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- However, AI-based tools can help researchers speed up the process. AI-powered tools can quickly identify relevant studies, extract key themes and concepts, and even generate summaries.
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Oct 8, 2021 · We conclude this section by outlining four aspects relevant to AI-based tools across the six steps of the literature review process: (1) evaluation and validity, (2) transparency and replicability, (3) compatibility for recombination, and (4) usability.
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Aug 17, 2024 · We identified relevant tools using two prominent software repositories (the Systematic Literature Review Toolbox and the Comprehensive R Archive Network) supplemented by manually searching relevant surveys for additional tools.
- Francisco Bolaños
Oct 8, 2024 · This guide shows you how you can use AI tools to help with aspects of the literature review process: Abstract and title creation. Scoping and protocol development. Searching. Screening. Data extraction. Synthesis.
- Gerit Wagner, Roman Lukyanenko and Guy Par ́e
- Step 3: Screening for inclusion
- Step 5: Data extraction
- A research agenda
- Level I: Supporting infrastructure
- Level II: Methods and tools
- Step 6: Data analysis and interpretation.
- Level III: Research practice
- Declaration of con icting interests
- Funding
- Notes
- Author Biographies
Abstract 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...
In the screening step, authors work with the search results to dissociate the relevant papers from those that must be ex-cluded from the review. This step is typically divided into a rst (more inclusive) screening based on titles and abstracts fi and a second (more restrictive) screening based on full-texts (Templier and Par ́e, 2018). In manual sc...
Data extraction requires researchers to identify relevant fragments of qualitative and quantitative data and to transfer them to a (semi) structured coding sheet (Templier and Par ́e, 2018). It is more salient in descriptive reviews, scoping reviews, and reviews aimed at theory testing compared to reviews that are more selective and interpretive su...
In this section, we outline an agenda suggesting how IS researchers can focus and coordinate their efforts in ad-vancing AILRs. Nurturing a vibrant AILR tradition is a task for the entire scholarly community, including design science researchers, behavioral scientists, methodologists, re-viewers, and journal editors as well as authors of primary re...
Technical infrastructure can greatly facilitate or constrain AILRs. The diversity of infrastructure needed to support a vibrant AILR tradition within IS points to a wide range of related opportunities for research and design. We cover quality assurance, smart search technologies, and enhanced databases.
There are vast opportunities for methodological and tool-centric research on AILRs. We outline promising avenues for research and design in each individual step of the review process and offer complementary recommendations on cross-cutting concerns, covering the need for advancing evaluation studies, conceptions of validity, and the notion of trans...
Regarding the nal fi step, we focus on knowledge integration and inductive theory development. With knowledge integration still posing challenges for prospective authors, we call for the development of discipline-specic algorithms to address fi issues especially prevalent in IS, such as similarity as-sessment of IS measurement items or IS construct...
AILRs require broader considerations, which we believe, should involve the entire IS community, including authors of papers surveyed by AILRs, their reviewers, community thought leaders, and innovators interested in improving the way we conduct research. We highlight two broad streams of discussion pertaining to standardization and sharing.
fl The author(s) declared no potential con icts of interest with re- fl spect to the research, authorship, and/or publication of this article.
The author(s) received no nancial support for the research, au- fi thorship, and/or publication of this article.
We use the term“tool in a broad sense, covering applications ” offering graphical user interfaces (GUI), statistical packages, as well as programming libraries, for example. For the sake of completeness, we recognize that some tools support multiple steps of the review process (e.g., Covidence, Rayyan QCRI, Parsifal, SRDB.PRO, and SESRA). These too...
Gerit Wagner is a postdoctoral fellow at HEC Montr ́eal. His research focuses on literature reviews, the impact of re-search methods, digital health, and digital platforms for knowledge work. His research has been published in in-ternational journals, including the Journal of Strategic In-formation Systems, Journal of Medical Internet Research, Inf...
3 days ago · Recent advancements in artificial intelligence (AI) enable the rapid collection and organization of academic research with the push of a button, prompting a reevaluation of the role and value of human-generated systematic literature reviews (SLRs). In this editorial, we explore the value of the human element in producing SLRs, a method originally designed to reduce human bias in literature ...
Jun 9, 2022 · relevant to AI-based tools across the six steps of the lit- erature review process: (1) evaluation and validity, (2) transparency and replicability, (3) compatibility for re-
Oct 18, 2024 · This overlap indicates that the AI system was able to identify reviewers that editors considered appropriate, validating its potential utility in the selection process (Table 2 summarizes the key performance metrics of the AI-assisted reviewer selection process). The accuracy varied across disciplines, with higher rates of agreement in fields like Chemistry (58%) and lower in areas like Public ...
