Leveraging lecturers’ intelligence for student engagement enrichment in blended learning courses

References

• Adekola, J., Dale, V. H., & Gardiner, K. (2017). Development of an institutional framework to guide transitions into enhanced blended learning in higher education. Research in Learning Technology, 25, 1–16. https://doi.org/10.25304/rlt.v25.1973
   Web of Science ®Google Scholar
• Agyeiwaah, E. (2022). An exploratory sequential mixed methods design: A research design for small tourism enterprises in Ghana. In F. Okumus, S. M. Rasoolimanesh, and S. Jahani (Ed.), Advanced Research Methods in Hospitality and Tourism (pp. 25–45). Emerald Publishing Limited. https://doi.org/10.1108/978-1-80117-550-020221003
   Google Scholar
• Alkhatib, O. J. (2018). An interactive and blended learning model for engineering education. Journal of Computers in Education, 5(1), 19–48. https://doi.org/10.1007/s40692-018-0097-x
   Web of Science ®Google Scholar
• Al-Qatawneh, S., Eltahir, M. E., & Alsalhi, N. R. (2020). The effect of blended learning on the achievement of HDE students in the methods of teaching Arabic language course and their attitudes towards its use at Ajman University: A case study. Education and Information Technologies, 25(3), 2101–2127. https://doi.org/10.1007/s10639-019-10046-w
   Web of Science ®Google Scholar
• Amabile, T. M. (1982). Social psychology of creativity: A consensual assessment technique. Journal of Personality and Social Psychology, 43(5), 997–1013. https://doi.org/10.1037/0022-3514.43.5.997
   Web of Science ®Google Scholar
• Arbaugh, J. B. (2000). Virtual classroom versus physical classroom: An exploratory study of class discussion patterns and student learning in an asynchronous internet-based MBA course. Journal of Management Education, 24(2), 213–233. https://doi.org/10.1177/105256290002400206
   Google Scholar
• Asarta, C. J., & Schmidt, J. R. (2015). The choice of reduced seat time in a blended course. The Internet and Higher Education, 27, 24–31. https://doi.org/10.1016/j.iheduc.2015.04.006
   Web of Science ®Google Scholar
• Astakhova, M. N., & Porter, G. (2015). Understanding the work passion–performance relationship: The mediating role of organizational identification and moderating role of fit at work. Human Relations, 68(8), 1315–1346. https://doi.org/10.1177/0018726714555204
   Web of Science ®Google Scholar
• Astin, A. W. (1984). Student involvement: A developmental theory for higher education. Journal of College Student Personnel, 25(4), 297–308.
   Web of Science ®Google Scholar
• Baranova, T. A., Kobicheva, A. M., Tokareva, E. Y., & Mokhorov, D. (2022). The relationship between students’ psychological security level, academic engagement and performance variables in the digital educational environment. Education and Information Technologies, 27(7), 9385–9399. https://doi.org/10.1007/s10639-022-11024-5
   PubMed Web of Science ®Google Scholar
• Bedenlier, S., Bond, M., Buntins, K., Zawacki-Richter, O., & Kerres, M. (2020). Facilitating student engagement through educational technology in higher education: A systematic review in the field of arts and humanities. Australasian Journal of Educational Technology, 36(4), 126–150. https://doi.org/10.14742/ajet.5477
   Web of Science ®Google Scholar
• Bernard, R. M., Borokhovski, E., Schmid, R. F., Tamim, R. M., & Abrami, P. C. (2014). A meta-analysis of blended learning and technology use in higher education: from the general to the applied. Journal of Computing in Higher Education, 26(1), 87–122. https://doi.org/10.1007/s12528-013-9077-3
   Web of Science ®Google Scholar
• Boelens, R., De Wever, B., & Voet, M. (2017). Four key challenges to the design of blended learning: A systematic literature review. Educational Research Review, 22, 1–18. https://doi.org/10.1016/j.edurev.2017.06.001
   Web of Science ®Google Scholar
• Bollen, K. A., & Stine, R. A. (1992). Bootstrapping goodness-of-Fit measures in structural equation models. Sociological Methods & Research, 21(2), 205–229. https://doi.org/10.1177/0049124192021002004
   Web of Science ®Google Scholar
• Bond, M., & Bedenlier, S. (2019). Facilitating student engagement through educational technology: Towards a conceptual framework. Journal of Interactive Media in Education, 2019(1), 1–14. https://doi.org/10.5334/jime.528
   Web of Science ®Google Scholar
• Bouteraa, M., Raja Hisham, R. R. I., & Zainol, Z. (2022). Challenges affecting bank consumers’ intention to adopt green banking technology in the UAE: a UTAUT-based mixed-methods approach. Journal of Islamic Marketing, 14(10), 2466–2501. https://doi.org/10.1108/JIMA-02-2022-0039
   Web of Science ®Google Scholar
• Brislin, R. W. (1983). Cross-cultural research in psychology. Annual Review of Psychology, 34(1), 363–400. https://doi.org/10.1146/annurev.ps.34.020183.002051
   Google Scholar
• Browne, M. W., & Cudeck, R. (1992). Alternative Ways of Assessing Model Fit. Sociological Methods & Research, 21(2), 230–258. https://doi.org/10.1177/0049124192021002005
   Web of Science ®Google Scholar
• Byrne, B. M. (1989). A Primer of LISREL: Basic Applications and Programming for Confirmatory Factor Analytic Models. Springer-Verlag.
   Google Scholar
• Cao, W. (2023). A meta-analysis of effects of blended learning on performance, attitude, achievement, and engagement across different countries. Frontiers in Psychology, 14, 1212056. https://doi.org/10.3389/fpsyg.2023.1212056
   PubMed Web of Science ®Google Scholar
• Chen, X. L., Zou, D., Xie, H. R., & Su, F. (2021). Twenty-five years of computer-assisted language learning: A topic modeling analysis. Language Learning & Technology, 25(3), 151–185.
   Web of Science ®Google Scholar
• Chickering, A. W., & Gamson, Z. F. (1991). Appendix A: Seven principles for good practice in undergraduate education. New Directions for Teaching and Learning, 1991(47), 63–69. https://doi.org/10.1002/tl.37219914708
   Google Scholar
• Chin, W. W. (1998). The Partial Least Squares Approach to Structural Equation Modeling. Lawrence Erlbaum Associates.
   Google Scholar
• Chiparausha, B., Onyancha, O. B., & Ezema, I. J. (2022). Factors influencing the use of social media by academic librarians in Zimbabwe: a UTAUT model analysis. Global Knowledge, Memory and Communication, 73(1/2), 142–160. https://doi.org/10.1108/GKMC-09-2021-0151
   Web of Science ®Google Scholar
• Chiu, T. K. (2022). Applying the self-determination theory (SDT) to explain student engagement in online learning during the COVID-19 pandemic. Journal of Research on Technology in Education, 54(sup1), S14–S30. https://doi.org/10.1080/15391523.2021.1891998
   Web of Science ®Google Scholar
• Chiu, T. K. F. (2023). Student engagement in K-12 online learning amid COVID-19: A qualitative approach from a self-determination theory perspective. Interactive Learning Environments, 31(6), 3326–3339. https://doi.org/10.1080/10494820.2021.1926289
   Web of Science ®Google Scholar
• Chyung, S. Y. Y., Roberts, K., Swanson, I., & Hankinson, A. (2017). Evidence-Based Survey Design: The Use of a Midpoint on the Likert Scale. Performance Improvement, 56(10), 15–23. https://doi.org/10.1002/pfi.21727
   Google Scholar
• Coates, H. (2007). A model of online and general campus‐based student engagement. Assessment & Evaluation in Higher Education, 32(2), 121–141. https://doi.org/10.1080/02602930600801878
   Google Scholar
• Creswell, J. W., & Poth, C. N. (2018). Qualitative Inquiry and Research Design Choosing among Five Approaches (4th ed.). SAGE Publications.
   Google Scholar
• Creswell, J., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). Sage publications.
   Google Scholar
• Creswell, J. W. (2014). Research Design: qualitative, Quantitative and Mixed Methods Approaches (4th ed.). Sage publications.
   Google Scholar
• Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Plenum Publishing Co.
   Google Scholar
• Deci, E. L., & Ryan, R. M. (2000). The “What” and “Why” of Goal Pursuits: Human Needs and the Self-Determination of Behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
   Web of Science ®Google Scholar
• Dewi, C. K., Mohaidin, Z., & Murshid, M. A. (2020). Determinants of online purchase intention: a PLS-SEM approach: evidence from Indonesia. Journal of Asia Business Studies, 14(3), 281–306. https://doi.org/10.1108/JABS-03-2019-0086
   Web of Science ®Google Scholar
• Diaconis, P., & Efron, B. (1983). Computer-intensive methods in statistics. Scientific American, 248(5), 116–130. https://doi.org/10.1038/scientificamerican0583-116
   Web of Science ®Google Scholar
• Duff, A., Hancock, P., & Marriott, N. (2020). The role and impact of professional accountancy associations on accounting education research: An international study. The British Accounting Review, 52(5), 100829. https://doi.org/10.1016/j.bar.2019.03.004
   Web of Science ®Google Scholar
• Fisher, R., Perényi, Á., & Birdthistle, N. (2021). The positive relationship between flipped and blended learning and student engagement, performance and satisfaction. Active Learning in Higher Education, 22(2), 97–113. https://doi.org/10.1177/1469787418801702
   Web of Science ®Google Scholar
• Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
   Web of Science ®Google Scholar
• Fredricks, J. A. (2011). Engagement in School and Out-of-School Contexts: A Multidimensional View of Engagement. Theory into Practice, 50(4), 327–335. https://doi.org/10.1080/00405841.2011.607401
   Web of Science ®Google Scholar
• Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School Engagement: Potential of the Concept, State of the Evidence. Review of Educational Research, 74(1), 59–109. https://doi.org/10.3102/00346543074001059
   Web of Science ®Google Scholar
• Gao, B. W., Jiang, J., & Tang, Y. (2020). The effect of blended learning platform and engagement on students’ satisfaction——the case from the tourism management teaching. Journal of Hospitality, Leisure, Sport & Tourism Education, 27, 100272. https://doi.org/10.1016/j.jhlste.2020.100272
   Web of Science ®Google Scholar
• Gardner, H. (1987). The theory of multiple intelligences. Annals of Dyslexia, 37(1), 19–35. https://doi.org/10.1007/bf02648057
   PubMedGoogle Scholar
• Gardner, H., & Hatch, T. (1989). Educational Implications of the Theory of Multiple Intelligences. Educational Researcher, 18(8), 4–10. https://doi.org/10.3102/0013189X018008004
   Google Scholar
• Garrison, D. R., & Kanuka, H. (2004). Blended learning: Uncovering its transformative potential in higher education. The Internet and Higher Education, 7(2), 95–105. https://doi.org/10.1016/j.iheduc.2004.02.001
   Google Scholar
• Graham, C. R., Woodfield, W., & Harrison, J. B. (2013). A framework for institutional adoption and implementation of blended learning in higher education. The Internet and Higher Education, 18, 4–14. https://doi.org/10.1016/j.iheduc.2012.09.003
   Web of Science ®Google Scholar
• Gubman, E. (2004). From engagement to passion for work: The search for the missing person. Human Resource Planning, 27(3), 42–46.
   Google Scholar
• Guskey, T. R. (2002). Professional Development and Teacher Change. Teachers and Teaching, 8(3), 381–391. https://doi.org/10.1080/135406002100000512
   Google Scholar
• Hair, J. F., Hult, G. T. M., & Ringle, C. M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). Sage Publications.
   Google Scholar
• Halverson, L. R., & Graham, C. R. (2019). Learner engagement in blended learning environments: A conceptual framework. Online Learning, 23(2), 145–178. https://doi.org/10.24059/olj.v23i2.1481
   Web of Science ®Google Scholar
• Halverson, L. R., Graham, C. R., Spring, K. J., Drysdale, J. S., & Henrie, C. R. (2014). A thematic analysis of the most highly cited scholarship in the first decade of blended learning research. The Internet and Higher Education, 20, 20–34. https://doi.org/10.1016/j.iheduc.2013.09.004
   Web of Science ®Google Scholar
• Heilporn, G., Lakhal, S., & Bélisle, M. (2021). An examination of teachers’ strategies to foster student engagement in blended learning in higher education. International Journal of Educational Technology in Higher Education, 18(1), 25. https://doi.org/10.1186/s41239-021-00260-3
   PubMed Web of Science ®Google Scholar
• Heilporn, G., Lakhal, S., & Bélisle, M. (2022). Examining effects of instructional strategies on student engagement in blended online courses. Journal of Computer Assisted Learning, 38(6), 1657–1673. https://doi.org/10.1111/jcal.12701
   Web of Science ®Google Scholar
• Henrie, C. R., Bodily, R., Manwaring, K. C., & Graham, C. R. (2015). Exploring intensive longitudinal measures of student engagement in blended learning. International Review of Research in Open and Distributed Learning, 16(3), 131–155.
   Web of Science ®Google Scholar
• Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems, 116(1), 2–20. https://doi.org/10.1108/IMDS-09-2015-0382
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1007/s11747-014-0403-8
   Web of Science ®Google Scholar
• Heo, H., Bonk, C. J., & Doo, M. Y. (2022). Influences of depression, self-efficacy, and resource management on learning engagement in blended learning during COVID-19. The Internet and Higher Education, 54, 100856. https://doi.org/10.1016/j.iheduc.2022.100856
   PubMed Web of Science ®Google Scholar
• Hidayat, W., Wahyudin, W., & Prabawanto, S. (2018). Improving students’ creative mathematical reasoning ability students through adversity quotient and argument driven inquiry learning.Journal of Physics: Conference Series, 948,012005. https://doi.org/10.1088/1742-6596/948/1/012005
   Google Scholar
• Hilliard, L. P., & Stewart, M. K. (2019). Time well spent: Creating a community of inquiry in blended first-year writing courses. The Internet and Higher Education, 41, 11–24. https://doi.org/10.1016/j.iheduc.2018.11.002
   Web of Science ®Google Scholar
• Hinkin, T. R. (1995). A Review of Scale Development Practices in the Study of Organizations. Journal of Management, 21(5), 967–988. https://doi.org/10.1177/014920639502100509
   Web of Science ®Google Scholar
• Ho, V. T., Wong, S.-S., & Lee, C. H. (2011). A Tale of Passion: Linking Job Passion and Cognitive Engagement to Employee Work Performance. Journal of Management Studies, 48(1), 26–47. https://doi.org/10.1111/j.1467-6486.2009.00878.x
   Web of Science ®Google Scholar
• Hosseini, M. M., Egodawatte, G., & Ruzgar, N. S. (2021). Online assessment in a business department during COVID-19: Challenges and practices. The International Journal of Management Education, 19(3), 100556. https://doi.org/10.1016/j.ijme.2021.100556
   Web of Science ®Google Scholar
• Hrastinski, S. (2019). What do we mean by blended learning? TechTrends, 63(5), 564–569. https://doi.org/10.1007/s11528-019-00375-5
   Web of Science ®Google Scholar
• Huma, Z-e., Hussain, S., Thurasamy, R., & Malik, M. I. (2017). Determinants of cyberloafing: a comparative study of a public and private sector organization. Internet Research, 27(1), 97–117. https://doi.org/10.1108/IntR-12-2014-0317
   Web of Science ®Google Scholar
• Huy, P. Q., & Phuc, V. K. (2021). Accounting Information Systems in Public Sector towards Blockchain Technology Application: The Role of Accountants’ Emotional Intelligence in the Digital Age. Asian Journal of Law and Economics, 12(1), 73–94. https://doi.org/10.1515/ajle-2020-0052
   Web of Science ®Google Scholar
• Huy, P. Q., & Phuc, V. K. (2023a). Unfolding sustainable auditing ecosystem formation path through digitalization transformation: How digital intelligence of accountant fosters the digitalization capabilities. Heliyon, 9(2), e13392. https://doi.org/10.1016/j.heliyon.2023.e13392
   PubMed Web of Science ®Google Scholar
• Huy, P. Q., & Phuc, V. K. (2023b). Sustainable Decision Making in The Time of Uncertainty: Does Moral Intelligence Make It Different? Pacific Asia Journal of the Association for Information Systems, https://aisel.aisnet.org/pajais_preprints/7
   Google Scholar
• Johri, R., Misra, R. K., & Bhattacharjee, S. (2016). Work passion: Construction of reliable and valid measurement scale in the Indian context. Global Business Review, 17(3_suppl), 147S–158S. https://doi.org/10.1177/0972150916631206
   Google Scholar
• Kahu, E. R. (2013). Framing student engagement in higher education. Studies in Higher Education, 38(5), 758–773. https://doi.org/10.1080/03075079.2011.598505
   Web of Science ®Google Scholar
• Kardipah, S., & Wibawa, B. (2020). A fipped-blended learning model with augmented problem based learning to enhance students’ computer skills. TechTrends, 64(3), 507–513. https://doi.org/10.1007/s11528-020-00506-3
   Web of Science ®Google Scholar
• Karmaker, C. L., Aziz, R. A., Palit, T., & Bari, A. B. M. M. (2023). Analyzing supply chain risk factors in the small and medium enterprises under fuzzy environment: Implications towards sustainability for emerging economies. Sustainable Technology and Entrepreneurship, 2(1), 100032. https://doi.org/10.1016/j.stae.2022.100032
   Google Scholar
• Kuh, G. D. (2009). The national survey of student engagement: Conceptual and empirical foundations. New Directions for Institutional Research, 2009(141), 5–20. https://doi.org/10.1002/ir.283
   Google Scholar
• Lasekan, O. A., Pachava, V., Godoy Pena, M. T., Golla, S. K., & Raje, M. S. (2024). Investigating factors influencing students’ engagement in sustainable online education. Sustainability, 16(2), 689. https://doi.org/10.3390/su16020689
   Web of Science ®Google Scholar
• Lawson, M. A., & Lawson, H. A. (2013). New conceptual frameworks for student engagement research, policy, and practice. Review of Educational Research, 83(3), 432–479. https://doi.org/10.3102/0034654313480891
   Web of Science ®Google Scholar
• Legault, L., Ray, K., Hudgins, A., Pelosi, M., & Shannon, W. (2017). Assisted versus asserted autonomy satisfaction: Their unique associations with wellbeing, integration of experience, and conflict negotiation. Motivation and Emotion, 41(1), 1–21. https://doi.org/10.1007/s11031-016-9593-3
   Web of Science ®Google Scholar
• Lehmkuhl, G., Gresse von Wangenheim, G., Helena Martins-Pacheco, L., Borgatto, A. F., & Da Cruz Alves, N. (2021). SCORE – A model for the self-assessment of creativity skills in the context of computing education in K-12. Informatics in Education, 20(2), 231–254. https://doi.org/10.15388/infedu.2021.11
   Web of Science ®Google Scholar
• Liao, H., Zhang, Q., Yang, L., & Fei, Y. (2023). Investigating relationships among regulated learning, teaching presence and student engagement in blended learning: An experience sampling analysis. Education and Information Technologies, 28(10), 1–29. https://doi.org/10.1007/s10639-023-11717-5
   Web of Science ®Google Scholar
• Lima, F. D., Lautert, S. L., & Gomes, A. S. (2021). Contrasting levels of student engagement in blended and non-blended learning scenarios. Computers & Education, 172, 104241. https://doi.org/10.1016/j.compedu.2021.104241
   Web of Science ®Google Scholar
• Lin, T.-C., & Huang, C.-C. (2008). Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit. Information & Management, 45(6), 410–417. https://doi.org/10.1016/j.im.2008.06.004
   Web of Science ®Google Scholar
• Low, M. C., Lee, C. K., Sidhu, M. S., Lim, S. P., Hasan, Z., & Lim, S. C. (2023). Blended learning for engineering education 4.0: Students’ perceptions and their learning difficulties. Computer Applications in Engineering Education, 31(6), 1705–1722. https://doi.org/10.1002/cae.22668
   Web of Science ®Google Scholar
• Mahmud, M. S., Rahman, M. M., Lima, R. P., & Annie, E. J. (2021). Outbound medical tourism experience, satisfaction and loyalty: lesson from a developing country. Journal of Hospitality and Tourism Insights, 4(5), 545–564. https://doi.org/10.1108/JHTI-06-2020-0094
   Google Scholar
• Mandernach, B. J. (2015). Assessment of student engagement in higher education: A synthesis of literature and assessment tools. International Journal of Learning, Teaching and Educational Research, 12(2), 1–14.
   Google Scholar
• Mantai, L., & Calma, A. (2022). Beyond assuring learning: Greater challenges ahead for management educators. The International Journal of Management Education, 20(3), 100723. https://doi.org/10.1016/j.ijme.2022.100723
   Web of Science ®Google Scholar
• Manwaring, K. C., Larsen, R., Graham, C. R., Henrie, C. R., & Halverson, L. R. (2017). Investigating student engagement in blended learning settings using experience sampling and structural equation modeling. The Internet and Higher Education, 35, 21–33. https://doi.org/10.1016/j.iheduc.2017.06.002
   Web of Science ®Google Scholar
• Maria Josephine Arokia Marie, S. (2021). Improved pedagogical practices strengthens the performance of student teachers by a blended learning approach. Social Sciences & Humanities Open, 4(1), 100199. https://doi.org/10.1016/j.ssaho.2021.100199
   Google Scholar
• Matlin, M. W. (2014). Cognitive Psychology (5th ed.). John Wiley.
   Google Scholar
• Matore, M. E. E. M., Khairani, A. Z., & Razak, N. A. (2020). Development and psychometric properties of the adversity quotient scale: An analysis using rasch model and confirmatory factor analysis. Revista Argentina de Clínica Psicológica, XXIX(5), 574–591. https://doi.org/10.24205/03276716.2020.1055
   Google Scholar
• Milheim, W. D. (2006). Strategies for the design and delivery of blended learning courses. Educational Technology, 46(6), 44–47.
   Google Scholar
• Moghavvemi, S., Phoong, S. W., & Phoong, S. Y. (2023). Technology Usage and Students Performance: The Influence of Blended Learning. In Ł. Tomczyk (Eds.), New Media Pedagogy: Research Trends, Methodological Challenges and Successful Implementations. NMP 2022. Communications in Computer and Information Science (Vol. 1916, pp. 237–253). Springer.
   Google Scholar
• Moradimokhles, H., & Hwang, G.-J. (2020). The effect of online vs. blended learning in developing English language skills by nursing student: An experimental study. Interactive Learning Environments, 30(9), 1653–1662. https://doi.org/10.1080/10494820.2020.1739079
   Web of Science ®Google Scholar
• Muztaba, M., Bahri, S., & Farizal, F. (2020). The effects of adversity quotient and spiritual quotient on teacher performance. Asian Journal of Science Education, 2(1), 64–70. https://doi.org/10.24815/ajse.v2i1.15983
   Google Scholar
• Nevitt, J., & Hancock, G. (2001). Performance of bootstrapping approaches to model test statistics and parameter standard error estimation in structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 8(3), 353–377. https://doi.org/10.1207/S15328007SEM0803_2
   Web of Science ®Google Scholar
• Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). McGraw-Hill.
   Google Scholar
• Oduro, S., & Haylemariam, L. G. (2019). Market orientation, CSR and financial and marketing performance in manufacturing firms in Ghana and Ethiopia. Sustainability Accounting, Management and Policy Journal, 10(3), 398–426. https://doi.org/10.1108/SAMPJ-11-2018-0309
   Web of Science ®Google Scholar
• Osman, Z., & Sentosa, I. (2013). Mediating effect of customer satisfaction on service quality and customer loyalty relationship in Malaysian rural tourism. International Journal of Economics Business and Management Studies, 2(1), 25–37.
   Google Scholar
• Owston, R., & York, D. N. (2018). The nagging question when designing blended courses: Does the proportion of time devoted to online activities matter? The Internet and Higher Education, 36, 22–32. https://doi.org/10.1016/j.iheduc.2017.09.001
   Web of Science ®Google Scholar
• Pascarella, E. T., & Patrick, T. T. (2005). How College Affects Students: A Third Decade of Research. Jossey-Bass.
   Google Scholar
• Patrick, H., & Williams, G. C. (2012). Self-determination theory: its application to health behavior and complementarity with motivational interviewing. The International Journal of Behavioral Nutrition and Physical Activity, 9(1), 18. https://doi.org/10.1186/1479-5868-9-18
   PubMed Web of Science ®Google Scholar
• Phan, T., McNeil, S. G., & Robin, B. R. (2016). Students’ patterns of engagement and course performance in a Massive Open Online Course. Computers & Education, 95, 36–44. https://doi.org/10.1016/j.compedu.2015.11.015
   Web of Science ®Google Scholar
• Philipsen, B., Tondeur, J., Roblin, N. P., Vanslambrouck, S., & Zhu, C. (2019). Improving teacher professional development for online and blended learning: A systematic meta -aggregative review. Educational Technology Research and Development, 67(5), 1145–1174. https://doi.org/10.1007/s11423-019-09645-8
   Web of Science ®Google Scholar
• Pike, G. R. (2006). The Convergent and Discriminant Validity of NSSE Scalelet Scores. Journal of College Student Development, 47(5), 550–563. https://doi.org/10.1353/csd.2006.0061
   Web of Science ®Google Scholar
• Prahmana, R. C. I., Hartanto, D., Kusumaningtyas, D. A., Ali, R. M., & Muchlas. (2021). Community radio-based blended learning model: A promising learning model in remote area during pandemic era. Heliyon, 7(7), e07511. https://doi.org/10.1016/j.heliyon.2021.e07511
   PubMed Web of Science ®Google Scholar
• Puspitacandri, A., Warsono, W., Roesminingsih, E., Soesatyo, Y., & Susanto, H. (2020). The effects of intelligence, emotional, spiritual and adversity quotient on the graduates quality in Surabaya shipping polytechnic. European Journal of Educational Research, 9(3), 1075–1087. https://doi.org/10.12973/eu-jer.9.3.1075
   Google Scholar
• Reeve, J. (2013). How students create motivationally supportive learning environments for themselves: The concept of agentic engagement. Journal of Educational Psychology, 105(3), 579–595. https://doi.org/10.1037/a0032690
   Web of Science ®Google Scholar
• Reeve, J., & Tseng, C.-M. (2011). Agency as a fourth aspect of students’ engagement during learning activities. Contemporary Educational Psychology, 36(4), 257–267. https://doi.org/10.1016/j.cedpsych.2011.05.002
   Web of Science ®Google Scholar
• Ristiana, M. G., Istianah, E., & Pratama, D. F. (2020). Adversity quotient and logical thinking skills of prospective primary school teachers. Journal of Physics: Conference Series, 1657(1), 012002. https://doi.org/10.1088/1742-6596/1657/1/012002
   Google Scholar
• Rothbard, N. P., & Edwards, J. R. (2003). Investment in work and family roles: a test of identity and utilitarian motives. Personnel Psychology, 56(3), 699–729. https://doi.org/10.1111/j.1744-6570.2003.tb00755.x
   Web of Science ®Google Scholar
• Sanusi, S. (2017). Performance of FIQH teachers in the learning process at Madrasah Tsanawiyah (MTs) in Palopo–South Sulawesi. Advanced Science Letters, 23(11), 10903–10905. https://doi.org/10.1166/asl.2017.10182
   Google Scholar
• Saunders, M., Lewis, P., & Thornhill, A. (2012). Research Methods for Business Students (6th ed.). Pearson Education Ltd.
   Google Scholar
• Saunders, R. (2013). The role of teacher emotions in change: Experiences, patterns and implications for professional development. Journal of Educational Change, 14(3), 303–333. https://doi.org/10.1007/s10833-012-9195-0
   Google Scholar
• Sekaran, U., & Bougie, R. J. (2019). Research Methods for Business: A Skill Building Approach (8th ed.). Wiley and Sons.
   Google Scholar
• Sekreter, G. (2019). Emotional Intelligence as a Vital Indicator of Teacher Effectiveness. International Journal of Social Sciences & Educational Studies, 5(3), 286–302.
   Google Scholar
• Shin, Y., Park, J., & Lee, S. G. (2018). Improving the integrated experience of in-class activities and fne-grained data collection for analysis in a blended learning class. Interactive Learning Environments, 26(5), 597–612. https://doi.org/10.1080/10494820.2017.1374980
   Web of Science ®Google Scholar
• Sigit, D. V., Suryanda, A., Suprianti, E., & Ichsan, I. Z. (2019). The effect of adversity quotient and gender to learning outcome of high school students. International Journal of Innovative Technology and Exploring Engineering, 8(6), 34–37.
   Google Scholar
• Singh, J., Steele, K., & Singh, L. (2021). Combining the best of online and face-to-face learning: Hybrid and blended learning approach for COVID-19, post vaccine, & post-pandemic world. Journal of Educational Technology Systems, 50(2), 140–171. https://doi.org/10.1177/00472395211047865
   Google Scholar
• Singh, K., & Parveen, S. (2018). Impact of adversity quotient on learning behaviour among secondary school students. Indian Journal of Public Health Research & Development, 9(12), 1773–1779. https://doi.org/10.5958/0976-5506.2018.02247.7
   Google Scholar
• Singh, S., & Srivastava, S. (2018). Moderating effect of product type on online shopping behaviour and purchase intention: An Indian perspective. Cogent Arts & Humanities, 5(1), 1–27. https://doi.org/10.1080/23311983.2018.1495043
   Web of Science ®Google Scholar
• Skinner, E. A., Kindermann, T. A., & Furrer, C. J. (2009). A Motivational Perspective on Engagement and Disaffection. Educational and Psychological Measurement, 69(3), 493–525. https://doi.org/10.1177/0013164408323233
   Web of Science ®Google Scholar
• Smith, K., & Hill, J. (2019). Defining the nature of blended learning through its depiction in current research. Higher Education Research & Development, 38(2), 383–397. https://doi.org/10.1080/07294360.2018.1517732
   Web of Science ®Google Scholar
• Sollosy, M., & McInerney, M. (2022). Artificial intelligence and business education: What should be taught. The International Journal of Management Education, 20(3), 100720. https://doi.org/10.1016/j.ijme.2022.100720
   Web of Science ®Google Scholar
• Spring, K. J., Graham, C. R., & Hadlock, C. A. (2016). The current landscape of international blended learning. International Journal of Technology Enhanced Learning, 8(1), 84–102. https://doi.org/10.1504/IJTEL.2016.075961
   Web of Science ®Google Scholar
• Stein, J., & Graham, C. R. (2020). Essentials for blended learning: A standards-based guide. Routledge.
   Google Scholar
• Stoltz, P. G. (1997). Adversity quotient: turning obstacles into opportunities. John Wiley & Sons.
   Google Scholar
• Tansiongco, L. A., & Ibarra, F. P. (2020). Educational leader’s adversity quotient, management style and job performance: Implications to school leadership. Indonesian Research Journal in Education, 4(2), 386–401. https://doi.org/10.22437/irje.v4i2.9264
   Google Scholar
• Taylor, M. C., Atas, S., & Ghani, S. (2019). Alternate dimensions of cognitive presence for blended learning in higher education. International Journal of Mobile and Blended Learning, 11(2), 1–18. https://doi.org/10.4018/IJMBL.2019040101
   Web of Science ®Google Scholar
• Taylor, M., Vaughan, N., Ghani, S. K., Atas, S., & Fairbrother, M. (2018). Looking back and looking forward: A glimpse of blended learning in higher education from 2007–2017. International Journal of Adult Vocational Education and Technology, 9(1), 1–14. https://doi.org/10.4018/IJAVET.2018010101
   Google Scholar
• Tetteh, L. A., Krah, R., Ayamga, T. A., Ayarna-Gagakuma, L. A., Offei-Kwafo, K., & Gbade, V. A. (2023). Covid-19 pandemic and online accounting education: the experience of undergraduate accounting students in an emerging economy. Journal of Accounting in Emerging Economies, 13(4), 825–846. https://doi.org/10.1108/JAEE-07-2021-0242
   Web of Science ®Google Scholar
• Tinsley, H. E. A., & Tinsley, D. J. (1987). Uses of factor analysis in counseling psychology research. Journal of Counseling Psychology, 34(4), 414–424. https://doi.org/10.1037/0022-0167.34.4.414
   Web of Science ®Google Scholar
• Trigueros, R., García-Tascón, M., Gallardo, A. M., Alías, A., & Aguilar-Parra, J. M. (2020). The Influence of the teacher’s prosocial skills on the mindwandering, creative intelligence, emotions, and academic performance of secondary students in the area of physical education classes. International Journal of Environmental Research and Public Health, 17(4), 1437. https://doi.org/10.3390/ijerph17041437
   PubMed Web of Science ®Google Scholar
• Trowler, V., & Trowler, P. (2010). Student engagement evidence summary. The Higher Education Academy.
   Google Scholar
• Tsankov, N., & Damyanov, I. (2017). Education majors’ preferences on the functionalities of E-learning platforms in the context of blended learning. International Journal of Emerging Technologies in Learning (iJET), 12(05), 202–209. https://doi.org/10.3991/ijet.v12i05.6971
   Google Scholar
• Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application, 11(2), 5–40.
   Google Scholar
• Vallerand, R. J., & Houlfort, N. (2003). Passion at work: Emerging perspectives on values in organizations. Information Age Publishing.
   Google Scholar
• Vallerand, R. J., Blanchard, C., Mageau, G. A., Koestner, R., Ratelle, C., Leonard, M., Gagne, M., & Marsolais, J. (2003). Les passions de l‘âme: On obsessive and harmonious passion. Journal of Personality and Social Psychology, 85(4), 756–767. https://doi.org/10.1037/0022-3514.85.4.756
   PubMed Web of Science ®Google Scholar
• Vallerand, R. J., Salvy, S. J., Mageau, G. A., Elliot, A. J., Denis, P. L., Grouzet, F. M. E., & Blanchard, C. (2007). On the role of passion in performance. Journal of Personality, 75(3), 505–533. https://doi.org/10.1111/j.1467-6494.2007.00447.x
   PubMed Web of Science ®Google Scholar
• Vanslambrouck, S., Zhu, C., Lombaerts, K., Philipsen, B., & Tondeur, J. (2017). Students’ motivation and subjective task value of participating in online and blended learning environments. The Internet and Higher Education, 36, 33–40. https://doi.org/10.1016/j.iheduc.2017.09.002
   Web of Science ®Google Scholar
• Vollet, J. W., Kindermann, T. A., & Skinner, E. A. (2017). In peer matters, teachers matter: Peer group influences on students’ engagement depend on teacher involvement. Journal of Educational Psychology, 109(5), 635–652. https://doi.org/10.1037/edu0000172
   Web of Science ®Google Scholar
• Voogt, J., & Roblin, N. P. (2012). A comparative analysis of international frameworks for 21st century competences: Implications for national curriculum policies. Journal of Curriculum Studies, 44(3), 299–321. https://doi.org/10.1080/00220272.2012.668938
   Web of Science ®Google Scholar
• Walia, C. (2019). A dynamic definition of creativity. Creativity Research Journal, 31(3), 237–247. https://doi.org/10.1080/10400419.2019.1641787
   Web of Science ®Google Scholar
• Wang, M.-T., Fredricks, J. A., Ye, F., Hofkens, T. L., & Linn, J. S. (2016). The Math and Science Engagement Scales: Scale development, validation, and psychometric properties. Learning and Instruction, 43, 16–26. https://doi.org/10.1016/j.learninstruc.2016.01.008
   Web of Science ®Google Scholar
• Wang, X., Liu, M., Tee, S., & Dai, H. (2021). Analysis of adversity quotient of nursing students in Macao: A cross-section and correlation study. International Journal of Nursing Sciences, 8(2), 204–209. https://doi.org/10.1016/j.ijnss.2021.02.003
   PubMed Web of Science ®Google Scholar
• Wanner, T., & Palmer, E. (2015). Personalising learning: Exploring student and teacher perceptions about flexible learning and assessment in a flipped university course. Computers & Education, 88, 354–369. https://doi.org/10.1016/j.compedu.2015.07.008
   Web of Science ®Google Scholar
• Webb, A., McQuaid, R. W., & Webster, C. W. R. (2021). Moving learning online and the COVID-19 pandemic: a university response. World Journal of Science, Technology and Sustainable Development, 18(1), 1–19. https://doi.org/10.1108/WJSTSD-11-2020-0090
   Web of Science ®Google Scholar
• Wijaya, T. T., Cao, Y., Bernard, M., Rahmadi, I. F., Lavicza, Z., & Surjono, H. D. (2022). Factors influencing microgame adoption among secondary school mathematics teachers supported by structural equation modelling-based research. Frontiers in Psychology, 13, 952549. https://doi.org/10.3389/fpsyg.2022.952549
   PubMed Web of Science ®Google Scholar
• Wilkin, C. (2022). Developing critical reflection: An integrated approach. The British Accounting Review, 54(3), 101043. https://doi.org/10.1016/j.bar.2021.101043
   Web of Science ®Google Scholar
• Yu, Z. G., Xu, W., & Sukjairungwattana, P. (2022). Meta-analyses of differences in blended and traditional learning outcomes and students’ attitudes. Frontiers in Psychology, 13, 926947. https://doi.org/10.3389/fpsyg.2022.926947
   PubMed Web of Science ®Google Scholar
• Zhang, K. Z. K., Cheung, C. M. K., & Lee, M. K. O. (2014). Examining the moderating effect of inconsistent reviews and its gender differences on consumers’ online shopping decision. International Journal of Information Management, 34(2), 89–98. https://doi.org/10.1016/j.ijinfomgt.2013.12.001
   Web of Science ®Google Scholar
• Zhao, Y., Sang, B., & Ding, C. (2022). The roles of emotional intelligence and adversity quotient in life satisfaction. Current Psychology, 41(12), 9063–9072. https://doi.org/10.1007/s12144-021-01398-z
   Web of Science ®Google Scholar
• Zhong, Q., Wang, Y., Lv, W., Xu, J., & Zhang, Y. (2022). Self-regulation, teaching presence, and social presence: Predictors of students’ learning engagement and persistence in blended synchronous learning. Sustainability, 14(9), 5619. https://doi.org/10.3390/su14095619
   Web of Science ®Google Scholar
• Zhu, C. (2017). University student satisfaction and perceived effectiveness of a blended learning course. International Journal of Learning Technology, 12(1), 66–83. https://doi.org/10.1504/IJLT.2017.083996
   Google Scholar
• Zientek, L. R., & Thompson, B. (2007). Applying the bootstrap to the multivariate case: bootstrap component/factor analysis. Behavior Research Methods, 39(2), 318–325. https://doi.org/10.3758/bf03193163
   PubMed Web of Science ®Google Scholar