Process mining enables organizations to gain datadriven insights into their business processes. An increasing number of organizations are either launching process mining initiatives or expanding the current scope and areas of application. Despite the growing adoption of process mining among practitioners, several challenges remain, such as the lack of clear value propositions. While recent studies have examined factors influencing value identification, little attention has been given to what affects individual business users in generating insights from process mining. As value creation is driven by the business user of process mining output, it is essential that they are able to make sense and interpret its outputs into actionable insights. In this paper, we present the results of an interview study with process mining business users. Based on the interview data, we derive the factors that influence the use of process mining outputs in order to obtain insights. We report on the factors grouped into three main categories: (1) process mining knowledge, (2) tooling and visualization, and (3) business and domain knowledge. We then discuss practical implications of these factors for practitioners, highlighting both PM output design-related considerations and contextual factors, such as user training and clearly defined analysis goals, that influence how process mining outputs are interpreted and used.

Process performance analysis based on event logs is a core task of process mining. Typical tools enrich a directly-follows graph with statistics on waiting times between activities. Such projection may reveal process issues that manifest as a high average waiting time between activities. However, the purely control-flow-oriented view disregards the influence of actor behavior on process performance and may lead to a distorted analysis. Typically, projected measures aggregate the waiting time it takes for disparate types of actor behavior to a single measure: a direct continuation of the work by the same actor, a continuation of the work by the same actor after being interrupted by another case, or a handover to another actor. For a handover, the receiving actor may decide to prioritize activities in other cases before starting the work. Hence, two similar waiting time measures may imply very different dynamics of the actors’ behavior. The paper contributes a method to systematically decompose the regular control-flow performance measure into more fine-grained performance measures based on such behavioral mechanisms of actors. We leverage event knowledge graphs as a joint representation of actor and control flow perspectives to derive features for the behavioral mechanisms and systematically analyze them. The evaluation of the features on a loan application process shows that they provide clearly interpretable performance insight compared to the potentially misleading average waiting times.