title: I Like, I Cite? Do Facebook Likes Predict the Impact of Scientific Work?
abstract: Due to the increasing amount of scientific work and the typical delays in publication, promptly assessing the impact of scholarly work is a huge challenge. To meet this challenge, one solution may be to create and discover innovative indicators. The goal of this paper is to investigate whether Facebook likes for unpublished manuscripts that are uploaded to the Internet could be used as an early indicator of the future impact of the scientific work. To address our research question, we compared Facebook likes for manuscripts uploaded to the Harvard Business School website (Study 1) and the bioRxiv website (Study 2) with traditional impact indicators (journal article citations, Impact Factor, Immediacy Index) for those manuscripts that have been published as a journal article. Although based on our full sample of Study 1 (N = 170), Facebook likes do not predict traditional impact indicators, for manuscripts with one or more Facebook likes (n = 95), our results indicate that the more Facebook likes a manuscript receives, the more journal article citations the manuscript receives. In additional analyses (for which we categorized the manuscripts as psychological and non-psychological manuscripts), we found that the significant prediction of citations stems from the psychological and not the non-psychological manuscripts. In Study 2, we observed that Facebook likes (N = 270) and non-zero Facebook likes (n = 84) do not predict traditional impact indicators. Taken together, our findings indicate an interdisciplinary difference in the predictive value of Facebook likes, according to which Facebook likes only predict citations in the psychological area but not in the non-psychological area of business or in the field of life sciences. Our paper contributes to understanding the possibilities and limits of the use of social media indicators as potential early indicators of the impact of scientific work.
authors.affiliation: Chair for Strategy and Organization, TUM School of Management, Technische Universität München, Munich, Bavaria, Germany.
authors.forename: Stefanie
authors.initials: S
authors.lastname: Ringelhan
authors.affiliation: Chair for Strategy and Organization, TUM School of Management, Technische Universität München, Munich, Bavaria, Germany.
authors.forename: Jutta
authors.initials: J
authors.lastname: Wollersheim
authors.affiliation: Chair for Strategy and Organization, TUM School of Management, Technische Universität München, Munich, Bavaria, Germany; Bavarian State Institute for Higher Education Research and Planning, Munich, Bavaria, Germany.
authors.forename: Isabell M
authors.initials: IM
authors.lastname: Welpe
mesh_terms: D015706:Bibliometrics; D005544:Forecasting; D006801:Humans; D020407:Internet / Q000706:statistics & numerical data*; D055811:Journal Impact Factor; D008373:Manuscripts as Topic; D011642:Publications / Q000706:statistics & numerical data / Q000639:trends; D012586:Science / Q000706:statistics & numerical data / Q000639:trends; D061108:Social Media / Q000706:statistics & numerical data*; D060756:Social Networking