W4408108904 Working papers AI × management and organizations

Reputation, Referrals, (and Artificial Intelligence)

Yi Chen · Thomas Jungbauer · Mark Satterthwaite

Also recorded as: doi:10.2139/ssrn.5163738 · ssrn:5163738

ssrn abstract page · 2025-03-04

Recorded claims
3
coded from the inspected source
Evidence classes
2
ssrn-abstract-page, ssrn-ejournal-listing
Recorded limitations
4
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 develops a dynamic market model in which horizontally specialized experts decide whether to refer mismatched problems while reputation affects future demand.

confidence: high ssrn-abstract-page apw-e1-ssrn-backfill

Scope: abstract-level model; unit: expert and referral market; period and denominator: not applicable

It reports that the steady-state market generally produces inefficient referrals and specialization, and that AI diagnosis and treatment discourage referrals and further increase problem-expertise mismatch.

confidence: high ssrn-abstract-page apw-e1-ssrn-backfill

Scope: author-reported theoretical result in the described model

At inspection time, the public Organizations & Markets: Formal & Informal Structures all-years search for artificial intelligence displayed this work among 50 postings.

confidence: high ssrn-ejournal-listing apw-e1-ssrn-backfill

Scope: universe: filtered O&M Formal & Informal Structures listing; unit: posting; period: all years through 2026-08-20; denominator: 50 displayed postings

Structured relationships

In-corpus citations

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

Cites (0)

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

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 evidence only; full text was not inspected and no PDF was downloaded.
  • O&M membership is evidenced by the retained public Formal & Informal Structures listing result. Its result URL is session-contextual and omits the journal_id; the retained bytes display the exact family label, query, and 50-posting denominator.
  • The result is model-specific; the abstract reports no empirical validation or population denominator.
  • No paper-specific license statement was displayed; the site-wide footer reserves rights, so the record paraphrases only.

Source independence

ssrn-only