Organizational culture, climate and IC: an interaction analysis
Bibliographic record
Abstract
Purpose This study aims to empirically investigate the role of organizational culture and climate in supporting intellectual capital (IC) management systems. Specifically, it seeks to investigate the relationship between organizational characteristics (culture and climate) and IC management systems in the Middle East (Iran and Lebanon) and Canada. Design/methodology/approach Data were gathered via a survey instrument and statistical analysis was used to test for significance between dependent and independent variables. Then a two‐stage hierarchical multiple regression was used to test for the nature and effects of country of origin as a moderating variable. Findings The findings suggest that both culture and climate play significant roles in developing management systems for IC. In addition, for country, when organizational climate improves, Middle Eastern respondents perceived an even greater improvement in IC management systems compared to their Canadian counterparts. Originality/value There is limited research that has been undertaken to compare developed and developing countries with regard to the influence of organizational characteristics on IC management systems. This research is timely given the recent publication of the Arab Human Development Report and the Arab Knowledge Report . This study provides insight into the ability of organizations in the Middle East to develop a knowledge base and reduce the knowledge gap between the Arab world and countries currently classified as knowledge intensive.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".