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  • Are Consumers Efficient and Effective Users of Generative AI? Mixed-methods insights into consumer decision-making

Zitiervorschlag
Zitiervorschlag

Kaiser, C., Kaiser, J., Schallner, R., Schneider, S. (2025): Are Consumers Efficient and Effective Users of Generative AI? Mixed-methods insights into consumer decision-making. Proceedings of the EURAM 2025 Conference, Florence, Italy.

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Jahr

2025

Autorinnen und Autoren
Dr. Carolin Kaiser,
Dr. Jakob Kaiser,
René Schallner,
Prof. Dr. Sabrina Schneider
Titel der Publikation
Are Consumers Efficient and Effective Users of Generative AI? Mixed-methods insights into consumer decision-making
Publikation
Peer-reviewed

Are Consumers Efficient and Effective Users of Generative AI? Mixed-methods insights into consumer decision-making

This study explores how consumers engage with generative AI when searching for and processing product and service information, particularly compared to traditional search engines. Using a mixed-methods approach, the research finds that consumers are more efficient and effective when using generative AI tools (e.g., ChatGPT) over traditional search engines (e.g., Google) for purchase decisions. Qualitative analysis reveals that initial skepticism toward AI diminishes as users recognize its utility, leading to more confident decision-making. Key insights include: (1) effective human-AI collaboration can develop organically, even without guidance; (2) consumers become more receptive to generative AI as they experience its benefits; and (3) AI-enabled tools enhance consumer capabilities, forming "superteams" that empower users to tackle complex tasks. The study contributes to human-AI collaboration research by highlighting how generative AI transforms consumer decision-making, not just professional workflows.

Autorinnen und Autoren

Kontakt

Head of Artificial Intelligence

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Zitiervorschlag

Kaiser, C., Kaiser, J., Schallner, R., Schneider, S. (2025): Are Consumers Efficient and Effective Users of Generative AI? Mixed-methods insights into consumer decision-making. Proceedings of the EURAM 2025 Conference, Florence, Italy.

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