The measurement concept for the structural equation model (SEM)

Multiple regression is the method that was used to predict changes in the dependent variable in response to changes in the several independent variables (Forza, 2002). While regression considers only one dependent variable and one aggregate error term, SEM can handle multiple dependent variables as well as error terms for all dependent and independent variables in the structural model (Kline, 2011). Thus, SEM can estimate a series of interrelated dependence relationships simultaneously. Although multiple regression is useful to examine the relationship between independents and a dependent variable, it cannot directly

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propose potential relationships in a model that are justified and interpreted substantively by theories (Cheng, 2001).

Based on the work of Anderson and Gerbing (1988), the model was tested using a two- stage structural equation model. First, confirmatory factor analysis (CFA) was performed to evaluate construct validity regarding convergent and discriminant validity using AMOS 18.0. In this stage, construct reliability (CR) and the average variance extracted (AVE) for the validity test were considered. In the second stage, structural equation model (SEM) analysis was employed to test the research hypotheses empirically.

CFA is generally used to provide a confirmatory test of a study’s measurement theory and test how well the measured variables represent a smaller number of constructs (Hair et al., 2010). Therefore, the study used confirmatory factor analysis since a proposed model was formed by a theory based on links between structures and item measures. CFA examines the relationships between proposed item measures and a related latent construct to assess the unidimensionality of each construct (Kim and Mueller, 1978). In other words, the proposed item measures may load only on the one proposed associated construct (Swafford et al., 2008).

After examining the reliability and validity across the constructs, this research examined how well the data fit the model by proving that badness or goodness-of-fit measurements met recommended levels. Root mean square error approximation (RMSEA) and standardised root mean residual (SRMR) consider the levels of residuals in measurements. RMSEA is an estimate of the discrepancy between the model, with optimally chosen parameter estimates, and the population covariance matrix. SRMR reflects the discrepancy between the predicted (i.e. model-implied) and observed (i.e. sample) covariance matrix. RMSEA is an especially typical measure for overall model fit and a smaller value of RMSEA represents a better

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model fit. The recommended maximum values for SRMR and RMSEA is 0.08 (Hair et al., 2010).

The other two measures typically used, the comparative fit index (CFI) and the goodness- of-fit index (GFI), both have recommended minimum thresholds of 0.90 (Hair et al., 2010; Kline, 2011). The goodness-of-fit index (GFI) indicates the overall degree of fit (measure of fit between the hypothesised model and the observed covariance metrics). In addition, Segars and Grover (1993) recommend the ratio of χ² to the degree of freedom as less than 3.0 to indicate a reasonable fit.

4.6. CHAPTER SUMMARY

This chapter began by presenting the research design employed in this research. Then, research strategies for testing hypotheses in this study were explained in terms of which data sources and questionnaire designs were presented. In the section on questionnaire design, construct measurements, scale development of the type of customisation and the level of product variety, and the questionnaire development procedure were explained. In addition, the items and resources employed for the research’s constructs were presented. It has to be noted that all of the items used in this research were adapted from the relevant literature to eliminate concerns regarding the reliability and validity of the constructs. Furthermore, the questionnaire went through a comprehensive assessment procedure to guarantee its efficiency and validity prior to being formally utilised in this study.

In the next section on data collection strategy, the sample used for the study, the data collection methodology and the sample size were described. In this process, the questionnaire was sent to 1,950 potential informants and as a result 364 usable responses were received. The final section of this chapter illustrated statistical strategies for data analysis. General

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measurement concepts employed in this research, such as reliability, validity, ANOVA and cluster analysis, were explained. Then, measurement concepts for SEM, such as CFA, CR, AVE, GFI, CFI, SRMR and RMSEA, were also described.

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CHAPTER FIVE

SURVEY APPLICATION AND RESULTS

5.1. INTRODUCTION

The main objectives of the survey were to investigate the impact product variety has on business functions with respect to a type of customisation, and also to investigate the supply chain design to support the management of variety increases by testing the relationship between a variety control strategy (VCS) and supply chain performance. The survey also aimed to determine how variety-related strategy and supply chain performance differences depend on the level of customisation. These are achieved by evaluating: (1) the extent of product variety effects on business function performance for various types of customisation; (2) how variety control strategies influence supply chain performance depending on the level of customisation; and (3) the differences in variety-related strategy and supply chain performance that depend on the level of customisation.

This chapter contains four sections. Section 5.2 provides general descriptive statistics for respondents’ and manufacturers’ characteristics. After data screening in Section 5.3, Section 5.4 presents the analysis of the impact of increasing variety on business function performance, as determined through the use of the ANOVA test on the data gathered by the UK’s manufacturers. Section 5.5 presents the results of a structural equation model (SEM) used to manage an increase in variety, which was applied using confirmatory factor analysis (CFA) on all of the data gathered in the UK and Korea. Next, Section 5.6 presents the results of differences in strategy and performance, as determined through the use of EFA and the t-test on all of the data gathered in the UK and Korea. Combined samples (Korea and the UK) are

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employed for investigating Q2 and Q3, while separated sample is used for answering Q1 and Q4. Therefore, differences between the two countries were investigated separately and can not affect the analysis of SEM in Section 5.5 and t-test in Section 5.6. Finally, Section 5.7 summarises the survey results and the related findings.