The increasing number of journals makes it difficult to decide the right venue for manuscript submission. This becomes more complicated as the selection criteria may vary from one discipline to another. Therefore, appropriate cross‐disciplinary studies are required to understand the exact concerns that dominate a particular field. The current study compares 16 factors that influence journal choices between medicine and social sciences using the answers given to a global survey of 235 open access journal authors. The results reveal that authors of both areas consider ‘peer reviewed’ status as the most important factor while showing the least interest to the ‘number of annual subscribers’ of the journal. However, compared to social science authors, those in the discipline of medicine give significantly more consideration to (1) impact factor, (2) the inclusion of the journal in abstracting and indexing services, (3) publisher's prestige, and (4) online submission with tracking facility. The factors that were identified can be categorized for both disciplines as reflecting the reputation of a journal, performance or production issues, and reliability and demand characteristics of their publication choice. The editors and publishers can use these findings to attract the best manuscripts as the study reveals the author's perception of the journal's status. The results can also be used to design recommender systems for journal submission for new authors in a discipline.

PurposeStudying the nature of research progress in interrelated research domains is important for evaluating the research productivity and to understand the current trends of the area of research. This study aims to examine a research domain that combines library and information science with information systems (IS).Design/methodology/approachQuartile 1 journals that cover both subject domains in SCImago were selected for the study. Bibliographic records of the publications during 2010 and 2019 were retrieved from the Scopus database. VOSviewer data visualization tool was used to perform citation, coauthorship, bibliographic coupling, cocitation and co-occurrence analysis. In addition, descriptive and inferential statistics were exploited.FindingsThe absence of a consistent association between the number of documents authored and the number of citations received by a researcher was an important finding of the study. The strong association of authors regardless of the different topics they researched and the trend of increasing interest on collaborative research were also highlighted. Moreover, the authors who received the highest number of citations were not always the first authors of the documents which received the most citations. The documents published in information management, information theory and IS journals attained the most citations. The journals, institutions and countries with the highest number of documents and citations were also revealed by the research. Electrical engineering departments showed a higher research productivity, while they were utilized more compared to that of other departments. IS and management, information theory, communication, information retrieval, geographic-based IS and bioinformatics were the six major research areas of the considered domain.Originality/valueThis is the first study related to examining the research progress in a combined subject domain using multiple aspects including, individual performance, institutional progress, geographical contribution and so on. Identifying the major research areas in the combined subject domain can also be considered a novel contribution to the field.

Purpose The purpose of this paper is to develop a journal recommender system, which compares the content similarities between a manuscript and the existing journal articles in two subject corpora (covering the social sciences and medicine). The study examines the appropriateness of three text similarity measures and the impact of numerous aspects of corpus documents on system performance. Design/methodology/approach Implemented three similarity measures one at a time on a journal recommender system with two separate journal corpora. Two distinct samples of test abstracts were classified and evaluated based on the normalized discounted cumulative gain. Findings The BM25 similarity measure outperforms both the cosine and unigram language similarity measures overall. The unigram language measure shows the lowest performance. The performance results are significantly different between each pair of similarity measures, while the BM25 and cosine similarity measures are moderately correlated. The cosine similarity achieves better performance for subjects with higher density of technical vocabulary and shorter corpus documents. Moreover, increasing the number of corpus journals in the domain of social sciences achieved better performance for cosine similarity and BM25. Originality/value This is the first work related to comparing the suitability of a number of string-based similarity measures with distinct corpora for journal recommender systems.

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