IDEAS home Printed from https://ideas.repec.org/a/wly/apsmbi/v24y2008i5p439-458.html
   My bibliography  Save this article

REBUS‐PLS: A response‐based procedure for detecting unit segments in PLS path modelling

Author

Listed:
  • V. Esposito Vinzi
  • L. Trinchera
  • S. Squillacciotti
  • M. Tenenhaus

Abstract

Structural equation models (SEMs) make it possible to estimate the causal relationships, defined according to a theoretical model, linking two or more latent complex concepts, each measured through a number of observable indicators, usually called manifest variables. Traditionally, the component‐based estimation of SEMs by means of partial least squares (PLS path modelling, PLS‐PM) assumes homogeneity over the observed set of units: all units are supposed to be well represented by a unique model estimated on the overall data set. In many cases, however, it is reasonable to expect classes made of units showing heterogeneous behaviours to exist. Two different kinds of heterogeneity could be affecting the data: observed and unobserved heterogeneity. The first refers to the case of a priori existing classes, whereas in unobserved heterogeneity no information is available either on the number of classes or on their composition. If a group structure for the statistical units is given, the aim of the analysis is to search for any differences in the behaviours of the a priori given classes. In PLS‐PM this would mean studying the effect of directly observed moderating variables, i.e. estimating as many (local) models as there are classes. Unobserved heterogeneity, instead, implies identifying classes of units (a priori unknown) having similar behaviours. Such heterogeneity is captured by an unobserved (latent) discrete moderating variable defining both the number of classes and the class membership. A new method for unobserved heterogeneity detection in PLS‐PM is proposed in this paper: response‐based procedure for detecting unit segments in PLS‐PM (REBUS‐PLS). REBUS‐PLS, according to PLS‐PM features, does not require distributional hypotheses and may lead to local models that are different in terms of both structural and measurement models. An application of REBUS‐PLS on real data will be shown. Copyright © 2008 John Wiley & Sons, Ltd.

Suggested Citation

  • V. Esposito Vinzi & L. Trinchera & S. Squillacciotti & M. Tenenhaus, 2008. "REBUS‐PLS: A response‐based procedure for detecting unit segments in PLS path modelling," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 439-458, September.
    • Handle: RePEc:wly:apsmbi:v:24:y:2008:i:5:p:439-458
    DOI: 10.1002/asmb.728
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asmb.728
    Download Restriction: no
    File URL: https://libkey.io/10.1002/asmb.728?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Carsten Hahn & Michael D. Johnson & Andreas Herrmann & Frank Huber, 2002. "Capturing Customer Heterogeneity Using A Finite Mixture Pls Approach," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 54(3), pages 243-269, July.
    2. Kamel Jedidi & Harsharanjeet S. Jagpal & Wayne S. DeSarbo, 1997. "Finite-Mixture Structural Equation Models for Response-Based Segmentation and Unobserved Heterogeneity," Marketing Science, INFORMS, vol. 16(1), pages 39-59.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sarstedt, Marko & Radomir, Lăcrămioara & Moisescu, Ovidiu Ioan & Ringle, Christian M., 2022. "Latent class analysis in PLS-SEM: A review and recommendations for future applications," Journal of Business Research, Elsevier, vol. 138(C), pages 398-407.
    2. Fosso Wamba, Samuel & Bhattacharya, Mithu & Trinchera, Laura & Ngai, Eric W.T., 2017. "Role of intrinsic and extrinsic factors in user social media acceptance within workspace: Assessing unobserved heterogeneity," International Journal of Information Management, Elsevier, vol. 37(2), pages 1-13.
    3. Boussougou Boussougou, Guy & Sanz Sanz, Esther & Napoléone, Claude & Martinetti, Davide, 2021. "Identifying agricultural areas with potential for city connections: A regional-scale methodology for urban planning," Land Use Policy, Elsevier, vol. 103(C).
    4. Cheong Kim & Hyeon Gyu Jeon & Kun Chang Lee, 2020. "Discovering the Role of Emotional and Rational Appeals and Hidden Heterogeneity of Consumers in Advertising Copies for Sustainable Marketing," Sustainability, MDPI, vol. 12(12), pages 1-20, June.
    5. Picón-Berjoyo, Araceli & Ruiz-Moreno, Carolina & Castro, Ignacio, 2016. "A mediating and multigroup analysis of customer loyalty," European Management Journal, Elsevier, vol. 34(6), pages 701-713.
    6. Fordellone, Mario & Vichi, Maurizio, 2020. "Finding groups in structural equation modeling through the partial least squares algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 147(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sarstedt, Marko & Salcher, André, 2007. "Modellselektion in Finite Mixture PLS-Modellen," Discussion Papers in Business Administration 1394, University of Munich, Munich School of Management.
    2. Ringle, Christian M. & Sarstedt, Marko & Schlittgen, Rainer & Taylor, Charles R., 2013. "PLS path modeling and evolutionary segmentation," Journal of Business Research, Elsevier, vol. 66(9), pages 1318-1324.
    3. Joti kumari & Jai Kumar, 2023. "Influence of motivation on teachers’ job performance," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.
    4. Ringle, Christian M., 2006. "Segmentation for path models and unobserved heterogeneity: The finite mixture partial least squares approach," MPRA Paper 10734, University Library of Munich, Germany.
    5. Jan-Michael Becker & Christian Ringle & Marko Sarstedt & Franziska Völckner, 2015. "How collinearity affects mixture regression results," Marketing Letters, Springer, vol. 26(4), pages 643-659, December.
    6. Schlittgen, Rainer & Ringle, Christian M. & Sarstedt, Marko & Becker, Jan-Michael, 2016. "Segmentation of PLS path models by iterative reweighted regressions," Journal of Business Research, Elsevier, vol. 69(10), pages 4583-4592.
    7. Sarstedt, Marko & Radomir, Lăcrămioara & Moisescu, Ovidiu Ioan & Ringle, Christian M., 2022. "Latent class analysis in PLS-SEM: A review and recommendations for future applications," Journal of Business Research, Elsevier, vol. 138(C), pages 398-407.
    8. Fordellone, Mario & Vichi, Maurizio, 2020. "Finding groups in structural equation modeling through the partial least squares algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 147(C).
    9. Ioana Gutu & Daniela Tatiana Agheorghiesei & Alexandru Tugui, 2023. "Assessment of a Workforce Sustainability Tool through Leadership and Digitalization," IJERPH, MDPI, vol. 20(2), pages 1-30, January.
    10. Yu Ding & Wayne S. DeSarbo & Dominique M. Hanssens & Kamel Jedidi & John G. Lynch & Donald R. Lehmann, 2020. "The past, present, and future of measurement and methods in marketing analysis," Marketing Letters, Springer, vol. 31(2), pages 175-186, September.
    11. Allen, Jaime & Muñoz, Juan Carlos & Ortúzar, Juan de Dios, 2019. "On evasion behaviour in public transport: Dissatisfaction or contagion?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 626-651.
    12. Sarstedt, Marko & Wilczynski, Petra & Melewar, T.C., 2013. "Measuring reputation in global markets—A comparison of reputation measures’ convergent and criterion validities," Journal of World Business, Elsevier, vol. 48(3), pages 329-339.
    13. Kim, Suwon, 2018. "Snack-media platform market segmentation based on user heterogeneity: A Q-methodology approach," 22nd ITS Biennial Conference, Seoul 2018. Beyond the boundaries: Challenges for business, policy and society 190357, International Telecommunications Society (ITS).
    14. Nalan Basturk & Richard Paap & Dick van Dijk, 2008. "Structural Differences in Economic Growth," Tinbergen Institute Discussion Papers 08-085/4, Tinbergen Institute.
    15. Mahsa Samsami & Ralf Wagner, 2021. "Investment Decisions with Endogeneity: A Dirichlet Tree Analysis," JRFM, MDPI, vol. 14(7), pages 1-19, July.
    16. Christian Homburg, 2007. "Betriebswirtschaftslehre als Empirische Wissenschaft — Bestandsaufnahme und Empfehlungen," Schmalenbach Journal of Business Research, Springer, vol. 59(56), pages 27-60, January.
    17. Heungsun Hwang & Marc Tomiuk, 2010. "Fuzzy clusterwise quasi-likelihood generalized linear models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 4(4), pages 255-270, December.
    18. Amaral, Christopher & Kolsarici, Ceren, 2020. "The financial advice puzzle: The role of consumer heterogeneity in the advisor choice," Journal of Retailing and Consumer Services, Elsevier, vol. 54(C).
    19. Zhong Li & Shan Jiang & Ruibo He & Yihan Dong & Zijin Pan & Chengzhong Xu & Fangfang Lu & Pei Zhang & Liang Zhang, 2018. "Trajectories of Hospitalization Cost Among Patients of End-Stage Lung Cancer: A Retrospective Study in China," IJERPH, MDPI, vol. 15(12), pages 1-12, December.
    20. Sarstedt, Marko & Ringle, Christian M. & Smith, Donna & Reams, Russell & Hair, Joseph F., 2014. "Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers," Journal of Family Business Strategy, Elsevier, vol. 5(1), pages 105-115.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:apsmbi:v:24:y:2008:i:5:p:439-458. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)1526-4025 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.