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, (i) to quantify the extent to which the JQI variables explain the work engagement scores, and (ii) to determine which JQI variables most afect 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 fndings include (i) that the work engagement index is mainly infuenced 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 efcient human resources strategies that improve both individual wellbeing and company performance in the accounting sector

A socially responsible consumption index based on non-linear dimensionality reduction and global sensitivity analysis

Higher-order factor analysis is a statistical method that consists of repeating steps of factor analysis. Studies of this type allow researchers and practitioners to visualize the hierarchical structure of the concept being studied. Unfortunately, the Socially Responsi- ble Consumer (SRC) research community still remains unable to construct a second-order SRC index. Most researchers argue that the statistical requirements for the construction of the second-order index are not met. They typically try to construct the second-order index by applying linear factor analysis techniques. It is worth mentioning that this is a widespread practice in the social sciences. In this manuscript, we aim to show how bet- ter indices can be created by applying non-linear dimensionality reduction techniques. Speci cally, we have modi ed the Unsupervised Extreme Learning Machine (UELM) method to promote orthogonality in the basis function space. These methods are able to model interactions among the input variables, but unfortunately, they are usually consid- ered black boxes. To overcome this limitation, we propose the use of Global Sensitivity Analysis (GSA) techniques, which are able to estimate the importance of each variable by itself and in conjunction with the others. To test the methodology, we have used a sample of 703 Spanish consumers and a multidimensional SRC metric that considers both social and environmental issues. As expected, the non-linear techniques tend to enhance the results provided by the linear techniques