Conflating Antecedents and Formative Indicators: A Comment on Aguirre-Urreta and Marakas

Published Online:https://doi.org/10.1287/isre.2014.0543

References

  • Aguirre-Urreta MI, Marakas GM (2014) Research note—Partial least squares and models with formatively specified endogenous constructs: A cautionary note. Inform. Systems Res.25(4):761–778.LinkGoogle Scholar
  • Bagozzi RP (2011) Measurement and meaning in information systems and organizational research: Methodological and philosophical foundations. MIS Quart. 35(2):261–292.CrossrefGoogle Scholar
  • Becker JM, 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.CrossrefGoogle Scholar
  • Becker JM, Rai A, Rigdon EE (2013) Predictive validity and formative measurement in structural equation modeling: Embracing practical relevance. Proc. 34th Internat. Conf. Inform. Systems, Milan, http://aisel.aisnet.org/icis2013/proceedings/ResearchMethods/5/.Google Scholar
  • Bollen KA, Davis WR (2009) Causal indicator models: Identification, estimation, and testing. Structural Equation Modeling: A Multidisciplinary J. 16(3):498–522.CrossrefGoogle Scholar
  • Diamantopoulos A (2011) Incorporating formative measures into covariance-based structural equation models. MIS Quart. 35(2):335–358.CrossrefGoogle Scholar
  • Diamantopoulos A, Winklhofer HM (2001) Index construction with formative indicators: An alternative to scale development. J. Marketing Res. 38(2):269–277.CrossrefGoogle Scholar
  • Diamantopoulos A, Riefler P, Roth KP (2008) Advancing formative measurement models. J. Bus. Res. 61(12):1203–1218.CrossrefGoogle Scholar
  • Dijkstra TK, Schermelleh-Engel K (2013) Consistent partial least squares for nonlinear structural equation models. Psychometrika 79(4):585–604.CrossrefGoogle Scholar
  • Fornell C, Bookstein FL (1982) Two structural equation models: LISREL and PLS applied to exit-voice theory. J. Marketing Res. 19(4):440–452.CrossrefGoogle Scholar
  • McDonald RP (1996) Path analysis with composite variables. Multivariate Behavioral Res. 31(2):239–270.CrossrefGoogle Scholar
  • Petter S, Straub D, Rai A (2007) Specifying formative constructs in information systems research. MIS Quart. 31(4):623–656.CrossrefGoogle Scholar
  • Rigdon EE (2012) Rethinking partial least squares path modeling: In praise of simple methods. Long Range Planning 45(5–6):341–358.CrossrefGoogle Scholar
  • Ringle CM, Sarstedt M, Straub D (2012) A critical look at the use of PLS-SEM in MIS Quarterly. MIS Quart. 36(1):iii–viii.CrossrefGoogle Scholar
  • Steiger JH (1990) Some additional thoughts on components, factors and factor indeterminacy. Multivariate Behavioral Res. 25(1):41–45.CrossrefGoogle Scholar
  • Velicer WF, Jackson DN (1990) Component analysis versus common factor analysis: Some issues in selecting an appropriate procedure. Multivariate Behavioral Res. 25(1):1–28.CrossrefGoogle Scholar
  • Wetzels M, Odekerken-Schröder G, van Oppen C (2009) Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quart. 33(1):177–195.CrossrefGoogle Scholar
  • Wold HO (1982) Soft modeling: The basic design and some extensions. Jöreskog KG, Wold H, eds. Systems Under Indirect Observation: Causality, Structure, Prediction (Part II) (North-Holland, Amsterdam), 1–54.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.