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Ai Components For Performance Measurement - A Bibliometric Approach

Author

Listed:
  • RADU VALENTIN

    (VALAHIA UNIVERSITY OF TARGOVISTE, ROMANIA)

  • CROITORU IONUT MARIUS

    (NATIONAL UNIVERSITY OF SCIENCE AND TECHNOLOGY POLITEHNICA BUCHAREST, ROMANIA)

  • TABIRCA ALINA IULIANA

    (VALAHIA UNIVERSITY OF TARGOVISTE, ROMANIA)

  • STOICA SILVIU-IONEL

    (VALAHIA UNIVERSITY OF TARGOVISTE, ROMANIA)

Abstract

This study employs a bibliometric approach to analyze the landscape of artificial intelligence (AI) components used in performance measurement. As organizations increasingly leverage AI for optimizing processes and decisionmaking, understanding the trends in AI components becomes imperative. The identified AI components are classified based on their roles in enhancing performance measurement, offering insights into the prevalent methodologies and emerging technologies. The bibliometric analysis encompasses a comprehensive review of scholarly articles, conference papers, and patents, systematically exploring the evolving field. In this research, the methodology involves data extraction from reputable academic databases and patent repositories, followed by applying bibliometric techniques to quantify and visualize key aspects. The findings of this study contribute to the existing knowledge by mapping the intellectual structure of AI components for performance measurement.

Suggested Citation

  • Radu Valentin & Croitoru Ionut Marius & Tabirca Alina Iuliana & Stoica Silviu-Ionel, 2023. "Ai Components For Performance Measurement - A Bibliometric Approach," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 6, pages 286-300, December.
    • Handle: RePEc:cbu:jrnlec:y:2023:v:6:p:286-300
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    References listed on IDEAS

    as
    1. Jean-Marie John-Mathews, 2022. "Some critical and ethical perspectives on the empirical turn of AI interpretability," Post-Print hal-03395823, HAL.
    2. Jayraj V. Vaghasiya & Carmen C. Mayorga-Martinez & Jan Vyskočil & Martin Pumera, 2023. "Black phosphorous-based human-machine communication interface," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    3. John-Mathews, Jean-Marie, 2022. "Some critical and ethical perspectives on the empirical turn of AI interpretability," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    4. K A H Kobbacy & S Vadera & M H Rasmy, 2007. "AI and OR in management of operations: history and trends," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(1), pages 10-28, January.
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