Examining Indonesian College Students’ Behavioral Intention of Using Moodle App for E-Learning Platform

Siti Nur  Azizah; Tono Suwartono; Sri  Nurhayati; Endang  Sungkawati; Nur'aeni Nur'aeni

doi:10.48161/qaj.v4n3a668

Authors

Siti Nur Azizah Department of Accounting, Faculty of Economics and Business, Universitas Muhammadiyah Purwokerto, Jl. Raya Dukuh Waluh No. 1, Purwokerto 53182, Indonesia;
Tono Suwartono Department of English Language Teaching, Faculty of Teacher Training and Education, Universitas Muhammadiyah Purwokerto, Jl. Raya Dukuh Waluh No. 1, Purwokerto 53182, Indonesia;
Sri Nurhayati Department of Community Education, Faculty of Education, IKIP Siliwangi, Jl. Terusan Jend. Sudirman No. 1, Bandung 40614, Indonesia;
Endang Sungkawati Department of Management, Faculty of Economics and Business, Universitas Wisnuwardhana, Jl. Wisnuwardhana No. 6, Malang 65146, Indonesia;
Nur’aeni Nur’aeni Department of Psychology, Faculty of Psychology, Universitas Muhammadiyah Purwokerto, Jl. Raya Dukuh Waluh No. 1, Purwokerto 53182, Indonesia;

DOI:

https://doi.org/10.48161/qaj.v4n3a668

Abstract

The Covid-19 crisis has forced educational institutions to conduct online learning. However, the technology readiness and acceptance of the human resource to this shift towards distance education via Learning Management System (LMS) remained in question. This is where the gap lies between the govt’s policy and LMS implementation reality. The current study aims to explore the Moodle LMS acceptance among undergraduate students throughout the Archipelago Indonesia during the pandemic and beyond by adopting a Unified Theory of Acceptance and Usage of Technologies (UTAUT) model to better explain the students' behavioral intentions. Data has been gathered from 510 undergraduate students via online questionnaire with the help of Google Form. We used adapted questionnaire and tried it out before being administered. Using the Partial Least Squares - Structural Equation Modelling to analyze the data, this study has found that the original UTAUT constructs, except for effort expectancy and facilitating conditions, can influence the intention of using the Moodle LMS. This study also has revealed that both computer self-efficacy and other-efficacy directly affect the intention of utilizing Moodle application for e-learning platform. Furthermore, experience positively moderated computer-self efficacy and negatively other-efficacy as hypothesized. The findings indicate that Performance Expectancy and Social Influences are confirmed to have contributed to UTAUT Model, while Effort Expectancy and Facilitating Condition are not. In addition, constructs within Social Cognitive Theory, i.e. Other Efficacy, Computer Self Efficacy and Behavioral Intention are totally confirmed both directly and indirectly. With regard to the findings, practical recommendations have also been given at the end.

Downloads

Download data is not yet available.

Author Biography

Tono Suwartono, Department of English Language Teaching, Faculty of Teacher Training and Education, Universitas Muhammadiyah Purwokerto, Jl. Raya Dukuh Waluh No. 1, Purwokerto 53182, Indonesia;

Suwartono – better known as Tono SUWARTONO in the academic and publication sphere - is a Professor of English Language Teaching at Universitas Muhammadiyah Purwokerto, Indonesia. He holds a bachelor's degree in English Language Teaching, a master’s degree in Linguistics for Language Teaching, and a Ph.D in Language Education. In addition, he is an internationally certified TESOL trainer. Since his first debut in the 1990s, he has conducted countless research studies and extensively published articles in national, international, as well as reputable international journals. He has travelled internationally to present at conferences and published them in conference proceedings. He has also productively authored books. He has trained thousands of teachers nationwide. His workshops include those on educational research, academic writing, test and testing and educational evaluation, learning and teaching methodology, language education, Teaching English as a Second Language (TESL), Teaching English as Foreign Language (TEFL/TESOL), and motivational programs. Besides working for the Govt programs such as Independent Watch Team for the school national examination and Instructor and Supervisor for Teacher Certification Program, he also serves as a peer reviewer for several journals within Indonesia and beyond, including seven internationally reputed journals indexed in Scopus. For full profile and detail, please visit all "Websites & Social links."

References

Lwoga, E. T., & Komba, M. (2015). Antecedents of continued usage intentions of web-based learning management system in Tanzania. Education + Training, 57(7), 738-756.

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.

Gunasinghe, A., Hamid, J. A., Khatibi, A., & Azam, S. M. F. (2020). The viability of UTAUT-3 in understanding the lecturer's acceptance and use of virtual learning environments. International Journal of Technology Enhanced Learning, 12(4), 458-481.

Fianu, E., Blewett, C., & Ampong, G. O. (2020). Toward the development of a model of student usage of MOOCs. Education + Training, 62(5), 521-541.

McKeown, T., & Anderson, M. (2016). UTAUT: Capturing differences in undergraduate versus postgraduate learning? Education + Training, 58(9), 945-965.

Posese-Okesene, V. (2017). E-learning: The use of Moodle. International Journal for e-Learning Security, 7(1), 540-548.

Wang, Z., Sharma, P. N., & Cao, J. (2016). From knowledge sharing to firm performance: A predictive model comparison. Journal of Business Research, 69(10), 4650-4658.

Salloum, S. A., Mhamdi, C., Al Kurdi, B., & Shaalan, K. (2018). Factors affecting the adoption and meaningful use of social media: A Structural Equation Modeling approach. International Journal of Information Technology and Language Studies, 2(3), 96-109.

Mahande, R. D., & Malago, J. D. (2019). An e-learning acceptance evaluation through UTAUT model in a postgraduate program. Journal of Educators Online, 16(2).

Kayali, M. H., & Alaaraj, S. (2020). Adoption of cloud-based e-learning in developing countries: A combination of DOI, TAM, and UTAUT. International Journal of Contemporary Management and Information Technology, 1(1), 1-7.

Nair, S., Sagar, M., Sollers, J. III, Consedine, N., & Broadbent, E. (2015). Do slumped and upright postures affect stress responses? A randomized trial. Health Psychology, 34(6), 632–641.

Mikalef, P., Ilias, P. O., Giannakos, M., Krogstie, J., & Lekakos, G. (2016). Big data and strategy: A research framework. MCIS 2016 Proceedings, 50.

Bandura, A. (2011). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1-26.

Lent, R., & Lopez, F. (2002). Cognitive ties that bind: A tripartite view of efficacy beliefs in growth-promoting relationships. Journal of College Student Development, 21, 256-286.

Jackson, B., & Beauchamp, M. (2010). Efficacy beliefs in coach–athlete dyads: Prospective relationships using actor–partner interdependence models. Applied Psychology: An International Review, 59(2), 220-242.

Arif, M. S., Yeow, S. M., Zakuan, N., Jusoh, A., & Bahari, A. Z. (2012). The effects of computer self-efficacy and technology acceptance model on behavioral intention in internet banking systems. Procedia - Social and Behavioral Sciences, 57, 448-452.

Yang, H. L., & Lin, S. L. (2019). The reasons why elderly mobile users adopt ubiquitous mobile social service. Computers in Human Behavior, 93, 62-75.

Zheng, J., & Li, S. (2020). What drives students’ intention to use tablet computers: An extended technology acceptance model. International Journal of Educational Research, 102.

Costa, L. C. M., Maher, C. G., McAuley, J. H., Hancock, M. J., & Smeets, R. J. E. M. (2011). Self-efficacy is more important than fear of movement in mediating the relationship between pain and disability in chronic low back pain. European Journal of Pain, 15(2), 213-219.

Rogowska, A., Zmaczyńska-Witek, B., Zmaczyńska-Witek, M., & Kardasz, Z. (2020). The mediating effect of self-efficacy on the relationship between health locus of control and life satisfaction: A moderator role of movement disability. Disability and Health Journal, 13(4).

Padilla-Góngora, D., López-Liria, R., Díaz-López, M. P., Aguilar-Parra, J. M., & Vargas-Muñoz, M. E. (2017). Habits of the elderly regarding access to the new information and communication technologies. Procedia - Social and Behavioral Sciences, 237, 1412-1417.

Chang, V., Yang, W., & Wills, G. (2020). Research investigations on the use or non-use of hearing aids in smart cities. Technological Forecasting and Social Change, 153.

Cassaino, M., O’Sullivan, V., Kenny, R., & Setti, A. (2018). Disabilities moderate the association between neighborhood urbanity and cognitive health: Results from the Irish longitudinal study on ageing. Disability and Health Journal, 11(3), 359-366.

Gogan, M. L., Sirbu, R., & Draghici, A. (2015). Aspects concerning the use of the Moodle platform – case study. Procedia Technology, 19(1), 1142-1148.

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.

Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs: Prentice-Hall.

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

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111-1132.

Ajzen, I. (1991). The theory of planned behavior. Behavior and Human Decision Processes, 50(2), 179-211.

Hong, S., Thong, J. Y. L., & Tam, K. Y. (2006). Understanding continued information technology usage behavior: A comparison of three models in the context of mobile Internet. Decision Support Systems, 42(3), 1819-1834.

Thompson, R. L., Higgins, C. A., & Howell, J. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 124-143.

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215.

Venkatesh, V., Thong, J., & 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.

Isradilla, I., & Indrawati. (2017). Analysis of user acceptance towards online transportation technology using UTAUT 2 model: A case study in Uber, Grab and Go-Jek in Indonesia. International Journal of Science and Research, 6(7), 1479-1482.

Madigan, R., Louw, T., Wilbrink, M., Schieben, A., & Merat, M. (2017). What influences the decision to use automated public transport? Using UTAUT to understand public acceptance of Automated Road Transport Systems. Transportation Research Part F: Traffic Psychology and Behavior, 50, 55-64.

Mensah, I. K., Tianyu, Z., Zeng, G., & Chuanyong, L. (2019). Determinants of the continued intention of college students in China to use Di mobile car-sharing services. SAGE Open, 9(4).

Tran, V., Zhao, S., Diop, E. B., & Song, W. (2019). Travelers’ acceptance of electric car sharing systems in developing countries: The case of China. Sustainability, 11(19).

Chen, J., Li, R., Gan, M., Fu, Z., & Yuan, F. (2020). Public acceptance of driverless buses in China: An empirical analysis based on an extended UTAUT model. Discrete Dynamics in Nature and Society.

Gao, S., Li, Y., & Guo, H. (2019). Understanding the adoption of bike sharing systems: By combining technology diffusion theories and perceived risk. Journal of Hospitality and Tourism Technology, 10(3).

Almunawar, M. N., Anshari, M., & Lim, S. A. (2020). Customer acceptance of ride-hailing in Indonesia. Journal of Science and Technology Policy Management, 12(3), 443-462.

Compeau, D., & Higgins, C. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211.

Hasan, B. (2006). Delineating the effects of general and system-specific computer self-efficacy beliefs on IS acceptance. Information & Management, 43(5), 565-571.

Lee, S. (2014). Examining the factors that influence early adopters' smartphone adoption: The case of college students. Telematics and Informatics, 31(2), 308-318.

Lee, S., & Lee, K. (2018). Factors that influence an individual's intention to adopt a wearable healthcare device: The case of a wearable fitness tracker. Technological Forecasting and Social Change, 129, 154-163.

Yang, H., & Lin, S. (2019). The reasons why elderly mobile users adopt ubiquitous mobile social service. Computers in Human Behavior, 93, 62-75.

Balapour, A., Reychav, I., Shaberwal, R., & Azuri, J. (2019). Mobile technology identity and self-efficacy: Implications for the adoption of clinically supported mobile health apps. International Journal of Information Management, 49, 58-68.

Beatty, D., Beauchamp, M., & Dunlop, W. L. (2011). Examining the influence of other-efficacy and self-efficacy on personal performance. Journal of Sport & Exercise Psychology, 33(4), 586-593.

Beauchamp, M., & Whinton, L. (2005). Self-efficacy and other-efficacy in dyadic performance: Riding as one in equestrian eventing. Journal of Sport & Exercise Psychology, 27(2), 245-252.

Bitner, M. (1990). Evaluating service encounters: The effects of physical surroundings and employee responses. Journal of Marketing, 54(2), 69-82.

Bitner, M., Faranda, W., Hubbert, A., & Zeithaml, V. (1997). Customer contributions and roles in service delivery. International Journal of Service Industry Management, 8(3), 193-205.

Al-Hakim, Z. T., Sengupta, S., & Cuny, C. (2020). Impact of shared history on customers’ service evaluations. Journal of Retailing and Consumer Service, 55, 949-966.

Ye, J., Zheng, J., & Yi, F. (2020). A study on users' willingness to accept mobility as a service based on UTAUT model. Technological Forecasting and Social Change, 157, 120066.

Sanmukhiya, C. (2020). A PLS-SEM approach to the UTAUT model. Annals of Social Sciences & Management Studies, 6(1), 001-003.

Bawack, R., & Kamdjoug, J. (2017). Adequacy of UTAUT in clinician adoption of health information systems in developing countries: The case of Cameroon. International Journal of Medical Informatics, 109(1), 15-22.

Chen, G., Eden, D., & Gully, S. (2001). Validation of a new general self-efficacy scale. Organizational Research Methods, 4(1), 62-83.

Dwivedi, Y., Rana, N., Tamilmani, K., & Raman, R. (2020). A meta-analysis based modified unified theory of acceptance and use of technology (Meta-UTAUT): A review of emerging literature. Current Opinion in Psychology, 36, 13-18.

Chin, W. (1998). Issues and opinion on structural equation modelling. MIS Quarterly, 22(1), 7-16.

Hair, J., Risher, J., Sarstedt, M., & Ringle, C. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 3(1).

Diamantopoulos, A., & Siguaw, J. A. (2006). Formative Versus Reflective Indicators in Organizational Measure Development: A Comparison and Empirical Illustration. British Journal of Management, 17(4), 263-282.

Dijkstra, T., & Henseler, J. (2015). Consistent partial least squares path modelling. MIS Quarterly, 39(2).

Hartono, J., & Abdillah, W. (2016). PLS (partial least square) concept & application for empirical study. Yogyakarta: BPFE.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135.

Hair, J., Hollingsworth, C., Randolph, A., & Chong, A. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3).

Malik, G., & Rao, A. (2019). Extended expectation-confirmation model to predict continued usage of odor/ride hailing apps: Role of perceived value and self-efficacy. Information Technology & Tourism, 21, 461–482.

Jackson, B., & Beauchamp, M. R. (2010). Efficacy beliefs in coach–athlete dyads: Prospective relationships using actor–partner interdependence models. Applied Psychology: An International Review, 59(2), 220–242.