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Purpose: This study explores how process managers perceive the adoption, use and management of process mining in practice. While research in process mining predominantly focuses on the technical aspects, our work highlights organizational and managerial implications. Design/methodology/approach: We report on a focus group study conducted with process managers from various industries in Central Europe. This setting allowed us to gain diverse and in-depth insights about the needs and expectations of practitioners in relation to the adoption, use and management of process mining. Findings: We find that process managers face four central challenges. These challenges are largely related to four stages; (1) planning and business case calculation, (2) process selection, (3) implementation, and (4) process mining use. Research implications: We point to research opportunities in relation to the adoption, use and management of process mining. We suggest that future research should apply interdisciplinary study designs to better understand the managerial and organizational implications of process mining. Practical implications: The reported challenges have various practical implications at the organizational and managerial level. We explore how existing BPM frameworks can be extended to meet these challenges. Originality/value: This study is among the first attempts to explore process mining from the perspective of process managers. It clarifies important challenges and points to avenues for future research.
Accepted for publication in the Business Process Management Journal
Adoption, Use and Management of Process Mining in Practice*
Thomas Grisold1, Jan Mendling2, Markus Otto1, Jan vom Brocke1
1 University of Liechtenstein (;;
2 Vienna University of Economics and Business (
Purpose: This study explores how process managers perceive the adoption, use and
management of process mining in practice. While research in process mining predominantly
focuses on the technical aspects, our work highlights organizational and managerial
Design/methodology/approach: We report on a focus group study conducted with process
managers from various industries in Central Europe. This setting allowed us to gain diverse
and in-depth insights about the needs and expectations of practitioners in relation to the
adoption, use and management of process mining.
Findings: We find that process managers face four central challenges. These challenges are
largely related to four stages; (1) planning and business case calculation, (2) process selection,
(3) implementation, and (4) process mining use.
Research implications: We point to research opportunities in relation to the adoption, use
and management of process mining. We suggest that future research should apply
interdisciplinary study designs to better understand the managerial and organizational
implications of process mining.
Practical implications: The reported challenges have various practical implications at the
organizational and managerial level. We explore how existing BPM frameworks can be
extended to meet these challenges.
Originality/value: This study is among the first attempts to explore process mining from the
perspective of process managers. It clarifies important challenges and points to avenues for
future research.
Keywords: Process mining, organizational implications, focus group, leadership, governance, data
* Please cite as:
Grisold, T., Mendling, J., Otto, M. & vom Brocke, J. (forthc.). Adoption, Use and Management
of Process Mining in Practice. Business Process Management Journal.
Note: This article has been accepted for publication in the Business Process Management Journal but has not been through
the copyediting, typesetting, pagination and proofreading process. This may lead to differences between this version and the
final version.
Accepted for publication in the Business Process Management Journal
1. Introduction
Process mining has been gaining increasing attention in practice. By the end of 2019, Celonis,
a commercial process mining vendor, had an estimated net worth of 2.5 billion US dollars and
received the prestigious “Deutscher Zukunftspreis” (German Future Award), awarded by the
President of Germany. Recent claims suggest that the market will grow tenfold over the next
five years (Gartner, 2019). Yet, the existing research on process mining is little concerned with
how process managers use, understand and evaluate process mining tools, and how
organizations generate value from process mining. There is a fertile research discourse on
process mining. While some works in this discourse set out to illuminate the practical aspects
of process mining (Turner et al., 2012, Thiede et al., 2018, Emamjome et al., 2019, van der
Aalst, 2019, van der Aalst et al., 2007, De Weerdt et al., 2013), the majority of research is
concerned with technical aspects, e.g. developing and improving process mining techniques
(Augusto et al., 2018, Tax et al., 2019, Di Ciccio et al., 2017, Yeshchenko et al., 2018).
So far, research on process mining has hardly investigated management questions (vom
Brocke et al., 2020). For the practical application of process mining, it is important to
understand the following questions; how do process managers perceive potential benefits of
process mining tools? How do organizations generate value from process mining? What
potentials, threats and risks do those observe who deal with business process work in their
everyday jobs? Such management questions are largely unanswered. Overall, we observe that
the academic debate has brought limited attention to process managers’ needs in terms of
process mining (van der Aalst, 2019).
This paper addresses this research gap. We invite a practitioner’s voice (Bartunek and Rynes,
2014) to the process mining discourse and aim to better understand the needs, challenges
and perceived potentials of process mining in practice. Research can gain additional insights
about a phenomenon through “the advice and perspectives of key stakeholders (researchers,
users, clients, sponsors, and practitioners)” (Van de Ven, 2007). This means that insights from
practice can help researchers to extend and develop new theories and identify future research
directions (Sutton and Staw, 1995). Therefore, we report the results of a focus group study
that we conducted with process managers who are using process mining or are intending to
use it.
Our study reveals four central themes related to the adoption, management and use of
process mining: (1) process managers are in a strong need of approaches to plan and estimate
the expected outcomes of process mining initiatives, (2) they miss guidance when selecting
the right processes for process mining, (3) they perceive problems with respect to defining the
measures of success, and (4) there are various concerns regarding data availability and
privacy. Based on these observations, we describe a number of implications for process mining
research. Considering the far-reaching effects of this technology (e.g. increased transparency
in organizational work), we propose that future research should explore the use of process
mining from a descriptive perspective. Discourses and concepts from the organizational
sciences provide suitable means for doing so.
Accepted for publication in the Business Process Management Journal
We will proceed as follows. In the next section, we will discuss and summarize research on
process mining. Then, we provide methodological details of the focus group study we are
reporting on. Subsequently, we summarize the main findings that emerged. Finally, we discuss
challenges and opportunities for process mining research that arise from insights by process
2. Theoretical Background
2.1 Process Mining
The field of business process management (BPM) is concerned with the design, analysis and
improvement of business processes (Dumas et al., 2018, vom Brocke and Rosemann, 2010).
In light of the increasing use of information systems in business process work, process mining
has emerged as a promising technology to visualize, analyze and improve business processes.
Process mining tools exploit event log data, that is, structured traces of activities that were
carried out and recorded with digital technologies. For example, when actors use an ERP
system to accomplish tasks, they leave digital traces. When these traces are aligned with
specific cases, they reveal the different sequences of how a process was executed. This
information can be used to apply various automatic analysis techniques of process mining to
obtain in-depth insights (van der Aalst, 2012). Complementary to technologies such as
Enterprise Resource Planning (ERP), Workflow Management (WFM) and Customer
Relationship Management (CRM) systems, process mining tools support the execution,
monitoring and analysis of business processes (van der Aalst and Weijters, 2004). Process
mining techniques integrate concepts from model-based process analysis (e.g. simulation) and
data-oriented analysis techniques (e.g. data mining and machine learning).
Process mining has been applied for a broad spectrum of processes in different industries
(Thiede et al., 2018, dos Santos Garcia et al., 2019). As applications are advancing and
improving, their implementation and general usage are becoming more complex and more
widespread across different domains (Diba, 2019). One of the key benefits is that process
mining results reveal in detail how a business process really works, as opposed to what
analysts document in handbooks or process participants report in interviews (Bolt and van der
Aalst, 2015, Dumas et al., 2018). Beyond that, increasing uptake is fueled by the wide
availability of digital trace data and the demand to optimize the company's business processes
and performance (van der Aalst, 2012). Process mining can provide the grounds to find
additional levers for improving process work, for example, through robotic process
automation (Geyer-Klingeberg et al., 2018) or with tools for predictive analytics (Breuker et
al., 2016).
2.2 Dominant Themes in Process Mining Research
Research on process mining has been primarily emerging from computer science, and most
research continues in this domain (Thiede et al., 2018). In this area, there are numerous works
that invent and improve algorithms and process mining techniques. These works aim to
achieve more precise measures (Huang and Kumar, 2012, Polyvyanyy et al., 2018), detect
Accepted for publication in the Business Process Management Journal
different process dynamics (Bose et al., 2013), integrate social and organizational information
(Song and van der Aalst, 2008) and account for contextual factors (Yeshchenko et al., 2018).
It has also been noted that process mining poses several challenges, particularly for non-
experts (van der Aalst, 2011, vom Brocke and Mendling, 2018). These challenges refer, for
example, to selecting appropriate process mining vendors (Turner et al., 2012), identifying
suitable processes for process mining (Thiede et al., 2018), cleaning and integrating data
(Andrews et al., 2018, Dumas et al., 2018), and making process mining an ongoing and
continuous effort (van der Aalst, 2011).
The first two aspects, i.e. selecting process mining vendors and choosing appropriate
processes, have been investigated in previous studies. Turner et al. (2012) analyze process
mining vendors and offer recommendations for selecting the appropriate solution. In a similar
vein, Emamjome et al. (2019) investigate the diffusion of process mining tools and analyze
their features. Thiede et al. (2018) explore how process mining is used with respect to
different types of services, and they conclude that researchers should collaborate more
closely with practitioners in order to understand their needs and expectations. Diba (2019)
suggests that process mining initiatives can be divided into certain stages; planning, data and
process discovery, analysis and knowledge transfer (also see Rebuge and Ferreira, 2012). He
argues that process mining has far-reaching implications on the organizational and managerial
level, and there is little guidance how these challenges can be addressed, and he also calls for
more research on the practical aspects of process mining.
There are several works that discuss how process mining projects can be organized. For
instance, van Eck et al. (2015) and Aguirre et al. (2017) describe methodologies for carrying
out such projects, and Maruster and van Beest (2009) provide a methodology for redesigning
processes by the help of process mining. Examples of domain-specific proposals in healthcare
are Rebuge and Ferreira (2012) and Fernandez-Llatas et al. (2015). Mans et al. (2013) have
identified success factors for process mining projects in healthcare. In the accounting domain,
Jans et al. (2013) applied process mining techniques to enrich audit evidence during a financial
statement audit.
What is largely missing so far, however, is research on how process managers perceive process
mining, how they adopt the technology and how they integrate it into their information
systems landscape.
2.3 Business Impact of Process Mining
Process mining is a form of a business intelligence system (Chen et al., 2012). The use of
business intelligence systems has been associated with positive effects on organizations, e.g.
higher performance and productivity (Müller et al., 2018, Richards et al., 2019). Broadly
speaking, these effects are enabled through enhanced decision-making in organizations
(Wixom and Watson, 2010), which is facilitated by data visualizations and factual evidence
(Shollo and Galliers, 2016, Olszak, 2016). In this context, process mining has been linked to
various opportunities for performance improvement, generating an increasing interest in
practice (Davenport and Spanyi, 2019). Vendors report that major corporations are using
process mining, including Airbus, BMW, Uber, SAP, Ernst & Young, among others. A recent
Accepted for publication in the Business Process Management Journal
market analysis by Gartner (2019) reviews 19 commercial vendors of process mining solutions.
The report identifies a number of use cases where process mining is applied. These include (1)
business process improvement, (2) auditing and compliance, (3) process automation, (4)
digital transformation and (5) IT operations. In Table 1, we summarize success stories of
process mining and organize them according to the use cases identified by Gartner (2019).
These cases were collected from major vendors as well as consulting reports. The table
highlights that process mining is used across various industries and with respect to different
Use Case
Business Process
Lana Labs1
The Interroll Group
implemented process
mining to gain insights
into their production
processes. They identified
more than 3000 variations
in the process.
Business Process
Initial experiments of
PGGM, a Dutch pension
provider, suggest that
process mining can lead
to expected time savings
of 66% across processes.
Business Process
Health Care
Lana Labs1
Process mining was
implemented to improve
patient-centered care and
communication among
different functions in the
context of emergency
hospitalization. Waiting
time for patients
decreased by 80%. Overall
patient-centered care was
Auditing and
Telekom AG
Deutsche Telekom uses
process mining in their
purchase-to-pay process.
According to the
company, they decreased
their cash discount loss by
Accepted for publication in the Business Process Management Journal
20%. Further benefits are
associated with increased
transparency and the
standardization of the
process across
Auditing and
Vodafone uses process
mining to improve
standardization in their
purchase-to-pay and
process. The company
increased the number of
error-free purchases by
more than 10%, and the
market entry time by
Auditing and
EY Ernst &
Audit Assurance
More than 150 clients use
process mining for
auditing and compliance.
EY reports that the client
audit preparation time
decreased by 50%.
Furthermore, customer
satisfaction was increased
due to higher
transparency and
automated insights in
audit risks and
compliance breaches.
(South Korea)
LOEN used process mining
to analyze user behavior
on their online music
streaming platform. Based
on their interaction
patterns with the
platform, users were
categorized into five
segments. This allowed
the company to
Accepted for publication in the Business Process Management Journal
understand users
motivations to engage
with the service. These
insights are used for
marketing purposes and
development of services.
Energias de
Portugal (EDP)
Process mining is applied
to various processes to
support the organization’s
digital transformation. By
providing joint access to
operations, process
mining supports agility
across departments and
frictionless operations.
IT Operations
(One of
leading life
Analyzing the customer
onboarding process, the
company could identify
opportunities and
requirements for robotic
process automation. This
led to a new business
application set-up and
savings of 42% in full-time
equivalents (FTE). The
design of a new business
issue process led to
savings of 66% in FTEs.
IT Operations/
Business Process
Software AG
The number of contracts
that could be processed
without additional
inquiries could be
increased by 28%.
Complaint rates
decreased by 60%.
Furthermore, process
costs decreased by 20%.
Table 1: A collection of process mining use cases as provided by major vendors and reports
Accepted for publication in the Business Process Management Journal
The outcomes summarized in Table 1 show substantial benefits that companies have obtained
from adopting process mining tools. Two notes of caution are warranted. First, these cases
are success stories. This means that they showcase achievements at the upper end of the
spectrum. As such, they reflect a strong and intentional selection bias of vendors to report
only the most impressive results. Second, the narrative of these success stories is often
reconstructed at hindsight. However, companies likely face high uncertainty about potential
costs and benefits before engaging with process mining.
3. Methodology: Focus Group
To understand the needs of process managers in relation to process mining, we adopted a
focus group design (Krueger and Casey, 2014, Bandara et al., 2007). Focus group designs
support exploratory and participatory research studies as they draw on cooperative work and
"active collaboration between evaluators and participants during all or most steps of the
evaluation process" (Filipowska et al., 2009). Thereby, this approach allows us to develop
insights which often go beyond established methods (O’Raghallaigh et al., 2012). For example,
as compared to conventional interview techniques, focus groups provide a context in which
participants can articulate, discuss and extend their viewpoints (Krueger and Casey, 2014).
When confronted with other opinions, participants may feel encouraged to reflect on and
articulate underlying assumptions. The articulation of these assumptions may lead to
reflection processes in other participants, and so on. This triggers a dynamical and mutual
exchange (Wibeck et al., 2007). A focus groups can thus be seen as a collective sense-making
process about a specific topic that unfolds across participants and researchers (Wilkinson,
1998, Wibeck et al., 2007). This setting allows us to create a learning community to foster
exchange between researchers and practitioners (Van de Ven, 2007).
3.1 Participants
Data collection took place in November 2019 in Zurich, Switzerland. The study was conducted
as part of a workshop that was organized by Signavio, a commercial provider of BPM software
and process mining applications. The workshop was intended to create a space for
practitioners who consider the adoption of process mining or who are already using it.
The 22 participants were representatives from organizations from different industries, such
as health care and financial services. An overview is provided in Table 2. The profiles of these
participants largely cover the tasks that are typically assigned to process managers. More
specifically, they were responsible for the management of business processes in their
respective organization, including process analysis, redesign, implementation (Dumas et al.,
2018). While Table 2 shows that the responsibilities for process management activities were
tied to different job profiles and core tasks in their organizations (Müller et al., 2016b), all
participants had broader responsibilities in relation to the adoption, use or management of
process mining initiatives. This included project management, coordination with stakeholders
and communication with process vendors. Mind that more stakeholders will be involved in
process mining initiatives, including e.g. specialists in data analytics. Those may be able to
Accepted for publication in the Business Process Management Journal
provide complementary insights into specific aspects of the implementation (e.g. challenges
and issues with respect to data cleaning, see Suriadi et al., 2017). However, the main focus of
this study was on the broader organizational and managerial implications of process mining.
Since the participants came from different backgrounds, we could gain rich insights into the
interactions and dependencies of process mining and different organizational settings
(Bandara et al., 2007). The resulting diversity within the focus group contributed to the validity
and reliability of the results (Van Greunen et al., 2010).
Jan Mendling
Vienna University of
Economics and Business,
Research on technical and
organizational aspects of
process mining
Jan vom Brocke
University of Liechtenstein
Research on managerial and
organizational implications
of process mining
Thomas Grisold
University of Liechtenstein
Research on organizational
and theorizing-related
implications of process
Markus Otto
University of Liechtenstein
Research on business
opportunities related to
process mining
22 participants
20 participants worked in
Switzerland, one participant
worked in Germany, and
one participant worked in
Background in various
- Health care (23%)
- Professional
services/consulting (18%)
- Financial services (14%)
- Remainders are from
consumer products,
Sports & entertainment,
higher education &
research, industrial
machinery and
components, public
All participants had
competences regarding the
adoption and use of process
Core responsibilities:
- Management of business
process, quality and
excellence (41%)
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- Executive management
and head of business
- Process engineering
- Others (18%)
The represented
organizations had different
sizes, ranging from small-to-
medium companies to large
Organizational sizes:
- 1-50 employees (32%)
- 201-1000 employees
- 1001-5000 employees
- 5001-10.000 employees
- 10.000+ employees (27%)
- n/a (5%)
Table 2: Information about the participants of the focus group
3.2 Design and Procedure
We followed Krueger and Casey (2014) who provide a number of suggestions to conduct and
moderate a focus group. They assert that the following points facilitate an efficient procedure;
(1) providing a friendly and engaging presentation, (2) offering the opportunity to contribute
and involving all participants, (3) exposing participants to different opinions and statements
and inviting them to reflect on them, (4) highlighting clear communication rules, and finally,
(5) listening attentively to other views while controlling the own individual view.
The agenda was divided into four parts. First, two of the authors gave a one-hour presentation
about the technical and operational as well as organizational and managerial implications of
process mining. We emphasized that, from a research point of view, process mining can only
support decision making in organizations by providing insights into how a process is carried
out. However, the results need to be interpreted and translated into managerial actions
(Dumas et al., 2018). Furthermore, it was stressed that process mining has an impact on the
six core elements of BPM (vom Brocke and Rosemann, 2010). In particular, people need to be
able to use the technology in their everyday tasks. This session was concluded with a 30-
minutes Q&A session.
Second, we asked all participants to propose questions that they find relevant and important
to discuss in relation to process mining. Of a total number of 12 proposed questions, the
participants selected four questions for in-depth discussion:
1. How can one create and calculate a business case for process mining?
2. Which processes should be selected for process mining?
3. How can one align process mining initiatives?
4. What is important to consider with respect to data availability?
Accepted for publication in the Business Process Management Journal
Third, all participants split up into groups, each discussing one of the questions. We asked
them to share their experiences and viewpoints, and summarize the main points of their
discussions. One author was assigned to each table in order to facilitate the discussion. The
break-out sessions lasted for 30 minutes.
Fourth, each group was asked to summarize their discussions on a flipchart to then present
and discuss it in the plenary session. The summaries concerned both findings and open
questions, which were directed to the academics and other process managers. This part lasted
for 60 minutes.
4. Findings
In the following, we summarize the key outcomes along the four questions that were
discussed during the break-out sessions. In essence, these questions refer to the phases of
process mining adoption, including planning, process selection, implementation and process
mining use.
4.1 How can one create and calculate a business case for process mining?
We found that participating process managers are aware of the general benefits associated
with process mining. They see the benefit of process mining in improving work efficiency. For
example, they associate process mining with cost and expense reduction by identifying steps
that are obsolete or can be automatized. However, they reported to have difficulties in
assessing and predicting specific outcomes when using it.
The participants acknowledged that process mining, due to its utilization of digital traces,
allows for quantification and verification of various improvement opportunities. Established
approaches to improve process work are considered inaccurate and require extensive effort
(e.g. interviews with process participants), and process mining is said to provide fast and
accurate measures of certain improvements. The participants mentioned the opportunity to
investigate process deviations. They highlighted the possibility to contrast better process
performances against worse process performances, and to explore causes and underlying
reasons (e.g. through interviews). Furthermore, they stressed that collective decisions can be
made on the grounds of objective measures. This was considered important because
oftentimes, their decisions rely on subjective interpretations. They indicated that this may be
problematic as not all process participants may comply with decisions taken to improve the
In short, participants saw business value in process mining in terms of opportunities for
analyzing the process. The see particular relevance in evidence-based decision-making.
However, the participating process managers highlighted that they cannot measure the value
of process mining. This, in turn, makes it difficult for them to pitch it to senior decision-makers,
such as board members.
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4.2 Which processes should be selected for process mining?
This question was debated in terms of the quantity of data that needs to be available for
process mining. Participants pointed out that process mining makes only sense when a process
has a high number of repetitions and one can identify variations. In light of the large
investments associated with the adoption of process mining, process managers stressed that
they need to be able to convince decision-makers. For doing so, they need to establish
agreement on what they expect from process mining. This implies agreement under what
circumstances a process runs according to desired standards. One process manager who was
involved in the implementation of a process mining initiative in a large manufacturing
company in Switzerland emphasized that it is important to precisely pin down the process
steps that should be analyzed. He explained that this hinges on a specific problem formulation,
and implies that the process is being abstracted to some extent. He pointed out that many
process managers are unaware of how the level of abstraction influences interpretations
about the process. In a similar vein, one participant claimed that process mining does not
automatically lead to continuous and on-going use once it has been implemented. He stressed
that process mining will likely fall into oblivion if there is no strategy for how it should
contribute to the mid- and long-term success of the company.
Second, it was discussed which kind of processes can or should be mined. Process managers
agreed that it is less about the type of process (e.g. order-to-cash). Rather, they reported that
it is more important that the process fulfills certain criteria. For them, the primary question
turned out to be, if (and how much) data is produced within a process. Underlying this
reasoning was the observation that a sufficient amount of data is the basis to make meaningful
decisions. Furthermore, they also find it important to consider the number of people involved
in the process. One process manager argued that the more people are involved, the easier it
is to generate effective insights. There was agreement among all participants that the
question of which process is chosen ultimately depends on the size of the organization, its
strategic goals, etc.
To summarize, process managers were less interested in what type of process should be
mined but what properties a process has to exhibit in order to be applicable for process
mining. These include, for example, the amount of data produced, and the variation that may
arise. They perceive issues in determining this and oftentimes, they can only rely on the
specifications by commercial software vendors.
4.3 How can one align process mining initiatives?
This question was discussed with respect to the insights that can be expected from process
mining. Central was the question how process mining aligns with the strategy and other
operations in the organization.
Process managers discussed how process mining can be aligned with the strategy and goals of
an organization. They agreed that process mining has to consider the people who use the
technology as they need awareness and certain capabilities to use the technology. To see how
well process mining is embedded in the organization, one process manager suggested to
measure how often process mining is used. Furthermore, process mining data enables them
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to observe if actors from different units increase cooperation and exchange of information.
Culture was considered important because process mining creates transparency across units.
A supportive organizational culture incorporating values, such as trust and openness has been
reported important to allow for productive process mining use.
Finally, they reported that governance has to be considered. Two central questions were (1)
who should be involved in the implementation of process mining initiatives, and (2) who
should be responsible for managing them (e.g. interpreting the data and making decisions)?
A recurrent theme was that smaller process mining initiatives should be set off to iteratively
align operational capabilities.
In terms of operationalizing the measurement strategy, two process managers who have been
accompanying a process mining project emphasized that is important to check if the data are
accurate and complete. They suggested that initial activities should be directed to the
validation of event log data. Since organizations often use multiple information systems, they
pointed out that one should assess the correspondence of these data (e.g. in PM and SAP).
One participant reported that the appealing visualizations of process mining may lead
decision-makers to believe that there are straight-forward ways to improve a process.
However, he recalled that process mining use can even be detrimental if the data are
What is missing is, according to the reports of the process managers, a guide on how certain
variables can inform decision-making. For example, they suggested that one should be able to
select specific variables with respect to overall availability, potential outcomes, etc.
To summarize, process managers suggested that the alignment of process mining has
managerial and organizational implications.
4.4 What is important to consider with respect to data availability?
Again, process managers highlighted that they need to ensure that a process produces a
sufficient amount of event log data. They emphasized that there may be fractions in the data
when processes run across different systems. They suggested that any process mining
activities need to be aligned with higher organizational goals. One process manager stressed
that higher goals define the respective data needs. Some process managers pointed out that
parts of a process may be neither digitalized nor integrated in a process mining application.
They stressed that this leads to a lack of information. Process managers appreciated the idea
to consider additional tasks in process mining, e.g. if one writes an e-mail to confirm an order,
or uses Excel to update the inventory, it would be interesting to see how often these steps
occur, how long they take, etc. They stressed that additional information would add
explanatory value to understanding process behavior.
A second point that was discussed refers to the implications of process mining for privacy. A
central theme here was that process mining produces data that is collected across
organizational units. Process managers reported that data collection and integration are often
complex and cumbersome efforts because there are strong implications for data protection.
For example, one process manager asserted that it may be unclear who the owner of the data
is. One process manager who was involved in a process mining project in his company argued
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that there should be a transparent data policy before any process mining initiative is rolled
out. There was common agreement among the participants that this becomes even more
important in light of the General Data Protection Regulation (GDPO) of the European Union.
Furthermore, process managers reported that there might arise mistrust since many activities
of actors are being recorded and people feel monitored. Therefore, it was stated that process
mining can have an impact on the culture and the working atmosphere in the organization.
Finally, process managers stressed that there is an asymmetry in terms of the permission to
access and use of relevant data. They mentioned that those teams which are responsible for
data integration often have difficulties to obtain the data since they are not involved in the
decision-making processes. Following the accounts of process managers, a significant delay in
process mining projects is tied to organizational barriers, rather than technical ones.
To conclude, process mining was associated with privacy issues that can affect process mining
initiatives. Furthermore, it was mentioned that the transparency enabled through process
mining has implications on the organizational level.
4.5 Summary of Findings
Table 3 summarizes the findings obtained through the focus group. It organizes key insights
along four major stages of process mining adoption, namely (1) planning and business case
calculation, (2) process selection, (3) implementation, and (4) process mining use.
We organize the claims of process managers along two dimensions, namely perceived benefits
and recommendations that were reported by process managers, and issues that are perceived
at different stages.
Stages from planning to
process mining use
Perceived benefits and
Perceived issues
Planning and business case
Business value is created
when process mining is
used to (continuously)
analyze business
Decisions can be made
based on evidence
through real data
Process managers do not
know how to calculate
the business value of
process mining activities
They need guidance to
convince decision-
Process selection
Rather than the types of
processes, process
managers consider he
process properties
It is unclear what
process properties are
There is an asymmetry in
terms of the permission
to access and use of
relevant data
Delays can occur due to
data access, which is
Accepted for publication in the Business Process Management Journal
often tied to
organizational barriers
A sufficient amount of
data must exist
Data needs should be
defined with respect to
strategic goals
There are data fractions
when process run on
different systems
Te am s w ho ar e
responsible for data
integration often have
difficulties to obtain the
data since they are not
involved in the decision-
Process mining use
PM should align with the
strategy and other
operations in the
It needs to be ensured
that data has high
quality and is complete
Should be a transparent
data policy
Process managers miss
information about how
certain variables can
inform decision-making
It is important to cope
with the increased
transparency created
through process mining
Tra nspare nc y m ay le ad
to distrust and perceived
Table 3: Findings from the focus group study
5. Implications and Opportunities for Research
The vivid discussions during data collection provided us with the opportunity to gain
numerous insights into how process mining is perceived and used in practice. Drawing on
these insights, we see four central implications arising for future research.
5.1 Effort and Cost-Benefit Assessment
All workshop participants were aware of the benefits that process mining can deliver to the
organization, including the enhanced analysis of processes and improvement of process
efficiency (dos Santos Garcia et al., 2019). However, throughout the discussions, participants
expressed a need for evidence that process mining provides concrete and measurable value
for the organization. What are the benefits as compared to the relatively high costs? In what
ways can these benefits be measured? How did other companies improve through process
mining initiatives? What is the business impact of process mining?
While research has been concerned with positive outcomes of process mining (Jans et al.,
2013), it remains unexplored how the effects of process mining can be translated into
increased revenue and reduced costs. Such insights have been obtained in related areas, e.g.
Accepted for publication in the Business Process Management Journal
business intelligence systems and big data use (Trieu, 2017). For example, investigating the
exploitation of big data for firm performance, Müller et al. (2018) conducted an econometric
analysis and found that firms utilizing big data analytics increase their firm productivity by 3-
7%. They also found that these effects vary across industry domains. Future research could
analyze the performance of organizations after implementing process mining. This can further
be analyzed and compared across industries, e.g. manufacturing (Son et al., 2014), insurance
companies (Nguyen et al., 2014), health care (Rojas et al., 2016), among others. Such insights
could also lead to the development of use cases where process mining has been implemented
in certain sectors. Mind that the success stories reported in Table 1 are collected from vendor
and consulting reports. They provide a limited picture in terms of (1) details regarding the
initiatives and outcomes, and (2) pitfalls and downsides. Future research may take a stronger
focus on use cases, developing detailed accounts of how organizations realized process mining
To conclude, insights regarding the practical use of process mining could provide the grounds
to investigate when and why certain techniques are more applicable, which could be both
interesting for research and practice.
5.2 Organizational implications of process mining
The participating process managers mentioned that process mining initiatives have an impact
on the organizational level. This has also been reported by previous works on business
intelligence systems. For example, Shollo and Galliers (2016) observed that the
implementation of a business intelligence system leads to learning and emerging knowledge
sharing practices across the organization. In a similar vein, Mikalef and Korgstie (2020) find
that big data analytics have a positive impact on both incremental and radical process
innovation. These works also stress that organizational culture plays an important role in the
implementation of such initiatives. In this vein, recent estimates suggest that 60-70% of
implementations fail, and these failures seem to arise from organizational and cultural issues
(Olszak, 2016). Process managers reported that process mining leads to higher transparency
and may evoke privacy concerns. Therefore, it has to fit to the organizational culture, or vice
versa, an organization has to ensure that its cultural values align with this technology in order
to achieve an impact on processes (Schmiedel et al., 2015, Štemberger et al., 2018). In that
regard, Spiegel et al. (2017) introduced the concept of “embedded culture”, meaning that
management methods come with embedded values, which have to be in line with the
organization. Arguably, the same holds for the use of process mining. On the one hand, values
such as trust, openness, and excellence will be supportive for introducing this technology. On
the other hand, the technology will require an organization to integrate these values into the
daily work. This might even require organizations to unlearn established routines and
assumptions (Grisold et al., 2020a). Additional research is needed to examine the cultural
values associated with process mining.
From a research perspective, we see a subtler implication here. Traditionally, BPM research
has been concerned with producing prescriptive knowledge to facilitate, guide and manage
process work in organizations (Mendling et al., in press). This is also reflected in process mining
Accepted for publication in the Business Process Management Journal
research where the majority of works aim to improve the accuracy of process mining
techniques (dos Santos Garcia et al., 2019). Considering that process mining is associated with
changes in the broader organizational context, future research can take a descriptive stance
towards the implementation and use of process mining. This is emphasized by research in the
organizational sciences which has repeatedly found that the implementation of new
technologies in process work leads to significant, and often unintended changes in
organizations (e.g. Berente et al., 2016). Adopting methods, theories and research designs
from the organizational sciences to explore the implementation and use of process mining
may lead to a broader understanding of how this technology alters work practices (Pentland
and Feldman, 2008, Mendling et al., in press). This can be illustrated with regards to the
increased transparency created through process mining. Based on the process managers’
reports, a higher degree of transparency may simultaneously evoke both positive and negative
implications (Farjoun, 2010). On the one hand, transparency may increase communication
patterns (Dittrich et al., 2016) and coordination (Dionysiou and Tsoukas, 2013) among actors.
At the same time, it may lead to distrust as actors feel that they are under surveillance and
expose resistance (Ball, 2005). Understanding such dynamics, in turn, will help to develop
better prescriptive theories about the implementation of process mining in organizations
(Mendling et al., 2020).
5.3 Leadership and governance implications of process mining
The role of leadership and governance has also been discussed in the focus group. Since
process mining provides rapid feedback and detailed insights into how processes are carried
out, it is important to deal with this information in appropriate ways. This, however, poses
two challenges.
First, process mining provides a detailed analysis of steps that are taken while value is
generated. Traditional management activities might often be taken in relation to the
outcomes of a process. For example, when managers recognize that a process creates above-
average outcomes, they may reward actors who were involved in the process (and vice versa,
if performance was bad, they may impose sanctions). Process mining can enable new
management approaches that rely on real-time information about process activities.
Decisions can be made while the process is running. However, responding to and utilizing such
information requires managers to be able to interpret the data in meaningful ways. We see
great potential to explore and illuminate leadership practices and required skills in the era of
data-driven BPM (Davenport and Spanyi, 2019) .
Furthermore, process managers mentioned that it can be challenging to ensure on-going and
continuous use of process mining. This raises the question how governance structures should
be designed to fully exploit process mining benefits (vom Brocke et al., 2014). One aspect that
seems relevant in that regard is that many process mining tools grant access to a number of
people who are involved in the process, and hence, it might be useful to involve them in
decision-making processes. For example, one might find that actors do not adhere to standard
procedures but found more efficient ways to carry out the process (Mertens et al., 2016). In
order to understand why this is the case, it is important that everyone who is involved in the
Accepted for publication in the Business Process Management Journal
process can articulate their reasoning as well as the surrounding contextual factors (Grisold et
al., 2020b). This information, in turn, might help to make better decisions in the future or
inform process redesign efforts (Groß et al., 2019). In consequence, organizations may set in
place new governance structure. For instance, one might introduce meetings involving
multiple stakeholders in order to review and discuss insights generated through process
mining (Müller et al., 2016a).
5.4 Data availability and privacy issues related to process mining
Process managers reported that the availability of data plays an important role. Of particular
interest is the question how they can collect and integrate additional relevant data, e.g. via
sensors, in order to further develop process mining capabilities. Companies will need to
develop data collection strategies, starting with an analysis of the information needs or in
wider terms the analytical opportunities and defining which data to collect from existing
sources, just as well as for which data to establish new sources (Beverungen et al., 2020).
Process managers also reported on issues connected to privacy. When collecting and
integrating data for process mining, it is often not clear who the data owner is. Furthermore,
even when data ownership is clear, it has to be clarified if/to what extent the data can be
used. Some claims suggest that traditional privacy models need to be aligned with the
requirements arising from big data. For example, data ownership is often not clear (Perreault,
2015) and the increasing amount of available data leads to surveillance opportunities (Hugl,
2010). In that regard, previous works suggested how privacy-preserving features can be built
into algorithms (Li and Sarkar, 2006).
A few studies have started to address privacy issues in relation to process mining (Liu et al.,
2016, Mannhardt et al., 2018, Mannhardt et al., 2019, Fahrenkrog-Petersen et al., 2019, Pika
et al., 2020). For example, Mannhardt et al. (2019) present a protection model for event data
and highlight potential privacy threats as well as means to protect against them. In light of the
ever-changing regulations and data protection laws, we expect that this topic deserves
continuous and in-depth contributions, accounting for specific regions and countries. For
example, the introduction of the General Data Protection Regulation (GDPR) by the European
Union has drastically changed the possibilities for the use and analysis of personal data. Such
regulations can have implications for process mining use, and research could explicitly take
such developments into account, e.g. when developing new algorithms. Future research could
open up a transdisciplinary dialogue with the law field in order to translate legal regulations
into concrete recommendations for process mining researchers.
6. Conclusion
In this article, we presented insights from a focus group study with process managers on the
adoption, use and management of process mining. The study provided us with numerous
insights about how practitioners perceive and use this technology. This allowed us to reflect
on how the topic is debated in research, and how research can develop new questions and
Accepted for publication in the Business Process Management Journal
study contexts. We identified four major themes which are relevant for process managers.
These include:
(1) What are the benefits of process mining, and how can they be calculated?,
(2) How can one account for the organizational and managerial implications of process
(3) What are leadership and governance implications of process mining? and
(4) How to tackle data availability and privacy issues?
In light of the growing popularity of process mining, and the associated opportunities to
improve business operations, we believe that these questions will gain increasing relevance
both in research and practice. We suggest that future research should explore the
organizational and managerial implications of process mining, and address the role of culture
and governance. Furthermore, we see opportunities for transdisciplinary research
approaches, particularly with respect to the business economics side of process mining as well
as the legal aspects (e.g. in terms of data privacy). Finally, the managerial and organizational
implications revealed in this study may inform research on the technical aspects of process
mining. For example, the integration of various contextual factors is important for decision-
making (Grisold et al., 2020b, Yeshchenko et al., 2018).
Our study comes with limitations. In particular, the small focus group size and the strong
emphasis on the Swiss and German market may provide a partial view on process mining
initiatives. Furthermore, our focus group study was limited to one afternoon. Additional and
more in-depth may be gained through longer studies or other study designs, e.g. Delphi
studies or surveys. Furthermore, the results point to general implications of process mining
adoption, use and management. More in-depth insights into individual cases are important to
understand how organizations realize process mining initiatives.
This work was co-funded by the Erasmus+ programme of the European Union [2019-1-LI01-
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... Against this backdrop, in particular, process mining (PM) has gained traction in recent years as a BDA technique to discover, monitor, and improve business processes based on event data that are available in organizations' information systems (IS) (Badakhshan et al., 2022). Hence, PM allows organizations to create unprecedented and continuous transparency of their end-to-end business processes as the foundation for process improvements (Grisold et al., 2020). As organizations increasingly adopt PM, its market volume has increased and is expected to reach $2.3 billion by 2025, with an annual growth rate of 33% (Biscotti et al., 2021). ...
... However, despite high expectations and widespread use in practice, organizations still struggle to implement and realize value from PM. For example, organizations fail to implement PM due to technical factors, such as availability and quality of event data, but also due to organizational factors, such as a lack of guidance on required process properties for successful PM implementation (Grisold et al., 2020). In addition, surveys show that only 9% of the organizations using PM achieved the desired improvement for their use case (Galic & Wolf, 2021). ...
... These observations indicate that organizations, first, struggle to identify and establish the antecedents necessary for implementing PM use cases, and second, lack guidance in assessing and realizing value from PM use cases. Still, organizations need to overcome these challenges not only to adopt but, in particular, to continuously identify and implement successful PM use cases where the expected benefits outweigh the required implementation effort (Grisold et al., 2020). ...
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Process mining (PM) has gained traction as a Big Data Analytics technique to discover, monitor, and improve business processes based on event data that are available in organizations' information systems. However, despite high expectations and widespread use in practice, organizations still struggle to implement and realize value from PM. In particular, organizations, first, are challenged to identify and establish the antecedents necessary for implementing PM use cases, and second, lack guidance in identifying and assessing valuable PM use cases. Even though initial studies investigated sociotechnical factors influencing the adoption, implementation, and value of PM on the organizational level, knowledge in the field is still fragmented, and we lack a systematic understanding of how organizations can assess antecedents for and value potentials of PM to identify valuable use cases. Thus, building on a design science research approach, we address this research gap by developing and evaluating a structured framework drawing on the taxonomy development method of Nickerson et al. (2013) for assessing PM use cases based on their antecedents and expected value potentials. We iteratively develop and evaluate the taxonomy grounded in theory by drawing on PM literature and related research fields and practice by conducting twelve semi-structured interviews at a German manufacturing corporation to apply and evaluate the taxonomy. Consequently, our study contributes to research on the organizational implementation and use of PM and enables researchers and practitioners to understand, operationalize, and assess the factors influencing the selection of PM use cases.
... These studies report on results obtained and illustrate the value of process mining in industry (Corallo et al., 2020). However, an important aspect impacting how an organisation accepts such results (Grisold et al., 2021) relates to how the results are visualised (Sirgmets et al., 2018;Basole et al., 2015). ...
... As companies define process KPIs with consideration to their business needs, this helps evaluate the Visualising improvement opportunities relevancy of the improvement opportunity. This finding is in line with previous research of Grisold et al. (2021), stating that the application of process mining in an organisation should be aligned with its strategy. However, our study provides additional insight into specific factors and representations to consider when applying process mining for identifying and visualising process improvement opportunities. ...
... However, while an improvement opportunity can change process performance, it can be challenging to implement the required changes due to a lack of financial, technological or human resources. This is confirmed by previous works (Bitomsky et al., 2019;Grisold et al., 2021) that highlight the need for organisations to consider multidimensional effects of improvement projects, that is to confirm that the investments required for the changes will yield sufficient returns. Therefore, while visualising the impact of improvements on the process performance, it is also advisable to consider other factors, such as cost and effort of implementation. ...
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Purpose When improving business processes, process analysts can use data-driven methods, such as process mining, to identify improvement opportunities. However, despite being supported by data, process analysts decide which changes to implement. Analysts often use process visualisations to assess and determine which changes to pursue. This paper helps explore how process mining visualisations can aid process analysts in their work to identify, prioritise and communicate business process improvement opportunities. Design/methodology/approach The study follows the design science methodology to create and evaluate an artefact for visualising identified improvement opportunities (IRVIN). Findings A set of principles to facilitate the visualisation of process mining outputs for analysts to work with improvement opportunities was suggested. Particularly, insights into identifying, prioritising and communicating process improvement opportunities from visual representation are outlined. Originality/value Prior work focuses on visualisation from the perspectives – among others – of process exploration, process comparison and performance analysis. This study, however, considers process mining visualisation that aids in analysing process improvement opportunities.
... Thus, PM might allow firms to foster and act on shared E2E process understanding. However, research on PM has, thus far, rather focused on the technical basis than on implications for process knowledge (Badakhshan et al., 2022;Grisold et al., 2020). This raises the question: How do firms use PM to create and act on a shared E2E process understanding at the firm level? ...
... Although PM is restricted to processes recorded in IS, it is currently considered the leading technology for E2E process visibility (Badakhshan et al., 2022). Yet, due to its novelty, our understanding of PM's application in firms is limited (Grisold et al., 2020), leaving questions about how PM supports the emergence and enactment of shared E2E process understanding. ...
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Firms struggle with improving end-to-end (E2E) processes due to difficulties in establishing shared E2E process understanding across firm levels. Creating behavioral visibility into processes might provide a solution, but traditional methods are limited in effectiveness. Thus, process mining (PM), offering data-driven process discovery and measurement, shows promise, but its implications on creating and acting on a shared E2E process understanding remain unclear. Addressing this gap, we conduct a single case study at a manufacturing firm guided by theories of organizational learning and organizational routines. Our preliminary findings reveal how the data-driven behavioral visibility through PM enables four mechanisms within and between the individual, team, and firm levels to create a shared E2E process understanding and change. We contribute to business process management and PM research by showing how firms use PM to overcome challenges in the multi-level process of creating and acting on a shared E2E process understanding.
... Firstly, process mining supports the visualization of processes and defines how each one of them contributes to organizational value streams (Leno et al., 2021). According to Grisold et al. (2021), a key issue in process mining is process selection based on the unique properties processes exhibit, e.g. how much data is being handled and produced by each process, which affects automation feasibility. ...
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Purpose Robotic process automation (RPA) has recently emerged as a technology focusing on the automation of repetitive, frequent, voluminous and rule-based tasks. Despite a few practical examples that document successful RPA deployments in organizations, evidence of its economic benefits has been mostly anecdotal. The purpose of this paper is to present a step-by-step method to RPA investment appraisal and a business case demonstrating how the steps can be applied to practice. Design/methodology/approach The methodology relies on design science research (DSR). The step-by-step method is a design artefact that builds on the mapping of processes and modelling of the associated costs. Due to the longitudinal nature of capital investments, modelling uses discounted cashflow and present value methods. Empirical grounding characteristic to DSR is achieved by field testing the artefact. Findings The step-by-step method is comprised of a preparatory step, three modelling steps and a concluding step. The modelling consists of compounding the interest rate, discounting the investment costs and establishing measures for comparison. These steps were applied to seven business processes to be automated by the case company, Estate Blend. The decision to deploy RPA was found to be trivial, not only based on the initial case data, but also based on multiple sensitivity analyses that showed how resistant RPA investments are to changing circumstances. Practical implications By following the provided step-by-step method, executives and managers can quantify the costs and benefits of RPA. The developed method enables any organization to directly compare investment alternatives against each other and against the probable status quo where many tasks in organizations are still carried out manually with little to no automation. Originality/value The paper addresses a growing new domain in the field of business process management by capitalizing on DSR and modelling-based approaches to RPA investment appraisal.
... Process mining comprises methods to analyze event data generated in information systems during the execution of business processes. Process mining is quickly growing in adoption, and so is its business impact [9]. ...
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Like other analytic fields, process mining is complex and knowledge-intensive and, thus, requires the substantial involvement of human analysts. The analysis process unfolds into many steps, producing multiple results and artifacts that analysts need to validate, reproduce and potentially reuse. We propose a system supporting the validation, reproducibility, and reuse of analysis results via analytic provenance and data awareness. This aims at increasing the transparency and rigor of exploratory process mining analysis as a basis for its stepwise maturation. We outline the purpose of the system, describe the problems it addresses, derive requirements and propose a design satisfying these requirements. We then demonstrate the feasibility of the central aspects of the design.
... Scholars, however, also recognized that changing business processes does not always work out as intended. The implementation of a new technology, for instance, can lead to unintended changes in business processes (Berente et al., 2016;Grisold et al., 2020a). Moreover, process participants do not always enact a process model as it has been designed. ...
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This paper examines how the use of process mining in organizations can promote change in real-world business processes via data-based process models. Drawing on routine dynamics research, we conceptualize process models as artifacts that organizational members can use to change the business processes (i.e., routines) which they perform, and we theorize how such change is possible. Our arguments (a) suggest an emergent change approach to process mining, (b) advance social business process management by unpacking the social influence of process models, and (c) suggest guidelines for practitioners that apply process mining in organizations.
Traditionally, Performance Management (PM) is considered one of the core functions of management accounting, focused on the results of business units and primarily based on financial measures. However, with the growing emphasis on process orientation and the implementation of Business Process Management (BPM), traditional PM needs to be adapted to measure what is managed, i. e. business processes. To achieve this, process-oriented organizations rely on a Process Performance Measurement System (PPMS), with Process Mining as the state-of-the-art tool for monitoring and improving processes.In theory, the Process Mining-supported PPMS should be well integrated into the PM System (PMS), and process performance should be measured holistically, i.e. by both quantitative and qualitative figures. However, in practice it remains unclear whether these criteria are being met and whether management accounting is involved in the utilization of Process Mining and the development of a holistic PPMS. To address this research gap, a multiple case study within the German energy industry was conducted. Drawing on data from 33 semi-structured interviews, this paper presents a five-stage maturity model for the implementation of a holistic, Process Mining-supported PPMS and examines how management accounting can promote progression along this path. Due to its interdisciplinary nature, this study further contributes to research by demonstrating that the involvement of management accounting is not only beneficial to the success of Process Mining and BPM, but also crucial to the management accounting profession itself.KeywordsManagement AccountingProcess MiningProcess Performance MeasurementBusiness Process ManagementMaturity Model
Process mining generates valuable insights into business processes through the analysis of event logs. However, event logs are commonly subject to various data quality issues which hinder the success of process mining initiatives in organizations. Identical timestamp errors, for example, occur when multiple events of a process instance mistakenly share the same timestamp. This error causes discovered process models to be unrepresentative and process performance analysis results to be misleading. To address this problem, we propose a method for automatically repairing identical timestamp errors in event logs. To that end, we combine existing method components for error detection and reordering of erroneous events with a novel approach for repairing timestamps based on Generative Adversarial Networks. To allow for a rigorous evaluation, we instantiate our approach as a software prototype, and use it to repair a total of six real-life and artificial event logs with overall 30 variations. Thereby, we show that the proposed method shows improved results compared to alternative approaches for repairing identical timestamp errors in event logs.
Detailed recordings of employee behaviour can give organisations valuable insights into their work processes. However, recording techniques each have their advantages and disadvantages in terms of their obtrusiveness for participants, the richness of information they capture, and the risks that are involved. In an effort to systematically compare recording techniques, we conducted a multiple-case study at a multinational professional services organisation. We followed six participants for a working day, comparing the outcomes from non-participant observation, screen recording, and timesheet techniques. We generated 136:04 h of data and 849 records of activities. We identified 58 differences between the techniques. The results show that the use of only one technique will not produce a complete and accurate record of the activities that occur on the screen (online), in the hallway (offline), and in the extra hours (overtime). Therefore, it is vital to choose a technique wisely, taking into account the type of information it does not capture. Furthermore, this study identifies some open challenges with respect to accurately recording employee behaviour.KeywordsEmployee BehaviourWork PatternsData Collection TechniquesObservationScreen RecordingTimesheet
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The behavioural comparison of systems is an important concern of software engineering research. For example, the areas of specification discovery and specification mining are concerned with measuring the consistency between a collection of execution traces and a program specification. This problem is also tackled in process mining with the help of measures that describe the quality of a process specification automatically discovered from execution logs. Though various measures have been proposed, it was recently demonstrated that they neither fulfil essential properties, such as monotonicity, nor can they handle infinite behaviour. In this article, we address this research problem by introducing a new framework for the definition of behavioural quotients. We prove that corresponding quotients guarantee desired properties that existing measures have failed to support. We demonstrate the application of the quotients for capturing precision and recall measures between a collection of recorded executions and a system specification. We use a prototypical implementation of these measures to contrast their monotonic assessment with measures that have been defined in prior research.
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Following recent calls to extend our understanding of organizational unlearning, we gain empirical insights into how the process unfolds in practice. Based on the findings of a study with change consultants in Europe, we conceptualize two forms of organizational unlearning. First, open-ended unlearning refers to instances where organizational knowledge is intentionally discarded, but the outcomes of the change process are not known. Second, goal-directed unlearning refers to situations where organizations implement specified knowledge structures that are incompatible with established ones. We also find that both forms of organizational unlearning necessitate preparatory work and interventions that increase their likelihood to succeed. Outlining the implications of the organizational unlearning concept in organizational practice contributes to a better understanding of existing research. We further discuss implications for theory and outline avenues for future research.
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Business Process Management is a boundary-spanning discipline that aligns operational capabilities and technology to design and manage business processes. The Digital Transformation has enabled human actors, information systems, and smart products to interact with each other via multiple digital channels. The emergence of this hyper-connected world greatly leverages the prospects of business processes – but also boosts their complexity to a new level. We need to discuss how the BPM discipline can find new ways for identifying, analyzing, designing, implementing, executing, and monitoring business processes. In this research note, selected transformative trends are explored and their impact on current theories and IT artifacts in the BPM discipline is discussed to stimulate transformative thinking and prospective research in this field.
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The potential of big data analytics in enabling improvements in business processes has urged researchers and practitioners to understand if, and under what combination of conditions, such novel technologies can support the enactment and management of business processes. While there is much discussion around how big data analytics can impact a firm's incremental and radical process innovation capabilities, we still know very little about what big data analytics resources firms must invest in to drive such outcomes. To explore this topic, we ground this study on a theory-driven conceptualisation of big data analytics based on the resource-based view (RBV) of the firm. Based on this conceptualisation, we examine the fit between the big data analytics resources that underpin the notion, and their interplay with organisational contextual factors in driving a firm's incremental and radical process innovation capabilities. Survey data from 202 chief information officers and IT managers working in Norwegian firms are analysed by means of fuzzy set qualitative comparative analysis (fsQCA). Results show that under different combinations of contextual factors the significance of big data analytics resources varies, with specific configurations leading to high levels of incremental and radical process innovation capabilities.
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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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Process mining has been successfully applied in the healthcare domain and has helped touncover various insights for improving healthcare processes. While the benefits of process miningare widely acknowledged, many people rightfully have concerns about irresponsible uses of personaldata. Healthcare information systems contain highly sensitive information and healthcare regulationsoften require protection of data privacy. The need to comply with strict privacy requirements mayresult in a decreased data utility for analysis. Until recently, data privacy issues did not get muchattention in the process mining community; however, several privacy-preserving data transformationtechniques have been proposed in the data mining community. Many similarities between datamining and process mining exist, but there are key differences that make privacy-preserving datamining techniques unsuitable to anonymise process data (without adaptations). In this article, weanalyse data privacy and utility requirements for healthcare process data and assess the suitabilityof privacy-preserving data transformation methods to anonymise healthcare data. We demonstratehow some of these anonymisation methods affect various process mining results using three publiclyavailable healthcare event logs. We describe a framework for privacy-preserving process mining thatcan support healthcare process mining analyses. We also advocate the recording of privacy metadatato capture information about privacy-preserving transformations performed on an event log.
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Process mining techniques use event data to show what people, machines, and organizations are really doing. Process mining provides novel insights that can be used to identify and address performance and compliance problems. In recent years, the adoption of process mining in practice increased rapidly. It is interesting to see how ideas first developed in open-source tools like ProM, get transferred to the dozens of available commercial process mining tools. However, these tools still resort to producing Directly-Follows Graphs (DFGs) based on event data rather than using more sophisticated notations also able to capture concurrency. Moreover, to tackle complexity, DFGs are seamlessly simplified by removing nodes and edges based on frequency thresholds. Process-mining practitioners tend to use such simplified DFGs actively. Despite their simplicity, these DFGs may be misleading and users need to know how these process models are generated before interpreting them. In this paper, we discuss the pitfalls of using simple DFGs generated by commercial tools. Practitioners conducting a process-mining project need to understand the risks associated with the (incorrect) use of DFGs and frequency-based simplification. Therefore, we put these risks in the spotlight.
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Privacy regulations for data can be seen as a major driver for data sovereignty measures. A specific example for that is the case of event data that is recorded by information systems during the processing of entities in domains such as e-commerce or healthcare. Since such data, typically available in the form of event log files, contains personalized information on the specific processed entities, it can expose sensitive information that may be attributed back to individuals. In recent years, a plethora of methods have been developed to analyze event logs under the umbrella of process mining. However, the impact of privacy regulations on the technical design as well as the organizational application of process mining has been largely neglected. In this paper, we set out to develop a protection model for event data privacy, which lifts the well-established notion of differential privacy. Starting from common assumptions on the event logs used in process mining, we study potential privacy leakages and means to protect against them. We show at which stages of privacy leakages, a protection model for event logs shall be used. We instantiate the notion of differential privacy for process discovery methods, i.e., algorithms that aim at the construction of a process model from an event log. The general feasibility of our approach is demonstrated by its application to two publicly available real-life events logs.