The plausibility of computing the h-index of scholarly productivity and impact using reference-enhanced databases

Purpose - This paper aims to provide a general overview, to be followed by a series of papers focusing on the analysis of pros and cons of the three largest, cited-reference-enhanced, multidisciplinary databases (Google Scholar, Scopus, and Web of Science) for determining the h-index. Design/methodology/approach - The paper focuses on the analysis of pros and cons of the three largest, cited-reference-enhanced, multidisciplinary databases (Google Scholar, Scopus and Web of Science). Findings - The h-index, developed by Jorge E. Hirsch to quantify the scientific output of researchers, has immediately received well-deserved attention in academia. The theoretical part of his idea was widely embraced, and even enhanced, by several researchers. Many of them also recommended derivative metrics based on Hirsch's idea to compensate for potential distortion factors, such as high self-citation rates. The practical aspects of determining the h-index also need scrutiny, because some content and software characteristics of reference-enhanced databases can strongly influence the h-index values. Originality/value - The paper focuses on the analysis of pros and cons of the three largest, cited-reference-enhanced, multidisciplinary databases.