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The development of an AI journal ranking based on the revealed preference approach

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  • Serenko, Alexander

Abstract

This study presents a ranking of 182 academic journals in the field of artificial intelligence. For this, the revealed preference approach, also referred to as a citation impact method, was utilized to collect data from Google Scholar. This list was developed based on three relatively novel indices: h-index, g-index, and hc-index. These indices correlated almost perfectly with one another (ranging from 0.97 to 0.99), and they correlated strongly with Thomson's Journal Impact Factors (ranging from 0.64 to 0.69). It was concluded that journal longevity (years in print) is an important but not the only factor affecting an outlet's ranking position. Inclusion in Thomson's Journal Citation Reports is a must for a journal to be identified as a leading A+ or A level outlet. However, coverage by Thomson does not guarantee a high citation impact of an outlet. The presented list may be utilized by scholars who want to demonstrate their research output, various academic committees, librarians and administrators who are not familiar with the AI research domain.

Suggested Citation

  • Serenko, Alexander, 2010. "The development of an AI journal ranking based on the revealed preference approach," Journal of Informetrics, Elsevier, vol. 4(4), pages 447-459.
    • Handle: RePEc:eee:infome:v:4:y:2010:i:4:p:447-459
    DOI: 10.1016/j.joi.2010.04.001
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    1. Bras-Amorós, Maria & Domingo-Ferrer, Josep & Torra, Vicenç, 2011. "A bibliometric index based on the collaboration distance between cited and citing authors," Journal of Informetrics, Elsevier, vol. 5(2), pages 248-264.
    2. Alfonso Ibáñez & Pedro Larrañaga & Concha Bielza, 2011. "Using Bayesian networks to discover relationships between bibliometric indices. A case study of computer science and artificial intelligence journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(2), pages 523-551, November.
    3. Sicilia, Miguel-Angel & Sánchez-Alonso, Salvador & García-Barriocanal, Elena, 2011. "Comparing impact factors from two different citation databases: The case of Computer Science," Journal of Informetrics, Elsevier, vol. 5(4), pages 698-704.
    4. Ricardo Arencibia-Jorge & Rosa Lidia Vega-Almeida & José Luis Jiménez-Andrade & Humberto Carrillo-Calvet, 2022. "Evolutionary stages and multidisciplinary nature of artificial intelligence research," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5139-5158, September.
    5. Chih-Fong Tsai & Chihli Hung, 2013. "Popular research topics in multimedia," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(1), pages 465-479, April.
    6. Zhao Zhai & Ming Shan & Amos Darko & Albert P. C. Chan, 2021. "Corruption in Construction Projects: Bibliometric Analysis of Global Research," Sustainability, MDPI, vol. 13(8), pages 1-21, April.
    7. Serenko, Alexander & Bontis, Nick, 2011. "What's familiar is excellent: The impact of exposure effect on perceived journal quality," Journal of Informetrics, Elsevier, vol. 5(1), pages 219-223.
    8. Rob Law & Daniel Leung, 2020. "Journal impact factor: A valid symbol of journal quality?," Tourism Economics, , vol. 26(5), pages 734-742, August.
    9. Bilal Manzoor & Idris Othman & Juan Carlos Pomares, 2021. "Digital Technologies in the Architecture, Engineering and Construction (AEC) Industry—A Bibliometric—Qualitative Literature Review of Research Activities," IJERPH, MDPI, vol. 18(11), pages 1-26, June.
    10. Serenko, Alexander & Dohan, Michael, 2011. "Comparing the expert survey and citation impact journal ranking methods: Example from the field of Artificial Intelligence," Journal of Informetrics, Elsevier, vol. 5(4), pages 629-648.
    11. Lior Rokach, 2012. "Applying the Publication Power Approach to Artificial Intelligence Journals," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(6), pages 1270-1277, June.

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