Signals convey information to marketplace participants regarding the unobservable quality of a product. Whenever product quality if unobservable prior to purchase, there is the risk of adverse selection. Problems of hidden information also occur in the consumer marketplace when the consumer is unable to verify the quality of a good prior to purchase. The sending, receiving, and interpretation or signals are potential ways to overcome the problem of adverse selection. In general, there is a lack of empirical evidence for signaling hypothesis, particularly that which links signaling to business performance outcomes. This research proposes that reputation serves as a marketplace signal to convey unobservable information about products offered for sale. Signaling hypotheses are tested in a network context, examining the influence of signals throughout a network of buyers and sellers in a marketplace. There are many situations where a signal does not affect just one sender and one receiver; multiple constituencies may be aware of and react to a given signal. This study incorporates the actions of seller side principals, seller side agents, and buyer side agents when examining marketplace signals and provides a new perspective and better vantage point from which to test signaling theory. The research setting for this study is the world’s largest individual marketplace for Thoroughbred yearlings. Several sources of secondary data are employed. These openly available published sources of information were selected as representative of the information that would typically be available to marketplace principals and agents to use in planning interactions in this unique live auction marketplace. The findings from his study indicate that the reputation of seller side principals and agents affect the eventual business performance outcomes as measured by final price brought at auction for goods. Specifically, seller side principals and agents who have developed a reputation for producing or selling high-priced or high-performing goods will be rewarded in the marketplace with relatively higher prices for their goods. Buyer side agents who are more central in the marketplace will pay relatively higher prices for goods. Evidence suggests that more central seller side agents will receive relatively higher prices for their goods.

Prior literature suggests that CEOs capitalize on their position within the hierarchy of all business executives, resulting in various – both positive and negative – firm outcomes. Using a novel data set on golf outings to measure the quality of a CEO's informal (vs. formal) network, as measured by the CEO's network centrality, this study examines whether well-connected CEOs generate private gains through insider trades. Results suggest that, among golfing CEOs, CEOs with higher quality informal networks generate significantly higher insider trading profits on sales of their firms' stock, consistent with more famous, powerful, and influential CEOs possessing superior information. The paper continues by delineating a channel through which private information flow to network participants by documenting significantly different golf patterns of CEOs during the two weeks before material firm events become public while showing that CEOs generate noticeably higher insider trading profits from stock trades executed during the two weeks following these golf outings. This study highlights a setting in which shareholders are at risk of wealth transfer and illustrates the potential limitations of regulation concerning insider trading.

The existing theoretical and empirical literature to investigate the existence of local market power is typically based on spatial competition models in the tradition of Hotelling's (1929) linear city and Salop's (1979) circular city. In models of this kind, strong assumptions are made that lead to a spatial homogeneity (symmetry) of firms in a highly stylized one-dimensional market space. However, some of these assumptions are hardly satisfied in many (retail) markets. The present thesis builds on a recent model by Chen and Riordan (2007), in which the market is characterized by a star-shaped graph with a central intersection. In an extension of Chen and Riordan, I distinguish between firms close to the center and firms in the periphery of a spatial market. This spatial heterogeneity leads to an asymmetric competition between firms. A central firm directly competes with a larger number of firms than remote firms do. The implications of the theoretical model are tested in two empirical applications to the retail gasoline market of Vienna and Austria. Using station level data on diesel prices, I estimate price reaction functions for gasoline stations in two different approaches. In the first approach the Austrian retail gasoline market is divided into numerous highly localized and delimited markets. The second approach analyzes the metropolitan area of Vienna and treats the whole market as one big network of gasoline stations, which are connected through the road network. In both approaches I apply econometric spatial autoregressive (SAR) models. The estimated parameters of the slopes of the reaction functions are used to evaluate the impact of individual gasoline stations on equilibrium market prices depending on their location within the market (network). All results obtained provide evidence for (more) central suppliers serving as a stronger reference in pricing than (rather) remote suppliers. Thus, the assumption of a symmetry in spatial competition which is usually implied by spatial competition models in theoretical and applied research, is rejected. (author's abstract)

We empirically investigate the importance of centrality (holding a central position in a spatial network) for strategic interaction in pricing for the Austrian retail gasoline market. Results from spatial autoregressive models suggest that the gasoline station located most closely to the market center - defined as the 1-median location - exerts the strongest effect on pricing decisions of other stations. We conclude that centrality influences firms' pricing behavior and further find that the importance of centrality increases with market size. (authors' abstract) / Series: Department of Economics Working Paper Series

We highlight the importance of "centrality" for pricing. Firms characterized by a more central position in a spatial network are more powerful in terms of having a stronger impact on their competitors' prices and on equilibrium prices. These propositions are derived from a simple theoretical model and investigated empirically for the retail gasoline market of Vienna, Austria. We compute a measure of network centrality based on the locations of gasoline stations in the road network. Results from a spatial autoregressive model show that prices of gasoline stations are more strongly correlated with prices of central competitors.

As Internet communities such as question-answer (Q&A) forums and online social networks (OSNs) grow in prominence as knowledge sources, traditional editorial filters are unable to scale to their size and pace. This absence hinders the exchange of knowledge online, by creating an understandable lack of trust in information. This mistrust can be partially overcome by a forum by consistently providing reliable information, thus establishing itself as a reliable source. This work investigates how algorithmic approaches can contribute to building such a community of voluntary experts willing to contribute authoritative information. This work identifies two approaches: a) reducing the cost of participation for experts via matching user queries to experts (question recommendation), and b) identifying authoritative contributors for incentivization (authority estimation). The question recommendation problem is addressed by extending existing approaches via a new generative model that augments textual data with expert preference information among different questions. Another contribution to this domain is the introduction of a set of formalized metrics to include the expert's experience besides the questioner's. This is essential for expert retention in a voluntary community, and has not been addressed by previous work. The authority estimation problem is addressed by observing that the global graph structure of user interactions, results from two factors: a user's performance in local one-to-one interactions, and their activity levels. By positing an intrinsic authority 'strength' for each user node in the graph that governs the outcome of individual interactions via the Bradley-Terry model for pairwise comparison, this research establishes a relationship between intrinsic user authority, and global measures of influence. This approach overcomes many drawbacks of current measures of node importance in OSNs by naturally correcting for user activity levels, and providing an explanation for the frequent disconnect between real world reputation and online influence. Also, while existing research has been restricted to node ranking on a single OSN graph, this work demonstrates that co-ranking across multiple endorsement graphs drawn from the same OSN is a highly effective approach for aggregating complementary graph information. A new scalable co-ranking framework is introduced for this task. The resulting algorithms are evaluated on data from various online communities, and empirically shown to outperform existing approaches by a large margin. / text

We empirically investigate the importance of centrality (holding a central position in a spatial network) for strategic interaction in pricing for the Austrian retail gasoline market. Results from spatial autoregressive models suggest that the gasoline station located most closely to the market center - defined as the 1-median location - exerts the strongest effect on pricing decisions of other stations. We conclude that centrality influences firms' pricing behavior and further find that the importance of centrality increases with market size.

Plant–pollinator network studies have uncovered important generalities in the structure of these communities, rapidly advancing our understanding of the underlying drivers of such a structure. In spite of this, however, it is still unclear how changes in structural network properties influence overall plant pollination success. One key limitation is the lack of information on the relationship between network structural properties and aspects of pollination and plant reproductive success. Here, we estimate four plant species network structural metrics (interaction strength, weighted degree, closeness centrality, and specialization level), commonly used to describe their importance within plant–pollinator networks, at two different sites, and evaluate their effects on pollen deposition and pollen tube success. We found a positive effect of plant–pollinator specialization and a negative effect of closeness centrality on heterospecific pollen load size. We also found a marginal negative effect of closeness centrality on pollen tube success. Our results suggest that increasing plant–pollinator specialization within nested communities (pollinated by one or very few generalist insect species) may result in high levels of heterospecific pollen transfer. Furthermore, the differential effects of plant–pollinator network metrics on pollination success (pollen receipt and pollen tube success), highlight the need to integrate quantity (e.g. visitation rate) and quality (e.g. pollen delivery) aspects of pollination to achieve a more mechanistic understanding of the relationship between plant–pollinator network structure and function. Such knowledge is key to evaluate the resilience and stability of plant–pollinator communities and the services they provide in the face of increasing human disturbances.

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京都大学 / 新制・課程博士 / 博士(工学) / 甲第23864号 / 工博第4951号 / 新制||工||1773(附属図書館) / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 藤井 聡, 教授 宇野 伸宏, 准教授 松中 亮治 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM