Attitudes of business students toward artificial intelligence in the context of an underdeveloped country

Datos Bibliográficos

ID22434883
AutoresYohannes Workeaferahu Elifneh (0000-0001-8759-0450, Addis Ababa University, autor de correspondencia)
Año2026
Páginas1-17
Fecha de publicación2026-06-01
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaGlobal Knowledge, Memory and Communication (JOURNAL)
Identificadores de la revistaISSN: 2514-9342 • E-ISSN: 2514-9350
EditorialEmerald (PUBLISHER)
DOI10.1108/gkmc-08-2025-0587
OpenAlexW7162991648
IdiomaEN
Referencias citadas75

Purpose There is an increasing interest to examine the subject of artificial intelligence (AI) in the context of Higher Education Institutions (HEIs). Specifically, research examining students’ attitudes toward artificial intelligence (SATAI) has emerged as a crucial area of investigation. Accordingly, this study aims to examine the attitudes of business students toward AI in an underdeveloped country context, where there is a paucity of similar studies. Design/methodology/approach This study employed exploratory, descriptive research design which is an appropriate approach to understanding AI perceptions in an under-researched context where empirical evidence remains scarce. Quantitative data were collected using the SATAI Scale, involving 267 participants from public universities in Ethiopia. The analysis employed statistical techniques, including descriptive analysis, independent sample t-tests and one-way analysis of variance (ANOVA). Findings The findings show that business students exhibited positive attitudes toward AI. Analysis of the independent sample t-test and one-way ANOVA revealed no statistically significant differences in attitudes between male and female business students and across different educational levels (undergraduate, masters and PhD levels). Research limitations/implications This study focused solely on business students at public universities in Ethiopia. Future research could examine private institutions or other disciplines. Although descriptive statistics, t-tests and ANOVA were used, advanced techniques could provide deeper insights. Future studies may also delve to explore how positive attitudes toward AI translate into sustainable educational practices. Originality/value By extending AI research to underexplored contexts, this study helps narrow the empirical gap in underdeveloped countries and offers valuable insights for educators, researchers, policymakers, curriculum developers and other stakeholders seeking to integrate AI into higher education.

Curriculum · Descriptive statistics · Developing country · Empirical research · Perception · AI in Service Interactions · Ethics and Social Impacts of AI · Teaching and Learning Programming