The importance of organizational learning for organizational sustainability
Bibliographic record
Abstract
Purpose This Special Issue is intended to heighten awareness of the importance of organizational learning in addressing the demands of organizational sustainability, and in particular triple bottom line (TBL) sustainability. A definition of TBL sustainability is provided, together with an exploration of the practical issues relevant to adopting organizational learning in addressing it. By exploring research and practitioner viewpoints bearing on sustainability‐related applications of organizational learning, this Special Issue aims to help organizations remove barriers to achieving sustainability goals and catalyze the progress for an organization on its sustainability journey. Design/methodology/approach General sustainability‐related concerns and challenges associated with organizational learning are reviewed, and individual authors voice their understanding of the application of organizational leaning to particular aspects of sustainability based on their research, their case studies, and the extant literature. Findings Findings include enhanced understanding of the incompatibility of single‐ and double loop learning in TBL sustainability contexts, and the required emphasis on double‐loop learning to progress sustainability aims successfully. The effectiveness of dialogic interaction is described in achieving a transition towards sustainability in people, organizations and society as a whole. How individual worldviews called “our ecological selves” allow creation of the conditions for confronting global environmental challenges is explained. Contributions are made to the understanding of hybrid organizations through the case of a Brazilian networked organization, and a paradox view of management based on the theories of organizational learning and managerial cybernetics is applied to enlighten the understanding of sustainability. The learning and adaptive system of the US commercial aviation industry is explored and the application of such a system in an organization operating according to triple bottom line sustainability principles is described. Originality/value The opinions and research presented provide new and unique understanding of how organizational learning may contribute to organizational sustainability. Further value is added via the assessment of means to progress the sustainability ideal, the identification of barriers, and the many practical examples of means to facilitate progress toward that ideal.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".