Proceedings of the 5th International Conference on Research in Human Resource Management
Year: 2024
DOI:
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Mapping The Future: Predictive Insights into Employee Turnover and Satisfaction Dynamics
Irina Safiulina, Hajer Rabii
ABSTRACT:
Consulting firms often experience unique advantages and disadvantages related to employee turnover. On one hand, a dynamic workforce can bring fresh perspectives and innovative ideas, fostering a culture of agility and adaptability. On the other hand, high turnover rates can disrupt project continuity and strain client relationships, ultimately impacting overall performance. This study utilizes data sets, gathered through comprehensive surveys conducted in 2021 and 2024. These surveys consist of 120 unique questions covering various areas, including Autonomy & Empowerment, Career Progression, Collaboration, Communication, Company Leadership & Trust, Engagement & Loyalty, Company Values, Pay & Benefits, Quality of Service, Work Environment, Recognition, Resources, Strategy & Strategy Alignment, Supportive Management, Sustainability, Diversity & Inclusion, and Training & Development. The primary objective is to develop a machine learning model using 2021 data to predict employee turnover in 2024 and calculate the probability of turnover. The contrasting contexts of 2021, a year marked by the COVID-19 pandemic, and 2024, with its different challenges, offer a unique perspective on shifts in employee satisfaction. In addition to overall satisfaction, this research aims to identify specific reasons for employee departure that go beyond compensation. By exploring these satisfaction trends, we seek to provide actionable insights for improving employee retention strategies.
keywords: continuous improvement; employee retention; machine learning; predictive modeling; work environment