The DOI matches its Crossref record; 270 of the 270 references it cites were found in a registry or an index.
Sensemaking in Organizations: Taking Stock and Moving Forward
Abstract
Sensemaking is the process through which people work to understand issues or events that are novel, ambiguous, confusing, or in some other way violate expectations. As an activity central to organizing, sensemaking has been the subject of considerable research which has intensified over the last decade. We begin this review with a historical overview of the field, and develop a definition of sensemaking rooted in recurrent themes from the literature. We then review and integrate existing theory and research, focusing on two key bodies of work. The first explores how sensemaking is accomplished, unpacking the sensemaking process by examining how events become triggers for sensemaking, how intersubjective meaning is created, and the role of action in sensemaking. The second body considers how sensemaking enables the accomplishment of other key organizational processes, such organizational change, learning, and creativity and innovation. The final part of the chapter draws on areas of difference and debate highlighted throughout the review to discuss the implications of key tensions in the sensemaking literature, and identifies important theoretical and methodological opportunities for the field.
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“In a situation that causes high levels of uncertainty and feelings of vulnerability like the COVID-19 outbreak, people actively search for up-to-date information to fill the information gap and, thus, to make sense of the situation (Fischer-Preßler et al., 2019; Maitlis & Christianson, 2014).”
From Understanding user responses to the COVID-19 pandemic on Twitter from a terror management theory perspective: Cultural differences among the US, UK and India (2022) · cites this paper as Maitlis & Christianson, 2014
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Metadata
- Authors
- Maitlis, S., & Christianson, M.
- Affiliations
- University of British Columbia (CA)
- Venue
- Academy of Management Annals
- Publisher
- Academy of Management
- Published
- 2014-01
- Volume
- 8
- Issue
- 1
- Pages
- 57-125
- ISSN
- 1941-6067 (ISSN-L)
- 1941-6520
- OpenAlex
- W4245597957
- Cited by
- 1,764
- References
- 268
- Citations by year
- 2026: 332 · 2025: 187 · 2024: 167 · 2023: 192 · 2022: 182 · 2021: 188 · 2020: 160 · 2019: 139 · 2018: 87 · 2017: 55 · 2016: 39 · 2015: 31 · 2014: 3
- FWCI
- 93.84
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Academy of Management Annals (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Management and Organizational Studies
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Organizational Learning and Leadership
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Appreciative Inquiry and Organizational Change
- Keywords
- sensemaking, organizational sensemaking, ambiguity, intersubjective meaning, organizational change, organizational learning, creativity and innovation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
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.