A framework towards bias-free contextual productivity assessment

  • Susan George
  • Hiran H. Lathabai
  • Thara PrabhakaranEmail author
  • Manoj Changat


Productivity assessment of various actors is one of the major concerns of Scientometrics and is vital for many applications that include policymaking. Popular productivity indices are not suitable for the determination of productivity of actors within a research context. A framework for the generation of metrics for contextual productivity assessment based on network approach has been recently proposed. However, that framework used full counting or full credit allocation, which incurs inflationary and equalizing bias. Schemes such as fractional and harmonic counting could reduce inflationary bias and harmonic counting has a repute of minimizing equalizing bias. As the existing framework for contextual productivity assessment is prone to inflationary and equalizing bias, empowering it with the provision to determine the right credit allocation scheme might take us closer to the achievement of a bias-free framework. In this work, a method to quantify the biases and to decide the right credit allocation scheme is introduced and using this we revamp the existing framework. As a case study, the productivity of inventors in the field ‘Wireless Power Transmission’ is determined. Implications from the real-world case study signify the effectiveness of the framework.


Bias-free productivity assessment Contextual productivity assessment Fractional counting Harmonic counting Patent-inventor network Affiliation networks 



This work used the facility provided by ‘Scientometric lab’ (Order No. Pl.A1/Annual plan 16-17/Imp.plan/16 dated. 29/11/2016), Department of Futures Studies, University of Kerala.


  1. Albert, M. B., Avery, D., Narin, F., & McAllister, P. (1991). Direct validation of citation counts as indicators of industrially important patents. Research Policy, 20(3), 251–259.CrossRefGoogle Scholar
  2. Batagelj, V. (2012). Social network analysis, large-scale. In A. Robert Meyers (Ed.), Computational complexity: Theory, techniques, and applications (pp. 2878–2897). New York: Springer.CrossRefGoogle Scholar
  3. Batagelj, V., & Cerinšek, M. (2013). On bibliographic networks. Scientometrics, 96(3), 845–864.CrossRefGoogle Scholar
  4. Berker, Y. (2018). Golden-ratio as a substitute to geometric and harmonic counting to determine multi-author publication credit. Scientometrics, 114(3), 839–857.CrossRefGoogle Scholar
  5. Bonacich, P. (2007). Some unique properties of eigenvector centrality. Social Networks, 29(4), 555–564.CrossRefGoogle Scholar
  6. Borgatti, S. P. (2005). Centrality and network flow. Social Networks, 27(1), 55–71.MathSciNetCrossRefGoogle Scholar
  7. De Nooy, W., Mrvar, A., & Batagelj, V. (2018). Exploratory social network analysis with Pajek. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  8. Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131–152.MathSciNetCrossRefGoogle Scholar
  9. Egghe, L., Rousseau, R., & Van Hooydonk, G. (2000). Methods for accrediting publications to authors or countries: Consequences for evaluation studies. Journal of the American Society for Information Science, 51(2), 145–157.CrossRefGoogle Scholar
  10. Ernst, H., Leptien, C., & Vitt, J. (2000). Inventors are not alike: The distribution of patenting output among industrial R&D personnel. IEEE Transactions on Engineering Management, 47(2), 184–199.CrossRefGoogle Scholar
  11. Garfield, E. (1955). Citation indexes for science: A new dimension in documentation through association of ideas. Science, 122(3159), 108–111.CrossRefGoogle Scholar
  12. Garfield, E. (1957). Breaking the subject index barrier—A citation index for chemical patents. Journal of the Patent Office Society, 39, 583.Google Scholar
  13. Garfield, E. (1972). Citation analysis as a tool in journal evaluation. Science, 178(4060), 471–479.CrossRefGoogle Scholar
  14. Guan, J. C., & Gao, X. (2009). Exploring the h-index at patent level. Journal of the American Society for Information Science and Technology, 60(1), 35–40.CrossRefGoogle Scholar
  15. Hagen, N. (2009). Harmonic publication and citation counting: Sharing authorship credit equitably-not equally, geometrically or arithmetically. Scientometrics, 84(3), 785–793.CrossRefGoogle Scholar
  16. Hagen, N. T. (2008). Harmonic allocation of authorship credit: Source-level correction of bibliometric bias assures accurate publication and citation analysis. PLoS One, 3(12), e4021.CrossRefGoogle Scholar
  17. Hagen, N. T. (2013). Harmonic coauthor credit: A parsimonious quantification of the byline hierarchy. Journal of Informetrics, 7(4), 784–791.CrossRefGoogle Scholar
  18. Hansen, D., Shneiderman, B., & Smith, M. A. (2010). Analyzing social media networks with NodeXL: Insights from a connected world. Los Altos: Morgan Kaufmann.Google Scholar
  19. Harhoff, D., Narin, F., Scherer, F. M., & Vopel, K. (1999). Citation frequency and the value of patented inventions. Review of Economics and Statistics, 81(3), 511–515.CrossRefGoogle Scholar
  20. Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569–16572.CrossRefGoogle Scholar
  21. Hodge, S. E., Greenberg, D. A., & Challice, C. (1981). Publication credit. Science, 213, 950.Google Scholar
  22. Hoel, E. G., Heng, W.-L., & Honeycutt, D. (2005). High performance multimodal networks. In International symposium on spatial and temporal databases (pp. 308–327). Springer.Google Scholar
  23. Kim, J., & Diesner, J. (2014). A network-based approach to coauthorship credit allocation. Scientometrics, 101(1), 587–602.CrossRefGoogle Scholar
  24. Kosmulski, M. (2006). A new Hirsch-type index saves time and works equally well as the original h-index. ISSI Newsletter, 2(3), 4–6.Google Scholar
  25. Kuan, C.-H., Huang, M.-H., & Chen, D.-Z. (2011). Ranking patent assignee performance by h-index and shape descriptors. Journal of Informetrics, 5(2), 303–312.CrossRefGoogle Scholar
  26. Lathabai, H. H., Prabhakaran, T., & Changat, M. (2014). Affiliations network analysis in scientific citations: A case study of information technology for engineering. In 2014 International conference on data science & engineering (ICDSE) (pp. 151–156). IEEE.Google Scholar
  27. Lathabai, H. H., Prabhakaran, T., & Changat, M. (2017). Contextual productivity assessment of authors and journals: A network scientometric approach. Scientometrics, 110(2), 711–737.CrossRefGoogle Scholar
  28. Levine, L. (1986). Prolific inventors—A bibliometric analysis. Scientometrics, 10(1–2), 35–42.CrossRefGoogle Scholar
  29. Lindsey, D. (1980). Production and citation measures in the sociology of science: The problem of multiple authorship. Social Studies of Science, 10(2), 145–162.CrossRefGoogle Scholar
  30. Liu, X. Z., & Fang, H. (2012a). Fairly sharing the credit of multi-authored papers and its application in the modification of h-index and g-index. Scientometrics, 91(1), 37–49.CrossRefGoogle Scholar
  31. Liu, X. Z., & Fang, H. (2012b). Modifying h-index by allocating credit of multi-authored papers whose author names rank based on contribution. Journal of Informetrics, 6(4), 557–565.CrossRefGoogle Scholar
  32. Lotka, A. J. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences, 16(12), 317–323.Google Scholar
  33. Manohar, M., Lathabai, H., George, S., & Prabhakaran, T. (2018). Wire-free electricity: Insights from a techno-futuristic exploration. Utilities Policy, 53, 3–14.CrossRefGoogle Scholar
  34. Narin, F. (1994). Patent bibliometrics. Scientometrics, 30(1), 147–155.CrossRefGoogle Scholar
  35. Narin, F., & Breitzman, A. (1995). Inventive productivity. Research Policy, 24(4), 507–519.CrossRefGoogle Scholar
  36. Newman, M. E. J. (2008). Mathematics of Networks. In S. N. Durlauf & L. E. Blume (Eds.), The New Palgrave Dictionary of Economics (pp. 4059–4064). London: Palgrave Macmillan.Google Scholar
  37. Osório, A. (2018). On the impossibility of a perfect counting method to allocate the credits of multi-authored publications. Scientometrics, 116(3), 2161–2173.CrossRefGoogle Scholar
  38. Prabhakaran, T., Lathabai, H. H., & Changat, M. (2015). Detection of paradigm shifts and emerging fields using scientific network: A case study of information technology for engineering. Technological Forecasting and Social Change, 91, 124–145.CrossRefGoogle Scholar
  39. Price, D. (1981). Multiple authorship. Science, 212(4498), 986–986.CrossRefGoogle Scholar
  40. Tesla, N. (1908). The future of the wireless art. In W. W. Massie & C. R. Underhill (Eds.), Wireless Telegraphy & Telephony (pp. 67–71). New York: D. Van Nostrand.Google Scholar
  41. Tesla, N. (1914). Apparatus for transmitting electrical energy. US Patent 1,119,732.Google Scholar
  42. Tesla, N. (1927). World system of wireless transmission of energy. Telegraph and Telephone Age, 20, 457–460.Google Scholar
  43. Trajtenberg, M. (1990). A penny for your quotes: Patent citations and the value of innovations. The Rand Journal of Economics, 21, 172–187.CrossRefGoogle Scholar
  44. USPTO-OPET (published on May 31, 2019). Retrieved July 19, 2019, from
  45. Van Hooydonk, G. (1997). Fractional counting of multiauthored publications: Consequences for the impact of authors. Journal of the American Society for Information Science, 48(10), 944–945.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2019

Authors and Affiliations

  1. 1.Department of Futures StudiesUniversity of KeralaThiruvananthapuramIndia

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