Systematic Literature Review: The Use of SEM in Business and Social Sciences – Insights from ABAC Journal 2021–2024

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Detaylı Bibliyografya
Yayımlandı:ABAC Journal vol. 45, no. 2 (Apr-Jun 2025), p. 1
Yazar: Napontun, Kittipong
Diğer Yazarlar: Sophachit, Worawalan, Senachai, Prarawan
Baskı/Yayın Bilgisi:
Assumption University Press
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Full Text - PDF
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024 7 |a 10.59865/abacj.2025.5  |2 doi 
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045 2 |b d20250401  |b d20250630 
100 1 |a Napontun, Kittipong 
245 1 |a Systematic Literature Review: The Use of SEM in Business and Social Sciences – Insights from ABAC Journal 2021–2024 
260 |b Assumption University Press  |c Apr-Jun 2025 
513 |a Journal Article 
520 3 |a Structural Equation Modeling (SEM) is a crucial analytical instrument in business and social sciences, allowing researchers to examine intricate correlations between observable and latent variables while reducing measurement errors. This study conducts a systematic literature review (SLR) of SEM-related research published in the ABAC Journal from 2021 to 2024. The review classifies research articles according to their application domains, estimating techniques, software utilization, theoretical frameworks, and geographical study sites. Research indicates that SEM is mostly employed in marketing, management, travel and tourism, finance, human resource management, education, accounting, and economics. Maximum Likelihood Estimation (MLE) is the predominant method, followed by Partial Least Squares-Structural Equation Modeling (PLS-SEM) and Integrated Generalized Structured Component Analysis (IGSCA). AMOS and SmartPLS are identified as the favored SEM software. The research underscores the growing utilization of hybrid SEM methodologies, which combine factor-based and component-based models to enhance analytical flexibility. These findings offer significant insights for researchers and practitioners, assisting them in choosing suitable SEM approaches according to research aims and data attributes. Future research should investigate the use of IGSCA and conduct comparative assessments of various SEM estimate techniques across varied data situations to improve methodological progress in business and social science research. 
653 |a Software 
653 |a Literature reviews 
653 |a Structural equation modeling 
653 |a Systematic review 
653 |a Social sciences 
653 |a Errors 
653 |a Measurement errors 
653 |a Social research 
653 |a Human resources management 
653 |a Resource management 
653 |a Tourism 
653 |a Measurement 
653 |a Maximum likelihood method 
653 |a Marketing 
653 |a Business 
653 |a Literary criticism 
700 1 |a Sophachit, Worawalan 
700 1 |a Senachai, Prarawan 
773 0 |t ABAC Journal  |g vol. 45, no. 2 (Apr-Jun 2025), p. 1 
786 0 |d ProQuest  |t ABI/INFORM Global 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3222185271/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3222185271/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch