Data were analysed using a set of statistical tools i.e., frequency, percentage, T test, one-way ANOVA, and principal component analysis (PCA) with the Microsoft Excel (2010) and Statistical Package for Social Sciences (SPSS) version 25.0. T test and one-way ANOVA were performed to determine the relation among the studied variables with the respondent’s demographic characteristics. Generally, the T test is performed to determine whether there is any statistically significant difference between the means of two groups, while the one-way ANOVA is used to determine whether there are any statistically significant differences between the means of three or more independent groups (Howell 2002; Kim 2015). Furthermore, PCA is a data reduction tool that demonstrates each potentiality of the parameters and their confidence level in large sample datasets (Bodrud-Doza et al.2020a). PCA was performed using Varimax rotation with Kaiser Normalization, in order to maximize the sum of the variance of the factor coefficients (Bodrud-Doza et al. 2019b), which better explains the studied factors. Before conducting the PCA, Kaiser-Maier-Olkin (KMO) and Bartlett’s sphericity tests were used to confirm the necessity of this analysis. The results of the KMO test (that is, 0.832) and Bartlett’s sphericity test (that is, p < 0.01) suggest that our datasets are fit for PCA (Bodrud-Doza et al. 2020b; Islam et al. 2002a; Shammi et al. 2020). The number of factors chosen was based on Kaiser’s principle; that is, only factors with eigenvalues > 1.0 were considered. A detail flowchart describing the research methodology is presented in Fig. 2.

Flowchart showing the research methodology

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