The growing concentration of the population in urban areas presents great challenges for sustainability. Within this process, mobility emerges as one of the main generators of externalities that hinder the achievement of the Sustainable Development Goals. The transition of cities towards innovations in sustainable mobility requires progress in different dimensions, whose interaction requires research. Likewise, it is necessary to establish whether the experiences developed between cities with different contexts can be extrapolated. Therefore, the purpose of this study was to identify how the conditions that determine a city’s readiness to implement urban mobility innovations could be combined. For this, qualitative comparative analysis was applied to a model developed using the multi-level perspective, analyzing 60 cities from different geographical areas and with a different gross domestic product per capita. The R package Set Methods was used. The explanation of the readiness of cities to implement mobility innovations is different to the explanation of the readiness negation. While readiness is explained by two solutions, in which only regime elements appear, the negation of readiness is explained by five possible solutions, showing the interaction between the landscape and regimen elements and enacting the negation of innovations as a necessary condition. The cluster analysis shows us that the results can be extrapolated between cities with different contexts.
With a methodological approach, this article explores the application of data mining to the user-generated content of tourist accommodation on infomediation platforms and social networks. Its objective is to present an algorithm that allows the identification of service characteristics relevant to guest satisfaction and trust. Our study processes unstructured, natural-language data about Airbnb and hotel stays (the final dataset was 12,236 Airbnb sentences and 12,200 hotel sentences from 2018 until September 25 2021). Among the results is a computational algorithm that uses BERTopic to identify latent themes (or topics) in the narratives. Secondly, our analysis applies a Zero-shot classification approach for classifying guest reviews into labels related to guests' satisfaction and trust. Thirdly, we execute a Principal Component Analysis to investigate the sufficiency relationships between extracted topics, customer satisfaction, and trust-based labels. To sum up, and as practical implications, our study adds to the knowledge about the sharing economy by providing insights for developing marketing policies and a better understanding of hospitality services.
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