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Experimental approaches are gaining in popularity across disciplines, ranging from behavioral sciences to economics. In this chapter, we discuss the advantages and disadvantages of field experiments and review their use by scholars to study routine dynamics. Based on these, we suggest that field experiments hold further promise to study routines given their potential to develop and test theory, while achieving internal and external validity. To further the adoption of field experiments to study routines, we outline a five-step procedure, including research questions and hypotheses, context and research setting, treatment and design, measurement and statistical tests, and managing field experiments. We conclude by discussing potential research questions and contexts suitable for field experiments.

This chapter offers an introduction to Routine Dynamics as a particular approach to studying organizational phenomena. We provide a brief description of the genealogy of research on routines; starting with the work of the management scholar Fredrick Taylor (1911) and the pragmatist philosopher John Dewey (1922) at the beginning of the last century, to the works of the Carnegie School on standard operating procedures around the middle of the last century, to the economics-based Capabilities approach and finally the practice-based approach of Routine Dynamics around the turn of the century. We also discuss the advantages of conceptualizing patterns of action as “routines”, as compared to “practices”, “processes”, “activities” or “institutions”. In particular, we highlight that the concept of routines directs the researcher’s attention to certain specificities of particular action patterns, such as task orientation, sequentiality of actions, recurrence and familiarity as well as attempts at reflexive regulation. We also introduce and explain the key concepts of the Routine Dynamics perspective and how they have developed over time.

In this chapter, we compare Routine Dynamics and Strategy as Practice based on an extended literature review. Routine Dynamics and Strategy as Practice are distinctive communities of thought in organization studies that exhibit a number of striking parallels: both subscribe to the overall “practice turn” in the social sciences, seek to bring the human being back in, and focus predominantly on the level of action involved in organizational routines and strategy, respectively. In our comparison of similarities and differences of Routine Dynamics and Strategy as Practice, we focus on their empirical domains, underlying theoretical perspectives, research frameworks, levels of analysis, and empirical methods employed. Based on that, we discuss what Routine Dynamics can learn from Strategy as Practice and vice versa. We conclude with some general reflections on the future relation between the two research communities and develop an agenda for future research that facilitates cross-fertilizations between the two research communities.

An organisation’s event logs can give great insight into factors that affect the execution of their business processes by comparing different process cohorts. We have recently presented ProcessProfiler3D, a novel tool for such comparisons that supports interactive data exploration, automatic calculation of performance data and visual comparison of multiple cohorts. The approach enables the intuitive discovery of differences and trends in cohort performance. To better support the interpretation of these differences in the context of process execution we now extended the tool with a novel visualisation technique that enables the visualisation of case execution and timing in a way that provides context to such a performance analysis.

This paper presents the integration of a plugin for interactive and incremental business process repair into the Apromore advanced business process analytics platform. Given a model and an event log representing the behavior of a process as input, the plugin describes encountered behavioral differences between them as natural language statements, as well as graphical representations displayed over the model. The graphical representation of the differences provides visual guidance on how to repair the model so that it better reflects the observed behavior. Subsequently, the users have the option to select the discrepancies that need to be resolved and how to resolve them. The users can choose whether to follow the suggestion provided by the tool or to repair the discrepancies differently. The differences are updated with every repair applied over the model, resulting in an iterative procedure where the user has the ultimate control over the extent and the implementation of the changes over the model.

This paper introduces Nirdizati: A web-based application for generating predictions about running cases of a business process. Nirdizati is a configurable full-stack web application that supports users in selecting and tuning prediction methods from a list of implemented algorithms and enables the continuous prediction of various performance indicators at runtime. The tool can be used to predict the outcome, the next events, the remaining time, or the overall workload per day of each case of a process. For example, in a lead-to-order process, Nirdizati can predict which customer leads will convert to purchase orders and when. In a claim handling process, it can predict if a claim decision will be made on time or late. The predictions, as well as real-time summary statistics about the process executions, are presented in a dashboard that offers multiple visualization options. Based on these predictions, process participants can act proactively to resolve or mitigate potential process performance violations. The target audience of this demonstration includes process mining researchers as well as practitioners interested in exploring the potential of predictive process monitoring.