Adopting Sustainability Competencies in Management Education—A Scoping Review of Progress
Bibliographic record
Abstract
There is growing urgency to address society’s complex issues, many of which are incorporated within the Sustainable Development Goals (SDGs). Higher education has a special role and a responsibility to support and promote these goals and to prepare students for the complex challenges they will face as future leaders. The SDG framework helps students understand SDGs, but special competencies are necessary to address them effectively. Sustainability competencies (SCs) impart the personal/emotional development missing from current programming, but higher education institutions (HEIs) have been reluctant to introduce them into their curricula. Meanwhile, graduating students are ill-prepared for the complex problems, such as sustainability, that they will face as new managers and leaders. Our research question focused on identifying essential evidence that would support the implementation of SCs in HEIs. Our purpose was to raise awareness of the need for action in improving sustainability education and to assist in moving the issue forward. To enhance reading, we purposefully included multiple sections that capture and highlight the essential information. We employed a scoping review (SR) to scope out the relevant literature that supported a credible model for SCs and determine whether consensus was evident among scholars for such a model. Contrary to a commonly expressed theme in the literature, the results revealed that scholarly opinion had converged around a framework proposed by Wiek, Withycombe and Redman in 2011 and their 2021 update. A thematic analysis identified the key barriers preventing integration in HEIs, including the absence of a comprehensive policy to direct the implementation and sustain the change. We discuss these barriers and how they may be addressed. Integrating SCs into ME responds to SDG 4 (quality education). The results are intended to generate action regarding the need to integrate SCs in ME—sooner rather than later. The conclusions drawn respond to SDG 4 (quality education). The study serves to increase awareness of the issues and barriers preventing the much-needed transformation of ME in HEIs and stimulate discussion and potential action. Further research may involve a systematic review to inform much-needed policy and implementation.
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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 | 0.052 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.037 | 0.035 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".