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Cited by 914

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

MARKING TIME: PREDICTABLE TRANSITIONS IN TASK GROUPS.

Gersick, C. J. G.·1989·Academy of Management Journal

Abstract

A new model of group development suggests that groups ' attention to time and pacing is an important catalyst of their progress through cre-ative projects. In this laboratory study, groups were videotaped as they produced creative products and then interviewed about replays of se-lected portions of the tapes. Participants ' efforts to pace themselves were explored in depth, with special focus on a key feature of the model, a major transition in groups ' approach toward their work at the midpoint of their allotted time. The appropriateness of laboratory sim-ulation for studying midpoint transitions was also assessed. The labo-ratory results mirrored and extended the field-based model; they showed how groups make deliberate attentional shifts at their temporal midpoints, what differences exist between pacing patterns in the first and second halves of groups ' life spans, and what happens when tran-sitions fail. Implications are drawn for theory, practice, and research. Organizations often rely on small groups when they need an innovation

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Metadata

Authors
Gersick, C. J. G.
Venue
Academy of Management Journal
Published
1989-06-01
Volume
32
Issue
2
Pages
274-309
ISSN
  • 0001-4273 (ISSN-L)
  • 1948-0989
OpenAlex
W2024451743
Cited by
914
References
3
FWCI
13.79
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: MARKING TIME: PREDICTABLE TRANSITIONS IN TASK GROUPS. https://app.cochat.ai/papers/10.2307/256363
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Gersick, C. J. G. (1989). MARKING TIME: PREDICTABLE TRANSITIONS IN TASK GROUPS. Academy of Management Journal. https://doi.org/10.2307/256363

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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.