Essays on supplier responsiveness and buyer firm value - Nyenrode ...

2.6 The Structural Model
The second part in our model estimation is assessing the structural model. The SEM
technique was chosen because it is an important tool for academic research as well as
for analyzing survey data and covariance structures in data (Hair et al., 2006). We
follow the seven stages of structural equation modeling that were developed by Hair
et al. (2006).
The first stage is developing a modeling strategy. We had two alternative
strategies from which to choose while developing our modeling strategy. The first
alternative is developing a single model development strategy (i.e., also called a
confirmatory model strategy) and the second alternative is developing a competing
model strategy. The benefit of the first alternative is that it leads a researcher to focus
on one model and, in turn, this leads to a rigorous application of the SEM technique.
In tandem, a single model development strategy suffers from the lack of identification
of alternative models (Hair et al., 2006). The benefit of the second alternative is that it
allows a researcher to assess alternative theories, but we had already evaluated
alternative theories during our literature review and synthesis. Moreover, we have
already arrived at a specific model that we wanted to test. Hence, a single model
strategy best suited our current needs.
We also dealt with whether we had enough evidence to determine causality.
The most important criterion is a sound theoretical reasoning that we have developed
in the earlier section on our hypotheses development and literature synthesis. Supplier
responsiveness, IdRR, buyer satisfaction, supplier brand value, and buyer firm value
have statistical associations and cause and effect antecedence based on prior research
that was covered in the literature synthesis. We chose the most important variables
after our literature synthesis, thus significantly reducing the possibility of a lack of
alternative causal variables. A limitation of this approach is that we are not able to
investigate all other possible models that our research might include. While
specifying the initial model we had to adhere to the rule of five observations for each
measured variable in the model (Hair et al., 2006, p. 166). Our initial model with 39
items and four latent constructs and one observed construct did not meet this criterion.
With the help of modification indices, this was later reduced to 10 measured variables
and five constructs. This gave us a ratio of about 16 respondents for every variable
(164/10), which exceeds the minimum recommended limit of five respondents for
every item (Hair et al., 2006, p. 166). Our four latent concepts and one observed
construct are fewer than the maximum recommended of 20 for using the SEM
technique.
In the second and third stages, we constructed a path diagram of causal
relationships. We have one exogenous construct, which is supplier responsiveness.
We have four endogenous constructs, namely IdRR, satisfaction, supplier brand
value, and buyer firm value. We also tested for unidimensionality. Our results
indicated that our constructs load on more than one factor and, therefore,
unidimensionality is not a concern in the current study.
In the fourth stage of our research, we determined the input matrix type and
estimated the proposed model. We choose the covariance matrix because it is a true
test of theory (Hair et al., 2006, p. 603). The correlation matrix is not suited for theory
testing as it only represents a pattern of relationships. We used the maximum
likelihood estimation method and our sample size of 164 was above the recommended
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