Introducing a scientific productivity model based on the experts’ viewpoint and testing it in the world’s top authors based on highly cited indicator

Osareh, Farideh; Soheili, Faramarz; Keshvari, Mariam

doi:10.22055/slis.2021.31955.1677

Introducing a scientific productivity model based on the experts’ viewpoint and testing it in the world’s top authors based on highly cited indicator

Document Type : Original Article

Authors

Farideh Osareh ¹

Faramarz soheili ²

Mariam Keshvari ³

¹ Department of Knowledge and Information Science, Shahid Chamran University of Ahvaz, Iran

² Department of Knowledge and Information Science, Payame Noor University, Tehran, Iran

³ PhD of Knowledge and Information Science, Shahid Chamran University of Ahvaz, Iran

https://doi.org/10.22055/slis.2021.31955.1677

Abstract

Background and Objectives: One of the most important goals of scientometrics is the evaluation and analysis of scientific components. These evaluations are done for different purposes and one of the most important of them is to identify "top authors"; That is, those who are prominent in their field of science due to the quantity and quality of scientific and research activities. A review of the literature shows that the identification of top authors, which has been done with two approaches: quantitative (scientometric indicators) and qualitative (expert opinions) - and rarely combined - is growing towards a quantitative approach. However, some related works, while warning about purely quantitative evaluations, state that quantitative evaluations show an incomplete picture of scientific productivity, and that quality evaluation is necessary, and emphasize the importance of the role of experts. On the other hand, in the literature, the identification of top authors is based on the evaluation of scientific productivity, Currently, scientific productivity in the field of scientometrics is measured according to the level of publications and citations, although citation and publication categories cannot give a complete picture of scientific productivity - according to the economic definition of this concept (output to output ratio) - But these two categories are currently the most important issues in the relevant texts; Therefore, it seems that a comparison between quantitative and qualitative approaches to evaluating the scientific productivity of top authors can help a more comprehensive understanding of the evaluation of top authors.the most important goal of the present article is the designing of the top authors’ scientific productivity model so as to achieve a combined set of quantitative and qualitative indicators and items influencing the scientific productivity and take a step towards the improvement of the scientific productivity’s evaluation.
Methodology: The present study has been conducted based on a mixed (quantitative-qualitative) method; the qualitative section uses the ideas and notions of the experts in the scientific productivity domain and the quantitative section deals with the application of the statistical tests and scientometrics’ indicators. Two statistical populations have taken part in the present study: 12 domestic and foreign experts in the scientific productivity field; 235 highly cited authors from around the globe. The study data have been collected using checklist, questionnaires and Clarivate Analytics-Web of Science database. To analyze the data, SPSS 19 and LISREL 8 were used. The scientific productivity model has been verified based on the experts’ ideas in the population of the top authors using second order confirmatory factor analysis.
Findings: The present study’s findings indicate that the scientific productivity model based on the experts’ notions in population of the studied top authors features a favorable goodness of fit. Considering the factor loads in the confirmatory factor analysis, the “bibliometrics component” with a factor load of 1, the “individual component” with a factor load of 0.69 and the “organizational component” with a factor load of 0.63 in the population of highly cited authors are influential in the scientific productivity model. The summing of the ideas from the three studied sample volumes indicates that “the time dedicated to the scientific activities”, “the number of the authors’ received citations” and “the scientific prestige of the affiliate organization” are respectively the most important items in the individual, bibliometrics and organizational components influencing the scientific productivity of the top authors.
Discussion: The findings of the present study indicate that a collection of organizational, individual and bibliometric factors influence the scientific productivity of the top authors and the tri-component model features a favorable goodness of fit. Findings of the present study show that the bibliometric component on the one hand affects on scientific productivity and on the other hand, is the product of the interaction between individual and organizational components, and the final output of scientific productivity. The top authors in the present study are more cited than their colleagues and peers based on the highly cited indicator. Of course, according to relevant research, highly cited authors are probably highly published. Scientific success in the present age seems to require familiarity with the existing competitive culture of citation and publishing. According to the results obtained in the present study, it is thought that in the community of the top authors, they are familiar with the publishing and citation culture, collaborates with scholarly journals (as a reviewer or editorial board member), selects journals of high academic standing for publication, and recognizes the importance of communicating with world-renowned authors. The result shows that the role of the organizational component is less than the other two components; Emphasising on publishing more and receiving more citations can be both from organizational factors and from internal and individual motivations. Combination of the quantitative and qualitative items can offer a more thorough image of the status of the individuals’ scientific productivity; the items offered in this model can be employed as solution by the individuals and organizations for enhancing scientific productivity.

Keywords

Scientific Productivity

Top Authors

Citation Indicators

Highly Cited Ayuthors

Confirmatory Factor Analysis

Subjects

Scientometrics

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). The contribution of star scientists to overall sex differences in research productivity. Scientometrics, 81(1), 137–156. https://doi.org/10.1007/s11192-008-2131-7

Addis, E., & Pagnini, C. (2010). Scientific excellence. Meta-analysis of gender and science research–Topic report, pp. 1-90, Retrieved 2018, Dec. 25, from http://www. genderandscience. org/doc/TR5_Excellence. Pdf.

Aksnes, D. W., & Taxt, R. E. (2004). Peer reviews and bibliometric indicators: a comparative study at a Norwegian university. Research Evaluation, 13(1), 33-41. https://doi.org/10.3152/147154404781776563

Ali Beigi, A. H. (2008). Research Productivity Analysis of Faculty Members: A Case Study of Razi University, Research and planning in Higher Education, 13(4), 125-155. [In Persian]

Allison, P. D., & Long, J. S. (1990). Departmental effects on scientific productivity. American Sociological Review, 55(4), 469-478. https://doi.org/10.2307/2095801

Bean, J. P. (1982). A causal model of faculty research productivity. Annual Meeting of the American Educational Research Association, (pp. 3-30). New York: American Educational Research Association.

Ben-Porath, Y. (1967). The production of human capital and the life cycle of earnings. Political Economy, 75(4), 352-365. https://doi.org/10.1086/259291

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. https://doi.org/10.1097/00001888-200503000-00006

Boardman, C., & Bozeman, B. (2015). Academic faculty as intellectual property in university-industry research alliances. Economics of Innovation and New Technology, 24(5), 403-420. https://doi.org/10.1080/10438599.2014.988499

Bornmann, L., De Moya Anegón, F., & Leydesdorff, L. (2010). Does scientific advancement lean on the shoulders of mediocre research? An investigation of the Ortega hypothesis. In Eleventh International Conference on Science and Technology Indicators (p. 43), Netherlands: University of Leiden. https://doi.org/10.1371/journal.pone.0013327

Brocato, J. J. (2002). The research productivity of family medicine department faculty: A national study. (Unpublished doctoral dissertation). Michigan State University, East Lansing, United States.

Creswell, J. W. (1985). Faculty research performance: Lessons from the sciences and the social sciences. Washington: Association for the Study of Higher Education.

Dundar, H., & Lewis, D. R. (1998). Determinants of research productivity in higher education. Research in Higher Education, 39(6), 607-631. https://doi.org/10.1023/A:1018705823763

Edwards, S. A., & McCarrey, M. W. (1973). Measuring Performance of Researchers. Research Management, 16(1), 34-41. https://doi.org/10.1080/00345334.1973.11756168

Fahimnia, F., Noroozi Chakoli., A. R., & Bamir, M. (2017). Investigating the effect of individual and organizational factors on research productivity for faculty members in Tehran University. Scientometics Research, 2(4), 15-26.

Finkelstein, M. J. (1984). The American academic profession: A synthesis of social scientific inquiry since World War II. Columbus: Ohio State University Press. https://archive.org/details/americanacademic0000fink/mode/2up?q=%22malcolm+cleveland%22

Garfield, E. (1973). More of Forecasting Noble Prizes and the Most Cited Scientists of 1972! Current Contents, 40(1), 5-7.

Hedjazi, Y., & Behravan, J. (2011). Study of factors influencing research productivity of agriculture faculty members in Iran. Higher Education, 62(5), 635-647. https://doi.org/10.1007/s10734-011-9410-6

Kenna, R., Mryglod, O., & Berche, B. (2017). A scientists' view of scientometrics: Not everything that counts can be counted. Condens Matter Phys, 20(1), 20-65.‏ https://doi.org/10.5488/CMP.20.13803

Kosmulski, M. (2018). Are you in top 1%? Scientomerics, 114(2), 557-565. https://doi.org/10.1007/s11192-017-2526-4

Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607-610. https://doi.org/10.1177/001316447003000308

Lancho-Barrantes, B. S., Ceballos, H. G., & Cantú-Ortiz, F. J. (2019). Factors that influence scientific productivity from different countries: A causal approach through multiple regression using panel data. BioRxiv. https://doi.org/10.1101/558254

Larivière, V., Macaluso, B., Archambault, É., & Gingras, Y. (2010). Which scientific elites? On the concentration of research funds, publications and citations. Research Evaluation, 19(1), 45-53. https://doi.org/10.3152/095820210X492495

Lawshe CH. (1975). A Qualitative Approach to Content Validity. Personnel Psychology, 28(8), 563-75. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x

Lotka, A. J. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Science, 16 (12), 317-323.

Más-Bleda, A., Thelwall, M., Kousha, K., & Aguillo, I. (2014). Successful researchers publicizing research online: An outlink analysis of European highly cited scientists' personal websites. Journal of Documentation, 70(1), 148-172. https://doi.org/10.1108/JD-12-2012-0156

Mendez, E. (2012). What's in good? International Development Research Centre, 2-29. Retrieved 2018, Dec. 15, from: https: //idl–bnc–Idrc.dspaced irect .org /bitstream /handle /10625/50267/IDL-50267.pdf.

Nayeri, M. D., & Rostami, M. (2016). A Model on Knowledge Workers Performance Evaluation. Review of Knowledge Economy, 3(1), 1-13. https://doi.org/10.18488/journal.67/2016.3.1/67.1.1.13

Ortega, J. L. (2017). Are peer-review activities related to reviewer bibliometric performance? A scientometric analysis of Publons. Scientometrics, 112(2), 947-962. https://doi.org/10.1007/s11192-017-2399-6

Price, D. J. (1970). Citation measures of hard science, soft science, technology, and nonscience. Communication Among Scientists and Engineers, 13(1), 3-22.

Ruiz-Castillo, J. (2016). Research output indicators are not productivity indicators. Informetrics. https://doi.org/10.1016/j.joi.2016.04.004

Saberi, M., Mohammad Khani, K., & Arasteh, H. R. (2016). Investigating the influential factors on scientific productivity of faculty and presenting a model to improve it (a case study of Azas Islamic University 8 region), Research in Educational Systems, 29(9), 55-81. [In Persian]

Sahel, J. A. (2011). Quality versus quantity: assessing individual research performance. Science Translational Medicine, 3(84), 1-4. https://doi.org/10.1126/scitranslmed.3002249

Sandström, U., & Van den Besselaar, P. (2016). Quantity and/or quality? The importance of publishing many papers. PloS One, 11(11), 149. https://doi.org/10.1371/journal.pone.0166149

Teodorescu, D. (2000). Correlates of faculty publication productivity: A cross-national analysis. Higher Education, 39(2), 201-222. https://doi.org/10.1023/A:1003901018634

Tijssen, R. J. (2003). Scoreboards of research excellence. Research Evaluation, 12(2), 91-103. https://doi.org/10.3152/147154403781776690

Tijssen, R. J., Visser, M. S., & Van Leeuwen, T. N. (2002). Benchmarking international scientific excellence: Are highly cited research papers an appropriate frame of reference? Scientometrics, 54(3), 381-397. https://doi.org/10.1023/A:1016082432660

Tol, R. S. (2013). Identifying excellent researchers: A new approach. Informetrics, 7(4), 803-810. https://doi.org/10.1016/j.joi.2013.06.003

Uzzi, B., Mukherjee, S., Stringer, M., & Jones, B. (2013). Atypical combinations and scientific impact. Science, 342(6157), 468-472. https://doi.org/10.1126/science.1240474

Van Noorden, R. (2011). A profusion of measures: Scientific performance indicators are proliferating--leading researchers to ask afresh what they are measuring and why. Richard Van Noorden surveys the rapidly evolving ecosystem. Nature, 465(7300), 864-867. https://doi.org/10.1038/465864a

Van Raan, A. F. (2006). Comparison of the Hirsch-index with standard bibliometric indicators and with peer judgment for 147 chemistry research groups. Scientometrics, 67(3), 491-502. https://doi.org/10.1556/Scient.67.2006.3.10