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• 1 2017-01-21T13:57:23-08:00 COMPLEXITY THEORY 13 Complexity theory may allow organizational researchers identify the emergences of structures and dynamics of economic systems and its organizational forms that support connectivity and growth plain 2017-03-23T14:16:09-07:00 Resources such as the Tech City Maps are fundamental for organizational researchers in terms of data access as it gives the appropriate context for analysis. Nevertheless, in order to identify the emergence of structures and organizational forms that facilitate the connectivity and growth of the system, that is, to understand the micro dynamics of the system, Roberta Comunian reminds us the value of complexity theory. It is, therefore, advisable to consider the Digital Economy clusters, not just business communities but also complex systems. According to Roberta Comunian, any system can be considered ‘complex’ when its elements interact in a nonlinear way, which means that “it is not possible to forecast the behavior of the system simply by having knowledge of its components” (2010:5). Comunian also compiled a comprehensive list of principles of complex systems (Figure 4). Any system that displays the characteristics outlined in her table of principles can be also considered ‘complex’. Comunian also suggests that, in order to understand complex adaptative systems, what is needed first is to identify human and non-human agents interacting within it. Complexity theory may allow organizational researchers identify the emergence of structures and dynamics of economic systems and its organizational forms that support connectivity and growth (Comunian, 2010:7)
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  The collective organization, such as the Hull Digital Economy Cluster, are often better positioned than individuals to influence the feedback that “brings about the emergence of new organizational collectives”, such as the entrepreneurship networks within the Hull Digital Economy ecosystem. According to Chilles and Meyer (2001 and 2004) these new organizational collectives “play an integral role in the management of self-organizing processes, with implications for collective entrepreneurship in new industry formation” (Chiles et al., 2004:515). Many other scholars hold the view that complexity theory may fulfill the needs to understand the entrepreneurial practice at an organizational level (Eisenhardt, 2002; Chiles et al., 2004; McKelvey, 2004). McKelvey stated that “as actors in a disequilibrium market process,(...) entrepreneurs continually generated novelty through their discovery of market gaps (Kirzner 1973) and creative imagination (Lachmann 1986), but their alertness/creativity was not unfettered; (...) constrained and shaped by the local culture (Scott 2001)” (McKelvey, 2004:314). This shift from equilibrium seeking to order creation (disequilibrium market process) is a key element for understanding complexity in the context of startup culture. (McKelvey, 2004:314). Hinterberger (1994 quoted in McKelvey, 2004: 317) also critiques the traditional reliance on equilibrium assumption from a different perspective linked both to competitive context and socio-economic actors, and uncover four elements that challenge the equilibrium assumption.
  1. Rapid changes in the competitive context of firms do not allow the kinds of extended equilibria seen in biology and classical physics.
  2. There is more and more evidence that the future is best characterized by ‘‘disorder, instability, diversity, disequilibrium, and nonlinearity’’ (Hinterberger, 1994:37)
  3. Firms are likely to experience changing basins of attraction—that is, the effects of different equilibria.
  4. Agents coevolve to create higher level structures that become the selection contexts for subsequent agent behaviors. (McKelvey, 2004:317)
  It has been already stated how complexity theory may allow organizational researchers identify the emergence of structures and dynamics of economic systems and its organizational forms that support connectivity and growth (Comunian, 2010:7), according to Fuller and Warren, “explanations for the emergence, existence, and sustainability of particular patterns (firms, networks, industries) should” as they argue “be grounded in an understanding of the dynamics operating through multiple levels” (Fuller and Warren, 2006:958). The multilevel approach summarized from Fuller and Warren and described in Figures 5  explain how micro processes generate macro-order and how organizational research in entrepreneurship and complexity theory may operate. Other researchers such as Tsoukas and Chia believe this feature would provide scholars “with a fuller understanding of the dynamics of change” (Tsoukas and Chia 2002:568). Furthermore, there are three domains that give an account of the relation between entrepreneurial practice and the structures that want to be observed. Those domains are, according to Fuller and Warren (2006:959).
  • The reflexive identity of the entrepreneur: represented through the language of mental models, motivations, and everyday practice.
  • The recursive (regular/repeating/established) activities of the entrepreneur’s firm.
  • The recursive activities in a network of firms (for instance a technological cluster, or particular industry) in which the entrepreneurial firm is engaged
  
  In relation to the domains outlined above, Fuller and Lewis (2003) states the fact that the firm's chosen strategy and its relationships are closely bound, and that interaction with others moderates the innovative entrepreneurial practices, leading to emergent properties that arise as a response to the interactions within the system. In relation to the impact of the relationships and interactions between the firm and the system, Lave and Wenger (1991) and later Wenger (1998) argue that “developing a practice, of any kind, requires the formation of a community (however loosely defined) whose members can engage with one another and thus acknowledge and legitimise each other as participants—a process of becoming, not just encountering—the social process view in other words” (in Fuller & Warren, 2006:964).