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- W2611090952 abstract "This paper provides empirical evidence that machine learning algorithms can successfully replace humans in some core processes that fall within HR tasks at business schools, and probably also in other types of organizations. Narxoz University in Almaty, Kazakhstan, has implemented the KPI system, with strategic goals cascaded to organizational units and then to individual faculty and staff members. Wage increase and promotion decisions are linked to a semi-annual faculty and staff performance review, which is in turn based on the KPI assessment, quality of teaching, research output and additional achievements. We used data analyzed by Narxoz’s HR Committee and decisions taken by the Committee’s to estimate logit and CART models that recommend wage raise decisions. We showed that these simple machine learning models could replicate HR Committee decisions with good accuracy. Moreover, we also showed that faculty members selected for wage raise by machine learning algorithms achieve better results than faculty promoted by the HR Committee. This paper also presents the benefits of using data science techniques in HR processes at business schools. Firstly, this technique leads to discovery of the actual decision processes that have taken place. Secondly, it allows to verify whether decisions are taken in line with internal procedures, or regardless of such procedures, and allows to verify their time consistency. Thirdly, such analysis provides good grounds for a transparent discussion about improvement of HR processes. Finally, modeling of HR decisions allows to create evidence-based policies and helps forging a learning organization culture. It is especially important in universities and business schools, given that human capital is of such strategic importance. We conclude that analyzing HR processes using data science tools should become part of the best practices, while also being reflected in the business schools accreditation standards." @default.
- W2611090952 created "2017-05-12" @default.
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- W2611090952 date "2017-01-01" @default.
- W2611090952 modified "2023-10-17" @default.
- W2611090952 title "Can Computers Run HR Function at Business Schools?" @default.
- W2611090952 doi "https://doi.org/10.2139/ssrn.2960572" @default.
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