Origin and emergence of entrepreneurship as a research field

This paper seeks to map out the emergence and evolution of entrepreneurship as an independent field in the social science literature from the early 1990s to 2009. Our analysis indicates that entrepreneurship has grown steadily during the 1990s but has truly emerged as a legitimate academic discipline in the latter part of the 2000s. The field has been dominated by researchers from Anglo-Saxon countries over the past 20 years, with particularly strong representations from the US, UK, and Canada. The results from our structural analysis, which is based on a core document approach, point to five large knowledge clusters and further 16 sub-clusters. We characterize the clusters from their cognitive structure and assess the strength of the relationships between these clusters. In addition, a list of most cited articles is presented and discussed

Detecting h-index manipulation through self-citation analysis

The h-index has received an enormous attention for being an indicator that measures the quality of researchers and organizations. We investigate to what degree authors can inflate their h-index through strategic self-citations with the help of a simulation. We extended Burrell’s publication model with a procedure for placing self-citations, following three different strategies: random self-citation, recent self-citations and h-manipulating self-citations. The results show that authors can considerably inflate their h-index through self-citations. We propose the q-index as an indicator for how strategically an author has placed self-citations, and which serves as a tool to detect possible manipulation of the h-index. The results also show that the best strategy for an high h-index is publishing papers that are highly cited by others. The productivity has also a positive effect on the h-index

Self-citations at the meso and individual levels: effects of different calculation methods

This paper focuses on the study of self-citations at the meso and micro (individual) levels, on the basis of an analysis of the production (1994–2004) of individual researchers working at the Spanish CSIC in the areas of Biology and Biomedicine and Material Sciences. Two different types of self-citations are described: author self-citations (citations received from the author him/herself) and co-author self-citations (citations received from the researchers’ co-authors but without his/her participation). Self-citations do not play a decisive role in the high citation scores of documents either at the individual or at the meso level, which are mainly due to external citations. At micro-level, the percentage of self-citations does not change by professional rank or age, but differences in the relative weight of author and co-author self-citations have been found. The percentage of co-author self-citations tends to decrease with age and professional rank while the percentage of author self-citations shows the opposite trend. Suppressing author self-citations from citation counts to prevent overblown self-citation practices may result in a higher reduction of citation numbers of old scientists and, particularly, of those in the highest categories. Author and co-author self-citations provide valuable information on the scientific communication process, but external citations are the most relevant for evaluative purposes. As a final recommendation, studies considering self-citations at the individual level should make clear whether author or total self-citations are used as these can affect researchers differently

A Rejoinder on Energy versus Impact Indicators

Citation distributions are so skewed that using the mean or any other central tendency measure is ill-advised. Unlike G. Prathap's scalar measures (Energy, Exergy, and Entropy or EEE), the Integrated Impact Indicator (I3) is based on non-parametric statistics using the (100) percentiles of the distribution. Observed values can be tested against expected ones; impact can be qualified at the article level and then aggregated.Comment: Scientometrics, in pres

The influence of self-citation corrections on Egghe's g index

The g index was introduced by Leo Egghe as an improvement of Hirsch's index h for measuring the overall citation record of a set of articles. It better takes into account the highly skewed frequency distribution of citations than the h index. I propose to sharpen this g index by excluding the self-citations. I have worked out nine practical cases in physics and compare the h and g values with and without self-citations. As expected, the g index characterizes the data set better than the h index. The influence of the self-citations appears to be more significant for the g index than for the h index.Comment: 9 pages, 2 figures, submitted to Scientometric

A New Approach to Analyzing Patterns of Collaboration in Co-authorship Networks - Mesoscopic Analysis and Interpretation

This paper focuses on methods to study patterns of collaboration in co-authorship networks at the mesoscopic level. We combine qualitative methods (participant interviews) with quantitative methods (network analysis) and demonstrate the application and value of our approach in a case study comparing three research fields in chemistry. A mesoscopic level of analysis means that in addition to the basic analytic unit of the individual researcher as node in a co-author network, we base our analysis on the observed modular structure of co-author networks. We interpret the clustering of authors into groups as bibliometric footprints of the basic collective units of knowledge production in a research specialty. We find two types of coauthor-linking patterns between author clusters that we interpret as representing two different forms of cooperative behavior, transfer-type connections due to career migrations or one-off services rendered, and stronger, dedicated inter-group collaboration. Hence the generic coauthor network of a research specialty can be understood as the overlay of two distinct types of cooperative networks between groups of authors publishing in a research specialty. We show how our analytic approach exposes field specific differences in the social organization of research.Comment: An earlier version of the paper was presented at ISSI 2009, 14-17 July, Rio de Janeiro, Brazil. Revised version accepted on 2 April 2010 for publication in Scientometrics. Removed part on node-role connectivity profile analysis after finding error in calculation and deciding to postpone analysis

Bias in the journal impact factor

The ISI journal impact factor (JIF) is based on a sample that may represent half the whole-of-life citations to some journals, but a small fraction (<10%) of the citations accruing to other journals. This disproportionate sampling means that the JIF provides a misleading indication of the true impact of journals, biased in favour of journals that have a rapid rather than a prolonged impact. Many journals exhibit a consistent pattern of citation accrual from year to year, so it may be possible to adjust the JIF to provide a more reliable indication of a journal's impact.Comment: 9 pages, 8 figures; one reference correcte

The fruits of collaboration in a multidisciplinary field

Collaboration between researchers and between research organizations is generally considered a desirable course of action, in particular by some funding bodies. However, collaboration within a multidisciplinary community, such as the Computer–Human Interaction (CHI) community, can be challenging. We performed a bibliometric analysis of the CHI conference proceedings to determine if papers that have authors from different organization or countries receive more citations than papers that are authored by members of the same organization. There was no significant difference between these three groups, indicating that there is no advantage for collaboration in terms of citation frequency. Furthermore, we tested if papers written by authors from different organizations or countries receive more best paper awards or at least award nominations. Papers from only one organization received significantly fewer nominations than collaborative papers