Organizational characteristics fostering intellectual capital in Canada and the Middle East
Bibliographic record
Abstract
Purpose This study sets out to examine how organizational characteristics are related to intellectual capital and how these variables are different between Canadian and Middle East contexts. Design/methodology/approach A questionnaire was developed to measure the four major study constructs, i.e. intellectual capital, culture, climate, and organizational traits. Each of these constructs was represented by a number of subscales that were subjected to ANOVA and correlations to test the hypotheses. Findings The analysis showed that all three categories of characteristics (culture, climate, and other traits) are significantly correlated with IC management. The results also indicated significant differences in all organizational characteristics and IC management between Canada and the Middle East. Research limitations/implications Culture, climate, and other traits are important enablers for the effective management of IC. Although the research tested three culture variables, four climate variables, and two other traits, future research should investigate these variables and the interactions among them more thoroughly. Practical implications The results have implications for organizations operating in different international contexts. Managers can use the results for more effective and efficient management of organizational characteristics that would foster IC management. Originality/value The research provides a comprehensive study of enablers of effective IC management, an area of study that has not received much attention in the past. It also provides insight as to why effective IC management may be more successful in certain countries.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".