Factors Influencing the Intention to Participate in Thammasat University Library Information Literacy Activities

Main Article Content

Sukanya Panyainkaew

Abstract

    This study examined factors influencing participation in Thammasat University Library information literacy activities (IL) to formulate guidelines for organizationally improving them. Data was collected through an online questionnaire validated for content accuracy (IOC) and reliability (Cronbach’s Alpha = 0.884) by questionnaire from 145 participants, including students, instructors, and staff who had attended IL; the data was analyzed by percentage, frequency, mean, and standard deviation.
      Results showed that IL participation interest was impacted at a high or highest level by attitudes toward participation, behavioral intention, and perceived self-efficacy, usefulness. Social influence was rated at a high level. Mean scores follow: attitudes toward participation (4.49–4.52); perceived usefulness (4.42–4.47); perceived self-efficacy (4.30–4.41); behavioral intention (4.19–4.42); and social influence (3.21–3.89) and attitudes toward participation is the most impactful variable, exerting significantly positive influence on behavioral intention.

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How to Cite
Panyainkaew, S. (2026). Factors Influencing the Intention to Participate in Thammasat University Library Information Literacy Activities . PULINET Journal, 13(2), R168-R184. https://doi.org/10.66692/pulinet.13.2.3346
Section
Research Articles

References

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., & Fishbein, M. (1975). A bayesian analysis of attribution processes. Psychological Bulletin, 82(2), 261-277. https://doi.org/10.1037/h0076477

Al-Adwan, A. S., Li, N., Al-Adwan, A., Abbasi, G. A., Albelbisi, N. A., & Habibi, A. (2023). “Extending the Technology Acceptance Model (TAM) to predict university students’ intentions to use metaverse-based learning platforms”. Education and Information Technologies, 28(11) , 15381–15413. https://doi.org/10.1007/s10639-023-11816-3

Bamgbose, A. A., Ibrahim, H. M., & Musa, S. (2024). Information literacy and learning in the emerging digital landscape: A theoretical review. Library Philosophy & Practice (e-journal). 8125. https://digitalcommons.unl.edu/libphilprac/8125

Barz, N., Benick, M., Dörrenbächer-Ulrich, L., & Perels, F. (2024). Students’ acceptance of e-learning: Extending the technology acceptance model with self-regulated learning and affinity for technology. Discover Education, 3(1), 114. https://doi.org/10.1007/s44217-024-00195-7

Chen, G., Shuo, C., Chen, P., & Zhang, Y. (2022). An empirical study on the factors influencing users’ continuance intention of using online learning platforms for secondary school students by big data analytics. Mobile Information Systems, 2022(1), 9508668. https://doi.org/10.1155/2022/9508668

Diseiye, O., Ukubeyinje, S. E., Oladokun, B. D., & Kakwagh, V. V. (2024). Emerging technologies: Leveraging digital literacy for self-sufficiency among library professionals. Metaverse Basic and Applied Research, 3, 59. https://doi.org/10.56294/mr202459

Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146

Khan, M. J., Reddy, L. K. V., Khan, J., Narapureddy, B. R., Vaddamanu, S. K., Alhamoudi, F. H., Vyas, R., Gurumurthy, V., Altijani, A. A. G., & Chaturvedi, S. (2023). Challenges of e-Learning: Behavioral intention of academicians to use e-Learning during COVID-19 crisis. Journal of Personalized Medicine, 13(3). 555. https://doi.org/10.3390/jpm13030555

Liao, C. H. (2024). Exploring social media determinants in fostering pro-environmental behavior: Insights from social impact theory and the theory of planned behavior. Front Psychol, 15. https://doi.org/10.3389/fpsyg.2024.1445549

Mailizar, M., Almanthari, A., & Maulina, S. (2021). Examining teachers’ behavioral intention to use e-Learning in teaching of mathematics: An extended TAM model. Contemporary Educational Technology, 13(2), ep298. https://doi.org/10.30935/cedtech/9709

Rosli, M. S., & Saleh, N. S. (2024). Predicting the acceptance of metaverse for educational purposes in universities: A structural equation model and mediation analysis of the extended technology acceptance model. SN Computer Science, 5(6), 688. https://doi.org/10.1007/s42979-024-03015-9

Sanches, T., Lopes, C., & Antunes, M. L. (2022). Critical thinking in information literacy pedagogical strategies: New dynamics for higher education throughout librarians’ vision. In Universitat Politècnica de València (Ed.), Proceedings of the 8th International Conference on Higher Education Advances (HEAd’22) (pp. 489-496). Spain. http://dx.doi.org/10.4995/HEAd22.2022.14476

Trixa, J., & Kaspar, K. (2024). Information literacy in the digital age: Information sources, evaluation strategies, and perceived teaching competences of pre-service teachers. Frontiers in Psychology, 15, 1-22. https://doi.org/10.3389/fpsyg.2024.1336436