HR Analytics (HRA), a growing field within Human Resource Management, uses AI, social sciences, and statistics to analyze company data and facilitate decision-making. Despite its prominence, HRA faces skepticism about its effectiveness. HRA progresses through the descriptive, predictive, prescriptive and autonomous phases. Most HR departments remain in the descriptive phase due to several challenges, including a lack of specialized knowledge and high IT costs. However, successful enterprise applications of HRA enable data-driven decisions and predictions, such as employee churn. This study employs a mixed methods case study approach to determine the effectiveness of HRA in the commercial air transport sector in Portugal. Qualitative semi-structured interviews identify the main challenges in people management, while quantitative analysis uses multiple linear regression on data from a questionnaire to 369 professionals from Portuguese airlines to explore the factors that influence turnover intentions. Key findings include the devaluation of HR at airlines, misalignment between business and HR strategies, and high turnover intentions among ground staff, exacerbated by the pandemic. The study confirms that career satisfaction, leadership, work-life balance, and pay significantly affect turnover intentions. This study contributes by validating a new scale of job satisfaction for the Portuguese air transport sector, highlighting the value of mixed methodologies and providing practical recommendations for HR policies. Keywords: #hranalytics #casestudy #airlines #mixedmethods #AI #employeechurn

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HR Analytics,Mixed methods,airlines,case study,phd thesis,phd

• Economics and Business - Social Sciences