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  1. 1. A Classification of Internet Retail Stores Peter Spiller and Gerald L. Lohse Department of Operations and Information Management The Wharton School of the University of Pennsylvania 1319 Steinberg Hall - Dietrich Hall Philadelphia, Pennsylvania 19104-6366 office phone: (215) 898-8541 FAX number: (215) 898-3664 e-mail: lohse@ wharton.upenn.edu Peter Spiller was a visiting scholar at the Wharton School of the University of Pennsylvania,completing his master of industrial engineering from the Technical University of Berlin, Germany.He studied the implications of Internet marketing and electronic commerce. After finishing histhesis in spring 1997, he became a consultant with McKinsey and Co. in Duesseldorf, Germany. Gerald L. Lohse is an Assistant Professor of Operations and Information Management at theWharton School of the University of Pennsylvania. His research interests include electronicshopping and on-line information services, cognitive models of decision processes, impact ofdecision support systems on decision processes, human-computer interaction, and visualization ofinformation. He has published over 30 articles, book chapters, and reports on these topics.Current projects include Internet yellow pages directories, personalized on-line retail catalogs thatdynamically adapt to consumer preferences and basic research on how people perceive andprocess information from displays. Acknowledgment We are grateful to Dr. Vladimir Zwass, Steven O. Kimbrough and two anonymous reviewersfor their helpful comments and suggestions on earlier drafts of this manuscript.
  2. 2. A Classification of Internet Retail StoresAbstract We describe an empirical method for classifying Internet retail sites for electronic commerce.The technique used 35 observable Internet retail site attributes and features of on-line stores asthe raw data to classify the on-line stores into meaningful groups. This paper presents aclassification of on-line retail stores based upon an August 1996 convenience sample of 137Internet retail stores. Descriptive statistics for 44 variables provide a snapshot of key attributesand features of on-line stores. Subsequent cluster and factor analysis identified five distinct Webcatalog interface categories: super stores, promotional stores, plain sales stores, one page storesand product listings. On-line stores differ primarily on the three dimensions: size, serviceofferings, and interface quality. A preponderance of the stores in the study had limited productselection, few service features, and poor interfaces. This categorization provides a betterunderstanding of the strategies pursued in Internet-based marketing and will be helpful forInternet retail store designers as well as for researchers to structure and target further analyses inthis domain. Keywords: Internet marketing; on-line catalog; electronic store fronts; cyberstore; cybermall;electronic shopping; classification; cluster analysis; factor analysis 1
  3. 3. Introduction According to Hoffman, Novak, and Chatterjee [15] on-line retail stores ‘offer direct salesthrough an electronic channel via an electronic catalog or other more innovative format’ As with .the physical store and paper catalogs, on-line stores differ in terms of the variety and types ofproducts offered, price, advertising and promotional efforts and service. On-line stores also differin their catalog or store design and layout. Table 1 summarizes some of the analogies betweenretail stores, paper catalogs and on-line catalogs that aided the variable definition. Insert Table 1 here To help understand the different strategies adopted by retail Web sites, this exploratoryresearch structures and classifies on-line retail stores into meaningful groups. A classification ofretail Web sites will help retail Web site developers understand their design alternatives as well asthe functional requirements of a site. For example, what are potential design alternatives for on-line stores? Is there a set of factors or dimensions (e.g., size or service) that can characterize aretail Web site? How do Internet retail sites differentiate their retail store from the competition?By classifying Internet retail stores, we hope to (1) structure this domain for further systematicinquiry, (2) provide concepts for developing theories, (3) identify anomalies, and (4) predict futureresearch directions [19]. Hoffman, Novak, and Chatterjee [15] and Cappel and Myerscough [7] have suggested broadclassifications of Internet sites for electronic commerce. They classified Internet sites on thehighest possible level that can be summarized as (1) on-line store fronts or catalogs sellingproducts or establishing marketplace awareness, (2) content sites providing information andsupport, and (3) Web traffic control sites, such as malls and search engines. However, both 2
  4. 4. classification schemes are not based on empirical analysis, but upon a functional analysis of broadcharacteristics of commercial Web sites [7, 15]. Our research builds on the classifications outlined above [7, 15] and concentrates on anempirically-based classification of retail on-line stores. A great amount of variation in on-linestores can be observed either on the Internet or on proprietary on-line services. Depending on theretailer’ intention, on-line catalogs focus on a mixture of pure selling, on increasing marketplace sawareness or providing how-to information, or just on providing some incentives to order a papercatalog. Some retailers see their on-line presence as an opportunity for a more personalcommunication with the customer, others use it mainly to learn about the medium and todetermine its possibilities (for a discussion of different catalogers’ goals see [30]). A classificationof on-line store interfaces provides a better understanding of how the electronic stores embodymany of the retail store attributes that customers perceive, such as the atmosphere, convenience,friendliness, tidiness, organization, and product display. Some product categories, such as software, financial services or travel, are widely viewed asbeing successful on the Internet today [23, 29]. To grow on-line sales, the Internet must attract acritical mass of mainstream shoppers, not just addicted specialists shopping for particularproducts; therefore, we chose to focus on on-line retail stores selling womens apparel. Accordingto a Simmons survey of almost 200,000 adults, clothing accounts for the greatest proportion ofcatalog merchandise purchased in the US (34% in 1995) [40]. Women’ apparel and accessories shold the largest share of consumer spending, accounting for 19% of all dollars spent on catalogpurchases. Thus, on-line womens apparel stores are an important segment of retail catalog sales. This paper contributes to a growing literature on electronic commerce. The statisticalsummary of on-line store features and attributes support many of the findings of the survey of 3
  5. 5. consumer reactions to electronic shopping by Jarvenpaa and Todd [16]. Many of the problemsassociated with on-line shopping (e.g., finding products in the store and ordering the product) [9]originate in the retail Web site design. The statistical methodology provides an objective,empirical technique for clustering stores into meaningful groups. The technique also providesdevelopers of commercial Web sites a methodology for eliciting functional requirements for on-line stores by 1) identifying major store design features, 2) organizing on-line stores intomeaningful clusters that invite comparison of various design alternatives and 3) helping clientsunderstand the competitor’ store designs. s This paper describes an empirical study of 137 Internet stores selling women’ apparel that scategorized different on-line sales approaches, user interface styles, and services used in on-linestores. First, the study surveys 44 Internet apparel Web site features. Descriptive statisticssummarize key features of the Web sites surveyed. We measured interface functionality, theordering and payment possibilities, customer service offerings and additional features that attractcustomers to the site. Second, we categorized some general approaches for Internet store designsby analyzing patterns in descriptive attributes of the on-line stores surveyed. Using the survey ofstore features and subsequent cluster and factor analysis, we identify five distinct strategies thatcharacterize Web retail sites. The following section defines on-line store variables used in the study. Next, we describe thesampling method and statistical analyses. After presenting descriptive statistics on characteristicsof the on-line stores in the survey, we discuss the results of our classification of Internet stores.Finally, we raise a set of future design issues for Internet stores. 4
  6. 6. Identification of Characteristic On-line Store Attributes First, we defined a set of variables that describe and differentiate on-line stores. Analogies fromthree domains helped define the variables. The most obvious source for on-line store designvariables is the literature on the design and evaluation of paper-based catalogs. We also measuredattributes shoppers consider when selecting a retail store. Shopping an on-line store is certainlymuch more like shopping a paper catalog than shopping a retail outlet because both involve maildelivery of the purchases and in both cases customers can not touch and smell the items. On theother hand, on-line shopping can incorporate new interactive features to catalog shopping, such asemail inquiries to sales representatives, discussion forums for customers or voice and videoapplications. In this sense, on-line stores are somewhere in between paper catalogs and retailstores, but also add completely new dimensions to shopping like automated price comparisons,unlimited “shelf” spaces, and so on. We recorded these “extra” features but did not use them todescribe or categorize the on-line stores because they were too rare in the stores we surveyed. Inaddition to these attributes, we examined attributes of human computer interfaces such as theinterface clarity and navigation.Paper Catalog Design Variables Research on paper catalog design and evaluation primarily emphasizes the importance oflayout factors to distinguish selling and not selling catalogs. Several authors suggest guidelinesfor design and evaluation [17 (pp. 243-250), 35, 36]. Most of these concentrate on page layout,typography, copy, art-design and reader involvement of the catalogs. However, we did not try tooperationalize any subjective attributes of the layout quality into descriptive variables. Anotherfeature is the “look and feel” of the catalog. This includes picture placements, merchandisegrouping, exclamations, and front and back covers [14, 37]. The paper catalog’ cover page s 5
  7. 7. translates into the on-line catalog’ home page. Both the cover page and the home page aim to sexcite shoppers about the catalog and to generate interest to flip through the pages. Thus, wedescribe home page features, such as the use of images, the home page image size and pagelength. Page lengths were measured in screen pages. The browser was expanded to fill the full screen.We defined a screen page as the browser1 screen area on a 1024x768 17” monitor that can belooked at without scrolling the page2. Pages were scrolled and the number of screen pages fittingon the Web-page was counted. Image sizes represent the sum of all images on one screen page,not including graphical buttons. We did not measure the size of image files in bytes because filesizes largely depend on image resolutions and file formats (gif, jpg, etc.). Further, screen areaallows comparisons between image use in paper media to on-line media. We also measuredwhether images in the catalog were used for product display and menu buttons (image overlay),only for product display, or not at all. We also coded whether the catalog used thumbnail images.This captures the level of catalog sophistication in terms of the image use.Attributes Considered by Shoppers in Retail Store Selection Electronic shopping incorporates many of the same characteristics as normal shopping, such asdepartmental product organization and browsing possibilities. Thus, we measured attributes thatshoppers consider when patronizing a retail store. A great amount of research was done on the evaluation of department stores by consumers.Berry [6] empirically identified a number of attributes using a mail survey. May [22] andLindquist [18] emphasized the importance of the retail stores’ image. Lindquist distinguishedstore image components in functional qualities, such as the merchandise selection, price ranges,credit policy and the store layout, as well as psychological attributes that were associated with the 6
  8. 8. customer’ feelings about the store. In our research, we only concentrated on functional qualities sbecause we did not survey actual customers. More recent research developed models predicting customer store choice behavior. Severalauthors apply consideration set theory to store choice modeling [11, 24, 39]. Others usehierarchical models [1] or threshold models [20] for predicting customers’ store choice behavior.Yet, most authors use store attributes described by Lindquist’ summary [18]. His attribute list is sa compilation from 26 researchers in this field. We adopt these store attribute groups fromLindquist. Merchandise includes product variety, quality, guarantees, and price. Serviceexamines general and sales clerk service, merchandise return, and credit and payment policies.Promotion explores sales, advertising, and promotion displays. Convenience includes generalconvenience in the store, like store organization or store hours. Arnold et al. [2, 3] suggested afew additional store attributes associated with store convenience: ease of navigating through thestore and a fast checkout. A number of Lindquist’ functional attributes were excluded from our sresearch (clientele of the store, physical facilities, store atmosphere, institutional factors). Theyare either irrelevant in the on-line shopping environment or were not related to our survey of on-line catalog features. Merchandise The overall selection of the merchandise can be characterized by the totalnumber of products and the number of product categories. The number of product categories isnot easy to define across different stores since stores offering different types of products will havevery different levels of product categories. In one store, mens, womens and childrens clothingmight represent three high-level product categories; in the next case, it might be business attire,casual and sporting goods. Thus, in order to assess the selection, or broadness of the stores, weused the number of hierarchical store levels as a proxy variable for the number of product 7
  9. 9. categories. Information about the quality of the goods includes guarantees and order informationsuch as information on shipping and returns. Service Interactive customer services are very important for on-line stores [34]. Serviceinformation included whether the store featured any gift services, a frequently asked questionssection, company information, the information content provided for the average product (this canbe considered as the information that is accessible to the customer when talking to a salesrepresentative in the store), a sales representative’ phone number or email, and whether the store sfeatured a feedback section, offered help on the product-size selection or featured extra questionsin the order form to get to know the customer. Promotion Different store promotions were observed. Next to frequent buyer schemes, weconsidered appetizer information like magazines or lotteries and also links to other sites aspromotion. This strategy of magazine catalogs or “Magalogs” is also increasingly used in papercatalogs [26]. Another variable recorded the existence of a “What’ new?” section that can either sbe seen as promotion or general service. Navigation Stores featuring a deep hierarchy of levels had to provide tools to easily navigatethe store. To shop, consumers navigate from the home page to end product pages containingimages and descriptions of the products. Organization is a prerequisite for navigability [21]. Theinterface structure, its different hierarchical levels, and their organization should be apparent tothe user and easy to understand. In on-line stores, navigability can be supported by features suchas search or browse functions. Providing product indices or a site index also greatly enhances thefeeling of “knowing where you are” in an interface. Browsing and navigation capabilities of anInternet retail store also include the number of modes to shop the store (by brand, price,department), and convenience during checkout in an on-line store. 8
  10. 10. User Interface Consistency is considered as one of the most basic usability principles ofcomputer interfaces [28, 31, 38]. Many authors distinguish three types of consistency: internalconsistency, external consistency with other interfaces familiar to the user, and consistency withreal world features [13]. In our research, we surveyed whether consistent menu bars were inplace on all pages. The use of different colors to guide users of a computer interface has received a lot of attention(for color in HCI see [21, 27]; for color in on-line marketing see [10]). We surveyed thebackground color, texture, or pattern of the home page. Texture and color have been shown toinfluence consumer choice behavior3. Help functions are most often related to helping users recover from errors or find a particularfunction in the documentation [28, p. 148]. We analyzed whether stores offered any initial helpfor customers to shop their store. This included help information about the store’ navigation or sthe use of ordering features like a shopping cart function. Altogether, 44 attributes were used to evaluate each on-line stores. Appendix A contains thechecklist that describes all 35 variables used in the cluster and factor analyses. Nine additionalvariables were collected for descriptive statistics only.Methods We used a convenience sample of on-line stores in our survey. Internet search engines such asYahoo, Alta Vista and Infoseek were used to find single stores or comprehensive lists of stores.Two thousand stores were screened by looking either at the description provided by the searchengine or at the store itself. All stores selling more than five items of any kind of clothing werebookmarked for further consideration. Stores selling just one product in different variations (e.g.,T shirt stores) were not considered. Finally, 137 stores offering women’ apparel for sale were s 9
  11. 11. selected for comparison. This convenience sample certainly does not claim to cover all Internetapparel stores, though it should be quite representative for the type of apparel stores currently on-line. The on-line stores were selected from July 15th to July 28th, 19964. The first author coded all store variables using a standardized checklist. Given the rapidlychanging nature of the sites, the reliability of the coding was tested post hoc using a randomsample of 42 of the Web sites 137 sites. The first author and a research assistant recoded allmeasures for each of these 42 Web sites. Using Cronbach alpha, inter-rater reliability over thissubsample was 0.818. Due to resource limitations, we did not count individual words associated with each interfacefeature. Instead, we counted the total number of screen pages and full text lines that containedrelated information. Full text lines were adjusted to reflect different font sizes and frames. A posthoc analysis of a random sample of 14 of the 137 Web sites surveyed found a high correlation (r =0.98) between line count and word count. Thus, line count is a reasonable approximation forword count. Data analysis for the survey is twofold. The first analyses provide descriptive statistics on thedata collected. The second analyses classify and group the surveyed on-line stores using clusterand factor analysis. Principal factor analysis was used to reduce the number of variablesdescribing the on-line stores to a few components. This analysis identified three factors thatcharacterize the on-line stores in the survey. The cluster and factor analysis excluded descriptive variables not considered useful tocharacterize an on-line store. These excluded variables are those coding membershiprequirements, store locators, payment methods, price policies, and whether an order form for printcatalogs was available. This data was only recorded for descriptive reasons, not to classify the 10
  12. 12. on-line stores. The four variables coding different links of the on-line stores were summarized ina single variable (LINKS). Product and non-product related appetizers were summarized in thevariable APPETS.ResultsDescriptive features of on-line apparel stores Store size may influence consumer satisfaction of the on-line shopping experience. Most on-line stores surveyed were reasonably small. 62% have less than 50 products for sale. Only 5%had more than 500 products, including L.L. Bean with approximately 1,600 and ShoppersAdvantage with nearly 250,000 products offered in the on-line store. 78% of the on-line stores offered goods at prices on par with paper catalogs or store prices; noon-line stores appeared to charge a premium for on-line sales. The remaining 22% sold at adiscount on the Internet. These mostly included firms relying solely on the Internet for generatingsales. Company information presented in the on-line stores varied greatly. 15% of the on-line storesprovided a “store locator” to find the company’ retail stores. 22% offered an order form to srequest a printed version of the catalog. Almost one third did not provide any information on thecompany’ history, policies or background; 80% had less than 10 lines of information. This is a ssurprising number since customers want to know who they are dealing with and sending the creditcard information to [12]. For companies with an established reputation, this is not an issue.However, for new “virtual companies” solely operating on the Internet, this is one important wayto establish credibility of the business. Only a few of the on-line stores did not make use of images. About half of the on-line storesnot only used images for product display, but also used them for navigation with image overlay 11
  13. 13. links. This is especially true for home pages and menu bars. Image size averaged about 60 cm2on home and end product pages each which is equivalent to 11% of screen area. The medianimage size lies between 5 and 8% of total screen area. While we measured the quantity of theimages, we did not assess the quality of the images. For example, Ridgon’ description in the Wall sStreet Journal [33] emphasizes the problem of image quality, “. . . after waiting 5 minutes forsome fuzzy pictures to appear, I soon realized that the technology has not caught up with myimagination.” Thus, there are tradeoffs among image size, image quality, and downloading time. Page length, measured in multiples of a normal screen size, was one screen page in most cases.Lists of products on end product pages or lists of product indices on navigation pages werelonger than one screen page. More sophisticated on-line stores tended to fit pages on singlescreens. Most retailers also tried to display only single products on end product pages. Incontrast, mail-order catalogs often group clothing outfits on the same page, making it easier tocoordinate a multi-item purchase. While most on-line stores displayed only one product perimage, some on-line stores displayed sets of products on one picture. Text informationaccompanying the product images varied depending on the product type and on-line store. Whilesome products such as trekking goods or clothing that claimed to be made of special fibers had alot more text information, 50% of the on-line stores surveyed averaged less than three lines of textper product. This small amount is surprising given the ability of an on-line Web site to make a lotof product information easily available to consumers. Due to the limited product range of most on-line stores, the number of hierarchies was three orless for 75% of the on-line stores. Few sites made use of thumbnail images. Thumbnails can befound in on-line stores where customers can be characterized as not being very sure about the 12
  14. 14. actual good which they intend to buy (e.g., gift or lingerie catalogs). Background color was whiteor gray for 42% of the sites. 42% used patterns or texture. Interface navigation is supported by consistent menu bars, search functions and indices.However, only a few stores had product search functions, site or product indices. This is certainlydue to the small size of most stores. Additionally, 24% of the sites surveyed did not haveconsistent menus to navigate through the pages. Ordering is done using a shopping cart metaphor in 30% of cases; only one third accepted off-line orders. Five of all 137 on-line stores required a membership to shop. Already, 29% offeredon-line ordering with credit card data submission as the only way to pay. 59% offered this as anoption. The next most popular payment method was phone ordering, with 12% of the on-linestores relying solely on this alternative. Very few sites offered frequent buyer incentives or usedadditional personal questions in their order form to learn more about their customers. Thisinformation is readily available to on-line service providers such as America Online andCompuServe. It is also becoming available on the Internet as the use of cookie technologybecomes more widespread. Additional customer service information includes help on the interface usage or product sizeselection, FAQ- and “What’ new” - sections. A great number of sites do not provide any of these sservices. These results are very similar to those for interactive service offerings like contactphone numbers or email addresses of sales representatives or feedback sections. 47% of the sitesdo not offer an email address for interactive service. Customers will probably return to an on-line store site if it offers incentives. These appetizers,which attract customers to come back to the site, consist of built in features like magazines,lotteries and links to other interesting sites. 76% of the sites do not offer any appetizers. Those 13
  15. 15. found most often include magazines with product related articles, glossaries, travel or otherproduct related tips, and lottery games. Interestingly, very few stores made use of links between appetizer and product pages or amongdifferent product pages. By not displaying multiple products on the same screen, Internet storesrestrict the comparison of products or the purchase of associated items [5]. Links in Internetstores can overcome this problem by directing the customer to related products. For example, theSpiegel site (www.spiegel.com) features some very elaborate magazine pages with many links toassociated products elsewhere in the store. 55% of the stores do not have any links to other areas and only 8% provide links to more thantwo of the following areas: product related sites, affiliates, regional sites and Internet sources. Afew sites had links to competitors (malls, related Internet shops) but they were not comprehensiveenough to be useful for product and price comparisons. Of the 20% of the sites having somelinks, most had links to search engines, Netscape or MS Explorer, or to the consulting companythat designed the Web site.Cluster analysis of store attributes Prior studies by Day and Heeler [8] and Finn and Louviere [11] used cluster analysis tosegment stores or shopping centers. Day and Heeler used variables like the number of employees,the number of store checkouts, customer demographics, in-store promotions, individual productsales and total store sales to cluster regional retail stores. They also used subsequent principalfactor analysis to reduce the number of variables to a few dimensions. Finn and Louviereconsidered the variables proposed in Arnold et al. [3] to arrive at a segmentation of localshopping centers. Two important store attributes identified by Finn and Louviere were the qualityof service and a wide selection of products. Cluster analysis is a purely empirical method of 14
  16. 16. classification but it is widely employed in marketing for market, product, and customersegmentation. Guidelines for the application of cluster analysis in marketing can be found in Punjand Steward [32]. Before computing the cluster analysis with the SYSTAT Software Package [41], all intervaland ordinal level data were standardized to an interval from zero to one. Standardization makesoverall level and variation comparable across variables [41]. The standardized variables weretransformed into normalized Euclidean distances. Hierarchical clusters were generated using thecombined set of Euclidean distances. In the calculation, we first used the single linkage methodthat adds extreme cases only at the end. However, this procedure did not identify any extremecases for deletion from the data set. It is essential to realize that each clustering technique is biased towards finding clusterspossessing certain characteristics related to cluster size or dispersion (variance). Ward’ minimum svariance technique (as well as the k-means method) tends to find clusters with roughly the samenumber of observations per cluster. Average linkage tends to find clusters of equal variance.Single linkage is notorious for creating groups with many singletons or outliers. Milligan’ (1981) sreview of clustering methods found that the Ward method had the best overall performance. Ittends to produce groups of similar size and usually provides very good results [4, p. 298]. Thus,the actual clustering was conducted using Wards method. A tree diagram of the cluster data shows five distinct clusters with sizes ranging from 20 to 34cases. These initial major branches of the tree are the focus of our discussion. We did notdescribe distinct subgroups within these five major clusters. For each of the five clusters, Table 2shows the means for each of the 35 variables used in the clustering. We make no claim that thereare only five categories of on-line storefronts or that they are optimal in any sense. We merely 15
  17. 17. state that the five categories we have identified are meaningful groups with similarcharacteristics5. Next, we describe each of the five clusters in more detail and give some specificexamples from each cluster. Insert Table 2 here Super Stores The first cluster contains most of the largest stores in terms of total productnumber and number of pages. The score for the average store size is 54% higher than that of thesecond ranked cluster. This is also reflected in the number of levels between home page andproduct pages, where the score is 70% higher. Average information for the customer is mostextensive, including information about the company, about ordering, gift services and “What’snew?” - sections. The number of extra appetizers and customer-care features such as feedback oraccess to sales representatives are also more extensive. Most stores in this cluster have a product index or a search function. This is certainlycorrelated with the stores’ size. Small sites selling only a few products would not need a searchfunction or a product index. Super Stores also provide the most text information for eachproduct. Number of products on product pages is small with most sites displaying only oneproduct per page. The corresponding page length is one screen page in most cases. The L.L. Bean on-line store, Land’ End, Spiegel, Online Sports, J.C. Penney, Shoppers sAdvantage and Service Merchandise can be found in this cluster. They all try to attract customerswith a broad supply of extra information and offer a sophisticated interface with many short pagesand navigation tools. Promotional Store Front Stores in this cluster also score high on customer information andappetizer variables. Most provide information pages about the products, as well as on unrelated 16
  18. 18. topics like the environment (AWEAR) or lifestyle issues (Tara Thralls). The numbers of productsare small. There are no striking features that characterize the user interface. The cluster includes AWEAR, Tara Thralls, Madeleine Vionnet’ Scarves, Wickers, Cheyenne sOutfitters, Real Bodies, Utilities Design Match and The Old Hide House. Most sell very fewproducts, but offer a lot of information on the company or other issues. The focus of the store ismore on promoting general company awareness than on generating on-line sales. Plain Sales Stores The third cluster comprises a number of relatively large stores with no orlittle extra product information. While company information content is still average, most storesof this cluster offer no appetizers, links or additional customer services. On the other hand, interface navigation is quite elaborate for most “Plain Sales” stores. Theyall use thumbnail images. Average size of a product image is largest in this category. Many storesfeature a product index or a browse function. Each product page tends to be long and showsmore than one product in most cases. Stores in this cluster make abundant use of graphics for product display. Products do notrequire explanations, extra information on usage or other additional information. Special WWWfeatures like links are hardly used. This cluster includes many boutiques like Milano, Rhondi,First Lady, Dock of the Bay, Clothes Horse and Leather & Gift Outlet, as well as other stores thatemphasize a product image in their marketing. The latter group includes for example swimwearand lingerie sites, such as Skinzwear or Sophie la Nuit. One Page Stores The two variables distinguishing this cluster from others are the number oflevels between home page and product pages, and the home page length. The number of levels isvery small across most sites in this cluster. Home page length is very large for half of the on-line 17
  19. 19. stores. The on-line stores are all relatively small and feature little extra information, links orappetizers. The cluster actually consists of two different subgroups. The first one is stores relying heavilyon browse functions to navigate among products. For this reason, numbers of hierarchical levelsare low. This group includes boutiques such as Mariam Apparel, Lims or KHLA. The secondgroup contains stores only consisting of one long home page, displaying a list of products belowan introductory statement. Examples for this group are Alaska Mountaineering, Australian Coat,Al’ Texas Jeans and Close To You. However, the cluster also contains a few stores, such as sMarissa Fabiani, that are very extensive and have several levels. Product Listings The number of levels is below average for most stores in this cluster. Littleadditional information and few appetizers are available. Store size is average in comparison toother categories. Product listings have large numbers of products on end product pages and agreater page length. Average product image size is smaller than in any other cluster. Almost noneof the stores feature navigation tools like indices or a search function. Products are found in listson product pages. The average store in this cluster contains only the home page with links to certain productcategories and additionally one long page for each category, displaying a list or directory of smallproduct images and their description. Images are not enlargeable. The page also containsordering information. Stores in this cluster include Rocky Mountain Outfitters, Seabury’sWomen’ Golf, The Dress Connection, Dance Supplies, Full Swing Golf of Alaska and Fisher sHenney Naturals. The example stores mentioned above represent their groups fairly well. Of course, any clusterincludes some cases that might appear to fit better into a different category. Thus, our results 18
  20. 20. require a few caveats as noted above. Table 3 summarizes the main features of the five apparelstore groups identified by our analysis. Insert Table 3 hereStore dimensions from principal factor analysis While cluster analysis identified five broad categories of apparel on-line stores, principal factoranalysis identified a few high level dimensions characterizing the stores. The goal of this analysisis to summarize the data set from the survey as accurately as possible using a few factors. Inaddition to the variables used in the cluster analysis, aggregate variables have been defined byadding the values of binary variables describing a similar category. For example, the threevariables coding the different navigation tools (PRODIDX, SITEIDX, BROWSE) were groupedinto one variable reflecting the stores’ navigation capabilities. The advantage of this procedure isthat it reduces variance in the data, thereby yielding clearer identifiable factors. Not all variablescollected in the survey were used in the factor analysis because variables that do not contribute tobasic factors tend to deteriorate results. For this reason, the variables describing page lengths andpicture sizes were excluded. Table 4 lists the basic variables and all aggregate variables used in theprincipal factor analysis. The factors were rotated automatically using the varimax algorithm. TheScree Test suggests three factors. Results for the three factor model is shown in Table 5. Insert Table 4 here Insert Table 5 here Seven variables load particularly high on the first factor. They describe the total number ofproducts, the number of levels, the amount of company information presented, the amount ofinformation regarding ordering and using the interface, whether there is a search function or 19
  21. 21. different modes to shop in and the number of customer care features like gift services, access tosales representatives and feedback possibilities. The first six variables are related to the store’ssize. Customer care is related to store size. Larger stores provide more customer care features.We refer to the first factor as Store Size. This factor explains 20.4% of the total variance. The second factor, Service, includes the variables LINKS, APPETS, PRINFO andTEXTLENGTH. LINKS counts the number of hyperlinks to related information. APPETSmeasures appetizers such as magazines, travel information, sale items and games. TEXTLENGdescribes the information content available for individual products, PRINFO stands for generalproduct information and services such as help on the product’ size selection. In a retail world sstore, this information is available from the sales clerk. They are all associated with appetizersand information supplied for the customers’convenience and attraction. The third factor comprises variables reflecting the interface design. The variable MENUBARSdescribes whether the store has consistent text or image menu bars on all pages whileIMAGEUSE scores higher if images are not only used for product display but also as navigationand orientation tools in the store. IDXBROW reflects the availability of site and product indicesas well as browsing functions to locate products and information in the store. A “What’ new” or sa FAQ section, summarized in the variable CUSTINFO, also provide the customer withinformation about the store and its features. These variables measure interface quality andconsistency, hence we call the factor Interface Quality. In effect, the three factors Store Size, Customer Services and Interface Quality capture a greatshare of the variance in the data explaining 46.2% of total variance. As there are only threecomponents, we plot the stores in the three dimensional space spanned by the factors (Figure 1). Insert Table 6 here 20
  22. 22. Insert Figure 1 here The five categories found in the cluster analysis can be roughly identified. The Super Storesare clearly segregated from the other stores by their size. The stores within this cluster still differin terms of services and interface appearance. Shoppers Advantage for example is weak on theServices scale as it does not offer many additional features which are not product related. Land’sEnd and Spiegel with their extensive magazines score high on this dimension. L.L. Bean and J.C.Penney have the highest interface quality due to very good menu bars and navigation capabilities.The Service Merchandise Store is quite far away from this group, mainly due to its smaller sizewith only 31 products. We described Promotional Stores as having few products and lots of additional information.Thus, this cluster can be best identified in the Size versus Services projection. Tara Thralls andAWEAR score especially high on the Services dimension while all stores in this cluster have lessthan the average size. Plain Sales Stores can be found below average on the Services dimension.They also score medium to low on the Size dimension as they are still small in comparison to theSuper Stores cluster. The interface is better than average. The One Page Store cluster can be found on the negative axis of all three dimensions.Services, Size and Interface Quality are below average for most stores. Product Listings arebigger in size and offer more services than One Page Stores. Still, the Interface Quality is alsobelow average as can be seen from the graph.Discussion and Summary The Internet and the WWW are changing rapidly. Stores will become more sophisticated andincorporate additional features in the future. Today, video, audio and instant access to highquality images are limited basically by low bandwidths. Future on-line stores can become much 21
  23. 23. more interactive, involving, for example, voice mail inquiries to sales representatives. Intelligenton-line stores might direct consumers automatically to products complementary to a specificperson’ purchase history (for example, see http://www.firefly.net/places.fly). Despite all these spossible changes, retailers will still have different intentions. There is no simple on-line storedesign to accommodate everyone and each retailer will follow a different marketing strategy. This research identified five different on-line store design strategies: Super Stores, PromotionalStores, Plain Sales Stores, One Page Stores and Product Listings. They differ mainly in terms ofthe store’ size, the quantity of extra information and appetizers, and the interface design sincluding consistency, page lengths, image sizes and navigation capabilities. We concentrated on asubset of Internet stores for women’ apparel and did not study non-apparel stores to generalize sour findings. We assume that the basic approaches concerning the stores’ design will not differvery much for other types of stores. Further research is needed to justify any generalizationsempirically. We also did not determine the percentage of on-line retail stores for women’ apparel sthat would be classified as super stores, promotional stores, plain sales stores, one page stores andproduct listings. Additional research is needed to estimate those values. This research makes several contributions. First, it provides an interesting snapshot (July1996) of Internet store features. The descriptive statistics of these on-line store attributessuggests many opportunities to improve the experience for consumers. As noted in Table 6, manyof the descriptive statistics of on-line store attributes resonate with the findings by Jarvenpaa andTodd [16] in their survey of consumer reactions to electronic shopping on the World Wide Web.We also found few products in a typical store, a low overall quality of the pictures in the on-linestore, a dearth of product information in the on-line stores as compared to traditional paper 22
  24. 24. catalogs, only a small amount of store policy information available from an on-line store, and fewinterface features to help find goods and services during goal directed shopping. Second, the paper documents a useful statistical methodology for clustering on-line stores intomeaningful groups. This exploratory data analysis structured the on-line stores into meaningfulgroups using observable attributes and features of on-line stores as the raw data. While differentclustering techniques for analyzing data can and do produce different taxonomies, the goal is toidentify groups that are more similar within a cluster than between clusters. We believe thatclustering provides an objective, empirical basis for classification of Internet sites for electroniccommerce. Third, the technique provides developers of Web sites a methodology for eliciting functionalrequirements from their clients. Our technique will help developers characterize attributes andfeatures of alternative Web sites to 1) facilitate the definition of requirements, 2) identify majordesign features, 3) structure the design space into meaningful clusters to facilitate theconsideration of various design alternatives, and 4) help clients position their Web sitestrategically by understanding the competing on-line store designs. The classification has important implications for on-line store designers. Retailers can comparetheir own catalogs with those in the survey. Measures for each of the 35 variables shown inAppendix A can be added to a data file containing the means shown in Table 2. Using a statisticalpackage like Systat [41], a matrix of Euclidean distances can be computed and the data can beclustered for comparison. Retailers then could compare their catalog to the categories weidentified and determine whether it has key features associated with particular categories. Our list of features and attributes in Table 2 and Appendix A also serves as a check-list forInternet store designers. The check-list can be used to survey peer group on-line stores. Do 23
  25. 25. these on-line stores incorporate new features that appear useful for the consumer? Docompetitors emphasize particular features like special services or high quality images? Comparingthe on-line store with similar competitors’ on-line stores generates ideas about how to improvethe store’ design. s The classification provides a better understanding of the design space in which the storesoperate. It reveals promising Internet store models that are not yet existent. Retailers might notwant to follow one of the standard strategies, but may want to differentiate themselves from thecompetition. The classification can help envision new strategies that combine elements of knownapproaches to uniquely position their on-line store’ marketing strategy. For example, the sclassification did not find any One Page Stores that provided high service levels or a high qualityinterface. This is an anomaly given the large number of niche women’ clothing boutiques in a stypical shopping mall. Why don’ such stores exist on the Internet? Is it because small retail tstores lack the expertise needed to develop high quality on-line stores with good service? Or arethe costs of developing such a Web site prohibitive to small retail stores? Such questions lead to a larger research agenda exploring the effectiveness of different types ofstores identified in our classification. This might involve measuring effectiveness as a function ofsales per unit of traffic, total units of traffic, and total sales for the various types of stores. In theirsurvey of consumer reactions to electronic shopping, Jarvenpaa and Todd [16] did not examinereactions to different types of store separately. It is not clear whether consumers prefer to shop atlarge stores or small stores; at discount stores or department stores or how the type of on-linestore impacts consumer behavior. According to surveys of retail stores [17], large stores shouldattract more customers than small stores. From the perspective of an on-line retailer, it would beimportant to know whether large stores are more effective than small stores in converting store 24
  26. 26. traffic into sales. More generally, this raises the question about the relationship betweeneffectiveness and the categories of stores identified in the research (Super Stores, PromotionalStorefronts, Plain Sales Stores, One Page Stores and Product Listings). By structuring thisdomain of inquiry, the classification delineates on-line stores into similar groups. Effectivenesscan be examined within and between groups. For example, are Super Stores more effective thanPlain Sales Stores? Within stores identified as Super Stores, what determines the effectiveness ofa particular Super Store compared to other Super Stores? In Super Stores, a search feature mayimprove sales. It is not at all clear that a search feature would impact sales in One Page Stores.The store categories permit a more detailed comparison of effectiveness measures among stores.Thus, the classification will aid future research in this domain. Analyzing the evolution of Web stores can lead to insights about what makes a successful on-line store. The three store dimensions we identified raise some questions for future research. Service In electronic commerce, the physical store is replaced by an on-line store. The salesclerk is now a help button or a product index on the screen display. What services can increasecustomer loyalty to an on-line store? Are consumers more likely to make a purchase from an on-line store if it offers more than one mode of shopping? What type of product information attractsconsumers? Table 6 suggests a general lack of attention to customer service in existing on-linestores. Better presentation of customer service information will be essential in the redesign andrevisions of existing on-line stores. Size How important is product variety? Are niche boutiques better than Super Stores? Manyof the stores surveyed were small, but it is not clear whether this is a good or bad characteristic.Consumers are more likely to find specific products in a small on-line store. In large on-linestores, it is not clear how best to facilitate browsing and navigation. More importantly, a better 25
  27. 27. use of links among products in a on-line store may enhance the shopping experience. Consumersmight be more likely to make a purchase when a related product is linked. Interface The user interface design is essential for conducting business online. How easy is itfor the consumer to find what they want in the store? Is there an optimal number of screen levelsconsumers are willing to navigate before giving up and leaving? If they find a product, how easy isit to buy the product online? Clearly there are interface problems associated with navigation,search, and the ordering process in many existing on-line stores. Since sales can be adversely influenced by poor on-line store design, it is essential to quantifythe effects of different layout, organizational, browsing and navigation features on Internet storetraffic and sales. Managers, production staff and merchant partners should not assume customersdo not want an item if it is not selling. Nor should they conclude that a poor response to a givenstore design is because of the merchandising mix. It is important to take a harder look at thepossible relationship between poor selling items and store design and layout. This is especiallyimportant given the replacement of retail store personnel by various user interface features in on-line stores. Could customers be having a tough time wading through the screens? Can customersfind what they want in the stores? Are customers aware of what products are in the stores? Afterall, diligence in browsing a store is not a virtue on-line retailers should expect from theircustomers. Because of the cost involved with launching a new store or changing an existing storedesign, it is important to allocate product development resources to on-line store features thatimprove store traffic and sales. In our future research, we hope to prioritize store redesign effortsto those features with the greatest impact on Internet store traffic and sales by quantifying thebenefits of specific design features. 26
  28. 28. Endnotes1 The browser Netscape Explorer 2.02 showed both Toolbar and Location in the window.2 In the USA, 23.8% of the monitors are 17 inch; 33.4% are 14 inch; 39.9% are 15 inch; and 2.8% are 19 inches or larger (June 24, 1997 PC Magazine; p. 173). The long term trend is towards larger monitors.3 [Mandel and Johnson 1997 http://www-marketing.wharton.upenn.edu/ec/project.html]4 A complete list of all the catalogs used in the study is available from the authors.5 To address concerns about the distance measure, we re-examined the data using two other distance measures. Gamma distances were computed using 1 minus the Goodman-Kruskal gamma correlation coefficient. This is used typically for rank order or ordinal scales. Pearson distances were computed using 1 minus the Pearson product-moment correlation coefficient. This is used typically for any quantitative data. As a basis for comparison we specified 5 clusters (using this option in Systat) and saved the cluster number in a Systat file. We compared the cluster of each on-line store using each new distance metric to the clusters produced using Euclidean distances. Using the Gamma distance measure, 93 stores out of 137 (68%) were in the same cluster as those found using Euclidean distances. Using the Pearson distance measure, 95 stores out of 137 (69%) were in the same cluster as those found using Euclidean distances. More importantly, the interpretation and nature of each cluster (on-line store category) remains substantively intact.6 The browser Netscape Explorer 2.02 showed toolbar and location at a resolution of 1024x768.References1. Ahn, K. H., and Ghosh, A. Hierarchical models of store choice. International Journal of Retailing, 4, 5 (1989), 39-52.2. Arnold, S. J.; Ma, S.; Tigert, D. J. A comparative analysis of determinant attributes in retail store selection. Advances in Consumer Research, Association for Consumer Research, 5 (1977), 663-667.3. Arnold, S. J.; Tae, H. O.; Tigert, D. J. Determinant attributes in retail patronage: seasonal, temporal, regional, and international comparisons. Journal of Marketing Research, 20, 2 (May 1983), 149-157.4. Backhaus, K.; E. Bernd; P. Wulff; W. Rolf. Multivariate Analysemethoden. Berlin: Springer, 8th edition, 1996.5. Baty, J. B. II and Lee, R. M. Intershop: Enhancing the vendor/customer dialectic in electronic shopping. Journal of Management Information Systems, 11(4), (1995), 9-31.6. Berry, L. J. The components of department store image: A theoretical and empirical analysis. Journal of Retailing, 45, 1 (Spring 1969), 3-20.7. Cappel, J. J. and Myerscough, M. A. World Wide Web uses for electronic commerce: Toward a classification scheme. [URL: http://hsb.baylor.edu/html/ramsower/ais.ac.96 /papers/aisor1- 3.htm]. 27
  29. 29. 8. Day, G. S. and Heeler, R. M. Using cluster analysis to improve marketing experiments. Journal of Marketing Research, 8, 3 (August 1971), 340-347.9. Editor. Shopping on the Internet: And a wary Christmas to you. The Economist, December 21, 1996, 33-34, 38.10. English-Zemke, P. Using color in on-line marketing tools. IEEE Transactions on Professional Communication, 31, 2 (June 1988), 70-74.11. Finn, A. and Louviere, J. Shopping-center patronage models: Fashioning a consideration set segmentation solution. Journal of Business Research, 21, 3 (November 1990), 259-275.12. Fram, E. H. and Grady, D. B. Internet buyers - Will the surfers become buyers. Direct Marketing, 58, 6 (October 1995), 63-65.13. Grudin, J. The case against user interface consistency. Communications of the ACM, 32, 10 (October 1989), 1164-1173.14. Hayes, L. Barrie Pace is in the business of style. Catalog Age, 10, 9 (September 1993), 134, 136.15. Hoffman, D. L., Novak, T. P., and Chatterjee, P. Commercial scenarios for the Web: Opportunities and challenges. Journal of Computer-Mediated Communication, Special Issue on Electronic Commerce, 1, 3 (1996) [http://www.usc.edu/dept/annenberg/journal.html].16. Jarvenpaa, S. L. and Todd, P. A. Consumer reactions to electronic shopping on the World Wide Web. International Journal of Electronic Commerce, (1997), 1, 2, 59-88.17. Lewis, H. G. Direct marketing strategies and tactics. Chicago, IL: The Dartnell Corporation, 1992.18. Lindquist, J. D. Meaning of image. Journal of Retailing, 50, 4 (Winter 1974-1975), 29-38.19. Lohse, G. L.; Biolsi, K.; Walker, N.; Rueter, H. H. A classification of visual representations. Communications of the ACM, 37, 12 (December 1994), 36-49.20. Malhotra, N. K. A threshold model of store choice. Journal of Retailing, 59, 2 (Summer 1983), 3-21.21. Marcus, A. Principles of effective visual communication for graphical user interface design, in: Baecker, Ronald M. et al. (ed.). Human-Computer Interaction: Toward the Year 2000. San Francisco, CA: Morgan Kaufmann Publishers, Inc, 1995, 425-441.22. May, E. G. Practical applications of recent retail image research. Journal of Retailing, 50, 4 (Winter 1974-1975), 15-20.23. McCartney, S. Poised for takeoff. Wall Street Journal, New York, (June 17, 1996), R6.24. Miller, H. J. Consumer search and retail analysis. Journal of Retailing, 69, 2 (Summer 1993), 160-192.25. Milligan, G.W. (1981). A review of monte carlo tests of cluster analysis, Multivariate Behavioral Research, 16, 379-407.26. Morris-Lee, J. Push-pull marketing with magalogs. Direct Marketing, 56, 2 (June 1993), 23- 26, 47. 28
  30. 30. 27. Murch, G. Color graphics - blessing or ballyhoo. Excerpt in: Baecker, Ronald M. et al. (ed.). Human-Computer Interaction: Toward the Year 2000. San Francisco, CA: Morgan Kaufmann, (1995), 442-443.28. Nielsen, J. Usability Engineering. Cambridge, MA: Academic Press, 1993.29. O’Connell, V. Stock answer. Wall Street Journal, New York, (June 17, 1996), R8.30. Pellet, J. The future of electronic retail. Direct Marketing, 59, 7 (January 1996), 36-46.31. Polson, P. G. The consequences of consistent and inconsistent user interfaces, in: Guindon, R. (ed.). Cognitive Science and its Application for Human-Computer Interaction. Hillsdale, NJ: Lawrence Erlbaum, (1988), 59-108.32. Punj, G. and Steward, D. W. Cluster analysis in marketing research: Review and suggestions for application. Journal of Marketing Research, 20, 2 (May 1983), 134-148.33. Ridgon, J. Caught in the Web. Wall Street Journal, (June 17, 1996) R14.34. Sandberg, J. Making the sale. Wall Street Journal, (June 17, 1996) R6.35. Schmid, J. Did you like your last catalog?. Catalog Age, 10, 4 (May 1996), 101-104.36. Schmid, J. Good catalog design drives response. Target Marketing, 16, 11 (November 1993), 10-11.37. Shasho-Jones, G. Cover story: A step-by-step guide to designing effective catalog covers. Catalog Age, 8, 11 (November 1991), 107-108.38. Shneiderman, B. Designing the User Interface. Reading, MA: Addison Wesley, 1987.39. Spiggle, S. and Sewall, M. A. A choice sets model of retail selection. Journal of Marketing, 51, 2 (April 1987), 97-111.40. Direct Marketing Association 1995-96 Statistical fact book. New York, NY: Direct Marketing Association, 1997.41. Wilkinson, L. SYSTAT: The System for Statistics. Evanston, IL: SYSTAT, Inc., 1990. 29
  31. 31. Retail Store Paper Catalog On-line CatalogSales clerk service Product descriptions, sales clerk Product descriptions, information pages, gift on the phone, information pages services, special search functionsStore promotion Special offers, lotteries, sale Special offers, on-line games and lotteries, catalogs links to other sites of interest, appetizersStore window displays Front and back cover Catalog home pageStore atmosphere Copy quality, graphics, product Interface consistency, catalog organization, arrangement, perceived image interface and graphics qualityAisle products Products on first 2-4 spreads and Featured products on each hierarchical level of the middle spread the catalogStore layout Page and product arrangement Screen depth, browse and search functions, indices, image mapsNumber of floors in the Catalog organization Hierarchical levels of the catalogstoreNumber of store entrances Frequency of mailings, number of Number of links to a particular catalogand store outlets / branches unique catalogs mailedCheckout cashier Order form, 1-800 phone number On-line shopping basket or order formSee and touch of the Limited to image quality and Limited to image quality and description,merchandise description future potential for sound and video Table 1 Analogies between retail stores, paper catalogs and on-line catalogs 30
  32. 32. Plain One Super Promotional Sales Page ProductNo. Variable* Min Max Stores Stores Stores Stores Listings Number Stores 20 30 34 30 23 1 CATSIZE 1 5 3.35 2.07 2.53 2.10 2.22 2 HIERARCH 0 6 4.08 2.80 2.78 2.45 2.54 3 ORDERINF 0 4 1.75 1.00 0.32 0.57 0.17 4 GIFTSERV 0 1 0.35 0.00 0.00 0.03 0.04 5 FAQ 0 1 0.15 0.07 0.15 0.03 0.04 6 COMPINFO 0 4 2.85 2.07 1.32 1.37 1.39 7 TEXTLENG 0 20 5.10 4.10 3.26 2.92 2.98 8 SALEREP 0 1 0.65 0.43 0.53 0.00 1.00 9 FEEDBACK 0 1 0.90 0.77 0.06 0.00 0.00 10 GETKNOW 0 1 0.15 0.03 0.03 0.03 0.00 11 XTRAINF 0 1 0.30 0.53 0.00 0.07 0.09 12 SIZESELE 0 1 0.55 0.17 0.26 0.13 0.17 13 FREQBUY 0 1 0.30 0.00 0.03 0.03 0.04 14 LINKS 0 8 1.30 1.33 0.82 0.60 0.87 15 BANNER 0 1 0.25 0.00 0.12 0.10 0.04 16 APPETS 0 4 1.50 0.73 0.29 0.60 0.35 17 WHATNEW 0 1 0.30 0.00 0.06 0.00 0.04 18 SHOPMOD 0 1 0.35 0.03 0.00 0.03 0.00 19 SITEIDX 0 1 0.10 0.00 0.03 0.10 0.00 20 PRODIDX 0 2 1.20 0.40 0.85 0.10 0.04 21 PRODSEAR 0 1 0.20 0.03 0.03 0.00 0.04 22 BROWSE 0 1 0.20 0.17 0.29 0.30 0.04 23 PRODLINK 0 1 0.15 0.10 0.03 0.03 0.00 24 PRODSELE 0 3 1.85 1.23 1.18 1.27 1.17 25 PRODNUMB 1 25 3.18 3.15 4.43 3.52 5.37 26 MENUBARS 0 2 1.55 1.27 1.00 0.90 1.04 27 BACKGRO 0 3 1.55 1.03 1.15 1.27 0.70 28 HELPINTF 0 4 1.45 0.20 0.12 0.00 0.13 29 HPLENGTH 0 11 1.34 1.33 1.83 2.55 1.72 30 NAVILENG 0 8 1.84 1.44 1.04 0.51 0.72 31 EPROPLEN 0 12 1.64 1.94 2.14 1.70 2.63 32 HPIMAG 0 1 0.15 0.12 0.11 0.11 0.09 33 PRODIMAG 0 1 0.11 0.08 0.16 0.10 0.07 34 IMAGEUSE 0 2 1.95 1.47 1.50 1.30 1.17 35 THUMBS 0 1 0.85 0.43 1.00 0.00 0.00 * See Appendix A for a complete variable descriptionTable 2: Means for each of the five on-line store categories for all 35 variables used in thecluster analysis 31
  33. 33. Strategy Main Features Examples1. Super Store •large catalog size L.L.Bean, Land’ End, Spiegel, Online s(20 stores) •ample extra information and Sports, J.C. Penney, Shoppers Advantage, appetizers (magazines) Service Merchandise •navigation tools •pages fit on one screen page2. Promotional Store •limited product range AWEAR, Tara Thralls, Madeleine Vionnet’ s,(30 stores) •extensive company information Cheyenne Outfitters, Real Bodies, Utilities •ample appetizers and links Design Match, The Old Hide House3. Plain Sales Catalog •medium or large catalog size Milano, Rhondi, First Lady, Dock of the Bay,(34 stores) •large images, use of thumbnails Clothes Horse, Leather & Gift Outlet, • appetizers or links no Skinzwear, Sophie la Nuit4. One Page Catalog •limited catalog size Mariam Apparel, Lims, KHLA; Alaska(30 stores) •few hierarchical levels Mountaineering, Australian Coat, Al’ Texas s •product browse function Jeans, Close To You • page catalog one5. Product Listing •medium catalog size Rocky Mountain Outfitters, Seabury’ The s,(23 stores) •long product pages / lists Dress Connection, Dance Supplies, Full •small product images Swing Golf of Alaska, Fisher Henney •few hierarchical levels Naturals Table 3 Classification of five major on-line catalog strategies 32
  34. 34. Aggregate Variable Consists of Variables (simple unweighted sum)Interface Navigation SITEIDX, PRODIDX, BROWSE[IDXBROW] site-index, product-index, browse functionSearch Tool and Shopping Modes PRODSEAR, SHOPMOD[PSEASHMO] product search function, shopping in more than one mode?Customer Information WHATNEW, FAQ[CUSTINFO] what’ new section, FAQ section sCustomer Care GETKNOW, SALEREP, FEEDBACK, GIFTSERV[CUSTCARE] get to know the customer questions, access to sales rep, feedback section, gift servicesAppetizers APPET1, APPET2[APPETS] product related appetizers, not product related appetizersAll Links to Other Areas PRODLKS, COMPLKS, REGIOLKS, INTERLKS[LINKS] product, competitor, geographical region or Internet linksAdditional Product Information XTRAINFO, SIZESELE[PRINFO] extra product information, help on product size selectionBasic Variables used in Principal Factor AnalysisCATSIZE COMPINFO IMAGEUSE TEXTLENGHIERARCH MENUBARS HELPINTF ORDERINF Table 4 Fifteen variables used in the principal factor analysis 33
  35. 35. Factor 1 Factor 2 Factor 3Size Services Interface QualityCATSIZE LINKS MENUBARSHIERARCH APPETS IMAGEUSECOMPINFO PRINFO CUSTINFOORDERINFO TEXTLENG IDXBROWHELPINTFPSEASHMOCUSTCARE 20.4% 14.0% 11.8%Table 5 Results of the factor analysis (percentages denote explained variances) 34
  36. 36. Catalog Attribute Descriptive Statistics Consumer Reaction from Jarvenpaa & Todd (1997) MerchandiseCatalog size: number of •62% have less than 50 products •31% were disappointed withdifferent products •Only 5% had more than 500 products product varietyPrice •78% had prices comparable with paper •18% felt that prices seem to be catalogs or store prices higher on the Web •22% sold at a discountQuality •33% did not provide any company policy •Absence of familiar brand information (returns, guarantees, etc). name merchandiseProduct descriptions, use of •50% had less than 3 lines of text per •Need better descriptions andimages, text length product pictures of the products ServiceGeneral service, gift services, •Only 19% offered extra product information •80% had at least one negativeFAQ on product related •80% had less than 10 lines of text about the comment about customerquestions, and information company’ history, reputation, policies, etc. s service on the Webabout the company •Only 9% had a FAQ section •Hidden shipping costsSales clerk service, phone •47% did not offer email for interactive •Merchants did not anticipatenumber, email of sales reps, service consumer queries and requestsfeedback section, get to know •95% did not have links on end product for policy informationthe customer questions, help pages to related products •Responsiveness determined ifon product-size selection •Only 25% had help for size selection it was a good or bad siteOrder information, •Only 30% used a shopping cart metaphor •41% noted a lack ofmerchandise return, credit, •Most required manual re-entry of product information on returns,and payment policies information onto an order form delivery time, guarantees, etc.Fast checkout •41% did not offer on-line ordering with a •Some sites had hard to follow credit card ordering directions PromotionAdvertising links to other •76% did not offer incentives or appetizers to •“You never get a sale”sites, use of banner ads, attract and retain customers •“You never get to see head-to-appetizers to attract •Only 6% offered a “What’ New” section s head competition that you seecustomers, sales promotion in a mall” Interface QualityNavigation indices and •4% had a site index •44% felt goal directedbrowsing, ease to find items, •6% had a product search function shopping and productnumber of shopping modes, •Only 6% allowed multiple shopping modes comparisons are difficult onconvenience •Only 22% had a product browsing function the WebConsistency of the interface •24% did not have consistent menu bars •“This is not for computer illiterate people.”Providing help •12% had on-line help for interface usage •“I had places I wanted to go and couldn’ understand how” tUse of images for navigation, •Less than 8% of the total screen area •Inability to adequatelysize of images, enlargeable contained images or graphics visualize the productsthumbnail pictures •Over 50% used images for navigation Table 6 Comparison of attributes of on-line catalogs with a survey of consumer reactions to electronic shopping from Jarvenpaa and Todd (1997) 35
  37. 37. Super Stores Promotional Stores Product Listings Plain Sales Stores One Page StoresFigure 1 Apparel Internet catalogs classified by Size, Services and Interface (To accommodate store names, only a subset of the 137 stores are displayed) 36
  38. 38. Appendix A Variables used in the Internet survey Data Coding and examples Data Collection / CommentsMerchandise1. size, number of different 1: 6 - 10 products The number was counted and summed products [CATSIZE] 2: 11-50 products for all product categories in the on-line 3: 51 - 100 products store. 4: 101 - 500 products 5: 501 - products2. number of hierarchies average number The straightest path through the general between home page and transition network of the catalog was end product page evaluated for 6 different products. [HIERARCH]3. order information - 0: less than 4 lines The total number of lines providingshipping / ordering / product 1: 5-20 lines information about guarantees and the quality / return information 2: 21 - 40 lines ordering process was counted.[ORDERINF] 3: 41 - 60 lines 4: 61 lines & greaterService4. gift services 0: no Wrapping, gift certificates[GIFTSERV] 1: yes5. FAQ on product related 0: no Did the store provide an frequently questions [FAQ] 1: yes asked questions section on product related questions or the company?6. company information 0: 0 - 11 lines The total number of lines providingmission statement, history, 1: 11 - 20 lines company information was counted. policies 2: 21 - 40 lines 3: 41 - 80 lines[COMPINFO] 4: 81 lines & greater7. text length on “end product average number of lines for 6 Any full line on the screen was counted pages” end product pages as one line, single items of information[TEXTLENG] (e.g. “waterproofed”) were counted as 0.5 lines.8. phone number / email of 0: none Did the on-line store offer any way to sales reps 1: yes contact a sales representative, by email,[SALEREP] phone or fax?9. feedback section 0: no Did the retailer ask for feedback (email[FEEDBACK] 1: yes comments (mailto), blank email form, email questionnaire, phone)?10. extra get-to-know-the- 0: no Any personal questions about the buyer customer questions 1: yes on the order form were rated 1. [GETKNOW]11. extra product information 0: no Any information on care, maintenance,[XTRAINF] 1: yes use of products etc. was rated 1.12. help on product-size 0: no Stores presenting tables, figures, etc. selection [SIZESELE] 1: yes with product sizes were rated 1. 37
  39. 39. Promotion13. frequent buyer incentives 0: no Any discounts or gifts associated with[FREQBUY] 1: yes higher sales per customer.14. links to product related Four variables, one for each of Some sites offered a large number of information sites, the link categories (see left), links in one of the categories (e.g., to competitors, regional were defined as follows: general Internet sites like search sources or general Internet 0: no links engines). This was considered less sites 1: 1 - 5 links useful than offering links in different[LINKS] 2: 6 or more links categories which was reflected in the The variables were summed to coding scheme. The variable LINKS form one LINKS variable. eventually took values from 0 to 8.15. extra banner ads on the 0: no Ads for other companies or the store pages [BANNER] 1: yes itself were rated 1.16. product-related and non- Two variables, one for product- Examples for product-related appetizers product related appetizers related and one for non-product- are: hot product of the day, mailing list, related appetizers were coded: sale items.[APPETS] 0: none Examples for non-product-related 1: one closely product related appetizers are: magazines, travel item information, guest books, games. 2: more than one item or total content more than 10 pages The variables were summed to form one APPETS variable.17. “What’ new” section s 0: no A section introducing new products,[WHATNEW] 1: yes catalog features or news was rated 1.Other Store Variables18. shopping in different 0: no Were there different strategies the modes 1: yes customer can use to find a product (store[SHOPMOD] organization by product category, gender, price level, etc.)?19. site index 0: no A site index gives an overview of the[SITEIDX] 1: yes structure and hierarchies of all pages.20. product index 0: no Stores providing comprehensive lists of 1: yes, text all their products, in addition to the[PRODIDX] 2: yes, with thumbnails departmental product organization, were rated 1 or 2.21. product search function 0: no Any inter-catalog search function was[PRODSEAR] 1: yes rated 1.22. product image browse 0: no A browse function that enables shoppers function [BROWSE] 1: yes to navigate directly between products, without changing store levels.23. links between related 0: no Links between related products or links product pages 1: yes between products and additional[PRODLINK] information (magazine pages, games, etc.) was rated 124. product selection 0: off-line 1 refers to an on-line form which has to 1: on-line form to fill manually be filled out manually; 2 was a form[PRODSELE] 2: on-line clickable form consisting of pull-down menus listing 3: shopping cart all product options. An on-line shopping cart only requires the shopper to hit an order button on end product pages. 38
  40. 40. Interface Variables25. number of products on end average number The number was counted on 6 different product page end product pages. [PRODNUMB]26. menu bars on all pages 0: no Menu bars had a home page button and 1: yes, text at least one further navigation button.[MENUBARS] 2: yes, image overlays The buttons had to be placed consistently on all pages of one level. Buttons using image overlays were considered more sophisticated.27. background color or 0: no color, white /gray In case of changing background colors pattern 1: one color on different pages, the modal level was 2: pattern recorded.[BACKGRO] 3: texture28. help on interface usage 0: no Any full line of text on the screen was 1: 1 - 11 lines counted as one line.[HELPINTF] 2: 11 - 20 lines 3: 21 - 40 lines 4: 41 - lines29. page length of home page length in screen pages Taking the normal screen size as reference6, total page length was[HPLENGTH] estimated as a multiple of this size by scrolling the page down. If the interface used inter-page links, page length was only measured down to the line where the first link pointed to.30. page length of navigation average length in screen pages average length in screen pages page [NAVILENG]31. page length of end product average length in screen pages average length in screen pages page [EPROPLEN]32. image size on the home measured as a percentage of the Image size was determined by page total visible screen area (549 measuring width and height of all home square cm) page images, excluding buttons.[HPIMAG]33. image size on “end product mean of 6 pictures; measured as Image size was determined by pages” a percentage of the total visible measuring width and height of an[PRODIMAG] screen area average end product image.34. use of images 0: none Did the catalog use images for product 1: to show products only display only, or for graphical buttons,[IMAGEUSE] 2: image overlay and product too? display35. use of enlargeable thumb- 0: no The use of any enlargeable image was nail pictures [THUMBS] 1: yes rated 1. 39