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The purpose of this paper is to evaluate the status of organizational climate of Iranian academic libraries and map its structural equation model. Organizational climate is one of the main indicators of organizational health and dynamic.

This research was an applied survey. The statistical population of the research included all staff working in 96 central libraries of Iranian governmental universities (N = 520). A self-administered questionnaire was used for data collection. Some descriptive and inferential statistical methods applied for data analysis in SPSS and factor analysis in LISREL software were used for modelling organizational climates in the libraries.

The t values of factor analysis pass showed that the effect of all indicators of ClimateQUAL on organizational climate was significant. The factor loading of the pass model showed that among the nine indicators, climate for psychological safety with 5.24, climate for innovation with 5.06 and climate for leadership with 4.93 had a higher effect on organizational climate of the libraries. Considering the values of goodness-of-fit indicators, the path model has an optimal status in all indicators, but that of RMSEA in which the status is relatively acceptable. The observed data relatively matched the theoretical model.

The t values of factor analysis pass showed that the effect of all indicators of ClimateQUAL on organizational climate was significant. The factor loading of the pass model showed that among the nine indicators, climate for psychological security with 5.24, climate for innovation with 5.06 and climate for leadership with 4.93 had a higher effect on organizational climate of the libraries. Considering the values of goodness-of-fit indicators, the path model has an optimal status in all indicators, except the RMSEA in which the status is relatively acceptable. The observed data relatively matched the theoretical model.

This paper aims to evaluate some of the known scientific indexes by using virtual data and proposes a new index, named multiple h-index (mh-index), for removing the limits of these variants.

Citation report for 40 researchers in Babol, Iran, was extracted from the Web of Science and entered in a checklist together with their scientific lifetimes and published ages of their papers. Some statistical analyses, especially exploratory factor analysis (EFA) and structural correlations, were done in SPSS 19.

EFA revealed three factors with eigenvalues greater than 1 and explained variance of over 96 per cent in the studied indexes, including the mh-index. Factors 1, 2 and 3 explained 44.38, 28.19 and 23.48 of the variance in the correlation coefficient matrix, respectively. The m-index (with coefficient of 90 per cent) in Factor 1, a-index (with coefficient of 91 per cent) in Factor 2 and h- and h2-indexes (with coefficients of 93 per cent) in Factor 3 had the highest factor loadings. Correlation coefficients and related comparative diagrams showed that the mh-index is more accurate than the other nine variants in differentiating the scientific impact of researchers with the same h-index.

As the studied variants could not satisfy all limits of the h-index, scientific society needs an index which accurately evaluates individual researcher’s scientific output. As the mh-index has some advantages over the other studied variants, it can be an appropriate alternative for them.