WHEN THE PROFILE BECOMES THE PERSON: A SYSTEMATIC REVIEW AND BIBLIOMETRIC ANALYSIS OF EMPLOYEE DATAFICATION

Abstract

Employee datafication transforms workers’ activities, characteristics and experiences into digital representations that inform organizational decisions. Although these representations can support systematic management, they raise questions about what employee profiles capture, what they omit and how they acquire authority. This study aims to map the thematic structure of employee datafication research and connect its principal debates through the creation, use and governance of employee profiles. A systematic literature review was conducted using Scopus. The final corpus comprised 786 articles, which were analyzed through the bibliometric technique called keyword co-occurrence. The clusters encompass employee analytics and human resource decision-making; algorithmic labour control and employment relations; monitoring and organizational justice; algorithmic work design and employee adaptation; recognition technologies and employee experiences; and employment contexts and working conditions. Considering that, three major themes emerge, referring to the adequacy and fairness of employee representations, the organizational conditions shaping algorithmic control, and employee influence over data collection and decision use. These themes position datafication as a connected problem of representation, work design and governance. Its contribution is an integrative perspective connecting data capture, inference, decision use and contestation, with employee profiles providing a common focus across the mapped research streams. Organizations should assess whether collected indicators meaningfully represent the work being evaluated, preserve relevant contextual information and clearly communicate the purposes of data use. Practical recommendations include accessible procedures for correcting employee records, reviewing consequential decisions and involving employee representatives in monitoring design. Evaluations of analytics systems should also consider fairness, autonomy and working conditions alongside operational benefits.