W4409696353 Peer-reviewed research AI × management and organizations

The transparency dilemma: How AI disclosure erodes trust

Oliver Schilke · Martin Reimann

Also recorded as: doi:10.1016/j.obhdp.2025.104405

Organizational Behavior and Human Decision Processes

Recorded claims
1
coded from the inspected source
Evidence classes
1
google-scholar-result
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.

The work examines whether disclosing generative-AI use across work tasks compromises trust in the user, with the retained result snippet identifying workplace actors including supervisors.

confidence: moderate google-scholar-result apw-c1-obot-journals

Scope: search-result snippet and bibliographic metadata; publisher abstract not inspected

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

  • Crossref supplied bibliographic metadata but no abstract.
  • The work-context determination uses a full Elsevier abstract rendered in the retained Google Scholar result because ScienceDirect was access-blocked; publisher full text was not inspected.
  • Crossref supplied only year and month, so published_date is null rather than an inferred day.

Source independence

corroborated:google-scholar-result