Exploring Drivers of Staff Engagement in Healthcare Organizations Using Tree-Based Machine Learning Algorithms

Staff engagement in the work environment is vital to organizational success. Engaged staff are motivated and indulged in their work as they have a sense of belonging, commitment, and loyalty toward their employer, which eventually leads to better performance and outcomes. While various organizationa...

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Bibliographic Details
Published in:IEEE transactions on engineering management Vol. 70; no. 8; pp. 2988 - 2997
Main Authors: Al-Nammari, Ragheb, Simsekler, Mecit Can Emre, Gabor, Adriana Felicia, Qazi, Abroon
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Boosting
Data analytics
Decision trees
employee engagement
gradient boosting (GB)
Health care
healthcare management
Machine learning
machine learning (ML)
Managers
Medical services
Organizational aspects
organizational culture
Organizations
Prediction algorithms
Prediction models
Predictive models
Radio frequency
random forest (RF)
Resource allocation
Safety
safety culture
staff engagement
Teamwork
Training
Working conditions
ISSN:0018-9391, 1558-0040
Online Access:Get full text
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Summary:Staff engagement in the work environment is vital to organizational success. Engaged staff are motivated and indulged in their work as they have a sense of belonging, commitment, and loyalty toward their employer, which eventually leads to better performance and outcomes. While various organizational factors are related to staff engagement, limited research is available regarding what drives staff engagement and the degree of their importance in healthcare. Leveraging data-driven approaches, in this article, we employ three machine learning algorithms, random forests, gradient boosting, and extra trees, to identify the relative importance of organizational factors affecting staff engagement. We use hospital-level aggregate survey data from hospitals in the U.K. While staff engagement is the outcome variable, the following factors are used as organizational factors in the prediction model and feature importance analysis: equality, diversity, and inclusion, safety culture, health and wellbeing, immediate managers, quality of appraisals, quality of care, bullying and harassment, violence, and team working. All the algorithms provide comparable prediction results with similar feature importance ranking with respect to prediction accuracy. The results suggest that safety culture is the most influential factor related to staff engagement, followed by the team working. Healthcare managers and decision makers can benefit from this data-driven application to make informed decisions in resource allocation and prioritization efforts to improve staff engagement.
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ISSN:0018-9391
1558-0040
DOI:10.1109/TEM.2022.3209879