Purpose-This paper aims to consider the most important criteria of e-book acquisition in academic libraries. The survey seeks these reasons on the basis of two aspects: e-book features for users and for information professionals or libraries. Moreover, restrictions on e-books acquisition are surveyed with regard to information professionals' attitude. Design/methodology/approach-The survey research method is used in this study with questionnaire and interview used for data gathering. The statistical population consisted of 60 information professionals working at state universities of metropolitan Tehran. Findings-The results show that high storage capacity and multimedia capability are the most important features in acquiring e-books on the basis of academic user needs. Also, the main criteria of acquiring e-books from the professionals' point of view of fostering their interests are: occupying little space, ease of selection and simultaneous circulation. Moreover, this study shows that the main restrictions of e-book acquisition are: hardware and software restrictions, the lack of a union catalog for Iranian e-books, low level of user familiarity, hackers and even eye-fatigue when reading. Originality/value-This study takes the form of unique research that surveys the e-book acquisition criteria in Iran on the basis of two aspects (user and library) with focus on information professionals' attitude, simultaneously. In addition, there has not been any research on e-books in Iran, and this study is the first.

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Highly cited papers are influenced by external factors that are not directly related to the document's intrinsic quality. In this study, 50 characteristics for measuring the performance of 68 highly cited papers, from the Journal of The American Medical Informatics Association indexed in Web of Science (WOS), from 2009 to 2019 were investigated. In the first step, a Pearson correlation analysis is performed to eliminate variables with zero or weak correlation with the target (“dependent”) variable (number of citations in WOS). Consequently, 32 variables are selected for the next step. By applying the Ridge technique, 13 features show a positive effect on the number of citations. Using three different algorithms, i.e., Ridge, Lasso, and Boruta, 6 factors appear to be the most relevant ones. The "Number of citations by international researchers", "Journal self-citations in citing documents”, and "Authors' self-citations in citing documents”, are recognized as the most important features by all three methods here used. The "First author's scientific age”, "Open-access paper”, and "Number of first author's citations in WOS" are identified as the important features of highly cited papers by only two methods, Ridge and Lasso. Notice that we use specific machine learning algorithms as feature selection methods (Ridge, Lasso, and Boruta) to identify the most important features of highly cited papers, tools that had not previously been used for this purpose. In conclusion, we re-emphasize the performance resulting from such algorithms. Moreover, we do not advise authors to seek to increase the citations of their articles by manipulating the identified performance features. Indeed, ethical rules regarding these characteristics must be strictly obeyed.

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