The C2B2C model mitigates trust risks in secondhand transactions by enabling platforms to intervene deeply in logistics activities, such as pickup, inspection, delivery, and after-sales services, thereby making logistics service quality a critical determinant of user experience and platform competitiveness. However, the multinode and high-uncertainty nature of C2B2C logistics processes renders user needs more sensitive and exhibits pronounced nonlinear characteristics. Against this background, this study employs text-based corpus analysis and the Kano model to identify user demand attributes in C2B2C secondhand e-commerce logistics services. The impacts of different service elements on user satisfaction are further evaluated using Better-Worse coefficients and four-quadrant analysis. On the basis the results, four attractive logistics service demands are identified: timely pickup response, rapid return collection, flexible delivery modification, and transparent logistics information. Subsequently, the TRIZ methodology is introduced to translate these demand characteristics into actionable logistics optimization strategies. Scenario-based simulations are then conducted, combining two-proportion Z-tests and bootstrap resampling, to assess potential improvements in satisfaction following strategy implementation. Results indicate that under reasonable improvement scenarios, the proposed TRIZ-based strategies yield statistically significant or marginally significant improvements in user satisfaction across all four key logistics services. Overall, this study develops an integrated logistics service improvement and validation framework that combines corpus analysis, the Kano model, and TRIZ, providing a systematic decision-support reference for optimizing logistics operations and enhancing user experience in C2B2C secondhand e-commerce platforms.
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