Application of Gis in Small and Medium Enterprises
Metha, Sanjay S., Leipnik, Mark, Maniam, Balasundram, Journal of Business and Entrepreneurship
Geographic Information Systems (GIS) is a powerful technology that allows for spatial and statistical analysis of many issues that pertain to Small and Medium Enterprises (SMEs). In particular, GIS can facilitate unit location in relation to local and regional demographics, allocation of sales territories given a spatially distributed client or customer base, and optimal location of distribution facilities or delivery routes. With respect to competition between potential units and existing units of the same or competing companies, GIS is being incorporated into the decision making process by many retailers. This paper discusses the ways in which GIS can be used by SMEs and discusses current and potential methods and their strengths and limitations. Sources of additional information related to GIS are also provided. Overall, GIS offers SMEs a powerful analytical tool. Increasing use of this technology is to be expected and dissemination of information related to GIS is desirable to promote adoption and appropriate use of GIS by the business community.
Geographic Information Systems (GIS) is a term applied to a suite of computer programs designed to facilitate input, storage, manipulation, and analysis of spatial data and related attribute data (Burough, 1986). GIS have been in use since the mid 1960's (Tomlinson, Calkins, & Marble, 1976). The most common applications of this technology have been in natural resource management (by forestry and water resources management agencies), in infrastructure and facilities management, in land records management, and by property appraisal districts. More recently, many public utilities and municipal governments have embraced the technology (Goodchild, 1991; Estes, 1995). GIS is being applied in thousands of disparate ways, from mapping crime incident locations to tracking nuclear submarines (GIS World, 1996).
Applications in business decision making and market research have been a more recent phenomenon. According to Goodchild (1991, p. 3), "Given the importance of location in marketing, it is perhaps surprising that there has been so little discussion of GIS in the market research literature." With the assistance of this technology, marketers can practice micromarketing (i.e., develop marketing programs targeted to consumers in small geographic regions). Much of the analysis is conducted by large organizations and specialized consulting firms, which developed the speciality term geodemographics (i.e., combining geographic and demographic analysis for market research). The availability of lower cost software and more powerful and inexpensive hardware has prompted increased application of GIS by smaller firms worldwide. In the U.S., these factors, coupled with availability of standardized nationwide data, have led to a proliferation of GIS use in business applications. The availability of the 1990 census of the Topologically Integrated Geographic Encoding and Referencing (TIGER) system data set, developed by the U.S. Census Bureau, has been a particularly important factor (Martin, 1996). TIGER data is used with commercial PC-based GIS packages, such as Mapinfo and ARCVTEW, in business location analysis, in territory allocation studies, in efforts to identify target populations for market segmentation, in advertising, and in direct mail (ESRI, 1997; Francica, 1997). These PC-based GIS packages, some of which cost as little as $250, along with easy to access data of Metropolitan Statistical Areas (MSAs) or counties on CD-ROMs, low cost plotters, and computers, have rendered GIS-based spatial analysis a financially feasible approach for any size business.
For the purpose of this paper, we will use the government's classification of small, medium, and large enterprises (Hodgetts & Kuratko, 1998). The Office of Management and Budget uses number of employees as the unit of measure to segregate business into small (1-99) , medium (100-499), and large (>500). Large firms operating in a range of ownership formats have increasingly embraced GIS for both strategic and tactical planning purposes. Small and Medium Enterprises (SMEs) in general are just starting to adopt GIS. Many large firms are using GIS to gain market share, some of which will inevitably be at the expense of SMEs. One particular area where GIS is efficacious is the analysis of customer demographics, a type of analysis that is crucial to relationship marketing. Relationships and understanding of the unique characteristics of customers is a sustainable competitive advantage many SMEs have over their larger competitors. Therefore, using available tools to further strengthen relationship-based marketing is an essential strategy to remain competitive. Nevertheless, SMEs have not extensively used GIS for marketing or other business-related purposes. The limited use or lack of use of this technology by SMEs is no longer due to financial and technological limitations, but rather a result of limited dissemination of information about the technology.
GIS applications to analysis of SME-related issues have lagged other business applications, which began to utilize GIS in the late 1980's (Goodchild, 1991). Users have adopted the technology later than users with applications involving infrastructure and natural resources management by as much as two decades (Tomlinson, Calkin, & Marble, 1976). However, SMEs are now starting to embrace the technology and use it to study important issues. For example, franchisees benefit from GIS analysis performed by a parent company (franchisor) on their behalf. In many cases rationalizing and optimizing distribution and allocation of territories is performed using GIS to manage various types of spatial analysis. GIS analysis can be used to determine optimal locations, allocate trade areas, streamline distribution, and study the competition between existing units and proposed units. This analysis is performed either internally (i.e., by the franchisors or franchisee) or externally (i.e., by a consulting firm). Because franchising involves a long-term commitment of substantial resources by franchisors and franchisees, the limited effort involved in performing GIS-based analysis can generally be justified. Other SMEs that could benefit from GIS include firms whose delivery of services involves substantial expenditures for travel and whose travel time to customers or client locations is a significant cost factor; for example, pest control, landscaping services, home repair firms, in-home health care providers, florists, and some restaurants. All of these firms could benefit from analysis of customer locations in relation to routes, traffic patterns, and firm locations.
Presently, GIS is primarily used by two distinct groups. On one hand, large organizations (e.g., McDonald's, Texaco, Ford Motor Company, Coca-Cola, Ace Hardware Corporation, and Gold's Gym Inc.) are using GIS to study optimal location of outlets and allocation of territories to distributors. Conversely, specialized consulting firms (e.g., Thompson & Associates and Business Location Research) conduct GIS-based analyses for all large, medium, and small enterprises (Rando, 1998). Most types of analyses utilize the spatial and database analysis capabilities standard in commonly used GIS packages. However, the analysis may also employ data specifically gathered for the study and/or use of specialized algorithms programmed into the GIS.
GIS DATA AND SPATIAL ANALYSIS TOOLS
The TIGER data set from the U.S. Census Bureau, which relates census demographics (e.g., age, income, and education) with street location and addressing data and geographic subdivision boundaries (e.g., zip code zones, census tracts, county and city boundaries), has been the biggest single source of data for business GIS. TIGER line files represent streets as segments structured so that beginning and ending address ranges for the left and right sides of each block are stored in a topological structure. TIGER also contains boundary files that include the boundaries of census blocks, block groups, tracts, and MSAs. In addition, political boundaries, such as county and state boundaries, are stored. The U.S. Census Bureau continually updates streets. The attribute data associated with TIGER includes summary information for census blocks and full detail of population characteristics for block groups and census tracts. Of this demographic data, perhaps that relating to income, employment, age, gender, and housing issues, are most pertinent to business.
The 2000 census, which will allow TIGER demographic data to be updated for the first time since 1992, will be available by spring 2001. Because this data set has become increasingly popular for customer and trade area analysis, the 2000 TIGER will also include, as a separate boundary file, layer data on zip code tabulation areas. So, customer zip code data can be used to determine the demographics of the zip code zones from which customers are derived. Currently, population and demographic characteristics for zip code zones can only be determined indirectly. The census bureau is making the 1998 TIGER data available on CD-ROM; a set of seven CDs, with each CD covering from three to eight states, costs $490. More information of TIGER data can be obtained on the Census Bureau's web site at www.census.gov/geo/www/TIGER/index.html. Although TIGER data is limited by the ten-year intervals between census enumerations, several firms supply enhanced and statistically updated data. Firms such as GDT, BLR, Wessex, First Street, and Claritas supply specialized TIGER data in convenient formats ideal for marketing studies.
Other commercial data sets include locations of business categorized by SIC code, nationwide radio and television broadcast markets and their characteristics, and the Potential Rating Index for Zip Markets (PRIZM) by Claritas Corporation. PRIZM data assumes that people who live near one another are similar in many ways. PRIZM combines census data, nationwide consumer surveys about shopping and lifestyle, and purchase data to identify market segments. PRIZM uses colorful names (e.g., young influentials, blue blood estates, and upstarts and seniors) to describe 62 segments that can be identified by zip codes and census tracts (Levy & Weitz, 1998).
View and Query
Examples of standard spatial analysis functions used in the study of SMEs include, at the most basic level, spatially driven view and query of locations coupled with analysis of associated attribute (descriptive and statistical) data. For example, a partial map of a MSA with major features and transportation routes (typically surface streets possibly categorized with data on traffic densities) would be viewed along with the locations of all restaurants. The GIS data would be queried to identify all fast food outlets serving hamburgers and these would be highlighted, typically by changing the color with which they were represented. All related attribute data for the selected restaurants would be available for further query and analysis. A common use of the view and query function is to help understand the geographic extent and spatial factors affecting trade areas (Patterson, 1997).
A more sophisticated analysis would employ the buffer zone generation function in the GIS to create a region of interest (i.e., a zone extending a specified distance from a feature or features of interest). All features falling within this region of interest would then be identified. Both features (units) within the zone or outside the zone can be easily determined. For example, a buffer zone five miles in radius could be generated around all dry cleaning establishments within a region. The areas not currently within five miles of a dry cleaner would be readily identified. Thus, potentially underserved areas could be identified. The simple case of a circle with a given radius extending out from a point feature representing a unit location can be generated. A more sophisticated buffer zone can also be generated by either using city-block distances or travel times to determine all areas within 20 minutes of a given set of features rather than a simple fixed distance. The latter variation requires that data on average speeds and connectivity of streets (e.g., the existence of one-way streets and non-connecting overpasses) be present in the representation of the street network stored in the GIS (DeMers, 1997). A variant of buffer zone generation has been added into the latest release of the ARC VIEW Business Analyst module from ESRI. This function takes scattered customer location data and generates a statistically derived envelope that encloses a user-specified proportion of all probable customers. This shrink-wrapped polygon, which indicates the trade areas from which most customers are likely to come, can facilitate advertising and locational decisions.
Another important standard function of commercial GIS packages is polygon overlay. In this function, data about a variety of themes is stored in separate coregistered layers (sometimes referred to as levels, coverages, or themes). These layers can be overlaid and the intersection and proximity of features in one layer can be compared with the corresponding feature of other layers. A typical example would involve a GIS that contained transportation information, location of the actual store, proposed locations for a new store, and demographic data of the region, each stored in separate layers (i.e., four layers in total). As illustrated in the previous example, store locations (points) could be overlaid on the street network (lines), which could be overlaid by the polygonal areas representing census tracts, block groups, or zip code zones. Analysis of the combined data would then be feasible. For example, the average income of the residents in census tracts within which there is an existing unit could be readily determined (ESRI, 1997).
Spatial analysis could also be performed on all the data simultaneously. The demographics of areas within a specified distance or travel time or the demographics of underserved areas, could be analyzed and statistically tabulated. For example, the average income for all census block groups within five miles of a dozen potential locations that are currently underserved by competing stores, could be identified. Demographic data can also be analyzed in a stepwise fashion by adding additional polygons to a region of interest by linking contiguous areas together. Thus, an area within a travel time or given region of a community can be constructed and the demographics of the areas constituting this new zone can be combined and analyzed. For example, a region containing 200,000 people can be assembled that is approximately 45 minutes from a potential store location. The demographics of this zone (which may contain many tracts) can then be analyzed. It is even possible to allocate population within a census tract or zip code of an area falling within a zone and outside a zone. Because TIGER data is available for the entire U.S., the limitations of using data solely associated with MSAs can be overcome and new regional groupings can be created that cross state boundaries and take into account economic, cultural, social, physiographic issues and/or incorporate larger or smaller trade areas. For example, the region west of the coastal mountains from Ventura, California to the Mexican border, or a region composed of Dallas, Houston, Austin, and San Antonio, could be created and then analyzed as a whole (i.e., target market). Smaller trade areas that occupy less than a whole census tract or cut across census tract boundaries can also be assembled. This can be accomplished by using block group and even block level data or by assuming an average population density and performing the allocation based on portioning the area of the tract falling inside and outside the prescribed zone(s) (Faintich, 1996).
Determination of trade areas is one of the most promising applications of GIS for SMEs. By mapping customer locations, the spatial distribution of customers can be assessed. Furthermore, by analysis of the related attribute data concerning buying patterns, magnitude of sales, and customer demographics, it is possible to develop customer profiles and to define existing trade areas. Once trade areas have been delineated and characterized, they can be examined to determine marketing and service delivery strategies. For example, if most customers come from a particular community, then promotional efforts can be better targeted on maintaining the locality and increasing the penetration into this commimity. Conversely, if few customers come from a targeted community, the marketing efforts should refocus on this community. The decision of which interpretation to select could be guided by analyzing the demographics of each community or region with TIGER data. If the demographic data indicate that the existing community has relatively few new potential customers, then expansion will require catering to more far-flung customers. Alternatively, if analysis of the demographic data shows that there are many new customers within the community from which most current customers are drawn, then further efforts within that more spatially limited area will likely be more effective (Patterson, 1997).
Trade area delineation can also help firms understand the distances their customers are willing to travel, which can help firms decide whether to grow market share by either (1) running some type of promotional activity or the like to lure additional customers, or (2) opening a new unit. Understanding the trade area is also important for firms that deliver services to their customers. If the bulk of customers are within a limited distance of the firm's office, then abandoning efforts to cater to the few outlying customers may be cost effective because travel times to render service to these customers are frequently not recovered in higher fees. Conversely, if despite limited efforts to target an outlying group of customers, significant business is obtained, then additional promotional efforts and/or establishment of a unit or service dispatch location closer to these customers may be warranted. Regardless, knowing the spatial extent of customers, the attributes of customers, and the demographics of the region, can help to target marketing efforts and streamline the delivery of services for all SMEs (Thrall & Del Valle, 1996).
The demographic data available from the U.S. Census Bureau can be supplemented with information on customer profiles, purchasing habits, or other customer specific data. For example, many large retailers (e.g., Radio Shack, Toys ".3" Us, and Service Merchandise) track the zip codes and/or telephone numbers of all customers and map this with information on store locations and demographics of the areas from which their customers are drawn. Databases on consumer credit, business locations and characteristics, and consumer buying patterns and preferences are also available. These databases are frequently linked and analyzed using GIS (Thrall & Del Valle, 1996).
More sophisticated types of GIS analysis depend on the use of specialized applications software capable of network analysis and/or spatial optimization. The former type of analysis typically involves finding the shortest path through an interconnected network, usually representing streets. The network analysis software can account for travel times, capacities, and information on volume of travel on a given transportation artery (Lupien, 1987). Network modeling has been used along with GIS to route delivery vehicles (e.g., FEDEX, UPS), buses (e.g., Independent School Districts), garbage trucks, emergency response routing of fire trucks, ambulances and police cars, and even recovery of road-killed animals (e.g., city of Austin, Texas Solid Waste Management Department).
Business applications of network analysis/routing models are increasing dramatically, particularly for trucking and delivery of in-home services (Dinkier, 1997). Networking software can be used to determine the aggregate travel time for all persons living within a sales territory or trade area defined by a distance or by the boundaries of a city or region to a potential retail outlet. For example, Sears, Roebuck and Company, uses GIS along with network modeling to route and coordinate home repair and appliance installation services. The Sears application has recently been incorporated in a routing analysis product by ESRI for use with ARCVIEW and ARCVIEW business analyst products.
Spatial optimization is a special form of the mathematical optimization technique employed in the operations research context for decades. Typically, spatial optimization relies on GIS with imbedded algorithms (e.g., simplex algorithms) to optimally locate facilities and/or allocate territories given an objective function and constraint(s) that can be weighted. This approach has been used to locate infrastructure (e.g., roads, pipelines) or facilities (e.g., substations, road maintenance centers). Business applications of GIS linked to spatial optimization programs for location/allocation analysis has been available for over a decade. These applications have been increasing, particularly for large retail outlets (e.g., Walmart, Home Depot) (Goodchild, 1984). Because spatial optimization is complex and requires much site-specific data, it is rarely used in current business applications, although the potential for this approach-for example, in selecting among multiple potential locations for a unit, given a variety of factors-is high.
EXAMPLES OF SPECIFIC BUSINESS APPLICATIONS
Most SMEs currently using GIS are firms in fields such as surveying, landscape architecture, urban planning, and market research. These firms use GIS as a primary part of their business rather than as a tool to determine how to improve distribution and promotion of a product or service. Nevertheless, there are several exceptions.
* An advertising and printing firm specializing in direct mail of political and commercial materials. This three-person firm uses TIGER data along with customer information from mailing lists. In particular, demographic information of registered voters and their voting patterns is used to selectively target direct mail by zip code to potential voters.
* An adventure travel firm tracks its worldwide customer locations with GIS and maintains a web site that uses web-based GIS server technology to allow customers to view a map of potential vacation areas and query attribute data and view video clips over the Internet.
* A commercial real-estate leasing firm employs GIS to identify suitable rental locations for its clientele, which are primarily professional corporations. The GIS stores TIGER-based street maps of the area surrounding rental properties and aerial photographs of the area, and facilitates demographic analysis of the types of customers in proximity to each rental property.
* A manufacturer of customized titanium golf clubs uses detailed demographic data and customer data bases and industry information about golf pro-shops to target advertising and marketing efforts at a selective affluent clientele.
* A check-cashing firm uses TIGER data to identify areas with demographic characteristics that include a high proportion of low-income persons to determine new check-cashing-store locations.
Many specialized businesses have responded to the growth of demand for business application of GIS. Although most clients of consulting firms such as Channel Marketing of Texas, Decision Support Services of New York, Matrix Research of Wisconsin, and Thompson Associates of Michigan, are larger firms, SMEs increasingly use the services and expertise of these firms. For small business people short on time and capital investment, and leery of new technologies, these consultants offer an ideal alterative. According to David Bunten, industry segment manager for retail and commercial real estate at ESRI, another alternative that will be increasingly important in coming years is the Internet. Several firms are offering GIS-based spatial analysis and database access over the Internet. Various simple forms of spatial analysis and mapping are available for modest fees, included geocoding of customer addresses. Confidentiality of customer information submitted to such sites is a major impediment to more rapid adoption of the promising technological approach.
Although many SMEs conduct GIS analysis in-house or rely on consulting firms to perform GIS-based market geodemographic analysis for a fee, an increasing number of firms find that local development agencies (e.g., Small Business Development Centers) or chambers of commerce can do much of the analysis at little or no cost. Sometimes governmental agencies will perform GIS-based analysis on a cost recovery basis. For example, to target telephone solicitations for replacement air conditioners, a small air conditioner sales and service firm in the College Station, Texas area uses the Brazos County appraisal district's GIS to extract information on age, value, and square footage of homes. The appraisal district merely charges for time and materials.
Many franchisors now use GIS. In most franchise systems (e.g., restaurants, hotels and motels, printing services, convenience stores, laundry services, real estate services, and health and beauty aid), a continuous business relationship exists, which includes everything from providing the actual product to providing marketing strategy, site selection, training, operating manuals, cooperative advertising programs, and quality control (U.S. Department of Commerce, 1993). Many fast food franchisors use GIS to estimate the effect on sales and revenues of a proposed unit on existing units. Texaco studies these and other locational issues as a service to franchisees (Long, 1996).
Many franchisors employ specialized consulting firms to conduct similar studies. A specific example of how GIS is being used to assist franchisors in better decision making is illustrated by the activities of Thompson & Associates (a consulting firm based in Ann Arbor, Michigan). This firm specializes in market research applications of GIS and related technologies. Dominos Pizza Inc., Chickfil-A, Burger King Corporation, Baskin-Robbins, Long John Silvers, and Dunkin Donuts, are among its clients that have extensive franchise operations (J. Horton, personal communication, 1998).
Cannibalization of existing franchises and feasibility of new units in developed markets is routinely analyzed by Thompson & Associates, with GIS as a key analytical tool. A typical study involves entering a proposed unit location into a GIS, which contains street location data and various landmarks as features. Locations of competitors may also be included in the GIS. An in-store survey is conducted, where a GIS-generated map of the area is provided to the customer at the time of sale. The customer is asked to state where he/she has come from (i.e., home or office) and to locate their residence or workplace on the map. Typically, the map extends no more than five miles in any direction from the store location. The customer marks his/her approximate residential location on the map and the map is linked with the records of sale information. The map contains a reference grid that is used to group responses. The distance from respondents' residential or workplace reference grid cells to the proposed and/or existing unit is determined by the GIS. This information and the purchase value are used to estimate the distance customers are willing to travel to the existing unit and their expenditures once they arrive.
The impact of new units on existing units can be evaluated with the spatial analysis capabilities of GIS and survey information. To estimate the cannibalization of a new unit on an existing unit, GIS-based information is used with proprietary formulae, which relate sales lost by existing units to customer locations, the value of purchases, and the proposed locations for competing units. For example, units in a mall rarely compete with units in a pad (i.e., freestanding units on mall property). Furthermore, some types of fast food franchise systems (e.g., hamburger chains) draw more proximate customers than other types of chains (e.g., pizza parlors). In other words, customers are willing to travel farther (and spend more) for a pizza than for a hamburger (J. Horton, personal communication, 1998).
The validity of conclusions based on this grid-map-based approach is most defensible in cases where travel in all directions is equally easy and distance is the only factor accounting for fall-off in visitation. In an urban area with a grid-iron street pattern and connected two-way streets, the analysis is more valid than in rural areas or in areas with limited accessibility. This approach may be modified to cover a larger area or to expand the zone of study to include areas that are easier to reach due to freeways or arterial streets, and conversely exclude or change the relative weighting on areas that are less accessible (Martin, 1996).
The use of a grid map to geo-code customer locations is only one option. To geo-code customer locations, zip code information or street address information can be used with built-in utilities in most commercial GIS packages (Culpepper & Johnson, 1998). However, zip code zones are too large for studies of fast-food customers, as these zones may contain entire communities. Instead, zip code plus four data can be used to track such customers (e.g., mail order customers and business from other firms) (Wendelken, 1996). Unfortunately, most consumers are generally ignorant of this more specific information, just as they are ignorant of their census track or residential block group. Street addresses can be readily geo-coded, using TIGER data, with tools built into all commercial GIS packages (Johnson 1998). However, customers' locations are determined/approximated by interpolation, using the beginning and ending street address for each block. Thus, in rural areas where blocks are long and population density is not uniform, this method is inaccurate. A host of other problems affect geo-coding street addresses, including (1) street addresses may not be readily divulged by casual customers, (2) geo-coding of street addresses is problematic in rapidly growing areas, and (3) addresses are more laborious to keypunch than grid coordinates (Lam, 1983). Therefore, as a practical matter, either a zip code, a phone number code, or a grid map coordinate, is likely to be more convenient for geo-coding.
The use of GIS by SMEs is in its infancy. With minor modifications, many spatial analysis techniques developed and used for other applications are applicable to SMEs. As a result of lower costs, greater user friendliness, and more powerful spatial analysis capabilities, GIS software is affordable to an ever increasing number of SMEs (Goodchild, 1991).
There are many sources of additional information on GIS, including the trade journal Business Geographies and other academic journals in Geography. Associations such as the Urban and Regional Information Systems Association (URISA), as well as vendors of GIS software, can help new and potential users. The principal vendors of GIS software used in business applications include Mapinfo Corporation of Chicago, Illinois, The Environmental Systems Research Institute (ESRI) of Redlands, California (vendor of the ARCVIEW, ARCVIEW Business Analyst and ARC/INFO GIS), and Intergraph Corporation of Huntsville, Alabama (vendor of MGE, Geomedia, and VISTAmap).
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Sanjay S. Metha
Sam Houston State University
Sanjay S. Metha is an Assistant Professor of Marketing at Sam Houston State University. He has taught at The University of Texas-Permian Basin, University of North Texas, University of Texas-San Antonio, and Angelo State University. His non-academic experience includes working with several food franchising systems. Dr. Metha has presented over 45 papers at regional, national, and international conferences. He has published over 40 articles in various journals, including Journal of Asian Business, The Cornell H.R.A. Quarterly, Journal of Professional Services Marketing, Health Marketing Quarterly, Journal of Customer Services in Marketing and Management, and Journal of Business Strategies. He holds a B.S. in Mathematics and an M.B.A. from Angelo State University, and an M.S. and Ph.D. from the University of North Texas.
Mark R. Leipnik is an Assistant Professor in Geography at Sam Houston State University and Director of a GIS research lab. His research interests include application of spatial analysis to business and marketing research and analysis of spatial and demographic determinants of consumer behavior. He has also published research concerning the application of GIS to criminal-justice-related issues and the use of GIS and related technologies in environmental assessment. Dr. Leipnik has work as a GIS and/or environmental consultant and/or analyst for local, county, state, and federal governments and for large and small businesses and consulting firms. He holds undergraduate degrees in Business Economics and Environmental Studies from the University of California at Santa Barbara, an M.B.A. from the Jones Graduate School of Administration at Rice University, and a Ph.D. from the University of California at Santa Barbara.
Balasundram Maniam is an Assistant Professor of Finance at Sam Houston State University. He taught at Texas A&M International University from 1991-1997, where he was awarded the Scholar of the Year in the College of Business Administration for 1996-1997. Dr. Maniam's non-academic experience includes working at the Standard Chartered Bank in Malaysia. He holds M.B.A. andaB.S. in computer science from Arkansas State University. He holds a Ph. D. from the University of Mississippi. Dr. Maniam has presented over 50 papers at regional, national, and international conferences. He has published over 40 articles in journals such as Managerial Finance, Journal of Applied Business Research, Industrial Management & Data Systems, Journal of International Finance, and The International Journal of Business Disciplines.…
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Publication information: Article title: Application of Gis in Small and Medium Enterprises. Contributors: Metha, Sanjay S. - Author, Leipnik, Mark - Author, Maniam, Balasundram - Author. Journal title: Journal of Business and Entrepreneurship. Volume: 11. Issue: 2 Publication date: October 1999. Page number: 77+. © Association for Small Business and Entrepreneurship Oct 2008. Provided by ProQuest LLC. All Rights Reserved.
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