Background: This study aims to provide researchers and practitioners with a more elaborate instrument to measure turnover intentions based on the planned behaviour theory model. The questionnaire assesses 5 distinct aspects of turnover intentions (i.e., subjective social status, organisational culture, personal orientation, expectations, and career growth).

Methods: In this cross-sectional study (comprise of 2 studies in one) a wave survey design was applied to a large diversity of workers drawn from the staff of universities, banks, hospitals, factories, and telecommunication companies. Exploratory factor analysis (EFA) was applied the identify the sub-dimensions and Cronbach's alpha to assess the reliability of the first study. In the second study, for the Confirmatory factor analysis to establishing structural model of the dimensions.

Results: We demonstrate the reliability, factor structure, and validity evidence based on internal structure and relationship with other variables of the new measure among two samples (N = 622; N = 433). Twenty-five items with 5 factors were extracted to represent a broader perspective of turnover intention scale.

Conclusions: In total, the study indicates that the assessment can be used to reliably assess several major indicators of turnover intentions. Therefore, improved employees' evaluations and reduced loss of valuable staff as a result of avoidable measures in considering the interests of workers.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10496226PMC
http://dx.doi.org/10.1186/s40359-023-01303-2DOI Listing

Publication Analysis

Top Keywords

turnover intentions
16
factor analysis
8
study
5
expanded-multidimensional turnover
4
intentions
4
intentions scale
4
scale development
4
development validation
4
validation background
4
background study
4

Similar Publications

Aim: To explore the influence of emotional intelligence and organisational commitment (OC) on clinical nurses' turnover intention (TI) and to provide intervention strategies to reduce the turnover rate of nursing staff and maintain the stability of the nursing team.

Design: A cross-sectional descriptive study was conducted with nurses (n = 452) in a tertiary hospital in Kaifeng City, Henan Province, China.

Methods: The project was conducted in July 2023.

View Article and Find Full Text PDF

Background: Physician well-being and workforce retention within the healthcare system is of critical importance. Understanding physicians' intent to leave the organization will inform efforts on optimizing the physician workforce. In this study, we examine the association of burnout and specific drivers of burnout on turnover intentions.

View Article and Find Full Text PDF

Background: Critical care nurses are vulnerable to depression, which not only lead to poor well-being and increased turnover intention, but also affect their working performances and organizational productivity as well. Work related factors are important drivers of depressive symptoms. However, the non-liner and multi-directional relationships between job demands-resources and depressive symptoms in critical care nurses has not been adequately analyzed.

View Article and Find Full Text PDF

Background: Globally, low back pain (LBP) is responsible for disability among 60.1 million people. Health workers face a higher likelihood of being exposed to LBP compared to employees in the construction and manufacturing sectors.

View Article and Find Full Text PDF

Aim: The objectives of this study were to determine the prevalence of burnout risk and intention-to-leave among intensive care unit (ICU) nurses and analyse the association of these with workload and work environment.

Design: A cross-sectional survey of nurses working in ICUs was conducted in France between 15 January 2024 and 15 April 2024 alongside a longitudinal assessment of workload during the same period.

Methods: ICU nurse workload was assessed using the Nursing Activities Score (NAS).

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!