Using Latent Variables for Confirmatory Composite Analysis


  • Chanta Jhantasana Valaya Alongkorn Rajabhat University Under the Royal Patronage


Partial Least Squares Path Modeling, Confirmatory Factor Analysis, Confirmatory Composite Analysis


In general, all constructs in a confirmatory factor analysis (CFA) are latent variables. Should all constructs also be emergent variables, a hypothetical construct stemming from a latent variable that has received little attention in studies, then a confirmatory composite analysis (CCA) is a possibility. This study employed latent variables as emergent variables in order to conduct a CCA. The latent variables were related to an individual's traits, attitudes, or behavioral notions, such as satisfaction, trust, or loyalty. An emergent variable is composed of data on capabilities, values, indices, therapies, and interventions, as well as an artifact or design idea. CFA was used to analyze the satisfaction, trust, and loyalty of 200 Lazada and Shopee customers, and additional emergent variables were created from latent variables for the CCA. The study demonstrates that emergent variables can arise from latent variables and that CCA is more accurate than CFA.


Download data is not yet available.


Aaker, D. A., & Equity, M. B. (1991). The free press. New York.

Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. The Leadership Quarterly, 21(6), 1086-1120.

Arnett, D. B., Laverie, D. A., & Meiers, A. (2003). Developing parsimonious retailer equity indexes using partial least squares analysis: A method and applications. Journal of Retailing, 79(3), 161-170.

Benitez, J., Henseler, J., Castillo, A., & Schuberth, F. (2020). How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Information & Management, 57(2), 103-168.

Bollen, K. A., & Bauldry, S. (2011). Three Cs in measurement models: Causal indicators, composite indicators, and covariates. Psychological Methods, 16(3), 265.

Cadogan, J. W., & Lee, N. (2013). Improper use of endogenous formative variables. Journal of Business Research, 66(2), 233-241.

Chaudhuri, A., & Holbrook, M. B. (2001). The chain of effects from brand trust and brand affect to brand performance: The role of brand loyalty. Journal of Marketing, 65, (2), 81-93.

Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281.

Diamantopoulos, A., & Riefler, P. (2011). Using formative measures in international marketing models: A cautionary tale using consumer animosity as an example. Advance in International Marketing,22, 11-30.

Dijkstra, T. K., & Henseler, J. (2015a). Consistent and asymptotically normal PLS estimators for linear structural equations. Computational Statistics & Data Analysis, 81, 10-23.

Dijkstra, T. K., & Henseler, J. (2015b). Consistent partial least squares path modeling. MIS Quarterly, 39(2), 1-A5

Ehigie, B. O. (2006). Correlates of customer loyalty to their bank: a case study in Nigeria. International Journal of Bank Marketing, 24 (7), 494-508.

Geyskens, I., Steenkap, J. E. B. M., & Kumar, N. (1999). A Meta-analysis of satisfaction in marketing channel. Journal of Marketing Research, 36(2), 223-238

Guenzi, P., Johnson, M. D., & Castaldo, S. (2009). A comprehensive model of customer trust in two retail stores. Journal of Service Management, 20(3), 290-316.

Hart, C. W. & Johnson, M. D. (1999). Growing the trust relationship. Marketing Management, 8 (1), 8-19.

Hair Jr, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101-110.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: International version. New Jersey, Pearson.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis, cengage learning, andover. Hampshire, United Kingdom.

Henseler, J. (2017a). ADANCO 2.0.1.: user manual. https://www. support/user-manual/

Henseler, J. (2017b). Bridging design and behavioral research with variance-based structural equation modeling. Journal of Advertising, 46(1), 178-192.

Henseler, J., & Schuberth, F. (2020). Using confirmatory composite analysis to assess emergent variables in business research. Journal of Business Research, 120, 147-156.

Henseler, J., Ringle, C., & Sinkovics, R. (2009). The use of partial least squares path modeling in international marketing. Advance in International Marketing, 20, 277-319.

Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Ketchen, D., Hair, J. F., Hult, T. M., & Calantone, R. J. (2014). Common beliefs and reality about PLS: Comments on Ronkko and Evermann (2013). Organizational Research Methods, 17(2), 182-209.

Horppu, M., Kuivalainen, O., Tarkiainen, A. & Ellonen, H.-K. (2008). Online satisfaction, trust and loyalty and the impact of the offline parent brand. Journal of Product & Brand Management, 17(6), 403-413.

Hu, L. T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to under parameterized model misspecification. Psychological Methods, 3(4), 424.

Hubona, G. S., Schuberth, F., & Henseler, J. (2021). A clarification of confirmatory composite analysis (CCA). International Journal of Information Management, 61, 102399.

Hult, G. T. M., Hair Jr, J. F., Proksch, D., Sarstedt, M., Pinkwart, A., & Ringle, C. M. (2018). Addressing endogeneity in international marketing applications of partial least squares structural equation modeling. Journal of International Marketing, 26(3), 1-21.

Izogo, E. E. (2015). Determinants of attitudinal loyalty in Nigerian telecom service sector: Does commitment play a mediating role?. Journal of Retailing and Consumer Services, 23, 107-117.

Jhantasana, C. (2022). Intrinsic and extrinsic motivation for university staff satisfaction: Confirmatory composite analysis and confirmatory factor analysis. Asia Social Issues, 15(2), 249810.

Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34, 183-202.

Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1-10.

Kock, N. (2017). Factor-based SEM building on consistent PLS: An information systems illustration. Laredo, TX: ScriptWarp Systems.

Marcoulides, G. A., & Chin, W. W. (2013). You write, but others read Common methodological misunderstandings in PLS and related methods. In Ed New Perspectives in partial least squares and related methods (pp. 31-64). Springer, New York, NY.

Müller, T., Schuberth, F., & Henseler, J. (2018). PLS path modeling–a confirmatory approach to study tourism technology and tourist behavior. Journal of Hospitality and Tourism Technology. 1757-9880.

Nitzl, C. (2016). The use of partial least squares structural equation modeling (PLS-SEM) in management accounting research: Directions for future theory development. Journal of Accounting Literature, 37, 19-35.

Nitzl, C., Roldan, J. L., & Cepeda, G. (2016). Mediation analysis in partial least squares path modeling: Helping researchers discuss more sophisticated models. Industrial Management & Data Systems, 116(9). 1849-1864.

OECD. (2006). Handbook on Constructing Composite Indicators. 42495745.pdf

Parasuraman, A., Zeithaml, V. A., & Malhotra, A. (2005). ES-QUAL: A multiple-item scale for assessing electronic service quality. Journal of Service Research, 7(3), 213-233.

Picon, A., Castro, I. & Roldan, J. L. (2014). The relationship between satisfaction and loyalty: A mediator analysis. Journal of Business Research, 67(5), 746-751.

Raykov, T. & Marcoulides, G. A. (2006). A First Course in Structural Equation Modeling, 2nd Edn. Mahaw: Lawrence Erlbaum Associates.

Reinartz, W., Krafft, M., & Hoyer, W. D. (2004). The customer relationship management process: Its measurement and impact on performance. Journal of Marketing Research, 41(3), 293-305.

Richter, N. F., Cepeda-Carrión, G., Roldán Salgueiro, J. L., & Ringle, C. M. (2016). European management research using partial least squares structural equation modeling (PLS-SEM). European Management Journal, 34 (6), 589-597.

Rigdon, E. E. (2016). Choosing PLS path modeling as an analytical method in European management research: A realist perspective. European Management Journal, 34(6), 598-605.

Rigdon, E. E., Sarstedt, M., & Ringle, C. M. (2017). On comparing results from CB-SEM and PLS-SEM: Five perspectives and five recommendations. Marketing: ZFP – Journal of Research and Management, 39(3), 4-16.

Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2018). Partial least squares structural equation modeling in human resource management research, The International Journal of Human Resource Management, 31(12),1617-1643.

Roemer, E., Schuberth, F., & Henseler, J. (2021). HTMT2–an improved criterion for assessing discriminant validity in structural equation modeling. Industrial Management & Data Systems, 121(12), 2637-2650.

Rönkkö, M., & Evermann, J. (2013). A critical examination of common beliefs about partial least squares path modeling. Organizational Research Methods, 16(3), 425-448.

Rönkkö, M., McIntosh, C. N., & Antonakis, J. (2015). On the adoption of partial least squares in psychological research: Caveat emptor. Personality and Individual Differences, 87, 76-84.

Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (2016). Estimation Issues with PLS and CBSEM: Where the bias lies!. Journal of Business Research, 69(10), 3998-4010.

Sarstedt, M., Hair Jr, J. F., Cheah, J. H., Becker, J. M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal (AMJ), 27(3), 197-211.

Schuberth, F. (2020). Confirmatory composite analysis using partial least squares: Setting the record straight. Review of Managerial Science, 1-35.

Schuberth, F., Henseler, J., & Dijkstra, T. K. (2018). Confirmatory composite analysis. Frontiers in Psychology, 9, 2541.

Schumacker., R. E., & Lomax, R. G. (2016). A beginner's guide to structural equation modeling: 4th Edition. New York, NY: Routledge.

Shankar, V., Urban, G. L., & Sultan, F. (2002). Online trust: A stakeholder perspective, concepts, implications, and future directions. The Journal of Strategic Information Systems, 11(3-4), 325-344.

Shmueli, G., Ray, S., Velasquez Estrada, J. M. & Chatla, S. B. (2016). The elephant in the room: Evaluating the predictive performance of PLS models. Journal of Business Research, 69(10), 4552-4564.

Soper, D. S. (2021). A-priori sample size calculator for structural equation models [Software].

Tran, T. T. (2019). On the factors affecting the development of e-commerce in Vietnam: Case study of Lazada, Shopee, and Tiki. International Journal of Advanced and Applied Sciences, 6(4), 45-52.

Urban, G. L., Amyx, C., & Lorenzon, A. (2009). Online trust: State of the art, new frontiers, and research potential. Journal of Interactive Marketing, 23(2), 179-190.

Yoon, S.-H. (2007). Determinants of online service satisfaction and their impacts on behavioral intentions. Journal of Korea Trade, 11(3), 23-52.

Yoon, S.-J. (2002). The antecedents and consequences of trust in online-purchase decisions. Journal of Interactive Marketing, 16(2), 47-63.

Yu, X., Zaza, S., Schuberth, F., & Henseler, J. (2021). Counterpoint: Representing forged concepts as emergent variables using composite-based structural equation modeling. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 52(SI), 114-130.

Zeng, F., Hu, Z., Chen, R., & Yang, Z. (2009). Determinants of online service satisfaction and their impacts on behavioural intentions. Total Quality Management & Business Excellence, 20(9), 953-969.




How to Cite

Jhantasana, C. . (2023). Using Latent Variables for Confirmatory Composite Analysis. Creative Business and Sustainability Journal, 44(2), 22–40. Retrieved from



Research Articles