This study looks into how consumer satisfaction in the smart home appliance market is affected by digital transformation in urban homes in Indore, Madhya Pradesh, India. Consumer purchasing criteria and post-purchase satisfaction measures have significantly changed as a result of the quick spread of Internet of Things (IoT)-enabled devices, AI-driven automation, and cloud-integrated ecosystems. 200 urban household respondents from prominent residential areas of Indore, such as Vijay Nagar, Palasia, Saket, and Rajendra Nagar, were given a structured questionnaire using a five-point Likert scale. The study assesses how functional automation features, perceived security risks, and user-interface (UI) usability affect total customer satisfaction. Multiple Linear Regression and One-Way ANOVA were used for hypothesis testing, and Cronbach's Alpha (α = 0.847) was calculated to confirm instrument reliability. The results show that while perceived security and connection concerns have a substantial negative impact (β = −0.210, p < 0.012), functional automation features (β = 0.422, p < 0.001) and UI ease of use (β = 0.315, p < 0.004) are significant positive drivers of satisfaction. For appliance makers and merchants navigating Central India's changing regional digital markets, the report offers specific strategic recommendations.
- Balakrishna, S., & Sharma, R. (2021). Urban consumerism and smart device adoption in Tier-2 Indian cities. Journal of Consumer Markets, 38(4), 412–428. https://doi.org/10.1108/JCM-2020-0231
- Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
- Bitner, M. J., Brown, S. W., & Meuter, M. L. (2000). Technology infusion in service encounters. Journal of the Academy of Marketing Science, 28(1), 138–149. https://doi.org/10.1177/0092070300281013
- Chang, Y. P., Yan, J., Zhang, J., & Luo, J. (2014). Online pharmacy patronage: The moderating role of product involvement. Computers in Human Behavior, 40, 1–14. https://doi.org/10.1016/j.chb.2014.07.025
- Cochran, W. G. (1977). Sampling techniques (3rd ed.). John Wiley & Sons.
- Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
- 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
- Foroudi, P., Melewar, T. C., & Gupta, S. (2016). Linking corporate logo, corporate image, and reputation: An examination of consumer perceptions in the UK. Journal of Business Research, 69(2), 853–862. https://doi.org/10.1016/j.jbusres.2015.07.01
- Grewal, D., Roggeveen, A. L., &Nordfält, J. (2017). The future of retailing. Journal of Retailing, 93(1), 1–6. https://doi.org/10.1016/j.jretai.2016.12.008
- Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. Journal of Marketing, 60(3), 50–68. https://doi.org/10.1177/002224299606000304
- Hubert, M., Blut, M., Brock, C., Zhang, R. W., Koch, V., & Riedl, 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-12-2016-0726
- Kim, T., Chiu, W., & Chow, B. (2019). Sport participation behaviour and digital media use: How do fans become consumers of digital sport content? Sport, Business and Management, 9(1), 59–77. https://doi.org/10.1108/SBM-06-2018-0037
- Kumar, V., & Lim, J. (2008). How do decision criteria of consumers evolve over time? A longitudinal study. Journal of the Academy of Marketing Science, 36(4), 481–501. https://doi.org/10.1007/s11747-007-0078-1
- Lee, S. H., Yoon, C., & Park, M. (2020). AI voice assistant adoption for smart home devices: The mediating role of perceived intelligence. Information Technology & People, 34(5), 1449–1476. https://doi.org/10.1108/ITP-03-2020-0143
- Mani, Z., & Chouk, I. (2018). Consumer resistance to innovation in services: Challenges and barriers in the internet of things era. Journal of Product Innovation Management, 35(5), 780–807. https://doi.org/10.1111/jpim.12463
- McKinsey & Company. (2022). India's digital consumer boom: The next frontier. McKinsey Global Institute. https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights
- Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.
- Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.1177/ 002224378001700405
- Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12–40.
- Park, E., del Pobil, A. P., & Kwon, S. J. (2017). The role of Internet of Things (IoT) in smart cities: Technology roadmap-oriented approaches. Sustainability, 9(9), 1656. https://doi.org/ 10.3390/su9091656
- 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
- Raman, P., & Kumar, N. (2022). Digital divide and digital inclusion in Tier-2 Indian cities: Evidence from Madhya Pradesh. Journal of Information Technology & Politics, 19(2), 145–162. https://doi.org/10.1080/19331681.2021.1983542
- Rathore, A. (2020). Post-pandemic shift in household appliance consumption in India. Indian Journal of Marketing, 50(8), 7–21. https://doi.org/10.17010/ijom/2020/v50/i8/152956
- Rogers, E. M. (1995). Diffusion of innovations (4th ed.). Free Press.
- Saini, R., & Gupta, A. (2023). Technology adoption trends in Central Indian cities: A consumer perspective. Management and Labour Studies, 48(1), 34–52. https://doi.org/10.1177/ 0258042X221131479
- Shin, D. H. (2014). A socio-technical framework for Internet-of-Things design: A human-centered design for the Internet of Things. Telematics and Informatics, 31(4), 519–531. https://doi.org/10.1016/j.tele.2014.02.003
- Singh, R., & Srivastava, M. (2018). Predictors of Internet-enabled technology adoption in Central India: A regional validation study. Asia Pacific Journal of Marketing and Logistics, 30(4), 812–830. https://doi.org/10.1108/APJML-06-2017-0111
- 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. https://doi.org/10.2307/30036540
- Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Qi Dong, J., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901. https://doi.org/10.1016/j.jbusres.2019.09.022
- 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. https://doi.org/10.1016/j.im.2004.07.001
- Yang, H., Lee, H., & Zo, H. (2018). 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
- 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. https://doi.org/10.1177/ 002224298805200302
- Zhou, T. (2011). An empirical study of initial trust in mobile banking. Internet Research, 21(5), 527–540. https://doi.org/10.1108/10662241111176353.