Predictors and Outcomes of Successful Localization in the Aviation Industry: The Case of Oman
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it