[AISWorld] Call for BISE Special Issue Process Mining at the Enterprise Level

[J Mendling]

Call for Papers

Business & Information Systems Engineering

Special Issue on Process Mining at the Enterprise Level
(See http://www.bise-journal.com/)


Theme

Process Mining is a novel technology that helps enterprises to better
understand their business processes. Over the last 20 years, there has
been intensive research into various process mining techniques. These
techniques support the automatic discovery of business process models
from event log data, the checking of conformance between specified and
observed behavior, the identification of various variants of a
business process, non-compliant behavior, performance-relevant
insights, and so forth.

Research on process mining has mostly focused on devising new or
better algorithms (see van der Aalst 2016; Augusto et al. 2019). There
are a few exceptions, among others the following. Van der Aalst et al.
(2007) were the first ones to discuss process mining from the
perspective of applications in industrial practice. Jans et al. (2014)
applied process mining techniques to enrich audit evidence during a
financial statement audit. Vom Brocke and Mendling (2018) present
various applications of process mining in hospitals, insurances,
software usability analysis, and logistics.

In recent years, process mining has found its way into enterprise
software (Dumas et al. 2019), and has, thus, become available for
companies in their daily practice of business process management.
Companies like Celonis, Fluxicon, Signavio, and Software AG are among
the roughly 20 companies that Gartner monitors. Kerremans (2019) from
Gartner identifies business process improvement, auditing and
compliance, process automation, digital transformation, and IT
operations (in order of decreasing importance) as practical use cases
for process mining in enterprise settings.

Some contributions have been made towards understanding process mining
from an enterprise perspective. For instance, van Eck et al. (2015)
and Aguirre et al. (2017) describe methodologies how process mining
projects can be conducted and Maruster and van Beest (2009) provide a
methodology how business processes can be redesigned with the help of
process mining. Examples of domain-specific proposals in healthcare
are Rebuge and Ferreira (2012) and Fernandez-Llatas et al. (2015).
Grisold et al. (2020) have introduced process mining as a strategy of
inquiry to study in organizational science and to study organizational
change in specific. Mans et al. (2013) have identified success factors
for process mining projects. However, what is largely missing so far
is research on how enterprises adopt process mining technology and how
they integrate it into their information systems landscape.


Invited Contributions

The ambition behind this special issue is to fill the described
research gap. We invite original contributions that investigate how
enterprises actually adopt and use process mining software. We are
specifically interested in submissions that make use of the extensive
set of theories that have been discussed in information systems
research and organizational science. These contributions are expected
to utilize empirical research methods such as case studies, surveys,
experiments (to name but a few) towards leveraging insights into how
and why enterprises succeed or fail when adopting and using process
mining software.

Topics that are welcomed include, but are not limited to the following:

- How is process mining used and adopted at the enterprise level?

- What is the potential of using various types of data in process mining?

- How does process mining complement other approaches and technologies?

- How do enterprises build suitable data sets?

- What are the implications for management of using process mining?

- Which governance structures do enterprises develop for process mining?

- How do enterprises calculate the business case of process mining?

- How does process mining change organizational culture?

- How does process mining change the required skill sets of tool users?

- How is process mining integrated into the IT landscape?

- How is process mining integrated with existing business process 
methodologies?

- How is process mining adopted in specific application domains, e.g.,
accounting, health, finance, HR, tax, etc.?

- How is process mining used to support digital transformation initiatives?

- What strategic implications for enterprises emerge from process mining 
usage?

- What is the business impact of adopting process mining?

- What is the overall business value of process mining?

- What is the transformative nature of process mining at the enterprise 
level?


Submission Guidelines

Please submit papers by 1 Nov 2020 at the latest via the journal’s
online submission system (http://www.editorialmanager.com/buis/).
Please observe the instructions regarding the format and size of
contributions to Business & Information Systems Engineering (BISE).
Papers should adhere to the submission general BISE author guidelines
(http://www.bise-journal.com/author_guidelines).

All papers will be reviewed anonymously (double-blind process) by at
least two referees with regard to relevance, originality, and research
quality. In addition to the editors of the journal, including those of
this special focus, distinguished international scholars will be
involved in the review process.


Schedule

- Submission Deadline: 01 November 2020

- Author Notification 1: 06 January 2021

- Completion Revision 1: 01 March 2021

- Author Notification 2: 15 April 2021

- Completion Revision 2: 22 May 2021

- Final Workshop Meeting: with ECIS 2021 in Timisoara, Romania,
(mandatory of acceptance candidates)


Editors of the Special Issue

- Jan vom Brocke, University of Liechtenstein, Vaduz, Liechtenstein

- Mieke Jans, Hasselt University, Hasselt, Belgium

- Jan Mendling, Vienna University of Economics and Business, Vienna,
Austria (corresponding editor: jan.mendling at wu.ac.at)

- Hajo A. Reijers, Utrecht University, Utrecht, The Netherlands


References

Aguirre S, Parra C, Sepúlveda M (2017) Methodological proposal for
process mining projects. Int J Bus Process Integr Manag 8(2):102-113

Augusto A, Conforti R, Dumas M, La Rosa M, Maggi FM, Marrella A,
Mecella M, Soo A (2019) Automated discovery of process models from
event logs: review and benchmark. IEEE Trans Knowl Data Eng
31(4):686-705

Dumas M, La Rosa M, Mendling J, Reijers HA (2018) Fundamentals of
business process management, 2nd edn. Springer, Heidelberg

Fernández-Llatas C, Lizondo A, Monton E, Benedí J-M, Traver V (2015)
Process mining methodology for health process tracking using real-time
indoor location systems. Sensors 15(12):29821-29840

Grisold T, Wurm B, Mendling J, vom Brocke J (2020) Using process
mining to support theorizing about change in organizations. In:
Proceedings HICSS 2020

Jans M, Alles MG, Vasarhelyi MA (2014) A field study on the use of
process mining of event logs as an analytical procedure in auditing.
Account Rev 89(5):1751-1773

Kerremans M (2019) Market guide for process mining. Gartner Report

Mans R, Reijers HA, Berends H, Bandara W, Prince R (2013) Business
process mining success. In: Proceedings ECIS 2013, pp 89 ff

Maruster L, van Beest NRTP (2009) Redesigning business processes: a
methodology based on simulation and process mining techniques. Knowl
Inf Syst 21(3):267-297

Rebuge A, Ferreira DR (2012) Business process analysis in healthcare
environments: a methodology based on process mining. Inf Syst
37(2):99-116

van der Aalst WMP (2016) Process mining – data science in action, 2nd
edn. Springer, Heidelberg

van der Aalst WMP, Reijers HA, Weijters AJMM, van Dongen BF, Alves de
Medeiros AK, Song M, Verbeek HMW (2007) Business process mining: an
industrial application. Inf Syst 32(5):713-732

van Eck ML, Lu X, Leemans SJJ, van der Aalst WMP (2015) PM^2 : A
Process Mining Project Methodology. In: Proceedings CAiSE 2015, pp
297-313

vom Brocke J, Mendling J (2018) Business process management cases,
digital innovation and business transformation in practice. Springer,
Heidelberg


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