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.
Integrity signals
Registry
Cites
Statistics
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.
Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,706 · see citing works
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 · see citing works
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 · see citing works
Statistical Power Analysis for the Behavioral Sciences
Muller, K. E., & Cohen, J. (1989) · Technometrics · doi:10.2307/1270020 · Cited by 99,588 · see citing works
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033 · see citing works
(2013) · Cited by 44,563 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Self-citationA Theory of Cognitive Dissonance
Festinger, L. (1957) · doi:10.1515/9781503620766 · Cited by 24,241 · see citing works
Pls-Sem: indeed a silver bullet
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011) · Journal of Marketing Theory and Practice · doi:10.2753/mtp1069-6679190202 · Cited by 21,631 · see citing works
Self-citationSPSS and SAS procedures for estimating indirect effects in simple mediation models
Preacher, K. J., & Hayes, A. F. (2004) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03206553 · Cited by 17,383 · see citing works
Asymptotic Confidence Intervals for Indirect Effects in Structural Equation Models
Sobel, M. E. (1982) · Sociological Methodology · doi:10.2307/270723 · Cited by 12,810 · see citing works
(1998) · Cited by 12,725 · see citing works
Reconsidering Baron and Kenny: myths and truths about mediation analysis
Zhao, X., Lynch, J. G., & Chen, Q. (2010) · Journal of Consumer Research · doi:10.1086/651257 · Cited by 12,583 · see citing works
\nThe use of partial least squares path modeling in international marketing
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009) · Radboud Repository (Radboud University) · doi:10.1108/s1474-7979(2009)0000020014 · Cited by 11,790 · see citing works
Self-citationPartial least squares structural equation modeling (PLS-SEM): An emerging tool in business research
F. Hair Jr, J., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014) · European Business Review · doi:10.1108/ebr-10-2013-0128 · Cited by 11,728 · see citing works
Self-citationPLS-regression: a basic tool of chemometrics
Wold, S., Sjöström, M., & Eriksson, L. (2001) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(01)00155-1 · Cited by 10,084 · see citing works
An assessment of the use of partial least squares structural equation modeling in marketing research
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-011-0261-6 · Cited by 8,088 · see citing works
Self-citationBootstrap Methods and their Application
Davison, A. C., & Hinkley, D. V. (1997) · doi:10.1017/cbo9780511802843 · Cited by 7,350 · see citing works
Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003) · Information Systems Research · doi:10.1287/isre.14.2.189.16018 · Cited by 6,479 · see citing works
Tenenhaus, M., Vinzi, V. E., Chatelin, Y.-M., & Lauro, C. (2005) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2004.03.005 · Cited by 6,312 · see citing works
MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007) · Annual Review of Psychology · doi:10.1146/annurev.psych.58.110405.085542 · Cited by 5,688 · see citing works
Assessing measurement model quality in PLS-SEM using confirmatory composite analysis
Hair, J. F., Howard, M. C., & Nitzl, C. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2019.11.069 · Cited by 5,446 · see citing works
Self-citationPLS-SEM or CB-SEM: updated guidelines on which method to use
Jr., J. F. H., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017) · International Journal of Multivariate Data Analysis · doi:10.1504/ijmda.2017.087624 · Cited by 5,441 · see citing works
Self-citationPredictive model assessment in PLS-SEM: guidelines for using PLSpredict
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019) · European Journal of Marketing · doi:10.1108/ejm-02-2019-0189 · Cited by 4,618 · see citing works
Self-citationAn updated and expanded assessment of PLS-SEM in information systems research
Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017) · Industrial Management & Data Systems · doi:10.1108/imds-04-2016-0130 · Cited by 4,043 · see citing works
Self-citationKlemelä, J. (2018) · Wiley series in probability and statistics · doi:10.1002/9781119409137.ch4 · Cited by 3,718 · see citing works
How to Specify, Estimate, and Validate Higher-Order Constructs in PLS-SEM
Sarstedt, M., Hair, J. F., Cheah, J., Becker, J.-M., & Ringle, C. M. (2019) · Australasian Marketing Journal (AMJ) · doi:10.1016/j.ausmj.2019.05.003 · Cited by 3,676 · see citing works
Self-citationPartial least squares structural equation modeling
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017) · Handbook of Market Research · doi:10.1007/978-3-319-05542-8_15-1 · Cited by 3,285 · see citing works
Self-citationA Primer on Partial Least Squares Structural Equation Modeling
Ketchen, D. J. (2013) · Long Range Planning · doi:10.1016/j.lrp.2013.01.002 · Cited by 3,251 · see citing works
Moderation in Management Research: What, Why, When, and How
Dawson, J. (2013) · Journal of Business and Psychology · doi:10.1007/s10869-013-9308-7 · Cited by 3,029 · see citing works
Consistent Partial Least Squares Path Modeling1
Dijkstra, T. K., & Henseler, J. (2015) · MIS Quarterly · doi:10.25300/misq/2015/39.2.02 · Cited by 2,898 · see citing works
AIC model selection using Akaike weights
Wagenmakers, E.-J., & Farrell, S. (2004) · Psychonomic Bulletin & Review · doi:10.3758/bf03206482 · Cited by 2,684 · see citing works
Minimum sample size estimation in PLS‐SEM: The inverse square root and gamma‐exponential methods
Kock, N., & Hadaya, P. (2018) · Information Systems Journal · doi:10.1111/isj.12131 · Cited by 2,621 · see citing works
Mediation analysis in partial least squares path modeling
Nitzl, C., Roldán, J. L., & Cepeda‐Carrión, G. (2016) · Industrial Management & Data Systems · doi:10.1108/imds-07-2015-0302 · Cited by 2,618 · see citing works
Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (2012) · Long Range Planning · doi:10.1016/j.lrp.2012.09.008 · Cited by 2,454 · see citing works
Self-citationSarstedt, M., Ringle, C. M., Smith, D., Reams, R., & Hair, J. F. (2014) · Journal of Family Business Strategy · doi:10.1016/j.jfbs.2014.01.002 · Cited by 2,212 · see citing works
Self-citationHeuristics versus statistics in discriminant validity testing: a comparison of four procedures
Franke, G. R., & Sarstedt, M. (2019) · Internet Research · doi:10.1108/intr-12-2017-0515 · Cited by 2,211 · see citing works
Self-citationCochran, W. G., & الفتاح, ع. ا. ع. (1978) · Al-Maǧallaẗ Al-Miṣriyyaẗ Lil Dirāsāt Al-Tiǧāriyyaẗ /Al-Maǧallaẗ Al-Miṣriyyaẗ Lil Dirāsāt Al-Tiǧāriyyaẗ · doi:10.21608/alat.1978.219785 · Cited by 2,203 · see citing works
Latent Variable Path Modeling with Partial Least Squares
Lohmöller, J.-B. (1989) · Physica-Verlag eBooks · doi:10.1007/978-3-642-52512-4 · Cited by 2,149 · see citing works
Representing and Testing Organizational Theories: A Holistic Construal
Bagozzi, R. P., & Phillips, L. W. (1982) · Administrative Science Quarterly · doi:10.2307/2392322 · Cited by 2,064 · see citing works
Simultaneous Factor Analysis in Several Populations
Jöreskog, K. G. (1971) · Psychometrika · doi:10.1007/bf02291366 · Cited by 2,051 · see citing works
Partial least squares structural equation modeling in HRM research
Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2020) · The International Journal of Human Resource Management · doi:10.1080/09585192.2017.1416655 · Cited by 1,943 · see citing works
Self-citationGalit Shmueli, A., Ray, S., Estrada, J. M. V., & Chatla, S. B. (2015) · Journal of Business Research · doi:10.1016/j.jbusres.2016.03.049 · Cited by 1,904 · see citing works
Self-citationEstimation issues with PLS and CBSEM: Where the bias lies!
Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (2016) · Journal of Business Research · doi:10.1016/j.jbusres.2016.06.007 · Cited by 1,889 · see citing works
Self-citationRethinking some of the rethinking of partial least squares
Hair, J. F., Sarstedt, M., & Ringle, C. M. (2019) · European Journal of Marketing · doi:10.1108/ejm-10-2018-0665 · Cited by 1,839 · see citing works
Self-citationDiamantopoulos, A., Sarstedt, M., Fuchs, C., Wilczynski, P., & Kaiser, S. (2012) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-011-0300-3 · Cited by 1,812 · see citing works
Self-citationA Beginner's Guide to Partial Least Squares Analysis
Haenlein, M., & Kaplan, A. (2004) · Understanding Statistics · doi:10.1207/s15328031us0304_4 · Cited by 1,714 · see citing works
Ali, F., Rasoolimanesh, S. M., Sarstedt, M., Ringle, C. M., & Ryu, K. (2017) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-10-2016-0568 · Cited by 1,620 · see citing works
Self-citationSwitching barriers and repurchase intentions in services
Jones, M. A., Mothersbaugh, D. L., & Beatty, S. E. (2000) · Journal of Retailing · doi:10.1016/s0022-4359(00)00024-5 · Cited by 1,571 · see citing works
Peng, D. X., & Lai, F. (2012) · Journal of Operations Management · doi:10.1016/j.jom.2012.06.002 · Cited by 1,532 · see citing works
PLS-SEM or CB-SEM: updated guidelines on which method to use
Hair, J. F., Matthews, L. M., Matthews, R. L., et al. (2017) · International Journal of Multivariate Data Analysis · doi:10.1504/ijmda.2017.10008574 · Cited by 1,489 · see citing works
Self-citationPartial least squares regression and projection on latent structure regression (PLS Regression)
Abdi, H. (2010) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.51 · Cited by 1,446 · see citing works
Partial Least Squares Structural Equation Modeling
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021) · Springer eBooks · doi:10.1007/978-3-319-05542-8_15-2 · Cited by 1,408 · see citing works
Self-citationContemporary Approaches to Assessing Mediation in Communication Research
Preacher, K. J., & Hayes, A. F. (2008) · Sage eBooks · doi:10.4135/9781452272054.n2 · Cited by 1,346 · see citing works
A Concise Guide to Market Research
Mooi, E., & Sarstedt, M. (2011) · Springer eBooks · doi:10.1007/978-3-642-12541-6 · Cited by 1,342 · see citing works
Self-citationStructural model robustness checks in PLS-SEM
Sarstedt, M., Ringle, C. M., Cheah, J.-H., Ting, H., Moisescu, O. I., & Radomir, L. (2020) · Tourism Economics · doi:10.1177/1354816618823921 · Cited by 1,271 · see citing works
Self-citationHenseler, J., & Chin, W. W. (2010) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705510903439003 · Cited by 1,240 · see citing works
Aguinis, H., Beaty, J. C., Boik, R. J., & Pierce, C. A. (2005) · Journal of Applied Psychology · doi:10.1037/0021-9010.90.1.94 · Cited by 1,216 · see citing works
Testing Moderating Effects in PLS Path Models: An Illustration of Available Procedures
Henseler, J., & Fassott, G. (2009) · Springer eBooks · doi:10.1007/978-3-540-32827-8_31 · Cited by 1,201 · see citing works
Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results
Streukens, S., & Leroi-Werelds, S. (2016) · European Management Journal · doi:10.1016/j.emj.2016.06.003 · Cited by 1,120 · see citing works
A GENERAL METHOD FOR ESTIMATING A LINEAR STRUCTURAL EQUATION SYSTEM*
Jöreskog, K. G. (1970) · ETS Research Bulletin Series · doi:10.1002/j.2333-8504.1970.tb00783.x · Cited by 1,109 · see citing works
Collinearity, Power, and Interpretation of Multiple Regression Analysis
Mason, C. H., & Perreault, W. D. (1991) · Journal of Marketing Research · doi:10.2307/3172863 · Cited by 1,067 · see citing works
Bridging Design and Behavioral Research With Variance-Based Structural Equation Modeling
Henseler, J. (2017) · Journal of Advertising · doi:10.1080/00913367.2017.1281780 · Cited by 1,059 · see citing works
Interpretation of Formative Measurement in Information Systems Research1
Cenfetelli, R. T., & Bassellier, G. (2009) · MIS Quarterly · doi:10.2307/20650323 · Cited by 1,051 · see citing works
Beyond a tandem analysis of SEM and PROCESS: Use of PLS-SEM for mediation analyses!
Sarstedt, M., Hair, J. F., Nitzl, C., Ringle, C. M., & Howard, M. C. (2020) · International Journal of Market Research · doi:10.1177/1470785320915686 · Cited by 1,034 · see citing works
Self-citationCollinearity, Power, and Interpretation of Multiple Regression Analysis
Mason, C. H., & Perreault, W. D. (1991) · Journal of Marketing Research · doi:10.1177/002224379102800302 · Cited by 1,026 · see citing works
Trizano-Hermosilla, Í., & Alvarado, J. M. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.00769 · Cited by 986 · see citing works
Rethinking Partial Least Squares Path Modeling: In Praise of Simple Methods
Rigdon, E. E. (2012) · Long Range Planning · doi:10.1016/j.lrp.2012.09.010 · Cited by 915 · see citing works
Executing and interpreting applications of PLS-SEM: updates for family business researchers
Hair, J. F., Astrachan, C. B., Moisescu, O. I., Radomir, L., Sarstedt, M., Vaithilingam, S., & Ringle, C. M. (2021) · Journal of Family Business Strategy · doi:10.1016/j.jfbs.2020.100392 · Cited by 860 · see citing works
Self-citationImproving Our Understanding of Moderation and Mediation in Strategic Management Research
Aguinis, H., Edwards, J. R., & Bradley, K. J. (2016) · Organizational Research Methods · doi:10.1177/1094428115627498 · Cited by 843 · see citing works
On Comparing Results from CB-SEM and PLS-SEM: Five Perspectives and Five Recommendations
Rigdon, E. E., Sarstedt, M., & Ringle, C. M. (2017) · Marketing ZFP · doi:10.15358/0344-1369-2017-3-4 · Cited by 805 · see citing works
Self-citationChin, W. W., Cheah, J., Liu, Y., Ting, H., Lim, X., & Cham, T. (2020) · Industrial Management & Data Systems · doi:10.1108/imds-10-2019-0529 · Cited by 802 · see citing works
A comparative study of cb-sem and pls-sem for theory development in family firm research
Astrachan, C. B., Patel, V. K., & Wanzenried, G. (2014) · Journal of Family Business Strategy · doi:10.1016/j.jfbs.2013.12.002 · Cited by 766 · see citing works
Does PLS Have Advantages for Small Sample Size or Non-Normal Data?1
Goodhue, D. L., ', L., & Thompson (2012) · MIS Quarterly · doi:10.2307/41703490 · Cited by 750 · see citing works
Robustness of partial least-squares method for estimating latent variable quality structures
Cassel, C. M., Hackl, P., & Westlund, A. H. (1999) · Journal of Applied Statistics · doi:10.1080/02664769922322 · Cited by 717 · see citing works
Interpretation of formative measurement in information systems research
Cenfetelli, R. T., & Bassellier, G. (2009) · Journal of the Association for Information Systems · doi:10.5555/2017434.2017440 · Cited by 706 · see citing works
Tips to use partial least squares structural equation modelling (PLS-SEM) in knowledge management
Cepeda‐Carrión, G., Cegarra‐Navarro, J., & Cillo, V. (2018) · Journal of Knowledge Management · doi:10.1108/jkm-05-2018-0322 · Cited by 702 · see citing works
Predictive Analytics in Information Systems Research
Shmueli, G., & Koppius, O. (2010) · doi:10.2139/ssrn.1606674 · Cited by 676 · see citing works
Rigdon, E. E. (2016) · European Management Journal · doi:10.1016/j.emj.2016.05.006 · Cited by 676 · see citing works
Hair, J. F., Ringle, C. M., Gudergan, S. P., Fischer, A., Nitzl, C., & Menictas, C. (2018) · BuR - Business Research · doi:10.1007/s40685-018-0072-4 · Cited by 670 · see citing works
Self-citationWhen predictors of outcomes are necessary: guidelines for the combined use of PLS-SEM and NCA
Richter, N., Schubring, S., Hauff, S., Ringle, C. M., & Sarstedt, M. (2020) · Industrial Management & Data Systems · doi:10.1108/imds-11-2019-0638 · Cited by 667 · see citing works
Self-citationPartial Least Squares Path Modeling
Henseler, J. (2017) · International series in quantitative marketing · doi:10.1007/978-3-319-53469-5_12 · Cited by 661 · see citing works
A critical look at the use of SEM in international business research
Richter, N., Sinkovics, R. R., Ringle, C. M., & Schlaegel, C. (2016) · International Marketing Review · doi:10.1108/imr-04-2014-0148 · Cited by 643 · see citing works
Self-citationHow collinearity affects mixture regression results
Becker, J.-M., Ringle, C. M., Sarstedt, M., & Völckner, F. (2014) · Marketing Letters · doi:10.1007/s11002-014-9299-9 · Cited by 581 · see citing works
Self-citationNitzl, C. (2016) · Journal of Accounting Literature · doi:10.1016/j.acclit.2016.09.003 · Cited by 580 · see citing works
Liengaard, B. D., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2020) · Decision Sciences · doi:10.1111/deci.12445 · Cited by 580 · see citing works
Self-citationCheah, J.-H., Sarstedt, M., Ringle, C. M., Ramayah, T., & Ting, H. (2018) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-10-2017-0649 · Cited by 566 · see citing works
Self-citationComponents and Parameters of Corporate Reputation — An Empirical Study
Schwaiger, M. (2004) · Schmalenbach Business Review · doi:10.1007/bf03396685 · Cited by 546 · see citing works
The Essentials of Business Research Methods
Hair, J. F., & Page, M. L. (2015) · Routledge eBooks · doi:10.4324/9781315716862 · Cited by 517 · see citing works
Self-citationThe ML and PLS techniques for modeling with latent variables : Historical and comparative aspects
Jöreskog, K. G. (1982) · Cited by 472 · see citing works
Cole, D. A., & Preacher, K. J. (2013) · Psychological Methods · doi:10.1037/a0033805 · Cited by 469 · see citing works
A Concise Guide to Market Research
Sarstedt, M., & Mooi, E. (2014) · Springer texts in business and economics · doi:10.1007/978-3-642-53965-7 · Cited by 448 · see citing works
Self-citationA Critical Examination of Common Beliefs About Partial Least Squares Path Modeling
Rönkkö, M., & Evermann, J. (2013) · Organizational Research Methods · doi:10.1177/1094428112474693 · Cited by 442 · see citing works
MODERATION ANALYSIS: ISSUES AND GUIDELINES
Memon, M. A., Cheah, J., Ramayah, T., Ting, H., Chuah, F., & Cham, T. (2019) · Journal of Applied Structural Equation Modeling · doi:10.47263/jasem.3(1)01 · Cited by 433 · see citing works
Willaby, H. W., Costa, D. S. J., Burns, B. D., MacCann, C., & Roberts, R. D. (2015) · Personality and Individual Differences · doi:10.1016/j.paid.2014.09.008 · Cited by 419 · see citing works
Essential new PLS-SEM analysis methods for your entrepreneurship analytical toolbox
Manley, S. C., Hair, J. F. J., Williams, R. I. J., & McDowell, W. C. (2020) · International Entrepreneurship and Management Journal · doi:10.1007/s11365-020-00687-6 · Cited by 405 · see citing works
Self-citationOn the use of partial least squares path modeling in accounting research
Lee, L. S., Petter, S., Fayard, D., & Robinson, S. N. (2011) · International Journal of Accounting Information Systems · doi:10.1016/j.accinf.2011.05.002 · Cited by 395 · see citing works
ESTIMATING MODERATING EFFECTS IN PLS-SEM AND PLSc-SEM: INTERACTION TERM GENERATION*DATA TREATMENT
Becker, J.-M., Ringle, C. M., & Sarstedt, M. (2018) · Journal of Applied Structural Equation Modeling · doi:10.47263/jasem.2(2)01 · Cited by 375 · see citing works
Self-citationThis fast car can move faster: a review of PLS-SEM application in higher education research
Ghasemy, M., Teeroovengadum, V., Becker, J.-M., & Ringle, C. M. (2020) · Higher Education · doi:10.1007/s10734-020-00534-1 · Cited by 345 · see citing works
Self-citationThe error term in formative measurement models: interpretation and modeling implications
Diamantopoulos, A. (2006) · Journal of Modelling in Management · doi:10.1108/17465660610667775 · Cited by 343 · see citing works
Path Analysis with Composite Variables
McDonald, R. P. (1996) · Multivariate Behavioral Research · doi:10.1207/s15327906mbr3102_5 · Cited by 334 · see citing works
A Concise Guide to Market Research
Sarstedt, M., & Mooi, E. (2018) · Springer texts in business and economics · doi:10.1007/978-3-662-56707-4 · Cited by 328 · see citing works
Self-citationNext-generation prediction metrics for composite-based PLS-SEM
Hair, J. F. (2020) · Industrial Management & Data Systems · doi:10.1108/imds-08-2020-0505 · Cited by 314 · see citing works
Self-citationMEDIATION ANALYSIS: ISSUES AND RECOMMENDATIONS
Memon, M. A., Cheah, J.-H., Ramayah, T., Ting, H., & Chuah, F. (2018) · Journal of Applied Structural Equation Modeling · doi:10.47263/jasem.2(1)01 · Cited by 305 · see citing works
Statistical Inference with PLSC Using Bootstrap Confidence Intervals1
Aguirre-Urreta, M. I., & Rönkkö, M. (2018) · MIS Quarterly · doi:10.25300/misq/2018/13587 · Cited by 304 · see citing works
In Defense of Causal-Formative Indicators: A Minority Report
Bollen, K., & Diamantopoulos, A. (2015) · Psychological methods · doi:10.1037/met0000056 · Cited by 300 · see citing works
The use of sampling methods in advertising research: a gap between theory and practice
Sarstedt, M., Bengart, P., Shaltoni, A. M., & Lehmann, S. (2017) · International Journal of Advertising · doi:10.1080/02650487.2017.1348329 · Cited by 294 · see citing works
Self-citationPrediction‐Oriented Model Selection in Partial Least Squares Path Modeling
Sharma, P. N., Galit Shmueli, A., Sarstedt, M., Danks, N. P., & Ray, S. (2018) · Decision Sciences · doi:10.1111/deci.12329 · Cited by 294 · see citing works
Self-citationAn Empirical Comparison of the Efficacy of Covariance-Based and Variance-Based SEMPreprint
Reinartz, W., Haenlein, M., & Henseler, J. (2009) · SSRN Electronic Journal · doi:10.2139/ssrn.1462666 · Cited by 287 · see citing works
Partial Least Squares (PLS) Methods: Origins, Evolution, and Application to Social Sciences
Mateos-Aparicio, G. (2011) · Communication in Statistics- Theory and Methods · doi:10.1080/03610921003778225 · Cited by 286 · see citing works
Structural modeling of heterogeneous data with partial least squares
Rigdon, E. E., Ringle, C. M., & Sarstedt, M. (2010) · Review of marketing research · doi:10.1108/s1548-6435(2010)0000007011 · Cited by 269 · see citing works
Self-citationTesting moderating effects in PLS path models with composite variables
Fassott, G., Henseler, J., & Coelho, P. S. (2016) · Industrial Management & Data Systems · doi:10.1108/imds-06-2016-0248 · Cited by 257 · see citing works
Worse than measurement error: Consequences of inappropriate latent variable measurement models.
Rhemtulla, M., van Bork, R., & Borsboom, D. (2019) · Psychological Methods · doi:10.1037/met0000220 · Cited by 257 · see citing works
PLS-SEM for Software Engineering Research
Russo, D., & Stol, K.-J. (2021) · ACM Computing Surveys · doi:10.1145/3447580 · Cited by 255 · see citing works
Estimating regression models of finite but unknown order
Geweke, J. F., & Meese, R. A. (1981) · Journal of Econometrics · doi:10.1016/0304-4076(81)90091-9 · Cited by 242 · see citing works
On the value relevance of customer satisfaction. Multiple drivers and multiple markets
Raithel, S., Sarstedt, M., Scharf, S., & Schwaiger, M. (2011) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-011-0247-4 · Cited by 241 · see citing works
Self-citationSarstedt, M., Wilczynski, P., & Melewar, T. C. (2012) · Journal of World Business · doi:10.1016/j.jwb.2012.07.017 · Cited by 239 · see citing works
Self-citationLatent Variables and Indices: Herman Wold’s Basic Design and Partial Least Squares
Dijkstra, T. K. (2009) · Springer eBooks · doi:10.1007/978-3-540-32827-8_2 · Cited by 238 · see citing works
Latent Variable Path Modeling with Partial Least Squares.
Kroonenberg, P. M., & Lohmöller, J.-B. (1990) · Journal of the American Statistical Association · doi:10.2307/2290049 · Cited by 233 · see citing works
Rethinking Partial Least Squares Path Modeling: Breaking Chains and Forging Ahead
Rigdon, E. E. (2014) · Long Range Planning · doi:10.1016/j.lrp.2014.02.003 · Cited by 229 · see citing works
Hair, J. F., & Sarstedt, M. (2021) · The Journal of Marketing Theory and Practice · doi:10.1080/10696679.2020.1860683 · Cited by 224 · see citing works
Self-citationConfirmatory Composite Analysis
Schuberth, F., Henseler, J., & Dijkstra, T. K. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.02541 · Cited by 215 · see citing works
Measurement for the Social Sciences
Rossiter, J. R. (2010) · Springer eBooks · doi:10.1007/978-1-4419-7158-6 · Cited by 206 · see citing works
Explanation Plus Prediction—The Logical Focus of Project Management Research
Hair, J. F., & Sarstedt, M. (2021) · Project Management Journal · doi:10.1177/8756972821999945 · Cited by 206 · see citing works
Self-citationHwang, H., Sarstedt, M., Cheah, J., & Ringle, C. M. (2019) · Behaviormetrika · doi:10.1007/s41237-019-00085-5 · Cited by 205 · see citing works
Self-citationA structured review of partial least squares in supply chain management research
Kaufmann, L., & Gaeckler, J. (2015) · Journal of Purchasing and Supply Management · doi:10.1016/j.pursup.2015.04.005 · Cited by 199 · see citing works
Estimating Regression Models of Finite but Unknown Order
Geweke, J. F., & Meese, R. A. (1981) · International Economic Review · doi:10.2307/2526135 · Cited by 185 · see citing works
A Primer on the Conditional Mediation Analysis in PLS-SEM
Cheah, J., Nitzl, C., Roldán, J. L., Cepeda‐Carrión, G., & Gudergan, S. P. (2021) · ACM SIGMIS Database the DATABASE for Advances in Information Systems · doi:10.1145/3505639.3505645 · Cited by 184 · see citing works
Causal Indicator Models: Identification, Estimation, and Testing
Bollen, K. A., & Davis, W. R. (2009) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705510903008253 · Cited by 179 · see citing works
Rasoolimanesh, S. M., Wang, M., Roldán, J. L., & Kunasekaran, P. (2021) · Journal of Hospitality and Tourism Management · doi:10.1016/j.jhtm.2021.07.013 · Cited by 173 · see citing works
Psychological ownership in social media influencer marketing
Pick, M. (2020) · European Business Review · doi:10.1108/ebr-08-2019-0165 · Cited by 168 · see citing works
The case of partial least squares (PLS) path modeling in managerial accounting research
Nitzl, C., & Chin, W. W. (2017) · Journal of Management Control · doi:10.1007/s00187-017-0249-6 · Cited by 161 · see citing works
Dijkstra, T. K. (2014) · Long Range Planning · doi:10.1016/j.lrp.2014.02.004 · Cited by 152 · see citing works
Dorfman, R., & Theil, H. (1961) · The Review of Economics and Statistics · doi:10.2307/1927302 · Cited by 151 · see citing works
PLS-Based Model Selection: The Role of Alternative Explanations in Information Systems Research
Sharma, P. N., Sarstedt, M., Galit Shmueli, A., Kim, K. H., & Thiele, K. O. (2019) · Journal of the Association for Information Systems · doi:10.17705/1jais.00538 · Cited by 142 · see citing works
Self-citationPredictions from Partial Least Squares Models
Danks, N. P., & Ray, S. (2018) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-78756-699-620181003 · Cited by 139 · see citing works
Self-citationEberl, M. (2009) · Springer eBooks · doi:10.1007/978-3-540-32827-8_22 · Cited by 135 · see citing works
Danks, N. P., Sharma, P. N., & Sarstedt, M. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2020.03.019 · Cited by 135 · see citing works
Self-citationYou Write, but Others Read: Common Methodological Misunderstandings in PLS and Related Methods
Marcoulides, G. A., & Chin, W. W. (2013) · Springer proceedings in mathematics & statistics · doi:10.1007/978-1-4614-8283-3_2 · Cited by 125 · see citing works
Improper use of endogenous formative variables
Cadogan, J. W., & Lee, N. J. (2012) · Journal of Business Research · doi:10.1016/j.jbusres.2012.08.006 · Cited by 118 · see citing works
"Haters Gonna Hate": PLS and Information Systems Research
Petter, S. (2018) · ACM SIGMIS Database the DATABASE for Advances in Information Systems · doi:10.1145/3229335.3229337 · Cited by 118 · see citing works
On the adoption of partial least squares in psychological research: Caveat emptor
Rönkkö, M., McIntosh, C. N., & Antonakis, J. (2015) · Personality and Individual Differences · doi:10.1016/j.paid.2015.07.019 · Cited by 116 · see citing works
Prediction in HRM research–A gap between rhetoric and reality
Sarstedt, M., & Danks, N. P. (2021) · Human Resource Management Journal · doi:10.1111/1748-8583.12400 · Cited by 116 · see citing works
Self-citationDiscriminant validity of the customer-based corporate reputation scale: some causes for concern
Radomir, L., & Moisescu, O. I. (2019) · Journal of Product & Brand Management · doi:10.1108/jpbm-11-2018-2115 · Cited by 115 · see citing works
Assessing the validity of secondary data proxies for marketing constructs
Houston, M. B. (2003) · Journal of Business Research · doi:10.1016/s0148-2963(01)00299-5 · Cited by 108 · see citing works
SIMULTANEOUS FACTOR ANALYSIS IN SEVERAL POPULATIONS*
Jöreskog, K. G. (1970) · ETS Research Bulletin Series · doi:10.1002/j.2333-8504.1970.tb00790.x · Cited by 108 · see citing works
Selecting single items to measure doubly concrete constructs: A cautionary tale
Sarstedt, M., Diamantopoulos, A., Salzberger, T., & Baumgartner, P. (2015) · Journal of Business Research · doi:10.1016/j.jbusres.2015.12.004 · Cited by 100 · see citing works
Self-citationPLS-Based Model Selection: The Role of Alternative Explanations in Information Systems Research
Sharma, P. N., Sarstedt, M., Galit Shmueli, A., Kim, K. H., & Thiele, K. O. (2019) · tub.dok (Hamburg University of Technology) · doi:10.17005/1.jais.00538 · Cited by 98 · see citing works
Self-citationRigdon, E. E., Becker, J.-M., & Sarstedt, M. (2019) · Multivariate Behavioral Research · doi:10.1080/00273171.2018.1535420 · Cited by 90 · see citing works
Self-citationConflating Antecedents and Formative Indicators: A Comment on Aguirre-Urreta and Marakas
Rigdon, E. E., Becker, J.-M., Rai, A., Ringle, C. M., Diamantopoulos, A., Karahanna, E., Straub, D. W., & Dijkstra, T. K. (2014) · Information Systems Research · doi:10.1287/isre.2014.0543 · Cited by 63 · see citing works
Self-citationBayonne-Sopo, E., Marin-Garcia, J. A., & Alfalla-Luque, R. (2020) · Journal of Industrial Engineering and Management · doi:10.3926/jiem.3416 · Cited by 63 · see citing works
Quantify uncertainty in behavioral research
Rigdon, E. E., Sarstedt, M., & Becker, J.-M. (2020) · Nature Human Behaviour · doi:10.1038/s41562-019-0806-0 · Cited by 61 · see citing works
Self-citationDiamantopoulos, A., & Riefler, P. (2011) · Advances in international marketing · doi:10.1108/s1474-7979(2011)0000022004 · Cited by 57 · see citing works
Partial Least Squares Path Modeling
Latan, H., Joseph F. Hair, Jr., & Noonan, R. (2023) · Springer eBooks · doi:10.1007/978-3-031-37772-3 · Cited by 55 · see citing works
Models with latent variables: LISREL versus PLS
Schneeweiß, H. (1991) · Statistica Neerlandica · doi:10.1111/j.1467-9574.1991.tb01300.x · Cited by 52 · see citing works
Multigroup Analysis in Information Systems Research using PLS-PM
Klesel, M., Schuberth, F., Niehaves, B., & Henseler, J. (2022) · ACM Sigmis Database · doi:10.1145/3551783.3551787 · Cited by 36 · see citing works
Parceling Cannot Reduce Factor Indeterminacy in Factor Analysis: A Research Note
Rigdon, E. E., Becker, J.-M., & Sarstedt, M. (2019) · Psychometrika · doi:10.1007/s11336-019-09677-2 · Cited by 29 · see citing works
Self-citationAccounting for Uncertainty in the Measurement of Unobservable Marketing Phenomena
Rigdon, E. E., & Sarstedt, M. (2022) · Emerald Group Publishing Limited eBooks · doi:10.1108/s1548-643520220000019003 · Cited by 20 · see citing works
Self-citationThe Impact of Missing Values on PLS, ML and FIML Model Fit
Grimm, M. S., & Wagner, R. (2020) · Kobra (Universitätsbibliothek Kassel) · doi:10.17170/kobra-202107074257 · Cited by 13 · see citing works
The Piggy in the Middle: The Role of Mediators in PLS-SEM-based Prediction
Danks, N. P. (2021) · Trinity's Access to Research Output (TARA) (Trinity College Dublin) · doi:10.1145/3505639.3505644?casa_token=moqzdpxic_gaaaaa:l0se_rarq8purjg-sbrwqkshxfrxo0cjfilv73lgka4p2er_mnyb0_qytjnkeu0nno2xhnv417gvrw · Cited by 1 · see citing works
Self-citationUse of partial least squares (PLS) in strategic management research: a review of four recent studies
Hulland, J. (1999) · Strategic Management Journal · doi:10.1002/(sici)1097-0266(199902)20:2
Homburg (2001) · doi:10.1002/1520-6793(200101)18:1
Flagged first, then by citations
7 of the 171 references couldn’t be matched to a known paper.
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
Venkatesh, V., & Morris, M. G. (2000) · MIS Quarterly · doi:10.2307/3250981 · Cited by 4,382 · see citing works
Hair, J., & Alamer, A. (2022) · Research Methods in Applied Linguistics · doi:10.1016/j.rmal.2022.100027 · Cited by 2,833 · see citing works
Self-citationSarstedt, M., Hair, J. F., Pick, M., Liengaard, B. D., Radomir, L., & Ringle, C. M. (2022) · Psychology and Marketing · doi:10.1002/mar.21640 · Cited by 1,633 · see citing works
Self-citationPartial Least Squares Structural Equation Modeling
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2022) · Handbook of Market Research · doi:10.1007/978-3-319-57413-4_15 · Cited by 1,483 · see citing works
Self-citationAl-Adwan, A. S., Li, N., Al-Adwan, A., Abbasi, G. A., Albelbisi, N. A., & Habibi, A. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-11816-3 · Cited by 637 · see citing works
“PLS-SEM: indeed a silver bullet” – retrospective observations and recent advances
Sarstedt, M., Hair, J. F., & Ringle, C. M. (2022) · The Journal of Marketing Theory and Practice · doi:10.1080/10696679.2022.2056488 · Cited by 705 · see citing works
Self-citationMultigroup analysis of more than two groups in PLS-SEM: A review, illustration, and recommendations
Cheah, J., Amaro, S., & Roldán, J. L. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.113539 · Cited by 380 · see citing works
Impact of Employees' Workplace Environment on Employees' Performance: A Multi-Mediation Model
Gu, Z., Chupradit, S., Ku, K. Y., Nassani, A. A., & Haffar, M. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.890400 · Cited by 327 · see citing works
Wang, S., Sun, Z., & Chen, Y. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11338-4 · Cited by 305 · see citing works
Choi, S., Jang, Y., & Kim, H. (2023) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2022.2049145 · Cited by 294 · see citing works
Review of Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook
Edeh, E. J., Lo, W., & Khojasteh, J. (2022) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2022.2108813 · Cited by 265 · see citing works
Rahim, N. I. M., Iahad, N. A., Yusof, A. F., & Al-Sharafi, M. A. (2022) · Sustainability · doi:10.3390/su141912726 · Cited by 310 · see citing works
Kamalanon, P., Chen, J., & Le, T.-T.-Y. (2022) · Sustainability · doi:10.3390/su14020689 · Cited by 254 · see citing works
Reviewing the SmartPLS 4 software: the latest features and enhancements
Cheah, J., Magno, F., & Cassia, F. (2023) · Journal of Marketing Analytics · doi:10.1057/s41270-023-00266-y · Cited by 288 · see citing works
Qin, Y., Omar, B., & Musetti, A. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.932805 · Cited by 237 · see citing works
Lee, S., Lee, N., Lee, T. J., & Hyun, S. S. (2023) · Journal of Sustainable Tourism · doi:10.1080/09669582.2023.2175836 · Cited by 241 · see citing works
Pham, H. C., Duong, C. D., & Nguyen, G. K. H. (2024) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2024.103758 · Cited by 222 · see citing works
Alsyouf, A., Lutfi, A., Alsubahi, N., Alhazmi, F. N., Al‐Mugheed, K., Anshasi, R. J., Alharbi, N. I., & Albugami, M. A. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20021347 · Cited by 268 · see citing works
Shahadat, M. M. H., Nekmahmud, M., Ebrahimi, P., & Fekete-Farkas, M. (2023) · Global Business Review · doi:10.1177/09721509221137199 · Cited by 247 · see citing works
Wanner, J., Herm, L.-V., Heinrich, K., & Janiesch, C. (2022) · Electronic Markets · doi:10.1007/s12525-022-00593-5 · Cited by 240 · see citing works
Arya, V., Sambyal, R., Sharma, A., & Dwivedi, Y. K. (2023) · Journal of Consumer Behaviour · doi:10.1002/cb.2214 · Cited by 226 · see citing works
Is Chatgpt a menace for creative writing ability? An experiment
Niloy, A. C., Akter, S., Sultana, N., Sultana, J., & Rahman, S. I. U. (2024) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12929 · Cited by 198 · see citing works
Le, T. T., Tran, P. Q., Lam, N. P., Tra, M. N. L., & Uyen, P. H. P. (2023) · Operations Management Research · doi:10.1007/s12063-023-00411-x · Cited by 196 · see citing works
Tian, W., Ge, J., Zhao, Y., & Zheng, X. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1268549 · Cited by 232 · see citing works
The Government Metaverse: Charting the Coordinates of Citizen Acceptance
Al‐Adwan, A. S. (2024) · Telematics and Informatics · doi:10.1016/j.tele.2024.102109 · Cited by 187 · see citing works
Al‐Emran, M., AlQudah, A. A., Abbasi, G. A., Al-Sharafi, M. A., & Iranmanesh, M. (2023) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2023.3237789 · Cited by 178 · see citing works
Li, K. (2023) · Sustainability · doi:10.3390/su15065221 · Cited by 157 · see citing works
Filieri, R., Açikgöz, F., & Du, H. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2023.113774 · Cited by 177 · see citing works
Zhang, L., Anjum, M. A., & Wang, Y. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2184594 · Cited by 160 · see citing works
Shaikh, F., Afshan, G., Anwar, R. S., Abbas, Z., & Channa, K. A. (2023) · Asia Pacific Journal of Human Resources · doi:10.1111/1744-7941.12385 · Cited by 174 · see citing works
Bucea-Manea-Țoniş, R., Kuleto, V., Gudei, S. C. D., Lianu, C., Lianu, C., Ilić, M. P., & Păun, D. G. (2022) · Sustainability · doi:10.3390/su14105842 · Cited by 183 · see citing works
From concerns to benefits: A comprehensive study of ChatGPT usage in education
Jo, H. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00471-4 · Cited by 146 · see citing works
“One of the principal benefits of PLS lies in its fewer constraints concerning sample size distribution and residuals relative to covariance-based structural equation techniques such as LISREL and AMOS, as highlighted by (Hair et al., 2021).”
BackgroundMethodEvaluation of Formative Measurement Models
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021) · Classroom companion: business · doi:10.1007/978-3-030-80519-7_5 · Cited by 284 · see citing works
Self-citationHang, Y., Sarfraz, M., Khalid, R., Öztürk, İ., & Tariq, J. (2022) · Economic Research-Ekonomska Istraživanja · doi:10.1080/1331677x.2022.2026243 · Cited by 142 · see citing works
Aftab, J., Abid, N., Sarwar, H., Amin, A., Abedini, M., & Veneziani, M. (2023) · Corporate Social Responsibility and Environmental Management · doi:10.1002/csr.2654 · Cited by 153 · see citing works
Sarfraz, M., Öztürk, İ., Yoo, S., Raza, M. A., & Han, H. (2023) · Humanities and Social Sciences Communications · doi:10.1057/s41599-023-01799-4 · Cited by 166 · see citing works
Zhang, Q., Zada, M., Khan, S., Haddad, H., Al-Ramahi, N. M., & Nureldeen, W. A. (2024) · Sustainability · doi:10.3390/su16209021 · Cited by 136 · see citing works
Schöggl, J., Stumpf, L., & Baumgartner, R. J. (2023) · Business Strategy and the Environment · doi:10.1002/bse.3593 · Cited by 133 · see citing works
Kassa, B. Y., & Worku, E. K. (2025) · Journal of Open Innovation Technology Market and Complexity · doi:10.1016/j.joitmc.2025.100474 · Cited by 194 · see citing works
Xu, Y., Du, J., Khan, M. A. S., Jin, S., Altaf, M., Anwar, F., & Sharif, I. (2022) · Frontiers in Environmental Science · doi:10.3389/fenvs.2022.779629 · Cited by 130 · see citing works
Alrabai, F. (2022) · Journal of Multilingual and Multicultural Development · doi:10.1080/01434632.2022.2053138 · Cited by 136 · see citing works
The technology acceptance model and adopter type analysis in the context of artificial intelligence
Ibrahim, F., Münscher, J., Daseking, M., & Telle, N.-T. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1496518 · Cited by 182 · see citing works
Artificial Intelligence and Industry 4.0: Catalysts for Sustainable Organizational Development
Al-Momani, A. M. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-75091-5_13 · Cited by 124 · see citing works
Ishaq, M. I., Sarwar, H., Aftab, J., Franzoni, S., & Raza, A. (2023) · Business Strategy and the Environment · doi:10.1002/bse.3557 · Cited by 121 · see citing works
Lee, H., Shin, M., Yang, J., & Chock, T. M. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2374100 · Cited by 141 · see citing works
“…results from PLS-SEM confirmed that our research model has a good fit: APC ¼ 0.220 ( p < 0.05), suggesting a moderate positive relationship between variables (Cohen, 2013; Mohamed et al., 2018), and ARS ¼ 0.248 ( p < 0.05), indicating our model has acceptable explanatory power (Hair et al., 2021).”
InfluentialFactors influencing business intelligence adoption: evidence from Jordan
Jaradat, Z., Al-Dmour, A., Alshurafat, H., Al-Hazaima, H., & Shbail, M. O. A. (2022) · Journal of Decision System · doi:10.1080/12460125.2022.2094531 · Cited by 120 · see citing works
Gupta, V. (2024) · Systems · doi:10.3390/systems12030103 · Cited by 120 · see citing works
Salah, M., Alhalbusi, H., Ismail, M. M., & Abdelfattah, F. (2023) · Current Psychology · doi:10.1007/s12144-023-04989-0 · Cited by 135 · see citing works
Zaman, U., Koo, I., Abbasi, S., Raza, S. H., & Qureshi, M. G. (2022) · Sustainability · doi:10.3390/su14116441 · Cited by 117 · see citing works
Jain, R. M., Garg, N., & Khera, S. N. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.893691 · Cited by 155 · see citing works
Testing an adoption model for Industry 4.0 and sustainability: A Malaysian scenario
Jayashree, S., Reza, M. N. H., Malarvizhi, C. A., Gunasekaran, A., & Rauf, M. A. (2022) · Sustainable Production and Consumption · doi:10.1016/j.spc.2022.02.015 · Cited by 116 · see citing works
An, S., Eck, T., & Yim, H. (2023) · Sustainability · doi:10.3390/su15010832 · Cited by 114 · see citing works
Xueyun, Z., Mamun, A. A., Masukujjaman, M., Rahman, M. K., Gao, J., & Yang, Q. Y. (2023) · Scientific Reports · doi:10.1038/s41598-023-42591-3 · Cited by 167 · see citing works
Du, L., & Lv, B. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12835-4 · Cited by 141 · see citing works
Determinants of Students’ Satisfaction with AI Tools in Education: A PLS-SEM-ANN Approach
Almufarreh, A. (2024) · Sustainability · doi:10.3390/su16135354 · Cited by 146 · see citing works
Wang, S., & Zhang, H. (2025) · Renewable Energy · doi:10.1016/j.renene.2025.122855 · Cited by 113 · see citing works
The Impact of Technologies of Traceability and Transparency in Supply Chains
Khan, M., Parvaiz, G. S., Dedahanov, A. T., Abdurazzakov, O. S., & Rakhmonov, D. A. (2022) · Sustainability · doi:10.3390/su142416336 · Cited by 112 · see citing works
Thathsarani, U. S., & Jianguo, W. (2022) · Information · doi:10.3390/info13080390 · Cited by 112 · see citing works
Dong, X., Zhao, H., & Li, T. (2022) · Sustainability · doi:10.3390/su14074374 · Cited by 129 · see citing works
Lavidas, K., Voulgari, I., Papadakis, S., Athanassopoulos, S., Anastasiou, A., Filippidi, A., Komis, V., & Karacapilidis, N. I. (2024) · Information · doi:10.3390/info15060314 · Cited by 165 · see citing works
Açikgöz, F., Elwalda, A., & de Oliveira, M. J. (2023) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2022.100152 · Cited by 114 · see citing works
Sarwar, Z., Gao, J., & Khan, A. U. (2023) · Asia Pacific Journal of Management · doi:10.1007/s10490-023-09879-4 · Cited by 107 · see citing works
Does ESG Impact Firms’ Sustainability Performance? The Mediating Effect of Innovation Performance
Zhou, S., Rashid, M. H. U., Zobair, S. A. M., Sobhani, F. A., & Siddik, A. B. (2023) · Sustainability · doi:10.3390/su15065586 · Cited by 107 · see citing works
Zhu, Y. D., Zhang, H., Siddik, A. B., Zheng, Y., & Sobhani, F. A. (2023) · Systems · doi:10.3390/systems11090461 · Cited by 107 · see citing works
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021) · Classroom companion: business · doi:10.1007/978-3-030-80519-7_7 · Cited by 106 · see citing works
Self-citationTowards data-driven decision making: the role of analytical culture and centralization efforts
Szukits, Á., & Móricz, P. (2023) · Review of Managerial Science · doi:10.1007/s11846-023-00694-1 · Cited by 166 · see citing works
Wang, S., & Zhang, H. (2025) · Systems · doi:10.3390/systems13010043 · Cited by 106 · see citing works
Blockchain Technology Enhances Sustainable Higher Education
Bucea-Manea-Țoniş, R., Martins, O., Bucea-Manea-Țoniș, R., Gheorghiţă, C., Kuleto, V., Ilić, M. P., & Simion, V. E. (2021) · Sustainability · doi:10.3390/su132212347 · Cited by 125 · see citing works
Xia, Z., Wu, D., & Zhang, L. (2022) · Sustainability · doi:10.3390/su14106283 · Cited by 120 · see citing works
Abdurrahman, A., Gustomo, A., & Prasetio, E. A. (2024) · Journal of Open Innovation Technology Market and Complexity · doi:10.1016/j.joitmc.2024.100215 · Cited by 148 · see citing works
Soomro, R., Al-rahmi, W., Dahri, N. A., Almuqren, L., Almogren, A. S., & Aldaijy, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-86464-3 · Cited by 112 · see citing works
Abourokbah, S. H., Mashat, R. M., & Salam, M. A. (2023) · Sustainability · doi:10.3390/su15043636 · Cited by 104 · see citing works
Hossain, I., Nekmahmud, M., & Fekete‐Farkas, M. (2022) · Sustainability · doi:10.3390/su14116513 · Cited by 103 · see citing works
Using SOR framework to explore the driving factors of older adults smartphone use behavior
Huang, T. (2023) · Humanities and Social Sciences Communications · doi:10.1057/s41599-023-02221-9 · Cited by 102 · see citing works
A comparative analysis of multivariate approaches for data analysis in management sciences
Ahmed, R. R., Štreimikienė, D., Štreimikis, J., & Šikšnelytė-Butkienė, I. (2024) · E+M Ekonomie a Management · doi:10.15240/tul/001/2024-5-001 · Cited by 123 · see citing works
Trust in Social Media: Enhancing Social Relationships
Hatamleh, I. H. M., Safori, A. O., Habes, M., Tahat, O. M., Ahmad, A. K., Abdallah, R. A.-Q., & Aissani, R. A. (2023) · Social Sciences · doi:10.3390/socsci12070416 · Cited by 129 · see citing works
Shamszare, H., & Choudhury, A. (2023) · Healthcare · doi:10.3390/healthcare11162308 · Cited by 113 · see citing works
Alabdali, M. A., Yaqub, M. Z., Agarwal, R., Alofaysan, H., & Mohapatra, A. K. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.141670 · Cited by 100 · see citing works
Sarstedt, M., Adler, S. J., Ringle, C. M., Cho, G., Diamantopoulos, A., Hwang, H., & Liengaard, B. D. (2024) · Journal of Product Innovation Management · doi:10.1111/jpim.12738 · Cited by 99 · see citing works
Self-citationModeling the interplay of EFL learners’ basic psychological needs, grit and L2 achievement
Shirvan, M. E., & Alamer, A. (2022) · Journal of Multilingual and Multicultural Development · doi:10.1080/01434632.2022.2075002 · Cited by 101 · see citing works
Kuleto, V., Ilić, M. P., Stănescu, M., Ranković, M., Šević, N. P., Păun, D. G., & Teodorescu, S. (2021) · Sustainability · doi:10.3390/su132111814 · Cited by 97 · see citing works
Dai, X., Siddik, A. B., & Tian, H. (2022) · Sustainability · doi:10.3390/su142013607 · Cited by 95 · see citing works
Quiet quitting during COVID-19: the role of psychological empowerment
Lu, M., Mamun, A. A., Chen, X., Yang, Q. Y., & Masukujjaman, M. (2023) · Humanities and Social Sciences Communications · doi:10.1057/s41599-023-02012-2 · Cited by 127 · see citing works
Barakat, B., Milhem, M., Naji, G. M. A., Alzoraiki, M., Muda, H. B., Ateeq, A. A., & Abro, Z. (2023) · Sustainability · doi:10.3390/su151914144 · Cited by 96 · see citing works
How to Apply Necessary Condition Analysis in PLS-SEM
Richter, N., Hauff, S., Ringle, C. M., Sarstedt, M., Kolev, A. E., & Schubring, S. (2023) · Springer eBooks · doi:10.1007/978-3-031-37772-3_10 · Cited by 95 · see citing works
Self-citationAljumah, A. (2023) · Cogent Business & Management · doi:10.1080/23311975.2023.2270813 · Cited by 149 · see citing works
Partial Least Squares Structural Equation Modeling
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2025) · Springer eBooks · doi:10.1007/978-3-319-05542-8_15-3 · Cited by 115 · see citing works
Self-citationAdvancing algorithmic bias management capabilities in AI-driven marketing analytics research
Akter, S., Sultana, S., Mariani, M. M., Wamba, S. F., Spanaki, K., & Dwivedi, Y. K. (2023) · Industrial Marketing Management · doi:10.1016/j.indmarman.2023.08.013 · Cited by 116 · see citing works
Chu, S.-C., Kim, H., & Kim, Y. (2022) · International Journal of Advertising · doi:10.1080/02650487.2022.2138057 · Cited by 91 · see citing works
Sanusi, I. T., Ayanwale, M. A., & Chiu, T. K. F. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-12250-1 · Cited by 89 · see citing works
Gudergan, S. P., Moisescu, O. I., Radomir, L., Ringle, C. M., & Sarstedt, M. (2024) · Journal of Business Research · doi:10.1016/j.jbusres.2024.115087 · Cited by 126 · see citing works
Self-citationSergeeva, O. V., Zheltukhina, M. R., Shoustikova, T., Tukhvatullina, L. R., Dobrokhotov, D. A., & Kondrashev, S. V. (2025) · Contemporary Educational Technology · doi:10.30935/cedtech/16039 · Cited by 131 · see citing works
Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Almuqren, L., Almgren, A. S., Alshimai, A., & Al‐Adwan, A. S. (2025) · IEEE Access · doi:10.1109/access.2025.3560567 · Cited by 106 · see citing works
Bhilat, E. M. E., Jaouhari, A. E., & HAMIDI, L. S. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.123149 · Cited by 85 · see citing works
Jo, H. (2023) · Information Development · doi:10.1177/02666669231202764 · Cited by 99 · see citing works
Gopinathan, S., Kaur, A. H., Veeraya, S., & Raman, M. (2022) · Sustainability · doi:10.3390/su14116844 · Cited by 127 · see citing works
Mohammed, M., Shafiq, N., Elmansoury, A., Al‐Mekhlafi, A. A., Rached, E. F., Zawawi, N. A. W. A., Haruna, A., Rafindadi, A. D., & Ibrahim, M. B. (2021) · Sustainability · doi:10.3390/su131910660 · Cited by 97 · see citing works
The Impact of Green Entrepreneurship on Social Change and Factors Influencing AMO Theory
Mia, M. M., Rizwan, S., Zayed, N. M., Nitsenko, V. S., Miroshnyk, O., Kryshtal, H. O., & Остапенко, Р. (2022) · Systems · doi:10.3390/systems10050132 · Cited by 100 · see citing works
Soomro, R. B., Memon, S. G., Dahri, N. A., Al-Rahmi, W. M., Aldriwish, K., Salameh, A. A. M., Al‐Adwan, A. S., & Saleem, A. (2024) · Sustainability · doi:10.3390/su16177351 · Cited by 80 · see citing works
The power of AI: enhancing customer loyalty through satisfaction and efficiency
Singh, P., & Singh, V. (2024) · Cogent Business & Management · doi:10.1080/23311975.2024.2326107 · Cited by 108 · see citing works
- 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