This study examined the effects of recommendation rationale presentation type on consumers’ usage intention toward size recommendation services and investigated the moderating role of fashion involvement in this relationship. A scenario-based online experiment was conducted to simulate a size selection situation in an online fashion shopping context. Participants received experimental stimili featuring a unisex white short-sleeved T-shirt purchase scenario, where size recommendation services used either collaborative filtering or content-based filtering. The stimuli were designed to reflect differences in the presentation of recommendation grounds to consumers. Data were collected from 200 participants through an online survey conducted between December 11 and 13, 2023. The moderating effect of fashion involvement was examined using the PROCESS macro (Model 1). The results showed that fashion involvement significantly moderated the relationship between recommendation rationale presentation type and usage intention. Specifically, no significant difference was found between the two presentation types at the mean and lower levels of fashion involvement. However, the content-based filtering type showed significantly higher usage intention at higher levels of fashion involvement. Therefore, consumer acceptance of size recommendation services should be understood not as a straightforward comparison of presentation types, but as a conditional relationship influenced by consumer characteristics such as fashion involvement.

Abstract
I. Introduction
Ⅱ. Review of Literature
    1. Size recommendation services and usage intention
    2. Differences in recommendation acceptance byrecommendation rationale presentation type
    3. Conditional effects of recommendationrationale presentation type: A fashion involvementand information processing perspective
Ⅲ. Research Model and Hypothesis
    1. Research model
    2. Hypothesis
Ⅳ. Research Method
    1. Experimental design and procedure
    2. Experimental stimuli
    3. Sample and data collection
    4. Measures
    5. Data analysis
Ⅴ. Results
    1. Measurement model
    2. Hypothesis testing
    3. Conditional effects analysis
Ⅵ. Discussion and Conclusion
References

• Sangwoo Seo(Professor, Dept. of Fashion Business, Jeonju University, Korea) | 서상우 (전주대학교 패션산업학과 교수) Corresponding author