When the Wait Isn’t So Bad: the Interacting Effects of Website Delay, Familiarity, and Breadth

1. MOIS 508 Dr. Dina Rateb When the Wait Isn’t So Bad: the Interacting Effects of Website Delay, Familiarity, and Breadth By: Dina EL Touby ID: 900060001

2. Introduction • Websites became so popular that almost everyone uses them for research & online shopping. However with all its popularity, it has one major drawback, frequent delay when moving from one page to the other. • Delay is considered the most commonly experienced problem with the web leading many to call it the “Worldwide Wait”. • A study was made among 160 undergraduate students to show the relation between delay and 2 other variables (breadth & familiarity) and their effects on user’s performance, attitudes & behavioral intentions. The results were significant.

3. Delay, Familiarity & Breadth • Familiarity & site breadth are hypothesized to interact with delay and dampen its negative effects on users. • Delay: • Effects • Tolerable level • Breadth: • Broad Strategy: increasing the number of hyperlinks, decreasing the number of clicks& page loads. • Deep strategy: : decreasing the number of hyperlinks, increasing the number of clicks& page loads • Familiarity • Familiarity & Breadth dampen the effects of Delay on two important outcomes; user’s performance & intentions to return. (behavioral intentions is mediated by attitudes)

4. Hypothesis • Hypothesis # 1: Breadth & Familiarity will dampen the total cost of delay on overall performance of a user in a set of search tasks. • Hypothesis # 2: Familiarity & Breadth will dampen the negative effects of delay on behavioral intentions, but the relationship will mediated by attitudes

5. Research Methodology • The study was constructed in an experimental setting to control delay, site depth & familiarity and to allow measurements of outcome variables. • A completely counterbalanced fully factorial design sites were constructed.

6. Operationalization of Independent Variables • Delay: it was manipulated with Javascript code to provide a constant eight-second delay per page for the slow-site and no such delay for the fast site. • Familiarity: two artificial websites were created. • The familiar site contained images, process and descriptions of products available in everywhere to be familiar to the subjects. • The unfamiliar website contained fictitious software products, accessories and their categories were meaningless. • Breadth: Websites were created with 2 hierarchal structures for 81 bottom-level page. • “Broad” Type: it was created with 2 levels to traverse (9 hyperlinks per page). • “Deep” Type: it was created with 4 levels to traverse (3 hyperlinks per page).

7. Operationalization of Dependent Variables • Attitudes: were measured by averaging the responses to a set of seven nine point likert type questions. • Performance: it was measured by counting how many of the nine search tasks were accomplished. The tasks required subjects to visit each third of the site’s hierarchy the same number of times providing a reasonably balanced overall view of the site. • Behavioral Intentions: they were measured using the average of 2 questions addressing two related future behaviors. • Question # 1: How readily the subject would visit the site again? • Question # 2: How likely the subject would recommend the site for others to visit?

8. Procedure • The websites were created and their 32 combinations were written on CDs to control the browser’s response time. • Everything in the laboratory was the exact same to make sure there are no unwanted errors. • Participants were asked to complete the required 9 tasks for each website in order and to answer the questions given. • Answers were assessed as correct or incorrect by comparing subjects’ answers against the facts on the site. Data about the actual number of clicks were recorded automatically.

9. Results • After the end of the experiment, the analysis began with multivariate ANOVA for both attitudes & performance and univariate ANOVA for each one separately. Tests of mediation of attitudes between the experimental factors & behavioral intentions were also conducted. • One test was according to Baron & kenney & another was based on PLS. *ANOVA: Analysis of Variance.

10. Baron & Kenney Results • The given table shows the results of the Multivariate tests of Hypothesis 1&2. The tests reveal that each three way interaction was significant. Overall MANOVA: Attitudes & Performances

11. Baron & Kenney Results Cont’d • Means for the dependent variables across the main treatments presented in the following tables. • Each experiment factor had the intended & expected effects on both dependent variables. Means across the Delay Treatment Means across the Familiarity Treatment Means across the Breadth Treatment

12. PLS Approach • In the PLS approach, the 3 variables were reduced to 1 factor that is called “Cost”. A code for the “low-cost” was assigned by collecting the following conditions together; fast, familiar & broad. Another code was assigned for the “high-cost” for the following conditions; slow, unfamiliar & deep. PLS model without Attitudes as a Mediator PLS model with hypothesized Paths

13. Limitations • The subjects were college students & the results might not generalize to the rest of the population. • The task might not represent typical tasks that online shoppers undertake. • In real world, it is rare to find an ideally modeled site like the one used in the experiment when doing causal information search. • Analysis was limited by the inability to use structural equation modeling to test the complete model in one simultaneous analysis. • Incentives of the laboratory browsers and home shopper might differ. Home shoppers might switch their desires if they got frustrated of not finding what they want, its not the case with the subjects

14. Implications & Conclusions • The study of Delay, Familiarity & Breadth factors have proven that these three factors should be studied together to get the optimum results and each one separately is not enough. • The three factors were effective in predicting users’ performances in, and attitudes about navigating a shopping site. Further more, attitudes fully mediated the effect of the factors on behavioral intentions.

15. Thank You