In this section we consider the present in light of the recent publications in the field. Many of these argue that emerging IT
capabilities and their adoption in business and society have given rise to a step change in the complexity, dynamism, uncertainty
and unpredictability of social, political and economic systems. They suggest that developing requisite SIS capabilities
to deal with these changes will entail a paradigm shift, and they proffer methodological and conceptual alternatives to enable
such a shift to take place (e.g. El Sawy et al., 2010; Nevo and Wade, 2010; Oh and Pinsonneault, 2007; Peppard and
Ward, 2004; Tanriverdi et al., 2010; Wade and Hulland, 2004).
For the purposes of this discussion we focus on a selection of papers from the 2000s that offer alternatives, both for formulating
the challenge that SIS scholars face, and the approaches for dealing with these challenges (El Sawy et al., 2010; Tanriverdi
et al., 2010; Nevo and Wade, 2010; Oh and Pinsonneault, 2007). All of them challenge or reframe the concepts
embedded in what Chen et al. (2010) identified as the three most persistent threads in SIS research over the past three decades:
alignment of SIS with Business Strategy, SISP and IS for competitive advantage. Their assertions, theorisations and recommendations
are based on the premise that the emerging competitive landscape (resulting from the advances in the
capabilities and ubiquity of digital technologies and their deployment) is complex and characterised by increased turbulence
and dynamism, and that the dominant approaches of past decades are inadequate for strategising in this emergent context.
The common challenge for SIS, they assert, is to develop new perspectives, methodologies and strategies for dealing with this
dynamic context.
Common across these scholars is the view that we need to adopt a holistic systems perspective: Nevo and Wade
(2010) base their arguments on a combination of systems thinking and RBV, whilst Oh and Pinsonneault (2007), Tanriverdi
et al. (2010) and El Sawy et al. (2010) use concepts from Complexity Science and complex systems thinking. This
move is motivated by the belief that it is no longer possible for organisations to isolate endogenous dynamics and
deployment of resources from the changes that are happening in the environment. These authors assert the importance
of understanding the nature of the dynamic relationship between the organisation and its environment (which contains
other, heterogeneous organisations and resources interacting with each other). At a more general level there is a concern
with the need for systemic theory building to understand the dynamics of such relationships- for example El Sawy et al.
(2010) advocate the use of configuration theories (as alternatives to the more commonly used variance theories and process
theories) for defining the patterns of unfolding interactions at ‘‘the confluence among environmental turbulence, dynamic
capabilities and IT systems’’ (which they label as the phenomenon of digital ecodynamics), whilst Oh and
Pinsonneault (2007) in the course of exploring different conceptual and analytical approaches for assessing the strategic
value of IT, highlight the efficacy of non-linear approaches for understanding relationships between alignment and performance
in turbulent environments.
Whilst they differ in their focus and prescriptions, the ideas of these authors draw on the systems concepts summarised
in Table 2 to characterise the systemic complexity and to ground their recommendations for future directions in SIS
research.
Prominent in the current discourse are the challenges posed for SIS theory and practice by the non-linear dynamics, emergence
and the open, non-equilibrium nature of systems: together these features give rise to uncertainty, unpredictability and
turbulence in the competitive landscape, making alignment and SISP problematic.
132 Y. Merali et al. / Journal of Strategic Information Systems 21 (2012) 125–153
4.1. Alignment
The SIS concept of alignment has received a great deal of attention throughout the decades, and perspectives on alignment
have changed progressively- as Chan (2002) points out, alignment is a complex, dynamic process with a moving target.
Oh and Pinsonneault’s (2007) analysis, focusing on the non-linearity of complex systems provides a succinct perspective on
the implications of non-linearity:
‘‘ . . . nonlinear perspectives suggest that organizations are dynamic systems that never settle down and are continuously on the
move. As a result, organizations are likely to be in disequilibrium states in which no deterministic and simple linear solutions are
present. . . even a small difference in the degree of fit between business strategy and IT can lead to large variations in organizational
performance…In fact, a sustainable ‘‘perfect’’ alignment may be an illusionary concept, given the speed and magnitude
of change in business and technological environments’’ (p. 246).
In addition to the challenge of attaining ‘‘perfect’’ alignment, environmental turbulence poses the difficulty of selecting
the most suitable dimensions for alignment. McLaren et al. (2011) and El Sawy et al. (2010) also consider the problem of
selecting dimensions for alignment and advocate using configurational theories (Meyer et al., 1993) to bound the number
of combinations of dimensions to consider. Tanriverdi et al. (2010) on the other hand, argue that the dynamism and uncertainty
of the ‘‘dancing’’, rugged competitive landscape necessitates abandoning the quest for alignment and replacing it with
a quest for co-evolution. However, this then raises the challenge of selecting the dimensions for co-evolutionary fit for which
they do not propose a solution.
4.2. SISP and competitive advantage
Common amongst scholars focusing on the challenges for SISP, strategising and competitive positioning in the face of the
inherent complexity, turbulence and dynamism of the competitive landscape, is the question of defining how SIS can contribute
to competitive positioning here. The papers cited in this section highlight the reflexive relationship between a firm
and its environment – strategic moves by the firm can impact on, and possibly shape changes in, the structure and dynamics
of the environment (e.g. as other firms respond by imitation or innovation), and changes in the environment may impact on
the firm’s resource base, structure and behaviour. Whilst this dynamic existed in the past, its impact is exacerbated by the
increased complexity (in terms of the number and diversity of firms and resources that can interact, and combinatorial possibilities
afforded by richness and reach of digital technologies) and the rapid pace of IT-related change in the competitive
landscape.
A common approach is to advocate the adoption of co-evolutionary strategies to retain viability in this context: to
adapt and evolve, continually developing new capabilities and relationships that are well-aligned with the changing
opportunities for competitive positioning in the dynamic context. Relating this to extant literature in SIS, Tanriverdi
et al. (2010) advocate reframing the quests for alignment, integration and competitive advantage respectively as quests
for co-evolution, re-configuration (of business processes, products and services, and the contracts, resources, and transactions
associated with them), and renewal, entailing what may be interpreted as a degree of Shumpeterian destruction
(destabilising old sources of competitive advantage, and dismantling out-moded capabilities and endowments) and a
capacity for re-invention to remain competitive in the changing landscape. Their argument for renewal parallels the
Table 2
Complex systems concepts.
Complex System Complex systems are open, non-linear systems, composed of many (often heterogeneous), partially connected
components that interact with each other through a diversity of feedback loops
Complexity The complexity of the system arises from its composition: it comprises a large number of heterogeneous entities (e.g.
individuals, groups, organisations, nations) that have varying degrees of interconnectivity and interdependence.
Relationships may be asymmetric and vary in nature, strength, stability and persistence. The variation in connectivity
and the degree and nature of the interdependence may be across time or space
Non-equilibrium
Dynamics
The system is characterised by non-equilibrium dynamics. It is also open – its components interact with each other and
with those in the environment (which contains other, heterogeneous organisations and resources interacting with each
other): these interactions may be asymmetric, they are contingent on prevailing conditions and local sensitivities, and
vary over time. Fluxes in and out of the system vary, and system stability is predicated on mutual adjustments between
components within and across system boundaries
Emergence The observed system and its behaviour at the macro-level is an emergent phenomenon: the local interactions of
components at lower levels give rise to a collective macro-level behaviour that is different in scale and kind to the
properties of the individual components at the lower levels
Non-linear Dynamics The complex, networked nature of the relationships between components gives rise to non-linear dynamics – small
changes in one location can be transmitted and amplified through the network of connections to produce large changes
at the system level
Complex Adaptive
Systems (CAS)
CAS are complex systems that embody the characteristics defined above, and they have the capacity to adapt in the face
of environmental perturbations whilst retaining their integrity and identity
Y. Merali et al. / Journal of Strategic Information Systems 21 (2012) 125–153 133
earlier discourse on dynamic capabilities in the strategic management literature (Teece et al., 1997; Teece, 2006) and
underlines the transient nature of competitive advantage in the face of environmental turbulence. Whilst they use the
CAS concept to describe the nature of Complex Adaptive Business Systems (CABS) they overlook the potential of CABS
to exploit IT in adaptive strategies that involve external players or the development of higher level collective structures
for stabilisation in turbulent environments. El Sawy et al. (2010) on the other hand emphasise the potential of exploiting
mutual dependencies of diverse components in ecological constructs.
Two common themes run through the SIS literature discussed in this section: the interconnected or networked nature
of the whole system and the need for holistic systems concepts and constructs to articulate the dynamic aspects of the
interactions and (co)evolution of organisations in, and with, their dynamic contexts. Below we look more closely at the
network motif and at the suitability of complexity science for furnishing the requisite systems concepts that the SIS
literature calls for.
4.3. The network motif
The network motif is apparent in the wider management literature with a discernible shift from focusing solely on the
firm as a unit of organisation to focusing on networks of firms, from considerations of industry-specific value systems to
considerations of networks of value systems, and from the concept of discrete industry structures to the concept of ecologies
(see for example Burgelman, 1991; Buchanan, 2002; Lewin and Volberda, 1999; Merali, 2006; Merali and McKelvey,
2006; Seidl, 2007).
The SIS literature suggests that the dynamism in the competitive terrain requires firms to be agile and reconfigure
their resource base and organisation in a co-evolutionary fashion to keep up with demands of the changing landscape
(Sambamurthy et al., 2003; Weill et al., 2002). Tanriverdi et al. (2010) advocate a strategy of re-configuration and renewal
in order to maintain such a fit. We argue that this is not a viable proposition for four reasons- firstly, discontinuous
change in the environment may demand the acquisition of new capabilities that cannot be developed endogenously, secondly
the pace of change in the environment may be too rapid to allow for the cycle of dismantling existing resource
bundles and sources of competitive advantage to create the requisite new capabilities, thirdly it would irrevocably destroy
relational capital built up over time, and fourthly it would be a wasteful process in contexts with high degrees
of perturbation.
We suggest that a more viable approach would be to adopt a network form of organisation of resources, with a network
that spanned organisational boundaries and connected with diverse others, through diverse relationships (varying in
strength, longevity and nature), in a constellation that optimised adaptive potential. This entails developing strong and
long-lived relationships with some collaborators, more transient relationships with others, and deciding which resources
and capabilities need to be sequestered within organisational boundaries, which ones can be shared with others, and which
can be acquired from others. The central idea for this strategy is to create a structure which embodies the requisite potential
for ecologically stable relationships alongside more transient ones to deliver the requisite bundle of resources for effective
positioning in the prevalent context.
To deal with the problem of provisioning for the future in a dynamic context we propose a parallel investment
based on real options thinking – i.e. to make small investments in a number of different resources that may become
valuable in the future. This approach should aim to ensure that the constellation contains the requisite variety and
micro-diversity that will support the dynamic configuration of viable resource bundles in the face of environmental
turbulence.
A further level of complexity arises because of the reflexive relationship between the firm and its environment –
the firm is a component of the multi-dimensional, multi-level nature of the competitive landscape, and the macrolevel
properties of the whole system emerge from the dynamics of locally situated, inter-connected components. As
identified in the literature (e.g. El Sawy et al., 2010; Kauffman, 1993; Tanriverdi et al., 2010; Pavlou and El Sawy,
2006, 2010) if the landscape is a rugged one, it is possible for firms with limited visibility of the landscape to get stuck
in suboptimal niches.
A fundamental problem in this situation is firstly, one of deciding what the requisite level of investment in heterogeneity
should be, and second, what an efficient strategy would be to explore the competitive landscape in order to identify regions
of superior performance. This second problem is identical to that posed by March (1991) in his identification of myopic firms
that under-invested in exploratory learning (Levinthal and March, 1993).
In this discussion we used the network approach to address the issue of competitive positioning in SIS, but the approach is
equally relevant for exploring issues of SISP, particularly in the current IT landscape with its diverse population of technological
capabilities and platforms, service providers and media choices to cater for a diverse and heterogeneous population of
clients and user groups.
4.4. The Science of Complex Systems for articulating system characteristics in the SIS domain
Overall, the ‘‘paradigm shift’’ advocated in the papers discussed here is predicated on the premise that the present
and the future for SIS entails navigating a competitive landscape characterised by complexity, dynamism, uncertainty
and unpredictability of social, political and economic systems. All the authors advocate taking a holistic, systemic
134 Y. Merali et al. / Journal of Strategic Information Systems 21 (2012) 125–153
perspective and offer constructs and concepts to explicitly address the reflexive relationship between the organisations
and their environment.
The use in these papers of concepts from Complexity Science to articulate the rugged and dynamic nature of the competitive
landscape, the adoption of the CAS paradigm to articulate the characteristics of dynamic, viable business systems, and
the engagement with consequences of non-linear dynamics illustrates the relevance of these concepts for the future SIS research
agenda.
Here we outline the concepts from Complexity Science and the network paradigm that can be used to articulate the
system characteristics and dynamics that scholars in the 2010+ era have engaged with, and we propose that
going forward, Complexity Science can provide a scaffolding and core concepts in the future trajectory of SIS, whilst
adoption of the network paradigm constitutes a natural progression for the co-evolution of physical and social
technologies.
Over the years SIS scholars dealing with dynamic contexts have emphasised the importance of change, transformation
and adaptation. Concepts like punctuated equilibrium, ambidexterity, co-evolution and emergence have been used to
characterise the process of change since the 1990s, and more recently writers have drawn on Complexity Science concepts
to articulate organisational behaviours and interactions in dynamic an uncertain contexts.
In response to the call by scholars in the 2010+ era for a holistic systemic approach in SIS we propose that the complex
systems paradigm offers a generic definition for systems of the kind that these writers describe. The use of complex systems
concepts in SIS literature to date has been rather piecemeal, with different authors selectively using particular concepts to
focus on specific aspects of SIS. It has also been largely descriptive, used to define or characterise behaviours or characteristics
of dynamic systems and their states. However, in addition to providing a language and concepts for describing the phenomenology
of complex systems and their behaviours, Complexity Science is also concerned with explaining the network
mechanisms that underpin this phenomenology.
By definition complex systems are essentially network systems (Merali, 2004)- the network of a complex system embodies
a set of heterogeneous nodes (embodying resources, capacities to act, etc.) which have the potential to be connected in a
variety of ways through diverse relationships (or links).
More specifically complex systems are open, non-linear systems, composed of many (often heterogeneous), and partially
connected components that interact with each other through a diversity of feedback loops. Their complexity derives from the
partially connected nature of the network and the non-linear network dynamics which make the behaviour of these systems
difficult to predict (Casti, 1997). The non-linearity of these systems means that small changes in inputs can have dramatic
and unexpected effects on outputs.
This construct serves to explain the link between the structure and dynamics of systems at all scales, and underpins
macro-level behaviours (such as punctuated equilibrium) displayed by complex systems.
The concept of CAS is highly relevant for articulating the dynamic characteristics of digitally connected organisational
forms (see Merali, 2004, 2006) for comprehensive review of Complexity Science and IS). CAS adapt and evolve in the process
of interacting with their environments. They have the potential (capacity) for both adaptation and transformation through
the dynamic adjustment of local negative and positive feedback loops. Adaptation at the macro-level (the ‘whole’ system) is
characterised by emergence and self-organisation based on the local adaptive behaviour of the system’s constituents. The
relationship between the system and the environment is a reflexive one: changes in the system both shape and are shaped
by changes in the environment. The CAS paradigm imposes the need to consider the dynamics and mutually defining consequences
of the relationship between the system and its environment, taking us from issues of simple adaptation to issues
of co-adaptation and co-evolution in dynamic contexts.
From a complexity perspective, viability3 in dynamic contexts is predicated on access to the requisite variety of responses to
match the demands of the context. The network thus constitutes the locus of diversity generation, because it has the potential to
be ‘‘rewired’’ according to contingencies, and its potential diversity is greater than that displayed at any particular moment in
time (Merali, 2005). The adaptive potential is conferred by
the micro-diversity of the components,
the existence of the requisite degree of connectivity between nodes and
the capacity for spontaneous re-configuration of the pattern of linkages.
Complexity Science provides a generic framework for the study of complex systems in dynamic contexts, and as such provides
a scaffolding for the development of more specific concepts and models for describing and explaining particular behaviours
and phenomena of interest to SIS scholars. For example, the construct of CAS and its associated network dynamics
accommodates the constructs for defining both, ambidexterity and punctuated equilibrium.
3 Not all nodes are equally connected, and individual nodes may be connected to a number of different nodes at any given time. The connectivity of
individual nodes may change over time: depending on the task at hand, attendant constraints and proclivities, individual nodes activate particular connections
in the network at particular times (Merali, 2006). The global network form at any given moment is a manifestation of the collective pattern of interconnections:
over time we can expect to observe a dynamic network topology, with individual constellations in the network becoming activated selectively as and when
needed for particular collaborative and transactional contingencies.
Y. Merali et al. / Journal of Strategic Information Systems 21 (2012) 125–153 135
4.5. Modelling CAS
Whilst the concept of using the network approach is a relatively simple one, the behaviour of CAS is difficult to predict
because of their sensitivity to initial conditions and the potential for non-linear responses to contextual perturbations. To
understand the mechanisms underpinning the dynamics we need to access descriptions of the system at multiple scales
from the micro to the macro at the same time.
Complexity Science offers the modelling approaches for exploring the dynamics of such non-deterministic systems,
revealing the way that the micro- and macro-level relationships play out over time. Agent-based computational modelling
has characteristics that are particularly useful for studying contextually embedded systems. An agent based model is comprised
of individual ‘agents’ (e.g. firms) commonly implemented as software objects (Casti, 1997; Holland, 1995, 1998).
Agent objects have states and rules of behaviour. They can be endowed with requisite resources, traits, behaviours and rules
for interacting with, and adapting to, each other. Typically agent-based models deploy a diversity of agents to represent the
constituents of the focal system, and the modeller defines the environmental parameters that are of interest as the starting
conditions for the particular study. Repeated runs of the model reveal collective states or patterns of behaviour as they
emerge from the interactions of entities over time. Agent-based modelling facilitates the inclusion of micro-diversity (e.g.
the rationality of agents can be limited, agents can be made diverse so there is no need to appeal to representative agents,
payoffs may be noisy and information can be local), allowing us to study the diversity of (local) behaviours at fine scales and
to observe the emergence of the global characteristics at the large scale. Running the model furnishes us with an entire
dynamical history of the process under study.
- The future trajectory of SIS research
Our earlier definition of the SIS domain as being responsible for the co-evolution of Social and Physical Technologies
based on Nelson’s theorising, and the discussion of CAS and Networks in the last section leads us to make the following four
propositions for the future of the SIS domain.
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