PurposeThe purpose of this paper is to explore if e-commerce customers change their preferred last-mile delivery, when they are provided with additional information on the environmental and social sustainability impact of the available last-mile delivery options.Design/methodology/approachWe conduct a stated-preference survey and apply McNemar test on the collected data.FindingsThe results show that displaying the environmental and social impacts of last-mile deliveries influences E-commerce customers, and generally makes them more likely to choose a more sustainable last-mile delivery.Research limitations/implicationsThe main limitations are (1) the possible inconsistency between participants' intentions stated in the survey and their actual behaviour in real life and (2) the possibility of participants denying socially undesirable behaviours. Further research can study e-commerce customers' real behaviour.Practical implicationsE-retailers and logistics companies could implement transparent information sharing on the delivery sustainability impact on all three sustainability pillars.Social implicationsThe suggested transparent information sharing has the potential to change customers' behaviours towards more sustainable deliveries.Originality/valueWe provide a new approach in investigating customers' preferences on last-mile deliveries, by giving E-customers the chance of making choices between different deliveries, not only based on the economic factors (as in common practice nowadays) but also based on the environmental and social factors.

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PurposeThe paper aims to develop (1) a comprehensive framework for classifying crowdshipping business models and (2) a taxonomy of currently implemented crowdshipping business models.Design/methodology/approachThe business models of 105 companies offering crowdsourced delivery services are analysed. Cluster analysis and principal component analysis are applied to develop a business model taxonomy.FindingsA detailed crowdsourced delivery business model framework with 74 features is developed. Based on it, six distinct clusters of crowdshipping business models are identified. One cluster stands out as the most appealing to customers based on social media metrics, indicating which type of crowdshipping business models is the most successful.Research limitations/implicationsDetailed investigations of each of the six clusters and of recent crowdshipping business model developments are needed in further research in order to enhance the derived taxonomy.Practical implicationsThis paper serves as a best-practices guide for both start-ups and global logistics operators for establishing or further developing their crowdsourced delivery business models.Originality/valueThis paper provides a holistic understanding of the business models applied in the crowdshipping industry and is a valuable contribution to the yet small amount of studies in the crowd logistics field.

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