Factors Impacting Male Student’s Attitude and Intention to Use Mobile Learning In Guizhou, China
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Purpose: This research aims to examine the factors impacting attitude, and intention to use mobile learning for male college students in Guizhou, China. The conceptual framework proposes a causal relationship among perceived usefulness, perceived ease of use, compatibility, perceived enjoyment, attitude, cognitive need, social influence, and intention to use. Research design, data, and methodology: The researcher adopted the quantitative method (n=500), distributing questionnaires to male college students who use mobile learning at the Guizhou Institute of Technology. The sampling procedure includes judgmental and stratified random sampling in selecting students who use three mobile learning platforms. The Structural Equation Model and Confirmatory Factor Analysis were used for the data analysis, including model fit, reliability, and validity of the constructs. Results: The results demonstrate that perceived usefulness and perceived ease of use have a significant impact on attitude. Compatibility has a significant impact on perceived enjoyment. Furthermore, compatibility, perceived enjoyment, attitude, cognitive need, and social influence have a significant impact on intention to use. Conclusions: Eight hypotheses were proven to fulfill research objectives. So, perceived usefulness, perceived ease of use, compatibility, perceived enjoyment, cognitive need, and social influence are advised to supply an assessment to examine the level of intention to use mobile learning at Guizhou Institute of Technology.
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References
Ackoff, R. L. (1953). The Design of Social Research (1st ed.). University of Chicago Press.
Al-Gahtani, S., & King, M. (1999). Attitudes, satisfaction, and usage: factors contributing to each in the acceptance of information technology. Behavior & Information Technology, 18(4), 277-297. https://doi.org/10.1080/014492999119020
Aliberti, M. J., Cenzer, I. S., Smith, A. K., Lee, S. J., Yaffe, K., & Covinsky, K. E. (2019). Risk assessment of adverse outcomes for older adults: the need to include both physical and cognitive elements. Journal of the American Geriatrics Society, 67, 477-483. https://doi.org/10.1111/jgs.15683
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. https://doi.org/10.5901/mjss.2015.v6n4s1p268
Alsaleh, D. A., Elliott, M. T., Fu, F. Q., & Thakur, R. (2019). Cross-cultural differences in the adoption of social media. Journal of Research in Interactive Marketing, 13(1), 119-140. https://doi.org/10.1108/jrim-10-2017-0092
Andoh, C. B. (2018). Predicting students’ intention to adopt mobile learning. Journal of Research in Innovative Teaching & Learning, 11(2), 178–191.
Awang, Z. (2012). Structural equation modeling using AMOS graphic (1st ed.). Penerbit Universiti Teknologi MARA.
Azizi, S. M., & Khatony, A. (2019). Investigating factors affecting medical sciences students’ intention to adopt mobile learning. BMC Medical Education, 19(1), 1-10. https://doi.org/10.1186/s12909-019-1831-4
Bagozzi, R. P., & Lee, K. H. (2002). Multiple routes for social influence: the role of compliance, internalization, and social identity. Social Psychology Quarterly, 65(3), 226-247. https://doi.org/10.2307/3090121
Batts, B. L., & Herring, C. (2013, June 21). An Exploration of the Relationship between Social Media Use and Engagement among African American Student Leaders, Hampton University, http://search.proquest.com/docview/ 1438180982?accountid=10612
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
Buabeng-Andoh, C., & Baah, C. (2020). Pre-service teachers’ intention to use learning management system: an integration of UTAUT and TAM. Interactive Technology and Smart Education, 17(4), 455-474. https://doi.org/10.1108/itse-02-2020-0028
Cheng, Y. M. (2014). An Exploration into Cost-Influencing Factors on Construction Projects. International Journal of Project Management, 32, 850-860. https://doi.org/10.1016/j.ijproman.2013.10.003
Chong, A. Y. L. (2013). Predicting m-commerce adoption determinants: a neural network approach. Expert Systems with Applications, 40(2), 523-530.
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., & Warshaw, P. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111-1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x
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
Ding, M. S., & Hampe, J. F. (2004). Reconsidering the challenges of M- payments: A roadmap to plotting the potential of the future M Commerce market. In Proceedings 16th Bled ecommerce Conference, Bled, Slovenia, 8(3), 164-183.
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 (1st ed.). John Wiley & Sons, Ltd. 9
Ganjeh, R. S., Memarian, H., Khosravil, M. H., & Mojarab, M. (2019). A comparison between effects of earthquake and blasting on stability of my slopes: a case study of Chadormalu open-pit mine. Journal of Mining and Environment (JME), 10(1), 223-240.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed). Pearson.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Pearson Prentice Hall.
Hashim, K. F., Tan, F. B., & Rashid, A. (2015). Adult learners’ intention to adopt mobile learning: a motivational perspective. British Journal of Educational Technology, 46(2), 381-390. https://doi.org/10.1111/bjet.12148
Hernandez, J. M., & Mazzon, J. A. (2006). Adoption of internet banking: proposition and implementation of an integrated methodology approach. International Journal of Bank Marketing, 25(2), 72-88.
Hirschman, E. C. (1984). Experience seeking: a subjective perspective for consumption. Journal of Business Research, 12(1), 115-136. https://doi.org/10.1016/0148-2963(84)90042-0
Holak, S. L., & Lehmann, D. R. (1990). Purchase intentions and the dimensions of innovation: an exploratory model. The Journal of Product Innovation Management, 7(1), 59-75. https://doi.org/10.1111/1540-5885.710059
Hossain, M. N., Talukder, M. S., Khayer, A., & Bao, Y. K. (2020). Investigating the factors driving adult learners’ continuous intention to use M-learning application: a fuzzy-set analysis. Journal of Research in Innovative Teaching & Learning, 16(1), 245-270. https://doi.org/10.1108/jrit-09-2019-0071
Huang, J.-H., Lin, Y.-R., & Chuang, S.-T. (2007). Elucidating user behavior of mobile learning A perspective of the extended technology acceptance model. Journal of The Electronic Library, 25(5), 585-598. https://doi.org/10.1108/02640470710829569
Hubley, A. (2014). Discriminant validity. In A. C. Michalos (Ed.), Encyclopedia of quality of life and well-being research (pp. 1664–1667). Springer.
Klobas, J. E., & Clyde, L. A. (2001). Social influence and internet use. Library Management, 22(1/2), 61-68. https://doi.org/10.1108/01435120110358943
Lai, J. Y., & Ulhas, K. R. (2012). Understanding acceptance of dedicated e-textbook applications for learning. The Electronic Library, 30(3), 321-338. https://doi.org/10.1108/02640471211241618
Lee, C. Y., Tsao, C. H., & Chang, W. C. (2015). The relationship between attitude toward using and customer satisfaction with mobile application services: an empirical study from the life insurance industry. Journal of Enterprise Information Management, 28(5), 680-697. https://doi.org/10.1108/jeim-07-2014-0077
Lee, M. K. O., Cheung, C. M. K., & Chen, Z. (2005). Acceptance of internet-based learning medium: the role of extrinsic and intrinsic motivation. Information & Management, 42(8), 1095-1104. https://doi.org/10.1016/j.im.2003.10.007
Lei, P. W., & Wu, Q. (2007). Introduction to structural equation modeling: Issues and practical considerations. Educational Measurement: Issues and Practice, 26(3), 33-43. https://doi.org/10.1111/j.1745-3992.2007.00099.x
Leon, S. (2018). Service mobile apps: A millennial generation perspective. Industrial Management & Data Systems, 118(9), 1837–1860. https://doi.org/10.1108/imds-10-2017-0479
Letchumanan, M., & Tarmizi, R. (2011). Assessing the intention to use e-book among engineering undergraduates in University Putra, Malaysia, Malaysia. Journal of Library Hi Tech, 29(3), 512-528. https://doi.org/10.1108/07378831111174459
Liebana-Cabanillas, F., Muñoz-Leiva, F., & Sánchez-Fernández, J. (2018). A global approach to the analysis of user behavior in mobile payment systems in the new electronic environment. Service Business, 12(1), 25-64. https://doi.org/10.1007/s11628- 017-0336-7.
Lin, F. F., & Lee, G. G. (2006). Effects of socio-technical factors on organizational intention to encourage knowledge sharing. Management Decisions, 44(1), 74-88. https://doi.org/10.1108/00251740610641472
Loetscher, T., Potter, K. J., Wong, D., & Das Nair, R. (2019). Cognitive rehabilitation for attention deficits following stroke. Cochrane Database of Systematic Reviews, 11(1), 1-10.
Maxwell, J. A. (1996). Qualitative Research Design: An Interactive Approach London (1st ed.). Applied Social Research Methods Series.
Milošević, I., Zivkovic, D., Manasijevic, D., & Nikolić, D. (2015). The effects of the intended behavior of students in the use of M-learning. Computers in Human Behavior, 51(1), 207-215. https://doi.org/10.1016/j.chb.2015.04.041
Mohamed, A. M., Anders, J., & Schneider, C. (2020). Monitoring of Changes in Land Use/Land Cover in Syria from 2010 to 2018 Using Multitemporal Landsat Imagery and GIS. Land 2020, 9(7), 226-230. https://doi.org/10.3390/land9070226
Mondi, M., Woods, P., & Rafi, A. (2007). Students’ ‘uses and gratification expectancy’ conceptual framework in relation to E-learning resources. Asia Pacific Education Review, 8(3), 435-449. https://doi.org/10.1007/bf03026472
Mostafa, A. E. (2020). Mobile learning during the era of COVID-19. Presentación Revista virtual, 61(1-2), 1-10. https://doi.org/10.35575/rvucn.n61a1
Myhill, C. E. (2002). ICT for access to information services for disabled people: an overview of projects and services at Gateshead Libraries Service. Program: electronic library and information systems, 36(3), 176-181. https://doi.org/10.1108/00330330210440467
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.
Olson, M. A., & Kendrick, R. V. (2008). Origins of attitudes, book: attitudes and attitude change. in Crano, W.D. and Prislin, R. (Eds), Attitudes and Attitude Change, Psychology Press, New York, NY.
Park, E., & Kim, K. J. (2013). User acceptance of long-term evolution (LTE) services an application of extended technology acceptance model. Program: Electronic library and information systems, 47(2), 188-205. https://doi.org/10.1016/j.ijinfomgt.2008.07.001
Park, E., & Kim, K. J. (2014). An integrated adoption model of mobile cloud services: exploration of key determinants and extension of technology acceptance model. Telematics and Informatics, 31(3), 376-385. https://doi.org/10.1016/j.tele.2013.11.008
Pedroso, C. B., Silva, S. 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. http://dx.doi.org/10.1016/j.ijpe.2016.08.035.
Peng, X. B., Berseth, G., & Van De Panne, M. (2016). Terrain-adaptive locomotion skills using deep reinforcement learning. ACM Transactions on Graphics, 35(1), 81-85. https://doi.org/10.1145/2897824.2925881
Phonthanukitithaworn, C., Sellitto, C., & Fong, W. M. (2016). An investigation of mobile payment (m-payment) service in Thailand. Asia-Pacific Journal of Business Administration, 8(1), 37-54. https://doi.org/10.1108/APJBA-10-2014-0119
Pinho, J. C. M. R., & Soares, A. M. (2011). Examining the technology acceptance model in the adoption of social networks. Journal of Research in Interactive Marketing, 5(2/3), 116-129. https://doi.org/10.1108/17505931111187767
Rauniar, R., Rawski, G., Yang, J., & Johnson, B. (2013). Technology acceptance model (TAM) and social media usage: an empirical study on Facebook. Journal of Enterprise Information Management, 27(1), 6-30. https://doi.org/10.1108/jeim-04-2012-0011
Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Free Press.
Saha, G. C., & Theingi, T. (2009). Service quality, satisfaction, and behavioural intentions: A study of low‐cost airline carriers in Thailand. Managing Service Quality: An International Journal, 19(3), 350-372. https://doi.org/10.1108/09604520910955348
Schierz, P., Schilke, O., & Wirtz, B. (2010). Understanding customer acceptance of mobile payment services: an empirical analysis. Journal of Electronic Commerce Research and Application, 9(3), 209-216. https://doi.org/10.1016/j.elerap.2009.07.005
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
Sharma, L., & Srivastava, M. (2020). Teachers’ motivation to adopt technology in higher education. Journal of Applied Research in Higher Education, 12(4), 673-692. https://doi.org/10.1108/jarhe-07-2018-0156
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
Siron, Y., & Wibowo, A., & Narmaditya, B. (2020). Factors affecting the adoption of e-learning in Indonesia: Lesson from Covid-19. Journal of Technology and Science Education, 10(2), 282. https://doi.org/10.3926/jotse.1025
Streiner, D., Norman, G., & Cairney, J. (2015). Health measurement scales: A practical guide to their development and use (5th ed.). Oxford University Press. https://doi.org/ 10.1093/med/9780199685219.001.0001
Suki, N. M., & Suki, N. M. (2011). Users’ behavior towards ubiquitous m-learning. Turkish Online Journal of Distance Education, 12(3), 118-129.
Sun, H., & Zhang, P. (2006). Causal relationships between perceived enjoyment and perceived ease of use: an alternative approach. Journal of the Association for Information Systems, 7(9), 618-645. https://doi.org/10.17705/1jais.00100
Tan, F. B., & Chou, J. P. C. (2008). The relationship between mobile service quality, perceived technology compatibility, and users’ perceived playfulness in the context of mobile information and entertainment services. International Journal of Human-Computer Interaction, 24(7), 649-671. https://doi.org/10.1080/10447310802335581
Teo, T. (2010). A path analysis of pre-service teachers’ attitudes to computer use: applying and extending the technology acceptance model in educational context. Interactive Learning Environments, 18(1), 65-79. https://doi.org/10.1080/10494820802231327
Teo, T. S. H., & Pok, S. H. (2003). Adoption of WAP-enabled mobile phones among internet users. Omega, 31(6), 483-498. https://doi.org/10.1016/j.omega.2003.08.005
Thong, J. Y. L., Hong, S., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64(9), 799-810. https://doi.org/10.1016/j.ijhcs.2006.05.001
Thongsri, N., Shen, L., Bao, Y. K., & Alharbi, L. M. (2018). Integrating UTAUT and UGT to explain behavioral intention to use M-learning A developing countries perspective. Journal of Systems and Information Technology, 20(3), 278-297. https://doi.org/10.1108/jsit-11-2017-0107
Venkatesh, V. (2000). Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365. https://doi.org/10.1287/isre.11.4.342.11872
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. https://doi.org/10.2307/30036540
Wu, B., & Zhang, C. (2014). Empirical study on continuance intentions towards E-Learning 2.0 systems. Behavior & Information Technology, 33(10), 1027-1038. https://doi.org/10.1080/0144929x.2014.934291
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
Yang, H., & Yoo, Y. (2004). It is all about attitude: revisiting the technology acceptance model. Decision Support System, 38(1), 19-31. https://doi.org/10.1016/s0167-9236(03)00062-9
Yap, S. F. C., & Lee, C. K. C. (2014). Leveraging the power of online social networks: a contingency approach. Marketing Intelligence & Planning, 32(3), 345-374.
Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The Behavioral Consequences of Service Quality. Journal of Marketing, 60(2), 31-46. https://doi.org/10.2307/1251929
Zhang, L., Zhu, J., & Liu, Q. (2012). A meta-analysis of mobile commerce and the moderating effect of culture. Computers in Human Behavior, 28(5), 1902-1911. https://doi.org/10.1016/j.chb.2012.05.008