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PurposeThis paper aims to explore the characteristics of knowledge diffusion of library and information science to reveal its development trend and influence on other disciplines.Design/methodology/approachBased on the ESI discipline classification, this paper measures the knowledge diffusion from the library and information science to other disciplines over the last 24 years using indicators in four dimensions: breadth, intensity, speed and theme of knowledge diffusion.FindingsThe results show that the knowledge diffusion breadth of library and information science is wide, spreading to 21 ESI disciplines; the knowledge spread mainly concentrates in four soft or applied disciplines, and yet partially inter-disciplinary, and the knowledge diffusion intensity to each ESI discipline is parabolic whose highest point is mostly in 2004–2005; the speed of spreading to the 21 ESI disciplines is faster and faster, and the articles at the highest speed of knowledge diffusion are basically published after 2005; the knowledge diffusion themes are becoming increasingly diverse, deepening and specialization over time.Originality/valueThis paper modifies the relevant indicators of knowledge diffusion and constructs a measurement framework of knowledge diffusion from four aspects: breadth, intensity, speed and theme. The research method can also be used to explore the characteristics of knowledge absorption of a discipline from other ones.

As indicator weights obtaining is often difficult in all types of evaluation, this paper describes an approach to improve the indicator weights of scientific and technological competitiveness evaluation of Chinese universities. As a public institution funded by Chinese government, the research center for Chinese science evaluation of Wuhan University has completed five annual evaluations for the scientific and technological competitiveness of Chinese universities since 2005, whose abundant and reliable data motivated us to try to improve the weights obtained by the AHP (analytical hierarchy process). Based on these data, we calculated the objective weights of the indicator using the representative mathematical methods of the least square and the variation coefficient. As the weights of AHP can be influenced by the knowledge, experience and preference of experts and the calculated objective weights neglect the subjective judgement information, we integrated the subjective and objective weights by respectively using the additive and multiplicative model to reflect both the subjective considerations of experts and the objective information, and obtained three kinds of integrative weights. Finally, we selected the integrative weights of multiplicative model as the best weights by comparing and analyzing the evaluation results in 2005 and 2009 of each kind of weights. The results show that the evaluation effect of the weights of multiplicative model is indeed the best for all types of Chinese universities among these kinds of weights, and the experts and university principals enquired also basically reached a consensus on the university rankings of the integrative weights of multiplicative model.

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