Process Mining as a Strategy of Inquiry: Understanding Design Interventions and the Development of Business Processes

Process (re-)design and improvement are important aspectsof the Business Process Management (BPM) life-cycle. Yet, there is lit-tle empirical evidence on how design interventions materialize in actualprocess execution, leading to repeated failure of such initiatives. In thisdissertation I use the emerging affordances of process mining algorithmsto address this important limitation. In particular, I devise a methodthat combines process mining and grounded theory to study processualphenomena. Consequently, this method is applied to investigate changein business processes. This thesis contributes to the body of knowledgein BPM and bordering disciplines by demonstrating how process min-ing can be used as a method to study processual phenomena. Furtherthis research sheds light on the impact of design interventions on actualprocess execution and vica versa

Will Algorithms Replace Managers? A Systematic Literature Review on Algorithmic Management

Algorithms increasingly take on tasks traditionally performed by humans. They not only serve as co-workers to their human counterparts but increasingly take over management tasks by supervising and coordinating human workers – a phenomenon referred to as Algorithmic Management (AM). There is a growing research interest in this topic, but currently, the field lacks an overview and thorough understanding of what types of managerial work algorithms already perform. We address this with a structured literature review. We find that the automation of management work and workers’ responses to it (so-called algoactivism) have received the most attention. However, the configuration of AM systems has so far received little systematic attention. We further analyze which management functions algorithms perform. We find that while algorithms primarily supervise and investigate workers, coordinating interdependent workers and tasks has not been addressed. We propose several avenues for future research

Towards Routines Mining – Designing and Implementing the Argos Miner, a Design Science Artifact for Studying Routine Dynamics with Process Mining

Digital artifacts increasingly support actors in carrying out organizational routines. These artifacts leave digital trace data, that is, time-stamped data about what actions actors performed. While extant research on routines largely builds on qualitative methods, the increasing ubiquitousness and prevalence of trace data enable novel methodological opportunities. However, several challenges currently hinder the adoption of trace data in empirical research on routines in general and their dynamics in particular. Promising approaches such as process mining are neither designed for nor sensitive to the concept of routines. In this paper, we follow a design science research approach to develop the first iteration of an artifact, which we coin Argos Miner. This artifact is based on process mining algorithms and overcomes challenges inherent in adopting process mining in routine dynamics research. It enables scholars to capture reality in flight by analyzing routine dynamics using a computational, mixed-methods approach

DEVELOPMENT OF A MEASUREMENT SCALE FOR BUSINESS PROCESS STANDARDIZATION

Process-oriented companies face the dichotomy of process standardization versus process diversity. On the one hand, multinational companies try to realize returns of scale by standardization. On the other hand, markets require businesses to adapt to local needs and government regulations. As of to-day, there is no framework available to measure the degree of process standardization. This is both a problem for companies that want to assess their degree of standardization as well as for research that aims to investigate standardization and its connection with other concepts. In this paper, we address this research gap from the perspective of scale development. We utilize a well-acknowledged method for devising a measurement instrument to specifically and directly measure the degree of standardiza-tion in business processes. Various application scenarios and future research areas are pointed out

The Connection between Process Complexity of Event Sequences and Models discovered by Process Mining

Process mining is a research area focusing on the design of algorithms that can automatically provide insights into business processes by analysing historic process execution data, known as event logs. Among the most popular algorithms are those for automated process discovery, whose ultimate goal is to generate the best process model that summarizes the behaviour recorded in the input event log. Over the past decade, several process discovery algorithms have been proposed but, until now, this research was driven by the implicit assumption that a better algorithm would discover better process models, no matter the characteristics of the input event log. In this paper, we take a step back and question that assumption. Specifically, we investigate what are the relations between measures capturing characteristics of the input event log and the quality of the discovered process models. To this end, we review the state-of-the-art process complexity measures, propose a new process complexity measure based on graph entropy, and analyze this set of complexity measures on an extensive collection of event logs and corresponding automatically discovered process models. Our analysis shows that many process complexity measures correlate with the quality of the discovered process models, demonstrating the potential of using complexity measures as predictors for the quality of process models discovered with state-of-the-art process discovery algorithms. This finding is important for process mining research, as it highlights that not only algorithms, but also connections between input data and output quality should be studied

Using Process Mining to Support Theorizing About Change in Organizations

Process mining refers to a family of algorithms used to computationally reconstruct, analyze and visualize business processes through event log data. While process mining is commonly associated with the improvement of business processes, we argue that it can be used as a method to support theorizing about change in organizations. Central to our argument is that process mining algorithms can support inductive as well as deductive theorizing. Process mining algorithms can extend established theorizing in a number of ways and in relation to different research agendas and phenomena. We illustrate our argument in relation to two types of change: endogenous change that evolves over time and exogenous change that follows a purposeful intervention. Drawing on the discourse of routine dynamics, we propose how different process mining features can reveal new insights about the dynamics of organizational routines

DIGITAL TRACE DATA RESEARCH IN INFORMATION SYSTEMS: OPPORTUNITIES AND CHALLENGES

Digital trace data research is an emerging paradigm in Information Systems (IS). Whether for theory development or theory testing, IS scholars increasingly draw on data that are generated as actors use information technology. Because they are ‘digital’ in nature, these data are particularly suitable for computational analysis, i.e. analysis with the aid of algorithms. In turn, this opens up new possibilities for data analysis, such as process mining, text mining, and network analysis. At the same time, the increasing use of digital trace data for research purposes also raises questions and potential issues that the research community needs to address. For example, one key question is what constitutes a valid contribution to the body of knowledge and how digital trace data research influences our collective identity as a field? In this panel, we will discuss opportunities and challenges associated with digital trace data research. Reflecting on the panelists’ and the audience’s experience, we will point to strategies to mitigate common pitfalls and outline promising research avenues

Digital Twins of Organizations: A Socio-Technical View on Challenges and Opportunities for Future Research

Digital twins of organizations are software models that leverage operational and other data streams in order to dynamically monitor, analyze and improve organizational activities over time. Despite surging interest in practice, there is little research about this emerging topic. In this report, we draw from a panel discussion that has taken place at the International Conference on Business Process Management in 2021. Panelists and discussants included scholars from the information systems field, organization science and computer science. Summarizing and integrating the variety of involved perspectives, we present a socio-technical view on this emerging phenomenon. We point to several implications for future research

Researching Digital Entrepreneurship: Current Issues and Suggestions for Future Directions

This report documents the outcomes of a professional development workshop (PDW) held at the 40th International Conference on Information Systems in Munich, Germany. The workshop focused on identifying how information systems (IS) researchers can contribute to enriching our knowledge about digital entrepreneurship—that is, the point at which digital technologies and entrepreneurship intersect. The PDW assembled numerous IS researchers working on different aspects of digital entrepreneurship. Jointly, we delineated digital entrepreneurship from related phenomena and conceptualized the different roles that digital technologies can have in entrepreneurial endeavors. We also identified relevant strategies, opportunities, and challenges in conducting digital entrepreneurship research. This report summarizes the shared views that emerged from the interactions at the PDW and our collaborative effort to write this report. The report provides IS researchers interested in digital entrepreneurship with food for thought and a foundation for future research

Fire ant social chromosomes: Differences in number, sequence and expression of odorant binding proteins.

Variation in social behavior is common yet our knowledge of the mechanisms underpinning its evolution is limited. The fire ant Solenopsis invicta provides a textbook example of a Mendelian element controlling social organization: alternate alleles of a genetic element first identified as encoding an odorant binding protein (OBP) named Gp-9 determine whether a colony accepts one or multiple queens. The potential roles of such a protein in perceiving olfactory cues and evidence of positive selection on its amino acid sequence made it an appealing candidate gene. However, we recently showed that recombination is suppressed between Gp-9 and hundreds of other genes as part of a >19 Mb supergene-like region carried by a pair of social chromosomes. This finding raises the need to reassess the potential role of Gp-9. We identify 23 OBPs in the fire ant genome assembly, including nine located in the region of suppressed recombination with Gp-9. For six of these, the alleles carried by the two variants of the supergene-like region differ in protein-coding sequence and thus likely in function, with Gp-9 showing the strongest evidence of positive selection. We identify an additional OBP specific to the Sb variant of the region. Finally, we find that 14 OBPs are differentially expressed between single- and multiple-queen colonies. These results are consistent with multiple OBPs playing a role in determining social structure