Author(s): KIM, T., LEE, G., SEO, D., PARK, J., YUN, J., Institution: KOREA INSTITUTE OF INDUSTRIAL TECHNOLOGY, Country: KOREA, SOUTH, Abstract-ID: 1098

Introduction
The global fitness industry is undergoing rapid transformation toward AI-driven, personalized health management services. However, consumer acceptance of AI technologies varies significantly across cultures and regulatory frameworks, particularly concerning data privacy and system integration. This study quantified English consumer preferences for AI-based smart fitness systems and estimated willingness to pay for key service attributes, providing empirical evidence for market entry strategies within a stringent data protection environment.

Methods
A choice experiment (DCE) was conducted with 200 adults residing in England via online survey in October 2025. The experimental design incorporated five attributes with multiple levels: AI personalization (3 levels), equipment integration (3 levels), mobile ecosystem compatibility (3 levels), data privacy (3 levels), and monthly subscription price (4 levels: £30–£120). A conditional logit model was employed to estimate marginal willingness to pay (MWTP) for each attribute level.

Results
The conditional logit model revealed that English consumers prioritize ecosystem integration and data security over AI personalization. Four non-price attributes achieved statistical significance: complete mobile ecosystem integration (MOB3: β = 1.019, p < 0.01) emerged as the strongest utility driver, followed by enhanced data privacy (PRIV2: β = 0.932, p < 0.01; PRIV3: β = 0.881, p < 0.01) and partial equipment integration (EQ2: β = 0.912, p < 0.05). These attributes yielded substantial MWTP estimates: £38.03 for complete mobile integration, £34.04 for equipment connectivity, and £33–£35 for privacy enhancements. Notably, all AI personalization levels (AI2, AI3) failed to achieve statistical significance, indicating limited perceived value. The price coefficient was highly significant (β = -0.0268, p < 0.001), with aggregate willingness to pay for optimal configurations ranging £50–£100 monthly. These findings demonstrate a clear preference hierarchy wherein functional integration and data protection substantially outweigh algorithmic sophistication in consumer decision-making.

Conclusion
Market success in England requires prioritizing integration capabilities and data governance over advanced AI functionalities. Mobile ecosystem compatibility and data privacy constitute primary value drivers, whereas AI personalization offers limited competitive advantage. Market entry strategies should emphasize GDPR-compliant security, seamless cross-device interoperability, and equipment connectivity. Investment in sophisticated AI algorithms should remain secondary to establishing trust through demonstrable privacy practices and reliable cross-platform integration, reflecting consumers' preference for practical utility over technological novelty.