W4413451183 Peer-reviewed research AI × management and organizations

Optimal Integration: Human, Machine, and Generative AI

Hongda Zhong

Also recorded as: doi:10.1287/mnsc.2024.07401

Management Science

Recorded claims
1
coded from the inspected source
Evidence classes
1
crossref-api-metadata
Recorded limitations
2
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.

The abstract models how error correction and error creation shape authority and sequencing among humans, machines, and generative AI.

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

Scope: abstract-level

Structured relationships

In-corpus citations

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

Cites (1)

Cited by (0)

No in-corpus incoming citation is available in the current enrichment coverage.

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; full text was not inspected.
  • The publisher surface was not queried after the lane’s quarantined dynamic-challenge response; no independent publisher-page verification was attempted.

Source independence

publisher-only