W4395701305 Peer-reviewed research AI × management and organizations

Accommodating Machine Learning Algorithms in Professional Service Firms

James R. Faulconbridge · Atif Sarwar · Martin Spring

Also recorded as: doi:10.1177/01708406241252930

Organization Studies · 2024-05-23

Recorded claims
2
coded from the inspected source
Evidence classes
1
crossref-api-metadata
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 studies accountants and lawyers and identifies accommodation as a professional-service-firm response that both justifies machine-learning adoption and addresses algorithmic opacity and professional resistance.

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

Scope: abstract-level professional-service-firm study

The abstract identifies organizational accommodation practices that enable or inhibit adoption of machine learning in accounting and legal professional-service work.

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.

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; no full text inspected
  • SAGE public DOI-page probes returned HTTP 403 during the lane, so claims rely on retained Crossref publisher-deposited metadata
  • 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