The relationship between learning capability and organizational performance
Bibliographic record
Abstract
Purpose The purpose of this paper is to present a meta‐analysis of a subset of published empirical research papers that measure learning capability and link it to organizational performance. It also seeks to examine both financial and non‐financial performance. Design/methodology/approach In a search of published research on learning capability and organizational performance, the authors identified 33 articles that met criteria for inclusion in the meta‐analysis. Both objective and perceptual measures of organizational performance were considered to be acceptable. The data were analyzed using the Hunter and Schmidt meta‐analysis software. Findings The findings support a positive relationship between learning capability and organizational performance, with stronger results for non‐financial than financial performance. This has significant implications for justifying the investment in building a learning capability in organizations. Recommendations for managers are provided, such as the use of learning capability measures and the need to measure performance. Research limitations/implications The paper discusses the implications of these results for further theory building and development to advance knowledge in the field. This includes addressing the need for new research designs, the issue of causality, potential mediating effects and the impact of context in better understanding this complex relationship. It suggests that research is also needed to increase our understanding of how to effectively build this learning capability. Originality/value This meta‐analysis provides empirical evidence to support the value of building a learning capability in 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 | 0.037 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.013 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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".