The DOI matches its Crossref record; 164 of the 171 references it cites were found in a registry or an index.
Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook
Open accessAbstract
This is an open-access step-by-step guide to analyzing PLS models using R, especially using the SEMinR package and with in-depth case studies.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The text we found for this paper looks like another document, so the self-check wasn’t run.
Citations
Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,893
Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.2307/3151312 · Cited by 109,314
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190
Statistical Power Analysis for the Behavioral Sciences
Muller, K. E., & Cohen, J. (1989) · Technometrics · doi:10.2307/1270020 · Cited by 99,603
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
(2013) · Cited by 44,563
A new criterion for assessing discriminant validity in variance-based structural equation modeling
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0403-8 · Cited by 38,459
Self-citationStatistical power analyses using G∗Power 3.1: tests for correlation and regression analyses
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009) · Behavior Research Methods · doi:10.3758/brm.41.4.1149 · Cited by 37,615
When to Use and How to Report the Results of PLS-SEM
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019) · European Business Review · doi:10.1108/ebr-11-2018-0203 · Cited by 27,541
Self-citationA Theory of Cognitive Dissonance
Festinger, L. (1957) · doi:10.1515/9781503620766 · Cited by 24,241
Research works printed but not linked: 119
- Henseler, J. (2021). Composite-based structural equation modeling: Analyzing latent and emergent vari- ables. New York, NY: Guilford Press.
- Mehmetoglu, M., & Venturini, S. (2021). Structural equation modelling with partial least squares using Stata and R. London: Routledge.
- Memon, M. A., Ramayah, T., Cheah, J.-H., Ting, H., Chuah, F., & Cham, T. H. (2021). PLS-SEM statistical programs: A review. Journal of Applied Structural Equation Modeling, 5(1), i-xiv.
- Monecke, A., & Leisch, F. (2012). semPLS: Structural equation modeling using partial least squares. Journal of Statistical Software, 48(3), 1-32.
- Ramayah, T., Cheah, J.-H., Chuah, F., Ting, H., & Memon, M. A. (2016). Partial least squares struc- tural equation modeling (PLS-SEM) using SmartPLS 3.0: An updated and practical guide to sta- tistical analysis. Kuala Lumpur: Pearson Malaysia.
- Sarstedt, M., & Cheah, J. H. (2019). Partial least squares structural equation modeling using Smart- PLS: A software review. Journal of Marketing Analytics, 7(3), 196-202.
- 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.
- Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295-358). Mahwah, NJ: Erlbaum.
- Chin, W. W., & Newsted, P. R. (1999). Structural equation modeling analysis with small samples using partial least squares. In R. H. Hoyle (Ed.), Statistical strategies for small sample research (pp. 307-341). Thousand Oaks, CA: Sage.
- Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155-159.
- do Valle, P. O., & Assaker, G. (2016). Using partial least squares structural equation modeling in tour- ism research: A review of past research and recommendations for future applications. Journal of Travel Research, 55(6), 695-708. Chapter 1 • An Introduction to Structural Equation Modeling 1
- Ghasemy, M., Teeroovengadum, V., Becker, J.-M., & Ringle, C. M. (2020). This fast car can move faster: A review of PLS-SEM application in higher education research. Higher Education, 80, 1121-1152.
- Guttman, L. (1955). The determinacy of factor score matrices with implications for five other basic problems of common-factor theory. British Journal of Statistical Psychology, 8(2), 65-81.
- Hair, J. F., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442-458.
- 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.). Thousand Oaks, CA: Sage.
- Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-151.
- Hair, J. 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.
- Hair, J. F., & Sarstedt, M. (2019). Composites vs. factors: Implications for choosing the right SEM method. Project Management Journal, 50(6), 1-6.
- 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 partial least squares: Comments on Rönkkö & Evermann (2013). Organizational Research Methods, 17(1), 182-209.
- In K. G. Jöreskog & H. Wold (Eds.), Systems under indirect observation, part II (pp. 119-130). Amsterdam: North-Holland.
- Khan, G., Sarstedt, M., Shiau, W.-L., Hair, J. F., Ringle, C. M., & Fritze, M. (2019). Methodological research on partial least squares structural equation modeling (PLS-SEM): A social network analysis. Internet Research, 29(3), 407-429.
- Marcoulides, G. A., & Saunders, C. (2006). PLS: A silver bullet? MIS Quarterly, 30(2), iii-ix.
- Rasoolimanesh, S. M., Ringle, C. M., Sarstedt, M., & Olya, H. (2021). The combined use of predic- tion-oriented approaches: Partial least squares-structural equation modeling and fuzzyset quali- Chapter 1 • An Introduction to Structural Equation Modeling 1 tative comparative analysis. International Journal of Contemporary Hospitality Management, 33(5), 1571-1592.
- Richter, N. F., Cepeda Carrión, G., Roldán, J. L., & Ringle, C. M. (2016). European management research using partial least squares structural equation modeling (PLS-SEM): Editorial. European Management Journal, 34(6), 589-597.
- Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). A critical look at the use of PLS-SEM in MIS Quarterly. MIS Quarterly, 36(1), iii-xiv.
- Roldán, J. L., & Sánchez-Franco, M. J. (2012). Variance-based structural equation modeling: Guidelines for using partial least squares in information systems research. In M. Mora, O. Gelman, A. L. Steenkamp, & M. Raisinghani (Eds.), Research methodologies, innovations and philosophies in software systems engineering and information systems (pp. 193-221).
- Sarstedt, M., Hair, J. F., Cheah, J.-H., Becker, J.-M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order models. Australasian Marketing Journal, 27(3), 197-211.
- Sarstedt, M., Hair, J. F., Nitzl, C., Ringle, C. M., & Howard, M. C. (2020). Beyond a tandem analysis of SEM and PROCESS: Use PLS-SEM for mediation analyses! International Journal of Market Research, 62(3), 288-299.
- Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (2016). Estimation issues with PLS and CB-SEM: Where the bias lies! Journal of Business Research, 69(10), 3998-4010.
- Usakli, A., & Kucukergin, K. G. (2018). Using partial least squares structural equation modeling in hospitality and tourism: Do researchers follow practical guidelines? International Journal of Contemporary Hospitality Management, 30(11), 3462-3512.
- Wold, H. (1982). Soft modeling: The basic design and some extensions. In K. G. Jöreskog & H. Wold (Eds.), Systems under indirect observations, part II (pp. 1-54). Amsterdam: North-Holland.
- Wold, H. (1985). Partial least squares. In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of statistical sciences (pp. 581-591). New York, NY: John Wiley.
- Zeng, N., Liu, Y., Gong, P., Hertogh, M., & König, M. (2021). Do right PLS and do PLS right: A critical review of the application on PLS in construction management research. Frontiers of Engineering Management, 8(3), 356-369.
- Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295-358). Mahwah, NJ: Erlbaum.
- Hair, J. F., Binz Astrachan, C., Moisescu, O. I., Radomir, L., Sarstedt, M., Vaithilingam, S., & Ringle, C. M. (2020). Executing and interpreting applications of PLS-SEM: Updates for family business researchers. Journal of Family Business Strategy, 12(3), 100392. Chapter 1 • An Introduction to Structural Equation Modeling 1
- Jöreskog, K. G., & Wold, H. (1982). The ML and PLS techniques for modeling with latent variables: Historical and comparative aspects. In H. Wold & K. G. Jöreskog (Eds.), Systems under indirect observation, part I (pp. 263-270). Amsterdam: North-Holland.
- Rigdon, E. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long Range Planning, 45(5-6), 341-358.
- Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205.
- Wold, H. (1982). Soft modeling: The basic design and some extensions. In K. G. Jöreskog & H. Wold (Eds.), Systems under indirect observations, part II (pp. 1-54). Amsterdam: North-Holland. References
- Eklöf, J. A., & Westlund, A. H. (2002). The pan-European customer satisfaction index program: Current work and the way ahead. Total Quality Management, 13(8), 1099-1106.
- Hornik, K., & Leisch, F. (2002). Vienna and R: Love, marriage and the future. In R. Dutter (Ed.), Festschrift 50 Jahre Österreichische Statistische Gesellschaft (pp. 61-70). Vienna: Österreichische Statistische Gesellschaft.
- Rigdon, E. E., Sarstedt, M., & Becker, J.-M. (2020). Quantify uncertainty in behavioral research. Nature Human Behaviour, 4, 329-331. Chapter 2 • Overview of R and RStudio RStudio Team. (2021). RStudio: Integrated development for R [computer software]. Boston, MA: RStudio, PBC. Retrieved from: http://www. rstudio. com/
- Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205.
- Wickham, H., & Grolemund, G. (2016). R for data science. Sebastopol, CA: O'Reilly Media. Retrieved from: https://r4ds. had. co. nz/ Suggested Readings
- Grolemund, G. (2014). Hands-on programming with R: Write your own functions and simulations. Sebastopol, CA: O'Reilly Media.
- Wickham, H. (2019). Advanced R. Boca Raton, FL: CRC press. Retrieved from: https://adv-r. hadley. nz/
- Wickham, H., & Grolemund, G. (2016). R for data science. Sebastopol, CA: O'Reilly Media. Retrieved from: https://r4ds. had. co. nz/ References
- Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295-358). Mahwah, NJ: Erlbaum.
- 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.). Thousand Oaks, CA: Sage.
- Hair, J. 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.
- Helm, S., Eggert, A., & Garnefeld, I. (2010). Modelling the impact of corporate reputation on cus- tomer satisfaction and loyalty using PLS. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications in market- ing and related fields (Springer Handbooks of Computational Statistics Series) (Vol. II, pp. 515- 534). Berlin: Springer.
- Rigdon, E. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long Range Planning, 45(5-6), 341-358.
- Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling and more. Version 0.5-12 (beta). Journal of Statistical Software, 48(2), 1-36.
- Streukens, S., & Leroi-Werelds, S. (2016). Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrapping results. European Management Journal, 34(6), 618-632.
- Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205.
- Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295-358). Mahwah, NJ: Erlbaum.
- 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.). Thousand Oaks, CA: Sage.
- Hair, J. 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.
- Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205. References
- Aguirre-Urreta, M. I., & Rönkkö, M. (2018). Statistical inference with PLSc using bootstrap confi- dence intervals. MIS Quarterly, 42(3), 1001-1020.
- Dijkstra, T. K. (2014). PLS' Janus face-response to professor Rigdon's 'rethinking partial least squares modeling: In praise of simple methods. Long Range Planning, 47(3), 146-153.
- Hair, J. F., Hult, T., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Thousand Oaks: Sage.
- Hair, J. 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.
- Streukens, S., & Leroi-Werelds, S. (2016). Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrapping results. European Management Journal, 34(6), 618-632.
- Hair, J. F., Hult, T., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Thousand Oaks: Sage.
- Hair, J. 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.
- Hair, J. F., Sarstedt, M., & Ringle, C. M. (2019). Rethinking some of the rethinking of partial least squares. European Journal of Marketing, 53(4), 566-584.
- Aguirre-Urreta, M. I., & Rönkkö, M. (2018). Statistical inference with PLSc using bootstrap confi- dence intervals. MIS Quarterly, 42(3), 1001-1020.
- Bollen, K. A., & Diamantopoulos, A. (2017). In defense of causal-formative indicators: A minority report. Psychological Methods, 22(3), 581-596.
- Cheah, J. H., Sarstedt, M., Ringle, C. M., Ramayah, T., & Ting, H. (2018). Convergent validity assessment of formatively measured constructs in PLS-SEM. International Journal of Contemporary Hospitality Management, 30(11), 3192-3210.
- Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295-336.
- Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155-159.
- Diamantopoulos, A., Sarstedt, M., Fuchs, C., Wilczynski, P., & Kaiser, S. (2012). Guidelines for choosing between multi-item and single-item scales for construct measurement: A predictive validity perspective. Journal of the Academy of Marketing Science, 40(3), 434-449.
- Eberl, M. (2010). An application of PLS in multi-group analysis: The need for differentiated corpo- rate-level marketing in the mobile communications industry. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of Partial Least Squares: Concepts, Methods and Applications. (Springer handbooks of computational statistics series, vol. II) (pp. 487-514). Berlin: Springer.
- Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149-1160.
- Grimm, M. S., & Wagner, R. (2020). The impact of missing values on PLS, ML and FIML model fit. Archives of Data Science, Series A, 6(1), 04.
- Hair, J. F., Hult, T., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Thousand Oaks, CA: Sage.
- Hair, J. 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.
- 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.
- Sarstedt, M., Wilczynski, P., & Melewar, T. (2013). Measuring reputation in global markets: A com- parison of reputation measures' convergent and criterion validities. Journal of World Business, 48(3), 329-339.
- Schwaiger, M., Sarstedt, M., & Taylor, C. R. (2010). Art for the sake of the corporation: Audi, BMW Group, DaimlerChrysler, Montblanc, Siemens, and Volkswagen help explore the effect of spon- sorship on corporate reputations. Journal of Advertising Research, 50(1), 77-90.
- Hair, J. F., Hult, T., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Thousand Oaks, CA: Sage.
- Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (2016). Estimation issues with PLS and CBSEM: Where the bias lies! Journal of Business Research, 69(10), 3998-4010. Chapter 5 • Evaluation of Formative Measurement Models References
- Aguirre-Urreta, M. I., & Rönkkö, M. (2018). Statistical inference with PLSc using bootstrap confi- dence intervals. MIS Quarterly, 42(3), 1001-1020.
- Becker, J.-M., Ringle, C. M., Sarstedt, M., & Völckner, F. (2015). How collinearity affects mixture regression results. Marketing Letters, 26(4), 643-659.
- Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference: Understanding AIC and BIC in model selection. Sociological Research Methods, 33(2), 261-304.
- Chin, W. W., Cheah, J.-H., Liu, Y., Ting, H., Lim, X.-J., & Cham, T. H. (2020). Demystifying the role of causal-predictive modeling using partial least squares structural equation modeling in infor- mation systems research. Industrial Management & Data Systems, 120(12), 2161-2209.
- Hair, J. F. (2021). Next generation prediction metrics for composite-based PLS-SEM. Industrial Management & Data Systems, 121(1), 5-11.
- Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-151.
- Hair, J. 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.
- Hair, J. F., & Sarstedt, M. (2021). Explanation plus prediction -The logical focus of project manage- ment research. Project Management Journal, 52(4), 319-322.
- Hair, J. F., Sarstedt, M., & Ringle, C. M. (2019). Rethinking some of the rethinking of partial least squares. European Journal of Marketing, 53(4), 566-584.
- Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., et al. (2014). Common beliefs and reality about partial least squares: Comments on Rönkkö & Evermann (2013). Organizational Research Methods, 17(1), 182-209.
- Memon, M. A., Cheah, J. H., Ramayah, T., Ting, H., Chuah, F., & Cham, T. H. (2019). Moderation analysis: Issues and guidelines. Journal of Applied Structural Equation Modeling, 3(1), i-xi.
- Rigdon, E. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long Range Planning, 45(5-6), 341-358.
- Sarstedt, M. & Danks, N. P. (2021). Prediction in HRM research-A gap between rhetoric and real- ity. Human Resource Management Journal, forthcoming.
- Sarstedt, M., Hair, J. F., Cheah, J.-H., Becker, J.-M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order models. Australasian Marketing Journal, 27(3), 197-211.
- Shmueli, G., Ray, S., Estrada, J. M. V., & Chatla, S. B. (2016). The elephant in the room: Predictive performance of PLS models. Journal of Business Research, 69(10), 4552-4564.
- Streukens, S., & Leroi-Werelds, S. (2016). Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results. European Management Journal, 34(6), 618-632.
- Suggested Reading Chin, W. W., Cheah, J.-H., Liu, Y., Ting, H., Lim, X.-J., & Cham, T. H. (2020). Demystifying the role of causal-predictive modeling using partial least squares structural equation modeling in infor- mation systems research. Industrial Management & Data Systems, 120(12), 2161-2209.
- Hair, J. F., Hult, T., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Thousand Oaks, CA: Sage.
- 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.
- 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.). Thousand Oaks, CA: Sage.
- Nitzl, C., Roldán, J. L., & Cepeda Carrión, G. (2016). Mediation analysis in partial least squares path modeling. Industrial Management & Data Systems, 119(9), 1849-1864.
- 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.). Thousand Oaks, CA: Sage.
- Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regres- sion-based approach (2nd ed.). New York, NY: Guilford Press.
- Nitzl, C., Roldán, J. L., & Cepeda Carrión, G. (2016). Mediation analysis in partial least squares path modeling. Industrial Management & Data Systems, 119(9), 1849-1864.
- Preacher, K. J., & Hayes, A. F. (2008). Contemporary approaches to assessing mediation in communi- cation research. In A. F. Hayes, D. Slater, & L. B. Snyder (Eds.), The SAGE sourcebook of advanced data analysis methods for communication research (pp. 13-54). Thousand Oaks, CA: Sage.
- Sarstedt, M., Hair, J. F., Nitzl, C., Ringle, C. M., & Howard, M. C. (2020). Beyond a tandem analysis of SEM and PROCESS: Use of PLS-SEM for mediation analyses! International Journal of Market Research, 62(3), 288-299.
- Zhao, X., Lynch, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197-206. Chapter 7 • Mediation Analysis References
- Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable model- ing approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189-217.
- Diamantopoulos, A., Sarstedt, M., Fuchs, C., Kaiser, S., & Wilczynski, P. (2012). Guidelines for choosing between multi-item and single-item scales for construct measurement: A predictive validity perspective. Journal of the Academy of Marketing Science, 40(3), 434-449.
- Gardner, R. G., Harris, T. B., Li, N., Kirkman, B. L., & Mathieu, J. E. (2017). Understanding "it depends" in organizational research: A theory-based taxonomy, review, and future research agenda concerning interactive and quadratic relationships. Organizational Research Methods, 20(4), 610-638.
- 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.). Thousand Oaks, CA: Sage.
- Memon, M. A., Cheah, J.-H., Ramayah, T., Ting, H., Chuah, F., & Cham, T. H. (2018). Moderation analysis: Issues and guidelines. Journal of Applied Structural Equation Modeling, 3(1), i-ix.
- Sarstedt, M., Diamantopoulos, A., Salzberger, T., & Baumgartner, P. (2016). Selecting single items to measure doubly-concrete constructs: A cautionary tale. Journal of Business Research, 69(8), 3159-3167.
- 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.). Thousand Oaks, CA: Sage.
- Memon, M. A., Cheah, J.-H., Ramayah, T., Ting, H., Chuah, F., & Cham, T. H. (2018). Moderation analysis: Issues and guidelines. Journal of Applied Structural Equation Modeling, 3(1), i-ix.
- J. F. Hair Jr. et al., Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R, Classroom Companion: Business, https://doi.org/10.1007/978-3-030-80519-7 · DOI
Cited by
Sorted by citations
Items 601–700 of 1,024
Le, T. T., Tran, P. Q., & Dhar, B. K. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.144387 · Cited by 25
Factors influencing intentions to use QRIS: A two-staged PLS-SEM and ANN approach
Ramayanti, R., Azhar, Z., & Azman, N. H. N. (2024) · Telematics and Informatics Reports · doi:10.1016/j.teler.2024.100185 · Cited by 25
Ismail, I. J. (2025) · Cogent Social Sciences · doi:10.1080/23311886.2024.2441400 · Cited by 25
“SEM is a multivariate statistical tool recognized for its ability to deal with simple and complex models containing latent variables (Hair et al., 2021).”
Bocean, C. G. (2025) · Agronomy · doi:10.3390/agronomy15010198 · Cited by 25
Sun, J., Wu, Q., Ma, Z., Zheng, W., & Hu, Y. (2025) · Educational Technology Research and Development · doi:10.1007/s11423-025-10495-w · Cited by 38
“The average variance extracted (AVE) value is commonly assumed to be greater than 0.5 to imply that the latent variables have ideal convergent validity (Hair et al., 2021).”
InfluentialObeng, H. A., Atan, T., & Arhinful, R. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-13056-2 · Cited by 25
“The values range from 0 to 1, indicating the effectiveness of the independent factors in predicting the outcome of the dependent variable [98, 99]”
Eldin, A. Y. S., Elnour, A., Hassan, R. O. A., & Awad, A. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-05784-x · Cited by 25
Rashed, M., Uddin, M. K., Islam, M. F., Faisal-E-Alam, M., Tushar, H., & Ahmed, M. R. (2025) · Global Journal of Flexible Systems Management · doi:10.1007/s40171-025-00471-x · Cited by 33
“Considering the two-stage approach suggested by (Hair Jr et al., 2021), this analysis first evaluates the The initial stage involved assessing the measurement items and their associated exogenous variables.”
Ayoub, H. S., & Sopuru, J. C. (2026) · Sustainability · doi:10.3390/su18031157 · Cited by 25
Rural smartness: Its determinants and impacts on rural economic welfare
Mukti, I. Y., Henseler, J., Aldea, A., Govindaraju, R., & Iacob, M. (2022) · Electronic Markets · doi:10.1007/s12525-022-00526-2 · Cited by 25
Factors affecting “employees’ creativity”: the mediating role of intrinsic motivation
Yesuf, Y. M., Getahun, D. A., & Debas, A. T. (2023) · Journal of Innovation and Entrepreneurship · doi:10.1186/s13731-023-00299-8 · Cited by 25
Revisiting after-class boredom via exploratory structural equation modeling
Pawlak, M., Solhi, M., Shirvan, M. E., Kruk, M., & Taherian, T. (2023) · IRAL - International Review of Applied Linguistics in Language Teaching · doi:10.1515/iral-2022-0151 · Cited by 25
Pan, S., Jung, S., & Suo, S. (2023) · Journal of Broadcasting & Electronic Media · doi:10.1080/08838151.2023.2224477 · Cited by 27
Nguyễn, X. H., Nguyen, K. L., Nguyen, T., Nguyen, T. T. H., & Ta, V. L. (2023) · Sustainability · doi:10.3390/su151612371 · Cited by 36
Martínez‐Falcó, J., Marco‐Lajara, B., Zaragoza‐Sáez, P., & Sánchez‐García, E. (2023) · Agribusiness · doi:10.1002/agr.21846 · Cited by 25
Mustafa, S., Long, Y., & Rana, S. (2023) · Scientific Reports · doi:10.1038/s41598-023-37239-1 · Cited by 25
Customer satisfaction in the pet food subscription-based online services
Lima, D., Ramos, R. F., & Oliveira, P. M. (2024) · Electronic Commerce Research · doi:10.1007/s10660-024-09807-8 · Cited by 32
Nokes, B. T., Orr, J. E., White, S. L., Luu, S., Chen, Z., Alex, R. M., Sands, S. A., Wojeck, B. S., Owens, R. L., Malhotra, A., et al. (2024) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00925.2023 · Cited by 28
Green intellectual capital and green supply chain performance: do external pressures matter?
Buhaya, M. I., & Metwally, A. B. M. (2024) · Cogent Business & Management · doi:10.1080/23311975.2024.2349276 · Cited by 25
Sarma, P., Alam, M., & Begum, I. A. (2022) · GM crops & food · doi:10.1080/21645698.2022.2140678 · Cited by 26
Modeling the Enablers of Consumers’ E-Shopping Behavior: A Multi-Analytic Approach
Yang, H., Luo, Y., Qiu, Y., Zou, J., Masukujjaman, M., & Ibrahim, A. M. (2023) · Sustainability · doi:10.3390/su15086564 · Cited by 30
Wei, F., Zhang, H., Whaley, J. E., & Kim, Y. (2023) · Sustainability · doi:10.3390/su151511722 · Cited by 27
León-Gómez, A., Jaén, J. M. S., Mercedes, P.-M., & Sánchez, H. H. G. (2023) · Environment Development and Sustainability · doi:10.1007/s10668-023-04025-y · Cited by 35
Novotny, M., Dodds, R., & Walsh, P. R. (2023) · International Journal of Tourism Research · doi:10.1002/jtr.2605 · Cited by 26
The determinants of public acceptance of telemedicine apps: an innovation diffusion perspective
Liu, D., Son, S., & Cao, J. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1325031 · Cited by 24
Veiga, P. M., Ferreira, J. J., Zhang, Z., & Liu, Y. D. (2024) · Journal of Computer Information Systems · doi:10.1080/08874417.2023.2297031 · Cited by 25
Factors shaping the adoption of sustainable vegan diets
Shah, S., & Thanki, H. (2024) · International Journal of Consumer Studies · doi:10.1111/ijcs.13034 · Cited by 27
Obeid, A., Ibrahim, R. B., & Fadhil, A. (2024) · IEEE Access · doi:10.1109/access.2024.3373190 · Cited by 33
Lin, Y., Wang, W., & Hsieh, M.-J. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12700-4 · Cited by 24
Madhavan, M., Sharafuddin, M. A., & Wangtueai, S. (2024) · Administrative Sciences · doi:10.3390/admsci14060110 · Cited by 35
Akhtar, M. W., Karatepe, O. M., Rescalvo‐Martin, E., & Rizwan, M. (2024) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2024.103897 · Cited by 26
Li, W., & Liu, Y. (2024) · Urban forestry & urban greening · doi:10.1016/j.ufug.2024.128517 · Cited by 24
Memorable digital-free tourism experiences: Antecedents and outcomes
Sthapit, E., Ji, C., Yang, P., & Woosnam, K. M. (2024) · Journal Of Vacation Marketing · doi:10.1177/13567667241282022 · Cited by 24
“Since the Stoner-Geisse Q 2 values for MDFTE, EWB, EXS, HWB and PA were 0.063, 0.165, 0.066, 0.024 and 0.164, respectively, which are greater than the threshold value of zero (Hair et al., 2021), exhibiting predictive relevance of the proposed relations in the model.”
InfluentialInvernizzi, A., Renzetti, S., van Thriel, C., Rechtman, E., Patrono, A., Ambrosi, C., Mascaro, L., Corbo, D., Cagna, G., Gasparotti, R., et al. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-03108-2 · Cited by 25
Arata, S., Sugiuchi, M., Yazawa, R., Funatsu, H., & Kawakubo, S. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.112180 · Cited by 24
Building a sustainable future: BIM’s role in construction, logistics, and supply chain management
Alsehaimi, A., Waqar, A., Alrasheed, K. A., Bageis, A. S., Almujibah, H. R., Benjeddou, O., & Khan, A. M. (2024) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2024.103103 · Cited by 24
Shang, Y., & Ma, L. (2024) · Acta Psychologica · doi:10.1016/j.actpsy.2024.104550 · Cited by 24
Drivers of Port Competitiveness Among Low-, Upper-, and High-Income Countries
Mdanat, M., Alhur, M., Bwaliez, O. M., Samawi, G. A., & Khasawneh, R. (2024) · Sustainability · doi:10.3390/su162411198 · Cited by 24
Khaddam, A. A., & Alhanatleh, H. (2024) · Innovative Marketing · doi:10.21511/im.20(4).2024.19 · Cited by 24
Liu, D., Yousaf, Z., & Rosak-Szyrocka, J. (2024) · Sustainability · doi:10.3390/su162411173 · Cited by 24
Bocean, C. G. (2025) · Applied Sciences · doi:10.3390/app15020816 · Cited by 33
Razzak, M. R., Kharusi, S. A., Umrani, W. A., & Riyami, S. A. (2025) · Journal of Family Business Management · doi:10.1108/jfbm-01-2025-0016 · Cited by 24
Qu, C., & Kim, E. (2025) · Sustainability · doi:10.3390/su17051873 · Cited by 45
Adoption of AI in construction design: Insights from UTAUT2 and TOE frameworks
Katebi, A., & Tehrani, M. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.104981 · Cited by 27
AI Adoption for Collaboration: Factors Influencing Inclusive Learning Adoption in Higher Education
Alyoussef, I. Y., Drwish, A. M., Albakheet, F. A., Alqahtani, R. H. A., & Technology, A. A. A. E. (2025) · IEEE Access · doi:10.1109/access.2025.3567656 · Cited by 46
Buenadicha-Mateos, M., Sánchez-Hernández, M., & González-López, Ó. R. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.792606 · Cited by 24
Bucea-Manea-Țoniş, R., VASILE, L. M., Stănescu, R., & Moanta, A. D. (2022) · Sustainability · doi:10.3390/su14074339 · Cited by 29
Dinç, M. S., Zaim, H., Hassanin, M., & Alzoubi, Y. I. (2022) · Human Systems Management · doi:10.3233/hsm-211563 · Cited by 24
Rezaei, G., Hosseini, S. M. H., & Sana, S. S. (2022) · Sustainability · doi:10.3390/su141610444 · Cited by 24
Sozoniuk, M., Park, J., & Lumby, N. (2022) · Sustainability · doi:10.3390/su141710874 · Cited by 24
Antecedents influencing SNS addiction and exhaustion (fatigue syndrome): focusing on six countries
Kim, J.-H., Jung, S.-H., & Choi, H.-J. (2022) · Behaviour and Information Technology · doi:10.1080/0144929x.2022.2136531 · Cited by 26
Misbauddin, S. M., Alam, M., Karmaker, C. L., Nabi, M. N. U., & Hasan, M. M. (2023) · Sustainability · doi:10.3390/su15032146 · Cited by 25
Li, K., Ji, C., He, Q., & Rastegar, R. (2023) · International Journal of Tourism Research · doi:10.1002/jtr.2584 · Cited by 24
Who Is Going Green? Determinants of Green Investment Intention in the Saudi Food Industry
Aliedan, M. M., Alyahya, M. A., Elshaer, I. A., & Sobaih, A. E. E. (2023) · Agriculture · doi:10.3390/agriculture13051047 · Cited by 24
García-Murillo, G., Hernández, P. N., & Rodriǵuez, R. S. (2023) · Behavioral Sciences · doi:10.3390/bs13050419 · Cited by 32
Bernal, J., Schreiber, S., Menze, I., Ostendorf, A., Pfister, M., Geisendörfer, J., Nemali, A., Maaß, A., Yakupov, R., Peters, O., et al. (2023) · Alzheimer s Research & Therapy · doi:10.1186/s13195-023-01243-4 · Cited by 23
Gu, F., Zhu, Z., & Ali, S. (2023) · Processes · doi:10.3390/pr11051412 · Cited by 24
Ying, W., Yasmin, F., & Akbar, A. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1184185 · Cited by 47
Amiruddin, A., Baharuddin, F. R., Takbir, T., & Setialaksana, W. (2023) · SAGE Open · doi:10.1177/21582440231214375 · Cited by 95
Digital Competence in Islamic Education for Lifelong Learning
Abubakari, M. S., & Kalinaki, K. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-1410-4.ch001 · Cited by 24
Chang, M.-Y., Hsia, W.-J., & Chen, H. (2024) · Nutrients · doi:10.3390/nu16081162 · Cited by 24
Sohail, M. T., & Chen, S. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.990785 · Cited by 23
Psychometric Properties of the Perceived Collective Family Efficacy Scale in Algeria
Sabah, A., Aljaberi, M. A. A., Lee, K., & Lin, C. (2023) · Healthcare · doi:10.3390/healthcare11192691 · Cited by 23
Hyun, H., Hong, E., Ahn, J., & Park, J. (2024) · Journal of Travel & Tourism Marketing · doi:10.1080/10548408.2023.2239837 · Cited by 25
Making Sense of AI Benefits: A Mixed-method Study in Canadian Public Administration
Madan, R., & Ashok, M. (2024) · Information Systems Frontiers · doi:10.1007/s10796-024-10475-0 · Cited by 42
Bag, S., Rahman, M. S., Sharma, J., Chiarini, A., Srivastava, S. K., & Gupta, S. (2024) · Production Planning & Control · doi:10.1080/09537287.2024.2327344 · Cited by 30
Yang, H., Fan, W., & Tsai, M.-C. (2024) · Sustainability · doi:10.3390/su16062537 · Cited by 23
Gogia, E. H., Shao, Z., Khan, K., Rehman, M. Z., Haddad, H., & Al-Ramahi, N. M. (2024) · BMC Psychology · doi:10.1186/s40359-024-01873-9 · Cited by 35
Hassan, T. H., Fazia, C., Abdelmoaty, M. A., Janzakov, B., GOZNER, M., Almakhayitah, M. Y., Saleh, M. I., Aleedan, M. H., Abdou, A. H., & Salem, A. E. (2024) · Discover Sustainability · doi:10.1007/s43621-024-00354-8 · Cited by 42
Digital Transformation Driving SME Business Model Innovation
Liu, K. P., Chung, K. W., Chiu, W., & Chen, G. (2024) · Journal of Global Information Management · doi:10.4018/jgim.350191 · Cited by 50
Predicting the Quiet Quitting Intention Among the Generation Z Workforce in Hotel Industry
Xueyun, Z., Yang, Q. Y., & Mamun, A. A. (2024) · Journal of Quality Assurance in Hospitality & Tourism · doi:10.1080/1528008x.2024.2393336 · Cited by 36
“The VIF values ranged between 1.324 and 2.946, which did not exceed the threshold value of 3, as recommended by J. F. Hair et al. (2021).”
InfluentialAnjum, T., Tautiva, J. A. D., Zaheer, M. A., & Heidler, P. (2024) · Education Sciences · doi:10.3390/educsci14090983 · Cited by 39
Perera, R. L., & Badir, Y. F. (2024) · Business Strategy and the Environment · doi:10.1002/bse.4074 · Cited by 23
Ahmad, M. M., & Wang, G. (2025) · Applied Sciences · doi:10.3390/app15020518 · Cited by 25
Liu, N., Deng, W., & Ayub, A. F. M. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13349-3 · Cited by 36
“Typically, VAF > 80% indicates full mediation, 20% ≤ VAF ≤ 80% suggests partial mediation and VAF 20% implies no mediation (Hair et al., 2021b).”
Indriyani, E. P., Suhariadi, F., Lestari, Y. D., Aldhi, I. F., Rahmawati, E., Hardaningtyas, D., & Abbas, A. (2025) · Sustainability · doi:10.3390/su17031194 · Cited by 37
Umar, S. B., Ahmad, J., Bukhori, M. A. B. M., Ali, K. A. M., & Hussain, W. M. H. W. (2025) · Sustainability · doi:10.3390/su17062445 · Cited by 36
Zhang, Q., Wong, P. P. W., & Wang, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-99447-1 · Cited by 37
He, S., & Ren, Y. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1571279 · Cited by 31
Chumnumporn, K., Udomvitid, K., Ueki, Y., Intalar, N., Pongsathornwiwat, A., Pannakkong, W., Komolavanij, S., & Jeenanunta, C. (2025) · Sustainability · doi:10.3390/su17156988 · Cited by 41
Li, Y., Ren, Y., Du, Z., Li, M., & Jiang, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-19213-1 · Cited by 45
Yang, J., Xie, W., & Ni, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-21977-5 · Cited by 23
“Instead of specifying relationships between multiple independent and dependent constructs in a path model 27–29 .”
Key Factors Influencing Loyalty and Satisfaction Toward ERP: Mediating Role of Flow
Jo, H. (2022) · Journal of the Knowledge Economy · doi:10.1007/s13132-022-00981-4 · Cited by 28
Sukma, N., & Leelasantitham, A. (2022) · Frontiers in Environmental Science · doi:10.3389/fenvs.2022.918981 · Cited by 29
A community sustainability ecosystem modeling for water supply business in thailand
Sukma, N., & Leelasantitham, A. (2022) · Frontiers in Environmental Science · doi:10.3389/fenvs.2022.940955 · Cited by 25
Kankhuni, Z., Ngwira, C., Sepula, M. B., & Kapute, F. (2022) · Journal of Teaching in Travel & Tourism · doi:10.1080/15313220.2022.2128983 · Cited by 25
Moisescu, O. I., Gică, O. A., & Herle, F. (2022) · Behavioral Sciences · doi:10.3390/bs12110411 · Cited by 23
Chau, T. H. P., Tran, Y. T. T., & Le, T. D. (2022) · Cogent Business & Management · doi:10.1080/23311975.2022.2140746 · Cited by 39
Gruzd, A. A., & Hernández-García, Á. (2022) · Behaviour and Information Technology · doi:10.1080/0144929x.2022.2152366 · Cited by 24
Ivaşcu, L., Ali, W., Khalid, R., & Raza, M. (2022) · Energies · doi:10.3390/en16010297 · Cited by 23
Alenazi, S. A., & AlAnazi, T. M. B. (2023) · Sustainability · doi:10.3390/su15031952 · Cited by 23
Helal, E. A., Hassan, T. H., Abdelmoaty, M. A., Salem, A. E., Saleh, M. I., Helal, M. Y. I., Abuelnasr, M. S., Mohamoud, Y. A., Abdou, A. H., Radwan, S. H., et al. (2023) · Journal of risk and financial management · doi:10.3390/jrfm16030210 · Cited by 23
Kumar, S., Raj, R., Salem, I., Singh, E. P., Goel, K., & Bhatia, R. (2023) · Asian Business & Management · doi:10.1057/s41291-023-00230-9 · Cited by 26
Seroka‐Stolka, O. (2023) · Business Strategy and the Environment · doi:10.1002/bse.3469 · Cited by 23
Wang, Y., Zhu, J., Xu, Z., Dai, X., Chen, K., & Ying, W. (2023) · Scientific Reports · doi:10.1038/s41598-023-39658-6 · Cited by 28
Shafique, M. N., Rashid, A., Yeo, S. F., & Adeel, U. (2023) · Sustainability · doi:10.3390/su151511979 · Cited by 27
Predicting consumer purchase intention on electric cars in India: Mediating role of attitude
Mukesh, & Narwal, M. (2023) · Business Strategy & Development · doi:10.1002/bsd2.289 · Cited by 23
Seroka‐Stolka, O. (2023) · Sustainability · doi:10.3390/su151914257 · Cited by 23
Zarifis, A., & Fu, S. (2023) · Digital · doi:10.3390/digital3040018 · Cited by 25
Swathi, K. S., Barkur, G., & Somu, G. (2023) · Cogent Business & Management · doi:10.1080/23311975.2023.2264579 · Cited by 23
Metadata
- Authors
- Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S.
- Affiliations
- University of South Alabama (US)
- Hamburg University of Technology (DE)
- Otto-von-Guericke-Universität Magdeburg (DE)
- Trinity College Dublin (IE)
- National Tsing Hua University (TW)
- ORCID
- Venue
- Classroom Companion: Business
- Publisher
- Springer International Publishing
- Published
- 2021
- ISSN
- 2662-2866 (ISSN-L)
- 2662-2874
- OpenAlex
- W3210383951
- Cited by
- 10,343
- References
- 171
- Citations by year
- 2026: 3947 · 2025: 2779 · 2024: 1862 · 2023: 1196 · 2022: 498 · 2021: 60
- FWCI
- 418.20
- Type
- book
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Classroom companion: business (publishedVersion, cc-by)
- Directory of Open access Books (OAPEN Foundation) (submittedVersion, other-oa)
- Trinity's Access to Research Output (TARA) (Trinity College Dublin) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- partial least squares structural equation modeling, PLS-SEM, R programming language, structural equation modeling, case studies
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review