A Multi-domain Approach Toward Adaptations of Socio-technical Systems: The Dutch Railway Case-Part 1

Abstract—Socio-technical systems are highly complex as they
contain a number of domains each of which including numerous
interdependent elements. With such complexity, policy makers
and managers need to adapt (make incremental changes in) sociotechnical
systems, and currently available approaches for such
adaptations are rare. Consequently, and to fill this gap, this paper
takes a multi-domain approach based upon Design Structure and
Multi-domain matrices to develop a multi-domain model of sociotechnical
system. Moreover, that model is analyzed according to
both the change propagation measures of the non-human domain
and the information processing view of the stakeholder domain of
socio-technical systems. Application of this method for the Dutch
railway system is discussed in Part 2 of this paper.

Grounded in the general systems theory [47], sociotechnical
systems conceptualize systems as consisting of two
independent, but linked, systems: a technical system and a
social system [32]. The former is composed of equipment and
processes, while the latter consists of people and relationships
[27]. For instance, while an infrastructure is mainly a complex
engineering system, but it is a socio-technical system: in
addition to the fact that it contains many hardware/technical
elements, it also involves people in different roles/positions
In recent years the increasing availability of compututational
tools has enabled gathering of (and analysing) data
about the socio-technical systems. Similarly, in this paper,
we present a multi-domain approach that aims to identify
performance-enhancing adaptations in the domains of sociotechnical
systems. The core ideas of our approach relies
on the four distinct notions. First, rather than planning a
socio-technical system, identifying adaptation possibilities is
recommended [3]. Second, socio-technical systems include
several inter-related domains (e.g., stakeholders, functional,
technical), and thus, a multi-domain approach [11] toward
those systems appears to be plausible. Third, change lies at the
heart of safety critical systems like power plants, and railway
systems [16], and hence, change propagation measures can
be used to examine the non-human (e.g., technical) elements
of socio-technical systems. Fourth, those results obtained
from analysing the non-human domain, and the information
processing view of organizational systems [18] can be used to
examine stakeholders coordination/communication structures.
The rest of this paper is organized as follows. The next
section discusses the streams of relevant literature. Then, the
method and its steps are presented in details. Finally, the
discussion and conclusion sections end our paper.

In this section, we present a short review of the streams of
literature that examine (a) socio-technical systems, (b) design
structure matrix, and (c) change propagation in technical
systems. These three topics are selected as they constitute the
core parts of both the context and the proposed method in this
A. Socio-technical systems and Adaptation
Early attempts to study socio-technical systems are those
by [44], [17]. They state that those systems can only be
understood when social, psychological, environmental and
technological systems are assessed as a whole. In streams of
the relevant literature, different definitions have been provided
for socio-technical systems. For instance, these systems are
considered to “involve both complex physical-technical systems
and networks of interdependent actors” [12]. As another
field specific definitions can be the one discussed in the
Information Systems (IS) field, where IS are contemplated as “
socio-technical systems involving the interplay of technology
components (hardware and software), people (with cognitive
capabilities and associated shortcomings), data (to capture
real-life situations) and organizational issues (processes and
management)” (page 284 in [20]).
In order to describe socio-technical systems (STS), scholars
have examined the common attributes of those systems. In
general, common features of STS include (1) large number of
elements [8], (2) nonlinear interactions [19], [36], [39], [48],

adaptive capacity [28], feedback loops [30], [29], and emergent
properties [37].
Another relevant aspect is that since socio-technical systems
are highly complex, a deliberate and comprehensive and
outcome-oriented planning process may not be possible for
such systems [3]. Moreover, situations in which deliberate
design is possible and effective from those situations in which
it may not be, should be distinguished (ibid). Thus, evolutionary
models that allow for learning and adaptation can be
an alternative for analysing/improving socio-technical systems.
Such adaptive methods will be in line with the previously
stated principle of socio-technical designs (“minimal critical
specification of rules”) that demands no more to be specified
than what is absolutely essential in such systems [1].
B. Design Structure Matrix and Change Propagation
The Design Structure Matrix (DSM) is a popular visualization
tool for system/processes modeling, especially for purposes
of decomposition and integration [5]. A DSM displays
the relationships between elements (e.g., technical, human,
etc) of a system in a compact manner: A square matrix with
identical row and column labels (that represent the elements
of a system). Cells inside a DSM are either zero and one:
presence of one (zero) indicates that the column element has
(no) impact (or interaction) on the row element (see [5]). One
of the main advantages of DSM models is their simplicity, as
complex tools quickly become challenging to represent and
understand [6].
The DSM tool can be used in various domains to model
interactions within one particular domain (e.g., technical domain).
For instance, a product (or technical) DSM illustrates its
architecture which is “the arrangement of components interacting
to perform specified functions” (page 302 in [7]). Another
type of the DSM tools is the organizational (or stakeholders)
DSM in which an organization consists of “organizational
units” such as teams, departments, and individuals that connect
to each other according to reporting/lateral/information flow
relationships (ibid). Such organizational DSMs are considered
as stakeholders matrix that shows the list and interactions of
all human entities within a system. There are other DSM
types, and as discussed in below, process/activity DSM and
functional DSM can be developed and analysed for systems.
In addition to the domains themselves, their interactions
or across domains should be modelled. The multi-domain
matrix MDM is a matrix-based approach that relates/maps
two DSMs of two different domains [11]. For instance,
often, stakeholders/organizational units use/control/supervise
technical/physical components. Thus, one MDM can be a
stakeholdertechnical MDM matrix. The list of possible
DSM and MDM matrices is shown in Figure 1, and in below,
we briefly describe each of those matrices.
During developing and designing a new product, often a
change to one part of the product will bring changes to other
parts [9]. More formally, change propagation is seen as the
process by which a “change to one part or element of an
existing system configuration or design results in one or more additional changes to the system, when those changes would
not have otherwise been required” [15]. Having such process
in engineering systems enables designers and managers to
develop and operate those systems on schedule and within
budget [21].

Fig. 1: Engineering Systems Multi-Domain Matrix, adopted
from [2].

Change propagation research and literature relates to different
fields (e.g., management, engineering design, product
development, complexity), and have been growing in the
last decades. The special issue on engineering change in
the Journal of Research in Engineering Design [13] and a
comprehensive overview paper [23] illustrate parts of that
Two notions of the change propagation research need to
be highlighted as our paper utilizes them. On the one hand,
and from the perspective of change propagation, components
of a product (technical system) fall into different categories
[15], [21]: (1) constants that remain unaffected by change,
(2) absorbers that absorb more changes than they cause, (3)
carriers that absorb and cause a similar number of changes,
and (4) multipliers that generate more changes than they
absorb. On the other hand, scholars have developed different
frameworks and methods for assessing and managing change
propagation. For instance, taking DSM matrix into account,
the change prediction method evaluates how changes spread
through a product [9]. Relevantly, our paper uses a network
approach toward measuring and evaluating change propagation
within a non-human (technical) domain of a socio-technical

Socio-technical systems are highly complex, and as discussed
earlier, it is recommended to take adaptive approach
toward managing them [3]. In this paper, we presented a multidomain
approach that aims to identify performance-enhancing
adaptations in domains of socio-technical systems. At first,
using DSM and MDM matrices, our model builds a multidomain
perspective of socio-technical systems. In particular,
both of the stakeholders and non-human domain matrices are
In the next step, and for the non-human (technical) domain,
four categories of the elements that require different adaptation
strategies are identified based upon the change propagation
perspective. This aspect is highly related to many sociotechnical
systems, as change lies at the heart of safety critical
systems like power plants, railway systems [16]. Relevantly,
a network-view based model of inter-dependencies among the
non-human elements is developed, and then, overall influence
and susceptability scores of each element are calculated. Using
those scores, and with regard to the change propagation
aspect, the method classifies the non-human elements into
four classes: constants, absorbers, multipliers, and carriers.
Each of these categories imply different change propagation
behaviours, and hence, managerial actions. For instance, carriers
are risky components that need more resource, whereas,
constants are less risky and can be redesigned/adapted more
For the stakeholders domain, we take an information processing
view of an organizational system which expects such
systems’ information processing requirements to be matched
with their information processing capabilities [18]. Similarly,
we argue that in a socio-technical system, those non-human
elements that are classified according to change propagation
behavior impose different information processing requirements
on the overall performance system and its stakeholders. Thus,
our method calculates the overall information processing requirements
of each stakeholder. To that end, the results of nonhuman
domain analysis (or categories of the non-human elements)
are utilized, and therefore, change propagation features
are assumed to cause different levels of information processing
requirements for the involved stakeholders. Consequently, by
aggregation, and ordering the overall information processing
requirements of stakeholders, they are ordered in order to identify
those with highest/lowest overall information processing
According to the information processing view of organizations,
different organizational/coordination mechanisms (e.g.,
group meeting, reporting procedures) provide different levels
of information processing capability. For instance, it is argued
that as uncertainty increases, organizations use pre-established
plans/schedules for cordination than other coordination mechanisms
like group meeting and direct contacts [46]. With
these highlights, then, our method recommends to adapt coordination
among stakeholders such that those stakeholders
with high (low) overall information processing requirements
coomunicate/coordinate their decisions/actions based on the
coordination mechanisms that provide high (low) information
processing capabilities.
The presented method in this paper opens up at least two
directions for future research. First, while it is formulated
as a generic approach that focuses on domains of a sociotechnical
system, application of this method on the industrial
cases could reveal potentials/shortcomings of this method.
Therefore, Part 2 of this paper illustrates utilizing our method
for the Dutch railway system. Second, although this paper
aims to identify performance-enhancing adaptations in sociotechnical
systems, its approach could still be further enhance
by explicitly taking into account other rules/principles. As an
example, ethical rules in data/information domains could be
an alternative analysis to be included in the future extended
version of the presented method.

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