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Journal of Information Technology (2006) 21, 211–215& 2006 JIT Palgrave Macmillan Ltd. All rights reserved 0268-3962/06 $30.00 Using Complexity Science to effect aparadigm shift in Information Systemsfor the 21st century 1Warwick Business School, The University of Warwick, Coventry, UK;2UCLA Anderson School of Management, 110 Westwood Plaza, Los Angles OA 90095-148 U.S.A.
Correspondence: Y Merali,The Warwick Business School, University of Warwick, Coventry, UK.
Tel: þ 44 (0)24 7652 2456;Fax: þ 44 (0)24 7652 3719;E-mail: Yasmin.Merali@wbs.ac.ukJournal of Information Technology (2006) 21, 211–215. doi:10.1057/palgrave.jit.2000082 Thomas Kuhn (1962) is famous for his descriptions of emergent new network order is cheap. ‘Almost free’ science as consisting of long periods of ‘normal puzzle network changes can bring about transformational changes solving’ separated by brief periods of ‘paradigm shift’.
in the state of the world. (Baraba´si, 2002; Newman et al., Computers have been around for half a century, with 2006). The net effect of this is a perception that individuals Information Systems (IS) in firms existing for several and organizations have to deal with a world that is decades. Increasingly, we see various observers complain- increasingly dynamical, complex and uncertain, and that ing about normal puzzle solving in IS (Ciborra, 1994; their actions may have unintended consequences that Orlikowski and Iacono, 2001) at a time when people increasingly spend time in virtual worlds – business people This is reflected in the management literature where work more and more in virtual teams (VTs), while there are there has been a discernible shift from focusing solely on now special programs to rescue teenagers from total the firm as a unit of organization to focusing on networks of firms, from considerations of industry-specific value In the tradition of exploratory learning, this Special Issue systems to considerations of networks of value systems, and is intended to act as a catalyst to stimulate discussion and from the concept of discrete industry structures to the debate among those who see the need for a paradigm shift concept of ecologies. The fact that the terms ‘network in the IS community. To this end, we explore the economy’ and ‘network society’ (Castells, 1996) have contribution that complexity science can make to fostering become integrated into the management lexicon highlights such a shift in the IS discipline and its re-positioning in the the extent to which networking developments in the IS management field. The motivation for this Special Issue domain are implicated in the development of the wider comes from our observation that the IS and the information management arena. In particular, this shows up in the technology (IT) landscape is characterized by network literature on competitive dynamics where the network dominance and increasing complexity, coupled with the economy is characterized by competition in high-velocity possibility that this heralds a paradigm shift for IS research environments, speed of technological change and uncer- and practice. For those who are championing the paradigm tainty (Eisenhardt, 1990, Li and Atuahene-Gima, 2002).
shift, we think complexity science applications to IS, such Organizations, needing to shape and redefine their own as those presented in this Special Issue, offer hope.
competitive arena (Hayton, 2005), are confronted with the The network motif is a recursive one. First, the potency need to continually innovate (Tushman and O’Reilly, 1996; of discrete advances in hardware and software capabilities Autio et al., 2000, Hayton 2005). This brings with it the to generate significant change in business and society is challenges of working towards radical and incremental realized through the mobilization of network effects.
innovation, (Nambisan, 2002) while dealing with resource Second, technological advances escalate the potency of constraints (Barney, 1991; McDougall et al. 1994; Steven- network effects by continually enhancing the connectivity son, 1999) to achieve an efficacious balance of risk and and bandwidth of networks. Third, the growth of IT- return. The quest for coherent integration of social, enabled socio-economic networks is accompanied by economic, and IT networks has resulted in the convergence globalization and an increase in the number and hetero- of strategy, OD and IS research on issues of information geneity of players who can affect the dynamics of networks.
and informating, connectivity, coordination, competition, Recent work elucidating the relationship between network collaboration, learning and transformation at multiple topologies and network dynamics illustrates that the low levels of analysis in the networked world. These develop- cost of connectivity supported by the internet means that ments highlight the importance of trans-disciplinary Using complexity science to paradigm shift research, and the strong position of IS research in this development of paradigms for IS research in the networked world, and to explore the potential contribution of Complexity science is viewed as a source of concepts for complexity science to the development of ontological and enabling the trans-disciplinary exploration of complex epistemological constructs deployed to articulate the nature organization in the network economy and network society, and role of information, IS and IT in the networked world.
and for explaining the dynamics of networked systems at Our intention in this issue is not to present complexity different levels of description ranging from the micro- to science as a ready-made paradigm for IS research and the macro-level. It offers a powerful set of methods for practice, but to provoke a debate in the IS community explaining non-linear, emergent behaviour in complex about the adequacy of current approaches in dealing with systems.1 In the IS literature, there have recently been two the dynamics of the emergent networked world, using special issues dedicated to applications of complexity complexity science as the launch pad for what we hope will theory,2,3 both extolling the value of adopting the complex be a voyage of exploration in the best of learning traditions.
systems perspective in IS research and practice, but To this end, we have included papers that showcase the despondent at the slow rate of adoption of complexity utilization of complexity science concepts to explore problems in the networked world ranging from the We recognize three basic Schools of complexity science: micro- to the macro-level, covering issues of IS strategy European: Prigogine (Nicolis and Prigogine, 1989) and development, design and utilization. The papers cover the others (e.g., Haken, 1977; Cramer, 1993; Mainzer, 1994) strategic alignment of IS and the cultural and institutional focus on adaptive tension and the first critical value of the challenges that the new information and communication imposed energy in physical systems that sets off phase technologies (ICTs) pose for both state and corporate transitions. Prigogine is famous for his emergent ‘dissipa- bureaucracies confronted with the complexities of an tive structures’. Haken and Mainzer zero-in ‘order para- increasingly distributed social order.
meters’ that drive new order creation in one way or another Our seven articles stretch across the broadest landscape at the phase transition. This school is math intensive.
yet seen in the recent use of complexity science to further American: The Santa Fe Institute focuses on how new understand and respond to IS difficulties in organizations.
order arises in biological and social systems (Anderson They extend from Weberian bureaucracy and Boisot’s et al., 1988; Pines, 1988; Cowan et al., 1994; Arthur et al., Information-Space (I-Space) to the use of scale-free theories 1997). They focus more on the second critical value at the from econophysics; they reach from epistemology and so-called ‘edge of chaos’ where new order emerges. Here, axiology to a discussion of the advantages of agent-based new order emerges when heterogeneous agents – such as computational modelling; they range from business pro- biomolecules, organisms, people or social systems – are cesses to virtual teams (VTs); and they range from motivated by a drive for improved fitness or learning to introductory to state-of-the-art complexity concepts. Com- initiate connections with other agents. This is all it takes.
plexity science is put to use as a post-normal puzzle-solving A key method, computational modelling, begins with set of concepts and dynamics aimed at reinvigorating the Kauffman’s work on ‘spontaneous order creation’ (1969, entire IS fitness landscape. We have ordered the articles as 1993), resulting from heterogeneous agents and connectiv- much as possible so that their applications of complexity ity (see also Merali as well as Canessa and Riolo, this issue).
Econophysics: Here the focus is on how the order creation Yasmin Merali focuses on the emergence of the IS actually unfolds once the forces of emergent order creation domain as a central feature of the management research by self-organizing agents are set in motion (Zipf, 1949; landscape in a networked world. She shows that the West and Deering, 1995; Newman, 2005). Key parts of this emergence of the network economy and network society third aspect are fractal structures, power laws and scale-free necessitates a paradigm shift in the IS discipline, and that theory. An obvious example of a fractal structure is a complexity science offers the apposite concepts and tools cauliflower – its adaptive design is the same from the one for effecting such a shift. To avoid confusing fundamental large whole cauliflower down to its hundreds of tiny, almost complexity science concepts with the more colloquial uses invisible, florets. If plotted, this size-by-frequency ratio of complexity terminology, she provides an introduction to forms a Pareto distribution. A power law is a representation concepts from complexity science for scholars in the IS of the Pareto distribution in a double-log graph.4 Andriani field who are unacquainted with complexity science.
and McKelvey (2005) report 80 kinds of power laws, Yasmin then proceeds to explore the utility of these including 40 pertaining to organizations and other social concepts for developing IS theory and practice for the phenomena. Power laws frequently signify underlying emergent networked world. She starts with an overview of fractal structures. Why do fractal structures happen? the networked world, highlighting the features that have led Andriani and McKelvey (2006) discuss some 15 kinds of to the current interest in complexity science across the scale-free theories explaining why some adaptive phenom- management field. Then she defines the information and ena use the same causal dynamic at multiple levels. This is systems characteristics of the dynamical networked world.
opposite to the practice of using different disciplines in The dynamics of emergence are predicated on micro- organizations from bottom to top: individuals/psychology, diversity, and fine-grained representations are essentially groups/social psychology, networks/sociology, organiza- descriptive models of the detailed complexity of the world tions/organization theory, industries/IO economics and and its dynamics. However, to understand the dynamics of emergence, we need to access representations at different The purpose of this Special Issue is to explore the utility levels of granularity and abstraction simultaneously.
of complexity science and its concepts in advancing the Computational modelling allows us to discover how Using complexity science to paradigm shift macro-level properties and behaviours of systems emerge clan-like networks that characterize China’s social and from micro-level diversity and dynamics. Modelling economic evolution, for example, point to possible alter- emerges as the principal research tool for complex systems.
natives? To answer these questions, we must first briefly Yasmin concludes by discussing the contribution, the consider how knowledge is structured and shared within complexity science can make to the development of and between organizations and how this might affect the ontological and epistemological frameworks and computa- way that such organizations get institutionalized. We can tional modelling methods for IS in the networked world.
then explore the effect that ICTs might have on this process.
Peter Allen and Liz Varga explain the coevolution of IS Petru Curs¸eu observes that research on VTs has and the processes that underpin the construction and proliferated in the last few decades. However, he argues, development of IT from a complex systems perspective.
few clear and consistent theoretical attempts to integrate Their analysis highlights the process of emergence in IS.
the literature on VTs in a systemic way have emerged. His Evolution operates at the microscopic level; in organiza- article uses the complex adaptive systems (CAS) perspec- tions, this is the individual or agent, and each agent has an tive to integrate the literature on emergent states in VTs.
idiosyncratic view of the organization, using to some extent According to this framework, VT effectiveness depends on personal constructs in dealing with the reality of organiza- the interaction between three levels of dynamics: local, tional life. The agents also have their own view as to how global and contextual. Team cognition, trust, cohesion and they know what they know, that is, an epistemology. Peter conflict are described as states that emerge from the and Liz argue that agents’ epistemology is their IS, which is interactions among the VT members. Furthermore, as parts seen as more than the IT system they use. The IS of each of global dynamics, they impact on VT effectiveness, and at agent coevolves, by interaction with other agents, based on the same time, they are influenced by the outcomes of the the agent’s view of reality; it adds to the agent’s view of VT. Petru also shows how insights from this bi-directional reality, it refines it and it enables learning. The interaction causality as well as other benefits of using the CAS of all agents constitutes the organization. Even more framework work to improve our understanding of VT importantly, each agent is motivated by different values dynamics. Finally, his article also provides an overview of and interests. This is their axiology. If the motivations of artificial simulation models as well as simulation results many agents in an organization are similar, we may speak concerning the emergence of the four states described in of a shared culture. It is the axiology of an agent that the CAS framework. He also discusses several ways to motivates them to learn and develop their IS. This agent- improve the accuracy of the simulation models using based axiological framework is essential to understanding empirical data collected in real VTs.
the evolution of organizations. It is the interaction of agents Richard Vidgen and Xiaofeng Wang note that new that build consensus as to the shared reality of the technologies, notably service-oriented architectures and organization, and this in turn affects each agent’s ability Web services, are enabling what they call the third wave of and motivation to evolve the organization’s IS further. In business process management (BPM). Other observers addition, they propose that it is time that IT systems claim that BPM is informed by complexity theory and that included modelling capabilities, based on multi-agent business processes can evolve and adapt to changing representations of the organization and its context, to business circumstances. BPM adherents suggest that the explore and support strategic thinking and decision business/IT divide will be obliterated through a process- centric approach to systems development. Richard and Max Boisot starts with the question: How did bureau- Xiaofeng first explore the evolution of BPM and its cracies evolve? Bureaucracies in the Weberian mould, associated technologies. Then they turn to complexity whether of the state or corporate type, are rational-legal theory for new ideas about how distributed multi-agent structures organized to deliver order, stability and predict- processes, emergence, chaos and self-organization will ability. He attributes these changes to post-medieval further inform the scientific application of third-wave developments in ICTs. Then he asks: Could the new ICTs process management over the coming decade. They use that have appeared over the past 20 years have a similar coevolutionary theory to understand the business/IT impact on 21st century organizations? And, what kind of relationship. Specifically, Richard and Xiaofeng apply challenge does this pose 21st century organizations? To Stuart Kauffman’s NKC model to a business process address these questions, Max first presents a conceptual ecosystem to bring out the implications of coevolution for framework, the I-Space, which allows us to explore the the theory and practice of BPM and for the relationship relationship between how knowledge is structured and how between business and IT. They argue that a wider view of it flows within and between populations of agents. His the business process ecosystem is needed to take account of paper then examines what cultural and institutional the social perspective as well as the human/non-human challenges the new ICTs pose for both state and corporate bureaucracies, confronted as they are with the complexities Enrique Canessa and Rick Riolo state right off that of an increasingly distributed social order. Would such a organizations that make use of computer information development necessarily presage a further extension of systems are prototypical CAS. They show how a key either state or corporate bureaucracy? If not, what might it complexity science method, agent-based modelling (ABM), presage? Ever since Coase’s seminal 1937 paper, the can be used to study the impact of two different modes of options, whether applied at the level of the firm or at the use of computer-mediated communication (CMC) on level of the state, have tended to be framed exclusively as organizational culture (OC) and performance. The ABM either bureaucratic hierarchies or competitive markets.
includes stylized representations of (1) agents commu- Are such institutional forms our only options? Might the nicating with other agents to complete tasks; (2) an OC Using complexity science to paradigm shift consisting of the distribution of agent traits, changing as limitations of traditional approaches in IS, particularly agents communicate; (3) the effect of OC on communica- with regard to the dynamics of emergence and the tion effectiveness (CE) and (4) the effect of CE on task attribution of causality in complex systems.
completion times, that is, performance. If CMC is used in a The compelling argument for complexity science is that it broad mode, that is, to contact and collaborate with many provides a wide and powerful lens to define and move new agents, the development of a strong OC is slowed, around the multi-dimensional ‘problem’ and ‘solution’ leading to decreased CE and poorer performance early on.
spaces in a dynamic way, at multiple levels of abstraction.
If CMC is used in a local mode, repeatedly contacting the In the IS domain, we have a legacy of approaches rooted in same agents, a strong OC develops rapidly, leading to heterogeneous philosophical schools, and complexity increased CE and high performance early on. However, if science offers a number of philosophical openings for CMC is used in a broad mode over longer time periods, a connecting with these schools (Merali, 2004). The mission strong OC can develop over a wider set of agents, leading to of this Special Issue is to stimulate a review of the an OC that is stronger than an OC which develops with ontological and epistemological bases for the concepts local CMC use. Thus the broad use of CMC results in overall and methods that the IS community subscribes to, and to CE and performance that is higher than is generated by explore avenues for future developments.
local use of CMC. Enrique and Rick end their article with Finally, for IS scholars ready to take the paradigm-shift an excellent discussion of how the dynamics generated by plunge, we offer complexity science as an apt means for an ABM can lead to a deeper understanding of the moving toward a more dynamical theoretical and metho- behaviour of a CAS, thereby allowing researchers to better dological platform better suited for studying IS dynamics at design empirical longitudinal studies.
the dawn of the 21st century. Collectively, the authors of the Hind Benbya and Bill McKelvey focus on IS misalign- assembled articles provide a meaty introduction to com- ment – a significant problem in a changing world. Despite plexity principles along their way toward studies of more years of attention, IS misalignment remains a critical and idiosyncratic aspects of IS, business and organizational chronic unsolved problem in today’s complex and turbu- lent world. They argue that the coevolutionary andemergent nature of alignment has rarely been taken intoconsideration in IS research and that this is the funda- mental reason behind why IS alignment is so difficult. Hind 1 See Anderson (1999) and Maguire et al. (2006) for a review of and Bill present a view of IS alignment in organizations that the utilization of complexity concepts in organizational theory draws and builds on complexity theory and especially its literature, and Merali (2004) for a review of complexity concepts focus on coevolution-based self-organized emergent beha- viour and structure. This, they suggest, provides important 2 Communications of the ACM, 2005, 48(5): Special Issue on insights for dealing with the emergent nature of IS alignment. Their view considers business/IS alignment as 3 Information Technology and People, 2006, 19(1): Special Issue a series of adjustments at three levels of analysis: individual, operational and strategic and suggests several 4 Power laws are also defined by their fixed exponent. They often enabling conditions – principles of adaptation and scale- take the form of rank/size expressions such as F$N Àb, where F free dynamics – aimed at speeding up the adaptive is frequency, N is rank (the variable) and b the exponent, is coevolutionary dynamics among the three levels. Instead constant. In exponential functions the exponent is the variable of focusing upon simple cause–effect deterministic logic, Hind and Bill suggest a chain of causal dynamics: first,organizational effectiveness is a function of IS alignment;second, IS alignment is a function of coevolutionary dynamics spreading across three levels: individual users,business and IS subcomponents, and top-level business Anderson, P. (1999). Complexity Theory and Organization Science, strategy; third, adaptation via coevolution is a function of Anderson, P.W., Arrow, K.J. and Pines, D. (eds.) (1988). The Economy as an several first principles of efficacious adaptation; fourth, the Evolving Complex System, Reading, MA: Addison-Wesley.
first principles are best achieved via scale-free dynamics; Andriani, P. and McKelvey, B. (2005). Beyond Gaussian Averages: Redirecting and fifth, IS alignment leadership begins when managers set organization science toward extreme events and power laws, Presented at the Academy of Management Annual Meeting, Honolulu, HA, August.
Collectively, these papers provide a ‘taster’ or sampler for Andriani, P. and McKelvey, B. (2006). On the Relevance of Extremes vs Means in Organization Science: Some theory, research, statistics, and power law those who are new to complexity science. They show how implications, Working paper, Durham, UK: Durham Business School, concepts from complexity theory can be used to explore and yield insights into issues central to the IS domain. It is Arthur, W.B., Durlauf, S.N. and Lane, D.A. (eds.) (1997). The Economy as an important to note that we do not see complexity science as Evolving Complex System, Proceedings of the Santa Fe Institute, Vol. XXVII.
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