Three essays on people analytics using big data and artificial intelligence

Abstract

Interest in people analytics has been growing as people data sources expand, people data values increase, and more HR tech vendors offer analytics and reporting. This thesis aims to provide theoretical contributions and actionable implications by applying people analytics using big data and artificial intelligence to peer assessment, developmental feedback, and AI recruiting process. To do this, the first study examines how direct and transparent communications via the reply and the use of carbon copy (cc) measured by email big data analysis influences the perceptions in the context of peer assessment, respectively. The second study examines how specificity and quality of developmental feedback, measured by a range of state-of-the-art machine learning techniques, influence intended effort. The third study examines the difference in the predictive ability of two likeability scores on job interview performance: automated video interview likeability assessments developed based on labeled data by HR professionals and the current employees’ video interview likeability assessments based on asynchronous video interviews.