chunk1

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
This email address is being protected from spambots. You need JavaScript enabled to view it.
The Cognitive Artificial Intelligence Systems Laboratory
at CLI, Cambridge, England

Home | About Us | Events | Our Team | Contributors | Peer Reviewers | Editing Services | Books | Contact | Online Access

© 2009 Addleton Academic Publishers. All Rights Reserved.

 
Joomla templates by Joomlashine