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The second and third hypothesis were related to age and gender. Table 6 shows the results of the whole sample and the experimental group for gender. The mean rank for the whole sample does not differ greatly across the two groups. Consequently, Table 6 portrays a non- significant coefficient of 0.468 (z-statistic = 0.080). Therefore there is no difference between the mean ranks of both males and females in correctly judging e-mails from banks. Additional tests were run with the experimental and the control group (using an Independent sample t-test) in order to see whether there were any differences in the groups which took part in the experiment. The experimental group showed a greater different mean rank for gender. Still, Table 6 displayed a non-significant coefficient of 0.178 (z-statistic = 0.923). In accordance with the experimental group, the control group also did not show any significance 0.323 (t-statistic = 0.472) according

to Table 7. Therefore, there is no indication that gender does have an effect on correctly judging e-mails from banks. Therefore hypothesis 2 is rejected.

Table 6: The mean rank statistics of the amount of correctly judged bank e-mails across gender and the test statistics of an equal mean rank of the amount of correctly judged bank e-mails

between males and females.

Table 6

Mean rank statistics across groups

1 tailed Z-test for equality of mean ranks Gender X ̅ rank Sum of ranks N Mann- Whitney U Z P-value The amount of correctly

judged bank e-mails (whole sample)

Male 13.89 250.00 14

79.00 0.080 0.468 Female 14.22 128.00 13

The amount of correctly judged bank e-mails (experimental group)

Male 6.25 62.50 10

7.50 0.923 0.178 Female 9.50 28.50 3

Note. * P < α = 0.10, one-tailed; ** P < α = 0.05, one-tailed and *** P < α = 0.01, one-tailed.

Table 7: The mean statistics of the amount of correctly judged bank e-mails across gender and the test statistics of an equal mean of the amount of correctly judged bank e-mails between males

and females.

Table 7

Mean statistics across groups

Levene’s test for equality of variances

T-test for equality of means

Gender X̅ Std. F Sig. T

Sig (1-

tailed) N The amount of

correctly judged bank e-mails (control group) – equal variances assumed Male 7.83 1.329 0.232 0.639 0.472 0.323 6 Female 8.13 0.991 8

Equal variances not assumed

0.452 0.331

Note. * P < α = 0.10, one-tailed; ** P < α = 0.05, one-tailed and *** P < α = 0.01, one-tailed.

The third hypothesis was related to the influence of age on phishing susceptibility. The results of the tests can be seen in Table 8 and 9. Table 8 depicts the results for the whole sample and the experimental group. At first, the rank statistics of the whole sample show similar mean

ranks, with the younger age group having a greater sum of ranks because of the higher N. Subsequently Table 8 depicts a non-significant coefficient of 0.428 (z-statistic = 0.182). Therefore there is no difference in correctly judging bank e-mails across the younger and older age group in the whole sample.

Table 8: The mean rank statistics of the amount of correctly judged bank e-mails across the age groups and the test statistics of an equal mean rank of the amount of correctly judged bank e-

mails between the older and younger age groups.

Table 8

Mean ranks statistics across groups

1 tailed Z-test for equality of mean ranks Age X̅ rank Sum of ranks N Mann- Whitney U Z P-value The amount of correctly

judged bank e-mails (whole sample) Older age groups 14.31 114.50 8 73.50 0.182 0.428 Young age groups 13.87 263.50 19 The amount of correctly

judged bank e-mails (experimental group) Older age groups 12.50 12.50 1 0.50 1.020 0.154 Young age groups 6.54 78.50 12

Note. * P < α = 0.10, one-tailed; ** P < α = 0.05, one-tailed and *** P < α = 0.01, one-tailed.

Additional analysis were performed to check whether the experimental or control group portrayed a different sign. Test results of the experimental group can be seen in Table 8 as well. In this instance, interpreting the non-significant coefficient of 0.154 (z-statistic = 1.020) from this test is not valid and reliable because of this small N (N=1) in the older age group for the experimental group. Thus, no conclusions can be made about the experimental group. The results of the independent sample t-test for the control group can be seen in Table 9. Table 9 depicts a small difference between the means of both groups, in favor of the younger age group. Hence, the control group depicts a non-significant coefficient of 0.323 (t-statistic = 0.467). Thus, the

hypothesis 3 is rejected. Consequently there is no significant difference between different age groups in terms of correctly judging bank related e-mails.

Table 9: The mean statistics of the amount of correctly judged bank e-mails across the age groups and the test statistics of an equal mean of the amount of correctly judged bank e-mails

between the older and younger age groups.

Table 9

Mean statistics across groups

Levene’s test for equality of variances T-test for equality of means Age X̅ Std. F Sig. T Sig (1- tailed) N The amount of correctly judged bank e-mails – equal variances assumed Older age groups 8.14 1.464 2.229 0.161 0.467 0.323 7 Younger age groups 7.86 0.690 7 Equal variances not assumed 0.467 0.326

Note. * P < α = 0.10, one-tailed; ** P < α = 0.05, one-tailed and *** P < α = 0.01, one-tailed.

Additional analysis of the data portrayed that a participant’s relational status had an effect on the amount of bank e-mails which are correctly judged. Table 10 displays the results of this test in which the dependent variable was the phishing susceptibility. The grouping variable was a dummy variable: a 0 indicated being in a relationship and a 1 indicated being single. At first, Table 10 displays a higher mean rank for being in relationship across all three tests.

Consequently, the whole sample displays a significant coefficient of 0.023 (z-statistic = 2.000). Therefore there is a significant difference in the mean ranks between being single or being in a relationship, in favor of the group in a relationship. Consequently being in a relationship has a positive effect on correctly judging bank e-mails.

Table 10 also shows additional analysis of the experimental group and the control group, with the goal to study which group being in a relationship mattered the most. The results of these additional tests depict that especially being in a relationship matters most in the experimental group due to the significant coefficient of 0.076 (t-statistic = 1.433) at an alpha of 0.1. Therefore the focus should be on single persons after the hints of Veiligbankieren.nl had a more intensive

marketing campaign. The opposite holds as well. Focusing the marketing campaign on either of these groups beforehand does not have a significantly higher positive effect in comparison to the other group because of the insignificant value of the control group.

Table 10: The mean rank statistics of the amount of correctly judged bank e-mails across the relationship status and the test statistics of an equal mean rank of the amount of correctly judged

bank e-mails between being in a relationship and single.

Table 10

Mean rank statistics across groups

1 tailed Z-test for equality of mean ranks Relationship status X̅ rank Sum of ranks N Mann- Whitney U Z P-value The amount of correctly

judged bank e-mails (whole sample)

In a

relationship 16.11 290.00 18 _{56.00 2.000 0.023**} Single 9.78 88.00 9

The amount of correctly judged bank e-mails (experimental group)

In a

relationship 8.00 72.00 9 _{9.00 1.433 0.076*} Single 4.75 19.00 4

The amount of correctly judged bank e-mails (control group)

In a

relationship 8.33 75.00 9 _{15.00 0.890 0.187} Single 6.00 30.00 5

Note. * P < α = 0.10, one-tailed; ** P < α = 0.05, one-tailed and *** P < α = 0.01, one-tailed.