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.

在本文中，提出了一种非线性的多维（基于机器学习的）会计索引，该索引将工作参与度得分（根据会计的看法）与七个工作质量指数（JQI）（由Eurofound提出）相关联。该研究的目标有两个方面，即（i）量化JQI变量解释工作参与度分数的程度，以及（ii）确定哪些JQI变量对工作参与度分数的影响最大。表现最佳的回归模型取得了具有竞争力的均方根百分比，突出表明所选变量主要决定了工作投入值。其他重要发现包括：（i）工作投入指数主要受社会环境指数影响；（ii）技能，自由裁量力和前景指数对促进会计师的工作投入也至关重要。人力资源从业人员可以使用所采用的工具来提出有效的人力资源战略，以改善会计部门的个人福祉和公司绩效。