Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 482

The DOI matches its Crossref record; 87 of the 87 references it cites were found in a registry or an index.

Capability Traps and Self-Confirming Attribution Errors in the Dynamics of Process Improvement

Repenning, Nelson P., Sterman, John D.·2002·Administrative Science Quarterly

Abstract

To better understand the factors that support or inhibit internally focused change, we conducted an inductive study of one firm's attempt to improve two of its core business processes. Our data suggest that the critical determinants of success in efforts to learn and improve are the interactions between managers' attributions about the cause of poor organizational performance and the physical structure of the workplace, particularly delays between investing in improvement and recognizing the rewards. Building on this observation, we propose a dynamic model capturing the mutual evolution of those attributions, managers' and workers' actions, and the production technology. We use the model to show how managers' beliefs about those who work for them, workers' beliefs about those who manage them, and the physical structure of the environment can coevolve to yield an organization characterized by conflict, mistrust, and control structures that prevent useful change of any type.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2307/3094806

Cites

87 of 87 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 87 references

Cited by

See all 482

Metadata

Authors
Repenning, N. P., & Sterman, J. D.
Venue
Administrative Science Quarterly
Publisher
JSTOR
Published
2002-06
Volume
47
Issue
2
Pages
265-295
ISSN
  • 0001-8392 (ISSN-L)
  • 1930-3815
OpenAlex
W3122062862
Cited by
482
References
87
FWCI
10.93
Type
journal article
Language
en
Open access
closed

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 ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Capability Traps and Self-Confirming Attribution Errors in the Dynamics of Process Improvement. https://app.cochat.ai/papers/10.2307/3094806
The paper
Repenning, N. P., & Sterman, J. D. (2002). Capability Traps and Self-Confirming Attribution Errors in the Dynamics of Process Improvement. Administrative Science Quarterly. https://doi.org/10.2307/3094806

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.