Keywords

Employee Turnover, Hospitality Sector, Hotel, Comparative Analysis, Skill Development, Work-Life Balance, Neural Networks, Supervisory Practices, Career Progression, Resource Constraints, Employee Performance, Drivers of Employee Turnover, Workforce Retention.

Introduction

In the dynamic and demanding landscape of the hospitality industry, employee turnover is a persistent and critical challenge, particularly in India. This challenge is amplified in Hyderabad, a burgeoning hospitality hub, where retaining skilled talent is a significant hurdle for hoteliers. The hotel industry’s operational nature, characterized by long working hours, high stress, and diverse functional demands, exacerbates this issue. Turnover rates are especially high among frontline employees in departments like kitchen, housekeeping, and food and beverage services, which face substantial manpower challenges and excessive workloads. These roles often serve as stepping stones for employees seeking better opportunities or career advancement, whereas support functions like HR, finance, and sales experience greater stability (Ray).

Existing literature identifies a plethora of factors contributing to turnover, including competition, better prospects, lack of career development opportunities, insufficient pay and benefits, personal reasons, resource constraints, rigid organizational policies, and inadequate Employment Conditions. While these studies provide valuable insights, there is limited research focusing on how these factors vary across different hotel categories and their nuanced impact in specific locations like Hyderabad.

This study addresses these gaps by comparing drivers of employee turnover between 3-4 star and 5-5-star deluxe hotels in Hyderabad. It leverages neural network analysis to uncover patterns and insights that traditional methods may overlook. By integrating advanced analytics with a comparative framework, the research not only contributes to the understanding of turnover dynamics in the hospitality sector but also provides actionable insights for industry stakeholders. The findings aim to inform strategic interventions tailored to distinct hotel segments, promoting workforce retention and organizational stability in this vital industry.

The hotel business is known for having the highest staff turnover rates in the private sector. The industry is demanding in nature, faces distinctive hurdles in retaining skilled talent due to various factors. This challenge is amplified in Hyderabad, a burgeoning hospitality hub, where retaining skilled talent is a significant hurdle for hoteliers. Existing literature identifies a plethora of factors contributing to turnover, including competition, better prospects, lack of career development opportunities, insufficient pay and benefits, personal reasons, resource constraints, rigid organizational policies, and inadequate Employment Conditions. While these studies provide valuable insights, there is limited research focusing on how these factors vary across different hotel categories and their nuanced impact in specific locations like Hyderabad.

Employee turnover is particularly high among associates working in departments such as Food production, Rooms Division, Restaurant services which face substantial manpower challenges, leading to excessive workload pressures. Employees in these departments frequently change jobs to enhance their professional profiles. Conversely, roles in supportive functions like HR, Finance, and Sales experience greater stability, benefiting both organizational growth and individual career development. Stability tends to increase at higher organizational levels, while the lower hierarchy sees frequent turnover as employees seek better opportunities and career growth.

Employees leave their positions for a variety of reasons, including seeking better opportunities, limited prospects for career development, insufficient pay and benefits, and personal circumstances. Existing literature identifies numerous factors contributing to turnover challenges, such as intense competition, lack of job security, family responsibilities, childcare, motherhood, absence of fringe benefits, resource limitations, inflexible organizational policies, restricted career advancement opportunities, lack of autonomy, low motivation, and unfavorable Employment Conditions.

This study addresses these gaps by comparing dimensions of employee turnover between 3-4 star and 5-5-star deluxe hotels in Hyderabad. It leverages neural network analysis to uncover patterns and insights that traditional methods may overlook. By integrating advanced analytics with a comparative framework, the research not only contributes to the understanding of turnover dynamics in the hospitality sector but also provides actionable insights for industry stakeholders. The findings aim to inform strategic interventions tailored to distinct hotel segments, promoting workforce retention and organizational stability in this vital industry.

Literature Review

Employment Conditions are critical to employee performance, particularly in the hospitality sector. (Jayaweera, 2015) highlights that favorable environmental factor, such as a conducive workplace and adequate infrastructure, significantly enhance job motivation and productivity. Two-factor theory further emphasizes the importance of hygiene factors, including pay, supervision, and Employment Conditions. When these factors are not addressed, employees often experience dissatisfaction, which impedes motivation and increases the likelihood of turnover. Studies by (Poulston, 2009) and Di Pietro & Condly (2007) found a direct link between poor Employment Conditions and employee turnover, noting that dissatisfaction with pay, supervisors, and workplace environments often drives employees to seek alternative employment opportunities.

Career advancement is another key determinant of employee retention. According to (Spagnoli & Weng, 2019), career advancement entails achieving long-term job success through opportunities that align with an individual's professional goals. The significance of career adaptability in mediating the relationship between work social support and turnover intentions has been highlighted in studies such as (Karatepe & Olugbade, 2017). Employees who perceive opportunities for career growth and development are more likely to exhibit career satisfaction and lower intentions to leave. (Joshi & Kanthe, 2022) further underline that structured career planning and development initiatives not only address employees’ professional aspirations but also significantly reduce turnover rates by ensuring a clear path for growth.

Fringe benefits play a pivotal role in shaping employee satisfaction and loyalty. Defined as additional compensation beyond direct wages, such as housing allowances, medical insurance, and paid holidays, these benefits have been shown to improve employee retention (Gold, 2007). (Soon et al., 2008) found that fringe benefits positively influence organizational commitment by fostering a sense of value and security among employees. Bhargava & Singhal, 2014 advocate for greater flexibility in designing benefits packages, suggesting that customized benefits can effectively meet diverse employee needs, thereby bridging retention gaps.

Supervisory practices are also instrumental in employee retention. Gordon et al., 2018 identified that supportive supervision significantly enhances employee well-being, reducing turnover intentions. Transformational leadership, as discussed by (Chen & Wu, 2017), fosters trust and minimizes psychological contract breaches, which are critical for employee retention. The role of effective leadership is further emphasized by (Mhlanga, 2018), who identified teamwork, appreciation, and fair treatment as key motivational factors for reducing turnover. Similarly stress the importance of fair and unbiased supervision in creating a positive organizational culture that supports employee retention (Dogru et al., 2023).

Despite extensive research on these factors, comparative analyses of turnover across hotel categories in the Indian context remain limited. Hyderabad, as a growing hospitality hub, offers a unique setting to study these dynamics. The turnover patterns between mid-scale (3-4 star) and luxury (5-star deluxe) hotels are underexplored, leaving gaps in tailored HR strategies for these segments. Moreover, existing studies predominantly employ traditional statistical methods that fail to capture the intricate, non-linear relationships between multiple turnover factors (Erbasi & Arat, 2012).

This study addresses these gaps by employing neural networks to analyze turnover predictors, offering a novel methodological approach that uncovers complex interactions among factors. By focusing on both academic insights and practical HR interventions, the study contributes to a deeper understanding of turnover dynamics in Hyderabad’s hospitality sector and beyond (Ghani et al., 2022).

Objectives

• To identify the key factors influencing employee turnover in selected hotels.

• To conduct a comparative analysis of turnover drivers across different hotel categories.

Hypothesis

• There are no significant differences in the drivers of employee turnover between 3-4 star and 5-star deluxe hotels in Hyderabad.

Methodology

This study explores the factors contributing to employee turnover in the hotel industry, identified through an extensive literature review as Employment Conditions, Career Progression, Supplementary Benefits, Supervisory Practices, and Motivational Factors. The research is a comparative analysis focusing on two categories of hotels in Hyderabad: 3- & 4-star hotels and 5- & 5-star deluxe hotels. Data were collected through convenience sampling, yielding 660 valid responses, with 300 responses from 3- & 4-star hotels and 360 from 5- & 5-star deluxe hotels (Chawla et al., 2024).

Convenience sampling was chosen due to practical limitations and the specific focus on Hyderabad hotels. While this method may reduce generalizability, it enabled the collection of diverse perspectives from employees across the selected hotel categories. Supplementary interviews were conducted to further validate the findings, making the methodology suitable for the study’s objectives (Rajashekar & Jain, 2024).

HRACC-classified hotels were selected for the study to ensure consistency and reliability in categorization. The HRACC (Hotel & Restaurant Approval and Classification Committee), under the Ministry of Tourism, Government of India, classifies hotels based on criteria such as facilities, services, and operational practices. For this study, 3- & 4-star hotels are grouped as mid-scale accommodations catering to business-class guests, while 5- & 5-star deluxe hotels represent the luxury segment, offering premium facilities and superior service quality. This categorization aligns with the study’s aim of comparing these two distinct segments (Satish & Eugene).

A feed-forward neural network model was utilized to analyse and predict the factors influencing employee turnover (Michael & Fotiadis, 2022). The model consisted of input, hidden, and output layers. The input layer processed variables such as Employment Conditions, career advancement opportunities, fringe benefits, supervisory practices, and motivational factors. The hidden layer, with three nodes, captured complex relationships through non-linear activation functions, and the output layer predicted turnover likelihood across the hotel categories (Davidson & Brindha, 2022).

To ensure model robustness, the dataset was divided into training and testing sets in an 80:20 ratio. The training set was used to develop the model, while the testing set validated its predictive accuracy (Herzberg et al., 2011). Cross-validation techniques were employed to prevent overfitting by iteratively training and validating the model on smaller data subsets. This systematic approach highlights the effectiveness of neural networks in analysing drivers of Employee Turnover, providing actionable insights into employee retention challenges (Figure 1).

Statistical Tools

ANOVA, or Analysis of Variance, is a statistical method utilized to compare the means of two or more groups to determine if significant differences exist among them. It examines the variability within the data to test the null hypothesis, which assumes no difference between group means, against the alternative hypothesis, which posits that at least one group mean is distinct. ANOVA employs an F-test to evaluate the ratio of variance between groups to the variance within groups. A significant F-value indicates that the between-group variance outweighs the within-group variance, leading to the rejection of the null hypothesis and concluding a significant difference in at least one group mean.

The general relationship in ANOVA is expressed as:

Total Variance = Between-group Variance + Within-group Variance

Mathematically:

SS_total = SS_between + SS_within

Where:

• SS_total represents the total sum of squared deviations from the overall mean.

• SS_between refers to the sum of squared deviations of group means from the overall mean, capturing variance attributed to differences between groups.

• SS_within denotes the sum of squared deviations within each group, accounting for variance due to individual differences within groups.

This framework allows ANOVA to determine if observed differences in means are statistically significant, helping to distinguish whether they are due to random chance or specific factors under investigation.

Neural Network Multilayer Perceptron (MLP) Prioritization

Multilayer Perceptron (MLP) neural networks are utilized to assess the significance of variables within a dataset. The architecture typically consists of three main components:

1. Input Layer: Each neuron represents an individual variable.

2. Hidden Layers: Comprising one or more layers where complex transformations are performed to capture intricate patterns.

3. Output Layer: This layer generates predictions or classifications for the target variables.

The network is trained using a loss function and optimization algorithm to minimize prediction errors. By examining the weights assigned to input neurons during the training process, the relative importance of variables can be determined. This prioritization identifies key factors influencing the model's output, offering valuable insights for further analysis (Immaneni & Sailaja, 2019).

Fundamental Equation of MLP Neurons

The mathematical representation for a single neuron in an MLP is as follows:

Where:

• inputs: Data values fed into the neuron.

• weights: Parameters determining the influence of each input.

• bias: A constant that adjusts the output.

• dot_product: The sum of element-wise products of inputs and corresponding weights.

• activation_function: A non-linear function applied to enable the network to model complex relationships.

In an MLP, the output of each neuron in the previous layer becomes the input for neurons in the next layer. The network learns by iteratively updating weights and biases through backpropagation, minimizing discrepancies between predicted and actual outputs.

MLP-based variable prioritization is a powerful tool for identifying influential factors in large datasets. By quantifying the impact of variables, it supports efficient decision-making and enhances model interpretability in research and applied scenarios (Figure 2).

This Diagram shows the neural network with input layer variable as career advancement. These inputs are processed through a hidden layer with seven nodes, which apply mathematical functions to capture complex relationships. The output layer predicts the likelihood of employee turnover for two hotel categories (3- & 4-star and 5- & 5-star deluxe). Blue and gray lines represent the weights of connections between layers. The model was trained and validated using the dataset, ensuring accuracy and insights into turnover factors.

Results and Discussion

Results of One-Way ANOVA for the Five Drivers of Employee Turnover

Employment Conditions

The analysis reveals a significant influence of employment conditions on employee turnover drivers. At a 5% level of significance, the between-groups sum of squares accounts for 15.897. The calculated F-value of 8.31 exceeds the critical value of 2.37, with a p-value of 0.001, indicating notable differences in employment conditions among 3- & 4-star hotels.

Career Progression

Career progression significantly affects employee turnover drivers. The between-groups sum of squares is 34.811, and the F-statistic is 17.575, accompanied by a p-value of 0.001. This signifies meaningful variations in career progression opportunities across 3- & 4-star hotels.

Supplementary Benefits

The findings highlight the substantial impact of supplementary benefits on employee turnover drivers. The between-groups variation accounts for 80.408 of the total sum of squares, with an F-statistic of 65.437 and a p-value of 0.001. This underscores significant differences in supplementary benefits among 3- & 4-star hotels.

Supervisory Practices

Supervisory practices also show a significant effect on employee turnover drivers. The between-groups sum of squares is 18.993, and the calculated F-statistic of 5.782 exceeds the critical value. A p-value of 0.001 confirms significant differences in supervision levels across 3- & 4-star hotels.

Motivational Factors

The analysis indicates a significant impact of motivational factors on employee turnover drivers. The between-groups sum of squares is 19.562, with an F-statistic of 6.223 and a p-value of 0.001. This result demonstrates substantial differences in motivational factors among 3- & 4-star hotels.

Results of One-Way ANOVA for Drivers of Employee Turnover in 5- and 5-Star Deluxe Hotels

Employment Conditions

The analysis indicates a significant impact of employment conditions on employee turnover. The between-groups variation contributes 21.714 to the total sum of squares, and the calculated F-value of 11.583 exceeds the critical value of 2.37. Additionally, the p-value of 0.001 confirms that employment conditions differ significantly among 5- and 5-star deluxe hotels.

Career Progression

Career progression shows a notable effect on employee turnover. The between-groups sum of squares is 4.812, and the F-statistic is 12.250 with a p-value of 0.001. This highlights significant variations in career progression opportunities across 5- and 5-star deluxe hotels.

Supplementary Benefits

The results reveal a significant influence of supplementary benefits on employee turnover. The between-groups variation accounts for 5.285 of the total sum of squares, with an F-statistic of 8.838 and a p-value of 0.001. These findings indicate substantial differences in supplementary benefits among 5- and 5-star deluxe hotels.

Supervisory Practices

Supervisory practices demonstrate a significant effect on employee turnover. The between-groups variation accounts for 34.075 of the total sum of squares, with an F-statistic of 12.088 and a p-value of 0.001. This underscores meaningful differences in supervisory practices across 5- and 5-star deluxe hotels.

Motivational Factors

Motivational factors also exhibit a significant impact on employee turnover. The between-groups variation is 21.322, with an F-statistic of 6.694 and a p-value of 0.001. This confirms significant differences in motivational factors among 5- and 5-star deluxe hotels.

These findings establish the statistical significance of the selected drivers of turnover. Further analysis using a neural network model will prioritize these factors and enable a comparative quantitative evaluation across hotel categories.

Neural Network Analysis of Drivers of Employee Turnover

This study examines the underlying causes of employee turnover within 3- and 4-star hotels as well as 5- and 5-star deluxe hotels by leveraging a neural network model for analysis. The dimensions analyzed include employment conditions, career progression, supplementary benefits, supervisory practices, and motivational factors. The neural network approach provides a structured and data-driven method to prioritize and understand the relative importance of these factors, offering deeper insights into the dynamics of employee turnover across different hotel categories.

The above provides a comparative analysis of Employment Conditions in 3- and 4-star hotels versus 5- and 5-star deluxe hotels. In 3- and 4-star hotels, "Fatigue caused by extended work hours" emerged as the primary factor driving employee turnover, with a normalized importance of 100%. Conversely, in 5- and 5-star deluxe hotels, "Lack of adequate tools and resources to perform tasks effectively" was identified as the leading contributor to turnover, also with a normalized importance of 100%. Notably, in 3- and 4-star hotels, all constraints under Employment Conditions displayed a contribution exceeding 95%, while in 5- and 5-star deluxe hotels, only one constraint reached this level of significance. This indicates that 3- and 4-star hotels need to address multiple factors comprehensively, whereas 5- and 5-star deluxe hotels should prioritize resolving issues related to insufficient tools and resources.

Career Progression

Challenges in career advancement for employees in the hotel industry are primarily attributed to limited opportunities for skill development, inadequate recognition of employee contributions, and restricted avenues for growth. These factors collectively hinder career progression and contribute significantly to employee dissatisfaction and turnover.

The study indicates there is higher turnover in the 3-star and 4-star hotels with respect to the score for the statement “Growth prospects in this organization are acceptable” whereas in 5 star and 5star deluxe hotel it is for the statement “Limited avenues for skill enhancement.” with respect to the “Career Progression”. This signifies there are more employees leaving the 3-& 4-star hotels due to less growth which is not up to mark satisfying the employees need. Employees are leaving the organization due to limited access to skill development opportunities, which hinders their career growth in 5- and 5-star deluxe hotels. Comparatively in 3-and 4-star turnover is more than the 5- and 5-star deluxe hotel’s turnover.

Supplementary Benefits

Hotel employees often face challenges related to bonuses, insufficient incentives, and leave policies, all of which fall under the category of Supplementary benefits compares the impact of supplementary benefits on employee retention in “3- and 4-star hotels” and “5- and 5-star deluxe hotels.” In 3- and 4-star hotels, the statement “Performance evaluation practices are adequate” was the most significant factor in reducing turnover, with normalized importance at 100%. In contrast, in 5- and 5-star deluxe hotels, the factor “Travel reimbursement policies meet expectations” contributed the most to retention, also with normalized importance at 100%.

The analysis highlights that in 3- and 4-star hotels, negative factors such as “Insufficient bonuses and rewards” and “Limited employee amenities” contribute more to turnover compared to positive aspects. Conversely, in 5- and 5-star deluxe hotels, positive aspects like adequate travel reimbursement policies play a greater role in retention. The findings suggest that 3- and 4-star hotels should focus on improving “Travel reimbursement policies meet expectations,” “Insufficient bonuses and rewards,” and “Limited employee amenities.” For 5- and 5-star deluxe hotels, the focus should remain on addressing “Insufficient bonuses and rewards” and “Limited employee amenities” to enhance employee satisfaction and reduce turnover.

Supervisory Practices

Supervisory practices significantly influence employee turnover. Key factors include the responsiveness to employee grievances, the effectiveness of supervisory roles, and the presence of communication barriers, all of which contribute to employee dissatisfaction and attrition.

The findings highlight that in 3- and 4-star hotels, the statement “Supervisory responsibilities are carried out effectively” ranked higher, indicating the importance of strong supervisory practices. In contrast, in 5- and 5-star deluxe hotels, “Perceived favoritism in supervision” emerged as a critical factor, suggesting it plays a larger role in employee decisions to leave. Overall, employees in 3- and 4-star hotels appear to experience lower turnover rates due to the greater significance of supervisory-related factors in these establishments. This underscores the need for targeted interventions to reduce favoritism, particularly in 5- and 5-star deluxe hotels.

Motivational factors

The study examines employee turnover by analyzing motivational factors, including limited opportunities for skill enhancement, challenges in retaining employees, and insufficient recognition of employee creativity. These factors collectively influence employee decisions to leave an organization. The key drivers of turnover identified through this analysis are presented below, categorized by specific segments.

It highlights key motivational factors in both categories of hotels. In both categories, “Employee innovation is recognized and valued”. Additionally, "Implementation of retention strategies" is observed to be more actively practiced in 3- and 4-star hotels. Overall, motivational factors appear to have a greater impact in the 3- and 4-star hotel segment than in the 5- and 5-star deluxe segment.

The data highlights distinct drivers of employee turnover between 3- and 4-star hotels and 5- and 5-star deluxe hotels, offering valuable insights into the underlying reasons for attrition in these segments. In 3- and 4-star hotels, employment conditions and supervisory practices emerge as the most significant factors influencing employee turnover, with normalized importance values of 100% and 98.50%, respectively. These findings indicate that employees in this category prioritize fair working conditions and effective management. Additionally, career progression (89%) and supplementary benefits (83.30%) are notable contributors to turnover, suggesting that limited growth opportunities and inadequate benefits also play a crucial role. Motivational factors, while less critical with a normalized importance of 72.30%, still contribute to attrition in these hotels.

In contrast, the primary drivers of turnover in 5- and 5-star deluxe hotels are supervisory practices and motivational factors, with normalized importance values of 100% and 99.20%, respectively. This indicates that issues related to management practices and employee engagement are pivotal in these upscale establishments. Employment conditions (92.90%) and supplementary benefits (90.10%) are also important, though slightly less influential compared to the aforementioned factors. Interestingly, career progression holds a significantly lower importance in this category, with a normalized importance of 36.90%, suggesting that employees in 5- and 5-star deluxe hotels perceive sufficient opportunities for growth and advancement.

These findings reveal that while supervisory practices are universally critical across both segments, the nature of employee concerns varies. In 3- and 4-star hotels, turnover is largely driven by structural issues such as working conditions, career growth, and benefits. In 5- and 5-star deluxe hotels, intrinsic factors like motivation and leadership quality play a more prominent role. This distinction underscores the importance of tailoring retention strategies to address the unique challenges and expectations of employees in each segment.

Findings

The effect of inadequate resources on turnover in 5- and 5-star deluxe hotels and employee fatigue brought on by extended work hours in 3- and 4-star hotels are in line with earlier studies conducted in the hospitality industry. For example, (Sridhar et al., 2018) emphasised that physical stressors are a major contributor to employee attrition and that appropriate HR policies are necessary to address high turnover rates. Similar to this, insufficient resources have been found to be a contributing cause to employee turnover in 3- and 4-star hotels, although a major problem in 5- and 5-star deluxe hotels has been the lack of trust between staff and their supervisors. These findings are consistent with (Gupta, 2019), who highlights that organisational trust and resource availability are significant elements that can contribute to high turnover in the hotel industry.

The study's focus on career development is consistent with past research that suggests mentorship programs can lower employee turnover by subtly meeting their desires for career advancement. According to (Pittman, 2020), matching management styles to employees' career goals can greatly improve retention. When taken as a whole, these results highlight how important professional growth possibilities are for reducing turnover.

The poll revealed that while appraisal practices in 3- and 4-star hotels were ineffectual when it came to fringe benefits, employees in 5- and 5-star deluxe hotels were dissatisfied with their trip allowances. These findings support those of (Belushi & Khan, 2017), who argue that financial rewards are important employee motivators and that effective incentive systems are crucial. Additionally, their study highlights the need of providing welfare benefits above and beyond monetary remuneration in order to improve employee wellbeing and morale.

Turnover was also significantly influenced by supervisory characteristics; bias in supervision was noted in 5- and 5-star deluxe hotels, whereas ineffective supervision was more common in 3- and 4-star hotels. Additionally, workers in hotels with three and four stars said they had a poor relationship with management. These results are consistent with those who investigated the connection between organisational culture and employee turnover and came to the conclusion that good management and supervisory techniques are critical to keeping workers in the hospitality sector.

Suggestions

Recent academic research underscores the importance of strategic human resource (HR) interventions in enhancing employee retention within the hospitality industry. Implementing robust talent management practices is crucial for retaining skilled employees. This involves identifying, developing, and retaining talent through effective recruitment, training, and career development programs. Such practices not only enhance employee satisfaction but also contribute to organizational success.

Fostering a positive work environment through employee engagement strategies is vital. This includes recognizing employee contributions, providing growth opportunities, and ensuring alignment between individual and organizational goals. Engaged employees are more likely to remain with the organization, reducing turnover rates.

Providing clear pathways for career progression and professional development helps in retaining top talent. Mentorship programs and regular performance appraisals can address employees' career growth needs, thereby reducing turnover intentions.

Offering competitive salaries and comprehensive benefits packages, including fringe benefits and travel allowances, is crucial. Financial rewards and welfare amenities play a significant role in motivating employees and enhancing retention.

Training supervisors to adopt fair and unbiased management practices can significantly impact employee retention. Effective supervision contributes to a positive organizational culture, which is instrumental in retaining employees.

By integrating these strategic HR interventions, organizations in the hospitality industry can address the multifaceted challenges of employee turnover, leading to improved retention rates and overall organizational performance.

Conclusion

This study highlights key factors influencing employee turnover in Hyderabad's hospitality sector, specifically in 3- & 4-star and 5- & 5-star deluxe hotels. The findings reveal significant differences in turnover drivers across these segments, such as physical stress and ineffective supervision in 3- & 4-star hotels and resource inadequacies and lack of trust in 5- & 5-star deluxe hotels. These insights underscore the need for tailored HR strategies that address segment-specific challenges.

Practical recommendations for hotel managers include improving work-life balance initiatives, implementing effective appraisal and incentive policies, and fostering a supportive supervisory culture. Investing in career development programs and employee engagement strategies can also mitigate turnover and enhance retention. Addressing these factors is critical for sustaining workforce stability and ensuring consistent service quality in the hospitality industry.

While the study provides valuable insights, it is not without limitations. The use of convenience sampling and a focus on Hyderabad hotels limit the generalizability of findings. Future research could explore turnover factors across different geographic locations and hotel categories, as well as assess the effectiveness of specific interventions such as mentorship programs or technological integration in HR practices. Additionally, longitudinal studies could offer a deeper understanding of how employee attitudes and organizational dynamics evolve over time.

By addressing these areas, future studies can build on the findings of this research, providing broader perspectives and actionable strategies to improve employee retention in the hospitality industry.

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Received: 21-Nov-2024, Manuscript No. AMSJ-24-15470; Editor assigned: 22-Nov-2024, PreQC No. AMSJ-24-15470(PQ); Reviewed: 20-Dec-2024, QC No. AMSJ-24-15470; Revised: 26-Dec-2024, Manuscript No. AMSJ-24-15470(R); Published: 23-Jan-2025