700 word discussion: Complexity is increasing as new technologies are emerging every day. This complexity impacts human experiences. Organizations are turning to digitally enabled solutions to assist with the emergence of digitalization.
700 words: Complexity of Information Systems Research in the Digital World. Complexity is increasing as new technologies are emerging every day. This complexity impacts human experiences. Organizations are turning to digitally enabled solutions to assist with the emergence of digitalization. Please review the article and define the various technologies that are emerging as noted in the article. Note how these emerging technologies are impacting organizations and what organizations can to do to reduce the burden of digitalization.
The paper must include a cover page, an introduction, a body with fully developed content, and a conclusion and a minimum of five peer-reviewed journal articles.
SPECIAL ISSUE: COMPLEXITY & IS RESEARCH
COMPLEXITY AND INFORMATION SYSTEMS RESEARCH
IN THE EMERGING DIGITAL WORLD1
Hind Benbya
Technology and Innovation Management, Montpellier Business School, 2300 Avenue des Moulins,
Montpellier 34185 Cedex 4, FRANCE {[emailprotected]}
Ning Nan
Sauder School of Business, University of British Columbia, 2053 Main Mall,
Vancouver V6T 1Z2 British Columbia, CANADA {[emailprotected]}
Hseyin Tanriverdi
McCombs School of Business, The University of Texas at Austin
Austin, TX 78712 U.S.A. {[emailprotected]}
Youngjin Yoo
Department of Design & Innovation, The Weatherhead School of Management, Case Western
Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106-7235 U.S.A. and
Warwick Business School, University of Warwick, Coventry CV4 7RL UNITED KINGDOM {[emailprotected]}
Complexity is all around us in this increasingly digital world. Global digital infrastructure, social media,
Internet of Things, robotic process automation, digital business platforms, algorithmic decision making, and
other digitally enabled networks and ecosystems fuel complexity by fostering hyper-connections and mutual
dependencies among human actors, technical artifacts, processes, organizations, and institutions. Complexity
affects human agencies and experiences in all dimensions. Individuals and organizations turn to digitally
enabled solutions to cope with the wicked problems arising out of digitalization. In the digital world, com-
plexity and digital solutions present new opportunities and challenges for information systems (IS) research.
The purpose of this special issue is to foster the development of new IS theories on the causes, dynamics, and
consequences of complexity in increasing digital sociotechnical systems. In this essay, we discuss the key
theories and methods of complexity science, and illustrate emerging new IS research challenges and oppor-
tunities in complex sociotechnical systems. We also provide an overview of the five articles included in the
special issue. These articles illustrate how IS researchers build on theories and methods from complexity
science to study wicked problems in the emerging digital world. They also illustrate how IS researchers lever-
age the uniqueness of the IS context to generate new insights to contribute back to complexity science.
1 Keywords: Complexity, sociotechnical systems, emergence, coevolution, chaos, scalable dynamics
digitalization
1
Hind Benbya and Ning Nan served as associate editors for the special issue. Huseyin Tanriverdi and Youngjin Yoo served as senior editors. William McKelvey
was a SE for the special issue but was unable to participate in the writing of the introductory essay.
DOI: 10.25300/MISQ/2020/13304 MIS Quarterly Vol. 44 No. 1, pp. 1-17/March 2020 1
Benbya et al./Introduction: Complexity & IS Research
Introduction
When we conduct a search on Google, it returns hundreds, of
thousands, results instantaneously. The results not only
reflect the interests of the one who is doing the search, but
also the millions of internet users who created or clicked on
hyperlinks of websites. As more users search, link, and click
with similar keywords, the results will continue to change
according to user location and search time. A search for
Korean restaurants in Munich, Germany, for example, gives
different results from a search in Cleveland, OH, USA. Con-
ducting the same search a day or two later also produces
different results. A simple Google search result is an emer-
gent property, a complex web of interactions among users,
websites, topics, advertisers, and many other social or tech-
nical entities. In short, our daily experience of using mundane
digital tools is a dynamic emergent outcome of complex
sociotechnical systems.
As early as 2010, the world-wide production of transistors has
exceeded that of rice, and is much cheaper (Lucas et al. 2012).
Deviceslarge and smallpowered by microprocessors and
connected by the internet are filling every inhabited corner of
the earth. Some of these devices are not just passively
waiting for commands; equipped with a powerful artificial
intelligence engine, they often act on their own. We already
see autonomous vehicles on the streets interacting with traffic
signals that respond to changing traffic patterns, in the midst
of human-controlled vehicles and pedestrians. Sprinklers are
connected to the weather service on the internet to control the
amount of water on a lawn. The temperature of millions of
houses is controlled by Nest connected to the Google Home
Assist service. Connected speakers recommend different
music playlists based on the time, location, and, of course,
your preference. Social network services also enable every
user as a potential content creator on the internet. Once
created, user-generated content can be liked, shared, and
mashed with other content by other users, often creating
unpredictably complex forms of diffusions. Digital platform
ecosystems such as Uber and AirBnB connect millions of
users and providers globally. More than 80% of movies
watched on Netflix are recommended by algorithms.2
These examples illustrate truly astonishing advances from the
humble start of computers in organizations in the early 20th
century. After merely a few decades, what once seemed to be
glorified calculators have evolved into digital technologies
that permeate our lives and work. These digital technologies
in turn foster new sociotechnical systems such as wikis, social
media, and platform ecosystems that are fundamentally
changing the way people work and live.
Not every technological invention has such a transformational
impact. What set apart digital technologies? At the heart of
digital technologies is symbol-based computation. Bistrings
(0s and 1s) provide a standard form of symbols to encode
input, process, and output of a wide variety of tasks (Faulkner
and Runde 2019). They reduce the design specificity of hard-
ware for operationalizing the symbol-based computation.
Furthermore, simplicity of bitstrings eases the effort to shrink
the size, reduce the cost, and increase the processing power of
hardware. Symbol-based computation provides a generali-
zable and applicable mechanism to unite the operations of
matter and the abstract mental processes (Lovelace 1842). It
lays the foundation for digital technology to rapidly advance
beyond the function of a calculator. More importantly,
symbol-based computation sets in motion the emergence of
complex sociotechnical systems.
Emanating from symbol-based computation are a few
complexity-inducing characteristics of digital technologies.
Embedded: as described by the vision for symbol-based
computation (Lovelace 1842; Shannon 1993, Turning
1950), digital capabilities are increasingly embedded in
objects that previously have pure material composition
(Yoo et al. 2012). Digital capabilities can encode and
automate abstract cognitive processes for converting new
information into adaptive changes of objects. They also
enable objects to provide decision support to adaptive
cognitive processes of social actors.
Connected: objects embedded with digital capabilities
and users of such objects can be connected into webs of
sociotechnical relations (Sarker et al. 2019) because
symbol-based computation homogenizes data (Yoo
2010). When information is shared in the webs of socio-
technical relations, abstract cognitive processes encoded
in objects or possessed by social actors become mutually
dependent.
Editable: digital technologies are editable (Kallinikos et
al. 2013; Yoo 2012) due to symbol-based computation.
This editability allows increasingly diverse cognitive
processes to be introduced into the webs of socio-
technical relations. Recurrent adaptation of diverse,
connected, and mutually dependent objects and social
actors can amplify or diminish an initial change in a
sociotechnical system, producing outcomes that defy
simple extrapolation from the initial change (Arthur
2015; Holland 1995; Page 2010). Complexity, therefore,
becomes a salient attribute of sociotechnical systems.
2
See https://mobilesyrup.com/2017/08/22/80-percent-netflix-shows-
discovered-recommendation/.
2 MIS Quarterly Vol. 44 No. 1/March 2020
Benbya et al./Introduction: Complexity & IS Research
Reprogrammable: through the separation of hardware
and software of symbol-based computation, digital tech-
nology is reprogrammable (Yoo et al. 2010). The same
hardware can perform different functions depending on
the software that runs on the device.
Communicable: digital technologies are communicable
by following a set of agreed-upon protocols (Lyytinen
and King, 2006; Yoo 2010). With the pervasive diffu-
sion of digital technologies, they now form a global
digital infrastructure (Tilson et al. 2010).
Identifiable: each and every device connected to the
digital infrastructure is uniquely identifiable through its
own unique address (Yoo 2010). The increasing digital
penetration leads to a higher degree of identifiability,
allowing for more granular manipulation levels of digital
objects.
Associable: digital objects are associable through shared
traits. The associability of distributed heterogeneous
devices and data allows one to identify emerging patterns
across different realms and geographies in a way that was
simply not possible in the past.
Digital technologies not only give rise to complex sociotech-
nical systems; they also distinguish sociotechnical systems
from other complex physical or social systems. While com-
plexity in physical or social system is predominantly driven
by either material operations or human agency, complexity in
sociotechnical systems arises from the continuing and
evolving entanglement of the social (human agency), the
symbolic (symbol-based computation in digital technologies),
and the material (physical artifacts that house or interact with
computing machines). The functions of digital technologies
and the roles of social actors are perpetually defined and
redefined by each other (Faulkner and Runde 2019; Zittrain
2006). This sociotechnical entanglement limits the generali-
zability of complexity insights obtained from nondigital
systems to complex digital systems. Furthermore, while
material operations or human agency either increase or
dampen complexity in physical or social systems, digital tech-
nologies can both mitigate and intensify complexity. This is
because individuals and organizations engaged with complex
sociotechnical systems often turn to digital technologies (e.g.,
data analytics) for solutions to complex problems. Yet, the
application of a solution can instigate a new round of digitally
enabled interactions that diminish the intended effect of the
solution. This dual effect of digital technologies on com-
plexity can produce dynamic interaction patterns and out-
comes that are qualitatively different from those in other
complex systems.
The distinct effects of digital technologies on complex socio-
technical systems present an important opportunity for infor-
mation systems (IS) researchers to extract novel insights
regarding the nature and relevance of digital technologies. IS
researchers can apply theories and methods from complexity
science to model observations that defy simple extrapolation
from initial changes in a sociotechnical system. In this essay,
we introduce key complexity theories such as emergence,
coevolution, chaos, and scalable dynamics as the most likely
foundation for IS researchers to rethink predictability, caus-
ality, boundary, and durability of observations in the digital
world. Subsequently, we explain how the centrality of
symbol-based computation in IS research paves the way for
IS-specific research themes to extend complexity science.
The articles in this special issue are briefly described to illus-
trate a few prominent themes such as IS development for
rapidly changing requirements and using digital technologies
to steer or tame complexity.
Complexity Science: Key Theories
and Methods
Complexity sciences origins lie in 50 years of research into
nonlinear dynamics in natural sciences and spans a variety of
scholarly disciplines including biology (Kauffman 1993),
chemistry (Prigogine and Stengers 1984), computer science
(Holland 1995; Simon 1962), physics (Gell-Mann 1995), and
economics (Arthur 1989). Developments across disciplines
over time resulted in a meta-theoretical framework within
which several theoretically consistent approaches and
methods can be integrated.
Complexity science theories and methods combine different
epistemologies (i.e., positivism, interpretivism, and realism)
to provide novel opportunities to question assumptions (e.g.,
equilibrium, stability, etc.), manage tensions and paradoxes,
and rethink the way we view many sociotechnical phenomena
at the center of our field. Their value is particularly promi-
nent when the research community faces new phenomena and
questions that do not lend themselves well to the traditional,
reductionist approaches.
Complexity Drivers and States
Complexity is an attribute of systems made up of large num-
bers of diverse and interdependent agents3 that influence each
3
These could range from molecules to individual human beings to organized
collectives.
MIS Quarterly Vol. 44 No. 1/March 2020 3
Benbya et al./Introduction: Complexity & IS Research
Figure 1. States of Complex Systems (Benbya and McKelvey 2011)
other in a nonlinear way and are constantly adapting to inter-
nal or external tensions (Holland 1995). Because such
systems are constantly evolving, they have a large degree of
unpredictability. They cannot, therefore, be understood by
simply examining the properties of a systems components.
Four key characteristics influence the level of complexity in
a system: (1) diversity, (2) adaptiveness, (3) connectedness,
and (4) mutual dependency among agents in the system (e.g.,
Cilliers 1998; Holland 1995). The nonlinear interplay of the
above four characteristics coupled with increased tension in
the form of external or internal challenges and/or oppor-
tunities drive the system from one state to another.
A system can exist or fluctuate between three states or
regions: stable at one extreme, chaos at the other, with an in-
between state called the edge of chaos (Kauffman 1995;
Lewin 1992). Figure 1 provides an illustration of the three
states.
Specifically, in the stable state, the diversity, adaptiveness,
connectedness,and mutual dependency of agents in the system
are all at low levels. Consequently, adaptive tensions are low
(Page 2010) and complexity is benign (Tanriverdi and Lim
2017). The system rapidly settles into a predictable and
repetitive cycle of behavior. In such stable systems, novelty
is rare. There is a tendency for stable systems to ossify.
As the diversity, adaptiveness, connectedness, and mutual
dependency levels of systems reach moderate levels, the com-
plexity level increases (Page 2010). Systems with increased
levels of complexity enter the so-called edge of chaos state
or a region of emergent complexity (Boisot and McKelvey
2010). By staying in this intermediate state, these systems
never quite settle into a stable equilibrium but never quite fall
apart. They exhibit continuous change, adaptation, coevolu-
tion and emergence (Kauffman 1993; Lewin 1992).
Increasing levels of tensions, beyond a certain threshold,
might result in chaos or extreme outcomes (e.g., catastrophes,
crises, etc.) which exhibit fractals, power laws, and scalable
dynamics. Chaotic systems never really settle down into any
observable patterns. Since they are sensitive to initial condi-
tions, they can amplify exponentially and have monumental
consequences (Gleick 1987).
Complexity Theories
As outlined above, many living systems (e.g., organisms,
neural networks, ecosystems) on the edge of chaos appear to
constantly adapt and self-organize to create configurations
that ensure compatibility with an ever-changing environment.
This perpetual fluidity is regarded as the norm in systems on
the edge of chaos; it can lead to processes and outcomes as
diverse as phase transitions, catastrophic failures, and unpre-
dictable outcomes (see Table 1). Complexity theories such as
emergence, coevolution, chaos, and extremes, as well as
scalable dynamics, offer an explanation of such processes and
outcomes.
4 MIS Quarterly Vol. 44 No. 1/March 2020
Benbya et al./Introduction: Complexity & IS Research
Table 1. Processes and Outcomes of Complex Systems
Complexity Theories Processes Outcomes
Emergence Disequilibrium situations: tensions,
triggers and small events outside the norm
Positive feedback and bursts of
amplification
Phase-transitions
Self-organization
Unpredictable outcomes: new structures,
patterns, and properties within a system (e.g.,
distributed leadership emergence), a new level
of analysis (e.g., a network), or a collective
phenomenon (e.g., collective action)
Emergence can take two forms: composition or
compilation
Coevolution Interdependency and boundary-crossing
interrelationships
Multilevel dynamics
Bidirectional or two-way causality
Mutual influences
Reciprocal adaptations and changes over time
Chaos Sensitivity to initial conditions
Constrained trajectory (e.g., strange
attractor)
Time-dependency and irreversible
dynamics
Catastrophic failures (e.g., systemic risk, cyber-
security breaches)
Escalation of causes leading to disastrous
societal consequences (e.g., disrupting lives on
a large scale)
Scalable Dynamics Instability and large variations
Single cause leading to a cascade of
interconnected events
Self-similarity across scales
Positive or negative extreme outcomes
Fractal dynamics
Power laws
Emergence
Emergence is a dynamic process of interactions among
heterogeneous agents that unfolds and evolves over time,
resulting in various kinds of unexpected novel individual- and
group-level configurations and/or broader social structures
(Benbya and McKelvey 2016). Complexity and organization
scholars have theorized such a dynamic process for some time
(Kozlowski et al. 2013; Plowman et al. 2007).
Systems-wide changes in natural open systems revealed how
unorganized entities in a given system, subjected to an exter-
nally imposed tension, can engage in far-from-equilibrium
dynamics. The entities can therefore self-organize into dis-
tinct phase transitions leading to a new higher-level order
(Prigogine and Stengers 1984).
Social systems put under tension, through recession, crisis,
organizational change, and so forth, can exhibit similar phase
transitions and emergent outcomes. As such, many social
scientists have made a direct mathematical parallel between
physical and social systems to deduce the process mech-
anisms inherent in micro interaction dynamics that yield the
higher-level order and its emergent novel outcomes. They
have identified two forms of emergence: composition or
compilation (Kozlowski and Klein 2000). In composition
models, emergent processes allow individuals perceptions,
feelings, and behaviors to become similar to one another.
Compilation models, on the other hand, capture divergence.
They characterize processes in which lower-level phenomena
are combined in complex and nonlinear ways to reflect unit-
level phenomena that are not reducible to their constituent
parts. The discovery of emergence involves either a post hoc
analysis of time series data (e.g., system behavior) and
conceptual tools that allow scholars to verify the existence of
emergence dynamics in systems, or an analytical mapping of
the sequential phases of emergence dynamics (e.g., Plowman
et al. 2007).
Interactions among sociotechnical entities yield many
emergent outcomes in information systems. Examples
include the collaborative creation of online order and tech-
nology affordances (e.g., Nan and Lu 2014), IS alignment
(Benbya et al. 2019), and new configurations among organi-
zation, platform, and participant dimensions (Benbya and
Leidner 2018). An emergence perspective offers a lens to
understand many unpredictable sociotechnical phenomena
that span individual, group, organizational, and societal levels
in the context of widening digitalization.
Coevolution
Coevolution refers to the simultaneous evolution of entities
and their environments, whether these entities being
organisms or organizations (McKelvey 2004). Ehrlich and
MIS Quarterly Vol. 44 No. 1/March 2020 5
Benbya et al./Introduction: Complexity & IS Research
Raven (1964) introduced the term coevolution to characterize
the mutual genetic evolution of butterflies, and associated
plant species. Such a process encompasses the twin notions
of interdependency and mutual adaptation, with the idea that
species or organizations evolve in relation to their environ-
ments, while at the same time these environments evolve in
relation to them.
In addition, to the above characteristics, coevolutionary
processes have three main properties. First, coevolutionary
phenomena are multilevel. They encompass at least two dif-
ferent levels of analysis. Second, coevolutionary phenomena
take time to manifest. This implies that longitudinal designs
are necessary to understand coevolutionary processes. Third,
bidirectional causality or two-way relationships (e.g., Yan et
al. 2019) are central to coevolutionary processes.
In IS research, coevolution theory has been used to theorize
the codesign of organizations and information systems
(Nissen and Jin 2007; Vidgen and Wang 2009), the alignment
of business and IT (Benbya et al. 2019; Benbya and
McKelvey 2006b; Tanriverdi, Rai, and Venkatraman 2010;
Vessey and Ward 2013), coevolution of business strategy
with the competitive landscape (Lee et al. 2010); and coevo-
lution of platform architecture, governance, and environ-
mental dynamics (Tiwana et al. 2010).
Chaos
Chaos theory was initially developed with Lorenzs (1963)
work in response to an anomaly in atmospheric science.
Chaotic systems are sensitive to initial conditions. This
sensitivity to initial conditions, called the butterfly effect,
implies that even a slight change, analogous to a butterflys
wing-beat, can lead to radical consequences on a much larger
scale.
In addition to being unstable and sensitive to initial condi-
tions, chaotic systems are deterministic because the systems
trajectory is constrained. Such chaotic systems possess a
strange attractor, a value or a set of values that system vari-
ables tend toward over time but never quite reach (Lorenz
1963). Sudden discontinuous shifts in chaotic systems drive
them from one attractor to another, leading thereby to catas-
trophes and disastrous societal consequences.
Chaos theory has been used to theorize social and organi-
zational dynamics as nonlinear chaotic systems by virtue of
their sensitivity to initial conditions. For example, McBride
(2005) used concepts of chaos theory to study the dynamic
interactions between information systems and their host
organizations. Guo et al. (2009) use chaos theory to develop
a framework to illustrate blog system dynamics arising from
micro (individual blog traffic dynamics) and macro (blogo-
sphere structure) levels. Hung and Tu (2014) provide an
empirical analysis of the applicability of chaos theory to
explain technological change processes. Tanriverdi and Lim
(2017) theorize about IS-enabled complexity vigilance
capabilities for detecting whether a complex ecosystem
approaches the edge of chaos/discontinuity.
Scalable Dynamics, Fractals,
and Power Laws
Scalable dynamics refer to self-similarity of underlying
patterns across different levels of analysis (Manderbrot et al.
1983). This notion of self-similarity across scales has become
a core tenet of complexity science and has led to various
theories to characterize how a single cause can scale up into
positive or negative extreme events and drive similar out-
comes at multiple levels (for reviews, Adriani and McKelvey
2006; Benbya and McKelvey 2011).
The dimensionality of such self-similarity across scales can be
measured using a mathematical mapping technique referred
to as fractals. In other terms, fractals measure the density
of a nonlinear data set, such as stock market behaviors or the
shape of a coastline (Casti, 1994). When such measures are
taken at increasing orders of magnitude, each fractal dimen-
sion is self-similar to the ones before and after it, meaning
that the underlying patterns are the same across levels of
analysis. These relationships are always governed by a power
law (Cramer 1993).
Fractal analysis has helped describe and explain different
changes that occur within similar patterns at multiple scales
across organizations, markets, and industries. For example,
Farjoun and Levin (2001) use a fractal analysis to characterize
industry dynamism over time and capture the rate, amplitude,
and unpredictability of change.
Methods
Research on complex sociotechnical systems has used a
variety of methods, some are well established while others are
just emerging. IS scholars have studied dynamics of complex
systems by using established research methods such as longi-
tudinal qualitative case studies (e.g., Benbya and Leidner
2018; Paul and McDaniel 2016), morphogenetic approaches
(e.g., Njihia and Merali, 2013), statistical methods for longi-
tudinal data analyses (e.g., Nan and Lu, 2014; Tanriverdi and
Du 2020; Tanriverdi, Roumani, and Nwankpa 2019). How-
ever, complex sociotechnical systems that operate far from
6 MIS Quarterly Vol. 44 No. 1/March 2020
Benbya et al./Introduction: Complexity & IS Research
equilibrium conditions also present challenges for some estab-
lished research methods such as closed-form analytical
modeling methods. As such, newer methods have emerged to
study under nonequilibrium conditions, complex interactions
among multiple variables, and multilevel causality. Those
new methods include agent-based simulation, the qualitative
comparative analysis (QCA) method, and dynamic network
modeling based on graph theories.
Agent-based simulation utilizes symbol-based computation to
precisely express a theory about a complexity concept such as
agents, interactions, and the environment involved in an emer-
gent process. The computational expression can then be used
to simulate and test the theory in controlled and replicable
ways. This methodological approach was advanced by the
Santa Fe Institute (SFI), a multidisciplinary research center
created in the mid-1980s (Waldrop 1992). Applications of
simulation methods include genetic algorithms (Holland
1995), cellular automata (Krugman 1996), NK landscape
models (Kauffman 1993), and a combination of several
approaches found in agent-based models (Carley and Svoboda
1996).
The QCA method allows researchers to identify how multiple
causal attributes combine into distinct configurations to
produce an outcome of interest, and to assess the relative
importance of each configuration to the same outcome (Ragin
and Rubinson 2009). It relies on the set-theoretic approach
and Boolean algebra to conceptualize and analyze causal
complexity described as equifinality, conjunctural causation,
and causal asymmetry (Ragin 2000, p. 103). Scholars from
different social science disciplines including the IS field have
advocated the use of QCA to embrace causal complexity that
is typical of social or sociotechnical systems (see El Sawy et
al. 2010; Fichman 2004; Misangyi et al. 2017; Park et al.
2020).
Dynamic network modeling focuses on interactions that are
the root cause of complexity in a phenomenon. Agents and
their interactions are modeled as nodes and edges in a net-
work. Dynamic network modeling enables researchers to
identify patterns of interactions among a population of agents
in a system. Scholars have been using tools like spatio-
temporal network modeling to understand how new edges are
formed (George et al. 2007; Taylor et al. 2010). For example,
complex patterns of evolution in a digital platform ecosystem
can be modeled as a network of third-party complements and
boundary resources (Um et al. 2015). Here, third-party com-
plementary products are modeled as agents interacting with
one another through shared boundary resources. Scholars
have used a similar approach to explore the relationship
between consumers and brands (Zhang et al. 2016) and to
understand the emergent nature of social relationships using
relational event network (Schecter et al. 2017). Another
important tool based on dynamic network modeling is net-
work community (Sekara et al. 2016). A network community
is set of densely connected nodes (Newman and Girvan
2004). For example, scholars have used network community
to discover dynamic emerging patterns of routines (Pentland
et al. 2020).
Implications of Complexity
for IS Research
The increased levels of complexity in sociotechnical systems
in the context of widening digitalization creates numerous
opportunities and challenges for IS research. Due to the
distinct effects of digital technologies on complex socio-
technical systems, simply replicating middle-level theories
and models for complex physical, biological, or social
systems would not fully capture IS-specific complexity issues.
A fruitful approach for IS researchers is to use complexity
science as a meta-theoretical lens to rethink a few funda-
mental research challenges (see Table 2). In this section, we
discuss a few of the challenges as exemplified by the fol-
lowing questions:
Under what conditions is prediction feasible in complex,
sociotechnical systems?
What is the nature of causality in complex, socio-
technical systems?
How can researchers circumscribe the boundaries of a
complex, sociotechnical system to study?
How durable is newly discovered knowledge in complex,
sociotechnical systems?
Limits to Prediction in Complex
Sociotechnical Systems
Prediction of potential outcomes in a given sociotechnical
system is one of the perennial questions in IS literature. It has
become even more important with recent developments in big
data and artificial intelligence (AI) technologies. However,
complexity of sociotechnical systems present major chal-
lenges for prediction. Interactions among a diverse set of
connected, mutually dependent, and adaptive agents in a
sociotechnical system lead to the emergence of unexpected
outcomes that defy the extrapolation techniques at the heart of
prediction models. Properties of complex sociotechnical
systems, such as nonlinearity, self-organization, coevolution,
MIS Quarterly Vol. 44 No. 1/March 2020 7
Benbya et al./Introduction: Complexity & IS Research
Table 2. Implications of Complexity for IS Research
Issue Implication for IS Research
Prediction of behaviors
of complex systems
There are limits to the prediction of behaviors of complex sociotechnical systems. System-level
properties such as non-decomposability, nonlinearity, self-organization, and coevolution
inevitably lead to emergent, unpredictable system behaviors.
Prediction efforts of IS research should focus not on the ability to foresee specific, well-defined
system events in space and time (i.e., paths), but on the ability to anticipate the range of
possible behaviors the system might adopt (i.e., patterns).
Nature of causality in
complex systems
A linear view of causality between inputs and outputs of the complex sociotechnical system is
inadequate. There are multiple causal mechanisms and different forms of causality in complex
sociotechnical systems.
Three distinguishi
