INVESTIGATION ON THE USE BEHAVIOR OF MOBILE VIDEO APPS AMONG GEN Z STUDENTS IN CHONGQING, CHINA
Keywords:
Mobile Video Application, Generation Z, Science Students, Behavioral Intention, Use BehaviorAbstract
Mobile video applications have a second opportunity because of their convenience, real-time, and distance-free features. This research explores the factors that influence the use behavior of mobile video apps among generation Z in Chongqing, China. These factors are determined by perceived ease of use, usefulness, social influence, habit, facilitating conditions, behavioral intention, and user behavior. The researchers used quantitative research methods and non-probabilistic sampling as sampling tools. A total of 500 science college students studying and using mobile video apps in Chongqing, China, were invited to participate in the study. In this research, structural equation models (SEM) and confirmatory factor analysis (CFA) were used to model fit, reliability, and validity. The results show that perceived ease of use, social influence, and habit significantly affect the behavioral intention towards use behavior.
References
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
Akbar, F. (2013). What affects students’ acceptance and use of technology. https://figshare.com/articles/What_affects_students_acceptance_and_use_of_technology_/6686654
Al-Emran, M., & Teo, T. (2020). Do knowledge acquisition and knowledge sharing really affect e-learning adoption? An empirical study. Education and Information Technologies, 25(3), 1983-1998. https://doi.org/10.1007/s10639-019-10062-w
Awang, Z. (2012). Structural equation modeling using AMOS graphic (1st ed.). Penerbiy University Technology MARA.
Baptista, G., & Oliveira, T. (2016). A weight and a meta-analysis on mobile banking acceptance research. Computers in Human Behavior, 63, 480-489. https://doi.org/10.1016/j.chb.2016.05.074
Baptista, G., & Oliveira, T. (2017). Why so serious? Gamification impact in the acceptance of mobile banking services. Internet Research, 27(1), 118-139. https://doi.org/10.1108/intr-10-2015-0295
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246. https://doi.org/10.1037/0033-2909.107.2.238
Brown, S. A., Venkatesh, V., & Hoehle, H. (2015). Technology adoption decisions in the household: A seven-model comparison. Journal of the Association for Information Science and Technology, 66(9), 1933-1949. https://doi.org/10.1002/asi.23305
Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). Routledge Taylor & Francis Group.
Chen, L., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: an extended technology acceptance perspective. Information & Management, 39(8), 705-719. https://doi.org/10.1016/s0378-7206(01)00127-6
Chillakuri, B., & Mahanandia, R. (2018). Generation Z entering the workforce: the need for sustainable strategies in maximizing their talent. Human Resource Management International Digest, 26(4), 34-38. https://doi.org/10.1108/hrmid-01-2018-0006
Chua, P. Y., Rezaei, S., Gu, M.-L., Oh, Y., & Jambulingam, M. (2018). Elucidating social networking apps decisions: Performance expectancy, effort expectancy and social influence. Nankai Business Review International, 9(2), 118-142. https://doi.org/10.1108/nbri-01-2017-0003
Chun, H., Lee, H., & Kim, D. (2012). The Integrated Model of Smartphone Adoption: Hedonic and Utilitarian Value Perceptions of Smartphones Among Korean College Students. Cyberpsychology, Behavior, and Social Networking, 15(9), 473-479. https://doi.org/10.1089/cyber.2012.0140
Dahlberg, T., Guo, J., & Ondrus, J. (2015). A critical review of mobile payment research. Electronic Commerce Research and Applications, 14(5), 265-284. https://doi.org/10.1016/j.elerap.2015.07.006
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982
Deng, S., Liu, Y., & Qi, Y. (2011). An empirical study on determinants of web based question-answer services adoption. Online Information Review, 35(5), 789-798. https://doi.org/10.1108/14684521111176507
Dhiman, N., Arora, N., Dogra, N., & Gupta, A. (2019). Consumer adoption of smartphone fitness apps: an extended UTAUT2 perspective. Journal of Indian Business Research, 12(3), 363-388. https://doi.org/10.1108/jibr-05-2018-0158
Dwivedi, Y. K., Rana, N. P., Chen, H., & Williams, M. D. (2011). A Meta-analysis of the Unified Theory of Acceptance and Use of Technology (UTAUT). Governance and Sustainability in Information Systems. Managing the Transfer and Diffusion of IT, 155-170. https://doi.org/10.1007/978-3-642-24148-2_10
Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Contemporary Sociology, 6(2), 244. https://doi.org/10.2307/2065853
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/10.1177/002224378101800104
Gao, L., & Bai, X. (2014). A unified perspective on the factors influencing consumer acceptance of internet of things technology. Asia Pacific Journal of Marketing and Logistics, 26(2), 211-231. https://doi.org/10.1108/apjml-06-2013-0061
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2010). Multivariate data analysis (7th ed.). Prentice Hall.
Hew, J.-J., Lee, V.-H., Ooi, K.-B., & Wei, J. (2015). What catalyses mobile apps usage intention: an empirical analysis. Industrial Management & Data Systems, 115(7), 1269-1291. https://doi.org/10.1108/imds-01-2015-0028
Hu, X., & Lai, C. (2019). Comparing factors that influence learning management systems use on computers and on mobile. Information and Learning Sciences, 120(7/8), 468-488. https://doi.org/10.1108/ils-12-2018-0127
Joo, Y. J., Kim, N., & Kim, N. H. (2016). Factors predicting online university students' use of a mobile learning management system (m-LMS). Educational Technology Research and Development, 64(4), 611-630. https://doi.org/10.1007/s11423-016-9436-7
José Liébana-Cabanillas, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2014). Role of gender on acceptance of mobile payment. Industrial Management & Data Systems, 114(2), 220-240. https://doi.org/10.1108/imds-03-2013-0137
Kim, S. S., Malhotra, N. K., & Narasimhan, S. (2005). Research Note—Two Competing Perspectives on Automatic Use: A Theoretical and Empirical Comparison. Information Systems Research, 16(4), 418-432. https://doi.org/10.1287/isre.1050.0070
Lee, Y.-K., Park, J.-H., Chung, N., & Blakeney, A. (2012). A unified perspective on the factors influencing usage intention toward mobile financial services. Journal of Business Research, 65(11), 1590-1599. https://doi.org/10.1016/j.jbusres.2011.02.044
Li, Z., Ge, Y., Su, Z., & Huang, X. (2020). Audience leisure involvement, satisfaction and behavior intention at the Macau Science Center. The Electronic Library, 38(2), 383-401. https://doi.org/10.1108/el-07-2019-0176
Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How Habit Limits the Predictive Power of Intention: The Case of Information Systems Continuance. MIS Quarterly, 31(4), 705. https://doi.org/10.2307/25148817
Miller, L. J., & Lu, W. (2019). Gen Z is set to outnumber millennials within a year. available at: https://www.bloomberg.com/news/articles/2018-08-20/gen-z-to-outnumber-millennials-within-ayear-demographic-trends
Pedroso, R., Zanetello, L., Guimaraes, L., Pettenon, M., Goncalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry (São Paulo), 43(3), 37-40. https://doi.org/10.1590/0101-60830000000081
Ryback, R. (2016). From baby boomers to generation Z: a detailed look at the characteristics of each generation. Psychology Today, available at: https://www.psychologytoday.com/gb/blog/thetruisms-wellness/201602/baby-boomers-generation-z?amp
Samsudeen, S. N., & Mohamed, R. (2019). University students’ intention to use e-learning systems, A study of higher educational institutions in Sri Lanka. Interactive Technology and Smart Education,16(3), 219-238.
San Martín, H., & Herrero, Á. (2012). Influence of the user's psychological factors on the online purchase intention in rural tourism: Integrating innovativeness to the UTAUT framework. Tourism Management, 33(2), 341-350. https://doi.org/10.1016/j.tourman.2011.04.003
Sharma, G. P., Verma, R. C., & Pathare, P. (2005). Mathematical modeling of infrared radiation thin layer drying of onion slices. Journal of Food Engineering, 71(3), 282-286. https://doi.org/10.1016/j.jfoodeng.2005.02.010
Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. In M.A. Lange (Ed.), Leading - Edge psychological tests and testing research (pp. 27-50). Nova.
Teo, T., Lee, C. B., & Chai, C. S. (2007). Understanding pre-service teachers' computer attitudes: applying and extending the technology acceptance model: Understanding pre-service teachers' computer attitudes. Journal of Computer Assisted Learning, 24(2), 128-143. https://doi.org/10.1111/j.1365-2729.2007.00247.x
Tjondronegoro, D., Wang, L., & Joly, A. (2007). Delivering a Fully Interactive Mobile TV. International Journal of Web Information Systems, 2(3/4), 197-211. https://doi.org/10.1108/17440080780000300
Venkatesh, V., Brown, S. A., Maruping, L. M., & Bala, H. (2008). Predicting different conceptualizations of system use: the competing roles of behavioral intention, facilitating conditions, and behavioural expectation. MIS Quarterly, 32(3), 438-502.
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
Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157. https://doi.org/10.2307/41410412
Wu, J.-H., & Wang, Y.-M. (2006). Measuring KMS success: A respecification of the DeLone and McLean's model. Information & Management, 43(6), 728-739. https://doi.org/10.1016/j.im.2006.05.002
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Suvarnabhumi Institute of Technology
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
บทความที่ได้รับการตีพิมพ์เป็นลิขสิทธิ์ของวารสารวิชาการ สถาบันเทคโนโลยีแห่งสุวรรณภูมิ
ข้อความที่ปรากฏในบทความแต่ละเรื่องในวารสารวิชาการเล่มนี้เป็นความคิดเห็นส่วนตัวของผู้เขียนแต่ละท่านไม่เกี่ยวข้องกับสถาบันเทคโนโลยีแห่งสุวรรณภูมิ และคณาจารย์ท่านอื่นๆในสถาบันฯ แต่อย่างใด ความรับผิดชอบองค์ประกอบทั้งหมดของบทความแต่ละเรื่องเป็นของผู้เขียนแต่ละท่าน หากมีความผิดพลาดใดๆ ผู้เขียนแต่ละท่านจะรับผิดชอบบทความของตนเองแต่ผู้เดียว
