Purpose : The presented study explored the critical antecedents of blended learned adoption by students in India. It extended the unified theory of acceptance and use of technology (UTAUT) with attitude (ATT) and self-management of learned (SL) as additional constructs.

Methodology : The proposed model was tested empirically using confirmatory factor analysis (CFA) and structural equation modeling (SEM). The data were collected through a questionnaire from 383 New Delhi, Indian students for a period from January 2020 to December 2021.

Findings : The analysis of the data revealed that effort expectancy (EE), performance expectancy (PE), facilitating conditions (FC), and SL had significant positive effects on behavioral intention (BI) and ATT toward blended learning. The impact of socially influenced (SI) and PE on BI and ATT, respectively, was statistically not significant; FC exerted a positive influence on EE. Further, ATT was an important factor in creating BI as well as for actual usage (AU) of blended technology. The impact of BI on AU was also positive and significant.

Practical Implications : The present study made an important contribution to the extant literature by proposing a modified framework for identifying the students’ BI and actual use of blended learning. The study was expected to provide useful insights into the formulation, promotion, and implementation of blended learning in educational institutions in India. In light of the ongoing advancements in technology, it was imperative to proactively foresee and effectively manage the issues that may arise concerning its integration within the field of education. This research would facilitate institutions in anticipation of forthcoming transformations within the educational domain.

Originality : The originality of this study resides in its focus on how two mediators—ease of use and usefulness—played a pivotal role in changing students’ perceptions of blended learning. Notably, while social influence and performance expectations had no statistically significant effect on BI and ATT, respectively, enabling situations had a favorable effect on effort expectations.

1. Aggarwal, R. (2017). Economics of e-learning in higher education: The Indian case. Prabandhan: Indian Journal of Management, 10(6), 40–48. https://doi.org/10.17010/pijom/2017/v10i6/115374
2. Ahmad, S. (2020). Digital initiatives for access and quality in higher education: An overview. Prabandhan: Indian Journal of Management, 13(1), 9–18. https://doi.org/10.17010/pijom/2020/v13i1/149944
3. 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
4. Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological Bulletin, 82(2), 261–277. https://doi.org/10.1037/h0076477
5. Ali, W. (2020). Online and remote learning in higher education institutes: A necessity in light of COVID-19 pandemic. Higher Education Studies, 10(3), 16–25. https://doi.org/10.5539/hes.v10n3p16
6. Andersson, A., & Grönlund, Å. (2009). A conceptual framework for e-learning in developing countries: A critical review of research challenges. The Electronic Journal of Information Systems in Developing Countries, 38(1), 1–16. https://doi.org/10.1002/j.1681-4835.2009.tb00271.x
7. Arora, A. K., & Srinivasan, R. (2020). Impact of pandemic COVID-19 on the teaching – learning process: A study of higher education teachers. Prabandhan: Indian Journal of Management, 13(4), 43–56. https://doi.org/10.17010/pijom/2020/v13i4/151825
8. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16, 74–94. http://dx.doi.org/10.1007/BF02723327
9. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.
10. Boyle, T., Bradley, C., Chalk, P., Jones, R., & Pickard, P. (2003). Using blended learning to improve student success rates in learning to program. Journal of Educational Media, 28(2–3), 165–178. https://doi.org/10.1080/1358165032000153160
11. Briz-Ponce, L., Pereira, A., Carvalho, L., Juanes-Méndez, J. A., & García-Peñalvo, F. J. (2017). Learning with mobile technologies – students' behavior. Computers in Human Behavior, 72, 612–620. https://doi.org/10.1016/j.chb.2016.05.027
12. Bruff, D. O., Fisher, D. H., McEwen, K. E., & Smith, B. E. (2013). Wrapping a MOOC: Student perceptions of an experiment in blended learning. MERLOT Journal of Online Learning and Teaching, 9(2), 187–199.
13. Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming (3rd ed.). Routledge. https://doi.org/10.4324/9781315757421
14. Castro, R. (2019). Blended learning in higher education: Trends and capabilities. Education and Information Technologies, 24(4), 2523–2546. https://doi.org/10.1007/s10639-019-09886-3
15. Ceesay, L. B. (2021). Learning beyond the brick and mortar: Prospects, challenges, and bibliometric review of e-learning innovation. Jindal Journal of Business Research, 10(1), 33–48. https://doi.org/10.1177/22786821211000190
16. Chan, M. M., Plata, R. B., Medina, J. A., Alario-Hoyos, C., Rizzardini, R. H., & de la Roca, M. (2018). Analysis of behavioral intention to use cloud-based tools in a MOOC: A technology acceptance model approach. Journal of Universal Computer Science, 24(8), 1072–1089. https://doi.org/10.3217/jucs-024-08-1072
17. Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modeling. Education and Information Technologies, 25(5), 3443–3463. https://doi.org/10.1007/s10639-020-10159-7
18. Chatterjee, S., Majumdar, D., Misra, S., & Damaševičius, R. (2020). Adoption of mobile applications for teaching-learning process in rural girls' schools in India: An empirical study. Education and Information Technologies, 25, 4057–4076. https://doi.org/10.1007/s10639-020-10168-6
19. Chin, W. W. (1998). Commentary: Issues and opinions on structural equation modeling. MIS Quarterly, 22(1), 7–16. http://www.jstor.org/stable/249674
20. Cubeles, A., & Riu, D. (2018). The effective integration of ICTs in universities: The role of knowledge and academic experience of professors. Technology, Pedagogy and Education, 27(3), 339–349. https://doi.org/10.1080/1475939X.2018.1457978
21. 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
22. Dey, P., & Bandyopadhyay, S. (2019). Blended learning to improve quality of primary education among underprivileged school children in India. Education and Information Technologies, 24(3), 1995–2016. https://doi.org/10.1007/s10639-018-9832-1
23. Dhawan, S. (2020). Online learning: A panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems, 49(1), 5–22. https://doi.org/10.1177/0047239520934018
24. 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(3), 719–734. https://doi.org/10.1007/s10796-017-9774-y
25. Evans, J. R., & Mathur, A. (2018). The value of online surveys: A look back and a look ahead. Internet Research, 28(4), 854–887. https://doi.org/10.1108/IntR-03-2018-0089
26. Farid, S., Ahmad, R., Alam, M., Akbar, A., & Chang, V. (2018). A sustainable quality assessment model for the information delivery in e-learning systems. Information Discovery and Delivery, 46(1), 1–25. https://doi.org/10.1108/IDD-11-2016-0047
27. Gamal Aboelmaged, M. (2010). Predicting e-procurement adoption in a developing country: An empirical integration of technology acceptance model and theory of planned behaviour. Industrial Management & Data Systems, 110(3), 392–414. https://doi.org/10.1108/02635571011030042
28. Gan, C. L., & Balakrishnan, V. (2018). Mobile technology in the classroom: What drives student-lecturer interactions? International Journal of Human–Computer Interaction, 34(7), 666–679. https://doi.org/10.1080/10447318.2017.1380970
29. Geng, S., Law, K. M. Y., & Niu, B. (2019). Investigating self-directed learning and technology readiness in blending learning environment. International Journal of Educational Technology in Higher Education, 16, Article 17. https://doi.org/10.1186/s41239-019-0147-0
30. Ghazal, S., Al-Samarraie, H., & Aldowah, H. (2018). “I am still learning”: Modeling LMS critical success factors for promoting students' experience and satisfaction in a blended learning environment. IEEE Access, 6, 77179–77201. https://dx.doi.org/10.1109/ACCESS.2018.2879677
31. Greenspoon, P. J., & Saklofske, D. H. (1998). Confirmatory factor analysis of the multidimensional students' life satisfaction scale. Personality and Individual Differences, 25(5), 965–971. https://doi.org/10.1016/S0191-8869(98)00115-9
32. Gupta, K. P., & Maurya, H. (2022). Adoption, completion, and continuance of MOOCs: A longitudinal study of students' behavioural intentions. Behaviour & Information Technology, 41(3), 611–628. https://doi.org/10.1080/0144929X.2020.1829054
33. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis (Vol. 5, pp. 207–219). Prentice-Hall.
34. Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In R. R. Sinkovics & P. N. Ghauri (eds.), New challenges to international marketing (Advances in International Marketing, Vol. 20). Emerald Group Publishing Limited. https://doi.org/10.1108/S1474-7979(2009)0000020014
35. Kanojia, M., Shukla, B., & Wali, A. (2022). Critical factors to technology development in Indian academia: An ISM-MICMAC approach. Prabandhan: Indian Journal of Management, 15(5), 8–26. http://doi.org/10.17010/pijom/2022/v15i5/169578
36. Kanwal, F., & Rehman, M. (2017). Factors affecting e-learning adoption in developing countries–empirical evidence from Pakistan's higher education sector. IEEE Access, 5, 10968–10978. https://doi.org/10.1109/ACCESS.2017.2714379
37. Khechine, H., Raymond, B., & Augier, M. (2020). The adoption of a social learning system: Intrinsic value in the UTAUT model. British Journal of Educational Technology, 51(6), 2306–2325. https://doi.org/10.1111/bjet.12905
38. Kundu, A. (2021). A sound framework for ICT integration in Indian teacher education. International Journal of Teacher Education and Professional Development (IJTEPD), 4(1), 49–67. https://doi.org/10.4018/IJTEPD.2021010104
39. Lazar, I. M., Panisoara, G., & Panisoara, I. O. (2020). Digital technology adoption scale in the blended learning context in higher education: Development, validation and testing of a specific tool. PLoS ONE, 15(7), e0235957. https://doi.org/10.1371/journal.pone.0235957
40. Lin, J.-W., & Lin, H.-C. K. (2019). User acceptance in a computer-supported collaborative learning (CSCL) environment with social network awareness (SNA) support. Australasian Journal of Educational Technology, 35(1), 100–115. https://doi.org/10.14742/ajet.3395
41. Liyanagunawardena, T. R., Adams, A. A., & Williams, S. A. (2013). MOOCs: A systematic study of the published literature 2008-2012. The International Review of Research in Open and Distributed Learning, 14(3), 202–227. https://doi.org/10.19173/irrodl.v14i3.1455
42. Martín-García, A. V., Martínez-Abad, F., & Reyes-González, D. (2019). TAM and stages of adoption of blended learning in higher education by application of data mining techniques. British Journal of Educational Technology, 50(5), 2484–2500. https://doi.org/10.1111/bjet.12831
43. Mosunmola, A., Mayowa, A., Okuboyejo, S., & Adeniji, C. (2018). Adoption and use of mobile learning in higher education: The UTAUT model. In, Proceedings of the 9th International Conference on E-Education, E-Business, E-Management and E-Learning (pp. 20–25). https://doi.org/10.1145/3183586.3183595
44. Nath, A., Behura, A. K., Kumar, R., Kanak, T., & Sundararajan, M. (2019). Technology advancement, teachers' efforts, and know - how as key factors to improve students' creativity for excellence in education management. Prabandhan: Indian Journal of Management, 12(11), 7–20. https://doi.org/10.17010/pijom/2019/v12i11/148408
45. Panigrahi, R., Srivastava, P. R., & Sharma, D. (2018). Online learning: Adoption, continuance, and learning outcome—A review of literature. International Journal of Information Management, 43, 1–14. https://doi.org/10.1016/j.ijinfomgt.2018.05.005
46. Pardamean, B., & Susanto, M. (2012). Assessing user acceptance toward blog technology using the UTAUT model. International Journal of Mathematics and Computers in Simulation, 6(1), 203–212.
47. Parkes, M., Stein, S., & Reading, C. (2015). Student preparedness for university e-learning environments. The Internet and Higher Education, 25, 1–10. https://doi.org/10.1016/J.IHEDUC.2014.10.002
48. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
49. Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), Article 22. https://doi.org/10.1186/s41039-017-0062-8
50. Pradeepkumar, J. E. I., & Panchanatham, N. (2011). Amazing impact of e-learning education in rural India. Prabandhan: Indian Journal of Management, 4(5), 43–49. https://doi.org/10.17010/pijom/2011/v4i5/62453
51. Prasad, P. W. C., Maag, A., Redestowicz, M., & Hoe, L. S. (2018). Unfamiliar technology: Reaction of international students to blended learning. Computers & Education, 122, 92–103. https://doi.org/10.1016/j.compedu.2018.03.016
52. Rogers, E. (2003). Diffusion of innovations (5th ed.). Free Press.
53. Sangeeta, & Tandon, U. (2021). Factors influencing adoption of online teaching by school teachers: A study during COVID-19 pandemic. Journal of Public Affairs, 21(4), e2503. https://doi.org/10.1002/pa.2503
54. Smith, P. J., Murphy, K. L., & Mahoney, S. E. (2003). Towards identifying factors underlying readiness for online learning: An exploratory study. Distance Education, 24(1), 57–67. https://doi.org/10.1080/01587910303043
55. Sultana, J. (2020). Determining the factors that affect the uses of Mobile Cloud Learning (MCL) platform blackboard – a modification of the UTAUT model. Education and Information Technologies, 25(1), 223–238. https://doi.org/10.1007/s10639-019-09969-1
56. Šumak, B., & Šorgo, A. (2016). The acceptance and use of interactive whiteboards among teachers: Differences in UTAUT determinants between pre- and post-adopters. Computers in Human Behavior, 64, 602–620. https://doi.org/10.1016/j.chb.2016.07.037
57. Taber, K. S. (2018). The use of Cronbach's alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273–1296. https://doi.org/10.1007/s11165-016-9602-2
58. Tan, P. J. B. (2013). Applying the UTAUT to understand factors affecting the use of English e-learning websites in Taiwan. SAGE Open, 3(4). https://doi.org/10.1177/2158244013503837
59. Tandon, U., Kiran, R., & Sah, A. N. (2016). Customer satisfaction using website functionality, perceived usability and perceived usefulness towards online shopping in India. Information Development, 32(5), 1657–1673. https://doi.org/10.1177/0266666915621106
60. Tarhini, A., Masa'deh, R., Al-Busaidi, K. A., Mohammed, A. B., & Maqableh, M. (2017). Factors influencing students' adoption of e-learning: A structural equation modeling approach. Journal of International Education in Business, 10(2), 164–182. https://doi.org/10.1108/JIEB-09-2016-0032
61. Teo, T., Zhou, M., Fan, A. C. W., & Huang, F. (2019). Factors that influence university students' intention to use Moodle: A study in Macau. Educational Technology Research and Development, 67(3), 749–766. https://doi.org/10.1007/s11423-019-09650-x
62. Thomas, T., Singh, L., & Renville, D. (2020). The utility of the UTAUT: An application to mobile learning adoption in the Caribbean. International Journal of Education and Development using Information and Communication Technology, 16(2), 122–143.
63. Tseng, T. H., Lin, S., Wang, Y.-S., & Liu, H.-X. (2019). Investigating teachers' adoption of MOOCs: The perspective of UTAUT2. Interactive Learning Environments, 30(4), 635–650. https://doi.org/10.1080/10494820.2019.1674888
64. Uğur, N. G., & Turan, A. H. (2018). E-learning adoption of academicians: A proposal for an extended model. Behavior & Information Technology, 37(4), 393–405. https://doi.org/10.1080/0144929X.2018.1437219
65. 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
66. Venkatesh, V., Brown, S. A., & Bala, H. (2013). Bridging the qualitative-quantitative divide: Guidelines for conducting mixed methods research in information systems. MIS Quarterly, 37(1), 21–54. https://doi.org/10.25300/MISQ/2013/37.1.02
67. 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
68. 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
69. Virani, S. R., Saini, J. R., & Sharma, S. (2023). Adoption of massive open online courses (MOOCs) for blended learning: The Indian educators' perspective. Interactive Learning Environments, 31(2), 1060–1076. https://doi.org/10.1080/10494820.2020.1817760
70. 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. https://doi.org/10.1111/j.1467-8535.2007.00809.x
71. Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety. Educational and Psychological Measurement, 73(6), 913–934. https://doi.org/10.1177/0013164413495237
72. Yakubu, M. N., Dasuki, S. I., Abubakar, A. M., & Kah, M. M. O. (2020). Determinants of learning management systems adoption in Nigeria: A hybrid SEM and artificial neural network approach. Education and Information Technologies, 25(5), 3515–3539. https://doi.org/10.1007/s10639-020-10110-w
73. Yeop, M. A., Yaakob, M. F. M., Wong, K. T., Don, Y., & Zain, F. M. (2019). Implementation of ICT Policy (blended learning approach): Investigating factors of behavioural intention and use behaviour. International Journal of Instruction, 12(1), 767–782. https://doi.org/10.29333/iji.2019.12149a
74. Ying, A. N. L., & Yang, I. (2017). Academics and learners' perceptions on blended learning as a strategic initiative to improve student learning experience. MATEC Web of Conferences, The 9th International Unimas Stem Engineering Conference (ENCON 2016) “Innovative Solutions for Engineering and Technology Challenges” 04005. https://doi.org/10.1051/matecconf/20178704005
75. Zhang, Z., Cao, T., Shu, J., & Liu, H. (2022). Identifying key factors affecting college students' adoption of the e-learning system in mandatory blended learning environments. Interactive Learning Environments, 30(8), 1388–1401. https://doi.org/10.1080/10494820.2020.1723113