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

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In the third step, we used maximum likelihood estimati<strong>on</strong> in AMOS to<br />

estimate the model. The recommended sample size is between 100-150, but valid<br />

results have also been found with smaller sample sizes of up to 50 (Hair et al.,<br />

2006).Therefore, since our sample size of 164 is over the 100 threshold <strong>and</strong> well<br />

above the minimum size of 50, we decided to use the maximum likelihood opti<strong>on</strong>.<br />

Another technique that we could have used was asymptomatically distributi<strong>on</strong> free,<br />

which offers an advantage over all other techniques since it does not require normally<br />

distributed data. However, as the data we are using is within the acceptable range of<br />

the normal limit, this issue is not a c<strong>on</strong>siderati<strong>on</strong> for us. In additi<strong>on</strong>, asymptomatically<br />

distributi<strong>on</strong> free requires larger sample sizes than our current sample size of 164.<br />

Therefore, we decided not to use it. We also used the opti<strong>on</strong>s of fitting the saturated<br />

<strong>and</strong> independence models, minimizati<strong>on</strong> history, st<strong>and</strong>ardized estimates, <strong>and</strong> squared<br />

multiple correlati<strong>on</strong>s. The latter two comp<strong>on</strong>ents would eventually help us to estimate<br />

the average variance that was extracted <strong>and</strong> the reliability of the findings. The<br />

st<strong>and</strong>ardized residual covariances are less than two, which means the measurement<br />

model is acceptable.<br />

Another criteri<strong>on</strong> is that the st<strong>and</strong>ardized regressi<strong>on</strong> weights should be above<br />

0.5 (i.e., preferably 0.7). All of our measures, as presented in Table 2.19, fulfill this<br />

criteria. A close examinati<strong>on</strong> of the st<strong>and</strong>ardized regressi<strong>on</strong> weights within the CFA<br />

model indicates that n<strong>on</strong>e is extremely close to <strong>on</strong>e. This result provides evidence that<br />

multicollinearity is not a problem with our data. The highest st<strong>and</strong>ardized regressi<strong>on</strong><br />

weight we have is 0.838.<br />

Table 2.19 represents the average variance extracted (AVE) for each the<br />

c<strong>on</strong>structs in the model tested. AVE is the shared amount of variance indicators have<br />

with a c<strong>on</strong>struct. The minimal acceptable level as a rule of thumb is 50%. Two of our<br />

c<strong>on</strong>structs <strong>buyer</strong> satisfacti<strong>on</strong> <strong>and</strong> IdRR pass the 50% threshold. Supplier<br />

resp<strong>on</strong>siveness is close to the 50% threshold <strong>and</strong> <strong>supplier</strong> br<strong>and</strong> <strong>value</strong> does has bad<br />

c<strong>on</strong>struct validity. However, in Chapter 4 when we repeat this analysis using a sub<br />

sample of publically listed companies from the data used in Chapter 2 <strong>and</strong> we find<br />

that the c<strong>on</strong>struct <strong>supplier</strong> br<strong>and</strong> <strong>value</strong> satisfies the criteria of surpassing the 0.5<br />

threshold for AVE.<br />

Table 2.19 Average Variance Extracted<br />

C<strong>on</strong>struct Observed Variable (SRW) Squared Multiple Correlati<strong>on</strong>s<br />

Supplier Resp<strong>on</strong>siveness SIQ (.773) .598<br />

Supplier Resp<strong>on</strong>siveness CustNPD (.643) .414<br />

Supplier Resp<strong>on</strong>siveness CF (.631) .398<br />

Average Variance Extracted 47 %<br />

IdRR CCC (.682) .465<br />

IdRR IM (.796) .633<br />

Average Variance Extracted 54 %<br />

Supplier Br<strong>and</strong> Value AssBr<strong>and</strong> (.645) .415<br />

Supplier Br<strong>and</strong> Value RP (.579) .335<br />

Average Variance Extracted 37.5%<br />

Buyer Satisfacti<strong>on</strong> Sales (.838) .703<br />

Buyer Satisfacti<strong>on</strong> Fairness (695) .483<br />

Average Variance Extracted 59.2 %<br />

SRW=St<strong>and</strong>ardized Regressi<strong>on</strong> Weights.<br />

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