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

4.7 Discussion
We start this discussion section with an important clarification. The purpose of this
current paper was not to take an absolute position on whether objective or subjective
measurement was superior, nor was the intention to enter into a philosophical debate
about whether any measures could ever be considered objective. Rather, it was to
determine whether objective and subjective performance measures reinforce each other.
We have not found evidence to indicate that this occurs in our current data set.
For this paper, we adopt the theoretical lens of market orientation. We argue that
both objective and subjective data can be used in tandem for the model in Chapter 2.
Hence, we expected to find that regardless of the data type used in the model, supplier
responsiveness reduces IdRR and results in improved buyer firm performance. Our
current research draws on prior research in market orientation and B2B literature, and our
results contribute to both literature streams. In the rest of this section, we discuss the
measurement model hypothesis results and the structural model hypothesis results.
4.7.1 Measurement Model Hypothesis
In our hypothesis 1(a), we predicted that our measurement model would be similar to the
model in Chapter 2. At least in terms of fit statistics, our prediction was correct. Overall,
our fit statistics were acceptable. The objective model has a slightly worse RMSEA (0.5)
than the model in chapter 2 RMSEA (0.67). The model in this Chapter has a GFI and CFI
of .937 and .906, whereas the Chapter 2 has a .95 for GFI and .96 for CFI. The
differences are minor differences between both CFAs in terms of goodness of fit indices.
This is because the data used in the CFA in Chapter 4 is a subsample of the data used in
Chapter 2. Furthermore, our construct reliability and construct validity are acceptable for
the CFA of Chapter 4. The construct reliability and construct validity are also acceptable
for the CFA of Chapter 2 except for the variable supplier brand value. This difference in
construct validity can be explained as managers publically listed firms understand
supplier brand value as a variable of importance whereas this importance is not of such a
great concern to managers of unlisted companies. For supplier responsiveness in Chapter
4 the average variance extracted (AVE) was 66.90% and our construct reliability (CR)
was 0.857 compared with the 47% AVE of the model in Chapter 2 and a CR of 0.72. For
IdRR, we got an AVE of 63.35% and CR of 0.774 compared with the AVE of 54% of the
model in Chapter 2 and CR of 0.71. For buyer satisfaction the AVE is 62.12% in Chapter
4 and a CR of 0.76, whereas the model in chapter 2 had an AVE of 59.2% and a CR of
0.74. For supplier brand value, we had an AVE of 61.65% and a CR of 0.76 compared
with the model in Chapter 2 that had an AVE of 37.5% and a CR of 0.54.
Finding confirmatory factor analysis studies for comparison has been challenging
because of the limited number of similar studies. At a general level, numerous intraorganization
market orientation studies have been undertaken, but inter-organization
studies are very scarce, and studies examining objective and subjective data in marketing
with confirmatory factor analysis are even more limited (Kohli & Jaworski, 1990;
Kohli et al., 1993). In addition to the confirmatory factor analysis, we also conducted a
bivariate correlation analysis between subjective and objective measures for comparison
purposes. Like Jaworski and Kohli (1993), we found an insignificant correlation between
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