Unpacking Drivers that Influence Behavioral Intention to Use AI among Private University Students in Bangladesh: Using UTAUT2 Model Approach

This study identifies the primary determinants influencing private university students’ behavioral intention to use AI for studies and assignments. The study also explains the percentage of behavioral intention. For this purpose, this study surveyed 181 private university students in Bangladesh. The conceptual model for this study was developed using the UTAUT2 model. Using PLS-SEM, this study found that hedonic motivation (0.36), effort expectancy (0.16), performance expectancy (0.16), and social influence (0.13) are the drivers that significantly influence students’ behavioral intention to use AI. Habit and facilitating conditions do not significantly affect students’ behavioral intention to use AI for their studies and preparing assignments. The strongest driver among all is hedonic motivation, and the least influential driver is habit. The study's findings on Bangladeshi students will influence AI technology developers to tailor their tools to the preferences of both Bangladeshi and South Asian students

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Publication Details

Journal
Journal of Information Systems Engineering & Management
Published
2025-04-26
DOI
https://doi.org/10.52783/jisem.v10i40s.7245
Primary Topic
Air Quality Monitoring and Forecasting
Type
article
Field-Weighted Citation Impact
0.00
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article

Unpacking Drivers that Influence Behavioral Intention to Use AI among Private University Students in Bangladesh: Using UTAUT2 Model Approach

Andréia V. Faria
Journal of Information Systems Engineering & Management
Air Quality Monitoring and Forecasting
article

Unpacking Drivers that Influence Behavioral Intention to Use AI among Private University Students in Bangladesh: Using UTAUT2 Model Approach

Andréia V. Faria
article en

Abstract

This study identifies the primary determinants influencing private university students’ behavioral intention to use AI for studies and assignments. The study also explains the percentage of behavioral intention. For this purpose, this study surveyed 181 private university students in Bangladesh. The conceptual model for this study was developed using the UTAUT2 model. Using PLS-SEM, this study found that hedonic motivation (0.36), effort expectancy (0.16), performance expectancy (0.16), and social influence (0.13) are the drivers that significantly influence students’ behavioral intention to use AI. Habit and facilitating conditions do not significantly affect students’ behavioral intention to use AI for their studies and preparing assignments. The strongest driver among all is hedonic motivation, and the least influential driver is habit. The study's findings on Bangladeshi students will influence AI technology developers to tailor their tools to the preferences of both Bangladeshi and South Asian students

Journal of Information Systems Engineering & ManagementVol. 10(40s)
Openalex Percentile: Top 92%
Air Quality Monitoring and Forecasting
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