30, August 2025

Hybrid Analytics for Hospitality Excellence: An Empirical Study on the Impact of Integrating Statistical and Machine Learning Models on Hotel Performance

VOLUME-9; ISSUE-8; AUG-2025

Author(s): 1. Reema Khan, 2. Dr. Seema Shrimali, 3. Dr. Divya Hiran

Authors Affiliations:

  1. Ph. D. Research Scholar, Faculty of Computer Science, Pacific Academy of Higher Education and Research University, Udaipur, Rajasthan, India ;
  2. 2. Assistant Professor, Pacific Academy of Higher Education and Research University, Udaipur, Rajasthan, India;
  3. 3.Professor, Department of Home Science, Govt. Meera Girls College, Udaipur, Rajasthan, India, divyahiran123@gmail.com

DOIs:10.2017/IJRCS/202508012     |     Paper ID: IJRCS202508012


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The hospitality industry operates in a dynamic environment marked by fluctuating demand, intense competition, and evolving guest expectations, making accurate forecasting and data-driven decision-making essential. Traditionally, hotels have relied on statistical models for forecasting and revenue management, valued for their interpretability but limited in handling complex, non-linear patterns. The advent of machine learning (ML) offers greater predictive power and flexibility, though adoption has been hampered by interpretability concerns, technical complexity, and resource constraints. Hybrid analytics—integrating statistical rigor with ML adaptability—has emerged as a promising approach, yet empirical evidence in hospitality remains scarce.

This study investigates the adoption, perceived value, and performance impact of hybrid analytics in Indian hotels, drawing on Technology Acceptance Model (TAM) and Technology–Organization–Environment (TOE) frameworks. A cross-sectional survey of 342 hotel professionals across diverse property types reveals that hybrid analytics adoption significantly improves forecasting accuracy (15–18% higher) and positively correlates with key performance metrics such as revenue per available room (RevPAR) and guest satisfaction. Organizational readiness partially mediates these effects, highlighting the importance of skills, infrastructure, and a data-driven culture.

The findings contribute to theory by empirically validating the superiority of hybrid approaches over single-method models and identifying readiness as a critical enabler. For practitioners, the study offers a strategic roadmap emphasizing phased adoption, explainable AI tools, and alignment with sustainability goals. Policy recommendations include incentivizing adoption, developing industry-specific AI guidelines, and capacity-building initiatives. The research underscores that hybrid analytics is not a peripheral innovation but a strategic necessity for competitive advantage in an increasingly data-driven hospitality landscape.

hospitality industry , machine learning (ML)

Reema Khan, Dr. Seema Shrimali, Dr. Divya Hiran (2025); Hybrid Analytics for Hospitality Excellence: An Empirical Study on the Impact of Integrating Statistical and Machine Learning Models on Hotel Performance, International Journal of Research Culture Society,    ISSN(O): 2456-6683,  Volume – 9,   Issue –  8,  Pp.75-83.       Available on – https://ijrcs.org/

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