The Determinants of Behavior Intentions to Use Chinese Animation and Comics Platforms of Senior Students in Chengdu, China
DOI:
https://doi.org/10.14456/shserj.2023.42Keywords:
Satisfaction, Performance, Behavioral Intention, Animation And Comics PlatformsAbstract
Purpose: This study aims to investigate the determinants of behavioral intentions to use Chinese animation and comics platforms of senior students in Chengdu, China. The conceptual framework includes perceived ease of use, perceived usefulness, attitude, trust, satisfaction, performance, and behavioral intention. Research design, data, and methodology: This research applied a quantitative method to the distributed questionnaire to 500 senior students in three selected universities. Purposive, stratified random, and convenience sampling was conducted to collect the data. The index of item-objective congruence (IOC) and a pilot test (n=30) by Cronbach alpha coefficient reliability test were applied. In addition, the data were analyzed by confirmatory factor analysis (CFA) and structural equation modeling (SEM). Results: All nine hypotheses are supported by research hypotheses and testing results. Perceived ease of use significantly impacts perceived usefulness and attitude. Perceived usefulness significantly impacts attitude and behavioral intention. Behavioral intention is impacted by attitude and satisfaction. Additionally, satisfaction is significantly related to trust and performance. Finally, trust also has a significant impact on perceived ease of use. Conclusions: The findings contribute to a new knowledge of what the young generation considers using animation and comics platforms. Thus, platform developers can exploit the results for the better development to enhance users’ experience.
References
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.
Anderson, J. C., & Narus, J. A. (1990). A model of distributor firm and manufacturer firm working partnerships. Journal of Marketing, 54(1), 42-58. https://doi.org/10.2307/1252172
Awang, Z. (2012). Structural equation modeling using AMOS graphic (1st ed.). Penerbit University Teknologi MARA.
Baki, R., Birgoren, B., & Aktepe, A. (2018). A Meta Analysis of Factors Affecting Perceived Usefulness and Perceived Ease of Use in The Adoption of E-Learning Systems. Turkish Online Journal of Distance Education, 19(4), 4-42.
https://doi.org/10.17718/tojde.471649
Bala, M., Shehu, R., & Lawal, M. (2010). Determination of the level of some heavy metals in water collected from two pollution - prone irrigation areas around Kano Metropolis. Bayero Journal of Pure and Applied Sciences, 1(1), 36-38.
https://doi.org/10.4314/bajopas.v1i1.57511
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
Butcher, J. N., Graham, J. R., Ben-Porath, Y. S., Tellegen, A., Dahlstrom, W. G., & Kaemmer, B. (2001). Minnesota Multiphasic Personality Inventory-2: Manual for administration, scoring, and interpretation, revised edition. University of Minnesota Press.
Byon, K., & Zhang, J. (2010). Development of a scale measuring destination image. Marketing Intelligence & Planning, 28(2), 508-532. https://doi.org/10.1108/02634501011053595
Chang, K.-C., Kuo, N.-T., Hsu, C. L., & Cheng, Y.-S. (2014). The Impact of Website Quality and Perceived Trust on Customer Purchase Intention in the Hotel Sector: Website Brand and Perceived Value as Moderators. International Journal of Innovation, Management and Technology, 5(4), 255-260. https://doi.org/10.7763/IJIMT.2014.V5.523
Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63(1), 160-175.
Cigdem, H., & Öztürk, M. (2016). Factors Affecting Students' Behavioral Intention to Use LMS in Turkish Post-Secondary Vocational School. International Review of Research in Open and Distance Learning, 17(3), 276-295.
https://doi.org/10.19173/irrodl.v17i3.2253
Cohen, P. J. (1969). Decision procedures for real and p‐Adic fields. Communications on Pure and Applied Mathematics, 22(1), 131-151. https://doi.org/10.1002/cpa.316022020
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
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
Doney, P. M., & Cannon, J. P. (1997). An examination of the nature of trust in buyer–seller relationships. Journal of Marketing, 61(2), 35-51. https://doi.org/10.2307/1251829
Dwyer, F. R., Schurr, P. H., & Oh, S. (1987). Developing buyer–seller relationships. Journal of Marketing, 51(2), 11-27.
https://doi.org/10.2307/1251126
Fan, K.-K., & Feng, T.-T. (2021). Sustainable Development Strategy of Chinese Animation Industry. Sustainability, 13(13), 7235. http://dx.doi.org/10.3390/su13137235
Feng, D., Xiang, C., Vongurai, R., & Pibulcharoensit, S. (2022). Investigation on Satisfaction and Performance of Online Education Among Fine Arts Major Undergraduates in Chengdu Public Universities. AU-GSB E-JOURNAL, 15(2), 169-177. https://doi.org/10.14456/augsbejr.2022.82
Fishbein, M., & Ajzen, I. (1977). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Philosophy and Rhetoric, 10(2), 130-132.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Pearson Prentice Hall.
Huang, P.-L., Lee, B., & Chen, C.-C. (2017). The influence of service quality on customer satisfaction and loyalty in B2B technology service industry. Total Quality Management & Business Excellence, 30(4), 1-17.
https://doi.org/10.1080/14783363.2017.1372184
Hussain, K., Khan, A., & Bavik, A. (2003). The effects of job performance on frontline employee job satisfaction and quitting intent: the case of hotels in Turkish Republic of Northern Cyprus. EMU Journal of Tourism Research, 4(1), 83-94.
Jeong, M., & Lambert, C. (2001). Adaptation of an information quality framework to measure customers’ behavioral intentions to use lodging Web sites. International Journal of Hospitality Management, 20(2), 129-146.
https://doi.org/10.1016/S0278-4319(00)00041-4
Kaplanidou, K., & Gibson, H. J. (2010). Predicting Behavioral Intentions of Active Event Sport Tourists: The Case of a Small-Scale Recurring Sports Event. Journal of Sport & Tourism, 15(2), 163-179.
Kim, M., & Qu, H. (2014). Travelers' behavioral intention toward hotel self-service kiosks usage. International Journal of Contemporary Hospitality Management, 26(2), 225-245. https://doi.org/10.1108/IJCHM-09-2012-0165
Lee, N. J., Chae, S. M., Kim, H., Lee, J. H., Min, H. J., & Park, D. E. (2016). Mobile-Based Video Learning Outcomes in Clinical Nursing Skill Education: A Randomized Controlled Trial. Computers, informatics, nursing: CIN, 34(1), 8-16. https://doi.org/10.1097/CIN.0000000000000183
Li, L. (2011). Understanding Chinese animation industry: The nexus of media, geography and policy. Creative Industries Journal, 3(3), 189-205. https://doi.org/10.1386/cij.3.3.189_1
Moorman, C., Deshpande, R., & Zaltman, G. (1993). Factors Affecting Trust in Market Research Relationships. Journal of Marketing, 57(1), 81-101. https://doi.org/10.2307/1252059
Oliver, R. L. (1980). A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions. Journal of Marketing Research, 17(4), 460-469. https://doi.org/10.1177/002224378001700405
Özgen, C., & Reyhan, S. (2020). Satisfaction, utilitarian performance and learning expectations in compulsory distance education: A test of mediation effect. Educational Research and Reviews, 15(6), 290-297. https://doi.org/10.5897/ERR2020.3995
Pedroso, C. B., da Silva, A. L., & Tate, W. L. (2016). Sales and Operations Planning (S&OP): Insights from a multi-case study of Brazilian Organizations. International Journal of Production Economics, 182, 213-229. https://doi.org/10.1016/j.ijpe.2016.08.035
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. Nova.
Soper, D. S. (2022, May 24). A-priori Sample Size Calculator for Structural Equation Models. Danielsoper.
www.danielsoper.com/statcalc/default.aspx
Stanislow, J. (2020, March 11). 5G Impact on Virtual Reality (VR) and Augmented Reality (AR). LinkedIn.
https://www.linkedin.com/pulse/5g-impact-virtual-reality-vr-augmented-ar-jeff-stanislow/
Stoel, L., & Lee, K. H. (2003). Modeling the effect of experience on student acceptance of web-based courseware. Internet Research, 13(5), 364-374. https://doi.org/10.1108/10662240310501649
Sun, P.-C., Tsai, R., Finger, G., Chen, Y.-Y., & Yeh, D. (2008). What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183-1202. https://doi.org/10.1016/j.compedu.2006.11.007
Teo, T., Wong, S., & Chai, C. (2008). A Cross-cultural Examination of the Intention to Use Technology between Singaporean and Malaysian pre-service Teachers: An Application of the Technology Acceptance Model (TAM). Educational Technology & Society, 11(4), 265-280.
Venkatesh, V., & Davis, F. D. (1996). A Model of the Antecedents of Perceived Ease of Use: Development and Test. Decision Sciences, 27(3), 451-481. http://dx.doi.org/10.1111/j.1540-5915.1996.tb01822
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Wu, B., & Chen, X. (2017). Continuance Intention to Use MOOCs: Integrating the Technology Acceptance Model (TAM) and Task Technology Fit (TTF) Model. Computers in Human Behavior, 67, 221-232. https://doi.org/10.1016/j.chb.2016.10.028
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
Yim, B., & Byon, K. (2018). The Influence of Emotions on Game and Service Satisfaction and Behavioral Intention in Winning and Losing Situations: Moderating Effect of Identification with the Team. Sport Marketing Quarterly, 27(2), 93-107.
https://doi.org/10.32731/SMQ.272.062018.03
Zeithaml, V., Berry, L., & Parasuraman, A. (1996). The Behavioral Consequences of Service Quality. Journal of Marketing, 60(2), 31-46. http://dx.doi.org/10.2307/1251929
Zhang, J. (2014). The impacts of trust and feelings on knowledge sharing among Chinese employees. New England Journal of Entrepreneurship, 17(1), 21-28. https://doi.org/10.1108/NEJE-17-01-2014-B003
Zhang, S., Zhao, J., & Tan, W. (2008). Extending TAM for Online Learning Systems: An Intrinsic Motivation Perspective. Tsinghua Science & Technology, 13(3), 312-317. https://doi.org/10.1016/S1007-0214(08)70050-6
Zhou, T. (2011). Examining the critical success factors of mobile website Adoption. Online Information Review, 35(4), 636-652.
