Design and Theoretical Specification of an AI-Enabled Framework for Burnout and Turnover-Intention Risk Assessment in Technology Firms

Abstract

This design-stage study aims to create and theoretically specify an artificial intelligence (AI)-enabled conceptual framework for assessing burnout risk and turnover intention in technology companies. Previous studies have examined burnout or employee turnover using organisational, psychosocial, behavioural, and AI-based approaches separately; however, limited research has integrated these dimensions into a unified framework. The gap inspires the present study at the design stage, which is a theoretical development of an integrated framework for understanding burnout risk and turnover intention in technology organisations. A priori power analysis was performed, and a sample size of at least 171 participants was determined. The study follows Design Science Research (DSR) methodology for artifact development, supported by bibliometric mapping to identify relevant research trends and theoretical foundations. The results of the bibliometrics helped to select the constructs, integrate them with theories, design questionnaires, and identify gaps in studies. The proposal is based on the models of Bianchi, Maslach and Menezes, consisting of 35 observed items from 5 constructs, which is equal to 28 proposed facets. Its main contribution is the development of a theoretically grounded framework that supports proactive approaches for managing burnout risk and employee turnover intention in technology organisations. The proposed framework may support proactive HR decision-making, while its organisational effectiveness and economic impact, including potential reductions in turnover-related costs, require further evaluation.