Stem accounting: Effects of traditional and big data education, learning and intelligence on the accounting student's achievement

STEM accounting has emerged as a significant accounting profession's evolution. Big data, as a part of pivotal technology part of STEM, has become ubiquitous and can revolutionize traditional accounting education. This study, in alliance with the STEM paradigms, aims to empirically compare the effect of “traditional accounting education” and “Big Data-based accounting education” on accounting students' achievement (ASA) in various accounting courses, when students' “learning” and “intelligence”, as mediating and moderating variables, are incorporated in this relation. By developing an accounting education's moderated - mediation model, adopting the pretest-posttest design and utilizing the experimental approach, we formed two experimental groups with traditional and Big Data-based methods participating in various accounting courses. For each course, one group was also considered as a “control” group. The study population encompasses all accounting students studying at the undergraduate level of a big university in Iran. The study sample includes 330 students. Applying structural equation modeling, our findings indicate that both Big Data-based and traditional education methods maintain a significant positive effect on ASA; but, the Big Data-base's effect is greater. However, when “learning” is used as the mediating variable, the Big Data's effect is not significant for any of the courses, and there is a very minor mediation; while the traditional method's effect is significant and leads to a medium mediation. Emotional intelligence, as a moderating variable, has no significant effect on the relationship between traditional and Big Data-based methods and the ASA. However, cognitive intelligence has an effect on some accounting courses. When intelligence and teaching methods' interaction is examined, the situation does not change and the emotional intelligence's variable is still unaffected. Although Big Data is useful for STEM accounting education, it is not always statistically significant and preferred in the education's process and its significance is contingent on the type of accounting courses and student's cognitive characteristics (such as learning). The findings' implications for educators and the future of accounting education are to synthesize Big Data and other contemporary IT information techniques in accounting courses and teaching. The role of the student's cognitive intelligence is also potent in this context. This study not only contributes to the extant research on Big Data accounting education but also sheds light on the STEM's accounting literature.