DOI QR코드

DOI QR Code

Call for an Open Discussion on Empirical Viability of Causal Indicators

  • Kim, Gi Mun (School of Business, Chungnam National University) ;
  • Shin, Bong Sik (Management Information Systems, San Diego State University) ;
  • Grover, Varun (College of Business and Behavioral Science, Clemson University) ;
  • Howell, Roy D. (Rawls College of Business, Texas Tech University) ;
  • Kim, Ki Joo (Department of Global Business Administration, Konyang University)
  • Received : 2017.10.27
  • Accepted : 2017.12.23
  • Published : 2017.12.31

Abstract

Over the past decade, we have witnessed Serious Debates in MISQ and Other Journals Between Two Camps that have Differing Views on the use of Causal Indicators to Measure Constructs. There is the Camp that advocates Causal Indicators (ADVOCATE) and the Camp that opposes Their Usage (OPPONENT). The Debates have been primarily centered on the OPPONENT's Argument that the Meaning of a Latent Variable is determined by its Outcome Variables. However, Little Effort has been made to Validate the ADVOCATE's Dispute (Against the OPPONENT's Arguments) that the Meaning of a Latent Variable is decided by its Causal Indicators if there is no Misspecification. Our Study precisely examines the Integrity of the Argument. For this, we empirically examine how the two Primary Psychometric Properties-Comprehensiveness and Interrelationship-of Causal Indicators Influence Theory Testing between Latent Variables through Three Different Tests (i.e., Comprehensive Test, Interrelationship Test, and Mixed Test). Conducted on Two Different Datasets, Our Analysis Consistently Reveals that Structural Path Coefficients are Hardly Sensitive to the Changes (i.e., Misspecification) in the Properties of Causal Indicators. The Discovery offers Important Evidence that the Sound Theoretical Logic of a Causal Model is not in Sync with the Empirical Mechanism of Parameter Estimation. This Underscores that a Latent Variable Formed by Causal Indicators is empirically an elusive notion that is Difficult to Operationalize. As Our Results have Significant Implications on the Integrity of Numerous IS studies which have conducted Theory or Hypothesis Testing Using Causal Indicators, we strongly advocate Open Discussions among Methodologists regarding Our Findings and Their Implications for Both Published IS Research and Future Practices.

Keywords

References

  1. Lee, N., Cadogan, J. and Chamberlain, L., “The MIMIC Model and Formative Variables: Problems and Solutions,” AMS Review, Vol. 3, No. 1, pp. 3-17, 2013. https://doi.org/10.1007/s13162-013-0033-1
  2. Hardin, A. M. and Chang, J. C-J., "Does Existing Measurement Theory Support the Use of Composite and Causal Indicators in Information Systems Research?" DATA BASE for Advances in Information Systems, Vol. 44, No. 4, pp. 56-65, 2013. https://doi.org/10.1145/2544415.2544419
  3. Bainter, S. A. and Bollen, K. A., "Interpretational Confounding or Confounded Interpretations of Causal Indicators?" Measurement: Interdisciplinary Research and Perspectives, Vol. 12, No. 4, pp. 125-140, 2014. https://doi.org/10.1080/15366367.2014.968503
  4. Diamantopoulos, A., “Incorporating Formative Measures into Covariance-based Structural Equation Models,” MIS Quarterly, Vol. 35, No. 2, pp. 335-358, 2011. https://doi.org/10.2307/23044046
  5. Howell, R. D., Breivik, E., and Wilcox, J., Formative Measurement: A Critical Perspective. DATA BASE for Advances in Information Systems, Vol. 44, No. 4, pp. 44-54, 2013. https://doi.org/10.1145/2544415.2544418
  6. Bainter, S. A. and Bollen, K. A., “Moving Forward in the Debate on Causal Indicators: Rejoinder to Comments,” Measurement: Interdisciplinary Research and Perspectives, Vol. 13, No. 1, pp. 63-74, 2015. https://doi.org/10.1080/15366367.2015.1016349
  7. Howell, R. D., "What is the Latent Variable in Causal Indicator Models?" Measurement: Interdisciplinary Research and Perspectives, Vol. 12, No. 4, pp. 141-145, 2014. https://doi.org/10.1080/15366367.2014.980105
  8. Rigdon, E. E., “Lee, Cadogan, and Chamberlain: An Excellent Point . . . But what about that Iceberg?,” AMS Review, Vol. 3, No. 1, pp. 24-29, 2013. https://doi.org/10.1007/s13162-013-0034-0
  9. Bagozzi, R. P., “Measurement and Meaning in Information Systems and Organizational Research: Methodological and Philosophical Foundations,” MIS Quarterly, Vol. 35, No. 2, pp. 261-292, 2011. https://doi.org/10.2307/23044044
  10. Coltman, T., Devinney, T. M., Midgley, D. F. and Venaik, S., “Formative versus Reflective Measurement Models: Two Applications of Formative Measurement,” Journal of Business Research, Vol. 6, No. 12, pp. 1250-1262, 2008.
  11. Bollen, K. A., “Evaluating Effect, Composite, and Causal Indicators in Structural Equation Models,” MIS Quarterly, Vol. 35, No. 2, pp. 359-372, 2011. https://doi.org/10.2307/23044047
  12. Blalock, H. M., Causal Inferences in Nonexperimental Research, Chapel Hill, NC: University of North Carolina Press. 1964
  13. Curtis, R. F. and Jackson, E. F., "Multiple Indicators in Survey Research," American Journal of Sociology, Vol. 68, pp. 195-204, 1962. https://doi.org/10.1086/223309
  14. Edwards, J. R., and Bagozzi, R. P., “On the Nature and Direction of Relationships Between Constructs and Measures,” Psychological Methods, Vol. 95, No. 2, pp. 155-74, 2000.
  15. Jarvis, C. B., MacKenzie, S. B., and Podsakoff, P. M., “A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research,” Journal of Consumer Research, Vol. 30, No. 2, pp. 199-218, 2003. https://doi.org/10.1086/376806
  16. Podsakoff, P., MacKenzie, S., Podsakoff, N., & Lee, J.. "The Mismeasure of Man(agement) and its Implications for Leadership Research," The Leadership Quarterly, Vol. 14, pp. 615-656, 2003. https://doi.org/10.1016/j.leaqua.2003.08.002
  17. MacKenzie, S. B., Podsakoff, P. M., and Jarvis, C. B., “The Problem of Measurement Model Misspecification in Behavioral and Organizational Research and Some Recommended Solutions,” Journal of Applied Psychology, Vol. 90, No. 4, pp. 710-730, 2005. https://doi.org/10.1037/0021-9010.90.4.710
  18. Howell, R. D., Breivik, E., and Wilcox, J. B., “Reconsidering Formative Measurement,” Psychological Methods, Vol. 12, No. 2, pp. 205-218, 2007. https://doi.org/10.1037/1082-989X.12.2.205
  19. Bagozzi, R. P., “On the Meaning Formative Measurement and How It Differs From Reflective Measurement: Comment on Howell, Breivik, and Wilcox (2007),” Psychological Methods, Vol. 12, No. 2, pp. 229-237, 2007. https://doi.org/10.1037/1082-989X.12.2.229
  20. Bollen K. A., “Interpretational Confounding Is Due to Misspecification, Not to Type of Indicator: Comment on Howell, Breivik, and Wilcox (2007),” Psychological Methods, Vol. 12, No. 2, pp. 219-228, 2007. https://doi.org/10.1037/1082-989X.12.2.219
  21. Kim, G., Shin, B., and Grover, V., “Investigating Two Contradictory Views of Formative Measurement in Information Systems Research,” MIS Quarterly, Vol. 34, No. 2, pp. 345-365, 2010. https://doi.org/10.2307/20721431
  22. Edwards, J. R., “The Fallacy of Formative Measurement,” Organizational Research Methods, Vol. 14, No. 2, pp. 370-388, 2011. https://doi.org/10.1177/1094428110378369
  23. Hardin, A. M., and Marcoulides, G. A., “A Commentary on the Use of Formative Measurement,” Educational and Psychological Measurement, Vol. 71, No. 5, pp. 753-764, 2011. https://doi.org/10.1177/0013164411414270
  24. Markus, K. A., “Unfinished Business in Clarifying Causal Measurement: Commentary on Bainter and Bollen,” Measurement: Interdisciplinary Research and Perspectives, Vol. 12, No. 4, pp. 146-150, 2014. https://doi.org/10.1080/15366367.2014.980106
  25. Rhemtulla, M., van Bork, R., and Borsboom, D., “Calling Models With Causal Indicators “Measurement Models” Implies More Than They Can Deliver,” Measurement: Interdisciplinary Research and Perspectives, Vol. 13, No. 1, pp. 59-62, 2015. https://doi.org/10.1080/15366367.2015.1016343
  26. MacKenzie, S. B., Podsakoff, P., and Podsakoff, N., “Construct Measurement and Validation Procedures in MIS and Behavioral Research: Integrating New and Existing techniques,” MIS Quarterly, Vol. 35, No. 2, pp. 293-334, 2011. https://doi.org/10.2307/23044045
  27. Petter, S., Rai, A., and Straub, D., “The Critical Importance of Construct Measurement Specification: A Response to Aguirre-Urreta and Marakas,” MIS Quarterly, Vol. 36, No. 1, pp. 147-155, 2012.
  28. Widaman, K. F., “Much Ado about Nothing-or At Best, Very Little,” Measurement: Interdisciplinary Research and Perspectives, Vol. 12, No. 4, pp. 165-168, 2014. https://doi.org/10.1080/15366367.2014.980109
  29. Diamantopoulos A., Riefler, P., and Roth, K. P., “Advancing Formative Measurement Models,” Journal of Business Research, Vol. 61, No. 12, pp. 1203-1218, 2008. https://doi.org/10.1016/j.jbusres.2008.01.009
  30. Kim, S. H., and Kim, J. K., “Impact of Privacy Concern and Institutional Trust on Privacy Decision Making: A Comparison of E-Commerce and Location-Based Service,” Journal of the Korea Industrial Information Systems Research, Vol. 22, No. 1, pp. 69-87, 2017. https://doi.org/10.9723/jksiis.2017.22.1.069
  31. Soh, H. C., and Kim, J. K., “Influence of Information Security Activities of Financial Companies on Information Security Awareness and Information Security Self Confidence: Focusing on the Mediating Effect of Information Security Awareness,” Journal of the Korea Industrial Information Systems Research, Vol. 22, No. 4, pp. 45-64, 2017. https://doi.org/10.9723/JKSIIS.2017.22.4.045
  32. Kang, S. R., Nam, S. H., and Yang, H. D., “Investigating the Influence of the Perceived Cloud Service Risks on the Intention to Use the Abandonment Option: The Moderation Effect of IS Maturity and the Mediation Effect of Cloud Service Satisfaction,” Journal of the Korea Industrial Information Systems Research, Vol. 22, No. 4, pp. 65-77, 2017. https://doi.org/10.9723/JKSIIS.2017.22.4.065