CHAPTER 5: DATA ANALYSIS
5.6 CONFIRMATION OF DIMENSIONALITY
5.6.2 Confirmatory Factor Analysis (CFA) Results
5.6.2.1 Measurement Model of Perceived Usefulness (PU)
The measurement model of perceived usefulness (PU) obtained from the CFA procedure is presented in Figure 5.8.
FIGURE 5.8 PERCEIVED USEFULNESS (PU) MEASUREMENT MODEL
AMOS version 17 was used to produce the measurement model of PU. As can be seen in Figure 5.8, the model presents the PU construct as reflected by eight indicators (PU1 to PU8) with all loadings above 0.5. This indicated high levels of convergence. To fully confirm the convergent validity, AVE and model fit indices were calculated (see Table 5.19). The procedure formulated by Fornell and Larcker (1981) was used to calculate AVE.
TABLE 5.19 AVERAGE VARIANCE EXTRACTED (AVE) - PERCEIVED USEFULNESS
ITEM LOADING
PU1 0.64 PU2 0.75 PU3 0.75 PU4 0.62 PU5 0.76 PU6 0.72 PU7 0.58 PU8 0.7
AVE = 0.63
As shown in Table 5.19 above, AVE for PU was above 0.5, demonstrating good convergent validity and confirming that more than 50% of the variance of PU was due to its indicators.
The model fit indices presented in Table 5.20 resulted in an acceptable fit of measurement model for PU. The IFI and CFI values were both equal to 0.91, whereas GFI was 0.92.
These results reflected a good model fit according to the parameters suggested in Table 5.18, supporting the AVE analysis explained above and confirming the convergent validity for PU.
TABLE 5.20 MODEL FIT ANALYSIS – PERCEIVED USEFULNESS (PU) Goodness-of-fit
criterion Acceptable level Value
Model fit
C MIN/df Ratio 2 to 1 or 3 to 1 11.96 Chi-square value Sig. Level > 0.05 0.00
RMSEA < 0.05 0.13
Model comparison
IFI > 0.9 0.91
CFI > 0.9 0.91
GFI > 0.9 0.92
NFI > 0.9 0.9
5.6.2.2 Measurement Model of Perceived Ease Of Use (PEOU)
The measurement model of perceived ease of use (PEOU) obtained through the CFA procedure is presented in Figure 5.9.
FIGURE 5.9 PERCEIVED EASE OF USE (PEOU) MEASUREMENT MODEL
As shown in Figure 5.9, the model presents the PEOU construct as reflected by four indicators (PEOU1 to PEOU4). The loadings of PEOU1 to PEOU4 were above 0.5, indicating high levels of convergence. To fully confirm the convergent validity, AVE and model fit indices were calculated (see Table 5.21).
TABLE 5.21 AVERAGE VARIANCE EXTRACTED (AVE) – PERCEIVED EASE OF USE
As shown in Table 5.21, average variance extracted for PEOU items was above 0.5, demonstrating good convergent validity. Based on this result, it was confirmed that more than 50% of the variance of PEOU was due to its indicators.
The model fit indices presented in Table 5.22 resulted in an acceptable fit of the measurement model for PEOU. Nine statistical tests met the fit criteria, as the results in Table 5.18 reveal. They included CMIN/df (degree of freedom) (0.419), significant level of the chi-square value (0.0000), RMSEA (0.0000), IFI (1.002), CFI (1.00), GFI (0.999), AGFI
ITEM LOADING PEOU1 0.80 PEOU2 0.69 PEOU3 0.78 PEOU4 0.44
AVE = 0.61
(0.997), TLI (1.005)and NFI (0.999). These findings supported the AVE analysis explained above and confirmed the convergent validity for PEOU.
TABLE 5.22 MODEL FIT ANALYSIS - PERCEIVED EASE OF USE (PEOU) Goodness-of-fit
criterion Acceptable level Value
Model fit
C MIN/df Ratio 2 to 1 or 3 to 1 0.419 Chi-square value Sig. Level > 0.05 0.000
RMSEA < 0.05 0.000
Model comparison
IFI > 0.9 1.002
CFI > 0.9 1.000
GFI > 0.9 0.999
AGFI > 0.9 0.997
5.6.2.3 Measurement Model of Perceived Enjoyment (PE)
The measurement model of perceived enjoyment (PE) obtained from the CFA procedure is presented in Figure 5.10.
FIGURE 5.10 PERCEIVED ENJOYMENT (PE) MEASUREMENT MODEL
As shown in Figure 5.10, the model depicts the PE construct as reflected by six indicators (PE1 to PU6), with all loadings above 0.5 indicating high convergence. Average variance extracted was then calculated and used to confirm convergent validity for PE.
TABLE 5.23 AVERAGE VARIANCE EXTRACTED (AVE) - PERCEIVED ENJOYMENT
As shown in Table 5.23, AVE for PE was above 0.5, demonstrating good convergent validity. This result confirmed that more than 50% of the variance of PE was due to its indicators.
The model fit indices presented in Table 5.24 resulted in an acceptable fit of the measurement model for PE. The GFI (0.98), AGFI (0.95) and CFI (0.98) were acceptable and the RMSEA was just under recommended levels, supporting the AVE analysis explained above and confirming the convergent validity for PE.
TABLE 5.24 MODEL FIT ANALYSIS – PERCEIVED ENJOYMENT (PE) Goodness-of-fit
criterion Acceptable level Value
Model fit
C MIN/df Ratio 2 to 1 or 3 to 1 5.56 Chi-square value Sig. Level > 0.05 0.00
RMSEA < 0.05 0.08
Model comparison
IFI > 0.9 0.98
CFI > 0.9 0.98
GFI > 0.9 0.98
AGFI > 0.9 0.95
5.6.2.4 Measurement Model of Usage Intention (UI) ITEM LOADING
PE1 0.72 PE2 0.61 PE3 0.88 PE4 0.92 PE5 0.85 PE6 0.67
AVE = 0.77
The measurement model of usage intention (UI) obtained from the CFA procedure is presented in Figure 5.11.
FIGURE 5.11 USAGE INTENTION (UI) MEASUREMENT MODEL
As shown in Figure 5.11, the model presents the UI construct as reflected by four indicators (UI1 to UI4), with all loadings above 0.5, thus indicating high convergence levels. To fully confirm the convergent validity, AVE for UI was accordingly calculated (see Table 5.25).
TABLE 5.25 AVERAGE VARIANCE EXTRACTED – USAGE INTENTION
As revealed in Table 5.25, average variance extracted for UI items was above 0.5, demonstrating good convergent validity. This result confirmed that more than 50% of the variance of UI was due to its indicators.
The model fit indices presented in Table 5.26 resulted in an acceptable fit of the measurement model for UI. CFI value was equal to 0.96 reflecting a good model fit according to the parameters suggested in Table 5.18. With the RMSEA value just under recommended levels, other indices including GFI and NFI also indicated adequate model
ITEM LOADING UI1 0.59 UI2 0.86 UI3 0.92 UI4 0.85
AVE = 0.82
fit, with a value of 0.96 for both of these indices. These results supported the AVE analysis explained above and confirmed the convergent validity for UI.
TABLE 5.26 MODEL FIT ANALYSIS – USAGE INTENTION (UI) Goodness-of-fit
criterion Acceptable level Value
Model fit
C MIN/df Ratio 2 to 1 or 3 to 1 33.96 Chi-square value Sig. Level > 0.05 0.00
RMSEA < 0.05 0.22
Model comparison
CFI > 0.9 0.96
GFI > 0.9 0.96
AGFI > 0.9 0.78
5.6.2.5 Measurement Model of Technology Readiness (TR)
The measurement model of technology readiness (TR) obtained from the confirmatory factor analysis procedure is presented in Figure 5.12.
FIGURE 5.12 TECHNOLOGY READINESS MEASUREMENT MODEL
As shown in Figure 5.12, the model depicts the TR construct as reflected by its four parcelled indicators. Three indicators (Parcels 2, 3 and 4) demonstrated factor loadings of above 0.5, indicating high levels of convergence. On the other hand, the loading for Parcel 1 was lower than 0.5 (0.23). However, there is a strong theoretical argument to keep Parcel 1 in the model as stated in the literature. Conceptually, technology readiness has to be measured by its four factors. The first two factors refer to the ‘contributor component’
that strengthens a user’s technology readiness. The other two are the ‘inhibitors’ that
suppress technology readiness. These factors interact to create a certain psychological state in oneself, and a particular attitude toward new technology, called ‘technology readiness’. The exclusion of one factor could potentially result in incompleteness of the instrument for measuring an integrative multidimensional attitude-related construct like TR. Therefore, technology readiness in this thesis will still be measured by the four parcels. Its convergent and discriminant validity were further examined by the AVE and measurement model fit analysis, reported in Tables 5.27 and 5.28. respectively.
TABLE 5.27 AVERAGE VARIANCE EXTRACTED FOR TECHNOLOGY READINESS Parcel 1 Parcel 2 Parcel 3 Parcel 4
Parcel1 0.53
Parcel2 0.00 0.52
Parcel3 0.03 0.34 0.43
Parcel4 0.03 0.03 0.19 0.53
Table 5.27 presents the measurements of AVE for the TR parcels. Three of the four parcels—Parcels 1, 2 and 4—demonstrated values of over 0.5, confirming good convergent validity. AVE for Parcel 3 was 0.43, thus below the acceptable cut-off value of 0.5. However, the convergent validity of Parcel 3 was supported by the strong theoretical foundation to be accepted for further analysis. As has been explained previously in this section, in order to validly measure technology readiness, one must include all of its four dimensions. Therefore, it was decided to include Parcel 3 as a necessary TR dimension for the comprehensively measurement of technology readiness. This treatment was well supported by an adequate discriminant validity.
The examination of discriminant validity for the TR measurement model was conducted based on the assessment procedure initially proposed by Fornell and Larcker (1981). This procedure requires the AVE of each construct to be greater than the variance shared between each construct and the other constructs. Adopting Fornell and Lacker’s procedure, in the case of TR (Table 5.27) the AVE measurements (principal diagonal) were compared with the correlation-square of each construct with the other constructs, to ensure that the former exceeded the latter (Carrasco & Foxall, 2006). The results of the comparison strongly confirmed discriminant validity.
As can be seen in Table 5.27, the average variance extracted for Parcel 1 (0.53) was greater than the correlation-square between Parcel 1 and the other parcels. The average variance extracted for Parcel 2 (0.52) was greater than the correlation-square between Parcel 2 and the other parcels. The average variance extracted for Parcel 3 (0.43) was greater than the correlation-square between Parcel 3 and the other parcels. Finally, the
average variance extracted for Parcel 4 (0.53) was greater than the correlation-square between Parcel 4 and the other parcels.
TABLE 5.28 MODEL FIT ANALYSIS – TECHNOLOGY READINESS (TR) Goodness-of-fit
criterion Acceptable level Value
Model fit
C MIN/df Ratio 2 to 1 or 3 to 1 1.014 Chi-square value Sig. Level > 0.05 0.363
RMSEA < 0.05 0.005
Model comparison
CFI > 0.9 1.000
GFI > 0.9 1.000
AGFI > 0.9 1.000
In the same direction, the model fit analysis results presented in Table 5.28, including CMIN/df (1.014), significant level of chi-square value (0.363), RMSEA (0.005), CFI (1.0), GFI (1.0), AGFI (1.0), TLI (1.0) and NFI (0.994), indicated an acceptable fit, and thus confirmed the convergent and discriminant validity.
5.6.2.6 Measurement Model of Consumer Perceived Value (CPV)
The measurement model of consumer perceived value (CPV) obtained from the CFA procedure is presented in Figure 5.13.
FIGURE 5.13 CONSUMER PERCEIVED VALUE (CPV) MEASUREMENT MODEL
As shown in Figure 5.13 above, the model indicates that the CPV construct is reflected by four parcelled indicators with all loadings of above 0.5, indicating high levels of
convergence. To further confirm the convergent and discriminant validity, AVE and model fit analysis was conducted.
TABLE 5.29 AVERAGE VARIANCE EXTRACTED – CONSUMER PERCEIVED VALUE Parcel 1 Parcel 2 Parcel 3 Parcel 4
Parcel1 0.58
Parcel2 0.32 0.58
Parcel3 0.44 0.23 0.66
Parcel4 0,04 0,01 0.18 0.61
As shown in Table 5.29, average variance extracted for each of the four CPV parcelled factors was above 0.5, confirming good convergent validity. Furthermore, the comparison between AVE measurements (principal diagonal) with the correlations-square of each construct with the other constructs nicely confirmed discriminant validity. As revealed in Table 5.29, the AVE for Parcel 1 (0.58) was greater than the correlation-square between Parcel 1 and the other CPV parcelled factors. The AVE for Parcel 2 (0.58) was greater than the correlation-square between Parcel 2 and the other CPV factors. The AVE for Parcel 3 (0.66) was greater than the correlation-square between Parcel 3 and the other CPV factors. Finally, the AVE for Parcel 4 (0.61) was greater than the correlation-square between Parcel 4 and the other CPV factors.
TABLE 5.30 MODEL FIT ANALYSIS Goodness-of-fit
criterion Acceptable Level Value
Model fit
C MIN/df Ratio 2 to 1 or 3 to 1 45.89 Chi-square value Sig. Level > 0.05 0.000
RMSEA < 0.05 0.250
Model comparison
CFI > 0.9 0.87
GFI > 0.9 0.93
The model fit indices presented in Table 5.30 showed that GFI value of 0.93 was acceptable and CFI value was just under the acceptable levels. As posited by Doll, Xia and Torkzadeh (1994), the criteria for judging model fit are relative rather than absolute and there are no standard cutoff values for evaluating model-data fit and the existence of higher-order constructs. Therefore it was decided to implement the CFA model for CPV
for further analysis in this study. To support our decision, we also took into consideration the high reliability of each CPV dimension (QUA = 0.810, EMO = 0.888, PRC = 0.716, SOC = 0.906), an acceptable internal consistency reliability and the evidence of content and construct validity. In other words, the measurement model used to evaluate CPV in this thesis was based on the four parcelled CFA model above.
5.6.2.7 Measurement Model of Schwartz’s Cultural Orientation
Exploratory factor analysis (EFA) results suggested two parcelled factors to be used in measuring Schwartz’s individual cultural orientation. The first factor was interpreted as conservatism and the second one refers to openness to change. The measurement model using CFA was not applied to these factors because the CFA procedure requires a minimum number of three manifest variables to form or reflect one latent construct. The validity of the two Schwartz factors was substantially supported by the theoretical concept behind Schwartz’s value system.
The two dimensions were considered to be higher-order value types. These groups originally resulted from projections of the polarity between individualism and collectivism (Schwartz 1992). Here, it can be understood that the structure of Schwartz’s value system still resembled the basic human requirement to serve personal (individual) needs and social needs, or, in other words, to be an individualist or a collectivist. Consequently, any measurement of individual cultural orientation that adopts Schwartz’s value system in particular would have to follow the polarisation based on the two opposing dimensions.
For this reason, the measurement model used to evaluate individual cultural orientation in this thesis was based on two parcelled dimensions.