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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
1
Jan vom Brocke
University of Liechtenstein
jan.vom.brocke@uni.li
Wil M.P. van der Aalst
RWTH Aachen
wvdaalst@pads.rwth-aachen.de
Thomas Grisold
University of Liechtenstein
thomas.grisold@uni.li
Waldemar Kremser
Radboud University
w.kremser@fm.ru.nl
Jan Mendling
Humboldt University
jan.mendling@hu-berlin.de
Brian Pentland
Michigan State University
Pentland@broa d.msu.edu
Jan Recker
University of Hamburg
jan.christof.recker@uni-
hamburg.de
Maximilian Roeglinger
University of Bayreuth
maximilian.roeglinger@fim-rc.de
Michael Rosemann
QUT Brisbane
m.rosemann@qut.edu.au
Barbara Weber
University of St.Gallen
barbara.weber@unisg.ch
Abstract
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.
1
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
Electronic copy available at: https://ssrn.com/abstract=3916817
2
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
change?”.
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
Electronic copy available at: https://ssrn.com/abstract=3916817
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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
Example
Causal processes (one event or
process contributes to the
production of another event or
process)
Seed ge rmination
Thought-sequencing process
(do this, then that)
Solving an
equation
Ceremonial process
Baptism
Performatory p rocess
Playing poker
Biological processes
Mitosis
Mental processes
Perceiving
Political p rocesses
Voting
Mathematical processes
Differential
equation
Productive process
Manufacturing
process
Problem-solving process
Solving a criminal
case
Social-stylization processes
Performing a
wedding
Owned process (follows from
thing or su bject, intentional)
Musician
performs a piece
of musik
Unowned process (non-
intentional, do not come from
subject or thing)
Thunderstorm
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
Electronic copy available at: https://ssrn.com/abstract=3916817
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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
activities.
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.
Electronic copy available at: https://ssrn.com/abstract=3916817
6
Phase
Process Science Activities
Goals
Exemplary Methods
Discovery
Capturing and
describing
processes
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.
Explanation
Understanding
why, how and
when a
process
unfolds
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
context.
Intervention
Intervening
and shaping
the process
into desired
direction
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
data.
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,
2000).
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.
Perspective
Contributing to Process Science
Focus
Exemplary Discipline
Human
Cognitive and
affective states of
people and their
change over time.
§ Psychology
§ Neuroscience
§ Anthropology
Social
Social interactions
and how they change
over time
§ Social Science
§ Organization
Science
§ Information
Systems Research
Environmental
Changes in man-
made and non-
owned constructed
or occurring systems
§ Natural Science
§ Urban Science
§ Architecture
Political
The governance of
social behaviors and
change
§ Political Science
§ Law
§ Ethics
Economic
Economic factors
influencing
processes, including
mechanisms of value
creation, in
particular, the
production,
distribution, and
consumption of
goods and services
§ Management
Science
§ Decision Science
§ Organization
Science
§ Economics
Technological
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
interventions.
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
evolves.
Electronic copy available at: https://ssrn.com/abstract=3916817
8
Acknowledgments
We thank all members of the process science
community who provided important feedback on
earlier ideas. Find more information here:
www.process-science.net.
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: ...
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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
... While research in this field has been mainly concerned with technical matters, several recent works called for research around managerial and organizational aspects of process mining [e.g., 10], in order to leverage the full potential of process mining [11]. Understanding the organizational perspective involved in process mining is crucial to capitalize on the possible benefits of the technology [4,7]. ...
... To this end, recent research has focused on the practical implications of process mining, including project success factors [6], methodologies to conduct projects [9], case studies, or Delphi studies [7]. Despite these works, we are only beginning to understand how process mining is adopted, used, and managed in practice [3,10], and what potentials it bears for identifying, understanding, and intervening into processes [5,11]. Specifically, there is a lack of research around the capabilities and competencies that are required to successfully implement and scale process mining. ...
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