DOI: https://doi.org/10.14710/medstat.16.1.76-87

SIMULATION STUDY FOR UNDERSTANDING THE PERFORMANCE OF PARTIAL LEAST SQUARES–MODIFIED FUZZY CLUSTERING (PLSMFC) IN FINDING GROUPS UNDER STRUCTURAL EQUATION MODEL

*Moch. Abdul Mukid scopus  -  Department of Statistics, Universitas Diponegoro, Indonesia
Bambang Widjanarko Otok  -  Department of Statistics, Institut Teknologi Sepuluh Nopember, Indonesia
Suparti Suparti  -  Department of Statistics, Universitas Diponegoro, Indonesia
Open Access Copyright (c) 2023 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
In structural equation modeling (SEM), it is usually assumed that all observations follow only one model. This becomes irrelevant if the observations contain natural groups, each of which has a different SEM model. Mukid et al (2002) have proposed the partial least squares-modified fuzzy clustering method (PLSMFC) as a way to find groups of observations and at the same time estimate the parameters of the SEM model. This research aims to understand the performance of the PLSMFC method in finding groups of observations characterized by different forms of structural equation models. The goal was achieved by conducting a simulation study involving factors such as SEM model specification and number of clusters. The procedure used is to force the generated data into a different number of segments. The segment validity measures used are the fuzziness performance index (FPI) and normalized classification entropy (NCE). The correct number of segments is indicated by the smallest FPI and NCE values. Based on simulation studies, it is known that the PLSMFC method can detect segments accurately, especially if the size of the segments used to reallocate observations is larger than the number of segments used to generate the data.
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Keywords: Unobserved Heterogenity; Partial Least Squares; Fuzzy Clustering

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  1. Aktepe, A., Ersöz, S., & Toklu, B. (2015). Computers & Industrial Engineering Customer Satisfaction and Loyalty Analysis with Classification Algorithms and Structural Equation Modeling. Computers & Industrial Engineering, 86, 95–106
  2. Ansari, A., Jedidi, K., & Jagpal, S. (2014). A Hierarchical Bayesian Treating Equation Methodology for Structural Models in Heterogeneity. Marketing Science, 19(4), 328–347
  3. Bai Gokarna, V., Mendon, S., Thonse Hawaldar, I., Spulbar, C., Birau, R., Nayak, S., & Manohar, M. (2022). Exploring the Antecedents of Institutional Effectiveness: A Case Study of Higher Education Universities in India. Economic Research-Ekonomska Istrazivanja , 35(1), 1162–1182
  4. Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The Fuzzy c-Means Clustering Algorithm. Computers & Geosciences, 10(2), 191–203
  5. Chuah, S. H. W., Tseng, M. L., Wu, K. J., & Cheng, C. F. (2021). Factors Influencing The Adoption of Sharing Economy in B2B Context in China: Findings from PLS-SEM and fsQCA. Resources, Conservation and Recycling, 175(September), 105892
  6. Fordellone, M. & Vichi, M. (2020). Finding Groups in Structural Equation Modeling Through The Partial Least Squares Algorithm. Computational Statistics and Data Analysis, 147, 106957
  7. Hahn, C.; Johnson, M.D.; Herrmann, A.; Huber, F. Capturing Customer Heterogeneity using a Finite Mixture PLS Approach. Schmalenbach Bus. Rev. 2002, 54, 243–269
  8. Heiser, W. J., & Groenen, P. J. F. (1997). Cluster Differences Scaling with A Within-Clusters Loss Component and A Fuzzy Successive Approximation Strategy to Avoid Local Minima. In Psychometrika (Vol. 62, pp. 63–83)
  9. Hidayat, R. N., Poernomo, E., Waluyo, M., & Otok, B. W. (2018). The Model of Risk of Travel Ticket Purchasing Decisions on Marketing Communication Mix in Online Site Using Structural Equation Modeling. International Journal of Civil Engineering and Technology, 9(5), 847–856
  10. Hwang, H., DeSarbo, W. S., & Takane, Y. (2007). Fuzzy Clusterwise Generalized Structured Component Analysis. Psychometrika, 72(2), 181–198
  11. Jajuga, K. (1986). Linear Fuzzy Regression. Fuzzy Sets and Systems, 20(3), 343–353
  12. Jedidi, K., Jagpal, H. S., & Desarbo, W. S. (1997). Finite-Mixture Structural Equation Models for Response-Based Segmentation and Unobserved Heterogeneity. Marketing Science, 16(1), 39–59
  13. Lamberti, G., Aluja, T. B., & Sanchez, G. (2016). The Pathmox Approach for PLS Path Modeling Segmentation. Applied Stochastic Models in Business and Industry, 32(4), 453–468
  14. Lohmöller, J. B. (2013). Latent Variable Path Modeling with Partial Least Squares. Berlin: Physica-Verlag HD
  15. McBratney, A. B., & Moore, A. W. (1985). Application of Fuzzy Sets to Climatic Classification. Agricultural and Forest Meteorology, 35(1–4), 165–185
  16. Mukid, M. A., Otok, B. W., & Suharsono, A. (2022). Segmentation in Structural Equation Modeling Using a Combination of Partial Least Squares and Modified Fuzzy Clustering. Symmetry, 14(11)
  17. Otok, B. W., Sustrami, D., Hastuti, P., Purhadi, Sutikno, Purnami, S. W., & Suharsono, A. (2018). Structural Equation Modeling The Environment, Psychology, Social Relationships Against Physical Health in Determination Quality of Elderly Community Surabaya. International Journal of Civil Engineering and Technology, 9(12), 926–938
  18. Sarstedt, M. (2008). A Review of Recent Approaches for Capturing Heterogeneity in Partial Least Squares Path Modelling. Journal of Modelling in Management, (July 2008). https://doi.org/10.1108/17465660810890126
  19. Sarstedt, M., Radomir, L., Moisescu, O. I., & Ringle, C. M. (2022). Latent Class Analysis in PLS-SEM: A Review and Recommendations for Future Applications. Journal of Business Research, 138(September 2021), 398–407
  20. Schlittgen, R., Ringle, C. M., Sarstedt, M., & Becker, J. (2016). Segmentation of PLS Path Models by Iterative Reweighted Regressions. Journal of Business Research, 69(10), 4583–4592. https://doi.org/10.1016/j.jbusres.2016.04.009
  21. Vinzi, V. E., Trinchera, L., Squillacciotti, S., & Tenenhaus, M. (2008). REBUS-PLS: A Response-Based Procedure for Detecting Unit Segments in PLS Path Modelling. Applied Stochastic Models in Business and Industry, 24, 439–458. https://doi.org/10.1002/asmb
  22. Wedel, M., & Steenkamp, J. B. E. M. (1989). A Fuzzy Clusterwise Regression Approach to Benefit Segmentation. International Journal of Research in Marketing, 6(4), 241–258. https://doi.org/10.1016/0167-8116(89)90052-9

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