This dissertation presents an alternative understanding to current works exploring the creative class. Extant views of the creative class portray it as a concentration of individuals and organizations producing clusters of interconnected cultural activities fostering positive socioeconomic change in the communities where they are located. By contrast, this dissertation articulates the creative class as time evolving geographical organizing of networked creative individuals whose presence over time in a community may or may not foster positive socioeconomic change. The creative class is thus conceptualized as contingent and continuously evolving processes whose emergence at any one point in time may or may not be sustainable over time. Framed theoretically through a nexus between strategic management, economic geography, and economic sociology, the unfolding of a creative class is explored as location specific phenomenon illustrating mutually co-constructing processes of organizations and their environment. It focuses –as its exemplar- on local socioeconomic processes enacted by an assortment of artists and artisans in a small New England (USA) former mill town. A case study was derived from data collected for over four years in fieldwork through a multi-method approach. Underpinned by interpretative notions, methodology included participative ethnography and social network analyses, where quantitative and qualitative data functioned in a complementary way. Exploring relationships between artists and artisans and their organizing attempts to become members of the community, observations focused on mundane situations through which these processes were enacted. Social network methodologies contributed to mapping processual linkages between community members, while further ethnographic work contextualized relationships uncovered through social network analyses. The resulting case study presents a narrative about the unfolding of a potential creative class as dynamic bottom-up phenomenon whose socioeconomic consequences cannot be guaranteed by formal planning. Artists and artisans struggle to become a community of creative practice and become acknowledged as such by their neighbors when their organizing opens up socioeconomic change. These processes, which may lead to a sustainable cultural economy in this location, are not independent or exogenous to the place. They are part of the local history, influenced by shared and ongoing socioeconomic processes, and specific to locality.

This thesis represents a synthesis of relational learning and causal discovery, two subjects at the frontier of machine learning research. Relational learning investigates algorithms for constructing statistical models of data drawn from of multiple types of interrelated entities, and causal discovery investigates algorithms for constructing causal models from observational data. My work demonstrates that there exists a natural, methodological synergy between these two areas of study, and that despite the sometimes onerous nature of each, their combination (perhaps counterintuitively) can provide advances in the state of the art for both. Traditionally, propositional (or "flat") data representations have dominated the statistical sciences. These representations assume that data consist of independent and identically distributed (iid) entities which can be represented by a single data table. More recently, data scientists have increasingly focused on "relational" data sets that consist of interrelated, heterogeneous entities. However, relational learning and causal discovery are rarely combined. Relational representations are wholly absent from the literature where causality is discussed explicitly. Instead, the literature on causality that uses the framework of graphical models assumes that data are independent and identically distributed. This unexplored topical intersection represents an opportunity for advancement --- by combining relational learning with causal reasoning, we can provide insight into the challenges found in each subject area. By adopting a causal viewpoint, we can clarify the mechanisms that produce previously identified pathologies in relational learning. Analogously, we can utilize relational data to establish and strengthen causal claims in ways that are impossible using only propositional representations.

This dissertation aims to advance our understanding of workplace aggression by developing and testing two models based on the stereotype content model (SCM) and adopting the social network analysis approach. Specifically, two studies of the dissertation focus on (a) unfavorable social evaluations stemming from competence stereotypes, (b) stereotype-driven negative emotions as a mechanism to explain the relationship between competence and workplace aggression, and (c) the role of informal workplace relationships in predicting workplace aggression. Study one investigates the nonlinear relationship between job performance and exposure to workplace aggression and two distinct mediating mechanisms at high and low levels of job performance. High performers provoke jealousy, and low performers provoke contempt from coworkers, both of which are positively associated with exposure to workplace psychological aggression. The study tested these relations using data from a sample of 187 teachers from educational institutions in India and found support for the curvilinear relationship between performance and workplace psychological aggression and the mediating mechanisms of jealousy and contempt for high and low performers, respectively. Study two examines the role of workplace social ties (advice and friendship ties) in predicting workplace aggression. Results from data collected at 2-time points from 248 individuals in 21 workgroups largely supported the proposition that highly competent employees become victims of covert aggression and low competence employees become victims of overt aggression. Interestingly, the findings suggest that advice-giving and friendship ties mitigate the experience of aggression by reducing coworkers’ envy. However, advice-seeking aggravates overt aggression by increasing coworkers’ contempt. / Dissertation / Doctor of Philosophy (PhD)

The Paradise Papers represent one of the largest public data leaks comprising 13.4 million con_dential electronic documents. A dominant theory presented by Neal (2014) and Gri_th, Miller and O'Connell (2014) concerns the use of these offshore services in the relocation of intellectual property for the purposes of compliance, privacy and tax avoidance. Building on the work of Fernandez (2011), Billio et al. (2016) and Kou, Peng and Zhong (2018) in Spatial Arbitrage Pricing Theory (s-APT) and work by Kelly, Lustig and Van Nieuwerburgh (2013), Ahern (2013), Herskovic (2018) and Proch_azkov_a (2020) on the impacts of network centrality on _rm pricing, we use market response, discussed in O'Donovan, Wagner and Zeume (2019), to characterise the role of offshore services in securities pricing and the transmission of price risk. Following the spatial modelling selection procedure proposed in Mur and Angulo (2009), we identify Pro_t Margin and Price-to-Research as firm-characteristics describing market response over this event window. Using a social network lag explanatory model, we provide evidence for social exogenous effects, as described in Manski (1993), which may characterise the licensing or exchange of intellectual property between connected firms found in the Paradise Papers. From these findings, we hope to provide insight to policymakers on the role and impact of offshore services on securities pricing.

Loneliness has long been understood as a part of the human condition, however, its ability to be measured remains a struggle, much due to its subjective nature and many complexities. As loneliness has recently been highlighted as a global health concern in need of intervention, an accurate measurement of the phenomenon is perhaps more crucial than ever. This study aims to give an in-depth understanding of loneliness, in particular how it pertains to a practical tool of measurement. The text discusses limitations of previous measurements and analyzes which areas of loneliness still remain to be underrepresented in established measuring scales. To reach the goal of the study and find the best way to measure loneliness, we implemented our own loneliness scale using both questions from established questionnaires as well as our own formulated questions. Three distinct categories within loneliness emerged- social networks, actual loneliness and experienced belonging. These, along with relevant descriptive factors of the participants, were used to measure loneliness. The final measurement scale was shared in the form of an online questionnaire to a total of 479 participants. The results showed all categories to be distinct aspects of all significant to one another and in the measurement of loneliness as a whole. Both socioeconomic, participants origin, as well as age-related factors also proved to be relevant in measuring loneliness but they did not have the same predictive strength as the main three variables. Furthermore, this scale included previously underrepresented questions which aimed to distinguish trait and state loneliness and situational loneliness-triggers. These all proved significant in the wider category of experienced belonging but did not warrant a distinct category of its own. The final loneliness scale measures both more subjective and qeualitative as well as objective and quantitative aspects of loneliness. As the measurement showed good internal consistency, it can be considered a valid and reliable tool to gain an in-depth understanding of loneliness.

Developments such as the ubiquity of electronic networks and the breadth of digital platforms beyond simple forums have lead to major economic and social transformations worldwide (Agarwal et al, 2008). This research will explore developments in the area of social networking using Internet technology in order to determine factors that lead to success. Despite considerable growth among the number of online communities available, very few are successful at retaining members and continued usage by its members (Ma & Agarwal, 2007). The purpose of this dissertation is to determine factors leading to a successful social networking site (SNS). Through the unification of the IS success model and social capital theory, a new model is develop called the SNS success model. The result of this study support eight hypotheses with one hypothesis unsupported. It was shown that content quality, system quality, and trust positively affect social capital and user satisfaction. User satisfaction positively affects continued use intention. Social capital was shown to support user satisfaction but the relationship with continued use intention was non-significant. Further investigation demonstrated that the relationship between social capital and continued use intention was fully mediated by user satisfaction. This study contributes to knowledge by developing a model displaying success factors for SNS success. Further, it demonstrates the relationship between social capital and continued use intention through the mediation of user satisfaction. This study serves as a foundation of research in the emerging area of SNSs. It also has practical implications for practitioners to help SNS administrators understand factors that influence usage. Recommendations for future research and practical implications for SNS administrators are also discussed.

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