Data-driven Automated Decision-Making in Assessing Employee Performance and Productivity: Designing and Implementing Workforce Metrics and Analytics
Data-driven Automated Decision-Making in Assessing Employee Performance and Productivity: Designing and Implementing Workforce Metrics and Analytics

Summary/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.

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