Matching Items (8)
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- All Subjects: Business Administration
- Language: English
- Creators: Gu, Bin
Description
The advancement of information and communication technology (ICT) has significantly transformed how people communicate and obtain information in both their personal and professional lives. In the realm of digitally-facilitated social interaction, this dissertation calls for attention to the importance of information technology artifacts (IT artifacts) on social welfare by showcasing that the careful design and usage of IT artifacts have the potential to enhance the effectiveness, efficiency, and fairness of social interactions. The first study centers around addressing the cold-start issue that often arises when new products are introduced. Specifically, I investigate how machine-generated content can enhance the equity of new products. Analyzing data from Kaggle.com, my research demonstrates that the use of machine-generated content is effective in tackling the cold-start problem by increasing the adoption of the product in the initial phase. Additionally, my findings reveal that machine-generated content can also reduce information asymmetry for users regarding the datasets or associated providers. As a result, these outcomes provide strong evidence supporting the use of machine-generated content to enhance equity in online communities. The second research investigates the impact of a platform’s decision to impose application fees on enhancing the quality of matching results in an online labor market. Based on data obtained from Freelancer.com, my analysis demonstrates that the implementation of application costs serves as a motivator for workers to submit fewer but more selective bids. This, in turn, increases the likelihood of employers offering contracts, as workers are less likely to apply casually or without much thought. Overall, these results indicate that application costs can enhance the efficiency of the matching process. In the third study, I examine whether gender differences exist in telework adjustment as a response to disasters and to what extent such adjustments can help reduce gender inequality, using the COVID-19 pandemic as an example. The study's findings reveal the following: 1) Female workers exhibit a higher rate of telework adjustment than their male counterparts by 7% after accounting for differences in job sorting, and female workers are more responsive to external constraints. 2) Telework adjustment can help mitigate gender inequality in the labor market.
ContributorsHou, Jingbo (Author) / Chen, Pei-Yu (Thesis advisor) / Gu, Bin (Committee member) / Hong, Yili (Kevin) (Committee member) / Arizona State University (Publisher)
Created2023
Description
Small and medium-sized enterprises (SMEs) have emerged as a vital force in the economic development of our country, significantly influencing national economic growth, employment rates, market competition, and innovation. Consequently, the ability of SMEs to achieve stable performance growth is crucial for socio-economic development. Previous research has identified factors such as political connections, cash flow, capital, and information technology as significant contributors to business growth. However, SMEs often lack these resources or cannot control them, making existing research inadequate for guiding the performance growth of SMEs.To address this issue, this study is grounded in the resource-based view and constructs a theoretical model on the impact of SME strategic completeness on performance growth. This model is based on literature research and in-depth interviews. It highlights the mediating roles of market adaptability, operational agility, and employee proactivity. Additionally, the model examines the moderating effects of competitive intensity, strategic implementation capability, and management digitalization. The study collected 203 valid SME samples for analysis and hypothesis testing, yielding the following conclusions:
(1)The more comprehensive the business strategy, the better the adaptability to external markets, the higher the operational agility, and the stronger the initiative of employees.
(2)Better market adaptability leads to faster performance growth.
(3)Higher operational agility accelerates performance growth.
(4)Greater employee initiative enhances performance growth.
(5)The stronger the external competitive intensity faced by a business, the more effective a well-developed strategy is in improving market adaptability, operational agility, and employee initiative.
(6)Stronger strategic implementation capabilities further enhance the effectiveness of a well-developed strategy in improving market adaptability and employee initiative.
(7)A higher level of management digitalization enhances the effects of market adaptability, operational agility, and employee initiative, thereby boosting business performance.
This study offers theoretical and practical value by clarifying the paths, mechanisms, and boundary conditions through which strategic completeness promotes business performance growth, enriching the theoretical system of SME performance growth. Furthermore, by integrating the cutting-edge concept of management digitalization into the research on strategic completeness and performance growth, this study contributes to the development and refinement of the research framework. Practically, it provides strategic recommendations for SMEs on how to enhance business performance growth through strategic completeness.
ContributorsZhang, Lei (Author) / Wang, Lili (Thesis advisor) / Guo, Qigui (Thesis advisor) / Gu, Bin (Committee member) / Arizona State University (Publisher)
Created2024
Description
Two-sided online platforms are typically plagued by hidden information (adverse selection) and hidden actions (moral hazard), limiting market efficiency. Under the context of the increasingly popular online labor contracting platforms, this dissertation investigates whether and how IT-enabled monitoring systems can mitigate moral hazard and reshape the labor demand and supply by providing detailed information about workers’ effort. In the first chapter, I propose and demonstrate that monitoring records can substitute for reputation signals such that they attract more qualified inexperienced workers to enter the marketplace. Specifically, only the effort-related reputation information is substituted by monitoring but the capability-related reputation information. In line with this, monitoring can lower the entry barrier for inexperienced workers on platforms. In the second chapter, I investigate if there is home bias for local workers when employers make the hiring decisions. I further show the existence of home bias from employers and it is primarily driven by statistical inference instead of personal “taste”. In the last chapter, I examine if females tend to have a stronger avoidance of monitoring than males. With the combination of the observational data and experimental data, I find that there is a gender difference in avoidance of monitoring and the introduction of the monitoring system increases the gender wage gap due to genders differences in such willingness-to-pay for the avoidance of monitoring. These three studies jointly contribute to the literature on the online platforms, gig economy and agency theory by elucidating the critical role of IT-enabled monitoring.
ContributorsLiang, Chen, Ph.D (Author) / Gu, Bin (Thesis advisor) / Hong, Yili (Thesis advisor) / Chen, Peiyu (Committee member) / Arizona State University (Publisher)
Created2019
Description
The recent changes in the software markets gave users an unprecedented number
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.
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.
ContributorsKanat, Irfan (Author) / Santanam, Raghu (Thesis advisor) / Vinze, Ajay (Thesis advisor) / Gu, Bin (Committee member) / Arizona State University (Publisher)
Created2016
Description
By collecting and analyzing more than two million tweets, U.S. House Representatives’ voting records in 111th and 113th Congress, and data from other resources I study several aspects of adoption and use of Twitter by Representatives. In the first chapter, I study the overall impact of Twitter use by Representatives on their political orientation and their political alignment with their constituents. The findings show that Representatives who adopted Twitter moved closer to their constituents in terms of political orientation.
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.
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.
ContributorsMosuavi, Seyedreza (Author) / Gu, Bin (Thesis advisor) / Vinzé, Ajay S. (Committee member) / Shi, Zhan (Michael) (Committee member) / Arizona State University (Publisher)
Created2016
Description
With the fast development of Chinese capital market, an increasing number of institutions and retail investors invest through professional managers. The key to evaluating investment manager’s skill and performance persistence largely lies in portfolio style research and attribution analysis.
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.
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.
ContributorsZhan, Yuyin (Author) / Gu, Bin (Thesis advisor) / Wang, Jiang (Thesis advisor) / Wang, Tan (Committee member) / Arizona State University (Publisher)
Created2016
Description
Digital media refers to any form of media which depends on electronic devices for its creation, distribution, view, and storage. Digital media analytics involves qualitative and quantitative analysis from the business to understand users’ behaviors. This technique brings disruptive changes to many industries and its path of economic disruption is getting wider and wider. Under the context of the increasingly popular digital media market, this dissertation investigates what are the best content delivery strategy and the new cultural phenomenon: Internet Water Army. The first essay proposes a theory-guided computational approach that consolidates distinct data sources spanning unstructured text, image, and video data, systematically measures modes of persuasion, and unveils the multimedia content design strategies for crowdfunding projects. The second essay studies whether using the Internet Water Army helps sales and under what conditions it helps. This study finds that the Internet water army helps product sales at both post-level and fans-level. The effect is largely reflected by changing the number of emotional fans. Furthermore, the earlier to purchase the water armies, more haters, likers, and neutral fans it can attract. The last essay builds a game model to study the trade- off between honestly promoting the product according to their evaluation and catering to the consumer’s prior belief on the product quality to stay on the market as long as possible. It provides insights on the optimum usage of promotion on social media and demonstrate how conventional wisdom about negative reviews will hurt business may be misleading in the presence of social media. These three studies jointly contribute to the crowdfunding and social media studies literature by elucidating the content delivery strategy, and the impact and purchasing strategy of the Internet Water Army.
ContributorsYIN, XUEYAN (Author) / Chen, Pei-Yu (Thesis advisor) / Gu, Bin (Committee member) / Shi, Zhan (Committee member) / Benjamin, Victor (Committee member) / Arizona State University (Publisher)
Created2020
Description
Online product ratings offer consumers information about products. In this dissertation, I explore how the design of the rating system impacts consumers’ sharing behavior and how different players are affected by rating mechanisms. The first two chapters investigate how consumers choose to share their experiences of different attributes, how their preferences are reflected in numerical ratings and textual reviews, whether and how multi-dimensional rating systems affect consumer satisfaction through product ratings, and whether and how multi-dimensional rating systems affect the interplay between numerical ratings and textual reviews. The identification strategy of the observational study hinges on a natural experiment on TripAdvisor when the website reengineered its rating system from single-dimensional to multi-dimensional in January 2009. Rating data on the same set of restaurants from Yelp, were used to identify the causal effect using a difference-in-difference approach. Text mining skills were deployed to identify potential topics from textual reviews when consumers didn’t provide dimensional ratings in both SD and MD systems. Results show that ratings in a single-dimensional rating system have a downward trend and a higher dispersion, whereas ratings in a multi-dimensional rating system are significantly higher and convergent. Textual reviews in MDR are in greater width and depth than textual reviews in SDR. The third chapter tries to uncover how the introduction of monetary incentives would influence different players in the online e-commerce market in the short term and in the long run. These three studies together contribute to the understanding of rating system/mechanism designs and different players in the online market.
ContributorsLiu, Ying (Author) / Chen, Pei-Yu (Thesis advisor) / Hong, Yili (Thesis advisor) / Gu, Bin (Committee member) / Arizona State University (Publisher)
Created2018