Direct and indirect effect of last mile logistics performance on user intention of crowdsourced delivery services

• (LMLP) affects use intention (UI) via perceived confidence (PT) and performance expectation (PE). • A SEM model using partial least squares was applied in the analysis. • Good last-mile logistics performance was not key for intention to use. • Delivery managers should focus on compliance, not just speed or reliability. The objective of this study was to identify whether Last Mile Logistics Performance directly influences the Intention to Use Crowdsourcing Platforms or whether it does so through mediating variables such as Performance Expectation and Perceived Trust. The study was quantitative, with a correlational scope and a cross-sectional design, and through probabilistic sampling, it was applied to a sample of 721 Ecuadorian consumers who had recently used crowdsourcing platforms. The data collection instrument was a questionnaire based on previous studies and validated through a panel of experts in Research and Logistics Management. To test the hypothesized model, convergent and discriminant validity tests were performed, as well as structural equation modeling. The results showed that Last Mile Logistics Performance does not directly influence the Intention to Use these platforms. However, it has a significant impact on Performance Expectation and Perceived Trust, which in turn influence consumers’ Intention to Use. This finding highlights the importance of logistics quality as a key antecedent in creating expectations and trust in platforms. The results provide relevant information for business decision-making in the field of logistics and the use of crowdsourcing platforms, emphasizing the need to strengthen user trust and manage their expectations to improve the adoption of these services.