Web Systems Design and Online Consumer Behavior [Electronic resources] نسخه متنی

Empirical Results

Model’s Hypotheses

As mentioned earlier, in the first part of the model (focused on switching costs’
antecedents) we hypothesize that:

H1: The 5Ps are positively related to perceived positive
switching costs

Following this approach, we suppose (in the second part of the model, focused on the
consequences of the customer perception of positive switching costs) that:

H2: Perceived positive switching cost are positively
associated with customer satisfaction, repurchase intentions, cognitive and behavioral
loyalty

H3a: Customer satisfaction and cognitive loyalty (in a
digital environment) are positively associated with cognitive lock-in strategies

As well as:

H3b: Repurchase intentions and behavioral loyalty (in a
digital environment) are positively associated with behavioral lock-in strategies

Research Methodology

Data Collection and Sampling Procedure

Our empirical analysis followed two steps: in the
first part standard scale development procedures were followed in the development of the
multidimensional switching costs scale. Scale items for the 5Ps were developed based on the
guidelines suggested by Churchill (1979) and Gerbing and Anderson (1988). But also in-depth
interviews with managers from a sample of 15 firms from IT (B2B) sector (three e-suppliers and 12
of their e-customers [that had experienced shopping online experiences with all the three
esuppliers]) were conducted to define the scale items. A panel of five marketing faculty reviewed
the items for clarity and face validity. Then item-total correlation, Cronbach’s Alpha and
exploratory factor analysis were examined: thus the original items were refined and items not
meaningfully were deleted. At this step (concluded the exploratory analysis) we were ready to
administer the questionnaire to a sample of 180 e-customers [that had experienced shopping online
from at least two of the original 3 e-suppliers]. The answering rate was quite high (about 86%).
Our analysis followed mainly the structural equation modeling procedure.

Analyses and Results

The hypotheses were tested using multiple multivariate analysis methodologies (we used SPSS
11.0 and LISREL 8.54). As mentioned earlier, firstly we conducted an exploratory factor analysis to
determine whether the scale items loaded as expected. We then calculated Cronbach’s alphas for the
scale items to ensure that they exhibited satisfactory levels of internal consistency (Table 16-1). We
refined the scales by deleting items that did not load meaningfully on the underlying construct and
those that did not highly correlate with other items measuring the same construct.

Antecedents of Perceived Positive Switching Costs
Feeling

Hypothesis H1 refers to the impact of the 5Ps on the customer perceived feeling of positive
switching costs (during his online shopping expeditions), stemming from the Web site’s elements. To
parsimoniously capture the joint impact of the 5Ps, we used the following multiplicative
model:

PSC = g₀ *
P₁^g1 * P₂^g2 * P₃^g3 * P₄^g4
* P₅^g5 (1)

Where: PSC = customer perceived feeling of positive switching costs; P₁
= perceived usefulness; P₂ = perceived ease of use;
P₃ = perceived simplicity of Web site interface design; P₄
= best perceived customer service; P₅ = positive perceived
feeling with the Web site. Equation (1) was linearized by logarithmic transformation and the
log-transformed model was estimated using ordinary least squares (OLS). Correlations between the
variables are reported in
Table
16-1.

Parameter g₀ is the intercept, and
parameters g₁ to g₅ capture the impact of the 5Ps on PSC Results of
the regression analysis are presented in
Table 16-2.
A summary consideration of the results indicates that all the parameters estimated are significant
at p < .05, and in the predicted direction. The adjusted R² of the
model is 0.73. We calculated the variance inflation factor to check for multicollinearity: the
average VIF is 2.66, ranging from 2.11 and 3.21, well below the recommended cutoff of 10 (Neter,
Wasserman & Cutner, 1985). Thus our hypothesis (H1) that the 5Ps are positively related to
customer feeling of positive switching costs stemming from Web site’s elements is supported. The
elasticity of PSC respect to the 5Ps ranges from 0.07 for perceived ease of use to 0.42 for
positive perceived feeling with the Web site.

Consequences on Customer Satisfaction, Repurchase
Intentions, Cognitive and Behavioral Loyalty

Hypotheses H2 focuses on the consequences of
customer-perceived positive switching costs. To test the impact of PSC on customer satisfaction (=
CS), repurchase intentions (= RI), cognitive loyalty (CL) and behavioral loyalty (BL), the
hypotheses were tested by the use of structural equation modeling (SEM, this time using LISREL
8.54); the path diagram, the separate structural equations and regression analyses with the latent
variable scores will be reported. The collected evidence from all these analyses will be used for
the conclusion regarding the stated hypothesis. The path diagram (Figure 16-2)
shows that PSC have a positive direct effect, as hypothesized, on all the considered four
dimensions. All estimates have t-values larger than three and are thus statistically significant.
The structural equations (reduced models) as well as the multiple regression analysis (MRA) confirm
this result, by generating a high R² (0.87).

Figure 16-2: Relationship between
customer perceived feeling of positive switching costs and customer satisfaction (CS), repurchase
intentions (RI), cognitive loyalty (CI), behavioral loyalty
(BI)

Although c² measure should show
a lower value (c² = 12.52, df = 172,
p < 0.005), since c² is very
sensitive to sample size, a large number of other indices indicate a good fit of the model.
Table 16-3
shows some of those indices along with reported guidelines for a good model.

Possible Correlates with Cognitive and Behavioral Lock-In
Strategies

Hypotheses H3a and H3b focus instead on the possible correlation between CS, RI, CL, BL and
cognitive and behavioural strategies in a digital environment. In order to test their possible
correlation, the hypotheses were also tested in this case by the use of SEM. The path diagram, the
separate structural equations and regression analyses with the latent variable scores are reported
below. The path diagram (Figure 16-3) shows, as hypothesized, that customer satisfaction and
cognitive loyalty are correlated higher with cognitive lock-in strategies (than with behavioral
lock-in), whereas repurchase intentions and behavioral loyalty better correlate with behavioral
lock-in strategies (than with cognitive ones). All estimates have t-values larger than three and
are thus statistically significant. The structural equations (reduced models) as well as the
multiple regression analysis (MRA) confirm this result, by generating a high R²
(0.76).

Figure 16-3: Relationship between
customer satisfaction (CS), repurchase intentions (RI), cognitive loyalty (CI), behavioral loyalty
(BI) and cognitive (CLC)/behavioral (BLC) lock-in strategies (For simplicity we will show only the
significant values [values <0.40 are omitted]. We report in large dashed arrows and irregular
dashed arrows the significant pathways.)

Also in this case c² measure
should show a lower value (c² =
14.73, df = 165, p < 0.005). But a large number of other indices indicate a good fit of the
model (Table
16-4 shows some of those indices along with reported guidelines for good model.).