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Links among perceived service quality, patient satisfaction and behavioral intentions in the urgent care industry: Empirical evidence from college students.

Description: Patient perceptions of health care quality are critical to a health care service provider's long-term success because of the significant influence perceptions have on customer satisfaction and consequently organization financial performance. Patient satisfaction affects not only the outcome of the health care process such as patient compliance with physician advice and treatment, but also patient retention and favorable word-of-mouth. Accordingly, it is a critical strategy for health care organizations to provide quality service and address patient satisfaction. The urgent care (UC) industry is an integral part of the health care system in the United States that has been experiencing a rapid growth. UC provides a wide range of medical services for a large group of patients and now serves an increasing population. UC is becoming popular because of the convenient locations, extended hours, walk-in policy, short waiting times, and accessibility. A closer examination of the current health care research, however, indicates that there is a paucity of research on urgent care providers. Confronted with the emergence of the urgent care industry and the increasing demand for urgent care, it is necessary to understand how patients perceive urgent care providers and what influences patient satisfaction and retention. This dissertation addresses four areas relevant to the above mentioned issues: (1) development of an instrument to measure perceived service quality in the urgent care industry; (2) identification of the determinants of patient satisfaction and behavioral intentions; (3) empirical examination of the relationships among perceived service quality, patient satisfaction and behavioral intentions; and (4) comparison of the perceived service quality across several primary urgent care providers, such as urgent care centers, hospital emergency departments, and primary care physicians' offices. To validate this new instrument and examine the hypothesized relationships proposed in this study, an electronic web based survey was designed and administered to college ...
Date: August 2009
Creator: Qin, Hong
Partner: UNT Libraries

Impact of Forecasting Method Selection and Information Sharing on Supply Chain Performance.

Description: Effective supply chain management gains much attention from industry and academia because it helps firms across a supply chain to reduce cost and improve customer service level efficiently. Focusing on one of the key challenges of the supply chains, namely, demand uncertainty, this dissertation extends the work of Zhao, Xie, and Leung so as to examine the effects of forecasting method selection coupled with information sharing on supply chain performance in a dynamic business environment. The results of this study showed that under various scenarios, advanced forecasting methods such as neural network and GARCH models play a more significant role when capacity tightness increases and is more important to the retailers than to the supplier under certain circumstances in terms of supply chain costs. Thus, advanced forecasting models should be promoted in supply chain management. However, this study also demonstrated that forecasting methods not capable of modeling features of certain demand patterns significantly impact a supply chain's performance. That is, a forecasting method misspecified for characteristics of the demand pattern usually results in higher supply chain costs. Thus, in practice, supply chain managers should be cognizant of the cost impact of selecting commonly used traditional forecasting methods, such as moving average and exponential smoothing, in conjunction with various operational and environmental factors, to keep supply chain cost under control. This study demonstrated that when capacity tightness is high for the supplier, information sharing plays a more important role in effective supply chain management. In addition, this study also showed that retailers benefit directly from information sharing when advanced forecasting methods are employed under certain conditions.
Date: December 2009
Creator: Pan, Youqin
Partner: UNT Libraries

Supply Chain Network Planning for Humanitarian Operations During Seasonal Disasters

Description: To prevent loss of lives during seasonal disasters, relief agencies distribute critical supplies and provide lifesaving services to the affected populations. Despite agencies' efforts, frequently occuring disasters increase the cost of relief operations. The purpose of our study is to minimize the cost of relief operations, considering that such disasters cause random demand. To achieve this, we have formulated a series of models, which are distinct from the current studies in three ways. First, to the best of our knowledge, we are the first ones to capture both perishable and durable products together. Second, we have aggregated multiple products in a different way than current studies do. This unique aggregation requires less data than that of other types of aggregation. Finally, our models are compatible with the practical data generated by FEMA. Our models offer insights on the impacts of various parameters on optimum cost and order size. The analyses of correlation of demand and quality of information offer interesting insights; for instance, under certain cases, the quality of information does not influence cost. Our study has considered both risk averse and risk neutral approaches and provided insights. The insights obtained from our models are expected to help agencies reduce the cost of operations by choosing cost effective suppliers.
Date: May 2013
Creator: Ponnaiyan, Subramaniam
Partner: UNT Libraries

The Impact of Cloud Based Supply Chain Management on Supply Chain Resilience

Description: On March 2011 a destructive 9.0-magnitude earthquake and tsunami along with nuclear explosions struck northeastern Japan; killing thousands of people, halting industry and crippling infrastructure. A large manufacturing company operating outside of Japan received the news in the middle of the night. Within a few hours of the tsunami hitting Japan, this manufacturer’s logistics team ran global materials management reports to communicate the precise status of the products originating from Japan to their entire global network of facilities. With this quick and far reaching communication the manufacturer was able to launch a successful contingency plan. Alternative suppliers, already existing as part of their global network, were evaluated and used to mitigate Japan’s disruptive impact. The resiliency of this manufacturer’s trusted network of supply chain trading partners allowed for minimum disruptions, saving countless money and maintaining continuity for its end-to-end supply chain. This manufacturer was part of a cloud-based supply chain that provided the catalyst to quickly shift its resources to allay the impact of no longer being able to receive product from Japan. Today's supply chains are global and complex networks of enterprises that aim to deliver products in the right quantity, in the right place, and at the right time in an increasingly volatile and unpredictable environment. To cope with internal and external supply chain instability and disruptions, supply chains need to be resilient to survive. A supply chain's ability to collaboratively share information with its supply chain partners is one of the most important factors that enhance a supply chain’s resilience. Cloud based supply chain management (SCM) creates a platform that enables collaborative information sharing that helps to identify, monitor and reduce supply chain risks, vulnerabilities and disruptions. However, supply chain academics and practitioners are at its infancy in understanding the capabilities of cloud based supply chains and ...
Date: August 2015
Creator: Kochan, Cigdem Gonul
Partner: UNT Libraries

Comparing Latent Dirichlet Allocation and Latent Semantic Analysis as Classifiers

Description: In the Information Age, a proliferation of unstructured text electronic documents exists. Processing these documents by humans is a daunting task as humans have limited cognitive abilities for processing large volumes of documents that can often be extremely lengthy. To address this problem, text data computer algorithms are being developed. Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) are two text data computer algorithms that have received much attention individually in the text data literature for topic extraction studies but not for document classification nor for comparison studies. Since classification is considered an important human function and has been studied in the areas of cognitive science and information science, in this dissertation a research study was performed to compare LDA, LSA and humans as document classifiers. The research questions posed in this study are: R1: How accurate is LDA and LSA in classifying documents in a corpus of textual data over a known set of topics? R2: How accurate are humans in performing the same classification task? R3: How does LDA classification performance compare to LSA classification performance? To address these questions, a classification study involving human subjects was designed where humans were asked to generate and classify documents (customer comments) at two levels of abstraction for a quality assurance setting. Then two computer algorithms, LSA and LDA, were used to perform classification on these documents. The results indicate that humans outperformed all computer algorithms and had an accuracy rate of 94% at the higher level of abstraction and 76% at the lower level of abstraction. At the high level of abstraction, the accuracy rates were 84% for both LSA and LDA and at the lower level, the accuracy rate were 67% for LSA and 64% for LDA. The findings of this research have many strong implications for the ...
Date: December 2011
Creator: Anaya, Leticia H.
Partner: UNT Libraries

Supply Chain Network Evolution: Demand-based Drivers of Interfirm Governance Evolution

Description: Which form of exchange governance performs better in a dynamic environment? This remains an unanswered question in the transaction cost analysis (TCA) and relational exchange literatures. Some researchers purport that transactional governance provides superior performance by providing firms the flexibility to change suppliers. Others suggest that relational governance leads to superior performance because of the willingness of both parties to adapt. Reviews of TCA have turned up ambivalent empirical findings with regard to the effects of uncertainty despite a track record of strong empirical support for other predictions. Because most of TCA and relational exchange theories' predictions enjoy strong support, this research builds upon these theories to propose a theoretical modeling framework for a dynamic environment in a supply chain network (SCN) setting. This dissertation extends TCA and relational exchange to a dynamic, network environment. It uses the approach of building a simulation in order to study in detail the relationship between key exchange factors and the selection of transactional and relational exchange governance over time. This research effort extended TCA theory with a complex adaptive model of supply chain network governance evolution that attempts to link environmental, network, production, firm and exchange factors in a continuously evolving loop. The proposed framework expands transaction cost analysis' explanatory power. Results partially support past scholarly proposal that uncertainty functions as an antecedent of asset specificity rather than as an independent construct affecting governance outcome dependent upon which form of uncertainty is being considered. The successful simulation of supply chain networks as complex adaptive systems shift the focus from deterministic, confirmatory models of exchange to an exploratory, positive model. Instead of exchange governance as an outcome, it is the catalyst of the evolutionary process.
Date: August 2007
Creator: Gravier, Michael J.
Partner: UNT Libraries

Reliable Prediction Intervals and Bayesian Estimation for Demand Rates of Slow-Moving Inventory

Description: Application of multisource feedback (MSF) increased dramatically and became widespread globally in the past two decades, but there was little conceptual work regarding self-other agreement and few empirical studies investigated self-other agreement in other cultural settings. This study developed a new conceptual framework of self-other agreement and used three samples to illustrate how national culture affected self-other agreement. These three samples included 428 participants from China, 818 participants from the US, and 871 participants from globally dispersed teams (GDTs). An EQS procedure and a polynomial regression procedure were used to examine whether the covariance matrices were equal across samples and whether the relationships between self-other agreement and performance would be different across cultures, respectively. The results indicated MSF could be applied to China and GDTs, but the pattern of relationships between self-other agreement and performance was different across samples, suggesting that the results found in the U.S. sample were the exception rather than rule. Demographics also affected self-other agreement disparately across perspectives and cultures, indicating self-concept was susceptible to cultural influences. The proposed framework only received partial support but showed great promise to guide future studies. This study contributed to the literature by: (a) developing a new framework of self-other agreement that could be used to study various contextual factors; (b) examining the relationship between self-other agreement and performance in three vastly different samples; (c) providing some important insights about consensus between raters and self-other agreement; (d) offering some practical guidelines regarding how to apply MSF to other cultures more effectively.
Date: August 2007
Creator: Lindsey, Matthew Douglas
Partner: UNT Libraries

Bank Loans as a Financial Discipline: A Direct Agency Cost of Equity Perspective

Description: In a 2004 study, Harvey, Lin and Roper argue that debt makers with a commitment to monitoring can create value for outside shareholders whenever information asymmetry and agency costs are pronounced. I investigate Harvey, Lin and Roper's claim for bank loans by empirically testing the effect of information asymmetry and direct agency costs on the abnormal returns of the borrowers' stock around the announcement of bank loans. I divide my study into two main sections. The first section tests whether three proxies of the direct agency costs of equity are equally significant in measuring the direct costs associated with outside equity agency problems. I find that the asset utilization ratio proxy is the most statistically significant proxy of the direct agency costs of equity using a Chow F-test statistic. The second main section of my dissertation includes and event study and a cross-sectional analysis. The event study results document significant and positive average abnormal returns of 1.01% for the borrowers' stock on the announcement day of bank loans. In the cross sectional analysis of the borrowers' average abnormal stock returns, I find that higher quality and more reputable banks/lenders provide a reliable certification to the capital market about the low level of the borrowers' direct agency costs of equity and information asymmetry. This certification hypothesis holds only for renewed bank loans. In other words, in renewing the borrowers' line of credit, the bank/lender is actually confirming that the borrower has a low level of information asymmetry and direct costs of equity. Given such a certificate from the banks/lenders, shareholders reward the company/borrower by bidding the share price up in the capital market.
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Date: December 2006
Creator: Hijazi, Bassem
Partner: UNT Libraries