Data Analysis

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Control variables. First, we controlled for supply market dynamism by including the degree of variability of changes in the supplier’s market, such as rapidly changing technology or fluctuations in product availability (Cannon & Perreault Jr., 1999; Achrol & Stern, 1988).

Significant supply market dynamism can create uncertainty and risk for a buying customer, and thus may increase the customer’s willingness to offer favorable prices (Roa & Monroe, 1996).

This construct describes the extent of changes in the product market in recent years, and we use four indicators (product features and specs, vendor support services, technology used by suppliers, and product availability) to measure (Cannon & Perreault Jr., 1999). All indicators were measured on a seven-point scale from “major significant” to “minor significant.” The Cronbach alpha measure of reliability for this construct is 0.79.

Second, we controlled for the variability of product quality by testing the degree of variability in product quality in the supply marketplace. If the product market is considerable variability in product quality, customers who are concerned about quality will be willing to offer favorable prices to assure quality (Rao & Monroe, 1996). The variable of one scale was judged by comparing to other purchases the customer makes, the product is “highly variable in quality = 7” to “not variable in quality = 1” (seven-point scale).

4.5 Data Analysis

The analysis for testing the proposed hypotheses was carried out in two stages. Initially, the reliability and construct validity of independent and dependent constructs were evaluated using Cronbach’s  coefficients and confirmatory factor analysis (CFA). After reliability and construct validity were established, composite scores were used to reflect the dimensions of the underlying constructs, and to test the hypotheses using structural equation modeling (SEM).

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4.5.1 Reliability.

For all seven multiple-item scales, the coefficient  for each set of items was computed to assess the reliability of the measures. All of the scales demonstrate acceptable reliability above 0.70 (see Table 1), following criteria suggested by DeVellis (2003).

4.5.2 Measurement validity.

Following Anderson and Gerbing (1988), CFA was carried out to assess the validity of all of construct measures included in this study. As shown in Table 1, the model provides an acceptable fit (2(674) =1930.69, CFI=0.94, NNFI=0.94, RMSEA=0.093). Moreover, all factor loadings were statistically significant at the 5% level, and most of the factor loadings exceed the arbitrary 0.5 standard (Fornell & Larcker, 1981). Thus these measures demonstrate adequate convergent validity. In terms of discriminant validity, this study used the methods suggested by Fornell and Larcker (1981). As shown in Table 3, the square root of the average variance extracted (AVE) (ranging from 0.687 through 0.866) is greater than most of the corresponding correlations (ranging from 0.102 through 0.660), which indicates adequate discriminant validity.

4.5.3 Structural equation model.

With the acceptable measurement model established, we proceeded to estimate the structural model. We use data collected from 217 foreign customers to test the hypothesized relationships through path analysis using LISREL methodology and provide the results (Joreskog, 1977). As shown in Figure 2, the overall fit statistics indicate an adequate fit of the model to the data (2 (18) =55.21, CFI=0.91, GFI=0.95, RMSEA=0.09).

As per our findings, the hypothesized relationship between a customer’s expectation of relationship continuity and its willingness to accommodate emerging market suppliers with favorable prices is positive and significant (=.12 p<0.05), which supports Hypothesis 1.

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Hypothesis 2 postulates that a customer’s perceived threat of quality failure has negative effects on its tendency of continuing cooperative relationship with a supplier. Consist with our claim, a customer’s perceived threat of the quality failure does reduce a customer’s expectation of relationship continuity (= -.26, p<0.001). Hypothesis 3, which claims that a customer’s expectation of relationship continuity is also driven by its perceived value of a relationship with emerging market suppliers (=.22, p<0.01), is supported. In line with our claim, we found no difference in terms of the impact of a customer’s perceived threat of quality failure and relationship value on its expectation of relationship continuity (χ2 (19) =55.50, Δχ2 = 0.29, df =1, p >.05).

We found a different effect of a supplier’s brand investment on its customer’s perceived threat of quality failure and relationship value, in line with Hypothesis 4. Our findings show that a supplier’s brand investment reduces a customer’s perceived threat of quality failure (= -.32, p<0.001), as expressed in Hypothesis 4a. On the contrary, a supplier’s brand investments was expected to affect a customer’s perceived relationship value, and that is also supported (=.29, p<0.001), as expressed in Hypothesis 4b. Moreover, we found no significant difference in terms of the impact of a supplier’s brand investment on its customer’s perceived threat of quality failure and relationship value (χ2 (19) =55.43, Δχ2 = 0.22, df =1, p >.05).

Hypothesis 5a and 5b, which claim that a supplier’s capabilities have significant and negative effects on a customer’s perceived threat of quality failure (= -.48, p<0.001), but positive effects on a customer’s perceived relationship value (=.41, p<0.001). In addition, there is no significant difference in terms of the impact of a supplier’s competences on its customer’s perceived threat of quality failure and relationship value (χ2 (19) =55.85, Δχ2 = 0.64, df =1, p

>.05)

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Hypotheses 6 also relate a supplier’s TSIs to a customer’s perceived value from suppliers.

We also found negative effect of a supplier’s TSIs on a customer’s perceived threat of quality failure (= -.13, p<0.05), but positive effect on a customer’s perceived relationship value (= .25, p<0.001), as predicted by Hypotheses 6a and 6b. Furthermore, there is no significant difference in terms of the impact of a supplier’s TSIs on its customer’s perceived threat of quality failure and relationship value (χ2 (19) =56.79, Δχ2 = 1.58, df =1, p >.05)

Finally, Hypothesis7 postulates that a supplier’s relational norms have a positive impact on a customer’s perception of relationship value with the supplier. In line with our claim, relationship value was found to be driven strongly by the supplier’s relational norms (= .30, p<0.001): thus there is empirical support for Hypothesis 7.

4.5.4 Competing models: Examining other direct or indirect paths.

Since our goal is to untangle direct versus indirect effects within a complex chain of constructs, it is important to verify that other paths are not significant. One possibility is that the customer’s perceived threat of product quality and value of relationships may directly affect the willingness of their customers to offer better prices. The issue is important because (1) we model the customer’s long term orientation as an important mediator of the impacts of customer’s perceived value and its willingness of offering favorable prices. If either proves false, the chain of direct and indirect effects would be significantly different, having major theoretical and practical implications. To test the alternative model, the direct links between (customer’s perceived threat of product quality)-(customer’s willingness to offer favorable prices) and (customer’s perceived value of relationships)-(customer’s willingness to offer favorable prices) were added. The difference in χ2 was not significant, χ2 (16) =54.03, Δχ2 = −1.18, df =2, p >.05).

Therefore, the customer’s perceived value affects its willingness of offering favorable prices is

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indirect.

Another possible challenge to the hypothesized model is that emerging market supplier’s strategies (i.e., supplier’s brand investment, supplier’s capabilities, supplier’s TSIs and supplier’s relational norms) may direct affect its customer’s willingness to offer favorable prices. To test this assertion, the links from the supplier’s strategies and its customer’s willingness to offer favorable prices were freed (six new paths). The results show that the difference in chi-square was not significant, χ2 (12) = 44.83, Δχ2 = −10.38, df = 6, p > .05). Thus, emerging market suppliers can receive more favorable prices indirectly through decreasing the customer’s perceived threat of product quality, increasing customer’s perceived value of relationships, and expectation of relationship continuity.