Abstract
This paper aims to understand the influence of institutional and organisational embeddedness on research productivity of Italian sociologists. We looked at all records published by Italian sociologists in Scopus from 1973 to 2016 and reconstructed their co-authorship patterns. We built an individual productivity index by considering the number and type of records, the impact factor of journals in which these records were published and each record’s citations. We found that sociologists who co-authored more frequently with international authors were more productive and that having a stable group of co-authors had a positive effect on the number of publications but not on citations. We found that organisational embeddedness has a positive effect on productivity at the group level (i.e., sociologists working in the same institute), less at the individual level. We did not found any effect of the scientific disciplinary sectors, which are extremely influential administratively and politically for promotion and career in Italy. With all caveats due to several limitations of our analysis, our findings suggest that internationalisation and certain context-specific organisational settings could promote scientist productivity .
Similar content being viewed by others
Notes
“Agenzia Nazionale di Valutazione del Sistema Universitario e della Ricerca”.
Scientific disciplinary sectors established by MIUR: "General sociology, Sociology of culture and communication, Economic sociology, Environmental sociology, Political sociology and Sociology of law and social change".
In order to ensure the full correspondence between MIUR and Scopus records, we not only automatically checked the correspondence with MIUR names and Scopus profile with multiple criteria and step-by-step procedures. We also cross-checked manually each conflicting or absent case by a group of three independent assistants. As emphasized by (Abramo and D’Angelo 2011b; Pepe and Kurtz 2012; De Stefano et al. 2013), this is a time consuming and hard task but is the only way to reduce mistakes, also sometimes due to surname changes (e.g., marriage or divorce) and homonyms.
To do so we wrote an R (2016) script that interacted with Scopus API. First, we searched each of these authors’ last and first names in Scopus to see if they had official profiles. When available, we extracted all publications records of these authors throughout their scientific career. We started data gathering by sending search queries to Scopus API on July 27th 2016, while from September 8th 2016 we started gathering Scopus CSV exports of all available information on publications for each author through Scopus web interface to build links with data from API interface and cover up differences and shortages. To manipulate the data and modeling it, we have used Base (2016), Dplyr (2016), Igraph (2006), lme4 (2015), stargazer (2015), ggplot2 (2009) and Stringdist (2014) packages in R to write data cleaning and statistical analysis procedures.
References
Abramo, G., Cicero, T., & D’Angelo, C. A. (2013). Individual research performance: A proposal for comparing apples to oranges. Journal of Informetrics, 7(2), 528–539. https://doi.org/10.1016/j.joi.2013.01.013.
Abramo, G., & D’Angelo, C. A. (2011a). Evaluating research: From informed peer review to bibliometrics. Scientometrics, 87(3), 499–514.
Abramo, G., & D’Angelo, C. A. (2011b). National-scale research performance assessment at the individual level. Scientometrics, 86(2), 347–364. https://doi.org/10.1007/s11192-010-0297-2.
Abramo, G., & D’Angelo, C. A. (2014). How do you define and measure research productivity? Scientometrics, 101(2), 1129–1144. https://doi.org/10.1007/s11192-014-1269-8.
Abramo, G., D’Angelo, C. A., & Caprasecca, A. (2009). Gender differences in research productivity: A bibliometric analysis of the Italian academic system. Scientometrics, 79(3), 517–539.
Abramo, G., D’Angelo, C. A., & Di Costa, F. (2008). Assessment of sectoral aggregation distortion in research productivity measurements. Research Evaluation, 17(2), 111–121. Retrieved from http://rev.oxfordjournals.org/content/17/2/111.short.
Abramo, G., D’Angelo, C. A., & Di Costa, F. (2011). Research productivity: Are higher academic ranks more productive than lower ones? Scientometrics, 88(3), 915–928.
Abramo, G., D’Angelo, C. A., & Di Costa, F. (2017). The effects of gender, age and academic rank on research diversification. Scientometrics, 1–15.
Abramo, G., D’Angelo, C. A., & Rosati, F. (2016a). A methodology to measure the effectiveness of academic recruitment and turnover. Journal of Informetrics, 10(1), 31–42.
Abramo, G., D’Angelo, C. A., & Rosati, F. (2016b). The north–south divide in the Italian higher education system. Scientometrics, 109(3), 2093–2117. https://doi.org/10.1007/s11192-016-2141-9.
Agrawal, A., McHale, J., & Oettl, A. (2017). How stars matter: Recruiting and peer effects in evolutionary biology. Research Policy, 46(4), 853–867.
ANVUR. (2014). Confronto tra dimensione e qualita delle strutture universita. Retrieved from http://www.anvur.org/rapporto/stampa.php.
Azoulay, P., Ganguli, I., & Zivin, J. G. (2017). The mobility of elite life scientists: Professional and personal determinants. Research Policy, 46(3), 573–590.
Baccini, A., & De Nicolao, G. (2016). Do they agree? Bibliometric evaluation versus informed peer review in the Italian research assessment exercise. Scientometrics, 108(3), 1651–1671.
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01.
Batista, P. D., Campiteli, M. G., & Kinouchi, O. (2006). Is it possible to compare researchers with different scientific interests? Scientometrics, 68(1), 179–189.
Becher, T., & Trowler, P. (2001). Academic tribes and territories: Intellectual enquiry and the culture of disciplines. London: McGraw-Hill Education.
Beerkens, M. (2013). Facts and fads in academic research management: The effect of management practices on research productivity in australia. Research Policy, 42(9), 1679–1693.
Bellotti, E., Guadalupi, L., & Conaldi, G. (2016a). Comparing fields of sciences: Multilevel networks of research collaborations in Italian Academia. In Multilevel network analysis for the social sciences (pp. 213–244). Springer.
Bellotti, E., Kronegger, L., & Guadalupi, L. (2016b). The evolution of research collaboration within and across disciplines in Italian Academia. Scientometrics, 109(2), 783–811. https://doi.org/10.1007/s11192-016-2068-1.
Berlemann, M., & Haucap, J. (2015). Which factors drive the decision to opt out of individual research rankings? An empirical study of academic resistance to change. Research Policy, 44(5), 1108–1115.
Blackburn, R. T., Behymer, C. E., & Hall, D. E. (1978). Research note: Correlates of faculty publications. Sociology of Education, 132–141.
Bland, C. J., Center, B. A., Finstad, D. A., Risbey, K. R., & Staples, J. G. (2005). A theoretical, practical, predictive model of faculty and department research productivity. Academic Medicine, 80(3), 225–237.
Bland, C. J., Ruffin, M. T., et al. (1992). Characteristics of a productive research environment: Literature review. Academic Medicine, 67(6), 385–397.
Bland, C. J., Seaquist, E., Pacala, J. T., Center, B., & Finstad, D. (2002). One school’s strategy to assess and improve the vitality of its faculty. Academic Medicine, 77(5), 368–376.
Bornmann, L. (2010). Towards an ideal method of measuring research performance: Some comments to the Opthof and Leydesdorff (2010) paper. Journal of Informetrics, 4(3), 441–443. https://doi.org/10.1016/j.joi.2010.04.004.
Burrows, R. (2012). Living with the h-index? Metric assemblages in the contemporary academy. The Sociological Review, 60(2), 355–372.
Burt, R. S. (2005). Brokerage and closure: An introduction to social capital. Oxford: Oxford University Press.
Butts, C. T. (2016). Sna: Tools for social network analysis. Retrieved from https://CRAN.R-project.org/package=sna.
Chatzimichael, K., Kalaitzidakis, P., & Tzouvelekas, V. (2016). Measuring the publishing productivity of economics departments in Europe. Scientometrics, 1–20.
Coile, R. C. (1977). Lotka’s frequency distribution of scientific productivity. Journal of the American Society for Information Science, 28(6), 366–370.
Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695. Retrieved from http://igraph.org.
de Price, D. J. S. (1970). Citation measures of hard science, soft science, technology, and nonscience. In C. E. Nelson & D. K. Pollock (Eds.), Communication among scientists and engineers (pp. 3–22). Lexington, MA: Heath.
De Rijcke, S., Wouters, P. F., Rushforth, A. D., Franssen, T. P., & Hammarfelt, B. (2016). Evaluation practices and effects of indicator use—a literature review. Research Evaluation, 25(2), 161–169.
De Stefano, D., Fuccella, V., Vitale, M. P., & Zaccarin, S. (2013). The use of different data sources in the analysis of co-authorship networks and scientific performance. Social Networks, 35(3), 370–381.
Edwards, M. A., & Roy, S. (2017). Academic research in the 21st century: Maintaining scientific integrity in a climate of perverse incentives and hypercompetition. Environmental Engineering Science, 34(1), 51–61.
Egghe, L. (2010). The hirsch index and related impact measures. Annual Review of Information Science and Technology, 44(1), 65–114.
Ellwein, L. B., Khachab, M., & Waldman, R. (1989). Assessing research productivity: Evaluating journal publication across academic departments. Academic Medicine, 64(6), 319–325.
Faraway, J. L. (2005). Extending the linear model with r: Generalized linear, mixed effects an nonparametric regression models. Cambridge: CRC Press.
Fox, M. F. (1983). Publication productivity among scientists: A critical review. Social Studies of Science, 13(2), 285–305.
Garfield, E. (1980). Premature discovery or delayed recognition-why. Current Contents, 21, 5–10.
Geuna, A., & Piolatto, M. (2016). Research assessment in the UK and Italy: Costly and difficult, but probably worth it (at least for a while). Research Policy, 45(1), 260–271.
Hakala, J., & Ylijoki, O.-H. (2001). Research for whom? Research orientations in three academic cultures. Organization, 8(2), 373–380.
Halevi, G., Moed, H., & Bar-Ilan, J. (2017). Suitability of Google scholar as a source of scientific information and as a source of data for scientific evaluation—Review of the literature. Journal of Informetrics, 11(3), 823–834.
Hâncean, M.-G., & Perc, M. (2016). Homophily in coauthorship networks of east European sociologists. Scientific Reports, 6, 36152.
Hancock, K. J., & Baum, M. (2010). Women and academic publishing: Preliminary results from a survey of the ISA membership. In The international studies association annual convention, new orleans, la.
Hicks, D., Wouters, P., Waltman, L., De Rijcke, S., & Rafols, I. (2015). The Leiden manifesto for research metrics. Nature, 520(7548), 429.
Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102, 16569–16572.
Hirsch, J. E. (2010). An index to quantify an individual’s scientific research output that takes into account the effect of multiple coauthorship. Scientometrics, 85(3), 741–754.
Hlavac, M. (2015). Stargazer: Well-formatted regression and summary statistics tables. Cambridge, USA: Harvard University. Retrieved from http://CRAN.R-project.org/package=stargazer.
Jonkers, K., & Tijssen, R. (2008). Chinese researchers returning home: Impacts of international mobility on research collaboration and scientific productivity. Scientometrics, 77(2), 309–333.
Jung, J., Bozeman, B., & Gaughan, M. (2017). Impact of research collaboration cosmopolitanism on job satisfaction. Research Policy, 46, 1863–1872.
Katz, J. S., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26(1), 1–18.
Khabsa, M., & Giles, C. L. (2014). The number of scholarly documents on the public web. PLoS ONE, 9(5), e93949.
Khor, K. A., & Yu, L. G. (2016). Influence of international coauthorship on the research citation impact of young universities. Scientometrics, 107(3), 1095–1110.
Kronegger, L., Mali, F., Ferligoj, A., & Doreian, P. (2011). Collaboration structures in Slovenian scientific communities. Scientometrics, 90(2), 631–647.
Kuzhabekova, A. (2011). Impact of co-authorship strategies on research productivity: A social-network analysis of publications in russian cardiology (PhD thesis). University of Minnesota.
Lamont, M. (2009). How professors think. Cambridge: Harvard University Press.
Lazega, E., Jourda, M.-T., Mounier, L., & Stofer, R. (2008). Catching up with big fish in the big pond? Multi-level network analysis through linked design. Social Networks, 30(2), 159–176.
Leenders, R. T. A. (2002). Modeling social influence through network autocorrelation: Constructing the weight matrix. Social Networks, 24(1), 21–47.
Leydesdorff, L., Park, H. W., & Wagner, C. (2014). International coauthorship relations in the social sciences citation index: Is internationalization leading the network? Journal of the Association for Information Science and Technology, 65(10), 2111–2126.
Long, J. S. (1978). Productivity and academic position in the scientific career. American Sociological Review, 889–908.
Long, J. S., & McGinnis, R. (1981). Organizational context and scientific productivity. American Sociological Review, 422–442.
Meho, L. I., & Yang, K. (2007). Impact of data sources on citation counts and rankings of LIS faculty: Web of Science versus Scopus and Google Scholar. Journal of the Association for Information Science and Technology, 58(13), 2105–2125.
Narin, F., Stevens, K., & Whitlow, E. (1991). Scientific co-operation in Europe and the citation of multinationally authored papers. Scientometrics, 21(3), 313–323.
Narin, F., & Whitlow, E. S. (1991). Measurement of scientific cooperation and coauthorship in cec-related areas of science. Commission of the European Communities Directorate-General Telecommunications, Information Industries and Innovation.
National agency for the evaluation of the university and research systems. (2013). Retrieved from http://www.unive.it/nqcontent.cfm?a_id=161248.
Nederhof, A. J. (2006). Bibliometric monitoring of research performance in the social sciences and the humanities: A review. Scientometrics, 66(1), 81–100.
Nygaard, L. P. (2015). Publishing and perishing: An academic literacies framework for investigating research productivity. Studies in Higher Education, 1–14.
Opthof, T., & Leydesdorff, L. (2010). Caveats for the journal and field normalizations in the CWTS (“Leiden”) evaluations of research performance. Journal of Informetrics, 4(3), 423–430. https://doi.org/10.1016/j.joi.2010.02.003.
Pepe, A., & Kurtz, M. J. (2012). A measure of total research impact independent of time and discipline. PLoS ONE, 7(11), e46428.
Provasi, G., Squazzoni, F., & Tosio, B. (2012). Did they sell their soul to the devil? Some comparative case-studies on academic entrepreneurs in the life sciences in Europe. Higher Education, 64(6), 805–829.
Prpić, K. (2002). Gender and productivity differentials in science. Scientometrics, 55(1), 27–58.
R Core Team. (2016). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/.
Ramsden, P. (1994). Describing and explaining research productivity. Higher Education, 28(2), 207–226.
Rumsey, A. R. (2006). The association between co-authorship network structures and successful academic publishing among higher education scholars.
Shapin, S. (2009). The scientific life: A moral history of a late modern vocation. Chicago: University of Chicago Press.
Smith, M. (1958). The trend toward multiple authorship in psychology. American Psychologist, 13(10), 596.
Snijders, T., & Bosker, R. (1999). Multilevel analysis: An introduction to basic and advanced multilevel modeling. London: Sage.
Stack, S. (2004). Gender, children and research productivity. Research in Higher Education, 45(8), 891–920.
Stergiou, K. I., & Lessenich, S. (2014). On impact factors and university rankings: From birth to boycott. Ethics in Science and Environmental Politics, 13(2), 101–111.
Timmermans, S., & Epstein, S. (2010). A world of standards but not a standard world: Toward a sociology of standards and standardization. Annual Review of Sociology, 36, 69–89.
Turri, M. (2014). The new Italian agency for the evaluation of the university system (anvur): A need for governance or legitimacy? Quality in Higher Education, 20(1), 64–82.
van der Loo, M. (2014). The stringdist package for approximate string matching. The R Journal, 6(1), 111–122. Retrieved from https://CRAN.R-project.org/package=stringdist.
Weick, K. E. (2016). Perspective construction in organizational behavior. Annual Review of Organizational Psychology and Organizational Behavior, (0).
Whitley, R. (2003). Competition and pluralism in the public sciences: The impact of institutional frameworks on the organisation of academic science. Research Policy, 32(6), 1015–1029.
Wickham, H. (2009). Ggplot2: Elegant graphics for data analysis. Springer-Verlag New York. Retrieved from http://ggplot2.org.
Wickham, H., & Francois, R. (2016). Dplyr: A grammar of data manipulation. Retrieved from https://CRAN.R-project.org/package=dplyr.
Wilsdon, J., Allen, L., Belfiore, E., Campbell, P., Curry, S., Hill, S., … others. (2015). The metric tide: Report of the independent review of the role of metrics in research assessment and management. hefce.
Zuur, A., Ieno, E., Walker, N., Saveliev, A., & Smith, G. (2009). Mixed effects models and extensions in ecology with r. gail m, krickeberg k, samet jm, tsiatis a, wong w, editors. New York, NY: Spring Science and Business Media.
Author information
Authors and Affiliations
Corresponding author
Appendix
Appendix
This appendix includes further data and results that complement the analysis shown in the article. In particular, it provides details on our multilevel and macro level models.
Regression models comparison
Table 4 compares different multilevel models that we run with the same random effects structure as those presented in the Table 1. These versions included our institutional embeddedness variables as fixed effects. Results confirmed the importance of academic status (i.e., younger scientists are more productive), certain gender effects and stable co-authorship patterns on the number of publications. They also confirmed geographical, localisation effects.
Table 5 compares macro level models that ruled out the potential difference between sociologists working in the same universities to see the results between universities. Given that we had a considerable number of these association (78), we wanted to check if this could have biased our analysis. Results suggest that the findings presented in the article were statistically robust.
Rights and permissions
About this article
Cite this article
Akbaritabar, A., Casnici, N. & Squazzoni, F. The conundrum of research productivity: a study on sociologists in Italy. Scientometrics 114, 859–882 (2018). https://doi.org/10.1007/s11192-017-2606-5
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-017-2606-5