Paper Number
ECIS2026-2644
Paper Type
CRP
Abstract
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 German-speaking countries. Results show that AI labels significantly reduce trust relative to human labels and decrease application intention relative to both human- and unlabeled stimuli. Further, attitude toward AI moderates these effects as individuals with more positive AI attitudes react less negatively to AI labels and show higher application intentions for human-labeled descriptions. The present findings demonstrate that authorship disclosure acts as a consequential signal in early recruitment and highlight reputational risks associated with AI-generated content.
Recommended Citation
Stahl, Hendrik; Koch, Gabriel; and Kranz, Johann, "First Impressions Always Last: How AI Disclosure On Job Descriptions Shapes Trust and Application Intention" (2026). ECIS 2026 Proceedings. 17.
https://aisel.aisnet.org/ecis2026/genai/genai/17
First Impressions Always Last: How AI Disclosure On Job Descriptions Shapes Trust and Application Intention
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 German-speaking countries. Results show that AI labels significantly reduce trust relative to human labels and decrease application intention relative to both human- and unlabeled stimuli. Further, attitude toward AI moderates these effects as individuals with more positive AI attitudes react less negatively to AI labels and show higher application intentions for human-labeled descriptions. The present findings demonstrate that authorship disclosure acts as a consequential signal in early recruitment and highlight reputational risks associated with AI-generated content.
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