Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 860

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Perspectives on Models of Job Performance

Viswesvaran, Chockalingam, Ones, Deniz S.·2000·International Journal of Selection and Assessment

Abstract

Contemporary models of job performance are reviewed. Links between task performance, contextual performance, organizational citizenship behaviors, counterproductivity and organizational deviance are pointed out. Measurement issues in constructing generic models applicable across jobs are discussed. Implications for human resource management in general, and performance appraisal for selection and assessment in particular, are explored. It is pointed out that the different dimensions or facets of individual job performance hypothesized in the literature are positively correlated. This positive manifold suggests the presence of a general factor which represents a common variance shared across all the dimensions or facets. Although no consensus exists in the extant literature on the meaning and source of this shared variance (i.e., the general factor), rater idiosyncratic halo alone does not explain this general factor. Future research should explain the common individual differences determinants of performance dimensions.

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 · 10.1111/1468-2389.00151

Cites

1 of 1 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 1 references

Metadata

Authors
Viswesvaran, C., & Ones, D. S.
Venue
International Journal of Selection and Assessment
Publisher
Wiley
Published
2000-12
Volume
8
Issue
4
Pages
216-226
ISSN
  • 0965-075X (ISSN-L)
  • 1468-2389
OpenAlex
W2048435811
Cited by
860
References
1
FWCI
13.30
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: Perspectives on Models of Job Performance. https://app.cochat.ai/papers/10.1111/1468-2389.00151
The paper
Viswesvaran, C., & Ones, D. S. (2000). Perspectives on Models of Job Performance. International Journal of Selection and Assessment. https://doi.org/10.1111/1468-2389.00151

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.