The DOI matches its Crossref record; 82 of the 82 references it cites were found in a registry or an index.
Dealing with information overload: a comprehensive review
Open accessAbstract
Information overload is a problem that is being exacerbated by the ongoing digitalization of the world of work and the growing use of information and communication technologies. Therefore, the aim of this systematic literature review is to provide an insight into existing measures for prevention and intervention related to information overload. The methodological approach of the systematic review is based on the PRISMA standards. A keyword search in three interdisciplinary scientific databases and other more practice-oriented databases resulted in the identification of 87 studies, field reports, and conceptual papers that were included in the review. The results show that a considerable number of papers have been published on interventions on the behavioral prevention level. At the level of structural prevention, there are also many proposals on how to design work to reduce information overload. A further distinction can be made between work design approaches at the level of information and communication technology and at the level of teamwork and organizational regulations. Although the identified studies cover a wide range of possible interventions and design approaches to address information overload, the strength of the evidence from these studies is mixed.
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Cites
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Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement
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A Global Measure of Perceived Stress
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Organizational Information Requirements, Media Richness and Structural Design
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“Factors with the potential to buffer the detrimental effects of techno-stressors on employee well-being are discussed under the label of techno-inhibitors (Ragu-Nathan et al., 2008; Estrada-Muñoz et al., 2022).”
BackgroundTechnostress: Technological Antecedents and Implications1
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Don’t Be Fooled: The Misinformation Effect of Explanations in Human–AI Collaboration
Spitzer, P., Holstein, J., Morrison, K., Holstein, K., Satzger, G., & Kühl, N. (2026) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2025.2574511 · Cited by 12
Using aggregated AI detector outcomes to eliminate false-positives in STEM-student writing.
Hyatt, J.-P. K., Bienenstock, E. J., Firetto, C. M., Woods, E. R., & Comus, R. C. (2025) · Advances in Physiology Education · doi:10.1152/advan.00235.2024 · Cited by 4
Metadata
- Authors
- Arnold, M., Goldschmitt, M., & Rigotti, T.
- Venue
- Frontiers in Psychology
- Publisher
- Frontiers Media SA
- Published
- 2023-06-21
- Volume
- 14
- Pages
- 1122200-1122200
- ISSN
- 1664-1078 (ISSN-L)
- OpenAlex
- W4381548764
- Cited by
- 291
- References
- 82
- FWCI
- 75.62
- Type
- journal article
- Language
- en
- Open access
- gold
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
