). Springer.
73.
ShangliaoS. (2022). Smart home market in India. Statista. https://www.statista.com/topics/6903/smart-home-market-in-india/
74.
Shanthana LakshmiS., & GuptaD. (2021). The smart set: A study on the factors that affect the adoption of smart home technology. Machine Learning for Predictive Analysis: Proceedings of ICTIS2020, 443–450.
75.
ShimJ. T., Van SlykeC., JiangJ. J., & JohnsonR. D. (2004). Does trust reduce concern for information privacy in e-commerce. Proceedings of the 7th Annual Conference of the Southern Association for Information Systems, 37, 227–234.
76.
ShuhaiberA., & MashalI. (2019). Understanding users’ acceptance of smart homes. Technology in Society, 58, 101110. https://doi.org/10.1016/j.techsoc.2019.01.003
77.
SinagaM. (2019). Adoption of IoT at home in Indonesia[Master’s thesis, University of Twente].
78.
SinghD., PsychoulaI., KropfJ., HankeS., & HolzingerA. (2018). Users’ perceptions and attitudes towards smart home technologies, M. Mokhtari, B. Abdulrazak, & H. Aloulou (Eds), (pp. 203–214). Springer International Publishing. https://doi.org/10.1007/978-3-319-94523-1_18
79.
SitkinS. B., & RothN. L. (1993). Explaining the limited effectiveness of legalistic “remedies” for trust/distrust. Organization Science, 4(3), 367–392.
80.
StraubE. T. (2009). Understanding technology adoption: Theory and future directions for informal learning. Review of Educational Research, 79(2), 625–649.
81.
SuryadevaraN. K., & MukhopadhyayS. C. (2015). Smart homes. Springer.
82.
SutterM., & KocherM. G. (2007). Trust and trustworthiness across different age groups. Games and Economic Behavior, 59(2), 364–382. https://doi.org/10.1016/j.geb.2006.07.006
83.
TamilmaniK., RanaN. P., WambaS. F., & DwivediR. (2021). The extended Unified Theory of Acceptance and Use of Technology (UTAUT2): A systematic literature review and theory evaluation. International Journal of Information Management, 57, 102269.
84.
TarkomaS. (2011). Internet of Things Strategic Research Agenda (IoT-SRA). Finnish Strategic Centre for Science, Technology, and Innovation: For Information and Communications (ICT) Services, Businesses, and Technologies.
85.
ThongJ. Y. L., HongS. J., & TamK. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human Computer Studies, 64(9), 799–810. https://doi.org/10.1016/j.ijhcs.2006.05.001
VassliL. T., & FarshchianB. A. (2018). Acceptance of health-related ICT among elderly people living in the community: A systematic review of qualitative evidence. International Journal of Human–Computer Interaction, 34(2), 99–116. https://doi.org/10.1080/10447318.2017.1328024
88.
VenkateshV., & BalaH. (2008). Technology Acceptance Model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
89.
VenkateshV., & DavisF. D. (2000). Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
90.
VenkateshV., MorrisM. G., DavisG. B., & DavisF. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
91.
VenkateshV., SykesT. A., & ZhangX. (2011). “Just what the doctor ordered”: A revised UTAUT for EMR system adoption and use by doctors. 2011 44th Hawaii International Conference on System Sciences, 1–10.
92.
VenkateshV., ThongJ. Y. L., & XuX. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly: Management Information Systems, 36(1), 157–178. https://doi.org/10.2307/41410412
93.
WagnerK., NimmermannF., & Schramm-KleinH. (2019). Is it human? The role of anthropomorphism as a driver for the successful acceptance of digital voice assistants. Proceedings of the 52nd Hawaii International Conference on System Sciences.
94.
WarshawP. R. (1980). A new model for predicting behavioral intentions: An alternative to Fishbein. Journal of Marketing Research, 17(2), 153–172. https://doi.org/10.1177/002224378001700201
95.
WeckM., & AfanassievaM. (2022). Toward the adoption of digital assistive technology: Factors affecting older people’s initial trust formation. Telecommunications Policy, 47(2), 102483.
96.
WilliamsM. D., RanaN. P., & DwivediY. K. (2015). The unified theory of acceptance and use of technology (UTAUT): A literature review. Journal of Enterprise Information Management, 28(3), 443–488.
97.
YoonH. S., & OcceñaL. G. (2015). Influencing factors of trust in consumer-to-consumer electronic commerce with gender and age. International Journal of Information Management, 35(3), 352–363. https://doi.org/10.1016/j.ijinfomgt.2015.02.003
98.
ZahediF., & SongJ. (2008). Dynamics of trust revision: Using health infomediaries. Journal of Management Information Systems, 24(4), 225–248. https://doi.org/10.2753/MIS0742-1222240409
99.
ZaidanA. A., & ZaidanB. B. (2020). A review on intelligent process for smart home applications based on IoT: Coherent taxonomy, motivation, open challenges, and recommendations. Artificial Intelligence Review, 53(1), 141–165. https://doi.org/10.1007/s10462-018-9648-9
100.
ZhangT., TaoD., QuX., ZhangX., LinR., & ZhangW. (2019). The roles of initial trust and perceived risk in public’s acceptance of automated vehicles. Transportation Research Part C: Emerging Technologies, 98, 207–220.
101.
ZhouT. (2011a). An empirical examination of initial trust in mobile banking. Internet Research, 21(5), 527–540. https://doi.org/10.1108/10662241111176353
102.
ZhouT. (2011b). The effect of initial trust on user adoption of mobile payment. Information Development, 27(4), 290–300. https://doi.org/10.1177/0266666911424075
