摘要 中國自1970年代末期經濟改革開放以來，經濟快速成長，導致2012年中國的GDP已經超過了8兆美元，成為繼美國之後的世界第二經濟大國。隨著經濟的成長，中國也逐漸由製造業為主的經濟體，朝向發展服務業和內需型產業轉變。中國人民所得增加，消費能力增強，近幾年量販業在中國蓬勃發展，相較之下臺灣量販店目前的展店速度與營收成長趨緩。 本研究以在兩岸都設有賣場的量販店業者潤泰集團大潤發及法商家樂福為研究對象，蒐集這兩家業者在臺灣和中國兩地的相關資料，以市場定位、經營理念、經營策略、以及經營績效等，深入整理與分析，再運用深度訪談，透過競爭策略五力分析及SWOT分析，探討量販店業者如何運用兩岸不同的市場及不同經營策略創造績效。研究結果顯示不論是五力分析或是SWOT分析，中國大潤發的競爭力最強。反之，在五力分析中台灣家樂福所受影響最大，其中尤其以購買者的議價能力、替代品或服務的威脅，以及現有廠商的競爭程度等方面所受影響較大。另一方面，在SWOT分析中，中國家樂福較居於劣勢，而所受的威脅也最強，主要原因為門市集客力弱及面臨租金成本提高的壓力。 關鍵字：大潤發、家樂福、經營策略、經營績效、五力分析 / Abstract Since its reform in late 1970s, China has experienced high economic growth. With GDP over 8 trillion USD, in 2012 China became the second largest economy ranked only behind the USA. Accompanying with such growth, China slowly transited from a manufacturing-based into a service and domestic-based economy. As the economic condition improves, people’s consumption power increases, which drives rapid growth of the hypermarket industry in China; and as contrast, the Taiwanese hypermarket industry is reaching a plateau during the same period of time. In this research, a comparative study is conducted with hypermarket industries in China and Taiwan. Taking Carrefour and RT-Mart chains, that operate on both sides of the Taiwan Strait, as samples, this study employs SWOT analysis and Michael Porter’s Five Forces Model to investigate the business strategy and performance differences. In particular, this study collects business data of said companies, including company’s vision, market position, business strategy, and operational performance, and the exploration was supplemented by in-depth interviews with the high-ranking staff members in the two companies. The findings suggest that RT-Mart China is the most competitive confirmed by both SWOT and Five Forces analyses while Carrefour Taiwan is impacted by the buyer bargain power and threat of substitutes and services. In addition, via SWOT analysis, it is found that Carrefour China is in a weak position since it is suffering from high rental costs and inability to gather customers. Keywords: RT-Mart, Carrefour, business strategy, business performance, Michael Porter’s five forces analysis

In South Africa various financial institutions and independent vendors have developed residential property valuation models to estimate the current value of historically traded properties. A natural extension to these models has been to develop historical property price indices. In this dissertation, three of the four approaches to developing property price indices will be examined. Through back–testing and other statistical methods, the most accurate and robust approach will be determined. The four major approaches available are the mean valuation per suburb, the median valuation per suburb, the repeat sales approach and hedonic regression. The mean valuation per suburb approach can be biased because of outliers in property prices. However, outliers in property prices will not influence the median valuation per suburb approach, but in cases where property values in a suburb have a skewed distribution, the valuation amount could be distorted. Neither of the above mentioned shortcomings influences the repeat sales or the hedonic regression approach. To follow the hedonic regression approach, the characteristics of the property need to be known. In South Africa, however, the available property data lacks detailed characteristics of traded properties. This dissertation will therefore focus on the first three methods. The repeat sales approach measures the growth in property prices by applying a generalized linear model to properties that have traded more than once. This approach is only possible if there is a representative amount of repeat sales able to fit a model. The focus of this project will be on the repeat sales approach, but all three the approaches discussed will be analysed to prove that the repeat sales approach is the most accurate in developing a property price index for properties in South Africa. / Thesis (M.Sc. (Risk Analysis))--North-West University, Potchefstroom Campus, 2011.

In South Africa various financial institutions and independent vendors have developed residential property valuation models to estimate the current value of historically traded properties. A natural extension to these models has been to develop historical property price indices. In this dissertation, three of the four approaches to developing property price indices will be examined. Through back–testing and other statistical methods, the most accurate and robust approach will be determined. The four major approaches available are the mean valuation per suburb, the median valuation per suburb, the repeat sales approach and hedonic regression. The mean valuation per suburb approach can be biased because of outliers in property prices. However, outliers in property prices will not influence the median valuation per suburb approach, but in cases where property values in a suburb have a skewed distribution, the valuation amount could be distorted. Neither of the above mentioned shortcomings influences the repeat sales or the hedonic regression approach. To follow the hedonic regression approach, the characteristics of the property need to be known. In South Africa, however, the available property data lacks detailed characteristics of traded properties. This dissertation will therefore focus on the first three methods. The repeat sales approach measures the growth in property prices by applying a generalized linear model to properties that have traded more than once. This approach is only possible if there is a representative amount of repeat sales able to fit a model. The focus of this project will be on the repeat sales approach, but all three the approaches discussed will be analysed to prove that the repeat sales approach is the most accurate in developing a property price index for properties in South Africa. / Thesis (M.Sc. (Risk Analysis))--North-West University, Potchefstroom Campus, 2011.

The retail industry is considered to be a very competitive industry in the United States since there are so many players in the almost saturated retail markets that provide similar products and services at similar price levels to customers. Market selection has been identified as an important strategy to differentiate a retailer in this competitive market. Therefore in this thesis, we describe a conceptual framework to evaluate retailers investment opportunities in dynamic, competitive retail markets. The objective is to describe a conceptual investment analysis framework to address the strategic aspects of a retailer s investment opportunity as well as the dynamic uncertainty of a retail market in a single framework. This conceptual framework outlines a strategic view towards retail stores as flexible assets of a retail enterprise. This conceptual framework is general and can be adjusted and applied to investments options in other services. In addition, we develop an integrated investment analysis approach based on dynamic programming to explore retailers investment behaviors in dynamic markets. The objective is to determine retailers optimal investment thresholds in noncompetitive and competitive markets. We consider two retailers to illustrate our approach and use a simple game theory treatment to address competition in retail markets. We use our integrated investment analysis model based on a real options methodology to evaluate the apparent tendency for the small discount retailer invests earlier in a new developing market due to the competition effect from the large discount retailer. This early entry gives the small retail a first-mover advantage and delays the big retailer s entry into the competitive market. In addition, we conduct sensitivity analysis to characterize how significantly the values of our model parameters impact the retailers investment decisions. We also develop an integrated investment analysis approach based on contingent claims analysis to explore retailers investment behaviors in dynamic markets. The objective is to determine retailers optimal investment thresholds in noncompetitive and competitive markets. The equivalent risk neutral evaluation approach is presented in this thesis as an extended version of the contingent claims analysis approach, which facilitates the market-oriented valuation of the retailer s investment option in dynamic markets. Sensitivity analysis is conducted to study how retailers optimal investment thresholds change as the values of parameters in this equivalent risk neutral evaluation approach change. The relationship between the dynamic programming and the equivalent risk neutral evaluation approach is also summarized in this thesis to identify the similarities and the differences between these two investment analysis approaches. One of the most important objectives of this comparison is to determine in what market conditions the choice of investment analysis approach is critical and dramatically changes the retailer s optimal investment threshold. Finally, we empirically examine an important aspect of our theoretical work that the big retailer invests and opens a store relatively later in markets with a small retailer compared to markets without a small retailer. In addition, the big retailer opens a store at relatively higher retail market potential in markets with a small retailer compared to markets without a small retailer. In this thesis, we discuss some empirical evidence to support these theoretical results. We chose Wal-Mart and Dollar General as the big and small retailers, respectively, in our empirical study. Our empirical results do not validate the theory and just provide supporting evidence for our theoretical works.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-10-18T14:46:23Z No. of bitstreams: 1 DEYZI SANTOS GOUVEIA - TESE PPGEP 2013..pdf: 23818629 bytes, checksum: 1446eed173142071f0a7597927cef5f1 (MD5) / Made available in DSpace on 2018-10-18T14:46:23Z (GMT). No. of bitstreams: 1 DEYZI SANTOS GOUVEIA - TESE PPGEP 2013..pdf: 23818629 bytes, checksum: 1446eed173142071f0a7597927cef5f1 (MD5) Previous issue date: 2013-02-25 / Capes / Realizou-se esta pesquisa com o objetivo de analisar a composição lipídica do óleo de coco catolé (Syagrus oleracea Mart.) quanto ao perfil de ácidos graxos, identidade do óleo e possíveis variações decorrentes da degradação térmica. Para este fim também foram realizados os seguintes procedimentos: caracterização físico-química da amêndoa; secagem nas temperaturas de 40, 50, 60 e 70°C e levantamento da cinética nessas temperaturas, aplicando-se os modelos de Page, Henderson & Pabis, Lewis, Wang & Singh e Midilli et al; extração do óleo da amêndoa do coco catolé in natura e procedentes dos 4 níveis de secagens; caracterização físico-química do óleo (índice de acidez, índice de peróxido, índice de refração, índice de saponificação e índice de iodo) segundo metodologia proposta pela AOCS (2009); determinação da viscosidade cinemática do óleo a 40 °C e composição em ácidos graxos láurico (C12:0), mirístico (C14:0), palmítico (C16:0), esteárico (C18:0), oleico (C18:l) e linoleico (C18:2) do óleo, por cromatografia gasosa; avaliação da estabilidade térmica do óleo de coco catolé através da termogravimetria (TG); avaliação oxidativa do óleo utilizando-se o método Rancimat; avaliação do estresse térmico, segundo estudo da degradação dos componentes graxos do óleo de coco catolé devido à sua permanência durante 5 minutos, nas temperaturas de 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180°C, respectivamente, concluiu-se que: O teor lipídico, proteico e de carboidratos do coco catolé fornece cerca de 30,23% das necessidades calóricas de uma dieta de 2.000 Kcal de um indivíduo adulto; dentre os modelos testados o de Midilli et al. apresentou os maiores valores de R2 e os menores valores de DQM, para todas as temperaturas ajustando-se melhor aos dados observados; o óleo apresenta baixa possibilidade de deterioração oxidativa e boa estabilidade térmica com início de degradação em 203,1 °C; pela análise das curvas TG/DTG constatou-se a existência de duas etapas de decomposição térmica nos intervalos de 203,1 a 469,9 °C e 469,9 a 586,1 °C, com respectivas perdas de massa de 95,9 e 4,1 %; não houve variações relevantes com o aumento da temperatura no percentual do perfil de ácidos graxos do constituinte C 12:0 (ácido láurico), presente em maior quantidade no óleo. / This research was conducted with the objective of analyzing the lipid composition of catole coconut oil (Syagrus oleracea Mart.) with regard to the fatty acid profile, oil identity and possible variations resulting from thermal degradation. For this purpose the following procedures were performed: chemical and physico-chemical characterization of almond; drying (pre-extraction) at temperatures of 40, 50, 60 and 70 °C; drying kinetics in these temperatures, applying Page, Henderson & Pabis, Lewis, Wang & Singh e Midilli et al. models; extraction of catole coconut oil of fresh almond and coming from 4 levels of drying; physicochemical characterization of oil (water content, acid value, peroxide value, refractive index, saponification number, iodine number and density), according to the methodology proposed by AOCS (2009) and, finally, fatty acid of oil composition (lauric (C12:0), myristic (C14:0), palmitic (C16:0), stearic (C18:0), oleic (C18:l) and linoleic (CI 8:2)) by gas chromatography; evaluating the thermal stability of the catole coconut oil using thermogravimetry (TG); Review of oxidative oil using the Rancimat method; kinematic viscosity determination of oil at 40 °C; evaluation of thermal stress, according to the degradation study of the fatty acids components from catole coconut oil due to the permanence of this by 5 minutes, at temperatures of 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180 °C, respectively. It was concluded: The content of lipid, protein and carbohydrate of catole coconut provides approximately 30.23% of the caloric needs of a 2,000 kcal diet for an adult; among the models tested, Midilli et al. models had the highest R2 values and lower values of DQM, for all temperatures, adjusting better to the observed data; The oil has a low possibility of oxidative deterioration and good thermal stability with degradation beginning at 203.1 °C; by analysis of the TG / DTG curves was observed the existence of two stages of thermal decomposition at intervals from 203.1 to 469.9 °C and 469.9 to 586.1 °C, with respective mass losses of 95.9 and 4,1%; there were no significant variations in percentage of the fatty acid profile of constituent C 12:0 (lauric acid), present in larger quantities in catole coconut oil, with temperature increase.

Customer knowledge plays a vital part in organisations today, particularly in sales and marketing processes, where customers can either be channel partners or final consumers. Managing customer data and/or information across business units, departments, and functions is vital. Frequently, channel partners gather and capture data about downstream customers and consumers that organisations further upstream in the channel require to be incorporated into their information systems in order to allow for management information delivery to their users. In this study, the focus is placed on manufacturing organisations using third party distribution since the flow of information between channel partner organisations in a supply chain (in contrast to the flow of products) provides an important link between organisations and increasingly represents a source of competitive advantage in the marketplace. The purpose of this study is to determine whether there is a significant difference in the use of sales and marketing data marts as management information delivery mechanisms in manufacturing organisations in different industries, particularly the pharmaceuticals and branded consumer products. The case studies presented in this dissertation indicates that there are significant differences between the use of sales and marketing data marts in different manufacturing industries, which can be ascribed to the industry, both directly and indirectly. / Thesis (MIS(Information Science))--University of Pretoria, 2002. / Information Science / MIS / unrestricted

No description available.

Darbas apima banko klientų kredito istorinių duomenų dėsningumų tyrimą. Pirmiausia nagrinėjamos banko duomenų saugyklos, siekiant kuo geriau perprasti bankinius duomenis. Vėliau naudojant banko duomenų imtis, kurios apima kreditų grąžinimo istoriją, siekiama įvertinti klientų nemokumo riziką. Tai atliekama adaptuojant algoritmus bei programinę įrangą duomenų tyrimui, kuris pradedamas nuo informacijos apdorojimo ir paruošimo. Paskui pritaikant įvairius klasifikavimo algoritmus, sudarinėjami modeliai, kuriais siekiama kuo tiksliau suskirstyti turimus duomenis, nustatant nemokius klientus. Taip pat siekiant įvertinti kliento vėluojamų mokėti paskolą dienų skaičių pasitelkiami regresijos algoritmai bei sudarinėjami prognozės modeliai. Taigi darbo metu atlikus numatytus tyrimus, pateikiami duomenų vitrinų modeliai, informacijos srautų schema. Taip pat nurodomi klasifikavimo ir prognozavimo modeliai bei algoritmai, geriausiai įvertinantys duotas duomenų imtis. / This work is about analysing regularities in bank clients historical credit data. So first of all bank information repositories are analyzed to comprehend banks data. Then using data mining algorithms and software for bank data sets, which describes credit repayment history, clients insolvency risk is being tried to estimate. So first step in analyzis is information preprocessing for data mining. Later various classification algorithms is used to make models wich classify our data sets and help to identify insolvent clients as accurate as possible. Besides clasiffication, regression algorithms are analyzed and prediction models are created. These models help to estimate how long client are late to pay deposit. So when researches have been done data marts and data flow schema are presented. Also classification and regressions algorithms and models, which shows best estimation results for our data sets, are introduced.

The purpose of this research is to examine the following research question: how has enforcement of Resource Conservation and Recovery Act (RCRA) adapted to the Big Box business system innovation? Additionally, the study explored the possible nature of regulatory choke points that may emerge from the enforcement of RCRA in the Big Box retail system. This study used contingency theory to establish a foundation for analysis of the Big Box business system innovation through identification of structural elements, external influences, and their subsequent interactions associated with the Big Box retail system in terms of environmental compliance with the RCRA enforced by the United States (US) Environmental Protection Agency (EPA). This research employed an embedded comparative case study design using the comparison of two Big Box firms, Walmart Stores, Inc. and Target Corporation, nationally and for the following states with opposing enforcement strategies: Arizona, Kentucky, Missouri, and Texas. The data used was obtained from third-party federal or firm-maintained sources. Findings indicate Walmart adheres to the structural models developed using contingency theory principles and incurs more impacts from regulatory agencies due to the enforcement of RCRA. Furthermore, it was observed that inspections of the firms are not distributed throughout the organizational structural elements by all states. Additionally, the use of different enforcement strategies resulted in the emergence of regulatory choke points by Arizona, Kentucky, and Texas; however, Missouri appears to balance enforcement without causing a regulatory choke point. This research has identified that the enforcement of RCRA has not universally adapted to the demands of the Big Box business system innovation. Agency implications, firm implications, directions for further research, and continued development of a regulatory choke point theory are discussed.

The main goal of this thesis is to summarize and introduce general theoretical concepts of Data Warehousing by using the systems approach. The thesis defines Data Warehousing and its main areas and delimitates Data Warehousing area in terms of higher-level area called Business Intelligence. It also describes the history of Data Warehousing & Business Intelligence, focuses on key principals of Data Warehouse building and explains the practical applications of this solution. The aim of the practical part is to perform the evaluation of theoretical concepts. Based on that, design and build Data Warehouse in environment of an existing company. The final solution shall include Data Warehouse design, hardware and software platform selection, loading with real data by using ETL services and building of end users reports. The objective of the practical part is also to demonstrate the power of this technology and shall contribute to business decision-making process in this company.