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TRUVACE RECORD VERSION
record: TRV-2026-2572
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T07:55:43.460392Z
status: published
lens: p_space
sector: labor
headline: First Impressions Always Last: How AI Disclosure On Job Descriptions Shapes Trust and Application Intention
dek: Artificial Intelligence (AI) is increasingly used to automate communication in organizations, including the creation of job descriptions. Yet little is known about how disclosing AI authorship affects potential applicants’ reactions. Drawing on signaling theory, this study examines whether authorship labels on job descriptions shape trust in the organization and intention to apply. We conduct a 3x1 between-subjects online vignette experiment (AI vs. human vs. no label) with 651 working-age participants from Germ…
gain_title: (none)
problem_title: In an online experiment with 651 working-age people in German-speaking countries, job descriptions labelled as AI-written reduced trust in the employer compared with human-labelled ones and lowered intention to apply compared with both human-labelled and unlabelled ones.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: In an online experiment with 651 working-age people in German-speaking countries, job descriptions labelled as AI-written reduced trust in the employer compared with human-labelled ones and lowered intention to apply compared with both human-labelled and unlabelled ones.
problem_evidence: AI labels significantly reduce trust relative to human labels and decrease application intention relative to both human- and unlabeled stimuli.
quick_read: A vignette experiment with 651 working-age participants from German-speaking countries tested how people react when a job description carries an AI label, a human label or no label. AI labels lowered trust in the organization compared with human labels, and lowered application intention compared with both human-labelled and unlabelled descriptions.

The result matters for employers that use AI to automate communication, including drafting job descriptions. The authors describe authorship disclosure as a consequential signal in early recruitment and point to reputational risks tied to AI-generated content.

Attitudes toward AI changed the size of the effect. People with more positive views of AI reacted less negatively to AI labels and showed higher application intentions for human-labelled descriptions. The study measured stated intentions, not actual applications.
limitation: The study used an online vignette experiment with participants from German-speaking countries, so it measured reactions to hypothetical job descriptions rather than real application behaviour.
tag: Evidence-backed problem
key_points: The study used a 3x1 between-subjects online vignette experiment with three conditions: AI label, human label and no label. | It involved 651 working-age participants from German-speaking countries. | AI labels significantly reduced trust in the organization relative to human labels. | AI labels decreased application intention relative to both human-labelled and unlabelled job descriptions. | Attitude toward AI moderated the effects: people with more positive AI attitudes reacted less negatively to AI labels. | The authors drew on signaling theory to frame authorship disclosure as a signal in early recruitment.
rundown: The experiment randomly showed participants job descriptions with one of three authorship labels. Researchers measured two outcomes: trust in the organization and intention to apply.

Compared with human labels, AI labels cut trust. For application intention, AI labels produced lower scores than both human labels and no label, so disclosing AI authorship carried a measurable cost even against descriptions with no stated author.

Individual views of AI shaped the reaction. Participants with more positive AI attitudes reacted less negatively to AI labels and showed higher application intentions for human-labelled descriptions. The authors conclude that disclosure works as a consequential early recruitment signal and carries reputational risk.
sources:
- peer_reviewed | Journal of the Association for Information Systems | https://aisel.aisnet.org/ecis2026/genai/genai/17 | 2026-06-14
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