This study focuses on the importance of intrinsic and extrinsic work rewards among women and men in 12 post-industrial nations in the Global North. Guiding my analyses was Esping-Andersen’s theoretical framework and the following three main research questions: (1) how individual attributes and national policies influence the salience individuals assign to intrinsic and extrinsic rewards; (2) how individual attributes and national policies differ from each other in relative magnitude as predictors of the value individuals assign to intrinsic and extrinsic rewards; and (3) how individual attributes and national policies impact the importance individuals assign to intrinsic and extrinsic rewards differs by gender. For the micro level analysis, I used data from the 2005 International Social Survey Program Work Orientation Module. The twelve countries included in the analysis are Australia, Denmark, Belgium, Canada, Finland, France, Germany, Ireland, Norway, Sweden, United Kingdom, and the United States. Macro level policy data are drawn from the 2005 Social Expenditure Database and maternity leave data are from the 2005 International Network on Leave Policy and Research. Analysis was performed using Stata regression with the cluster command. While not all variables included in the model were statistically significant, the general hypotheses were supported with the following results: (1) micro level variables (education, income, and employment) and macro level variables (paid family leave and the percentage of GDP spent on childcare and pre-primary education) increased the importance individual’s assign to intrinsic rewards; (2) the lack of human capital increases an individual’s emphasis on extrinsic rewards; (3) while macro level variables have a far greater impact on the importance individuals assign to intrinsic work rewards, both micro and macro level factors are important for explaining the maximum possible variation in the importance individuals assign to intrinsic work rewards; and (4) gender does not change the value an individual assigns to intrinsic or extrinsic rewards. This study represents a new, more comprehensive approach to studying the relationships among micro-level factors, structural opportunities and constraints, intrinsic and extrinsic work rewards, and gender. A review of the literature shows no other studies of this scope.

Die Netzwerkanalyse zielt darauf ab, nicht-triviale Erkenntnisse aus vernetzten Daten zu gewinnen. Beispiele für diese Erkenntnisse sind die Wichtigkeit einer Entität im Verhältnis zu anderen nach bestimmten Kriterien oder das Finden des am besten geeigneten Partners für jeden Teilnehmer eines Netzwerks - bekannt als Maximum Weighted Matching (MWM). Da der Begriff der Wichtigkeit an die zu betrachtende Anwendung gebunden ist, wurden zahlreiche Zentralitätsmaße eingeführt. Diese Maße stammen hierbei aus Jahrzehnten, in denen die Rechenleistung sehr begrenzt war und die Netzwerke im Vergleich zu heute viel kleiner waren. Heute sind massive Netzwerke mit Millionen von Kanten allgegenwärtig und eine triviale Berechnung von Zentralitätsmaßen ist oft zu zeitaufwändig. Darüber hinaus ist die Suche nach der Gruppe von k Knoten mit hoher Zentralität eine noch kostspieligere Aufgabe. Skalierbare Algorithmen zur Identifizierung hochzentraler (Gruppen von) Knoten in großen Graphen sind von großer Bedeutung für eine umfassende Netzwerkanalyse. Heutigen Netzwerke verändern sich zusätzlich im zeitlichen Verlauf und die effiziente Aktualisierung der Ergebnisse nach einer Änderung ist eine Herausforderung. Effiziente dynamische Algorithmen sind daher ein weiterer wesentlicher Bestandteil moderner Analyse-Pipelines. Hauptziel dieser Arbeit ist es, skalierbare algorithmische Lösungen für die zwei oben genannten Probleme zu finden. Die meisten unserer Algorithmen benötigen Sekunden bis einige Minuten, um diese Aufgaben in realen Netzwerken mit bis zu Hunderten Millionen von Kanten zu lösen, was eine deutliche Verbesserung gegenüber dem Stand der Technik darstellt. Außerdem erweitern wir einen modernen Algorithmus für MWM auf dynamische Graphen. Experimente zeigen, dass unser dynamischer MWM-Algorithmus Aktualisierungen in Graphen mit Milliarden von Kanten in Millisekunden bewältigt. / Network analysis aims to unveil non-trivial insights from networked data by studying relationship patterns between the entities of a network. Among these insights, a popular one is to quantify the importance of an entity with respect to the others according to some criteria. Another one is to find the most suitable matching partner for each participant of a network knowing the pairwise preferences of the participants to be matched with each other - known as Maximum Weighted Matching (MWM). Since the notion of importance is tied to the application under consideration, numerous centrality measures have been introduced. Many of these measures, however, were conceived in a time when computing power was very limited and networks were much smaller compared to today's, and thus scalability to large datasets was not considered. Today, massive networks with millions of edges are ubiquitous, and a complete exact computation for traditional centrality measures are often too time-consuming. This issue is amplified if our objective is to find the group of k vertices that is the most central as a group. Scalable algorithms to identify highly central (groups of) vertices on massive graphs are thus of pivotal importance for large-scale network analysis. In addition to their size, today's networks often evolve over time, which poses the challenge of efficiently updating results after a change occurs. Hence, efficient dynamic algorithms are essential for modern network analysis pipelines. In this work, we propose scalable algorithms for identifying important vertices in a network, and for efficiently updating them in evolving networks. In real-world graphs with hundreds of millions of edges, most of our algorithms require seconds to a few minutes to perform these tasks. Further, we extend a state-of-the-art algorithm for MWM to dynamic graphs. Experiments show that our dynamic MWM algorithm handles updates in graphs with billion edges in milliseconds.

The prevalence of work and family in people’s lives combined with limited time and resources often results in a moral decision between work and family, posing a dilemma between hedonistic values for work and altruistic values for family. This study aimed to understand the processes of decision-making in work-family dilemmas and tested three approaches. Therefore, the construal-level theory, time perspectives and logic of appropriateness were introduced. In line with research on construal-level theory and dilemmas, the impact of psychological distance and construal level theory on the decision was examined. Operationalizing social distance, participants were asked to either advise a closely related person (group 1) or someone they just met (group 2) on four work-family dilemmas. The total sample consisted of 212 participants from Germany (49.5%), Sweden (35.8%) and other countries (14.2%). t-tests between the two treatment groups did not reveal significant differences in the dilemma advice (p > .05). Exploratory analyses did not find time perspectives to be related to the decision (p > .005) but work-family centrality was found to be significantly associated to the decision-making in the dilemma (p <.005). With the limitations of the study in mind, the construal level theory could not be supported but values have shown to impact attitudinal decisions, supporting the logic of appropriateness. In work-family conflicts, it, therefore, does not matter whom one advises but whether the advisor values work or family more, influences the given advice. Based on this study, implications for further research are pointed out.

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The purpose of the present study was to assess if combining the two frameworks would account for more variance in PTSS than could be accounted for using the frameworks separately. An online community sample from Amazon.com's Mechanical Turk (N = 437), who reported experiencing a prior traumatic event, completed measures that reflected the constructs of narrative centrality, negative affectivity, and death concerns, along with a measure of PTSS. PTSS was regressed on the latent variables of death concerns, narrative centrality, and negative affectivity, along with the latent variable interactions between narrative centrality*death concerns and narrative centrality*negative affectivity. Death concerns was not be predictive of PTSS, whereas narrative centrality and negative affectivity were found to uniquely and interactively account for 77% of the variance in PTSS. Death concerns was found to be a separate construct from negative affectivity. The implications of these findings for the two frameworks are discussed along with future directions. By considering aspects of narrative centrality and negative affectivity, substantial portions of PTSS can be accounted for.

Purpose – This study aims to look into how foreignness and outsidership affect the market entry of a SME and the individual firm's ability to access the correct network as well as look at which support structures are available for the individual firms entering into a new market. Theory/Design & Research Questions – By looking through the lens of Social Network theory, aspects of trust, legitimacy, nodes, foreignness and outsidership, this study has looked into the journey of a large hamburger chain in Sweden that has successfully entered into different markets outside of Sweden. The research questions explored were (i) how SMEs can overcometheir liabilities and leverage network connections to form strategic partnerships with other firms or organizations, and (ii) how centrality of nodes can be utilized by SMEs to gain access to valuable information and resources, and (iii) how intermediary networks can support SMEs in their efforts to enter new markets, and what policies and programs have proven effective in this regard. Methodology – The data was gathered using a non-probability sampling by conducting a semi-structured qualitative interview and was complemented with secondary sources from the company profile. Findings – Depending on the size of the firm the level of ability to access networks can differ. Smaller firms are more inclined to seek help from intermediaries compared to larger firms. Furthermore actors that are more central have been found to have a key role for firms entering foreign markets.

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No description available.

This dissertation illustrates how to detect the rater centrality effect in a simulation study that approximates data collected in large scale performance assessment settings. It addresses three research questions that: (1) which of several centrality-detection indices are most sensitive to the difference between effect raters and non-effect raters; (2) how accurate (and inaccurate), in terms of Type I error rate and statistical power, each centrality-detection index is in flagging effect raters; and (3) how the features of the data collection design (i.e., the independent variables including the level of centrality strength, the double-scoring rate, and the number of raters and ratees) influence the accuracy of rater classifications by these centrality-detection indices. The results reveal that the measure-residual correlation, the expected-residual correlation, and the standardized deviation of assigned scores perform better than the point-measure correlation. The mean-square fit statistics, traditionally viewed as potential indicators of rater centrality, perform poorly in terms of differentiating central raters from normal raters. Along with the rater slope index, the mean-square fit statistics did not appear to be sensitive to the rater centrality effect. All of these indices provided reasonable protection against Type I errors when all responses were double scored, and that higher statistical power was achieved when responses were 100% double scored in comparison to only 10% being double scored. With a consideration on balancing both Type I error and statistical power, I recommend the measure-residual correlation and the expected-residual correlation for detecting the centrality effect. I suggest using the point-measure correlation only when responses are 100% double scored. The four parameters evaluated in the experimental simulations had different impact on the accuracy of rater classification. The results show that improving the classification accuracy for non-effect raters may come at a cost of reducing the classification accuracy for effect raters. Some simple guidelines for the expected impact of classification accuracy when a higher-order interaction exists summarized from the analyses offer a glimpse of the "pros" and "cons" in adjusting the magnitude of the parameters when we evaluate the impact of the four experimental parameters on the outcomes of rater classification. / Ph. D.

Mycorrhizal networks (MNs), or the networks of fungal mycelia that connect plants to each other, are vital in contributing to the well-being of ecosystems. They not only assist in the transport of nutrients across an ecosystem, but also help protect an ecosystem from disease and adverse conditions. However, more research into these networks is needed and modelling these networks as graphs can help us achieve this. By applying centrality analysis and performing k-core partitioning on these networks, we are able to identify the trees that are most important and central to a MN and observe the effects of removing these trees. We also perform random partitioning on these networks and compare the results to the k-core partitioning results. We found that these networks are fairly resilient to the removal of a single keystone individual, but this can disrupt the interconnectedness of a MN in a dry (xeric) moisture regime. We also found that these networks are less resilient to k-core partitioning. In a network of trees divided up by age cohorts, the maximal k-core subgraph contained a mix of trees that mostly belonged to older cohorts and were linked to one specific fungal genet. This could influence conservation efforts for not only a few older trees, but also some younger trees and potentially specific fungal genets. When removing the maximal k-core subgraphs for networks in dry (xeric) and moist (mesic) moisture regimes, the network became disconnected for the xeric graph and still somewhat connected for the mesic graph. So, mycorrhizal networks could possibly be more resilient to this k-core partitioning in an area where the soil is moist rather than dry.