Manuscript Title:

ENHANCING EMPLOYEE ENGAGEMENT: LEVERAGING AI - SENTIMENTAL ANALYSIS AND INSIGHTS

Author:

ARTHI MEENA, Dr. MRUTHYUNJAYA SHARMA

DOI Number:

DOI:10.5281/zenodo.13833195

Published : 2024-09-23

About the author(s)

1. ARTHI MEENA - Assistant Professor, Cambridge Institute of Technology.
2. Dr. MRUTHYUNJAYA SHARMA - Professor and Head of Department, RNS Institute of Technology, Bengaluru.

Full Text : PDF

Abstract

In today’s rapidly evolving hybrid workplace, employee engagement has emerged as a critical factor for organizational success. Engaged employees are more productive, go the extra mile of work, and get infused into their task accomplishments. Not Every employee remains consistently engaged and the Organization cannot constantly monitor each individual to assess their level of engagement in hybrid work mode. It is a quite challenging task. According to Gallup 2022, only 15 % of employees are engaged at work and the remaining are not engaged and disengaged. The gap is large when it comes to engaging employees in hybrid work mode. To comprehend this, Organizations are employing AI technology which has proven beneficial in gaining deeper insights into their employees, thereby enhancing their understanding of their workforce. The main aim of the paper is to understand how AI technology sentimental analysis advocates employee engagement and track employees towards better performance, productivity, and retention. The second aim is to comprehend the role of AI–sentimental analysis in engagements. A qualitative approach using data from linkden, feedback survey and deep interview method of 30 IT working employees. Primary data is collected with the help of a structured questionnaire and reports are recorded and transformed into sentimental labels. The study showed that employers could easily identify employee opinions; and perception feelings towards the job and proactively take action towards engaging employees by providing feedback and skill development programs.


Keywords

Employee Engagement, Artificial Intelligence, Sentimental analysis, Hybrid work mode and Retention.