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Benefit-Risk Perceptions and Public GenAI Acceptance: A Survey Experiment Based on the Emerging Technology Acceptance
Ning Ma1, Songtao Lin1, Xinyu Dong1
1School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an 710049, China.
Public acceptance of generative artificial intelligence (GenAI) hinges on balancing perceived benefits like usefulness against risks such as property loss. Understanding these trade-offs is crucial for emerging AI technologies.
Area of Science:
- Social Sciences
- Technology and Innovation
- Artificial Intelligence Ethics
Background:
- Emerging Technology Acceptance Model provides a framework for understanding user adoption.
- Generative Artificial Intelligence (GenAI) presents unique benefits and risks influencing public perception.
- Differentiated benefit-risk perceptions are key to analyzing GenAI acceptance.
Purpose of the Study:
- To investigate the factors influencing public acceptance of Generative Artificial Intelligence (GenAI).
- To integrate perceived benefits and risks into a unified analytical framework based on the Emerging Technology Acceptance Model.
- To examine the moderating effects of technology usage behavior and regulatory trust on GenAI acceptance.
Main Methods:
- Three scenario-based survey experiments were conducted.
- Data were collected from Chinese internet users in an online survey context.
- Statistical analyses examined the relationships between perceived benefits, risks, and GenAI acceptance.
Main Results:
- Perceived usefulness and ease of use positively influenced GenAI acceptance.
- Perceived social welfare loss, personal property loss, and technical-ethical conflict negatively impacted acceptance.
- Personal property loss demonstrated the strongest inhibitory effect on acceptance.
Conclusions:
- GenAI acceptance is shaped by a complex interplay of perceived benefits and risks.
- Technology usage behavior and regulatory trust influence risk sensitivity more than benefit sensitivity.
- Findings offer insights into the benefit-risk trade-offs crucial for emerging AI technologies.
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