Formell eller normativ legitimitet

The backward stepwise entry method was adopted for the binary logistic regression analysis (i.e., out-come is binary – yes/no). Four significant predictors were found explaining over 30% of the total varia-tion in the yes/no response (Negelkerke = 0.305 – see Table7.6).

Table 7.6: Undergraduate students’ (non-IT) WCT model – testing fitness to the data Pseudo R-Square

Cox and Snell .224

Nagelkerke .305

McFadden .191

The values shown in Table7.6show that this model is not as powerful as the one generated for the young urban professionals (30–40) user group. The author suggests that other significant factors influencing the willingness to complete a task exist for this group of users, especially when competing service providers exist (unlike in the e-government domain). A discussion on this is presented in Section 7.3.3.

Table 7.7: Undergraduate students’ (non-IT) WCT model – testing goodness of fit Chi-Square df Sig

Pearson 21.224 24 .625

Deviance 21.849 24 .588

Table 7.8: Undergraduate students’ (non-IT) WCT model – likelihood ratio tests

Effect Chi-Square df Sig

Before deciding on a regression model to use, the outcome variable was tested to determine whether it follows a normal distribution. As shown in Table7.9the Shapiro-Wilk value for workload (outcome) is .911, strongly rejecting the null-hypothesis that the population is normally distributed (p-value< 0.05).

This was also confirmed through a visual check by plotting the data using a histogram (see Figure7.6).

Table 7.9: Undergraduate students’ (non-IT) PEW model – tests of normality

Kolmogorov-Smirnov Shapiro-Wilk Statistic df Sig Statistic df Sig

.128 684 .000 .911 684 .000

A Generalised Linear Model with Gamma distribution was therefore adopted since it fits better to right skewed distributions. The Gamma Regression model identifies three significant predictors whereby

Figure 7.6: Undergraduate students’ (non-IT) PEW model – normality test for workload (visually right skewed, denoting a non-normal distribution)

Interruptions to daily routines (I) is the best predictor followed by Recall (ItR) and Delays (D). All have a large Chi-square value (i.e., coefficients are statistically significant to the model). The expected perceived workload when there are no interruptions (I) is 35.20% less than when interruptions are introduced (see Table7.1) . Similarly when there are no delays (D = 0), perceived workload is expected to be 10.71%

less than when there are major delays (D = 2). For every additional unit increase in recall (ItR), perceived workload is expected to increase by 2.63%.

Table 7.10: Undergraduate students’ (non-IT) PEW model – test of model effects

Type III

Source Wald Chi-Square df Sig

(Intercept) 160.595 1 .000

D 21.393 2 .000

I 58.618 1 .000

ItR 33.741 1 .000

Tables7.11and7.12provide more information on the model’s goodness of fit, including the Pearson Chi-Square measure as well as the Likelihood Ratio Chi-Square (see Section4.2.2).

Table 7.11: Undergraduate students’ (non-IT) PEW model – testing goodness of fit Value df Value/df

Deviance 336.065 607 .554

Pearson Chi-Square 277.408 607 .457

7.3. Evaluation and Findings 189 Table 7.12: Undergraduate students’ (non-IT) PEW model – omnibus test

Likelihood Ratio Chi-Square df Sig

217.339 4 .000

7.3.2 Evaluation of task completion predictions

It was decided to follow up this study with a simple user evaluation exercise. An online questionnaire was distributed among undergraduate students through social media and email. This questionnaire contained screenshots from the three blogging engines’ sign-up pages and for each screenshot respondents had to indicate whether they would consider signing-up (or otherwise) while indicating the reasons behind their decisions. 15 respondents completed this questionnaire, and comparisons were then made between reported behaviour and model predictions:

Enrolment process at Blog.com The WCT model predicted that 76.3% would be willing to enrol and use the service. The user evaluation exercise has shown that 78.6% of the respondents would not be put off by the enrolment process and would be willing to use this blogging engine – see Figure 7.7

Enrolment process at WordPress.com The WCT model predicted that 63.3% would be willing to enrol and use the service. The user evaluation exercise has shown that 71.4% of the respondents would not be put off by the enrolment process and would be willing to use this blogging engine – see Figure7.8

Enrolment process at LiveJournal.com The WCT model predicted that 70.2% would be willing to en-rol and use the service. The user evaluation exercise has shown that only 42.9% of the respondents would not be put off by the enrolment process and would be willing to use this blogging engine – see Figure7.9.

Comments on the third case were varied, and the discrepancy between the predicted value and users’ indications could be explained by a number of factors, including interface attractiveness (when compared to the alternative options) and privacy concerns. These are some of the students’

responses:

• “It seems too serious & the layout for filling in the information is a bit dull.” [Questionnaire respondent]

• “My answer is not really related to the information asked for in the registration process, it is more due to the fact that I did not like the user interface... it’s not welcoming.. So I would only register if I wouldn’t have managed to find better sources.” [Questionnaire respondent]

– see the Replaceability (R) behavioural modifier discussed in Section4.1.1.

• “But the combined information is quite a bit, enough to put you in a demographic. Probably results in targeted advertising. Also, whole feel is too formal.” [Questionnaire respondent]

• “The same old boring registration process and layout (one field for every row). The ‘Captcha’

at the end of the form can be really frustrating sometimes; at points I would not be able to get it right!” [Questionnaire respondent]

Figure 7.7: Feedback from actual users and the predictions generated via Sentire. This chart shows the percentage of undergraduate students (non-IT) who would be willing to enrol on Blog.com

Figure 7.8: Feedback from actual users and the predictions generated via Sentire. This chart shows the percentage of undergraduate students (non-IT) who would be willing to enrol on WordPress.com

Figure 7.9: Feedback from actual users and the predictions generated via Sentire. This chart shows the percentage of undergraduate students (non-IT) who would be willing to enrol on LiveJournal.com

User interface (UI) design plays a crucial role in the way an e-service is perceived by this group of users and could potentially affect their decision making process. This is especially true with services that have a high replaceability (↑R) factor (i.e., competing service providers) and do not carry any legal usage requirement (i.e., ToS 1 and 2). This goes beyond the impact that identity processes alone can have on users. Portraying the right image can help to improve user perception, while softening the negative impact created by demanding enrolment tasks. In this case study, the undergraduate students (non-IT) user group took interface attractiveness into consideration while evaluating their options. The following are some comments from different respondents focusing on the look and feel of enrolment pages as well as on the underlying workload.

• “Very simple and short process with a nice UI” [Questionnaire respondent]

7.3. Evaluation and Findings 191

• “It makes registering very easy and it has a clean and simple interface.” [Questionnaire respon-dent]

• “[It has] an attractive sign-up form” [Questionnaire respondent]

• “Quite straight-forward to use and very user friendly mostly because of the big boxes used.”

[Questionnaire respondent]

• “It seems too serious and the layout for filling in the information is a bit dull.” [Questionnaire respondent]