AI กับภาวะหมดไฟในการปฏิบัติงาน: กรณีศึกษา บุคลากรมหาวิทยาลัยของรัฐแห่งหนึ่งในจังหวัดสงขลา

Nunnapat Songdecha; Chetsada Noknoi

Authors

Nunnapat Songdecha Thaksin University
Chetsada Noknoi Thaksin University

Keywords:

Artificial Intelligence, Burnout, Occupational Performance, Public University Staff

Abstract

This research article aims to: 1. explore staff experiences and perceptions of using artificial intelligence (AI) in their work, 2. examine the impact of AI on occupational burnout, and 3. propose human resource management strategies in the AI era to mitigate burnout at a public university in Songkhla Province, Thailand. Employing a qualitative approach, data were collected through in-depth interviews with 11 key informants, including administrators, academic staff, and support personnel with at least five years of work experience. Data were analyzed using content analysis, complemented by triangulation to ensure trustworthiness.

Findings were as follows: AI reduced repetitive tasks, enhanced work efficiency, and increased job satisfaction while alleviating stress. However, older staff or those less proficient in technology experienced difficulties and stress in using AI. Enhancing digital skills, providing psychological support and motivation, and designing flexible work systems emerged as critical mechanisms for reducing occupational burnout.

References

Bakker, A. B., & Demerouti, E. (2007). The job demands–resources model: State of the art. Journal of Managerial Psychology, 22(3), 309–328.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.

Brynjolfsson, E. & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. New York, NY: W. W. Norton & Company.

Creswell, J. W. & Báez, J. (2021). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Thousand Oaks, CA: SAGE Publications.

Creswell, J. W. & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). Thousand Oaks, CA: SAGE Publications.

Davenport, T. H. & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

Demerouti, E. et al. (2001). The job demands–resources model of burnout. Journal of Applied Psychology, 86(3), 499–512.

Hobfoll, S. E. (1989). Conservation of resources: A new attempt at conceptualizing stress. American Psychologist, 44(3), 513–524.

Huang, M.-H. & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30–50.

Joungtrakul, T. (2020). Qualitative research methods and applications. Bangkok: Chulalongkorn University Press.

Lichtman, M. (2013). Qualitative research for the social sciences. Thousand Oaks, CA: SAGE Publications.

Lincoln, Y. S. & Guba, E. G. (1985). Naturalistic inquiry. Beverly Hills, CA: Sage Publications.

Maslach, C. & Jackson, S. E. (1981). The measurement of experienced burnout. Journal of Occupational Behaviour, 2, 99–113.

Moustakas, C. (1994). Phenomenological research methods. Thousand Oaks, CA: SAGE Publications.

Patton, M. Q. (2015). Sampling, qualitative (purposeful). In J. D. Wright (Ed.), International encyclopedia of the social & behavioral sciences (2nd ed.). (pp. 812–817). Amsterdam, Netherlands: Elsevier.

Phakdee, P. & Kaewjomnong, K. (2021). Thematic and content analysis in qualitative research. Journal of Behavioral Science, 16(1), 1–15.

Salanova, M. et al. (2013). The gain spiral of resources and work engagement: Sustaining a positive worklife. Work & Stress, 27(1), 1–17.

Sutthinarakorn, P. (2019). Qualitative research: Concepts and processes (2nd ed.). Bangkok: Chulalongkorn University Press.

Tams, S. et al. (2021). Worker stress in the age of digital transformation: The moderating role of IT self-efficacy. European Journal of Information Systems, 30(1), 1–23.

Trist, E. L. & Bamforth, K. W. (1951). Some social and psychological consequences of the longwall method of coal-getting. Human Relations, 4(1), 3–38.

Venkatesh, V. et al. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

_____. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328–376.