Scientific productivity refers to the productivity of scientists in their research performance. In other words, the term concerns how much output scientists produce within a certain time period, or compared to the inputs that are utilized for the research. The major outputs from research are publications, patents, inventions, and product developments. However, especially in research institutions, productivity more directly refers to publication or publishing productivity since most research results are reported as forms of publication. Therefore, being ”more or less productive” simply indicates that a scientist produces more or fewer publications than do others. Scholarly journal articles, books, conference papers, and mono graphs are included in publication counts. Among the publication forms, peer reviewed journal articles are most frequently used as a productivity measure.
Three different methods are used for counting publications: normal count (also called standard count), fractional count (also called adjusted count), and first author count (also called straight count). In normal count, each of the co authors is given full credit for the multi authored publications regardless of how much each contributed to the publications. This counting method is most often used due to the convenience of data collection. However, inflating the number of publications is a major disadvantage. In contrast, in a fractional count, each publication is counted as one divided by the number of co authors. The main purpose of using fractional count is to remove an overestimation of normal count and estimate the individual contribution to a publication. But the process of fractional counting is very tedious since all the co authors are not always identified. First author count is another way to remove the inflated credit by normal count and to determine the individual’s major contribution to publications. This method only recognizes publications in which the individual has contributed as the main author.
While publication counts deal with the quantity of research performance, citation counts, on the other hand, address the quality of publications. It is often said that the more qualified the publication, the greater the number of citations. However, citation count is less frequently used for measuring productivity because it is not a direct output but shows the impact or influence of publications.
Since scientific productivity is of interest not only to academics but also for public policy in more recent times, what determines scientific productivity has been extensively studied in many disciplines. The literature identifies many determinants including psychological characteristics, demographic characteristics, environ mental characteristics, cumulative advantages, and reinforcement. Psychological characteristics indicate that motivation, inner compulsion, capacity to work hard, cognitive and perceptual style, and work habits all affect the productivity of scientists significantly. It is almost always true that productive scientists have strong motivation and orientation for research.
Among the demographic characteristics, age is an important predictor for productivity. Early studies found that a productivity peak occurs in scientists’ late thirties and early forties and thereafter declines steadily. By contrast, some recent studies identify another productivity peak around age 50. Although there are many different life cycle models of productivity, they should be interpreted appropriately in the con text of a specific discipline. For example, the productivity cycle in physics is different from that in sociology. In most cases, scientists become less productive as they age, especially after 50. It is also often pointed out that the age effect is attributed to age and not to the possibility that older scientists have different attributes, values, or access to resources than younger members. Like age, gender is also an important variable affecting productivity difference. Early studies commonly pointed out that women tend to have somewhat lower publication rates than men. But more recent studies do not agree with this proposition, rather believing that sex differences in publication productivity are negligible, with the exception of women with young children. In a similar vein, one recent study found that sex differences in the number of publications increase during the first decade of the career, but are reversed later in the career.
Environmental and organizational factors also play a significant role in determining scientific productivity. For example, prestigious institutions tend to have more research resources and many “star” scientists in more specific research fields. The advantages are likely to help their scientists to collaborate more easily with experts inside the organization and also to attract more joint research and research grants from outsiders. Especially when research projects require more expensive equipment and infrastructure (not only physical but also human capital), the institutional capacity and external supports significantly affect research performance.
Cumulative advantage and reinforcement theories explain the difference of scientific productivity among scientists by using a concept of “feedback” processes. They propose that prior exceptional performance is conducive to later performance. The ideas are largely based on Merton’s so called ”Matthew effect” in science: once scientists receive recognition from their colleagues, they accrue additional advantages as they progress through their careers. The advantages typically begin with doctoral training in a prestigious department. The training, in turn, leads to a position in a major research university amply supplied with adequate resources for research. The initial appointment has a major impact on later productivity, and in turn, the prestige of second department and subsequent productivity. Cumulative advantage deals with resources and prestige of institutions, whereas reinforcement addresses the feedback one receives from successful publication of works, works being cited, and formal and informal praise from colleagues. Reinforcement theory typically explains that when scientists publish, the recognition they receive for the contribution stimulates further publication.
Although publication productivity measures scientists’ research performance efficiently, it still loses many aspects of scientists’ research performance. In particular, the outputs of teaching and mentoring as important research activities are often neglected in measuring scientific productivity. So a better measurement needs to be developed to include broader aspects of scientific activity.
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- Lee, S. & Bozeman, B. (2005) The Impact of Research Collaboration on Scientific Productivity. Social Studies of Science 35: 673-702.
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- Long, S. (1992) Measure of Sex Differences in Scientific Productivity. Social Forces 71(1): 159-78.
- Merton, R. K. (1973) The Sociology of Science: Theoretical and Empirical Investigations. University of Chicago Press, Chicago.
- Pelz, D. & Andrews, F. (1966) Scientists in Organizations: Productive Climate for Research and Development. Wiley, New York.
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