Characterizing rural resident acceptance of drone delivery: a large language model (llm) empowered approach

Abstract

With the increasing demand for rural logistics services and the notable disparities in service provision between urban and rural areas, there arises a compelling need to explore innovative drone-based delivery solutions. This thesis aims to uncover the challenges hindering the adoption of drone-based delivery, due to technological and physical barriers, which consequently affect service quality for rural residents. Such disparities amplify concerns regarding delivery equity and residents' acceptance of potential drone delivery services. Our research presents an inaugural investigation into residents' direct willingness and sentiment toward drone delivery services in rural areas using a Large Language Model (LLM)-empowered machine learning framework. Leveraging the LLM-driven Light Gradient-Boosting Machine (LightGBM) method, our prediction model mitigates cognitive bias and enhances the predictive accuracy of residents' acceptance categories compared to traditional ordinal logistic regression models. This thesis advances the understanding of rural residents' acceptance of drone delivery services, uncovering pertinent challenges within the rural logistic landscape and the evolution of the drone delivery market. Moreover, it reveals the gap between the supply of rural drone delivery services and the demand from the rural consumer base, exploring the intricate interplay between socioeconomic factors and delivery preferences. This approach fosters a drone-based delivery ecosystem that inclusively benefits all rural residents, irrespective of their geographical location.