Estimating and assessing second-order constructs using PLS-PM: the case of composites of composites
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Florian Schuberth | Jörg Henseler | Manuel E. Rademaker | J. Henseler | Florian Schuberth | M. Rademaker
[1] Jan-Bernd Lohmöller,et al. Latent Variable Path Modeling with Partial Least Squares , 1989 .
[2] Theo K. Dijkstra,et al. On the extraction of weights from pairwise comparison matrices , 2013, Central Eur. J. Oper. Res..
[3] P. Bentler,et al. Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .
[4] Jose Benitez-Amado,et al. IT-enabled knowledge ambidexterity and innovation performance in small U.S. firms: The moderator role of social media capability , 2018, Inf. Manag..
[5] Florian Schuberth,et al. Using confirmatory composite analysis to assess emergent variables in business research , 2020, Journal of Business Research.
[6] S. Mulaik,et al. EVALUATION OF GOODNESS-OF-FIT INDICES FOR STRUCTURAL EQUATION MODELS , 1989 .
[7] Detmar W. Straub,et al. Common Beliefs and Reality About PLS , 2014 .
[8] Sang M. Lee,et al. Green supply chain management and organizational performance , 2012, Ind. Manag. Data Syst..
[9] Marko Sarstedt,et al. How to Specify, Estimate, and Validate Higher-Order Constructs in PLS-SEM , 2019, Australasian Marketing Journal.
[10] Andreas Ritter,et al. Structural Equations With Latent Variables , 2016 .
[11] Wynne W. Chin,et al. On the use, usefulness, and ease of use of structural equation modeling in MIS research: a note of caution , 1995 .
[12] K. Law,et al. Multidimensional constructs in structural equation analysis: An illustration using the job perception and job satisfaction constructs , 1999 .
[13] Jason Bennett Thatcher,et al. Conceptualizing models using multidimensional constructs: a review and guidelines for their use , 2012, Eur. J. Inf. Syst..
[14] Elena Karahanna,et al. Time Flies When You're Having Fun: Cognitive Absorption and Beliefs About Information Technology Usage , 2000, MIS Q..
[15] Meng U. Taing,et al. Recommendations for improving the construct clarity of higher-order multidimensional constructs , 2012 .
[16] J. Henseler. Bridging Design and Behavioral Research With Variance-Based Structural Equation Modeling , 2017 .
[17] Paulo Duarte,et al. Methods for modelling reflective-formative second order constructs in PLS , 2018, Journal of Hospitality and Tourism Technology.
[18] Ainin Sulaiman,et al. Understanding impulse purchase in Facebook commerce: does Big Five matter? , 2017, Internet Res..
[19] Fujun Lai,et al. Information orientation and its impacts on information asymmetry and e-business adoption: Evidence from China's international trading industry , 2006, Ind. Manag. Data Syst..
[20] P. Bentler,et al. Comparative fit indexes in structural models. , 1990, Psychological bulletin.
[21] Jörg Henseler,et al. Modeling Reflective Higher-Order Constructs using Three Approaches with PLS Path Modeling: A Monte Carlo Comparison , 2007 .
[22] Jacob Cohen,et al. Problems in the Measurement of Latent Variables in Structural Equations Causal Models , 1990 .
[23] Geoffrey S. Hubona,et al. Using PLS path modeling in new technology research: updated guidelines , 2016, Ind. Manag. Data Syst..
[24] Edward E. Rigdon,et al. Comment on “Improper use of endogenous formative variables” , 2014 .
[25] Laura Guitart Tarrés,et al. Impact of human resources on supply chain management and performance , 2015, Ind. Manag. Data Syst..
[26] K. Jöreskog. A General Method for Estimating a Linear Structural Equation System. , 1970 .
[27] Thurasamy Ramayah,et al. A comparison of five reflective–formative estimation approaches: reconsideration and recommendations for tourism research , 2018, Quality & Quantity.
[28] H. Akaike,et al. Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .
[29] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[30] James H. Steiger,et al. Understanding the limitations of global fit assessment in structural equation modeling , 2007 .
[31] Alain Yee-Loong Chong,et al. An updated and expanded assessment of PLS-SEM in information systems research , 2017, Ind. Manag. Data Syst..
[32] Shawn Bauldry,et al. Three Cs in measurement models: causal indicators, composite indicators, and covariates. , 2011, Psychological methods.
[33] Galit Shmueli,et al. To Explain or To Predict? , 2010, 1101.0891.
[34] Florian Schuberth,et al. How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research , 2020, Inf. Manag..
[35] A. Basilevsky,et al. Factor Analysis as a Statistical Method. , 1964 .
[36] Dianne M. Finkelstein,et al. A Beginner's Guide to Structural Equation Modeling , 2005, Technometrics.
[37] Edward E. Rigdon,et al. Rethinking Partial Least Squares Path Modeling: In Praise of Simple Methods , 2012 .
[38] Michel Tenenhaus,et al. PLS path modeling , 2005, Comput. Stat. Data Anal..
[39] Jörg Henseler,et al. Confirmatory Composite Analysis , 2018, Front. Psychol..
[40] Jacob Cohen,et al. A power primer. , 1992, Psychological bulletin.
[41] Jacob Cohen. Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.
[42] Huseyin Uzunboylu,et al. The life satisfaction of teachers at work place, research of structural equation modelling regarding general and organized cynicism , 2018 .
[43] Gaby Odekerken-Schröder,et al. Using PLS path modeling for assessing hierarchial construct models: guidelines and impirical illustration , 2009 .
[44] Jörg Henseler,et al. Consistent and asymptotically normal PLS estimators for linear structural equations , 2014 .
[45] Florian Schuberth,et al. Investigating the effects of tourist engagement on satisfaction and loyalty , 2019, The Service Industries Journal.
[46] Byunghak Leem,et al. The effect of the supply chain social capital , 2013, Ind. Manag. Data Syst..
[47] Pratyush Nidhi Sharma,et al. Prediction: Coveted, Yet Forsaken? Introducing a Cross-Validated Predictive Ability Test in Partial Least Squares Path Modeling , 2020, Decis. Sci..
[48] Florian Schuberth,et al. cSEM: Composite-Based Structural Equation Modeling , 2020 .
[49] P. Barrett. Structural equation modelling : Adjudging model fit , 2007 .
[50] T Raykov,et al. On Structural Equation Model Equivalence. , 1999, Multivariate behavioral research.
[51] W. G. Brown,et al. Multicollinearity problems and ridge regression in sociological models , 1975 .
[52] S. Ross,et al. An exploration of motives in sport video gaming , 2006 .
[53] Bradley Wilson,et al. Using PLS to Investigate Interaction Effects Between Higher Order Branding Constructs , 2010 .
[54] Joe F. Hair,et al. Assessing measurement model quality in PLS-SEM using confirmatory composite analysis , 2020 .
[55] K. Jöreskog. A general approach to confirmatory maximum likelihood factor analysis , 1969 .
[56] F. Bookstein,et al. Two Structural Equation Models: LISREL and PLS Applied to Consumer Exit-Voice Theory , 1982 .
[57] Susana Pérez López,et al. Information Technology Competency, Knowledge Processes and Firm Performance , 2012, Ind. Manag. Data Syst..
[58] Jose Benitez-Amado,et al. How information technology influences opportunity exploration and exploitation firm's capabilities , 2018, Inf. Manag..
[59] Wen-Lung Shiau,et al. Methodological research on partial least squares structural equation modeling (PLS-SEM) , 2019, Internet Res..
[60] J. Edwards. Multidimensional Constructs in Organizational Behavior Research: An Integrative Analytical Framework , 2001 .
[61] Joseph F. Hair,et al. When to use and how to report the results of PLS-SEM , 2019, European Business Review.
[62] Jörg Henseler,et al. Investigating the moderating role of fit on sports sponsorship and brand equity. , 2007 .
[63] Taeke Klaas Dijkstra. Latent variables in linear stochastic models , 1981 .
[64] Martin Wetzels,et al. Hierarchical latent variable models in PLS-SEM: guidelines for using reflective-formative type models , 2012 .
[65] Stanley A. Mulaik,et al. First Order or Higher Order General Factor , 1997 .
[66] R. Stine,et al. Bootstrapping Goodness-of-Fit Measures in Structural Equation Models , 1992 .
[67] Li-Chun Hsu,et al. Investigating community members' purchase intention on Facebook fan page: From a dualistic perspective of trust relationships , 2017, Ind. Manag. Data Syst..
[68] Kenneth W. Green,et al. The impact of JIT-II-selling on organizational performance , 2007, Ind. Manag. Data Syst..
[69] Scott E. Maxwell,et al. Multivariate group comparisons of variable systems: MANOVA and structural equation modeling. , 1993 .
[70] Theo K. Dijkstra,et al. A Perfect Match Between a Model and a Mode , 2017 .
[71] Taehun Kim,et al. The effect of knowledge on system integration project performance , 2008, Ind. Manag. Data Syst..
[72] D. Straub,et al. Editor's comments: a critical look at the use of PLS-SEM in MIS quarterly , 2012 .
[73] P. Bentler,et al. Significance Tests and Goodness of Fit in the Analysis of Covariance Structures , 1980 .
[74] Edward E. Rigdon,et al. Choosing PLS path modeling as analytical method in European management research: A realist perspective , 2016 .
[75] Jörg Henseler,et al. Testing Moderating Effects in PLS Path Models. An Illustration of Available Procedures , 2005 .
[76] Pratyush Nidhi Sharma,et al. Model selection uncertainty and multimodel inference in partial least squares structural equation modeling (PLS-SEM) , 2020 .
[77] K. Yuan. Fit Indices Versus Test Statistics , 2005, Multivariate behavioral research.
[78] Jörg Henseler,et al. Estimating hierarchical constructs using consistent partial least squares: The case of second-order composites of common factors , 2017, Ind. Manag. Data Syst..
[79] Herman Wold,et al. Soft modelling: The Basic Design and Some Extensions , 1982 .
[80] J. Henseler. Partial least squares path modeling: Quo vadis? , 2018, Quality & Quantity.
[81] Nastaran Hajiheydari,et al. Mobile application user behavior in the developing countries: A survey in Iran , 2018, Inf. Syst..
[82] Rex B. Kline,et al. Principles and Practice of Structural Equation Modeling , 1998 .
[83] Nick Lee,et al. Improper use of endogenous formative variables , 2013 .
[84] Marko Sarstedt,et al. Rethinking some of the rethinking of partial least squares , 2019, European Journal of Marketing.
[85] Pratyush Nidhi Sharma,et al. PLS-Based Model Selection: The Role of Alternative Explanations in Information Systems Research , 2019, J. Assoc. Inf. Syst..
[86] R. Beran,et al. Bootstrap Tests and Confidence Regions for Functions of a Covariance Matrix , 1985 .
[87] Yu Zhou,et al. Organizational use of the internet: Scale development and validation , 2005, Internet Res..
[88] Ainin Sulaiman,et al. Factors influencing the use of social media by SMEs and its performance outcomes , 2015, Ind. Manag. Data Syst..
[89] T. Dijkstra,et al. Consistent Partial Least Squares for Nonlinear Structural Equation Models , 2013, Psychometrika.
[90] Joel Waldfogel,et al. Introduction , 2010, Inf. Econ. Policy.