Running Head Pathways of Opportunity PATHWAYS OF OPPORTUNITY IN DYNAMIC ORGANIZATIONAL NETWORKS
How do organizational networks change continuously to provide new opportunities for members? We view organizations as complex adaptive systems embedded in heterogeneous networks consisting of different kinds of nodes (such as people, machines, projects, and heterogeneous components of the modern technological environment) and consider new opportunities to reside in the creation of new pathways linking previously unconnected nodes. We identify two broad classes of ideology relevant to opportunity creation in organizational networks: serendipity and goal directedness. These ideologies operate as explicit and tacit theories of networking, affecting the addition of new nodes as well as the creation and rearrangement of ties. Pathways of Opportunity 3 Organizations can be understood as networks of procedures, puzzles, interpretations, and behaviors that provide opportunities for sense making (Weick, 1979: 4) These fluid and shifting networks are at least in part constituted in terms of tacit or explicit theories concerning what people expect to experience. Managers and other occupants of formal leadership positions who expect the world to be stable and routine may well help confirm this expectation for themselves and others by structuring experience as a series of habitual routines. Theories about organizations concerning their design and their operation may have even more powerf ul self fulfilling prophecies (cf. Ferraro, Pfeffer, Sutton, 2005) To the extent that organizational theories emphasize stability rather than change, routines rather than serendipity, and control rather than self organization, organizations themselves may not only appear to be inertial, but may also be constituted according to taken for granted theoretical prescriptions. Thus, organizational research that posits the importance of routines and inertial processes (cf. Hannan Freeman, 1977) is likely to both see and experience the world of organizations in terms of stability of organizational forms. Organizations as complex adaptive systems embedded in environments both internal and external are often described in terms of bundles of routines that store the cumulative results of learning (e.g. Kilduff, 1992) This emphasis on the recursiveness by which systems self learn and self organize is a starting point for understanding the process of opportunity creation within dynamic, open system organizational networks. Complexity theory seeks to understand how system level self organization emerges from interactions among nodes in the system (Anderson, 1999; Eisenhardt Bhatia, 2002; Kauffman, 1993) In drawing from complexity theory, we propose a view of Pathways of Opportunity 4 organizational systems that includes the following characteristics associated with complex systems (cf. Cilliers, 1998: 3 5) a) The organization contains a large number of interacting nodes (cf. McKelvey, 1999) such nodes comprising both individual people as well as other heterogenous elements such as technologies (cf. Carley, 2002) b) Small investments of effort (such as efforts to establish new connections between nodes) are capable of non linear yields (such as dramatically increased connectivity across the organizational system) c) Opportunity creation and destruction are recursive processes in that activities initiated by particular nodes are likely to cycle back into the vicinity of these nodes. d) The organization, to the extent that it is understood as a complex network system, operates far from equilibrium, requiring a constant flow of energy: "equilibrium is another word for death" (Cilliers, 1998: 4) e) The historical memory of the system (embedded in routines) undergoes constant change. f) Each node has only local knowledge no god like purview of the whole system is possible. Thus, complexity emerges from the interactions of heterogenous nodes it cannot be imposed by an omniscient organizational designer. We draw from these complexity ideas throughout the chapter to explore how leadership can be understood in terms of continual opportunity creation in a system of emergent complexity. From this perspective, opportunity creation can be facilitated or hindered by those in formal leadership positions. Not all organizations exhibit the dynamic properties of continual opportunity creation indeed, we think that network stagnation is the more likely outcome for most organizations most of the time, given the well known human aversion to constant change. But, just as at the individual level, people are interested in the extent to which exceptional states of 'flow' can be attained in the production of work Pathways of Opportunity 5 (Csikszentmihalyi, 1991) so, at the organizational level, organizational leaders are likely to be interested in the extent to which exceptional states of dynamic opportunity creation can be attained. We consider organizations as transformational engines that generate opportunities for members to learn from each other and from organizational resources such as data bases, procedures, and goals. Given that an organization functions in part as a dedicated arrangement of specialized resources (Lawrence Lorsch, 1967) we consider how the arrangement of specialized resources leads to the emergence of opportunity generation. Specifically, given the self organizing, emergent characteristics of complex systems, our research question is: How can leadership arrange for organizational networks to change continuously, providing new opportunities for members, and avoiding the fate of stagnation? In order to answer this question we first must define what we mean by an organizational network in this complexity context. HETEROGENEOUS NETWORKS A network can be defined as a set of nodes and the relations that connect them. The nodes in any specific network are often assumed to be of the same kind. Thus, we have studies of interpersonal networks in which the nodes are people (e.g. Mehra, Kilduff, Brass, 2001) and interorganizational networks in which the nodes are organizations or organizational units (e.g. Tsai, 2002) But we have almost no studies of heterogeneous networks in which different kinds of nodes and their relationships are studied simultaneously. In fact, a key assumption in previous network studies is that nodes are homogeneous or, at least, at the same level (Wasserman Faust, 1994) This Pathways of Opportunity 6 assumption simplifies the job of conceptualizing or defining the boundary of a network but ignores the possibility of interactions among different kinds of nodes or across different levels. As a result, previous studies tend to find that networks are stable or relatively inert over time because the way nodes are defined in these studies excludes cross level effects of network change and development. To more meaningfully observe change and new opportunities in networks, we need to consider a broader set of nodes. For example, in an organization where employees utilize different technologies and work on different projects, we should not narrowly focus on "people" as the nodes in the organizational network without considering "technologies" and "projects" as nodes as well. Although research in the Actor Network Theory (A NT) tradition (Law Hassard, 1999) has described the importance of studying connections between people, machines, texts, projects, and other heterogeneous components of the modern technological environment, this research tradition appears to have had almost no influence on social network. Whereas A NT research is deliberately qualitative in its pursuit of connections that are considered important from the point of view of the participant observer, and eschews the use of quantitative methods (Law Hassard, 1999) social network research is relentlessly quantitative in its use of mathematical algorithms to detect network structure. Without abandoning the advantages of quantitative modeling, it may be possible to incorporate more heterogeneous components within standard social network analyses. (Indeed, in pursuit of optimal organizational design considerations, computational modeling approaches have incorporated diverse network elements within what is termed the "metamatrix" see Carley, 2002, for a Pathways of Opportunity 7 review. And the discussion of heterogenous agents in interaction with each other and with artifacts in Axelrod and Cohen, 1999 explores organizational implications of a complexity perspective. We know from A NT research that large scale technological projects can produce self organization among human actors whose relations with each other are subservient to relations with the many dynamic aspects of project management (Knorr Cetina, 1981) In such circumstances, there appears to be no need for conventional leadership initiatives. We also know that people have intense relationships with texts and machines of all kinds (Latour, 1996) Thus, in considering what entities to include in an organizational network, the analyst is faced with a boundary choice that differs from the usual dilemma omnipresent in social network research. Whereas social network researchers are usually concerned at the beginning of a research project with the question of which nodes of the same kind to include (Wasserman Faust, 1994: 31) in organizational research the analyst is less concerned with the boundary of the population, because the formal boundary of the organization can be taken to contain the relevant set of internal entities. However, within that formal boundary there is a potentially unlimited set of heterogeneous nodes that could include entities such as people, routines, cognitions, ideologies, texts, and machines. Network Regions Heterogeneity refers not only to the different types of nodes but also encompasses different network regions within the overall organizational network. Figure 1 (adapted from Dorogovtsev and Mendes, 2003: 20) illustrates the typical topography of a complex Pathways