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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- All Subjects: Influencing factors
- Creators: Shen, Wei
- Creators: Gu, Bin
Description中国水环境行业当前正处在以质量驱动、效率提升为主导的发展阶段，为积极响应国家政策以及环境发展导向，平衡公众日益增长的公共品需求同公共品短缺、低效之间的矛盾，抓住市场发展机遇，提高企业市场竞争中的核心能力，水环境行业必须要明确资本驱动、效率导向、服务标准提高要求下的价值流方向，加快行业发展动力的创新改革。因此，本文立足政府充分授权下的水环境企业战略联盟模式（具体体现为BOT模式）影响因素研究，包括如下几部分内容：
第一，界定政府充分授权下水环境企业战略联盟内涵，分析其形成的理论基础、水环境企业战略联盟的类型、发展差异性及战略联盟动因。通过梳理战略联盟理论国内外研究现状回顾及评述，提出政府充分授权下水环境企业战略联盟模式研究的主要问题。
第二，探索政府充分授权下水环境企业战略联盟模式的影响因素。通过对水环境基础设施战略联盟项目合同关键内容的深入分析，识别出政府充分授权下水环境企业战略联盟模式的关键影响因素。
第三，实证分析各关键因素对政府充分授权下水环境企业战略联盟模式效果的影响。运用回归分析方法对项目规模、政府政策、监督管理、激励机制、风险分配和投资回报对联盟模式效果的影响进行实证检验，验证了各影响因素对政府充分授权下水环境企业战略联盟模式效果的正向作用。
最后，对政府充分授权下水环境企业战略联盟模式影响因素及作用研究的结论进行总结。
第一，界定政府充分授权下水环境企业战略联盟内涵，分析其形成的理论基础、水环境企业战略联盟的类型、发展差异性及战略联盟动因。通过梳理战略联盟理论国内外研究现状回顾及评述，提出政府充分授权下水环境企业战略联盟模式研究的主要问题。
第二，探索政府充分授权下水环境企业战略联盟模式的影响因素。通过对水环境基础设施战略联盟项目合同关键内容的深入分析，识别出政府充分授权下水环境企业战略联盟模式的关键影响因素。
第三，实证分析各关键因素对政府充分授权下水环境企业战略联盟模式效果的影响。运用回归分析方法对项目规模、政府政策、监督管理、激励机制、风险分配和投资回报对联盟模式效果的影响进行实证检验，验证了各影响因素对政府充分授权下水环境企业战略联盟模式效果的正向作用。
最后，对政府充分授权下水环境企业战略联盟模式影响因素及作用研究的结论进行总结。
ContributorsLi, Zhensheng (Author) / Pei, Ker-Wei (Thesis advisor) / Yu, Xiaoyun (Thesis advisor) / Shen, Wei (Committee member) / Arizona State University (Publisher)
Created2019
Description随着移动互联网的快速发展，共享经济在我国产业结构转型升级中作用愈来愈大，IT服务的供给和需求已逐渐卷入共享经济发展的浪潮。本文以我国产业互联网生态建设中共享经济应用的典型IT技术服务生态平台——S公司为例，系统研究线上IT技术服务模式下客户满意度的构成和影响因素。针对当前IT服务需求与资源的不均衡情况，S公司将社会化协作、共享经济等新型理念植入线上IT共享服务平台，将分散在全国范围内不同行业、不同地域、不同层次IT服务需求（客服IT服务外包）和供给（工程师、服务商资源、备品备件资源等）进行资源整合，通过APP打通了传统方式下客户需求和IT服务资源供给间的通道，并部署了云端IT服务资源池，持续为客户提供高质量的IT服务。
本文构建了客户所处不同阶段满意度量表，并结合S公司线上IT服务平台模式特征建立客户满意度影响因素研究模型，并从客户层面、工程师层面、平台层面入手构建了不同阶段实证模型。本文主要从两方面着手：第一，构建系统化的客户评价量表，并借助问卷调查法获取有效数据，为后续实证储备资源；第二，通过构建多元线性回归模式，实证检验客户对工程师的满意度的影响因素和作用机理。实证结果发现：（1）客户层面，客户自身对产品或服务价值感知的认可度越高，客户的整体满意度越高；（2）实证结果并未发现工程师年龄、性别、历史接单量对客户满意度具有显著的正向或负向影响，工程师级别越高客户满意度水平越高，交易后教育程度对客户满意度具有正向影响，工程师优质的服务评价对客户满意度具有正向影响，工程师投诉率越高客户满意度越低，订单的延期或终止（特别是由工程师发起）会大大降低客户的满意度；（3）平台层面，平台下载量越高，客户的整体满意度越高。
本文的研究框架和实证结论不仅可以丰富学术界有关线上IT服务平台的客户评价体系，而且有助于从企业提升客户粘性、提升平台流量等角度系统分析影响客户粘性的主要因素，为线上共享服务平台的发展提供借鉴。
ContributorsCao, Hongyi (Author) / Shao, Benjamin (Thesis advisor) / Li, Xianglin (Thesis advisor) / Gu, Bin (Committee member) / Arizona State University (Publisher)
Created2021
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