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«Researchers have endeavored to increase understanding of the relationships between investments in information systems (IS), competitive advantage, ...»

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6.1.7. Cognition. Cognitions are schemas or knowledge structures used by individuals and groups to make sense of information and environments (Walsh, 1995). A particular knowledge structure ―represents organized knowledge about a given concept or type of stimulus‖ (Fiske & Taylor, 1984, p. 149). In other words, cognition is used by individuals and groups to make sense of their world. Individuals and groups develop cognitive schemas ranging from particular roles individuals might play (e.g., a police officer) to behavior based schemas (e.g., how to behave in class) (Elsbach et al., 2005).

There is a long history of research in individual and group-level cognition in various contexts (for a review, see Walsh, 1995), and studies have shown the impact of cognitive schemas on organizational processes and outcomes (Elsbach et al., 2005; Kaplan, 2008).

Of particular interest in the current study is the idea of distributed cognition (Aranda & Easterbrook, 2006; Boland et al, 1994; Flor & Hutchins, 1991; Hollan et al., 2000;

Hutchins, 1991; Rogers, 1994).

Distributed Cognition. Flor and Hutchins (1991) and Hutchins (1991) suggest that the distributed cognition approach views a cognitive system not at its most basic individual level, but as a system composed of individual and relevant artifacts. Hutchins (1991) posits that we can never understand collective outcomes by studying only what is understood at the individual level. He uses the example of the successful completion of an airplane flight. All of the individual agents and the relevant artifacts that go into the successful completion of the flight must be understood in unity, as individual agents and

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cognition is interested in the interdependencies among individuals and the necessity of such interdependencies in facilitating successful coordinated outcomes. Distributed cognition posits that cognitive processes may be distributed across social groups, may involve coordination between internal and external structure, and may be spatially and temporally distributed (Hollan et al., 2000).

Distributed cognition is designed to study cognition in socially situated contexts (Rogers, 1994). Several properties characterize distributed cognition. Aranda and Easterbrook (2006) suggest that distributed cognition should not be used to study cognition in constrained settings, such as a laboratory experiment, due to the importance of examining the dynamic nature of cognitive interaction. Secondly, analyzing the artifacts people use to accomplish their cognitive tasks is important. Third, a key consideration is identifying the paths that information follows to reach the people that need it. Members of a group start out with various fragments of information or knowledge; then, those fragments of information and knowledge are shared through varying forms of communication until they reach the people who need them. Finally, cognitive work may be examined on two levels, the actual resolution of cognitive problems or analysis of learning and structuring activities that take place among group members.

Study II of this dissertation seeks to examine the way in which social networks are formed around social computing and communications technologies and serve as the connecting mechanism among the cognitions distributed among FCI‘s management team.

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native environment of the focal firm to allow observation of the true nature of dynamic cognitive interaction.

Chapter seven provides a description of the research methodology employed in Study II of this dissertation.

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While the overarching methodology employed in this dissertation remains qualitative, three research methods have been incorporated. Thus, the research methodology employed makes a unique contribution to research in its own right.

Grounded Theory has been used in Study I to identify the central concepts, build theory and explain the general role of information systems in competitive actions and firm performance. Study II builds upon the Grounded Theoretical findings in Study I to incorporate both Social Network Analysis (SNA) and Centering Resonance Analysis (CRA). These two methods will be used to extend Grounded Theory by examining the collective and interactive nature of managers in conceiving, enacting and executing competitive actions in the context of managerial social networks, social computing, and communications technologies.

7.1. Rationale for Research Methodology Social network analysis (SNA) has been employed to provide a method which would allow an examination of the role of social computing and communications technologies in the infrastructure of the social network. SNA tends to focus upon the ties between actors in a network, largely disregarding the attributes of the individuals making up the nodes of the network. Using a one-mode social network analysis is a somewhat problematic approach when used on its own in the context of Study II, as it concentrates

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information distributed among individuals in the network. More importantly as onemode SNA is primarily concerned with structural patterns, it ignores the purpose of the network, or the social network constructed as a platform for aggregated experience, knowledge and information that is distributed throughout the cognitions of individuals inherent in the network structure as a whole. Furthermore, a one-mode social network analysis does not necessarily take into account the mechanisms by which social networks are formed or the vehicles by which knowledge and information can flow more or less efficiently throughout the network structure. Therefore, two-mode social network analysis was conducted in an effort to learn the mechanisms by which the social network infrastructure is supported.

While social network analysis tools can be used to identify social network nodes and the structural patterns among them, great difficulty arises when attempting to examine the knowledge and information that flows among network participants.

Furthermore, the volume and multifariousness of data inherent in the social network structure can make many forms of such analysis prohibitive. Centering resonance analysis provides the analytical tools to examine both the cognitive structures of individuals and of groups of individuals, and to identify the major themes and concepts present in the group. However, centering resonance analysis on its own is limiting, as it does not take into account the structures of groups, positions of individuals in such structures or the ties amongst individuals in such groups.

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with the grounded theoretical process model in Study I to effectively address the research question in Study II. First, a one-mode social network analysis approach is employed to identify the individual managers and the ties among them in the context of each of the four stages inherent in the grounded theoretical process model – conceiving, enacting, executing, and firm performance. Second, a two-mode social network analysis approach is used to examine the social network infrastructure, or the primary media by which each social network is formed at each stage of the grounded theoretical process model and the vehicles by which knowledge and information flow throughout the network structure (i.e., face-to-face interactions, wireless communications devices, enterprise systems, etc.).

Third, centering resonance analysis is used to examine the concepts that are common among the managers at each stage of the grounded theoretical process model. Finally, the one-mode and two-mode networks are synthesized through a measure of IT Mediation Intensity to provide an integrated depiction of the social network structure that exists at each stage and the mechanisms inherent in the social network infrastructure. By utilizing and synthesizing these approaches, the question of how managers in a dominant firm use intrafirm social computing networks and communications technologies in conceiving, enacting and executing competitive actions and responses to improve firm performance was effectively addressed.

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Study II continues in the context of the focal firm, FCI. The primary unit of analysis in Study II remains the competitive action of the firm, which is defined as competitive actions and reactions formulated and enacted by the firm as either an aggressive competitive move or a direct response to the action of a competitor.

Competitive actions of FCI were examined through three types of data: managerial interviews, managerial observation, and internal and external documents, such as FCI‘s annual report, and relevant trade and industry publications, and in the context of social computing and communications technologies. Additional data collection took place through semi-structured and structured interviews with managers, observation of managers, and internal and external documents collected during the period 2008-2009.

Additional interview data was taped and transcribed to text. Transcribed interview data was used in the data analysis. The primary time frame of interest in this study remains the period 2006 – 2008.

7.3. Data Analysis Study I of this dissertation found four distinct stages in a competitive dynamics process: Conceiving, Enacting, Executing, Firm Performance. Findings from Study I indicate that in the context of competitive actions and responses, managers behave collectively, relying upon a culmination of the unique expertise, information and knowledge inherent in each individual manager. In order to answer the research question in Study II, the competitive action New Product Development identified in Study I was chosen as exemplary, as this competitive action is the most complex in this study. The

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individuals with widely varied responsibilities in terms of meeting organizational goals.

In the context of the competitive action, New Product Development, and in building upon the findings in Study I, three areas were addressed. First, as a necessary condition to study the role of social computing and communications technologies in competitive dynamics, one-mode social network analysis was used to construct managerial social networks at each stage of the competitive dynamics process, examining whether they are different or similar across the four stages of competitive activity identified in Study I. Secondly, by using two-mode social network analysis, the role of social computing and communications technologies in the network infrastructure at each stage of the competitive dynamics process was determined. Finally, centering resonance analysis was used to constructed and examine digitially-mediated aggregate managerial cognitions at each stage of the process model developed in Study I.

The ensuing sections provide detailed explanations of social network analysis and centering resonance analysis.

7.3.1. Social Network Analysis. Social network analysis (SNA) is a methodology used to examine patterns of communication among individuals and to understand the composition and role of social networks. SNA is used to understand information and knowledge flows within complex systems (Corman et al., 2002) such as organizations where coordination of individuals is required to reach desired goals (Mote, Jordan, Hage, and Whitestone, 2007). The goal of SNA is to understand the location of actors (nodes) within the network and to understand the relationships (ties) between

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represent channels for the flow of knowledge and information (Wasserman and Faust, 1994).

Rogers (1986) suggests that ―The essence of human behavior is the interaction through which one individual exchanges information with one or more other individuals‖ (p. 203) and that these ―communication flows‖ become patterned in terms of the interpersonal linkages created by the sharing of information among these ―interconnected individuals‖ (p. 203). The positions held in networks of interconnected individuals (as described by their relationships), influence an individual‘s exposure to and control over information (Burt, 1992; Haythornthwaite, 1996). Social network analysis seeks to identify relations among people, locate patterns amongst those relations, and interpret the effects of such relations and patterns upon communication and the transfer of information and knowledge. Barnes (1972) describes the social network concept as the intent ―…to discover how A, who is in touch with B and C, is affected by the relation between B and C…‖ (p. 3).

In social network analysis, it is important that the boundaries of the network to be studied are clearly defined. For example, a network under study might be bounded by participation in a specific activity such as a specific group of individuals who share a common interest, who meet some specific criteria, or as members of a community. As such it is common in social network analysis to know a priori the parameters that define a given network (Hanneman & Riddle, 2005).

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interest of the network analyst lies in the quality and quantity of ties between key individuals or nodes or the structure of networks. Ties between the nodes may have directional (send and/or receive) and strength (weak tie or strong tie) attributes (Hanneman & Riddle, 2005).

Unlike research methods such as surveys which isolate individuals from their social context by random sampling from a greater population, it is common in social network analysis to study a population in its entirety – i.e., top level managers in an organization or CIOs in a particular industry. Barton (1968) suggests that random sampling methods greatly limit the value of research by removing context, likening it to a biologist ―…putting his experimental animals through a hamburger machine and looking at every hundredth cell through a microscope; anatomy and physiology get lost, structure and function disappear, and one is left with cell biology‖ (p. 1). Social network analysis examines the individual node in the context of its position in a social structure rather than isolating the node. Hence, the network node has positional value in a social network only due to its relevant position to other nodes.

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