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High-order PLS path model for multi-group analysis: the prosumership service quality model

Author

Listed:
  • Enrico Ciavolino

    (University of Salento)

  • Sergio Salvatore

    (University of Salento)

  • Piergiorgio Mossi

    (University of Salento)

  • Gloria Lagetto

    (University of Salento)

Abstract

This study deepening the analysis of prosumership service quality model (PROSERV), a new model for assessing the customer satisfaction developed in order to take prosumership into account as a fundamental component of service quality and satisfaction (Ciavolino et al. in Int J Bus Soc 18(3):409–426, 2017b). In particular this study wants to demonstrate its factorial invariance by comparing the results obtained in several production sectors and using innovative PLS Path Modelling (PLS-PM) evaluation. The answers of 544 users of services (53.9% women; age: Mean $$=$$ = 40.77; SD $$=$$ = 13,270) in 4 sectors: banks, post offices, restaurants and medical centers were examined. A procedure of multi-group analysis based on PLS-PM with high order constructs has been implemented. The results show that in the overall satisfaction there is a substantial invariance between the different components considered by the PROSERV model.

Suggested Citation

  • Enrico Ciavolino & Sergio Salvatore & Piergiorgio Mossi & Gloria Lagetto, 2019. "High-order PLS path model for multi-group analysis: the prosumership service quality model," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2371-2384, September.
    • Handle: RePEc:spr:qualqt:v:53:y:2019:i:5:d:10.1007_s11135-018-0747-0
    DOI: 10.1007/s11135-018-0747-0
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    References listed on IDEAS

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    1. E. Ciavolino & J. J. Dahlgaard, 2009. "Simultaneous Equation Model based on the generalized maximum entropy for studying the effect of management factors on enterprise performance," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(7), pages 801-815.
    2. Enrico Ciavolino & Maurizio Carpita, 2015. "The GME estimator for the regression model with a composite indicator as explanatory variable," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 955-965, May.
    3. Enrico Ciavolino & Maurizio Carpita & Mariangela Nitti, 2015. "High-order PLS path model with qualitative external information," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(4), pages 1609-1620, July.
    4. Enrico Ciavolino & Mariangela Nitti, 2013. "Using the Hybrid Two-Step estimation approach for the identification of second-order latent variable models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(3), pages 508-526.
    5. M. Nitti & E. Ciavolino, 2014. "A deflated indicators approach for estimating second-order reflective models through PLS-PM: an empirical illustration," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(10), pages 2222-2239, October.
    6. Enrico Ciavolino & Maurizio Carpita & Amjad Al-Nasser, 2015. "Modelling the quality of work in the Italian social co-operatives combining NPCA-RSM and SEM-GME approaches," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(1), pages 161-179, January.
    7. Enrico Ciavolino & Amjad Al-Nasser, 2009. "Comparing generalised maximum entropy and partial least squares methods for structural equation models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(8), pages 1017-1036.
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    Cited by:

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