Influential Factors on the Utilization of E-Learning Systems among Second-Year Arts Major Students in Higher Vocational Colleges in Henan, China

Qingguo Zhang

Authors

Qingguo Zhang

Keywords:

Subjective Norm , Effort Expectancy, Perceived Usefulness, Behavioral Intention, Use Behavior

Abstract

Purpose: The objective of this study is to explore the factors impacting the use behavior of e-learning systems among second year of art majors in higher vocational colleges in Henan, China. The research framework introduces subjective norm, effort expectancy, internet experience, e-learning motivation, perceived usefulness, behavioral intention, and use behavior. Research design, data, and methodology: The researcher employed a quantitative research method and distributed questionnaires to 500 sophomore students in arts majors at a public higher vocational college in Henan province. The initial content validity and reliability assessment was carried out using Item Objective Consistency and Cronbach’s Alpha. After the data was collected, Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were employed to analyze the data, validate the model fit, and confirm the causal relationships between variables, testing the hypotheses for their reliability and validity. Results: The results of the study suggest that the use behavior in the e-learning system among second-year students majoring in arts at vocational colleges is significantly impacted by subjective norm, effort expectancy, internet experience, e-learning motivation, perceived usefulness, and behavioral intention. Conclusions: It is recommended to designing and promoting e-learning systems to enhance successful adoption and effective utilization.

Author Biography

Qingguo Zhang

School of Music, Henan Vocational Institute of Arts, China.

References

Abbad, M., Abbad, R., & Saleh, M. (2011). Limitations of e-commerce in developing countries: Jordan case. Education, Business and Society: Contemporary Middle Eastern Issues, 4(4), 280-291. https://doi.org/10.1108/17537981111190060

Abbasi, A., Kashif, R., & Amna, B. (2011). Islamic Work Ethics: How it Affects Business Performance. Actual Problems of Economics, 126(1), 1-10.

Ajzen, I. (1980). Understanding attitudes and predicting social behavior (1st ed.). Englewood cliffs.

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

Alalwan, A. A., Dwivedi, Y. K., & Williams, M. D. (2006). Customers’ intention and adoption of telebanking in Jordan. Information Systems Management, 33(2), 154-178, https://doi.org/10.1080/10580530.2016.1155950

AlAwadhi, S., & Morris, A. (2008, January). The Use of the UTAUT Model in the Adoption of E-government Services in Kuwait. Proceedings of the 41st annual Hawaii international conference on system sciences (HICSS 2008), 219-219. https://doi.org/10.1109/HICSS.2008.452

Alenezi, A. R., & Karim, A. (2010). An empirical investigation into the role of enjoyment, computer anxiety, computer self-efficacy and internet experience in influencing the students’ intention to use e-learning: A case study from Saudi Arabian governmental universities. Turkish Online Journal of Educational Technology-TOJET, 9(4), 22-34.

Al-Gahtani, S. S., Hubona, G. S., & Wang, J. (2007). Information technology (IT) in Saudi Arabia: Culture and the acceptance and use of IT. Information & management, 44(8), 681-691. https://doi.org/10.1016/j.im.2007.09.002

Ali, M., Chin-Hong, P., & Arif, I. (2015). Determinants of Internet Banking Adoption by Banks in Pakistan. Management and Organizational Studies, 4(4), 12. https://doi.org/10.5430/mos.v4n4p12

Al-Mamary, Y. H., & Shamsuddin, A. (2015). Testing of The Technology Acceptance Model in Context of Yemen. Mediterranean Journal of Social Sciences, 6(4), 1-10.

Alwahaishi, S., & Snášel, V. (2013). Consumers’ acceptance and use of information and communications technology: A UTAUT and flow based theoretical model. Journal of Technology Management and Innovation, 8(2), 61-73.

https://doi.org/10.4067/S0718-27242013000200005

Anandarajan, M., Simmers, C., & Igbaria, M. (2000). An exploratory investigation of the antecedents and impact of internet usage: an individual perspective, Behavior and Information Technology, 19(1), 69-85. https://doi.org/10.1080/014492900118803

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

Awwad, M. S., & Al-Majali, S. M. (2015). Electronic library services acceptance and use. The Electronic Library, 33(6), 1100-1120. http://dx.doi.org/10.1108/EL-03 2014-0057

Baldwin, R. E., & Tomiura, E. (2020). Thinking ahead about the trade impact of COVID-19. In R. Baldwin & B. W. di Mauro (Eds.), Economics in the Time of COVID-19 (pp. 59-71). Centre for Economic Policy Research.

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

Burns, N., & Grove, S. K. (1997). Instructor’s Manual to Accompany the Practice of Nursing Research: Conduct, Critique and Utilization (1st ed.). WB Saunders.

Byrne, B. M. (2013). Structural equation modeling with AMOS: basic concepts, applications, and programming (1st ed.). Routledge.

Cappel, J. J., & Hayen, R. L. (2004). Evaluating e-learning: A case study. Journal of Computer Information Systems, 44(4), 49-56.

Carey, K., & Blatnik, S. (2005). E-learning and economic development. Turkish Online Journal of Distance Education, 6(1), 22-33.

Castañeda, J. A., Montoso, F. J., & Luque, T. (2007). The dimensionality of customer privacy concern on the internet. Online Information Review, 31(4), 420-439. https://doi.org/10.1108/14684520710780395

Chang, C. C. (2013). Library mobile applications in university libraries. Library Hi Tech, 31(3), 478-492. https://doi.org/10.1108/lht-03-2013-0024

Chang, S. C., & Tung, F. C. (2008). An empirical investigation of students’ behavioural intentions to use the online learning course websites. British Journal of Educational Technology, 39(1), 71-83. https://doi.org/10.1111/j.1467-8535.2007.00742.x

Chaouali, W., Yahia, I. B., & Souiden, N. (2016). The interplay of counter-conformity motivation, social influence, and trust in customers’ intention to adopt Internet banking services: The case of an emerging country. Journal of Retailing and Consumer Services, 28, 209-218. https://doi.org/10.1016/j.jretconser.2015.10.007

Chen, S. Y., & Macredie, R. D. (2005). The assessment of usability of electronic shopping: A heuristic evaluation. International journal of Information management, 25(6), 516-532. https://doi.org/10.1016/j.ijinfomgt.2005.08.008

Chow, W. S., & Chan, L. S. (2008). Social network, social trust, and shared goals in organizational knowledge sharing. Information & management, 45(7), 458-465. https://doi.org/10.1016/j.im.2008.06.007

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.

Clark, R. C., & Mayer, R. E. (2023). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning. Performance Improvement, 42(5), 41-43. https://doi.org/10.1002/pfi.4930420510

Conati, C. (2002). Probabilistic assessment of user’s emotions in educational gams. Applied artificial intelligence, 16(7-8), 555-575. https://doi.org/10.1080/08839510290030390

Cooper, D. R., & Schindler, P. S. (2011). Qualitative research. Business research methods, 4(1), 160-182.

Coovert, M. D., & Goldstein, M. (1980). Locus of control as a predictor of users’ attitude toward computers. Psychological Reports, 47(3), 1167-1173. https://doi.org/10.2466/pr0.1980.47.3f.1167

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

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace 1. Journal of applied social psychology, 22(14), 1111-1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x

Debnath, A., Kobra, K. T., Rawshan, P. P., Paramita, M., & Islam, M. N. (2018). An Explication of acceptability of wearable devices in context of bangladesh: A user study. 2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud), 136-140.

Douglas, D. E., & Van Der Vyver, G. (2004). Effectiveness of e-learning course materials for learning database management systems: An experimental investigation. Journal of Computer Information Systems, 44(4), 41-48.

Farooq, M. S., Salam, M., Jaafar, N., Fayolle, A., Ayupp, K., Radovic-Markovic, M., & Sajid, A. (2017). Acceptance and use of lecture capture system (LCS) in executive business studies: Extending UTAUT2. Interactive Technology and Smart Education, 14(4), 329-348.

Fishbein, M., & Ajzen, I. (1975). Beliefs, attitude, intention, and behavior: an introduction to theory and research. 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.

Fox, R. J. (2010). Wiley International Encyclopedia of Marketing. John Wiley & Sons, Ltd.

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

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

Harandi, S. R. (2015). Effects of e-learning on Students’ Motivation. Procedia-Social and Behavioral Sciences, 181, 423-430. https://doi.org/10.1016/j.sbspro.2015.04.905

Haverila, M., & Barkhi, R. (2009). The Influence of Experience, Ability and Interest on e-Learning Effectiveness. European Journal of Open, distance and E-learning, 1, 1-10.

Herrero Crespo, A., San Martín Gutiérrez, H., & García de los Salmones, M. D. M. (2017). Explaining the adoption of social networks sites for sharing user-generated content: A revision of the UTAUT2. Computers in Human Behavior, 71, 1-10.

Hoyle, R. H. (Ed.). (1995). Structural equation modeling: Concepts, issues, and applications (1st ed.). Sage.

Hoyle, R. K. (2011). Structural equation modeling for social and personality psychology. Structural Equation Modeling for Social and Personality Psychology, 1-120. https://doi.org/10.4135/9781446287965

Hsu, C. L., & Lu, H. P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information & management, 41(7), 853-868. https://doi.org/10.1016/j.im.2003.08.014

Huang, Y. M., & Chiu, P. S. (2015). The effectiveness of a meaningful learning‐based evaluation model for context‐aware mobile learning. British Journal of Educational Technology, 46(2), 437-447. https://doi.org/10.1111/bjet.12147

Islam, M. T., & Selim, A. S. M. (2006). Current status and prospects for e-learning in the promotion of distance education in Bangladesh. Turkish Online Journal of Distance Education, 7(1), 1-10.

Johns, G. (1996). Organizational behavior: Understanding and managing life at work (1st ed.). HarperCollins College Publishers.

Keller, J., & Suzuki, K. (2004). Learner motivation and e-learning design: A multinationally validated process. Journal of educational Media, 29(3), 229-239. https://doi.org/10.1080/1358165042000283084

Kijsanayotin, B., Pannarunothai, S., & Speedie, S. M. (2009). Factors influencing health information technology adoption in Thailand's community health centers: Applying the UTAUT model. International journal of medical informatics, 78(6), 404-416. https://doi.org/10.1016/j.ijmedinf.2008.12.005

Kim, K.-H. (2010). Understanding the consistent use of internet health information. Online information review, 34(6), 875-891. https://doi.org/10.1108/14684521011099388

Kline, R. B. (2010). Principles and practice of structural equation modeling (1st ed.). Guilford Press.

Law, K. M., Lee, V. C., & Yu, Y. T. (2010). Learning motivation in e-learning facilitated computer programming courses. Computers & Education, 55(1), 218-228. https://doi.org/10.1016/j.compedu.2010.01.007

Lee, M. G. (2001). Profiling students’ adaptation styles in Web-based learning. Computers & Education, 36(2), 121-132. https://doi.org/10.1016/s0360-1315(00)00046-4

Lee, Y. C. (2006). An empirical investigation into factors influencing the adoption of an e‐learning system. Online information review, 30(5), 517-541. https://doi.org/10.1108/14684520610706406

Liao, Z., & Cheung, M. T. (2001). Internet-based e-shopping and consumer attitudes: an empirical study. Information & management, 38(5), 299-306. https://doi.org/10.1016/s0378-7206(00)00072-0

Liaw, S. S., Huang, H. M., & Chen, G. D. (2007). Surveying instructor and learner attitudes toward e-learning. Computers & education, 49(4), 1066-1080. https://doi.org/10.1016/j.compedu.2006.01.001

Lin, C. P., & Anol, B. (2008). Learning online social support: an investigation of network information technology based on UTAUT. CyberPsychology & behavior, 11(3), 268-272. https://doi.org/10.1089/cpb.2007.0057

Liu, I. F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, A. H. (2010). Extending the TAM model to explore the factors that affect intention to use an online learning community. Computers & education, 54(2), 600-610.

https://doi.org/10.1016/j.compedu.2009.09.009

Malhorta, N. K., & Birks, D. F. (2006). Marketing research: An applied approach. Prentice Hall.

Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information & management, 38(4), 217-230. https://doi.org/10.1016/s0378-7206(00)00061-6

Mtebe, J. S., & Raisamo, R. (2014). Challenges and instructors’ intention to adopt and use open educational resources in higher education in Tanzania. International review of research in open and distributed learning, 15(1), 249-271.

https://doi.org/10.19173/irrodl.v15i1.1687

Nicola, M., Alsafi, Z., Sohrabi, C., Kerwan, A., Al-Jabir, A., & Iosifidis, C. (2020). The socio-economic implications of the coronavirus and COVID-19 pandemic: A review. International Journal of Surgery, 78, 185-193.

https://doi.org/10.1016/j.ijsu.2020.04.018.

Nysveen, H., & Pedersen, P. E. (2004). An exploratory study of customers' perception of company web sites offering various interactive applications: moderating effects of customers’ Internet experience. Decision Support Systems, 37(1), 137-150. https://doi.org/10.1016/s0167-9236(02)00212-9

Onaolapo, S., & Oyewole, O. (2018). Performance Expectancy, Effort Expectancy, and Facilitating Conditions as Factors Influencing Smart Phones Use for Mobile Learning by Postgraduate Students of the University of Ibadan, Nigeria. Interdisciplinary Journal of E-Skills and Lifelong Learning, 14, 95-115. https://doi.org/10.28945/4085

O’Neal, P. W. (2007). Motivation of Health Behavior (1st ed.). Nova Publishers.

Palvia, S., Aeron, P., Gupta, P., Mahapatra, D., Parida, R., Rosner, R., & Sindhi, S. (2018). Online education: Worldwide status, challenges, trends, and implications. Journal of Global Information Technology Management, 21(4), 233-241.

https://doi.org/10.1080/1097198x.2018.1542262

Paola Torres Maldonado, U., Feroz Khan, G., Moon, J., & Jeung Rho, J. (2011). E‐learning motivation and educational portal acceptance in developing countries. Online Information Review, 35(1), 66-85. https://doi.org/10.1108/14684521111113597

Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning. Educational Technology & Society, 12(3),150-162.

Pedroso, R., Zanetello, L., Guimarães, L., Pettenon, M., Gonçalves, 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

Pierre, P. (1998). Distance learning in physical education teacher education. Quest, 50(4), 344-356

Pintrich, P. R., & Schrauben, B. (1992). Students' motivational beliefs and their cognitive engagement in classroom academic tasks. In D. H. Schunk & J. L. Meece (Eds.), Student perceptions in the classroom (pp. 149-183). Lawrence Erlbaum Associates.

Pipitwanichakarn, T., & Wongtada, N. (2021). Leveraging the technology acceptance model for mobile commerce adoption under distinct stages of adoption: A case of micro businesses. Asia Pacific Journal of Marketing and Logistics, 33(6), 1415-1436. https://doi.org/10.1108/APJML-10-2018-0448

Sair, S. A., & Danish, R. Q. (2018). Effect of performance expectancy and effort expectancy on the mobile commerce adoption intention through personal innovativeness among Pakistani consumers. Pakistan Journal of Commerce an social sciences (PJCSS), 12(2), 501-520.

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.

Saunders, M., Lewis, P., & Thornhill, A. (2016). Research methods for business students (7th ed.). Pearson Education.

Sharma, G. P., Verma, R. C., & Pathare, P. (2005). Mathematical modeling of infrared radiation thin layer drying of onion slices. Journal of Food Engineering, Journal of Food Engineering, 71(3), 282-286. https://doi.org/10.1016/j.jfoodeng.2005.02.010

Sharma, S. K., Al-Badi, A. H., Govindaluri, S. M., & Al-Kharusi, M. H. (2016). Predicting motivators of cloud computing adoption: A developing country perspective. Computers in Human Behavior, 62, 61-69. https://doi.org/10.1016/j.chb.2016.03.073

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.

Spears, N., & Singh, S. N. (2004). Measuring attitude toward the brand and purchase intentions. Journal of current issues & research in advertising, 26(2) ,53-66. https://doi.org/10.1080/10641734.2004.10505164

Speier, C., & Venkatesh, V. (2002). The hidden minefields in the adoption of sales force automation technologies. Journal of Marketing, 66(3), 98-111. https://doi.org/10.1509/jmkg.66.3.98.18510

Sun, P. C., Tsai, R. J., 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

Tarhini, A., Hone, K., Liu, X., & Tarhini, T. (2017). Examining the moderating effect of individual-level cultural values on users’ acceptance of e-learning in developing countries: a structural equation modeling of an extended technology acceptance model. Interactive Learning Environments, 25(3), 306-328. https://doi.org/10.1080/10494820.2015.1122635

Tarhini, A., Mohammed, A. B., & Maqableh, M. (2016). Modeling factors affecting student’s usage behaviour of e-learning systems in Lebanon. International Journal of Business and Management, 11(2), 299-299. https://doi.org/10.5539/ijbm.v11n2p299

Tavares, J., & Oliveira, T. (2018). New Integrated Model Approach to Understand the Factors That Drive Electronic Health Record Portal Adoption: Cross-Sectional National Survey. Journal of Medical Internet Research, 20(11), e11032.

https://doi.org/10.2196/11032

Taylor, S., & Todd, P. (1995). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International journal of research in marketing, 12(2), 137-155.

https://doi.org/10.1016/0167-8116(94)00019-k

Vallerand, R. J., Pelletier, L. G., Blais, M. R., Briere, N. M., Senecal, C., & Vallieres, E. F. (1992). The Academic Motivation Scale: A measure of intrinsic, extrinsic, and amotivation in education. Educational and psychological measurement, 52(4), 1003-1017. https://doi.org/10.1177/0013164492052004025

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

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. L., & 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

Wang, Y. S., & Shih, Y. W. (2009). Why do people use information kiosks? A validation of the Unified Theory of Acceptance and Use of Technology. Government information quarterly, 26(1), 158-165. https://doi.org/10.1016/j.giq.2008.07.001

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

Wut, E., Ng, P., Leung, K. S. W., & Lee, D. (2021). Do gamified elements affect young people’s use behavior on consumption-related mobile applications? Young Consumers, 22(3), 368-386.

Yadav, R., Sharma, S. K., & Tarhini, A. (2016). A multi-analytical approach to understand and predict the mobile commerce adoption. Journal of enterprise information management, 29(2), 222-237. https://doi.org/10.1108/jeim-04-2015-0034

Yang, H., Yu, J., Zo, H., & Choi, M. (2016). User acceptance of wearable devices: An extended perspective of perceived value. Telematics and Informatics, 33(2), 256-269. https://doi.org/10.1016/j.tele.2015.08.007

Yau, H. K., & Ho, T. C. (2015). The influence of subjective norm on behavioral intention in using e-learning: An empirical study in Hong Kong higher education. In Proceedings of the International Multiconference of Engineers and Computer Scientists, 2, 18-20.

Yazdanmehr, A., & Wang, J. (2016). Employees’ information security policy compliance: A norm activation perspective. Decision Support Systems, 92, 36-46. http://doi.org/10.1016/j.dss.2016.09.009

Yee, L. C. A. (2013). Understanding mobile commerce continuance intentions: An empirical analysis of Chinese consumers. Journal of Computer Information Systems, 53(4), 22-30. https://doi.org/10.1080/08874417.2013.11645647

Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in human behavior, 26(4), 760-767. https://doi.org/10.1016/j.chb.2010.01.013

Zuiderwijk, A., Janssen, M., & Dwivedi, Y. K. (2015). Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology. Government information quarterly, 32(4), 429-440.

https://doi.org/10.1016/j.giq.2015.09.005