Using confirmatory composite analysis to assess emergent variables in business research
Tài liệu tham khảo
Akaike, 1998, Information theory and an extension of the maximum likelihood principle, 199
Anderson, 1988, Structural equation modeling in practice: A review and recommended two-step approach, Psychological Bulletin, 103, 411, 10.1037/0033-2909.103.3.411
Bagozzi, 1988, On the evaluation of structural equation models, Journal of the Academy of Marketing Science, 16, 74, 10.1007/BF02723327
Barrett, 2007, Structural equation modelling: Adjudging model fit, Personality and Individual Differences, 42, 815, 10.1016/j.paid.2006.09.018
Benítez-Ávila, 2018, Interplay of relational and contractual governance in public-private partnerships: The mediating role of relational norms, trust and partners’ contribution, International Journal of Project Management, 36, 429, 10.1016/j.ijproman.2017.12.005
Benitez, 2020, How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research, Information & Management, 2, 103168, 10.1016/j.im.2019.05.003
Benitez, 2018, How information technology influences opportunity exploration and exploitation firm’s capabilities, Information & Management, 55, 508, 10.1016/j.im.2018.03.001
Benitez, 2018, Impact of information technology infrastructure flexibility on mergers and acquisitions, MIS Quarterly, 42, 25, 10.25300/MISQ/2018/13245
Beran, 1985, Bootstrap tests and confidence regions for functions of a covariance matrix, The Annals of Statistics, 13, 95, 10.1214/aos/1176346579
Bollen, 1989
Borsboom, 2008, Latent variable theory, Measurement: Interdisciplinary Research and Perspectives, 6, 25
Bozdogan, 1987, Model selection and Akaike’s information criterion (AIC): The general theory and its analytical extensions, Psychometrika, 52, 345, 10.1007/BF02294361
Braojos, 2019, How do social commerce-IT capabilities influence firm performance? Theory and empirical evidence, Information & Management, 56, 155, 10.1016/j.im.2018.04.006
Browne, 1993, Alternative ways of assessing model fit, 136
Cegarra-Navarro, J. G., Ruiz, F. J. A., Martínez-Caro, E., & Garcia-Perez, A. (in press). Turning heterogeneity into improved research outputs in international R&D teams. Journal of Business Research (p. in print). doi:10.1016/j.jbusres.2019.05.023.
Burnham, 2002
Cegarra-Navarro, 2019, An open-minded strategy towards eco-innovation: A key to sustainable growth in a global enterprise, Technological Forecasting and Social Change, 148, 119727, 10.1016/j.techfore.2019.119727
Chin, W. W. (2010). How to write up and report PLS analyses. In V. Esposito Vinzi, W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares (pp. 655–690). Berlin, Heidelberg: Springer. doi:10.1007/978-3-540-32827-8_29.
Choi, 2020, Bayesian generalized structured component analysis, British Journal of Mathematical and Statistical Psychology, 73, 347, 10.1111/bmsp.12166
Coan, 2010, Emergent ghosts of the emotion machine, Emotion Review, 2, 274, 10.1177/1754073910361978
Cohen, 1990, Problems in the measurement of latent variables in structural equations causal models, Applied Psychological Measurement, 14, 183, 10.1177/014662169001400207
Cole, 1993, Multivariate group comparisons of variable systems: MANOVA and structural equation modeling, Psychological Bulletin, 114, 174, 10.1037/0033-2909.114.1.174
Dijkstra, 1983, Some comments on maximum likelihood and partial least squares methods, Journal of Econometrics, 22, 67, 10.1016/0304-4076(83)90094-5
Dijkstra, T. K. (2015). All-inclusive versus single block composites. doi:10.13140/RG.2.1.2917.8082 working paper.
Dijkstra, 2017, A perfect match between a model and a mode, 55
Dijkstra, 2011, Linear indices in nonlinear structural equation models: Best fitting proper indices and other composites, Quality & Quantity, 45, 1505, 10.1007/s11135-010-9359-z
Dijkstra, 2015, Consistent partial least squares path modeling, MIS Quarterly, 39, 297, 10.25300/MISQ/2015/39.2.02
Edwards, 2001, Multidimensional constructs in organizational behavior research: An integrative analytical framework, Organizational Research Methods, 4, 144, 10.1177/109442810142004
Felipe, 2016, An explanatory and predictive model for organizational agility, Journal of Business Research, 69, 4624, 10.1016/j.jbusres.2016.04.014
Fisher, 1936, The use of multiple measurements in taxonomic problems, Annals of Eugenics, 7, 179, 10.1111/j.1469-1809.1936.tb02137.x
Foltean, 2019, Customer relationship management capabilities and social media technology use: Consequences on firm performance, Journal of Business Research, 104, 563, 10.1016/j.jbusres.2018.10.047
Fornell, 1981, Evaluating structural equation models with unobservable variables and measurement error, Journal of Marketing Research, 18, 39, 10.1177/002224378101800104
Fuller, 2016, Common methods variance detection in business research, Journal of Business Research, 69, 3192, 10.1016/j.jbusres.2015.12.008
Goodhue, 2017, A multicollinearity and measurement error statistical blind spot: Correcting for excessive false positives in regression and PLS, MIS Quarterly, 41, 667, 10.25300/MISQ/2017/41.3.01
Hair, 2020, Assessing measurement model quality in PLS-SEM using confirmatory composite analysis, Journal of Business Research, 109, 101, 10.1016/j.jbusres.2019.11.069
Hair, 2011, PLS-SEM: Indeed a silver bullet, Journal of Marketing Theory and Practice, 18, 139, 10.2753/MTP1069-6679190202
Hair, 2019, When to use and how to report the results of PLS-SEM, European Business Review, 31, 2, 10.1108/EBR-11-2018-0203
Henseler, 2012, Why generalized structured component analysis is not universally preferable to structural equation modeling, Journal of the Academy of Marketing Science, 40, 402, 10.1007/s11747-011-0298-6
Henseler, J. (2015a). Confirmatory composite analysis. In 2nd International symposium on partial least squares path modeling. Seville, Spain. June 16–19.
Henseler, J. (2015b). Confirmatory composite analysis. In 5th modern modeling methods conference. Storrs, CT, U.S.A. May 19–20.
Henseler, J. (2015c). Is the whole more than the sum of its parts? On the interplay of marketing and design research. Enschede: University of Twente. http://purl.utwente.nl/publications/95770.
Henseler, J. (2015d). Towards a framework for confirmatory empirical design research. In 5th CIM community workshop. Enschede, the Netherlands. September 1–2.
Henseler, 2017, Bridging design and behavioral research with variance-based structural equation modeling, Journal of Advertising, 46, 178, 10.1080/00913367.2017.1281780
Henseler, 2014, Common beliefs and reality about PLS: Comments on Rönkkö & Evermann (2013), Organizational Research Methods, 17, 182, 10.1177/1094428114526928
Henseler, 2016, Using PLS path modeling in new technology research: Updated guidelines, Industrial Management & Data Systems, 116, 2, 10.1108/IMDS-09-2015-0382
Hernández-Perlines, 2016, Training and business performance: The mediating role of absorptive capacities, SpringerPlus, 5, 2074, 10.1186/s40064-016-3752-6
Huang, 2017, Asymptotics of AIC, BIC, and RMSEA for model selection in structural equation modeling, Psychometrika, 82, 407, 10.1007/s11336-017-9572-y
Hu, 1998, Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification, Psychological Methods, 3, 424, 10.1037/1082-989X.3.4.424
Hwang, 2004, Generalized structured component analysis, Psychometrika, 69, 81, 10.1007/BF02295841
Hwang, 2017, Generalized structured component analysis with uniqueness terms for accommodating measurement error, Frontiers in Psychology, 8, 2137, 10.3389/fpsyg.2017.02137
Jöreskog, 1969, A general approach to confirmatory maximum likelihood factor analysis, Psychometrika, 34, 183, 10.1007/BF02289343
Kettenring, 1971, Canonical analysis of several sets of variables, Biometrika, 58, 433, 10.1093/biomet/58.3.433
Kline, 2015
Lehmann, 2003
Marcoulides, 2013, You write, but others read: Common methodological misunderstandings in PLS and related methods, 31
Martelo-Landroguez, 2019, Uncontrolled counter-knowledge: Its effects on knowledge management corridors, Knowledge Management Research & Practice, 17, 203, 10.1080/14778238.2019.1599497
Martín, 2002, Consistency and identifiability revisited, Brazilian Journal of Probability and Statistics, 16, 99
Mason, 1975, Multicollinearity problems and ridge regression in sociological models, Social Science Research, 4, 135, 10.1016/0049-089X(75)90008-3
Maydeu-Olivares, 2017, Assessing fit in structural equation models: A Monte-Carlo evaluation of RMSEA versus SRMR confidence intervals and tests of close fit, Structural Equation Modeling: A Multidisciplinary Journal, 25, 389, 10.1080/10705511.2017.1389611
McQuarrie, 1998
Mulaik, 1989, Evaluation of goodness-of-fit indices for structural equation models, Psychological Bulletin, 105, 430, 10.1037/0033-2909.105.3.430
Nuzzo, 2015, How scientists fool themselves – and how they can stop, Nature, 526, 182, 10.1038/526182a
Pearson, 1901, On lines and planes of closest fit to systems of points in space, The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2, 559, 10.1080/14786440109462720
Petter, 2007, Specifying formative constructs in Information Systems research, MIS Quarterly, 31, 623, 10.2307/25148814
Pittino, 2018, Psychological ownership, knowledge sharing and entrepreneurial orientation in family firms: The moderating role of governance heterogeneity, Journal of Business Research, 84, 312, 10.1016/j.jbusres.2017.08.014
Podsakoff, 2003, Common method biases in behavioral research: A critical review of the literature and recommended remedies, Journal of Applied Psychology, 88, 879, 10.1037/0021-9010.88.5.879
Preacher, 2012, The problem of model selection uncertainty in structural equation modeling, Psychological Methods, 17, 1, 10.1037/a0026804
Rademaker, M. & Schuberth, F. (2020). cSEM: Composite-based structural equation modeling. https://github.com/M-E-Rademaker/cSEMRpackageversion 0.1.0.9000.
Rasoolimanesh, 2019, Investigating the effects of tourist engagement on satisfaction and loyalty, The Service Industries Journal, 39, 559, 10.1080/02642069.2019.1570152
R Core Team, 2020
Reise, 1999, Measurement issues viewed through the eyes of IRT, 219
Rhemtulla, 2012, When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions, Psychological Methods, 17, 354, 10.1037/a0029315
Rhemtulla, 2020, Worse than measurement error: Consequences of inappropriate latent variable measurement models, Psychological Methods, 25, 30, 10.1037/met0000220
Rigdon, 2012, Rethinking partial least squares path modeling: In praise of simple methods, Long Range Planning, 45, 341, 10.1016/j.lrp.2012.09.010
Rigdon, 2020, Quantify uncertainty in behavioral research, Nature Human Behaviour, 4, 329, 10.1038/s41562-019-0806-0
Roldán, 2018, Antecedents and consequences of knowledge management perfomance: The role of IT infrastructure, Intangible Capital, 14, 518, 10.3926/ic.1074
Rueda, 2017, From traditional education technologies to student satisfaction in management education: A theory of the role of social media applications, Information & Management, 54, 1059, 10.1016/j.im.2017.06.002
Ruiz-Palomo, 2019, Family management and firm performance in family SMEs: The mediating roles of management control systems and technological innovation, Sustainability, 11, 3805, 10.3390/su11143805
Sajtos, 2016, Auxiliary theories as translation mechanisms for measurement model specification, Journal of Business Research, 69, 3186, 10.1016/j.jbusres.2015.12.007
Sanchez-Franco, 2019, Understanding relationship quality in hospitality services: A study based on text analytics and partial least squares, Internet Research, 29, 478, 10.1108/IntR-12-2017-0531
Sánchez-Polo, 2019, Overcoming knowledge barriers to health care through continuous learning, Journal of Knowledge Management, 23, 508, 10.1108/JKM-10-2018-0636
Sarstedt, 2016, Estimation issues with PLS and CBSEM: Where the bias lies!, Journal of Business Research, 69, 3998, 10.1016/j.jbusres.2016.06.007
Schermelleh-Engel, 2003, Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures, Methods of Psychological Research Online, 8, 23
Schuberth, F., Dijkstra, T.K., & Henseler, J. (2018a). Confirmatory composite analysis. In Meeting of the SEM working group. Amsterdam, the Netherlands. March 15–16.
Schuberth, F., Rademaker, M., & Henseler, J. (in press). Estimating and assessing second-order constructs using PLS-PM: the case of composites of composites. Industrial Management & Data Systems.
Schuberth, 2018, Confirmatory composite analysis, Frontiers in Psychology, 9, 2541, 10.3389/fpsyg.2018.02541
Schuberth, F. (in press). Confirmatory composite analysis using partial least squares: Setting the record straight. Review of Managerial Science. doi:10.1007/s11846-020-00405-0.
Schuberth, 2018, Partial least squares path modeling using ordinal categorical indicators, Quality & Quantity, 52, 9, 10.1007/s11135-016-0401-7
Schwarz, 1978, Estimating the dimension of a model, The Annals of Statistics, 6, 461, 10.1214/aos/1176344136
Thoemmes, 2018, Local fit evaluation of structural equation models using graphical criteria, Psychological Methods, 23, 27, 10.1037/met0000147
van Riel, 2017, Estimating hierarchical constructs using consistent partial least squares: The case of second-order composites of common factors, Industrial Management & Data Systems, 117, 459, 10.1108/IMDS-07-2016-0286
Vidaurre, 2013, Bayesian sparse partial least squares, Neural Computation, 25, 3318, 10.1162/NECO_a_00524
Wei, 2020, Supply chain information integration and firm performance: Are explorative and exploitative IT capabilities complementary or substitutive?, Decision Sciences, 51, 464, 10.1111/deci.12364
West, 2012, Model fit and model selection in structural equation modeling, 209
Williams, 2010, Method variance and marker variables: A review and comprehensive CFA marker technique, Organizational Research Methods, 13, 477, 10.1177/1094428110366036
Wold, H. O. A. (1973). Nonlinear iterative partial least squares (NIPALS) modelling. Some current developments. In P. R. Krishnaiah (Ed.), Proceedings of the 3rd international symposium on multivariate analysis (pp. 383–407). Dayton, OH.
Wolf, 2013, Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety, Educational and Psychological Measurement, 73, 913, 10.1177/0013164413495237
Yiu, 2020, Impact of service-dominant orientation on the innovation performance of technology firms: Roles of knowledge sharing and relationship learning, Decision Sciences, 51, 620, 10.1111/deci.12408
Yuan, 2005, Fit indices versus test statistics, Multivariate Behavioral Research, 40, 115, 10.1207/s15327906mbr4001_5
Yuan, 2015, Assessing structural equation models by equivalence testing with adjusted fit indexes, Structural Equation Modeling: A Multidisciplinary Journal, 23, 319, 10.1080/10705511.2015.1065414
