Promoting Online Education in Higher Education Institutions in Pakistan: Insights from the Technology Acceptance Model

Authors

  • Syed Mir Muhammad Shah Sukkur IBA University Pakistan
  • Faheem Gul Gilal Sukkur IBA University Pakistan
  • Raheela Haque Sukkur IBA University Pakistan
  • Elena Hunt Laurentian University, Canada

DOI:

https://doi.org/10.52461/sabas.v5i2.2424

Keywords:

PU, PEU, TAM, University students' attitude, Adoption of online education, Behavioral intention

Abstract

This study investigates the effect of perceived usefulness (PU) and perceived ease of use (PEU) on university students’ attitudes toward online education, their behavioral intention, and subsequent actual adoption/use of online education, using the Technology Acceptance Model (TAM) as a theoretical lens. It is also hypothesized students' geographic status (e.g., urban vs. rural) and educational levels (e.g., undergrad vs. grad) as moderating variables towards a positive effect of PU and PEU on actual adoption/use of online education through the mediation of attitude and behavioral intention. To this end, three hundred and thirty-one students were recruited from both public and private universities and sub-campuses in Pakistan, and the hypotheses were tested using structural equation modeling (SEM) and multigroup modeling in AMOS (Analysis of a moment structures in SPSS). SEM results supported the positive effect of PU and PEU on university students' attitudes toward online education, their behavioral intention, and subsequent actual adoption/use of online education. Finally, the important implications for policy and research are discussed.

Author Biographies

Syed Mir Muhammad Shah, Sukkur IBA University Pakistan

Department of Business Administration, Sukkur IBA University, Sukkur, Pakistan

Faheem Gul Gilal, Sukkur IBA University Pakistan

Department of Business Administration, Sukkur IBA University, Sukkur, Pakistan

Raheela Haque, Sukkur IBA University Pakistan

Department of Business Administration, Sukkur IBA University, Sukkur, Pakistan

Elena Hunt, Laurentian University, Canada

Laurentian University, Canada

References

Adigun, J., Onihunwa, J., Irunokhai, E., Sada, Y., & Adesina, O. (2015). Effect of Gender on Students' Academic Performance in Computer Studies in Secondary Schools in New Bussa, Borgu Local Government of Niger State. Journal of Education and practice, 6(33), 1-7.

Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In Action control (pp. 11-39). Springer, Berlin, Heidelberg.

Al-araibi, A. A. M., Nazri bin Mahrin, M., & Yusoff, R. C. M. (2019). Technological aspect factors of E-learning readiness in higher education institutions: Delphi technique. Education and Information Technologies, 24(1), 567-590.

Albort-Morant, G., Sanchís-Pedregosa, C., & Paredes Paredes, J. R. (2021). Online banking adoption in Spanish cities and towns. Finding differences through TAM application. Economic Research-Ekonomska Istraživanja, 1-19.

Al-Emran, M., Al-Maroof, R., Al-Sharafi, M. A., & Arpaci, I. (2020). What impacts learning with wearables? An integrated theoretical model. Interactive learning environments, 1-21.

Almaiah, M. A., & Alyoussef, I. Y. (2019). Analysis of the effect of course design, course content support, course assessment, and instructor characteristics on the actual use of the E-learning system. IEEE Access, 7, 171907-171922

Alsabawy, A. Y., Cater-Steel, A., & Soar, J. (2016). Determinants of perceived usefulness of e-learning systems. Computers in Human Behavior, 64, 843-858.

Bashir, I., & Madhavaiah, C. (2015). Consumer attitude and behavioral intention towards Internet banking adoption in India. Journal of Indian Business Research, 7(1), 67-102.

Baumann, C., Burton, S., Elliott, G., & Kehr, H. M. (2007). Prediction of attitude and behavioral intentions in retail banking. International Journal of Bank Marketing, 25(2), 102-116.

Bentler, P. M., & Speckart, G. (1979). Models of attitude-behavior relations. Psychological Review, 86(5), 452-464.

Bishnoi, V., & Sharma, R. (2009). Impact of TV advertising on buying behavior of rural and urban teenagers. JK Journal of Management & Technology, 1(1), 65-76.

Boca, G. D. (2021). Factors influencing students’ behavior and attitude towards online education during COVID-19. Sustainability, 13(13), 7469-7480.

Camacho, A., Alves, R. A., De Smedt, F., Van Keer, H., & Boscolo, P. (2021). Relations among motivation, behavior, and performance in writing: A multiple‐group structural equation modeling study. British Journal of Educational Psychology, 91(4), 1456-1480.

Cano, F., Pichardo, M. C., Berbén, A. B. G., & Fernández-Cabezas, M. (2021). An integrated test of multidimensionality, convergent, discriminant, and criterion validity of the course experience questionnaire: an exploratory structural equation modeling. Assessment & Evaluation in Higher Education, 46(2), 256-268.

Cao, J., Yang, T., Lai, I. K. W., & Wu, J. (2021). Student acceptance of intelligent tutoring systems during COVID-19: The effect of political influence. The International Journal of Electrical Engineering & Education. https://doi.org/10.1177/00207209211003270

Chang, I. H., & Chen, R. S. (2020). The impact of perceived usefulness on satisfaction with online parenting resources: the mediating effects of liking and online interaction. The Asia-Pacific Education Researcher, 29(4), 307-317.

Chiu, M. M., & McBride-Chang, C. (2006). Gender, context, and reading: A comparison of students in 43 countries. Scientific Studies of Reading, 10(4), 331-362.

Di Pietro, L., Di Virgilio, F., & Pantano, E. (2012). Social network for the choice of tourist destination: attitude and behavioral intention. Journal of Hospitality and Tourism Technology, 3(1), 60-76.

Doan, T. T. T. (2021). The effect of perceived risk and technology self-efficacy on online learning intention: An empirical study in Vietnam. The Journal of Asian Finance, Economics, and Business, 8(10), 385-393.

Eskasasnanda, I. D. P. (2017). Causes and Effects of Online Video Game Playing among Junior-Senior High School Students in Malang East Java. Komunitas: International Journal of Indonesian Society and Culture, 9(2), 191-202.

Fishbein, M. (2008). A reasoned action approach to health promotion. Medical Decision Making, 28(6), 834-844.

Fishbein, M., & Ajzen, I. (1975). BelieJ attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.

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

Gilal, F. G., Zhang, J., Gilal, R. G., & Gilal, N. G. (2020). Integrating intrinsic motivation into the relationship between product design and brand attachment: A cross-cultural investigation based on self-determination theory. European Journal of International Management, 14(1), 1-27.

Gilal, F. G., Zhang, J., Paul, J., & Gilal, N. G. (2019). The role of self-determination theory in marketing science: An integrative review and agenda for research. European Management Journal, 37(1), 29-44.

Godin, G. (1993). The theories of reasoned action and planned behavior: Overview of findings, emerging research problems and usefulness for exercise promotion. Journal of Applied Sport Psychology, 5(2), 141-157.

Goh, C. F., Hii, P. K., Tan, O. K., & Rasli, A. (2020). Why do university teachers use E-learning systems?. The International Review of Research in Open and Distributed Learning, 21(2), 136-155.

Grimmer, M., & Miles, M. P. (2017). With the best of intentions: a large sample test of the intention-behavior gap in pro‐environmental consumer behavior. International Journal of Consumer Studies, 41(1), 2-10.

Gupta, V., Khanna, K., & Gupta, R. K. (2018). A study on the street food dimensions and its effects on consumer attitude and behavioral intentions. Tourism Review, 73(3), 374-388.

Hair, J. et al., (1998) Multivariate Data Analysis, 5th edn (Englewood Cliffs, N. J: Prentice-Hall).

Hair Jr, J. F., Sarstedt, M., Matthews, L. M., & Ringle, C. M. (2016). Identifying and treating unobserved heterogeneity with FIMIX-PLS: part I–method. European Business Review.

Hahs-Vaughn, D. (2004). The impact of parents' education level on college students: An analysis using the beginning postsecondary students longitudinal study 1990-92/94. Journal of College Student Development, 45(5), 483-500.

Hamidi, H., & Chavoshi, A. (2018). Analysis of the essential factors for the adoption of mobile learning in higher education: A case study of students of the University of Technology. Telematics and Informatics, 35(4), 1053-1070.

Hamzah, H., & Ahmad Shaberi, H. S. (2021). Teaching and learning using the online platform a new experience. International Journal of Practices in Teaching and Learning (IJPTL), 1(2), 1-5.

Han, J. H., & Sa, H. J. (2021). Acceptance of and satisfaction with online educational classes through the technology acceptance model (TAM): The COVID-19 situation in Korea. Asia Pacific Education Review, 1-13.

Islamoglu, H., Kabakci Yurdakul, I., & Ursavas, O. F. (2021). Pre-service teachers’ acceptance of mobile-technology-supported learning activities. Educational Technology Research and Development, 69(2), 1025-1054.

Jaiyeoba, O. O., & Iloanya, J. (2019). E-learning in tertiary institutions in Botswana: apathy to adoption. The International Journal of Information and Learning Technology,10(2), 157-168.

James, R. (2001). Participation disadvantage in Australian higher education: An analysis of some effects of geographical location and socioeconomic status. Higher Education, 42(4), 455-472.

Johari, N., Mustaffha, N., Ripain, N., Zulkifli, A., & Ahmad, N. W. (2015). Students’ Acceptance of Online Learning in KUIS. In First International Conference on Economics and Banking (pp. 326-335).

Joo, Y. J., Park, S., & Lim, E. (2018). Factors influencing preservice teachers’ intention to use technology: TPACK, teacher self-efficacy, and technology acceptance model. Journal of Educational Technology & Society, 21(3), 48-59.

Joo, Y. J., So, H. J., & Kim, N. H. (2018). Examination of relationships among students' self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs. Computers & Education, 122, 260-272.

Jurinovich, S., & Domenici, V. (2022). Digital Tool for the Analysis of UV–Vis Spectra of Olive Oils and Educational Activities with High School and Undergraduate Students. Journal of Chemical Education.

Kalogiannakis, M., & Papadakis, S. (2019). Evaluating pre-service kindergarten teachers' intention to adopt and use tablets into teaching practice for natural sciences. International Journal of Mobile Learning and Organisation, 13(1), 113-127.

Kearney, M., Schuck, S., Aubusson, P., & Burke, P. F. (2018). Teachers’ technology adoption and practices: Lessons learned from the IWB phenomenon. Teacher Development, 22(4), 481-496.

Kemer, G., & Atik, G. (2012). Hope and social support in high school students from urban and rural areas of Ankara, Turkey. Journal of Happiness Studies, 13(5), 901-911.

Khan, A. K., Moss, S., Quratulain, S., & Hameed, I. (2018). When and how to subordinate performance leads to abusive supervision: A social dominance perspective. Journal of Management, 44(7), 2801-2826.

Kobul, M. K. (2022). Socioeconomic status influences Turkish digital natives’ internet use habitus. Behaviour & Information Technology, 1-19.

Lan, W. (2005). Self‐monitoring and its relationship with educational level and task importance. Educational psychology, 25(1), 109-127.

Lavidas, K., Komis, V., & Achriani, A. (2022). Explaining faculty members’ behavioral intention to use learning management systems. Journal of Computers in Education, 1-19.

Lawrence, J. E., & Tar, U. A. (2018). Factors that influence teachers’ adoption and integration of ICT in teaching/learning process. Educational Media International, 55(1), 79-105.

Leung, X. Y., & Cai, R. (2021). How pandemic severity moderates digital food ordering risks during COVID-19: An application of prospect theory and risk perception framework. Journal of Hospitality and Tourism Management, 47, 497-505.

Li, B. (2021). Ready for online? Exploring EFL teachers’ ICT acceptance and ICT literacy during COVID-19 in mainland China. Journal of Educational Computing Research, 07356331211028934.

Liu, Y., Hau, K. T., Liu, H., Wu, J., Wang, X., & Zheng, X. (2020). Multiplicative effect of intrinsic and extrinsic motivation on academic performance: A longitudinal study of Chinese students. Journal of personality, 88(3), 584-595.

Mailizar, M., Burg, D., & Maulina, S. (2021). Examining university students’ behavioral intention to use e-learning during the COVID-19 pandemic: An extended TAM model. Education and Information Technologies, 26(6), 7057-7077.

McCabe, G. A., Oltmanns, J. R., & Widiger, T. A. (2021). Criterion A scales: Convergent, discriminant, and structural relationships. Assessment, 28(3), 813-828.

Nayak, J. K. (2018). Relationship among smartphone usage, addiction, academic performance and the moderating role of gender: A study of higher education students in India. Computers & Education, 123, 164-173.

Olsson, D., Gericke, N., Boeve-de Pauw, J., Berglund, T., & Chang, T. (2019). Green schools in Taiwan–Effects on student sustainability consciousness. Global Environmental Change, 54, 184-194.

Onyema, E. M., Eucheria, N. C., Obafemi, F. A., Sen, S., Atonye, F. G., Sharma, A., & Alsayed, A. O. (2020). Impact of Coronavirus pandemic on education. Journal of Education and Practice, 11(13), 108-121.

Pal, D., & Patra, S. (2021). University students’ perception of video-based learning in times of COVID-19: A TAM/TTF perspective. International Journal of Human-Computer Interaction, 37(10), 903-921.

Panigrahi, R., Srivastava, P. R., & Sharma, D. (2018). Online learning: Adoption, continuance, and learning outcome—A review of the literature. International Journal of Information Management, 43, 1-14.

Pittalis, M. (2021). Extending the technology acceptance model to evaluate teachers’ intention to use dynamic geometry software in geometry teaching. International Journal of Mathematical Education in Science and Technology, 52(9), 1385-1404.

Purnawirawan, N., De Pelsmacker, P., & Dens, N. (2012). Balance and sequence in online reviews: How perceived usefulness affects attitudes and intentions. Journal of Interactive Marketing, 26(4), 244-255.

Rahmi, B. A. K. I., Birgoren, B., & Aktepe, A. (2018). A meta analysis of factors affecting perceived usefulness and perceived ease of use in the adoption of e-learning systems. Turkish Online Journal of Distance Education, 19(4), 4-42.

Rather, R. A. (2021). Demystifying the effects of perceived risk and fear on customer engagement, co-creation and revisit intention during COVID-19: A protection motivation theory approach. Journal of Destination Marketing & Management, 20, 100564.

Sánchez-Mena, A., Martí-Parreño, J., & Aldás-Manzano, J. (2019). Teachers’ intention to use educational video games: The moderating role of gender and age. Innovations in Education and Teaching International, 56(3), 318-329.

Schlebusch, C. L. (2018). Computer anxiety, computer self-efficacy, and attitudes towards the internet of first-year students at a South African University of Technology. Africa Education Review, 15(3), 72-90.

Sciarelli, M., Prisco, A., Gheith, M. H., & Muto, V. (2021). Factors affecting the adoption of blockchain technology in innovative Italian companies: an extended TAM approach. Journal of Strategy and Management.

Sebetci, Ö. (2015). A TAM-based model for e-government: A case for Turkey. International Journal of Electronic Governance, 7(2), 113-135.

Singer, J. (2020). Student stratification among a combination of school choice policies in Detroit. Journal of School Choice, 14(1), 122-153.

Siron, Y., Wibowo, A., & Narmaditya, B. S. (2020). Factors affecting the adoption of e-learning in Indonesia: Lesson from Covid-19. JOTSE: Journal of Technology and Science Education, 10(2), 282-295.

Song, H., Ruan, W. J., & Jeon, Y. J. J. (2021). An integrated approach to the purchase decision making process of food-delivery apps: Focusing on the TAM and AIDA models. International Journal of Hospitality Management, 95, 102943.

Sultan, I., Bardi, M. F., Baatta, A. M., Almaghrabi, S., & Mohammed, R. A. (2022). Medical Students’ Attitude Towards Robotic Surgery: A Cross-Sectional Survey. Journal of Medical Education and Curricular Development, 9, 23821205211066483.

Tandon, U. (2020). Factors influencing adoption of online teaching by school teachers: A study during COVID‐19 pandemic. Journal of Public Affairs, e2503.

Tang, Y. M., Chen, P. C., Law, K. M., Wu, C. H., Lau, Y. Y., Guan, J., ... & Ho, G. T. (2021). Comparative analysis of Student's live online learning readiness during the coronavirus (COVID-19) pandemic in the higher education sector. Computers & Education, 168, 104211.

UNESCO Report. (2020, March 4). COVID-19 educational disruption and response. UNESCO.

Unger, S., & Meiran, W. R. (2020). Student attitudes towards online education during the COVID-19 viral outbreak of 2020: Distance learning in a time of social distance. International Journal of Technology in Education and Science, 4(4), 256-266.

Ursavaş, Ö. F., & Reisoglu, I. (2017). The effects of cognitive style on Edmodo users’ behavior: A structural equation modeling-based multi-group analysis. The International Journal of Information and Learning Technology.

van Maarseveen, R. (2021). The urban-rural education gap: do cities indeed make us smarter?. Journal of Economic Geography, 21(5), 683-714.

Velayutham, S., Aldridge, J. M., & Fraser, B. (2012). Gender differences in student motivation and self-regulation in science learning: A multi-group structural equation modeling analysis. International journal of science and mathematics education, 10(6), 1347-1368

Webb, D., Soutar, G. N., Gagné, M., Mazzarol, T., & Boeing, A. (2022). Saving energy at home: Exploring the role of behavior regulation and habit. International Journal of Consumer Studies, 46(2), 621-635.

Yiakoumetti, A., Evans, M., & Esch, E. (2005). Language awareness in a bidialectal setting: The oral performance and language attitudes of urban and rural students in Cyprus. Language Awareness, 14(4), 254-260.

Yim, J. S. C., Moses, P., & Azalea, A. (2019). Predicting teachers’ continuance in a virtual learning environment with psychological ownership and the TAM: A perspective from Malaysia. Educational Technology Research and Development, 67(3), 691-709.

Zaccone, M. C., & Pedrini, M. (2019). The effects of intrinsic and extrinsic motivation on students learning effectiveness. Exploring the moderating role of gender. International Journal of Educational Management.

Zhang, W., Wang, Y., Yang, L., & Wang, C. (2020). Suspending classes without stopping learning: China’s education emergency management policy in the COVID-19 outbreak.

Zhou, L., Wu, S., Zhou, M., & Li, F. (2020). 'School’s Out, But Class’ On', The Largest Online Education in the World Today: Taking China’s Practical Exploration During The COVID-19 Epidemic Prevention and Control As an Example. Best Evid Chin Edu, 4(2), 501-519.

Zhu, Y., Wen, X., Chu, M., & Sun, S. (2022). Consumers’ intention to participate in food safety risk communication: A model integrating protection motivation theory and the theory of reasoned action. Food Control, 108993.

Published

2023-12-31

How to Cite

Shah, S. M. M., Gilal, F. G., Haque, R., & Elena Hunt. (2023). Promoting Online Education in Higher Education Institutions in Pakistan: Insights from the Technology Acceptance Model. South Asian Review of Business and Administrative Studies (SABAS), 5(2). https://doi.org/10.52461/sabas.v5i2.2424