Determinants of research efficiency in Canadian business schools: evidence from scholar-level data

Mehdi Rhaiem () and Nabil Amara ()
Additional contact information
Mehdi Rhaiem: Polytechnique Montréal
Nabil Amara: Université Laval

Scientometrics, 2020, vol. 125, issue 1, No 3, 53-99

Abstract: Abstract Using a large sample of faculty members of Canadian business schools, this article attempts to shed new light on the efficiency of academic research as measured, at the researcher’s level, by the peer-reviewed article counts and citations. Metrics on outputs from the Web of Science and from the Google Scholar databases, augmented by a survey data on factors explaining the productivity and impact performances of these faculty members, are used to assess their academic research efficiency and to perform an empirical investigation of the determinants of researchers’ efficiency, using the two-stage Bootstrap DEA approach. Results reveal that there is substantial room for improvements of technical efficiency, both across the eight fields considered in this study, and within each field. The analyses also enabled to identify determinants that might explain the academic efficiency gap between scholars across the eight research fields considered in this study, notably certification from independent agencies, seniority, sources of funding, affiliation to a business school with a doctoral program, and prestige and reputation of university of affiliation.

Keywords: Data envelopment analysis; Double bootstrap; Truncated regression; Academic research efficiency; Business scholars (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-020-03633-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:125:y:2020:i:1:d:10.1007_s11192-020-03633-z

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-020-03633-z

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().