Innovation Management Journal

Innovation Management Journal

Developing a Framework in Human Resource Management for Predicting Knowledge Workers’ Turnover Intention in Innovative Organizations: A Meta-Synthesis Approach

Authors
Seyed Asgher Jafari 1
Afshin Alipour 2
Ali Naeimi Khondabi 3
1 International Relations and Law Department, Faculty of Soft Technology and Management, Malek Ashtar University of Technology, Tehran, Iran
2 Assistant Professor, Department of Management, Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran
3 M.A. Student in Business Administration, Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran
10.22034/imj.2025.245382
Abstract
In innovative organizations, knowledge workers function as strategic assets and primary drivers of knowledge creation, transfer, and application, thereby playing a vital role in sustaining and enhancing competitive advantage. Consequently, their turnover intention can lead to adverse outcomes, including the loss of tacit knowledge, increased recruitment and training costs, and a reduced capacity for organizational innovation. This study aims to develop an innovative human resource management framework for predicting the turnover intention of knowledge workers in innovative organizations. To achieve this purpose, a qualitative meta-synthesis approach was employed to identify the dimensions and components influencing turnover intention. Through a systematic search of reputable scientific databases, 530 studies were initially screened, of which 31 eligible articles were selected and coded. The thematic synthesis of these texts resulted in the identification of 13 overarching dimensions and 36 subcomponents that collectively describe the multidimensional nature of turnover intention among knowledge workers. The credibility of the findings was enhanced using Lincoln and Guba’s four trustworthiness criteria. The resulting framework was subsequently applied in the parent study as the basis for feature engineering and the development of input variables, which were integrated into machine learning algorithms for predicting turnover intention. Accordingly, the findings of this research enrich the theoretical literature while providing a foundation for developing analytical and decision-support systems within data-driven human resource management.
Keywords
Knowledge workers
Turnover intention
Innovative organizations
Meta-synthesis
Machine learning