This thesis is based on three empirical studies of the director interlock network among Swedish firms listed on the Stockholm Stock Exchange, focusing on its consequences for firm behavior and its association with ownership structures. Director interlocks are created when directors serve on, and hence interlock, several boards. Director interlocks aggregate to a social network that not only connects most firms into a single component, but are, more essentially, also shown to affect firm behavior. The introductory chapter contains a review of the director interlock research as well as some suggestions for future research directions. Study I is an examination of the importance of director interlock relations for stock repurchases. Using parametric survival analysis, it is demonstrated that the decision to repurchase stocks is dependent on both the firms’ economic settings and their director interlocks. Study II examines the network impact on recruitment of new board members in the network of firms, directors, and owners. Using simulations, it is shown that directors with personal contact with a director and/or owner of the recruiting firm have a substantially increased probability of being recruited to the focal board. Study III examines the association between ownership and director interlocks. Re-analyses of the same network examined in Study II show that multiple director assignments are highly dependent on ownership interlocks. The three studies exemplify the real-world consequences of board interlocks and field a new understanding of the mechanisms behind their formation. Furthermore, the association between the director and ownership interlocks suggests that the ownership network (co-)produces some of the phenomena that have been attributed to the director network. These results underscore the need for further examination of director interlocks to bring the owners back into the analysis. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 3: Manuscript.</p>

Finding subgroups within social networks is important for understanding and possibly influencing the formation and evolution of online communities. This thesis addresses the problem of finding cohesive subgroups within social networks inferred from online interactions. The dissertation begins with a review of relevant literature and identifies existing methods for finding cohesive subgroups. This is followed by the introduction of the SCAN method for identifying subgroups in online interaction. The SCAN (Social Cohesion Analysis of Networks) methodology involves three steps: selecting the possible members (Select), collecting those members into possible subgroups (Collect) and choosing the cohesive subgroups over time (Choose). Social network analysis, clustering and partitioning, and similarity measurement are then used to implement each of the steps. Two further case studies are presented, one involving the TorCamp Google group and the other involving YouTube vaccination videos, to demonstrate how the methodology works in practice. Behavioural measures of Sense of Community and the Social Network Questionnaire are correlated with the SCAN method to demonstrate that the SCAN approach can find meaningful subgroups. Additional empirical findings are reported. Betweenness centrality appears to be a useful filter for screening potential subgroup members, and members of cohesive subgroups have stronger community membership and influence than others. Subgroups identified using weighted average hierarchical clustering are consistent with the subgroups identified using the more computationally expensive k-plex analysis. The value of similarity measurement in assessing subgroup cohesion over time is demonstrated, and possible problems with the use of Q modularity to identify cohesive subgroups are noted. Applications of this research to marketing, expertise location, and information search are also discussed.

Continuing medical education has been characterized as didactic and ineffective. This thesis explores the use of Knowledge Building theory, pedagogy, and technology to test an alternative model for physician engagement—one that emphasizes sustained and creative work with ideas. Several important conceptual changes in continuing medical education are implied by the Knowledge Building model—changes that extend the traditional approach through engagement in (a) collective responsibility for group achievements rather than exclusive focus on individual advancement and (b) work in design-mode, with ideas treated as objects of creation and assemblage into larger wholes and new applications, with extension beyond belief-mode where evidence-based acceptance or rejection of beliefs dominates. The goal is to engage physicians in “cultures of participation” where individual learning and collective knowledge invention or metadesign advance in parallel. This study was conducted in a continuing medical education End-of-Life Care Distance Education course, for family physicians, from 2004 to 2009. A mixed methods case study methodology was used to determine if social-mediated Knowledge Building improved physicians’ knowledge, and if so, what social network structural relationships and sociocognitive dynamics support knowledge improvement, democratization of knowledge, and a metadesign perspective. Traditional pre-/posttest learning measures across 4-years showed significant gains (9% on paired t-test = 5.34, p < 0.001) and large effect size (0.82). Social network analysis of ten 2008/2009 modules showed significant difference in density of build-on notes across groups. Additional results demonstrated a relationship between high knowledge gains and social network measures of centrality/distribution and cohesion. Correlation of posttest scores with centrality variables were all positive. Position/power analyses highlighted core-periphery sociocognitive dynamics between the facilitator and students. Facilitators most often evoked partner/expert relationships. Questions rather than statements dominated the discourse; discourse complexity was elaborated/compiled as opposed to reduced/dispersed. Themes beyond predefined learning objectives emerged and Knowledge Building principles of community responsibility, idea improvability, and democratization of knowledge were evident. Overall, results demonstrate the potential of collective Knowledge Building and design-mode work in continuing medical education, with individual learning representing an important by-product. There were no discernible decrements in performance, suggesting significant advantages rather than tradeoffs from engagement in Knowledge Building.

Continuing medical education has been characterized as didactic and ineffective. This thesis explores the use of Knowledge Building theory, pedagogy, and technology to test an alternative model for physician engagement—one that emphasizes sustained and creative work with ideas. Several important conceptual changes in continuing medical education are implied by the Knowledge Building model—changes that extend the traditional approach through engagement in (a) collective responsibility for group achievements rather than exclusive focus on individual advancement and (b) work in design-mode, with ideas treated as objects of creation and assemblage into larger wholes and new applications, with extension beyond belief-mode where evidence-based acceptance or rejection of beliefs dominates. The goal is to engage physicians in “cultures of participation” where individual learning and collective knowledge invention or metadesign advance in parallel. This study was conducted in a continuing medical education End-of-Life Care Distance Education course, for family physicians, from 2004 to 2009. A mixed methods case study methodology was used to determine if social-mediated Knowledge Building improved physicians’ knowledge, and if so, what social network structural relationships and sociocognitive dynamics support knowledge improvement, democratization of knowledge, and a metadesign perspective. Traditional pre-/posttest learning measures across 4-years showed significant gains (9% on paired t-test = 5.34, p < 0.001) and large effect size (0.82). Social network analysis of ten 2008/2009 modules showed significant difference in density of build-on notes across groups. Additional results demonstrated a relationship between high knowledge gains and social network measures of centrality/distribution and cohesion. Correlation of posttest scores with centrality variables were all positive. Position/power analyses highlighted core-periphery sociocognitive dynamics between the facilitator and students. Facilitators most often evoked partner/expert relationships. Questions rather than statements dominated the discourse; discourse complexity was elaborated/compiled as opposed to reduced/dispersed. Themes beyond predefined learning objectives emerged and Knowledge Building principles of community responsibility, idea improvability, and democratization of knowledge were evident. Overall, results demonstrate the potential of collective Knowledge Building and design-mode work in continuing medical education, with individual learning representing an important by-product. There were no discernible decrements in performance, suggesting significant advantages rather than tradeoffs from engagement in Knowledge Building.

Finding subgroups within social networks is important for understanding and possibly influencing the formation and evolution of online communities. This thesis addresses the problem of finding cohesive subgroups within social networks inferred from online interactions. The dissertation begins with a review of relevant literature and identifies existing methods for finding cohesive subgroups. This is followed by the introduction of the SCAN method for identifying subgroups in online interaction. The SCAN (Social Cohesion Analysis of Networks) methodology involves three steps: selecting the possible members (Select), collecting those members into possible subgroups (Collect) and choosing the cohesive subgroups over time (Choose). Social network analysis, clustering and partitioning, and similarity measurement are then used to implement each of the steps. Two further case studies are presented, one involving the TorCamp Google group and the other involving YouTube vaccination videos, to demonstrate how the methodology works in practice. Behavioural measures of Sense of Community and the Social Network Questionnaire are correlated with the SCAN method to demonstrate that the SCAN approach can find meaningful subgroups. Additional empirical findings are reported. Betweenness centrality appears to be a useful filter for screening potential subgroup members, and members of cohesive subgroups have stronger community membership and influence than others. Subgroups identified using weighted average hierarchical clustering are consistent with the subgroups identified using the more computationally expensive k-plex analysis. The value of similarity measurement in assessing subgroup cohesion over time is demonstrated, and possible problems with the use of Q modularity to identify cohesive subgroups are noted. Applications of this research to marketing, expertise location, and information search are also discussed.

The sharing of needles among injection drug users (IDUs) is a common route of Human Immunodeficiency Virus and Hepatitis C Virus transmission. Through the increased utilization of social network analysis, researchers have been able to examine how the interpersonal relationships of IDUs affect injection risk behaviour. This study involves a secondary analysis of data from a cross-sectional study of 156 IDUs from Winnipeg, Manitoba titled “Social Network Analysis of Injection Drug Users”. Multiple logistic regression analysis was used to assess the individual and the social network characteristics associated with needle sharing among the IDUs. Generalized Estimating Equations analysis was used to determine the injecting dyad characteristics which influence needle sharing behaviour between the IDUs and their injection drug using network members. The results revealed five key thematic findings that were significantly associated with needle sharing: (1) types of drug use, (2) socio-demographic status, (3) injecting in semi-public locations, (4) intimacy, and (5) social influence. The findings from this study suggest that comprehensive prevention approaches that target individuals and their network relationships may be necessary for sustainable reductions in needle sharing among IDUs. / February 2006

This dissertation research is focused on how we, as researchers, ‘influence’ others researchers. In particular, I am concerned with the notion of what constitutes the ‘influence’ of a scholar and how ‘influence’ is conferred upon scholars. This research is concerned with the construct called ‘scholarly influence’. Scholarly influence is of interest because a clear “theory of scholarly influence” does not yet exist. Rather a number of surrogate measures or concepts that are variable are used to evaluate the value of one’s academic work. ‘Scholarly influence’ is broken down into ‘ideational influence’ or the influence that one has through publication and the uptake of the ideas presented in the publication, and ‘social influence’ or the influence that one has through working with other researchers. Finally through the use of the definition of ‘scholarly influence’ this dissertation tries to commence a definition of ‘quality’ in scholarly work.

New social networking and social web tools are becoming available and are easing the process of customizing online social environments. With these developments in technology, core design efforts are being extended beyond usability for individual users and beginning to include notions of sociability for the engagement of communities of users. This thesis is an investigation of these developments. It is guided by the principal research question: how do you design for social engagement in an online social environment intended to facilitate interaction in a community of users? To address this question, this thesis presents a domain-community model developed from the communities of practice concept and the Work Domain Analysis model used in Cognitive Work Analysis. The domain-community model provides a basis for the design a composition of web components for an online social environment that will addresses issues of social engagement and domain effectiveness. In a case study, the domain-community model was used as a basis for the redesign of a social networking portal used by an international development leadership community called UCP-SARnet. A social network analysis of core members of UCP-SARnet was conducted before and after the portal was redesigned. From the social network analysis, it was concluded that the structure of UCP-SARnet was positively affected by the redesign: core group members reported they knew one another significantly more after the redesign of the website than before the redesign. User experience measures of the UCP-SARnet portal, website usage data, and a tally of website communication activity also changed significantly with the redesign of the website. This provided more evidence that a design informed by Cognitive Work Analysis and communities of practice produced a measurable effect on the structure of the UCP-SARnet online community. As such, this model can provide a basis for designers of online communities to more systematically account for social phenomena in relation to collective efforts in a given work domain. Furthermore, it is expected the effectiveness of the model can be taken forward with future work by refining the domain-community model, developing techniques to translate the model into interface concepts, and building practices for community-based research and design.

In recent years, many virtual communities have thrived with the rapid development of Internet. In the environment of virtual community, through the information exchange, members realize that they often have similar demands. In the case of online group-buying, buyers who have a common interest in a certain product group together to collect their collective power and thus get price discounts from suppliers. Hence online group-buying comprehends the need of interest, relationship, and transaction of virtual community. We are questioning whether the group-buying have invisible community relationships embedded in members¡¦ transaction activities. The purpose of this study is by the use of social network analysis to investigate the relationships of the network between initiators and participants and between co-participants. In particular we propose some measures based on social network analysis to help us understand the meanings of relationships of online group-buying network. According to the result of this study, we find the initiators have more power or resources to influence other members of the network between initiators and participants. And some active participants also have more power or resources to influence other participants of the network between co-participants. We also find the active initiators and participants would have more probability to occupy key positions of information flow.

This dissertation considers graph theoretic generalizations of the maximum clique problem. Models that were originally proposed in social network analysis literature, are investigated from a mathematical programming perspective for the first time. A social network is usually represented by a graph, and cliques were the first models of "tightly knit groups" in social networks, referred to as cohesive subgroups. Cliques are idealized models and their overly restrictive nature motivated the development of clique relaxations that relax different aspects of a clique. Identifying large cohesive subgroups in social networks has traditionally been used in criminal network analysis to study organized crimes such as terrorism, narcotics and money laundering. More recent applications are in clustering and data mining wireless networks, biological networks as well as graph models of databases and the internet. This research has the potential to impact homeland security, bioinformatics, internet research and telecommunication industry among others. The focus of this dissertation is a degree-based relaxation called k-plex. A distance-based relaxation called k-clique and a diameter-based relaxation called k-club are also investigated in this dissertation. We present the first systematic study of the complexity aspects of these problems and application of mathematical programming techniques in solving them. Graph theoretic properties of the models are identified and used in the development of theory and algorithms. Optimization problems associated with the three models are formulated as binary integer programs and the properties of the associated polytopes are investigated. Facets and valid inequalities are identified based on combinatorial arguments. A branch-and-cut framework is designed and implemented to solve the optimization problems exactly. Specialized preprocessing techniques are developed that, in conjunction with the branch-and-cut algorithm, optimally solve the problems on real-life power law graphs, which is a general class of graphs that include social and biological networks. Computational experiments are performed to study the effectiveness of the proposed solution procedures on benchmark instances and real-life instances. The relationship of these models to the classical maximum clique problem is studied, leading to several interesting observations including a new compact integer programming formulation. We also prove new continuous non-linear formulations for the classical maximum independent set problem which maximize continuous functions over the unit hypercube, and characterize its local and global maxima. Finally, clustering and network design extensions of the clique relaxation models are explored.