Cross tabulation between Academic level and Computer Self-Efficacy CSE)

Cross tabulations and chi-square tests were conducted between Academic level and Computer Self-Efficacy (CSE). The CSE construct is made up of 6 variables denoted as CSE_1, CSE_2, CSE_3, CSE_4, CSE_5 and CSE_6. The variables are;

CSE_1:I can complete a computer task only if someone assists me.

CSE_2: I can complete a computer task on my own without anyone’s assistance.

CSE_3: I can navigate through the Internet only if someone assists me.

CSE_4: I can navigate through the Internet on my own without anyone’s assistance. CSE_5: I can complete a CBA task only if someone assists me.

CSE_6: I can complete a CBA task on my own without anyone’s assistance.

The chi-square tests conducted between Academic level and the CSE variables showed that only two of the six CSE variables have significant relationships with Academic level (Table 4-22). These variables are CSE_2 and CSE_5. Therefore, crosstabs and inferences were made based on these two variables only.

Table 4-22: Chi-square test results between Academic level and Computer Self-Efficacy CSE_1 CSE_2 CSE_3 CSE_4 CSE_5 CSE_6

Academic Level

Chi-square value 16.309 23.104 13.154 14.406 22.372 16.291

Asymp. Sig. (p-value) 0.177 *0.027 0.358 0.276 *0.034 0.178

* p < 0.05 = significant relationship

The crosstabs between Academic level and CSE_2 showed that most of the respondents in their first year (64.9%), second year (79.4%), third year (85%) and honours academic level (100%) indicated that they can complete a computer task on their own without anyone’s assistance (Table 4-23). Also, the crosstabs between Academic level and CSE_5 showed that most of the respondents in each of the academic levels disagreed with the statement that they can complete a CBA task only if someone assists them (Table 4-24). This indicates that they do not need someone to assist them in order to complete a CBA task. Thus, it can be implied that, whether students are in their first year (new to CBA) or in the honours level, they are proficient enough to complete a computer task (including a CBA task) on their own without the assistance of anyone.

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Table 4-23: Crosstabs between Academic Level and Computer Self-Efficacy Q2 (CSE_2) Computer Self-Efficacy Q2

Total Strongly

Disagree Disagree Neutral Agree

Strongly Agree

Academic Level First year Count 6 15 30 41 53 145

% within Academic Level 4.1% 10.3% 20.7% 28.3% 36.6% 100.0% Second year Count 1 1 11 27 23 63 % within Academic Level 1.6% 1.6% 17.5% 42.9% 36.5% 100.0%

Third year Count 1 4 16 54 65 140

% within Academic Level 0.7% 2.9% 11.4% 38.6% 46.4% 100.0% Honours Count 0 0 0 1 1 2 % within Academic Level 0.0% 0.0% 0.0% 50.0% 50.0% 100.0% Total Count 8 20 57 123 142 350 % within Academic Level 2.3% 5.7% 16.3% 35.1% 40.6% 100.0%

Table 4-24: Crosstabs between Academic Level and Computer Self-Efficacy Q5 (CSE_5) Computer Self-Efficacy Q5

Total Strongly

Disagree Disagree Neutral Agree

Strongly Agree

Academic Level First year Count 52 37 31 12 13 145

% within Academic Level 35.9% 25.5% 21.4% 8.3% 9.0% 100.0% Second year Count 26 25 6 5 1 63 % within Academic Level 41.3% 39.7% 9.5% 7.9% 1.6% 100.0%

Third year Count 57 51 18 12 2 140

% within Academic Level 40.7% 36.4% 12.9% 8.6% 1.4% 100.0% Honours Count 1 0 1 0 0 2 % within Academic Level 50.0% 0.0% 50.0% 0.0% 0.0% 100.0% Total Count 136 113 56 29 16 350 % within Academic Level 38.9% 32.3% 16.0% 8.3% 4.6% 100.0%

4.8 Chi-square tests between the constructs used in this study

Six constructs were used in this study to investigate the perceptions of students about CBA. They are Computer Self-Efficacy (CSE), Perceived Ease-of-Use (PEOU), Perceived Usefulness (PU), Perceived Playfulness (PP), Facilitating Conditions (FC) and Behavioural Intention to Use (BI). In order to check if significant relationships exist between these constructs, chi-square tests were conducted. The results of the chi-square tests (as shown in

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the tables below) indicated that there are significant relationships (where p < 0.05) between some of the constructs.

The table below (Table 4-25) shows that all the six variables (PEOU_1, PEOU_2, PEOU_3, PEOU_4, PEOU_5 and PEOU_6) in the PEOU construct are significantly related to all the five variables (PU_1 to PU_5) in the PU construct. Thus, it can be implied that students’ perceived usefulness of CBA is determined by their perceived ease-of-use of CBA.

Table 4-25: Chi-square test result between PEOU and PU

* p < 0.05 = significant relationship

From the table below (Table 4-26), it can be inferred that all the variables in the PEOU construct have a significant relationship with all the variables in the CSE construct, and vice-versa. Thus, it can be implied that students’ perceived ease-of-use of CBA is determined by their computer self-efficacy.

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Table 4-26: Chi-square test result between PEOU and CSE

* p < 0.05 = significant relationship

The chi-square test result presented in Table 4-27, shows that significant relationships exist between PEOU and PP.

Table 4-27: Chi-square test result between PEOU and PP

* p < 0.05 = significant relationship

It can be seen in Table 4-28 that all the variables in the PEOU construct have a significant relationship with all the variables in the BI construct. It can thus be implied that students’ perceived ease-of-use of CBA determines their behavioural intention to use CBA.

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Table 4-28: Chi-square test result between PEOU and BI

* p < 0.05 = significant relationship

It can be seen in Table 4-29 that all the variables in the PP construct have a significant relationship with all the variables in the BI construct. Thus, it can be inferred that the perceived playfulness of CBA determines students’ behavioural intention to use CBA.

Table 4-29: Chi-square test result between PP and BI

* p < 0.05 = significant relationship

Also, it can be inferred from Table 4-30 that the perceived usefulness of CBA determines students’ behavioural intention to use CBA. This is because all the variables in the “perceived usefulness” construct have a significant relationship with all the variables in the “behavioural intention” construct.

Table 4-30: Chi-square test result between PU and BI

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From the table below (Table 4-31), few variables in the PEOU construct do not have a significant relationship with a few variables in the FC construct. However, most variables in the PEOU construct have a significant relationship with most variables in the FC construct, hence, significant relationships exist between PEOU and FC.

Table 4-31: Chi-square test result between FC and PEOU

* p < 0.05 = significant relationship

Similarly, as seen in Table 4-32, few variables in the PU construct do not have a significant relationship with few variables in the CSE construct. However, most variables in the PU construct have a significant relationship with most variables in the CSE construct, hence, significant relationships exist between PU and CSE.

Table 4-32: Chi-square test result between PU and CSE

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Furthermore, it can be inferred from Table 4-33 that students’ assessment preference determines their behavioural intention to use CBA. This is because all the variables in the “assessment preference” construct have a significant relationship with all the variables in the “behavioural intention” construct. Thus, if students prefer to undertake CBA, then they would most likely intend to undertake CBA frequently or in future.

Table 4-33: Chi-square test result between Assessment preference and BI

* p < 0.05 = significant relationship

From the chi-square test result shown in Table 4-34, it can be seen that there is no significant relationship between all the variables in both the “academic level” and “perceived ease-of- use” constructs. It can thus be implied that the academic level of students does not significantly influence their perceived ease-of-use of CBA.

Table 4-34: Chi-square test result between Academic Level and PEOU

PEOU_1 PEOU_2 PEOU_3 PEOU_4 PEOU_5 PEOU _6

Academic Level

Chi-square value 6.216 15.380 6.318 10.558 15.108 14.321

Asymp. Sig. (p-value) 0.905 0.221 0.899 0.567 0.236 0.281

4.9 Conclusion

This chapter showed the results of the analysis of the data obtained from the respondents. The “series mean” method was used to replace the missing data found in the data obtained, and the Kolmogorov Smirnov and the Shapiro-Wilk tests were used to check for the normality of the data. Descriptive statistics were presented using tables and graphs and inferential statistics were presented using cross tabulations and chi-square tests. The inferences of these statistics are shown at the end of each section. The next chapter presents how the results obtained have been interpreted in order to achieve the research objectives.

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