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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) · hlm. 133–148

DOI
10.15294/jeec.v14i1.20952
ISSN
2301-7341
Terbit
2025-07-02
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

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.

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  • Universitas Yudharta Pasuruan
  • Universitas Media Nusantara Citra
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Nina OktarinaAfiliasi tidak diketahui3
Amin PujiatiAfiliasi tidak diketahui3
Rusdarti RusdartiAfiliasi tidak diketahui3
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Indah AnisykurlillahAfiliasi tidak diketahui2
Puji Novita SariAfiliasi tidak diketahui2
Bestari Dwi HandayaniAfiliasi tidak diketahui2
Arief YuliantoUniversitas Media Nusantara Citra2
Nurul HayatiAfiliasi tidak diketahui2
Muhsin MuhsinAfiliasi tidak diketahui2
Achmad SlametAfiliasi tidak diketahui2
Partono ThomasAfiliasi tidak diketahui2
Miftakhu Nuril KhodijahUniversitas Yudharta Pasuruan1
Miftara Ainul MufidUniversitas Yudharta Pasuruan1
Wiwin Ainis RohtihUniversitas Yudharta Pasuruan1
Arif SantosoAfiliasi tidak diketahui1
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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/jeec/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 6 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<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>