Purpose In this paper, we discuss common pitfalls in producing review articles for publication in academic journals, offering guidance to minimize rejection rates. We highlight the dual core features of systematicity (i.e. rigor and transparency) and generativity (i.e. advancing knowledge) in review papers. Thereby, we aim to help researchers deal with the abundance of guidelines and create publishable literature reviews that meaningfully contribute to their fields. Additionally, we discuss the prospects and perils of incorporating advanced technologies, such as artificial intelligence (AI), in review research. Design/methodology/approach Drawing from an analysis of editorial guidelines, desk-rejection decisions and reviewer feedback, as well as our experience as authors, reviewers and editors, we identify six common pitfalls of literature reviews. For each pitfall, we discuss typical manifestations and mitigation strategies. We also incorporate illustrative examples of literature reviews that have successfully navigated these pitfalls. Findings We identify and discuss six common pitfalls: (1) lack of compelling motivation, (2) weak conceptual foundation, (3) poor research design, (4) flawed research method, (5) insufficient knowledge contributions and (6) poor paper crafting – which undermine systematicity and generativity. For each of the pitfalls, we put forward mitigation strategies, which collectively help improve systematicity and generativity. Additionally, we anticipate and discuss two (emerging) pitfalls related to AI and digital technologies in review research: irresponsible and ineffective use of AI. Again, we propose mitigation strategies. Originality/value We offer a structured framework to help researchers overcome common challenges in literature reviews and reduce the likelihood of rejection by leading academic journals.
Kembro et al. (Wed,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: