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Embry-Riddle Scholarly Commons · Journal article (JAAER)
Artificial Intelligence in Aviation: A Path Analysis
Attribution
This is the abstract and citation. Full text lives at Embry-Riddle Scholarly Commons — we link out rather than host. All credit to the authors and Embry-Riddle Aeronautical University.
Abstract
Verbatim from Embry-Riddle Scholarly Commons. Not paraphrased, not summarized.
The study applied the Technology Acceptance Model (TAM) to assess trust in artificial intelligence (AI) within the US commercial aviation industry. It found that ease of use and usefulness positively influenced attitudes toward AI, impacting users’ intention to use it. However, perceived usefulness did not significantly affect meaning, purpose, and mood positively correlated with trust in AI. In some cases, higher perceived usefulness led to lower trust, indicating the complexity of trust in AI in aviation. This study highlights the importance of trust in AI and suggests the need for further investigation in the aviation context. It also recommends expanding the framework of trustworthy AI to consider factors like algorithm transparency, explainability, and fairness for a more comprehensive understanding.
Authors
- Halawi, Leila, DBA Embry-Riddle Aeronautical University
- Miller, Mark D, Ed.D. Embry-Riddle Aeronautical University
- Holley, Sam J, Ph.D. Embry-Riddle Aeronautical University
Keywords
- Artificial Intelligence
- technology acceptance model
- trust
- path analysis
- commercial aviation
- Management Information Systems
- Social and Behavioral Sciences
Citation: Halawi, Leila, DBA, Miller, Mark D, Ed.D., Holley, Sam J, Ph.D. (2024). Artificial Intelligence in Aviation: A Path Analysis. Embry-Riddle Aeronautical University. Embry-Riddle Scholarly Commons ID oai:commons.erau.edu:jaaer-2061. https://commons.erau.edu/jaaer/vol33/iss4/10 ↗