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.
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…
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.
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…
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.