The rationality of explanation or human capacity? Understanding the impact of explainable artificial intelligence on human-AI trust and decision performance
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Perceived usefulness, perceived ease of use, and user acceptance of information technology
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From local explanations to global understanding with explainable AI for trees
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AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges
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“These agents are defined by their ability to perceive structured or unstructured inputs [46], to reason over contextual information [47,48], and to initiate actions toward achieving specific objectives, often acting as surrogates for human users or subsystems [49].”
AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges
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- Authors
- Wang, P., & Ding, H.
- Affiliations
- Central China Normal University (CN)
- Corresponding author
- Ding, Heng
- ORCID
- Venue
- Information Processing & Management
- Publisher
- Elsevier BV
- Published
- 2024-07
- Volume
- 61
- Issue
- 4
- Pages
- 103732
- ISSN
- 0020-0271 (ISSN-L)
- 0306-4573
- OpenAlex
- W4393965009
- Cited by
- 68
- References
- 13
- Citations by year
- 2026: 43 · 2025: 22 · 2024: 3
- FWCI
- 8.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Information Processing & Management (publishedVersion)
- Funders
- Fundamental Research Funds for the Central Universities
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- explainable AI, trust in AI, algorithmic transparency, sales forecasting, cross-border e-commerce, trust behavior, black box problem
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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