Measurement Model Development

Some interrelated statistical techniques are used to analyse the data as a supportive stream in measuring the fit. This section explores the reliability scores for the construct measures followed by confirmatory factor analysis (CFA). The reliability tests examine the internal consistency of the items in a measure to determine whether each observed variable should be retained, or any exclusion should be done.

The process follows the development of an individual measurement model for each construct measure to CFA and the overall measurement model to check the dimensionality of the construct and validity of the measures. A two-stage approach proposed by Gerbing and Anderson (1988) was used in confirmatory factor analysis.

A. Construct Validity

The constructs of supply chain relationships were obtained based on extensive literature reviews. These were adapted to develop components of the integrative structural model, to gain an understanding of hypothesised relationships among constructs, indicators and items, but only if they confirmed construct validity. The importance of ensuring the validity of the constructs has been emphasised by a number of authors, to address the issues of weak validation experienced by many research studies (Churchill, 1979; Malhotra, 2004; Gallagher et al., 2008; Hair et al., 2010). In terms of broad conception, validity refers to the extent to which an empirical measure adequately reflects the real meaning of the concept under consideration.

Through the implementation of CFA, construct validity in this study was first examined using a preliminary qualitative analysis to establish the framework of measurement model. This analysis was needed to determine whether the measurement model was to be constructed based on a reflective or formative model, particularly the constructs with multidimensional and multi-item structures. The implementation of each model would give different results, and therefore interpretation at this stage was crucially important.

In the reflective model, the latent variable influences the indicators, thus the direction of causality is from the construct to the indicators or measures; while in the formative model, the direction is from the measures to the construct (Jarvis et al., 2003).

A guideline proposed from Jarvis, Mackenzie and Podsakoff (2003) was used to establish the model. There were four criteria proposed by these researchers to determine whether the measurement model was reflective or formative. The first criterion relates to the direction of causality between the construct and its indicators. For reflective measurement models, the direction of causality flows from the construct to the measures, while the direction goes the opposite way for the formative models. The second criterion addresses the issue of the interchangeably of the indicators. The indicators need to be interchangeable for the reflective models, but not for formative models. The third criterion relates to the issue of whether the indicators should co-vary with each other. As for the reflective models, co-variation among the indicators is necessary, while in the formative models the covariance is unnecessary. The fourth criterion is referred to a question examining whether all measures are required to have the same antecedents and consequences.

Indicators in the reflective model should all have the same antecedents and consequences, because they reflect the same underlying construct and are believed to be interchangeable. On the other hand, the measures in the formative constructs do not have to be interchangeable, because they are not expected to have the same antecedents and consequences. Table 5-1 shows the difference between formative and reflective measurement models.

Table 5-1: Differences between Formative and Reflective Measurement Models

Formative Model Reflective Model

 Direction of causality is from measure to construct

 Direction of causality s from construct to measure

 No reason to expect the measures are correlated (internal consistency is not applied)

 However, attention should be given to nomological or criterion-related validity

 Measures are expected to be correlated (measures should possess internal consistency reliability)

 Dropping an indicator from the measurement model may alter the meaning of the construct

 Dropping an indicator from the measurement model does not alter the meaning of the construct  Takes the measurement error into

account at the construct level

 Takes the measurement error into account at the item level

 Constructs possesses surplus meaning

 Construct possesses surplus meaning

 Scale score does not adequately represent the construct

 Scale score does not adequately represent the construct

Source: Adapted from Jarvis et al.,(2003)

Applying the above criteria to the structure of partner’s characteristics capability,

alliance management capability and process capability, it was established that the measurement of these three constructs should be based on reflective models. Chapter 3 of this thesis describes the indicators of each construct from a broad perspective of literature and research done by previous authors. In summary, it can be concluded that

Formative Model Reflective Model Y1 Y2 Y3 Y1 Y2 Y3 Zeta1 e1 e2 e3

B. Model’s Unidimensionality

Further analysis in this thesis on construct validity refers to related issues such as unidimensionality. The unidimensionality of the model must be examined to confirm that a set of measured variables (or indicators) can be explained by only one underlying construct (Hair et al., 2010) . It can also be referred to as an internal-consistency reliability that concerns the homogeneity of the items comprising a scale; items must be correlated well with each other (DeVellis, 2012). Anderson and Gerbing (1988) explain that both unidimensionality and reliability are related, but are determined in different ways. According to them, “the unidimensionality of a scale can be evaluated by

examining the patterning of its component indicator correlations, whereas the reliability of a scale is determined by the number of items that define the scale and the reliabilities of those items” (Anderson & Gerbing, 1988, p. 190).

The assessment of the unidimensionality of each multiple-indicator construct should be performed prior to the assessment of construct reliability; both assessments (these being unidimensionality and construct reliability) are performed to confirm the usefulness of a scale (Anderson & Gerbing, 1988). Unidimensionality can also be measured through CFA to assess the internal and external consistency of a construct (Anderson & Gerbing, 1982; Anderson & Gerbing, 1988), and to analyse each measurement model for a first-order CFA construct. In this study, each critical factor of the research constructs was evaluated by factor analysing measurement instruments using Cronbach’s alpha reliability tests. According to Churchill (1979), coefficient or Cronbach’s alpha should be the first measure used to assess the quality of an

instrument. A cut of point (α= 0.7) for the alpha value suggested by Nunally and

Bernstein (1994) was used as a reasonable indicator of fit.

C. Convergent Validity

It is the degree to which measurement items of the same construct demonstrate a converged relationship, as indicated by the high proportion of variance shared among them. It refers to the extent to which multiple attempts measure the same concept with different methods are in agreement. To establish convergent validity, it is required to show measures that should be related are in reality related. This type of validity was observed in this thesis based on measurement model assessment conducted in accordance with the confirmatory factor analysis (CFA) procedure. The implementation of CFA to confirm convergent validity and evaluate a latent structure has received substantial justification in the literature (Churchill, 1979; DiStefano & Hess, 2005; Byrne, 2010).

As outlined in the CFA procedure, this thesis applied three assessment schemes to ensure convergent validity. First, the convergent of a common was assessed based on standardised factor loadings, which should be above 0.50 with statistical significance (Hair et al., 2006, 2010). Second, convergent validity was verified through the assessment of Average Variance Extracted (AVE), which had to be more or equal than 0.50 in order to achieve an adequate level (Fornell & Larcker, 1981; Vázquez-Carrasco & Foxall, 2006; Hair et al., 2010). Finally, the convergence was also reflected by measure of composite reliability (CR) which is greater than 0.7 and more than the

D. Discriminant Validity

Discriminant validity is the degree to which a concept differs from other concepts (Hair et al., 2010). It is the analysis of the distinction between two constructs, confirming that the hypothesised structural parts are free from discrepancy, and lead to an accurate result (Farrell & Rudd, 2009); this will allow greater confidence on the later interpretation of analysis findings (Farrell & Rudd, 2009). The observation of discriminant validity in this study was conducted by comparing square root of AVE with correlations shared between each indicator and the other indicator of the model (Fornell & Larcker, 1981; Vázquez-Carrasco & Foxall, 2006). A condition where the square root of AVE for each of the factors is greater than its shared variance with any of the other factors substantiated the discriminant validity (Fornell & Larcker, 1981; Schumacker & Lomax, 2008).

The above explained validity assurance must also be supported by adequate fit of each measurement model. To achieve this, an examination of model fit was performed. The fit indices summarised in Table 5.2 (see section 5.3.9) were used for this purpose. A fulfilment of the acceptable cut-off level of at least one commonly used index determined the model fit.