=Paper=
{{Paper
|id=Vol-3397/phd6
|storemode=property
|title=Enterprise Modelling of Rule Networks in Organisational Collaborations
|pdfUrl=https://ceur-ws.org/Vol-3397/phd6.pdf
|volume=Vol-3397
|authors=Jöran Lindeberg
|dblpUrl=https://dblp.org/rec/conf/emisa/Lindeberg23
}}
==Enterprise Modelling of Rule Networks in Organisational Collaborations==
https://ceur-ws.org/Vol-3397/phd6.pdf
PhD proposal: Enterprise modelling of rule networks
in organisational collaborations⋆
Jöran Lindeberg1,*
1
Department of Computer and Systems Sciences (DSV), Stockholm University, Borgarfjordsgatan 12, Kista, Stockholm
Abstract
Organisations and their activities are largely controlled by different types of rules. Rules can have
particular importance for organisational collaborations, for example in healthcare, in which negative
effects of rules at different levels are identified. Rules have interdependencies with other rules, forming
rule networks, which may be examined using systems and organisation theory. Rules can also be
modelled through enterprise modelling. However, present models seem to have limited support for
representing rule networks and understanding the emergence of unforeseen and undesired effects on
the boundaries of the action space of collaborating organisational units. This gap will be addressed
through the design of a modelling language with an accompanying method and tools, using Design
Science Research.
Keywords
organisational collaboration, enterprise modelling, business rule, rule network, design science, systems
thinking
1. Introduction
This paper is the first published description of a PhD project started in January 2023.
Organisations and their activities are largely controlled by different types of rules [1]. Al-
though rules are meant to bring about order, they can often have unintended and unexpected
consequences. While some rules have external origins, such as laws and regulations, others are
made by the organisational units themselves to fulfil their goals. The reason for some of these
internal rules is compliance with external rules. Most organisational rules have interdependen-
cies with other rules through e.g. explicit citations, forming rule networks that evolve over time
[2]. These networks together set boundaries that limit the action space of organisational units.
Rules can have particular importance for organisational collaborations, which horizontal
nature limits the degree of control that can be exercised by direct commands. One industry
that is both highly controlled by rules and reliant on organisational collaboration is healthcare,
making it an intriguing area for research about these two concepts. One problem in healthcare
is that many general practitioners find it nearly impossible to adhere to all applicable medical
recommendations [3, 4]. Another issue, in a Swedish context, is that the monetary incentives
13th International Workshop on Enterprise Modeling and Information Systems Architectures (EMISA), May 11 and 12,
2023 – Stockholm, Sweden
*
Corresponding author.
$ joran@dsv.su.se (J. Lindeberg)
0000-0001-7806-749X (J. Lindeberg)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
that drive the healthcare system make many practitioners take actions they believe are at odds
with their professional expertise [5, 6]. A third problem is that privacy regulation [7], most
notably the European Union’s General Data Protection Regulation (GDPR), makes it difficult for
caregivers to share data with each other about patients in inter-organisational care processes
[8]. The significance of the above issues is shown by the protracted debate about healthcare
that they have generated.
A contributing factor to all three aforementioned issues is that they arise from a complex
network of interdependent rules and regulations. The difficulty of comprehending and managing
these networks is the research problem addressed in this PhD project.
2. Knowledge Base
This section will present theoretical foundations for understanding rules as well as closer
definitions of some central concepts.
2.1. Perspectives from Sociology and Systems Thinking
Starting with a broad view, it is noteworthy that the growth of increasingly detailed rule
networks is intimately connected with the advancement of modern society. Already Emile
Durkheim observed in 1893 that "domestic law, from being originally simple, has become in-
creasingly complex" [9, p. 155]. Later, Max Weber, otherwise a strong proponent of bureaucracy,
cautioned that an iron cage of rules [10] could be humanity’s inescapable faith [11].
In a nearly empty world, i.e. loosely coupled system, it is easier to do local changes without
negative effects [1]. Hence, more rules increase the complexity of the system, and its manage-
ment. Like any system, organisations and organisational collaborations can be subject to the
emergence of new properties, qualitatively different from the properties of the components.
Some of these new properties, stemming from strong emergence cannot be foreseen [12].
An important point by Simon [13] is that advances in human knowledge can both increase
and decrease the amount of knowledge that needs to be mastered by professionals. In the latter
case, the needed knowledge is lessened as a result of the discovery of more universal laws. In
a similar manner, rules can both increase and decrease the residual variety [14] (cited in [15])
that an organisational unit has to manage.
2.2. Organisational collaboration
This research project understands organisational collaboration as a type of organisational coor-
dination distinct from both hierarchical chains of command and market mechanisms [7]. There
is typically no clear hierarchy among the actors, vague limitations of responsibility, and the
driving force is rather common goals than immediate return in forms e.g. revenue. Collab-
oration can be both intra-organisational (between units) and inter-organisational (between
organisations).
Collaboration occurs both around particular cases, e.g. the treatment of a patient, and when
designing general work conditions, such as when one organisational unit establishes a routine
that affects other units, as in the problem of medical guidelines in Section 1.
2.3. Rules and Enterprise Modelling
Rules, goals, and other aspects of an organisation can be modelled through enterprise modelling.
This technique is useful for e.g. visualizing the interdependence between various organizational
components and can involve analyzing, assessing and designing [16]. Two important parts of
enterprise modelling relating to this project are goal modelling and business rule modelling. Here
follows a brief description based on the 4EM method [16].
A goal model describes what an organisation or collaboration wants to achieve, and how
goals are interdependent. An example of a goal could be, say, "improved preventive care of
overweight patients." Some modelling languages, such as iStar [17], can express which actor
has which goal.
Business rules can be of three different types [16]. A derivation rule defines a concept based
on information about other concepts that are already present in the system. For instance, an
overweight patient could be defined as "a patient that has a Body Mass Index (BMI) above
25.0". An event-action rule states what to do in specific situations. For example, "if a patient is
overweight, perform the NICE guideline ’Physical activity: brief advice for adults in primary
care’". (NICE is a set of medical guidelines in the United Kingdom [3]). A constraint rule ensures
the information integrity of a system or sets limits on organisational behaviour. This could
entail, e.g., deciding that "all patients must be treated according to applicable NICE guidelines".
As this example shows, the implication of a particular rule on the boundaries of the action space
of an actor much depends on interdependent rules.
Compared to Sandkuhl et al. [16], this research project has a broader initial understanding of
organisational rules, including e.g. organisational culture and non-binding recommendations of
best practice. A working definition is: a control mechanism that defines the boundaries of the
action space of an organisational unit or role in a type of situation. This scope will likely be
narrowed during the research process.
2.4. Rule Networks
An apparently rather new concept in the field of organisations is what Zhu and Schulz [2] refer
to as rule networks. Their research aims at comprehending the growth and mutual citations
of medical guidelines. Citing Simon [13], Zhu and Schulz recognize that regulators, like all
humans, operate with bounded rationality in a myopic manner. Thus, the regulators will not be
able to predict all the consequences that new rules will have on all parts of an organisational
system.
There is more to rule interdependence, however, than citations. A second aspect is having the
same creator. A third aspect is governing the same actor or situation, in a reinforcing, neutral
or conflicting manner. A fourth aspect is common implications (both intended and actual, costs
as well as benefits). Just as for the definition of organisational rules in Section 2.3, this research
project has a broad initial understanding of rule interdependence, that encompasses all the
mentioned facets. Joining these concepts, the preliminary definition of a rule network for this
project is: a set of interdependent organisational rules that affects a situation.
3. Preliminary Research Goal
The section outlines the preliminary research goals of the PhD thesis project. They are likely to
change as the project unfolds.
The first research goal is to design a modelling language for organisational rule networks. As
for requirements, there are a number of potential perspectives to explore. First, the language
should represent the aspects of interdependence described in Section 2.4. Second, it should
represent feedback mechanisms of rule change. A rule maker must learn what the actual
consequences of a rule change was, compared to the intended. Third, the language should (if
feasible) allow for computations of quantifiable properties of a rule network, which helps in
understanding aggregated and emergent effects on organisational units.
The second goal is to design a method for using the modelling language to represent a rule
network of a particular organisation. The third goal is to make a tool for this purpose.
Ideally, an organisation would model all its rules and relevant external regulation as a rule
network. However, that is likely a poor use of resources. A more realistic approach would be to
start modelling the rules that seem to have the greatest impact on a concrete problem at hand,
and then expand the network until the benefits of improved understanding no longer seem to
outweigh the labour cost. As more problems with accompanying rules are added, the islands
will connect and form a growing landmass.
So what could constitute a concrete problem to tackle? One situation could be when practition-
ers seem unwilling to follow the rules. Modelling their rule networks can help in comprehending
the many — perhaps conflicting and overwhelming — demands they are dealing with. Another
scenario would be when an IT system needs to be designed for inter-organisational use in
a heavily regulated environment, such as a system for Health Information Exchange (HIE).
Modelling relevant rule networks will help in eliciting requirements to properly embed the
regulations in the system.
In the long run, the resulting artefacts could be part of the design of Digital Twins for
organisations. Digital twins have now evolved to the point where they can be used also for
sociotechnical systems [18].
4. Preliminary Research Methodology
The over-arching research framework will be Design Science Research (DSR) [19] as described by
Johannesson & Perjons [20]. The DSR cycle envisions the use of different research strategies and
methods being applied at various phases. The first step will be to conduct a systematic literature
study to complement the knowledge base with regard to enterprise modelling methods that have
been proposed for rules in organisational contexts. Capturing their limitations in detail will
allow a more precise appreciation of the research gap that the PhD project seeks to fill. To further
explicate the problem and elicit requirements, a case study of organisational collaboration in
healthcare in Region Stockholm is planned. After having designed the envisioned artefacts,
the case study can be continued for demonstration and evaluation. Note that in spite of the
chosen focus on collaboration in healthcare, the ambition is that the resulting artefacts will be
applicable to rule networks in general.
5. Concluding remarks
Rules and rule networks can have undesired and unforeseen effects in organisational collab-
orations, for example in the healthcare industry. This problem can be addressed through the
design of a modelling language for rule networks.
References
[1] J. G. March, H. A. Simon, Organizations, John Wiley & Sons, 1993.
[2] K. Zhu, M. Schulz, The dynamics of embedded rules: How do rule networks affect
knowledge uptake of rules in healthcare?, J. Manag. Stud. 56 (2019) 1683–1712.
[3] M. Johansson, G. Guyatt, V. Montori, Guidelines should consider clinicians’ time needed
to treat, BMJ (2023) e072953.
[4] N. Privett, S. Guerrier, Estimation of the time needed to deliver the 2020 USPSTF preventive
care recommendations in primary care, Am. J. Public Health 111 (2021) 145–149.
[5] M. Zaremba, Patientens pris: ett reportage om den svenska sjukvården och marknaden,
Weyler, 2013.
[6] H. Kjöller, Hanne Kjöller: Det allmänläkarna utsätts för är inget annat än byråkratisk
misshandel, DN. SE (2023).
[7] C. Auschra, Barriers to the integration of care in Inter-Organisational settings: A literature
review, Int. J. Integr. Care 18 (2018) 5.
[8] K. Fast Lappalainen, U. Fors, M. Henkel, C. Magnusson Sjöberg, E. Perjons, Digitalisering
inom vård och omsorg, Technical Report, Stockholm University, Stockholm, 2021.
[9] E. Durkheim, The Division of Labor in Society, Simon and Schuster, 1997.
[10] M. Weber, The Protestant Ethic and the Spirit of Capitalism, Routledge, 1930.
[11] M. Weber, Economy and Society: A New Translation, Harvard University Press, 2019.
[12] M. J. J. Colchester, Systems + Complexity An Overview, 1st edition ed., CreateSpace
Independent Publishing Platform, 2016.
[13] Simon, H. A., The Sciences of the Artificial, volume 3rd ed, The MIT Press, 1996.
[14] W. R. Ashby, An Introduction to Cybernetics, Springer New York, NY, 1956.
[15] J. P. Ríos, Systems thinking, organisational cybernetics and the viable system model, in:
J. Pérez Ríos (Ed.), Design and Diagnosis for Sustainable Organizations: The Viable System
Method, Springer Berlin Heidelberg, Berlin, Heidelberg, 2012, pp. 1–64.
[16] K. Sandkuhl, J. Stirna, A. Persson, M. Wißotzki, Enterprise Modeling, Springer Berlin
Heidelberg, 2014.
[17] F. Dalpiaz, X. Franch, J. Horkoff, istar 2.0 language guide (2016). arXiv:1605.07767.
[18] M. Caporuscio, F. Edrisi, M. Hallberg, A. Johannesson, C. Kopf, D. Perez-Palacin, Architec-
tural concerns for digital twin of the organization, in: Software Architecture, Springer
International Publishing, 2020, pp. 265–280.
[19] A. R. Hevner, S. T. March, J. Park, S. Ram, Design science in information systems research,
Miss. Q. 28 (2004) 75–105.
[20] P. Johannesson, E. Perjons, An Introduction to Design Science, Springer International
Publishing, 2014.