=Paper=
{{Paper
|id=Vol-2574/invited2
|storemode=property
|title=Meta-Study of Enterprise Modelling – Why and How (invited paper)
|pdfUrl=https://ceur-ws.org/Vol-2574/invited2.pdf
|volume=Vol-2574
|authors=Mijalche Santa,Geert Poels
|dblpUrl=https://dblp.org/rec/conf/vmbo/SantaP20
}}
==Meta-Study of Enterprise Modelling – Why and How (invited paper)==
https://ceur-ws.org/Vol-2574/invited2.pdf
Meta-Study of Enterprise Modelling -
Why and How
Mijalche Santa and Geert Poels
Faculty of Economics and Business Administration, Ghent University, Belgium
{mijalche.santa, geert.poels}@ugent.be
Abstract. In the literature there are calls for a paradigm shift and (re-)organizing
research on enterprise modelling in order to meet the challenges of modern
enterprises and increase the impact of the field. In this paper we argue that a meta-study
can strongly contribute in providing answers to these challenges. Wepresent how
the four components of a meta-study: meta-method analysis, meta-data analysis,
meta-theory analysis, and meta-synthesis can contribute to thedevelopment of a
theoretical framework that can provide a basis for futureresearch in enterprise
modelling. We also identify the research challenges to beovercome.
Keywords: Meta-study, Enterprise modelling, Theoretical framework
1 Introduction
The importance of conceptual modelling was understood as early as the mid to late
1960s [1]. Conceptual modelling focusses on “capturing and representing human
perceptions of the real world” in such a manner that they can be included in an
information system [2]. As such, conceptual modelling has always been an essential
part of developing information systems [3]. In the late 80’s, Enterprise Modelling
emerged as a discipline that investigated how to describe various aspects of an
enterprise [4]. Since then, it is at the core of the Management Information Systems
research domain and has been a subject of intensive research for about two decades [5].
In this period, a large body of research has been devoted to the development of
enterprise modelling approaches. As a result, the discipline starts to show signs of
modest maturity [6]. However, there are calls for changes in enterprise modelling
research. The authors of [7] conclude that “paradigm shift is needed for dealing
adequately with the challenges that modern enterprises face”. For example, one aspect
are the informal organizational elements, like values, beliefs, leadership, culture,
power, politics and others are largely neglected in the ‘enterprise model set’ [8]. One
reason can be that, historically, the rational approach has dominated the research in
Enterprise Modeling [9]. However, it is argued that this approach is essentially over
simplistic in nature and reduces complexity to an easier, simpler structure that does not
represent reality [10]. On the other hand, adding complexity to a model carries costs
and it is needed to investigate whether a balance can be achieved. This is especially
important in the new context of digital era, where the information and communication
technology we use now are different from the one in the past [11, 12]. For example, the
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Attribution 4.0 International (CC BY 4.0).
case of Internet of Things [13]. In this direction, in [5] there is a call for (re-)organizing
research on enterprise modelling in order to increase the impact of the field. One
suggestion is enterprise modelling approaches to depart the machine-like logic of the
organization [14]. Finally, enterprise modelling finds “their origin in the practitioners’
community and lack a solid scientific foundation, which hampers both theoretical and
practical progress” [15]. Recognizing this, members of the community call “to develop
new, appropriate theories, models, methods and other artifacts for the analysis, design,
implementation, and governance of enterprises by combining (relevant parts of)
management and organization science, information systems science, and computer
science…. The result of our efforts should be theoretically rigorous and practically
relevant” (cfr. the Enterprise Engineering Manifesto). The above calls for paradigm
shift raise different why questions: why Enterprise Modelling is not appropriately
developed to meet the current challenges, why it has lack of practical impact or why it
has lack of theoretical rigor.
Reflecting on these developments, we feel it is both timely and worthwhile to
examine the research on enterprise modelling. Based on the calls for paradigm shift,
our goal is to explore why is enterprise modelling research as currently conducted
emphasizing the wrong things or overlooking aspects that are worth investigating?
When we will perform that, we will engage in reflexive investigation for “what might
be”. To achieve this purpose, we propose that a meta-study on the enterprise modelling
literature needs to be performed.
A meta-study [16] enables the synthesis of published research, which includes a
systematic approach to the collection of studies, a critique of methodological
approaches, and a synthesis of findings. The goal is the development of a theoretical
framework that can provide a base for the future research in a discipline [17].
In this short paper we attempt to justify the usage of meta-study as an approach for
moving forward the discipline of Enterprise Modelling and to present the challenges in
the realization of such endeavor. As a result, we aim to initiate a discussion whether
meta-study is the right approach and how its challenges can be overcome.
2 Justification for using meta-study
The initial challenge of critically examining the state-of-the-art in enterprise modelling
comes from the breadth and depth of the discipline. Enterprise modelling is a generic
term which covers the set of activities, methods and tools related to developing models
for various aspects of an enterprise or a network of enterprises [4]. An enterprise model
may comprise a number of related “sub-models”, each focusing on a particular aspect
of the problem domain, such e.g. capabilities, processes, business rules,
concepts/information/data, vision/goals, strategies, business services, and
organizational structures [18]. Furthermore, enterprise modelling can have different
purposes, including human sense making and communication, computer assisted
analysis, and model deployment and activation [19]. All this pose three central, but
related, methodological challenges:
• How to analyze the primary research publications on enterprise modelling?
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• How to synthesize the primary research publications on enterprise modelling?
• How to move from synthesis to reflection of “what might be” in enterprise
modelling?
Disentangling the analysis, synthesis (“what is the story?”) and reflection (“what
might be?”) of the primary research publications is complicated because they are inter-
related. The work of these three aspects should not be seen as a linear, but as a back-
and-forth cyclical process. During the analysis we will identify data points that are base
for synthesizing to themes or dimensions, but this output is not definitive and can
change. The reflective engagement through a process of theoretical sensemaking might
require additional exploration and interpretation of the identified pool of primary
research. Thus, we go back to the starting point and move forward in the process of
analysis, synthesis and reflection. This cycle can be repeated many times. In this way
we can ensure that breadth and depth of enterprise modelling literature can be
appropriately integrated in answering the why questions and development of an
integrative frameworks and theories for enterprise modeling. In such conditions, using
meta-study and it four components could be a successful strategy.
Meta-study consists of four components [20]: meta-data analysis, meta-method
analysis, meta-theory analysis, and meta-synthesis. The first three components refer to
analytical phases, in which the findings, research designs, and theoretical frames of
primary research publications are compared and contrasted. In the meta-synthesis
phase, the findings of the analytical phases are considered in the light of the historical,
sociocultural, and disciplinary context in which the primary research was conducted.
This phase can be described as “digging deep to generate new knowledge about the
phenomenon under study” [20]. In this way, meta-synthesis challenges common
understandings of the phenomenon under study and the way in which it should be
studied [21]. It is through the process of meta-synthesis that first, we evaluate the calls
for changes in enterprise modelling research; and second, develop an integrated
theoretical framework that can guide the future work in this area. In other words, meta-
synthesis should help us evaluate whether enterprise modelling goes wrong in light of
the calls for change of direction made by some scholars. Based on such evaluation, we
should provide options for new directions. This is more easily said than done taking in
consideration the typical challenges faced by meta-study.
3 Meta-study challenges
The first challenge is selecting the primary research publications that will create the
data set that will be analyzed. The breadth and depth of enterprise modelling literature
raises not only the issue of comprehensiveness of the primary research that should be
addressed, but also the inclusion criteria that are applied in a meta-study (for example
goal of the research, methods, etc). When the data set is created, a second aspect that
needs to be taken in account is which studies in the data set should be excluded because
of a lack of rigor in the research or "How bad is too bad to be included in the meta-
study?" [21]. Before the meta-study starts, justified criteria of inclusion and exclusion
need to be provided.
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The second challenge is how based on the results from synthesis we move to
reflection of “what might be” in enterprise modelling. Most probably, for this we will
need a diverse set of experience, knowledge and perspectives. On one hand, there is a
need for people with strong experience and knowledge of enterprise modelling domain
(frameworks, methods, techniques) but they might be caught in that existing knowledge
and not be able to question the assumptions or look from different perspective. On the
other hand, you need inexperienced scholars or domain outsiders that can provide new
perspectives but might not be able to understand the logic behind the current research.
One option is to create a larger diverse research team. The benefits of having a diverse
team is beneficial because they can generate different perspectives and bring different
experience that can improve the end-product of the meta-study [21]. The problem here
is that the research might take too long due to integrating the experience, knowledge
and perspectives of the team members, but it can create a more representative
integrative framework. The second option is to have a narrow team that approximately
covers the required diverse set of experience, knowledge and perspectives and share
the results of analysis and synthesis with larger number of experts in the field and based
on their feedback, improve the end result of the meta study. This raises the question of
how to balance between quality and speed of performing the meta-study.
4 Conclusion
In this short paper, we make a proposal for meta-study of enterprise modelling. We
make an attempt to justify meta-study as an approach through which we can move
forward the discipline of enterprise modelling. We also present the challenges of
applying meta-study to study the domain of enterprise modelling. The goal is to open a
discussion with interested scholars on whether and how a meta-study of enterprise
modelling should be done.
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