# Evaluating the Perceived Quality of Mobile Banking Applications in Croatia: An Empirical Study
## 3 Methodology
### Apparatus  
Data were collected by administering a post-use questionnaire online through Google Forms in June 2022. The questionnaire comprised 4 items related to participants' demography (gender, age, occupation, and mobile banking application most commonly used for conducting financial transactions), 2 open-ended items meant for collecting data on the advantages and disadvantages of evaluated mobile banking applications, and 34 items designed for exploring facets of 8 constructs which constitute the research framework: responsiveness (3 items), efficiency (5 items), ease of use (5 items), usefulness (5 items), feedback quality (4 items), security of personal data (4 items), customer satisfaction (4 items), and behavioral intention (4 items). An initial pool of set forth 34 items can be found in Appendix A. Responses to questionnaire items were modulated on a five-point Likert scale (1-strongly disagree, 5-strongly agree).  
To assess the validity and reliability of the proposed research framework and to test the established hypotheses, we employed a partial least squares structural equation modeling (PLS-SEM) method. PLS-SEM maximizes the explained variance of the endogenous constructs by examining partial model relationships in a repeated sequence of ordinary least squares (OLS) regressions [41]. In PLS-SEM, construct scores are estimated as exact linear combinations of items that measure their aspects [52]. Our decision to use PLS-SEM rather than covariance-based SEM (CB-SEM) is based on the following three main reasons: (1) PLS-SEM does not require rigorous theoretical background, which makes it well suited for exploratory research [53]; (2) when the sample size is relatively small, PLS-SEM attains higher levels of statistical power compared to CB-SEM [54]; (3) the PLS-SEM algorithm transforms data that significantly depart from a normal distribution according to the central limit theorem thus making parameter estimations highly reliable [55]. PLS-SEM path analysis requires a minimum sample size of either [56]: (i) 10 times the largest number of items assigned to the most complex construct in the model or (ii) 10 times the largest number of exogenous constructs having an impact on an endogenous construct. In the proposed research model, the most complex constructs are measured with five items, while the largest number of exogenous constructs contributing to the endogenous construct is three. Given that the minimum required sample size for our study is 50, a sample size of 130 is considered adequate. The software tool SmartPLS 4.0.8.4 [57] was used to assess the psychometric features of both measurement and the structural model.  
Kruskal-Wallis test [58] is a non-parametric counterpart to a single-factor analysis of variance (one-way ANOVA). It does not assume a normal distribution of residuals which makes it suitable for comparing more than two independent samples [59]. In our empirical study, the Kruskal-Wallis test was used to determine whether there is a significant difference in the perceived quality among several age groups of mobile banking application users as well as among mobile banking applications that were involved in our study. Descriptive statistics were used to determine the pros and cons of examined mobile banking applications. The Kruskal-Wallis tests and descriptive statistics were carried out using the IBM SPSS 26 software [60].