ABSTRACT. This article presents an empirical study carried out to evaluate and analyze artificial intelligence-supported workplace decisions. Building our argument by drawing on data collected from Bright & Company, Corporate Research Forum, Deloitte, IBM Institute for Business Value, Management Events, McKinsey, and Top Employers Institute, we performed analyses and made estimates regarding to what extent organizations have been able to use human resource analytics to successfully predict business outcomes and take action to drive different outcomes (%). Data gathered from 4,700 respondents are tested against the research model by using structural equation modeling.
JEL codes: E24; J21; J54; J64

Keywords: big data algorithmic analytics; sensory and tracking technologies

How to cite: Nica, Elvira, Renata Miklencicova, and Eva Kicova (2019). “Artificial Intelligence-supported Workplace Decisions: Big Data Algorithmic Analytics, Sensory and Tracking Technologies, and Metabolism Monitors,” Psychosociological Issues in Human Resource Management 7(2): 31–36. doi:10.22381/PIHRM7220195

Received 7 July 2019 • Received in revised form 15 September 2019
Accepted 20 September 2019 • Available online 11 October 2019