Determinants Influencing the Adoption of New Information Technology Supporting Healthy Life Style: The Example of Wearable Self-Tracking Devices

Stanislava Grosová, Olga Kutnohorská, Marek Botek

Abstract

Purpose: The presented study aims to identify key factors affecting the adoption of wearable electronics in Czech women. The results of the study can give insight on how to design an optimized wearable self-tracking device.


Methodology/Approach: The well-established Unified Theory of Acceptance and Use of Technology (UTAUT) framework served as a baseline for research on key determinants of behavioral intention to use wearable self-tracking devices.


Findings: The strongest factor was identified as the habit. The second strongest predictor affecting behavioral intention and use was the construct of performance expectancy. Personal health motivation, as a factor reflecting the nature of the subject examined, was the third strongest factor. The determinants of price value, effort expectancy, and social impact influence the adoption and use of these products. Facilitating conditions, personal inovativeness, personal control over diet and hedonic motivations did not play a significant role.


Research Limitation/Implication: The tested sample included 808 interviewed women, but only from the Czech Republic. Scale already defined Eating control behavior as a measure of healthy lifestyle in terms of eating was the first usage in UTAUT 2 model.


Originality/Value of paper: The study aims primarily to uncover the determinants of the usage of wearable electronics. Secondarily, it extends the theoretical framework of UTAUT2 by testing personal factors such as personal inovativeness, personal motivation to health, and personal control of eating as variables explaining behavioral intention and usage.

References

Agarwal, R. and Prasad, J., 1997. The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Sciences, 28(3), pp.557-582. DOI: 10.1111/j.1540-5915.1997.tb01322.x.
Bruner, G., 2013. Marketing Scales Handbook: Multi-Item Measures for Consumer Insight Research (Volume 7). Fort Worth, Texas USA: GCBII Productions, LLC.
Chin, W.W., Marcolin, B.L. and Newsted, P.R., 2003. A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), pp.189-217. DOI: 10.1287/isre.14.2.189.16018.
CZSO (Czech Statistical Office), 2018a. Household Income and Living Conditions – 2017. 100 Století Statistiky, [online] Available at: < https://www.czso.cz/csu/stoletistatistiky/jak-jsou-na-tom-cesi-s-chudobou-obezitou-ci-sportovanim > [Accessed 06 February 2020].
CZSO (Czech Statistical Office), 2018b. Individuals in EU countries searching for health information on the Internet, 2017. [Využívání informačních a komunikačních technologií v domácnostech a mezi jednotlivci - 2018 > 8. Používání internetu k činnostem souvisejícím se zdravím] ČSÚ [online] Available at: < https://www.czso.cz/csu/czso/7-vyhledavani-vybranych-informaci-na-internetu-2gm1e9vx8n > [Accessed 06 February 2020].
Escobar-Rodriguez, T., Carvajal-Trujillo, E. and Monge-Lozano, P., 2014. Factors that influence the perceived advantages and relevance of Facebook as a learning tool: An extension of the UTAUT. Australasian Journal of Educational Technology, 30(2), pp.136-151. DOI: 10.14742/ajet.585.
Gao, Y., Li, H. and Luo, Y., 2015. An empirical study of wearable technology acceptance in healthcare. Industrial Management & Data Systems, 115(9), pp.1704-1723. DOI: 10.1108/imds-03-2015-0087.
Hair, J.F., Hult, G.M.T., Ringle, C.M. and Sarstedt, M., 2017. A primer on partial least squares structural equation modeling (PLS-SEM). 2nd ed. Los Angeles: SAGE Publications, Inc.
King, M. F. and Bruner, G.C., 2000. Social desirability bias: A neglected aspect of validity testing. Psychology & Marketing, 17(2), pp.79-103. DOI: 10.1002/(sici)1520-6793(200002)17:2<79::Aid-mar2>3.0.Co;2-0.
Okumus, B., Bilgihan, A. and Ozturk, A.B., 2016. Factors Affecting the Acceptance of Smartphone Diet Applications. Journal of Hospitality Marketing & Management, 25(6), pp.726-747. DOI: 10.1080/19368623.2016.1082454.
Pfeiffer, J., Entress-Fuersteneck, M.V., Urbach, N. and Buchwald, A., 2016. Quantify-me: Consumer Acceptance of Wearable Self-tracking Devices. In: ECIS, 24th European Conference on Information Systems (ECIS). Istanbul, Turkey, 12-15 July 2016. Augsburg: University of Augsburg, Bayreuth: University of Bayreuth.
Ringle, C.M., Wende, S. and Becker, J.M., 2015. SmartPLS 3. Boenningstedt: SmartPLS GmbH, [online] Available at: < http://www.smartpls.com > [Accessed 17 January 2020].
Shamim, M., Chiong, R., Bao, Y. and Malik, B., 2018. Acceptance and use predictors of fitness wearable technology and intention to recommend: An empirical study. Industrial Management & Data Systems, 119(1), pp.170-188. DOI: 10.1108/IMDS-01-2018-0009.
Tor, J. L. and Øystein, S., 2005. Impact of Personal Innovativeness on the Use of the Internet Among Employees at Work. Journal of Organizational and End User Computing (JOEUC), 17(2), pp.43-63. DOI: 10.4018/joeuc.2005040103.
Venkatesh, V., Morris, M.G., Davis, G.B. and Davis, F.D., 2003. User acceptance of information technology: Toward a unified view. Mis Quarterly, 27(3), pp.425-478. DOI: 10.2307/30036540.
Venkatesh, V., Thong, J.Y.L. and Xu, X., 2012. Consumer Acceptance and Use of Information Technology: Extending then Unified Theory of Acceptance and Use of Technology. Mis Quarterly, 36(1), pp.157-178. DOI: 10.2307/41410412.
Yuan, S., Ma, W., Kanthawala, S. and Peng, W., 2015. Keep Using My Health Apps: Discover Users' Perception of Health and Fitness Apps with the UTAUT2 Model. Telemedicine and E-Health, 21(9), pp.735-741. DOI: 10.1089/tmj.2014.0148.

Authors

Stanislava Grosová
grosovas@vscht.cz (Primary Contact)
Olga Kutnohorská
Marek Botek
Author Biography

Stanislava Grosová, University of Chemistry and Technology Prague

 

 

Grosová, S., Kutnohorská, O., & Botek, M. (2022). Determinants Influencing the Adoption of New Information Technology Supporting Healthy Life Style: The Example of Wearable Self-Tracking Devices. Quality Innovation Prosperity, 26(1), 24–37. https://doi.org/10.12776/qip.v26i1.1612

Article Details

No Related Submission Found