ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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In the first chapter, I identify several factors that affect how quickly information spreads on social media platforms. I utilized Twitter data from Hurricane Sandy, and the results indicate that the timing of information release and the influence of the content’s author determine information diffusion speed. The second chapter of this dissertation builds directly on the first study by also evaluating the rate at which social media content diffuses. A piece of content does not diffuse in isolation but, rather, coexists with other content on the same social media platform. After analyzing Twitter data from four distinct crises, the results indicate that other content’s diffusion often dampens a specific post’s diffusion speed. This is important for humanitarian organizations to recognize and carries implications for how they can coordinate with other organizations to avoid inhibiting the propagation of each other’s social media content. Finally, a user’s followers on social media platforms represent the user’s direct audience. The larger the user’s follower base, the more easily the same user can extensively broadcast information. Therefore, I study what drives the growth of humanitarian organizations’ follower bases during times of normalcy and emergency using Twitter data from one week before and one week after the 2016 Ecuador earthquake.
Considering that managerial risk taking is an important issue in strategic management research and agency theory has been widely adopted in academia and business worlds, it is imperative to clarify the mechanism behind the relationship between CEO power and risk taking. My study aims to fill this research gap. In this study I follow agency theory to take an employment security perspective and fully consider how CEOs’ concern about employment security is affected by their power and ownership structure to enrich the understanding of the effects of CEO power and ownership structure on risk taking. I fine-tune the key concept CEO power into the CEO power over board and introduce a key aspect of ownership structure - nontransient investor ownership. I further suggest that CEO power over board and nontransient investor ownership affect CEOs’ employment security and the resulting CEO risk taking. In addition, I consider a set of industry and firm characteristics as the boundary conditions for the effects of CEO power and nontransient investor ownership on CEO risk-taking. This set of industry and firm characteristics include industry complexity, industry dynamism, industry munificence and firm slack.
I test my theory using a large-scale, multi-year sample of U.S. publicly listed S&P 1500 firms between 2001 and 2017. My main hypotheses about the effects of CEO power over board and nontransient investor ownership on CEO risk taking receive strong support.
By using supervised machine learning and text mining techniques, I shift the focus to synthesizing the actual content shared by Representatives on Twitter to evaluate their effects on Representatives’ political polarization in the second chapter. I found support for the effects of repeated expressions and peer influence in Representatives’ political polarization.
Last but not least, by employing a recently developed dynamic network model (separable temporal exponential-family random graph model), I study the effects of homophily on formation and dissolution of Representatives’ Twitter communications in the third chapter. The results signal the presence of demographic homophily and value homophily in Representatives’ Twitter communications networks.
These three studies altogether provide a comprehensive picture about the overall consequences and dynamics of use of online social networking platforms by Representatives.
The current dissertation takes advantage of a unique dataset, uncover hidden investment style and trading behavior, understanding their source of excess returns, and establishing a more comprehensive methodology for evaluating portfolio performance and manager skills.
The dissertation focuses on quantitative analysis. Highlights three most important aspects. Investment style determines the systematic returns and risks of any portfolio, and can be assessed ex-ante; Transaction can be observed and modified during the investment process; and return attribution can be implemented to evaluate portfolio (managers), ex-post. Hence, these three elements make up a comprehensive and logical investment process.
Investment style is probably the most important factor in determining portfolio returns. However, Chinese investment managers are under constant pressure to follow the market trend and shift style accordingly. Therefore, accurately identifying and predicting each manager’s investment style proves critically valuable.
In addition, transaction data probably provides the most reliable source of information in observing and evaluating an investment manager’s style and strategy, in the middle of the investment process.
Despite the efficacy of traditional return attribution methodology, there are clear limitations. The current study proposes a novel return attribution methodology, by synthesizing major portfolio strategy components, such as risk exposure adjustment, sector rotation, stock selection, altogether. Our novel methodology reveals that investment managers do not obtain much abnormal returns through risk exposure adjustment or sector rotation. Instead, Chinese investment managers seem to enjoy most of their excess returns through stock selection.
In addition, we find several interesting patterns in Chinese A-share market: 1). There is a negative relationship between asset under management (AUM) and investment performance, beyond certain AUM threshold; 2). There are limited benefits from style switching in the long run; 3). Many investment managers use CSI 300 component stocks as portfolio ballast and speculate with CSI500 and Medium-and-Small board component stocks for excess returns; 4). There is no systematic negative relationship between portfolio turnover and investment performance; despite negative relationship within certain sub-samples and sectors; 5). It is plausible to construct out-performing portfolios with style index funds and ETFs.
of alternatives for any given task. In such a competitive environment, it is imperative
to understand what drives user behavior. To that end, the research presented in
this dissertation, tries to uncover the impact of business strategies often used in the
software markets.
The dissertation is organized into three distinct studies into user choice and post
choice use of software. First using social judgment theory as foundation, zero price
strategies effects on user choice is investigated, with respect to product features,
consumer characteristics, and context effects. Second, role of social features in
moderating network effects on user choice is studied. And finally, the role of social
features on the effectiveness of add-on content strategy on continued user engagement
is investigated.
The findings of this dissertation highlight the alignments between popular business
strategies and broad software context. The dissertation contributes to the litera-
ture by uncovering hitherto overlooked complementarities between business strategy
and product features: (1) zero price strategy enhances utilitarian features but not
non-utilitarian features in software choice, (2) social features only enhance network
externalities but not social influence in user choice, (3) social features enhance the
effect of add-on content strategy in extending software engagement.