ABSTRACT
The online food delivery service supply chain constitutes a crucial element in achieving sustainable development goals. With its prosperity, an increasing number of takeaway businesses are drawn to this sector. As their numbers rise, issues such as profitability resilience, environmental friendliness, and fulfillment of social responsibility emerge, posing potential disruptions to the service supply chain. Against this backdrop, our endeavor is to mine the sustainability of takeaway businesses using the triple bottom line. We propose a two-stage approach involving the Bayesian best-worst method and a data mining technique to derive the weights of sustainability criteria and the clusters of takeaway businesses. Subsequently, a case study is conducted focusing on takeaway businesses on the Ele.me platform in China. The results highlight economic sustainability as the most valued criterion, followed by social and environmental sustainability. Clustering outcomes reveal four distinct levels of sustainability, with a stronger performance in social sustainability compared to environmental and economic dimensions. Further discussions explore the relationship between sustainability levels, cuisine categories, and business size. Consequently, this study suggests an effective approach for advancing sustainability initiatives within the online food delivery service supply chain.
ABSTRACT
Service platform has developed rapidly in car-sharing, consumers often buy or own cars but not fully utilize and share them. Since the coronavirus pandemic has affected sales and people's attitudes towards car-sharing, which brought both opportunities and challenges to the platform and changed the operating mode of manufacturers, some traditional manufacturers have motivated to cooperate with third-party platform. In this paper, we develop an analytical framework to examine the pricing decisions and optimal mode selection of manufacturer under the COVID-19 epidemic. Considering the supply chain consists of a manufacturer and a third-party sharing platform. We analyze three scenarios including no sharing, customers-to-customers, and mixed sharing, then employ a game theoretic approach to get equilibrium solutions and analytically derive the optimal mode choice. Our analysis shows that when the operation and maintenance cost is low, manufacturer will join the third-party platform, and the sharing price increase in operation and maintenance cost, while the selling price decrease in operation and maintenance cost. When the value perception factor less than the threshold, the manufacturer will retain sales channel, and the selling demand decrease in value perception factor in the growing market, the sharing demand has the same trend, vice versa. Furthermore, we find that if the operation and maintenance cost is low and value perception factor is high, mixed sharing is the best choice for the manufacturer, while the manufacturer will choose no car-sharing when the value perception factor is relatively low.