CUSTOMER PRODUCT KNOWLEDGE AND ...

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CUSTOMER PRODUCT KNOWLEDGE AND INFORMATION DISPLAY PREFERENCES André P. Calitz Sherwin Barlow Nelson Mandela Metropolitan University P O Box 77000 Port Elizabeth, 6031 Tel No: +27 41 504 2639 Fax No: +27 41 504 2831 André[email protected]

Nelson Mandela Metropolitan University PO Box 77000 Port Elizabeth, 6031 Tel No: +27 41 504 2247 Fax No: +27 41 504 2831 [email protected]

ABSTRACT The product information displays or presentation modes used on e-commerce websites are factors which influence a customer’s buying decision and need to adapt depending on the customer’s knowledge about the specific products considered. The provision of the most appropriate product information display for a specific customer is beneficial, however, difficult to determine. Customer profiling in the e-commerce domain has provided several benefits for conducting business online, in creating a more personalised user interface for customers, improving customer satisfaction and presenting the required level of product information. In this study the authors designed and implemented an e-commerce website selling three different product ranges, namely groceries, wine and electrical products. The customer’s knowledge about each product range was determined utilising a tested questionnaire. A customer’s knowledge relating to each product category was classified as novice, intermediate or expert. The products in each product range used three different product information displays namely list, matrix and commented list in order to display product information to customers based on their product knowledge. Internet users were required to purchase products from the e-commerce website. The product information display (list, matrix or commented list) varied depending on the product knowledge classification (novice, intermediate or expert) of the customer. The product information displays presented were evaluated utilising on-line and written questionnaires, purchasing activities on the e-commerce website and eye-tracking evaluations. The research identified that a statistically significant relationship exists between a novice customers’ product knowledge level and the preference for the matrix product information display, but no statistical significant relationship was determined between the other product knowledge levels and product information displays. Keywords: Product information display, product knowledge, e-commerce.

1. INTRODUCTION The Internet has changed the way people conduct business activities and businesses on the Internet continuously seek new methods to effectively communicate with customers (Schneider, 2007). E-commerce has provided a way to build personalised relationships with customers using a customer profile. A customer profile is information about a customer’s demographics, sales history, characteristics and activities (Eirinaki and Vazirgiannis, 2003). Customer profiling has become an important tool in business today. Businesses use customer profiling to create a business customer database that can be utilised for market segmentation in order to identify new target markets and potential customers. A new development in customer profiling is Customer Relationship Management (CRM). CRM is used by businesses to create personalised relationships between the business and the customer to improve customer loyalty (Liu, Lin, Chen and Huang, 2001). Customer loyalty is a competitive advantage for businesses (Adomavicius and Tuzhilin, 2005). CRM is made possible by creating a customer profile for each customer interacting with the website. E-commerce has made Internet based advertising that includes personalised recommendations and customisation possible (Schafer, Konstan and Reidl, 2001). Businesses face many challenges regarding the level of personalisation to provide as these relationships cannot be too personal (Schafer et al., 2001). Research has shown that e-commerce is more competitive than any other conventional commerce because of the low entry barriers (Chang, Changchein and Huang, 2006). The way in which advertising, product information presentation and screen layout are implemented on an e-commerce system is an important factor leading to the website’s success level and affects the buyer’s buying decision (Sharp, Rodgers and Preece, 2007). The advertising technique could affect a visitor by changing a visit into a purchase increasing ecommerce website success (Schafer et al., 2001).The way in which information is presented can also affect customer buying behaviour to some extent therefore product information displays should be used efficiently (Hong, Thong and Tam, 2004). The product information displays are important factors’ influencing a customer’s buying decision (Hofgesang, 2007). Product information presentation modes mostly contain information such as the product name, description and price. These presentation modes change based on the category of products being displayed (Bettman and Kakkar, 1977). The

product information display modes used to display product information about a certain product might influence the customers buying decision (Hong et al., 2004). In this study, an e-commerce website was implemented selling three product categories, namely Grocery products, Wine and Electrical products. At the time of the research, wine, groceries and entertainment products were some of the items that were most commonly purchased on-line in South Africa (Goldstuck, 2007). The project utilised the customer information and browsing behaviour patterns to maintain a customer profile. The information captured is the customer’s personal information and the customer’s product knowledge levels for each product category. The product information display mode then adapts the computer user interface based on the customer’s product knowledge level for the product category. The product categories used three different product information displays layouts (presentation modes), namely list, matrix and commented list in order to display product information to customers based on their product knowledge levels. Product information interfaces mostly used on the Internet are the list and matrix information displays (Sharp et al., 2007) where the image based presentation display is preferred above only textual displays. The aim of the study was to implement a customer profiling system consisting of an ecommerce website that could be utilised to present product information using different product information displays and allowing customers to purchase products using a preferred presentation display. The limitations to the study include the use of a convenience sample, namely NMMU employees and a limited number of expert users per product category. The three product information displays, namely list, matrix and commented list were used to display product information to customers based on their product knowledge levels. The research objective of this paper is to determine whether a relationship exists between product information displays (presentation mode) and a customer’s product knowledge level. The research objectives and methodology are presented in Section 2. The growth of ecommerce, the implementation of the on-line store, the customer profiling module and the information display of products are presented in Section 3. Section 4 discusses the research study and results. Section 5 concludes and contextualises the findings and future research.

2. RESEARCH OBJECTIVES AND RESEARCH METHODOLOGY The research questions addressed in this study were: 

Does a relationship exist between a customer’s product knowledge level per product category and the product information display layout the customer prefers when browsing products on a website?



Do customers prefer the personalised product information displays the website provides when viewing product information?

The primary objective of this research was to implement an e-commerce website that allows customers to purchase products using preferred product information displays. The different product information interfaces (presentation modes) used on the website contained different levels of information about products, used different screen layouts which were linked to the customer’s product category knowledge level. The presentation mode was adapted for the customer based on his/her product knowledge level per product category. For example, a novice customer would receive the detailed matrix layout which contains a high level of product information. Three different product information displays, namely list, matrix and commented list were implemented per product category to display product information to customers based on their product knowledge levels. Customers purchased products on-line and the system adapted the product information presentation mode based on the customer’s product category knowledge and the customer’s product information display layout preferences. The specific research objectives of the study were to: 

Determine the product knowledge level (novice, intermediate or expert) per product category (groceries, wine and electrical products) for a customer; and



Determine if a relationship exists between a customer’s product knowledge level for a specific product category (groceries, wine and electrical goods) and the product information display layout (list, matrix and commented list) the customer prefers when purchasing products.

The hypothesis investigated in the study was as follows: 

H0: No relationship exists between a customer’s product knowledge levels and product information displays.



H1: A relationship exists between a customer’s product knowledge levels and product information displays.

The research methodology utilised in the study followed the traditional usability evaluations approach. A CSIS on-line store was created implementing an e-commerce website selling consumer products in the three categories, namely groceries, wine and electrical goods. Customers purchased products on-line and the system presented different product information displays (screens). Data gathering techniques included web-logging, pre- and postquestionnaires, an on-line product knowledge survey and eye-tracking. The data were statistically analysed and the results are presented in Section 4. 3. LITERATURE STUDY Customer profiling E-commerce has enabled on-line businesses to extend their customer base by obtaining access to global markets. E-commerce is buying and selling products, services and information using Internet technologies and electronic systems (Schneider, 2007). Businesses use different techniques to increase customer loyalty and turn website visitors into customers (Kim et al., 2006; Adomavicius and Tuzhilin, 2005). Customer profiling is a technique mostly used in online businesses to gain a customer’s trust and loyalty. The main uses of customer profiling are customisation, recommendation, personalisation and marketing. The customer profile contains information obtained from the customer. This information includes the customer’s personal data or the customer behaviour data when interacting with the e-commerce website (Schafer et al., 2001). A customer profile can be divided into factual information which is facts about the customer for example age, gender, name, geographical and behavioural information where this information is captured when a customer performs certain tasks on-line (Adomavius and Tuzhilin, 2005). The use of questionnaires is one explicit method most commonly used by an on-line business to obtain customer information, together with other techniques such as the use of cookies and web-log mining. Customer profiles have been implemented in various e-commerce systems but mostly to identify customer preferences and needs, for example, www.amazon.com (Amazon.com, 2010). A customer profile is established by implementing implicit and explicit feedback by on-line businesses (Jokela, Turpeinen, Kurki, Savia and Sulonen, 2001). Explicit feedback is simply determining factual information asking customers to register their details on the

website by using online questionnaires. Examples of on-line businesses that use explicit feedback to determine a customer’s profile are Amazon.com and Kalahari.net (Amazon.com, 2010; Kalahari.net, 2010). Figure 1 is a screen shot of explicit methods to obtain profile information from customers on Amazon.com. Figure 1: Explicit feedback customer profiling method (Amazon.com, 2010).

Implicit feedback is the web based application captures information about the customer as he or she interacts with the system. Web-log mining and cookies are techniques used by on-line businesses to prevent users from entering their information every time they enter the website. Web-log mining is the extraction of information from web-log files created and stored on the web server. Cookies are web-log files created at the server side but kept on the client machine (Nelte and Saul, 2000). Table 1: Needed customer profile components. Field Name

Amazon.com Edgars

Pick n Pay

Name Surname Title Gender DOB ID number Contact details Log on details Address details

Yes No No No Yes No Yes Yes No

Yes Yes Yes Yes Yes Yes Yes Yes Yes

Yes Yes Yes Yes Yes Yes Yes Yes Yes

Table 1 indicates the common customer profile information required by online businesses when opening a purchasing account. The table does not contain the customer product knowledge levels as part of the customer profile which this project will use. In this study, the following information for a customer is recorded:  Customer Details: Records the customer details that include biographical details, contact details and login information including a unique customer ID;  Log Details: The log details of the customer’s activities during the interaction session with the system. The log details will be used to update the customer profile;  Customer purchase: Keeps information about items customers have purchased in the past;  Sale: The recording of information relating to a single product purchase, the user interface settings, the product information and selected display mode. The information is used to determine the product information display mode most frequently used; and  Product: Product information and product categories. Product information display interfaces A customer needing to purchase a product has many external and internal factors which impact on the customer’s acquisition decision (Hofgesang, 2007). The customer’s information processing in memory plays a large role in the decision to acquire a product or not (Biehal and Chakravaarti, 1983).

Task structure differences then affect processing at acquisition.

Customer information processing differs in the way which product information is organised. Product information can be represented by brand or by product attribute. The product catalogues of e-commerce on-line stores should include more detailed information about products (Callahan and Koenemann, 2000). Callahan and Koenemann (2000) implemented a user interface called Info Zoom, a catalogue display that provides customers with a wide range of products where the customer then filters and compares products based on selected attributes. Early research has shown that customer choice is influenced by processing attributes relating to the way in which information is displayed and will be the key to future interface designs (Bettman and Kakkar, 1977). The reason is that customers consume information as it is displayed to them (Callahan and Koenemann, 2000). The way in which e-commerce websites display product information affects the buyer’s buying decision (Sharp et al., 2007). Businesses use customer profile information for many reasons, specifically for personalisation and recommendations. The customer’s profile can be

used to display products in a product information display layout the customer prefers. The customer profile information can be used to acquire or predict the customer’s preferences and a technique increasingly being utilised is the customisation of the user interface for a specific customer. E-commerce websites currently do not determine which presentation modes (product information displays) to use for customers viewing different product categories based on the customer’s product knowledge. Most e-commerce websites implement a single product information display mode with a link to a more detailed page containing information about the product being viewed. The product information displays (presentation modes) used on ecommerce websites are factors which influence customers’ buying decisions and overall satisfaction (Hong et al., 2004). Research conducted by Hong et al. (2004) has shown that the product information display mode has an effect on the customer’s browsing behaviour. The most common product information interfaces used by e-commerce websites are the list, the extended list and the matrix layouts. The image based presentation mode includes images on the website and this mode is preferred over a text based presentation mode where no images are included. Generally image based presentation modes outperform text based presentation modes (Hong et al., 2004). Images and/or text are included in all product information interfaces. The list product information display mode has been promoted to provide a more efficient browsing behaviour due to the easier recall of product information. The list product information display modes allow exploration of products and groups all products of a certain category together. This allows users to browse a category of products more easily, which positively affecting the customers browsing behaviour (Hong et al., 2004). The list information format is mostly used for browsing tasks where the matrix format is used for searching tasks (Hong et al., 2004). The matrix format is used when a customer purchasing products requires a large amount of information about the product being displayed. The detailed list information display mode consists of detailed descriptions of products and allows customers to add and view detailed information about a product. In summary, the three most commonly used product information displays (Hong et al., 2004) used in e-commerce and implemented in this study are: 

List layout (Figure 2 left) - contains a list of products with little detail about a product and mostly used by e-commerce website because it is easy for browsing;



Matrix layout (Figure 2 middle) - contains products which are displayed in a grid where this layout contains a fair amount of product information and mostly used to display technologic products and for searching; and



Commented list layout (Figure 2 right) - this is a layout which contains a list of products with a brief of the product details and mostly used in e-commerce for customers to add comments to products.

Figure 2: List layout, Matrix layout and Commented List layout implemented.

Figure 3 is an example of a matrix information display used by the Pick n Pay on-line shopping store for grocery products. Figure 3: Displaying grocery products in a matrix layout (Pick n Pay, 2010).

CSIS online store The CSIS on-line store was re-developed based on the design by Ntawanga et al. (2009). The three product categories implemented were groceries, wine and electrical goods. Electrical goods were included as a number of users are generally unfamiliar with this product category and might therefore have required additional product information before making a purchasing

decision. A number of changes were made on the user interface of the system to improve the quality and appearance (Figure 4). Figure 4: CSIS online store.

The user interface (Figure 5) for the creation of the initial customer profile was developed by Ntawanga et al. (2009) and utilised in this study. Each question has five options that are grouped per product category. Customer profiles that include static and dynamic information were established in accordance to research conducted by Ntawanga et al. (2009).

The

customer completed a questionnaire (Figure 5) and an initial profile consisting of all demographic details and product knowledge levels for each of the product categories was created. The product knowledge of a customer for each product category, namely groceries, wine and electrical products were updated when the customer shopped on-line. The customer profile is updated based on the dynamic information recorded; based on website usage data, product information utilised and display mode per product category selected. The customer’s profile is updated as the customer interacts with the website. An update to the customer’s product knowledge levels occurs when the customer purchases a certain amount of products from a certain category or when a customer selects a display mode explicitly. The customer is then allowed to add products to a basket in one of three product information interface layouts and check-out. The customer can also update generic information accordingly.

Figure 5: Questionnaire for customer’s initial product knowledge profile.

The questionnaire (Figure 5) was utilised to establish the level of product category knowledge contained specific questions on grocery products, electrical products and wine. Five questions for each product category were utilised and each question had to be scored (1=Not at all to 5=Always). The system rates the customer according to the total score for each product category. The system then uses the rating to determine the product information layout to display. Table 2 summarises the score and rating process (Ntawanga et al., 2009).

Table 2: Initial customer product knowledge level rating. Score Range

Customer Rating

Product Information Level

5-10

Novice

Matrix

11-19

Intermediate

Commented list

20-25

Advanced

List

The content display used the three different product information levels depending on the customer’s product category rating (Table 2). The three different information levels provide different display modes and display product information in three different layouts. The list layout information mode provides the least detail about a product where the matrix layouts provide more detail and allows more products to be viewed in a single row, thus allowing the

customers to search for products information more effectively. The commented list layout provides the most detail and a function to view additional information about products. Figure 6 shows a matrix product information display for a customer who has novice product knowledge for groceries. Figure 6: CSIS online store matrix product display for a grocery page.

4. THE RESEARCH STUDY AND RESULTS The user evaluation for this project consisted of a pilot study to determine whether users use different product information displays, followed by a main study to determine the relationship between product information displays and customer product knowledge levels. The pilot study consisted of a convenience sample (n=6) with staff and master students of the Computing Sciences Department of NMMU. Respondents were provided with a task list and a questionnaire. The evaluation consisted of a usability study which was used to obtain feedback on the systems usability and the usefulness of the different product information displays. The user evaluation (n=31) conducted in the main study evaluated the goal of the study, namely to determine whether a relationship exists between a customer’s product knowledge level for a product category and the product information display customers prefer when viewing the products. The users completed specified tasks and a number of metrics were measured at various stages in evaluations. The evaluation procedure consisted of customers completing an informed consent form, a demographical questionnaire and a pre and post task questionnaire.

Customers completed the registration process on-line in order to determine the user’s initial product knowledge levels based on the answers provided to on-line questionnaire (Figure 5). A task list was provided which contained instructions to purchase specific products from different product categories. In order to compare the product information displays users preferred, the CSIS on-line store was configured as two systems. The difference between the two systems was that system A provided the user with a default product information display interface as follows: 

List layout – for the grocery product category;



Detailed Matrix layout – for the electrical product category; and



Commented List layout- for the wine product category.

These default interfaces were chosen randomly to ensure no relationship exists between the interface and the customer’s product knowledge level for the product category. System B provided the users with a more personalised product information display based on the customer product knowledge level for a specific product category, as follows: 

Novice - Matrix layout for the selected product category;



Intermediate - Commented List layout for the selected product category; and



Expert - List layout for the selected product category.

The research assumption made was that if the user is presented with system A, which provides a default product information display interface, the user would change the interface to a personal preferred layout and when the user was presented with system B, the user would not change the interface. One half of the users were given System A first, followed by System B and the other half of the group System B first, followed by System A. The task list contained 18 tasks where the first 9 tasks were to purchase products on the first system presented to a participant. The products the participant purchased and the layout preferred were captured by the system. In the post questionnaire, participants were asked to rate the system according to various criteria using a 5-point Likert scale, which allowed quantitative data to be tabulated and visualised using different graphs. Eye tracking evaluations The results obtained from eye tracking provide evidence to support that the participants were focused on the detail in this layout preferably reading the information. The Gaze plots in Figure 7 show that the participants used the layout and the information provided. The layout

mostly preferred by participants when they viewed grocery products was the list layout and the matrix layout for electrical and wine products. This showed that the product information was usable and shows the participants used the layouts to support the results in the empirical study. Figure 7 (left) shows a heat map of participants using the commented list layout and Figure 7 (middle) the gaze plot of participants using the detailed matrix layout. The gaze plot of participants using list layout are presented in Figure 7 (right). Figure 7: Heat map (left), Gaze plot (middle) and a gaze plot (right).

Empirical evaluations In the main study the gender of the participants (n=31) were male (n=18) and female (n=13). All the participants were older than 19 years of age (reflecting a typical minimum age for users of an online shopping website) where 23% were older than 36 years. There were five different ethnicities/home languages and six different nationalities. The users had more than six years computer experience, 70% has more than six years Internet experience and 90% of the participants spend more than an hour on the Internet per day. This sample size (n=31) allowed that statistics be calculated such as t-tests and Chi-squared analysis. The participants (n=31) consisted mostly of participants with a novice product knowledge level for wine (n=23), intermediates for grocery (n=19) and electrical (n=12). A few participants were experts for the product categories (Figure 8). The users indicated that other products bought online mostly are books, music, games, scientific papers, articles and flights.

Figure 8: Main study user product knowledge (n=31).

A demographic questionnaire was presented to each participant which consisted of five sections A to E. Section A was basic demographical variables, Section B was based on computer experience, Section C focused on on-line shopping experience, Section D included the task lists for System A and B and Section E was the post questionnaire measuring the systems metrics as well as the system usability and the participants presentation layout preferences. The post questionnaire which measured the various criteria used a 5 point Likert scale. This scale was represented as: 1 - Strongly disagree and 5 - Strongly agree. The metrics which were captured in this evaluation were chosen to meet the study goal, which was to determine whether there is a relationship between the customer product knowledge levels and the product information display layouts they prefer. The metrics captured were system effectiveness, performance metrics and task success. The evaluation also contained a comparison of the two systems to determine which one was mostly preferred. Task list results The results tabulated are task success and task time for the activities for system A and B. Table 3: Task success information (n=31) Task success System Observed count A 229 B 246

Expected count 237,5 237,5

Chi-squared p-value 0.44

The results tabulated in Table 3 were calculated using the Chi-Squared test where the value obtained (t30 = 0.44, p < 0.05) using a 95% confidence interval shows that there was not a significant difference between the task successes for the two Systems. In Table 4 the task times were measured as interval data in seconds. The numbers 1-5 recorded as 1 is < 30 sec, 2 is 31-60 sec, 3 is 61-90 sec, 4 is 90-120 sec and 5 is > 120 sec. The average time range overall for System A was 2,63 seconds and 1,61 seconds for System B. The t-test on the task times (t30 = 14.66, p < 0.01) indicated that the task times were significantly different. It is clear that there was a slight decrease in task time between the systems, this could be due to the learnability of the system or that the users just performed better on System B overall. The average task time for system A’s task 2 was the highest overall where this task was to purchase an electrical product. This can be due to the users not changing the layout to the appropriate one in time. Table 4: Task times for System A and B (n=31) Task 1 2 3 4 5 6 7 8 9

Mean A 2.81 2.90 2.71 2.71 2.61 2.68 2.26 2.45 2.81

Mean B 1.97 1.77 1.42 1.42 1.65 1.58 1.42 1.87 1.45

Median A 3.00 3.00 3.00 3.00 3.00 3.00 2.00 2.00 3.00

Median B 2.00 2.00 1.00 1.00 1.00 1.00 1.00 2.00 1.00

Std.Dev A 0.91 0.79 0.78 0.69 0.92 0.75 0.73 0.77 0.98

Std.Dev B 0.80 0.80 0.62 0.67 0.75 0.67 0.62 0.72 0.72

Post-questionnaire results The questionnaires were based on the evaluation of the systems metrics. In Table 4 it was identified that participants were satisfied with the system and the product information displays they were presented with. The direct comparison of System A and B illustrates that participants preferred using System B which provided a product information display according to their product knowledge level. This illustrated that participants like a presentation which is personalised.

Table 5: System metrics for System A and B (n=31) Question (1- Strongly disagrees and 5- strongly agree)

Mean

Median

Std.Dev

1. The system speed was slow/fast? 2. I had enough information available to decide my purchase? 3. The layout of the product information matched my product knowledge level in System A? 4. The layout of the product information matched my product knowledge level in System B? 5. I prefer System A which had default displays or System B which had a display according to my product knowledge level? 6. I enjoyed using System A or System B because it was easier to find the product I had to purchase?

4.68

5.00

0.48

4.45

5.00

0.81

3.42

3.00

1.03

4.13

4.00

0.76

4.45

5.00

0.85

4.26

4.00

0.77

The first question in Table 5 obtained a high mean indicating that users found the system fast and question 2’s mean illustrates that there was enough information available to make purchase decisions. Question 3 presented a high standard deviation, proving that users’ opinions regarding whether layout matched their knowledge levels varied widely. Median values indicated that participants felt that the layouts of product information in System B provided a closer match to their preferences than the layouts provided by System A. The Likert scales for Question 5 and 6 had system A as a 1 rating and system B at rating 5. The means for question 5 and 6 were statistically significantly different from the intermediate value of 3 (Table 4), illustrating that those participants preferred using System B. This could be due to the system providing the product information displays according to the customer product knowledge level. This illustrates that participants preferred the system to provided personalised product information displays. In the final evaluation it has been identified that participants mostly preferred the list layout when viewing grocery products. This could be due to the fact that most participants have an expert or intermediate product knowledge level for groceries. Comments received from participants stating that when purchasing groceries they know what they need to purchase and do not require detailed product information.

Participant interface interaction results Figure 8: Preferred layout for product category (n=31).

The above figure represents the participant’s product knowledge levels and layouts they purchased products in a particular category. The first graph in Figure 8 (left) shows that of the 19% participants who were classified as novices for groceries and of this group over 70% of the sales for groceries were conducted using the matrix layout. Figure 8 (left) also indicates that the participants who were classified as intermediates and experts for groceries, also mostly preferred the matrix layout for their grocery purchases. Figure 8 (middle) shows that all the users classified as novices for electrical goods (32%) used the matrix layout for all electrical product purchases. The majority of the participants who were classified as intermediates and experts for electrical products also preferred the matrix layout. However, of the 39% of participants who were classified as intermediates for electrical products, a quarter of this group completed their purchases using the commented list layout. Figure 8 (right) indicates that 74% of the participants (n=31) in this study were classified as novices for the wine product category. 60% of the novice group completed the wine purchases using the matrix layout. The participants with an intermediate product knowledge level for wine generally preferred the commented list layout. It is clear from the graphs above that experts also had a greater percentage of their sales completed using the matrix layout all the time. The results from the Chi-squared test with a confidence interval of 95% are presented in Tables 6 to 8. The tables below show a statistically significant difference between the layout preferences for each class of participants as indicated by the p-values for the Chi-square tests being less than 0.05 for all classes of participants. Participants’ preferred layout was not equally split between the three layouts.

The results presented in Tables 6 to 8 show that participants preferred the matrix layout. The user interaction results section show that participants with any product knowledge level preferred using the matrix layout when purchasing a product from any category except for wine. The wine category participants with intermediate product knowledge levels used the commented list layout for wine. This can be due to the participants with an intermediate product knowledge level having a clear idea of the wine they needed to be purchase. The Chi-squared tests used further showed that a significant difference existed between layout choices and participants preferred the matrix layout overall. It can be deduced that there was a relationship between participants with a novice product knowledge level and the matrix layout. No statistically significant difference has been identified between the other product knowledge levels and product information displays where all participants with the other product knowledge levels have shown a preference for the matrix layout. The interpretation of the results concludes that the null hypothesis H0 cannot be accepted. Table 6: Chi-squared test for grocery product category (n=31) Layout List List(com) Matrix p-value

Novice (n=6) Observed Expected 33 33 17 33 50 33 0.000259287

Intermediate (n=19) Observed Expected 35 35 24 35 47 35 0.022692648

Expert (n=6) Observed Expected 17 33 33 33 50 33 0.000259287

Table 7: Chi-squared test for electrical product category (n=31) Layout List List(com) Matrix p-value

Novice (n=10) Observed Expected 10 33 0 33 90 33 9.42046E-33

Intermediate (n=12) Observed Expected 11 35 16 35 78 35 5.18895E-18

Expert (n=9) Observed Expected 33 33 0 33 67 33 1.68814E-15

Table 8: Chi-squared test for wine product category (n=31) Layout

Novice (n=23) Intermediate (n=6) Expert (n=2) Observed Expected Observed Expected Observed Expected List 2 37 17 36 0 33 List(com) 17 37 8 36 0 33 Matrix 87 37 83 36 100 33 p-value 6.18041E-25 5.87645E-21 1.34796E-44 In some cases, however, there is evidence suggesting a link between product knowledge and preferred layout. For example, a relationship clearly existed between participants with a novice product knowledge level and the matrix layout. Furthermore, there were indications

that participants preferred System B, which provided personalised levels of product information. Comments participants made were however captured as these comments affect the goal of this evaluation, where participants stated that:  “Even if I am an Expert I would still like to see details especially for electrical products.”  “I know which grocery products I want to purchase before hand and nutritional information is irrelevant to me.” Therefore this supports the reason for the non existence of relationships between the other product knowledge levels and product information displays. 5. CONCLUSIONS AND FUTURE RESEARCH E-commerce and customer profiling have become an integral part of modern businesses today. The use of customer profiling in e-commerce has brought about personalisation which improved customer satisfaction levels and loyalty (Adomavicius and Tuzhilin, 2005). The goals of this project were to successfully implement an e-commerce system including a customer profile, recording the customer’s product knowledge levels and purchasing behaviour and display settings. The CSIS on-line store presented three different product information displays depending on a customer’s product knowledge level. The interfaces were evaluated and the user testing consisted of two tests, the pilot study to determine whether the product information displays were useful and a main empirical evaluation to determine whether a relationship exists between a specific customer product knowledge level and a specific product information display. The results obtained from the empirical evaluation were that a statistical significant relationship exists between a novice product knowledge level and the matrix product information display mode presenting a large amount of detail about a product. There is no statistical significant relationship between other product knowledge levels interfaces and specific information display modes. No relationship exists between the intermediate and expert product knowledge levels and their related product information displays. The users in this study did however indicate that they would still want to see more detailed product information for the product categories for which they are classified as intermediates or experts. Participants in the study found the product information displays useful and agreed with the way in which the product knowledge levels were updated. The participants were highly satisfied with the personalised product information displays and preferred the system

providing personalised product information displays. The researchers further concluded that the reason there is no relationship between certain product knowledge levels and product information displays could be due to customers knowing which products they wanted to purchase. The user sample did also not include a large number of expert participants in specific product categories, for example electrical goods and therefore the results of this research cannot be generalised. Future research will test these findings on other systems and further investigate guidelines for systems to accommodate users with different product knowledge. Management need to take the research findings into consideration when developing new e-commerce applications or evaluating existing websites and specifically noting Internet customer’s product knowledge levels and product information display preferences. The use of product information displays should include enough information where this affects a customer’s buying decision and can lead to increases in sales and profitability. REFERENCES Adomavicius, G. and Tuzhilin., A., 2005. Personalisation Technologies: A ProcessOriented Perspective. Communications of the ACM , 48 (10), pp. 83-90. October 2005. Amazon.com, 2010. Amazon web services. [Online] Available at: www.amazon.com/gp/browse.html?node=3435361. [Accessed 20 February 2010]. Bettman, J. R. and Kakkar, P., 1977. Effects of Information Presentation Format on Consumer Information Acquisition Strategies, Journal of Consumer Research, 3, pp. 233240. Biehal, G. and Dipankar C., 1983. Information Accessibility as a Moderator of Consumer Choice. Journal of Consumer Research, 10 (1), pp. 1-14. Callaham, E. and Koenemann, J., 2000. A Comparative Usability Evaluation of User Interfaces for Online Product Catelogues. ACM EC'00, 17-20 October 2000, Minnesota, USA. Chang, S. E., Changchein, S.W. and Huang, R., 2006. Assessing users’ product specific knowledge for personalisation in e-commerce. Expert Systems with Applications 30, pp. 682-693. Eirinaki, M. and Vazirgiannis M., 2003. Web mining for web Personalisation. ACM transactions on Internet Technology 3 (1), pp. 1-27. February 2003. Goldstuck, A., 2007. Dreaming of an e-Christmas. [Online] Available at: http://www.worldwideworx.com/archives/59#more-59. [Accessed 30 July 2008].

Hofgesang, P.I., 2007. Web personalization through incremental individual profiling and support based user segmentation. In Proceedings of the IEEE/WIC/ACM international conference on web intelligence. USA, 2007. Silicon Valley. Hong, W., Thong, J.Y. and Tam, K.Y., 2004. Designing product listing pages on e-commerce websites: and examination of presentation mode and information format. International Journal of HCS, 1 (1), pp. 26-28. Jokela, S., Turpein, M., Kurki, T., Savia, E. and Sulonen, R., 2001. The role of structured content in a personalised news service. Proceedings of the 34th annual Hawaii international conference on systems science (HICSS-34). Washington DC.IEEE Computer Society: 7 January 2001. Kalahari.net, 2010. [Online] Available at: http://www.kalahari.net. [Accessed 28 March 2010]. Kim, J. W., Lee, K. M., Shaw, M. J., Chang, H., Nelson, M. and Easley, R. F., 2006. A preference scoring technique for personalised advertisements on Internet storefronts. Mathematical and Computer Modelling, 44 (1-2), pp. 3-15. July 2006. Liu, D., Lin, Y., Chen, C. and Huang, Y., 2001. Deployment of personalised e-catalogues. An agent-based framework integrated with XML metadata and user models. Journal of Network and Computer Applications, 24, pp. 201-228. Nelte , M and Saul, E., 2000. Cookies. Weaving the web into a state. The ACM Student Magazine. 7 (1), pp. 10-13. Ntawanga, F., Calitz A.P. and Barnard L., 2009. A Model for Dynamic Product Information Display as an Internet Service, Proceedings of the 3rd International Business Conference (IBC 2009), Zanzibar, 15-17 September 2009. Schafer, A.I., Konstan, J. and Reidl, J., 2001. E-commerce recommendation applications. Data Mining and Knowledge Discovery, 5 (1-2), pp. 115-113. Schneider, G.P., 2007. Electronic Commerce. 7th Annual edition. Boston: Thomson. Sharp, H., Rogers, Y. and Preece, J., 2007. Interaction Design, beyond human-computer interaction. 2nd Ed, John Wiley & Sons.