HR Analytics Adoption in the Indian Hospitality Industry: A Path to Data-Driven Decision Making

Abstract

Background: The rapid advancements in data-driven decision-making have revolutionized various industries, including the hospitality sector. Human Resource (HR) Analytics, a key component of this transformation, leverages data analysis to enhance workforce management, improve employee performance and drive strategic decision-making. However, the level of adoption of HR Analytics in the Indian hospitality industry remains an emerging and evolving concept. The hospitality industry, known for its dynamic and labour-intensive nature, heavily relies on effective human resource management. Challenges such as high employee turnover, skill shortages, job dissatisfaction and workforce mobility make HR Analytics an essential tool for improving efficiency and strategic workforce planning. Objectives: This study examines the key change factors influencing the adoption of Human Resource Analytics among Human Resource professionals in the Indian hospitality sector. It aims to analyse how individual perceptions, organizational culture and accessibility to HR technology impact the acceptance of HR Analytics. Methodology: A quantitative research design was adopted, data gathered through a structured questionnaire distributed via Google Forms. The study targeted HR professionals across various levels in the Indian hospitality industry, with a specific focus on metropolitan cities such as Delhi, Mumbai, Bangalore and Kolkata. A snowball sampling technique was used to reach participants. Out of the targeted 300 respondents, 75 valid responses were analysed. Statistical tools such as correlation analysis and linear regression were applied to assess the relationships between variables, including sense of capability, cultural influence, HR technology accessibility, data availability, risk framing strategy, ease of adoption and work performance enhancement. Results: The study found that six out of seven factors significantly influenced HR Analytics adoption. Work performance enhancement had the highest impact (Adjusted R² = 0.460, p < 0.05), followed by cultural influence (Adjusted R² = 0.307, p < 0.05) and HR technology accessibility (Adjusted R² = 0.258, p < 0.05). However, risk framing strategy showed no significant impact (p > 0.05). The study also confirmed that these factors are interrelated, highlighting the need for a comprehensive approach to HR Analytics implementation. Conclusion: The findings suggest that successful HR Analytics adoption in the Indian hospitality industry requires a multi-faceted approach, including employee training, leadership support, flexible work environments and investments in HR technology. Organizations must ensure that employees perceive HR Analytics as beneficial and easy to use, addressing concerns about data security and change resistance. Future research should explore the organizational-level adoption of HR Analytics and expand the sample size for broader insights.