Sosyal Bilimlerde PLS-YEM Kullanım Rehberi: Hiyerarşik Yapı Modellemesi ile Bir Uygulama

PLS algoritması ile kurulan Yapısal Eşitlik Modelleri, avantajlı ve kolaylaştırıcı yönleri ile sosyal bilimlerde artarak kullanılmaktadır. Sosyal bilimler araştırmacıları, PLS-YEM ile kurdukları araştırma modellerini SmartPLS başta olmak üzere birçok programda son kullanıcı olarak test etmektedirler. Ölçeklerin yapısı gereği, modellerde yer alan değişkenlerin büyük kısmı hiyerarşik çok boyutlu yapılardan oluşmaktadır. Bu çalışma, PLS-YEM kullanımında kullanıcının dikkat etmesi gereken noktalara dikkat çekmeyi amaçlamaktadır. Kontrol listesi ile bu yöntemin kullanımının kolaylaştırılması sağlanmaya çalışılmıştır. Ayrıca, ölçek yapısına göre hiyerarşik yapıların oluşturulması ve geçerlilik-güvenilirliğinin sağlanması gibi konularda üst-düzey yapı modelleme yaklaşımı için bir rehber sunulmuştur. Bu şekilde özellikle Türkçe literatürde rastlanmayan bir yol haritası ile hiyerarşik yapı modellemesinden yararlanacak gelecekteki araştırmalara uygulama, yorumlama ve raporlama konularında katkı sunulacağına inanılmaktadır.

Anahtar Kelimeler:

PLS-YEM, metodoloji, hiyerarşik yapı modellemesi, Smart-PLS

Pls-Sem Guide for Social Sciences: An Application with Hierarchical Component Modelling

PLS based Structural Equation Models are widens in usage thanks to their expedient and facilitator aspects. Social scientists test their models with PLS-SEM with many programs which are led by SmartPLS. Accordingly with structures of scales, most of the models consists hierarchical multi-dimensional variables. The current paper aims to indicate critical points to be considered while using PLS-SEM. The given checklist offers a list for requirements of convenient usage of PLS-SEM. Additionally, this study guides researchers to build hierarchical structures and to grant their validity & reliability. This knowledge fills the blank which is needed in the Turkish literature with a rare application of the higher-order constructing process. The contribution of this paper includes usage, interpratetion, and reporting issues for end-users.

Keywords:

PLS- SEM, methodology, Smart-PLS, hierarchical component modelling,

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Ab Hamid, M. R., Sami, W. ve Sidek, M. M. (2017). Discriminant Validity Assessment: Use of Fornell & Larcker criterion versus HTMT Criterion. Journal of Physics: Conference Series, 890(012163). https://doi.org/10.1088/1742-6596/890/1/012163
Astrachan, C. B., Patel, V. K. ve Wanzenried, G. (2014). A comparative study of CB-SEM and PLS-SEM for theory development in family firm research. Journal of family business strategy, 5(1), 116-128. https://doi.org/10.1016/j.jfbs.2013.12.002
Akter, S., D’Ambra, J. ve Ray, P. (2010). Service quality of mHealth platforms: development and validation of a hierarchical model using PLS. Electronic Markets, 20, 209-227. https://doi.org/10.1007/s12525-010-0043-x
Ali, F., Rasoolimanesh, S. M., Sarstedt, M., Ringle, C. M. ve Ryu, K. (2018). An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. International Journal of Contemporary Hospitality Management, 30(1), 514-538. https://doi.org/10.1108/IJCHM-10-2016-0568
Aybek, G. ve Karakaş, H. (2022). Use The Silver Bullet on The Right Beast: A Guide on Usage of PLS-SEM in Tourism and Gastronomy Studies. Advances in Hospitality and Tourism Research (AHTR), 10(2), 327-336. https://doi.org/10.30519/ahtr.1097884
Aybek, G. ve Özdemir, B. (2022). Effects of ethnic restaurant experience on prospective tourist intentions: Mediating role of food image. Tourism Management Perspectives, 44, 101034. https://doi.org/10.1016/j.tmp.2022.101034
Barclay, D., Thompson, W. ve Higgins, C. (1995). The Partial Least Squares (PLS) approach to causal modeling: Personal computer use as an illustration. Technology Studies, 2(2), 285-309.
Barrett, P. (2007). Structural equation modelling: Adjudging model fit. Personality and Individual differences, 42(5), 815-824. https://doi.org/10.1016/j.paid.2006.09.018
Bayat, B. (2014). Uygulamalı Sosyal Bilim Araştırmalarında Ölçme, Ölçekler Ve “Likert” Ölçek Kurma Tekniği. Gazi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 16(3), 1-24.
Becker, J. M., Klein, K. ve Wetzels, M. (2012). Hierarchical latent variable models in PLS-SEM: guidelines for using reflective-formative type models. Long range planning, 45(5-6), 359-394. https://doi.org/10.1016/j.lrp.2012.10.001
Becker, J. M., Cheah, J. H., Gholamzade, R., Ringle, C. M. ve Sarstedt, M. (2023). PLS-SEM’s most wanted guidance. International Journal of Contemporary Hospitality Management, 35(1), 321-346. https://doi.org/10.1108/IJCHM-04-2022-0474
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. New York: Academic Press.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/10.1037/0033-2909.112.1.155
Coltman, T., Devinney, T. M., Midgley, D. F. ve Venaik, S. (2008). Formative versus reflective measurement models: Two applications of formative measurement. Journal of Business Research, 61(12), 1250-1262. https://doi.org/10.1016/j.jbusres.2008.01.013
Crocetta, C., Antonucci, L., Cataldo, R., Galasso, R., Grassia, M. G., Lauro, C. N. ve Marino, M. (2021). Higher-order PLS-PM approach for different types of constructs. Social Indicators Research, 154, 725-754. https://doi.org/10.1007/s11205-020-02563-w
Cronbach, L. J. ve Meehl, P. E. (1955). Construct validity in psychological tests. Psychological bulletin, 52(4), 281-302. https://doi.org/10.1037/h0040957
Dash, G. ve Paul, J. (2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 173, 121092. https://doi.org/10.1016/j.techfore.2021.121092
Diamantopoulos, A. ve Siguaw, J. A. (2006). Formative versus reflective indicators in organizational measure development: A comparison and empirical illustration. British journal of management, 17(4), 263-282.https://doi.org/10.1111/j.1467-8551.2006.00500.x
do Valle, P. O. ve Assaker, G. (2016). Using partial least squares structural equation modeling in tourism research: A review of past research and recommendations for future applications. Journal of Travel Research, 55(6), 695-708. https://doi.org/10.1177/0047287515569779
Duarte, P. ve Amaro, S. (2018). Methods for modelling reflective-formative second order constructs in PLS: An application to online travel shopping. Journal of Hospitality and Tourism Technology, 9(3), 295-313. https://doi.org/10.1108/JHTT-09-2017-0092
Gaskin, J., Godfrey, S. ve Vance, A. (2018). Successful system use: It’s not just who you are, but what you do. AIS Transactions on Human-Computer Interaction, 10(2), 57-81. https://doi.org/10.17705/1thci.00104
Gontur, S., Gadi, P. D. ve Bagobiri, E. (2022). The moderating effect of positive word-of-mouth between service quality and customer loyalty in the hospitality sector: A PLS-SEM approach. Journal of Economics and Management, 44(1), 266-285. https://doi.org/10.22367/jem.2022.44.11
Hagger, M. S., Gucciardi, D. F. ve Chatzisarantis, N. L. (2017). On nomological validity and auxiliary assumptions: The importance of simultaneously testing effects in social cognitive theories applied to health behavior and some guidelines. Frontiers in psychology, 8, 1933. https://doi.org/10.3389/fpsyg.2017.01933
Hair, J. F., Ringle, C. M. ve Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202
Hair, J., Hollingsworth, C. L., Randolph, A. B. ve Chong, A. Y. L. (2017a). An updated and expanded assessment of PLS-SEM in information systems research. Industrial management & data systems, 117(3), 442-458. https://doi.org/10.1108/IMDS-04-2016-0130
Hair, J., Tomas, G., Hult, M., Ringle, C.M. ve Sarstedt., M. (2017b). A primer on partial least squares structural equation modeling. Los Angeles:Sage.
Hair, J. F., Matthews, L. M., Matthews, R. L. ve Sarstedt, M. (2017c). PLS-SEM or CB-SEM: updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107-123. https://doi.org/10.1504/IJMDA.2017.087624
Hair, J. F., Risher, J.J., Sarstedt, M. ve Ringle, 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
Henseler, J., Ringle, C.M. ve Sinkovics, R.R. (2009), “The use of partial least squares path modeling in international marketing”, Sinkovics, R.R. and Ghauri, P.N. (Ed.) New Challenges to International Marketing (Advances in International Marketing, Vol. 20), Emerald Group Publishing Limited, Bingley, pp. 277-319. https://doi.org/10.1108/S1474-7979(2009)0000020014
Hu, L. T. ve Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological methods, 3(4), 424-453. https://doi.org/10.1037/1082-989X.3.4.424
Kock, N. ve Hadaya, P. (2018). Minimum sample size estimation in PLS‐SEM: The inverse square root and gamma‐exponential methods. Information systems journal, 28(1), 227-261. https://doi.org/https://doi.org/10.1111/isj.12131
Liu, L., Li, C. ve Zhu, D. (2012). A new approach to testing nomological validity and its application to a second-order measurement model of trust. Journal of the Association for Information Systems, 13(12), 950-975. https://doi.org/10.17705/1jais.00320
Magno, F., Cassia, F. ve Ringle, C. M. (2022). A brief review of partial least squares structural equation modeling (PLS-SEM) use in quality management studies. The TQM Journal. https://doi.org/10.1108/TQM-06-2022-0197
Marcoulides, G. A. ve Saunders, C. (2006). Editor's comments: PLS: a silver bullet?. MIS quarterly, 30(2), iii-ix. https://doi.org/10.2307/25148727
Mateos-Aparicio, G. (2011). Partial least squares (PLS) methods: Origins, evolution, and application to social sciences. Communications in Statistics-Theory and Methods, 40(13), 2305-2317. https://doi.org/10.1080/03610921003778225
Matthews, L., Hair, J. O. E. ve Matthews, R. (2018). PLS-SEM: The Holy Grail for Advanced Analysis. The Marketing Management Journal, 28(1), 1-13.
Ngoc Ton, H. N., Shumshunnahar, M., Nhat Tu, T. N. ve Nguyen, P. T. (2023). Revisiting social capital and knowledge sharing processes in tertiary education: Vietnamese and Bangladeshi students as target populations. Cogent Social Sciences, 9(1), 2186579. https://doi.org/10.1080/23311886.2023.2186579
Paxton, P., Curran, P. J., Bollen, K. A., Kirby, J. ve Chen, F. (2001). Monte Carlo experiments: Design and implementation. Structural Equation Modeling, 8(2), 287-312. https://doi.org/10.1207/S15328007SEM0802_7
Peng, D. X. ve Lai, F. (2012). Using partial least squares in operations management research: A practical guideline and summary of past research. Journal of operations management, 30(6), 467-480. https://doi.org/10.1016/j.jom.2012.06.002
Rasoolimanesh, S. M. (2022). Discriminant validity assessment in PLS-SEM: A comprehensive composite-based approach. Data Analysis Perspectives Journal, 3(2), 1-8.
Rigdon, E. E., Sarstedt, M. ve Ringle, C. M. (2017). On comparing results from CB-SEM and PLS-SEM: Five perspectives and five recommendations. Marketing: ZFP–Journal of Research and Management, 39(3), 4-16.
Salgado, J. F. (2017). Bandwidth-fidelity dilemma. Encyclopedia of personality and individual differences, 1-4. https://doi.org/10.1007/978-3-319-28099-8_1280-1
Sarstedt, M., Ringle, C.M., Smith,D., Reams, R. ve Hair J.F. (2014). Partial Least Squares Structural Equation Modeling (PLS-SEM): A useful tool for family business researchers. Journal of Family Business Strategy, 5(1), 105-115. https://doi.org/10.1016/j.jfbs.2014.01.002
Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O. ve Gudergan, S. P. (2016). Estimation issues with PLS and CBSEM: Where the bias lies!. Journal of business research, 69(10), 3998-4010. https://doi.org/10.1016/j.jbusres.2016.06.007
Sarstedt, M. (2019). Der Knacks and a Silver Bullet. In: Babin, B.J., Sarstedt, M. (eds) The Great Facilitator (s. 155-164). Springer. https://doi.org/10.1007/978-3-030-06031-2_19
Sarstedt, M., Hair Jr, J. F., Cheah, J. H., Becker, J. M. ve Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian marketing journal, 27(3), 197-211. https://doi.org/10.1016/j.ausmj.2019.05.003
Schuberth, F., Rademaker, M. E. ve Henseler, J. (2020). Estimating and assessing second-order constructs using PLS-PM: the case of composites of composites. Industrial Management & Data Systems, 120(12), 2211-2241. https://doi.org/10.1108/IMDS-12-2019-0642
SmartPLS. (2023, 11 Mart). Model Fit. SmartPLS. https://www.smartpls.com/documentation/algorithms-and-techniques/model-fit/
Sosik, J. J., Kahai, S. S. ve Piovoso, M. J. (2009). Silver bullet or voodoo statistics? A primer for using the partial least squares data analytic technique in group and organization research. Group & Organization Management, 34(1), 5-36. https://doi.org/10.1177/1059601108329198
Tenenhaus, M., Amato, S. ve Esposito Vinzi, V. (2004). A global goodness-of-fit index for PLS structural equation modelling. In Proceedings of the XLII SIS scientific meeting, 1(2), 739-742.
van Riel, A. C., Henseler, J., Kemény, I. ve Sasovova, Z. (2017). Estimating hierarchical constructs using consistent partial least squares: The case of second-order composites of common factors. Industrial management & data systems, 117(3), 459-477. https://doi.org/10.1108/IMDS-07-2016-0286
Wetzels, M., Odekerken-Schröder, G. ve Van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS quarterly, 3(1), 177-195. https://doi.org/10.2307/20650284