Academy of Marketing Studies Journal (Print ISSN: 1095-6298; Online ISSN: 1528-2678)

Abstract

A Comparative Analysis of Employee Attrition Drivers In Hyderabad's Hotel Sector using Neural Networks

Author(s): Kiran Mayi Immaneni, Nagalakshmi Kundeti, Vidhya Aswath, Sara Shuttari and Sailaja V.N.

The hospitality industry has significant potential to maximize its accommodation capacity through both leisure and business travel. However, this potential is hindered by high levels of employee turnover, which poses a critical challenge to sustaining a committed workforce. This study examines the factors contributing to employee turnover, employing a comparative analysis across different hotel categories in Hyderabad. The research includes a sample size of 660 employees, encompassing both currently employed and recently resigned individuals. The study categorizes categorized into two segments: one comprising 3- and 4-star hotels, and the other including 5-star and 5-star deluxe hotels. Key findings show that physical stress from long workdays, a lack of possibilities for career progression, ineffective supervisory techniques, and a lack of simplified appraisal procedures are major causes of employee turnover in 3- and 4-star hotels. Conversely, in 5- and 5-star deluxe hotels, turnover is primarily influenced by inadequate resources, lack of trust, and limited skill development opportunities. Methodologically, the study employs ANOVA for statistical analysis and neural networks for predictive modeling, highlighting its analytical rigor. Addressing these challenges requires implementing strategies such as improving work-life balance, enhancing employee performance evaluations, providing skill development opportunities, and tailoring staff training programs to meet specific needs. These measures aim to reduce turnover and strengthen workforce commitment, ultimately supporting the sustainable growth of the hospitality sector.

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