7. Results and Discussion
7.2. Data Screening and Preliminary Analysis
Missing data are observations that have been lost or not recorded within the measuring instrument. The best method to prevent and reduce the chance of the missing data is to ensure the proper study design (Smuk 2015). Consequently, a preventive strategy was employed through using Google forms (Google 2021) to eliminate the negative impacts of missing data.
Google forms provide the ability to design all questions as mandatory to answer, so all returned questionnaires were considered valid since there was no missing data.
7.2.2. Straight-lining Behavior
Straight-lining behavior can reduce the data quality, and relates to the occurrence when the respondent (participant) provides similar answers for a list of a Likert-scale survey questions (Kim et al. 2019; Gogami et al. 2021; VanDerSchaaf, Daim & Basoglu 2021). In other words,
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the participant is mentally disengaged from the survey but still, he has to complete it. The participant’s provided responses can be considered not useable as they are not answering the question properly, so the affected answers will be coded as missing (Mirzaei et al. 2021). In the case of this study, 4 responses were dropped due to the straight-lining behaviour (variance = 0).
This result can be explained by the high levels of education (μ = 2.98, = .527) and experience (μ = 3.29, = .972), because people with less experience and less education are more likely to straight-line their answers (Kim et al. 2019). Therefore, the dataset after removing the straight- lined responses will be N = 263.
7.2.3. Analysis of Outliers
Outliers are observations that are breaking the pattern as exposed by the majority of the observations (Møller, von Frese & Bro 2005). They are cases with extreme values that can statistically misrepresent the data where value on one variable as “univariate outlier” or odd values of two or more variables as “multivariate outliers” (Aguinis, Gottfredson & Joo 2013).
(Aguinis, Gottfredson & Joo 2013) have classifed the outliers into various categories such as error outliers, model fit outliers, and interesting outliers. They have introduced the error outlier as data points that in a distance from other points and include observations that are not within the possible range of values. Model fit outliers include the influential cases that may impact the fit of the model.
In this research, there were no error outliers due to the strict Likert-scale and the functionality of Google forms to select the answer with no need to enter any value. On the other hand, 21 cases as influential outliers were recognized using the Mahalanobis Distance within the linear regression method in SPSS as recommended by (Tabachnick & Fidell 2013) to handle the multivariate outliers and followed by computing the value to calculate the probability of the outlier. Various critical Chi2 values for each variable, where p < .001 and df is 1, less than the
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quant and df as the degree of freedom for the number of variables against the dependent variable.
The 21 cases were eliminated as a recommended handling technique for the influential outliers (Aguinis et al. 2010; Aguinis, Gottfredson & Joo 2013; Mahapatra et al. 2020). Therefore, the dataset without considering the outliers for analysis will be N = 242.
7.2.4. Normality Testing
Testing normality is crucial procedure and relies on the normal distribution (Doornik & Hansen 2008; Field 2013). However, PLS-SEM method does not require testing for normality (Rönkkö et al. 2016; Hair et al. 2019). In this research, the normality was tested to eliminate any doubts regarding the normal distribution of the received responses. The normal distribution is presented by two measures: Skewness (to measure the symmetry of a distribution) and Kurtosis (to measure the peakiness or flatness of a distribution) (Field 2013). The acceptable ranges for both measures are (-2 and +2) (Field 2013; Tabachnick & Fidell 2013). According to (Kline 2011), the acceptable range for kurtosis can be (-3 and +3).
The results in Table 24 shows that the data for all constructs are normal distributed since all the values of skewness and kurtosis are within the acceptable threshold. The negative results of kurtosis measure indicate that the distribution of cases is relatively flat with thin tails (Platykurtic distributions), while the negative skewness results indicate the left side of the distribution is longer (Doornik & Hansen 2008; Tabachnick & Fidell 2013).
Table 24. Summary of the Descriptive Statistics.
Mean Std. Deviation Skewness Kurtosis
PE 3.3066 1.07358 -.338 -1.339
EE 3.3975 1.13320 -.563 -.865
FC 3.2531 1.25310 -.381 -1.255
SI 3.4050 1.22485 -.708 -.809
BI 3.3202 1.16982 -.490 -1.355
UB 3.2562 1.23329 -.373 -1.208
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AT 3.4990 1.15668 -.674 -.671
CSE 3.4545 1.20015 -.636 -.863
ANX 2.4804 1.33493 .555 -1.301
INV 3.3492 1.21986 -.628 -1.056
TRU 3.3320 1.16484 -.548 -.979
7.2.5. Analysis of Common Method Bias (CMB)
In the context of PLS-SEM, common method bias is a phenomenon that is caused by the measurement design as in the study instead of the studied model (Kock 2015). For instance, the instructions before starting the questionnaire can affect the participants and how to respond to each question. The CMB can be examined through automated procedure using SmartPLS by considering the calculation of variance inflation factor (VIF). VIF is a measure to calculate the amount of multicollinearity for a set multiple regression variables (Kock & Lynn 2012). All the values as shown in Table 25 shows that the VIF for the latent variables in the model are lower than the suggested threshold (3.3), which is an indication that the model is not contaminated by common method bias (Kock 2015).
Table 25. Results of VIF (CMB).
ANX AT BI CSE EE FC INV PE SI TRU UB
ANX 1.7045
AT 2.0322
BI 2.4214
CSE 1.8761 2.9403
EE 2.0454 2.8825 2.9191
FC 2.5597 2.4214
INV 1.7483
PE 2.0454 2.9059
SI 3.1646 3.0530
TRU 2.1690
UB