EMPIRICAL STUDY OF KNOWLEDGE SHARING AMONG MULTINATIONAL ACADEMICIANS
Bibliographic record
Abstract
Knowledge sharing among faculty members may enhance the quality of teaching and research activities. Despite the fact that a number of research has been conducted, to the best of our knowledge, this is the first work that focus on multinational academicians. The aim of this empirical study is to investigate ways and factors which contribute to knowledge sharing in the context of multinational academicians at universities. We performed this study at the Information Technology faculty at one of universities in Saudi Arabia, as a case study. The faculty employed academicians from 10 different countries including Malaysia, Jordanian, Egypt, Saudi Arabia, Tunisia, Pakistan, Yamen, Algeria and Canada. We used qualitative and quantitative approaches. The sample size of this study is N=40, and n=38 responded to the survey. Research results indicate 100% and 95% of academicians preferred to use phone calls and social media respectively, for knowledge sharing. Between 92% and 95% of the respondents have approved that elements of self-esteem which include satisfaction and feeling proud of oneself, respectively, are factors for knowledge sharing. Despite the fact that there is a strong relationship between trust and friendship, there is a need to substantiate that assumption because only 42% of the respondents shared based on a friendship relationship. Respondents also recognized appreciation and monetary rewards as motivation factors. The language used, lack of informal interactions, voluntary efforts are among barriers of knowledge sharing in this context. Findings of this study can be used a guideline for setting up a knowledge sharing mechanism by multinational higher education institutes.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".