Model analysis

The first step in evaluating the SEM model results was to determine how well the data fit the model based on multiple fit indices including the RMSEA, SRMR, GFI and CFI. The next step considered the statistical significance of the coefficients on the paths in the model. The model was used to investigate hypotheses 2-1 to 2-7 using the entire dataset. The same model with the same path links was then tested according to levels of customisation in order to test hypothesis 2-8. Therefore, K-mean cluster analysis was conducted according to the level of customisation. The mean centre for the low-customisation group was 2.15 (n = 207); the mean centre for the high-customisation group was 4.43 (n = 156).

154 5.5.6.1. Total sum model

The total model exhibited an acceptable model fit, and the paths demonstrated higher t- values with acceptable p-values. According to multiple fit indices, the data fit the proposed model. That is, GFI (0.904), CFI (0.939), RMSEA (0.057) and SRMR (0.051) exhibited acceptable fit in the model. The coefficient on the path between variety control strategy and supply chain flexibility had a value of 0.376 at the 0.001 significance level. The result supported the hypothesis H2-1 that variety control strategy improves supply chain flexibility (see Yeh and Chu, 1999; Van Hoek, 1999; Salvador et al., 2004; Nair, 2005). The path coefficient between variety control strategy and supply chain agility had a value of 0.156 at the 0.001 level of statistical significance, which supported hypothesis H2-2 that variety control strategy improves supply chain agility (see Yeh and Chu, 1991; Davila and Wouters, 2007; Jacobs et al., 2011b). For the path between supply chain flexibility and agility, the coefficient was 0.609 and was significant at the 0.001 level. This result also supported hypothesis H2-3, that supply chain flexibility improves supply chain agility (Swafford et al., 2006; Agarwal et al., 2006). Coefficients on the path from supply chain flexibility to cost efficiency and from supply chain flexibility to customer service had values of 0.238 and 0.259 at the 0.01 and 0.001 levels of significance, respectively. Hence, the results also supported hypotheses H2-4 that increased supply chain flexibility improves supply chain cost efficiency (see Narasimhan and Jayaram, 1998; Graves and Tomlin, 2003 Chan, 2003) and H2-6 that increased supply chain flexibility improves supply chain customer service (see Narasimhan and Jayaram, 1998; Vickery et al., 1999; Zhang et al., 2002). Coefficients on the paths from supply chain agility to cost efficiency and from supply chain agility to customer service had values of 0.267 and 0.228 at the 0.01 and 0.001 levels, respectively, which revealed that supply chain agility improves cost efficiency (see Hiroshi and David, 1999; Hallgren and Olhager, 2009) and customer service (see Hiroshi and David, 1999; Agarwal et

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al., 2006; Hallgren and Olhager, 2009) (H2-5 and H2-7). Table 5-12 displays regression weight with t and p-values. Figure 5-5 represents the SEM diagram with path coefficients, levels of significance and fit indices.

Table 5-12 Result of regression weights for the overall dataset

Hypothesis Weight

(Path Coefficient) t-value p-value

H2-1 Variety control strategy → SC Flexibility .376*** 7.247 .000 H2-2 Variety control strategy → SC Agility .156*** 3.303 .000 H2-3 SC Flexibility→ SC Agility .609*** 8.219 .000 H2-4 SC Flexibility → SC Cost Efficiency .238** 2.651 .008 H2-5 SC Agility→ SC Cost Efficiency .267** 3.056 .002 H2-6 SC Flexibility→ Customer Service .259*** 3.769 .000 H2-7 SC Agility → Customer Service .228*** 3.472 .000 * represents significant level p<0.05, ** p<0.01, *** p<0.001

Figure 5-5 Structural equation model for the overall dataset

Note: Fit indices: Ch-sq / df = 438.044/202=2.16, GFI = 0.904, SRMR = 0.051, RMSEA = 0.057, CFI = 0.939 * represents significant level p<0.05, ** p<0.01, *** p<0.001

SC Flexibility SC Agility Cost Efficiency Customer Service Variety Control Strategy 0.376*** 0.238** 0.156*** 0.609*** 0.228*** 0.267** 0.259***

156 5.5.6.2. Model for low customisation

In the low customisation cluster, the item measure had statistically significant factor loadings (>0.60) after the deletion of six item measures: FL2, FL5, CE4, CUS5, 6 and 8. Regarding fit indices, the CFA model had acceptable fit indices (χ²/df = 340.658/199 = 1.71, GFI = 0.873, CFI = 0.937, SRMR = 0.049, RMSEA = 0.059). Moreover, CFA showed acceptable CRs (>0.792) and AVEs (>0.56). In addition, each squared correlation between constructs was less than the AVE. Thus, the resulting statistics revealed strong evidence of both discriminant and convergent validity.

The fit of the structural equation model was examined with multiple fit indices (Ch-sq/df = 349.782/202 = 1.73, GFI = 0.870, SRMR = 0.055, RMSEA = 0.060, CFI = 0.934). Then, the significance of the coefficients on individual paths was considered statistically. Between variety control strategy and supply chain flexibility, the coefficient had a value of 0.380 at the 0.001 significance level, while the coefficient between variety control strategy and supply chain agility had a value of 0.172 (p <0.01). The coefficient between supply chain flexibility and supply chain was 0.642 (p <0.001). The coefficient on the path from flexibility to cost efficiency and from flexibility to customer service had values of 0.257 (p <0.05) and 0.292 (p <0.01), respectively. The coefficient between supply chain agility and cost efficiency had a value of 0.271 at the 0.05 significance level. In addition, supply chain agility also has a significant direct impact on customer service; however, the coefficient was relatively low (0.178) at the 0.1 level of significance (close to the 0.05 significance level). The results also indicate that supply chain flexibility and agility mediate the impact of a variety control strategy on cost efficiency and customer service. Table 5-13 displays regression weight with t and p-values. Figure 5-6 presents a diagram for the SEM with path coefficients, significance levels and fit indices.

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Table 5-13 Result of regression weights for low customisation

Hypothesis Weight

(Path Coefficient) t-value p-value H2-1 Variety control strategy → SC Flexibility .380*** 5.358 .000 H2-2 Variety control strategy → SC Agility .172** 2.827 .005 H2-3 SC Flexibility→ SC Agility .642*** 6.720 .000 H2-4 SC Flexibility → SC Cost Efficiency .257** 2.119 .008 H2-5 SC Agility→ SC Cost Efficiency .271* 2.341 .034 H2-6 SC Flexibility→ Customer Service .292** 2.941 .003 H2-7 SC Agility → Customer Service .178+ 1.893 .058

+

represents significant level p<0.1, * p<0.05, ** p<0.01, *** p<0.001

Figure 5-6 Structural equation model for low customisation

Note: Fit indices: Ch-sq/df = 349.782/202 = 1.73, GFI = 0.870, SRMR = 0.055, RMSEA = 0.060, CFI = 0.934

+

represents significant level p<0.1, * p<0.05, ** p<0.01, *** p<0.001

5.5.6.3. Model for high customisation

In the high customisation cluster, the item measures had statistically significant factor loadings (>0.60) after the deletion of six item measures: FL5, AG3, CE4, CUS4, 6 and 8. The CFA also yielded acceptable fit criteria (Ch-sq/df = 336.775/199 = 1.69, GFI = 0.842, CFI = 0.911, SRMR = 0.065, RMSEA = 0.067). In addition, CFA showed acceptable CRs (>0.745)

SC Flexibility SC Agility Cost Efficiency Customer Service Variety Control Strategy 0.380*** 0.257* 0.172** 0.642*** 0.178++ 0.271* 0.292**

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and AVEs (>0.50). Each squared correlation between constructs was less than the AVE. Hence, the results indicate evidence of both discriminant and convergent validity.

First, fit of SEM was confirmed through the use of acceptable fit indices (Ch-sq/df = 344.734/202 = 1.70, GFI = 0.840, SRMR = 0.071, RMSEA = 0.068, CFI = 0.908). Then the significance of coefficients was checked by path analysis. The coefficient between variety control strategy and supply chain flexibility had a value of 0.403 at the 0.001 significance level. Supply chain flexibility and agility showed a high coefficient value (0.572) at the 0.001 significance level. However, variety control strategy does not have a direct impact on supply chain agility (p>0.1). This explains that supply chain flexibility mediates the impact of a variety control strategy on supply chain agility. The coefficients on the path from supply chain flexibility to cost efficiency and from supply chain flexibility to customer service had values of 0.256 and 0.188, respectively, at the 0.05 level. The coefficients on the path from supply chain agility to cost efficiency and from supply chain agility to customer service represented values of 0.283 (p<0.05) and 0.346 (p<0.001), respectively. Furthermore, agility in a high customisation context has a stronger impact on cost efficiency (0.283>0.271) and customer service (0.346>0.178) than does agility in a low customisation context. Therefore, H2-8 was supported. Table 5-14 and Figure 5-7 display path coefficients, significance level, t values and fit indices.

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Table 5-14 Result of regression weights for high customisation

Hypothesis Weight

(Path Coefficient) t-value p-value

H2-1 Variety control strategy → SC Flexibility .403*** 3.830 .000 H2-2 Variety control strategy → SC Agility .125 1.366 .172 H2-3 SC Flexibility→ SC Agility .527*** 5.049 .000 H2-4 SC Flexibility → SC Cost Efficiency .256* 1.991 .047 H2-5 SC Agility→ SC Cost Efficiency .283* 1.989 .047 H2-6 SC Flexibility→ Customer Service .188* 2.200 .028 H2-7 SC Agility → Customer Service .346*** 3.470 .000 * represents significant level p<0.05, ** p<0.01, *** p<0.001

Figure 5-7 Structural equation model for high customisation

Note: Fit indices: Ch-sq / df = 344.734/ 202= 1.70, GFI = 0.840, SRMR = 0.071, RMSEA = 0.068, CFI = 0.908, * represents significant level p<0.05, ** p<0.01, *** p<0.001

5.6.

STRATEGY

AND

PERFORMANCE

DIFFERENCES

ACCORDING TO THE LEVEL OF CUSTOMISATION

Based on the cluster analysis in the proposed model, significant differences of all structures in this study according to level of customisation were investigated by employing the T-test. Therefore,exploratory factor analysis (EFA) for all variables was conducted first

SC Flexibility SC Agility Cost Efficiency Customer Service Variety Control Strategy 0.403*** 0.256 * 0.125 0.527*** 0.346*** 0.283* 0.188*

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to test validity. The t-test and correlation analysis were performed subsequently. Table 5-15 displays a descriptive representation of the main products for each cluster.

Table 5-15 Main products for each cluster

Manufacturing industry type Low customisation

High

customisation Total Valid %

Food, beverage, tobacco 17 9 26 7.2

Wood and furniture 17 15 32 8.8

Chemical materials and products 21 7 28 7.7

Non-metal mineral products 8 7 15 4.1

Fabricated metal products 14 19 33 9.1

Computer and communication products 16 10 26 7.2

Electronic parts and components 21 20 41 11.3

Electrical machinery and equipment 20 19 39 10.7

Transport equipment 27 11 38 10.5

Textiles and leather 2 6 8 2.2

Paper products 9 2 11 3.0

Machinery and equipment 14 18 32 8.8

Basic metal products 5 3 8 2.2

Clothing and footwear 6 5 11 3.0

Other 10 5 15 4.1

Total 207 156 363 100%

5.6.1 Measurement scale

First, partnership with suppliers (4 items) and customer relationships (4 items) represent the extent to which a company has partnered closely with suppliers and customers to provide products and services, respectively. Respondents were asked to “indicate the company’s level of agreement” using a five-point Likert scale (1 = strongly disagree and 5 = strongly agree). Second, competitive capability was investigated using two constructs: cost leadership (2 items) and differentiation (3 items). Respondents were asked to “indicate how well the company performs in each of the following compared to competitors” using a five point

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Likert scale ranging from “poor” to “excellent”. Last, the business performance measures included four variables: the return on sales, the return on assets, market share growth and sales growth. Respondents were asked to “indicate how well the company performs” using a five point Likert scale (1 = poor and 5 = excellent).