Predictors and Outcomes of Successful Localization in the Aviation Industry: The Case of Oman
Bibliographic record
Abstract
Localization has encountered substantial focus in academia as well as practice; however, scarce studies have empirically examined this theme within tourism-related sectors in Oman, including the aviation sector. That is why the purpose of this paper is to develop and test an integrated model of the key predictors and outcomes of successful localization within the aviation industry. It also evaluates the mediating role of knowledge sharing ability between human resources development (HRD) practices and localization as well as the moderating effect of organizational commitment on the link between localization and firm performance. This paper is based on primary data collected from 194 employees operating in the national aviation sector in Oman. Based on PLS-SEM, the results indicated that HRD practices (i.e., training, performance appraisal, and rewards) have a positive impact on expatriates’ ability to share knowledge with national staff, and thus positively impact the localization success. Additionally, the firm's performance is positively influenced by successful localization. Knowledge sharing does not mediate the link between HRD practices and successful localization, but the results confirmed the interactive impact of organizational commitment on the direct connection between localization and performance. The findings contribute significantly to the research community and provide practical guidelines and managerial implications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".