W4310490491 Peer-reviewed research AI × management and organizations

Turning words into numbers: Assessing work attitudes using natural language processing.

Andrew B. Speer · James Perrotta · Andrew P. Tenbrink · Lauren J. Wegmeyer · Angie Y. Delacruz · Jenna Bowker

Also recorded as: doi:10.1037/apl0001061

Journal of Applied Psychology

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.

The work applies natural language processing to the assessment of work attitudes.

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

Scope: title-level metadata; 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.

Cites (0)

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

Cited by (1)

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.
  • Scope and claim are limited to the title-level description.
  • Crossref supplied only year and month, so published_date is null rather than an inferred day.

Source independence

publisher-only