Factors Influencing Cities’ Publishing Efficiency

Abstract

Purpose Recently, a vast number of scientific publications have been produced in cities in emerging countries. It has long been observed that the publication output of Beijing has exceeded that of any other city in the world, including such leading centres of science as Boston, New York, London, Paris, and Tokyo. Researchers have suggested that, instead of focusing on cities’ total publication output, the quality of the output in terms of the number of highly cited papers should be examined. However, in the period from 2014 to 2016, Beijing produced as many highly cited papers as Boston, London, or New York. In this paper, another method is proposed to measure cities’ publishing performance by focusing on cities’ publishing efficiency (i.e., the ratio of highly cited articles to all articles produced in that city). Design/methodology/approach First, 554 cities are ranked based on their publishing efficiency, then some general factors influencing cities’ publishing efficiency are revealed. The general factors examined in this paper are as follows: the linguistic environment of cities, cities’ economic development level, the location of excellent organisations, cities’ international collaboration patterns, and their scientific field profile. Furthermore, the paper examines the fundamental differences between the general factors influencing the publishing efficiency of the top 100 most efficient cities and the bottom 100 least efficient cities. Findings Based on the research results, the conclusion can be drawn that a city’s publishing efficiency will be high if meets the following general conditions: it is in a country in the Anglosphere–Core; it is in a high-income country; it is home to top-ranked universities and/or world-renowned research institutions; researchers affiliated with that city most intensely collaborate with researchers affiliated with cities in the United States, Germany, England, France, Canada, Australia, and Italy; and the most productive scientific disciplines of highly cited articles are published in high-impact multidisciplinary journals, disciplines in health sciences (especially general internal medicine and oncology), and disciplines in natural sciences (especially physics, astronomy, and astrophysics). Research limitations It is always problematic to demarcate the boundaries of cities (e.g., New York City vs. Greater New York), and regarding this issue there is no consensus among researchers. The Web of Science presents the name of cities in the addresses reported by the authors of publications. In this paper cities correspond to the spatial units between the country/state level and the institution level as indicated in the Web of Science. Furthermore, it is necessary to highlight that the Web of Science is biased towards English-language journals and journals published in the field of biomedicine. These facts may influence the outcome of the research. Practical implications Publishing efficiency, as an indicator, shows how successful a city is at the production of science. Naturally, cities have limited opportunities to compete for components of the science establishment (e.g., universities, hospitals). However, cities can compete to attract innovation-oriented companies, high tech firms, and R&D facilities of multinational companies by for example establishing science parks. The positive effect of this process on the city’s performance in science can be observed in the example of Beijing, which publishing efficiency has been increased rapidly. Originality/value Previous scientometric studies have examined cities’ publication output in terms of the number of papers, or the number of highly cited papers, which are largely size dependent indicators; however this paper attempts to present a more quality-based approach.

Tackling the UK's Regional Economic Inequality Binding Constraints and Avenues for Policy Intervention

“…43 Surveys of firms' innovative activity across regions also suggest that there are large numbers of innovative firms outside London and the South East, for example the UK government's Innovation Survey (BEIS 2021). (György 2018). 44 Of course, our analysis here is at a high level: the effectiveness of public sector R&D support depends not only on the level of R&D spend but also the way the spending occurs, and more broadly the way regional innovation ecosystems are supported and developed (McCann and Ortega-Argilés 2013).…”

“…In our analyses we draw on a range of data sources, including the EU's ARDECO database, the OECD regional and national statistics databases, the University of Gothenburg Quality of Government EU regional dataset, UK government statistics from the Office for National Statistics, the Department for Transport, the Office of Rail and Road, the Department for Education, the British Business Bank, and OFCOM, individual-level survey data from the UK's Labour Force Survey, and data from private sector sources including UK Finance, BVA BDRC, TomTom, and INRIX. We also draw on data compiled and analysed by other researchers: educational outcomes data from the Department for Education, compiled and analysed by Britton, Waltmann, Xu, and van der Erve (2021), travel time data from TravelTime, compiled and analysed by , travel time data from Google Maps, compiled and analysed by Conwell, Eckert, and Mobarak (2022), private equity funding data from Beauhurst, compiled and analysed by Wilson, Kacer, and Wright (2019), peer to peer lending data from FundingCircle, compiled and analysed by Ekpu, Wright, Prashar, and Ri (2020) and Xu, Su, and Celler (2021), and academic publication data from Web of Science, compiled and analysed by György (2018).…”