Abdullatif, H., & Velázquez-Iturbide, J. Á. (2020). Relationship between motivations, personality traits and intention to continue using MOOCs. Education and Information Technologies, 1–19. https://doi.org/10.1007/s10639-020-10161-z
Agarwal, R., & Karahanna, E. (2000). Time flies when You’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665. https://doi.org/10.2307/3250951
Alraimi, K. M., Zo, H., & Ciganek, A. P. (2015). Understanding the MOOCs continuance: The role of openness and reputation. Computers and Education, 80, 28–38. https://doi.org/10.1016/j.compedu.2014.08.006
Al-Shaikhli, D., Jin, L., Porter, A., et al. (2021). Visualising weekly learning outcomes (VWLO) and the intention to continue using a learning management system (CIU): The role of cognitive absorption and perceived self-regulated learning. Education and Information Technologies, (2021). https://doi.org/10.1007/s10639-021-10703-z
Arnold, K. E., & Pistilli, M. D. (2012, April). Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 267–270).
Banerjee, A. V. (1992). A simple model of herd behavior. The Quarterly Journal of Economics, 107(3), 797–817. https://doi.org/10.2307/2118364
Barnes, S. J., Pressey, A. D., & Scornavacca, E. (2019). Mobile ubiquity: Understanding the relationship between cognitive absorption, smartphone addiction and social network services. Computers in Human Behavior, 90, 246–258. https://doi.org/10.1016/j.chb.2018.09.013
Basol, G., & Balgalmis, E. (2016). A multivariate investigation of gender differences in the number of online tests received-checking for perceived self-regulation. Computers in Human Behavior, 58, 388–397. https://doi.org/10.1016/j.chb.2016.01.010
Bodily, R., & Verbert, K. (2017). Review of research on student-facing learning analytics dashboards and educational recommender systems. IEEE Transactions on Learning Technologies, 10(4), 405–418. https://doi.org/10.1109/tlt.2017.2740172
Bodily, R., Ikahihifo, T. K., Mackley, B., & Graham, C. R. (2018). The design, development, and implementation of student-facing learning analytics dashboards. Journal of Computing in Higher Education, 30(3), 572–598. https://doi.org/10.1007/s12528-018-9186-0
Bozoglan, B., Demirer, V., & Sahin, I. (2014). Problematic internet use: Functions of use, cognitive absorption, and depression. Computers in Human Behavior, 37, 117–123. https://doi.org/10.1016/j.chb.2014.04.042
Çebi, A., & Güyer, T. (2020). Students’ interaction patterns in different online learning activities and their relationship with motivation, self-regulated learning strategy and learning performance. Education and Information Technologies. https://doi.org/10.1007/s10639-020-10151-1
Concannon, J. P., Serota, S. B., Fitzpatrick, M. R., & Brown, P. L. (2018). How interests, self-efficacy, and self-regulation impacted six undergraduate pre-engineering students’ persistence. European Journal of Engineering Education, 44(4), 484–503. https://doi.org/10.1080/03043797.2017.1422695
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008
Dijkstra, T. K., & Henseler, J. (2015). Consistent partial least squares path modeling. MIS Quarterly, 39(2), 297–316.
Duffy, M. C., & Azevedo, R. (2015). Motivation matters: Interactions between achievement goals and agent scaffolding for self-regulated learning within an intelligent tutoring system. Computers in Human Behavior, 52, 338–348. https://doi.org/10.1016/j.chb.2015.05.041
Festinger L. (1954). A theory of social comparison processes. Human Relations, 7(2):117–140. https://doi.org/10.1177/001872675400700202
García-Pérez, D., Fraile, J., & Panadero, E. (2020). Learning strategies and self-regulation in context: How higher education students approach different courses, assessments, and challenges. European Journal of Psychology of Education, 1–18. https://doi.org/10.1007/s10212-020-00488-z
Green, S. G., & Welsh, M. A. (1988). Cybernetics and dependence: Reframing the control concept. Academy of Management Review, 13(2), 287–301. https://doi.org/10.5465/amr.1988.4306891
Hall, P. A., & Fong, G. T. (2010). Temporal self-regulation theory: Looking forward. Health Psychology Review, 4(2), 83–92. https://doi.org/10.1080/17437199.2010.487180
Harvey, A. J., & Keyes, H. (2019). How do I compare thee? An evidence-based approach to the presentation of class comparison information to students using dashboard. Innovations in Education and Teaching International, 57(2), 163–174. https://doi.org/10.1080/14703297.2019.1593213
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing. Emerald Group Publishing Limited.
Hirshleifer, D., & Hong Teoh, S. (2003). Herd behaviour and cascading in capital markets: A review and synthesis. European Financial Management, 9(1), 25–66. https://doi.org/10.1111/1468-036X.00207
Jivet, I., Scheffel, M., Drachsler, H., & Specht, M. (2017). Awareness is not enough: Pitfalls of learning analytics dashboards in educational practice. In European conference on technology enhanced learning (pp. 82–96). Springer.
Jumaan, I. A., Hashim, N. H., & Al-Ghazali, B. M. (2020). The role of cognitive absorption in predicting mobile internet users’ continuance intention: An extension of the expectation-confirmation model. Technology in Society, 63, 101355. https://doi.org/10.1016/j.techsoc.2020.101355
Karlinsky-Shichor, Y., & Zviran, M. (2015). Factors influencing perceived benefits and user satisfaction in knowledge management systems. Information Systems Management, 33(1), 55–73. https://doi.org/10.1080/10580530.2016.1117873
Kia, F. S., Teasley, S. D., Hatala, M., Karabenick, S. A., & Kay, M. (2020). How patterns of students dashboard use are related to their achievement and self-regulatory engagement. In ACM international conference proceeding series (pp. 340–349). Association for Computing Machinery. https://doi.org/10.1145/3375462.3375472
Kim, J., Jo, I. H., & Park, Y. (2016). Effects of learning analytics dashboard: Analyzing the relations among dashboard utilization, satisfaction, and learning achievement. Asia Pacific Education Review, 17(1), 13–24. https://doi.org/10.1007/s12564-015-9403-8
Kitsantas, A., Baylor, A. L., & Hiller, S. E. (2019). Intelligent technologies to optimize performance: Augmenting cognitive capacity and supporting self-regulation of critical thinking skills in decision-making. Cognitive Systems Research, 58, 387–397.
Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in massive open online courses. Computers and Education, 104, 18–33. https://doi.org/10.1016/j.compedu.2016.10.001
Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford publications.
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration (ijec), 11(4), 1–10.
Kurtovic, A., Vrdoljak, G., & Hirnstein, M. (2021). Contribution to family, friends, school, and community is associated with fewer depression symptoms in adolescents - mediated by self-regulation and academic performance. Frontiers in Psychology, 11, 615249. https://doi.org/10.3389/fpsyg.2020.615249
Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation-confirmation model. Computers and Education, 54(2), 506–516. https://doi.org/10.1016/j.compedu.2009.09.002
Léger, P. M., Davis, F. D., Cronan, T. P., & Perret, J. (2014). Neurophysiological correlates of cognitive absorption in an enactive training context. Computers in Human Behavior, 34, 273–283. https://doi.org/10.1016/j.chb.2014.02.011
Liaw, S. S., & Huang, H. M. (2013). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers and Education, 60(1), 14–24. https://doi.org/10.1016/j.compedu.2012.07.015
Lux, T. (1995). Herd behaviour, bubbles and crashes. The Economic Journal, 105(431), 881. https://doi.org/10.2307/2235156
Maselli, M. D., & Altrocchi, J. (1969). Attribution of intent. Psychological Bulletin, 71(6), 445–454. https://doi.org/10.1037/h0027348
McNeish, D. (2018). Thanks coefficient alpha, we’ll take it from here. Psychological Methods, 23(3), 412.
Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359–374. https://doi.org/10.1016/j.chb.2014.07.044
Moreno, V., Cavazotte, F., & Alves, I. (2016). Explaining university students’ effective use of e-learning platforms. British Journal of Educational Technology, 48(4), 995–1009. https://doi.org/10.1111/bjet.12469
Neugebauer, J., Ray, D. G., & Sassenberg, K. (2016). When being worse helps: The influence of upward social comparisons and knowledge awareness on learner engagement and learning in peer-to-peer knowledge exchange. Learning and Instruction, 44, 41–52. https://doi.org/10.1016/j.learninstruc.2016.02.007
Peng, M. W., Sun, S. L., Pinkham, B., & Chen, H. (2009). The institution-based view as a third leg for a strategy tripod. Academy of Management Perspectives, 23(3), 63–81. https://doi.org/10.5465/AMP.2009.43479264
Presser, S., & Blair, J. (1994). Survey pretesting: Do different methods produce different results?. Sociological Methodology, 73–104.
Presser, S., Couper, M. P., Lessler, J. T., Martin, E., Martin, J., Rothgeb, J. M., & Singer, E. (2004). Methods for testing and evaluating survey questions. Public Opinion Quarterly, 68(1), 109–130.
Reimers, G., & Neovesky, A. (2015). Student focused dashboards: An analysis of current student dashboards and what students really want. In CSEDU 2015 - 7th international conference on computer supported education, proceedings, 1 (pp. 399–404). https://doi.org/10.5220/0005475103990404
Reynolds, Nina, Diamantopoulos, Adamantios, & Schlegelmilch, Bodo. (1993). Pre-Testing in questionnaire design: A review of the literature and suggestions for further research. International Journal of Market Research, 35(2), 1–11. https://doi.org/10.1177/147078539303500202.
Richardson, H. A., Simmering, M. J., & Sturman, M. C. (2009). A tale of three perspectives: Examining post hoc statistical techniques for detection and correction of common method variance. Organizational Research Methods, 12(4), 762–800.
Roca, J. C. (2008). Understanding e-learning continuance intention in the workplace: A self-determination theory perspective. Computers in Human Behavior, 24(4).
Roca, J. C., Chiu, C. M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the technology acceptance model. International Journal of Human Computer Studies, 64(8), 683–696. https://doi.org/10.1016/j.ijhcs.2006.01.003
Roemer, E., Schuberth, F., & Henseler, J. (2021). HTMT2–an improved criterion for assessing discriminant validity in structural equation modeling. Industrial Management and Data Systems.
Rönkkö, M., & Cho, E. (2022). An updated guideline for assessing discriminant validity. Organizational Research Methods, 25(1), 6–14.
Rouis, S., Limayem, M., & Salehi-Sangari, E. (2011). Impact of Facebook usage on students’ academic achievement: Role of self-regulation and trust. Electronic Journal of Research in Educational Psychology, 9(3), 961–994. https://doi.org/10.25115/ejrep.v9i25.1465
Schumacher, C., & Ifenthaler, D. (2018). Features students really expect from learning analytics. Computers in Human Behavior, 78, 397–407.
Schunk, D. H., & Ertmer, P. A. (2000). Self-regulation and academic learning. In Handbook of self-regulation (pp. 631–649). Elsevier. https://doi.org/10.1016/b978-012109890-2/50048-2
Sun, H. (2013). A longitudinal study of herd behavior in the adoption and continued use of technology. MIS Quarterly: Management Information Systems, 37(4), 1013–1041. https://doi.org/10.25300/MISQ/2013/37.4.02
Sun, J., & Rueda, R. (2012). Situational interest, computer self-efficacy and self-regulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43(2), 191–204. https://doi.org/10.1111/j.1467-8535.2010.01157.x
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53.
Tawafak, R. M., Romli, A. B., & Arshah, R. B. A. (2018). Continued intention to use UCOM: Four factors for integrating with a technology acceptance model to moderate the satisfaction of learning. IEEE Access, 6, 66481–66498. https://doi.org/10.1109/ACCESS.2018.2877760
Toohey, D., Mcgill, T., Berkelaar, C., Kadekodi, A., Kaminska, D., Lianto, M., & Power, N. (2019). Do students really want to know? Investigating the relationship between learning analytics dashboards and student motivation. In Proceedings of the 2019 InSITE conference (pp. 321–332). Informing Science Institute https://doi.org/10.28945/4352
Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application (JITTA), 11(2), 2.
Van Teijlingen, E. R., Rennie, A. M., Hundley, V., & Graham, W. (2001). The importance of conducting and reporting pilot studies: the example of the Scottish births survey. Journal of Advanced Nursing, 34(3), 289–295.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365.
Wang, W., Guo, L., & Sun, R. (2019). Rational herd behavior in online learning: Insights from MOOC. Computers in Human Behavior, 92, 660–669. https://doi.org/10.1016/j.chb.2017.10.009
Wu, J. H., Tennyson, R. D., & Hsia, T. L. (2010). A study of student satisfaction in a blended e-learning system environment. Computers and Education, 55(1), 155–164. https://doi.org/10.1016/j.compedu.2009.12.012
Yammarino, F. J., & Atwater, L. E. (1993). Understanding self-perception accuracy: Implications for human resource management. Human Resource Management, 32(2–3), 231–247. https://doi.org/10.1002/hrm.3930320204
Zhao, X., Tian, J., & Xue, L. (2020). Herding and software adoption: A re-examination based on post-adoption software discontinuance. Journal of Management Information Systems, 37(2), 484–509. https://doi.org/10.1080/07421222.2020.1759941
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2