PLS-SEM Method Articles

Do you know of any good PLS-SEM methods articles, preferably in top-level journals? Please send us your proposal to literature@smartpls.com in the following format:

Cassia, F., & Magno, F. (in press). Algorithmic unpredictability on social media platforms: A multimethod SEM study of its effects and mitigation strategies for exporting SMEs. Journal of Global Marketing.

Castillo, A., Rescalvo-Martin, E., & Karatepe, O. M. (in press). Finite mixture partial least squares (FIMIX-PLS) in service research. The Service Industries Journal.

Cheah, J.-H., Sarstedt, M., Hair, J. F., & Ringle, C. M. (in press). Consistent partial least squares structural equation modeling using SmartPLS. Structural Equation Modeling: A Multidisciplinary Journal.

Ehrlich, J. S., Kreienbaum, C., & Ringle, C. M. (in press). Looking at the bigger picture: On the relevance of null effects of model differences across time. Journal of Marketing Analytics.

Hair, J. F., Sabol, M., Islam, M. S., & Murshed, F. (in press). Conditional mediation (CoMe) models with PLS-SEM: An update, review, and best-practice recommendations. Australasian Marketing Journal.

Margalina, V.-M., Kreienbaum, C., Hair, J. F., Becker, J.-M., & Ringle, C. M. (2026). (in press). Multiple linear and logistic regression analysis: A SmartPLS 4 software tutorial. Journal of Marketing Analytics.

Becker, J.-M., Richter, N. F., Ringle, C. M., & Sarstedt, M. (2026). Must-have, or maybe not? A sensitivity-based extension to necessary condition analysis. Journal of Business Research, 206, Article 115920.

Bido, D.d.S., & Souza, C. A. (2026). Structural equation modeling: Is it still worth learning? Brazilian administration review. Volume 23, Issue 3, e260020.

Hair, J. F., Sharma, P. N., Chin, W. W., Sarstedt, M., & Ringle, C. M. (2026). A multimethod SEM framework for analyzing models with latent variables. Journal of Global Marketing, 39(2), 167–182.

Memon, M. A., Thurasamy, R., Cheah, J.-H., Ting, H., Kreienbaum, C., & Ringle, C. M. (2026). Moderated mediation analysis using SmartPLS: A tutorial for business researchers. Journal of Applied Structural Equation Modeling, 10(1), 1–28.

Angelelli, M., Ciavolino, E., Ringle, C. M., Sarstedt, M., & Aria, M. (2025). Conceptual structure and thematic evolution in partial least squares structural equation modeling research. Quality & Quantity, 59, 2753–2798.

Bauer, J., Mayer, A., Fuchs, C., & Schamberger, T. (2025). Misspecifications in structural equation modeling: The choice of latent variables, causal-formative constructs or composites. arXiv, Article 2507.21998.

Cefis, M., Angelelli, M., Carpita, M., & Ciavolino, E. (2025). Detecting causal relations among indicators with the CTA test: Simulations and applications. Social Indicators Research, 178, 393–417.

Ghazali, Z. M., & Yaacob, W. (2025). Integrated PLS-SEM-latent growth curve model: A new conditional time invariant method for analysing panel survey data. MethodsX, 15, Article 103635.

Gudergan, S. P., Moisescu, O. I., Radomir, L., Ringle, C. M., & Sarstedt, M. (2025). Special issue editorial: Advanced partial least squares structural equation modeling (PLS-SEM) applications in business research. Journal of Business Research, 188, Article 115087.

Guenther, P., Guenther, M., Ringle, C. M., Zaefarian, G., & Cartwright, S. (2025). PLS-SEM and reflective constructs: A response to recent criticism and a constructive path forward. Industrial Marketing Management, 128, 1–9.

Hair, J. F., Babin, B. J., Ringle, C. M., Sarstedt, M., & Becker, J.-M. (2025). Covariance-based structural equation modeling (CB-SEM): A SmartPLS 4 software tutorial. Journal of Marketing Analytics, 13, 709–724.

Hauff, S., Richter, N. F., & Ringle, C. M. (2025). Human resource management systems research – how to gain impactful insights through formative measurement and hierarchical component models. The International Journal of Human Resource Management, 36(4), 611–635.

Liengaard, B. D., Becker, J.-M., Bennedsen, M., Heiler, P., Taylor, L. N., & Ringle, C. M. (2025). Dealing with regression models’ endogeneity by means of an adjusted estimator for the gaussian copula approach. Journal of the Academy of Marketing Science, 53, 279–299.

Liu, Y., Chin, W. W., Cheah, J.-H., Hair, J. F., & Lyu, C. (2025). Tackling missing data in PLS-SEM: Strategies and insights for business research. Journal of Business Research, 201, Article 115739.

Sarstedt, M./ Ringle, C.M./ Hair, J.F.: Partial Least Squares Structural Equation Modeling. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of Market Research (pp. 1-56). Springer Nature Switzerland, 2025.

Amusa, L. B., & Hossana, T. (2024). An empirical comparison of some missing sata treatments in PLS-SEM. PLOS ONE, 19(1), Article e0297037.

Cepeda, G., Roldán, J. L., Sabol, M., Hair, J. F., & Chong, A. Y. L. 2024. (2024). Emerging opportunities for information systems researchers to expand their PLS-SEM analytical toolbox. Industrial Management & Data Systems, 124(6), 2230–2250.

Cheah, J.-H., Magno, F., & Cassia, F. (2024). Reviewing the SmartPLS 4 software: The latest features and enhancements. Journal of Marketing Analytics, 12(1), 97–107.

Gironda, J. T. (2024). Review of advanced issues in partial least squares structural equation modeling (second edition). Journal of Marketing Analytics, 12(1), 108–109.

Hair, J. F., Sarstedt, M., Ringle, C. M., Sharma, P. N., & Liengaard, B. D. (2024). Going beyond the untold facts in PLS–SEM and moving forward. European Journal of Marketing, 58(13), 81–106.

Hair, J. F., Sarstedt, M., Ringle, C. M., Sharma, P. N., & Liengaard, B. D. (2024). The shortcomings of equal weights estimation and the composite equivalence index in PLS-SEM. European Journal of Marketing, 58(13), 30–55.

Hauff, S., Richter, N. F., Sarstedt, M., & Ringle, C. M. (2024). Importance and performance in PLS-SEM and NCA: Introducing the combined importance-performance map analysis (cIPMA). Journal of Retailing and Consumer Services, 78, Article 103723.

Kurtaliqi, F., Lancelot Miltgen, C., Viglia, G., & Pantin-Sohier, G. (2024). Using advanced mixed methods approaches: Combining PLS-SEM and qualitative studies. Journal of Business Research, 172, Article 114464.

Lamberti, G. (2023). Hybrid multigroup partial least squares structural equation modelling: An application to bank employee satisfaction and loyalty. Quality & Quantity, 57, 683–705.

Legate, A. E., Ringle, C. M., & Hair, J. F. (2024). PLS-SEM: A method demonstration in the R statistical environment. Human Resource Development Quarterly, 35(4), 501–529.

Liengaard, B. D. (2024). Measurement invariance testing in partial least squares structural equation modeling. Journal of Business Research, 177, Article 114581.

Richter, N. F., & Tudoran, A. A. (2024). Elevating theoretical insight and predictive accuracy in business research: Combining PLS-SEM and selected machine learning algorithms. Journal of Business Research, 173, Article 114453.

Rigdon, E. E. (2024). Understanding composite-based structural equation modeling methods from the perspective of regression component analysis. Multivariate Behavioral Research, 59(4), 677–692.

Sarstedt, M., Adler, S. J., Ringle, C. M., Cho, G., Diamantopoulos, A., Hwang, H., & Liengaard, B. D. (2024). Same model, same data, but different outcomes: Evaluating the impact of method choices in structural equation modeling. Journal of Product Innovation Management, 41(6), 1100–1117.

Sarstedt, M., Richter, N. F., Hauff, S., & Ringle, C. M. (2024). Combined importance–performance map analysis (cIPMA) in partial least squares structural equation modeling (PLS–SEM): A SmartPLS 4 tutorial. Journal of Marketing Analytics, Journal of Marketing Analytics, 12, 746–760.

Sarstedt, M., & Moisescu, O.-I. (2024). Quantifying uncertainty in PLS-SEM-based mediation analyses. Journal of Marketing Analytics, 12(1), 87–96.

Sharma, P. N., Sarstedt, M., Ringle, C. M., Cheah, J.-H., Herfurth, A., & Hair, J. F. (2024). A framework for enhancing the replicability of behavioral MIS research using prediction oriented techniques. International Journal of Information Management, 78, Article 102805.

Shiau, W.-L., Liu, C., Cheng, X., & Yu, W.-P. (2024). Employees’ behavioral intention to adopt facial recognition payment to service customers: From status quo bias and value-based adoption perspectives. Journal of Organizational and End User Computing, 36(1), 1–32.

Vaithilingam, S., Ong, C. S., Moisescu, O. I., & Nair, M. S. (2024). Robustness checks in PLS-SEM: A review of recent practices and recommendations for future applications in business research. Journal of Business Research, 173, Article 114465.

Adler, S. J., Sharma, P. N., & Radomir, L. (2023). Toward open science in PLS-SEM: Assessing the state of the art and future perspectives. Journal of Business Research, 169, Article 114291.

Becker, J.-M., Cheah, J. H., Gholamzade, R., Ringle, C. M., & Sarstedt, M. (2023). PLS-SEM’s most wanted guidance. International Journal of Contemporary Hospitality Management, 35(1), 321–346.

Cheah, J.-H., Amaro, S., & Roldán, J. L. (2023). Multigroup analysis of more than two groups in PLS-SEM: A review, illustration, and recommendations. Journal of Business Research, 156, Article 113539.

Cho, G., Lee, J., Hwang, H., Sarstedt, M., & Ringle, C. M. (2023). A comparative study of the predictive power of component-based approaches to structural equation modeling. European Journal of Marketing, 57(6), 1641–1661.

Cook, D. R., & Forzani, L. (2023). On the role of partial least squares in path analysis for the social sciences. Journal of Business Research, 167, Article 114132.

Guenther, P., Guenther, M., Ringle, C. M., Zaefarian, G., & Cartwright, S. (2023). Improving PLS-SEM use for business marketing research. Industrial Marketing Management, 111, 127–142.

Legate, A. E., Hair, J. F., Chretien, J. L., & Risher, J. J. (2023). PLS-SEM: Prediction-oriented solutions for HRD researchers. Human Resource Development Quarterly, 34(1), 91–109.

Morgeson, F. V., Hult, G. T. M., Sharma, U., & Fornell, C. (2023). The American Customer Satisfaction Index (ACSI): A sample dataset and description. Data in Brief, 48, Article 109123.

Richter, N. F., Hauff, S., Kolev, A. E., & Schubring, S. (2023). Dataset on an extended technology acceptance model: A combined application of PLS-SEM and NCA. Data in Brief, 48, Article 109190.

Richter, N. F., Hauff, S., Ringle, C. M., Sarstedt, M., Kolev, A. E., & Schubring, S. (2023). How to apply necessary condition analysis in PLS-SEM. In H. Latan, J. F. Hair, & R. Noonan (Eds.), Partial least squares path modeling: Basic concepts, methodological issues and applications (pp. 267-297). Springer International Publishing.

Ringle, C. M., Sarstedt, M., Sinkovics, N., & Sinkovics, R. R. (2023). A perspective on using partial least squares structural equation modelling in data articles. Data in Brief, 48, Article 109074.

Russo, D., & Stol, K.-J. (2023). Don’t throw the baby out with the bathwater: Comments on “recent developments in PLS”. Communications of the Association for Information Systems, 52, 700–704.

Sabol, M., Hair, J. F., Cepeda, G., Roldán, J. L., & Chong, A. Y. L. (2023). PLS-SEM in information systems: Seizing the opportunity and marching ahead full speed to adopt methodological updates. Industrial Management & Data Systems, 123(12), 2997–3017.

Sarstedt, M., Hair, J. F., & Ringle, C. M. (2023). "PLS-SEM: Indeed a silver bullet" - retrospective observations and recent advances. Journal of Marketing Theory & Practice, 31, 261–275.

Sarstedt, M., Ringle, C. M., & Iuklanov, D. (2023). Antecedents and consequences of corporate reputation: A dataset. Data in Brief, 48, Article 109079.

Sharma, P. N., Liengaard, B. D., Hair, J. F., Sarstedt, M., & Ringle, C. M. (2023). Predictive model assessment and selection in composite-based modeling using PLS-SEM: Extensions and guidelines for using CVPAT. European Journal of Marketing, 57(6), 1662–1677.

Sharma, P. N., Liengaard, B. D., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2023). Extraordinary claims require extraordinary evidence: A comment on “the recent developments in PLS”. Communications of the Association for Information Systems, 52, 739–742.

Basco, R., Hair, J. F., Ringle, C. M., & Sarstedt, M. (2022). Advancing family business research through modeling nonlinear relationships: Comparing PLS-SEM and multiple regression. Journal of Family Business Strategy, 13(3), Article 100457.

Becker, J.-M., Proksch, D., & Ringle, C. M.:. (2022). Revisiting gaussian copulas to handle endogenous regressors. Journal of the Academy of Marketing Science, 50, 46–66.

Ciavolino, E., Aria, M., Cheah, J.-H., & Roldán, J. L. (2022). A tale of PLS structural equation modelling: Episode I— A bibliometrix citation analysis. Social Indicators Research, 164(3), 1323–1348.

Cho, G., Hwang, H., Sarstedt, M., & Ringle, C. M. (2022). A prediction-oriented specification search algorithm for generalized structured component analysis. Structural Equation Modeling: A Multidisciplinary Journal, 29(2), 229–240.

Cho, G., Schlägel, C., Hwang, H., Choi, Y., Sarstedt, M., & Ringle, C. M. (2022). Integrated generalized structured component analysis: On the use of model fit criteria in international management research. Management International Review, 62, 569–609.

Sarstedt, M., & Danks, N. P. (2022). Prediction in HRM research: A gap between rhetoric and reality. Human Resource Management Journal, 32(2), 485–513.

Sarstedt, M., Hair, J. F., Pick, M., Liengaard, B. D., Radomir, L., & Ringle, C. M. (2022). Progress in partial least squares structural equation modeling use in marketing research in the last decade. Psychology & Marketing, 39(5), 1035–1064.

Sarstedt, M., Radomir, L., Moisescu, O. I., & Ringle, C. M. (2022). Latent class analysis in PLS-SEM: A review and recommendations for future applications. Journal of Business Research, 138, 398–407.

Cataldo, R., Crocetta, C., Grassia, M. G., Lauro, N. C., Marino, M., & Voytsekhovska, V. (2021). Methodological PLS-PM framework for SDGs system. Social Indicators Research, 156(2), 701–723.

Cheah, J.-H., Nitzl, C., Roldán, J. L., Cepeda-Carrion, G., & Gudergan, S. P. (2021). A primer on the conditional mediation analysis in PLS-SEM. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 52(SI), 43–100.

Cheah, J., Roldán, J. L., Ciavolino, E., Ting, H., & Ramayah, T. (2021). Sampling weight adjustments in partial least squares structural equation modeling: Guidelines and illustrations. Total Quality Management & Business Excellence, 32(13-14), 1594–1613.

Crocetta, C., Antonucci, L., Cataldo, R., Galasso, R., Grassia, M. G., Lauro, C. N., & Marino, M. (2021). Higher-order PLS-PM approach for different types of constructs. Social Indicators Research, 154(2), 725–754.

Dash, G., & Paul, J. (2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 173, Article 121092.

Hair, J. F., Binz Astrachan, C., Moisescu, O. I., Radomir, L., Sarstedt, M., Vaithilingam, S., & Ringle, C. M. (2021). Executing and interpreting applications of PLS-SEM: Updates for family business researchers. Journal of Family Business Strategy, 12(3), Article 100392.

Hair, J. F., & Sarstedt, M. (2021). Explanation plus prediction—the logical focus of project management research. Project Management Journal, 52(4), 319–322.

Liengaard, B., Sharma, P N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2021). Prediction: Coveted, yet forsaken? Introducing a cross-validated predictive ability test in partial least squares path modeling. Decision Sciences, 53(2), 362–392.

Manley, S. C., Hair, J. F., Williams, R. I., & McDowell, W. C. (2021). Essential new PLS-SEM analysis methods for your entrepreneurship analytical toolbox. International Entrepreneurship and Management Journal, 17, 1805–1825.

Manosuthi, N., Lee, J. S., & Han, H. (2021). An innovative application of composite-based structural equation modeling in hospitality research with empirical example. Cornell Hospitality Quarterly, 62(1), 139–156.

Memon, M. A., Ramayah, T., Cheah, J.-H., Ting, H., Chuah, F., & Cham, T. H. (2021). PLS-SEM statistical program: A review. Journal of Applied Structural Equation Modeling, 5(1), i–xiii.

Rasoolimanesh, S. M., Ringle, C. M., Sarstedt, M., & Olya, H. (2021). The combined use of symmetric and asymmetric approaches: Partial least squares structural equation modeling and fuzzy-set qualitative comparative analysis. International Journal of Contemporary Hospitality Management, 33(5), 1571–1592.

Rasoolimanesh, S. M., Wang, M., Roldán, J. L., & Kunasekaran, P. (2021). Are we in right path for mediation analysis? Reviewing the literature and proposing robust guidelines. Journal of Hospitality and Tourism Management, 48, 395–405.

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.

Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial least squares structural equation modeling. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of market research. Springer.

Sharma, P. N., Shmueli, G., Sarstedt, M., Danks, N., & Ray, S. (2021). Prediction-oriented model selection in partial least squares path modeling. Decision Sciences, 52(3), 567–607.

Cheah, J., Ramayah, T., Memon, M. A., Chuah, F., & Ting, H. (2020). Multigroup analysis using SmartPLS: Step-by-step guidelines for business research. Asian Journal of Business Research, 10(3), 1–19.

Chin, W., Cheah, J.-H., Liu, Y., Ting, H., Lim, X.-J., &, & Cham Tat, H. (2020). Demystifying the role of causal-predictive modeling using partial least squares structural equation modeling in information systems research. Industrial Management & Data Systems, 120(12), 2161–2209.

Cho, G., Hwang, H., Sarstedt, M., & Ringle, C. M. (2020). Cutoff criteria for overall model fit indexes in generalized structured component analysis. Journal of Marketing Analytics, 8(4), 189–202.

García-Fernández, J., Fernández-Gavira, J., Sánchez-Oliver, A. J., Gálvez-Ruíz, P., Grimaldi-Puyana, M., & Cepeda-Carrion, G. (2020). Importance-performance matrix analysis (IPMA) to evaluate servicescape fitness consumer by gender and age. International Journal of Environmental Research and Public Health, 17(18), 1–19.

Hair, J. F. (2020). Next-generation prediction metrics for composite-based PLS-SEM. Industrial Management & Data Systems, 121(1), 5–11.

Hair, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. Also take a look here: https://www.unibw.de/ciss-en/methodpaper-nitzl-et-al.

Hwang, H., Sarstedt, M., Cheah, J. H., & Ringle, C. M. (2020). A concept analysis of methodological research on composite-based structural equation modeling: Bridging PLSPM and GSCA. Behaviormetrika, 47, 219–241.

Rhemtulla, M., van Bork, R., & Borsboom, D. (2020). Worse than measurement error: Consequences of inappropriate latent variable measurement models. Psychological Methods, 25(1), 30–45.

Richter, N. F., Schubring, S., Hauff, S., Ringle, C. M., & Sarstedt, M. (2020). When predictors of outcomes are necessary: Guidelines for the combined use of PLS-SEM and NCA. Industrial Management & Data Systems, 120(12), 2243–2267.

Rigdon, E. E., Sarstedt, M., & Becker, J.-M. (2020). Quantify uncertainty in behavioral research. Nature Human Behaviour, 4, 329–331.

Sarstedt, M., Ringle, C. M., Cheah, J. H., Ting, H., Moisescu, O. I., & Radomir, L. (2020). Structural model robustness checks in PLS-SEM. Tourism Economics, 26(4), 531–554.

Cepeda-Carrion, G., Cegarra-Navarro, J. G., & Cillo, V. (2019). Tips to use partial least squares structural equation modelling (PLS-SEM) in knowledge management. Journal of Knowledge Management, 23(1), 67–89.

Cheah, J.-H., Ting, H., Ramayah, T., Memon, M. A., Cham, T.-H., & Ciavolino, E. (2019). A comparison of five reflective–formative estimation approaches: Reconsideration and recommendations for tourism research. Quality & Quantity, 53, 1421–1458.

Hair, J. F., Ringle, C. M., Gudergan, S. P., Fischer, A., Nitzl, C., & Menictas, C. (2019). Partial least squares structural equation modeling-based discrete choice modeling: An illustration in modeling retailer choice. Business Research, 12, 115–140.

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). Factors versus composites: Guidelines for choosing the right structural equation modeling method. Project Management Journal, 50(6), 619–624.

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). Internet Research, 29(3), 407–429.

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–ix.

Rigdon, E. E., Becker, J.-M., & Sarstedt, M. (2019). Factor indeterminacy as metrological uncertainty: Implications for advancing psychological measurement. Multivariate Behavioral Research, 54(3), 429–443.

Rigdon, E. E., Becker, J.-M., & Sarstedt, M. (2019). Parceling cannot reduce factor indeterminacy in factor analysis: A research note. Psychometrika, 84(3), 772–780.

Sarstedt, M., & Cheah, J.-H. (2019). Partial least squares structural equation modeling using SmartPLS: A software review. Journal of Marketing Analytics, 7, 196–202.

Sarstedt, M., Hair, J. F., Cheah, J.-H., Becker, J.-M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal (AMJ), 27(3), 197–211.

Sharma, P. N., Shmueli, G., Sarstedt, M., & Thiele, K. O. (2019). PLS-based model selection: The role of alternative explanations in MIS research. Journal of the Association for Information Systems, 20(4), 346–397.

Shiau, W.-L., Sarstedt, M., & Hair, J. F. (2019). Internet research using partial least squares structural equation modeling (PLS-SEM). Internet Research, 29(3), 398–406.

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.

Avkiran, N. K., & Ringle, C. M. (Eds.). (2018). Partial least squares structural equation modeling: Recent advances in banking and finance. Springer.

Becker, J.-M., Ringle, C. M., & Sarstedt, M. (2018). Estimating moderating effects in PLS-SEM and PLSc-SEM: Interaction term GenerationxData treatment. Journal of Applied Structural Equation Modeling, 2(2), 1–21.

Cheah, J.-H., Sarstedt, M., Ringle, C. M., Ramayah, T., & Ting, H. (2018). Convergent validity assessment of formatively measured constructs in PLS-SEM: On using single-item versus multi-item measures in redundancy analyses. International Journal of Contemporary Hospitality Management, 30(11), 3192–3210.

Hult, G. T. M., Hair, J. F., Proksch, D., Sarstedt, M., Pinkwart, A., & Ringle, C. M. (2018). Addressing endogeneity in international marketing applications of partial least squares structural equation modeling. Journal of International Marketing, 26(3), 1–21.

Memon, M. A., Cheah, J.-H., Ramayah, T., Ting, H., & Chuah, F. (2018). Mediation analysis: Issues and recommendations. Journal of Applied Structural Equation Modeling, 2(1), i–ix.

Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017). Mirror, mirror on the wall: A comparative evaluation of composite-based structural equation modeling methods. Journal of the Academy of Marketing Science, 45(5), 616–632.

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.

Rigdon, E. E., Sarstedt, M., & Ringle, C. M. (2017). On comparing results from CB-SEM and PLS-SEM. five perspectives and five recommendations. Marketing ZFP, 39(3), 4–16.

Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Partial least squares structural equation modeling. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of market research. Springer.

Becker, J.-M., & Ismail, I. R. (2016). Accounting for sampling weights in PLS path modeling: Simulations and empirical examples. European Management Journal, 34(6), 606–617.

Cepeda Carrión, G., Henseler, J., Ringle, C. M., & Roldán, J. L. (2016). Prediction-oriented modeling in business research by means of PLS path modeling. Journal of Business Research, 69(10), 4545–4551.

Evermann, J., & Tate, M. (2016). Assessing the predictive performance of structural equation model estimators. Journal of Business Research, 69(10), 4565–4582.

Hair, J. F., Sarstedt, M., Matthews, L., & Ringle, C. M. (2016). Identifying and treating unobserved heterogeneity with FIMIX-PLS: Part I - method. European Business Review, 28(1), 63–76.

Henseler, J., Hubona, G. S., &, & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 1–19.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3), 405–431.

Matthews, L., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2016). Identifying and treating unobserved heterogeneity with FIMIX-PLS: Part II – A case study. European Business Review, 28(2), 208–224.

Nitzl, C., Roldán, J. L., & Cepeda Carrión, G. (2016). Mediation analysis in partial least squares path modeling: Helping researchers discuss more sophisticated models. Industrial Management & Data Systems, 119(9), 1849–1864.

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), 589–97.

Ringle, C. M., & Sarstedt, M. (2016). Gain more insight from your PLS-SEM results: The importance-performance map analysis. Industrial Management & Data Systems, 119(9), 1865–1886.

Sarstedt, M., Diamantopoulos, A., & Salzberger, T. (2016). Should we use single items? Better not. Journal of Business Research, 69(8), 3199–3203.

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.

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.

Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2016). Guidelines for treating unobserved heterogeneity in tourism research: A comment on marques and reis (2015). Annals of Tourism Research, 57(March), 279–284.

Shiau, W.-L., & Chau, P. Y. K. (2016). Understanding behavioral intention to use a cloud computing classroom: A multiple model-comparison approach. Information & Management, 53(3), 355–365.

Schlittgen, R., Ringle, C. M., Sarstedt, M., & Becker, J.-M. (2016). Segmentation of PLS path models by iterative reweighted regressions. Journal of Business Research, 69(10), 4583–4592.

Schubring, S., Lorscheid, I., Meyer, M., & Ringle, C. M. (2016). The PLS agent: Predictive modeling with PLS-SEM and agent-based simulation. Journal of Business Research, 69(10), 4604–4612.

Shmueli, G., Ray, S., Velasquez Estrada, J. M., & Chatla, S. B. (2016). The elephant in the room: Evaluating the predictive performance of PLS models. Journal of Business Research, 69(10), 4552–4564.

Dijkstra, T. K., & Henseler, J. (2015). Consistent partial least squares path modeling. MIS Quarterly (MISQ), 39(2), 297–316.

Dijkstra, T. K., & Henseler, J. (2015). Consistent and asymptotically normal PLS estimators for linear structural equations. Computational Statistics & Data Analysis, 81(1), 10–23.

Henseler, J., Sarstedt, M., & Ringle, C. M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.

Dijkstra, T. K., & Schermelleh-Engel, K. (2014). Consistent partial least squares for nonlinear structural equation models. Psychometrika, 79(4), 585–604.

Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121.

Rigdon, E. E., Becker, J.-M., Rai, A., Ringle, C. M., Diamantopoulos, A., Karahanna, E., Straub, D., & Dijkstra, T. (2014). Conflating antecedents and formative indicators: A comment on Aguirre-Urreta and marakas. Information Systems Research, 25(4), 780–784.

Ringle, C. M., Sarstedt, M., & Schlittgen, R. (2014). Genetic algorithm segmentation in partial least squares structural equation modeling. OR Spectrum, 36(1), 251–276.

Sarstedt, M., Ringle, C. M., & Hair, J. F. (2014). PLS-SEM: Looking back and moving forward. Long Range Planning, 47(3), 132–137.

Becker, J.-M., Rai, A, , Ringle, C. M., & Völckner, F. (2013). Discovering unobserved heterogeneity in structural equation models to avert validity threats. MIS Quarterly (MISQ), 37(3), 665–694.

Ringle, C. M., Sarstedt, M., Schlittgen, R., & Taylor, C. R. (2013). PLS path modeling and evolutionary segmentation. Journal of Business Research, 66(9), 1318–1324.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1-2), 1–12.

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(2), 182–209.

Henseler, J., & Sarstedt, M (2013). Goodness-of-fit indices for partial least squares path modeling. Computational Statistics, 28(2), 565–580.

Becker, J.-M., Klein, K., & Wetzels, M. (2012). Hierarchical latent variable models in PLS-SEM: Guidelines for using reflective-formative type models. Long Range Planning, 45(5-6), 359–394.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2012). Partial least squares: The better approach to structural equation modeling? Long Range Planning, 45(5-6), 312–319.

Henseler, J. (2012). Why generalized structured component analysis is not universally preferable to structural equation modeling. Journal of the Academy of Marketing Science, 40(3), 402–413.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2012). Using partial least squares path modeling in international advertising research: Basic concepts and recent issues. In S. Okazaki (Ed.), Handbook of research in international advertising (pp. 252-276). Edward Elgar Publishing.

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., Ringle, C. M., Henseler, J., &, & Hair, J. F. (2012). On the emancipation of PLS-SEM: A commentary on rigdon (2012). Long Range Planning, 47(3), 154–160.

Dijkstra, T. K., & Henseler, J. (2011). Linear indices in nonlinear structural equation models: Best fitting proper indices and other composites. Quality & Quantity, 45(6), 1505–1518.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152.

Rigdon, E. E., Ringle, C. M., Sarstedt, M., & Gudergan, S. P. (2011). Assessing heterogeneity in customer satisfaction studies: Across industry similarities and within industry differences. In Advances in International Marketing (Vol. 22, pp. 169-194). Emerald.

Sarstedt, M., Becker, J.-M., Ringle, C. M., & Schwaiger, M. (2011). Uncovering and treating unobserved heterogeneity with FIMIX-PLS: Which model selection criterion provides an appropriate number of segments? Schmalenbach Business Review (sbr), 63(1), 34–62.

Sarstedt, M., Henseler, J., & Ringle, C. M. (2011). Multigroup analysis in partial least squares (PLS) path modeling: Alternative methods and empirical results. In Advances in International Marketing (Vol. 22, pp. 195-218). Emerald.

Dijkstra, T. K. (2010). Latent variables and indices: Herman Wold’s basic design and partial least squares. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications (Vol. II, pp. 23-46). Springer.

Henseler, J., & Chin, W. W. (2010). A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Structural Equation Modeling, 17(1), 82–109.

Henseler, J., & Fassott, G. (2010). Testing moderating effects in PLS path models: An illustration of available procedures. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications (Vol. II, pp. 713-735). Springer.

Rigdon, E. E., Ringle, C. M., & Sarstedt, M. (2010). Structural modeling of heterogeneous data with partial least squares. In N. K. Malhotra (Ed.), Review of Marketing Research (Vol. 7, pp. 255-296). Emerald.

Ringle, C. M., Sarstedt, M., & Mooi, E. A. (2010). Response-based segmentation using FIMIX-PLS: Theoretical foundations and an application to American Customer Satisfaction Index data. In R. Stahlbock, S. F. Crone, & S. Lessmann (Eds.), Annals of Information Systems (Vol. 8, pp. 19-49). Springer.

Sarstedt, M., & Ringle, C. M. (2010). Treating unobserved heterogeneity in PLS path modelling: A comparison of FIMIX-PLS with different data analysis strategies. Journal of Applied Statistics, 37(8), 1299–1318.

Reinartz, W. J., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing, 26(4), 332–344.

Gudergan, S., Ringle, C. M., Wende, S., & Will, A. (2008). Confirmatory tetrad analysis in PLS path modeling. Journal of Business Research, 61(12), 1238–1249.