More Transparency and Better Control? Investigating User Sensemaking of Data-Driven Change Management Technologies

Facing pressure to change, organizations are increasingly adopting data-driven change management technologies (DDCMTs), digital systems that collect and analyze change-relevant data to inform and support change management. Due to their novelty and equivocality, users’ understanding and use of DDCMTs hinge on how they make sense of these technologies, a process that has received little empirical attention. Therefore, we present an interdisciplinary multiple-case study of three DDCMTs. Our analysis shows that users’ understandings of DDCMTs center on transparency and control but vary considerably. Further, we illustrate how, beyond their potential benefits, the digital characteristics of DDCMTs—editability, autonomy, and interactivity—generate equivocality that allows for multiple and divergent understandings. Thus, we advance research on digitally supported change management by explaining how users develop multifaceted and ambivalent understandings of DDCMTs, providing an important step toward understanding their use. We also offer practical guidance for the development, implementation, and effective use of DDCMTs.