Abramson, L. Y., Seligman, M. E. P., & Teasdale, J. D. (1978). Learned helplessness in humans: Critique and reformulation. Journal of Abnormal Psychology, 87(1), 49–74.
Alabdulkarim, A. A. (2020). Minimizing the bullwhip effect in a supply chain: A simulation approach using the beer game. Simulation: Transactions of the Society for Modeling and Simulation International, 00(0), 1–16. https://doi.org/10.1177/0037549720930284
Al-Maroof, R. S., Salloum, S. A., Al-Emran, M., & Shaalan, K. (2021). The influence of gamification on learning management systems: The mediating role of engagement and motivation. International Journal of Information and Learning Technology, 38(2), 130–153. https://doi.org/10.1108/IJILT-05-2020-0089
Almeida, M. M. K. de, Marins, F. A. S., Salgado, A. M. P., Santos, F. C. A., & da Silva, S. L. (2015). Mitigation of the bullwhip effect considering trust and collaboration in supply chain management: A literature review. International Journal of Advanced Manufacturing Technology, 77(1-4), 495–513. https://doi.org/10.1007/s00170-014-6444-9
Al-Sukhni, M., & Migdalas, A. (2025). The impact of information sharing factors on the bullwhip effect mitigation: A systematic literature review. Operational Research, 25, 36. https://doi.org/10.1007/s12351-025-00915-3
Asikainen, H., Salmela-Aro, K., Parpala, A., & Katajavuori, N. (2020). Learning profiles and their relation to study-related burnout and academic achievement among university students. Learning and Individual Differences, 78, Article 101781. https://doi.org/10.1016/j.lindif.2019.101781.
Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman and Company.
Braz, A. C., De Mello, A. M., Gomes, L. A. de V., & de Souza Nascimento, P. T. (2018). The bullwhip effect in closed-loop supply chains: A systematic literature review. Journal of Cleaner Production, 202, 376–389. https://doi.org/10.1016/j.jclepro.2018.08.042
Cassady, J. C., & Johnson, R. E. (2002). Cognitive test anxiety and academic performance. Contemporary Educational Psychology, 27(2), 270–295. https://doi.org/10.1006/ceps.2001.1094
Cazan, A.-M., & Indreica, S. E. (2014). Need for cognition and approaches to learning among university students. Procedia - Social and Behavioral Sciences, 127, 134–138. https://doi.org/10.1016/j.sbspro.2014.03.227
Cazan, A.-M. (2015). Learning motivation, engagement and burnout among university students. Procedia - Social and Behavioral Sciences, 187, 413–417. https://doi.org/10.1016/j.sbspro.2015.03.077
Chen, F., Drezner, Z., Ryan, J. K., & Simchi-Levi, D. (2000). Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information. Management Science, 46(3), 436–443.
Christopoulos, A., & Mystakidis, S. (2023). Gamification in education. Encyclopedia, 3(4), 1223–1243. https://doi.org/10.3390/encyclopedia3040089
Chrysafiadi, K., & Virvou, M. (2013). PeRSIVA: An empirical evaluation method of a student model of an intelligent e-learning environment for computer programming. Computers & Education, 68, 322-333.
Díaz-Burgos, A., García-Sánchez, J.-N., Álvarez-Fernández, M. L., & de Brito-Costa, S. M. (2024). Psychological and educational factors of digital competence optimization interventions pre- and post-COVID-19 lockdown: A systematic review. Sustainability, 16(1), 51. https://doi.org/10.3390/su16010051
Diefenbach, M. A., Weinstein, N. D., & O'Reilly, J. (1993). Scales for assessing perceptions of health hazard susceptibility. Health Education Research, 8(2), 181–192. https://doi.org/10.1093/her/8.2.181
Dubey, R., Bryde, D. J., Blome, C., Roubaud, D., & Giannakis, M. (2021). Facilitating artificial intelligence powered supply chain analytics through alliance management during the pandemic crises in the B2B context. Industrial Marketing Management, 96, 135–146. https://doi.org/10.1016/j.indmarman.2021.05.003
Durán Peña, J. A., Ortiz Bas, Á., & Reyes Maldonado, N. M. (2021). Impact of bullwhip effect in quality and waste in perishable supply chain. Processes, 9(7), 1232. https://doi.org/10.3390/pr9071232
Dweck, C. S. (2000). Self-theories: Their role in motivation, personality, and development. Psychology Press.
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... & Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Elliott, E. S., & Dweck, C. S. (1988). Goals: An approach to motivation and achievement. Journal of Personality and Social Psychology, 54(1), 5–12. https://doi.org/10.1037/0022-3514.54.1.5
Fawaz, M. A., & Samaha, A. (2020). E‐learning: Depression, anxiety, and stress symptomatology among Lebanese university students during COVID‐19 quarantine. Nursing Forum, 55(4), 520–525. https://doi.org/10.1111/nuf.12521
Gaier, S. (2015). Understanding why students do what they do: Using attribution theory to help students succeed academically. Research & Teaching in Developmental Education, 31(2), 6–19.
Govindan, K., Soleimani, H., & Kannan, D. (2015). Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. European Journal of Operational Research, 240(3), 603–626. https://doi.org/10.1016/j.ejor.2014.07.012
Govindan, K., Kannan, D., Jørgensen, T. B., & Nielsen, T. S. (2022). Supply Chain 4.0 performance measurement: A systematic literature review, framework development, and empirical evidence. Transportation Research Part E: Logistics and Transportation Review, 164, 102725. https://doi.org/10.1016/j.tre.2022.102725
Graham, S., & Folkes, V. S. (Eds.). (1990). Attribution theory: Applications to achievement, mental health, and interpersonal conflict. Lawrence Erlbaum Associates.
Graham, S., & Weiner, B. (1996). Theories and principles of motivation. In D. Berliner & R. Calfee (Eds.), Handbook of Educational Psychology (pp. 63–84). Macmillan.
Graham, S. (2020). An attributional theory of motivation. Contemporary Educational Psychology, 61, 101861. https://doi.org/10.1016/j.cedpsych.2020.101861
Grassini, S. (2023). Shaping the future of education: Exploring the potential and consequences of AI and ChatGPT in educational settings. Education Sciences, 13(7), 692. https://doi.org/10.3390/educsci13070692
Guest, D. E., Sanders, K., Rodrigues, R., & Oliveira, T. (2020). Signalling theory as a framework for analysing human resource management processes and integrating human resource attribution theories: A conceptual analysis and empirical exploration. Human Resource Management Journal, 31(3), 796–818. https://doi.org/10.1111/1748-8583.12326Guest
Heider, F. (1958). The psychology of interpersonal relations. Wiley.
Hewett, T. E., Ford, K. R., Xu, Y. Y., Khoury, J., & Myer, G. D. (2017). Effectiveness of neuromuscular training based on the neuromuscular risk profile. American Journal of Sports Medicine, 45(9), 2142–2147. https://doi.org/10.1177/0363546517700128
Hinton, P., McMurray, I., & Brownlow, C. (2014). SPSS Explained. London, England: Routledge Press.
Horwitz, E. K., Horwitz, M. B., & Cope, J. (1986). Foreign language classroom anxiety. The Modern Language Journal, 70(2), 125–132.
Hsu, T.-C., & Hwang, G.-J. (2023). Chatbot-assisted learning: A literature review of educational trends and opportunities. Educational Technology & Society, 26(1), 1–15.
Hu, Q., & Schaufeli, W. B. (2009). The factorial validity of the Maslach Burnout Inventory–Student Survey in China. Psychological Reports, 105(2), 394–408. https://doi.org/10.2466/PR0.105.2.394-408
Huang, C. F., Nien, W. P., & Yeh, Y. S. (2015). Learning effectiveness of applying automated music composition software in the high grades of elementary school. Computers & Education, 83, 74-89.
Hui, C., & Mahmud, A. (2023). Game-based learning in mathematics education: A systematic review. Journal of Learning Technologies, 15(1), 87–102.
Husman, J., & Lens, W. (1999). The role of the future in student motivation. Educational Psychologist, 34(2), 113–125. https://doi.org/10.1207/s15326985ep3402_4
Im, T., & Kang, M. (2019). Structural relationships of factors which impact on learner achievement in online learning environment. International Review of Research in Open and Distributed Learning, 20(1), 1–22. https://doi.org/10.19173/irrodl.v20i1.4012
Ivanov, D., Dolgui, A., Sokolov, B., Ivanova, M., & Fukuyo, M. (2018). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829–846. https://doi.org/10.1080/00207543.2018.1488086
Ivanov, D., & Dolgui, A. (2019a). Low-certainty-need (LCN) supply chains: A new perspective in managing disruption risks and resilience. International Journal of Production Research, 57(15–16), 5119–5136. https://doi.org/10.1080/00207543.2018.1521025
Ivanov, D., & Dolgui, A. (2019b). New disruption risk management perspectives in supply chains: Digital twins, the ripple effect, and resileanness. IFAC-PapersOnLine, 52(13), 337–342. https://doi.org/10.1016/j.ifacol.2019.11.138
Ivanov, D., & Dolgui, A. (2020). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control. Advance online publication. https://doi.org/10.1080/09537287.2020.1768450
Ivanov, D., & Dolgui, A. (2022). Stress testing supply chains and creating viable ecosystems. Operations Management Research, 15, 475–486. https://doi.org/10.1007/s12063-021-00194-z
Kirkpatrick, D. L. (1975). Evaluating training programs: Tata McGraw-Hill Education.
Kline, R. B. (2015). Principles and practice of structural equation modeling(4th ed.). New York: The Guilford Press.
Kshetri, N. (2018). 1 Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 39, 80–89. https://doi.org/10.1016/j.ijinfomgt.2017.12.005
Lee, H. L., Padmanabhan, V., & Whang, S. (1997). Information distortion in a supply chain: The bullwhip effect. Management Science, 43(4), 546–558. https://doi.org/10.1287/mnsc.43.4.546
Lefcourt, H. M., von Baeyer, C. L., Ware, E. E., & Cox, D. J. (1979). The Multidimensional-Multiattributional Causality Scale: The development of a goal specific locus of control scale. Canadian Journal of Behavioural Science, 11(4), 286–304.
Leiner, D. J., & Quiring, O. (2008). What interactivity means to the user: Essential insights into and a scale for perceived interactivity. Journal of Computer-Mediated Communication, 14(1), 127–155. https://doi.org/10.1111/j.1083-6101.2008.01434.x
Linnenbrink, E. A., & Pintrich, P. R. (2002). Motivation as an enabler for academic success. School Psychology Review, 31(3), 313–327. https://doi.org/10.1080/02796015.2002.12086158
Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410. https://doi.org/10.3390/educsci13040410
Lu, C. (2021). Research on bullwhip effect management in supply chain based on system dynamics. Journal of Physics: Conference Series, 1910(1), 012034. https://doi.org/10.1088/1742-6596/1910/1/012034
Machuca, J. A. D., & Barajas, R. P. (2004). The impact of electronic data interchange on reducing bullwhip effect and supply chain inventory costs. Transportation Research Part E: Logistics and Transportation Review, 40(3), 209–228.
Makransky, G., & Lilleholt, L. (2018). A structural equation modeling investigation of the emotional value of immersive virtual reality in education. Educational Technology Research and Development, 66(5), 1141–1164. https://doi.org/10.1007/s11423-018-9581-2
Mandler, G., & Sarason, S. B. (1952). A study of anxiety and learning. Journal of Abnormal and Social Psychology, 47(2S), 166–173. https://doi.org/10.1037/h0062855
Mariani, M., & Dwivedi, Y. K. (2024). Generative artificial intelligence in innovation management: A preview of future research developments. Journal of Business Research, 175, 114542. https://doi.org/10.1016/j.jbusres.2024.114542
Maslach, C., Jackson, S. E., & Leiter, M. P. (1997). Maslach Burnout Inventory: Third Edition. In C. P. Zalaquett, & R. J. Wood (Eds.), Evaluating STRESS: A book of Resources (pp. 91-218). Scarecrow Education.
Maslach, C., & Jackson, S. E. (1981). The measurement of experienced burnout. Journal of Occupational Behaviour, 2(2), 99–113. https://doi.org/10.1002/job.4030020205
Maslach, C., & Leiter, M. P. (2008). Early predictors of job burnout and engagement. Journal of Applied Psychology, 93(3), 498–512. https://doi.org/10.1037/0021-9010.93.3.498
May, R. W., Bauer, K. N., & Fincham, F. D. (2015). School burnout: Diminished academic and cognitive performance. Learning and Individual Differences, 42, 126–131. https://doi.org/10.1016/j.lindif.2015.07.015
Mayer, R. E. (2019). Thirty years of research on online learning. Applied Cognitive Psychology, 33(2), 152–159. https://doi.org/10.1002/acp.3482
Melchor, P. J. M., Lomibao, L. S., & Parcutilo, J. O. (2023). Exploring the potential of AI integration in mathematics education for Generation Alpha—Approaches, challenges, and readiness of Philippine tertiary classrooms: A literature review. Journal of Innovations in Teaching and Learning, 3(1), 39–44.
Meng, Q., & Zhang, Q. (2023). The influence of academic self-efficacy on university students’ academic performance: The mediating effect of academic engagement. Sustainability, 15(7), 5767. https://doi.org/10.3390/su15075767
Montenegro-Rueda, M., Fernández-Cerero, J., Fernández-Batanero, J. M., & López-Meneses, E. (2023). Impact of the implementation of ChatGPT in education: A systematic review. Computers, 12(8), 153. https://doi.org/10.3390/computers12080153
Owens, M., Stevenson, J., Hadwin, J. A., & Norgate, R. (2012). Anxiety and depression in academic performance: An exploration of the mediating factors of worry and working memory. School Psychology International, 33(4), 433–449. https://doi.org/10.1177/0143034311427433
Park, Y., & Jo, I.-H. (2019). Factors that affect the success of learning analytics dashboards. Educational Technology Research and Development, 67(6), 1547–1571. https://doi.org/10.1007/s11423-019-09693-0
Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315–341. https://doi.org/10.1007/s10648-006-9029-9
Perera, T., & Rupasinghe, T. D. (2015). Teaching supply chain simulation - From beginners to professionals. In Proceedings of the 2015 Winter Simulation Conference (pp. 3549–3553). IEEE. https://doi.org/10.1109/WSC.2015.7408514
Peterson, C. (1993). Helpless behavior. Behaviour Research and Therapy, 31(3), 289–295. https://doi.org/10.1016/0005-7967(93)90027-R
Pintrich, P. R., & Schunk, D. H. (2002). Motivation in education: Theory, research, and applications (2nd ed.). Merrill Prentice Hall.
Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95(4), 667–686. https://doi.org/10.1037/0022-0663.95.4.667
Plass, J. L., Homer, B. D., & Kinzer, C. K. (2015). Foundations of Game Based Learning. Educational Psychologist, 50(4), 258–283. https://doi.org/10.1080/00461520.2015.1122533
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(22). https://doi.org/10.1186/s41039-017-0062-8
Queiroz, M. M., & Wamba, S. F. (2019). Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA. International Journal of Information Management, 46, 70–82. https://doi.org/10.1016/j.ijinfomgt.2018.11.021
Rahman, M. H., Rahman, M. A., & Talapatra, S. (2020). The bullwhip effect: Causes, intensity, and mitigation. Production & Manufacturing Research, 8(1), 406–426. https://doi.org/10.1080/21693277.2020.1862722
Rejeb, A., Rejeb, K., Appolloni, A., Treiblmaier, H., & Iranmanesh, M. (2024). Exploring the impact of ChatGPT on education: A web mining and machine learning approach. The International Journal of Management Education, 22, 100932. https://doi.org/10.1016/j.ijme.2024.100932
Rotter, J. B. (1966). Generalized expectancies for internal versus external control of reinforcement. Psychological Monographs: General and Applied, 80(1), 1–28. https://doi.org/10.1037/h0092976
Salanova, M., Schaufeli, W., Martínez, I., & Bresó, E. (2010). How obstacles and facilitators predict academic performance: The mediating role of study burnout and engagement. Anxiety, Stress & Coping, 23(1), 53–70. https://doi.org/10.1080/10615800802609965
Salmela-Aro, K., Muotka, J., Alho, K., Hakkarainen, K., & Lonka, K. (2016). School burnout and engagement profiles among digital natives in Finland: A person-oriented approach. European Journal of Developmental Psychology, 13(6), 704–718. https://doi.org/10.1080/17405629.2015.1107542
Salmela-Aro, K., & Upadyaya, K. (2014). School burnout and engagement in the context of demands–resources model. British Journal of Educational Psychology, 84(1), 137–151. https://doi.org/10.1111/bjep.12018
Sarason, I. G. (1984). Stress, anxiety, and cognitive interference: Reactions to tests. Journal of Personality and Social Psychology, 46(4), 929–938. https://doi.org/10.1037/0022-3514.46.4.929
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial least squares structural equationmodeling. In C. Homburg, Klarmann, M., Vomberg, A. (Ed.), Handbook of Market Research (pp. 587-632). Switzerland AG: Springer Nature.
Schaufeli, W. B., & Bakker, A. B. (2004). Job demands, job resources, and their relationship with burnout and engagement: A multi-sample study. Journal of Organizational Behavior, 25(3), 293–315. https://doi.org/10.1002/job.248
Schaufeli, W. B., Salanova, M., González-Romá, V., & Bakker, A. B. (2002). The measurement of engagement and burnout: A two sample confirmatory factor analytic approach. Journal of Happiness Studies, 3(1), 71–92.
Schulman, P., Castellon, C., & Seligman, M. E. P. (1989). Assessing explanatory style: The content analysis of verbatim explanations and the Attributional Style Questionnaire. Behaviour Research and Therapy, 27(5), 505–512. https://doi.org/10.1016/0005-7967(89)90084-3
Schunk, D. H. (1984). Self-efficacy perspective on achievement behavior. Educational Psychologist, 19(1), 48–58. https://doi.org/10.1080/00461528409529281
Sweeney, P. D., Anderson, K., & Bailey, S. (1986). Attributional style in depression: A meta-analytic review. Journal of Personality and Social Psychology, 50(5), 974–991. https://doi.org/10.1037/0022-3514.50.5.974
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
Tang, C. S., Veelenturf, L. P., & van Donselaar, K. H. (2020). Bullwhip effect in supply chains: A review of methods, causes and mitigation strategies. International Journal of Production Research, 58(1), 1–17. https://doi.org/10.1080/00207543.2019.1675910
Tayie, S. (2005). Research Methods and Writing Research Proposals. Center for Advancement of Postgraduate Studies and Research in Engineering Sciences, Faculty of Engineering-Cario University(CAPSCU): Pathways to Higher Education
Tzafilkou, K., Perifanou, M., & Economides, A. A. (2021). Development and validation of a students’ remote learning attitude scale (RLAS) in higher education. Education and Information Technologies, 26, 6925–6952. https://doi.org/10.1007/s10639-021-10586-0
Vanany, I., Syamil, A., & Widyarto, A. R. (2016). Teaching supply chain management using an innovative practical game. International Journal of Technology, 7(5), 852–860.
Vrije Universiteit Amsterdam. (n.d.). Distribution logistics and supply chain management: Test questions & answer. tudeerSnel. Retrieved May 5, 2025, from https://www.studeersnel.nl/nl/document/vrije-universiteit-amsterdam/distribution-logistics-and-supply-chain-management/test-questions-answer/143214
Wang, L., & Li, W. (2024). The impact of AI usage on university students’ willingness for autonomous learning. Behavioral Sciences, 14(10), 956. https://doi.org/10.3390/bs14100956
Warr, P., & Downing, J. (2000). Learning strategies, learning anxiety and knowledge acquisition. British Journal of Psychology, 91(3), 311–333. https://doi.org/10.1348/000712600161853
Weiner, B. (1985). An attributional theory of achievement motivation and emotion. Psychological Review, 92(4), 548–573. https://doi.org/10.1037/0033-295X.92.4.548
Wilson, T. D., Damiani, M., & Shelton, N. (2002). Improving the academic performance of college students with brief attributional interventions. In J. Aronson (Ed.), Improving academic achievement (pp. 89–108). Academic Press.
Wolters, C. A., Fan, W., & Daugherty, S. G. (2013). Examining achievement goals and causal attributions together as predictors of academic functioning. The Journal of Experimental Education, 81(3), 295–321. https://doi.org/10.1080/00220973.2012.700498
Wu, P.-H., Hwang, G.-J., Milrad, M., Ke, H.-R., & Huang, Y.-M. (2012). An innovative concept map approach for improving students’ learning performance with an instant feedback mechanism. British Journal of Educational Technology, 43(2), 217–232. https://doi.org/10.1111/j.1467-8535.2010.01167.x
Yang, H.-J., & Farn, C. K. (2005). An investigation the factors affecting MIS student burnout in technical-vocational college. Computers in Human Behavior, 21(6), 917–932. https://doi.org/10.1016/j.chb.2004.03.001
Yang, Y., Lin, J., Liu, G., & Zhou, L. (2021). The behavioural causes of bullwhip effect in supply chains: A systematic literature review. International Journal of Production Economics, 236, 108120. https://doi.org/10.1016/j.ijpe.2021.108120
Zhang, Y., Yao, Z., & Wang, J. (2024). The influence of academic self-efficacy on learning engagement in online education in China: The mediating role of effort. Journal of Contemporary Educational Research, 8(10), 61–68.
Zhao, X., Xiong, Y., Yu, Y., & Wang, L. (2018). Bullwhip effect mitigation of green supply chain optimization in electronics industry. Journal of Cleaner Production, 180, 888–912. https://doi.org/10.1016/j.jclepro.2018.01.134
Zheng, L., Bhagat, K. K., Zhen, Y., & Zhang, X. (2020). The effectiveness of the flipped classroom on students’ learning achievement and learning motivation: A meta-analysis. Educational Technology & Society, 23(1), 1–15.
Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25(1), 82–91. https://doi.org/10.1006/ceps.1999.1016