=Paper=
{{Paper
|id=Vol-3758/paper-08
|storemode=property
|title=Explaining and Understanding Organizational Dynamics Using Digital Trace Data
|pdfUrl=https://ceur-ws.org/Vol-3758/paper-08.pdf
|volume=Vol-3758
|authors=Sophie Hartl
|dblpUrl=https://dblp.org/rec/conf/bpm/Hartl24
}}
==Explaining and Understanding Organizational Dynamics Using Digital Trace Data==
https://ceur-ws.org/Vol-3758/paper-08.pdf
Explaining and Understanding Organizational Dynamics
Using Digital Trace Data
Sophie Hartl
University of Liechtenstein, Fürst-Franz-Josef Strasse, 9490 Vaduz, Liechtenstein
Abstract
The pervasive use of digital trace data in information systems research presents significant
opportunities for exploring processes, changes, and temporal dynamics. Past research has
leveraged the vast amount of available data, characterized by its fine-grained nature and
temporal characteristics, to investigate process-related phenomena such as organizational
change and broader organizational dynamics. However, a comprehensive understanding of how
organizational dynamics intersect with and rely on digital trace data remains elusive. This
dissertation project addresses this gap by employing digital trace data and computational
techniques to analyze them, such as process mining, to elucidate the impact of organizational
dynamics on processes. The analysis involves examining how organizational change influences
both process dynamics and the organization itself. The research utilizes primarily
computational methods, particularly process mining, applied to data sets from financial
institutions in Central Europe. To complement the quantitative data, qualitative data is
incorporated, acknowledging the often limited nature of digital trace data which typically lacks
context.
Keywords
Digital trace data, organizational dynamics, change, process mining 1
1. Introduction
Recently, digital trace data-based research is gaining increasing attention in social
sciences [1] but especially also in information systems (IS) research [2], providing novel
means for investigating socio-technical phenomena [2, 3], not least because of their
characteristics, such as the inherent inclusion of temporal information [4]. Digital trace
data are the residuals or traces which arise from the interaction of a user with a digital
tool, an information system [5]. As the communication and collaboration with digital
technologies is arising, the amount of available digital trace data is more and more
increasing [6].
Proceedings of the Best BPM Dissertation Award, Doctoral Consortium, and Demonstrations & Resources Forum
co-located with 22nd International Conference on Business Process Management (BPM 2024), Krakow, Poland,
September 1st to 6th, 2024
sophie.hartl@uni.li (S.Hartl)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Not least due to these features, there is a burgeoning number of studies elucidating
different facets of research on digital trace data. For instance, various researchers
developed guidance on how to use digital trace data [e.g., 7, 8], or conducted
methodological analyses of the topic [e.g., 9]. With the increasing interest in researching
digital trace data, there is also a growing demand to utilize this data in order to gain a
better understanding of the changes and dynamics within companies, whether they are
related to processes, organization, or social factors. The research project examines how
organizational dynamics occurring in different processes and routines unfold and develop
over time by using digital trace data analyses. Besides that, it makes use of the capabilities
of digital trace data to capture the actual organizational dynamics. Digital trace data are
especially useful in analyzing the dynamics occurring around organizational phenomena
[10, 11, 12, 1]. Building on the emerging interest in digital trace data research, the
dissertation project aims to answer the following question: How can organizational
dynamics be explained and understood using digital trace data? In order to find an answer
to this research question, different methodological approaches are taken. To this end, with
this research project it is aimed to extend the existing literature on process research and
digital trace data research in two ways. First, to study dynamic changes in process
behaviour in the organizational context, it leverages the capability of digital trace data to
capture actual process behaviour. Second, by using digital traces and related
computational methods to analyze them, it sheds light on how organizational processes
dynamically unfold over time.
However, as digital trace data typically lacks context, it is difficult to explain how and
why these organizational changes occur. Despite the increasing interest in digital trace
data research and the growing number of empirical as well as conceptual studies [e.g., 3, 7,
8], there is a need for gathering deeper and broader insights into how digital traces can be
leveraged to study how processes are enacted and how they change over time.
Besides contributing to research in process mining and computationally intensive
theorizing, this research also contributes to business process management (BPM)
research. Following the recent research arguing that BPM initiatives should consider the
dynamics of the digital age [e.g., 29, 30] many research projects within this dissertation
project include a digital trace data analysis in order to emphasize the importance of taking
a dynamic perspective on process changes.
2. Research Methodology and Techniques
Recently, computationally intensive theorizing is getting more and more attention as a
new research paradigm which is based on analyzing or theorizing digital trace data with
computational methods helping to understand organizational phenomena in the
contemporary digital environment [2, 3]. Due to the novelty of the method, guidance on
conducting computationally intensive theorizing is only emerging in the last years [e.g.,
3,9]. Digital trace data research is characterized by utilizing data from users’ digital
interactions. This approach provides multiple benefits. Digital trace data, typically
unstructured and fine-grained on a large scale, allow researchers to theorize (processual)
phenomena by applying computational methods, as highlighted by Recker (2021), giving
them unprecedented capabilities to investigate phenomena at an unprecedented scale and
level of detail [28]. Furthermore, digital trace data contains temporal information and
therefore allows tracing changes in the process over time [28].
As the dissertation project focusses on process changes over time in the organizational
context, as well as the induced dynamics, computationally intensive theorizing with digital
trace data was used in different research projects, such as in the research on temporal
bracketing (see table 1), where digital trace data was used to explain changes in a digital
onboarding process.
3. Research Background
3.1. Digital Trace Data Research and Process Mining
Digital trace data can be described as the digital records of activities carried out
through information systems [3, 5, 13. As we increasingly use digital technologies for
organizational processes, but also in our communication and collaboration, the amount of
such data available is constantly increasing [6, 14]. Besides the large volume and fine-
granular nature, the data is typically characterized by the long time span for which this
data is commonly collected and hence available [1], providing profound insights into
socio-technical phenomena [e.g., 4, 15]. Digital trace data often takes the form of an event
log [16] which is structured as timestamp, activity and a corresponding case ID [16]. As it
contains temporal information, it is suitable for analyzing the dynamics unfolding around
organizational phenomena and changes of the process in different contexts [11, 17]. With
the increasing available data, the computational possibilities to analyze them are
constantly rising and getting more sophisticated. Especially in the area of information
systems, there is growing interest in discussing methodological guidance for digital trace
data studies [2, 3] and ensuring the quality of the datasets [13].
Digital trace data can be analyzed through different methods, such as machine learning
[9] or process mining [10], yielding insights into processes and their dynamics that would
not be discernible through traditional manual analysis techniques. Process mining is the
most common used computational method to analyze digital trace data in process
research and has gained in popularity and adoption in recent years [19]. It uses digital
trace data in the form of event logs that are captured in IS in order to analyze, evaluate and
ultimately improve business processes [20, 16]. The event log is used in process mining to
apply one of the three process mining techniques: process discovery, conformance
checking, and process enhancement [16]. Allowing to analyze the as-is process rather than
relying on a modeled process, comparing it with a to-be process model or even take
actions from the gathered process insights [16].
The research projects within the dissertation (see table 1) are also based on digital
trace data analysis, in particular with the help of process mining. For example, process
discovery techniques are used to calculate throughput times and identify loops or
bottlenecks. Furthermore, conformance checking techniques are often used in order to
check the compliance of processes with the predefined process model and rules.
3.2. Organizational Dynamics and Process Research
Organizational dynamics entail the continuously developing and interrelated aspects of
an organization, forming its structure, culture, and decision-making procedures [21].
Organizational change is a complex phenomenon unfolding over time and can occur as
intended but also unintended change [10]. Within the dissertation project, the term of
organizational dynamics covers any kind of organizational change leading to dynamics
within a process, routine or generally the organization. Both, BPM and routine dynamics
are research streams exploring processes using digital trace [22].
Rather than organizational dynamics, the term of process dynamics is more discussed
in research. With process dynamics the changes in a process structure over time are
described [7] and it can be measured using some kind of diachronic analysis [3]. This
relates to changes in patterns of (process) behaviour over time which can be captured e.g.,
with digital trace data. Pentland et al. (2021) for instance, mentioned mechanisms used for
theorizing process dynamics, which are patterning [23], endogenous change [24],
imbrication [25], and phase change [4]. Organizational Dynamics, or specifically process
dynamics can be investigated with digital trace data due to its temporal information
included [1, 17], as for instance also shown by Pentland et al. (2021) who studied process
dynamics based on digital trace data. With routine dynamics it is often referred to the
complexity of a routine, meaning the number of possible paths through which the routine
can be performed [7, 26] and thus also uses digital trace data. Computational techniques
such as process mining can help theorizing about change in organizations and therefore
also organizational dynamics [10]. Organizational dynamics and process research are two
sometimes intertwined concepts. Process research is proving to be useful for delving into
the intricacies of organizational dynamics and unravelling the mechanisms, patterns and
dynamics that drive organizational processes [12].
The research project as a whole is based on the conceptual and theoretical foundation
grounded in process research. Process research is an essential element and systematic to
examine how change and organizational phenomena unfold over time [12, 27]. Its purpose
is to reveal the mechanisms, patterns, and dynamics of processes and provide insights into
how and why things happen [12]. Digital trace data, which was previously described can
serve as the base for process research [8].
4. Current Research Status
During the first five semesters, there were several research projects started and
partially also finalized. In the following semester there are two projects that should be
finished while also writing up the dissertation. It is aimed to submit the dissertation in
March 2025 and finish the doctoral studies by summer 2025.
The papers published or accepted so far (see table 1) all included a digital trace data
analysis of an onboarding process of a financial institution. For instance, a framework has
been developed to apply temporal bracketing to digital trace data, following the growing
interest in temporal analysis [e.g., 7, 10, 18] in digital trace data and the increasing interest
in frameworks to guide such research [2, 9]. The following two research papers that were
accepted mainly dealt with the topic of complexity of an organizational routine, whereby
the digital trace data analysis from the onboarding process also formed the basis in each
case. And lastly, there was just previously a research paper accepted proposing a context
framework for sense-making of the process mining results. The aforementioned research
projects have collectively facilitated the formulation of an answer to the research question
on explaining the organizational dynamics with digital trace data. This was achieved
through an analysis and interpretation of digital trace data. The second part, the
understanding of organizational dynamics, is now to be covered with the help of a generic
framework for the implementation of such process science projects. In the table below the
current status of research projects related to the dissertation project is outlined.
Table 1
Overview of Ongoing and Finalized Research Projects
Title Authors Type & Outlet
Published or Accepted
Explaining Change with Hartl, S., Franzoi, S., Full Paper
Digital Trace Data: A Grisold, T., vom Hawaii International Conference
Framework for Temporal Brocke, J. on System Sciences (HICSS 2023)
Bracketing
Drivers of Complexity in Hartl, S., Franzoi, S., Full Paper
Organizational Routines Grisold, T., vom European Group for Organization
Brocke, J. Studies (EGOS 2023)
Effects of IT-based Changes Franzoi, S., Hartl, S., Full Paper
on the Complexity of an Grisold, T., vom Annual Meeting of the Academy of
Organizational Routine Brocke, J. Management 2024
A Context Framework for Grisold, T., van der Full Paper
Sense-making of Process Aa, H., Franzoi, S., International Conference on
Mining Results Hartl, S., Mendling, Process Mining (ICPM 2024)
J., vom Brocke, J.
In Conceptualization
Framework for Conducting Process Science Tbd – Finalization by 10/24
Studies in an Organization
Further Development of the Research on Tbd – Finalization by 09/24
Complexity of Organizational Routines
With this doctoral consortium it is aimed to gather feedback on the remaining open
research projects and on the current dissertation structure. More precisely, with the
doctoral consortium it is intended to gather feedback on the ongoing research about a
framework for conducting process science studies as this is currently still in
conceptualization. Ultimately, this exchange should facilitate the development of a more
coherent structure for the dissertation, as well as provide guidance on how to integrate
the disparate research findings into a unified topic. Since the research presented here also
touches on the field of organizational research, an exchange with scholars working in this
field would also be very helpful.
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