PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
June 6, 2024Prabandhan Indian Journal of Management5 citations

Unraveling Job Embeddedness in the Indian Hotel Sector : Investigating Turnover and Retention

View Full Paper
ADAnjali DimriPKPankaj KumarVJVijay Kumar Jain

Key Points

Key points are not available for this paper at this time.

Abstract

Purpose : The aim of conducting this study was to uncover the driving forces that encourage employees to remain in their current positions within the hotel industry. Given the industry's notably high turnover rate, the study sought to identify the elements contributing to employee retention, with a focus on the significance of job embeddedness and its components: fit, link, and sacrifice. Design/Methodology/Approach : Interviews were conducted face-to-face and in semi-structured formats with 36 ( N = 36) respondents employed at luxury hotels in the Uttarakhand region, employing purposive sampling techniques. N-Vivo software was used to evaluate the results once content analysis was selected as the methodology. Findings : Employee retention is impacted by job embeddedness in the community and company, according to the studies. The effects of each job embeddedness variable on employee retention were also examined in this study. Practical Implications : A key component of strong job retention is high job embeddedness. This strategy offered doable tactics for improving employee retention and claimed to reduce the turnover rates in this high-turnover industry. Originality/Value : The fit, link, and sacrifice dimensions of job embeddedness were examined in this initial study to determine staff retention in the high-turnover hotel business. This study's novel approach to job embeddedness provided the foundation for HRM research and practice.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Dimri et al. (2024) studied this question.

synapsesocial.com/papers/68e65d1eb6db6435875ebaf7https://doi.org/10.17010/pijom/2024/v17i6/173559
Ask AI
Helpful
Bookmark
Share
View Full Paper