플랫폼 서비스에서의 검색에 대한 이용자 신뢰도 및 만족도에 영향을 미치는 요인 분석 = An Empirical Analysis of User Trust and Satisfaction in AI-Driven Platform Search Services

국문 초록 (Abstract)

본 연구는 AI 알고리즘 기반 검색 서비스가 제공되는 다양한 플랫폼 환경에서 이용자의 검색 결과 신뢰도와 검색 기능 만족도에 영향을 미치는 요인을 실증적으로 분석하였다. 분석대상 플랫폼 유형에는 검색엔진, 전자상거래, 소셜네트워크서비스, 개인간 중고거래, 여행 및 숙박 예약, 동영상 공유 및 시청, 음식 주문 및 배달, 그리고 생성형 AI 챗봇 등 여덟 가지 서비스를 포함하였고, 2024년 12월에 20~69세 전국 성인 1,035명을 대상으로 시행한 온라인 설문조사 데이터를 다변량 다중회귀분석모델을 통해 분석하였다. 분석 결과, 이용자가 검색에서 중시하는 가치와 알고리즘에 대한 이해도와 인식 수준이 플랫폼 서비스 전반에서 검색 결과에 대한 신뢰도와 검색 기능의 만족도에 유의한 영향을 미치는 것으로 나타났다. 특히 검색 결과의 품질뿐 아니라 결과 생성 과정의 공정성과 투명성에 대한 기대가 충족될 때 검색 결과 신뢰도와 검색 기능 만족도가 높아지는 반면, 알고리즘 변화에 대한 사전고지 부족이나 검색 결과의 상업적 영향에 대한 우려가 있을 경우 신뢰도와 만족도가 저해되는 것으로 나타났다. 이러한 결과는 플랫폼 서비스에서 검색에 대한 신뢰도와 만족도를 높이기 위해서는 플랫폼 서비스에서의 검색 투명성 제고와 검색·추천 시스템의 작동원리 등에 대한 디지털 리터러시 교육이 필요함을 시사한다.

다국어 초록 (Multilingual Abstract)

Online information has grown exponentially in recent years, making search functions a critical gateway for accessing information and services (Carroll, 2014). Meanwhile, platform-based business models have expanded across diverse domains and now embed search features into a wide range of online services, altering how users seek information and make decisions. Whereas dedicated search engines (e.g., Google, Naver) once dominated information retrieval, users today often search within various platform environments – including e-commerce sites, social media, and video-sharing platforms – rather than relying solely on traditional search engines (Park et al., 2024). Search functionality has thus become integral to services from online shopping and travel booking to food delivery, underscoring its central role in the platform ecosystem. In parallel, the rise of artificial intelligence has fueled highly personalized recommendation systems. Unlike search, which requires active user queries, recommendation algorithms proactively present content or products to users, potentially reducing effort but raising concerns if recommendations are misaligned with user preferences. Both search and recommendation features now shape user choices on platforms. Despite their growing importance, however, most previous research on platform search and recommendation services has focused on technical aspects of algorithms, with relatively little attention to user-centric outcomes like perceived credibility or satisfaction (Park et al., 2008).
To address this gap, the present study empirically examines user trust and satisfaction with AI-driven search and recommendation services across eight major types of online platforms. It focuses on two key user-centric outcomes – search credibility (trust in the search results returned) and satisfaction with the platform’s search functionality – across environments ranging from search engines and e-commerce sites to social media, peer-to-peer marketplaces, travel and lodging portals, video-sharing services, food delivery apps, and generative AI chatbot services. This broad scope captures a diverse range of platform contexts where algorithmic search and recommendation features are integral.
Key predictors of trust and satisfaction include user characteristics (e.g., age, education, frequency of search use, and whether default search settings are adjusted), the primary aspect of search the user prioritizes (such as accuracy, speed, personalization, or transparency of result ranking), user understanding of AI recommendation algorithms (algorithmic literacy), and user perceptions of algorithmic transparency and fairness. The latter encompasses the importance of measures such as platforms explaining their ranking criteria, providing advance notice of algorithm changes, clearly labeling sponsored content, and explaining how user data are used – reflecting concerns about algorithmic transparency and accountability (Shin et al., 2022).
Data for the study were collected through an online survey conducted in December 2024 with a sample of 1,035 adults in South Korea aged 20–69. Respondents reported their usage of the aforementioned platform services and evaluated both the trustworthiness of search results and their satisfaction with the search function for each platform they used. These two outcome measures (perceived search trust and search satisfaction) were captured on five-point Likert scales for each platform type.
To ensure consistent comparison across platform types, the analysis focused on the 682 respondents who had experience with all eight services. Trust and satisfaction ratings differed by platform: participants reported the highest search trust and satisfaction for search engines and online shopping services, and the lowest for peer-to-peer marketplaces. A multivariate multiple regression was then applied to assess the combined effects of all predictors on search result trust and search satis...