The role of responsible management education (RME) in helping to address irresponsible practices: A case study from the global south
Bibliographic record
Abstract
The literature on Responsible Management Education (RME) has consistently advocated for its effectiveness in equipping future leaders with the essential knowledge and skills. Various studies (Nonet & Kassel, 2016; Russo et al., 2023; Eustachio et al., 2024) have supported the positive impact of RME on shaping ethical and sustainable behaviour (Zhang & Szerencsi, 2022) and thus, in impacting on reducing irresponsible or poor behaviours. This paper aims to investigate the underlying causes of irresponsible behaviour and propose ways in which an institution can leverage its existing initiatives on education for sustainable development (ESD) to mitigate such behaviours and promote a more responsible and sustainable learning environment. This research paper is part of an interpretive, inductive, qualitative study that aims to investigate the obstacles and catalysts for sustainability in higher education institutions (HEIs). Empirical evidence was obtained through thematic analysis of 43 semi-structured interviews conducted as part of a single case study in a Saudi Arabian higher education institution (HEI). This organisation has a strong history of integrating responsible management principles (PRME) into its teaching, research, and operations. Despite this, the study revealed that the institution still faces sustainability challenges due to certain irresponsible actions. Despite being a signatory of PRME, there are underlying challenges in behaviour. Consequently, it is crucial to raise awareness. Participants suggested that additional institutional support is necessary to cultivate awareness among students from the initial stages of their enrolment in the university until graduation. In addition, it is recommended by Mousa et al. (2023) that RME should incorporate community outreach activities outside of the university in order to effectively raise awareness and acquire additional skills. This paper is valuable because it is one of a few studies to address a gap in the literature on Responsible Management Education (RME). Specifically, it focuses on the barriers that PRME signatory institutions in Saudi Arabia face. Russo et al. (2023) verified that more than 75% of RME literature originates from the global north. This finding emphasises the need for additional empirical research from the global south, particularly from the Middle East and North Africa (MENA) region, where RME is in high demand (Mousa et al., 2020).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".