Bibliographic record
Abstract
Purpose A growing body of literature on organizational learning suggests that companies or organizations with a learning capability can gain a competitive advantage. The argument is that learning organizations are better at knowledge transfer and generating new knowledge to solve problems. The objective of this study is to examine empirically if learning companies are more competitive and better performers than their competitors. Design/methodology/approach This study examines a portfolio of learning companies and a set of their competitors, looking at their financial performance over a significant period. Learning companies were selected based on content analysis of the published literature. Competitors were selected from an existing top 500 companies listing matched to the learning company's business domain. This study compares their performance using both market and accounting financial data. Findings The data show that learning companies demonstrate strong performance in financial markets over time, beating the traditional market indexes in both bull and bear markets. The accounting data show similar results. On a majority of the financial measures, the long‐term financial performance of learning companies is significantly superior to that of their closest competitors. Research limitations/implications This study discusses and explores the implications of these results in studying the link between learning companies and organizational performance. A limitation of the study is the small sample size of learning companies in the study. Also some potential alternative explanations for their performance cannot be completely ruled out due to the longitudinal nature of the study. Originality/value This study shows that there is a positive link between learning capability and competitive advantage, as measured by long‐term market financial performance of a group of learning companies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".