Analyses of Employee Retention and Best Practices Strategies

Abstract

This research examines the employee lifecycle framework using a mixed-methods approach, utilising a primary dataset comprising 47 distinct sources. This corpus includes employee and HR management interviews, staff surveys, exit interviews, internal documentation, and Key Performance Indicator (KPI) results. Furthermore, the study incorporates a purposefully selected case group of individuals who have experienced burnout, ensuring an ethically grounded perspective on extreme workplace stress.

The collected data was from a mid-sized IT consultancy firm currently facing significant challenges with staff turnover and the associated costs of onboarding. Methodologically, the study employed Computer-Assisted Qualitative Data Analysis Software (CAQDAS) to synthesise the data. By applying Herzberg’s Two-Factor Theory alongside the WorldatWork Total Rewards Model, the analysis, based on the sample size, yielded empirical evidence suggesting that the organisation suffers from a transparency deficit, strained relationships, and tension between growth-based compensation and professional stagnation.

The findings indicate a demonstrable breach of the psychological contract. Through thematic coding aligned with the chosen theoretical frameworks, specific areas of vulnerability were identified, providing targeted insights to reduce turnover rates.

Cross-referencing these results reveals critical gaps necessitating executive intervention. While the prevailing internal trend prioritises expanding fringe benefits, this research suggests that such measures are insufficient to improve retention. Instead, focusing on core elements identified during the qualitative interviews is recommended. This approach offers a more ethically robust framework for employee well-being and establishes a sustainable foundation for the organisation’s future strategic decision-making.

The integration of modern technology is proposed to address these issues more efficiently and promptly. Specifically, implementing Generative AI (GenAI) engines is recommended to enable proactive intervention before systemic issues affect the existing workforce. By utilising these analytical tools to process feedback from surveys and other feedback mechanisms, the organisation can automate the generation of data visualisations and actionable insights. This technological approach allows management to identify emerging trends rapidly, enabling the formulation of precise improvement strategies that foster a more responsive and preventative organisational culture.