The Effect of Human Capital Development on Strategic Renewal in the Egyptian Hospitality Industry: The Moderating Role of Dynamic Capabilities
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
Purpose: the primary goal of this research is to provide insight into the effect of human capital development on strategic renewal in the Egyptian hotel industry. In addition, to examine the effect of dynamic capabilities as a moderating variable in the relationship between human capital development and strategic renewal. Methods and tools: questionnaires were distributed based on a simple sampling method and collected in the Egyptian hospitality industry. 310 questionnaires were distributed, and 204 usable samples were obtained, yielding a 66% response rate from those who agree to participate. The Partial Least Square-Structural Equation Modeling (PLS-SEM) method has been used for analyzing the data and testing our hypotheses. The analysis of this paper was done using SPSS V. 23 for both descriptive and inferential statistics and Smart PLS V.3.3.3 for PLS-SEM analysis. Results: this study indicates that human capital development has a statistically significant effect on Strategic Renewal; also, the findings observe that Dynamic Capabilities have a statistically significant impact on Strategic Renewal. And the Moderating Effect of dynamic capabilities not approved. Theoretical and empirical contribution: The conceptual model with statistical results that emphasizes the significance of human capital development for the strategic renewal of business enterprises in the Hospitality field of developing countries has been investigated in this study. Human capital development has previously been highlighted, but its impact on strategic renewal has not been extensively investigated. Besides, this study provides valuable insights for decision-makers. As recommends that hotel managers consider human capital development and present it as a vital part of strategy formulation.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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