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Simultaneous Equation Model based on the generalized maximum entropy for studying the effect of management factors on enterprise performance

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  • E. Ciavolino
  • J. J. Dahlgaard

Abstract

The aim of this paper is to study the effect of management factors on enterprise performance, considering a survey that the University Consortium in Engineering for Quality and Innovation has led. The relationships between management factors and enterprise performance are formalized by a Simultaneous Equation Model based on the generalized maximum entropy (GME) estimation method. The format of this paper is as follows. In Section 2, the data collected, the questionnaire evaluation, and the management model analytical formulation are introduced. In Section 3, the GME formulation is specified, showing the main characteristics of the estimation method. In Section 4, the results and a comparison among GME, partial least squares (PLS), and maximum likelihood estimation (MLE) is shown. In Section 5, concluding remarks are discussed.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:japsta:v:36:y:2009:i:7:p:801-815
    DOI: 10.1080/02664760802510026
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    References listed on IDEAS

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    1. Giovanni Portoso, "undated". "The indirect scaling into the customer satisfaction : An approach based on the alternative use of the exponential and the normal distribution," Working Papers 53, SEMEQ Department - Faculty of Economics - University of Eastern Piedmont.
    2. Giovanni Portoso, "undated". "A concentration indicator of the frequencies on the extremes for the ordinal categorial variables based on judgments," Working Papers 66, SEMEQ Department - Faculty of Economics - University of Eastern Piedmont.
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    Citations

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    Cited by:

    1. 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.
    2. Renato Civitillo & Paolo Ricci & Biagio Simonetti, 2019. "Management and performance of Non-Profit Institutions: finding new development trajectories—evidence from Italy," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2275-2290, September.
    3. Rosa Bernardini Papalia & Enrico Ciavolino, 2011. "GME Estimation of Spatial Structural Equations Models," Journal of Classification, Springer;The Classification Society, vol. 28(1), pages 126-141, April.
    4. Alessandro Gennaro & Matteo Reho & Tiziana Marinaci & Barbara Cordella & Marco Castiglioni & Cristina Liviana Caldiroli & Claudia Venuleo, 2023. "Social Environment and Attitudes toward COVID-19 Anti-Contagious Measures: An Explorative Study from Italy," IJERPH, MDPI, vol. 20(4), pages 1-15, February.
    5. Enrico Ciavolino & Antonio Calcagnì, 2014. "A generalized maximum entropy (GME) approach for crisp-input/fuzzy-output regression model," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3401-3414, November.
    6. E. Ciavolino & A. Calcagnì, 2015. "Generalized cross entropy method for analysing the SERVQUAL model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(3), pages 520-534, March.

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