Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 331

The DOI matches its OpenAlex record; 56 of the 56 references it cites were found in a registry or an index.

Human Capital, Shared Expertise, and the Likelihood of Impasse in Corporate Acquisitions

Russell Wayne Coff·2002·Journal of Management

Abstract

Human capital often cannot be acquired in efficient labor markets due to poor information or firm-specific skills that develop over time. Since such knowledge may be critical to firms building a strategic capability, it is not surprising that many acquisitions occur in human capital-intensive industries. Yet, the uncertainty associated with human capital increases the risk of overbidding. If the buyer bids conservatively, the target may reject the offer or rival bidders may emerge. In contrast, aggressive bidders may need to back out of the transaction if due diligence reveals unanticipated risks. Either way, impasse is more likely for targets in human capital-intensive industries. This study explores whether a shared expertise mitigates these hazards. Findings suggest that similar expertise is particularly important when acquiring human capital-intensive targets. Transactions involving unrelated buyers of such targets are less likely to close. This has implications for diversification theory and the resource-based view.

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

OpenAlex · 10.1016/s0149-2063(01)00127-1

Cites

56 of 56 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 56 references

Cited by

See all 331

Metadata

Authors
Coff, R. W.
Venue
Journal of Management
Publisher
SAGE Publishing
Published
2002-02-01
Volume
28
Issue
1
Pages
107-128
ISSN
0149-2063 (ISSN-L)
OpenAlex
W2136671122
Cited by
331
References
56
FWCI
36.51
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: Human Capital, Shared Expertise, and the Likelihood of Impasse in Corporate Acquisitions. https://app.cochat.ai/papers/10.1016/s0149-2063(01)00127-1
The paper
Coff, R. W. (2002). Human Capital, Shared Expertise, and the Likelihood of Impasse in Corporate Acquisitions. Journal of Management. https://doi.org/10.1016/s0149-2063(01)00127-1

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.