Determinants of Graduate Students' Behavioral Intention to Use Mobile Learning Platforms at Hunan Normal University
Abstract
Background and Aim: This research examined the factors affecting graduate students' behavioral intentions toward using a mobile learning platform. The study focused on several latent variables, including system quality (SQ), service quality (SVQ), information quality (IQ), perceived usefulness (PU), satisfaction (SA), attitude (ATT), and behavioral intention (BI). The goal of the study was to assess the degree to which each of these variables impacts the utilization of the mobile learning platform.
Materials and Methods: This research collected survey responses from 500 graduate students at a public university in Hunan, China, regarding their perspectives on using mobile learning platforms. Structural equation modeling (SEM) and confirmatory factor analysis (CFA) were employed to analyze the data.
Results: The data analysis results indicated that all hypothesized paths were statistically significant, demonstrating strong direct relationships between the variables in the model. Information quality, in particular, had the most substantial impact on perceived usefulness. These findings highlight the critical role of these factors in enhancing the effective use of mobile learning platforms in higher education, leading to increased student satisfaction and behavioral intentions. The study's strengths include a large sample size and the application of advanced statistical methods like SEM and CFA, offering a thorough evaluation of the model's validity. However, the study is limited by its focus on a single university, which may not capture the diverse experiences of students at other institutions. Moreover, while the study emphasizes key factors influencing mobile learning, it does not explore potential barriers or challenges students might encounter, such as technological issues or varying levels of digital literacy. Future research could address these aspects to gain a more comprehensive understanding of the effectiveness of mobile learning platforms.
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References
Ahn, T., Ryu, S., & Han, I. (2007). The impact of Web quality and playfulness on user acceptance of online retailing. Information & Management, 44(3), 263-275.
Ajzen, I. (1987). Attitudes, traits, and actions: Dispositional prediction of behavior in personality and social psychology. In Advances in experimental social psychology (Vol. 20, pp. 1-63). Elsevier.
Ajzen, I. (2005). Attitudes, personality, and behavior. McGraw-Hill Education (UK).
Al Mulhem, A. (2020). Investigating the effects of quality factors and organizational factors on university students’ satisfaction with e-learning system quality. Cogent Education, 7(1), 1787004.
Al-Fraihat, D., Joy, M., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102, 67-86.
Almaiah, M. A., & Alismaiel, O. A. (2019). Examination of factors influencing the use of mobile learning system: An empirical study. Education and Information Technologies, 24, 885-909.
Almaiah, M. A., Al-Khasawneh, A., & Althunibat, A. (2020). Exploring the critical challenges and factors influencing the E-learning system usage during the COVID-19 pandemic. Education and Information Technologies, 25, 5261-5280.
Al-Mamary, Y. H., & Shamsuddin, A. (2015). Testing of the technology acceptance model in the context of Yemen. Mediterranean Journal of Social Sciences, 6(4), 268-273. DOI: 10.5901/mjss.2015.v6n4s1p268
Almarashdeh, I. (2016). Sharing instructors experience of learning management system: A technology perspective of user satisfaction in a distance learning course. Computers in Human Behavior, 63, 249-255.
Al-Rahmi, W. M., Alzahrani, A. I., Yahaya, N., Alalwan, N., & Kamin, Y. B. (2020). Digital communication: Information and communication technology (ICT) usage for education sustainability. Sustainability, 12(12), 5052. doi: https://doi.org/10.3390/su12125052
Alshare, K. A., & Lane, P. L. (2011). Predicting student-perceived learning outcomes and satisfaction in ERP courses: An empirical investigation. Communications of the association for information systems, 28(1), 34. https://doi.org/10.17705/1CAIS.02834
Alzaza, N. S., & Yaakub, A. R. (2011). Students’ awareness and requirements of mobile learning services in the higher education environment. American Journal of Economics and Business Administration, 3(1), 95-100.
Asubonteng, P., McCleary, K. J., & Swan, J. E. (1996). SERVQUAL revisited: a critical review of service quality. Journal of Services Marketing, 10(6), 62-81.
Athiyaman, A. (1997). Linking student satisfaction and service quality perceptions: the case of university education. European journal of marketing, 31(7), 528-540.
Awang, Z. (2012). Structural equation modeling using AMOS graphic. Penerbit Universiti Teknologi MARA.
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological bulletin, 107(2), 238.
Bhattacharjee, A., & Sanford, C. (2006). Influence processes for information technology acceptance: An elaboration likelihood model. MIS Quarterly, 805-825.
Bhattacherjee, A. (2000). Acceptance of e-commerce services: the case of electronic brokerages. IEEE Transactions on Systems, Man, and cybernetics-Part A: Systems and humans, 30(4), 411-420.
Browne, B. A., Kaldenberg, D. O., Browne, W. G., & Brown, D. J. (1998). Student as customer: Factors affecting satisfaction and assessments of institutional quality. Journal of marketing for higher education, 8(3), 1-14.
Buabeng-Andoh, C. (2018). Predicting students’ intention to adopt mobile learning: A combination of the theory of reasoned action and technology acceptance model. Journal of Research in Innovative Teaching & Learning, 11(2), 178-191.
Buabeng-Andoh, C. (2021). Exploring University students’ intention to use mobile learning: A research model approach. Education and Information Technologies, 26(1), 241-256.
Chao, C.-M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10, 1652.
Chennamaneni, A., Teng, J. T., & Raja, M. (2012). A unified model of knowledge sharing behaviors: theoretical development and empirical test. Behavior & Information Technology, 31(11), 1097-1115.
Cheong, J. H., & Park, M. C. (2005). Mobile Internet acceptance in Korea. Internet research, 15(2), 125-140.
Chiu, C. M., Chiu, C. S., & Chang, H. C. (2007). Examining the integrated influence of fairness and quality on learners’ satisfaction and Web‐based learning continuance intention. Information Systems Journal, 17(3), 271-287.
Chow, M., Herold, D. K., Choo, T.-M., & Chan, K. (2012). Extending the technology acceptance model to explore the intention to use Second Life for enhancing healthcare education. Computers & Education, 59(4), 1136-1144.
Chuttur, M.Y. (2009). Overview of the Technology Acceptance Model: Origins, Developments, and Future Directions, Indiana University, USA. Sprouts: Working papers on Information Systems, 9.
Clemes, M. D., Gan, C. E., & Kao, T.-H. (2008). University student satisfaction: An empirical analysis. Journal of marketing for higher education, 17(2), 292-325.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. 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.
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: a ten-year update. Journal of Management Information Systems, 19(4), 9-30.
Demir, K., & Akpinar, E. (2018). The Effect of Mobile Learning Applications on Students' Academic Achievement and Attitudes toward Mobile Learning. Malaysian Online Journal of Educational Technology, 6(2), 48-59.
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21, 719-734.
Erasmus, E., Rothmann, S., & Van Eeden, C. (2015). A structural model of technology acceptance. SA Journal of Industrial Psychology, 41(1), 1-12.
Fang, Y., Zhao, J., Gao, M., & Ibrahim, N. M. (2023). Dropout factors of English mobile learning among Chinese college students based on the UTAUT2 model. Proceedings of the 7th International Conference on Education and Multimedia Technology,
Fishbein, M., & Ajzen, I. (1981). Attitudes and voting behavior: An application of the theory of reasoned action. Progress in applied social psychology, 1(1), 253-313.
Fornell, C. (1992). A national customer satisfaction barometer: The Swedish experience. Journal of Marketing, 56(1), 6-21.
Golnaz, R., Zainalabidin, M., Mad Nasir, S., & Eddie Chiew, F. (2010). Non-Muslims’ awareness of Halal principles and related food products in Malaysia. International food research journal, 17(3), 667-674.
Goode, M. M., Moutinho, L. A., & Chien, C. (1996). Structural equation modeling of overall satisfaction and full use of services for ATMs. International Journal of Bank Marketing, 14(7), 4-11.
Halawi, L. A., McCarthy, R. V., & Aronson, J. E. (2008). An empirical investigation of knowledge management systems' success. Journal of Computer Information Systems, 48(2), 121-135.
Hoi, V. N. (2020). Understanding higher education learners' acceptance and use of mobile devices for language learning: A Rasch-based path modeling approach. Computers & Education, 146, 103761.
Holden, H., & Rada, R. (2011). Understanding the influence of perceived usability and technology self-efficacy on teachers’ technology acceptance. Journal of research on technology in education, 43(4), 343-367.
Hsu, C.-L., & Lu, H.-P. (2004). Why do people play online games? An extended TAM with social influences and flow experience. Information & Management, 41(7), 853-868.
Huang, J. H., Lin, Y. R., & Chuang, S. T. (2007). Elucidating user behavior of mobile learning: A perspective of the extended technology acceptance model. The electronic library, 25(5), 585-598.
Hussein, M. H., Ow, S. H., Ibrahim, I., & Mahmoud, M. A. (2021). Measuring instructors continued intention to reuse Google Classroom in Iraq: a mixed-method study during COVID-19. Interactive Technology and Smart Education, 18(3), 380-402.
Kettinger, W. J., & Lee, C. C. (1994). Perceived service quality and user satisfaction with the information services function. Decision Sciences, 25(5‐6), 737-766.
Kim, S., Sung, J., Park, J., & Dittmore, S. W. (2015). The relationship among leisure attitude, satisfaction, and psychological well-being for college students. Journal of Physical Education and Sport, 15(1), 70.
King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740-755.
Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and psychological measurement, 30(3), 607-610.
Kuehn, K. W. (2008). Entrepreneurial intentions research Implications for entrepreneurship education. Journal of Entrepreneurship Education, 11, 87-98.
Lee, J.-W. (2010). Online support service quality, online learning acceptance, and student satisfaction. The internet and higher education, 13(4), 277-283.
Lee, M.-C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506-516.
Lee, S., & Kim, B. G. (2017). The impact of qualities of social network service on the continuance usage intention. Management Decision, 55(4), 701-729.
Legramante, D., Azevedo, A., & Azevedo, J. M. (2023). Integration of the technology acceptance model and the information systems success model in the analysis of Moodle's satisfaction and continuity of use. The International Journal of Information and Learning Technology, 40(5), 467-484.
Liker, J. K., & Sindi, A. A. (1997). User acceptance of expert systems: a test of the theory of reasoned action. Journal of Engineering and Technology Management, 14(2), 147-173.
Lin, H.-F. (2007). Measuring online learning systems success: Applying the updated DeLone and McLean model. Cyberpsychology & behavior, 10(6), 817-820.
Lin, J. C.-C., & Lu, H. (2000). Towards an understanding of the behavioral intention to use a web site. International journal of information management, 20(3), 197-208.
Lin, J. S. C., & Hsieh, P. l. (2006). The role of technology readiness in customers' perception and adoption of self‐service technologies. International Journal of Service Industry Management, 17(5), 497-517.
Locke, E. A. (1969). What is job satisfaction? Organizational behavior and human performance, 4(4), 309-336.
Machleit, K. A., & Mantel, S. P. (2001). Emotional response and shopping satisfaction: Moderating effects of shopper attributions. Journal of Business Research, 54(2), 97-106.
Marangunić, N., & Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal access in the information society, 14, 81-95.
Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information systems research, 2(3), 173-191.
McKechnie, S., Winklhofer, H., & Ennew, C. (2006). Applying the technology acceptance model to the online retailing of financial services. International Journal of Retail & Distribution Management, 34(4/5), 388-410.
Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359-374.
Molla, A., & Licker, P. S. (2001). E-commerce systems success: An attempt to extend and respecify the Delone and MacLean model of IS success. J. Electron. Commer. Res., 2(4), 131-141.
Nagy, J. T. (2018). Evaluation of Online Video Usage and Learning Satisfaction: An Extension of the Technology Acceptance Model. International Review of Research in Open and Distributed Learning, 19(1), 160-185. https://doi.org/10.19173/irrodl.v19i1.2886
Ndubisi, N. O., Gupta, O. K., & Massoud, S. (2003). Organizational learning and vendor support quality by the usage of application software packages: A study of Asian entrepreneurs. Journal of Systems Science and Systems Engineering, 12, 314-331.
Nelson, R. R., Todd, P. A., & Wixom, B. H. (2005). Antecedents of information and system quality: an empirical examination within the context of data warehousing. Journal of Management Information Systems, 21(4), 199-235.
O’cass, A., & Fenech, T. (2003). Web retailing adoption: exploring the nature of internet users Web retailing behavior. Journal of Retailing and Consumer Services, 10(2), 81-94.
Oliver, R. L. (1993). Cognitive, affective, and attribute bases of the satisfaction response. Journal of Consumer Research, 20(3), 418-430.
Ozkan, S., & Koseler, R. (2009). Multi-dimensional students’ evaluation of e-learning systems in the higher education context: An empirical investigation. Computers & Education, 53(4), 1285-1296.
Pai, F.-Y., & Huang, K.-I. (2011). Applying the technology acceptance model to the introduction of healthcare information systems. Technological forecasting and social change, 78(4), 650-660.
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.
Petter, S., DeLone, W., & McLean, E. (2008). Measuring information systems success: models, dimensions, measures, and interrelationships. European journal of information systems, 17(3), 236-263.
Rahman, M. M., & Sloan, T. (2015). Opportunities and challenges of M-commerce adoption in Bangladesh: An empirical study. Journal of Internet Banking and Commerce, 20(3), 1.
Rai, N., & Thapa, B. (2015). A study on purposive sampling method in research. Kathmandu: Kathmandu School of Law, 5(1), 8-15.
Renda dos Santos, L. M., & Okazaki, S. (2016). Planned e-learning adoption and occupational socialization in Brazilian higher education. Studies in Higher Education, 41(11), 1974-1994.
Seddon, P. B. (1997). A respecification and extension of the DeLone and McLean model of IS success. Information systems research, 8(3), 240-253.
Sharma, G., Verma, R., & Pathare, P. (2005). Mathematical modeling of infrared radiation thin layer drying of onion slices. Journal of Food Engineering, 71(3), 282-286.
Sharma, S. K., Gaur, A., Saddikuti, V., & Rastogi, A. (2017). Structural equation model (SEM)-neural network (NN) model for predicting quality determinants of e-learning management systems. Behavior & Information Technology, 36(10), 1053-1066.
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 Publishers.
Singh, A., Sharma, S., & Paliwal, M. (2021). Adoption intention and effectiveness of digital collaboration platforms for online learning: the Indian students’ perspective. Interactive Technology and Smart Education, 18(4), 493-514.
Sisouvong , V. ., & Pasanchay, K. . (2024). Mobile Learning: Enhancing Self-Directed Education through Technology, Wireless Networks, and the Internet Anytime, Anywhere. Journal of Education and Learning Reviews, 1(2), 39–50. https://doi.org/10.60027/jelr.2024.752
Sun, X. (2019). Designing mobile learning to create active learning and just-in-time learning experiences. EdMedia+ Innovate Learning,
Teo, H.-H., Chan, H.-C., Wei, K.-K., & Zhang, Z. (2003). Evaluating information accessibility and community adaptivity features for sustaining virtual learning communities. International Journal of human-computer Studies, 59(5), 671-697.
Teo, T., & Zhou, M. (2014). Explaining the intention to use technology among university students: A structural equation modeling approach. Journal of Computing in Higher Education, 26, 124-142.
Thongsri, N., Shen, L., & Bao, Y. (2019). Investigating factors affecting learner’s perception toward online learning: evidence from ClassStart application in Thailand. Behavior & Information Technology, 38(12), 1243-1258.
Urbach, N., & Müller, B. (2012). The updated DeLone and McLean model of information systems success. Information Systems Theory: Explaining and Predicting Our Digital Society, 1, 1-18.
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sciences, 39(2), 273-315.
Wang, C., Wang, W., & Wu, H. (2020). Association between medical students’ prior experiences and perceptions of formal online education developed in response to COVID-19: a cross-sectional study in China. BMJ open, 10(10), e041886.
Wang, W.-T., & Lai, Y.-J. (2014). Examining the adoption of KMS in organizations from an integrated perspective of technology, individuals, and organizations. Computers in Human Behavior, 38, 55-67.
Wang, W.-T., & Wang, C.-C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53(3), 761-774.
Wang, Y. S. (2008). Assessing e‐commerce systems success: a respecification and validation of the DeLone and McLean model of IS success. Information Systems Journal, 18(5), 529-557.
Wang, Y. S., Wu, M. C., & Wang, H. Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British journal of educational technology, 40(1), 92-118.
Weerasawainon, A., & Ye, Y. (2019). Exploring the Relationship between Undergraduate Students’ Uses of Mobile Assisted Learning and Their Learning Performance of Chinese as a Foreign Language in Thailand. Asian Interdisciplinary and Sustainability Review, 8(2), 235–242. Retrieved from https://so05.tci-thaijo.org/index.php/PSAKUIJIR/article/view/242940
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.
Wu, J.-H., Tennyson, R. D., & Hsia, T.-L. (2010). A study of student satisfaction in a blended e-learning system environment. Computers & Education, 55(1), 155-164.
Wu, P. F. (2011). A mixed methods approach to technology acceptance research. Journal of the AIS.
Xia, S., & Phongsatha, T. (2025). Mobile Learning Application Integration in the English as a Foreign Language Classroom to Improve College Students’ Learning Outcomes. International Journal of Sociologies and Anthropologies Science Reviews, 5(1), 75–90. https://doi.org/10.60027/ijsasr.2025.5157
Yang, H. C. (2013). Bon Appétit for apps: young American consumers' acceptance of mobile applications. Journal of Computer Information Systems, 53(3), 85-96.
Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2007). Technology acceptance: a meta‐analysis of the TAM: Part 1. Journal of modeling in management, 2(3), 251-280.
Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31-46.