Analysis of gender and age group differences

Some scholars have evidenced that gender and age differences exist in the context of technology adoption (Wang, Wu, Wang, 2009; Venkatesh et al., 2012). Therefore, non-parametric tests, i.e. the Mann–Whitney U-test and the Kruskal Wallis Test were used to test respectively whether there were gender or age differences in the population. Non-parametric tests were used because the data sample size was relatively small and researchers often disagree as to whether Likert scale data can be considered as normally distributed. Non-parametric tests are more robust in these circumstances as they make no assumption as to the distribution of the parent population and can be applied to smaller samples of data. (Norman, 2010; Alexander, William & Frances, 2005). The Mann–Whitney U-test was chosen for investigating gender because there were two gender groups whereas the Kruskal Wallis Test was chosen for investigating age group difference because there were the three age groups (Alexander, William & Frances, 2005). Though the sample sizes were different in terms of age group or gender, the Mann–Whitney U-test and the Kruskal Wallis Test could be used to test the difference in gender (Mann & Whitney, 1947; Breslow, 1970).

5.7.2.1 Gender difference

The hypotheses of gender difference in the scores of seven independent variables were as follows.

Hypothesis 1

 H0: There is no significant gender difference in the scores for performance

expectancy.

 HA: There is a significant gender difference in the scores for performance

expectancy. Hypothesis 2

 H0: There is no significant gender difference in the scores for effort

expectancy.

 HA: There is a significant gender difference in the scores for effort expectancy.

Hypothesis 3

 H0: There is no significant gender difference in the scores for hedonic

motivation.

 HA: There is a significant gender difference in the scores for hedonic

motivation. Hypothesis 4

 H0: There is no significant gender difference in the scores for habit.

 HA: There is a significant gender difference in the scores for habit.

Hypothesis 5

 H0: There is no significant gender difference in the scores for device usability.

 HA: There is a significant gender difference in the scores for device usability.

Hypothesis 6

 H0: There is no significant gender difference in the scores for social presence.

 HA: There is a significant gender difference in the scores for social presence.

Hypothesis 7

 H0: There is no significant gender difference in the scores for interactive

learning.

 HA: There is a significant gender difference in the scores for interactive

learning.

As shown in Table 5.8, the z-statistic of (1) performance expectancy is 0.249, (2) device usability is 0.102, (3) effort expectancy is 0.005, (4) hedonic motivation is 0.580, (5) social presence is 0.133, (6) habit is 0.604, and (7) interactive learning is 0.360.

Table 5.8: Mann–Whitney U-test Statisticsa

PE DU EE HM SP HT IL

Mann-Whitney U 1406.000 1318.000 1104.000 1514.500 1341.500 1520.500 1450.500

Wilcoxon W 2147.000 4973.000 1845.000 5169.500 4996.500 5175.500 5105.500

Z -1.153 -1.637 -2.806 -.554 -1.502 -.519 -.916

a. Grouping Variable: Gender

Therefore, the alternate hypotheses of performance expectancy, device usability, hedonic motivation, social presence, habit, and interactive learning are rejected at 5 per cent level of significance (p < 0.05). The null hypothesis of effort expectancy is rejected at 5 per cent level of significance (p < 0.05). Therefore, there is no evidence to reject the null hypothesis for performance expectancy, device usability, hedonic motivation, social presence, or interactive learning at 5 per cent level of significance (p < 0.05).

As shown in Figure 5.7, a Boxplot was created to examine the gender difference in the score of effort expectancy. The male box has about the same length as the whiskers whereas the female box is shorter than the length of the whiskers. Besides, the male box median (~5.0) exceeds the female box median (~4.6). The female appears to have a larger variability than the male. Male and female are reasonably symmetric. There is an outliner in Female.

Figure 5.1: The Boxplot of gender difference in the score of effort expectancy [EE]

5.7.2.2 Age group difference

In this study, we have three age groups (1) 18-21, (2) 22-24, and (3) 25-29. The hypotheses of age group difference in the scores of seven independent variables were as follows.

Hypothesis 1

 H0: There is no significant age group difference in the scores for performance

expectancy.

 HA: There is a significant age group difference in the scores for performance

expectancy. Hypothesis 2

 H0: There is no significant age group difference in the scores for effort

expectancy.

 HA: There is a significant age group difference in the scores for effort

expectancy. Hypothesis 3

 H0: There is no significant age group difference in the scores for hedonic

motivation.

 HA: There is a significant age group difference in the scores for hedonic

motivation. Hypothesis 4

 H0: There is no significant age group difference in the scores for habit.

 HA: There is a significant age group difference in the scores for habit.

Hypothesis 5

 H0: There is no significant age group difference in the scores for device

usability.

 HA: There is a significant age group difference in the scores for device

usability. Hypothesis 6

 H0: There is no significant age group difference in the scores for social

presence.

 HA: There is a significant age group difference in the scores for social

Hypothesis 7

 H0: There is no significant age group difference in the scores for interactive

learning.

 HA: There is a significant age group difference in the scores for interactive

learning.

As shown in Table 5.9, there are significant age group differences in the scores of performance expectancy, device usability, hedonic motivation, social presence, and interactive learning (p < 0.05).

Table 5.9: Kruskal Wallis Test Statisticsa,b

PE DU EE HM SP HT IL

Chi-Square 11.651 9.992 .441 7.023 8.301 .746 9.550

df 2 2 2 2 2 2 2

Asymp. Sig. .003 .007 .802 .030 .016 .689 .008 a. Kruskal Wallis Test

b. Grouping Variable: AgeGP

Therefore, the alternate hypotheses of effort expectancy and habit are rejected at 5 per cent level of significance. Besides, the null hypothesis of performance expectancy, device usability, hedonic motivation, social presence and interactive learning are rejected at 5 per cent level of significance (p < 0.05). In summary, there are significant age group differences in the scores of performance expectancy, device usability, hedonic motivation, social presence, and interactive learning except for effort expectancy or habit, at 5 per cent level of significance (p < 0.05).

In order to further understand the difference visually, as shown in Figure 5.2, Boxplots were created to examine the age difference in the score of performance expectancy, device usability, hedonic motivation, social presence and interactive learning. Table 5.8 shows the interpretation of Boxplots of different variables.

Table 5.10: The interpretation of Boxplots for different variables GP1: Group aged 18-21, GP2: Group aged 22-24, GP3: Group aged 25-29

Variable GP1 GP2 GP3 Fig 5.8 PE 5.00 3.00 5.00 (a) DU 4.75 6.00 6.00 (b) HM 4.60 5.00 5.60 (c) SP 4.80 5.45 6.00 (d) IL 4.70 5.00 3.00 (e)

For the variables DU, HM, and SP, there is a clear trend that GP2 and GP3 have higher average scores. In other words, the older the respondent, the higher the average score. One of the reasons is that different age groups may think in different ways during the adoption of technology (Venkatesh & Morris, 2000). It is also believed that younger users have higher levels of self-worth that make them less affected by others in the adoption of mobile learning (Wang, Wu & Wang, 2009). In terms of mobile learning pedagogical strategy, previous researchers recommend that the knowledge level of learners and challenges of mobile learning should be matched so that learners can benefit from it (Wang, Wu & Wang, 2009). Therefore, mobile learning using Facebook should be designed by (1) strengthening the smartphone’s usability, (2) improving the enjoyment, and (3) emphasizing the sense of online community. For variable PE, GP1 and GP3 have higher average scores. For variable IL, GP1 and GP2 have higher average scores. Though there are no obvious trends for these variables,

PE DU HM SP IL GP1 5 4.75 4.6 4.8 4.7 GP2 3 6 5 5.45 5 GP3 5 6 5.6 6 3 0 1 2 3 4 5 6 7 Sc o re

previous researchers believe that it is due to the challenge of mobile learning being lower than the skills of users, and hence they feel bored and have negative feedback (Kiili, 2005). Therefore, it is necessary to address the problems of mobile learning and

improve performance expectancy and interactive learning.

Figure 5.8: The Boxplots for age difference in the score of PE, DU, HM, SP and IL

(a) 5.00 3.00 5.00 (b) 6.00 _6.00 4.75 (c) 4.60 5.00 5.60 (d) 4.80 5.45 6.00

5.7.2.3 Summary of Non-parametric tests

The results of non-parametric tests showed that there were significant (1) gender differences in the score for effort expectancy and (2) age differences in the score for performance expectancy, device usability, hedonic motivation, social presence [SP], and interactive learning. The findings corroborate past literature about the existence of age and gender differences in technology acceptance (Wang, Wu, Wang, 2009; Venkatesh et al., 2012). Therefore, the age and gender differences will be further investigated and explored in qualitative research in section 6.