Abstract
Purpose:
Sharing mobility has emerged as a key component of sustainable urban transportation, yet existing research largely relies on survey-based methods that may not fully capture users’ lived experiences. This study seeks to identify and prioritise the key factors shaping the adoption of sharing mobility in urban settings and to develop a multidimensional conceptual framework that reflects how economic, cultural, sociodemographic, environmental, technological, and policy factors jointly influence adoption decisions.
Design/methodology/approach:
Drawing on more than 1,00,000 user-generated online reviews of sharing mobility services, this study adopts a data-driven text-mining approach. First, sentiment analysis and Latent Dirichlet Allocation (LDA) topic modelling are employed to uncover dominant themes, user concerns, and experiential drivers of sharing mobility usage. These insights are then synthesised into a multidimensional conceptual framework. To move beyond description and provide decision-oriented insights, the study applies the Ordinal Priority Approach (OPA), a multi-criteria decision-making technique, to prioritise the relative importance of the identified dimensions based on expert judgments.
Findings:
The analysis reveals that usability, trust, and cultural perceptions play a central role in shaping users’ acceptance of sharing mobility services. Five core themes emerge from user narratives: (i) service and transaction efficiency, (ii) trust and experiential reliability, (iii) overall service impressions, (iv) local language and informal user feedback, and (v) value sensitivity. The OPA-based prioritisation further highlights trust-related and service reliability factors as the most influential drivers of adoption, followed by economic and cultural considerations. The proposed framework illustrates how these dimensions interact to shape sharing mobility adoption across diverse urban contexts.
Researchlimitations/implications:
By integrating large-scale user-generated data with multi-criteria decision-making, this study offers a robust alternative to traditional survey-based approaches and contributes methodologically to the sharing mobility and sustainable transportation literature. While the study relies on secondary data sources, it captures authentic and evolving user perspectives at scale.
Practical implications:
The findings provide actionable insights for sharing mobility service providers and policymakers by identifying priority areas for intervention, particularly in building user trust, improving service reliability, and designing culturally responsive platforms.
Originality/value:
This research advances the sharing mobility literature by combining text mining, conceptual framework development, and OPA-MCDM in a single, integrated analytical pipeline. By translating large-scale user narratives into prioritised decision criteria, the study offers a novel and scalable approach to understanding sharing mobility adoption in urban environments.