W7155962288 Peer-reviewed research AI × management and organizations

Does AI Cheapen Talk? Theory and Evidence from Global Entrepreneurship and Hiring

Bo Cowgill · Pablo Hernández-Lagos · Nataliya Langburd Wright

Also recorded as: doi:10.1287/mnsc.2024.07027

Management Science · 2026-04-27

Recorded claims
1
coded from the inspected source
Evidence classes
1
crossref-api-metadata
Recorded limitations
3
stated, never hidden

From the hash-bound evidence releases

Recorded claims

Each claim states what the inspected source says, at the recorded location—bounded by its scope and graded by its confidence. Nothing here is a synthesis across works.

A model and experiments in hiring and start-up investing report that applicants' access to generative AI can reduce evaluators' screening accuracy, while some settings involving applicants from non-English-speaking countries show improved accuracy.

confidence: high crossref-api-metadata apw-c1-obot-journals

Scope: publisher-deposited abstract metadata; organizational hiring and entrepreneurial-investment screening; sample sizes and periods are not stated in the captured abstract

Structured relationships

In-corpus citations

Only source-supplied cites relationships whose two endpoints are admitted are shown. Invocation evidence remains a separate relationship.

Boundaries

Limitations & independence

Recorded at coding time, carried with the work forever. A claim without its limits is not evidence.

Recorded limitations

  • Abstract-level Crossref metadata only; the publisher landing page could not be retained because public retrieval was access-blocked.
  • The abstract does not state denominators or periods for the experiments; numerical effect sizes are not carried forward.
  • Full text was not inspected.

Source independence

publisher-only