Improving the Sensitivity of Research Publication Indices Using Exponential Function and the Area under Curve

In collecting and analyzing information about recent research papers, authors, and journals with their citation and indices, we noticed that in many cases such indices fail to reflect reality and are incapable to distinguish between different authors in some cases while they clearly have different research profiles. There are many research papers that can be found in literature that discussed the behavior and limitations of the current citation or publication indices. Most of them referred to the sensitivity problem and the fact that most indices neglect some publications during index calculation. These two problems are the most important limitations that may judge index fairness. In this paper, we proposed new indices; MIExp-Index using exponential function and MIArea-Index using the concept of the area under curve (AUC) in order to enhance the sensitivity and thus the fairness of index assessment. To demonstrate our two novel indices, data of real and hypothetical authors were used to demonstrate these two approaches. The results showed that both of the proposed indices are very sensitive for each single citation that the paper receives after being published. In addition, the two indices consider all authors’ publications, even the new ones, into account.