Lessons Learned from Institutionalization of ML (Machine Learning) Supported HR Services in the Existence of Multiple Institutional Logics

This study explores how an organization has successfully implemented ML-supported HR services to resolve high employee turnover problems in the IT sector. The empirical setting of the research is where contradicting institutional logics exist among technical, HR, and business groups regarding the ML model development and use of the model predictions in HR services. Institutional framework is used to identify the roles of organizational actors and the legitimacy structures in the organizational environments that can shape or constrain the ML led organizational changes. In institutional theories, technology adoption and organizational change are not only constrained by organizational context, but also fostered through organizational actors’ roles and efforts to increase the legitimacy for the change. This research found that when multiple contradicting institutional logics exist, legitimizing the establishment of an enabling environment for multiple logics to reconcile and for the project to move forward is critical. Industry-wide conditions, previous experiences with the pilot ML project, forming a TFT with clearly defined roles and responsibilities, and relevant KPIs are found to legitimize the HR team and the business division to collaborate with the technical personnel to launch ML-supported HR services.