Why review papers get rejected: common pitfalls and how to avoid them

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Titel: Why review papers get rejected: common pitfalls and how to avoid them
Autoren: Kembro, Joakim, Kunisch, Sven, Durach, Christian F.
Weitere Verfasser: Lund University, Other research environments, Centre for Retail and Logistics (REAL), Lunds universitet, Andra forskningsmiljöer, Centrum för handel och logistik (REAL), Originator, Lund University, Lund University School of Economics and Management, LUSEM, Department of Business Administration, Centre for Retail Research at Lund University, Lunds universitet, Ekonomihögskolan, Företagsekonomiska institutionen, Centrum för handelsforskning vid Lunds Universitet, Originator, Lund University, Faculty of Engineering, LTH, Departments at LTH, Department of Industrial and Mechanical Sciences, Engineering Logistics, Lunds universitet, Lunds Tekniska Högskola, Institutioner vid LTH, Institutionen för industri- och maskinvetenskaper, Teknisk logistik, Originator
Quelle: International Journal of Physical Distribution and Logistics Management. 55(11):163-192
Schlagwörter: Humanities and the Arts, Other Humanities, Humaniora och konst, Annan humaniora och konst, Languages and Literature, Språk och litteratur, Natural Sciences, Computer and Information Sciences, Naturvetenskap, Data- och informationsvetenskap (Datateknik), Social Sciences, Economics and Business, Samhällsvetenskap, Ekonomi och näringsliv
Beschreibung: 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.
Zugangs-URL: https://doi.org/10.1108/IJPDLM-03-2025-0125
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  Data: Why review papers get rejected: common pitfalls and how to avoid them
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  Data: <i>International Journal of Physical Distribution and Logistics Management</i>. 55(11):163-192
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  Data: 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.
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