Unravelling the Barriers of Human Resource Analytics: Multi-Criteria Decision-Making Approach

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Meenu Chaudhary, Loveleen Gaur, Amlan Chakrabarti, NZ Jhanjhi


Although Human resource analytics is considered a ‘game-changer’, most organisations have still not integrated analytics due to the organisational-related, job-related and finance-related barriers. Existing literature has focused on addressing the barriers and contributing to the literature through systematic literature review and structural equation modeling. However, little has been researched on the barriers and their degree of magnitude in the organisation. Multi-criteria decision-making (MCDM) techniques find a solution in complex scenarios that include multiple factors and criteria. This study aims to measure the magnitude of the barriers and determine the ranking of the retail & e-commerce, IT, BFSI, FMCG, and travel & transport sectors based on the adoption and implementation of analytics using quantitative techniques of MCDM. In the first phase of the study, the entropy weight method (EWM) and criteria importance through inter-criteria correlation (CRITIC) techniques are used to derive the objective weights of the barriers. In the second phase, rankings are derived for the five sectors using TOPSIS and Measurement Alternatives and Ranking according to the Compromise Solution (MARCOS) techniques.


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Meenu Chaudhary, Loveleen Gaur, Amlan Chakrabarti, NZ Jhanjhi