=Paper=
{{Paper
|id=Vol-2973/paper_185
|storemode=property
|title=A Conceptualisation of Process Mining Impacts
|pdfUrl=https://ceur-ws.org/Vol-2973/paper_185.pdf
|volume=Vol-2973
|authors=Azumah Mamudu
|dblpUrl=https://dblp.org/rec/conf/bpm/Mamudu21
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==A Conceptualisation of Process Mining Impacts==
https://ceur-ws.org/Vol-2973/paper_185.pdf
A Conceptualisation of Process Mining Impacts
Azumah Mamudu
Queensland University of Technology, 2 George Street, Brisbane City, QLD 4000, Australia
Abstract
As the process mining domain continues to see growing interest among various industry sectors globally,
it is expedient to study its impact in organisations. Currently, no models or frameworks exist specifically
to assess the impact of process mining. Although generic IT/ IS impact models or frameworks could
be adopted, they are unlikely to cater to the contextual nuances particular to the process mining envi-
ronment. This study aims to provide a clear conceptualised understanding of process mining impact
and its success factors and contextual elements. The extent to which these contextual factors contribute
to Process Mining impact will also be ascertained. Applying a multi-phased qualitative analysis ap-
proach, secondary data from published case studies and empirical data from real-life case studies will
be analysed to derive an empirically validated process mining impact model. Current research progress
includes a structured literature review on Success literature and building an a priori model from the
analysis of secondary case studies.
Keywords
Process mining success, process mining impact model, process mining impact, impact models
1. Background and Research Problem
Process mining is a field of techniques that extract insights from an organisation’s information
systems using readily available event logs [1, 2]. It draws from computational intelligence, data
mining and process science to enhance business processes. The key capabilities of process mining
techniques are their ability to automatically discover process models, monitor performance
indicators, identify bottlenecks and resource constraints in a business process and assess
regulatory performance [1, 3]. Several process mining tools and techniques have been developed
and applied in a variety of contexts with promising results [4]. It continues to experience a
growing interest in finance, software development, insurance, shared services, and many other
sectors [5, 6, 3]
The process mining market is estimated to continue to grow. Results from the 2021 Global
Process Mining Survey of 106 IT and business executives conducted by Deloitte1 indicated that
67% of the respondents have started implementing process mining. 87% of non-adopters are
planning to conduct pilot runs. 83% of organisations using process mining on a global scale
intend to expand their initiatives. In all, 84% of respondents believe process mining delivers
value. In 2019, Gartner2 estimated that new product license and maintenance revenue for
process mining was about $320 million. It was also estimated that by 2023, the global process
Proceedings of the Demonstration & Resources Track, Best BPM Dissertation Award, and Doctoral Consortium at BPM
2021 co-located with the 19th International Conference on Business Process Management, BPM 2021, Rome, Italy,
September 6-10, 2021
" azumah.mamudu@hdr.qut.edu.au (A. Mamudu)
0000-0003-0897-5112 (A. Mamudu)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings CEUR Workshop Proceedings (CEUR-WS.org)
http://ceur-ws.org
ISSN 1613-0073
1
https://www2.deloitte.com/de/de/pages/finance/articles/global-process-mining-survey-2021.html
2
https://www.gartner.com/doc/reprints?id=1-24ARMY34&ct=201002&st=sb
analytics market size will grow from $185 million in 2018 to $1.42 billion at a Compound Annual
Growth Rate (CAGR) of 50% 3 .
Considering current market trends and the attention of the BPM research community towards
the applicability of process mining in organisations [7], a unique opportunity is presented to
thoroughly investigate the nature of impact in the process mining domain. This forms the crust
of my research.
Using qualitative analysis techniques, this research seeks to conceptualise the notion of
impact in process mining through empirically-derived assessment models that elaborately
capture its key dimensions and contextual elements.
This study proposes a definition for process mining impact as a short- or long-term outcome or
series of outcomes, perceived by relevant stakeholders which is directly or indirectly attributable
to the implementation and use of a process mining tool in an organisation.
1.1. Research problem and questions
As process mining continues to proliferate the business environment, the evidence to support its
impact and contribution to organisations is mostly anecdotal. To the best of my knowledge, apart
from reports and use cases from practitioners, tool vendors and consultants [8], no empirically
designed models or frameworks exist for assessing the impact of process mining outcomes in
organisations. It could be argued that existing generic impact models could be used to measure
impact in process mining. However, such generic models or frameworks are less likely to
properly capture the unique contextual nuances that pertain to the process mining environment.
This could hinder a deep appreciation of the nature of impact that process mining provides
and inhibit its potential to provide optimal value as a technology investment [9]. Based on
this motivation, this research seeks to propose an extensive model that identifies and evaluates
process mining impact and its influencing contextual factors.
To achieve this, the following research questions are posed:
1. How can the impact of process mining be conceptualised (constituents and key dimen-
sions)?
2. What are the different contextual elements that can influence process mining impact?
3. How might the impact of process mining differ based on the contextual elements identified
in RQ 2?
1.2. Research contribution
This study is expected to make the following theoretical contributions to research.
• Provide a clear conceptualised understanding of process mining impact.
• Provide a synthesis of existing literature on contextual elements of process mining impact
to address RQ 3 (secondary contribution).
• Deliver a deep understanding of the changing nature of PM impact based on contextual
elements.
• Propose a set of artefacts, actionable principles and procedural guidelines for measuring
the interrelationships between process mining impact and contextual factors.
3
https://research.aimultiple.com/process-mining-stats/
2. Related Work
Current success research in process mining mainly explores critical success factors for adopting
process mining. A key literature on process mining success is [10]. They propose a model from
theoretical and empirical foundations which identifies success factors and measures for process
mining. Another relevant study focuses on the success factors for process mining adoption
in organisations [3]. They explore the enabling factors and challenges for the early stages
of adopting process mining tools by organisations. Elements such as speed, efficiency and
compliance are identified as key success factors in [11] for achieving automation and digital
transformation in process mining although their main study focus is not success factors.Two
other studies by [12] and [13] touch on the impact and value realisation of process mining, but
they provide no clear direction to achieving these objectives.
Success research in Business Process Management (BPM), the mother-domain of process
mining has been substantial, both theoretically and empirically. For instance, some BPM success
models, such as by [14], extend the DeLone and McLean IS success model to test systems
quality and use of BPMS applications in operational activities. Other models [15] are unique
to certain industry contexts such as banking. The works by [16] and [17] propose a holistic
BPM framework with detailed sub-constructs for achieving BPM success. Other such as [18]
consider the contribution of organisational roles and responsibilities to BPM success initiatives
while [19] empirically test how organisational characteristics such as culture can influence
success factors in BPM adoption. [20] investigate success factors for the individual stages of
BPM adoption. Finally, BPM critical success factors have been systematically assessed for the
Public Sector [21].
In the broader area of IS, success has been an active research area for many decades [22, 23, 24,
25]. However, it is understood differently among various organisational stakeholders [24, 25].
Different scopes and measures have been used in evaluating IS success which has further
complicated the understanding of the concept [22, 23].
The initial effort to unify diverse views of success in IS research was conducted by DeLone
and McLean in 1992. Currently the most cited and recognised success model in IS literature,
the DeLone and McLean model of IS success is a taxonomy of six dimensions which presents
a unified and integrated conceptualisation of the dependant variable [26, 27]. It identifies six
dimensions of IS success [22]. The DeLone and McLean IS success model has been examined by
other researchers such as [28], [29] and [30] who have either re-specified, extended or studied
the inter-relatedness of its dimensions with other independent variables.
Gable et al. [23] re-conceptualise IS success in a formative and multi-dimensional model
that provides a benchmark for monitoring IS performance based on current and anticipated
net benefits from an information system as perceived by relevant stakeholders. Their 2-part
four-dimensional model measures individual and organisational impact to date on one half, and
uses system quality and information quality to assess probable future impacts on the other half.
3. Relation of Work to BPM State of the Art in Research
From a research perspective, the strides made in process mining (a sub-domain of Business Pro-
cess Management), have naturally skewed towards the technical aspects such as the developing
and designing of algorithms [6, 7]. The more managerial aspects that study process mining
in practice still has significant research gaps. For instance, over the years, most case studies
about the application of process mining have touched on some issues relating to process mining
in the organisational context, process mining methodologies, process mining success factors
and process mining adoption [5, 7]. Other pertinent managerial topics relating to governance,
organisational culture and enterprise integration of process mining have received little to no
attention hence a call for research contributions [7] to fill some of these existing gaps.
4. Research Methodology
The study will employ a multi-phased qualitative study using secondary data from published case
studies and empirical data from selected multiple real life case studies. Qualitative techniques
will be used to inductively derive an a priori model which will be validated using primary data
from three to five case studies. Table 1 below captures a detailed representation of the research
design based on the respective research questions.
Research Nature of Research Research
SN Research input
questions enquiry methods output
How can the impact
- Structured Literature
of process mining be - Secondary case evidence; A clear conceptualised
Detailed Review;
RQ1 conceptualised - In-depth case studies understanding of process
investigation - Qualitative analysis
(constituents (empirical) mining impact
of case study data
and key dimensions)?
A synthesis of existing
What are the different
Literature on success literature on contextual
contextual elements that High level Structured Literature
RQ 2 factors and elements of process
can influence process investigation Review
process mining adoption mining impact to
mining impact?
address RQ 3.
- A deep understanding of the
changing nature of PM impact
How might the impact Process mining impact
based on contextual elements.
of process mining differ model with extended - Qualitative analysis
Detailed - Ascertain the extent to which
RQ 3 based on the contextual contextual elements of case study data;
investigation contextual factors contribute to
elements identified (moderating and - Expert interviews
Process Mining impact.
in RQ2? mediating factors)
- Provide a rich understanding
of outputs from RQ 1 and 2.
Table 1
Research Design
4.1. Research progress
The research is still in its early stages. A thorough literature search has been conducted using
specific success and impact related keywords to collate success and impact literature from the
process mining domain. Due to limited published literature, the search was expanded to BPM
and IS domains. The literature analysis outcomes were used to draft a structured literature
review. Exploratory studies have been conducted by qualitatively analysing secondary data
from 12 real-life best practice process mining use cases by [8]. An initial version of a conceptual
model for process mining impact has been derived. The next stage involves a confirmatory
analysis of the model using empirical data from in-depth case studies.
4.2. Open points and issues
• Feedback on the proposed research design and associated risks pertaining to the study
area.
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