The Impact of Big Data-based Information Quality on Perceived Usefulness and Intention to Use: Focusing on Platform Recommendation Services

Big data is a service that enables organizations and companies to analyze large amounts of data that cannot be analyzed in real-time to infer strategic value and apply it to their business to secure future competitive advantages.Therefore, this study aims to analyze the impact of information quality on perceived usefulness and the Intention to Use of recommendation services on big data-based platforms.The factors that determine information quality are categorized as timeliness, accuracy, variety, freshness, and reliability.To fulfill the research objectives, a survey was conducted among users over 20 years old who use platform recommendation services.Smart PLS 4.0, a structural equation software package, was used to conduct basic statistical analysis of the questionnaire and hypothesis testing.The results of the study are summarized as follows.First, timeliness, accuracy, variety, freshness and reliability had a significant effect on perceived usefulness.Second, Perceived Usefulness had a significant impact on Intention to Use.In this study, users of big data-based platform recommendation services evaluated their Intention to Use based on the information quality factors of freshness, reliability, accuracy, timeliness, and variety, in that order.These results suggest that recommendation service providers should provide users with the latest information and services that they can trust and accept.