HR Analytics in The Commercial Aviation Sector: a literature review

Abstract

HR Analytics is the area of Human Resources Management (HRM) that uses the techniques of artificial Intelligence, social sciences, and innovation to extract, treat, and present data for decision-making. This has been a big trend in HR in recent years, with already business cases, academic studies, and industry applications, but it is still in an early stage of development. The commercial aviation industry has people at the heart of its strategy, as it is a people-to-people service. But there are no satisfied customers without satisfied employees. In addition, the topic of aviation safety is a central one, and the sector has extensive regulations on the subject. Aviation safety is also based on human-machine interaction and crew resource management (CRM). People must therefore be in the core strategy of aviation companies because, for the provision of their service, the human factor in the interaction with the machine, the team, and the customer is a critical success factor. An appropriate approach to HR Analytics is to start with the problems that the business faces and, from there, create indicators and models that can measure them. In this paper, a sectoral and problem-based HR analytics is discussed, which is one of its main contributions. HR analytics techniques are descriptive, predictive, and prescriptive. Starting from the nature of the air operation and its problems, HR analytics is an essential strategy to better know and act on the evidence of aviation service and operation. The purpose of this publication is to understand what academic studies exist on the applications of HR Analytics to the aviation industry. Through a literature review, it was concluded that the aviation sector has also few academic studies about HR analytics; however, there are already some indicators and models that can point in the right direction for the future. This is an area that can help measure performance indicators of HR policies and provide management with important data for decision-making in human resources management. The results of this research are presented, future research paths are suggested, and the limitations of this study are pointed out.