Quality & Quantity

, Volume 53, Issue 3, pp 1421–1458 | Cite as

A comparison of five reflective–formative estimation approaches: reconsideration and recommendations for tourism research

  • Jun-Hwa Cheah
  • Hiram Ting
  • T. Ramayah
  • Mumtaz Ali Memon
  • Tat-Huei Cham
  • Enrico CiavolinoEmail author


In partial least squares structural path modelling, the reflective–formative type of hierarchical component models (HCMs) (also known as Higher-Order Model) have become a popular choice for researchers. However, current approaches to estimate the reflective–formative type of HCM are ambiguous especially when used as an endogenous construct or a mediator. This paper presents a comparison between five different approaches (repeated indicator, two types of two-stage, hybrid, and improved repeated indicator) with two different estimation modes (Mode A and Mode B) when modelling a mediator construct of a reflective–formative HCM in the structural model. By using a model based on stimulus–organism-response theory, an empirical application to the tourism field is adopted in this study. The proposed HCM model examines perceived relative advantages as a mediation of the relationship between Communicability and Intention to Purchase Travel Online. The findings suggest that the improved repeated indicator approach with Mode B estimation yields better path coefficients, goodness of fit, explained variance, and predictive relevance as compared to other approaches. The study provides valuable recommendations and guidelines for tourism researchers to properly conduct an HCM analysis.


PLS-PM Hierarchical component model (HCM) Reflective–formative Tourism 



The authors wish to thank Prof. Dr. Christian M. Ringle and Prof. Dr. Marko Sarstedt for their valuable comments on the earlier version of this manuscript.


  1. Aguirre-Urreta, M.I., Rönkkö, M.: Statistical inference with PLSc using bootstrap confidence intervals. MIS Q. 42(3), 1001–1020 (2018)CrossRefGoogle Scholar
  2. Ali, F., Rasoolimanesh, S.M., Sarstedt, M., Ringle, C.M., Ryu, K.: An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. Int. J. Contemp. Hosp. Manag. 30(1), 514–538 (2018)CrossRefGoogle Scholar
  3. Amaro, S., Duarte, P.: An integrative model of consumers’ intentions to purchase travel online. Tour. Manag. 46, 64–79 (2015)CrossRefGoogle Scholar
  4. Arora, R.: Validation of an SOR model for situation, enduring, and response components of involvement. J. Mark. Res. 19(4), 505–516 (1982)CrossRefGoogle Scholar
  5. Babin, B.J., Griffin, M., Hair, J.F.: Heresies and sacred cows in scholarly marketing publications. J. Bus. Res. 69(8), 3133–3138 (2016)CrossRefGoogle Scholar
  6. Baron, R.M., Kenny, D.A.: The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 51(6), 1173–1182 (1986)CrossRefGoogle Scholar
  7. Barroso, C., Picón, A.: Multi-dimensional analysis of perceived switching costs. Ind. Mark. Manag. 41(3), 531–543 (2012)CrossRefGoogle Scholar
  8. Bauldry, S., Bollen, K.A.: Tetrad: a set of Stata commands for confirmatory tetrad analysis. Struct. Equ. Model. Multidiscip. J. 23(6), 921–930 (2016)CrossRefGoogle Scholar
  9. Becker, J.M., Klein, K., Wetzels, M.: Hierarchical latent variable models in PLS-SEM: guidelines for using reflective–formative type models. Long Range Plan. 45(5–6), 359–394 (2012)CrossRefGoogle Scholar
  10. Becker, J.M., Rai, A., Rigdon, E.: Predictive validity and formative measurement in structural equation modeling: embracing practical relevance. In: Proceedings of the International Conference on Information Systems (ICIS) (2013)Google Scholar
  11. Bernini, C., Matteucci, M., Mignani, S.: A Bayesian multidimensional IRT approach for the analysis of residents’ perceptions toward tourism. Electron. J. Appl. Stat. Anal. 8(3), 272–287 (2015)Google Scholar
  12. Bigné, E., Sanz, S., Ruiz, C., Aldás, J.: Why some Internet users don’t buy air tickets online. Inf. Commun. Technol. Tourism 6, 209–221 (2010)Google Scholar
  13. Cadogan, J.W., Lee, N.: Improper use of endogenous formative variables. J. Bus. Res. 66(2), 233–241 (2013)CrossRefGoogle Scholar
  14. Cheah, J.H., Sarstedt, M., Ringle, C.M., Ramayah, T., Ting, H.: Convergent validity assessment of formatively measured constructs in PLS-SEM: on using single-item versus multi-item measures in redundancy analyses. Int. J. Contemp. Hosp. Manag. (2018).
  15. Chin, W.W.: How to write up and report PLS analyses. In: Esposito Vinzi, V., Chin, W., Henseler, J., Wang, H. (eds.), Handbook of Partial Least Squares, pp. 655–690. Springer, Berlin (2010)CrossRefGoogle Scholar
  16. Ciavolino, E.: General distress as second order latent variable estimated through PLS-PM approach. Electron. J. Appl. Stat. Anal. 5(3), 458–464 (2012)Google Scholar
  17. Ciavolino, E., Calcagnì, A.: Generalized cross entropy method for analysing the SERVQUAL model. J. Appl. Stat. 42(3), 520–534 (2015)CrossRefGoogle Scholar
  18. Ciavolino, E., Carpita, M.: The GME estimator for the regression model with a composite indicator as explanatory variable. Qual. Quant. 49(3), 955–965 (2015)CrossRefGoogle Scholar
  19. Ciavolino, E., Nitti, M.: Using the hybrid two-step estimation approach for the identification of second-order latent variable models. J. Appl. Stat. 40(3), 508–526 (2013)CrossRefGoogle Scholar
  20. Ciavolino, E., Carpita, M., Al-Nasser, A.: Modelling the quality of work in the Italian social co-operatives combining NPCA-RSM and SEM-GME approaches. J. Appl. Stat. 42(1), 161–179 (2015a)CrossRefGoogle Scholar
  21. Ciavolino, E., Carpita, M., Nitti, M.: High-order pls path model with qualitative external information. Qual. Quant. 49(4), 1609–1620 (2015b)CrossRefGoogle Scholar
  22. Ciavolino, E., Salvatore, S., Mossi, P., Lagetto, G.: High-order PLS path model for multi-group analysis: the prosumership service quality model. Qual. Quant. (2018). CrossRefGoogle Scholar
  23. Cohen, J.: Statistical Power Analysis for the Behavioral Sciences, 2nd edn. Lawrence Erlbaum Associates, Publishers, Mahwah (1988)Google Scholar
  24. Diamantopoulos, A., Siguaw, J.A.: Formative versus reflective indicators in organizational measure development: a comparison and empirical illustration. Br. J. Manag. 17(4), 263–282 (2006)CrossRefGoogle Scholar
  25. Diamantopoulos, A., Riefler, P., Roth, K.P.: Advancing formative measurement models. J. Bus. Res. 61(12), 1203–1218 (2008)CrossRefGoogle Scholar
  26. do Valle, P.O., Assaker, G.: Using partial least squares structural equation modeling in tourism research: a review of past research and recommendations for future applications. J. Travel Res. 55(6), 695–708 (2016)CrossRefGoogle Scholar
  27. Dolbier, C.L., Webster, J.A., McCalister, K.T., Mallon, M.W., Steinhardt, M.A.: Reliability and validity of a single-item measure of job satisfaction. Am. J. Health Promot. 19(3), 194–198 (2005)CrossRefGoogle Scholar
  28. Edwards, J.R.: Multidimensional constructs in organizational behavior research: an integrative analytical framework. Organ. Res. Methods 4(2), 144–192 (2001)CrossRefGoogle Scholar
  29. Fink, A.: How to Conduct Surveys: A Step-by-Step Guide. Sage, Thousand Oaks (2017)Google Scholar
  30. Franke, G., Sarstedt, M.: Heuristics versus statistics in discriminant validity testing: A comparison of four procedures. Internet Res. (forthcoming)Google Scholar
  31. Fuller, C.M., Simmering, M.J., Atinc, G., Atinc, Y., Babin, B.J.: Common methods variance detection in business research. J. Bus. Res. 69(8), 3192–3198 (2016)CrossRefGoogle Scholar
  32. Garson, G.D.: Partial Least Squares: Regression and Structural Equation Model. Statistical Associates Publishing, Asheboro (2016)Google Scholar
  33. Geisser, S.: A predictive approach to the random effect model. Biometrika 61(1), 101–107 (1974)CrossRefGoogle Scholar
  34. González Rodriguez, M.R., Sámper, R.M.: Analysis of the efficiency of Spanish travel agencies. Electron. J. Appl. Stat. Anal. 5(1), 60–73 (2012)Google Scholar
  35. Goodhue, D.L., Lewis, W., Thompson, R.: Does PLS have advantages for small sample size or non-normal data? MIS Q. 26(2), 981–1001 (2012)CrossRefGoogle Scholar
  36. Grewal, D., Monroe, K.B., Krishnan, R.: The effects of price-comparison advertising on buyers’ perceptions of acquisition value, transaction value, and behavioral intentions. J. Mark. 62(2), 46–59 (1998)Google Scholar
  37. Gudergan, S.P., Ringle, C.M., Wende, S., Will, A.: Confirmatory tetrad analysis in PLS path modeling. J. Bus. Res. 61(12), 1238–1249 (2008)CrossRefGoogle Scholar
  38. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E.: Multivariate Data Analysis. Prentice Hall, Upper Saddle River (2010)Google Scholar
  39. Hair, J.F., Sarstedt, M., Pieper, T.M., Ringle, C.M.: The use of partial least squares structural equation modeling in strategic management research: a review of past practices and recommendations for future applications. Long Range Plan. 45(5–6), 320–340 (2012a)CrossRefGoogle Scholar
  40. Hair, J.F., Sarstedt, M., Ringle, C.M., Mena, J.A.: An assessment of the use of partial least squares structural equation modeling in marketing research. J. Acad. Mark. Sci. 40(3), 414–433 (2012b)CrossRefGoogle Scholar
  41. Hair, J.F., Sarstedt, M., Ringle, C.M.: Partial least squares structural equation modeling: rigorous applications, better results and higher acceptance. Long Range Plan. 46(1), 1–12 (2013)CrossRefGoogle Scholar
  42. Hair, J., Hollingsworth, C.L., Randolph, A.B., Chong, A.Y.L.: An updated and expanded assessment of PLS-SEM in information systems research. Ind. Manag. Data Syst. 117(3), 442–458 (2017a)CrossRefGoogle Scholar
  43. Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M.A.: Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage, Thousand Oaks (2017b)Google Scholar
  44. Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M., Thiele, K.O.: Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods. J. Acad. Mark. Sci. 45(5), 616–632 (2017c)CrossRefGoogle Scholar
  45. Hair, J.F., Sarstedt, M., Ringle, C.M., Gudergan, S.P.: Advanced Issues in Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage, Thousand Oaks (2018)Google Scholar
  46. Henseler, J., Ringle, C.M., Sinkovics, R.R.: The use of partial least squares path modeling in international marketing. In: Sinkovics, R.R., Ghauri, P.N. (eds.), New Challenges to International Marketing, pp. 277–319. Emerald Group Publishing Limited (2009)Google Scholar
  47. Henseler, J., Dijkstra, T.K., Sarstedt, M., Ringle, C.M., Diamantopoulos, A., Straub, D.W., et al.: Common beliefs and reality about PLS: comments on Rönkkö and Evermann (2013). Organ. Res. Methods 17(2), 182–209 (2014)CrossRefGoogle Scholar
  48. Henseler, J., Ringle, C.M., Sarstedt, M.: A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 43(1), 115–135 (2015)CrossRefGoogle Scholar
  49. Higgins, C.A., Compeau, D.R., Meister, D.B.: From prediction to explanation: reconceptualizing and extending the perceived characteristics of innovating. J. Assoc. Inf. Syst. 8(8), 26 (2007)Google Scholar
  50. Holak, S.L., Lehmann, D.R.: Purchase intentions and the dimensions of innovation: an exploratory model. J. Prod. Innov. Manag. 7(1), 59–73 (1990)CrossRefGoogle Scholar
  51. Ingusci, E., Manuti, A., Callea, A.: Employability as mediator in the relationship between the meaning of working and job search behaviours during unemployment. Electron. J. Appl. Stat. Anal. 9(1), 1–16 (2016)Google Scholar
  52. Jarvis, C.B., MacKenzie, S.B., Podsakoff, P.M.: A critical review of construct indicators and measurement model misspecification in marketing and consumer research. J. Consum. Res. 30(2), 199–218 (2003)CrossRefGoogle Scholar
  53. Johnson, R.E., Rosen, C.C., Djurdjevic, E., Taing, M.U.: Recommendations for improving the construct clarity of higher-order multidimensional constructs. Hum. Resour. Manag. Rev. 22(2), 62–72 (2012)CrossRefGoogle Scholar
  54. Kaufmann, L., Gaeckler, J.: A structured review of partial least squares in supply chain management research. J. Purch. Supply Manag. 21(4), 259–272 (2015)CrossRefGoogle Scholar
  55. Kline, R.B.: Principles and Practice of Structural Equation Modeling. Guilford Press, New York (2016)Google Scholar
  56. Lee, N., Cadogan, J.W.: Problems with formative and higher-order reflective variables. J. Bus. Res. 66(2), 242–247 (2013)CrossRefGoogle Scholar
  57. Lee, L., Petter, S., Fayard, D., Robinson, S.: On the use of partial least squares path modeling in accounting research. Int. J. Account. Inf. Syst. 12(4), 305–328 (2011)CrossRefGoogle Scholar
  58. Li, L., Buhalis, D.: E-commerce in China: the case of travel. Int. J. Inf. Manag. 26(2), 153–166 (2006)CrossRefGoogle Scholar
  59. Limayem, M., Khalifa, M., Frini, A.: What makes consumers buy from Internet? A longitudinal study of online shopping. IEEE Trans. Syst. Man Cybern. A Syst. Hum. 30(4), 421–432 (2000)CrossRefGoogle Scholar
  60. Lohmöller, J.B.: Latent variable path analysis with partial least squares. Physica, Heidelberg (1989)CrossRefGoogle Scholar
  61. Marcoulides, G.A., Chin, W.W., Saunders, C.: When imprecise statistical statements become problematic: a response to Goodhue, Lewis, and Thompson. MIS Q. 36(3), 717–728 (2012)CrossRefGoogle Scholar
  62. Meharbian, A., Russell, J.A.: An Approach to Environment Psychology. MIT Press, Cambridge (1974)Google Scholar
  63. Morrisonn, A.M., Jing, S., O’Leary, J.T., Cai, L.A.: Predicting usage of the internet for travel bookings: an exploratory study. Inf. Technol. Tour. 4(1), 15–30 (2001)CrossRefGoogle Scholar
  64. Nitzl, C.: The use of partial least squares structural equation modelling (PLS-SEM) in management accounting research: directions for future theory development. J. Account. Lit. 37, 19–35 (2016)CrossRefGoogle Scholar
  65. Nitti, M., Ciavolino, E.: A deflated indicators approach for estimating second-order reflective models through PLS-PM: an empirical illustration. J. Appl. Stat. 41(10), 2222–2239 (2014)CrossRefGoogle Scholar
  66. Nitzl, C., Roldan, J.L., Cepeda, G.: Mediation analysis in partial least squares path modeling: helping researchers discuss more sophisticated models. Ind. Manag. Data Syst. 116(9), 1849–1864 (2016)CrossRefGoogle Scholar
  67. Peng, D.X., Lai, F.: Using partial least squares in operations management research: a practical guideline and summary of past research. J. Oper. Manag. 30(6), 467–480 (2012)CrossRefGoogle Scholar
  68. Podsakoff, P.M., Organ, D.W.: Self-reports in organizational research: problems and prospects. J. Manag. 12(4), 531–544 (1986)Google Scholar
  69. Polites, G.L., Roberts, N., Thatcher, J.: Conceptualizing models using multidimensional constructs: a review and guidelines for their use. Eur. J. Inf. Syst. 21(1), 22–48 (2012)CrossRefGoogle Scholar
  70. Richter, N.F., Sinkovics, R.R., Ringle, C.M., Schlaegel, C.: A critical look at the use of SEM in international business research. Int. Mark. Rev. 33(3), 376–404 (2016a)CrossRefGoogle Scholar
  71. Richter, N.F., Cepeda, G., Roldán, J.L., Ringle, C.M.: European management research using partial least squares structural equation modeling (PLS-SEM). Eur. Manag. J. 34(6), 589–597 (2016b)CrossRefGoogle Scholar
  72. Rigdon, E.E.: Rethinking partial least squares path modeling: in praise of simple methods. Long Range Plan. 45(5–6), 341–358 (2012)CrossRefGoogle Scholar
  73. Rigdon, E.E.: Comment on “Improper use of endogenous formative variables”. J. Bus. Res. 67(1), 2800–2802 (2014a)CrossRefGoogle Scholar
  74. Rigdon, E.E.: Rethinking partial least squares path modeling: breaking chains and forging ahead. Long Range Plan. 47(3), 161–167 (2014b)CrossRefGoogle Scholar
  75. Rigdon, E.E.: Choosing PLS path modeling as analytical method in European management research: a realist perspective. Eur. Manag. J. 34(6), 598–605 (2016)CrossRefGoogle Scholar
  76. Ringle, C.M., Sarstedt, M., Straub, D.W.: A critical look at the use of PLS-SEM in MIS quarterly. MIS Q. 36(1), iii–xiv (2012)CrossRefGoogle Scholar
  77. Ringle, C.M., Wende, S., Becker, J.M.: SmartPLS 3. SmartPLS GmbH, Boenningstedt (2015)Google Scholar
  78. Ringle, C.M., Sarstedt, M., Mitchell, R., Gudergan, S.P.: Partial least squares structural equation modeling in HRM research. Int. J. Hum. Resour. Manag. (2018). CrossRefGoogle Scholar
  79. Rönkkö, M., Evermann, J.: A critical examination of common beliefs about partial least squares path modeling. Organ. Res. Methods 16(3), 425–448 (2013)CrossRefGoogle Scholar
  80. Sarstedt, M., Ringle, C.M., Smith, D., Reams, R., Hair Jr., J.F.: Partial least squares structural equation modeling (PLS-SEM): a useful tool for family business researchers. J. Fam. Bus. Strategy 5(1), 105–115 (2014)CrossRefGoogle Scholar
  81. Sarstedt, M., Hair, J.F., Ringle, C.M., Thiele, K.O., Gudergan, S.P.: Estimation issues with PLS and CBSEM: where the bias lies! J. Bus. Res. 69(10), 3998–4010 (2016)CrossRefGoogle Scholar
  82. Sarstedt, M., Ringle, C.M., Hair, J.F.: Partial least squares structural equation modeling. In: Homburg, C., Klarmann, M., Vomberg, A. (eds.), Handbook of Market Research, pp. 1–40. Springer, Cham (2017)Google Scholar
  83. Schönemann, P.H., Wang, M.M.: Some new results on factor indeterminacy. Psychometrika 37(1), 61–91 (1972)CrossRefGoogle Scholar
  84. Shmueli, G., Ray, S., Velasquez Estrada, J.M., Chatla, S.: The elephant in the room: evaluating the predictive performance of PLS models. J. Bus. Res. 69(10), 4552–4564 (2016)CrossRefGoogle Scholar
  85. Slama, M.E., Tashchian, A.: Validating the SOR paradigm for consumer involvement with a convenience good. J. Acad. Mark. Sci. 15(1), 36–45 (1987)CrossRefGoogle Scholar
  86. Sosik, J.J., Kahai, S.S., Piovoso, M.J.: Silver bullet or voodoo statistics? A primer for using the partial least squares data analytic technique in group and organization research. Group Organ. Manag. 34(1), 5–36 (2009)CrossRefGoogle Scholar
  87. Stone, M.: Cross-validatory choice and assessment of statistical predictions. J. R. Stat. Soc. B (Methodological), 36(2), 111–147 (1974)CrossRefGoogle Scholar
  88. Streukens, S., Leroi-Werelds, S.: Bootstrapping and PLS-SEM: a step-by-step guide to get more out of your bootstrap results. Eur. Manag. J. 34(6), 618–632 (2016)CrossRefGoogle Scholar
  89. Teo, T.S., Yeong, Y.D.: Assessing the consumer decision process in the digital marketplace. Omega 31(5), 349–363 (2003)CrossRefGoogle Scholar
  90. Van Riel, A.C., Henseler, J., Kemény, I., Sasovova, Z.: Estimating hierarchical constructs using consistent partial least squares: the case of second-order composites of common factors. Ind. Manag. Data Syst. 117(3), 459–477 (2017)CrossRefGoogle Scholar
  91. Voorhees, C.M., Brady, M.K., Calantone, R., Ramirez, E.: Discriminant validity testing in marketing: an analysis, causes for concern, and proposed remedies. J. Acad. Mark. Sci. 44(1), 119–134 (2016)CrossRefGoogle Scholar
  92. Wanous, J.P., Reichers, A.E.: Estimating the reliability of a single-item measure. Psychol. Rep. 78(2), 631–634 (1996)CrossRefGoogle Scholar
  93. Wetzels, M., Odekerken-Schröder, G., Van Oppen, C.: Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration. MIS Q. 33(1), 177–195 (2009)CrossRefGoogle Scholar
  94. Willaby, H.W., Costa, D.S., Burns, B.D., MacCann, C., Roberts, R.D.: Testing complex models with small sample sizes: a historical overview and empirical demonstration of what partial least squares (PLS) can offer differential psychology. Personal. Individ. Differ. 84, 73–78 (2015)CrossRefGoogle Scholar
  95. Wilson, B.: Using PLS to investigate interaction effects between higher order branding constructs. In: Esposito Vinzi, V., Chin, W., Henseler, J., Wang, H. (eds.), Handbook of Partial Least Squares, pp. 621–652. Springer, Berlin (2010)CrossRefGoogle Scholar
  96. Wilson, B., Henseler, J.: Modeling reflective higher-order constructs using three approaches with PLS path modeling: a Monte Carlo comparison. In: Thyne, M., Deans, K.R., Gnoth, J. (eds.) Australian and New Zealand Marketing Academy Conference Proceedings, pp. 791–800. Department of Marketing, School of Business, University of Otago, Otago (2007)Google Scholar
  97. Wold, H.O.: Soft modelling: the basic design and some extensions. In: Jöreskog, K.G., Wold, H.O. (eds.), Systems under indirect observations: Part II. North-Holland, Amsterdam, pp. 1–54 (1982)Google Scholar
  98. Wold, H.O.: Partial Least Squares. In: Kotz, S., Read, C.B., Balakrishnan, N., Vidakovic, B., Johnson, N.L. (eds.), Encyclopedia of Statistical Sciences, Wiley (2006)Google Scholar
  99. Zhao, X., Lynch Jr., J.G., Chen, Q.: Reconsidering Baron and Kenny: myths and truths about mediation analysis. J. Consum. Res. 37(2), 197–206 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Faculty of Economics and ManagementUniversiti Putra Malaysia (UPM)SerdangMalaysia
  2. 2.Faculty of Hospitality and Tourism ManagementUCSI UniversityKuala LumpurMalaysia
  3. 3.School of ManagementUniversiti Sains Malaysia (USM)PenangMalaysia
  4. 4.Air University School of ManagementAir UniversityIslamabadPakistan
  5. 5.Faculty of Accountancy and ManagementUniversiti Tunku Abdul Rahman (UTAR)SelangorMalaysia
  6. 6.University of SalentoLecceItaly

Personalised recommendations