Matching Items (306)
Description
Executive compensation design involving equity shares has been widely used in Europe, the United States and other developed countries where the capital markets are relatively mature. In China, due to the differences in industries, ownership structure, stages of enterprise development, constraints faced by the firms, the executive compensation design using equity shares tends to vary accordingly. For the state-owned companies, the situations are more complex than others. This complexity has not been a focus of the past literature, particularly on the compensation contract design and its subsequent implementation. Based on Coase contract theorem, agency theory and human capital theory, I examined how different state-owned firms vary in their approaches on managerial stock compensation design using a case study approach. The thesis concludes with a summary of major findings and a discussion of policy implications.
ContributorsAn, Hongjun (Author) / Pei, Ker-Wei (Thesis advisor) / Chen, Hong (Thesis advisor) / Gu, Bin (Committee member) / Arizona State University (Publisher)
Created2016
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
Description
This thesis aims to investigate the impacts of foreign banks’ management model on their degree of localization and operating efficiency. I decompose their management model into five major factors, including two formative factors and three reflective factors. The two formative factors are (1) strategic orientation and (2) target customers, and the three reflective factors are (1) top management team composition, (2) organizational structure, and (3) managerial authority and incentives. I propose that the formative factors influence foreign banks’ degree of localization, as demonstrated by the reflective factors, which subsequently influence foreign banks’ operating efficiency in China.
To test the above proposition, I conduct the empirical analysis in three steps. In the first step, I investigate foreign banks’ management model by surveying 13 major foreign banks locally incorporated in Mainland China. The results suggest that these 13 foreign banks can be categorized into three distinct groups based on their management model: intergrators, customer-followers, and parent-followers. The results also indicate that intergrators have the highest level of localization while parent-followers have the lowest level of localization.
In the second step, I conduct DEA (Data Envelope Analysis) and CAMEL (Capital Adequacy, Asset Quality, Management, Earnings, Liquidity Analysis) to assess the operating efficiency of these 13 foreign banks. The assessment is conducted in two ways: 1) the inter-group comparison between foreign banks and local Chinese banks; 2) the intra-group comparison between the three distinct groups of foreign banks identified in the first step. The results indicates that the principal factor driving the operating efficiency of both local Chinese banks and foreign banks is the comprehensive technical efficiency, which includes both the quality of management and the quality of technical elements. I also find the uptrend of technical efficiency of the integrators is more stable than that of the other two groups of foreign banks.
Finally, I integrate the results from step one and step two to assess the relevance between foreign banks’ localization level and operating efficiency. I find that foreign banks that score higher in localization tend to have a higher level of operating efficiency. Although this finding is not conclusive about the causal relationship between localization and operating efficiency, it nevertheless suggests that the management model of the higher performing integrators can serve as references for the other foreign banks attempting to enhance their localization and operating efficiency. I also discuss the future trends of development in the banking industry in China and what foreign banks can learn from local Chinese banks to improve their market positions.
To test the above proposition, I conduct the empirical analysis in three steps. In the first step, I investigate foreign banks’ management model by surveying 13 major foreign banks locally incorporated in Mainland China. The results suggest that these 13 foreign banks can be categorized into three distinct groups based on their management model: intergrators, customer-followers, and parent-followers. The results also indicate that intergrators have the highest level of localization while parent-followers have the lowest level of localization.
In the second step, I conduct DEA (Data Envelope Analysis) and CAMEL (Capital Adequacy, Asset Quality, Management, Earnings, Liquidity Analysis) to assess the operating efficiency of these 13 foreign banks. The assessment is conducted in two ways: 1) the inter-group comparison between foreign banks and local Chinese banks; 2) the intra-group comparison between the three distinct groups of foreign banks identified in the first step. The results indicates that the principal factor driving the operating efficiency of both local Chinese banks and foreign banks is the comprehensive technical efficiency, which includes both the quality of management and the quality of technical elements. I also find the uptrend of technical efficiency of the integrators is more stable than that of the other two groups of foreign banks.
Finally, I integrate the results from step one and step two to assess the relevance between foreign banks’ localization level and operating efficiency. I find that foreign banks that score higher in localization tend to have a higher level of operating efficiency. Although this finding is not conclusive about the causal relationship between localization and operating efficiency, it nevertheless suggests that the management model of the higher performing integrators can serve as references for the other foreign banks attempting to enhance their localization and operating efficiency. I also discuss the future trends of development in the banking industry in China and what foreign banks can learn from local Chinese banks to improve their market positions.
ContributorsSun, Minjie (Author) / Shen, Wei (Thesis advisor) / Qian, Jun (Thesis advisor) / Pei, Ker-Wei (Committee member) / Arizona State University (Publisher)
Created2016
Description
A major challenge in automated text analysis is that different words are used for related concepts. Analyzing text at the surface level would treat related concepts (i.e. actors, actions, targets, and victims) as different objects, potentially missing common narrative patterns. Generalized concepts are used to overcome this problem. Generalization may result into word sense disambiguation failing to find similarity. This is addressed by taking into account contextual synonyms. Concept discovery based on contextual synonyms reveal information about the semantic roles of the words leading to concepts. Merger engine generalize the concepts so that it can be used as features in learning algorithms.
ContributorsKedia, Nitesh (Author) / Davulcu, Hasan (Thesis advisor) / Corman, Steve R (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2015
Description
The current study combines field study, survey study, and public financial reports, and conducts an in-depths comprehensive study of the cost of the global tire industry. By comparing the price and the total cost structure of standardized tire products, we investigate Chinese tire industry’s global competitiveness, especially in light of China’s fast increasing labor cost. By constructing a comprehensive cost index (CCI), this dissertation estimates the evolution and forecasts the trend of global tire industry’s cost structure. Based on our empirical analysis, we provide various recommendations for Chinese tire manufacturers, other manufacturing industries, and foreign trade policy makers.
ContributorsZhang, Ning (Author) / Zhu, Ning (Thesis advisor) / Shen, Wei (Thesis advisor) / Chen, Hong (Committee member) / Arizona State University (Publisher)
Created2015
Description
Background: As the growth of social media platforms continues, the use of the constantly increasing amount of freely available, user-generated data they receive becomes of great importance. One apparent use of this content is public health surveillance; such as for increasing understanding of substance abuse. In this study, Facebook was used to monitor nicotine addiction through the public support groups users can join to aid their quitting process. Objective: The main objective of this project was to gain a better understanding of the mechanisms of nicotine addiction online and provide content analysis of Facebook posts obtained from "quit smoking" support groups. Methods: Using the Facebook Application Programming Interface (API) for Python, a sample of 9,970 posts were collected in October 2015. Information regarding the user's name and the number of likes and comments they received on their post were also included. The posts crawled were then manually classified by one annotator into one of three categories: positive, negative, and neutral. Where positive posts are those that describe current quits, negative posts are those that discuss relapsing, and neutral posts are those that were not be used to train the classifiers, which include posts where users have yet to attempt a quit, ads, random questions, etc. For this project, the performance of two machine learning algorithms on a corpus of manually labeled Facebook posts were compared. The classification goal was to test the plausibility of creating a natural language processing machine learning classifier which could be used to distinguish between relapse (labeled negative) and quitting success (labeled positive) posts from a set of smoking related posts. Results: From the corpus of 9,970 posts that were manually labeled: 6,254 (62.7%) were labeled positive, 1,249 (12.5%) were labeled negative, and 2467 (24.8%) were labeled neutral. Since the posts labeled neutral are those which are irrelevant to the classification task, 7,503 posts were used to train the classifiers: 83.4% positive and 16.6% negative. The SVM classifier was 84.1% accurate and 84.1% precise, had a recall of 1, and an F-score of 0.914. The MNB classifier was 82.8% accurate and 82.8% precise, had a recall of 1, and an F-score of 0.906. Conclusions: From the Facebook surveillance results, a small peak is given into the behavior of those looking to quit smoking. Ultimately, what makes Facebook a great tool for public health surveillance is that it has an extremely large and diverse user base with information that is easily obtainable. This, and the fact that so many people are actually willing to use Facebook support groups to aid their quitting processes demonstrates that it can be used to learn a lot about quitting and smoking behavior.
ContributorsMolina, Daniel Antonio (Author) / Li, Baoxin (Thesis director) / Tian, Qiongjie (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05