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The Relationship Between Self-concept Clarity and Procrastination among College Students: A Mediation Chain Model

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Published:24 August 2023Publication History

ABSTRACT

In the post-pandemic era, university students face a future replete with uncertainties, engendering significant challenges in both their academic and personal lives. The uncertain environment and continuous online learning may contribute to the prevalence of procrastination among students. Therefore, in this era, it is crucial for college students to have a sense of self-assurance and self-control. Based on self-regulation theory, this study examines the issue of procrastination among college students from a new perspective. A total of 756 university students were surveyed using the Self-Concept Clarity Scale, the Pure Procrastination Scale, the Brief Self-Control Scale, and the Brief Self-Handicapping Scale. Data analysis was conducted using SPSS 26.0 and AMOS 26.0, and a chain mediation model was established (Isolated the influence of control variables). Results indicate that students' self-concept clarity not only directly negatively predicts procrastination but also affects it through self-control, self-handicapping, and their chain-mediated effects, with self-handicapping having the greatest impact. These findings suggest that schools and educators should focus on developing college students' self-concept clarity to alleviate their procrastination behavior, which can be achieved by enhancing self-control and preventing self-handicapping. This study provides guidance for schools and educators on promoting college students' self-awareness and self-management, laying the foundation for future prevention and intervention of procrastination among university students.

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    • Published in

      cover image ACM Other conferences
      ICBDE '23: Proceedings of the 2023 6th International Conference on Big Data and Education
      June 2023
      149 pages
      ISBN:9798400708220
      DOI:10.1145/3608218

      Copyright © 2023 Owner/Author

      This work is licensed under a Creative Commons Attribution International 4.0 License.

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      Association for Computing Machinery

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      • Published: 24 August 2023

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