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Analysis of influencing factors of students majoring in logistics from the perspective of planned behavior theory

Yunjia Li, Qi Ding

Article ID: 3376
Vol 10, Issue 1, 2025, Article identifier:

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Abstract

With the rapid development of global economic integration and e-commerce, the logistics industry has become a critical driver of economic growth, placing higher demands on the quality of logistics professionals. As the future backbone of the logistics industry, higher vocational students majoring in logistics play a crucial role in meeting these demands. However, their learning behavior is often affected by various internal and external factors, such as insufficient motivation, improper learning strategies, and limited resources.

This study explores the factors influencing the learning behavior of logistics vocational students from the perspective of the Theory of Planned Behavior (TPB). We focus on vocational students because they represent a key group that will directly impact the future development of the logistics industry. By understanding their learning behavior, we can identify strategies to improve their educational outcomes and, consequently, the quality of logistics professionals.

Our findings indicate that students' attitudes, subjective norms, and perceived behavioral control significantly influence their learning intentions and behaviors. Among these factors, subjective norms have the most substantial impact, highlighting the importance of social pressures and industry standards in shaping students' learning motivations. The study also reveals that learning behavior plays a key role in translating students' intrinsic factors into actual learning actions, partially mediating the influence of perceived behavioral control. However, gender and grade did not significantly moderate the relationships between TPB structures and learning intentions.

The significance of this study lies in its application of the TPB framework to the context of logistics vocational education, offering a novel perspective on understanding student learning behavior. The results provide valuable insights for educators to develop effective teaching strategies and learning support measures, ultimately contributing to the sustainable development of the logistics industry.


Keywords

learning behavior; logistics vocational students; planned behavior theory; learning intention; structural equation modeling

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References

1. Adelman, H. S., & Taylor, L. L. (2018). Addressing barriers to learning: In the classroom and schoolwide.

2. Altawallbeh, M., Soon, F., Thiam, W., & Alshourah, S. (2015). Mediating role of attitude, subjective norm and perceived behavioural control in the relationships between their respective salient beliefs and behavioural intention to adopt e-Learning among instructors in Jordanian universities. Journal of Education and Practice, 6(11), 152-159.

3. Barnard, R. W., & Henn, R. H. (2023). Overcoming learning obstacles: Strategies for supporting students with diverse needs. Open Access Library Journal, 10(8), 1-14.

4. Conner, M. (2020). Theory of planned behavior. Handbook of sport psychology, 1-18.

5. Couture, V., Faber, B., Gu, Y., & Liu, L. (2018). E-commerce integration and economic development: Evidence from China (Vol. 24383). Cambridge, MA: National Bureau of Economic Research.

6. Darling-Hammond, L., Flook, L., Cook-Harvey, C., Barron, B., & Osher, D. (2020). Implications for educational practice of the science of learning and development. Applied developmental science, 24(2), 97-140.

7. Han, X. (2022). The Training Mode of Logistics Management Talents under the Background of Internet Information Technology. Curriculum and Teaching Methodology, 5(4), 67-75.

8. Huang, F., Teo, T., & Zhou, M. (2020). Chinese students’ intentions to use the Internet-based technology for learning. Educational Technology Research and Development, 68, 575-591.

9. Hung, V. T. (2023). Recruitment of logistics and construction industry workers in Vietnam post-Covid-19 era. International Journal of Advanced Multidisciplinary Research and Studies, 3(2), 81-90.

10. Kashif, M., Zarkada, A., & Ramayah, T. (2018). The impact of attitude, subjective norms, and perceived behavioural control on managers’ intentions to behave ethically. Total Quality Management & Business Excellence, 29(5-6), 481-501.

11. Luo, L., & Kim, V. W. E. (2023). Analysis of the Factors Influencing the Quality of Logistics Talent Training in Vocational College in Zhejiang Province, with the application of PDCA Quality Management Theory in the Theoretical. International Journal of Management, Accounting, Governance and Education, 236-248.

12. McKinnon, A., Flöthmann, C., Hoberg, K., & Busch, C. (2017). Logistics competencies, skills, and training: a global overview.

13. Rehman, N., Huang, X., Sarwar, U., Fatima, H., & Maqbool, S. (2024). Exploring the impact of family, school and society on the perception and reputation of vocational training in Pakistan: a statistical analysis. Education+ Training.

14. Robledo, J. L. R., Arán, M. V., Sanchez, V. M., & Molina, M. Á. R. (2015). The moderating role of gender on entrepreneurial intentions: A TPB perspective. Intangible capital, 11(1), 92-117.

15. Senjahari, B., Desfitranita, D., & Kustati, M. (2021). Learning objectives and environments: How do they affect students’ motivation in English language learning?. Studies in English Language and Education, 8(2), 492-507.

16. Sentosa, I., & Mat, N. K. N. (2012). Examining a theory of planned behavior (TPB) and technology acceptance model (TAM) in internetpurchasing using structural equation modeling. Researchers World, 3(2 Part 2), 62.

17. Skoglund, E., Fernandez, J., Sherer, J. T., Coyle, E. A., Garey, K. W., Fleming, M. L., & Sofjan, A. K. (2020). Using the theory of planned behavior to evaluate factors that influence pharmD students’ intention to attend lectures. American journal of pharmaceutical education, 84(5), 7550.

18. Velayutham, S., Aldridge, J. M., & Fraser, B. (2012). Gender differences in student motivation and self-regulation in science learning: A multi-group structural equation modeling analysis. International journal of science and mathematics education, 10, 1347-1368.

19. Wang, C., Wang, H., Li, Y., Dai, J., Gu, X., & Yu, T. (2024). Factors influencing university students’ behavioral intention to use generative artificial intelligence: Integrating the theory of planned behavior and AI literacy. International Journal of Human–Computer Interaction, 1-23.

20. Fülöp, M. T., Breaz, T. O., He, X., Ionescu, C. A., Cordoş, G. S., & Stanescu, S. G. (2022). The role of universities' sustainability, teachers' wellbeing, and attitudes toward e-learning during COVID-19. Frontiers in Public Health, 10.

21. Maleki, S., Naeimi, A., Bijani, M., & Moghadam, N. S. (2025). Comparing predictive power of planned behavior and social cognition theories on students’ pro-environmental behaviors: The case of University of Zanjan, Iran. Journal of Cleaner Production, 486, 144386.

22. Shin, Y. H., Jung, S. E., Kim, H., & Im, J. (2025). College students’ willingness to pay more for local food: An extended decomposed theory of planned behavior approach. Journal of Foodservice Business Research, 28(1), 95-113.


DOI: https://doi.org/10.59429/esp.v10i1.3376
(29 Abstract Views, 9 PDF Downloads)

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