Proceedings of the 12th European Conference on Knowledge ...

Pavel Bogolyubov
fields but year and names more <strong>of</strong>ten than not involved extensive search on o<strong>the</strong>r databases such as
Emerald Insight and SpringerLink or using Google, since original records were <strong>of</strong>ten incomplete.
Citations were taken exclusively from Google Scholar: in general, <strong>the</strong>re was a well pronounced
disparity between <strong>the</strong> numbers on ABI and Google, sometimes by <strong>the</strong> factor <strong>of</strong> ten, and in all but two
or three cases <strong>the</strong> citation numbers on ABI were lower; at <strong>the</strong> same time, Google’s figures were
consistent with o<strong>the</strong>r databases.
Keywords, providing a basis for fur<strong>the</strong>r contents analysis, were predominantly ABIs for <strong>the</strong> sake <strong>of</strong>
consistency, however, in a limited number <strong>of</strong> cases <strong>the</strong>y had to be taken from o<strong>the</strong>r databases or
suggested upon reading <strong>the</strong> papers.
It is worth mentioning that all figures discussed in this paper were valid in November 2010; <strong>the</strong>y tend
to change fairly rapidly as I have seen several times during <strong>the</strong> few months <strong>of</strong> working on <strong>the</strong> paper,
and this is effectively a snapshot in time.
As far as processing <strong>the</strong> numbers is concerned, <strong>the</strong> intention was to use <strong>the</strong> aforementioned paper by
Serenko and Bontis dedicated to meta-analysis <strong>of</strong> <strong>the</strong> literature on Knowledge Management and
Intellectual Capital (Serenko and Bontis 2004) as a general template, hence <strong>the</strong> choice <strong>of</strong> <strong>the</strong>
database. The fundamental principle <strong>of</strong> assessing <strong>the</strong> productivity and impact as key indicators
remained in force, but a few things were done differently.
First <strong>of</strong> all, instead <strong>of</strong> limiting <strong>the</strong> search to a number <strong>of</strong> leading journals, I have included all available
records, for two reasons: from a technical point <strong>of</strong> view, my set was <strong>of</strong> a manageable size, and
besides, full search would provide a more comprehensive assessment <strong>of</strong> <strong>the</strong> entire field.
Second, I have chosen a straight count method <strong>of</strong> calculating <strong>the</strong> productivity – in o<strong>the</strong>r words, every
author gets a point for (co-) authoring a paper regardless <strong>of</strong> how many people are involved. It is worth
mentioning that <strong>the</strong>re are several o<strong>the</strong>r ways <strong>of</strong> doing it, such as based on author position on <strong>the</strong> list
(<strong>the</strong> fur<strong>the</strong>r down, <strong>the</strong> lower <strong>the</strong> proportion <strong>of</strong> credit is attributed), equal credit (everyone gets an equal
portion <strong>of</strong> <strong>the</strong> total <strong>of</strong> one point) and normalised page size (<strong>the</strong> bigger <strong>the</strong> paper, <strong>the</strong> better), all <strong>of</strong>
<strong>the</strong>m, however, with <strong>the</strong>ir own drawbacks and limitations. Fur<strong>the</strong>rmore, I believe that straight count
provides an answer to <strong>the</strong> question <strong>of</strong> how many research-active authors are in each institution and in
each country - in o<strong>the</strong>r words, it helps to identify <strong>the</strong> research locale.
Third, even though authors’ names were ga<strong>the</strong>red too, I have decided to discard <strong>the</strong>m at least for <strong>the</strong>
time being and not to calculate productivity and impact by name as adding little to answering <strong>the</strong> key
questions.
The citation impact was calculated starting from straight count, however, bearing in mind that citations
accumulate over <strong>the</strong> time, individual numbers were time-normalised to generate <strong>the</strong> Normalised
Citation Impact Index – NCII (Holsapple, Johnson et al. 1994) – simply put, <strong>the</strong> number <strong>of</strong> citations
divided by <strong>the</strong> paper’s age in years. Naturally, <strong>the</strong> methods has its own weaknesses – for instance, a
paper published in December 2009 would get twice less credit than one published a month later,
however, taking papers’ longevity into account in a more accurate way would be unrealistic given <strong>the</strong>
volume <strong>of</strong> data processed.
Publication counts were added up by institution and country to generate total numbers, whereas total
NCIIs were divided by corresponding total publication numbers to show average time-normalised
number <strong>of</strong> citations per author per paper.
As a final point, it is worth mentioning that given <strong>the</strong> amount <strong>of</strong> manual data processing involved, <strong>the</strong>
analysis has proven to be not a pastime for <strong>the</strong> fain<strong>the</strong>arted. An extensive amount <strong>of</strong> pro<strong>of</strong>reading
was involved to avoid duplication, especially in institutions’ names: for example, Oxford University
could be listed in three different ways (Oxford University, University <strong>of</strong> Oxford and The University <strong>of</strong>
Oxford – all three appearing in different places on <strong>the</strong> lists), not to mention <strong>the</strong> non-English names
that could be spelt differently in a number <strong>of</strong> languages. I regret to admit that despite multiple
accuracy checks, some minor errors and omissions may still remain due to a high volume and varied
format <strong>of</strong> data as well as <strong>the</strong> variety <strong>of</strong> sources involved. I would like to apologise in advance for any
errors, should <strong>the</strong>y come to light.
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