Computational Literature Reviews: Method, Algorithms, and Roadmap

• Published in: Organizational research methods Vol. 26; no. 1; pp. 107 - 138
• Main Authors: Antons, David, Breidbach, Christoph F., Joshi, Amol M., Salge, Torsten Oliver
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.01.2023; SAGE PUBLICATIONS, INC
• Subjects: Algorithms; Data processing; Literature reviews; computational literature reviews; text mining; systematic reviews; machine learning; artificial intelligence; CLR method
• ISSN: 1094-4281; 1552-7425
• DOI: 10.1177/1094428121991230

The substantial volume, continued growth, and resulting complexity of the scientific literature not only increases the need for systematic, replicable, and rigorous literature reviews, but also highlights the natural limits of human researchers’ information processing capabilities. In search of a solution to this dilemma, computational techniques are beginning to support human researchers in synthesizing large bodies of literature. However, actionable methodological guidance on how to design, conduct, and document such computationally augmented literature reviews is lacking to date. We respond by introducing and defining computational literature reviews (CLRs) as a new review method and put forward a six-step roadmap, covering the CLR process from identifying the review objectives to selecting algorithms and reporting findings. We make the CLR method accessible to novice and expert users alike by identifying critical design decisions and typical challenges for each step and provide practical guidelines for tailoring the CLR method to four conceptual review goals. As such, we present CLRs as a literature review method where the choice, design, and implementation of a CLR are guided by specific review objectives, methodological capabilities, and resource constraints of the human researcher.