Identifying and Prioritizing the Components of Disciplinary Behavior (Case Study: University Faculty Members) (in Persian)

Masoumeh Mohammadi (), Golamreza Bordbar (), Ali Morovi Sharif Abadi () and Omid Mahdieh ()
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Masoumeh Mohammadi: Yazd University
Golamreza Bordbar: Yazd University
Ali Morovi Sharif Abadi: Yazd University
Omid Mahdieh: University of Zanjan

Management and Development Process Quarterly (٠صلنامه ٠رایند مدیریت و توسعه), 2024, vol. 37, issue 1, 71-110

Abstract: Purpose: Employee discipline is one of the important and long-standing concepts in management, which has even continued in postmodern management approaches. The current article aimed to identify the components of the disciplinary behavior of the academic staff members using a mixed research methodology. Methodology: To identify the components and indicators of disciplinary behavior, we used meta-composition technique (study of documents) in the first stage, and in the second stage, we applied the fuzzy Delphi method with expert participants, some of whom with theoretical experience and some with practical experience. Then, from among them, twenty-five individuals were purposefully selected. Finally, for prioritizing and weighting the components and indicators of disciplinary behavior acquired from the five universities under study (Tehran, Zanjan, Yazd, Tabriz, and Ardabil universities), the Waspas and Critic methods were used in a row. Findings: The outcomes of the first and second stages of statistical computation have led to seven components of disciplinary behavior including: types of discipline, factors affecting disciplinary behavior, types of violations, disciplinary procedures, types of punishment, consequences of discipline, antecedents of discipline and disciplinary knowledge, all in all with 37 indicators. Finally, we are presenting the priority list of the five universities as follows: Tehran University, Zanjan University, Yazd University, Tabriz University, and Ardabil University.

Keywords: Academic Staff Members; Discipline; Meta-Combination Technique; Mixed Approach; Waspas. (search for similar items in EconPapers)
Date: 2024
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