Fundamental Framework for Task Mining Technology Adoption: Results from a Qualitative Empirical Study

Digitalization influences business processes and their management. Digital technologies such as Process Mining (PM) or Robotic Process Automation (RPA), which are becoming more and more prevalent, offer new possibilities for process analysis, monitoring, and automation. PM can further be helpful in identifying processes that are suitable for RPA. However, purely applying PM at the business data level often does not produce all the details required to assess RPA suitability. These necessary details can be supplemented by Task Mining (TM), a technology that can analyze tasks at a desktop data level, which opens further potential beyond the automation benefits. However, the currently available scientific literature on TM is scarce and empirical qualitative studies or case studies on the adoption of TM technology from a BPM perspective are missing. Therefore, this paper develops a fundamental framework for TM technology adoption based on a qualitative empirical study involving experts via semi-structured interviews, which are analyzed according to the qualitative content analysis based on the methodology of Mayring. The framework relates five potential benefits, two opportunities, four risks, and four basic requirements identified from the qualitative study. It thus provides a new valid starting point for future research efforts and serves as a guideline for managers in practice to evaluate a potential TM deployment in the company.