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

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Abstract

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.

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Notes

  1. PwC. Our 2019 Audit Quality Report. 2019. Available at: https://www.pwc.com/us/en/services/audit-assurance/assets/pwc-2019-audit-quality-report.pdf.

  2. KPMG. Our Investment in audit quality. 2015. Available online at: https://assets.kpmg/content/dam/kpmg/pdf/2016/04/our-investment-in-audit-quality.pdf.

  3. The second-order decomposition also includes the interactions by pairs of the input variables.

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Acknowledgements

The research work of F.F.N. is funded by the Spanish Ministry of Science under Project ENE2017-88889-C2-1-R.

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Authors and Affiliations

  1. Financial Economy and Accounting Department, Universidad Loyola Andalucía, Córdoba, Spain

    Jose Joaquin del Pozo-Antúnez & Horacio Molina-Sánchez

  2. Business Department, Universidad Loyola Andalucía, Córdoba, Spain

    Antonio Ariza-Montes

  3. Quantitative Methods Department, Universidad Loyola Andalucía, Córdoba, Spain

    Francisco Fernández-Navarro

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  1. Jose Joaquin del Pozo-Antúnez
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  2. Horacio Molina-Sánchez
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  4. Francisco Fernández-Navarro
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Correspondence to Francisco Fernández-Navarro.

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del Pozo-Antúnez, J.J., Molina-Sánchez, H., Ariza-Montes, A. et al. Promoting work Engagement in the Accounting Profession: a Machine Learning Approach. Soc Indic Res 157, 653–670 (2021). https://doi.org/10.1007/s11205-021-02665-z

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