The Composite Overfit Analysis Framework: Assessing the Out-of-Sample Generalizability of Construct-Based Models Using Predictive Deviance, Deviance Trees, and Unstable Paths
References
- (2013) Best-practice recommendations for defining, identifying, and handling outliers. Organ. Res. Methods 16(2):270–301.Crossref, Google Scholar
- (2019) Retire statistical significance. Nature 567(7748):305–307.Crossref, Google Scholar
- (2010) A survey of cross-validation procedures for model selection. Statist. Surveys 4:40–79.Crossref, Google Scholar
- (2005) Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, vol. 571 (John Wiley & Sons, New York).Google Scholar
- (1989) A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods. Biometrika 76(3):503–514.Crossref, Google Scholar
- (1998) The partial least squares approach to structural equation modeling. Marcoulides GA, ed. Modern Methods for Business Research (Lawrence Erlbaum Associates Publishers, Mahwah, NJ), 295–336.Google Scholar
- (2020) Underspecification presents challenges for credibility in modern machine learning. Preprint, submitted November 24, https://arxiv.org/abs/2011.03395.Google Scholar
- (2021) The piggy in the middle: The role of mediators in PLS-SEM-based prediction: A research note. ACM SIGMIS Database DATABASE Adv. Inform. Systems 52(SI):24–42.Google Scholar
- (2018) Predictions from partial least squares models. Ali F, Rasoolimanesh SM, Cobanoglu C, eds. Applying Partial Least Squares in Tourism and Hospitality Research (Emerald Publishing Limited, Bingley, UK), 35–52.Crossref, Google Scholar
- (1979) Distribution-free performance bounds for potential function rules. IEEE Trans. Inform. Theory 25(5):601–604.Crossref, Google Scholar
- TK (2009) Latent variables and indices: Herman Wold’s basic design and partial least squares. Handbook of Partial Least Squares: Concepts, Methods and Applications (Springer, Berlin, Heidelberg), 23–46.Google Scholar
- (2016) Assessing the predictive performance of structural equation model estimators. J. Bus. Res. 69(10):4565–4582.Crossref, Google Scholar
- Faul F, Erdfelder E, Buchner A, Lang AG (2009) Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behav. Res. Methods 41(4):1149–1160.Google Scholar
- (2011) An update and extension to SEM guidelines for administrative and social science research. MIS Quart. 35(2):iii–xiv.Crossref, Google Scholar
- (2020) Using outliers for theory building. Organ. Res. Methods 24(1):172–181.Crossref, Google Scholar
- (2010) Pursuing failure. Organ. Res. Methods 13(4):620–643.Crossref, Google Scholar
- (2006) The nature of theory in information systems. MIS. Quart. 30(3):611–642.Crossref, Google Scholar
- (2017a) An updated and expanded assessment of PLS-SEM in information systems research. Indust. Management Data Systems 117(3):442–458.Crossref, Google Scholar
- (2020) Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. J. Bus. Res. 109(2020):101–110.Crossref, Google Scholar
- (2019) Rethinking some of the rethinking of partial least squares. Eur. J. Marketing 53(4):566–584.Crossref, Google Scholar
- (2021) A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (Sage Publications, Thousand Oaks, CA).Crossref, Google Scholar
- (2017b) Mirror, mirror on the wall: A comparative evaluation of composite-based structural equation modeling methods. J. Acad. Marketing Sci. 45(5):616–632.Crossref, Google Scholar
- (2013) The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. (Springer, New York).Google Scholar
- (2017) Bridging design and behavioral research with variance-based structural equation modeling. J. Advertising 46(1):178–192.Crossref, Google Scholar
- (2016) Using PLS path modeling in new technology research: Updated guidelines. Indust. Management Data Systems 116(1):2–20.Crossref, Google Scholar
- (2014) Common beliefs and reality about PLS: Comments on Rönkkö and Evermann (2013). Organ. Res. Methods 17(2):182–209.Crossref, Google Scholar
- (2009) Regularized generalized structured component analysis. Psychometrika 74(3):517–530.Crossref, Google Scholar
- (2004) Generalized structured component analysis. Psychometrika 69(1):81–99.Crossref, Google Scholar
- (2013) An Introduction to Statistical Learning (Springer, New York).Crossref, Google Scholar
- (1996) LISREL 8: User’s Reference Guide (Scientific Software, Chicago).Google Scholar
- (2010) Variations of the kanban system: Literature review and classification. Internat. J. Production Econom. 125(1):13–21.Crossref, Google Scholar
- (1964) The Conduct of Inquiry: Methodology for Behavioral Science (Chandler Publishing, New York).Google Scholar
- Kock N, Hadaya P (2018) Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Inform. Systems J. 28(1):227–261.Google Scholar
- (2008) Isolation forest. 2008 Eighth IEEE Internat. Conf. Data Mining (IEEE Computer Society, Washington, DC), 413–422.Google Scholar
- (1998) Forecasting: Methods and Applications, 3rd ed. (Wiley, New York).Google Scholar
- (1983) A contextualist theory of knowledge: Its implications for innovation and reform in psychological research. Adv. Experiment. Soc. Psych. 16(1983):1–47.Google Scholar
- (1989) A Perspectivist Approach to the Strategic Planning of Programmatic Scientific Research (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
- (2014) Reflections on partial least squares path modeling. Organ. Res. Methods 17(2):210–251.Crossref, Google Scholar
- (2012) Introduction to Linear Regression Analysis, vol. 821 (John Wiley & Sons, New York).Google Scholar
- (2010) The Power of Positive Deviance: How Unlikely Innovators Solve the World’s Toughest Problems (Harvard Business School Press, Boston).Google Scholar
- (2011) Sensitivity analysis in structural equation models: Cases and their influence. Multivariate Behav. Res. 46(2):202–228.Crossref, Google Scholar
- (2009) Data Set Shift in Machine Learning (MIT Press, Cambridge, MA).Google Scholar
- Ray S, Danks NP, Calero Valdez A, (2022) seminr: Building and Estimating Structural Equation Models. R package version 2.3.2. Accessed February 7, 2023, https://cran.r-project.org/web/packages/seminr/.Google Scholar
- R Core Team (2022) The R project for statistical computing. R Foundation for Statistical Computing, Vienna. Accessed February 7, 2023, https://www.R-project.org/.Google Scholar
- (2012) Rethinking partial least squares path modeling: In praise of simple methods. Long Range Planning 45(5–6):341–358.Crossref, Google Scholar
- (2012) Editor’s comments: A critical look at the use of PLS-SEM in MIS Quarterly. MIS Quart. 36(1):iii–xiv.Crossref, Google Scholar
- (2016) Partial least squares path modeling: Time for some serious second thoughts. J. Oper. Management 47–48(2016):9–27.Crossref, Google Scholar
- (2016) Estimation issues with PLS and CBSEM: Where the bias lies! J. Bus. Res. 69(10):3998–4010.Crossref, Google Scholar
- (2019) cbsem: Simulation, estimation and segmentation of composite based structural equation models. R package version 1.0.0. Accessed January 22, 2020, https://cran.r-project.org/web/packages/cbsem/index.html.Google Scholar
- (2020) Data generation for composite-based structural equation modeling methods. Adv. Data Anal. Classification 14(2020):747–757.Crossref, Google Scholar
- (2019) PLS-based model selection: The role of alternative explanations in information systems research. J. Assoc. Inform. Systems 20(4):4.Google Scholar
- (2021) Prediction‐oriented model selection in partial least squares path modeling. Decision Sci. 52(3):567–607.Crossref, Google Scholar
- (2010) To explain or to predict? Statist. Sci. 25(3):289–310.Crossref, Google Scholar
- (2011) Predictive analytics in information systems research. MIS Quart. 35(3):553–572.Crossref, Google Scholar
- (2016) The elephant in the room: Predictive performance of PLS models. J. Bus. Res. 69(10):4552–4564.Crossref, Google Scholar
- (2019) Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. Eur. J. Marketing 53(11):2322–2347.Crossref, Google Scholar
- (2004) Toward the construct definition of positive deviance. Amer. Behav. Sci. 47(6):828–847.Crossref, Google Scholar
- (1996) Dispelling some myths about factor indeterminacy. Multivariate Behav. Res. 31(4):539–550.Crossref, Google Scholar
- (2019) From development to deployment: Data set shift, causality, and shift-stable models in health AI. Biostatistics 11(19):345–352.Google Scholar
- (2011) Regularized generalized canonical correlation analysis. Psychometrika 76(2):257–284.Crossref, Google Scholar
- (2010) Structural equation modeling in information systems research using partial least squares. J. Inform. Tech. Theory Appl. 11(2):5–40.Google Scholar
- (2000) A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Sci. 46(2):186–204.Link, Google Scholar
- (2012) Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quart. 36(1):157–178.Crossref, Google Scholar
- (2012) Exceptional boards: Environmental experience and positive deviance from institutional norms. J. Organ. Behav. 34(2):253–271.Crossref, Google Scholar
- (2017) Data Mining Practical Machine Learning Tools and Techniques, 3rd ed. (Morgan Kaufmann, Burlington, MA).Google Scholar
- (1975) Path models with latent variables: The NIPALS approach. Blalock HM, Aganbegian A, Borodkin FM, Boudon R, Capecchi V, eds. Quantitative Sociology: International Perspective on Mathematical and Statistical Modeling (Academic Press, Cambridge, MA), 307–357.Crossref, Google Scholar
- (1982) Soft modeling: The basic design and some extensions. Joreskog KG, Wold HOA, eds. Systems Under Indirect Observations: Part II (North-Holland, Amsterdam), 1–54.Google Scholar
- (1990) Positive Deviance in Child Nutrition: With Emphasis on Psychosocial and Behavioural Aspects and Implications for Development (United Nations University, Tokyo).Google Scholar
- (2015) Cross-validation for selecting a model selection procedure. J. Econometrics 187(1):95–112.Crossref, Google Scholar

