Do LIS experts select more appropriate journals than journal finders? A study about LIS journals?

The primary aim of the present study is to provide a comparative-analytical analysis of the proposed results of the Manuscript Matcher with the responses of Library and Information Science experts to select the most appropriate journal for manuscript submission. This study is a kind of applied research conducted using the survey-analytical method with a comparative approach. Data were collected using a researcher-made questionnaire. The designed questionnaire was sent to 38 Library and Information Science (LIS) experts. Moreover, snowball sampling was employed to select a sample of 38 people. Twelve articles published in 12 WOSCC-indexed LIS journals were randomly selected for the Manuscript Matcher analysis. Abstracts and bibliographic data of the articles were sent to the experts. Moreover, the abstracts and titles of the 12 articles were entered in the manuscript matcher, and the results were analyzed and compared with those obtained from experts' opinions. Frequency distribution, percentage, chi-squared (χ 2 ) test, and SPSS software (version 26) were employed for data analysis. More than 70% of the statistical population were assistant professors, 36.8% of experts had more than 40 published articles, 5% had received more than 300 citations, and 2.6% of LIS experts had an h-index above 20. Compared with 62% congruent responses of experts in the target journal selection, the Manuscript Matcher showed an inferior performance of 53% congruent responses. Furthermore, no significant relationship was found between the number of citations and the experts’ h-indexes with the rate of their congruencies in selecting the target journal. Experts were more successful than Manuscript Matcher in selecting the target journal. However, the Manuscript Matcher included comprehensive coverage of publications that can facilitate the selection of a journal for researchers due to the limited knowledge of all journal finders and the time-consuming distinct search processes.