Automation of systematic literature reviews: A systematic literature review

Dinter, Raymon van; Teki̇nerdoğan, Bedir; Çatal, Çağatay

doi:10.1016/j.infsof.2021.106589

Cited by 188 publications

(114 citation statements)

References 70 publications

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“…We collected the evidence by surveying previous literature reviews of AI-based tools (e.g., Al-Zubidy et al, 2017;Harrison et al, 2020;Jonnalagadda et al, 2015;Kohl et al, 2018;Marshall and Wallace, 2019;Tsafnat et al, 2014;Van Dinter et al, 2021) and online registries (i.e., www. systematicreviewtools.com).…”

Section: Artificial Intelligence-based Support For the Literature Review Processmentioning

confidence: 99%

Artificial intelligence and the conduct of literature reviews

Wagner

Lukyanenko

Paré

2021

Journal of Information Technology

119

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Artificial intelligence (AI) is beginning to transform traditional research practices in many areas. In this context, literature reviews stand out because they operate on large and rapidly growing volumes of documents, that is, partially structured (meta)data, and pervade almost every type of paper published in information systems research or related social science disciplines. To familiarize researchers with some of the recent trends in this area, we outline how AI can expedite individual steps of the literature review process. Considering that the use of AI in this context is in an early stage of development, we propose a comprehensive research agenda for AI-based literature reviews (AILRs) in our field. With this agenda, we would like to encourage design science research and a broader constructive discourse on shaping the future of AILRs in research.

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Section: Artificial Intelligence-based Support For the Literature Review Processmentioning

confidence: 99%

Artificial intelligence and the conduct of literature reviews

Wagner

Lukyanenko

Paré

2021

Journal of Information Technology

119

View full text Add to dashboard Cite

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“…For example, the Semantic Web for Earth and Environmental Terminology (SWEET) ontology [ 131 ] and the Natural Environment Research Council (NERC; assessed on 28 May 2021) vocabulary could be used for the automated merging of the terms resulting from the abstract, titles and keyword analysis. Similarly, natural processing language techniques can also be used to identify the most relevant terms, on which the map is built up [ 132 , 133 ].…”

Section: Discussionmentioning

confidence: 99%

Research Trends and Future Perspectives in Marine Biomimicking Robotics

Aguzzi

Costa

Funari

et al. 2021

Sensors

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Mechatronic and soft robotics are taking inspiration from the animal kingdom to create new high-performance robots. Here, we focused on marine biomimetic research and used innovative bibliographic statistics tools, to highlight established and emerging knowledge domains. A total of 6980 scientific publications retrieved from the Scopus database (1950–2020), evidencing a sharp research increase in 2003–2004. Clustering analysis of countries collaborations showed two major Asian-North America and European clusters. Three significant areas appeared: (i) energy provision, whose advancement mainly relies on microbial fuel cells, (ii) biomaterials for not yet fully operational soft-robotic solutions; and finally (iii), design and control, chiefly oriented to locomotor designs. In this scenario, marine biomimicking robotics still lacks solutions for the long-lasting energy provision, which presently hinders operation autonomy. In the research environment, identifying natural processes by which living organisms obtain energy is thus urgent to sustain energy-demanding tasks while, at the same time, the natural designs must increasingly inform to optimize energy consumption.

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“…We used SLR following the van van Dinter et al (2021) protocol and modified into the following steps: research questions specifications, research identification, literature search, selection review, data extraction, data synthesis and review writing; ensuring the standardisation, innovation and operability of the method. Therefore, it has developmental value to the research topic and research method system itself.…”

Section: Methodsmentioning

confidence: 99%

Risk Management: Exploring Emerging Human Resource Issues during the COVID-19 Pandemic

Zhong

Ding

et al. 2021

JRFM

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The unanticipated coronavirus disease 2019 (COVID-19) pandemic has hit global business heavily, disrupting the management of human resources across numerous industries. More than 500 articles (indexed in Scopus and the Web of Science) on the impact of the COVID-19 outbreak on emerging human resources issues and related practices were published from 1 January 2020 to 31 January 2021. In this study, we conduct a systematic literature review on emerging studies in the business and management field to explore what the emerging human resource issues are during the COVID-19 pandemic and propose related practices to solve these issues. The analysis of the published literature identifies nine main human resource issues across 13 industries. The findings of this study suggest that COVID-19 has enormous impact on conventional human resource management and requires the theoretical and empirical attention of researchers. The propositions nominate related human resource practices to deal with emerging human resources issues and identify several research venues for future studies in this field.

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Automation of systematic literature reviews: A systematic literature review

Cited by 188 publications

References 70 publications

Artificial intelligence and the conduct of literature reviews

Artificial intelligence and the conduct of literature reviews

Research Trends and Future Perspectives in Marine Biomimicking Robotics

Risk Management: Exploring Emerging Human Resource Issues during the COVID-19 Pandemic

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