Comparative Analysis Using SmartPLS, AMOS, and LISREL in Business Financial Management Research
DOI:
https://doi.org/10.26858/brain.v3i2.79Keywords:
SmartPLS, AMOS, LISREL, Statistical ComparisonAbstract
This study aims to compare the results of statistical data analysis using SmartPLS, AMOS, and LISREL in examining the relationships among Green Finance, Financial Literacy, Financial Management Behavior, and Business Sustainability. The study employed a quantitative explanatory research design using a dataset consisting of 150 MSME respondents and 20 measurement indicators, with five indicators representing each research construct. The same dataset, measurement indicators, and structural model were analyzed using three statistical approaches to identify the consistency and differences in the resulting estimates. The analysis focused on the coefficient of determination (R²), direct effects, indirect effects, p-values, and t-statistics. The results show that Green Finance has a significant relationship with Financial Management Behavior and Business Sustainability, while Financial Literacy has a significant relationship with Financial Management Behavior and Business Sustainability. Financial Management Behavior also has a significant relationship with Business Sustainability. In addition, Financial Management Behavior significantly mediates the relationships between Green Finance and Business Sustainability and between Financial Literacy and Business Sustainability. The p-values for all direct and indirect relationships were 0.000 across SmartPLS, AMOS, and LISREL, indicating a consistent pattern of statistical significance. However, differences were observed in the R² values and t-statistics generated by the three analytical approaches. For Business Sustainability, the R² values were 0.872, 0.766, and 0.894 for SmartPLS, AMOS, and LISREL, respectively, while the R² values for Financial Management Behavior were 0.798, 0.655, and 0.766. These findings indicate that different statistical estimation approaches may produce variations in numerical estimates and explanatory power while maintaining consistent substantive conclusions regarding the relationships among the research constructs. The study contributes to the methodological literature by demonstrating the importance of considering the analytical characteristics of statistical approaches when conducting quantitative research involving latent constructs and mediation relationships.
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References
Bongomin, G. O. C., Ntayi, J. M., & Malinga, C. A. (2020). Analyzing the relationship between financial literacy and financial inclusion by microfinance banks in developing countries: Social network theoretical approach. International Journal of Sociology and Social Policy, 40(11/12), 1257–1277. https://doi.org/10.1108/IJSSP-12-2019-0262
Clayton, M. F., & Pett, M. A. (2008). AMOS versus LISREL: One data set, two analyses. Nursing Research, 57(4), 283–292. https://doi.org/10.1097/01.NNR.0000313487.64412.be
Flammer, C. (2021). Corporate green bonds. Journal of Financial Economics, 142(2), 499–516. https://doi.org/10.1016/j.jfineco.2021.01.010
Fitriana, Sipayung, Leonardo Davidsi, & Pratama, Aryan Agus. (2026). Pengaruh Green Finance dan Financial Literacy terhadap Business Sustainability dengan Financial Behavior sebagai Variabel Mediasi. Media Mahardhika, 24(3), 562-274.
Hair, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (2nd ed.). Guilford Press.
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2–20. https://doi.org/10.1108/IMDS-09-2015-0382
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
Lusardi, A., & Mitchell, O. S. (2014). The economic importance of financial literacy: Theory and evidence. Journal of Economic Literature, 52(1), 5–44. https://doi.org/10.1257/jel.52.1.5
Morgan, P. J., & Long, T. Q. (2020). Financial literacy, financial inclusion, and savings behavior in Laos. Journal of Asian Economics, 68, 101197. https://doi.org/10.1016/j.asieco.2020.101197
Purwanto, A., Asbari, M., Santoso, T. I., Haque, M. G., & Nurjaya. (2019). Marketing research quantitative analysis for large sample: Comparing of LISREL, TETRAD, GSCA, AMOS, SmartPLS, WarpPLS, and SPSS. Jurnal Ilmiah Ilmu Administrasi Publik: Jurnal Pemikiran dan Penelitian Administrasi Publik, 9(2), 355–372. https://doi.org/10.26858/jiap.v9i2.22803
Schoenmaker, D., & Schramade, W. (2022). Principles of sustainable finance. Oxford University Press.
Thaha, A. F. (2020). Dampak COVID-19 terhadap UMKM di Indonesia. BRAND Jurnal Ilmiah Manajemen Pemasaran, 2(1), 147–153. https://ejournals.umma.ac.id/index.php/brand
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