Construct Validity and Confirmatory Factor Analysis (CFA)

The factors obtained based on the EFA results were adopted to develop components of the integrative structural model to gain understanding of the hypothesised relationships among constructs, indicators and items in influencing usage intention formation, but only if they performed 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 previous research studies (Churchill 1979; DiStefano & Hess 2005;

Gallagher, Ting & Palmer 2008; Hair et al. 2006; Malhotra 2004). In terms of a broad conception, validity refers to the extent to which an empirical measure adequately reflects the real meaning of the concept under consideration. More specifically, as quoted from Gallagher et al. (2008), construct validity concerns ‘whether or not operationalisation of a measure accurately reflects its construct (p.266). In other words, it reflects the degree to which a measure relates to other variables as expected within a system of theoretical relationships. Hair et al. (2006) posit the following four important components of construct validity: face or consenses validity, convergent validity, discriminant validity and nomological validity.

To examine construct validity through the implementation of CFA, a preliminary qualitative analysis to establish the framework of measurement model was firstly conducted. This analysis was needed to determine whether the measurement model was to be constructed based on a reflective or formative model, particularly for the constructs with multidimensional and multi-item structures, including TR and CPV. 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 by 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 criteria relates to the direction of causality between the construct and its indicators. For reflective measurement models, the direction of causality flows from the measures to the construct.

The direction goes to the opposite way in the formative models. The second criteria addresses the issue of the interchangeability of the indicators. The indicators need to be interchangable for reflective models; but they do not have be like that in the formative models. The third criteria relates to the issue of whether the indicators should covary with each other. In the reflective models, covaration among the indicators is necessary; while in the formative models the covariation is not necessary. The fourth criteria is referred to a question examining whether all of the measures are needed to have the same antecedents and consequences. Indicators in the reflective model should all have the same antecendents and consequences, because they reflect the same underlying construct and are believed to be interchangeable. On the other hand, the measures in the formative models do not have to be interchangeable because they are not expected to have the same antecedents and consequences.

Applying the above criteria to the structure of TR and CPV, we found that the measurement of these two constructs should be based on reflective models. We first revisited the concept of TR to explain why it needed to be constructed with a reflective model. It has been shown in chapter 2 that TR conceptually proposed as a predisposition or attitude towards a new technology is formed from the total feeling of a person, reflected by two opposite views about technology simultaneously occurring in that individual (Parasuraman, 2000). In other words, TR is an individual’s holistic attitudinal position about technology; therefore it is an aggregated construct which should be measured by using all of its indicators. TR can not be appropriately measured by using only one or some of all of its indicators because TR is the combination of all relevant beliefs reflected in four components, consisting of two contributors and two inhibitors. At this point we can see that TR requires all of its dimensions and indicators to have the same consequences and covary with each other. Having this situation in this measurement model structure, we further understand that the direction of causality is from the overall attitude TR construct to its indicators, reflecting how the construct is operationalised in explaining someone’s predisposition towards embracing a new technology. This approach gained strong support from many previous studies in which the use of the reflective model in measuring attitude has been widely accepted. As reported by Jarvis et al (2003) in their article, on page 200, the measurement of attitude using the reflective model has been agreed to be an appropriate method in producing an expected outcome. As a result, we concluded that the

construction of a measurement model for TR needs to apply the reflective model structure.

The same decision was also made in determining the reflective model to be used in constructing the CPV measurement model. As has been explained in chapter 3, this thesis supported the conception of value proposed by Sweeney et al (2001) who posited that the multiple value dimensions explained consumer purchase intention better than a single value dimension. The argument previously provided by Sheth, Newman and Gross (1991) endorsed this conception, stating that consumer choice is a function of multiple consumption value dimensions. They further explained that these value dimensions may not be independent but interelated and correlated. As a result, value is conceptualised as multidimensional constructs; namely, the CPV multiple scale item adopted in this thesis.

Taking into account the characteristics of this scale in which the dimensions may be interelated and correlated, it was decided that the measurement model for CPV was developed by using the reflective model. The implementation of the reflective model provided an opportunity to better understand how each dimension interacts with each other in reflecting the value construct, because all measurement items under this construct were allowed to covary with each other. Besides, allowing the items to covary with each other and interact as CPV dimension antecendents at the same construct level gave us a better insight into the possibility of discovering other significant value dimensions. This possibility was indicated by a number of researchers, as quoted by Sweney et al. who suspect that other unidentified value dimensions could be significantly present in the CPV multidimensional value construct.

Face or content validity represents an indicator of whether or not each construct’s item is understandable and a reasonable measure if viewed from a theoretical perspective (Gallagher, Ting & Palmer 2008; Hair et al. 2006). Content validity is usually assured in the early stages of a research process. In this thesis it was observed at the pre-test stage, as has been explained in Chapter 4.

Convergent validity 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. This type of validity was observed in this thesis based on the measurement model assessment conducted in accordance with the confirmatory factor analysis procedure. The implementation of CFA to confirm convergent validity and evaluate a latent structure has received substantial justification in the literature (Byrne 1998; Churchill 1979; DiStefano & Hess 2005; Hoyle & Panter 1993; Thompson & Daniel 1996). As outlined in the CFA procedure, this thesis applied three assessment schemes to ensure convergent validity. First, the convergence of a common point was assessed based on standardised factor loadings which should be over 0.50 with statistical

significance (Hair et al. 2006). Second, convergent validity was verified through the assessment of Average Variance Extracted (AVE), which had to reach 0.50 or higher in order to achieve an adequate level (Fornell & Larcker 1981; Hair et al. 2006; Vazquez-Carrasco & Foxall 2006). Finally, although not strictly required, the convergence was also reflected by measure reliability as indicated by the Cronbach’s alpha value of 0.7 or above. This was applied in particular to the constructs that were measured by parcelled dimensions.

The observation of discriminant validity in this thesis was conducted by comparing the AVE of each construct indicator with the variance shared between each indicator and the other indicators of the model (Fornell & Larcker 1981; Vazquez-Carrasco & Foxall 2006).

A condition where the AVE for each of the factors was greater than its shared variance with any of the other factors substantiated the discriminant validity (Carrascoa & Foxallb 2006; Fornell & Larcker 1981; Schumacker & Lomax 1996).

The above explained validity assurance must also be supported by an adequate fit of each measurement model. To achieve this, an examination of model fit was performed.

The fit indices summarised in Table 5.18 were used for this purpose. A fulfilment of the acceptable cut-off level of at least one commonly used index determined the model fit.

TABLE 5.18 CUT-OFF CRITERIA FOR SELECTED FIT INDICES

No Measure Fit criteria Definition

1 Chi-square

( χ²)  Non-significant ( χ²) at least p-value >0.05

The fundamental measure used in SEM to quantify the differences between the observed and estimated covariance matrices.

2 Normed Fit

Value > 0.95 indicates a good fit, and 0.90–

0.95 an adequate fit

A fit statistic that is less sensitive to sample size and indicates how well the specified model reproduces the covariance matrix among the indicator items (i.e. the similarity of the observed and estimated covariance matrices).

4 AGFI (Adjusted Goodness-of-Fit Index)

Value > 0.95 indicates good fit, and 0.90–0.95 an adequate fit

An index representing goodness-of-fit for the degree of freedom.

5 RMSEA (Root Mean Square Error of Approximation)

Values < 0.05 indicate adequate fit

A measure that attempts to correct for the tendency of the X ² test statistic to reject models with large samples or a large number of observed variables.

6 NFI (Normed Fit Index)

Values > 0.95 indicate good fit, and 0.90–0.95 an adequate fit

A ratio of the differences in the X ² value for the fitted model and a null model divided by the X ² value for the null model.

7 CFI (Comparative Fit Index)

Values > 0.95 indicate good fit, and 0.90–0.95 an adequate fit

An incremental fit index that is an improved version of the normed fit index (NFI)

8 TLI (Tucker-Lewis Index)

Values > 0.95 indicate good fit, and 0.90–0.95 an adequate fit

A comparative index between proposed and null models adjusted based on degrees of freedom

9 IFI (Incremental

Fit Index) Values > 0.95 indicate good fit, and 0.90–0.95 adequate fit

An index interpreted similarly to TLI and CFI

10 AIC (Akaike Information Criterion)

Values closer to 0 show

better fit A parsimonious measure used as a comparative index between alternative models

Source: Gallagher, Ting and Palmer (2008); Kline (2005); Schreiber et al. (2006)