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Cited by 356

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

Daily performance at work: feeling recovered in the morning as a predictor of day‐level job performance

Open access
Binnewies, Carmen, Sonnentag, Sabine, Mojza, Eva J.·2009·Journal of Organizational Behavior

Abstract

This study examined the state of being recovered in the morning (i.e., feeling physically and mentally refreshed) as a predictor of daily job performance and daily compensatory effort at work. Ninety‐nine employees from public service organizations completed a general survey and two daily surveys on pocket computers over the course of one workweek. Hierarchical linear modeling showed that being recovered in the morning was positively related to daily task performance, personal initiative, and organizational citizenship behavior and negatively related to daily compensatory effort at work. Relationships between the state of being recovered and day‐specific job performance were moderated by job control. For persons with a high level of job control, the relationship between being recovered and daily performance was stronger. Copyright © 2008 John Wiley & Sons, Ltd.

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Crossref · 10.1002/job.541

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Metadata

Authors
Binnewies, C., Sonnentag, S., & Mojza, E. J.
Venue
Journal of Organizational Behavior
Publisher
Wiley
Published
2008-12-18
Volume
30
Issue
1
Pages
67-93
ISSN
  • 0894-3796 (ISSN-L)
  • 1099-1379
OpenAlex
W2025956004
Cited by
356
References
14
FWCI
26.04
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Daily performance at work: feeling recovered in the morning as a predictor of day‐level job performance. https://app.cochat.ai/papers/10.1002/job.541
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Binnewies, C., Sonnentag, S., & Mojza, E. J. (2009). Daily performance at work: feeling recovered in the morning as a predictor of day‐level job performance. Journal of Organizational Behavior. https://doi.org/10.1002/job.541

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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.