Bir Pazarlama Araştırmaları Aracı Olarak Yapısal Eşitlik Modellemesi: YEM Kullanıcıları İçin Kritik Konular ve Sorunlu Uygulamalar Üzerine Bir Kılavuz

Year 2021, Volume: 2 Issue: 2, 65 - 77, 27.12.2021

Umut Ünal

https://doi.org/10.52693/jsas.1015831

Cited By: 4

Abstract

Yapısal eşitlik modellemesi (YEM), sosyal bilimlerde, özellikle de pazarlama alanında son yıllarda kullanımı giderek artan çok güçlü bir çok değişkenli istatistiksel analiz tekniğidir. Bu modern analiz yönteminin yaygınlaşan kullanımının bir sonucu olarak, YEM kullanıcılarının karşılaştığı sorunlar dikkat çekmeye başlamış ve SEM literatüründe bu sorunlar kapsamlı bir şekilde irdelenmiştir. Bu makalenin amacı, daha yapılmış inceleme çalışmalarından faydalanarak SEM literatüründe tespit edilmiş sorunlar hakkında geniş kapsamlı bir tarama yapmak, farklı çalışmalarda ele alınmış çeşitli konuları bir araya getirerek araştırma kriterlerini genişletmek ve ampirik bir analiz yaparak söz konusu sorunların ne derece çözümlendiğini göstermektir. Tespit edilen sorunlu uygulamaların yanı sıra, literatürde bu uygulamalara yönelik önerilmiş olan çözüm yollarını sunması sayesinde bu çalışma, YEM kullanıcıları için temel bir kılavuz niteliği taşımaktadır.

Keywords

Yapısal Eşitlik Modellemesi, Kılavuz, Yanlış uygulamalar, Alan taraması

Structural Equation Modeling as a Marketing Research Tool: A Guideline for SEM Users About Critical Issues and Problematic Practices

Abstract

Structural equation modeling (SEM) is a very powerful multivariate statistical technique that has increasingly been used in social sciences, particularly in marketing. As a consequence of the widespread use of this contemporary analysis method, several issues that SEM users face have become a matter of concern, which are discussed thoroughly in SEM literature. This paper aims to conduct an extensive review of these issues by benefitting from the previous review works, broaden the research criteria by bringing together the issues that are separately addressed in those previous studies, and make an empirical analysis to demonstrate how well these problems are dealt with. Along with the problematic practices identified, the solutions suggested in the literature are presented. By that, this study serves as a basic guideline for SEM users.

Keywords

Structural equation modeling, Critical review, Guideline, Mispractices

References

• [1] R. P. Bagozzi and Y. Yi, “Specification, evaluation, and interpretation of structural equation models,” Journal of the Academy of Marketing Science, vol. 40, no. 1, Jan. 2012, doi: 10.1007/s11747-011-0278-x.
• [2] W. W. Chin, R. A. Peterson, and S. P. Brown, “Structural Equation Modeling in Marketing: Some Practical Reminders,” Journal of Marketing Theory and Practice, vol. 16, no. 4, Sep. 2008, doi: 10.2753/MTP1069-6679160402.
• [3] M. Sarstedt, C. M. Ringle, and J. F. Hair, “PLS-SEM: Looking Back and Moving Forward,” Long Range Planning, vol. 47, no. 3, pp. 132–137, Jun. 2014, doi: 10.1016/J.LRP.2014.02.008.
• [4] R. Weston and P. A. Gore, “A Brief Guide to Structural Equation Modeling,” The Counseling Psychologist, vol. 34, no. 5, Sep. 2006, doi: 10.1177/0011000006286345.
• [5] J. J. Hox and T. M. Bechger, “Introduction Structural Equation Modeling An Introduction to Structural Equation Modeling 1 What is Structural Equation Modeling?,” 1998.
• [6] G. T. M. Hult et al., “An Assessment of the Use of Structural Equation Modeling in International Business Research,” Research Methodology in Strategy and Management, vol. 3. pp. 385–415, 2006. doi: 10.1016/S1479-8387(06)03012-8.
• [7] J. F. Hair Jr., M. L. D. da S. Gabriel, and V. K. Patel, “AMOS Covariance-Based Structural Equation Modeling (CB-SEM): Guidelines on Its Application as a Marketing Research Tool,” Brazilian Journal of Marketing, vol. 13, no. 2, pp. 44–55, May 2014, doi: 10.5585/remark.v13i2.2718.
• [8] C. B. Astrachan, V. K. Patel, and G. Wanzenried, “A comparative study of CB-SEM and PLS-SEM for theory development in family firm research,” Journal of Family Business Strategy, vol. 5, no. 1, pp. 116–128, Mar. 2014, doi: 10.1016/J.JFBS.2013.12.002.
• [9] C. L. Shook, D. J. Ketchen, G. T. M. Hult, and K. M. Kacmar, “An assessment of the use of structural equation modeling in strategic management research,” Strategic Management Journal, vol. 25, no. 4, Apr. 2004, doi: 10.1002/smj.385.
• [10] F. J. Martínez-López, J. C. Gázquez-Abad, and C. M. P. Sousa, “Structural equation modelling in marketing and business research: Critical issues and practical recommendations,” European Journal of Marketing, vol. 47, no. 1, pp. 115–152, Feb. 2013, doi: 10.1108/03090561311285484.
• [11] H. Baumgartner and C. Homburg, “Applications of structural equation modeling in marketing and consumer research: A review,” 1996.
• [12] P. M. Bentler, “Multivariate Analysis with Latent Variables: Causal Modeling,” Annual Review of Psychology, vol. 31, no. 1, Jan. 1980, doi: 10.1146/annurev.ps.31.020180.002223.
• [13] Y. Koubaa, R. S. Tabbane, and R. C. Jallouli, “On the use of structural equation modeling in marketing image research,” Asia Pacific Journal of Marketing and Logistics, vol. 26, no. 2, pp. 315–338, 2014, doi: 10.1108/APJML-10-2013-0113.
• [14] V. Doğan, “PAZARLAMA ARAŞTIRMACILARININ YAPISAL EŞİTLİK MODELİ ANALİZİ UYGULAMALARI: SORUNLAR VE ÖNERİLER,” Journal of Administrative Sciences, vol. 16, no. 32, pp. 201–230, 2018.
• [15] D. Frías-Navarro and M. P. Soler, “Exploratory factor analysis (EFA) in consumer behavior and marketing research,” Suma Psicológica, vol. 19, pp. 47–58, 2012.
• [16] Kline Rex B., Principles and Practice of Structural Equation Modeling, 4th ed. New York: The Guilford Press, 2016.
• [17] J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate Data Analysis, 8th ed. Hampshire, UK: Cengage Learning EMEA, 2019. [Online]. Available: www.cengage.com/highered
• [18] L. Ding, W. F. Velicer, and L. L. Harlow, “Effects of estimation methods, number of indicators per factor, and improper solutions on structural equation modeling fit indices,” Structural Equation Modeling: A Multidisciplinary Journal, vol. 2, no. 2, Jan. 1995, doi: 10.1080/10705519509540000.
• [19] J. C. Anderson and D. W. Gerbing, “Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach,” 1988.
• [20] J. C. Loehlin, Latent Variable Models: An Introduction to Factor, Path, and Structural Analysis. Mahwah, NJ: Lawrence Erlbaum Associates, 1998.
• [21] P. M. BENTLER and C.-P. CHOU, “Practical Issues in Structural Modeling,” Sociological Methods & Research, vol. 16, no. 1, Aug. 1987, doi: 10.1177/0049124187016001004.
• [22] K. A. Bollen, Structural Equations with Latent Variables. New York, NY: Wiley Interscience, 1989.
• [23] L. Hu and P. M. Bentler, “Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives,” Structural Equation Modeling: A Multidisciplinary Journal, vol. 6, no. 1, Jan. 1999, doi: 10.1080/10705519909540118.
• [24] R. MacCallum, “Specification searches in covariance structure modeling.,” Psychological Bulletin, vol. 100, no. 1, 1986, doi: 10.1037/0033-2909.100.1.107.
• [25] M. S. Garver and J. T. Mentzer, “Logistics research methods: Employing structural equation modeling to test for construct validity,” Journal of Business Logistics, vol. 20, no. 1, pp. 33–57, 1999.
• [26] C. Fornell and D. F. Larcker, “Evaluating Structural Equation Models with Unobservable Variables and Measurement Error,” Journal of Marketing Research, vol. 18, no. 1, Feb. 1981, doi: 10.1177/002224378101800104.
• [27] J. Henseler, C. M. Ringle, and M. Sarstedt, “A new criterion for assessing discriminant validity in variance-based structural equation modeling,” Journal of the Academy of Marketing Science, vol. 43, no. 1, Jan. 2015, doi: 10.1007/s11747-014-0403-8.
• [28] J. F. Hair Jr., L. M. Matthews, R. L. Matthews, and M. Sarstedt, “PLS-SEM or CB-SEM: updated guidelines on which method to use,” International Journal of Multivariate Data Analysis, vol. 1, no. 2, 2017, doi: 10.1504/IJMDA.2017.087624.
• [29] P. M. Podsakoff, S. B. MacKenzie, J. Y. Lee, and N. P. Podsakoff, “Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies,” Journal of Applied Psychology, vol. 88, no. 5. pp. 879–903, Oct. 2003. doi: 10.1037/0021-9010.88.5.879.
• [30] N. Kock, “Common method bias in PLS-SEM: A full collinearity assessment approach,” 2015.
• [31] P. J. Jordan and A. C. Troth, “Common method bias in applied settings: The dilemma of researching in organizations,” Australian Journal of Management, vol. 45, no. 1, pp. 3–14, Feb. 2020, doi: 10.1177/0312896219871976.
• [32] P. M. Podsakoff and D. W. Organ, “Self-Reports in Organizational Research: Problems and Prospects,” Journal of Management, vol. 12, no. 4, Dec. 1986, doi: 10.1177/014920638601200408.
• [33] R. C. MacCallum, M. Roznowski, and L. B. Necowitz, “Model modifications in covariance structure analysis: The problem of capitalization on chance.,” Psychological Bulletin, vol. 111, no. 3, 1992, doi: 10.1037/0033-2909.111.3.490.
• [34] K. A. Bollen and R. A. Stine, “Bootstrapping Goodness-of-Fit Measures in Structural Equation Models,” Sociological Methods & Research, vol. 21, no. 2, Nov. 1992, doi: 10.1177/0049124192021002004.
• [35] A. J. Tomarken and N. G. Waller, “Structural equation modeling: Strengths, limitations, and misconceptions,” Annual Review of Clinical Psychology, vol. 1. pp. 31–65, 2005. doi: 10.1146/annurev.clinpsy.1.102803.144239.
• [36] D. Tofighi and D. P. MacKinnon, “Monte Carlo Confidence Intervals for Complex Functions of Indirect Effects,” Structural Equation Modeling: A Multidisciplinary Journal, vol. 23, no. 2, Mar. 2016, doi: 10.1080/10705511.2015.1057284.
• [37] J.-B. E. M. Steenkamp and H. Baumgartner, “On the use of structural equation models for marketing modeling,” International Journal of Research in Marketing, vol. 17, no. 2–3, Sep. 2000, doi: 10.1016/S0167-8116(00)00016-1.
• [38] J. B. Schreiber, A. Nora, F. K. Stage, E. A. Barlow, and J. King, “Reporting Structural Equation Modeling and Confirmatory Factor Analysis Results: A Review,” The Journal of Educational Research, vol. 99, no. 6, Jul. 2006, doi: 10.3200/JOER.99.6.323-338.
• [39] R. Hermida, “The problem of allowing correlated errors in structural equation modeling: concerns and considerations,” Computational Methods in Social Sciences, vol. 3, no. 1, pp. 5–17, 2015.
• [40] A. J. Tomarken and N. G. Waller, “Potential problems with ‘well fitting’ models.,” Journal of Abnormal Psychology, vol. 112, no. 4, 2003, doi: 10.1037/0021-843X.112.4.578.
• [41] R. Shah and S. M. Goldstein, “Use of structural equation modeling in operations management research: Looking back and forward,” Journal of Operations Management, vol. 24, no. 2, pp. 148–169, Jan. 2006, doi: 10.1016/J.JOM.2005.05.001.
• [42] L. J. Cronbach and R. J. Shavelson, “My Current Thoughts on Coefficient Alpha and Successor Procedures,” Educational and Psychological Measurement, vol. 64, no. 3, Jun. 2004, doi: 10.1177/0013164404266386.
• [43] I. Rodríguez-Ardura and A. Meseguer-Artola, “Editorial: How to Prevent, Detect and Control Common Method Variance in Electronic Commerce Research,” Journal of theoretical and applied electronic commerce research, vol. 15, no. 2, 2020, doi: 10.4067/S0718-18762020000200101.
• [44] J. Henseler, G. Hubona, and P. A. Ray, “Using PLS path modeling in new technology research: updated guidelines,” Industrial Management & Data Systems, vol. 116, no. 1, Feb. 2016, doi: 10.1108/IMDS-09-2015-0382.
• [45] G. W. Cheung and R. S. Lau, “Testing Mediation and Suppression Effects of Latent Variables,” Organizational Research Methods, vol. 11, no. 2, Apr. 2008, doi: 10.1177/1094428107300343.
• [46] D. P. MacKinnon, C. M. Lockwood, and J. Williams, “Confidence Limits for the Indirect Effect: Distribution of the Product and Resampling Methods,” Multivariate Behavioral Research, vol. 39, no. 1, Jan. 2004, doi: 10.1207/s15327906mbr3901_4.