paper

1. The impact of new IT adoption on Business
Process Management.
Tahir Ahmad
Department of Business Informatics and Operations Management,
Faculty of Economics and Business Administration,
Ghent University, Ghent, Belgium
Tahir.Ahmad@UGent.be
Abstract— Business Process Management is
dynamically changing in the era of IT and Digital
Innovations. This research focused on developing a
standardized decision tool/matrix having ability to
assist Best Fitting Technology Adoption decisions in
ambidextrous BPM environment. This study mainly
composed of three parts starting from systematic
literature review, moving towards theory development
and testing, and finally building a decision tool/matrix.
Systematic Literature Review (SLR) protocols will be
followed to explain an historical view of BPM and IT
alignment, by using NVivo tool, by developing nodes
and themes. Case studies and empirical analysis will be
performed to develop Process-Technology Fit theory
and testing it by using Structured Equation Modelling
(SEM) techniques. Further, we build artefacts and
validate them using Design Science Research (DSR).
Outcomes of this research includes presentation of
novel research agenda’s in the field of Ambidextrous
BPM, enhanced and validated form of Task-technology
Fit theory as Process-Technology Fit theory in the
context of Best Fitting Technology Adoption in
ambidextrous BPM environment. Another, foremost
output will be a decision tool or matrix that practically
assist BPM practitioners and technology adoption
decision makers to choose best fitting technology in
accordance with specific BPM capabilities.
Keywords—Business Process Management, Digital
Innovation, Ambidextrous BPM, Technology Adoption,
Design Science Research
I. INTRODUCTION
In this technological era, Business Process
Management (BPM ) is a dynamic field that is rising
through digital innovations. Digital innovations are
making our lives easier and support business units at
the decision, design and process level in
organizations. Attempts for business process
efficiency were initiated from the concept of business
process re-engineering (BPR), lean manufacturing,
six sigma, and total quality management (TQM) with
continuous improvement [1]. These management
tools and techniques are frequently used in business
process management. After the internet infusion,
enterprise resource planning (ERP) and decision
support systems (DSS) provided backing to BPM,
leading to Business Process Management Systems
(BPMS) [2]. Many studies have focused on the BPM
lifecycle, BPM capability and maturity models, and
BPMS. Business process modeling and design
remain highlighted areas, developing new modelling
languages, design tools and information
technologies.
Nowadays, an ambidextrous BPM approach is
trending in organizations to bring innovation in
business processes, resulting in smart and intelligent
BPM. It includes both exploitative and explorative
BPM approaches [3].
Information Technology (IT) or Information
Communication Technology (ICT) brings innovation
and a mindset of change into BPM. New IT includes,
for instance, cloud computing or green computing,
big data analytics, artificial intelligence (AI), Internet
of Things (IoT), industry 4.0, blockchains and agile
platforms penetrating in business processes [4][5].
Such emerging technologies enable business
processes to perform healthier by increasing the
capabilities for an optimal utilization of time and
resources within a specific process. These new
technologies are being used in each step of the BPM
lifecycle, namely from the discovery phase
throughout monitoring and control [6]. The
technologies also influence the BPM core elements,
including strategic alignment, governance, people
and culture. New IT is thus not limited to enhancing
the IT capabilities of BPM, but also enhancing
managerial competences [7].
This study’s emphasis is on three main research
areas. First, the BPM and IT co-evolution will be
scrutinized through a detailed literature review to
understand the current stage of BPM and IT. The
review also intends to dig out novel research
paradigms. Secondly, we will determine the possible
978-1-7281-4844-1/19/$31.00 ©2019 IEEE
Authorized licensed use limited to: University of Gent. Downloaded on October 12,2021 at 12:55:42 UTC from IEEE Xplore. Restrictions apply.

2. paradigms that organizations may follow to select the
best fitting technologies for BPM. By extending the
scope of the Task Technology Fit (TTF) theory [8]
towards a Process Technology Fit theory, new
theoretical foundations will be established to present
a solution how to choose the best fitting technology
in different BPM application areas to enhance
organizational performance.
Thirdly, we attempt to develop a decision
mechanism that has the ability to select the best fitting
technology/ies in specific scenarios and find an
optimal fit in accordance with an organizational
context.
In subsequent sections, we elaborate on the
problem statement, research questions, research
objectives, current research level, research methods
and the study’s timeline.
II. PROBLEM STATEMENT
Rahimi, Møller, & Hvam, [9] discussed the
integration of IT and BPM among organizational
planning levels, such as operational, managerial and
strategic levels. While similar studies on the IT
integration in the BPM discipline have been
published [9], no systematic research is available that
investigates the evolution of BPM along with IT
alignment for the purpose of identifying IT trends and
benefits of digital innovations in BPM in
ambidextrous environment. Likewise, no decision
tool or matrix is available or presented for real-time
decision making regarding the best fitting technology
use in business processes management. Information
Technology/-ies have revolutionized BPM. A
paradigm shift is witnessed from old and legacy BPM
approaches, towards an explorative type of BPM [1].
For instance, organizations practicing TQM, Six
Sigma, lean manufacturing and continues
improvement techniques are actually applying
exploitative BPM [10]. Presently, however, they are
progressively shifting towards more efficient paths,
processes, technologies and paradigms such as agile
BPM, IoT, industry 4.0, ITIL v4 and blockchains [1].
In this situation, there is a dire need of understanding
the BPM-IT success path as well as to specify the role
of new IT/technologies with particular BPM core
capabilities to achieve maximum advantages of
digital technologies. For instance, “Blockchain
technology might change governance towards a more
externally oriented model of self-governance based
on smart contracts” [11, p.11], hence governance
capability of BPM could be changed with blockchain
technology. Moreover, a comprehensive decision
tool/matrix is also required to choose the best fitting
technology in ambidextrous BPM environment of
organization’s business context.
III. RESEARCH QUESTIONS
Keeping in view the above circumstances, we will
be answering the following research questions:
RQ1. What is the state of the research regarding
the link between BPM and IT/technologies?
RQ2. How do organizations adopt the best fitting
technologies for specific business processes, given
the organization’s business context?
RQ3. How can a decision tool be build and tested
that selects the best fitting technology/-ies for
specific business processes, given an organization’s
business context?
IV. RESEARCH OBJECTIVES
Our research objectives to perform this study are
as follows.
RQ1. To benefit from the historical BPM-IT link
by investigating the current state of research and to
explore novel research avenues by means of a
research agenda(s) that focus on new IT contributions
in BPM explorative shift.
RQ2. To investigate the specific technology
characteristics as well as business process capabilities
to explain how organizations can adopt the best-
fitting technology/-ies for specific business process to
attain organizational performance in ambidextrous
BPM environment.
RQ3. To develop and test a decision tool/matrix
that can assist business process managers in choosing
the most appropriate technology/-ies as taking into
account the requirements or needs of a particular
business process used in an organization.
V. PROPOSED RESEARCH METHODS
To respond research questions, both qualitative
and quantitative research methodologies will be used.
Systematic Literature Review, interviews, case
studies, a large-scale survey and design-science
research, methodologies will be used mainly. We
continue with proposing the research methods per
RQ.
A. Methodology for RQ1 (In-Progress)
In RQ1, we will apply the systematic literature
review technique of [12] to analyze the BPM-IT co-
evolution in more detail, and to demonstrate
emerging themes along with existing BPM
frameworks including the BPM life cycle [13], BPM
core elements [14] BPM context factors [15] and
Research Agenda [16]. We will use various keywords
such as “Business Process Management”,
“Information technology”, “Agile BPM”, “Artificial
Intelligence”, Ambidexterity”, Social Media”,
“Digital Innovations”, “Internet of Things”, “Cloud
Computing”, “Industry 4.0”, “ITIL v4”,
“Blockchains”, “Explorative BPM” and other
keywords that covers new IT and explorative BPM
prospective. We will use NVivo tool recent version,
to create codes, nodes and themes to perform this
Authorized licensed use limited to: University of Gent. Downloaded on October 12,2021 at 12:55:42 UTC from IEEE Xplore. Restrictions apply.

3. meta-analysis. Results will be demonstrated in
table(s), graph(s) and conceptual framework.
a) Output:
A better understanding of the BPM-IT link over
time, and the corresponding research avenues. This
output can be in the form of conceptual model based
on existing studies.
B. Methodology for RQ2
In our second study, we will conduct case studies
in organizations that already using exploitative and
explorative BPM approaches, or organizations in the
transformation phase of adopting explorative BPM
techniques. Mainly ten to 15 different case studies
will be targeted for a better understanding and to find
patterns, depending upon when data saturation will
be reached. After theory development, a large-scale
survey will be conducted in organizations of Belgium
and Pakistan, that fall under the above scope. A
survey instrument will be developed and a statistical
analysis (SEM) will be performed. Data will be
collected from BPM and IT managers and
practitioners by using purposive and quota sampling
techniques. The population will be first segmented
into mutually exclusive sub-groups, and then
purposive sampling can be used.
a) Proposed theoretical framework
The best fit between technology adoption and BPM
can be explained in the light of Task Technology Fit
theory which says that technology is useless if it is
not fulfilling expectations of user/practitioner.
Technology has positive impact on individual
performance, and technology characteristics should
be aligned with tasks or processes performed by the
user or system [17]. Efficient BPM should capable to
trigger the desired changes in organization for
continues improvement and competitive advantage.
Technology is also being used to enhance the
ambidextrous BPM capabilities both exploitative and
explorative. To predict process behavior, technology
plays a vital role in process improvement and
innovation. This process initiated by technology
automatically explores the character of improvement
in business process management [1].
Companies abruptly using Social Media, Cloud
Computing, Internet of Things, Artificial Intelligence
in decision making, and Big Data Analytics in
operations and processes. Facebook, Linkedin, Uber,
WhatsApp, Bitcoin and Paypal are technological
innovations that reshaped business processes
according to the need of 21st
century customers [1].
In a nutshell, we extend Task-Technology Fit Theory
in shape of Process-Technology Fit Theory on the
basis of literature as well as statistical evidence.
Organizational context and technological context are
significant indicators of best fitting technology in
BPM. Similarly, the environmental factors such as
competitive pressure, trading partners and legal
obligations are paramount factors for new technology
adoption. Therefore, we will develop theory
accordingly.
First, a tentative research model is developed as
shown in figure 1, which reflects the possible output
of this section in the form of theoretical framework
(that will be developed further in more detail sub-
factors). At this moment, it shows that Process
Characteristics, Technology Characteristics and
Ambidextrous BPM Characteristics have a role in the
adoption of the Best Fitting Technology Adoption.
The best fitting technology adoption will leads to
boost organizational performance. Hence, Best fitting
Technology Adoption is also act as a mediator
between Process Characteristics, Technology
Characteristics, Ambidextrous BPM characteristics
and Organizational Performance. Environmental
Factors behave as moderator between relationship of
Process Characteristics, Technology Characteristics,
Ambidextrous BPM Characteristics, with Best fitting
Technology Adoption. It also moderate the relation
of Best Fitting technology Adoption and
Organizational Performance.
Therefore, the following tentative hypothesis are
developed to test later by using case studies and
large-scale survey analysis.
H1: Best Fitting Technology Adoption mediates the
relationship between Process Characteristics and
Organizational Performance.
H2: Best Fitting Technology Adoption mediates the
relationship between Technology Characteristics
and Organizational Performance.
H3: Best Fitting Technology Adoption mediates the
relationship between Ambidextrous BPM
Characteristics and Organizational Performance.
H4: Environmental Factors moderates the
relationship between Process Characteristics and
Best Fitting Technology Adoption.
H5: Environmental Factors moderates the
relationship between Technology Characteristics
and Best Fitting Technology Adoption.
H6: Environmental Factors moderates the
relationship between Ambidextrous BPM
Characteristics and Best Fitting Technology
Adoption.
H7: Environmental Factors moderates the
relationship between Best Fitting Technology
Adoption and Organizational Performance.
H8: Process Characteristics and Technology
characters influence each other.
Authorized licensed use limited to: University of Gent. Downloaded on October 12,2021 at 12:55:42 UTC from IEEE Xplore. Restrictions apply.

4. These tentative hypotheses will be tested by both case
studies and interviews with BPM practitioners as well
as quantitatively with large-scale survey based on a
survey instrument; the research model is developed
illustrated in figure 1. Hence, the best technology and
process fit could become attainable. Modifications in
hypotheses and the details of process, technology and
environmental sub-characteristics will be explored
and presented during this research phase. Moreover,
findings will highlight how an organization can adopt
the best fitting technologies for specific business
processes.
b) Output:
The intended output will be an extension of the Task-
Technology Fit theory [8] towards a validated
Fig. 1. Research Model
Process-Technology Fit Theory (e.g. by changing
Task characteristics into Process characteristics, plus
“Ambidextrous BPM characteristics”, and by adding
context factors such as organization size and sector),
in order to ultimately uncover adoption patterns. This
theory will not focus on particular technologies
(which will change fast over time), but rather on
technology characteristics.
C. Methodology for RQ3
Due to the pragmatic nature of RQ3, we will use
the design-science research methodology [18] in our
third study. This study focuses on developing
artifacts (i.e. construct, model, method, instantiation)
for finding the best fitting technology use in BPM
and to develop a decision matrix/tool having ability
to provide guidelines to practitioner and BPM users.
This will be the two way process as we develop
model based on previous studies, case studies, large-
scale surveys and validate them with real-time
scenarios in companies that will be in transformation
phase of adopting explorative BPM approach, will be
targeted for this purpose. Experience of practical
solutions will be documented and designed
iteratively by field studies and action research.
a) Output:
A validated decision tool/matrix or aid that can
give advice to select the best matching technology for
an organization’s specific process characteristics and
contextual factors. In other words, this third study
intends to support the theory from RQ2 with a
practical decision tool for organizations.
VI. TIMELINE
This PhD research is composed of three parts.
Ideally, on average, one academic year will be
allocated to each phase. The timeline of this study is
given in Table I. So far, the PhD is sampling and
reviewing the articles to be considered in the SLR
study of RQ1.
Fig. 1. Research Model
Process Characteristics
Ambidextrous BPM
Characteristics
Technology Characteristics
Best fitting Technology
Adoption
Environmental Factors
Organizational
performance
H1
H4
H5
H6
H7
H2
H3
H8
Authorized licensed use limited to: University of Gent. Downloaded on October 12,2021 at 12:55:42 UTC from IEEE Xplore. Restrictions apply.

5. TABLE I TIMELINE
ACKNOWLEDGEMENT
This PhD project will be organized by Ghent
University (Belgium) under the supervision of Prof.
dr. Amy Van Looy.
REFERENCES
[1] M. Rosemann, “Proposals for future BPM research
directions,” Lect. Notes Bus. Inf. Process., vol. 181
LNBIP, pp. 1–15, 2014.
[2] S. Sabrina and P. Ivan, “Development of ERP and
other large Business Systems in the context of new
Trends and Technologies,” Vallis Aurea, vol. 3, no. 2,
2017.
[3] R. Rialti, G. Marzi, M. Silic, C. Ciappei, and C.
Ciappei, “Ambidextrous organization and agility in
big data era The role of business process management
systems,” 2018.
[4] D. M. E.-D. M. Hussein, M. Hamed, and N. Eldeen,
“A Blockchain Technology Evolution Between
Business Process Management (BPM) and Internet-of-
Things (IoT),” Int. J. Adv. Comput. Sci. Appl., vol. 9,
no. 8, pp. 442–450, 2018.
[5] D. Ruiz-Fernández, D. Marcos-Jorquera, V. Gilart-
Iglesias, V. Vives-Boix, and J. Ramírez-Navarro,
“Empowerment of patients with hypertension through
BPM, IoT and remote sensing,” Sensors (Switzerland),
vol. 17, no. 10, 2017.
[6] D. Suša Vugec, K. Tomii-Pupek, and V. Bosilj
Vukši, “Social business process management in
practice: Overcoming the limitations of the traditional
business process management,” Int. J. Eng. Bus.
Manag., vol. 10, pp. 1–10, 2018.
[7] W. P. Wong, M. Tseng, and K. H. Tan, “Total Quality
Management Business Excellence A business
process management capabilities perspective on
organisation performance,” no. October 2014, pp. 37–
41, 2013.
[8] P. Trkman, “The critical success factors of business
process management,” Int. J. Inf. Manage., vol. 30,
no. 2, pp. 125–134, 2010.
[9] F. Rahimi, C. Møller, and L. Hvam, “Business process
management and IT management: The missing
integration,” Int. J. Inf. Manage., vol. 36, no. 1, pp.
142–154, 2016.
[10] H. Santos and C. Alves, “A2BP: A Method for
Ambidextrous Analysis of Business Process,” Proc.
19th Int. Conf. Enterp. Inf. Syst., no. April, pp. 227–
238, 2017.
[11] J. Mendling, I. Weber, wil van der Aalst, jan vom
Brocke, C. Cabanillas, F. Daniel, S. Debois, claudio
di Ciccio, M. Dumas, S. Dustdar, A. Gal, L. García-
bañuelos, G. Governatori, R. Hull, and marcello la
rosa, “Blockchains for Business Process Management
- Challenges and Opportunities,” ACM Trans. Manag.
Inf. Syst., vol. 9, no. 1, pp. 31–35, 2018.
[12] B. Kitchenham, “Procedures for Performing
Systematic Reviews,” 2004.
[13] M. Dumas, M. La Rosa, J. Mendling, and H. A.
Reijers, Fundamentals of Business Process
Management. 2013.
[14] M. Rosemann and J. Brocke, The Six Core Elements
of Business Process Management. 2015.
[15] J. Vom Brocke, S. Zelt, and T. Schmiedel, “On the
role of context in business process management,” Int.
J. Inf. Manage., vol. 36, no. 3, pp. 486–495, 2016.
[16] A. Van Looy and G. Poels, “A Practitioners ’ Point Of
View On How Digital Innovation Will Shape The
Future Of Business Process Management: Towards A
Research Agenda .,” in Proceedings of the 52nd
Hawaii International Conference on System Sciences |
2019 A, 2019, vol. 6, no. gap 2, pp. 6448–6457.
[17] D. L. Goodhue and R. L. Thompson, “Task-
Technology Fit and Individual,” vol. 19, no. 2, pp.
213–236, 1995.
[18] K. Peffers, T. Tuunanen, M. A. Rothenberger, and S.
Chatterjee, “A Design Science Research Methodology
for Information Systems Research,” J. Manag. Inf.
Syst., vol. 24, no. 3, pp. 45–78, 2007.
Timeline
Phase -I
Task Start Date End Date
Initial Article Readings 24-09-18 28-02-19
Systematic Literature Review 01-11-18 29-04-19
Analyzing Results 13-04-19 05-06-19
Phase 1 Write-up 22-04-19 30-06-19
Phase -II
Task Start Date End Date
Build initial theoretical model 01-07-19 20-08-19
Case studies 01-08-19 31-10-19
Large scale survey 01-10-19 27-02-20
Analyzing Results and build a revised
theoretical model
01-02-20 22-03-20
Phase 2 Write-up 16-02-20 30-04-20
Phase -III
Task Start Date End Date
Developing artefacts 01-05-20 31-07-20
Validation through various testing
techniques
01-08-20 30-11-20
Modifications 01-12-20 30-01-21
Developing a Decision tool/Matrix/Aid
path
01-02-21 31-03-21
Phase-III Write-up 01-03-21 15-04-21
Final Thesis Write-up 16-04-21 30-06-21
Final Defense Fall 2021
Authorized licensed use limited to: University of Gent. Downloaded on October 12,2021 at 12:55:42 UTC from IEEE Xplore. Restrictions apply.