For the purpose of conducting the hypothesis tests, the regression analysis in SPSS is utilized as a mean to learn the results. Two levels of analysis are shown as the
first level is the average of sub-variables. For instance, the emotional and epistemic value is averaged, composing the internal value. As for the second level, each subordinate is analyzed independently.
The first hypothesis is denoted H1, with repurchase intention being the dependent variable and satisfaction being the independent variable. The results show that although RP2 Pis low, it is still significant with a p-value of 0.000, with 1% of significance and the t value 11.425. Table 4.11 shows the detailed description of H1.
Table 4.11: Test Results for H1
Dependent variable Repurchase Intention
Independent variable RP2P β t p-value
Satisfaction 0.271 .520 11.425 .000***
The next hypotheses are H2, H3 and H4, as presented in Figure 3-1. They are internal value towards satisfaction, external value towards satisfaction and functional value towards satisfaction. The results show that all three variables show significant effects with internal and functional has a p-value of 0.000 with 1% of significance.
Although the p-value of external value was only 0.097, it is still significant with 10%
of significance. Detailed statistics will be shown in table 4.12.
Table 4.12: Tests Results for H2, H3 and H4.
Dependent variable Satisfaction
Independent variable RP2P β t p-value
Internal value .257 5.514 .000***
External value .074 1.666 .097*
Functional value
.525
.512 11.453 .000***
Note:
*p<0.1;
Note:
*p<0.1;
**p<0.05;
***p<0.01
Since the model argues that information visibility has mediating effects, we need to identify if the relationship of information visibility towards satisfaction is significant.
If not, then there is no need to test information visibility as a mediator. Taking the satisfaction as the dependent variable and information visibility as the independent variable, we got results that proved it is 1% significant. See table 4.13 for specific numbers.
Table 4.13: Tests Results for Information Visibility towards Satisfaction
Dependent variable Satisfaction
Independent variable β t p-value
Information visibility .470 10.003 .000***
Another aspect to confirm is the internal value, external value and function value needs to show significance towards information visibility. Therefore, we can add in the information visibility to the independent variable and compare the results to the previous tests. It showed that all three variables are 1% significant. Test results are displayed below.
Table 4.14: Results for Internal, External and Functional towards Information Visibility
Dependent variable Information visibility
Independent variable β t p-value
Internal value .155 2.696 .007***
External value .203 3.715 .000***
Functional value .288 5.241 .000***
Note:
*p<0.1;
**p<0.05;
***p<0.01 Note:
*p<0.1;
**p<0.05;
***p<0.01
The last step is adding in the mediator, information visibility to the independent variable as this can let us understand whether information visibility has a mediation effect or not. If the t value decreased, or the significance has changed weaker, this indicates that information visibility has mediation effect on the relationship between dependent variables and dependent one. On the other hand, if the t value increased, it means that there is no effect of information visibility. H5 will serve as the hypothesis whether information visibility has mediation effect on internal value. H6 is associated with external value and H7 with functional value. Table 4.15 presents the results with information visibility added as the independent variable.
Table 4.15: Test Results for H5, H6 and H7
Satisfaction Dependent variable
Model 1 Model 2
Independent variable β t p-value β t p-value
IV .257 5.514 .000*** .236 5.073 .000***
EV .074 1.666 .097* .046 1.032 .303
FV .512 11.453 .000*** .472 10.309 .000***
Mediator: InV .137 3.199 .002***
RP2P .525 .539
IV: Internal value; EV: External value; FV: Functional value; InV: Information visibility
Based on the results, we can conclude that information visibility has a partially mediating effect towards internal and functional value. The t value decreased from 5.514 to 5.073 for internal and 11.453 to 10.309 for the function value (see Model 1 and 2). Although it was not a significant drop in the numbers, it still has some, but little mediation effects. This implies that consumers regard significantly internal and functional value as important factors of purchase satisfaction, regardless that information is sufficient or not. As for the external value, the t value went down
Note:
*p<0.1;
**p<0.05;
***p<0.01
from 1.666 to 1.032 and the p-value went from 10% level of significance to non-significant. Therefore, this stresses that information visibility has a full mediating effect towards external value. In other words, information visibility can alternate the effects of external value to satisfaction. For a more concrete explanation, social and conditional value will be likely to influence the purchase satisfaction, and the same time they are ignored if consumers have sufficient information.
In the first level the test results showed that every value is significant and that information visibility does have a mediating effect on external value in the model. In the next stage, second level test uses each subordinate independently to run the regression analysis. That is, each subordinate coordinates with their respective t and p-value. Emotional value is denoted H2a and epistemic is H2b and so on. Of all the eight independent variables, every one of them was tested significant with seven of them displaying a 1% level of significance. Table 4.16 shows the results for the second level of regression analysis.
Table 4.16: Test Results for H2a-H4d
Dependent variable Satisfaction
Independent variable RP2P β t p-value
Emotional .463 8.424 .000***
Epistemic .167 3.040 .003***
Social .299 5.594 .000***
Conditional .215 4.027 .000***
Price .216 4.651 .000***
Quality .214 3.719 .000***
Need .112 2.014 .045**
Usage
.540
.297 5.259 .000***
Note:
*p<0.1;
**p<0.05;
***p<0.01
Then, the mediator, information visibility kicks in to be the independent variable as well. After this, we can compare it to the previous analysis to learn whether information visibility has mediating effects in the second level. Again, if the t value has decreased, or the significance level has become weaker, then we can conclude that information visibility has mediating effects. H5a serves as the hypothesis for emotional value towards satisfaction with information visibility being the mediator all the way to H7d is usage towards satisfaction. Table 4.17 shows the detailed description.
Table 4.17: Results for H5a-H7d
Satisfaction Dependent variable
Model 3 Model 4
Independent variable β t p-value β t p-value
Emotional .463 8.424 .000*** .390 7.281 .000***
Epistemic .167 3.040 .003*** .111 2.100 .036**
Social .299 5.594 .000*** .240 4.739 .000***
Conditional .215 4.027 .000*** .101 1.923 .055*
Price .216 4.651 .000*** .175 3.790 .000***
Quality .214 3.719 .000*** .179 3.174 .002***
Need .112 2.014 .045** .103 1.899 .058*
Usage .297 5.259 .000*** .269 4.863 .000***
Mediator: InV .134 3.098 .002***
RP2P .540 .552
InV: Information visibility
The information visibility shows significant and it has partially mediating effects on emotional, social, price, quality and usage (see Model 3 and 4). These five variables’ T value decreased somewhat and has a small weakening effect on the p-value. The epistemic variable’s p-value went from .003 to .036 and the need variable increased from .045 to .058 where both of which dropped one stage of
Note:
*p<0.1;
**p<0.05;
***p<0.01
significance level, from 1% level of significance to 5% significant. Among all, the conditional value changed the most, dropping two significance levels from 1% to 10%
significant and the T value decreased dramatically. From this, we can conclude that information visibility can alternate the effects from these three variables and that it has full mediation.