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Structural equation modelling: a silver bullet for evaluating public service motivation

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  • Raffaela Palma

    (Institutions at the University of Naples “Federico II”)

  • Enrica Sepe

    (Institutions at the University of Naples “Federico II”)

Abstract

The growing interest in public employee motivation and performance as a way to improve the quality of public service suggests the need to analyse different relationships. The overall goal of this work is to examine Public Service Motivation (PSM) and establish how it affects two positive outcomes, job satisfaction and individual performance, and two negative outcomes, resigned satisfaction and burnout. Firstly, this study aims to verify whether PSM positively influences employee job satisfaction and individual performance and, secondly, if PSM can reduce the risk of the two negative outcomes: burnout, which is a cause of people leaving their jobs, and resigned satisfaction, which in the long term leads to burnout. This analysis is based on a sample of 296 Italian teachers working in state primary and secondary schools. The collected data are analysed through structural equation modelling to establish the significant relationships among the mentioned variables. Moreover, partial least squares multi-group analysis was applied to investigate if there were significant differences between the two groups that belong to the variable relating to employee job tenure. Finally, suggestions for future research are offered based on the obtained results.

Suggested Citation

  • Raffaela Palma & Enrica Sepe, 2017. "Structural equation modelling: a silver bullet for evaluating public service motivation," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 729-744, March.
    • Handle: RePEc:spr:qualqt:v:51:y:2017:i:2:d:10.1007_s11135-016-0436-9
    DOI: 10.1007/s11135-016-0436-9
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    References listed on IDEAS

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    1. William Meredith, 1993. "Measurement invariance, factor analysis and factorial invariance," Psychometrika, Springer;The Psychometric Society, vol. 58(4), pages 525-543, December.
    2. Herman Wold, 1980. "Model Construction and Evaluation When Theoretical Knowledge Is Scarce," NBER Chapters, in: Evaluation of Econometric Models, pages 47-74, National Bureau of Economic Research, Inc.
    3. Trui P. S. Steen & Mark R. Rutgers, 2011. "The double-edged sword," Public Management Review, Taylor & Francis Journals, vol. 13(3), pages 343-361, March.
    4. Jeannette Taylor & Jonathan H. Westover, 2011. "Job Satisfaction in The Public Service," Public Management Review, Taylor & Francis Journals, vol. 13(5), pages 731-751, June.
    5. Jan Kmenta & James B. Ramsey, 1980. "Evaluation of Econometric Models," NBER Books, National Bureau of Economic Research, Inc, number kmen80-1, March.
    6. Henry Kaiser, 1958. "The varimax criterion for analytic rotation in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 23(3), pages 187-200, September.
    7. Jörg Henseler & Marko Sarstedt, 2013. "Goodness-of-fit indices for partial least squares path modeling," Computational Statistics, Springer, vol. 28(2), pages 565-580, April.
    8. Tenenhaus, Michel & Vinzi, Vincenzo Esposito & Chatelin, Yves-Marie & Lauro, Carlo, 2005. "PLS path modeling," Computational Statistics & Data Analysis, Elsevier, vol. 48(1), pages 159-205, January.
    9. Nina Mari van Loon & Wouter Vandenabeele & Peter Leisink, 2015. "On the bright and dark side of public service motivation: the relationship between PSM and employee wellbeing," Public Money & Management, Taylor & Francis Journals, vol. 35(5), pages 349-356, September.
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    Cited by:

    1. AMENDOLA, Francesca, 2019. "he Public Service Motivation: Lessons from the Literature," CELPE Discussion Papers 158, CELPE - CEnter for Labor and Political Economics, University of Salerno, Italy.
    2. Sahar Awan & Germà Bel & Marc Esteve, 2018. "“The benefits of PSM: an oasis or a mirage?”," IREA Working Papers 201825, University of Barcelona, Research Institute of Applied Economics, revised Oct 2018.

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