Examining the success factors of smart watch : a behavioral perspective on consumers

• Autorzy: Lin Chih Wei , Yang Chin-Cheng , Sia Wei Yeng , Tang Kai-Yu

Identyfikatory

DOI

10.17512/pjms.2019.20.2.31

Warianty tytułu

PL

Badanie czynników sukcesu inteligentnego zegarka : behawioralna perspektywa konsumentów

• Języki publikacji: EN

Abstrakty

EN

The purpose of this study was to investigate factors influencing consumers’ intention to use Apple Watch by applying Decomposed Theory of Planned Behavior (DTPB). The subject of the study was the Apple Watch users in Taiwan. The researcher used purposive and snowball sampling as the sampling method, a total of 350 surveys were issued, 327 valid surveys were collected and the effective response rate was 93.43%. The collected data were analyzed by confirmatory factor analysis and structural equation model. The results are illustrated as follow. First, the overall behavior model of Apple Watch users constructed by Decomposed Theory of Planned Behavior in this study fitted well. Second, the research model has well explanatory power to wearable device users, which is R² of attitudes, subjective norms and perceived behavioral control on behavior intentions have reached 63%. Third, perceived usefulness and perceived playfulness are the most two important influencing factors on attitudes. Interpersonal influence is the most important influencing factor to subjective norm. The most influencing factor of perceived behavioral control is facilitating condition.

PL

Celem tego badania było zbadanie czynników wpływających na zamiar korzystania przez Apple z zegarka przez zastosowanie dekompozycji teorii planowanego zachowania (DTPB). Przedmiotem badania byli użytkownicy Apple Watch na Tajwanie. Badacz zastosował celowe i śnieżkowe pobieranie próbek jako metodę pobierania próbek, wykonano w sumie 350 badań, zebrano 327 ważnych badań, a efektywny odsetek odpowiedzi wyniósł 93,43%. Zebrane dane przeanalizowano za pomocą potwierdzającej analizy czynnikowej i modelu równań strukturalnych. Wyniki zostały zilustrowane w następujący sposób. Po pierwsze, ogólny model zachowania użytkowników Apple Watch skonstruowany przez Dekomponowaną Teorię Planowanego Zachowania (DTPB) w tym badaniu dobrze skonstruowany. Po drugie, model badawczy wyjaśnia użytkownikom tych urządzeń, subiektywnych norm i postrzeganej kontroli behawioralnej nad zamiarami zachowań osiągnął 63%. Po trzecie, postrzegana użyteczność i postrzegana zabawa to dwa najważniejsze czynniki wpływające na postawy użytkowników.

Słowa kluczowe

EN

perceived playfulness; compatibility; technology acceptance model; innovation diffusion theory

PL

postrzegana użyteczność; kompatybilność; model akceptacji technologii; teoria dyfuzji innowacji

• Wydawca: Czestochowa University of Technology
• Czasopismo: Polish Journal of Management Studies
• Rocznik: 2019
• Tom: Vol. 20, No. 2
• Strony: 368--378
• Opis fizyczny: Bibliogr. 27 poz., rys.

Twórcy

autor

Lin Chih Wei

• Department of Business Administration, Chaoyang University of Technology, Taiwan

autor

Yang Chin-Cheng

• Department of Business Administration, Chaoyang University of Technology, Taiwan

autor

Sia Wei Yeng

• Department of Business Administration, Chaoyang University of Technology, Taiwan

autor

Tang Kai-Yu

• Department of Business Administration, Chaoyang University of Technology, Taiwan

Bibliografia

• 1. Appledaily. (2012). 40% of Apple users is a high-paying group. Retrieved from http://www.appledaily.com.tw/appledaily/article/headline/20120130/33987761/
• 2. Bhattacherjee, A. (2000). Acceptance of e-commerce services: the case of electronic brokerages. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 30(4), 411-420.
• 3. Bollen, K. A., & Stine, R. A. (1992). Bootstrapping Goodness-of-Fit Measures in Structural Equation Models. Sociological Methods & Research, 21(2), 205-229.
• 4. Chen, P. W. (2012). Examining Consumers’ Intention to Use Mobile Phone Applications: A Decomposed Theory of Planned Behavior Perspective. National Central University, Taiwan.
• 5. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and Intrinsic Motivation to Use Computers in the Workplace1. Journal of Applied Social Psychology, 22(14), 1111-1132.
• 6. Deng, M. H. (2015). Using Innovation Diffusion Model to Forecast Wearable Device in the Next Generation. Chung Yuan Christian University, Taiwan.
• 7. DIGITIMES. (2016). Smart phone back trend, wearable device starts to run. Retrieved from http://www.digitimes.com.tw/tw/dt/n/shwnws.asp?cnlid=13&id=0000471677_W0J5WR5B8767IW6GMQZ38
• 8. Gartner. (2016). Gartner Says Worldwide Wearable Devices Sales to Grow 18.4 Percent in 2016. Retrieved from http://www.gartner.com/newsroom/id/3198018
• 9. Hair, J. T., R.E., A., Tatham, R. L., & Black, W. C. (1997). Multivariate Data Analysis with Readings (3rd, Ed.). New York: Macmillan.
• 10. Hung, S.-Y., Tsai, J. C.-A., & Chou, S.-T. (2016). Decomposing perceived playfulness: A contextual examination of two social networking sites. Information & Management, 53(6), 698-716.
• 11. IHS Technology. (2015). Apple Watch Success Needed for a Smartwatch Mega Boom, New IHS Report Says. Retrieved from https://technology.ihs.com/530842/apple-watch-success-needed-for-a-smartwatch-mega-boom-new-ihs-report-says
• 12. Ingham, J., Cadieux, J., & Mekki Berrada, A. (2015). e-Shopping acceptance: A qualitative and meta-analytic review. Information & Management, 52(1), 44-60.
• 13. Lu, C. C. (2010). Research on the Attractive Factors of Pleasurable Products and Pleasure Durability. National Cheng Kung University, Taiwan.
• 14. Mäntymäki, M., Merikivi, J., Verhagen, T., Feldberg, F., & Rajala, R. (2014). Does a contextualized theory of planned behavior explain why teenagers stay in virtual worlds? International Journal of Information Management, 34(5), 567-576.
• 15. Moon, J.-W., & Kim, Y.-G. (2001). Extending the TAM for a World-Wide-Web context. Information & Management, 38(4), 217-230.
• 16. Msaed, C., Al-Kwifi, S. O., & Ahmed, Z. U. (2017). Building a comprehensive model to investigate factors behind switching intention of high-technology products. Journal of Product & Brand Management, 26(2), 102-119.
• 17. Oh, S. H., Kim, Y. M., Lee, C. W., Shim, G. Y., Park, M. S., & Jung, H. S. (2009). Consumer adoption of virtual stores in Korea: Focusing on the role of trust and playfulness. Psychology and Marketing, 26(7), 652-668.
• 18. Rogers, E. M. (1983). Diffusion of Innovation (3rd ed.). New York: The Free Press of Glencoe.
• 19. Shaffer, D. (2016). Higher income U.S. states use Apple iPhones; lower income states use Samsung Galaxy phones. Retrieved from MacDailyNews website: http://macdailynews.com/2016/09/27/higher-income-u-s-states-use-apple-iphones-lower-income-states-use-samsung-galaxy-phones/
• 20. Shimp, T. A., & Kavas, A. (1984). The Theory of Reasoned Action Applied to Coupon Usage. Journal of Consumer Research, 11(3), 795-809.
• 21. Taylor, S., & Todd, P. (1995a). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International Journal of Research in Marketing, 12(2), 137-155.
• 22. Taylor, S., & Todd, P. A. (1995b). Understanding Information Technology Usage: A Test of Competing Models. Information Systems Research, 6(2), 144-176.
• 23. Torkzadeh, G., Koufteros, X., & Pflughoeft, K. (2003). Confirmatory Analysis of Computer Self-Efficacy. Structural Equation Modeling: A Multidisciplinary Journal, 10(2), 263-275.
• 24. Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39(2), 273-315.
• 25. Wakefield, R. L., & Whitten, D. (2006). Mobile computing: a user study on hedonic/utilitarian mobile device usage. European Journal of Information Systems, 15(3), 292-300.
• 26. West, S. G., Finch, J. F., & Curran, P. J. (1995). Structural equation models with nonnormal variable s: Problems and remedies. (Thousand Oaks, Ed.). California, USA: Sage Publications.
• 27. Wu, M. Y., & Jiang, P. S. (2014). A Study of Usage Intention of Digital TV Based on Decomposed Theory of Planned Behavior. Journal of Data Analysis, 9(1), 147-168.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).