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dc.contributor.authorBen Yahia, Nesrine
dc.contributor.authorHlel, Jihen
dc.contributor.authorColomo-Palacios, Ricardo
dc.identifier.citationIEEE Access. 2021, 9, 60447-60458.en_US
dc.description.abstractIn the era of data science and big data analytics, people analytics help organizations and their human resources (HR) managers to reduce attrition by changing the way of attracting and retaining talent. In this context, employee attrition presents a critical problem and a big risk for organizations as it affects not only their productivity but also their planning continuity. In this context, the salient contributions of this research are as follows. Firstly, we propose a people analytics approach to predict employee attrition that shifts from a big data to a deep data context by focusing on data quality instead of its quantity. In fact, this deep data-driven approach is based on a mixed method to construct a relevant employee attrition model in order to identify key employee features influencing his/her attrition. In this method, we started thinking ‘big’ by collecting most of the common features from the literature (an exploratory research) then we tried thinking ‘deep’ by filtering and selecting the most important features using survey and feature selection algorithms (a quantitative method). Secondly, this attrition prediction approach is based on machine, deep and ensemble learning models and is experimented on a large-sized and a medium-sized simulated human resources datasets and then a real small-sized dataset from a total of 450 responses. Our approach achieves higher accuracy (0.96, 0.98 and 0.99 respectively) for the three datasets when compared previous solutions. Finally, while rewards and payments are generally considered as the most important keys to retention, our findings indicate that ‘business travel’, which is less common in the literature, is the leading motivator for employees and must be considered within HR policies to retention.en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.subjectdeep people analyticsen_US
dc.subjectemployee attritionen_US
dc.subjectpolicies recommendationen_US
dc.titleFrom Big Data to Deep Data to support people analytics for employee attrition predictionen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.subject.nsiVDP::Teknologi: 500en_US
dc.source.journalIEEE Accessen_US

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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal