Validation of a short form of the Inventory of Organizational Learning Facilitators
Bibliographic record
Abstract
Purpose This paper aims to develop a short form of the Inventory of Organizational Learning Facilitators (IOLF) by using the same factors as the long form to test the equivalence between two language versions (English and French) and to explore executives’ attitudes toward organizational learning (OL). Design/methodology/approach The structure of the long-form IOLF is based on five factors found in previous work: knowledge acquisition and transformation; OL culture; learning-focused leadership; OL support; and strategic management of new knowledge and learning. Two surveys of Canadian Federal Government executives assessed their perception of OL facilitators, organizational commitment, cynicism and intention to leave the organization. Correlational pattern analysis, conducted after confirmatory factor analyses, assessed the equivalence of the two language versions. Findings The short-form IOLF replicated the factor structure of previous work and demonstrated satisfactory internal consistency. Correlations showed equivalence between and across languages. Significant correlations with outcome variables, albeit in a cross-sectional design, supported predictive validity. Research limitations/implications This conceptually valid instrument can be adapted to English- and French-speaking populations. It can test hypotheses about the relationship between OL facilitators and individual, collective and organizational outcomes. The findings stem from self-report data in a cross-sectional design and require further research. Practical implications The short-form IOLF can quickly identify areas for improvement and monitor the evolution of an organization’s learning abilities. Originality/value This quick, efficient tool assesses OL context and can indicate factors likely to influence OL. This study offers empirically driven insights into conditions that influence executives’ attitudes.
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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 | 0.024 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".