Skip to main content
SpringerLink
Log in

Classification of growth models based on growth rates and its applications

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

In this paper, growth models are classified and characterised using two types of growth rates: from time t to t+1 and from time t to 2t. They are interesting in themselves but can also be used for a quick prediction of the type of growth model that is valid in a particular case. These ideas are applied on 20 data sets collected byWolfram, Chu andLu. We determine (using the above classification as well as via nonlinear regression techniques) that the power model (with exponent>1) is the best growth model for Sci-Tech online databases, but that Gompertz-S-shaped distribution is the best for social sciences and humanities online databases.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. B.C. Brookes, The growth, utility and obsolescence of scientific periodical literature. Journal of Documentation, 26(4), (1970) 283–294.

    Google Scholar 

  2. F.E. Croxton, D.J. Cowden,Applied General Statistics, Pitman & Sons, London, 1955.

    Google Scholar 

  3. D.J. De Solla Price,Little Science, Big Science, Columbia University Press, New York and London, 1963.

    Google Scholar 

  4. L. Egghe, I.K. Ravichandra Rao, Citation age data and the obsolescence function: Fits and explanationsInformation Processing and Management, 28 (2) (1992) 201–217.

    Article  Google Scholar 

  5. L. Egghe, R. Rousseau,Introduction to Informetrics, Elsevier, Amsterdam, 1990.

    Google Scholar 

  6. J. Tague, J. Beheshti, L. Rees-Potter, The law of exponential growth: evidence, implications and forecasts,Library Trends, 30(1) (1981) 125–149.

    Google Scholar 

  7. G.O. Ware, A general statistical model for estimating future demand levels of data-base utilization within an information retrieval organisation,Journal of the American Society for Information Sciences, 24 (1973) 261–164.

    Google Scholar 

  8. D. Wolfram, C.M. Chu, X. Lu, Growth of knowledge: bibliometric analysis using online database data.Informetrics 89/90,L. Egghe, R. Rousseau (Eds) 1990. Proceedings of the second international Conference on Bibliometrics, Scientometrics and Informetrics, London (Canada), 1989, 355–372.

Download references

Author information

Author notes

  1. I. K. Ravichandra Rao

    Present address: DRTC, R.V. College P.O., 8th Mile, Mysore Rd, 5600059, Bangalore, India

Authors and Affiliations

  1. LUC, Universitaire Campus, B-3590, Diepenbeek, (Belgium)

    L. Egghe & I. K. Ravichandra Rao

  2. UIA, Universiteitsplein 1, B-2610, Wilrijk, (Belgium)

    I. K. Ravichandra Rao

Authors
  1. L. Egghe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. I. K. Ravichandra Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

One of the authors (I.K.R. Rao) is grateful to the Belgian National Science Foundation (NFWO) for financial support in the period that he was a visiting professor in LUC.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Egghe, L., Ravichandra Rao, I.K. Classification of growth models based on growth rates and its applications. Scientometrics 25, 5–46 (1992). https://doi.org/10.1007/BF02016845

Download citation

  • Received:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02016845

Keywords

Search

Navigation