Mc Donalds and Burger King free essay sample

His seemingly endless enthusiasm and constant support helped me thru-out my program at Kent State University. I thank my other committee members Dr. Shawn Banasick and Dr. Chuanrong Zhang for their valuable comments and suggestions. I thank Dr. Milton Harvey and Mrs. Mary Lou Church for their affection and concerns. I am grateful to them and all my friends at McGilvrey Hall, for being the surrogate family during my years at Kent and their continued moral support thereafter. I thank Dr. MunroStasiuk, Dr. Schmidlin, Dr. Sheridan, Dr. Kaplan, Dr. Haley, Dr. Dymon, Dr. Bhardwaj and other faculty members in the Department of Geography for making the atmosphere in the department stimulating for research and academics. The Kent State University Library staffs are acknowledged for their efficiency and availability. A particular thanks to Edith Scarletto, Head of the Map Library, who helped me, gather the initial data required for the research. I would like to thank my friend Sathy for his help in formatting the entire text. We will write a custom essay sample on Mc Donalds and Burger King or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page I am forever indebted to my family, for their blessings and love and who have supported and encouraged me to do my best in all matters of life. Particular thanks, to my husband, Harsha, for his tireless support, love and affection and without whom I would have struggled to find the inspiration and motivation needed to complete this thesis. ix Last but not the least, I dedicate my thesis to my Grandmother â€Å"Jhaiji† who’s Blessings and loving support has encouraged me throughout my academic career and life. Sadly, Jhaiji left for her heavenly abode just a few days before the thesis was submitted. x Chapter 1 Introduction There has been a growing interest among the academia and the private sector for the use of GIS techniques in the analysis and planning of retail store network. Almost without exceptions, various retail organizations need to plan for complex consumer markets and keep up with competitions. Over the past few decades the methodologies used for research of sighting of retail outlets have become more sophisticated as a result of applicable modeling procedures being developed with GIS. This study conducts a retail location analysis of the relationship between the fast-food store performance of McDonald’s and Burger King and the various spatial and socio-economic factors of their respective catchment areas. Analytical procedures in GIS and statistical techniques have been applied to carry out the analysis in this study. In particular, study areas have been partitioned into a set of Thiessen polygons and into various spatial configurations using variable buffer polygons to emulate various spatial configurations of catchment areas (i. e. , trade areas) associated with each fast food store. The socio-economic profiles in the partitioned polygons have been analyzed with a series of regression models. The result of the study brought out a better understanding of how location factors influence the performance of 1 2 the stores as well as how the socio-economic attributes of the catchment areas affect the store revenues. 1. 1 Research Objectives: The main objective of this retail location analysis is to develop and apply methodology for analyzing the relationship between fast food store performance and the various socio-economic and demographic factors with various spatial configurations of their catchment areas in Portage and Summit Counties. The traditional role of GIS in retail demand-and-supply analysis has been to analyze market characteristics such as consumer demand, geodemographics, traffic flow, competitor locations, etc. and to search for an optimal location for a new retail outlet or to close retail outlets in over crowded markets. Knowing the geographical distributions of retail demand and supply is important in conducting marketing analysis using GIS analytical tools. GIS can overlay different data sets onto one another in an integrated environment. GIS analytical tools have been widely applied for exploring the relationships between demand and supply in many types of business practices, including operations of fast food restaurants. However, perhaps due to relatively low real estate costs and flexible rentals or perhaps due to the all too often time lag in adopting newly emerging technology, many retailers do not make use of sophisticated location analysis methods that are now available. Many a times, retailers follow the location decisions previously made by anchor retailers. The choice of a store location has a profound effect on the entire business of a retail operation. For picking an optimal store-site, it is necessary to utilize data of the demographics of that area (income, family size, age, ethnic composition, etc of the population), traffic patterns, and similar kind of retail outlets or competition in the area. These factors are basic to all retail location analysis. GIS tools can help to find the right site along wit h market penetration, market share and trade areas by combining aerial photos/maps, competitors’ locations, geodemographic factors, customer surveys and census data. GIS market analysis tools can also help to determine whether the products match the lifestyle and buying patterns of the customers. In this study- Retail Location Analysis: A Case Study of Burger King McDonald’s in Portage Summit Counties, Ohio, an analysis of catchment areas of the analyzed restaurants has been done using a series of regression models to analyze socio-economic and demographic factors in various spatial configurations of the study area. The study area has been partitioned to a set of Thiessen Polygons and also to sets of spatial configurations by using different buffering zones surrounding the retail outlets to create different proximity polygons for further analysis. Thiessen polygons define individual areas of influence around each service center, or in this case each fast food restaurant, in a set of points/locations of fast food outlets geocoded in such a way that any locations within a Thiessen polygon are closer to the polygon’s centroid (the retail outlet used to make up the polygon) than to any other retail outlet. Buffer polygons have been constructed around the fast food locations 4 based on various assumptions of how far the distances consumers may be willing to travel to receive fast food services. With the various spatial configurations of Thiessen polygons and buffer polygons as defined by the locations of retail outlets, regression models have been constructed to examine the importance of a set of selected socio-economic and geodemographic factors. The different regression models that use different independent variables as structured by both the Thiessen polygons and Buffer polygons have been done to see how well or poorly either of the two approaches capture the variations in the sales volumes of fast food stores. In today’s world of highly competitive market environment, it has become imperative that retailers must make use of spatial analytical technology to acquire new clientele, retain the existing/current customers, to enable market expansion, and to stay abreast with changing consumer tastes and requirements. Advances in GIS technology reiterates the fact that the future success of retail, real estate and restaurants will be determined to get a great extend by using this smart technology. 1. 2 Summary: Many successful businesses in the United States make use of GIS software to integrate, view and analyze data using geography. Use of GIS techniques enables retailers to understand and visualize spatial relationships and improves productivity and effectiveness of the business processes. The use of multiple regressions modeling in this study has been done to identify how the ethnic composition of population and median 5 household income in the service areas of Burger King and McDonald’s restaurants interact with one another to produce a specific sales outcome. Chapter 2 Problem Statements Retail location analysis is an important part in site selection of a retail store. â€Å"A trade area of a retail store is the geographical area from which it draws most of its customers and within which its market penetration is the highest†(Ghosh and McLafferty, 1987). Retail location analysis also helps to determine the focus areas for marketing promotional activities, highlights geographic weaknesses in the customer base and projecting future growth and expansion of the retail services (Berman and Evans, 2001). 2. 1 Size and Shape of the Retail Trade Area: The size of the retail trade area often depends on the nature of goods and services rendered at the retail outlets, along with the geographical distribution of other competing retail outlets. For instance, fast food restaurants like Burger King and McDonald’s sell goods and services that are popular, easily substituted and affordable by the majority of consumers create a smaller retail trade zone as compared to a specialty restaurant. Usually, retail trade zones are not geometrically regular, i. e. , a circle, a square or a polygon. Rather, the shape of the trade zone is based on road networks, geology and topography of the area, land use of the neighboring areas, etc. 6 7 When examining the way customers travel to make retail purchases, it is always necessary to take into consideration the distance that a customer has to travel. The distances that customers may be willing to travel are different, depending upon the type of object to be purchased. The number of trips undertaken by consumers and the travel time will be different based on specialty or commodity product (Salvaneschi, 1996). For purchasing a specialty product, which is generally expensive, unique or long lasting, the consumer is willing to travel over a longer distance. This tends to expand the trading area of that good or service. On the other hand, to purchase everyday supplies or common items consumers often prefer convenience, as the trips for such goods are frequent, distances are short and travel time is brief. For instance, people typically will not drive to another town for fast food, unless they are on way to or back from other destinations. According to consumer behavior studies the time availability of consumers is an important variable in the convenience and fast food market. Therefore, it should be an important part of market strategy (Darian and Cohen, 1995). In this thesis research, the study area is partitioned into polygons representing trade areas for further analysis. Several different approaches to creating trade areas are used. These include trade areas defined as buffer polygons surrounding fast food restaurants with widths of 1, 2 and 5 miles. In addition, partitioning the study area into a set of collectively inclusive but mutually exclusive Thiessen polygons with the restaurants as polygon centroid also generates trade areas. Generating buffers around features is a commonly used analytical procedure in GIS. Most buffering methods create simple-distance bound geometric buffers around the 8 features. Buffers surrounding retail outlets(or other service-rendering establishments) are also known as service areas, hinterlands or market areas and have useful in many geographical applications (Shaw, 1991; Sierra et al. , 1999; Van Wee et al. , 2001). A buffer delineates the area within a specified distance of a feature. It can be created from points, lines or polygons. The output buffers may be lines or polygons depending upon the features and their distance are specified in map units (Price, 2004). Concentric buffers represent the delineation of multiple levels of proximity. For example, different distances of 1 mile, 2 miles and 5 miles from the store can be used to generate buffer polygons around retail outlets. This type of concentric buffers may reveal patterns of market penetration in which the inner buffers often account for the largest proportion of customers while the density of customers decreases as one moves away from the outlet to the subsequent buffers. This distance-decay effect reflects the impact of geographic accessibility on store patronage. The actual size of the trade area for each store varies, depending on the location of the store. The sharper the distancedecay effect, the smaller would be the trade area for each of the fast food store. For this study a regression models are applied that relates sales outcomes (dependent variable) to many factors such as ethnic composition and median household income (independent variable) of population in the retail trade zones of the Burger King and McDonald’s in Portage and Summit Counties. These regression models show that Burger King’s annual sales are better explained by the included independent variables for buffers with widths of 1 and 2 miles than those of McDonald’s sales by the same set of variables. For a 5-mile buffer and Thiessen polygons, sales are better explained for 9 McDonald’s. Ethnic population and median household income for buffer polygons of 1 and 2 miles around the restaurants better explain annual sales for Burger King and polygons of 5-miles for McDonald’s. 2. Summary: Retail location analysis helps in site selection for a business outlet and in determining the performance of retail outlets in the trade area of the store. The trade area of the store reflects the socio-demographic characteristics of the clientele and is thus useful in determining the marketing strategies. The size and the shape of a retail trade area are determined by the nature of goods and services offered. Since fast food restaurants sell goods that can be easily substituted, majority of consumers form a small retail trade area. Ethnic composition of population and the median household income within the buffer polygons constructed around the fast food restaurants indicate how much time and distance consumers drive or travel to patronize these restaurants. Chapter 3 Literature Review During the past three decades, several important advancements have taken place in spatial-data analysis, data storage, retrieval and mapping. Geographic Information Systems have been very useful in tackling spatial analytic approaches and in forming an interface with the field of location science (Church, 2002). Several studies give an overview of the major impacts of GIS on works done in the field of location science in terms of model application, development and various methods that can be used for landuse suitability modeling (Malczewski, 2004). For example: GIS is now the most widely used software for analyzing, visualizing and mapping spatial data such as retail location analysis, transport networks, land-use patterns and census track data. Since GIS can be used to assemble large volumes of data from various sources with different map scales and in different coordinate systems, it is considered an important tool in location analysis. GIS can combine and simultaneously use several databases by transforming them into a common set of database (Pettit and Pullar, 1999). However, the use of GIS in location analysis involves the aspect of accuracy of representing real world situations in a GIS database. The notion of accuracy is the representation of geographical objects and representing socio-economic, cultural and political elements of the environment within which location analysis is done (Church, 2002). Not only is GIS used 10 11 as the source of input data for a location model, it has also been used as a means to present model results (Malczewski, 2004). . 1 GIS for Business and Service Sector Planning: The growing consumer orientation in business and service planning along with advances in GIS and spatial analysis techniques, have led to the promotion of the use of GIS in the area of business and service planning (Longley and Clark, 1995). Several books and articles assess the use of GIS for supporting business a nd service planning at the level of tactical and strategic decision-making (for example: Davies and Clarke, 1994; Benoit and Clarke, 1997; Clarke, 1998; Birkin, et al. , 2002). These studies aim to further explore and promote the use of GIS in the area of business and service planning by demonstrating the benefits of both methodological advances and evidence of benefits in GIS applications and spatial models in GIS. Business planning requires a critical review of geodemographic features and paying attention to requirements posed by endusers (Longley and Clark, 1995). By linking GIS and spatial analysis software, proprietary GIS can be applied to solving problems in several applications like retail location analysis, localized marketing, etc. This involves the integration of spatial models and GIS customized to the specific information needs of retail organizations for specific localities. Thus spatial modeling is used in the explanation and prediction of interaction between demand and supply for retail facilities and the search for suitable locations for retail outlets in an area. The major theme of these studies is the evolution of GIS towards a more flexible 12 and powerful spatial decision support system (DSS) or intelligent GIS (IGIS), applied in several service sectors, including retailing, financial services and health care. Marketing information systems (MKIS) are decision support systems targeted at marketingspecific decisions (Birkin, Clark and Clark, 1996). There is a realizable benefit in integrating GIS with MKIS because of its ability to provide map-based data presentation considered most effective for decision-makers (Ronald and Lawrence, 2004). 3. 2 GIS as a tool for Retail Location Decision: A dynamic and uncertain environment characterizes retailing and retail organizations as needing to plan for the complex consumer markets, while anticipating and reacting to competitions. This competitive nature of retail environment and the large number of techniques made use of by the retailers in locational planning, has led GIS to be used as an aid in strategic retail decision making and applications (Davies and Clark, 1994). GIS is used not just for location and catchments analysis but also for other retail sector issues such as category management, merchandising, marketing communications and relationship marketing (O’Malley, Patterson and Evans, 1997). Existing literature contains a practical framework and other important issues involved in retail network planning. GIS has contributed immensely in improving the efficiency and precision of retail planning and marketing. Since the 1960s methodologies used for retail outlet location research have become more sophisticated 13 as a result of modeling procedures brought about by GIS (Birkin, Clark and Clark, 2002). The US experience shows that the effective utilization of geospatial databases, and the development of decision support systems (DSS), is becoming a significant source of competitive advantage for retailers over those without. Some retailers further explore information opportunities afforded by GIS technology for their business practices. Rather than relying on customer information alone, they are now combining data from several sources simultaneously in a bid to better support their process of decision-making (Birkin, Clark and Clark, 2002). 3. 3 GIS Methodologies for Retail Location Studies: For analyzing the spatial structure of retail activities with location data at micro scale, a number of technologies are now widely available and utilized. These include application of methods such as Probability Density Function (PDF), Decision Support Systems (DSS), Spatial Interaction Models, Network Huff Model, Analysis of Variance (ANOVA) (Byrom, 2005), MATISSE (â€Å"Matching Algorithm, A Technique for Industrial Site Selection and Evaluation†), and RASTT (Retail Aggregate Space Time Trip Model) (Baker, 2003), and others. The Probability Density Function (PDF) of the retail stores is a function of how densities of the subject matters vary over specified dimension. If the specified dimension is time, the probability density function describes how such matter changes their frequencies and distribution over time. Alternatively, if the specified dimension is 14 locations (or space), the probability density function then describes how such matters vary in their spatial patterns. The PDF has been used to analyze the spatial structure of retailing (Sadahiro, 2001). Sadahiro tested the validity of this method by applying this method to the locational data of retail stores in Yokohama. This approach helps to measure the degree of agglomeration, spatial patterns, the relationship between the size and function of retail agglomerations and analyzes the spatial structure of retail agglomeration. Retailers for sales promotion activities and long-term strategic decision-making are increasingly developing GIS as DSS. GIS merges endogenous database by retailers and the exogenous databases sources to introduce retail decision- making and systems implementation (Nasirin and Birks, 2003). As an example, the examination of the experiences of some of the UK based retailers reflecting GIS implementation in retail location analysis shows a highly organized series of process management that has resulted as a result of this application. The Network Huff Model is formulated on a network with the shortest-path distance as an extension of the ordinary Huff (based on Euclidean distance) (Okabe and Okunuki, 2001). This computational method can be used for estimating the demand of retail stores on a street network in a GIS environment. Extending from the gravity model, the original and network Huff models use distances (Euclidean or shortest distance over a network) between retail outlets as inverse weights to estimate divisions of the entire market area into individual trade areas of the retail outlets. The benefits of 15 these models are the ability to meaningfully divide the studied space into a set of trade areas to support retail business operations. MATISSE is a knowledge-based decision support system (KBDSS) based on decision tables that can be used by industrial decision-makers and planners to assess the suitability of potential sites (Witlox, 2003). Witlox explains how a relational approach to the modeling of the site suitability concept can be implemented and tried to find all possible locations that meet the spatial production requirements based on the organizational characteristics of the firm. The growing interest of urban geographers and economic geographers in applying KBS, DSS and integrated system has been largely attributed to the development of computer systems. Computers are able to store, organize and process enormous amount of data as well as make possible the availability and accessibility of the domain-specific knowledge underlying the spatial problem. Witlox has identified three major categories of location factors at the highest level of decision-making. These three conditions are site conditions, investment and operating considerations and make up MATISSE’S head decision table. He points out that the experience with the construction of the system indicates that the developed procedure of knowledge in acquisition worked quite well, however, there are some problems with capturing of compensatory decision-making in terms of the decision table formalism. Nevertheless, the system is at a stage where it can be used in a straightforward manner.