Abstract
Studying online has currently evolved into a global ‘new normal’ trend in education. However, students’ intention to adopt online interactive behaviors is still relatively low, and few studies have attempted to obtain an in-depth understanding of students’ online interactive behaviors. This study aims to explain and predict students’ intention to adopt online interactions by synthesizing the theory of planned behavior (TPB) with cognitive and motivational factors. The proposed model was validated by surveying 368 junior high school students in China who had experienced formal online education for one semester. The results showed that perceived behavioral control was the strongest predictor of students’ adoption of online interactions, followed by subjective norms and attitudes, while cognitive and motivational factors were significant antecedents of the three core constructs of TPB. These findings of the study provide valuable insights to understand secondary school students’ learning behaviors in an online context and improve their adoption of online interactive behaviors.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action-control: From cognition to behavior (pp. 11–39). Springer.
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
Ajzen, I. (2011). The theory of planned behaviour: Reactions and reflections. Psychology & Health, 26(9), 1113–1127. https://doi.org/10.1080/08870446.2011.613995
Anthony Jnr, B., Kamaludin, A., Romli, A., Mat Raffei, A. F., AL Eh Phon, D. N., Abdullah, A., Leong Ming, G., Shukor, N. A., Shukri Nordin, M., & Baba, S. (2020). Predictors of blended learning deployment in institutions of higher learning: theory of planned behavior perspective. International Journal of Information and Learning Technology, 37(4), 179–196. https://doi.org/10.1108/IJILT-02-2020-0013
Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122–147. https://doi.org/10.1037/0003-066X.37.2.122
Bandura, A. (1986). Social foundations of though and action: A social cognitive theory. Prentice-Hall.
Bandura, A. (1997). Self-efficacy: The exercise of control. Freeman.
Bing, W., & Ping, T. A. (2008). A comparative analysis of learners interaction in the online learning management systems: Does national culture matter? Asian Association of Open Universities Journal, 3(1), 1–16. https://doi.org/10.1108/AAOUJ-03-01-2008-B001
Bol, L., & Garner, J. K. (2011). Challenges in supporting self-regulation in distance education environments. Journal of Computing in Higher Education, 23(2), 104–123. https://doi.org/10.1007/s12528-011-9046-7
Cheek, J. R., Bradley, L. J., Reynolds, J., & Coy, D. (2002). An intervention for helping elementary students reduce test anxiety. Professional School Counseling, 6(2), 162–164.
Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59(3), 1054–1064.
Chiu, C.-M., & Wang, E. T. G. (2008). Understanding web-based learning continuance intention: The role of subjective task value. Information & Management, 45(3), 194–201. https://doi.org/10.1016/j.im.2008.02.003
Chu, T.-H., & Chen, Y.-Y. (2016). With good we become good: Understanding e-learning adoption by theory of planned behavior and group influences. Computers & Education, 92–93, 37–52.
Conner, M., & Armitage, C. J. (1998). Extending the theory of planned behavior: A review and avenues for further research. Journal of Applied Social Psychology, 28(15), 1429–1464. https://doi.org/10.1111/j.1559-1816.1998.tb01685.x
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334. https://doi.org/10.1007/BF02310555
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equations models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: Global Edition (7th ed.). Pearson Higher Education.
Homer, P. M., & Kahle, L. R. (1988). A structural equation test of the value-attitude-behavior hierarchy. Journal of Personality and Social Psychology, 54(4), 638–646. https://doi.org/10.1037/0022-3514.54.4.638
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Kauffman, D. F. (2004). Self-regulated learning in web-based environments: Instructional tools designed to facilitate cognitive strategy use, metacognitive processing, and motivational beliefs. Journal of Educational Computing Research, 30(1–2), 139–161. https://doi.org/10.2190/AX2D-Y9VM-V7PX-0TAD
Kline, R. B. (2011). Convergence of structural equation modeling and multilevel modeling. In M. Williams & W. P. Vogt (Eds.), Handbook of methodological innovation in social research methods (pp. 562–589). Sage.
Kline, R. B. (2016). Methodology in the social sciences. Principles and practice of structural equation modeling (4th ed.). Guilford Press.
Lajoie, S. P., & Azevedo, R. (2006). Teaching and learning in technology-rich environments. In P. A. Alexander & P. H. Winne (Eds.), Handbook of Educational Psychology (pp. 803–821). Lawrence Erlbaum Associates Publishers.
Lee, J. C. K., Zhang, Z., & Yin, H. (2010). Using multidimensional Rasch analysis to validate the Chinese version of the motivated strategies for learning questionnaire (MSLQ-CV). European Journal of Psychology of Education, 25(1), 141–155.
Lee, M.-C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506–516. https://doi.org/10.1016/j.compedu.2009.09.002
Lehmann, T., Hähnlein, I., & Ifenthaler, D. (2014). Cognitive, metacognitive and motivational perspectives on preflection in self-regulated online learning. Computers in Human Behavior, 32, 313–323. https://doi.org/10.1016/j.chb.2013.07.051
Lung-Guang, N. (2019). Decision-making determinants of students participating in MOOCs: Merging the theory of planned behavior and self-regulated learning model. Computers & Education, 134, 50–62. https://doi.org/10.1016/j.compedu.2019.02.004
McKinnon, K. & Igonor, A. (2008). Explaining eLearning perceptions using the technology acceptance model and the theory of planned behavior. In: C. Bonk, M. Lee, & T. Reynolds (Eds.), Proceedings of E-Learn 2008–World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 2994–2999). Association for the Advancement of Computing in Education (AACE).
Mohammadyari, S., & Singh, H. (2015). Understanding the effect of e-learning on individual performance: The role of digital literacy. Computers & Education, 82, 11–25. https://doi.org/10.1016/j.compedu.2014.10.025
Muilenburg, L. Y., & Berge, Z. L. (2005). Student barriers to online learning: A factor analytic study. Distance Education, 26(1), 29–48. https://doi.org/10.1080/01587910500081269
Ndubisi, N. (2006). Factors of online learning adoption: A comparative juxtaposition of the theory of planned behaviour and the technology acceptance model. International Journal on E-Learning, 5(4), 571–591.
Newman, R. S. (1998). Adaptive help seeking: A role of social interaction in self-regulated learning. In S. A. Karabenick (Ed.), Strategic help seeking: Implications for learning and teaching (pp. 13–37). Lawrence Erlbaum Associates Publishers.
Pajares, F. (1996). Self-efficacy beliefs in academic settings. Review of Educational Research, 66(4), 543–578. https://doi.org/10.3102/00346543066004543
Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33–40. https://doi.org/10.1037/0022-0663.82.1.33
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
Prior, D. D., Mazanov, J., Meacheam, D., Heaslip, G., & Hanson, J. (2016). Attitude, digital literacy and self efficacy: Flow-on effects for online learning behavior. The Internet and Higher Education, 29, 91–97. https://doi.org/10.1016/j.iheduc.2016.01.001
Sawang, S., Sun, Y., & Salim, S. A. (2014). It’s not only what I think but what they think! The moderating effect of social norms. Computers & Education, 76, 182–189. https://doi.org/10.1016/j.compedu.2014.03.017
Shen, D., Cho, M.-H., Tsai, C.-L., & Marra, R. (2013). Unpacking online learning experiences: Online learning self-efficacy and learning satisfaction. The Internet and Higher Education, 19, 10–17. https://doi.org/10.1016/j.iheduc.2013.04.001
Shih, H.-P. (2008). Using a cognition-motivation-control view to assess the adoption intention for web-based learning. Computers & Education, 50(1), 327–337. https://doi.org/10.1016/j.compedu.2006.06.001
Sorgenfrei, C., & Smolnik, S. (2016). The effectiveness of e-learning systems: A review of the empirical literature on learner control. Decision Sciences Journal of Innovative Education, 14(2), 154–184. https://doi.org/10.1111/dsji.12095
Taipjutorus, W., Hansen, S., & Brown, M. (2012). Investigating a relationship between learner control and self-efficacy in an online learning environment. Journal of Open, Flexible, and Distance Learning, 16(1), 56–69.
UNESCO. (2020). COVID-19 educational disruption and response. Retrieved https://en.unesco.org/covid19/educationresponse
Wigfield, A., & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25, 68–81. https://doi.org/10.1006/ceps.1999.1015
Wong, J., Baars, M., Davis, D., Van Der Zee, T., Houben, G.-J., & Paas, F. (2019). Supporting self-regulated learning in online learning environments and MOOCs: A systematic review. International Journal of Human-Computer Interaction, 35(4–5), 356–373. https://doi.org/10.1080/10447318.2018.1543084
Ying, T., Tan, X., Ye, S., Ka, X., & Zhou, Y. (2020). Examining tourist mindfulness in museums: The roles of familiarity and motivation. Asia Pacific Journal of Tourism Research, 25(9), 981–996. https://doi.org/10.1080/10941665.2020.1819835
Yukselturk, E., & Bulut, S. (2007). Predictors for student success in an online course. Journal of Educational Technology & Society, 10(2), 71–83.
Zeidner, M. (1998). Test anxiety: The state of the art. Plenum Press.
Zeidner, M. (2010). Test anxiety. In I. B. Weiner & E. Craighead (Eds.), The Corsini encyclopedia of psychology (4th ed., pp. 1766–1768). Wiley.
Zhou, M. (2016). Chinese university students’ acceptance of MOOCs: A self-determination perspective. Computers & Education, 92–93, 194–203. https://doi.org/10.1016/j.compedu.2015.10.012
Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3), 329–339. https://doi.org/10.1037/0022-0663.81.3.329
Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25(1), 3–17. https://doi.org/10.1207/s15326985ep2501_2
Zimmerman, B. J., & Schunk, D. H. (2001). Self-regulated learning and academic achievement: Theoretical perspectives. Routledge.
Funding
This study was granted by Project No. Y201839174 from Zhejiang Provincial Education Department and Project No. 18jg20 from Wenzhou University, People's Republic of China.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
The authors declare that there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Pan, Y., Huang, Y., Kim, H. et al. Factors Influencing Students’ Intention to Adopt Online Interactive Behaviors: Merging the Theory of Planned Behavior with Cognitive and Motivational Factors. Asia-Pacific Edu Res 32, 27–36 (2023). https://doi.org/10.1007/s40299-021-00629-y
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40299-021-00629-y