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In the absence of a suitable citation index, this article explores the relative merits of alternative methods for the large-scale assessment of dissertation impact, using 150,740 UK doctoral dissertations from 2009\u20132018. Systematic methods for this were designed for Google Books, Scopus, Microsoft Academic, and Mendeley. Fewer than 1 in 8 UK doctoral dissertations had at least one Scopus (12%), Microsoft Academic (11%), or Google Books citation (9%), or at least one Mendeley reader (5%). These percentages varied substantially by subject area and publication year. Google Books citations were more common in the Arts and Humanities (18%), whereas Scopus and Microsoft Academic citations were more numerous in Engineering (24%). In the Social Sciences, Google Books (13%) and Scopus (12%) citations were important and in Medical Sciences, Scopus and Microsoft Academic citations to dissertations were rare (6%). Few dissertations had Mendeley readers (from 3% in Science to 8% in the Social Sciences) and further analysis suggests that Google Scholar finds more citations, but does not report information about all dissertations within a repository and is not a practical tool for large-scale impact assessment.<\/jats:p>","DOI":"10.1162\/qss_a_00042","type":"journal-article","created":{"date-parts":[[2020,3,25]],"date-time":"2020-03-25T09:30:56Z","timestamp":1585128656000},"page":"479-504","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":3,"title":["Google Books, Scopus, Microsoft Academic, and Mendeley for impact assessment of doctoral dissertations: A multidisciplinary analysis of the UK"],"prefix":"10.1162","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4827-971X","authenticated-orcid":false,"given":"Kayvan","family":"Kousha","sequence":"first","affiliation":[{"name":"Statistical Cybermetrics Research Group, School of Mathematics and Computer Science, University of Wolverhampton, Wulfruna Street, Wolverhampton WV1 1LY, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6065-205X","authenticated-orcid":false,"given":"Mike","family":"Thelwall","sequence":"additional","affiliation":[{"name":"Statistical Cybermetrics Research Group, School of Mathematics and Computer Science, University of Wolverhampton, Wulfruna Street, Wolverhampton WV1 1LY, UK"}]}],"member":"281","published-online":{"date-parts":[[2020,6,1]]},"reference":[{"key":"2025073014030096300_bib1","doi-asserted-by":"crossref","unstructured":"Aduku,  J., Thelwall,  M., & Kousha,  K. 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