Purpose The purpose of this paper is to provide a return on investment (ROI) based review of human resources (HR) analytics. The objectives of this paper are twofold: first, to offer an integrative analysis of the literature on the topic of HR analytics in order to provide scholars and practitioners a comprehensive yet practical ROI-based view on the topic; second, to provide practical implementation tools in order to assist decision makers concerning questions of whether and in which format to implement HR analytics by highlighting specific directions as to where the expected ROI may be found. Design/methodology/approach This paper is a review paper in which a four-step review and analysis methodology is implemented. Findings Study results indicate that empirical and conceptual studies in HR analytics generate higher ROI compared to technical- and case-based studies. Additionally, study results indicate that workforce planning and recruitment and selection are two HR tasks, which yield the highest ROI. Practical implications The results of this study provide practical information for HR professionals aiming to adopt HR analytics. The ROI-based approach to HR analytics presented in this study provides a robust tool to compare and contrast different dilemma and associated value that can be derived from conducting the various types of HR analytics projects. Originality/value A framework is presented that aggregates the findings and clarifies how various HR analytics tools influence ROI and how these relationships can be explained.

In this paper, we propose a comprehensive analytics framework that can serve as a decision support tool for HR recruiters in real-world settings in order to improve hiring and placement decisions. The proposed framework follows two main phases: a local prediction scheme for recruitments' success at the level of a single job placement, and a mathematical model that provides a global recruitment optimization scheme for the organization, taking into account multilevel considerations. In the first phase, a key property of the proposed prediction approach is the interpretability of the machine learning (ML) model, which in this case is obtained by applying the Variable-Order Bayesian Network (VOBN) model to the recruitment data. Specifically, we used a uniquely large dataset that contains recruitment records of hundreds of thousands of employees over a decade and represents a wide range of heterogeneous populations. Our analysis shows that the VOBN model can provide both high accuracy and interpretability insights to HR professionals. Moreover, we show that using the interpretable VOBN can lead to unexpected and sometimes counter-intuitive insights that might otherwise be overlooked by recruiters who rely on conventional methods.We demonstrate that it is feasible to predict the successful placement of a candidate in a specific position at a pre-hire stage and utilize predictions to devise a global optimization model. Our results show that in comparison to actual recruitment decisions, the devised framework is capable of providing a balanced recruitment plan while improving both diversity and recruitment success rates, despite the inherent trade-off between the two.

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How does smartphone use behavior affect quality of life factors? The following work suggests new insights into smartphone use behavior, mainly regarding two contradicting smartphone modes of use that affect quality of life in opposite ways. The Aware smartphone mode of use reflects an active lifestyle, while the Unaware mode of use reflects the use of the smartphone in conjunction with other activities. Using data from 215 individuals who reported their quality of life and smartphone use habits, we show that high levels of smartphone use in the Unaware mode of use have a significant negative effect on the quality of life. However, the results show a mild positive effect when the individual uses the smartphone in an aware mode of use. We identify three latent factors within the quality-of-life construct and measure the effect of the different smartphone modes of use on these quality-of-life factors. We find that (i) The functioning latent factor, which is an individual’s ability to function well in his or her daily life, is not affected by smartphone use behavior. In contrast, (ii) the competence latent factor, which is a lack of negative emotions or pain, and (iii) the positive feelings latent factor both show a clear effect with the smartphone Unaware mode of use. This implies that the unaware use of smartphones, which is its use in conjunction with other activities or late at night, can be related to lower levels of quality of life. Since smartphones currently serve as an interface between the self and the cyber space, as well as an interface between the self and other individuals online, these results need to be considered for social wellbeing in relation to digital human behavior, smartphone addiction and a healthy mode of use.

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