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Determinants of Trust in Artificial Intelligence in Indonesian Higher Education: A PLS-SEM Analysis Based on the Trustworthy Artificial Intelligence Framework

Journal of Information Systems Engineering and Business Intelligence · Vol. 12 No. 3 (2026) · pp. 615–628

DOI
10.20473/jisebi.12.3.615-628
ISSN
2443-2555, 2598-6333
Published
2026-10-28
Publisher
Universitas Airlangga
Language
eng
License
creativecommons.org/licenses/by/4.0

Abstract

Background: Trust in Artificial Intelligence (AI) is essential for adopting AI in higher education. However empirical validation of trust dimensions in Indonesian academia remains limited. Objective: This study examines the factors influencing academics’ trust in AI in Indonesian higher education using the trustworthy AI framework of the European Commission. Methods: A quantitative survey of 109 lecturers (response rate: 62.6%) was conducted using purposive sampling. Data were analyzed through partial least squares structural equation modeling (PLS-SEM). Results: Robustness is the only dimension with a significant positive effect on trust in AI, while fairness, explainability, transparency, and data privacy show no significant influence. These findings indicate that academics prioritize system reliability, stability, and performance over broader ethical dimensions. Conclusion: Unlike many previous studies conducted in developed countries where fairness, transparency, explainability, and privacy play important roles, this study found that only robustness significantly influences the trust of academics in AI. These findings provide contextual evidence that Trustworthy AI dimensions may operate differently across higher education environments in developing countries. Keywords: Artificial Intelligence, Trust, Robustness, Transparency, Fairness, Higher Education

Collaboration network of the authors

6 authors · 9 co-authorship links · 2 across different organisations

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  • Syarif Hidayatullah State Islamic University Jakarta
  • International Islamic University Malaysia
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Abd Rahman AhlanAffiliation unknown1
Mira KartiwiInternational Islamic University Malaysia1
Husni Teja SukmanaSyarif Hidayatullah State Islamic University Jakarta1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://e-journal.unair.ac.id/JISEBI/oai, first harvested 29 Okt 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Determinants of Trust in Artificial Intelligence in Indonesian Higher Education: A PLS-SEM Analysis Based on the Trustworthy Artificial Intelligence Framework</dc:title>
	<dc:creator>Yuniarto, Dwi</dc:creator>
	<dc:creator>Subiyakto, A'ang</dc:creator>
	<dc:creator>Rahman, Aedah Binti Abd.</dc:creator>
	<dc:description xml:lang="en-US">Background: Trust in Artificial Intelligence (AI) is essential for adopting AI in higher education. However empirical validation of trust dimensions in Indonesian academia remains limited.  
Objective: This study examines the factors influencing academics’ trust in AI in Indonesian higher education using the trustworthy AI framework of the European Commission. 
Methods: A quantitative survey of 109 lecturers (response rate: 62.6%) was conducted using purposive sampling. Data were analyzed through partial least squares structural equation modeling (PLS-SEM). 
Results: Robustness is the only dimension with a significant positive effect on trust in AI, while fairness, explainability, transparency, and data privacy show no significant influence. These findings indicate that academics prioritize system reliability, stability, and performance over broader ethical dimensions. 
Conclusion: Unlike many previous studies conducted in developed countries where fairness, transparency, explainability, and privacy play important roles, this study found that only robustness significantly influences the trust of academics in AI. These findings provide contextual evidence that Trustworthy AI dimensions may operate differently across higher education environments in developing countries. 
Keywords: Artificial Intelligence, Trust, Robustness, Transparency, Fairness, Higher Education </dc:description>
	<dc:publisher xml:lang="en-US">Universitas Airlangga</dc:publisher>
	<dc:date>2026-10-28</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://e-journal.unair.ac.id/JISEBI/article/view/78696</dc:identifier>
	<dc:identifier>10.20473/jisebi.12.3.615-628</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Information Systems Engineering and Business Intelligence; Vol. 12 No. 3 (2026): October; 615-628</dc:source>
	<dc:source>2443-2555</dc:source>
	<dc:source>2598-6333</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://e-journal.unair.ac.id/JISEBI/article/view/78696/41634</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 The Authors. Published by Universitas Airlangga.</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>