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The only constant in our world is change. Why is there not a field of science that explicitly studies continuous change? We propose the establishment of process science, a field that studies processes: coherent series of changes, both man-made and naturally occurring, that unfold over time and occur at various levels. Process science is concerned with understanding and influencing change. It entails discovering and understanding processes as well as designing interventions to shape them into desired directions. Process science is based on four key principles; it (1) puts processes at the center of attention, (2) investigates processes scientifically, (3) embraces perspectives of multiple disciplines, and (4) aims to create impact by actively shaping the unfolding of processes. The ubiquitous availability of digital trace data, combined with advanced data analytics capabilities, offer new and unprecedented opportunities to study processes through multiple data sources, which makes process science very timely.
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Process Science:
The Interdisciplinary Study of
Continuous Change
Jan vom Brocke
University of Liechtenstein
Wil M.P. van der Aalst
RWTH Aachen
Thomas Grisold
University of Liechtenstein
Waldemar Kremser
Radboud University
Jan Mendling
Humboldt University
Brian Pentland
Michigan State University
Jan Recker
University of Hamburg
Maximilian Roeglinger
University of Bayreuth
Michael Rosemann
QUT Brisbane
Barbara Weber
University of St.Gallen
The only constant in our world is change. Why is there
not a field of science that explicitly studies continuous
change? We propose the establishment of process
science, a field that studies processes: coherent series
of changes, both man-made and naturally occurring,
that unfold over time and occur at various levels.
Process science is concerned with understanding and
influencing change. It entails discovering and
understanding processes as well as designing
interventions to shape them into desired directions.
Process science is based on four key principles; it (1)
puts processes at the center of attention, (2)
investigates processes scientifically, (3) embraces
perspectives of multiple disciplines, and (4) aims to
create impact by actively shaping the unfolding of
processes. The ubiquitous availability of digital trace
data, combined with advanced data analytics
capabilities, offer new and unprecedented
opportunities to study processes through multiple data
sources, which makes process science very timely.
Cite as: vom Brocke, J., van der Aalst, W.M.P, Grisold, T., Kremser, W., Mendling, J., Pentland, B., Recker, J.,
Roeglinger, M., Rosemann, M. Weber, B. (2021). Process Science: The Interdisciplinary Study of Continuous Change.
Working Paper, available at SSRN Electronic Library, 2021.
1. Introduction
We live in an age of process. Many core
phenomena of our time speak to complex dynamics
involving change: Climate change, globalization, the
platformization of economies, as well as societal
movements including #meToo, #FridaysForFuture,
#blackLivesMatter, or political decisions, have in
common that we can learn a lot more about them if we
think of them as ongoing processes, rather than stable
objects or systems. Take the Covid-19 pandemic: At
the heart of the present pandemic is a virus (an object)
that is constantly changing: it is continually evolving
and mutating, and is tackled through waves of
pharmaceutical and non-pharmaceutical interventions.
Climate change has been an ongoing yet accelerating
progression of events that manifest in singular,
increasingly catastrophic events such as flooding,
bushfires, and drought. While societal movements
often start with catalyst events (think of George
Floyd’s death), it is the unfolding of collective action
which follows in response that generates political
pressure and, in some cases, mitigating action. In the
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economy, we have seen the rapid rise of platform
businesses, such as Uber, that do not offer new
products or services but change the way we produce
and consume them.
To study these and other contemporary
phenomena, we need to embrace the fact that the only
constant in our world is change. Phenomena unfold,
evolve and wane, and occur on a macro, meso and
micro level. Our world is not made up of things, it is
made up of processes that change everything around
us. However, a view that sees the world primarily as
flowing as opposed to being in a stable state is not
trivial. It goes against many of our deeply ingrained
assumptions that the world espouses stability and
permanence (Chia, 1999). The latter assumption has
been at the core of scientific investigation, focusing on
objects, their properties and relationships. In contrast,
an orientation towards processes—broadly defined as
the ordering of change—embraces a view of the world
that is evolving and becoming (Tsoukas & Chia,
2002). In a world where nothing is quite settled, two
new questions take centerstage. On the one hand, the
prime question of scientific understanding must
change from “what is?” to “how is it changing?”. On
the other hand, a new question emerges: as change
both occurs naturally and can be constructed
artificially, we need to ask: how can we influence
Process science seeks to foreground the
mechanisms and drivers that create, trigger, foster,
prevent, accelerate, or slow down processes.
Essentially, a focus on process pushes us to understand
how change unfolds. However, change is not only part
of the natural world around us, but also an artificial
construct shaped by human action. Therefore,
advancing our understanding of phenomena in terms
of their underlying processes also provides us with
new opportunities for influencing change. If we know
why, how and when certain changes occur, we can
design and study interventions. This is important as
many recent claims suggest that scientists should take
on the roles of real-world problem solvers (Gaieck,
Lawrence, Montchal, Pandori, & Valdez-Ward, 2020).
Extending Pettigrew (1997), process science
encourages scholars not only to capture processes in
flight—it also encourages them to change the direction
of the flight.
We conceptualize process science as the
interdisciplinary attempt to investigate the nature of
evolution, transition, and change on various levels of
abstraction. While every field is aware of processual
phenomena to some extent, there is no established
field that puts processes at its center. The goal of
process science is to reconcile methods, theories, and
approaches of various scientific fields to establish a
comprehensive understanding of processes as well as
means to design interventions to processes. Our
motivation to introduce process science is further
complemented by new means to study processes: the
ever-expanding datafication, which affects all areas of
our private and professional lives, generates
comprehensive data on processes dynamics; and
computational techniques from various disciplines
(Lazer et al., 2020; Simsek, Vaara, Paruchuri,
Nadkarni, & Shaw, 2019) enable the analysis of
process dynamics across various levels. Drawing on
various claims that the use of digital data yields
unprecedented opportunities for research (Lazer et al.,
2020), process science aims at integrating data from
diverse sources, including company data,
environmental data, body data, and many others.
Process science provides a platform for disciplines to
jointly advance the study of processual dynamics and
find ways to change them. Process science is not a
thing. We consider it as process science-ing: an
evolving process itself shaped by anyone who engages
with it.
2. Conceptualizing Process Science
Processes have been playing an important role in
various research domains (Recker, 2014). These
include psychology, linguistics, anthropology,
politics, economics, and others (Cornwell, 2015). In
the broadest sense, a process brings about change
through a sequence of temporally and causally related
activities or events. To this end, the term has been
appropriated by various disciplines in different ways
(Mendling, Berente, Seidel, & Grisold, 2021;
Pettigrew, 1997; Van de Ven & Poole, 1995). For
example, in the context of sociology, processes serve
to uncover the temporal aspects of a given
phenomenon, e.g. life trajectories (Abbott, 1995). In
contrast, computer science uses the term to depict
intended computational sequences to accomplish a
specific outcome. In the natural sciences, researchers
focus on processes to unravel mechanisms that explain
how certain phenomena evolve and lead to distinct
outcomes (Cornwell, 2015; Leenders, Contractor, &
DeChurch, 2016). Management and business research
emphasize the importance of designing processes to
enable business operations (Dumas, La Rosa,
Mendling, & Reijers, 2018; Hammer & Champy,
1993). As different research communities have applied
a process perspective to different phenomena, they
developed different methods to study them, and a
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cross-fertilization among research fields may lead to
new methods in order study how and why certain
phenomena evolve and change over time (Lazer et al.,
2020; Mendling et al., 2021; Simsek et al., 2019).
However, scientific discourses on processes continue
to be scattered across different fields (Abbott, 1995;
Mendling et al., 2021). In light of this, the core of
process science is an interdisciplinary field of study,
providing a platform to foster continuous exchange
across various isolated fields.
The acute relevance of process science is tied to
the changes and shifts associated with digital
technologies (Mendling, Pentland, & Recker, 2020).
Van der Aalst and Damiani (2015) identify four
historical logics in the context of operational process
research, namely (1) the study of single tasks, (2) a
focus on the process as a whole, (3) the use of
information technology to integrate and automate, and
(4) the study of devices that interconnected through
the internet, forming distributed systems such as in
smart manufacturing. Through the expanding means
provided by digital technologies, we see the
emergence of a fifth logic, in which processes become
central to understanding the dynamics of socio-
technical networks. It is not only that technical
infrastructure such as sensor technology, personal
digital assistants, and smart environments create
dynamics that transcend organizational containers, but
phenomena like social-media “shit storms”,
crowdsourcing, the Bitcoin hype, cyber bullying, the
Fridays for Future movement, spreading of fake news,
or self-organized disaster relief can hardly be grasped
without taking a process view as a starting point
(Mendling et al., 2020; Winter, Berente, Howison, &
Butler, 2014): More than ever, what we are observing
is continuously changing evolving and—at best
stable “for now” (Feldman, Pentland, D’Adderio, &
Lazaric, 2016).
The abundance of digital technologies also leads
to new opportunities to study processes and their
underlying dynamics. Digital traces produced by these
technologies offer insights into activities of actors that
would not have been possible to study before (Akemu
& Abdelnour, 2020), since manually obtaining traces
is not feasible at large scales. Digital trace data in
private as well as work-related contexts offer new
opportunities to study how phenomena evolve in terms
of underlying sequences of events (Pentland, Pentland,
& Calantone, 2017). This may open up a powerful
view to understand and predict how phenomena
change and behave over time (Lazer et al., 2020;
Oliver et al., 2020; Pentland et al., 2017). Using digital
trace data, we can study phenomena at different levels,
including the micro-, meso-, and macro-level (e.g.,
individual and organizational level). This can
complement established theories, e.g. in the social
sciences (Lazer et al., 2020). Embracing such
opportunities and establishing a dialogue across
disciplines to study processes from an integrated
viewpoint is at the core of process science.
Using the term ‘process science’, we draw on and
extend claims that have been made before. From a
computer science perspective, van der Aalst and
Damiani (2015) have used the term to denote “the
broader discipline that combines knowledge from
information technology and knowledge from
management sciences to improve and run operational
processes.” (p. 2). By this account, process science
extends data science which is “an inter-disciplinary
field that uses scientific methods, processes,
algorithms and systems to extract knowledge and
insights from many structural and unstructured data”.
Furthermore, Mendling (2016) used the term in the
context of business process management to call for
more scientific and empirical research in the field. The
term process science has also been used as a specific
field of engineering that is concerned with fluids and
circulation (Judd & Stephenson, 2002; Velis,
Longhurst, Drew, Smith, & Pollard, 2009). While
these works approach process science from within the
frame of a specific discipline, they share (for example)
an interventional perspective. In turn, we intend to
emphasize process science in terms of an
interdisciplinary study of processes. Process thinking
is put center stage and its use should not be limited to
a specific research discipline.
We define process science as follows:
Process science is the interdisciplinary study of
continuous change. By process, we mean a coherent
series of changes that unfold over time and occur at
multiple levels.
3. Key Tenets of Process Science
Process science emphasizes the following key
charactersistics; (1) process are in the focus, (2) we
scientifically investigate processes (3) through an
interdisciplinary lens, and (4) we intend to influence
and change processes to create impact. We will
explain these tenets in the following. Fig. 1 depicts a
core summary of process science.
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Fig. 1: Process Science Framework
At the core of process science is the study of processes
(focus). It aims to describe, explain and intervene in
processes (objective). Thereby, it embraces an
interdisciplinary viewpoint, integrating contributions
from various disciplines (perspective); some of these
disciplines are exemplified here.
3.1 Processes are in the focus
Process science offers an opportunity to
reconsider one of our basic assumptions: is the world
made of objects or processes? Across a wide range of
disciplines, we have been trained to think of object
first. For example, computer science and information
systems adopt the stance that processes change the
properties of objects that exist a priori (Wand &
Weber, 1993). Influential process modeling languages
such as UML and BPMN share this commitment to
representing “objects first (Chinosi & Trombetta,
2012; Fowler, 2004). Other disciplines, such as
biology, are beginning to question the object-first
perspective and consider a “process first” perspective.
Nicholson and Dupré (2018, p. 3) propose that “the
living world is a hierarchy of processes, stabilized and
actively maintained at different timescales.” They
argue that the entities we recognize as objects (e.g.,
cells or organisms) are the result of those processes. In
organization studies, the “process first” perspective
has also been proposed (Langley & Tsoukas, 2017;
Tsoukas & Chia, 2002).
In practice, processes and objects always co-exist:
the fire burns the wood and the wood fuels the fire.
However, the shift in perspective from object-first to
process-first affords a novel way to think about
familiar problems. For example, rather than focusing
on chickens and eggs, we could focus on the on-going
biochemical and evolutionary processes that bring
them both into existence. For our purposes, the
process-first perspective may provide a useful way to
see analogies across domains that have different
objects but similar processes.
The core of process science is to think about the
world in terms of processes. Table 1 exemplifies that
process science is concerned with a variety of
processes, such as political, mental, mathematical or
biological processes (Rescher, 2000). We distinguish
between different forms of processes according to (1)
broader criteria and (2) specific types of processes,
which all fall under the proposed definition of process
science. We also provide (3) specific examples for
each type of process. Within process science, we take
different perspectives to study these processes, which
can be informed by e.g. social sciences, such as
organizational sciences, or technical research, such as
computer science.
Distinction of process
Types of Process
Causal processes (one event or
process contributes to the
production of another event or
Seed ge rmination
Thought-sequencing process
(do this, then that)
Solving an
Ceremonial process
Performatory p rocess
Playing poker
Biological processes
Mental processes
Political p rocesses
Mathematical processes
Productive process
Problem-solving process
Solving a criminal
Social-stylization processes
Performing a
Owned process (follows from
thing or su bject, intentional)
performs a piece
of musik
Unowned process (non-
intentional, do not come from
subject or thing)
Tab. 1: Distinctions of process relevant for process
science (drawing on and extending Rescher, 2000)
It is important to note that process science
includes both “owned” and “unowned” processes
(Rescher, 2000). Processes are owned when they
involve agency and intention. Unowned processes
occur without the intentions of any agent. In very few
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cases, there will be a clear-cut distinction between
owned and non-owned processes. When looking at
real-world phenomena, owned and unowned processes
influence one another. Owned processes, such as
production processes, influence unowned processes,
such as environmental developments. Vice versa,
unowned processes have an impact on owned
processes, as it has been shown dramatically by the
Covid-19 pandemic. As process scientists, we aim to
study both forms of processes and how they interplay.
We consider the interplay of processes as a continuum
where processes within a phenomenon are owned and
unowned to different degrees (see Fig. 1). For
example, when studying the evolvement of the Covid-
19 pandemic, we are considering unowned process,
such as the emergence of the virus, as well as owned
processes, such as measures to keep it under control
(Oliver et al., 2020). Owned and unowned processes
may exhibit influences to different degrees at different
points in time.
Fig. 1: Taking an integrated view on different forms
of process in process science
3.2 A Science of Discovering, Explaining and
Intervening into Processes
Process science welcomes all approaches to
generating new scientific knowledge through
deduction, induction, and abduction. Its key idea is
that a focus on process advances our understanding of
various phenomena because it directs our attention to
underlying causal-temporal relations constituting a
specific phenomenon. When we know why and how a
specific process unfolds, we are better prepared to re-
direct and change it. Process science subsumes three
broad activities, which are depicted in Table 2.
Discovery emphasizes the detection of (emergent)
dynamics constituting the phenomenon of interest. It
can be challenging to detect emerging and evolving
processual dynamics and their significance may be
understood retrospectively (Chia, 1999). The
discovery activity capitalizes on the potentials of
digital trace data to explore all sorts of phenomena
(Lazer et al., 2020).
Explanation aims at understanding the dynamics
of processes. It explains how and why processes
unfold. Explanation activities seek to identify cause-
effect relations (Markus & Rowe, 2018), specifically
in relation to their situatedness, e.g., in temporal and
spatial contexts. Access to a wide range of data sources
will be beneficial, and again, the vast potentials
associated with digital trace data may come into play
(Lazer et al., 2009). Furthermore, an in-depth
understanding of a process enables predictions about
the possible future states of the process. Thereby, one
can anticipate patterns arising in the sequence of
activities and events in a specific context, or the
evolvement of a process in relation to certain
indicators and factors, such as performance indicators
in business environments (Poll, Polyvyanyy,
Rosemann, Röglinger, & Rupprecht, 2018; Vergidis,
Tiwari, Majeed, & Roy, 2007). In terms of
methodological approaches, it is important to establish
a comprehensive understanding of a process, for
example, by collecting and integrating contextual
information through complementary data sources,
such as observations.
Intervention aims at changing processes as they
unfold. This resonates with recent claims across
various fields that science should contribute more
strongly to solving real-world problems (Gaieck et al.,
2020; Oliver et al., 2020; Rose, 2018). Such
interventions build on the cause-effect relations
identified before, and can include one or many
measures to interfere with how the process seems
likely to unfold in the future. For instance, design-
oriented research can generate prescriptions on how to
organize a specific process, utilize a specific
technology or communicate process change to people
in order to meet specific objectives (Hevner, March,
Park, & Ram, 2004; Van Aken, 2005). Interventions
are based on an envisioned goal, e.g., to prevent a
process from causing damage. It aims for utility and
develops knowledge on how to solve problems related
to process interventions, presented e.g., by methods,
models or principles. Borrowing established
methodological approaches—such as design science
research in the information systems field—can
provide frameworks to plan and evaluate intervention
While these three activities are core to process
science, not all of them have to be necessarily involved
in a process science project. Depending on the
phenomenon, and the questions being pursued, a study
needs to make explicit its core focus: discovery,
explanation or intervention.
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Process Science Activities
Exemplary Methods
Capturing and
Techniques, such as process
mining, to create descriptive
representations of processes
using digital event data;
event-based architectures to
organize data collection and
storage as well as
computational methods to
analyze the data and to
identify patterns in processes.
why, how and
when a
Methods supporting sense-
making around processes in a
specific context, e.g.
qualitative empirical research
to study the context in which
a pattern is situated. Leads to
propositions or entire theories
on cause effect relations
embedded in a situational
and shaping
the process
into desired
Methods to develop and
evaluate interventions to
processes. Applying e.g.
design-oriented research,
developing interventions
based on explanatory research
and evaluating effects of such
interventions in process even t
Tab. 2: Process science activities
Process science progresses by systematically
making use of various and novel data sources. What is
important, however, is that these data reveal temporal
information to infer when they took place. We refer to
these data as “event data” as they reflect the
occurrence of something that happened at some point
in time (van der Aalst, 2016). Such data can come from
traditional qualitative research designs or from digital
trace data, such as time-stamped production data,
sensor data, or social media data (Lazer et al., 2020).
To understand the interplay of processes, it is
important to use data collected across different levels
of abstraction (Langley & Tsoukas, 2017; Rescher,
3.3 An Interdisciplinary Science
Process science is interdisciplinary. It is open to
all disciplines that can make contributions to describe,
understand and intervene in processes. We do not
suggest re-labeling existing fields or changing their
agendas, but rather, we envision that process science
integrates their contributions, their methods and
theories to study processes. It is only through looking
beyond single disciplines, and integrating such
disciplinary views and findings, that processes will be
understood more comprehensively. Similar arguments
have been made before. For example, Abbott (1995)
suggests that research in sociology can benefit from
importing technical models from operational research
to think about social processes. In a similar vein,
claims in the business process management field assert
that scholars should embrace openness, pluralism, and
integration of other processual views to advance
established views on process work (Kerpedzhiev,
König, Röglinger, & Rosemann, 2020).
Process science seeks to function as an interface
between disciplines, synthesizing assumptions and
methods to promote a holistic study of processes. If we
are interested, for instance, in lowering the
environmental load of our economic and social
behavior, it makes sense to not limit the view on
organizations or the environment but to study
processes within the economy and society to capture
all relevant effects, e.g. by synthesizing perspectives
from business, economy and environmental studies
(Hertz, Garcia, & Schlüter, 2020; Song, Sun, & Jin,
2017). Table 3 shows how process science integrates
a wide range of disciplines.
Contributing to Process Science
Exemplary Discipline
Cognitive and
affective states of
people and their
change over time.
§ Psychology
§ Neuroscience
§ Anthropology
Social interactions
and how they change
over time
§ Social Science
§ Organization
§ Information
Systems Research
Changes in man-
made and non-
owned constructed
or occurring systems
§ Natural Science
§ Urban Science
§ Architecture
The governance of
social behaviors and
§ Political Science
§ Law
§ Ethics
Economic factors
processes, including
mechanisms of value
creation, in
particular, the
distribution, and
consumption of
goods and services
§ Management
§ Decision Science
§ Organization
§ Economics
Applications and
algorithms involved
in the enactment,
capture, or analysis
of change
§ Computer
Science s
§ Engineering
§ Data Science
Tab. 3: Exemplary disciplines contributing to process
science from different perspectives
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One contemporary field of research that
exemplifies the key ambitions of process science is
process mining. Process mining has been developed to
analyze and visualize business process work by
processing event log data that occur when people
interact with information technology (van der Aalst et
al., 2011). Over the past years, process mining has
received considerable attention in research and
practice, leading to a rich repertoire of techniques and
algorithms (e.g. Augusto et al., 2018; van der Aalst,
2016). While process mining research has been
originally tied to the field of computer science, the
technology has attracted increasing interest from other
fields, such as management and organizational
research (Davenport & Spanyi, 2019). In addition to
this, recent claims stress that the functionalities of
process mining can also be used for other purposes,
such as research. Accordingly, it offers new
opportunities for theorizing in empirical research; for
example, the technology can be used to find patterns
in organizational change processes (Grisold, Wurm,
Mendling, & vom Brocke, 2020; Pentland, Vaast, &
Ryan Wolf, 2021) or explore working practices
(Malinova, Gross, & Mendling, 2019). Taken
together, process mining provides a good example for
what we envision to be at the core of process science:
a field of research that is strongly concerned with
analyzing processual phenomena blending the
interests of various research domains and exploiting
the potentials associated with digital trace data.
It should be noted that it may pose challenges for
different disciplines contributing to process science.
This is because they draw on different assumptions,
theories and methods. For instance, organizational
scientists draw on management science when studying
processes (Sydow & Schreyögg, 2013), but these
exclude perspectives on cognitive processes from their
analysis, as embraced, for example, by neuroscience
and psychology. Nonetheless, we believe that
accumulating knowledge from many disciplines will
be highly beneficial, as long as such views are made
transparent, and, thus, can be considered when
interpreting and discussing results and designing
3.4 A Science of Impact
Process science strives to make an impact.
Process science is inherently pragmatic as it strives to
create knowledge that has instrumental value in
solving real-word problems (Dewey, 1946). As such,
process science aims to produce knowledge that can
make an impact on people, organizations and society.
In light of the manifold and severe grand challenges
we are facing today (George, Howard-Grenville,
Joshi, & Tihanyi, 2016), process science should enable
the development of effective solutions, such as new
ways to organize processes as well as new ways to
intervene in processes.
The United Nations General Assembly, for
instance, has collected 17 interlinked goals designed
to be a “blueprint to achieve a better and more
sustainable future for all”, which are referred to as the
“Sustainable Development Goals” or simply the
“Global Goals”. These goals include, among others,
the end of poverty, good health and well-being, quality
education, gender equality, affordable and clean
energy, decent work and economic growth, as well as
peace, justice and strong institutions, to name but a
few. All of these goals are influenced by processes at
various levels, and accomplishing any one of these
goals is going to be a process itself. For instance, the
goal “good health and well-being” is dependent on
dynamics that cover both non-owned processes, as
illustrated (for example) by the spread of the
pandemic, as well as owned processes, e.g. measures
we take to improve the health and well-being of
people. True to its mission, process science can
investigate and design ways to influence the evolution
of these processes for the better.
As we have argued before, contributions are
enabled also by a rich and detailed understanding of
how and why processes unfold over time. Process
science embraces processes on various levels and in
different contexts, including both naturally evolving
and intentionally designed processes, and examines
how they interact over time. Insights we gain here shall
enable and guide interventions to affect the course of
things over time. Process science is not only about
capturing reality in flight—it is also about influencing
it while it unfolds (Pettigrew, 1997).
4. Conclusion
This paper introduces and conceptualizes a new
scientific field: process science. Process science is
concerned with the understanding of processes of
different kinds aiming to inform interventions to and
the design of processes. We have established
theoretical foundations for process science, and
provided reasons why this endeavor is very timely.
The next important step is to start process science-ing:
bringing process science to life and starting research
projects that embrace and advance the field. Process
science is in the making. Everyone who wants to
engage with it is welcome to shape the field as it
Electronic copy available at:
We thank all members of the process science
community who provided important feedback on
earlier ideas. Find more information here:
This work profited from funding of the Erasmus+
program by the European Union [2019-1-LI01-
KA203-000169]: “BPM and Organizational Theory:
An Integrated Reference Curriculum Design”.
We also wish to express our sincere gratitude to
both the Hilti Foundation and the Hilti Group for their
continuous support and inspiration for this work.
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... Furthermore, a comprehensive viewpoint to the multiple, socio-technical, elements involved in data-driven decision making is lacking [25], and there is little agreement in the literature on what and how to evaluate [17,41]. The aspect of time and the requirements for a longitudinal, or processual evaluation remains ignored, which would be imperative to capture the complex dynamics involving change related to multi-faceted decisions [5]. Accordingly, we set out with the following research question: ...
... Time highlights the processual nature of evaluation and refers to when and how often the evaluation is conducted, since the outcomes of the decision may vary across time. Decisions should be viewed from the perspective of process science which is concerned with understanding processes and influencing change in the desired directions over time [5]. One of the core requirements is to understand the emergent, situational, and holistic features of the decision, or the decision-making process, in its changing context [30], which adds to the necessity of a multi-faceted, process-oriented decision evaluation. ...
... The second DO highlights the importance of a process science perspective and capturing the changes in contexts, concepts, and consequences, as well as understanding how they evolve, interact, and unfold, through a processual evaluation across time [5]. The set of decisions and concepts involved in the evaluation, as well as the evaluation method, may differ according to the stage in time when the evaluation is made. ...
This paper addresses a need for developing ex-post evaluation for data-driven decisions resulting from collaboration between humans and machines. As a first step of a design science project, we propose four design objectives for an ex-post evaluation solution, from the perspectives of both theory (concepts from the literature) and practice (through a case of industrial production planning): (1) incorporate multi-faceted decision evaluation criteria across the levels of environment, organization, and decision itself and (2) acknowledge temporal requirements of the decision contexts at hand, (3) define applicable mode(s) of collaboration between humans and machines to pursue collaborative rationality, and (4) enable a (potentially automated) feedback loop for learning from the (discrete or continuous) evaluations of past decisions. The design objectives contribute by supporting the development of solutions for the observed lack of ex-post methods for evaluating data-driven decisions to enhance human-machine collaboration in decision making. Our future research involves design and implementation efforts through on-going industry-academia cooperation.KeywordsData-driven decisionsEx-post evaluationDesign objectivesCollaborative rationalityHuman-machine collaboration
... The control flow-connections between events, activities and actors-defines the logical structure of the business process, i.e., what types of activities will take place next given the past events and decisions. Second, while the initial structure of a process model may be stable, 5 the current state of a business process changes constantly as organizational actors perform the actions and make the decisions included in the process model (vom Brocke et al. 2021). These states can even change rapidly while the process is performed. ...
... There is vigorous competition in this market and continual refinements and extensions to product offerings. All models derived from process mining depend on the availability of accurate, detailed digital trace data that describe the running process (vom Brocke et al. 2021). In Table 1, we summarize three broad and widely used approaches to the creation of DTBP using process mining: imperative, declarative and object-centric. ...
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The recent rise of using digital representations for products and processes has created a movement to use ‘digital twins’ for organization design. We provide an overview of the notion of digital twin as a synchronized, real-time two-way interacting digital representation of the real-world phenomenon it is expected to replicate as a twin. The claim of a two-way causal connection between the real-world and the digital representation makes the current rhetoric about Digital Twins especially problematic. To grasp the challenges involved in Digital Twins of Organizations (DTO), we start from Digital Twins of Things (DTT) and Digital Twins of Business Processes (DTBP). We analyze and compare different kinds of digital twins using Peircean theory of semiotic relationships, which differentiate between signals, icons, and symbols. We posit that in order to fully model organizations as digital twins, an organization designer needs to model features of organizations that are not present in DTTs and DTBPs, such as agency, conflict, and emergence. Given the inevitable presence of symbolic phenomena, we speculate to what extent it is possible to move towards full DTOs, what characteristics broader DTOs need to have, and what benefits more extensive use of DTOs will offer for organization designers. We finally offer pointers towards a research agenda for DTOs that have the potential to improve organization designs and contribute to theory on organization design.
... BPR is considered the most value-adding phase in the BPM lifecycle [11,25,34]. Consequently, the BPM discipline seeks principles, methods, techniques, and tools to support this phase [30,34,37]. ...
The digital age entails challenges that pressure organisations to redesign their business processes for improved performance. A significant aspect of this effort is the appropriate assignment of human resources – or people – to tasks. Despite the importance, there is a lack of structured guidance on allocating people to tasks considering various performance considerations such as time, cost, quality and flexibility. This paper presents 15 human resource allocation patterns organised into five categories: resource capability, utilisation, reorganisation, productivity and collaboration. The pattern collection is designed to offer guidance on diverse strategies for human resource allocation, focusing on process redesign for performance improvement from a resource perspective. The research was conducted in a two-phase approach. In the first phase, a literature review was conducted to identify existing resource patterns and practices, synthesised into an initial catalogue of human resource allocation patterns. In the second phase, this catalogue was evaluated through expert interviews with ten practitioners. The patterns provide a repository of knowledge guiding academics and practitioners on different ways a person can be assigned to a task for improved process efficiency. These patterns form a strong foundation for future research in the area of human-centred business process redesign.
... Additionally, Grisold, et al. [19] show that BPM also can aid in process innovation and Brocke, et al. [20] take into account that organizations and their environments are always changing. Their viewpoint has ensured that BPM evolves into a broader process science approach. ...
Longitudinal Business Process Management (BPM) studies are rare. BPM maturity and process performance can be used to quantify an organization's BPM evolution. This research aims to examine the growth of BPM maturity over time and its impact on process performance inside an organization in continuous transformation. Over a seven-year period, BPM maturity and process performance were measured annually at a Dutch university. During this time, the organization has undergone an organizational restructuring with a focus on process management and has temporarily switched completely to digital education propelled by the Covid-19 crisis. Based on a repeated cross-sectional study (N = 921), the results present key BPM maturity features that are critical during disruptive organizational transformations. Furthermore, we found that BPM maturity is positively related to process performance throughout organizational changes during the period of our research.KeywordsBPM maturityProcess performanceOrganizational dynamicsLongitudinal research
... In this context, process mining is an enabler that can uncover the root causes of process inefciency by reconstructing and visualizing current business processes as they are with their various variations [3]. In principle, process-mining techniques use event log data to fnd the process model and check the suitability of the process model with predetermined procedures and the ability to improve the model with a variety of information about bottlenecks, decisions, and resource use [4]. In business processes, the resource use will certainly afect the related processes whether they can run smoothly or not [5]. ...
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The current business environment has no room for inefficiency as it can cause companies to lose out to their competitors, to lose customer trust, and to experience cost overruns. Business processes within the company continue to grow and cause them to run more complex. The large scale and complexity of business processes pose a challenge in improving the quality of process model because the effectiveness of time and the efficiency of existing resources are the biggest challenges. In the context of optimizing business processes with a process mining approach, most current process models are optimized with a trace clustering approach to explore the model and to perform analysis on the resulting process model. Meanwhile, in the event log data, not only the activities but also the other resources, such as records of employee or staff working time, process service time, and processing costs, are recorded. This article proposes a mechanism alternative to optimize business processes by exploring the resources that occur in the process. The mechanism is carried out in three stages. The first stage is optimizing the job shop scheduling method from the generated event log. Scheduling the time becomes a problem in the job shop. Utilizing the right time can increase the effectiveness of performance in order to reduce costs. Scheduling can be defined as the allocation of multiple jobs in a series of machines, in which each machine only does one job at a time. In general, scheduling becomes a problem when sequencing the operations and allocating them into specific time slots without prolonging the technical and capacity constraints. The second stage is generating the resource value that is recorded in the event log from the results of analysis of the previous stage, namely, job shop scheduling. The resource values are multivariate and then clustered to determine homogeneous clusters. The last stage is optimizing the nonlinear multipolynomials in the homogeneous cluster formed by using the Hessian solution. The results obtained are analyzed to get recommendations on business processes that are appropriate for the company’s needs. The impact of long waiting times will increase service costs, but by improving workload, costs can be reduced. The process model and the value of service costs resulting from the mechanism in the research can be a reference for process owners in evaluating and improving ongoing processes.
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Progressively, information systems (IS) researchers draw on digital trace data to capture the emergent dynamics of today’s digitalized world. Digital trace data enable researchers to generate highly-context specific insights into the features and dynamics of socio-technical phenomena. We suggest how IS researchers can use digital trace data to develop situated explanations, that is, explanations that capture the idiosyncratic features of real-world problems in order to generate impactful solutions to these problems. We outline five key principles to build situated explanations based on digital trace data. We make several suggestions on how the information system field can adjust its research and publication practices to embrace the development and dissemination of situated explanations.
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The demands of many public health contexts and challenges call for conditions that foster effective decision making. Policy designers must make appropriate choices appear viable, accessible, and beneficial. They can do this by harnessing transdisciplinary knowledge about behavioral tendencies, simultaneously integrating insights into end users and non-human agents, and employing design methods for system-level solutions. We propose a "choice triad" model to help practitioners frame transdisciplinary approaches to complex public health challenges and design effective conditions for choice. It has three lenses: choice posture, to reveal human and non-human agents' predispositions and inclinations; choice architecture, to improve immediate choice environments and encourage preferred actions; and choice infrastructure, to reveal the underlying system structures, processes, and policies that shape how potential public health solutions are accessed and supported. This approach promises to augment traditional design tools and expand current conceptions of available "economies of choice" when crafting behavioral public policy solutions. In combination, these lenses can provide a new conceptual syntax and working model to diagnose and develop solutions within complex public health settings. We introduce two examples to illustrate this model: the water crisis in Flint, Michigan, and Covid-19 vaccination efforts in the United States. Keywords: Design for policy, Systemic policy design, Behavioral design, Choice infrastructure, Choice triad model, Public health policy
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Creativity – developing something new and useful – is a constant challenge in the working world. Work processes, services, or products must be sensibly adapted to changing times. To be able to analyze and, if necessary, adapt creativity in work processes, a precise understanding of these creative activities is necessary. Process modeling techniques are often used to capture business processes, represent them graphically and analyze them for adaptation possibilities. This has been very limited for creative work. An accurate understanding of creative work is subject to the challenge that, on the one hand, it is usually very complex and iterative. On the other hand, it is at least partially unpredictable as new things emerge. How can the complexity of creative business processes be adequately addressed and simultaneously manageable? This dissertation attempts to answer this question by first developing a precise process understanding of creative work. In an interdisciplinary approach, the literature on the process description of creativity-intensive work is analyzed from the perspective of psychology, organizational studies, and business informatics. In addition, a digital ethnographic study in the context of software development is used to analyze creative work. A model is developed based on which four elementary process components can be analyzed: Intention of the creative activity, Creation to develop the new, Evaluation to assess its meaningfulness, and Planning of the activities arising in the process – in short, the ICEP model. These four process elements are then translated into the Knockledge Modeling Description Language (KMDL), which was developed to capture and represent knowledge-intensive business processes. The modeling extension based on the ICEP model enables creative business processes to be identified and specified without the need for extensive modeling of all process details. The modeling extension proposed here was developed using ethnographic data and then applied to other organizational process contexts. The modeling method was applied to other business contexts and evaluated by external parties as part of two expert studies. The developed ICEP model provides an analytical framework for complex creative work processes. It can be comprehensively integrated into process models by transforming it into a modeling method, thus expanding the understanding of existing creative work in as-is process analyses.
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Process mining enables organizations to uncover their actual processes, provide insights, diagnose problems, and automatically trigger corrective actions. Process mining is an emerging scientific discipline positioned at the intersection between process science and data science. The combination of process modeling and analysis with the event data present in today’s information systems provides new means to tackle compliance and performance problems. This chapter provides an overview of the field of process mining introducing the different types of process mining (e.g., process discovery and conformance checking) and the basic ingredients, i.e., process models and event data. To prepare for later chapters, event logs are introduced in detail (including pointers to standards for event data such as XES and OCEL). Moreover, a brief overview of process mining applications and software is given.
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Designing healthcare facilities and their processes is a complex task which influences the quality and efficiency of healthcare services. The ongoing demand for healthcare services and cost burdens necessitate the application of analytical methods to enhance the overall service efficiency in hospitals. However, the variability in healthcare processes makes it highly complicated to accomplish this aim. This study addresses the complexity in the patient transport service process at a German hospital, and proposes a method based on process mining to obtain a holistic approach to recognise bottlenecks and main reasons for delays and resulting high costs associated with idle resources. To this aim, the event log data from the patient transport software system is collected and processed to discover the sequences and the timeline of the activities for the different cases of the transport process. The comparison between the actual and planned processes from the data set of the year 2020 shows that, for example, around 36% of the cases were 10 or more minutes delayed. To find delay issues in the process flow and their root causes the data traces of certain routes are intensively assessed. Additionally, the compliance with the predefined Key Performance Indicators concerning travel time and delay thresholds for individual cases was investigated. The efficiency of assignment of the transport requests to the transportation staff are also evaluated which gives useful understanding regarding staffing potential improvements. The research shows that process mining is an efficient method to provide comprehensive knowledge through process models that serve as Interactive Process Indicators and to extract significant transport pathways. It also suggests a more efficient patient transport concept and provides the decision makers with useful managerial insights to come up with efficient patient-centred analysis of transportation services through data from supporting information systems.
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This collection of essays explores the metaphysical thesis that the living world is not ontologically made up of substantial particles or things, as has often been assumed, but is rather constituted by processes. The biological domain is organized as an interdependent hierarchy of processes, which are stabilized and actively maintained at different timescales. Even entities that intuitively appear to be paradigms of things, such as organisms, are actually better understood as processes. Unlike previous attempts to articulate processual views of biology, which have tended to use Alfred North Whitehead’s panpsychist metaphysics as a foundation, this book takes a naturalistic approach to metaphysics. It submits that the main motivations for replacing an ontology of substances with one of processes are to be looked for in the empirical findings of science. Biology provides compelling reasons for thinking that the living realm is fundamentally dynamic and that the existence of things is always conditional on the existence of processes. The phenomenon of life cries out for theories that prioritize processes over things, and it suggests that the central explanandum of biology is not change but rather stability—or, more precisely, stability attained through constant change. This multicontributor volume brings together philosophers of science and metaphysicians interested in exploring the consequences of a processual philosophy of biology. The contributors draw on an extremely wide range of biological case studies and employ a process perspective to cast new light on a number of traditional philosophical problems such as identity, persistence, and individuality.
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Data sharing, research ethics, and incentives must improve
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The world is blazing with change and digital innovation is fueling the fire. Process management can help channel the heat into useful work. Unfortunately, research on digital innovation and process management has been conducted by separate communities operating under orthogonal assumptions. We argue that a synthesis of assumptions is required to bring these streams of research together. We offer suggestions for how these assumptions can be updated to facilitate a convergent conversation between the two research streams. We also suggest ways that methodologies from each stream could benefit the other. Together with the three exemplar empirical studies included in the special issue on business process management and digital innovation, we develop a broader foundation for reinventing research on business process management in a world ablaze with digital innovation.
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Research on social‐ecological systems (SES) has highlighted their complex and adaptive character and pointed to the importance of recognizing their intertwined nature. Yet, we often base our analysis and governance of SES on static and independent objects, such as actors and resources which are not well suited to address complexity and intertwinedness. This bias, which is largely implicit, has its roots in substance ontologies that have influenced most of contemporary science. This paper argues that it is useful to critically reflect on this ontological grounding and develop SES research from a process ontological perspective. Key insights are that process ontological concepts such as process, event and possibility space are able to overcome the dichotomy between the social and the ecological and allow for a conceptualization of continuous change (dynamism) that enhances our understanding of SES as truly intertwined and complex systems. This will enable SES researchers to conceptualize problems as well as corresponding solutions in novel ways which will ultimately support the development of novel governance approaches, from rethinking the aims of policies from managing people towards managing relations between and among people and the natural system. To fully tap the potential which comes with a change in worldview towards a process ontology, changing research approaches and ways of abstracting are required.
The growing availability of digital trace data has generated unprecedented opportunities for analyzing, explaining, and predicting the dynamics of process change. While research on process organization studies theorizes about process and change, and research on process mining rigorously measures and models business processes, there has so far been limited research that measures and theorizes about process dynamics. This gap represents an opportunity for new information systems research. This research note lays the foundation for such an endeavor by demonstrating the use of process mining for diachronic analysis of process dynamics. We detail the definitions, assumptions, and mechanics of an approach that is based on representing processes as weighted, directed graphs. Using this representation, we offer a precise definition of process dynamics that focuses attention on describing and measuring changes in process structure over time. We analyze process structure over two years at four dermatology clinics. Our analysis reveals process changes that were invisible to the medical staff in the clinics. This approach offers empirical insights that are relevant to many theoretical perspectives on process dynamics.
In an effort to contribute to the recent debate around epistemological and methodological anarchism inspired by the thinking of Paul Feyerabend, we reflect on Habermas's pragmatist perspective of social science. We argue that the information systems field instantiates a sort of pluralism that goes beyond the relativistic conclusions of Feyerabend. This is evident through the different traditions of research into business processes and organizational routines. There is a healthy diversity of epistemological and methodological approaches in this research. Accompanying this diversity is an openness to novelty and change. Yet, at the same time, this does not necessitate the abandonment of rigor and a cumulative tradition implied by "anything goes." Anything does not go, and that's a good thing. There is not a singular, hegemonic approach to what constitutes strong information systems research, but neither have we devolved into anarchy.
Business process management (BPM) is a mature discipline that drives corporate success through effective and efficient business processes. BPM is commonly structured via capability frameworks, which describe and bundle capability areas relevant for implementing process orientation in organizations. Despite their comprehensive use, existing BPM capability frameworks are being challenged by socio-technical changes such as those brought about by digitalization. In line with the uptake of novel technologies, digitalization transforms existing and enables new processes due to its impact on individual behavior and needs, intra- and inter-company collaboration, and new forms of automation. This development led the authors to presume that digitalization calls for new capability areas and that existing frameworks need to be updated. Hence, this study explored which BPM capability areas will become relevant in view of digitalization through a Delphi study with international experts from industry and academia. The study resulted in an updated BPM capability framework, accompanied by insights into challenges and opportunities of BPM. The results show that, while there is a strong link between current and future capability areas, a number of entirely new and enhanced capabilities are required for BPM to drive corporate success in view of digitalization.