Promoting work Engagement in the Accounting Profession: a Machine Learning Approach

In this paper, a non-linear multi-dimensional (machine learning-based) index for accountants that relates work engagement scores (according to accountants’ perceptions) with the seven Job Quality Indices (JQI) (proposed by Eurofound) has been proposed. The goal of the research is two-fold, namely, (...

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Bibliographic Details
Published in:Social indicators research 2021-09, Vol.157 (2), p.653-670
Main Authors: del Pozo-Antúnez, Jose Joaquin, Molina-Sánchez, Horacio, Ariza-Montes, Antonio, Fernández-Navarro, Francisco
Format: Article
Language:English
Subjects:
Accountants
Artificial Intelligence
Cognitive style
Human Geography
Human resources
Indexes
Machine learning
Microeconomics
Original Research
Prospects
Public Health
Quality of Life Research
Social environment
Social Sciences
Sociology
Variables
Well being
Work
Work values
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Summary:In this paper, a non-linear multi-dimensional (machine learning-based) index for accountants that relates work engagement scores (according to accountants’ perceptions) with the seven Job Quality Indices (JQI) (proposed by Eurofound) has been proposed. The goal of the research is two-fold, namely, (i) to quantify the extent to which the JQI variables explain the work engagement scores, and (ii) to determine which JQI variables most affect the work engagement scores. The best performing regression model achieved a competitive root mean square percentage, highlighting that the selected variables primarily determine the work engagement values. Other important findings include (i) that the work engagement index is mainly influenced by the social environment index and (ii) that the skills and discretion and prospects indices are also crucial in the promotion of the work engagement of accountants. The instrument implemented could be employed by human resources practitioners to propose efficient human resources strategies that improve both individual well-being and company performance in the accounting sector.
ISSN:0303-8300
1573-0921
DOI:10.1007/s11205-021-02665-z