Antecedents of students’ behavioural intention to use generative artificial intelligence: Quantitative research

Objective: The article aims to identify factors that influence students’ behavioural intentions to use generative artificial intelligence (GenAI). Research Design & Methods: We proposed a research model based on the theory of planned behaviour, the technology acceptance model and a literature...

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Main Authors: Regina Lenart, Barbara A. Sypniewska, Jin Chen, Konrad Janowski
Format: Article
Language:English
Published: Cracow University of Economics 2024-03-01
Series:Entrepreneurial Business and Economics Review
Subjects:
Online Access:https://eber.uek.krakow.pl/eber/article/view/2580
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author Regina Lenart
Barbara A. Sypniewska
Jin Chen
Konrad Janowski
author_facet Regina Lenart
Barbara A. Sypniewska
Jin Chen
Konrad Janowski
author_sort Regina Lenart
collection DOAJ
description Objective: The article aims to identify factors that influence students’ behavioural intentions to use generative artificial intelligence (GenAI). Research Design & Methods: We proposed a research model based on the theory of planned behaviour, the technology acceptance model and a literature review. Findings: The results show that attitude, perceived usefulness, perceived quality, and perceived support from higher education institutions positively impact students’ behavioural intention to use GenAI. Implications & Recommendations: The findings allowed us to propose two practical implications for academic teachers and managers of higher education institutions. Firstly, we recommend supporting students in terms of their knowledge, skills and conscious use of GenAI. Comprehensive education and other forms of training may be of use here. Secondly, we recommend that educational establishments clearly define their expectations regarding students’ use of GenAI, particularly how and when they can safely use GenAI, not only during their studies. Contribution & Value Added: Our study offers a new multilevel model of students’ behavioural intentions to use generative GenAI. It enables the synthesis of our research results and the organisation of variables influencing students’ behavioural intention to use GenAI, as well as the relations between them. Furthermore, as far as we are aware, we are the first to encompass aspects of the perceived quality and ethics of students using GenAI in our research.
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publisher Cracow University of Economics
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spelling doaj-art-a3ddac804ca64da6b31ef8d7a33d25582025-08-20T02:53:17ZengCracow University of EconomicsEntrepreneurial Business and Economics Review2353-88212024-03-0113110.15678/EBER.2025.130101Antecedents of students’ behavioural intention to use generative artificial intelligence: Quantitative researchRegina Lenart0https://orcid.org/0000-0002-9266-9638Barbara A. Sypniewska1https://orcid.org/0000-0001-8846-1183Jin Chen2https://orcid.org/0000-0002-0156-9237Konrad Janowski3https://orcid.org/0000-0003-0838-9754Jagiellonian UniversityUniversity of Economics and Human Sciences in WarsawTsinghua UniversityUniversity of Economics and Human Sciences in Warsaw Objective: The article aims to identify factors that influence students’ behavioural intentions to use generative artificial intelligence (GenAI). Research Design & Methods: We proposed a research model based on the theory of planned behaviour, the technology acceptance model and a literature review. Findings: The results show that attitude, perceived usefulness, perceived quality, and perceived support from higher education institutions positively impact students’ behavioural intention to use GenAI. Implications & Recommendations: The findings allowed us to propose two practical implications for academic teachers and managers of higher education institutions. Firstly, we recommend supporting students in terms of their knowledge, skills and conscious use of GenAI. Comprehensive education and other forms of training may be of use here. Secondly, we recommend that educational establishments clearly define their expectations regarding students’ use of GenAI, particularly how and when they can safely use GenAI, not only during their studies. Contribution & Value Added: Our study offers a new multilevel model of students’ behavioural intentions to use generative GenAI. It enables the synthesis of our research results and the organisation of variables influencing students’ behavioural intention to use GenAI, as well as the relations between them. Furthermore, as far as we are aware, we are the first to encompass aspects of the perceived quality and ethics of students using GenAI in our research. https://eber.uek.krakow.pl/eber/article/view/2580generative artificial intelligenceGenAIstudentsantecedentsintentionSEM model
spellingShingle Regina Lenart
Barbara A. Sypniewska
Jin Chen
Konrad Janowski
Antecedents of students’ behavioural intention to use generative artificial intelligence: Quantitative research
Entrepreneurial Business and Economics Review
generative artificial intelligence
GenAI
students
antecedents
intention
SEM model
title Antecedents of students’ behavioural intention to use generative artificial intelligence: Quantitative research
title_full Antecedents of students’ behavioural intention to use generative artificial intelligence: Quantitative research
title_fullStr Antecedents of students’ behavioural intention to use generative artificial intelligence: Quantitative research
title_full_unstemmed Antecedents of students’ behavioural intention to use generative artificial intelligence: Quantitative research
title_short Antecedents of students’ behavioural intention to use generative artificial intelligence: Quantitative research
title_sort antecedents of students behavioural intention to use generative artificial intelligence quantitative research
topic generative artificial intelligence
GenAI
students
antecedents
intention
SEM model
url https://eber.uek.krakow.pl/eber/article/view/2580
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AT barbaraasypniewska antecedentsofstudentsbehaviouralintentiontousegenerativeartificialintelligencequantitativeresearch
AT jinchen antecedentsofstudentsbehaviouralintentiontousegenerativeartificialintelligencequantitativeresearch
AT konradjanowski antecedentsofstudentsbehaviouralintentiontousegenerativeartificialintelligencequantitativeresearch