Enhancing Job Satisfaction and Retention in IT Companies Through HR Analytics-Driven Employee Engagement
Employee retention and satisfaction are critical to organizational success in the highly competitive world of IT (Information Technology) enterprises. Job satisfaction, loyalty, and turnover rates may all be considerably increased by using Human Resource (HR) analytics to promote employee engagement. This research looks at employee engagement, job satisfaction, as well as retention intentions in the IT industry using a quantitative survey approach supported by descriptive and inferential statistics. Data was gathered from 500 workers in a variety of jobs inside IT companies using a stratified random sample technique, offering a thorough summary of the major variables affecting employee happiness and engagement. Descriptive statistics were used to show trends in employee satisfaction, while inferential statistics were employed to examine the relationships between work-life balance, career development opportunities, leadership quality, as well as employee retention. The value of HR analytics in enhancing retention is also examined in the research, with conflicting views found. According to the survey’s results, employee satisfaction fluctuates, with $\mathbf{2 0 \%}$ to $\mathbf{3 0 \%}$ of workers reporting high levels of satisfaction in areas like work-life balance and career advancement and a significant portion staying unhappy or indifferent. Additionally, $\mathbf{3 0 . 0 0 \%}$ of respondents agree that HR analytics significantly affects retention, although many workers think its full potential has not yet been reached. Future studies might examine how to further improve employee engagement and retention tactics in IT organizations by integrating machine learning algorithms with sophisticated HR analytics technologies.
Authors
- Snigdha Dwivedi
- Animesh Singh (ORCID: https://orcid.org/0000-0002-5287-7926)
Institutions
- Manav Rachna International Institute of Research and Studies (IN)
Publication Details
- Published
- 2024-11-28
- DOI
- https://doi.org/10.1109/icaiccit64383.2024.10912134
- Primary Topic
- AI and HR Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00