Authors

• Mojtaba Rajabi ¹
• S. Mostafa Rasoolimanesh ²
• Reza Andam ³
• Faezeh Abdollahnezhad ⁴

¹ Assistant Professor in Sport Management, Faculty of Physical Education and Sports Sciences, Shahrood University of Technology, Shahrood, Iran

² Professor and Director of Centre for Research and Innovation in Tourism (CRiT) Faculty of Social Sciences and Leisure Management, Taylors University, Malaysia

³ Associate Professor in Sport Management, Faculty of Physical Education and Sport Sciences, Shahrood University of Technology, Shahrood, Iran

⁴ PhD student in Sport Management, Faculty of Physical Education and Sports Sciences, Shahrood University of Technology, Shahrood, Iran.

Abstract

Objective: This study aims to systematically investigate the use of partial least squares-structural equation modeling (PLS-SEM) in sports management journals, and provide instructions and guidelines to apply this method.
Methodology:  This study is a systematic review, and secondary data was used. 183 studies of all PLS-SEM studies in 14 journals of sport management, published between 2020 and 2022, were analyzed in terms of reasons for using PLS-SEM, data characteristics, model characteristics, evaluation of measurement models, structural model evaluation, reporting, and using advanced analysis.
Results: The results show that the researchers have indicated a few reasons to apply PLS-SEM, which majority of articles highlighted small sample size and lack of data normality. The results identified several issues in reporting the measurement model assessment, especially for measurement evaluation of second-order models. In addition, the findings show that most of studies have not considered reporting the advanced criteria for evaluating of structural model.
Conclusion: The results of this study assist researchers, reviewers and editors of journals to be more mindful, in future studies regarding the reporting of the results using PLS-SEM and avoid incorrect conclusions.

Highlights

1. Ali F, Rasoolimanesh S M, Cobanoglu C. (Eds.). Applying partial least squares in tourism and hospitality research. Emerald Publishing, 2019.
2. Bodoff D, Ho S Y. Partial least squares structural equation modeling approach for analyzing a model with a binary indicator as an endogenous variable. Communications of the Association for Information Systems, 2016; 38(1), 23-32.
3. Bullock H E, Harlow L L, Mulaik S A. Causation issues in structural equation modeling research. Structural Equation Modeling: A Multidisciplinary Journal, 1994; 1(3), 253-267.
4. Hanushek E A, Jackson J E. Statistical methods for social scientists. Academic Press, 2013.
5. Khine M S. (Ed.). Application of structural equation modeling in educational research and practice, 2013; (Vol. 7). Rotterdam: SensePublishers.
6. Hwang H, Malhotra N K, Kim Y, Tomiuk M A, Hong S. A comparative study on parameter recovery of three approaches to structural equation modeling. Journal of Marketing Research, 2010; 47(4), 699-712.
7. Lastovicka J L, Thamodaran K. Common factor score estimates in multiple regression problems. Journal of Marketing Research, 1991;28(1), 105-112.
8. Ciavolino E, Aria M, Cheah J H, Roldán J L. A tale of PLS structural equation modelling: episode I—a bibliometrix citation analysis. Social Indicators Research, 2022; 1-26.
9. Richter N F, Cepeda-Carrion G, Roldán Salgueiro J L, Ringle C M. European management research using partial least squares structural equation modeling (PLS-SEM). European Management Journal, 2016a; 34 (6), 589-597.
10. Shmueli, G., Ray, S., Estrada, J. M. V., Chatla, S. B. The elephant in the room: Predictive performance of PLS models. Journal of business Research, 2016; 69(10), 4552-4564.
11. Rigdon E E, Sarstedt M, Ringle C M. On comparing results from CB-SEM and PLS-SEM: Five perspectives and five recommendations. Marketing: ZFP–Journal of Research and Management, 2017;39(3), 4-16.
12. Hair J F, Hollingsworth C L, Randolph A B, Chong A. Y. L. An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 2017a; 117(3), 442–458.
13. Rigdon E E. Choosing PLS path modeling as analytical method in European management research: A realist perspective. European Management Journal, 2016; 34(6), 598-605.
14. Sarstedt M, Hair J F, Ringle C M, Thiele K O, Gudergan S P. Estimation issues with PLS and CBSEM: Where the bias lies!. Journal of business research, 2016; 69(10), 3998-4010.
15. Hair JF, Sarstedt M, Ringle CM, et al. An Assessment of the Use of Partial Least Squares Structural Equation Modeling in Marketing Research. Journal of the Academy of Marketing Science, 2012b; 40(3): 414-433.
16. Sosik, J. J., Kahai, S. S., & Piovoso, M. J. Silver bullet or voodoo statistics? A primer for using the partial least squares data analytic technique in group and organization research. Group & Organization Management, 2009; 34(1), 5-36.
17. Richter, N. F., Cepeda, G., Roldán, J. L., & Ringle, C. M. European management research using partial least squares structural equation modeling (PLS-SEM). European Management Journal,  2015; 33(1), 1-3.
18. Ringle CM, Sarstedt M, Mitchell R, et al. Partial Least Squares Structural Equation Modeling in HRM Research. The International Journal of Human Resource Management forthcoming, 2019.
19. Hair JF, Hollingsworth CL, Randolph AB, et al. An Updated and Expanded Assessment of PLS-SEM in Information Systems Research. Industrial Management & Data Systems in press, 2016a.
20. Ringle CM, Sarstedt M and Straub DW. A Critical Look at the Use of PLS-SEM in MIS Quarterly. MIS Quarterly, 2012; 36(1): iii-xiv.
21. Peng DX and Lai F. Using Partial Least Squares in Operations Management Research: A Practical Guideline and Summary of Past Research. Journal of Operations Management, 2012; 30(6): 467–480.
22. Nitzl C. The Use of Partial Least Squares Structural Equation Modelling (PLS-SEM) in Management Accounting Research: Directions for Future Theory Development. Journal of Accounting Literature, 2016; 37(December): 19-35.
23. Hair JF, Sarstedt M, Pieper TM, et al. The Use of Partial Least Squares Structural Equation Modeling in Strategic Management Research: A Review of Past Practices and Recommendations for Future Applications. Long Range Planning, 2012a; 45(5-6): 320-340.
24. Ali F, Rasoolimanesh SM, Sarstedt M, et al. An Assessment of the Use of Partial Least Squares Structural Equation Modeling (PLS-SEM) in Hospitality Research. International Journal of Contemporary Hospitality Management, 2018; 30(1): 514-538.
25. Kaufmann, L., & Gaeckler, J. A structured review of partial least squares in supply chain management research. Journal of Purchasing and Supply Management, 2015; 21(4), 259-272.
26. Wold HOA. Soft Modeling: The Basic Design and Some Extensions. In: Jöreskog KG and Wold HOA (eds) Systems Under Indirect Observations: Part II. Amsterdam: North-Holland, 1982; 1-54.
27. Sarstedt M, Ringle CM and Hair JF. Partial Least Squares Structural Equation Modeling. In: Homburg C, Klarmann M and Vomberg A (eds) Handbook of Market Research, 2017a; Heidelberg: Springer.
28. Hair, J. F., Sarstedt, M., & Ringle, C. M. Rethinking some of the rethinking of partial least squares. European Journal of Marketing, 2019;53(4), 566-584.
29. Ghasemy, M., Teeroovengadum, V., Becker, J. M., & Ringle, C. M. This fast car can move faster: A review of PLS-SEM application in higher education research. Higher education, 2020; 80(6), 1121-1152.
30. Chin WW. PLS-Graph 3.0. Houston: Soft Modeling Inc, 2003.
31. Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., et al. Common beliefs and reality about PLS: comments on Rönkkö and Evermann Organizational Research Methods, 2014; 17(2), 182–209. https://doi.org/10.1177/1094428114526928.
32. Rönkkö, M. and Evermann, J. “A critical examination of common beliefs about partial least squares path modeling”, Organizational Research Methods, 2013; 6(3), 425-448.
33. Becker, J.-M. and Ismail, I.R. “Accounting for sampling weights in PLS path modeling: simulations and empirical examples”, European Management Journal, 2016; 34(6), 606-617.
34. Hair, J.F., Sarstedt, M., Ringle, C.M. and Gudergan, S.P. Advanced Issues in Partial Least Squares Structural Equation Modeling (PLS-SEM), Sage, Thousand Oaks, CA, 2018.
35. Nitzl, C., Roldán, J.L. and Cepeda, C.G. “Mediation analysis in partial least squares path modeling: helping researchers discuss more sophisticated models”, Industrial Management & Data Systems, 2016; 116 No. 9, pp. 1849-1864.
36. Chin, W. W. How to write up and report PLS analyses. In V. E. Vinzi, W. W, 2010.
37. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017b). A primer on partial least squares structural equation modeling (PLS-SEM) (2th ed.). Thousand Oaks: Sage, 2017b.
38. Garson, G. D. Partial least squares: regression and structural equation models. Asheboro: Statistical Associates Publishing, 2016.
39. Dash, G., & Paul, J. (2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 2021; 173, 121092.‏
40. Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. Partial least squares structuralequation modeling (PLS-SEM): An emerging tool in business research. European BusinessReview, 2014; 26(2), 106–121.
41. Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Routledge, 2013.
42. Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. Partial least squares structural equation modeling (PLS-SEM) using R: A workbook, 2021;(p. 197). Springer Nature.‏
43. Kock, N., & Hadaya, P. Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 2018; 28(1), 227–261.
44. Diamantopoulos, A., Sarstedt, M., Fuchs, C., Wilczynski, P. and Kaiser, S. “Guidelines for choosing between multi-item and single-item scales for construct measurement: a predictive validity perspective”, Journal of the Academy of Marketing Science, 2012; 40(3), 434-449.
45. Sarstedt, M., Diamantopoulos, A. and Salzberger, T. “Should we use single items? Better not”, Journal of Business Research, 2016a; 69, 3199-3203.
46. Sarstedt, M., Hair Jr, J. F., Cheah, J. H., Becker, J. M., & Ringle, C. M. How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian marketing journal, 2019; 27(3), 197-211.
47. Hulland, J. Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 1999; 20(2), 195–204.
48. Hair, J. F., Hult, T., Ringle, C. M., & Sarstedt, M. A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.), 2022; Thousand Oaks: Sage.
49. Dijkstra, T. K. Latent variables and indices: Herman Wold’s basic design and partial least In V. Esposito Vinzi, W. W, 2010.
50. Dijkstra, T. K. PLS’ Janus face–response to professor Rigdon’s ‘rethinking partial least. Squares modeling: In praise of simple methods. Long Range Planning, 2014; 47(3), 146–153.
51. Dijkstra, T. K., & Henseler, J. Consistent partial least squares path modeling. MIS Quarterly, 2015; 39(2), 297–316.
52. Henseler, J., Ringle, C.M. and Sarstedt, M. “A new criterion for assessing discriminant validity in variance-based structural equation modeling”, Journal of the Academy of Marketing Science, 2015; 43 No. 1, pp. 115-135.
53. Salajegheh, A., Biglari, N., & Andam, R. Clarification of Mediator Role of Positive Organizational Behavior in Relationship between Managers’ Sense of Humor with Employee’Creativity in Youth and Sport Offices of Kerman Province. Sport Management Studies, 2022; 13(70), 362-393. [Persian]
54. Norouzi Seyed Hossini, R., & Moradi, E. An investigation of consumption-commenications emotions affected by well-known sports mascots in explain consumers' behavioral intention: The moderating effect of gender. Communication Management in Sport Media, 2020; 7(4), 23-36. [Persian]
55. Akbari yazdi, H., roodbari, H., & abdolahi, S. The Impact of Experiential Marketing Dimension on Behavioral Intentions The Student Fans of Iranian Football Premier League. Sport Management and Development, 2020; 9(2), 122-138. Doi: 10.22124/jsmd.2020.4390. [Persian]
56. Mohammadi Y, elahi A, akbari yazdi H. Development and Validation of Scale to Measure Internal Branding in Sport Federations. 3 2022; 10 (36) :147-161 .[Persian]
57. Rajabi, M., Esfahani, Z., & Abdollahnezhad, F. Effect of Transformational Leadership on Employee Performance of Physical Education Teachers with Mediating Role of Identity and Work Engagement and Role of Moderated of Pro-active Personality. Human Resource Management in Sports, 2022; 9(2): 413-432. [Persian]
58. Alidoust Ghahfarokhi, E., Khosromanesh, R., Asadolahi, A., & Heidari, A. Investigating the impact of supporting sections on the main sector of Iran's sports industry using a holistic conceptual model. Contemporary Studies on Sport Management, 2022; 12(23), 35-48. Doi: 10.22084/smms.2020.21853.2637. [Persian]
59. Shariati, J. A. D., Seifpanahi Shabani, J., & Khosromanesh, R. Identify and study the status of trustees and the desired consequences of sports in Iran. Sport Management Journal, 2022; 14(2), 149-161. [Persian]
60. Hair, J. F., Ringle, C. M., & Sarstedt, M. PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 2011; 19, 139–151.
61. Becker, J.-M., Ringle, C. M., Sarstedt, M., & Völckner, F. How collinearity affects mixture regression results. Marketing Letters, 2015; 26(4), 643–659.
62. Mason, C. H., & Perreault, W. D. Collinearity, power, and interpretation of multiple regression analysis. Journal of Marketing Research, 1991; 28(3), 268–280.
63. Aguirre-Urreta, M. I., & Rönkkö, M. Statistical inference with PLSc using bootstrap confidence intervals. MIS Quarterly, 2018; 42(3), 1001–1020.
64. Shmueli, G., & Koppius, O. R. Predictive analytics in information systems research. MIS quarterly, 2011; 553-572.
65. Rigdon, E. E. Rethinking partial least squares path modeling: In praise of simple methods. Long range planning, 2012; 45(5-6), 341-358.
66. Raithel, S., Sarstedt, M., Scharf, S., & Schwaiger, M. On the value relevance of customer satisfaction. Multiple drivers and multiple markets. Journal of the Academy of Marketing Science, 2012; 40, 509-525.
67. Kenny, D. A. Moderation, 2015; http://davidakenny.net/cm/moderation.htm.
68. Sarstedt, M. & Danks, N. PPrediction in HRM research—A gap between rhetoric and reality.Human Resource Management Journal, forthcoming, 2021.
69. Chin, W., Cheah, J. H., Liu, Y., Ting, H., Lim, X. J., & Cham, T. H. Demystifying the role of causal-predictive modeling using partial least squares structural equation modeling in information systems research. Industrial Management & Data Systems, 2020;120(12), 2161-2209.
70. Sarstedt, M., Ringle, C. M., Smith, D., Reams, R., & Hair, J. FPartial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers. Journal of Family Business Strategy, 2014; 5, 105–115.
71. Khalili, M. R., Andam, R., & Rajabi, M. Effect of Ethical Leadership Style on Organizational Justice with Moderating Role of Work-Family Conflict among Physical Education Teachers. Human Resource Management in Sport Journal, 2021; 8(2), 309-325. [Persian]
72. Streukens, S., & Leroi-Werelds, S. Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results. European Management Journal, 2016; 34(6), 618–632.
73. Lohmöller, J. B., & Lohmöller, J. B. Predictive vs. structural modeling: Pls vs. ml. Latent variable path modeling with partial least squares, 1989; 199-226.
74. Goodness of Fit (GoF) - SmartPLS. From https://www.smartpls.com/documentation/algorithms-and-techniques/goodness-of-fit/. Retrieved 19 February 2023
75. Ringle, Christian M., Wende, Sven, & Becker, Jan-Michael. SmartPLS 4. Oststeinbek: SmartPLS. Retrieved from https://www.smartpls.com, 2022.

Keywords

• Partial least squares (PLS)
• Structural equation modeling (SEM)
• Sport Management
• Measurement model
• Structural model

Main Subjects

• فناوری‌های نوین و شبکه‌های اجتماعی و توسعه منابع انسانی در ورزش