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Cited by 295

The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.

When Friends Leave: A Structural Analysis of the Relationship between Turnover and Stayers' Attitudes

Krackhardt, David, Porter, Lyman W.·1985·Administrative Science Quarterly

Abstract

David Krackhardt and Lyman W. Porter It is argued in this paper that macro and micro perspectives can each benefit from the other. To demonstrate this, a current research issue in micro organizational behavior is analyzed with the help of theories in psychology, social psychology, and sociology. The specific question is: What effect does turnover in an organization have on the attitudes of those who remain in the organization? A longitudinal investigation of three fast-food restaurants explored this relationship against the background of the social network structures in each site. Among the findings was that the closer the employee was to those who left, the more satisfied and committed he or she became. The results underscore the importance of the structural context in studying micro phenomena, while at the same time they demonstrate the richness of micro theory in understanding why these phenomena occur.*

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Metadata

Authors
Krackhardt, D., & Porter, L. W.
Venue
Administrative Science Quarterly
Publisher
JSTOR
Published
1985-06
Volume
30
Issue
2
Pages
242
ISSN
0001-8392 (ISSN-L)
OpenAlex
W2082780579
Cited by
295
References
21
FWCI
6.21
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: When Friends Leave: A Structural Analysis of the Relationship between Turnover and Stayers' Attitudes. https://app.cochat.ai/papers/10.2307/2393107
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Krackhardt, D., & Porter, L. W. (1985). When Friends Leave: A Structural Analysis of the Relationship between Turnover and Stayers' Attitudes. Administrative Science Quarterly. https://doi.org/10.2307/2393107

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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.