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ABSTRACT. Employing recent research results covering big data-driven algorithmic decision-making in selecting and managing employees, and building our argument by drawing on data collected from Bright & Company, Corporate Research Forum, Deloitte, Management Events, McKinsey, and Top Employers Institute, we performed analyses and made estimates regarding data-driven approaches in organizations. Structural equation modeling was used to analyze the collected data.
JEL codes: E24; J21; J54; J64

Keywords: predictive analytics; workforce metrics; organizational human capital

How to cite: Meyers, Thomas D., Ladislav Vagner, Katarina Janoskova, Iulia Grecu, and Gheorghe Grecu (2019). “Big Data-driven Algorithmic Decision-Making in Selecting and Managing Employees: Advanced Predictive Analytics, Workforce Metrics, and Digital Innovations for Enhancing Organizational Human Capital,” Psychosociological Issues in Human Resource Management 7(2): 49–54. doi:10.22381/PIHRM7220198

Received 8 July 2019 • Received in revised form 13 September 2019
Accepted 15 September 2019 • Available online 11 October 2019

Thomas D. Meyers
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The Artificially Intelligent Algorithmic Systems
Research Unit at AAER, Frankfurt, Germany
Ladislav Vagner
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Faculty of Operation and Economics
of Transport and Communications,
Department of Economics,
University of Zilina, Zilina, Slovak Republic
Katarina Janoskova
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Department of Economics,
Faculty of Operation and Economics
of Transport and Communications,
University of Zilina, Zilina, Slovak Republic
Iulia Grecu
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Spiru Haret University, Constanța, Romania
Gheorghe Grecu
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Spiru Haret University, Constanța, Romania

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