The Effect of Students’ Self-Reported Information and Informatics Literacy on Financial Literacy and the Use of Online Payment

Abstract

In today’s world, where almost everything takes place in the virtual world, information and informatics, as well as financial literacy are becoming increasingly important. Although most of the university students are considered to be sufficiently information and informatics literate, the Covid-19 pandemic has confirmed how necessary it is to possess the skills and knowledge related to these literacies in order to maintain quality of life by using new financial technologies and be effective in various spheres of life. This study investigated whether there is connection between these two literacies and financial literacy of university students. Also, students’ demographic data, Internet use, agreement with statements concerning information and informatics literacy as well as the use of payment services before and after the Covid-19 pandemic was explored. In addition, the research aim was also to see if acceptable neural network model could be made for distinguishing students based on their reported financial literacy. Monte Carlo exact test showed that there is statistically significant association at the 0.05 level of significance between the self-reported informatics literacy and information literacy (p = .000, two-sided), age (p = .027, two-sided) and by making payments via digital wallets in 2021 (p = .007, two-sided) and 2020 year (p = .024, two-sided). Also, Monte Carlo exact test showed that there is statistically significant correlation at the 0.05 level of significance between respondents’ information literacy and their work experience (p = .005, two-sided) and who covers their life expenses (p=.019, two-sided). The Monte Carlo test also showed that both of these literacies have statistically significant relationship with financial literacy (p = .000, two-sided), but statistically significant relationship was not found between financial literacy and payments via digital wallets. Concerning the neural network approach, the obtained multilayer perceptron (MLP) neural network model gained overall efficiency of 97.5% in distinguishing students based on their level of financial literacy.