SỬ DỤNG PHƯƠNG PHÁP PLS – SEM ĐỂ KIỂM ĐỊNH MÔ HÌNH ĐA BIẾN

File: Tải bài viết
Title: Using PLS – SEM for testing multivariate model
Ngày nhận bài: 12/07/2023
Ngày xét duyệt: 11/08/2023
Ngày xuất bản: 11/08/2023
Tác giả: Việt Cao Quốc, Đoàn Thuận Phát
Trang: 24 - 32

Chuyên mục
QUẢN LÝ KINH TẾ

Tạp chí
Số 15 (08.2023)

Tóm tắt

Bài báo này trình bày phương pháp kiểm định mô hình đa biến bằng kỹ thuật có độ tin cậy và có nhiều ưu điểm nhất hiện nay, PLS-SEM. Đầu tiên, chúng tôi giới thiệu về PLS-SEM và một số ưu điểm quan trọng khi nhà nghiên cứu sử dụng để đánh giá mô hình nghiên cứu. Tiếp theo, lý thuyết đánh giá mô hình đo lường và đánh giá mô hình cấu trúc. Cuối cùng, một minh họa nhằm giúp cho nhà nghiên cứu và phản biện rút ra cách thức trình bày và các tiêu chí đánh giá khi công trình nghiên cứu của họ khi sử dụng PLS-SEM.

Abstract

This paper presents the method of testing multivariate models by PLS-SEM, a technique that has the most reliability and advantages nowadays. Firstly, an introduction to PLS-SEM is provided, along with its key merits when researchers assess their research models. Subsequently, the theories of measurement model assessment and structural model assessment are presented. Finally, an illustrative example is provided to aid researchers and reviewers in understanding the presentation and evaluation criteria when utilizing PLS-SEM for their research.

Từ khóa

PLS – SEM, CB – SEM, kiểm định mô hình đo lường, kiểm định mô hình cấu trúc.

PLS – SEM, CB – SEM, measurement model testing, structural model testing

Tài liệu tham khảo

Al Halbusi, H., Soto-Acosta, P., & Popa, S. (2022). Entrepreneurial passion, role models and self-perceived creativity as antecedents of e-entrepreneurial intention in an emerging Asian economy: The moderating effect of social media. Asia Pacific Journal of Management. https://doi.org/10.1007/s10490-022-09857-2

Bandura, A. (1986). Social foundations of thought and action : a social cognitive theory. In New Jersey: Prentice-Hall, 1986 (Vol. 16, Issue 1).

Barclay, D. W., Higgins, C. A., & Thompson, R. (1995). The partial least squares approach to causal modeling: personal computer adoption and use as 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

Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588–606. https://doi.org/10.1037/0033-2909.88.3.588

Blumberg, B. F., Cooper, D. R., & Schindler, P. S. (2014). Business Research Methods (4th ed.). McGraw - Hill.

Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). Routledge.

Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (Issue April, pp. 295–358). Erlbaum.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences. In Evaluation and Program Planning (2nd ed.). Lawrence Erlbaum Associates.

Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/10.1037/0033-2909.112.1.155

Cronbach, L. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.

Fornell, C. (1987). A second generation of multivariate analysis: Classification of methods and implications for marketing research. Review of Marketing, 1, 407–450.

Fornell, Claes., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312

Geisser, S. (1974). A Predictive Approach to the Random Effect Model. Biometrika, 61(1), 101. https://doi.org/10.2307/2334290

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Los Angeles: SAGE.

Hair, Joe F., Ringle, C. M., & 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, Joseph 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

Handayati, P., Wulandari, D., Soetjipto, B. E., Wibowo, A., & Narmaditya, B. S. (2020). Does entrepreneurship education promote vocational students’ entrepreneurial mindset? Heliyon, 6(11), e05426. https://doi.org/10.1016/j.heliyon.2020.e05426

Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Ketchen, D. J., Hair, J. F., Hult, G. T. M., & Calantone, R. J. (2014). Common Beliefs and Reality About PLS: Comments on Rönkkö and Evermann (2013). Organizational Research Methods, 17(2), 182–209. https://doi.org/10.1177/1094428114526928

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

Henseler, J., Ringle, C. M., & Sinkovics, R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20(4), 277–319. https://doi.org/10.1108/S1474-7979(2009)0000020014

Hofstede, G., Hofstede, G. J., & Minkov, M. (2010). Cultures and Organizations: Software of the Mind (3rd ed.). McGraw - Hill. https://doi.org/10.1007/s11569-007-0005-8

Hofstede, G., & McCrae, R. R. (2004). Personality and Culture Revisited: Linking Traits and Dimensions of Culture. Cross-Cultural Research, 38(1), 52–88. https://doi.org/10.1177/1069397103259443

Hulland, J. (1999). Use of Partial Least Squares ( Pls ) in Strategic Management Research : a Review of Four Recent Studies. Strategic Management Journal, 204(November 1996), 195–204.

Kock, N., & 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/10.1111/isj.12131

Krueger, N. F., Reilly, M. D., & Carsrud, A. L. (2000). Competing models of entrepreneurial intentions. Journal of Business Venturing, 15(5–6), 411–432. https://doi.org/10.1016/S0883-9026(98)00033-0

Lucas, W. A., Cooper, S. Y., Ward, T., & Cave, F. (2009). Industry placement, authentic experience and the development of venturing and technology self-efficacy. Technovation, 29(11), 738–752. https://doi.org/10.1016/j.technovation.2009.06.002

Obschonka, M., Silbereisen, R. K., & Schmitt-Rodermund, E. (2011). Successful Entrepreneurship as Developmental Outcome. European Psychologist, 16(3), 174–186. https://doi.org/10.1027/1016-9040/a000075

Ringle, Christian M., Wende, S., & Becker, J.-M. (2022). SmartPLS 4. Oststeinbek: SmartPLS. https://www.smartpls.com

Ringle, Christian Marc, Götz, O., Wetzels, M., & Wilson, B. (2009). On the Use of Formative Measurement Specifications in Structural Equation Modeling: A Monte Carlo Simulation Study to Compare Covariance-based and Partial Least Squares Model Estimation Methodologies. METEOR Research Memoranda (RM/09/014), September, 1–43. https://doi.org/10.2139/ssrn.2394054

Sarstedt, M., Ringle, C. M., Henseler, J., & Hair, J. F. (2014). On the Emancipation of PLS-SEM: A Commentary on Rigdon (2012). Long Range Planning, 47(3), 154–160.

Satorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In Latent variables analysis: Applications for developmental research (pp. 399–419). SAGE Publications Ltd.

Shmueli, G., Ray, S., Velasquez Estrada, J. M., & Chatla, S. B. (2016). The elephant in the room: Predictive performance of PLS models. Journal of Business Research, 69(10), 4552–4564. https://doi.org/10.1016/j.jbusres.2016.03.049

Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189

Stone, M. (1974). Cross-Validatory Choice and Assessment of Statistical Predictions. Journal of the Royal Statistical Society, 36(2), 111–147. https://doi.org/10.2307/2984809

Valaei, N., Rezaei, S., Ismail, W. K. W., & Oh, Y. M. (2016). The effect of culture on attitude towards online advertising and online brands: applying Hofstede’s cultural factors to internet marketing. International Journal of Internet Marketing and Advertising, 10(4), 270. https://doi.org/10.1504/IJIMA.2016.081346

Wang, Y.-S., Lin, S., Yeh, C.-H., Li, C.-R., & Li, H.-T. (2016). What drives students’ cyber entrepreneurial intention: The moderating role of disciplinary difference. Thinking Skills and Creativity, 22, 22–35. https://doi.org/10.1016/j.tsc.2016.08.003

Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2013). Business Research Methods. In Fort Worth: The Dryden Press. (9th ed.). Cengage Learning.