Attitude constructs

In this study, there were two attitudinal constructs measuring attitude from two different perspectives: ATD_Affections, which had four reflective items, was designed to collect information about respondents’ emotions/feelings towards the purchasing behaviour; ATD_Beliefs, which had eight reflective indicators, was designed to collect data regarding respondents’ beliefs/thoughts. Figure 5.3 illustrates the causality links from the two constructs to their representative indicators.

An examination of dimensionality and internal consistency reliability showed that the four items from the first aspect (i.e. ATD_Affections) were unidimensional, which suggested that they were measuring the same factor. The Cronbach’s alpha for ATD_Affections was .900, which was greater than .70, demonstrating good internal consistency reliability. Even though the Cronbach’s alpha for the eight items in ATD_Beliefs was high (.809), the eight items were not unidimensional. The factor analysis indicated that there were two factors extracted from the ATD_Beliefs; in other words, the eight items were measuring more than one factor. The component score coefficient matrix of the ATD_Beliefs suggested that the PremiumNeg (‘The higher cost of energy-saving light bulbs encourages people to carry on buying non-energy- saving ones’) was the item causing the problem. PremiumNeg was also the item that least correlated to the other seven items. After removing PremiumNeg, factor analysis showed that there was only one factor measured by the remaining seven items. The Cronbach’s alpha of the new ATD_Beliefs (with seven items, as illustrated in Panel A, Figure 5.3) was .834, greater than the desired minimum value of .70.

After confirming the items were unidimensional and their Cronbach’s alphas were greater than .70, the next step was to assess their convergent validity to see if the items could be used individually to represent their related constructs. The results suggested that the four items under the ATD_Affections could not be used individually

to measure ATD_Affections; nor could the seven items under the ATD_Beliefs be used individually to measure ATD_Beliefs.

According to the assessment of the ATD_Affections’ four-item congeneric measurement model (as illustrated in Panel A, Figure 5.3), the fit indices of the ATD_Affections’ congeneric model all indicated a poor fit to the data. The chi-square of the ATD_Affections congeneric model was 110.654 (df was 2) and the p-value was less than .001. Further, the p-value of the ATD_Affections’ bootstrapping was assessed to a value less than .001. These results indicated that the discrepancy between the proposed congeneric model (i.e. ATD_Affections and its related items) and the data was statistically significant. Other fit indices also indicated the same conclusion. The SRMR of the ATD_Affections’ four items was .0811, which was greater than the cut-off point (<.08). The values of the ATD_Affections’ CFI and TLI were also below the standard, at .885 and .654, respectively. The values of the ATD_Affections’ RMSEA and PCLOSE were .417 and less than .001, respectively, which also suggested that the model was a poor fit for the data (Table 5.2 shows the desired results for fit indices). The decision to drop the Pleasant indicator was made, based on its low factor loading (.66) and R-squared (.43) (Kline, 2011). The fit indices of the revised ATD_Affections’ congeneric measurement model (as illustrated in Panel B, Figure 5.3) suggested a good model fit. The revised ATD_Affections’ model’s chi-square value was .142 (df was 1), the p-value of the chi-square was .706 and its p-value of bootstrapping was 1.000. The other fit indices also aligned with the chi-square values, suggesting a good model fit (SRMR=.0018; CFI=1.000; TLI=1.004; RMSEA<.001; PCLOSE=.797). Therefore, this revised ATD_Affections’s measurement model was used in later data analyses.

The results of the ATD_Beliefs’ seven-item congeneric model (as illustrated in Panel A, Figure 5.3) showed that the seven remaining items’ factor loadings were statistically significant to the ATD_Beliefs variable. The chi-square for the ATD_Beliefs congeneric model was 126.531 (df is 14), the p-value was less than .001 and the p-value of its bootstrapping was also less than .001. The value of the ATD_Beliefs’ SRMR was .0687. The values of the construct’s CFI and TLI were .854 and .781, respectively. The value of the RMSEA was .141 and the PCLOSE value was less than .001 (Table 5.2 shows the desired results for fit indices). These all indicated that the model was not a good fit for the data. The decision to remove GovtMoney (‘It would be wise for the government to devote more money to supporting a strong energy-

saving programme’) and BulbPremium (‘Paying more for energy-saving light bulbs (than for conventional ones) is acceptable’) was made based on their low estimates on factor loadings (.59 and .48, respectively) and R-squared values (.35 and .23, respectively). Further, based on the modification indices generated by the AMOS, there was a correlation between Label_Com (‘I trust energy-saving claims made by manufacturers’) and Label_3Party (‘I trust an eco-label issued by an independent third party (e.g. Energy Star’)). Hence, a constraint was added to covary these two indicators.

Figure 5.3 ATD congeneric measurement models

Panel A

Panel B

The theoretical reasons for dropping these two indicators were: (1) the intensive nationwide advertising campaign (as discussed in Section 3.2.1) could have given the respondents the impression that the government has invested money into related

programmes, and (2) the price difference between an energy-saving light bulb and a non-energy-saving light bulbs may be too small to influence respondents’ attitudinal beliefs. This revised ATD_Beliefs measurement model (as illustrated in Panel B, Figure 5.3) had a good model fit, according to its fit indices (x2(4)=6.602, p=.158, bootstrapping

p =.871, CFI=.995, TLI=.988, RMSEA=.046, and PCLOSE=.467). Therefore, this revised ATD_Beliefs construct was used in later data analyses.