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Determinants of Behavioral Intention to Use Generative Artificial Intelligence on Economics Teachers in Semarang

Journal of Economic Education · Vol. 14 No. 1 (2025) · pp. 133–148

DOI
10.15294/jeec.v14i1.20952
ISSN
2301-7341
Published
2025-07-02
Publisher
Universitas Negeri Semarang
Language
eng

Abstract

This study aims to analyze the factors influencing economics teachers' behavioral intentions to use Generative Artificial Intelligence (GAI) in teaching, using the Unified Theory of Acceptance and Use of Technology (UTAUT) as the foundational framework. The respondents of the study were high school economics teachers in Semarang City, Central Java, Indonesia. Data were collected from 119 respondents using the accidental sampling technique. A questionnaire developed by previous researchers was used to gather research data. Structural Equation Modeling-Partial Least Squares (SEM-PLS) was employed as the data analysis method. The findings indicate that performance expectancy, effort expectancy, social influence, and facilitating conditions significantly determine the level of teachers' intention to use GAI in teaching. Furthermore, the Technological Pedagogical Content Knowledge (TPACK) variable serves as a moderating variable in the research model. The practical implications of this study suggest that educational institutions, teachers, and policymakers need to enhance understanding, provide training, and implement policies that support the integration of TPACK-based Generative AI. These efforts aim to encourage the effective use of this technology to improve the quality of teaching and learning.

Keywords

Collaboration network of the authors

40 authors · 68 co-authorship links · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • State University of Semarang
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Nurdian SusilowatiAffiliation unknown9
Puji Novita SariAffiliation unknown3
Nina OktarinaAffiliation unknown3
Amin PujiatiAffiliation unknown3
Rusdarti RusdartiAffiliation unknown3
Muhammad KhafidAffiliation unknown3
Sandy AriefAffiliation unknown3
Bestari Dwi HandayaniAffiliation unknown2
Arief YuliantoState University of Semarang2
Nurul HayatiAffiliation unknown2
Muhsin MuhsinAffiliation unknown2
Achmad SlametAffiliation unknown2
Partono ThomasAffiliation unknown2
Arif SantosoAffiliation unknown1
Sari LestariAffiliation unknown1
Galang Rahadian PrabowoAffiliation unknown1
Henny MurtiniAffiliation unknown1
Linda AgustinaAffiliation unknown1
Aditya HernawanAffiliation unknown1
Riswanda ImawanAffiliation unknown1
Aji KridawanAffiliation unknown1
Yacoeb Triandy HudoyoAffiliation unknown1
Imroatus SholikhahAffiliation unknown1
Ibrotul Lailatur RohmahAffiliation unknown1
Eko Adi SaputroAffiliation unknown1
Tia Pipit NaovilaAffiliation unknown1
Ajeng Rizka RiadianiAffiliation unknown1
Pristiani PristianiAffiliation unknown1
Rahmat SyarifullohAffiliation unknown1
Elyana Ayu SorayaAffiliation unknown1
Rastra BuanaAffiliation unknown1
Dany AriyantoAffiliation unknown1
Siti Nur EndahAffiliation unknown1
Ahmad Faris ArifianataAffiliation unknown1
Thiara FitriyaniAffiliation unknown1
Ina Sarah NormalitaAffiliation unknown1
Fitriana FitrianaAffiliation unknown1

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://journal.unnes.ac.id/journals/jeec/oai, first harvested 6 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Determinants of Behavioral Intention to Use Generative Artificial Intelligence on Economics Teachers in Semarang</dc:title>
	<dc:creator xml:lang="en">Sofyan Setiyawan</dc:creator>
	<dc:creator xml:lang="en">Agus Wahyudin</dc:creator>
	<dc:creator xml:lang="en">Amir Mahmud</dc:creator>
	<dc:subject xml:lang="en">Artificial Intelligence</dc:subject>
	<dc:subject xml:lang="en">Performance Expectancy</dc:subject>
	<dc:subject xml:lang="en">Effort Expectancy</dc:subject>
	<dc:subject xml:lang="en">Social Influence</dc:subject>
	<dc:subject xml:lang="en">, Facilitating Conditions</dc:subject>
	<dc:subject xml:lang="en">TPACK</dc:subject>
	<dc:subject xml:lang="en">Behavioral Intention</dc:subject>
	<dc:description xml:lang="en">This study aims to analyze the factors influencing economics teachers' behavioral intentions to use Generative Artificial Intelligence (GAI) in teaching, using the Unified Theory of Acceptance and Use of Technology (UTAUT) as the foundational framework. The respondents of the study were high school economics teachers in Semarang City, Central Java, Indonesia. Data were collected from 119 respondents using the accidental sampling technique. A questionnaire developed by previous researchers was used to gather research data. Structural Equation Modeling-Partial Least Squares (SEM-PLS) was employed as the data analysis method. The findings indicate that performance expectancy, effort expectancy, social influence, and facilitating conditions significantly determine the level of teachers' intention to use GAI in teaching. Furthermore, the Technological Pedagogical Content Knowledge (TPACK) variable serves as a moderating variable in the research model. The practical implications of this study suggest that educational institutions, teachers, and policymakers need to enhance understanding, provide training, and implement policies that support the integration of TPACK-based Generative AI. These efforts aim to encourage the effective use of this technology to improve the quality of teaching and learning.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2025-07-02</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/jeec/article/view/20952</dc:identifier>
	<dc:identifier>10.15294/jeec.v14i1.20952</dc:identifier>
	<dc:source xml:lang="en">Journal of Economic Education; Vol. 14 No. 1 (2025): June 2025; 133 - 148</dc:source>
	<dc:source>2301-7341</dc:source>
	<dc:source>10.15294/jeec.v14i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/jeec/article/view/20952/4604</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Journal of Economic Education</dc:rights>
</oai_dc:dc>