E-Commerce Web Objects:
Importance and Expected Placement
Jeremy Markum Busyandfit.com Richard H. Hall
Abstract
This study had two goals: 1) to determine the importance of given e-commerce web objects for users making online buying decisions, and 2) to identify users expectations as to where they would most expect these objects to be located on a well-designed
e-commerce site. Ninety-nine participants were recruited from undergraduate classes at the University of Missouri – Rolla and one hundred three participants, who were members of an on-line fitness e-commerce site (busyandfit.com), volunteered. Participants first rated ten e-commerce web objects on the degree to which each is important in making buying decisions. They then selected one of nine (3 x 3) quadrant locations on a web page, where they would most expect a given web object to be located on a well-designed e-commerce site. The results indicate that the most important objects to shoppers are those that allow them to make purchases quickly in a few steps, while those that encourage exploration and provide help are rated much lower. As for expected locations, user location schemas are largely consistent for e-commerce web object locations, and these expectations are also consistent with previous research. Therefore, the placement of objects in the design of an e-commerce site, consistent with user expectations, should be a relatively
Introduction
E-Commerce Site Design
Ecommerce, both business-to-business (B2B), and online consumer shopping, are becoming an increasingly large part of the economy. Wired News (2003) reports that sales over the internet increased 28.2 percent in the fourth quarter of 2002, compared to fourth quarter in 2001, for a total of $14.33 billion dollars.
There are many reasons for Ecommerce’s growing popularity with consumers (Nielsen, 1999):
• Easy to place an order
• Large selection of products
• Cheaper prices
• Faster service & delivery
• Detailed and clear information about what is being offered
• No sales pressure
• Easy payment procedures
One report posits that ecommerce visitor satisfaction is as high as 64% (Retail Forward, 2002). However, according to this same report, “…only 2 percent of online shoppers report their online shopping experience to be 'frustration-free'.” Further, it’s important to note that a very small percentage of shoppers actually shop online. For example,
according to Bernard (2002)
“Only a small percentage of people actually purchase merchandise online. In fact, the percentage of people that actually buy online is approximately 3 to 5 percent,”
This frustration is most likely limiting Ecommerce sales, and slowing the overall adoption of the online shopping process by most consumers. Incidentally, this 3-5 percent figure given by Bernard represents what is known as a conversion rate. The conversion rate for an ecommerce site is simply the number of unique visitors to the site, divided by the total number of sales. For traditional bricks and mortar storefronts, conversion approaches 70 percent or more (Bernard, 2002).
Several researchers and web “gurus” have proposed reasons for ecommerce’s lackluster performance in converting visitors to customers. For example, Copas (2003) suggest that one reason for the lack of customers is a lack of understanding of Internet shoppers’ personalities. Geissler, Zinkhan, and Watson (2001), offer another explanation. They propose that ineffective communication due to ill-managed information complexity is to blame for many failures on the web. They report that complexity:
• increases with the # of distinguishable elements
• varies inversely with the degree to which several elements are responded to as a unit. Chaparro (2002) suggests that poor scenario design present in many shopping carts on the web might be to blame. Chaparro (2002) illustrates the unique challenges shopping cart designers face when trying to streamline the online shopping process, by comparing online shopping task-flow, to traditional shopping. In her prototype scenario, shopping in a store requires seven steps, while shopping via the web requires 13 including, for example: create an account, enter name and email, enter shipping address, enter billing address, choose shipping method, enter credit card info, and review order and final price. Not only does completing a transaction online require more steps, but some of the steps are not necessarily intuitive. Additionally, steps such as having to enter a shipping address, and credit card info make some visitors nervous with issues like security. The Los Angeles News Room (2003) lists the following as primary concerns for people shopping online:
• security
• privacy
• too complicated
• don’t want to wait for delivery
A third explanation for the difficulties faced by online shoppers at ecommerce web sites is that the structure of web sites is inconsistent with users’ schemas.
“An essential ingredient in constructing the content of a website is knowing the typical users' mental model or 'schema' for the characteristic location of web objects on a website. Knowledge of this schema and constructing a site that reflects this should aid in the site's accessibility. This, in turn, should produce more accurate and faster information retrieval, as well as greater satisfaction with the site [emphasis added]. However, little is known about the average users' schema for the location of web objects on a typical website.” (Bernard, 2001)
Perhaps this lack of knowledge concerning where to place web object, which can be thought of as a noun or logical grouping of either information or function within a web page, is contributing to the ecommerce conversion problem.
Jacob Nielsen (1999) proposes that a fundamental problem is that designers violate users expectations by not following web conventions, which are established by the most visited web sites.
“Web design is easy: If you are thinking about how to design a certain page element, all you have to do is to look at the twenty most-visited sites on the Internet and see how they do it.
• If 90% or more of the big sites do things in a single way, then this is the de-facto standard and you have to comply. Only deviate from a design standard if your alternative design has at least 100% higher measured usability.
• If 60-90% of the big sites do things in a single way, then this is a
strong convention and you should comply unless your alternative design has at least 50% higher measured usability.
• If less than 60% of the big sites do things in a single way, then there are no dominant conventions yet and you are free to design in an alternative way. Even so, if there are a few options, each of which are used by at least 20% of big sites, you should limit yourself to choosing one of these reasonably well-known designs unless your alternative design has at least 25% higher measured usability than the best of the choices used by the big sites.” (Nielsen, 1999)
Within the context of Bernard’s schema explanation, Nielson is suggesting that users’ schemas are formed through their experience with commonly visited web sites.
Such a view is consistent with Shim, Shin, & Nottingham, (2002) who argue that online shopper behavior and schemas are learned from past interactions with ecommerce sites. They suggest that, if past learning helps build a particular mental model for how an ecommerce site should operate, then future visits to sites which don’t conform to this model could lead shoppers to abandon these new sites.
Empirical Investigation of User Schemas and Web Objects
The question of the ideal place to put web objects in order to conform to a visitor’s schema is in part answered by the following advice from Bernard (2000). In his report he suggests:
“An important step in organizing the content of a website is to place the information on the website according to how individuals typically view information.”
Bernard then goes on to reference a study by the Poynter Institute, which found that website visitors first focused on headlines, then article summaries, and finally captions.
“They [the Poynter Institute] also found that users were twice as likely to fixate on the text rather than on the images in their initial visit to a site. This, of course, conflicts with the common practice of using images to convey important information in lieu of text. Consequently, the best way to insure that a user will grasp the content and meaning of a web page is to have well formulated titles and headers that are placed around the images.” (Bernard, 2000)
Then, in the same article, and borrowing from Jacob Nielsen, Bernard reports: “Moreover, users often choose to ignore an area within a web page because it is in a low information-priority area, such as the bottom of a page (Nielsen, 1999a) or they believe the information is typically of no real interest to them.” (Bernard, 2000)
While expert guidelines and heuristics are useful, it’s important, of course, to empirically investigate users’ web object schemas either through self-report or more specific behaviors.
One might think that the best way to determine how to arrange ecommerce web objects in alignment with user’s schemas for online shopping would be to just show them several different layouts and then ask them which one they prefer. Nielsen (1998) warns of the danger in this approach, however. He reports how in one study, when users were asked to rate their level of preference for different web page templates, they preferred a template which actually hindered their performance on an assigned task. For this reason, a bottom up approach, which allows the user to “create” a template, would be preferable. An approach which utilizes the preference of multiple users would seem to have an even a better chance of capturing a “universal” schema that a majority of ecommerce site visitors would intuitively grasp.
Bernard (2001) takes just such an approach to determining users’ schemas in terms of expected web-object location. He had participants place cards representing web objects on an x-y placement grid in order to establish participant’s geometric expectations for the placement of web objects. In this study he found that there was little difference in where experienced and novice users expected web objects to be and that, further, all users were generally consistent in where they expected the objects to be located. The expected locations of the five web-objects were as follows: 1) Home page was expected in the upper left corner; 2) internal links were expected on the left side of the page; 3) external links were expected on the right side, or bottom of the lefts side of the page; 4) internal search engine was expected the upper and bottom-center of the page (this was less consistent); and 5) advertisements were expected in the upper middle of the page.
Our research was a replication and extension of Bernard’s approach. The research extended Bernard’s work in three basic ways: First, our geometric grid method differed in that we simplified the participants’ task, so that they could focus more on the content and expected location. Bernard used a physical grid with cards, while our study was completed online with participants viewing a real web page divided into quadrants. In addition, we included nine quadrants rather than fifty-six used in Bernard’s research, we did not allow web-objects to overlap, nor did we allow the objects to cover more than one quadrant. A second, important difference was that our focus was specifically on e-commerce sites, rather than generic web sites. While the Bernard study included five generic web objects, we extended this to ten objects and included objects such as “shopping cart” and “order button”, which are unique to the e-commerce environment.
Third, we also asked users to rate the importance of each of the web objects in terms of their impact on purchasing.
Importance of Web-Objects in Shopping Behavior
When investigating users schemas and e-commerce web objects another fundamental questions arises, which is: Which web objects should be considered? A number of investigators have addressed this question, by asking shoppers which factors are most important to them in their online shopping.
In a different study from the one discussed above, Bernard (2002) described various types of product information to consider on an e-commerce site. He asked shoppers to rate the degree to which each of these web objects would increase their likelihood to purchase. His results are presented in Table 1 below.
Feature % of shoppers indicating
that the feature increases purchase likelihood
Close-up product images 44%
Product availability 39%
Product comparison guides 34%
Search function 30%
1-800 Number to contact customer service representative 25% Product reviews/evaluations by online shoppers 24%
Catalog quick order 24%
Table 1. Features most likely to increase the likelihood of online purchase. (Bernard, 2002)
Tilson, Dong, Martin, & Kieche (1998) investigated a number factors, by asking shoppers to rate their importance. The ten most important factors were (in order of importance): 1. Credit card security
2. Easy return/exchange methods 3. Detailed descriptions of items 4. Pricing
5. Secure personal information 6. Pictures
7. General security concerns 8. Simple search methods 9. Alternate order methods 10. Appealing graphics
In order to capture information on users’ perceptions of the importance of web objects with information on expected location, we also included a survey in our study, which
asked participants to rank the importance of each e-commerce web object as to their impact on purchasing.
Research Questions
This research addressed two questions:
1. How important do users rate given web objects in impacting purchasing?
2. Where do users report that they expect to find common e-commerce web objects on a “well-designed” user interface?
Method
Participants
The participants were 99 students from the University of Missouri – Rolla, in lower level undergraduate classes in Information Science and Technology and Business
Administration, who participated for extra credit in class. In addition, 103 on-line volunteers who were members of the busyandfit.com web site
(http://www.busyandfit.com) participated.
Procedure
Participants were first asked to respond to a ten-item questionnaire. They were asked to respond to the following question: “How important do you feel each of the web objects listed is to you in making an online buying decision?” They responded to this question on a ten point Likert scale, which ranged from: 1) Not at all important to 10) Very important. They were then presented with a representation of a browser screen divided into nine quadrants (3 x 3) of equal size. Participants were asked where on the grid they most expect to find the given web objects used in the prior survey, on a “well designed” e-commerce site. For each web-object they selected a number from one to nine that represented each of the nine quadrants on the web page.
Results
Experimental Question 1: Web Object Importance
A one way repeated measures ANOVA was performed to determine if the means of the ratings assigned to each web object varied significantly from one another. The
independent variable was the ten web-objects and the dependent variable was the
importance rating. The ANOVA was significant F(1,201) = 4035.16, p < .001 The means are displayed in Figure 1 below.
7.87 7.81 7.41 7.01 6.75 6.11 5.935.87 3.57 2.77 2.5 3.5 4.5 5.5 6.5 7.5 8.5 Importance Rating Link to Merchandise Order Button Shopping Cart Search Login to Register Internal Links Help Links
Link to Home Page External Links Banner Ad
Figure 1. Importance Ratings as a Function of Object
Tukey post-hoc tests were computed to determine mean differences among the ratings, and these can be summarized as follows (greater than sign “>” signifies that a given mean was significantly higher than another):
1. Links to Merchandise > Search and lower ratings 2. Order Button > Login to Register and lower ratings
3. Shopping cart and Search > Internal Links and lower ratings 4. Login to Register > Link to Home Page and lower ratings
5. Internal Links, Help Links, and Link to Home Page > External Links and Banner Ad
Experimental Question 2: Web Object Location
In order to determine which location was expected, a quadrant count was calculated for each object. For each object we determined the number of users that selected each of the nine quadrants as the expected location. In order to determine whether or not given location(s) were preferred significantly more than others, a progressive series of chi-square tests were computed for each object in which the cell count for the quadrant that was preferred most was compared with the cell count for the quadrant that was preferred second most. If this test was not significant at the p < .01 level, then a second test was computed in which the cell count for the preferred quadrant was compared with the third most preferred, and this continued until a significant chi-square was reached. In eight of the ten cases, the preferred quadrant was found to have a significantly higher cell count than the second most preferred quadrant. With the internal link object, the preferred quadrant count was significantly higher than the third most preferred, and with the order button, the fifth highest quadrant was significantly different from the most preferred. These results are displayed in Table 2. Figure 2 is a summary of preferences displayed as a single “prototype” page, and Figure 3 is a graphical depiction of the top three preferred quadrants for each object with percentage of users selecting each.
Web object Preferred Quadrant(s) (% selected) Significant Chi-Square Banner Add 2(49%) Χ2 (1) = 44.885, p < .001 Shopping Cart 3(49%) Χ2 (1) = 50.70, p < .001 External Link 7(35%) Χ2 (1) = 9.48, p < .01 Help Link 3(42%) Χ2 (1) = 32.73, p < .001 Home Link 1(47%) Χ2 (1) = 40.16, p < .001 Internal Link 4(33%), 1(22%) Χ2 (1) = 54.38, p < .001 Merchandise Link 5(44%) Χ2 (1) = 9.27, p < .01 Login 1(40%) Χ2 (1) = 21.63, p < .001 Order Button 3(44%), 8(44%), 9(40%), 6(33%) Χ2 (1) = 13.07, p < .001 Search 3(80%) Χ2 (1) = 14.13, p < .001
Table 2. Web objects, the preferred quadrant with percentage of users selecting the quadrant, and the significant chi-square that occurred when the preferred quadrant was
progressively compared with cell counts of the quadrants with the next highest cell quadrants. (Note that quadrant numbers begin with 1 in the upper left and then run from
left to right with the lower right labeled as quadrant 9.)
Figure 2. Preferred Quadrants for E-Commerce Web Pages (Parentheses indicated where a single, significantly preferred quadrant was not found)
Banner Ad Shopping Cart External Link
Help Link Home Link Internal Link
Merchandise Link Merchandise Link
Order Button Search
Discussion
With respect to the first experimental question, it appears that the importance of a given web object is primarily a function of how important the object is for allowing a user to make a specific purchase quickly. The links to merchandise, shopping cart, and order button were rated as the top three most important objects. Such a rating fits well with the scenario that occurs when a user comes to a sight knowing what she wants with the intention of buying and completing the transaction. First, such a user would find the merchandise, second, the merchandise is placed in a cart, and, third, the user orders. It’s also interesting to note that these three top rated objects have traditional brick and mortar shopping analogs. Even though logging in to register is necessary with most e-commerce sites, both merchandise links and order button were rated significantly higher. Logging in to register is, of course, not a part of the traditional bricks and mortar shopping experience. This suggests that the traditional, non-internet, schema for buying is very powerful even for online shopping. It’s also interesting to note that links that are not directly associated with the one-stop shopping task, including internal and external, and even help links were rated quite low by the users, which further supports the picture of a shopper on a mission, as opposed to one who wants to explore related sites, or even learn about how to use the site. The low rating of the help button indicates that users expect the site to be usable and straightforward, they do not expect a help link to be an important part of the purchasing experience. Not surprisingly, banner ads were rated lower than any web object.
As far as the second experimental question, the results were clear-cut. Users appear to have surprisingly consistent schemas with respect to where they expect web objects to be located on “well-designed” e-commerce pages. With eight of the ten objects, the most popular quadrant was selected significantly more than all other quadrants and with one other the top quadrant was selected significantly more than all other quadrants but one. Figure 2 illustrates the “prototype” e-commerce site based on these results. Home and login links are in the top left. Internal and external links are along the left side; merchandise links are in the middle; shopping help and search are all expected in the top right, and the banner add is, of course, at the top in the middle. The one noted exception to this consistent user agreement is the order button, where three of the ten quadrants got largely equivalent numbers of users selecting them as the expected location (Figure 3). In fact there was even a fourth quadrant that did not differ significantly from the preferred quadrant, which is quadrant 6 (middle of the right side). The inconsistency with expectations with the order button is particularly interesting, given that it is one of the most important objects, according to the importance ratings. This suggest that a major stumbling block with an e-commerce scenario may be that users do not know how to purchase a given item once it has been selected. It further suggests that designers should make an effort to make the order button particularly salient, since the user does not know specifically where to look. With respect to the other objects, for the most part, a user does know where to look and these results indicate that a page with objects located as they are in Figure 2, will fit most consistently the user’s schema.
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