ABSTRACT. Despite the relevance of data-driven automated decision-making in assessing employee performance and productivity, only limited research has been conducted on this topic. Using and replicating data from Bright & Company, Corporate Research Forum, Deloitte, Management Events, McKinsey, and Top Employers Institute, I performed analyses and made estimates regarding current data practices at high-performing organizations (%) and the extent to which workers will be affected by hiring, displacing, contracting and retraining (%). The results of a study based on data collected from 4,300 respondents provide support for my research model. Using the structural equation modeling and employing the probability sampling technique, I gathered and analyzed data through a self-administrated questionnaire.
JEL codes: E24; J21; J54; J64

Keywords: data-driven decision-making; performance; productivity; metrics; analytics

How to cite: Wingard, Devin (2019). “Data-driven Automated Decision-Making in Assessing Employee Performance and Productivity: Designing and Implementing Workforce Metrics and Analytics,” Psychosociological Issues in Human Resource Management 7(2): 13–18. doi:10.22381/PIHRM7220192

Received 15 July 2019 • Received in revised form 22 September 2019
Accepted 26 September 2019 • Available online 11 October 2019

Devin Wingard
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The Cognitive Artificial Intelligence Systems Laboratory
at CLI, Cambridge, England

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