Automatizing the Assignment of the Submitted Manuscripts to Reviewers: A Systematic Review of Research Texts

Purpose: To systematicly review the automatazation of the assignment of the submitted manuscripts to reviewers in order to identify the status of research studies in this field in terms of types of evidence of expertise, types of retrieval models used, and the research gaps, and finally some suggestions for has been offered for future research. Method: The current research followed the systematic review framework presented by Kitchenham & Charters (2007). To collect the research data, scientific databases were searched by using related keywords, and thus 50 records in English from 1992 to 2018 were retrieved. After applying the preliminary refinements to the entrance and exit criteria to the study and applying the experts’ reviews, 43 English research studies were selected for systematic review. Then, a checklist was designed and the required information was extracted from the studies. Findings: The systematic review showed that the studies on automatization of the assignment of the submitted manuscripts to reviewers typically used four sources as the evidence of the candidate's expertise, including: the candidates’ self-expression, the candidates’ documents, collaborative networks and scientific relations between the candidates and the hybrid resource. In addition, Boolean model, expanded Boolean model, Fuzzy model, Vector Space model, Probabilistic model, the Probabilistic Latent Semantic Indexing, the Latent Dirichlet allocation, and Author-Subject model were among the information retrieval models used in the expert reviewer finding systems. The lack of attention to the design of the expert finding system for reviewers in national journals and conferences, lack of attention to knowledge resources, and lack of attention to the users' views on the design of the existing systems were among the research gaps in this field. Originality/value: The use of hybrid resources in extracting the candidate's expert evidence, using knowledge resources, and paying attention to the users' perspectives in designing the expert finding system for reviewers can help to improve the performance of the existing systems. This research presents an overall picture of both the measures thus far taken and the measures should be taken in desiging the expert reviewer finding systems.