The Asessment of Liberal Arts Students’ Behavioral Intention and Use Behavior of Mobile Video Apps in Chongqing, China

Authors

  • Ran Wei

DOI:

https://doi.org/10.14456/shserj.2023.5
CITATION
DOI: 10.14456/shserj.2023.5
Published: 2023-06-09

Keywords:

Mobile Video Applications, Generation Z, Students, Behavioral Intention, Use behavior

Abstract

Purpose: Generation Z people are reported to be main users of Internet and mobile video applications. Thus, this study aims to assess the influencing factors of behavioral intention and use behavior towards mobile video apps, using a case of Gen Z students in liberal arts in Chongqing, China. Research design, data and methodology: The researchers used quantitative research methods and nonprobability sampling techniques including purposive, quota and convenience samplings for the data collection. 500 college students in liberal arts program who have been using mobile video apps in Chongqing, China, were invited to participate in the study. Item Objective Congruence (IOC) Index and Cronbach’s Alpha reliability were approved before the data collection process. Afterwards, structural equation model (SEM) and confirmatory factor analysis (CFA) were used for the data analysis and results. Results: Perceived ease of use, social influence, habit and facilitating conditions have a significant affect behavioral intention. Furthermore, behavioral intention significantly affects use behavior. On the other hand, perceived usefulness has no significant effect on behavioral intention. Conclusions: This study validates factors impacting Gen Z’s adoption on mobile video editing applications which mobile developers are recommended to emphasize this major group of user’s use behavior for the better development of the apps.

Author Biography

Ran Wei

College of Computer and Information Science College of Software, Southwest University, China.

References

Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology- a replication. MIS Quarterly, 16(2), 227-247.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.

Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behaviour (1st ed.). Prentice-Hall.

Akbar, F. (2013, April 4). What affects students’ acceptance and use of technology?. Figshare.

https://figshare.com/articles/What_affects_students_acceptance_and_use_of_technology_/6686654

Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., Lal, B., & Williams, M. D. (2015). Consumer adoption of internet banking in Jordan: examining the role of hedonic motivation, habit, self-efficacy and trust. Journal of Financial Services Marketing, 20(2), 145-157.

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, 1983-1998.

Arrieta, B. U., Peña, A. I. P., & Medina, C. M. (2019). The moderating effect of blogger social influence and the reader’s experience on loyalty toward the blogger. Online Information Review, 43(3), 326-349.

Awang, Z. (2012). Structural equation modeling using AMOS graphic (5th ed.). Universiti Teknologi Mara Kelantan.

Bagozzi, R. P., & Lee, K. H. (2002). Multiple routes for social influence: the role of compliance, internalization, and social identity. Social Psychology Quarterly, 65(3), 226-247.

Baptista, G., & Oliveira, T. (2016). A weight and a meta-analysis on mobile banking acceptance research. Computers in Human Behavior, 63(10), 480-489.

Baptista, G., & Oliveira, T. (2017). Why so serious? Gamification impact in the acceptance of mobile banking services. Internet Research, 27(1), 118-139.

Bassiouni, D., & Hackley, C. (2014). “Generation Z” children's adaptation to digital consumer culture: A critical literature review. Journal of Customer Behaviour, 13(2), 113-133.

Beland, L.-P., Brodeur, A., & Wright, T. (2020). The short-term economic consequences of Covid-19: exposure to disease, remote work and government response. IZA Institute of Labor Economics. https://www.iza.org/publications/dp/13159/the-short-term-economic-consequences-of-covid-19-exposure-to-disease-remote-work-and-government-response

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

Bradbury-Jones, C., & Isham, L. (2020). The pandemic paradox: the consequences of COVID-19 on domestic violence. Journal of Clinical Nursing, 29(13-14), 2047-2049.

https://doi.org/10.1111/ jocn.15296

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.

Chau, P. Y. K., & Hu, P. J. H. (2001). Information technology acceptance by individual professionals: a model comparison approach. Decision Sciences, 32(4), 699-719.

Chen, L., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: an extended technology acceptance perspective. Information and Management, 39(8), 705-719.

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.

China Internet Network Information Center. (2021, February 3). Statistical report on Internet Development in China. http://www.cac.gov.cn/2021-02/03/c_1613923423079314.htm

Chopdar, P. K., Korfiatis, N., Sivakumar, V. J., & Lytras, M. D. (2018). Mobile shopping apps adoption and perceived risks: a cross-country perspective utilizing the unified theory of acceptance and use of technology. Computers in Human Behavior, 86, 109-128.

Chua, P. Y., Rezaei, S., Gu, M., 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.

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.

Dalhberg, T., Guo, J., & Ondrus, J. (2015). A critical review of mobile payment research. Electronic Commerce Research and Applications, 14(5), 265-284.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.

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.

De Haan, E., Kannan, P. K., Verhoef, P. C., & Wiesel, T. (2018). Device switching in online purchasing: examining the strategic contingencies. Journal of Marketing, 82(5), 1-19.

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.

Dhiman, N., Arora, N., Dogra, N., & Gupta, A. (2020). Consumer adoption of smartphone fitness apps: an extended UTAUT2 perspective. Journal of Indian Business Research, 12(3), 363-388.

Dhir, A., Kaur, P., & Rajala, R. (2018). Why do young people tag photos on social networking sites? Explaining user intentions. International Journal of Information Management, 38(1), 117-127.

Dwivedi, Y., Rana, N., Chen, H., & Williams, M. (2011). A meta-analysis of the Unified theory of acceptance and use of technology (UTAUT). In M. Nüttgens, A. Gadatsch, K. Kautz, I. Schirmer & N. Blinn (Eds.), Governance and Sustainability in Information Systems. Managing the Transfer and Diffusion of IT (pp. 155-170). Springer.

Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. (1st ed.). Addison-Wesley.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.

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.

Gupta, A., Dogra, N., & George, B. (2018). What determines tourist adoption of smartphone apps? An analysis based on the UTAUT-2 framework. Journal of Hospitality and Tourism Technology, 9(1), 50-64. https://doi.org/10.1108/JHTT-02-2017-0013.

Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Prentice-Hall.

Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2010). Multivariate data analysis (7th ed.). Prentice Hall.

Haroon, O., & Rizvi, S. (2020). COVID-19: media coverage and financial markets behavior-a sectoral inquiry. Journal of Behavioral and Experimental Finance, 27(1), 10-43.

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.

Hsu, C. L., & Lu, H. P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information and Management, 41(7), 853-868.

Huang, C. Y., & Kao, Y. S. (2015). UTAUT2 based predictions of factors influencing the technology acceptance of phablets by DNP. Mathematical Problems in Engineering, 1, 1-23. http://dx.doi.org/10.1155/2015/603747.

Huang, Y. M. (2015). Exploring the factors that affect the intention to use collaborative technologies: the differing perspectives of sequential/global learners. Australasian Journal of Educational Technology, 31(3), 278-292.

Huh, H. J., Kim, T., & Law, R. (2009). A comparison of competing theoretical models for understanding acceptance behavior of information systems in upscale hotels. International Journal of Hospitality Management, 28(1), 121-134.

Huili, Y., & Chunfang, Z. (2011). The analysis of influencing factors and promotion strategy for the use of mobile banking. Canadian Journal of Social Science, 7(2), 60-63.

Hu, X., & Lai, C. (2019). Comparing factors that influence learning management systems use on computers and mobile. Information and Learning Sciences, 120(7/8), 468-488.

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.

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.

Kumar, R. G., Rejikumar, G., & Ravindran, D. S. (2012). An empirical study on service quality perceptions and continuance intention in mobile banking context in India. Journal of Internet Banking and Commerce, 17(1), 1-22.

Lai, C. (2013). A framework for developing self-directed technology use for language learning. Language Learning and Technology, 17(2), 100-122.

Lanier, K. (2017). 5 Things HR professionals need to know about generation Z: thought leaders share their views on the HR profession and its direction for the future. Strategic HR Review, 16(6), 288-290.

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.

Liébana-Cabanillas, F. J., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2014). Role of gender on acceptance of mobile payment. Industrial Management & Data Systems, 114(2), 220-240.

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), 705-737.

Liu, Y., & Li, H. (2009). Mobile internet diffusion in China: an empirical study. Industrial Management & Data Systems, 110(3), 309-324.

Li, Z., Ge, Y., Su, Z., & Huang, X. H. (2020). Audience leisure involvement, satisfaction and behavior intention at the Macau Science Center. The Electronic Library, 38(2), 383-401.

Lua, J., Liub, C., & Weic, J. (2016). How important are enjoyment and mobility for mobile applications?. The Journal of Computer Information Systems, 57(1), 1-12.

Martin, H. S., & Herrero, A. (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.

Miller, L. J., & Lu, W. (2019). Gen Z is set to outnumber millennials within a year. Bloomberg.

https://www.bloomberg.com/news/articles/2018-08-20/gen-z-to-outnumber-millennials-within-ayear-demographic-trends

Morris, M. G., & Dillon, A. (1997). How user perceptions influence software use. IEEE Software, 14(4), 58-65.

Mütterlein, J. E., Kunz, R., & Baier, D. (2019). Effects of lead-usership on the acceptance of media innovations: A mobile augmented reality case. Technological Forecasting and Social Change. 145(11), 113-124.

Nagel, L. (2020). The influence of the COVID-19 pandemic on the digital transformation of work. International Journal of Sociology and Social Policy 40(9/10), 861-875.

National Bureau of Statistics. (2020, November 27). Statistics on university student enrollment in 2020.

https://data.stats.gov.cn/easyquery.htm?cn=C01&zb=A0M0F02&sj=2020

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, 43(3), 37-40.

Qin, A. (2020). China may Be beating the coronavirus, at a painful Cost. NY Times.

https://www.nytimes.com/2020/03/07/world/asia/china-coronavirus-cost.html

Ryback, R. (2016, June 22). From baby boomers to generation Z: a detailed look at the characteristics of each generation. Psychology Today.

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.

Schierz, P. G., Schilke, O., & Wirtz, B. W. (2010). Understanding consumer acceptance of mobile payment services: an empirical analysis. Electronic Commerce Research and Applications, 9(3), 209-216.

Sekaran, U. (1992). Research Methods for Business-A skill building approach (2nd ed.). John Wiley and Sons, Inc.

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.

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.

Soper, D. S. (2022, May 24). A-priori Sample Size Calculator for Structural Equation Models. Daniel Soper.

www.danielsoper.com/statcalc/default.aspx

Suh, B., & Han, I. (2002). Effect of trust on customer acceptance of internet banking. Electronic Commerce and Applications, 1(3), 247-263.

Taherdoost, H. (2017). Determining sample size; How to calculate survey sample size. International Journal of Economics and Management Systems, 2, 237-239.

Taylor, S., & Todd, P. (1995). Assessing IT usage: the role of prior experience. MIS Quarterly, 19(4), 561-70.

Teo, T. S. H., Lim, V. K. G., & Lai, R. Y. C. (1999). Intrinsic and extrinsic motivation in internet usage. Omega: International Journal of Management Science, 27(1), 25-37.

Teo, T., Lee, C. B., & Chai, C. S. (2008). Understanding pre-service teachers’ computer attitudes: applying and extending the technology acceptance model. Journal of Computer Assisted Learning, 24(2), 128-143.

Teo, T., & Noyes, J. (2014). Explaining the intention to use technology among pre-service teachers: a multi-group analysis of the unified theory of acceptance and use of technology. Interactive Learning Environments, 22(1), 51-66.

Thompson, R. L., Higgins, C. A., & Howell, J. M. (1994). Influence of experience on personal computer utilization: testing a conceptual model. Journal of Management Information Systems, 11(1), 167-187.

Tjondronegoro, D., Wang, L., & Joly, A. (2006). 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 behavioural intention, facilitating conditions, and behavioural expectation. MIS Quarterly, 32(3), 438-502.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46(2), 186-204.

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.

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), 157-178.

Wilder-Smith, A., & Freedman, D. O. (2020). Isolation, quarantine, social distancing and community containment: pivotal role for old-style public health measures in the novel coronavirus outbreak. Journal of Travel Medicine, 27(2), 1-4.

Wu, J. H., & Wang, Y. M. (2006). Measuring KMS success: A respecification of the DeLone and McLean’s model. Information and Management, 43(6), 728-739.

https://doi.org/10.1016/j.im.2006.05.002

Yi, M., Jackson, J., Park, J., & Probst, J. (2016). Understanding information technology acceptance by individual professionals: toward an integrative view. Information and Management, 43(3), 350-363.

Downloads

Published

2023-06-09

How to Cite

Wei, R. (2023). The Asessment of Liberal Arts Students’ Behavioral Intention and Use Behavior of Mobile Video Apps in Chongqing, China. Scholar: Human Sciences, 15(1), 38-50. https://doi.org/10.14456/shserj.2023.5