Under pressure: how time constraints, task complexity, and AI reliability shape human-AI interaction.
This study investigates the impact of time pressure on human-AI collaboration in knowledge work environments. Drawing on dual-processing frameworks, specifically the heuristic-systematic model, we conducted an online experiment (n = 228) in which participants evaluated AI-generated responses across...
| Publicado en: | Behaviour & Information Technology Vol. 45; no. 11; pp. 2597 - 2622 |
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| Autores principales: | , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jul2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=195034166&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195034166 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0144929X B6Q jtl: Behaviour & Information Technology issn: 0144929X maglogo: Y pubinfo: dt: Jul2026 vid: 45 iid: 11 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 195034166 190170541 195034166 195034166 10.1080/0144929X.2025.2587732 195034166 ppf: 2597 ppct: 25 formats: tig: atl: Under pressure: how time constraints, task complexity, and AI reliability shape human-AI interaction. aug: au: Hermanns, Lukas Teubner, Timm affil: Digital Service Engineering, Technical University Berlin, Berlin, Germany sug: subj: Artificial Intelligence User-Computer Interface Time Factors Stress, Psychological Decision Making Task Performance and Analysis Human Male Female Adolescence Adult Middle Age Aged Empirical Research Hallucinations Linear Regression Confidence Intervals T-Tests Mediation Analysis Data Analysis Software Descriptive Statistics Funding Source Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: This study investigates the impact of time pressure on human-AI collaboration in knowledge work environments. Drawing on dual-processing frameworks, specifically the heuristic-systematic model, we conducted an online experiment (n = 228) in which participants evaluated AI-generated responses across a series of tasks varying in complexity and correctness of AI responses. Participants subjected to a time-pressured treatment were compared to a control group with unlimited evaluation time. Our findings reveal that time pressure shifts cognitive processing from a systematic to a heuristic mode as expected, leading to diminished performance due to a reduced capacity to discriminate between correct and faulty AI responses. However, while both increased task complexity and AI advice faultiness independently impair human-AI-team performance, their interaction with time pressure suggests a more nuanced effect than predicted by traditional models. The results contribute to a refined understanding of cognitive dynamics in human-AI teaming and offer practical insights for designing AI systems and work environments that support effective decision-making under pressure. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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