Growth of research literature in scientific specialities. A modelling perspective

Abstract

The paper discusses the application of three well known diffusion models and their modified versions to the growth of publication data in four selected fields of S&T. It is observed that all the three models in their modified versions generally improve their performance in terms of parameter values, fit statistics, and graphical fit to the data. The most appropriate model is generally seen to be the modified exponential-logistic model. Growth of knowledgeThe understanding of the process of growth of knowledge in research specialities and its modelling has challenged bibiiometricians and sociologists for long. Over the years, some literature has appeared in this area. Gilbert 1 has reviewed the existing literature on the indicators of growth of knowledge in scientific specialities and lists many ways of measuring it, noting their strengths and limitations and commenting on their use.There are two approaches that have normally been considered in understanding knowledge growth: Qualitative and quantitative. Qualitative approach suggests structural or descriptive models of knowledge growth, while descriptive models use social phenomenon to explain diffusion and creation of knowledge. Quantitative approach is a more recent phenomenon, and have relied on summarisation statistics to .describe observed behaviour, while others apply growth and technology diffusion models and bibliometric/scientometric techniques.The growth of scientific knowledge generally takes the form of logistic curve. The successive phases of knowledge growth represented by logistic growth curve are: (a) a

Modeling the behaviour of science and technology: self-propagating growth in the diffusion process

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Continental-scale patterns and climatic drivers of fruiting phenology: A quantitative Neotropical review

“…An exponential growth in publication rate of phenological studies (as shown by a least square fit to an exponential model, R 2 = 0.96) is a common behavior for the fields of Ecology, Biometeorology or Evolution ( Fig. 1) among others, particularly after 1995 (Gupta et al, 1997;Vinkler, 2010). Even so, this increased rate of publication also shows a burgeoning interest in phenological research, more recently motivated by its insights into climate change (Cleland et al, 2007;Parmesan, 2007;Ibáñez et al, 2010;Keatley and Hudson, 2010;Chambers et al, 2013;Richardson et al, 2013).…”

Bibliometrics-based foresight on renewable energy production

“…The technology life cycle is central regarding bibliometric diffusion analysis, and the majority of bibliometric diffusion studies are based on Watts and Porter's (1997) life cycle indicators. Bibliometric data can be used in determining the stage of the technology life cycle, as the growth of scientific knowledge has been observed as resembling the diffusion process, more precisely the diffusion S-curve (Gupta et al, 1997). Rogers defined the diffusion of an innovation as a process in which the innovation spreads into a social system in time, through communication between individuals (Rogers, 1962).…”