A Study on the Relationship Between School Members’ Intellectual Capital, Organizational Learning, Leadership Behavior, and School Performance: A Structural Equation Modeling Approach
Bibliographic record
Abstract
The purpose of this study was to identify the relationship structure among school members’ intellectual capital, organizational learning, principals’ leadership behavior, and school performance, taking 288 teachers from elementary and high schools in Taiwan as a research sample. The validated questionnaires were employed to survey school teachers’ perspectives on these four constructs. All participants completed 53 items of validated instruments including Organizational Learning Inventory (OLI), Intellectual Capital Inventory (ICI), Leadership Behavior Inventory (LBI), and School Performance Inventory (SPI). The construct as well as the significant relationship between variables examined using SPSS 21 and Amos software package to conduct structural equation modeling (SEM). The result of a confirmatory factor analysis confirmed several fixed factors of the variables. The second findings of the study indicated that there was a significant and positive correlation among organizational learning, intellectual capital, principals’ leadership behavior, and school performance. In the light of the findings, this paper discusses the importance of organizational learning and principals’ leadership behavior in order to improve school performance. Implications, suggestions, and recommendations for teachers, policy makers, and educational stakeholders were discussed.
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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 | 0.004 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".