Exploring What Drives Undergraduates at Xihua University in Chengdu to Stick with Short Video Apps

Siqi Li

Authors

Siqi Li

Keywords:

Facilitating Condition, Satisfaction, Continuance Intention, Short Video Application, China

Abstract

Purpose: This research paper investigates the factors impacting user continuance intention on short video applications among undergraduates from Xihua University in Chengdu, China. The conceptual framework proposed a causal relationship among information sharing, information seeking, social interaction, entertainment, facilitating condition, and satisfaction impacting continuance intention. Research design, data, and methodology: The researcher applied the quantitative method (n=500), distributing questionnaires to undergraduates from Xihua University. The sampling process involved multi-stage sampling, including judgmental sampling to select four majors’ undergraduates of Xihua University, followed by stratified random sampling to proportionately allocate the sample size across these four majors and conclude with convenience sampling for distributing the questionnaire. The Structural Equation Model (SEM) and Confirmatory Factor Analysis (CFA) were used for the data analysis, and they included model fit, reliability, and validity of the constructs. Results: The results explicated that continuance intention was significantly and directly impacted by information seeking, social interaction, entertainment, facilitating condition, and satisfaction. Satisfaction strongly impacts continuance intention, followed by entertainment, information seeking, facilitating conditions, and social interaction. Moreover, it is indirectly impacted by information sharing. Conclusions: Companies and developers are suggested to ensure that the attributes of information sharing, information seeking, entertainment, and social interaction are available when using the app, and the app offers diverse, engaging, and high-quality content to meet users’ viewing needs and interests.

Author Biography

Siqi Li

School of Innovation and Entrepreneurship, Xihua University, China.

References

Abdillah, W., & Jogiyanto, O. (2015). Partial Least Square (PLS) Alternatif Structural Equation Modelling (SEM) dalam Penelitian Bisnis (1st ed.). Penerbit Andi.

Al-Mamary, Y. H., & Shamsuddin, A. (2015). Testing of the technology acceptance model in context of Yemen. Mediterranean Journal of Social Sciences, 6(4), 268-273. https://doi.org/10.5901/mjss.2015.v6n4s1p268

Awang, Z. (2012). Structural equation modeling using AMOS graphic (1st ed.). Penerbit Universiti Teknologi MARA.

Bano, S., Cisheng, W., Khan, A. N., & Khan, N. A. (2019). WhatsApp use and students’ psychological well-being: role of social capital and social integration. Children and Youth Services Review, 103, 200-208. https://doi.org/10.1016/j.childyouth.2019.06.002

Bao, Z. (2016). Exploring continuance intention of social networking sites. Aslib Journal of Information Management, 68(6), 736-755. https://doi.org/10.1108/ajim-05-2016-0064

Beaudry, A., & Pinsonneault, A. (2010). The other side of acceptance: Studying the direct and indirect effects of emotions on information technology use. MIS Quarterly, 34(4), 689-710. https://doi.org/10.2307/25750701

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

Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation Confirmation Model. MIS Quarterly, 25, 351-370. https://doi.org/10.2307/3250921

Blumler, J. G., & Katz, E. (1974). The uses of mass communication (1st ed.). Sage.

Bruggeman, H., Hiel, A. V., Hal, G. V., & Dongen, S. V. (2019). Does the use of digital media affect psychological well-being? An empirical test among children aged 9 to 12. Computers in Human Behavior, 101, 104-113. https://doi.org/10.1016/j.chb.2019.07.015

Byrne, B. M. (2010). Structural Equation Modeling with Amos: Basic Concepts, Applications, and Programming (2nd ed.). Taylor and Francis Group.

Chen, M., & Qi, X. (2015). Members’ satisfaction and continuance intention: A socio-technical perspective. Industrial Management & Data Systems, 115(6), 1132-1150. https://doi.org/10.1108/imds-01-2015-0023

Chen, X., & Liu, Y. (2023). Influencing factors of young people’s short video switching behavior based on grounded theory. Library Hi Tech. https://doi.org/10.1108/LHT-09-2022-0207

CNNIC. (2023, March). The 51st Statistical Report on China’s Internet Development https://www.cnnic.com.cn/IDR/ReportDownloads/

Dang, V. T. (2020). Social networking site involvement and social life satisfaction: The moderating role of information sharing. Internet Research, 31(1), 80-99. https://doi.org/10.1108/intr-04-2019-0167

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

Deters, F. G., & Mehl, M. R. (2013). Does posting Facebook status updates increase or decrease loneliness? An online social networking experiment. Social Psychological and Personality Science, 4, 579-586. https://doi.org/10.1177/1948550612469233

Dholakia, U. M., Bagozzi, R. P., & Pearo, L. K. (2004). A Social Influence Model of Consumer Participation in Network- and Small-Group-Based Virtual Communities. International Journal of Research in Marketing, 21, 241-263.

https://doi.org/10.1016/j.ijresmar.2003.12.004

Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook "friends": Social capital and college students' use of online social network sites. Journal of Computer-Mediated Communication, 12(4), 1143-1168.

https://doi.org/10.1111/j.1083-6101.2007.00367.x

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

George, D., & Mallery, P. (2003). SPSS for Windows Step by Step: A Simple Guide and Reference. 11.0 Update (4th ed.). Allyn & Bacon.

Guinea, A. O., & Markus, M. L. (2009). Why break the habit of a lifetime? Rethinking the roles of intention, habit, and emotion in continuing information technology use. MIS Quarterly, 33(3), 433-444. https://doi.org/10.2307/20650303

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

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective (7th ed.). Pearson Education.

Hoyle, R. H. (2011). Structural equation modeling for social and personality psychology (1st ed.). Sage.

Hsieh, J. J. P.-A., Rai, A., & Keil, M. (2008). Understanding digital inequality: Comparing continued use behavioral models of the socio-economically advantaged and disadvantaged. MIS Quarterly, 32(1), 97-126.

Hsu, M., Tien, S., Lin, H., & Chang, C. (2015). Understanding the roles of cultural differences and socio-economic status in social media continuance intention. Information Technology & People, 28(1), 224-241. https://doi.org/10.1108/ITP-03-2013-0034

Humbani, M., & Wiese, M. (2019). An integrated framework for the adoption and continuance intention to use mobile payment apps. International Journal of Bank Marketing, 37(2), 646-664. https://doi.org/10.1108/IJBM-03-2018-0072

Hussein, R., & Hassan, S. (2017). Customer engagement on social media: How to enhance continuation of use. Online Information Review, 41(7), 1006-1028. https://doi.org/10.1108/OIR-12-2016-0292

Jason. (2019). Video sharing in China: Most popular Chinese video sites and platforms in 2019. QPSOFTWARE. https://qpsoftware.net/blog/china-video-sharing-platforms

Kang, Y. S., & Lee, H. (2010). Understanding the role of an IT artifact in online service continuance: An extended perspective of user satisfaction. Computers in Human Behavior, 26(3), 353-364. https://doi.org/10.1016/j.chb.2009.11.005

Katz, E., Blumler, J. G., & Gurevitch, M. (1974). Uses and gratifications research. The Public Opinion Quarterly, 37(4), 509-523. https://doi.org/10.1086/268109

Ko, H., Cho, C. H., & Roberts, M. S. (2005). Internet uses and gratifications: A structural equation model of interactive advertising. Journal of Advertising, 34(2), 57-70. https://doi.org/10.1080/00913367.2005.10639155

Korgaonkar, P. K., & Wolin, L. D. (1999). A multivariate analysis of web usage. Journal of Advertising Research, 39(2), 53-62. https://doi.org/10.2501/JAR-39-2-53-62

Lee, C. S., & Ma, L. (2012). News sharing in social media: The effect of gratifications and prior experience. Computers in Human Behavior, 28(2), 331-339. https://doi.org/10.1016/j.chb.2011.10.002

Lee, S., Manthiou, A., Jeong, M., Tang, L., & Chiang, L. (2015). Does consumers’ feeling affect their quality of life? Roles of consumption emotion and its consequences. International Journal of Tourism Research, 17(4), 409-416.

https://doi.org/10.1002/jtr.2037

Lin, H., Fan, W., & Chau, P. Y. K. (2014). Determinants of users’ continuance of social networking sites: A self-regulation perspective. Information & Management, 51(5), 595-603. https://doi.org/10.1016/j.im.2014.01.006

Liu, Q., Shao, Z., Tang, J., & Fan, W. (2019). Examining the influential factors for continued social media use. Industrial Management & Data Systems, 119(5), 1104-1127. https://doi.org/10.1108/IMDS-06-2018-0293

Makri, K., & Schlegelmilch, B. B. (2017). Time orientation and engagement with social networking sites: A cross-cultural study in Australia, China, and Uruguay. Journal of Business Research, 80, 155-163. https://doi.org/10.1016/j.jbusres.2017.07.014

Mileva, G. (2023, January 23). 10 short-form video trends to watch out for in 2023. https://influencermarketinghub.com/short-form-video/

Nysveen, H., & Pedersen, P. E. (2016). Consumer adoption of RFID-enabled services: Applying an extended UTAUT model. Information Systems Frontiers, 18(2), 293-314. https://doi.org/10.1007/s10796-015-9607-5

Park, N., Kee, K., & Valenzuela, S. (2009). Being immersed in social networking environment: Facebook groups, uses and gratifications, and social outcomes. Cyberpsychology & Behavior, 12(6), 729-733. https://doi.org/10.1089/cpb.2009.0003

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.

Roca, J. C., Chiu, C.-M., & Martinez, F. J. (2006). Understanding e-learning continuance intention: An extension of the technology acceptance model. International Journal of Human-Computer Studies, 64(8), 683-696.

https://doi.org/10.1016/j.ijhcs.2006.01.003

Ryan, K. M. (2017). Vertical video: Rupturing the aesthetic paradigm. Visual Communication, 17(2), 245-261.

https://doi.org/10.1177/1470357217736660

Ryan, T., Chester, A., Reece, J., & Xenos, S. (2014). The uses and abuses of Facebook: A review of Facebook addiction. Journal of Behavioral Addictions, 3(3), 133-148. https://doi.org/10.1556/JBA.3.2014.010

Santhanamery, T., & Ramayah, T. (2013). The effect of personality traits on user continuance intention of e-filing system. Journal of Economics, Business and Management, 1(1), 25-28. https://doi.org/10.7763/JOEBM.2013.V1.6

Scale, M.-S. (2008). Facebook as a social search engine and the implications for libraries in the twenty-first century. Library Hi Tech, 26(4), 540-556. https://doi.org/10.1108/07378830810920937

Shao, G. (2009). Understanding the appeal of user-generated media: A uses and gratification perspective. Internet Research, 19(1), 7-25. https://doi.org/10.1108/10662240910927775

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

Sheth, S., & Kim, J. (2017). Social media marketing: The effect of information sharing, entertainment, emotional connection, and peer pressure on attitude and purchase intentions. GSTF Journal on Business Review (GBR), 5(1), 62-70. https://doi.org/10.5176/2010-4804_5.1.232

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.

Smock, A. D., Ellison, N. B., Lampe, C., & Wohn, D. Y. (2011). Facebook as a toolkit: A uses and gratification approach to unbundling feature use. Computers in Human Behavior, 27(6), 2322-2329. https://doi.org/10.1016/j.chb.2011.07.001

Soper, D. (2006). Calculator: A-priori sample size for structural equation models. Daniel Soper. https://www.danielsoper.com/statcalc/calculator.aspx?id=89

Stafford, T. F., & Stafford, M. R. (2004). Determining uses and gratifications for the internet. Decision Sciences, 35(2), 259-288. https://doi.org/10.1111/j.1540-5915.2004.02554.x

Syn, S. Y., & Oh, S. (2015). Why do social network site users share information on Facebook and Twitter? Journal of Information Science, 41(5), 553-569. https://doi.org/10.1177/0165551515579587

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 behavioral expectation. MIS Quarterly, 32(3), 483-502. https://doi.org/10.2307/25148852

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. Y., & 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. https://doi.org/10.2307/41410412

von der Osten, B. (2021, December 24). Short-form video: The fundamental piece of your content strategy. Rockcontent. https://rockcontent.com/blog/short-form-video/

Wang, Y. (2020). Humor and camera view on mobile short-form video apps influence user experience and technology-adoption intent: An example of TikTok (Douyin). Computers in Human Behavior, 110, 106373. https://doi.org/10.1016/j.chb.2020.106373

Wheaton, B., Muthén, B., Alwin, D. F., & Summers, G. (1977). Assessing reliability and stability in panel models. Sociological Methodology, 8, 84-136. https://doi.org/10.2307/270754

Whiting, A., & Williams, D. (2013). Why people use social media: A uses and gratifications approach. Qualitative Market Research: An International Journal, 16(4), 362-369. https://doi.org/10.1108/QMR-06-2013-0041

Wright, C. (2017). Are beauty bloggers more influential than traditional industry experts?. Journal of Promotional Communications, 5(3), 303-322. https://doi.org/10.1080/21639659.2017.1378850

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

Wyzowl. (2023). Video Marketing Statistics 2023. https://www.wyzowl.com/video-marketing-statistics/#stat-5-5

Yan, Y., He, Y., & Li, L. (2023). Why time flies? The role of immersion in short video usage behavior. Frontiers in Psychology, 14, 1127210. https://doi.org/10.3389/fpsyg.2023.1127210

Zhao, L., & Lu, Y. (2012). Enhancing perceived interactivity through network externalities: An empirical study on micro-blogging service satisfaction and continuance intention. Decision Support Systems, 53(4), 825-834. https://doi.org/10.1016/j.dss.2012.03.002

Zheng, X., Liu, Y., & Wang, Y. (2013). The impacts of information quality and system quality on users' continuance intention in information-exchange virtual communities: An empirical investigation. Decision Support Systems, 56, 513-524. https://doi.org/10.1016/j.dss.2013.07.006

Zong, W., Yang, J., & Bao, Z. (2019). Social network fatigue affecting continuance intention of social networking services. Data Technologies and Applications, 53(1), 123-139.