Matching Items (6)
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- All Subjects: Banking
- All Subjects: Individual investors
- All Subjects: Information science
- Creators: Gu, Bin
- Member of: ASU Electronic Theses and Dissertations
Description
Social media offers a powerful platform for the independent digital content producer community to develop, disperse, and maintain their brands. In terms of information systems research, the broad majority of the work has not examined hedonic consumption on Social Media Sites (SMS). The focus has mostly been on the organizational perspectives and utilitarian gains from these services. Unlike through traditional commerce channels, including e-commerce retailers, consumption enhancing hedonic utility is experienced differently in the context of a social media site; consequently, the dynamic of the decision-making process shifts when it is made in a social context. Previous research assumed a limited influence of a small, immediate group of peers. But the rules change when the network of peers expands exponentially. The assertion is that, while there are individual differences in the level of susceptibility to influence coming from others, these are not the most important pieces of the analysis--unlike research centered completely on influence. Rather, the context of the consumption can play an important role in the way social influence factors affect consumer behavior on Social Media Sites. Over the course of three studies, this dissertation will examine factors that influence consumer decision-making and the brand personalities created and interpreted in these SMS. Study one examines the role of different types of peer influence on consumer decision-making on Facebook. Study two observes the impact of different types of producer message posts with the different types of influence on decision-making on Twitter. Study three will conclude this work with an exploratory empirical investigation of actual twitter postings of a set of musicians. These studies contribute to the body of IS literature by evaluating the specific behavioral changes related to consumption in the context of digital social media: (a) the power of social influencers in contrast to personal preferences on SMS, (b) the effect on consumers of producer message types and content on SMS at both the profile level and the individual message level.
ContributorsSopha, Matthew (Author) / Santanam, Raghu T (Thesis advisor) / Goul, Kenneth M (Committee member) / Gu, Bin (Committee member) / Arizona State University (Publisher)
Created2013
Description
Responding to the allegedly biased research reports issued by large investment banks, the Global Research Analyst Settlement and related regulations went to great lengths to weaken the conflicts of interest faced by investment bank analysts. In this paper, I investigate the effects of these changes on small and large investor confidence and on trading profitability. Specifically, I examine abnormal trading volumes generated by small and large investors in response to security analyst recommendations and the resulting abnormal market returns generated. I find an overall increase in investor confidence in the post-regulation period relative to the pre-regulation period consistent with a reduction in existing conflicts of interest. The change in confidence observed is particularly striking for small traders. I also find that small trader profitability has increased in the post-regulation period relative to the pre-regulation period whereas that for large traders has decreased. These results are consistent with the Securities and Exchange Commission's primary mission to protect small investors and maintain the integrity of the securities markets.
ContributorsDong, Xiaobo (Author) / Mikhail, Michael (Thesis advisor) / Hwang, Yuhchang (Committee member) / Hugon, Artur J (Committee member) / Arizona State University (Publisher)
Created2011
Description
Due to the growing popularity of the Internet and smart mobile devices, massive data has been produced every day, particularly, more and more users’ online behavior and activities have been digitalized. Making a better usage of the massive data and a better understanding of the user behavior become at the very heart of industrial firms as well as the academia. However, due to the large size and unstructured format of user behavioral data, as well as the heterogeneous nature of individuals, it leveled up the difficulty to identify the SPECIFIC behavior that researchers are looking at, HOW to distinguish, and WHAT is resulting from the behavior. The difference in user behavior comes from different causes; in my dissertation, I am studying three circumstances of behavior that potentially bring in turbulent or detrimental effects, from precursory culture to preparatory strategy and delusory fraudulence. Meanwhile, I have access to the versatile toolkit of analysis: econometrics, quasi-experiment, together with machine learning techniques such as text mining, sentiment analysis, and predictive analytics etc. This study creatively leverages the power of the combined methodologies, and apply it beyond individual level data and network data. This dissertation makes a first step to discover user behavior in the newly boosting contexts. My study conceptualize theoretically and test empirically the effect of cultural values on rating and I find that an individualist cultural background are more likely to lead to deviation and more expression in review behaviors. I also find evidence of strategic behavior that users tend to leverage the reporting to increase the likelihood to maximize the benefits. Moreover, it proposes the features that moderate the preparation behavior. Finally, it introduces a unified and scalable framework for delusory behavior detection that meets the current needs to fully utilize multiple data sources.
ContributorsLi, Chunxiao (Author) / Gu, Bin (Thesis advisor) / Chen, Pei-Yu (Committee member) / Xiong, Hui (Committee member) / Arizona State University (Publisher)
Created2019
Description
In this thesis I examine the opportunities and challenges faced by the community banks in China. Rooted in the local communities, community banks generally focus on serving the local residents, farmers, and micro and small business enterprises (MSBE) through relationship building. Although community banks tend to be small relative to the other financial institutions, their unique market positions and business strategies have helped them to survive the competition and secure some market shares. Thus, it is important to understand the business strategies of community banks and to explore their future business opportunities and challenges.
I first provide a brief overview about the importance of local communities, community economy, and community banking, on the basis of an analysis about mismatch in the demand and supply of community financial services due to information asymmetry. Next, I review and analyze how commercial banks have utilized different types of information in their operations. I classify the information used by commercial banks into different categories and discuss their importance to the operations of commercial banks. After that, I conduct a case analysis to illustrate the role of non-financial information in the development of community banks’ business strategy. I conclude this thesis with a discussion of how community banks can better utilize data analysis to develop their core competencies in the era of “Big Data”.
I first provide a brief overview about the importance of local communities, community economy, and community banking, on the basis of an analysis about mismatch in the demand and supply of community financial services due to information asymmetry. Next, I review and analyze how commercial banks have utilized different types of information in their operations. I classify the information used by commercial banks into different categories and discuss their importance to the operations of commercial banks. After that, I conduct a case analysis to illustrate the role of non-financial information in the development of community banks’ business strategy. I conclude this thesis with a discussion of how community banks can better utilize data analysis to develop their core competencies in the era of “Big Data”.
ContributorsHou, Funing (Author) / Li, Feng (Thesis advisor) / Wang, Jiang (Thesis advisor) / Gu, Bin (Committee member) / Arizona State University (Publisher)
Created2015
Description
During the past decade, the Chinese bond market has been rapidly developing. The percentage of bond to total social funding is constantly increasing. The structure and behavior of investors are crucial to the construction of China’s bond market. Due to specific credit risks, bond market regulation usually involves in rules to control investor adequancy. It is heatedly discussed among academia and regulators about whether individual investors are adequate to directly participate in bond trading. This paper focuses on the comparison between individual and institutional bond investors, especially their returns and risks. Based on the comparison, this paper provides constructive suggestions for China’s bond market development and the bond market investor structure.
ContributorsLiu, Shaotong (Author) / Gu, Bin (Thesis advisor) / Zhu, Ning (Thesis advisor) / Yan, Hong (Committee member) / Arizona State University (Publisher)
Created2016
Description
Using historical data from the third-party payment acquiring industry, I develop a statistical model to predict the probability of fraudulent transactions by the merchants. The model consists of two levels of analysis – the first focuses on fraud detection at the store level, and the second focuses on fraud detection at the merchant level by aggregating store level data to the merchant level for merchants with multiple stores. My purpose is to put the model into business operations, helping to identify fraudulent merchants at the time of transactions and thus mitigate the risk exposure of the payment acquiring businesses. The model developed in this study is distinct from existing fraud detection models in three important aspects. First, it predicts the probability of fraud at the merchant level, as opposed to at the transaction level or by the cardholders. Second, it is developed by applying machine learning algorithms and logistical regressions to all the transaction level and merchant level variables collected from real business operations, rather than relying on the experiences and analytical abilities of business experts as in the development of traditional expert systems. Third, instead of using a small sample, I develop and test the model using a huge sample that consists of over 600,000 merchants and 10 million transactions per month. I conclude this study with a discussion of the model’s possible applications in practice as well as its implications for future research.
ContributorsZhou, Ye (Author) / Chen, Hong (Thesis advisor) / Gu, Bin (Thesis advisor) / Chao, Xiuli (Committee member) / Arizona State University (Publisher)
Created2015