Validity

According to Ticehurst & Veal (2000), business research encounters difficulties about validity, particularly in the measurement of attitudes and behaviour since there are often doubts about the true meanings of responses made in surveys, interviews, and the self- reporting of behaviour. Therefore, it is necessary to validate the constructs of this study. Validity is the extent to which the data collected truly reflect the phenomenon being studied. According to Zikmund (2003), validity means “the ability of a scale to measure what was intended to be measured”. Punch (1998) pointed out that validity represents the relationship between the construct and its indicators. Three points relating to aspects of valid constructs were suggested by Nunnally & Bernstien (1994). First, the construct should be seen as a good representation of the domain observable related to the construct. Then, the construct should

143 represent the alternative measures. Last, the construct should be related to other constructs of interest. Sekaran (2003) suggested that several types of validity tests for testing the goodness of measures include content validity, criterion-related validity, and construct validity.

However, in this study, content validity and construct validity, used by many researchers, were chosen to establish the validity of the survey questionnaire (Thong 1999; Thong & Yap 1995).

4.6.5.1.1 Content Validity

Content validity or face validity is the first type used within this thesis. It is a method to evaluate the validity of an instrument by the judgement of a group of experts in order to ensure that the questionnaire has an adequate and representative group of questions that reflect the real meaning of the concept (Cavana, Delahaye & Sekaran 2001; Zikmund 2003). This assesses the correspondence between the individual items and the concept through ratings by expert judges, and pre-tests with multiple sub-populations or other means (Hair et al. 2006).

In this study, the content validity of the survey questionnaire was considered because it was tested by means of a pre-test approach using research professionals and IT managers in the ERP user organisation as described earlier. In addition, development of the questionnaire of this study was based on the results of the short interview (exploratory study) and the findings from the relevant literature review on innovation adoption. Cavana, Delahaye & Sekaran (2001) suggested that content validity can be achieved from doing literature and conducting qualitative research. It was ensured that the survey questionnaire would provide data relating to accepted meanings of the concepts involved. Thus, content validity was achieved by generating the items from the conceptual background and through obtaining experts’ opinions of the items.

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4.6.5.1.2 Construct Validity

Construct validity is another type used for an assessment of the questionnaire’s capability to record data that accurately reflects the theory upon which the questionnaire is based on Sekaran (2003). In other words, construct validity testified that the instrument did tap the concept as theorised. Thus, this measure of validity refers to developing correct and adequate operational measures for the concept being tested (Yin 1994). Sekaran (2003) suggested that convergent validity examines whether the measures of the same construct are correlated highly, otherwise discriminant validity determines that the measures of a construct have not correlated too highly with other constructs. Most researchers test the construct validity by means of convergent and discriminant (Campbell & Fiske 1959; Peter 1981). Thus, construct validity is established in this thesis by analysing convergent validity and discriminant validity.

Various methods have been recommended for assessing convergent and discriminant validity: factor analysis (exploratory factor analysis (EFA)); correlation analysis, and even advanced procedure (e.g. confirmatory factor analysis (CFA) in Structural Equation Modelling (SEM)). For example, to test for convergent and discriminant validity, Kim & Frazier (1997) employ a confirmatory factor model, whereas (Heidi & John (1988) use correlation and regression analysis.

For the purpose of this thesis, convergent and discriminant validity have been assessed by using correlation and performing CFA. Convergent validity is synonymous with criterion with criterion validity (Zikmund 2003). Peter (1981) suggests that a high internal consistency through inter-item correlation (e.g. reliability tests) provides support for construct validity. Correlation analysis is one way of creating construct validity for this thesis. It implies that items that are indicators of a specific construct should converge or share a high proportion of

145 variance in common (Hair et al. 2006). In other words, it assesses the degree to which

measures of the same concept are correlated, with high correlation indicating that the scale is measuring it intended concept. Thus, it could be suggested that reliability is also an indicator of convergent validity.

In addition, to demonstrating convergent validity, the magnitude of the direct structural relationship between item and latent construct (or factor) should be statistically different from zero (Holmes-Smith, Cunningham & Coote 2006). The final items (not including deleted items) should be loaded highly on one factor (Anderson & Gerbing 1988), with a factor loading of 0.50 or greater (Hair et al. 2006).

Next, discriminant validity was also used to test construct validity. Discriminant validity refers to the extent to which a construct differs from other constructs (Hair et al. 2006). A measure has discriminant validity when there is a low correlation with measures of dissimilar concepts (Zikmund 2003). High discriminant validity provides evidence that a construct is unique and captures the same phenomenon that was captured by other constructs.

Discriminant validity has two methods employed in this thesis: 1) examining the single-factor congeneric model; and 2) conducting CFA. Thus, construct validity was used to enhance the model (through goodness-of-fit results from CFA), and fits to the data adequately (Hsieh & Hiang 2004). Results related to construct validity have been reported in Chapter 6.