The impact of AI recommendation quality on service satisfaction: the moderating roles of standardization and customization

Purpose This study aims to investigate how information quality and system quality influence the effectiveness of artificial intelligence (AI)-based recommendation service platforms. It integrates traditional information technology service quality (SQ) metrics with recommendation SQ measures, focusing on their impact on user satisfaction and behavior. This study further examines the moderating effects of standardization and customization on these relationships. Design/methodology/approach This study uses structural equation modeling to analyze data from 978 users of AI recommendation services. It evaluates the direct impacts of information quality (completeness, accuracy and format) and system quality (reliability, flexibility and timeliness) on recommendation quality (RQ). Findings The findings show significant positive effects of information quality and system quality on the quality of AI-generated recommendations, enhancing user satisfaction. This satisfaction is crucial for promoting continuous intention to use and positive word-of-mouth (WOM). This study also finds that standardization positively moderates the impact of RQ on WOM, whereas customization strengthens the relationship between satisfaction and continuous intention to use. Originality/value This research emphasizes the importance of quality metrics in shaping the efficacy of AI-based recommendation systems and highlights the need for a balance between standardization and customization to optimize user engagement and satisfaction. The findings offer valuable insights for AI service developers and marketers, emphasizing the significance of customized, high-quality recommendations to ensure sustained user engagement.