Comparison of Mediation Effects on Interaction and Multigroup Approach in Structural Equation Modeling PLS in Case of Bank Mortgage

Authors

  • Ulfah Maisaroh Departement of Statistics, University of Brawijaya
  • Adji Achmad Rinaldo Fernandes Departement of Statistics, University of Brawijaya
  • Atiek Iriany Departement of Statistics, University of Brawijaya
  • Mohammad Ohid Ullah Department of Statistics, Shahjalal University of Science and Technology http://orcid.org/0000-0003-2336-3018

DOI:

https://doi.org/10.31764/jtam.v8i1.19919

Keywords:

Interaction Approach, Mediation Effect, Moderation, Multigroup Approach, Structural Equation Modeling.

Abstract

“Structural Equation Modeling is one of multivariate statistical method that used to explain multiple relationships between latent variables simultaneously to test a mediation model to conduct a formal test on mediation effects. Application PLS-SEM for exploratory research and theory development are increasing. Under certain conditions, the effect of exogenous variables on endogenous variable is also strengthened or weakened by moderating variable. In SEM, there are two approaches in analyzing moderation variables, namely the interaction method and the multigroup method. This article aims to compare the mediation effect on interaction approaches and multigroup approaches in Structural Equation Modeling. The data used is the case of timeliness of Bank X mortgage payments. In this article, statistical methods are evaluated to compare indirect effect between groups and examine indirect effect on each group. It was concluded that Collectability Status moderates the indirect relationship between Capital and the Timeliness of Payment through Willingness to Pay. Debtors with current collectability status more strongly effect the Timeliness of Payment than debtors with incurrect collectability status. Theresults of testing indirect effects on moderation with interaction and multigroup approaches are not much different. In the multigroup approach, the bootstrap interval bias is smaller than the bootstrap interval bias in the interaction approach. The Q-square Predictive Relevance value in both methods is quite high, indicating that the model is good. On the Current Collectibility Status group Q^2 is 89.3%, in the incurrect Collectibility Status Q^2 is 84.2%. While in the interaction approach, Q^2 is 70.4%. Researcher recommend a multigroup approach to data that has categorical moderation variables because differences between groups can be directly observed without adding interaction variables in the model.â€

References

Annas, S., Ruliana, & Sanusi, W. (2022). Structural Equation Modeling for Analyzing The Technology Acceptance Model of Students in Online Teaching During the Covid-19 Pandemic. MEDIA STATISTIKA, 15(1), 104-115. doi:https://doi.org/10.14710/medstat.15.1.104-115

Becker, J., Rai, A., Ringle, C., & Völckner, F. (2013). Discovering Unobserved Heterogenity in Structural Equation Models to Avert Validity Threats. MISQ, 37(3), 665-694. doi:http://dx.doi.org/10.25300/MISQ/2013/37.3.01

Bollen, K. A. (1989). Structural Equations with Latent Variables. New York: Wiley.

Chan, W. (2007). Comparing Indirect Effects in SEM: A Sequential Model Fitting Method Using Covariance-Equivalent Specifications. Structural Equation Modeling: A Multidisciplinary Journal, 14(2), 326-346. doi:https://doi.org/10.1080/10705510709336749

Cheah, J.-H., Thurasamy, R., Memon, M. A., Chuah, F., & Ting, H. (2020). Multigroup Analysis using SmartPLS: Step-by-Step Guidelines for Business Research. Asian Journal of Business Research, 10(3), 1-19. doi:10.14707/ajbr.200087

Côté, K., Lauzier, M., & Stinglhamber, F. (2021). The Relationship Between Presenteeism and Job Satisfaction: A Mediated Moderation Model Using Work Engagement and Perceived Organizational Support. European Management Journal, 39, 270-278. doi:https://doi.org/10.1016/j.emj.2020.09.001

Dash, G., & Paul, J. (2021). CB-SEM vs PLS-SEM Methods for Research in Social Sciences and Technology Forecasting. Technological Forecasting and Social Change, 173. doi:https://doi.org/10.1016/j.techfore.2021.121092

Gelfand, M. J., Brett, J., Gunia, B. C., Imai, L., Huang, T.-J., & Hsu, B.-F. (2013). Toward a Culture-by-Context Perspective on Negotiation: Negotiating Teams in the United States and Taiwan. Journal of Applied Psychology, 98(3), 504 - 513. doi:https://doi.org/10.1037/a0031908

Gudergan, S., Ringle, C., Wende, S., & Will, A. (2008). Confirmatory Tetrad Analysis in PLS Path Modeling. Journal of Business Research, 61(12), 1238-1249. doi:https://doi.org/10.1016/j.jbusres.2008.01.012

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis A Global Perspective (7th ed.). Boston: Pearson. https://digitalcommons.kennesaw.edu/facpubs/2925/

Hair, J. F., Black, W. C., Babin, B. K., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). United Kingdom: Cengage. https://digitalcommons.kennesaw.edu/facpubs/2925/

Hair, J., Hollingsworth, C. l., Randolph, A. B., & Chong, A. Y. (2016). An Updates and Expanded Assessment of PLS-SEM in Information Systems Research. Industrial Management & Data Systems, 117(3), 442-259. doi: 10.1108/IMDS-04-2016-0130

Hayes, A. F. (2015). An Index and Test of Linear Moderated Mediation. Multivariat Behavioral Research, 50(1), 1-22. doi:https://doi.org/10.1080/00273171.2014.962683

Hayes, A. F., Montoya, A. K., & Rockwood, N. J. (2017). The Analysis of Mechanisms and Their Contingencies: Process versus Structural Equation Modeling. Australasian Marketing Journal, 25(1), 76-81. doi:https://doi.org/10.1016/j.ausmj.2017.02.001

Henseler, J., & Sarstedt, M. (2013). Goodness-of-fit Indices for Partial Least Square Path Modeling. Computational Statistics, 28(2), 565-580. doi:https://doi.org/10.1007/s00180-012-0317-1

Henseler, J., Ringle, C., & Sarstedt, M. (2016). Testing Measurement Invariance of Composites Using Partial Least Squares. International Marketing Review, 33(3), 405-431. doi:https://doi.org/10.1108/IMR-09-2014-0304

Igartua, J.-J., & Hayes, A. F. (2021). Mediation, Moderation, and Conditional Process Analysis: Concepts, Computations, and Some Common Confusions. The Spanish Journal of Psychology, 24(49), 1-23. doi:https://doi.org/10.1017/SJP.2021.46

Komunikasi, D. (2023, Mei 17). Bank Indonesia. (SHPR Triwulan I 2023: Perkembangan Harga Properti Residensial Meningkat Terbatas) Diambil kembali dari https://www.bi.go.id/id/publikasi/ruang-media/news-release/Pages/sp_2513023.aspx

Levant, R. F., Parent, M. C., McCurdy, E. R., & Bradstreet, T. C. (2015). Moderated Mediation of The Relationship Between Masculinity Ideology Outcome Expectations, and Energy Drink Use. Health Psychology, 34(11), 1100-1106. doi:http://dx.doi.org/10.1037/hea0000214

Lubis, K., Maulita, Y., & Sihombing, M. (2023). Grouping Mortgage Data By Job Using The Clustering Method. Nusantara Journal of Multidisciplinary Science, 1(3), 364-370. https://jurnal.intekom.id/index.php/njms/article/view/97

MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004). Confidence Limits fot the Indirect Effect: Distribution of the Product and Resampling Methods. Multivariate Behavioral Research, 39(1), 99-128. doi:https://doi.org/10.1207/s15327906mbr3901_4

Martens, S., Kamann, S., Dreizler, S., Göttgens, F., Husser, T.-O., Latour, M., . . . Weilbacher, P. M. (2023). Kinematic Differences Between Multiple Populations in Galatic Globular Clusters. Astronomy & Astrophysics, 1-26. doi:https://doi.org/10.1051/0004-6361/202244787

Miftahuddin, Putri, R. W., Setiawan, I., & Oktari, R. S. (2022). Modeling of Sea Surface Temperature Based on Partial Least Square - Structural Equation. MEDIA STATISTIKA, 14(2), 170-182. doi:https://doi.org/10.14710/medstat.14.2.170-182

Mueller, R. O., & Hancock, G. R. (2018). Structural Equation Modeling. In The Reviewer's Guide to Quantitative Methods in the Social Sciences (pp. 445-456). Routledge. https://www.taylorfrancis.com/chapters/edit/10.4324/9781315755649-33/structural-equation-modeling-ralph-mueller-gregory-hancock

Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and Resampling Strategies for Assessing and Comparing Indirect Effects in Multiple Mediator Models. Behavior Research Methods, 80(3), 879-891. doi:https://doi.org/10.3758/BRM.40.3.879

Rucker, D. D., McShane, B. B., & Preacher, K. J. (2015). A Researcher's Guide to Regression, Discretization, and Median Splits of Continuous Variables. Journal of Consumer Psychology, 25(4), 666-678. doi:https://doi.org/10.1016/j.jcps.2015.04.004

Ryu, E. (2015). Multiple-group Analysis Approach to Testing Group Difference in Indirect Effects. Behavior Research Methods, 47, 484-493. https://doi.org/10.3758/s13428-014-0485-8

Ryu, E., & Cheong, J. (2017). Comparing Indirect Effects in Different Groups in Single-Group and Multi-Group Structural Equation Models. Frontiers in Psychology, 8(747), 1-14. doi:https://doi.org/10.3389/fpsyg.2017.00747

Sarstedt, M., Henseler, J., & Ringle, C. (2011). Multigroup Analysis in Partial Least Squares (PLS) Path Modeling: Alternative Methods and Empirical Results. Advances in International Marketing, 69(6), 195-218. doi:https://doi.org/10.1108/S1474-7979(2011)0000022012

Solimun. (2010). Analisis Multivariat Pemodelan Struktural. Malang: CV Citra Malang.

Solimun, Fernandes, A. A., & Nurjannah. (2017). Metode Statistika Multivariat Pemodelan Persamaan Struktural (SEM) Pendekatan WarpPLS. Malang: UB Press. http://repository.um-palembang.ac.id/id/eprint/9177/

Tristanto, T. A., Nugraha, Waspada, I., Mayasari, & Kurniati, P. (2023). Sustainability Performance Impact of Corporate Performance in Indonesia Banking. Journal of Eastern European and Central Asian Research, 10(4), 668-678. doi:http://dx.doi.org/10.15549/jeecar.v10i4.1364

Published

2024-01-19

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Section

Articles