Factors Affecting Health Technology Acceptance via Social Media among Gen-Y in Songkhla Province
Main Article Content
Abstract
This research aimed to study: 1) the health attitude level; 2) perceived risk
of health; 3) online marketing mix factors; 4) health technology acceptance; and 5) factors affecting health technology acceptance via social media among Gen-Y in Songkhla province. The sampling group included 400 people. Data was collected using an online questionnaire. The statistics used in data analysis were frequency, percentage, mean, standard deviation, and multiple regression analysis. The results found that most of the representatives in the sample were females, with ages between 24 and 29, and with a bachelor's degree. Most worked for themselves or a company, and the pay ranged from 10,000 to 15,000 baht. Most respondents' social media access tools were smartphones, and the time when they utilized social media was from 6:00 a.m. to 9:00 p.m. The level of health-related attitudes was at its highest, with an average of 4.30. The average level of risk perception was high at 4.04. The level of online marketing mix elements was at a high level, with an average of 4.22. The level of acceptability of the use of health technology was 4.29. The factors affecting the acceptability of health technology through social media were: 1) personal service, 2) pricing, 3) channel, 4) marketing promotion, and 5) comprehension. The hypothesis testing revealed that health attitudes level, perceived risk of health and online marketing mix factors influenced health technology acceptance through social media and was statistically significant at the 0.05 level. The results of a study can enhance and innovate health technology via social media.
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References
Choketaworn, L. & Donkwa, K. (2017). Impacts of marketing mix and attitude toward clean food purchased decision of consumers in Nakhon Ratchasima Province. KKU Research Journal (Graduate Studies) Humanities and Social Sciences, 5(1), 79–91.
Churchill, E. F. (2012). Social media meaning. Proceeding of the 2012, international on Socially-aware multimedia. http://dl.acm.org/citation.cfm?id=2390876
Cronbach, L. J. (1970). Essentials of psychological test (5th ed.). Harper Collins.
Dachjiramanee, C. Suwannoi, T., Sukhaparamate, S. Jantarakolica, K. & Jantarakolica, T.(2022). Factors affecting the acceptance of Internet of Things (IoT) technology for residence. Journal of Humanities and Social Sciences Nakhon Phanom University, 12(3). 271-286.
Davis, F.D. (1989). Technology acceptance model, Technology acceptance model - IS Theory. http://is.theorizeit.org/wiki/Technology_acceptance_model
Electronic Transactions Development Agency. (2018). Checklist to know about online trading. Electronic Transactions Development Agency.
Euajarusphan, A. (2018). Media usage behavior by generation X and generation Y. The Journal of Social Communication Innovation, 6(1), 59-65.
Galib, H.M. & Steiger J. (2018). Predicting consumer behavior: An extension of technology acceptance model. International Journal of Marketing Studies, Canadian Center of Science and Education, 10(3), 1-73.
Jaiwong, S., Sartmoo, S., Worasesthaphong, T., & Kanittinsuttitong, N. (2022). Factors affecting technology acceptance and purchase decision on youtuber device through the platform. Narkbhutparitat Journal Nakhon Si Thammarat Rajabhat University, 14(3), 206-219.
Kacha, R. (2010). Lifestyle of subculture groups in Generation Y [Master’s thesis, Chulalongkorn University] http://cuir.car.chula.ac.th/handle/123456789/27315
Kamal, S.A., Shafiq, M. & Kakria P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60. DOI: 10.1016/j.techsoc.2019.101212
Kamonsupajinda, K. (2015). Social media use and generational identity: The differentiation between baby boomers generation vs. y generation [Unpublished master’s thesis]. Bangkok University.
Kanchanapha, C., & Phungbangkruay, J. (2020). The factors influencing clean food purchase intentions of generation Y in Bangkok. KKBS Journal of Business Administration and Accountancy, 4(2), 37–62.
Khangket, A. (2010). E-Commerce 6P marketing mix. http://drsuntzuweekly.com/it/-e-commerce-6p.
Leonwan, O. (2012). Factors affecting information technology acceptance: A case study of community development department, government complex chaeng watthana [Unpublished master’s thesis]. Rajamangala University of Technology Thanyaburi.
Liman, Q. & Sunalai, S. (2021). Factors affecting technology acceptance for mobile payment application of consumers in Bangkok. Suthiparithat Journal, 35(1), 158-174.
McKechnie, S., Winklhofer, H. & Ennew, C. (2006). Applying the technology acceptance model to the online retailing of financial services. International Journal of Retail & Distribution Management, 34(4), 388-410.
Miller, R.K. & Washington, K.D. (2017). Consumer behavior 2017-2018. Loganville, GA: Richard K Miller & Associates.
Panya, A. (2019). Online marketing mix factors affecting the decision to buy fashion clothes via social media (Facebook) of undergraduate students in the university district, Mueang District Chiang Mai Province [Unpublished master’s thesis]. Chiang Mai Rajabhat University.
Pengjarern, S. (2018). Factors affecting attitude on using social network of senior students at Kasetsart, Bangkhen Unversity. Vajira Nursing Journal, 18(2), 63–74.
Pisanpanich, C. (2011). The influence of materialism and self-awareness on impulsive buying behavior among Generation Y consumers [Unpublished master’s thesis]. Chulalongkorn University.
Pongput, S. (2013). Social Media: Applied Guidelines. http://library.senate.go.th/document/Ext6685/ 6685991_0004.PDF.
Poothong, T. (2018). Influence of personality and technology acceptance on intention to use e-books. Veridian E-Journal, 11(2), 3179-3193.
Prasitdechsakul, P. (2012). Transforming business after the COVID-19 crisis, KMA-Krungsri Mobile app. Bank of Ayudhya Public Company Limited.
Schiftman, L. & Kanuk, L. (2007). Consumer behavior (9th ed). Englewood Clifts, New Jersey, Prentice Hall.
Soosakulsing, W. & Rurkwararuk, W. (2020). Online marketing mix factors affecting the decision to buy fashion clothes through E-Commerce website in Mueang, Phitsanulok Province. Economics and Business Administration Journal Thaksin University, 12(1), 99-117.
Thonchai, Y. (2016). Factors affecting technology acceptance: a case study of reserve restaurant through mobile application [Unpublished master’s thesis]. Thammasat University.
Wang, Y., Wang, S., Wang, J., Wei, J. & Wang, C. (2018). An empirical study of consumers' intention to use ride-sharing services: Using an extended technology acceptance model. Spring Science Business Media, LLC.
Wangyen, S. (2020). Factors affecting technology adoption Used for processing accounting data A case study of the Federation of Thai Industries officials [Unpublished master’ thesis]. Dhurakij Pundit University.
Wittawatoran, S. (2007). Gen Y: Hold tight to make it work. Nation Multimedia Group Publishers.
Wiwatjarernwong, C. (2010). Online marketing mix. http://spssthesis.blogspot.sg
Wongnitchakul, V. (2007). Marketing Management. Bangkok University Press.
Wu, W.Y. & Ke, C.C. (2015). An online shopping behavior model integrating personality traits, perceived risk, and technology acceptance. Social Behavior and Personality, 43(1), 85-98. http://dx.doi.org/10.2224/sbp.2015.43.1.85
Zimbardo, P.G. & Leippe, M.R. (1991). The psychology of attitude change and social influence, American Psychological Association. Mcgraw-Hill Book Company.