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A framework for the Assessment of Research and its impacts

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  • Cinzia Daraio

    (Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG), University of Rome La Sapienza, Rome, Italy)

Abstract

This paper proposes a framework for the development of models for the assessment of research activities and their impacts. It distinguishes three dimensions: theory, methodology and data, each of which is further characterized by three main building blocks: education, research and innovation (theory); efficiency, effectiveness and impact (methodology); and availability, interoperability and \unit free" property (data). The different dimensions and their nine constituent building blocks are attributes of an overarching concept, denoted as "quality". Three additional quality attributes are identified as implementation factors (tailorability, transparency and openness) and three "enabling" conditions (convergence, mixed methods and knowledge infrastructures) complete the framework.The paper illustrates the complexity of the evaluation describing the generalized "implementation problem" in research assessment, according to the proposed framework. A framework is required to develop models of metrics. Models of metrics are necessary to assess the meaning, validity and robustness of metrics. The proposed framework can be a useful reference for the development of the ethics of research evaluation. Three examples of application, as well as further directions for future research are provided.

Suggested Citation

  • Cinzia Daraio, 2017. "A framework for the Assessment of Research and its impacts," DIAG Technical Reports 2017-04, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    • Handle: RePEc:aeg:report:2017-04
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    Cited by:

    1. Cinzia Daraio & Alessio Vaccari, 2019. "Sorting out Guidelines for a Good Evaluation of Research Practices.Towards the Assessment of Researcher’s Virtues," DIAG Technical Reports 2019-10, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    2. Daraio, Cinzia & Simar, Léopold & Wilson, Paul W., 2021. "Quality as a latent heterogeneity factor in the efficiency of universities," Economic Modelling, Elsevier, vol. 99(C).
    3. Cinzia Daraio & Leopold Simar & Paul W. Wilson, 2019. "Quality and its Impact on Efficiency," LEM Papers Series 2019/06, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    4. Cinzia Daraio & Simone Leo & Monica Scannapieco, 2022. "Accounting for quality in data integration systems: a completeness-aware integration approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1465-1490, March.
    5. Cinzia Daraio & Alessio Vaccari, 2020. "Using normative ethics for building a good evaluation of research practices: towards the assessment of researcher’s virtues," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 1053-1075, November.
    6. Cinzia Daraio & Alessio Vaccari, 2019. "Sorting out Guidelines for the Good Evaluation of Research Practices," DIAG Technical Reports 2019-02, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    7. Marco Angelini & Cinzia Daraio & Maurizio Lenzerini & Francesco Leotta & Giuseppe Santucci, 2020. "Performance model’s development: a novel approach encompassing ontology-based data access and visual analytics," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 865-892, November.
    8. Marco Angelini & Cinzia Daraio & Maurizio Lenzerini & Francesco Leotta & Giuseppe Santucci, 2019. "Performance Model’s development: A Novel Approach encompassing Ontology-Based Data Access and Visual Analytics," DIAG Technical Reports 2019-11, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    9. Moriah B. Bostian & Cinzia Daraio & Rolf Fare & Shawna Grosskopf & Maria Grazia Izzo & Luca Leuzzi & Giancarlo Ruocco & William L. Weber, 2018. "Inference for Nonparametric Productivity Networks: A Pseudo-likelihood Approach," DIAG Technical Reports 2018-06, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".

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