=Paper=
{{Paper
|id=Vol-3216/paper_131
|storemode=property
|title=Organizational Complexity: Insights from Digital Trace Data Research
|pdfUrl=https://ceur-ws.org/Vol-3216/paper_131.pdf
|volume=Vol-3216
|authors=Bastian Wurm
|dblpUrl=https://dblp.org/rec/conf/bpm/Wurm22
}}
==Organizational Complexity: Insights from Digital Trace Data Research==
https://ceur-ws.org/Vol-3216/paper_131.pdf
Organizational Complexity: Insights from Digital
Trace Data Research (Extended Abstract)
Bastian Wurm1
1
LMU Munich School of Management, Ludwigstr. 28, 80539 Munich, Germany
Abstract
This thesis investigates organizational complexity, i.e. the dynamic behavior that emerges from the
interaction of distinct parts of an organization. In particular, it examines structural complexity, i.e. how
organizations organize themselves as well as process complexity, i.e. how they carry out work. In this
extended abstract, we focus on the dissertation’s contributions to business process complexity that has
been conceptualized as the different ways to enact an organizational process. Bridging different streams
of Business Process Management (BPM) research, we develop and apply process mining techniques to
investigate how business processes complexity changes over time. This thesis offers several contributions
to BPM and related fields. First, we discuss how process mining can be used to research organizational
processes with digital trace data. Second, we apply process mining techniques to examine how the
complexity of organizational processes develops over time. Third, we develop a graph-based measure to
operationalize complexity captured in log data. We show that log complexity significantly influences the
quality of process models derived with state-of-the-art process discovery techniques.
Keywords
Organizational Complexity, Digital Trace Data, Process Complexity, Business Process Change, Graph
Entropy, Process Discovery
1. Motivation
Organizational complexity is the result of the interplay of interdependent parts of an organiza-
tion [1]. Research in this area has addressed, for instance, the effects of complexity on innovation
or organizational performance. Besides, there is an increasing interest in the complexity of
organizational processes and how it develops over time (e.g. [2, 3]).
This thesis is motivated by two key observations. First, process complexity is difficult
to observe as it involves many parts of an organization. Especially longitudinal studies are
scarce, as the problem of observability is multiplied. Second, information systems employed
in organizations produce increasing amounts of digital trace data that provide insights into
actions performed by organizational actors. It has been argued that this type of data can be
used for and might impact our research practices dramatically (e.g. [4, 5]).
To this end, this thesis capitalizes on the opportunities offered by digital trace data to answer
the research question: Which insights do digital trace data provide into organizational complexity
and its change over time?
BPM 2022 Best Dissertation Award, Doctoral Consortium, and Demonstration Resources Track
$ bastian.wurm@lmu.de (B. Wurm)
https://www.en.dmm.bwl.uni-muenchen.de/persons/professoren/wurm/index.html (B. Wurm)
0000-0002-1002-5397 (B. Wurm)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
15
While the thesis offers contributions to process complexity and structural complexity in
organizations, this extended abstract will focus on the studies on process complexity and their
contributions to BPM and related fields.
2. Business Process Complexity
We adopt the conceptualization of process complexity as a function of enactments [6, 7]. Fol-
lowing this understanding, processes are networks of interrelated activities. The more activities
in the process and the more connections among these exist, the more complex is a given process
[7]. To measure process complexity, Haerem and Pentland [7] propose the following formula
based on the work of Öser [8]: ∑︁ ∑︁
𝑡𝑖𝑒𝑠𝑔,𝑝
𝑔 𝑝
where p is the number of paths that can be taken to achieve a given goal (g). “The primary
understanding reflected in this measure is that task complexity is indexed by the number of
paths in the network of events that lead to the attainment of task outcomes” [7, p. 452]. This
measure reflects that the complexity of the task system is greater than its single components.
Based on this measure, Goh et al. [3] examine the complexity of scrum software development
processes. They find that increasing process complexity reflects requirements for the software.
Furthermore, Pentland et al. [2] demonstrate the effect of different variables in digitized
processes on process complexity. Surprisingly, they find that digitized processes over time
transition from paths with only small variations through bursts of complexity before they reach
a state in which they exhibit shorts paths with on-going, but limited variation.
It remains unclear how robust and generalizable the simulation findings by Pentland et al. [2]
are and whether the key assumptions made hold true in reality. As the authors themselves state
in [9], simulation is primarily a tool for hypothesis development warranting further empirical
examination and testing. In this thesis, we address the need for further empirical research on
process complexity. We detail our contributions in the following.
3. Contributions
3.1. Methodological Contributions
This work entails methodological contributions to process research [10, 11]. We have echoed
recent claims regarding the use of digital trace data for research purposes [4, 12]; we proposed
and employed process mining [13] as a method to study organizational processes that are
supported by information technology.
On the one hand, process mining enables scholars to derive action patterns based on digital
action traces and thereby reconstruct and understand how organizational work is carried out.
On the other hand, process mining can be employed to test hypotheses on how organizational
processes behave and change over time. More specifically, there are three key scenarios of how
process mining can be employed to derive insights from digital trace data. (1) Process discovery
can be used to derive a process model from digital action traces. (2) Conformance checking can
16
be applied to compare a process model with enacted process behavior as documented in the
log file. (3) Process drift algorithms can be employed to detect changes in how organizational
processes are carried out.
Process mining adds to existing procedures and data analysis techniques for process research
[14]. Traditional research methods are limited because researchers need to purposefully generate
and collect data about organizational phenomena. For example in the case of ethnographic
observations, a single scholar can be at one place only and cannot carry out observations
indefinitely. Hence, collected data are bound to one place and a limited period. In contrast,
process mining allows scholars to investigate organizational processes “in the wild” [15, p. 415]
and over extended periods of time. While process mining is useful to detect or test patterns of
action, we highlight that process mining should be complemented with interviews to further
derive contextual insights and substantiate explanations for how organizational processes
behave.
We have applied process mining to investigate how process complexity changes over time.
We provided detailed accounts of how we proceeded in our data analysis, specifically how we
employed techniques from the process mining of python library [16] to detect and select process
variants as well as how our own code complements these techniques. We did this not only for
reasons of transparency and replicability, but we hope that future research can draw on these
accounts as a means of guidance of how to apply process mining in research projects.
3.2. Contributions to Process Complexity
This dissertation provides contributions to research on process complexity [17]. By investigating
process complexity “in the wild” [15, p. 415], we further add to the understanding of how
process complexity changes over time. In comparison to Pentland et al. [2], we do not find
any indication for bursts, i.e. increases of several magnitudes, of process complexity. One
possible explanation for this is that activities in organizational processes cannot be arbitrarily
combined. Certain activities in organizational processes exhibit logical interdependencies, such
that one is required for the other to happen. For example, an invoice cannot be paid before it
is created. Furthermore, activities in organizational processes are interdependent, since one
activity provides context and signals that influence the enactment of a successive activity [18].
Thus, rather than arbitrarily, activities can be combined according to a Lego analogy: There is
only a limited set of combinations of Lego stones (activities) to reach a certain desired figure
(an outcome of an organizational process). Hence, in specific situations, a Lego stone cannot be
randomly chosen, but one must be chosen that fits with the set of stones already placed.
Additionally, to the process complexity measure proposed by Haerem et al. [7], we suggest
two additional measures to operationalize process complexity. We argue that the measure by
Haerem et al. [7] quantifies complexity in organizational processes in absolute terms. Since
this measure is based on the number of total ties, only minimal process variation will lead to
a high level of process complexity when a process is often enacted. If variation is stable, a
process that is enacted very often will exhibit a higher level of complexity, compared to the
very same process enacted once, only. For this reason, we introduce the measure of relative
complexity that normalizes complexity with respect to the number of enactments. As our
applications demonstrate, this distinction is useful, because it allows to differentiate between
17
the complexity that all process enactments produce and the complexity that each individual
enactment contributes. Particularly, in processes with large fluctuations in the number of cases,
relative complexity will provide a more nuanced picture.
More generally, this work contributes to building a bridge between Business Process Man-
agement and Routine Dynamics. Both fields of study have been referred to as “islands” of
process research [11, 19], investigating similar phenomena from different perspectives and with
little integration among both fields. By connecting Business Process Management and Routine
Dynamics, we contribute to progressing Business Process Management as a behavioral science
[11, 20].
3.3. Contributions to Process Mining
We have developed a new measure for process complexity based on graph entropy [21]. While
the measure by Haerem et al. [7] has been applied successfully in several studies [2, 17], there
are some conceptual issues. An important feature of complex systems is that they dynamically
co-evolve and may undergo exponential change. A measurement for complexity needs to
adequately represent the complexity of a given entity. However, exponentiality must remain a
feature of the entity to be studied, not of the measure itself. As a consequence, we propose a
graph-based measure that captures process complexity as it is enacted.
We further apply this measure to investigate the relationship between the complexity of
event sequences recorded in event logs and the quality of process models derived with process
discovery algorithms. We find that increases in log complexity decrease the quality of discovered
process models. More generally, our results point to the importance of the connection between
input data and outcomes of process mining algorithms.
Acknowledgments
I would like to acknowledge the continuous guidance and support of my supervisor Prof. Dr.
Jan Mendling, without whom this dissertation would not have been possible.
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