Approach to model testing

The research model was tested using the Partial least Square (PLS) Structural Equation Modelling (SEM) technique (using SmartPLS 2.0 software). PLS SEM was chosen because it did not require normal distributions, it could work with a small sample size, and it could test the moderation effect more effectively (Chin, 1998).

4.4.5.1 Construct validity

Construct validity refers to which indicators intended to measure a construct do accurately measure that particular construct (Hair, 2009). Construct validity can be confirmed by assessing content validity, convergent validity, and discriminant validity. All tests were carried out using SmartPLS 2.0 software.

Content validity refers to the extent to which a measure represents a construct correctly and fully covering the content of the construct (Hair, 2009). To ensure content validity, all items were adapted from prior researches (see discussion in section 4.3.4).

Convergent validity refers to the extent to which the indicators measuring the same construct correlate (Hair, 2009). Convergent validity can be measured by obtaining item reliability, internal consistency reliability, and the values of average variance

68 extracted (AVE). Item reliability is the standardised loading of an item on its

construct. Following Chin’s (1998) suggestion, a threshold value of .60 was used. Internal consistency reliability refers to the degree to which all items measuring the same construct produce similar results. It can be assessed using composite reliability or Cronbach’s alpha (Hair, 2009). In this study, both composite reliability and

Cronbach’s alpha were employed, and following Chin (1998), a threshold value of .70 was used. AVE is the average variance shared between a construct and its measures, and following Fornell and Larcker (1981), a threshold of .50 was used.

Discriminant validity refers to the extent to which the items measuring different constructs uncorrelated to each other (Gefen & Straub, 2005). At item level, all items should load on their own constructs higher than on other constructs in the model, at construct level, the square root of AVE of each construct should be higher than the correlations of the construct to other constructs (Fornell & Larcker, 1981).

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4.4.5.2 Approach to test model fit

The main model was tested following established procedure (Chin, Marcolin, & Newsted, 2003; Henseler & Fassott, 2010). Firstly convergent and discriminant validity were checked for all constructs, and items with low reliability and

cross-loading items were deleted. Then the main effects were tested using the PLS algorithm and bootstrapping procedures to assess the statistical significance of path coefficients. Following Chin (1998), 500 resamples were used in the bootstrapping procedure.

The model fit was assessed by the statistical significance of path coefficients and by the amount of variance explained in dependent variables. Chin (1998) suggests that the path coefficient value should be above .20 to be meaningful. In terms of effect size, Kline (2011) suggests that a path coefficient value close or below .10 should be considered as small, a value close to .30 should be considered as medium, and a value close or greater than .50 should be considered as large. In terms of the amount of variance explained in dependent variable, Chin suggests that a R2 value close

69 to .19 should be interpreted as weak, a value close to .33 should be considered as average, and a value close to .67 should be considered as substantial.

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4.4.5.3 Approach to compare groups

Chin (2003) suggests that groups can only be compared for differences when there are good model fits, data is not too non-normal, and when there is measurement invariance between models.

Measurement invariance was tested following the technique developed by PLS expert Professor Diogenes Bido (2007) based on the work of Maruyama (1997). The technique is shown below and was posted on SmartPLS forum.

ݐ ൌܮ݋ܽ݀݅݊݃ଵെ ܮ݋ܽ݀݅݊݃ଶ ටܵଵଶ൅ ܵଶଶ

Respectively, Loading1 and Loading2 are the loadings of each item, S1 and S2 are the

standard errors for the first and the second Group. Obtained t statistics are used to determine the statistical significance of difference between the item loadings in two groups. No significance (p>.05) suggests that there is measurement invariance between two groups; therefore the groups are suitable for comparison.

There are several techniques to assess the significance of the differences between the path coefficients of each group. On his PLS FAQ website, Chin suggests that when standard errors are equal, the t-statistic for the differences between the path

coefficients can be calculated using the following formula (Eberl, 2010; Keil et al., 2000; Kock, 2013). This approach was called the Pooled Standard Error method.

ݐ ൌ ߚଵെ ߚଶ ቌඨ ሺܰଵെ ͳሻଶ ሺܰଵ൅ ܰଶെ ʹሻ כ ܵଵ ଶ_൅ ሺܰଶെ ͳሻଶ ሺܰଵ൅ ܰଶെ ʹሻ כ ܵଶ ଶ_{ቍ כ ቆට}ͳ ܰ_ଵ൅ܰͳ_ଶቇ

70 Respectively, β1 and β2 are the path coefficient values, S1 and S2 are the standard

errors for the first and the second Group, N1 and N2 are the sample size of the first

and the second group.

When standard error for the groups is unequal, the t-statistic for the differences between the path coefficients can be calculated using the formula below. This is referred to as the Satterthwaite method (Eberl, 2010; Kock, 2013).

ݐ ൌ ߚଵെ ߚଶ ටܵଵଶ൅ ܵଶଶ

Respectively, β1 and β2 are the path coefficient values, S1 and S2 are the standard

errors for the first and the second Group.

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