مقاله انگلیسی رایگان در مورد تجارت الکترونیکی محرک مصرف کننده – امرالد ۲۰۱۸

Last-mile delivery has become a critical source for market differentiation, motivating retailers to invest in a myriad of consumer delivery innovations, such as buy-online-pickup-in-store, autonomous delivery solutions, lockers, and free delivery upon minimum purchase levels (Lim et al., 2017). Consumers care about last-mile delivery because it offers convenience and flexibility. For these reasons, same-day and on-demand delivery services are gaining traction for groceries (e.g. Deliv Fresh, Instacart), pre-prepared meals (e.g. Sun Basket), and retail purchases (e.g. Dropoff, Amazon Prime Now) (Lopez, 2017). To meet customer needs, parcel carriers are increasing investments into urban and automated distribution hubs (McKevitt, 2017). However, there is a lack of understanding as to how best to design last-mile delivery models with retailers turning to experimentations that, at times, attract scepticism from industry observers (e.g. Cassidy, 2017). For example, Sainsbury’s, Somerfield, and Asda established innovative pick centres, but closed them down within a few years (Fernie et al., 2010). eBay launched its eBay Now same-day delivery service in 2012, but in July 2015, it announced the closure of this programme. Google, likewise, opened and then closed its two delivery hubs for Google Express in 2013 and 2015, respectively (O’Brien, 2015). The development of these experimental last-mile logistics (LML) models, not surprisingly, created uncertainty within increasingly complicated and fragmented distribution networks. Without sustainable delivery economics, last-mile service provision will struggle to survive (as was the experience of Sainsbury’s, Somerfield, Asda, eBay, Google, and Webvan) with retailers increasingly challenged to find an optimal balance between pricing, consumer expectations for innovative new channels, and service levels (Lopez, 2017; McKevitt, 2017). Although several contributions have been made in the LML domain, the literature on LML models remains relatively fragmented, thus hindering a comprehensive and holistic understanding of the topic to direct research efforts. Hitherto, existing studies provide limited or no guidance on how contingency variables influence the selection of LML configurations (Agatz et al., 2008; Fernie et al., 2010; Mangiaracina et al., 2015; Lagorio et al., 2016; Savelsbergh and Van Woensel, 2016). Our paper addresses this knowledge deficiency by reviewing the disparate academic literature to capture key contingency and structural variables characterizing the different forms of last-mile distribution. We then theoretically establish the connection between these variables thereby providing a design framework for LML models. Our corpus is comprised of 47 papers published in 16 selected peer-reviewed journals during the period from 2000 to 2017. The review is performed from the standpoint of retailers operating LML. As such, some LML research streams are deliberately excluded, including issues related to public policy, urban traffic regulations, logistics infrastructure, urban sustainability, and environment.