UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item EXPLORING AND MODELING OF BIDDING BEHAVIOR AND STRATEGIES OF ONLINE AUCTIONS(2013) Guo, Wei; Rand, William; Jank, Wolfgang; Mathematics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Internet auctions, as an exemplar of the recent boom in e-commerce, are grow- ing faster than ever in the last decade. Understanding the reasons why bidders be- have a certain way allows invaluable insight into the auction process. This research focuses on methods for modeling, testing and estimation of bidders' behavior and strategies. I start my discussion with bid shading, which is a common strategy bidders believe obtains the lowest possible price. While almost all bidders shade their bids, at least to some degree, it is impossible to infer the degree and volume of shaded bids directly from observed bidding data. In fact, most bidding data only allows researchers to observe the resulting price process, i.e. whether prices increase fast (due to little shading) or whether they slow down (when all bidders shade their bids). In this work, I propose an agent-based model that simulates bidders with different bidding strategies and their interaction with one another. The model is calibrated (and hence properties about the propensity and degree of shaded bids are estimated) by matching the emerging simulated price process with that of the observed auction data using genetic algorithms. From a statistical point of view, this is challenging because it requires matching functional draws from simulated and real price processes. I propose several competing fitness functions and explore how the choice alters the resulting ABM calibration. The method is applied to the context of eBay auctions for digital cameras and show that a balanced fitness function yields the best results. Furthermore, in light of the discrepancy find from the model in bidders' be- havior and optimal strategies proposed from online auction literature. I extract empirical bidding strategies from auction winners and utilize the agent based model to simulate and test the performance of twenty-four different empirical and theo- retical strategies. The experiment results suggest that some empirical strategies perform robustly when compared to theoretical strategies and taking into account other bidders' ability to learn. In addition, I expended the online auction framework from single auction to multiple auction simulation, which acts as a platform for investigating and test- ing more complicated situations that involves the competition among concurrent auctions. This framework facilitates my investigation of bidders' switching behavior and enables me to answer a series questions. For example, is it beneficial for auction website to promote bidders' switching behavior? Will bidders and even sellers get any advantage from bidders' switching? What is the best auction recommendation strategy for online auction website to obtain higher profit and/or a better customer experience? Through careful experiment design, it has been showed that higher switching frequency leads to higher profit for auction website and reduces the price dispersion, which leads to reduced risk for both bidders and sellers. In addition, the best auction recommendation strategy is providing the five earliest closing auctions so that bidders can choose the lowest price auction.Item Words Matter: Essays on The Relationship Between Executive Word Choice and Investor Evaluation(2012) Guo, Wei; Goldfarb, Brent; Kirsch, David A; Business and Management: Management & Organization; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation examines the relationship between executive word choices and investor evaluations. Although the importance of language in organizations and the legitimating effect of language for new ventures has stimulated rich theoretical and empirical discussion, scholars still know little about whether, how, and when the language used by executives at established organizations influences external constituents (e.g., investors). I address these questions using two studies. In the first study, drawing from theories of persuasion and attitude change in social psychology, I examine the effect of emotional messages used by executives on investor evaluations and identify persuasion as one path by which executive language influences investors. In the second study, I combine two theoretical perspectives, the market signaling theory in economics and the construe-level theory in psychology, and investigate the effect of executives' use of realism words on investor evaluations. The second study identified signaling as another path by which executive language influences investors. Hypotheses from both studies were tested using a sample of 4,324 verbatim transcripts of 694 organizations' executive presentations at investor conferences between 2004 and 2010. This dissertation contributes to the strategy literature by providing an alternative theoretical framework that focuses on the psychological effect of executives' word choice, and by identifying two paths by which the language of executives in established organizations influence investor evaluations.