Organizational journeys toward strong cultures of sustainability: a qualitative inquiry
Bibliographic record
Abstract
Introduction: There is widespread belief that organizational culture plays a crucial role in transitioning organizations for sustainability, but we currently lack understanding of how supportive cultures develop. The goal of this study is to empirically investigate how a culture of sustainability (COS) develops within a varied sample of real-world organizations. Methods: A qualitative cross-sectional design was utilized in this study using 14 semi-structured qualitative interviews with leaders of organizations perceived as having a strong COS or being on a good path toward that. The interviews explored how the leaders from various organizations experienced the development process of a COS from the initial emergence to the time of the interview. The qualitative data were analyzed using template analysis combined with applying a team-based approach to open coding. Results: The results indicate that while COS development is not a direct, clear, or linear process, there are several common factors that descriptively capture the process of COS formation. The analysis revealed four general stages of COS development (emergence; visibility and engagement; institutionalization and system alignment; ingrained and habitualized practice) and three key contextual moderators (organizational characteristics; external stakeholders/societal culture; business case). Discussion: This study makes an important contribution to the limited empirical literature on the development of organizational culture over time. Understanding key factors, relationships between factors, and COS stages can help leaders establish realistic expectations and strategies for developing and strengthening COS within their organizations.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".