Predictably Satisfied? Using Data-Driven Technology to Promote Personalized Work Environments

Abstract

This paper explores the potential of increasingly accurate data-driven predictions for designing customized work environments. As an illustrative case study, I analyze unique individual-level panel data from Attuned, a Japanese company offering an AI-based tool to evaluate and enhance employee motivation. Developing a theoretical framework, I analyze how a principal reacts to the signals about an agent's (motivational) type conveyed by data-driven technology. I show that enhanced predictive accuracy and signal precision result in tailored management practices. Yet, this impact positively depends on the frequency of agent types and their similarity to others.

Keywords: Job satisfaction, Motivation, Artificial intelligence, People analytics

JEL Classification: L20, M10, M50, M54, O33