Content Validity

As mentioned prior to conducting the research study, content validity was

obtained through several steps. An extensive review of prior literature was conducted in the areas of supply chain management, service management and operations, information technology and performance. The scales were developed from this research and an evaluation of prior operationalized scales in these research areas. Next, three managers in the purchasing and sourcing arena were asked to review the items to evaluate the face validity of the measures. A final instrument was prepared after adjusting the questions based upon their feedback and that of this research’s dissertation committee. Some factors were pared to reduce the survey length. This work indicated that the resulting instrument represented the factors measured in the study.

4.3.4 Unidimensionality

Unidimensionality is obtained using confirmatory factor analysis in order to determine if the indicator variables are measuring a single theoretical construct. It can be evaluated by assessing several key fit indices to obtain an overall evaluation of the model’s fit to the data. These indices of fitness were obtained using the CALIS procedure in SAS version 9.2 for Windows. As suggested prior to commencing the

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survey, the number of indicator variables and constructs makes it difficult to be able to analyze the entire model at once. Given the number of responses received, three sub-models were evaluated (Moorman, 1995, Song, Dyer and Thieme 2006, Paulraj 2011), one for the services supply chain management factors, another for the three information technology impact factors and the last to measure the two performance factors (Tables 4.5a – 4.5c).

The first indicator is the ratio of the chi-square statistic to the degrees of freedom.

Here, some researchers recommend a ratio less than 3.0 (Hair et al, 1995) while others suggest a ratio less than 2.0 (Hatcher, 1994). Other measures of fit used in this study include adjusted goodness of fit (AGFI), root mean square residual (RMR), Bentler comparative fit index (CFI), and Bentler and Bonnet non-normed fit index (NNFI). When targeting values of AGFI > 0.80, RMR < 0.05 (or at least < 0.10), CFI > 0.90, and NNFI

> 0.90. When using these indicators, it can be seen in Table 4.5a that the services supply chain management measurement model meets three of the five measures, narrowly lagging with a CFI of 0.86 and an NNFI of 0.84. The information technology impact model meets four of the five measures, with an AGFI of 0.78 compared to the goal of 0.80. Lastly, the performance model meets three of the five measures. The AGFI is close at 0.77. The chi-square to degrees of freedom is 3.26, which is below the goal but within a reasonable level as mentioned by Marsch and Hocevar (1985). While these model statistics are not all beyond the ideal range, they are all very close, representing an adequate fit for a model of this scale.

125 4.3.5 Construct Validity

Construct validity measures the extent to which the items in the scale are affective for measuring the theoretical construct (Carmines and Zeller, 1979; Churchill, 1987). To perform a measurement of construct validity requires that the researcher not only

determine that each item measures the construct it was intended for but also to validate that the items do not measure any other factor. Combining tests of “convergent” and

“discriminant” validity ensure that this is accomplished.

Convergent Validity

Convergent validity was tested using both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). As with the unidimensionality analysis, the analysis broke up the overall model into three subgroups. With EFA, convergence is evaluated by determining if all eigenvalues are greater than 1.0 and all factor loadings exceed a

minimum of 0.30 (Hair et al, 1995). With CFA, convergence is determined by testing whether or not each individual item’s coefficient is greater than two times its standard error. The t-values for each item can also be evaluated to determine the strength of the relationship. T-values greater than 2.576 indicate a significance level of 0.01. Lastly, the inter-correlation (R²) value was reviewed. Items with a score below Flynn et al. (1994) recommended 0.30 were marked for possible deletion. R² is a measure of the proportion of variance identified in an observed variable as a ratio of the total variance in the construct being measured.

Convergent validity for the services supply chain management construct is maintained by an eigenvalue greater than 1.0. After completing the EFA, eight factors remained that met that criteria (Figure 4.1a). Meanwhile, Table 4.6a shows that all 34

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indicators had loadings well above 0.30 with only one factor loading less than 0.50. In Table 4.7a one can determine that all variables meet the requirement for coefficients (factor loadings) to be more than two times their standard error. We also see that the t-values and R² values are all very strong with no t-value less than 5.70. For the

information technology impact construct, the three eigenvalues greater than 1.0 are pictured in Figure 4.1b. Table 4.6a shows that all 15 indicators had loadings well above 0.30 as the lowest factor loading was 0.723. Table 4.7b proves that all variables meet the requirement for factor loadings to be more than two times their standard error and all t-values and R² values are very strong. Finally the performance measures separated into two factors, each with an eigenvalue greater than 1.0 (Figure 4.1c). The coefficients, t-values and R² values were all significant.

Discriminant Validity

Once again, EFA is utilized to analyze the models, this time to evaluate the discriminant validity of the items. By reviewing the factor loadings during the EFA, one can ascertain if a survey item loads on one and only one factor. If that factor is

hypothesized as the theoretical construct, then one can assume that the item is

appropriately measuring the theoretical factor. During the exploratory factor analysis stage, several indicators were discarded due to violations of this property. An entire theorized factor regarding Information Sharing was removed because the construct, while demonstrating strong reliability, did not explain a significant amount of variance and the individual indicators loaded heavily on secondary factors. These items were removed from the analysis and the cycle of review restarted.

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Going back to Table 4.6a – 4.6c, one can review the final item loadings. In the services supply chain management view (Table 4.6a) only two of the thirty-four factors have a loading greater than 0.40 on a second factor. For those two, EFCOM5 loaded on Factor 1 with a value of 0.425 and SBR4 loaded on Factor 5 with a value of 0.406. In both of these cases the primary factor was considered a stronger relationship. Meanwhile in the information technology model (Table 4.6b) no factors loaded over 0.40 on a second factor. Last, in the performance model (Table 4.6c) nine items loaded on a second factor with a value of 0.40 but none of these had a value greater than 0.50. In each case, the item loaded on its primary factor with a score of greater than or equal to 0.75. Thus we conclude that these results provide strong evidence for discriminant validity within the constructs.