AI COMPONENTS FOR PERFORMANCE MEASUREMENT - A BIBLIOMETRIC APPROACH

Valentin Radu, Croitoru Ionut Marius, Tabirca Alina Iuliana and Stoica Silviu-Ionel
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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

Annals - Economy Series, 2023, vol. 6, 286-300

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.

Keywords: AI; artificial intelligence; bibliometric; performance; information (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:cbu:jrnlec:y:2023:v:6:p:286-300

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