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What Do Experts Know About Forecasting Journal Quality? A Comparison with ISI Research Impact in Finance

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Abstract

Experts possess knowledge and information that are not publicly available. The paper is con¬cerned with forecasting academic journal quality and research impact using a survey of inter¬na¬tion¬al experts from a national project on ranking academic finance journals in Taiwan. A com¬par¬i¬son is made with publicly available bibliometric data, namely the Thomson Reuters ISI Web of Sci¬ence citations database (hereafter ISI) for the Business - Finance (hereafter Finance) category. The paper analyses the leading international journals in Finance using expert scores and quantifiable Re¬search Assessment Measures (RAMs), and highlights the similarities and differences in the expert scores and alternative RAMs, where the RAMs are based on alternative transformations of citations taken from the ISI database. Alternative RAMs may be calculated annually or updated daily to an¬swer the perennial questions as to When, Where and How (frequently) published papers are cited (see Chang et al. 2011a,b,c). The RAMs include the most widely used RAM, namely the classic 2-year impact factor including journal self citations (2YIF), 2-year impact factor excluding journal self citations (2YIF*), 5-year impact factor including journal self citations (5YIF), Immediacy (or zero-year impact factor, 0YIF), Eigenfactor, Article Influence, C3PO (Citation Performance Per Paper Online), h-index, PI-BETA (Papers Ignored - By Even The Authors), 2-year Self-citation Thresh¬old Approval Ratings (2Y-STAR), Historical Self-citation Threshold Approval Ratings (H-STAR), Impact Factor Inflation (IFI), and Cited Article Influence (CAI). As data are not available for 5YIF, Article Influence and CAI for 13 of the leading 34 journals considered, 10 RAMs are ana¬lysed for 21 highly-cited journals in Finance. The harmonic mean of the ranks of the 10 RAMs for the 34 highly-cited journals are also presented. It is shown that emphasizing the 2-year impact fac¬tor of a journal, which partly answers the question as to When published papers are cited, to the ex¬clu¬sion of other informative RAMs, which answer Where and How (frequently) published papers are cited, can lead to a distorted evaluation of journal impact and influence relative to the Harmonic Mean rankings. A linear regression model is used to forecast expert scores on the basis of RAMs that capture journal impact, journal policy, the number of high quality papers, and quantitative in¬form¬ation about a journal. The robustness of the rankings is also analysed.

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  • Chia-Lin Chang & Michael McAleer, 2013. "What Do Experts Know About Forecasting Journal Quality? A Comparison with ISI Research Impact in Finance," Working Papers in Economics 13/10, University of Canterbury, Department of Economics and Finance.
    • Handle: RePEc:cbt:econwp:13/10
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    1. Philip Hans Franses & Michael McAleer & Rianne Legerstee, 2009. "Expert opinion versus expertise in forecasting," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 63(3), pages 334-346, August.
    2. Chia-Lin Chang & Michael McAleer, 2013. "Ranking journal quality by harmonic mean of ranks: an application to ISI statistics & probability," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(1), pages 27-53, February.
    3. Chia-Lin Chang & Michael McAleer & Les Oxley, 2011. "What makes a great journal great in the sciences? Which came first, the chicken or the egg?," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(1), pages 17-40, April.
    4. Chia-Lin Chang & Esfandiar Maasoumi & Michael McAleer, 2016. "Robust Ranking of Journal Quality: An Application to Economics," Econometric Reviews, Taylor & Francis Journals, vol. 35(1), pages 50-97, January.
    5. Chia-Lin Chang & Michael McAleer & Les Oxley, 2011. "Great Expectatrics: Great Papers, Great Journals, Great Econometrics," Econometric Reviews, Taylor & Francis Journals, vol. 30(6), pages 583-619.
    6. Chia‐Lin Chang & Michael McAleer & Les Oxley, 2011. "What Makes A Great Journal Great In Economics? The Singer Not The Song," Journal of Economic Surveys, Wiley Blackwell, vol. 25(2), pages 326-361, April.
    7. Chia-Lin Chang & Michael McAleer & Les Oxley, 2011. "How are journal impact, prestige and article influence related? An application to neuroscience," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2563-2573, January.
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    1. Chia-Lin Chang & Michael Mcaleer, 2014. "Just How Good Are The Top Three Journals In Finance? An Assessment Based On Quantity And Quality Citations," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 9(01), pages 1-31.
    2. William C. Horrace & Christopher F. Parmeter, 2017. "Accounting for Multiplicity in Inference on Economics Journal Rankings," Southern Economic Journal, John Wiley & Sons, vol. 84(1), pages 337-347, July.
    3. Chia-Lin Chang & Michael McAleer, 2013. "Ranking Leading Econometrics Journals Using Citations Data from ISI and RePEc," Econometrics, MDPI, vol. 1(3), pages 1-19, November.
    4. Chia-Lin Chang & Michael McAleer, 2015. "Bibliometric Rankings of Journals Based on the Thomson Reuters Citations Database," Journal of Reviews on Global Economics, Lifescience Global, vol. 4, pages 120-125.
    5. Tombazos, Christis G. & Dobra, Matthew, 2014. "Formulating research policy on expert advice," European Economic Review, Elsevier, vol. 72(C), pages 166-181.
    6. Chia-Lin Chang & Michael McAleer, "undated". "Quality Weighted Citations Versus Total Citations in the Sciences and Social Sciences, with an Application to Finance and Accounting," Documentos de Trabajo del ICAE 2015-01, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico, revised Jan 2015.

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    More about this item

    Keywords

    Expert scores; Journal quality; RAMs; Impact factor; IFI; C3PO; PI-BETA; STAR; Eigenfactor; Article Influence; h-index; harmonic mean; robustness;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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