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Data Analytics and Machine Learning paradigm to gauge performances combining classification, ranking and sorting for system analysis

Author

Listed:
  • Andrea Pontiggia

    (Dept. of Management, Università Ca' Foscari Venice)

  • Giovanni Fasano

    (Dept. of Management, Università Ca' Foscari Venice)

Abstract

We consider the problem of measuring the performances associated with members of a given group of homogeneous individuals. We provide both an analysis, relying on Machine Learning paradigms, along with a numerical experience based on three conceptually different real applications. A keynote aspect in the proposed approach is represented by our data–driven framework, where guidelines for evaluating individuals’ performance are derived from the data associated to the entire group. This makes our analysis and the relative outcomes quite versatile, so that a number of real problems can be studied in view of the proposed general perspective.

Suggested Citation

  • Andrea Pontiggia & Giovanni Fasano, 2021. "Data Analytics and Machine Learning paradigm to gauge performances combining classification, ranking and sorting for system analysis," Working Papers 05, Department of Management, Università Ca' Foscari Venezia.
    • Handle: RePEc:vnm:wpdman:182
    as

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    References listed on IDEAS

    as
    1. Dmitri Kuksov & J. Miguel Villas-Boas, 2019. "The Performance Measurement Trap," Marketing Science, INFORMS, vol. 38(1), pages 68-87, January.
    2. J. Daniel Sherman & Robert T. Keller, 2011. "Suboptimal Assessment of Interunit Task Interdependence: Modes of Integration and Information Processing for Coordination Performance," Organization Science, INFORMS, vol. 22(1), pages 245-261, February.
    3. Aggarwal, Divya & Chandrasekaran, Shabana & Annamalai, Balamurugan, 2020. "A complete empirical ensemble mode decomposition and support vector machine-based approach to predict Bitcoin prices," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    4. Yue Maggie Zhou, 2013. "Designing for Complexity: Using Divisions and Hierarchy to Manage Complex Tasks," Organization Science, INFORMS, vol. 24(2), pages 339-355, April.
    5. Shannon W. Anderson & Amanda Kimball, 2019. "Evidence for the Feedback Role of Performance Measurement Systems," Management Science, INFORMS, vol. 65(9), pages 4385-4406, September.
    6. Margaret A. Abernethy & Henri C. Dekker & Jennifer Grafton, 2021. "The Influence of Performance Measurement on the Processual Dynamics of Strategic Change," Management Science, INFORMS, vol. 67(1), pages 640-659, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Performance Analysis; Data Analytics; Support Vector Machines; Human Resources;
    All these keywords.

    JEL classification:

    • M51 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - Firm Employment Decisions; Promotions
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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