Understanding consumer online shopping behaviour from the perspective of transaction costs

Table 5.21 Results of Structural Model When Controlling for Age, Gender, Income and Education Level 
Equation Predicted 
Variables 
Predictor 
Variables 
Hypothesis 
Beta weight 
T value 
R
2
 
1 
Perceived Consumer TCs 
Internet Access Availability 
Perceived Internet Expertise 
Online Buying Frequency 
Product Quality Concern 
E- Service Quality 
Reputation of Online Store 
Perceived Convenience 
Privacy and Security Concerns 
Age 
Gender 
Income 
Education Level 
H1a
-.052 
-2.948
**
.96 
H1b -.116 
-5.851
***
H1c -.228 
-9.072
***
H2a .057 2.330
*
H2c -.482 
-9.041
***
H2d -.074 -3.003
**
H3a -.087 
-4.239
***
H3b .048 
1.945 
(n.s.) 
.023 
1.407 
(n.s.) 
-.008 -.590 
(n.s.) 
-.007 -.458 
(n.s.) 
-.011 -.735 
(n.s.) 
2 
Online Purchase Behaviour 
Perceived Consumer TCs 
H4a 
-.834 
-30.524
***
.70 
3 Customer 
Loyalty 
Perceived 
Consumer TCs 
H4b 
-.563 
13.918
***
.76 
Customer Satisfaction 
H5 
.334 
8.854
***
4 Customer 
Satisfaction Perceived 
Consumer TCs 
H4c 
-.875 
-32.715
***
.77 
AVA
.80 
Note:
AVA: Average Variance Accounted for.
*** p < 0.001, ** p < 0.01 , * p < 0.05, n.s. p > .05 

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262
-.834 (-30.524
***
)
-.875 (-32.715
***
)
R
2
= 96%
R
2
= 70%
R
2
= 77% 
R
2
= 76% 
Figure 5.3 Structural Model with Control Variables Showing Results of Analysis 
 
 
Note: *** p < 0.001, ** p < 0.01 , * p < 0.05 
-.563 (-13.918
***
)
Consumer-related characteristics 
Internet Access Availability 
Perceived Internet Expertise
Online Buying Frequency
Online store- and product-related 
characteristics 
Product Quality Concern 
E- Service Quality
Reputation of Online Store
Online channel-related 
characteristics 
Perceived Convenience 
Privacy and Security Concerns
Perceived 
Consumer TCs of 
Online Shopping
Online 
Purchase 
Behaviour 
Customer 
Satisfaction 
Customer 
Loyalty 
-.116 (-5.851
***
)
-.052 (-2.948
**
)
.048 (1.945)
.057 (2.330
*
)
-.482 (-9.041
***
)
-.074 (-3.003
**
)
-.228 (-9.072
***
)
.334 (8.854
***
)
Age
Gender 
Income 
Education 
.023 (1.407) -.008 (-.590)
-.007 (-.458)
-.011 (-.735)
-.087 (-4.239
***
)

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263
5.7.2 Test of Causal Research Hypotheses 
When controlling for age, gender, income and education level of online shoppers in 
predicting consumer TCs, the results in Table 5.21 indicated that Internet access available 
(H1a) significantly affected consumer TCs (
β
= -.052, t = -2.948), thereby supporting H1a. 
Hypothesis 1b explicated the negative impact of perceived Internet expertise on consumer 
TCs. The results showed that consumer transaction costs was significantly influenced by 
perceived Internet expertise (
β
= -.116, t = -5.851). Thus, H1b was supported. Hypothesis 1c 
postulated the negative association between online buying frequency and consumer TCs. 
Results indicated that online buying frequency significantly affected consumer TCs (
β
= -
.228, t = -9.072), thus confirming H1c. H2a predicted a positive relationship between product 
quality concern and consumer TCs – this was supported (
β
= .057, t = 2.330). Also, as 
expected, e-service quality had a significant and negative impact on consumer TCs (
β
= -.482, 
t = -9.041), thus supporting H2c. As predicted in H2d, reputation of online store had a 
significant and negative influence on consumer TCs (
β
= -.074, t = -3.003), providing support 
for H2d. The results also show that perceived convenience significantly negatively 
influenced consumer TCs (
β
= -.087, t = -4.239), supporting H3a. Surprisingly, the researcher 
did not find the significant effect of privacy and security concerns on consumer TCs (
β
= .048, 
t = 1.945), thus H3b was not supported.
As presented in Table 5.21, when controlling for age, gender, income and education level in 
predicting consumer TCs, the results indicated that a negative and significant effect of 
consumer TCs on online purchase behaviour (
β
= -.834, t = -30.524), supporting H4a.
Similarly, the predicted negative relationship between consumer TCs and customer loyalty 
(H4b) was supported by the significant path with 
β
= -.563 (t = 13.918). H4c, which 
predicted a negative and significant effect of consumer TCs on customer satisfaction, was 

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264
also supported (
β
= -.875, t = -32.715). Table 5.21 revealed that the relationship between 
customer satisfaction and customer loyalty was positive and significant with a standardized 
path estimate of 
β
= -.334 (t = 8.854) and as such H5 was supported. 
Furthermore, the results shown in Table 5.21 indicate that the effects of the control variables 
on consumer TCs are all insignificant (t < 1.96, p > .05), which means that age, gender, 
income and education do not confound the relationships that have been specified in the 
structural model. Overall, one out of twelve estimates were consistent with the hypotheses, 
the results supported the theoretical model for controlling for demographic variables with a 
caveat for the path from privacy and security concerns to consumer TCs that was rejected.