Recommended Readings

Take a look at the following articles!

OPEN ACCESS! Longitudinal PLS-SEM: 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.

OPEN ACCESS! Online Shopping - Do Showroomers Differ?: Magno, F., Cassia, F., & Ringle, C. M. (in press). From an online shopping experience to online purchase intentions: Do showroomers differ? A study of a brand-owned D2C e-commerce store. Journal of Marketing Theory and Practice.

OPEN ACCESS! Linear and Logistic Regression Using SmartPLS: Margalina, V.-M., Kreienbaum, C., Hair, J. F., Becker, J.-M., & Ringle, C. M. (2026). Multiple linear and logistic regression analysis: A SmartPLS 4 software tutorial. Journal of Marketing Analytics.

OPEN ACCESS! Consistent PLS-SEM (PLSc-SEM) in SmartPLS: 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.

Multimethod SEM: 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.

Conditional mediation and moderated mediation: 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.

OPEN ACCESS! Multimethod SEM: 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.

OPEN ACCESS! Sensitivity-based extension to NCA: 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.

OPEN ACCESS! CB-SEM using SmartPLS: Bido, D.d.S., & Souza, C. A. (2026). Structural equation modeling: Is it still worth learning? Brazilian Administration Review, 23(3), Article e260020.

OPEN ACCESS! Moderated-meditation and conditional process analysis: 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.

OPEN ACCESS! New scale, PLS-SEM, and NCA: Rahman, S. M., Carlson, J., Chowdhury, N. H., Gudergan, S. P., Wetzels, M., Ringle, C. M., & Grewal, D. (2026). Omnichannel safe customer experience: How should it be measured? Does it affect customer well-being and retailers’ performance? Journal of Business Research, 202, Article 115760.

Ecological Research using PLS-SEM: Adeyeye, O. A., Hassaan, A. M., Yonas, M. W., Yawe, A. S., Nwankwegu, A. S., Yang, G., Yao, X., Song, Z., Kong, Y., Bai, G., & Zhang, L. (2026). The emerging application of partial least squares structural equation modelling in ecological research: An introductory overview. Environmental Modelling & Software, 202, Article 106988.

PLS-SEM in business research: Widaryanti, Wan, A., Sitawati, R., & Luhgiatno (2026). Exploring the application of PLS-SEM in business, management, and accounting research: A bibliometric approach. Quality & Quantity, 60, 2407–2434.

OPEN ACCESS! CB-SEM using SmartPLS tutorial: 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.

OPEN ACCESS! Word-of-Mouth: Moisescu, O.-I., Gică, O.-A., Herle, F.-A., Dan, I., & Sarstedt, M. (2025). Does one size fit all? The role of extraversion in generating electronic word-of-mouth through social media brand page engagement. Psychology & Marketing, 42(7), 1827–1847.

Missing values in PLS-SEM: 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.

PLS-SEM in the handbook of market research: Sarstedt, M., Ringle, C. M., & Hair, J. F. (2025). Partial least squares structural equation modeling. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of market research (pp. 1–56). Springer Nature Switzerland.

OPEN ACCESS! PLS-SEM research: 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.

Advances in PLS-SEM: 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.

PLS-SEM in supply chain management: Abbas, H., Salem, I. E., Akram, H. W., & Abbas, S. (2025). Exploring partial least squares structural equation modeling (PLS-SEM) applications in supply chain research: A bibliometric analysis and science mapping approach. Operations Research Forum, 6(142), 1–29.

OPEN ACCESS! Endogeneity and the Gaussian copula approach: 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.

PLS-SEM in retail market and consumer behavior research: Cheah, J.-H., & Hair, J. F. (2025). Explaining and predicting new retail market and consumer behavior habits using partial least squares structural equation modeling (PLS-SEM). Journal of Retailing and Consumer Services, 87, Article 104446.

OPEN ACCESS! PLS-SEM in HRM plus hierarchical component models: 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.

OPEN ACCESS! PLS-SEM and reflective constructs: 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.

OPEN ACCESS! CTA causal relations detection: 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.

OPEN ACCESS! Methodological uncertainty: 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.

OPEN ACCESS! Moving PLS-SEM forward: 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.

OPEN ACCESS! Shortcomings of equal weights: 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.

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

A permutation-based MGA approach for longitudinal data in PLS-SEM: Söllner, M., Mishra, A. N., Becker, J.-M., & Leimeister, J. M. (2024). Use IT again? Dynamic roles of habit, intention and their interaction on continued system use by individuals in utilitarian, volitional contexts. European Journal of Information Systems, 33(1), 80–96.

Replicability and Prediction: 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.

Composite-Based structural equation modeling: 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.

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

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

OPEN ACCESS! Review of the advanced PLS-SEM book (2e): 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.

NCA, PLS-SEM, and IPMA in combination: 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.

OPEN ACCESS! cIPMA in SmartPLS: 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, 12, 746–760.

Review of PLS-SEM studies in quality management: Magno, F., Cassia, F., & Ringle, C. M. (2024). A brief review of partial least squares structural equation modeling (PLS-SEM) use in quality management studies. The TQM Journal, 36(5), 1242–1251.

SmartPLS 4 software review article: 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.

PLS-SEM and machine learning: 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.

Mixed methods and PLS-SEM: 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.

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

PLS-SEM robustness checks: 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.

PLS-SEM in logistics and supply chain management: Wang, S., Cheah, J.-H., Wong, C. Y., & Ramayah, T. (2024). Progress in partial least squares structural equation modeling use in logistics and supply chain management in the last decade: A structured literature review. International Journal of Physical Distribution & Logistics Management, 54(7/8), 673–704.

PLS-SEM in human resource management: 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.

On the use of PLS-SEM in research articles: Petter, S., & Hadavi, Y. (2023). Use of partial least squares path modeling within and across business disciplines. In H. Latan, J. F. Hair, & R. Noonan (Eds.), Partial least squares path modeling: Basic concepts, methodological issues and applications (pp. 55–79). Springer International Publishing.

PLS-SEM in Information Systems: 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.

NCA in SmartPLS: 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.

Insightful arguments for the use of PLS-SEM: 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.

Weighted composites vs. CB-SEM: Deng, L., & Yuan, K.-H. (2023). Which method is more powerful in testing the relationship of theoretical constructs? A meta comparison of structural equation modeling and path analysis with weighted composites. Behavior Research Methods, 76(3), 646–678.

PLS-SEM and open science: 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.

OPEN ACCESS! On the use of PLS-SEM in business marketing research: 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.

Predictive power assessment in PLS-SEM: 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.

The legendary silver bullet: 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.

On the usefulness of PLS-SEM: 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.

Extraordinary claims: 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.

Most wanted PLS-SEM guidelines: 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.

Predictive power of components: 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.

OPEN ACCESS! On the use of PLS-SEM and HTMT update: 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.

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

OPEN ACCESS! American customer satisfaction index (ACSI) model data: 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.

OPEN ACCESS! Extended TAM data: 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.

PLS-SEM and prediction solutions: 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.

PLS-SEM in hospitality and tourism research: Liu, Y., Ting, H., & Ringle, C. (2023). Appreciation to and behavior intention regarding upscale ethnic restaurants. Journal of Hospitality & Tourism Research, 47(1), 235–256.

Review of PLS-SEM applications in marketing studies: 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.

PLS-SEM and NCA: Duarte, P., Silva, S. C., Linardi, M. A., & Novais, B. (2022). Understanding the implementation of retail self-service check-out technologies using necessary condition analysis. International Journal of Retail & Distribution Management, 50(13), 140–163.

PLS-SEM in international management research: Richter, N. F., Hauff, S., Ringle, C. M., & Gudergan, S. P. (2022). The use of partial least squares structural equation modeling and complementary methods in international management research. Management International Review, 62, 449–470.

PLS-SEM use in education research: Hair, J. F., & Alamer, A. (2022). Partial least squares structural equation modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), Article 100027.

Nonlinear relationships: 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.

Top-journal PLS-SEM application: Rahman, S. M., Carlson, J., Gudergan, S., Wetzels, M., & Grewal, D. (2022). Perceived omnichannel customer experience (OCX): Concept, measurement, and impact. Journal of Retailing, 98(4), 611–632. The web appendix shows how to report SmartPLS results

Search for the best model: 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.

Model fit: 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.

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

PLS-SEM in Sports Management: Cepeda-Carrión, G., Hair, J. F., Ringle, C. M., Roldán, J. L., & García-Fernández, J. (2022). Guest editorial: Sports management research using partial least squares structural equation modeling (PLS-SEM). International Journal of Sports Marketing and Sponsorship, 23(2), 229–240.

OPEN ACCESS! Endogeneity and Gaussian copula: 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.

Uncovering unobserved heterogeneity: 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.

The power of PLS-SEM: Petter, S., & Hadavi, Y. (2021). With great power comes great responsibility: The use of partial least squares in information systems research. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 52, 10–23.

Mediation and prediction: Danks, N. (2021). The piggy in the middle: The role of mediators in PLS-SEM-based prediction. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 52, 24–42.

Conditional mediation analysis: Cheah, J. H., Nitzl, C., Roldán, J. L., Cepeda Carrión, 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, 43–100.

Reflections on PLS-SEM: Hair, J. F. (2021). Reflections on SEM: An introspective, idiosyncratic journey to composite-based structural equation modeling. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 52, 101–113.

New PLS-SEM handbook article: 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.

PLS-SEM comparison: Yuan, K.-H., & Deng, L. (2021). Equivalence of partial-least-squares SEM and the methods of factor-score regression. Structural Equation Modeling: A Multidisciplinary Journal, 28(4), 557–571.

Update on PLS-SEM: 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.

PLS-SEM and fsQCA: 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.

PLS-SEM "how to" in entrepreneurship: 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.

Weighted PLS-SEM (WPLS): Cheah, J.-H., 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.

Predictive model selection test: 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.

Predictive model selection: 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.

PLS-SEM software review: 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.

Higher-order models: 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.

PLS-SEM in software engineering: Russo, D., & Stol, K.-J. (2021). PLS-SEM for software engineering research: An introduction and survey. ACM Computing Surveys, 54(4), 1–38.

Mediation analysis: 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.

PLS-SEM and binary data: Van der Schyff, K., Flowerday, S., & Lowry, P. (2020). Information privacy behavior in the use of Facebook apps: A personality-based vulnerability assessment. Heliyon, 6(8), 1–13.

Mediation and no need for PROCESS: 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.

PLS-SEM and NCA: 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.

PLS-SEM in Higher Education: 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.

PLS-SEM and future time perspectives: Chaouali, W., Souiden, N., & Ringle, C. M. (2020). Elderly customers' reactions to service failures: The role of future time perspective, wisdom and emotional intelligence. Journal of Services Marketing, 35(1), 65–77.

PLS-SEM in Operations Management: Bayonne, E., Marin-Garcia, J. A., & Alfalla-Luque, R. (2020). Partial least squares (PLS) in operations management research: Insights from a systematic literature review. Journal of Industrial Engineering and Management, 13(3).

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

SmartPLS multigroup analysis: 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.

Causal-predictive PLS-SEM: 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.

Necessary condition analysis (NCA) and PLS-SEM: 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.

PLS-SEM results assessment: 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.

Model fit: 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.

PLS-SEM and GSCA: 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.

IPMA application in hospitality management: Nunkoo, R., Teeroovengadum, V., Ringle, C. M., & Sunnassee, V. (2020). Service quality and customer satisfaction: The moderating effects of hotel star rating. International Journal of Hospitality Management, 91, Article 102414.

More common factor issues: 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.

Different views on CCA: Crittenden, V., Sarstedt, M., Astrachan, C., Hair, J. F., & Lourenco, C. E. (2020). Guest editorial: Measurement and scaling methodologies. Journal of Product & Brand Management, 29(4), 409–414.

CCA: 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

PLS-SEM in HRM: Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2020). Partial least squares structural equation modeling in HRM research. The International Journal of Human Resource Management, 31(12), 1617–1643.

Common factor issue: 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.

PLS-SEM software review: Sarstedt, M., & Cheah, J.-H. (2019). Partial least squares structural equation modeling using SmartPLS: A software review. Journal of Marketing Analytics, 7(3), 196–202.

Higher-order models: 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, 27(3), 197–211.

PLS-SEM in knowledge management: 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.

How to use PLSpredict?!: 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.

PLS-SEM results reporting: 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.

Some rethinking of the PLS-SEM rethinking: 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.

PLS-SEM research networks: Khan, G. F., Sarstedt, M., Shiau, W. L., Hair, J. F., Ringle, C. M., & Fritze, M. P. (2019). Methodological research on partial least squares structural equation modeling (PLS-SEM): An analysis based on social network approaches. Internet Research, 29(3), 407–429.

More on predictive model selection: 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.

Nonlinear effect in PLS-SEM: Ahrholdt, D. C., Gudergan, S., & Ringle, C. M. (2019). Enhancing loyalty: When improving consumer satisfaction and delight matters. Journal of Business Research, 94(1), 18–27.

PLS-SEM in environmental management: Kotilainen, K., Saari, U. A., Mäkinen, S. J., & Ringle, C. M. (2019). Exploring the microfoundations of end-user interests toward co-creating renewable energy technology innovations. Journal of Cleaner Production, 229, 203–212.

Formative and reflective measurement: 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.

Moderation analysis: 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.

OPEN ACCESS! PLS-SEM and binary data: 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, 115–142.

Something for PLS-SEM haters: Petter, S. (2018). "Haters gonna hate": PLS and information systems research. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 49(2), 10–13.

Convergent validity: 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.

Patient satisfaction: Rosenbusch, J., Ismail, I. R., & Ringle, C. M. (2018). The agony of choice for medical tourists: A patient satisfaction index model. Journal of Hospitality and Tourism Technology, 9(3), 267–279.

Endogeneity in PLS-SEM: 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.

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

PLS-SEM in hospitality research: Ali, F., Rasoolmanesh, S. M., Sarstedt, M., Ringle, C. M., & Ryu, K. (2018). An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. The International Journal of Contemporary Hospitality Management, 30(1), 514–538.

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

Mediation analysis: 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.

Mediation analysis: Cepeda Carrión, G., Nitzl, C., & Roldán, J. L. (2017). Mediation analyses in partial least squares structural equation modeling: Guidelines and empirical examples. In H. Latan & R. Noonan (Eds.), Partial least squares path modeling: Basic concepts, methodological issues and applications (pp. 173–195). Springer.

Segmentation: Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Treating unobserved heterogeneity in PLS-SEM: A multi-method approach. In H. Latan & R. Noonan (Eds.), Partial least squares path modeling: Basic concepts, methodological issues and applications (pp. 197–217). Springer.

OPEN ACCESS! CB-SEM and PLS-SEM: 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.

PLS-SEM performance: 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.

Prediction: 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.

PLS-SEM: 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.

OPEN ACCESS! CB-SEM and PLS-SEM: 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.

Mediation: 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.

Importance-performance map (IPMA): 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.

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

Weighted PLS: 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.

FIMIX-PLS segmentation: 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.

FIMIX-PLS tutorial: 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.

Dynamic PLS: 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.

Weighted regression segmentation: 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.

OPEN ACCESS! HTMT: 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.

FIMIX-PLS: Wilden, R., & Gudergan, S. P. (2015). The impact of dynamic capabilities on operational marketing and technological capabilities: Investigating the role of environmental turbulence. Journal of the Academy of Marketing Science, 43(2), 181–199.

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

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

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

The silver bullet!: 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.