Abstract
The Journal of Productivity Analysis (JPA) is a pioneering academic journal that aims to develop new methodologies for efficiency and productivity measurement and apply them into various fields. Collaboration between the contributing authors in JPA who are from various countries, institutes, and disciplines/fields makes it possible to affect the quality of articles. Drawing from bibliographic article information, this paper finds stylized facts from author and keyword networks, and the efficiency of JPA’s major authors. We then examine research collaboration effects in JPA by using a research impact measurement technique. Empirical findings show that author and keyword networks changed over time, and that collaboration across various authors, institutional types and continents is positively associated with research impact.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Battese GE, Coelli TJ (1992) Frontier production functions, technical efficiency and panel data: With application to paddy farmers in India. J Product Anal 3(1):153–169
Battese GE, Rao DSP, O’Donnell CJ (2004) A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies. J Product Anal 21(1):91–103
Borgatti SP, Everett MG, Johnson JC (2013) Analyzing social networks. SAGE Publications, London
Brandes U, Erlebach T (2005) Network analysis: methodological foundations. Springer-Verlag, Berlin
Cook WD, Seiford LM (2009) Data envelopment analysis (DEA)–Thirty years on. Eur J operational Res 192(1):1–17
Cooper WW, Park KS, Pastor JT (1999) RAM: A range adjusted measure of inefficiency for use with additive models, and relations to other models and measures in DEA. J Product Anal 11(1):5–42
Daraio C, Simar L (2005) Introducing environmental variables in nonparametric frontier models: a probabilistic approach. J Product Anal 24(1):93–121
Diewert WE, Smith AM (1994) Productivity measurement for a distribution firm. J Product Anal 5(4):335–347
Emrouznejad A, Parker BR, Tavares G (2008) Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA. Socio-Economic Planning. Sciences 42(3):151–157
Färe R, Grosskopf S, Lindgren B, Roos P (1992) Productivity changes in Swedish pharamacies 1980–1989: a non-parametric Malmquist approach. J Product Anal 3(1–2):85–101
Førsund RF, Sarafoglou N (2005) The tale of two research communities: the diffusion of research on productive efficiency. Int J Prod Econ 98(1):17–40
Fortunato S, Bergstrom CT, Börner K et al. (2018) Science of science. Science 359(6379):eaao0185
Greene W (2005) Fixed and random effects in stochastic frontier models. J Product Anal 23(1):7–32
Guimera R, Uzzi B, Spiro J, Amaral LAN (2005) Team assembly mechanisms determine collaboration network structure and team performance. Science 308(5722):697–702
Kumbhakar SC, Ortega-Argilés R, Potters L, Vivarelli M, Voigt P (2012) Corporate R&D and firm efficiency: evidence from Europe’s top R&D investors. J Product Anal 37(2):125–140
Lampe HW, Hilgers D (2015) Trajectories of efficiency measurement: a bibliometric analysis of DEA and SFA. Eur J Operational Res 240(1):1–21
Leahey E, Beckman CM, Stanko TL (2017) Prominent but less productive: The impact of interdisciplinarity on scientists’ research. Adm Sci Q 62(1):105–139
Lee BL, Wilson C, Pasurka CA, Fujii H, Managi S (2017) Sources of airline productivity from carbon emissions: an analysis of operational performance under good and bad outputs. J Product Anal 47(3):223–246
Lee J-D, Baek C, Kim H-S, Lee J-S (2014) Development pattern of the DEA research field: a social network analysis approach. J Product Anal 41(2):175–186
Leydesdorff L, Meyer M (2006) Triple helix indicators of knowledge-based innovation systems: Introduction to the special issue. Res Policy 35(10):1441–1449
Luke, DA (2015) A user’s guide to network analysis in R. Springer International Publishing, Switzerland
Olesen OB, Petersen NC (2016) Stochastic data envelopment analysis—A review. Eur J Operational Res 251(1):2–21
Seiford LM (1996) Data envelopment analysis: the evolution of the state of the art (1978–1995). J Product Anal 7(2):99–137
Simar L, Wilson PW (2000) Statistical inference in nonparametric frontier models: The state of the art. J Product Anal 13(1):49–78
Singh J, Fleming L (2010) Lone inventors as sources of breakthroughs: Myth or reality? Manag Sci 56(1):41–56
Tulkens H (1993) On FDH efficiency analysis: some methodological issues and applications to retail banking, courts, and urban transit. J Product Anal 4(1):183–210
Wang H-j, Schmidt P (2002) One-step and two-step estimation of the effects of exogenous variables on technical efficiency levels. J Product Anal 18(2):129–144
Wuchty S, Jones BF, Uzzi B (2007) The increasing dominance of teams in production of knowledge. Science 316(5827):1036–1039
Acknowledgement
This research was supported by Inha University (INHA-61571).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Choi, Hd., Oh, Dh. The importance of research teams with diverse backgrounds: Research collaboration in the Journal of Productivity Analysis. J Prod Anal 53, 5–19 (2020). https://doi.org/10.1007/s11123-019-00567-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11123-019-00567-4