FACTORS INFLUENCING PARTICIPATION IN UNIVERSITIES’ COLLABORATIVE TALENT CULTIVATION IN CHENGDU-CHONGQING ECONOMIC CIRCLE

ผู้แต่ง

  • Zijun Yi Graduate School of Business and Advanced Technology Management, Assumption University
  • Soonthorn Pibulcharoensit Graduate School of Business and Advanced Technology Management, Assumption University
  • Somsit Duangekanong Graduate School of Business and Advanced Technology Management, Assumption University

คำสำคัญ:

Talent cultivation, Chengdu-Chongqing Economic Circle, Talent training, Behavioral intention, Attitude

บทคัดย่อ

This research aims to illustrate factors influencing talents’ attitude and behavioral intention towards attending universities’ collaborative talent cultivation in Chengdu-Chongqing Economic Circle. The framework of this research proposed the relationships among perceived benefits (PB), perceived usefulness (PU), effort expectancy (EE), self-efficacy (SE), subjective norms (SN), attitude (ATT) and behavioral intention (BI). Six hypotheses were proposed accordingly in the framework. Quantitative method was adopted in this research. Multi-stage sampling approach including purposive or judgmental sampling, stratified sampling, purposive or convenience sampling and convenience sampling was used to carried out the survey. 480 online questionnaires were distributed to three universities in Chengdu. Confirmatory factor analysis (CFA) and structural equation model (SEM) were used to analyze the result and test the proposed hypotheses. The result explicated that perceived usefulness (PU) and self-efficacy (SE) have a significant impact on attitude, and subjective norms (SN) and attitude (ATT) have a significant impact on behavioral intention. Nevertheless, perceived benefits (PB) and effort expectancy (EE) have no significant impact on attitude. Hence, this research suggested to put more emphasis on changing talents’ attitude and subjective norms to increase their level of participation.

References

Agarwal, R., & Prasad, J. (1998). The antecedents and consequents of user perceptions in information technology adoption. Decision Support Systems, 22(1), 15-29.

Ajzen, I. (2012). The theory of planned behavior. In P. A. M. Van Lange, A. W. Kruglanski, & E. T. Higgins (Eds.), Handbook of theories of social psychology (pp. 438-459). Newbury Park, California: Sage.

Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: a theoretical analysis and review of empirical research. Psychological Bulletin, 84(5), 888-918.

Aksoy, P., & Gresham, F. M. (2020). Theoretical bases of “social-emotional learning intervention programs” for preschool children. International Online Journal of Education and Teaching (IOJET), 7(4). 1517- 1531.

Al-Debei, M. M., Akroush, M. N., & Ashouri, M.I. (2015). Consumer attitudes towards online shopping: The effects of trust, perceived benefits, and perceived web quality. Internet Research, 25(5), 707-733.

Alshare, K. A., Alomari, M. K., Lane, P. L., & Freeze, R. D. (2019). Development and determinants of end-user intention: usage of expert systems. Journal of Systems and Information Technology, 21(2), 166-185.

Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational Behavior and Human Decision Processes, 50(2), 248-287.

Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation confirmation mode. MIS Quarterly, 25(3), 351-370.

Bhattacherjee, A., & Premkumar, G. (2004). Understanding changes in belief and attitude toward information technology usage: a theoretical model and longitudinal test. MIS Quarterly, 28(2), 229-254.

Bhuasiri, W., Zo, H., Lee, H., & Ciganek, A. (2016). User acceptance of e-government services: Examining an e-tax filing and payment system in Thailand. Information Technology for Development, 22(4), 672-695.

Bollen, K. A. (1989). Structural equations with latent variables. New York: John Wiley & Sons.

Chen, B., & Lee, J. (2020). Household waste separation intention and the importance of public policy. International Trade, Politics and Development, 4(1), 61-79.

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

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

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

Gwebu, K. L., & Wang, J. (2011). Adoption of open-source software: the role of social identification. Decision Support Systems, 51(1),

-229.

Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis (6th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.

Harb, A. A., Fowler, D., Chang, H. J., Blum, S. C., & Alakaleek, W. (2019). Social media as a marketing tool for events. Journal of Hospitality and Tourism Technology, 10(1), 28-44.

Hsu, C., Yu, C., & Wu, C. (2014). Exploring the continuance intention of social networking websites: an empirical research. Information Systems and e-Business Management, 12(2), 139-163.

Joo, Y. K., & Kim, Y. (2017). Engineering researchers’ data reuse behaviours: a structural equation modelling approach. The Electronic Library, 35(6), 1141-1161.

Kim, D., Ferrin, D., & Rao, H. (2008). A trust-based consumer decision-making model in electronic commerce: the role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544-564.

Kim, E., & Kim, Y. (2004). Predicting online purchase intentions for clothing products. European Journal of Marketing, 38(7), 883-897.

Kim, Y., & Nah, S. (2018). Internet researchers’ data sharing behaviors: An integration of data reuse experience, attitudinal beliefs, social norms, and resource factors. Online Information Review, 42(1), 124-142.

Kim, Y., Choi, J., Park, Y., & Jiyoung Y. (2016). The adoption of mobile payment services for Fintech. International Journal of Applied Engineering Research, 11(2), 1058-1061.

Korgaonkar, P., & Wolin, L. (2002). Web usage, advertising, and shopping: relationship patterns. Internet Research: Electronic Networking Applications and Policy, 12(2),191-204.

Lee, H., & JinMa, Y. (2012). Consumer perceptions of online consumer product and service reviews: Focusing on information processing confidence and susceptibility to peer influence. Journal of Research in Interactive Marketing, 6(2), 110-132.

Leon, S., & Uddin, N. (2016). Finding supply chain talent: an outreach strategy. Supply Chain Management, 21(1), 20-44.

Li, X., Chen, Y., & Wang, Y. (2020). Chengdu-Chongqing Economic Circle: A China’s New Growth Pole. Retrieved from https://www.ichongqing.info/2020/05/22/ chengdu-chongqing-economic-circle-a-chinas-new-growth-pole/

Loiacono, E., & McCoy, S. (2018). When did fun become so much work: The impact of social media invasiveness on continued social media use?. Information Technology & People, 31(4), 966-983.

McKinney, L. N. (2004). Creating a satisfying internet shopping experience via atmospheric variables. International Journal of Consumer Studies, 28(3), 268-283.

Mohd Suki, N. (2016). Willingness of patrons to use library public computing facilities: insights from Malaysia. The Electronic Library, 34(5), 823-845.

Nwagwu, W. E., & Famiyesin, B. (2016). Acceptance of mobile advertising by consumers in public service institutions in Lagos, Nigeria. The Electronic Library, 34(2), 265-288.

Park, E. (2013). The adoption of tele-presence systems: Factors affecting intention to use tele-presence systems. Kybernetes, 42(6),

-887.

Park, S. Y. (2009). An analysis of the technology acceptance model in understanding University students’ behavioral intention to use e-learning. Dinamika Pendidikan, 12(1), 150-162.

Pedroso, R., Zanetello, L., Guimaraes, L., Pettenon, M., Goncalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor anlaysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry, 43(3), 37-40.

Polit, D., & Beck, C. (2014). Essentials of nursing research: Appraising evidence for nursing practice (8th ed.). Philadelphia, PA: Wolters Kluwer.

Rai, S., Ramamritham, K., & Jana, A. (2020). Identifying factors affecting the acceptance of government-to-government system in developing nations-empirical evidence from Nepal. Transforming Government: People, Process and Policy, 14(2), 283-303.

Roni, S., Merga, M. and Morris, J. (2020). Conducting Quantitative Research in Education. Singapore: Springer.

Sánchez, R. A., & Hueros, A., & Ordaz, M. G. (2013). E-learning and the University of Huelva: A study of WebCT and the technological acceptance model. Campus-Wide Information Systems. 30(2). Retrieved from https://doi.org/10.1108/10650741311306318

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 Science.

Soper, D. (n.d.). A priori sample size calculator for structural equation models. Free Statistics Calculators. Retrieved from https://www.danielsoper.com/ statcalc/calculator.aspx?id=89

Taber, K. S. (2018). The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education. Research Science Education, 48, 1273-1296. Retrieved from https://doi.org/10.1007/s11165-016-9602-2

Tan, Y., & Deng, R. (2020). 20 universities join University Consortium in Chengdu, Chongqing. Retrieved from http://www.chinadaily.com.cn/regional/ chongqing/ liangjiang/2020-05/14/content_37535931.htm.

Taylor, S., & Todd, P.A. (1995). Understanding information technology usage: a test of competing models. Information Systems Research, 6(2), 144-176.

Teo T. (2011). Technology Acceptance Research in Education. In: Teo T. (eds) Technology Acceptance in Education (pp. 1-5). Retrieved from https://doi.org/10.1007/978-94-6091-487-4_1

Tsai, W. C. (2012). A study of consumer behavioral intention to use e-books: the technology acceptance model perspective. Innovative Marketing, 8(4), 55-66.

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.

Verkijika, S. F., & De Wet, L. (2018). E-government adoption in sub-Saharan Africa, Electronic Commerce Research and Applications, Elsevier, 30, 83-93.

Wang, J. (2020). Chengdu-Chongqing Economic Circle University Alliance Set up. Retrieved from https://www.ichongqing.info/2020/05/16/chengdu-chongqing-economic-circle-university-alliance-set-up/

Wang, Z., Mao, Y., & Gale, F. (2008). Chinese consumer demand for food safety attributes in milk products. Food Policy, 33(1), 27-36.

Wheaton, B., Muthen, B., Alwin, D., F., & Summers, G. (1977). Assessing Reliability and Stability in Panel Models. Sociological Methodology, 8(1), 84-136.

Wolin, L., Korgaonkar, P., & Lund, D. (2002). Beliefs, attitudes, and behavior toward advertising. International Journal of Advertising, 21(1), 87-113.

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(2), 221-232.

Yoon, C., & Kim, H. (2013). Understanding computer security behavioral intention in the workplace: An empirical study of Korean firms. Information Technology & People, 26(4), 401-419.

Yousafzi, S. Y., Foxall, G. R., & Pallister, J. G. (2007). Technology acceptance: a meta-analysis of the TAM. Journal of Modelling in Management, 2(3), 251-280.

Zhu, G., Sangwan, S., & Lu, T. (2010). A new theoretical framework of technology acceptance and empirical investigation on self‐efficacy‐based value adoption model. Nankai Business Review International, (4), 345-372.

Zolait, A. (2014). The nature and components of perceived behavioural control as an element of theory of planned insights from Malaysia behaviour. Behaviour and Information Technology, 33(1), 65-85.

Downloads

เผยแพร่แล้ว

2022-06-30