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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- Genre: Doctoral Dissertation
- Creators: Shen, Wei
Description
China's city commercial banks were reorganized by the urban credit cooperatives in the same city in the 1990s. Although they are allowed to open branches outside the registered city, the location and the number of their branches have been strictly restricted. It is fatal to them to increase the competitiveness of their branches. Based on the diversity theory and its mechanism, in this study I examined the impact of source diversity of the senior management in the branches of the city commercial bank on the branches’ productivity and their asset yield. Invoking the resource-based theory and the social capital framework, the source diversity lead to the organization resources diversity and the organization knowledge diversity. The results demonstrate that the source diversity contribute to the branches’ competitiveness advantage. Both internal trained personnel and external introduction personnel are important for the branches’ top management team. But one of the two kinds of personnel is more suitable to their middle management team.
ContributorsZhang, Xiande (Author) / Gu, Bin (Thesis advisor) / Wang, Tan (Thesis advisor) / Shen, Wei (Committee member) / Arizona State University (Publisher)
Created2017
Description
Solar power, as an important part of renewable energy, has become one of the main choices for countries around the world in their energy strategic layout due to its cleanliness, renewability, and distributed attributes. In the context of the booming photovoltaic industry, China has emerged a large number of excellent photovoltaic companies, driving the whole industry to reduce costs and increase efficiency, making many contributions to the grid parity of photovoltaic power generation. In the development lifecycle of the photovoltaic industry, various companies choose different competitive strategies to deal with industry cyclical changes and external uncertainty based on their core competitiveness and market opportunities. Vertical integration is one of the strategic paths chosen by many photovoltaic companies. Therefore, it is an important issue to explore the impact of vertical integration on the development of Chinese photovoltaic companies.Based on the data of China's A-share listed photovoltaic companies from 2018 to 2022, this paper uses panel fixed effect model to empirically test the impact of vertical integration on corporate valuation, explores its influencing mechanism, and further analyzes the moderating effect of enterprise heterogeneity factors. The research in this paper shows that: (1) under other conditions unchanged, vertical integration significantly improves the valuation level of enterprises, and this positive impact will not change with the measurement method of enterprise valuation level. This is because the higher the vertical integration degree of enterprises, the stronger their ability to respond to external uncertainty. The more enterprises can obtain capital market preferences, the higher the enterprise valuation will be. This also means that the higher the vertical integration degree of photovoltaic enterprises, the higher their market share is, and they are more able to avoid the impact of external uncertainty, thus obtaining a higher valuation level in the secondary market. (2) The intermediary effect test shows that the channel for vertical integration of photovoltaic enterprises to affect enterprise valuation levels is to increase their market share. (3) Further heterogeneity analysis shows that enterprise profitability and enterprise size positively regulate the impact of vertical integration on enterprise valuation, while enterprise management shareholding ratio and enterprise operating cost ratio will weaken the positive promotion effect of vertical integration.
The research conclusions of this paper provide micro-empirical evidence for how photovoltaic companies can improve their enterprise valuation, and also provide some management references for other unlisted companies in the same industry.
Keywords: Photovoltaic enterprises; Vertical integration; Corporate valuation; Fixed effect model
ContributorsZheng, Ren (Author) / Shen, Wei (Thesis advisor) / Wu, Fei (Thesis advisor) / Zhao, Yanfei (Committee member) / Arizona State University (Publisher)
Created2024
Description
On January 30, 2019, the China Securities Regulatory Commission issued the Implementation Opinions on the Establishment of the Science and Technology Innovation Board on the Shanghai Stock Exchange and the Pilot Registration-based System, announcing the establishment of a new Science and Technology Innovation Board(STAR). The STAR Market is an important measure in China's capital market reform, aiming to promote the transformation of China's economy from a stage of rapid growth to a stage of high-quality development. The companies listed on the Science and Technology Innovation Board are mainly scientific and technological innovation enterprises that are at the forefront of the world's science and technology, the main battlefield of the economy, and the major needs of the country, in line with the national strategy, breaking through key core technologies, and with high market recognition. Since its launch on July 22, 2019, to May, 15, 2023, there are 522 companies have been listed on the STAR Market, with a total market capitalization of more than RMB 7 trillion. The successful listing of these enterprises will provide strong support for the deep integration of China's high-tech industries and strategic emerging industries.This paper analyzes the influencing factors of IPO listing pricing on the STAR Market, and studies 1478 companies listed on the three listing platforms of the STAR Market, ChiNext and Hong Kong stocks. Through descriptive statistical analysis and multivariate regression model, the influencing factors of the 1st day and the 20th day were empirically studied. The results of the study will provide a pricing reference for
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listed companies in the future, and provide a reference for policymakers to meet the expectations of the new regulatory reforms.
Through analysis of multiple factors includes but not limited as the NR,IPE, LEAD, ISCA, T10, AOL, BC, STL, RDI, CAGR, DTOR, these influencing factors have an important impact on the IPO of the STAR Market.
ContributorsHuang, Danyang (Author) / Shen, Wei (Thesis advisor) / Cheng, Shijun (Thesis advisor) / Jiang, Zhan (Committee member) / Arizona State University (Publisher)
Created2024
Description
Over the past two decades, propelled by urbanization, domestic investment and construction of commercial complexes have rapidly accelerated. This has led to a dramatic expansion of these complexes with swift operational iterations and related data changes. The impact of changing domestic and international financial policies, along with political environments, has seen e-commerce gradually seize the middle and low-end retail markets. Additionally, the global spread of the COVID-19 pandemic in the last three years has resulted in a substantial slowdown in domestic economic growth. Despite this, there is still developmental potential, prompting unprecedented attention to corporate investment and commercial operations.However, acquiring basic operational data in commerce is challenging, with inconsistent measurement standards among enterprises, hindering accurate and systematic judgments of operational performance. The factors influencing the operational performance of commercial complexes in China remain inadequately researched.At this juncture, the scientific measurement of commercial complex operational performance is crucial for their healthy development. This study explores the relationship between enterprise investment behavior, operational management behavior, and commercial complex operational performance. It measures influencing factors using resource configuration theory to control uncontrollable environmental factors, such as urban hierarchy, surrounding population, per capita GDP, surrounding commercial inventory and increment, and location planning support. Dynamic capability theory is then applied to investigate the impact of variables like the number of leases, area, brands, lease cost income, marketing activity types, activity funds, and activity time on operational performance. A model is established to analyze operational performance, contrasting significant variables before and after the pandemic, identifying factors affecting operational performance in early-stage investment and later-stage management strategies. Post-pandemic adjustments are suggested to adapt to changing environmental conditions.In the empirical research section, this paper validates the theoretical model through data analysis, studying the volatility of operational performance based on factors influencing commercial complexes. Integrating theoretical backgrounds, it analyzes investment and management strategies for enterprises in different situations, emphasizing key indicators. This provides enterprises with better choices for future projects and empowers commercial complex managers for effective future management, enhancing operational performance. The study offers a theoretical basis and guidance for promoting the healthy development of the market.
ContributorsHuang, Ke (Author) / Shen, Wei (Thesis advisor) / Zhu, Qigui (Thesis advisor) / Hu, Yu (Committee member) / Arizona State University (Publisher)
Created2024
Description
With the continuous development of the Chinese capital market over the past thirty years, the securities analyst industry has experienced a process of transformation from a reckless period to a golden time. One of the most important signals is that securities analysts are increasingly conducting research report providing long-term earnings forecasts for the company. However, current research on analysts is limited to their short-term forecasting behavior, and there is little on analysts' long-term earnings forecasts. Therefore, this article takes the research on analysts' long-term forecast reports issued by analysts on A-share listed companies, and conducts an empirical study on the analysts' forecasts accuracy and its influencing factors. First, the author combed the research literature related to analyst forecasts and selected variables from three dimensions, including company characteristics (financial indicators and non-financial indicators), analyst characteristics and affiliated institution characteristics; secondly, considering the high-dimensionality of the influencing factors, this paper uses the method of combining machine learning and traditional regression to conduct empirical research; finally, the research tested the heterogeneity of influencing factors from two perspectives, including time and industry.The results of this article show that the long-term profit forecasts of analysts in China have advantages over traditional statistical models. More than 60% of analysts
provide profit forecasts that are better than statistical models. Afterwards, when examining the factors that affected the accuracy of analysts’ forecasts, it found that although analyst and institutional characteristics affected analysts’ predictions to a certain extent, company characteristics are the most important variables among them all. As the time goes by, the influence of non-financial factors on forecast accuracy gradually decreasing, but analyst characteristics continue to strengthen. In addition, cyclical industries are more difficult to predict than companies in non-cyclical industries, and the difficulty of prediction will not be reduced with the analyst efforts. This research can help analysts optimizing their forecasting behavior and prompts investors to understand analysts' reports more deeply, which makes them using analyst forecast data to make investment decisions in a rationally ways, and it can also help to promote the securities pricing efficiency and development of Chinese capital market.
ContributorsRao, Gang (Author) / Shen, Wei (Thesis advisor) / Yan, Hong (Thesis advisor) / Hu, Jie (Committee member) / Arizona State University (Publisher)
Created2024