Vendor Performance Evaluation and Rating System Design for E-Commerce Platforms to Reduce Supply Chain Disruptions

Digital service platforms operating in high trust environments face increasing challenges in ensuring service reliability, quality consistency, and disruption prevention. Unlike product-based supply chains, service supply chains are characterized by intangibility, simultaneity, human dependency, and performance variability, which significantly increase vulnerability to human-driven disruptions such as cancellations, no-shows, delayed responsiveness, and compliance failures. Despite the growing importance of digital intermediaries in sectors such as online mental health services, structured vendor governance mechanisms remain underdeveloped, with many platforms relying heavily on reputation systems and customer ratings. This thesis develops a structured two stage vendor evaluation framework designed to enhance service supply chain stability in AI-powered digital platforms. Drawing upon service supply chain disruption theory, KPI performance measurement literature, and reputation system research, the study proposes an integrated governance model combining pre-onboarding capability based screening with post-onboarding weighted KPI performance monitoring. The pre-onboarding stage introduces a Pre-Onboarding Risk Index (PORI) based on professional competence, platform alignment, reliability commitment, and compliance standards. The post-onboarding stage applies a multidimensional KPI structure including service quality, responsiveness, reliability, and professional conduct and the calculation of Overall Vendor Performance Score (OVPS). Due to the launching phase of the Mindioo platform, the framework is validated through a structured simulation using hypothetical vendor profiles. The application demonstrates the logical coherence, operational feasibility, and categorization capability of the proposed model. Results indicate that integrating preventive screening with continuous performance monitoring provides a more robust governance mechanism than reliance on reputation systems alone and directly addresses human-driven disruption risks inherent in service based platforms. The study contributes to the extension of service supply chain disruption theory into digital service ecosystems and offers a structured managerial tool for vendor governance in AI-supported platforms. While empirical validation remains a future research direction, the proposed framework provides both theoretical advancement and practical applicability for enhancing stability, trust, and long-term sustainability in digital service supply chains.