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 environment. 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 approach, 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.
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 [
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.
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 [
To achieve this, the following research questions are posed: 1. How can the impact of process mining be conceptualised (constituents and key dimensions)? 2. What are the diferent contextual elements that can influence process mining impact? 3. How might the impact of process mining difer based on the contextual elements identified in RQ 2?
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. 3https://research.aimultiple.com/process-mining-stats/
Current success research in process mining mainly explores critical success factors for adopting
process mining. A key literature on process mining success is [
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 diferently among various organisational stakeholders [ 24, 25]. Diferent scopes and measures have been used in evaluating IS success which has further complicated the understanding of the concept [22, 23].
The initial efort 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.
From a research perspective, the strides made in process mining (a sub-domain of Business
Process Management), have naturally skewed towards the technical aspects such as the developing
and designing of algorithms [
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.
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How can the impact of process mining be RQ1 conceptualised (constituents and key dimensions)?
What are the diferent Literature on success RQ 2 ccmaoinnntiiennxgftluuimaelnpecalecetmp?reonctesssthat iHnivgehstliegvaetlion fparcotcoersssamndining adoption
How might the impact of process mining difer RQ 3 based on the contextual elements identified in RQ2?
Process mining impact Detailed cmoondteexltwuiatlheelexmteenndtesd investigation (moderating and mediating factors)
Research methods
Research output Structured Literature Review - Qualitative analysis of case study data; - Expert interviews
A synthesis of existing literature on contextual elements of process mining impact to address RQ 3. - A deep understanding of the changing nature of PM impact based on contextual elements. - Ascertain the extent to which contextual factors contribute to Process Mining impact. - Provide a rich understanding of outputs from RQ 1 and 2.
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 [
• Feedback on the proposed research design and associated risks pertaining to the study area. net/profile/Jerome_Geyer-Klingeberg/publication/326466901_Process_Mining_and_ Robotic_Process_Automation_A_Perfect_Match/links/5b4f787ea6fdcc8dae2b378c/ Process-Mining-and-Robotic-Process-Automation-A-Perfect-Match.pdf. [12] S. Decker, Data-driven business process improvement: An illustrative case study about the impacts and success factors of business process mining, Thesis, Jönköping International Business School, 2019. URL: https://www.diva-portal.org/smash/get/diva2: 1319791/FULLTEXT01.pdf. [13] J. Eggers, A. Hein, Turning big data into value: A literature review on business value realization from process mining, in: ECIS, 2020. [14] S. Poelmans, H. A. Reijers, J. Recker, Investigating the success of operational business process management systems, Information Technology and Management 14 (2013) 295– 314. 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