THE IMPACT OF AI-DRIVEN ANALYTICS ON IDENTIFYING SKILL GAPS AMONG TOURISM SECTOR EMPLOYEES AND ITS ROLE IN DEFINING ADVANCED JOB PERFORMANCE APPRAISAL RULES

Abstract

This study focuses on investigating and analyzing the transformative role played by AI-driven analytics as an independent variable in monitoring and diagnosing  skill gaps among human cadres working in tourism companies. The study proceeds from a fundamental problem represented in the inadequacy of traditional methods in keeping pace with the technological acceleration in the tourism sector, which has led to the emergence of gaps Cognitive and behavioral barriers to institutional excellence. The main objective of the study is to investigate how the outputs of intelligence (both predictive and descriptive) can be used in the engineering and formulation of the rules of Advanced Performance Appraisal, and transform them from mere rigid measurement tools to dynamic systems capable of providing real-time and objective feedback. To achieve this goal, the study relied on the descriptive-analytical approach, where a peer-reviewed scientific questionnaire was developed that was distributed to an intended sample of human resources managers and executives in tourism companies to collect raw data. The study adopts a set of main hypotheses that indicate that there is a direct and statistically significant interaction relationship between the accuracy of the analytical algorithms used in skills mining and the quality and reliability of the performance evaluation criteria used. The study concludes that the adoption of this technical model effectively contributes to bridging skill gaps by automatically linking the evaluation results with actual training needs, which reduces human bias and enhances the efficiency of human capital in facing the challenges of digital tourism.