Formative Vs. Reflective Measurement Model: Guidelines for Structural Equation Modeling Research

Article Sidebar

PDF
Cite as:
Mohd Hafiz Hanafiah, Formative Vs. Reflective Measurement Model: Guidelines for Structural Equation Modeling Research, Int. J. Anal. Appl., 18 (5) (2020), 876-889.

Main Article Content

Mohd Hafiz Hanafiah

Abstract

Various social sciences researchers have always debated the operationalisation of formative or a reflective measurement in Partial Least Squares Structural Equation Modeling (PLS-SEM). This paper aims to offer guidance on formative and reflective measurement model assessment in PLS-SEM. First, this paper explores and discuss the similarities and differences between the formative and reflective measurement model. Next, this paper reviews the practice of measurement model assessment for formative and reflective construct based on the latest methodological background. Finally, this paper proposes a set of guidelines in classifying the formative and reflective constructs and the steps in assessing the formative and reflective measurement model. This paper addresses the issue of measurement misspecification in PLS-SEM assessment by providing logical guidelines for researchers. This paper also helps to explain and suggest appropriate PLS-SEM assessment for researchers as they plan future research projects.

Article Details

References

  1. J.F. Hair, ed., A primer on partial least squares structural equations modeling (PLS-SEM), SAGE, Los Angeles, 2014.
  2. R.B. Kline, Principles and practice of structural equation modeling, Guilford Press, New York, 2015.
  3. J.F. Hair, C.M. Ringle, Sarstedt M. PLS-SEM: Indeed a silver bullet. J. Market. Theory Practice. 19(2)(2011), 139-152.
  4. X. Wang, L.M. Jessup, P.F. Clay. Measurement model in entrepreneurship and small business research: a ten year review. Int. Entrepren. Manage. J. 11(1)( 2015), 183-212.
  5. V.E. Vinzi, C.N. Lauro, S. Amato, PLS Typological Regression: Algorithmic, Classification and Validation Issues, in: H.-H. Bock, et al. (Eds.), New Developments in Classification and Data Analysis, Springer-Verlag, Berlin/Heidelberg, 2005: pp. 133-140.
  6. J.F. Hair Jr., L.M. Matthews, R.L. Matthews, M. Sarstedt, PLS-SEM or CB-SEM: updated guidelines on which method to use. Int. J. Multivar. Data Anal. 1(2)( 2017), 107-123.
  7. W.W. Chin, P.R. Newsted, Structural equation modeling analysis with small samples using partial least squares. Stat. Strat. Small sample Res. 1(1)( 1999), 307-341.
  8. R.R. Sinkovics, ed., New challenges to international marketing, Emarald, London, 2009.
  9. J.F. Hair, M. Sarstedt, C.M. Ringle, J.A. Mena. An assessment of the use of partial least squares structural equation modeling in marketing research. J. Acad. Market. Sci. 40(3)( 2012), 414-433.
  10. Qureshi I, Compeau D. Assessing between-group differences in information systems research: A comparison of covariance-and component-based SEM. MIS Quart. 33(2009), 197-214.
  11. A. Diamantopoulos, P. Riefler, K.P. Roth. Advancing formative measurement models. J. Bus. Res. 61(12)(2008), 1203-1218.
  12. A. Diamantopoulos, Incorporating formative measures into covariance-based structural equation models. MIS Quart. 35(2011), 335-358.
  13. K.A. Bollen, A. Diamantopoulos, Notes on measurement theory for causal-formative indicators: A reply to Hardin. Psychol. Meth. 22(2017), 605-608.
  14. T. Coltman, T.M. Devinney, D.F. Midgley, S. Venaik. Formative versus reflective measurement models: Two applications of formative measurement. J. Bus. Res. 61(12)( 2008), 1250-62.
  15. E.A. Khan, M.N.A. Dewan, M.M.H. Chowdhury. Reflective or formative measurement model of sustainability factor? A three industry comparison. Corp. Owner. Control. 13(2)( 2016), 83-92.
  16. K. Bollen, R. Lennox. Conventional wisdom on measurement: A structural equation perspective. Psychol. Bull. 110(2)( 1991), 305-314.
  17. A. Diamantopoulos, H.M. Winklhofer. Index construction with formative indicators: An alternative to scale development. J. Market. Res. 38(2)(2001), 269-277.
  18. R.P. Bagozzi, Y. Yi. On the evaluation of structural equation models. J. Acad. Market. Sci. 16(1)(1988), 74-94.
  19. C.B. Jarvis, S.B. MacKenzie, P.M. Podsakoff. A critical review of construct indicators and measurement model misspecification in marketing and consumer research. J. Consumer Res. 30(2)(2003), 199-218.
  20. A. Diamantopoulos, J.A. Siguaw. Formative versus reflective indicators in organisational measure development: A comparison and empirical illustration. Br. J. Manage. 17(4)(2006), 263-282.
  21. D. Borsboom, A.O.J. Cramer, R.A. Kievit, A.Z. Scholten, S. Franić. The end of construct validity. In R. W. Lissitz (Ed.), The concept of validity: Revisions, new directions, and applications (p. 135-170). IAP Information Age Publishing, 2009.
  22. R.G. Netemeyer, W.O. Bearden, S. Sharma, Scaling procedures: issues and applications, Sage Publications, Thousand Oaks, Calif, 2003.
  23. J.B. Wilcox, R.D. Howell, E. Breivik, Questions about formative measurement. J. Bus. Res. 61(12)(2008), 1219-1228.
  24. D. Borsboom, G.J. Mellenbergh, J. van Heerden, The theoretical status of latent variables., Psychol. Rev. 110(2003), 203-219.
  25. D.R. Allen, T. Finlayson, A. Abdul-Quader, A. Lansky. The role of formative research in the National HIV Behavioral Surveillance System. Public Health Rep. 124(1)(2009), 26-33.
  26. J.R. Macnamara. Research in public relations: A review of the use of evaluation and formative research. Asia-Pac. Public Relat. J. 1(1992), 2-11.
  27. J.C. Nunnally, I.H. Bernstein. Psychological theory. McGraw-Hill, New York, 1994.
  28. J. Hulland, Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strat. Manage. J. 20(2)(1999), 195-204.
  29. J.F. Hair, J.J. Risher, M. Sarstedt, C.M. Ringle. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 31(1)(2019), 2-24.
  30. C. Fornell, D.F. Larcker. Structural equation models with unobservable variables and measurement error: Algebra and statistics. Sage Publications Sage CA: Los Angeles, CA; 1981.
  31. J. Henseler, C.M. Ringle, M. Sarstedt. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Market. Sci. 43(1)(2015), 115-135.
  32. R.T. Cenfetelli, G. Bassellier. Interpretation of formative measurement in information systems research. MIS Quart. 33(2009), 689-707.
  33. S. Petter, D. Straub, A. Rai, Specifying formative constructs in information systems research. MIS Quart. 31(2007), 623-656.
  34. A.H. Westlund, M. Källström, J. Parmler. SEM-based customer satisfaction measurement: On multicollinearity and robust PLS estimation. Total Qual. Manage. 19(7-8)(2008), 855-869.
  35. W.W. Chin. The partial least squares approach to structural equation modeling. Mod. Meth. Bus. Res. 295(2)(1998), 295-336.
  36. G.R. Franke, K.J. Preacher, E.E. Rigdon. Proportional structural effects of formative indicators. J. Bus. Res. 61(12)(2008), 1229-1237.
  37. N. Roberts, J. Thatcher, Conceptualizing and testing formative constructs: tutorial and annotated example, SIGMIS Database. 40(2009), 9-39.
  38. K.A. Bollen, A. Diamantopoulos. In defense of causal-formative indicators: A minority report. Psychol. Meth. 22(3)(2017), 581-596.