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FrancoAngeli Journals

Articles

No. 1 (2025)

Reflective vs. Formative Measurement Models in Operations and Supply Chain Research

DOI
https://doi.org/10.3280/cgrds1-2025oa19037
Submitted
dicembre 15, 2024
Published
2025-05-05

Abstract

This research seeks to highlight a common mistake that researchers in the area of Operations and Supply Chain Management (O&SCM) make when selecting the measurement models in Structural Equation Modelling. In fat, the unproper selection of a measurement model in Structural Equation Modeling (SEM) research can lead to issues of model misspecification and non-valid findings. Therefore, this is the first study in O&SCM that highlights the differences between reflective and formative measurement models in SEM and invites researchers in this field to reflect and pay attention to the measurement model selection before diving into a statistical analysis.

References

  1. Alsheyadi A., Baawain A., Shaukat M.R. (2024). E-supply chain coordination and performance impacts: An empirical investigation. Production & Manufacturing Research, 12(1), 2379942. DOI: 10.1080/21693277.2024.2379942.
  2. Bollen K. and Davis W. (1994). Causal indicator models: identification, estimation, and testing, Paper presented at the American Sociological Association Convention, Miami.
  3. Bollen K., Lennox R. (1991). Conventional wisdom on measurement: a structural equation perspective, Psychological Bulletin, 110(2): 305-14.
  4. Bollen K.A. (1989), Structural Equation with Latent Variables, Wiley, New York, NY.
  5. Chand P., Kumar A., Thakkar J., Ghosh K.K. (2022). Direct and mediation effect of supply chain complexity drivers on supply chain performance: an empirical evidence of organizational complexity theory. International Journal of Operations & Production Management, 42(6): 797-825.
  6. De Giovanni P. (2012). Do internal and external environmental management contribute to the triple bottom line?. International Journal of Operations & Production Management, 32(3): 265-290.
  7. De Giovanni P., Cariola A. (2021). Process innovation through industry 4.0 technologies, lean practices and green supply chains. Research in Transportation Economics, 100869. DOI: 10.1016/j.retrec.2020.100869.
  8. De Giovanni P., Vinzi V.E. (2012). Covariance versus component-based estimations of performance in green supply chain management. International Journal of Production Economics, 135(2): 907-916. DOI: 10.1016/j.ijpe.2011.11.001.
  9. De Giovanni, P. (2020). When feature-based production capabilities challenge operations: Drivers, moderators, and performance. International Journal of Operations & Production Management, 40(2): 221-242. DOI: 10.1108/IJOPM-04-2019-0309.
  10. Diamantopoulos A., Siguaw J. (2006). Formative versus reflective indicators in organizational measure development: a comparison and empirical illustration, British Journal of Management, 17(4): 263-82.DOI: 10.1111/j.1467-8551.2006.00500.x.
  11. Diamantopoulos A., Winklhofer H. (2001). Index construction with formative indicators: an alternative to scale development, Journal of Marketing Research, 38(2): 269-77. DOI: 10.1509/jmkr.38.2.269.18845.
  12. Fornell C., Bookstein F.L. (1982). A comparative analysis of two structural equation models: LISREL and PLS applied to market data. In Fornell C. (Ed.). A second generation of multivariate analysis, vol. 1. New York: Praeger, pp. 289-324.
  13. Garver M.S. (2019). Threats to the validity of logistics and supply chain management research, Journal of Business Logistics, 40(1): 30-43. DOI: 10.1111/jbl.12203.
  14. Jarvis C.B., Mackenzie S.B., Podsakoff P.M., Mick D.G., Bearden W.O. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research, Journal of Consumer Research, 30(2): 199-218. DOI: 10.1086/376806.
  15. Jöreskog K., Goldberger A. (1975). Estimation of a model with multiple indicators and multiple causes of a single latent variable, Journal of American Statistics Association, 10: 631-9. DOI: 10.1080/01621459.1975.10482485.
  16. MacKenzie S., Podsakoff P., Jarvis C. (2005). The problem of measurement model misspecification in behavioural and organizational research and some recommended solutions, Journal of Applied Psychology, 90(4): 710-30. DOI: 10.1037/0021-9010.90.4.710.
  17. Malesios C., Dey P.K., Abdelaziz F.B. (2020). Supply chain sustainability performance measurement of small and medium sized enterprises using structural equation modeling. Annals of Operations Research, 294(1): 623-653.
  18. Petter S., Straub D., Rai A. (2007). Specifying formative construct in information system research, MIS Quarterly, 31(4): 623-656. DOI: 10.2307/25148814.
  19. Podsakoff P.M., MacKenzie S.B., Podsakoff N.P., Lee J.Y. (2003). The mismeasure of man (agement) and its implications for leadership research. Leadership Quarterly, 14: 615-656. DOI: 10.1016/j.leaqua.2003.08.002.
  20. Roh T., Noh J., Oh Y., Park K.S. (2022). Structural relationships of a firm’s green strategies for environmental performance: The roles of green supply chain management and green marketing innovation. Journal of cleaner production, 356, 131877. DOI: 10.1016/j.jclepro.2022.131877.
  21. Wallenburg C.M., Weber J. (2005). Structural equation modeling as a basis for theory development within logistics and supply chain management research. In Kotzab E., Seuring S., Müller M., Reiner G. (Eds.), Research methodologies in supply chain management, Springer, 171-186.
  22. Xu L., Peng X., Prybutok V. (2019). Formative measurements in operations management research: Using partial least squares. Quality Management Journal, 26(1): 18-31. DOI: 10.1080/10686967.2018.1542287.

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