INVESTIGATION ON THE USE BEHAVIOR OF MOBILE VIDEO APPS AMONG GEN Z STUDENTS IN CHONGQING, CHINA

Authors

  • Ran Wei College of Computer and Information Science College of Software, Southwest University

Keywords:

Mobile Video Application, Generation Z, Science Students, Behavioral Intention, Use Behavior

Abstract

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

2023-12-28

Issue

Vol. 9 No. 2 (2023): July-December, 2023

Section

Research Articles

License

Copyright (c) 2023 Suvarnabhumi Institute of Technology

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

บทความที่ได้รับการตีพิมพ์เป็นลิขสิทธิ์ของวารสารวิชาการ สถาบันเทคโนโลยีแห่งสุวรรณภูมิ

ข้อความที่ปรากฏในบทความแต่ละเรื่องในวารสารวิชาการเล่มนี้เป็นความคิดเห็นส่วนตัวของผู้เขียนแต่ละท่านไม่เกี่ยวข้องกับสถาบันเทคโนโลยีแห่งสุวรรณภูมิ และคณาจารย์ท่านอื่นๆในสถาบันฯ แต่อย่างใด ความรับผิดชอบองค์ประกอบทั้งหมดของบทความแต่ละเรื่องเป็นของผู้เขียนแต่ละท่าน หากมีความผิดพลาดใดๆ ผู้เขียนแต่ละท่านจะรับผิดชอบบทความของตนเองแต่ผู้เดียว