DECODING CONSUMER ATTITUDE TOWARDS SMART WEARABLE HEALTH DEVICES: AN INTEGRATED MODEL OF HEALTH, KNOWLEDGE, VALUE, AND CONSUMER CHARACTERISTICS
DOI:
https://doi.org/10.69980/c3zg4y82Keywords:
Smart Wearable Health Devices, Attitude, Health Consciousness, Perceived Benefits, Objective Knowledge, PLS-SEMAbstract
The rapid diffusion of digital health technologies has made smart wearable health devices like smartwatches, fitness bands, and related body-worn sensors an increasingly prominent category of consumer technology. This study develops and empirically tests an integrated model in which health consciousness, awareness, perceived benefits, consumer innovativeness, price sensitivity and objective knowledge are hypothesised to predict consumer attitude towards smart wearable health devices. Primary data were collected from 500 consumers in Delhi NCR using a structured, self-administered questionnaire and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) in SmartPLS 4. The measurement model demonstrated satisfactory reliability and validity, and the structural model explained 61.8 per cent of the variance in consumer attitude (R² = .618; Q²predict = .604). Health consciousness (β = .385), perceived benefits (β = .234), objective knowledge (β = .230) and awareness (β = .140) were found to exert significant positive effects on attitude, whereas consumer innovativeness (β = −.034) and price sensitivity (β = −.035) did not reach statistical significance. The findings suggest that health-related motivation and informed understanding of device functionality matter more to consumer evaluation than novelty-seeking or cost considerations.
References
1.Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215. https://doi.org/10.1287/isre.9.2.204
2.Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
3.Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1007/BF02723327
4.Bollen, K. A. (1989). Structural equations with latent variables. John Wiley & Sons.
5.Cheung, M. L., Chau, K. Y., Lam, M. H. S., Tse, G., Ho, K. Y., Flint, S. W., Broom, D. R., Tso, E. K. H., & Lee, K. Y. (2019). Examining consumers' adoption of wearable healthcare technology: The role of health attributes. International Journal of Environmental Research and Public Health, 16(13), 2257. https://doi.org/10.3390/ijerph16132257
6.Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Lawrence Erlbaum Associates.
7.Ciunova-Shuleska, A., Osakwe, C. N., Palamidovska-Sterjadovska, N., Ogunmokun, O., & Adeola, O. (2025). Fostering consumer acceptance of smart glasses: The moderating role of price sensitivity. Technology Analysis & Strategic Management, 37(12), 2771–2784. https://doi.org/10.1080/09537325.2024.2372780
8.Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
9.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
10.Efron, B., & Tibshirani, R. (1986). Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Statistical Science, 1(1), 54–75.
11.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/10.2307/3151312
12.Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61(1), 101–107. https://doi.org/10.1093/biomet/61.1.101
13.Gould, S. J. (1988). Consumer attitudes toward health and health care: A differential perspective. Journal of Consumer Affairs, 22(1), 96–118. https://doi.org/10.1111/j.1745-6606.1988.tb00214.x
14.Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
15.Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1–2), 1–12. https://doi.org/10.1016/j.lrp.2013.01.001
16.Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121. https://doi.org/10.1108/EBR-10-2013-0128
17.Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
18.Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage.
19.Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In R. R. Sinkovics & P. N. Ghauri (Eds.), Advances in International Marketing (Vol. 20, pp. 277–319). Emerald.
20.Henseler, J., Ringle, C. M., & Sarstedt, M. (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. https://doi.org/10.1007/s11747-014-0403-8
21.Lee, S. Y., & Lee, K. H. (2018). Factors that influence an individual's intention to adopt a wearable healthcare device: The case of a wearable fitness tracker. Technological Forecasting and Social Change, 129, 154–163. https://doi.org/10.1016/j.techfore.2018.01.002
22.Lichtenstein, D. R., Ridgway, N. M., & Netemeyer, R. G. (1993). Price perceptions and consumer shopping behavior: A field study. Journal of Marketing Research, 30(2), 234–245. https://doi.org/10.2307/3172830
23.Lunney, A., Cunningham, N. R., & Eastin, M. S. (2016). Wearable fitness technology: A structural investigation into acceptance and perceived fitness outcomes. Computers in Human Behavior, 65, 114–120. https://doi.org/10.1016/j.chb.2016.08.007
24.Menon, S. R., & Chavadi, C. A. (2022). A research-based approach to identify the health-conscious consumers in India. Vision: The Journal of Business Perspective. Advance online publication. https://doi.org/10.1177/09722629221100963
25.Park, C. W., Mothersbaugh, D. L., & Feick, L. (1994). Consumer knowledge assessment. Journal of Consumer Research, 21(1), 71–82. https://doi.org/10.1086/209386
26.Shin, G., Jarrahi, M. H., Fei, Y., Karami, A., Gafinowitz, N., Byun, A., & Lu, X. (2019). Wearable activity trackers, accuracy, adoption, acceptance and health impact: A systematic literature review. Journal of Biomedical Informatics, 93, 103153. https://doi.org/10.1016/j.jbi.2019.103153
27.Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B, 36(2), 111–147. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
28.Ulusoy, V., Hajdini, J., & Dirsehan, T. (2025). Deciphering wearable adoption: Insights from hospital image and consumer innovativeness. Journal of Technology in Behavioral Science. Advance online publication. https://doi.org/10.1007/s41347-025-00478-3
29.Wong, K. K.-K. (2013). Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Marketing Bulletin, 24(1), 1–32.
30.Yang, Q., Al Mamun, A., Hayat, N., Jingzu, G., Hoque, M. E., & Salameh, A. A. (2022). Modeling the intention and adoption of wearable fitness devices: A study using SEM-PLS analysis. Frontiers in Public Health, 10, Article 918989. https://doi.org/10.3389/fpubh.2022.918989
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
