The explosive growth of online shopping, particularly in South Korea, presents a challenge: consumers must assess product quality after purchase. This study focuses on the role of consumer trust in mitigating this risk, specifically examining how online reviews and shopping platforms influence purchase decisions for experience goods (products whose quality is only evident after use). Existing research highlights the importance of trust in online transactions, especially for experience goods, where uncertainty is high. Online reviews and reputable platforms are recognized as key factors in building this trust. However, inconsistencies exist in review guidelines across different countries and platforms, creating a need for investigation into which specific attributes consumers value most. This study aims to answer the following research question: Which attributes of online reviews and shopping platforms do consumers trust and ascribe economic value to when purchasing experience goods? It explores this question by considering brand value (famous vs. nonfamous brands) as a potential moderator.

The literature review explores the factors influencing trust in online shopping, focusing on online reviews and shopping platforms. Regarding online reviews, research emphasizes the importance of the number of reviews, star ratings, review types (text, picture, video), and text review length. Studies show that high star ratings and video reviews are particularly influential. The length of reviews also matters, with longer, more detailed reviews perceived as more trustworthy. The study also examines the role of shopping platforms in building consumer trust. Reputable platforms are shown to enhance consumer confidence. Finally, the literature highlights the interplay between brand value and trust, with famous brands generally enjoying higher levels of trust due to established reputation and reduced uncertainty. However, the relative importance of these factors for experience goods, especially concerning famous versus nonfamous brands, remains under-researched.

This study employs conjoint analysis to quantify consumer preferences and the economic value assigned to various online shopping attributes that impact trust. The choice-based conjoint analysis presented respondents with hypothetical product scenarios characterized by six attributes: price, number of reviews, star rating, review type, text review length, and shopping platform. These attributes were chosen based on existing literature and real-world online shopping platforms. A fractional factorial design with an orthogonal design from SPSS Statistics 25 was used to reduce the number of alternatives to 25. The respondents (528 consumers in South Korea, evenly distributed by age and gender) made choices between sets of five products, for both famous (Nike) and nonfamous brands. The data was analyzed using a multinomial logit model to estimate the relative importance (RI) of each attribute and the marginal willingness to pay (MWTP) for each attribute level. The RI indicates the influence of each attribute on consumer choice, while the MWTP quantifies the monetary value consumers are willing to pay for incremental improvements in each attribute. The study then separates the analysis for famous and nonfamous brands to compare the relative importance and MWTP of the attributes for each.

The results show that star rating is the most important factor (RI = 28.75%, MWTP = 2655.95 KRW per star) influencing consumer decisions for experience goods from famous brands, followed by the number of reviews (RI = 17.38%). Consumers show higher willingness to pay for premium reviews (over 300 words), picture and video reviews, and trusted shopping platforms (online platforms and open markets) compared to personal shopping malls. For nonfamous brands, similar patterns emerge, but the relative importance and MWTP for all attributes are significantly higher compared to famous brands. Consumers are willing to pay more for all attributes (number of reviews, star ratings, picture and video reviews, premium reviews, and shopping platforms) when purchasing from nonfamous brands than famous brands. This suggests that in the absence of brand recognition, consumers rely heavily on reviews and platform reputation to reduce uncertainty and make purchase decisions, even at a higher price. The MWTP for 100 reviews was 642.80 KRW for famous brands and 776.30 KRW for nonfamous brands, indicating a greater reliance on reviews for nonfamous brands.

The findings highlight the crucial role of online reviews and shopping platforms in building consumer trust, particularly for experience goods. The high importance of star ratings and the number of reviews underscore the power of social proof and peer influence in mitigating the uncertainty associated with online purchases. The higher MWTP for trusted platforms suggests that consumers are willing to pay a premium for reduced risk and increased confidence. The significant difference between famous and nonfamous brands highlights the importance of overcoming uncertainty, especially when prior experience is absent. For nonfamous brands, relying on strong reviews and reputable platforms becomes a critical strategy for attracting consumers and achieving higher prices.

This study demonstrates the significant impact of online reviews and platform reputation on consumer trust and purchase decisions for experience goods. The results provide valuable insights for retailers, particularly regarding pricing strategies and the strategic use of reviews and shopping platforms. Future research could extend this analysis to other product categories (search goods) and explore the moderating role of other factors, such as consumer demographics or product risk perceptions. Using a mixed logit model might provide a more nuanced analysis of the heterogeneity in consumer preferences.

The study's limitations include its focus solely on experience goods and the limitations inherent in conjoint analysis (use of fractional factorial design, potential for attribute non-attendance). The price range used in the survey could also be expanded in future studies. Furthermore, the analysis is limited to the South Korean context, and future research should investigate the generalizability of these findings across different cultural contexts and platforms. Finally, the study uses the multinomial logit model; future research could employ a mixed logit model to account for unobserved heterogeneity among consumers.