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The rise in online home delivery services (OHDS) has had a significant impact on how urban services are supplied and used in recent years. Studies on the spatial accessibility of OHDS are emerging, but few is known about the temporal dimension of OHDS accessibility as well as the geographic and socioeconomic differences in the spatiotemporal accessibility of OHDS. This study measures the spatiotemporal accessibility of four types of OHDS, namely leisure, fresh and convenient, medical, and catering services. The geographic and socioeconomic disparities in the spatiotemporal accessibility of these four types of OHDS are then identified using spatial statistical methods and the Kruskal-Wallis test (K-W test). The case study in Nanjing, China, suggests that: 1) spatiotemporal accessibility better reflects the temporal variation of OHDS accessibility and avoids overestimation of OHDS accessibility when only considering its spatial dimension. 2) The spatiotemporal accessibility of OHDS varies geographically and socioeconomically. Neighborhoods located in the main city or neighborhoods with higher housing prices, higher population density, and higher point of interest (POI) mix have better OHDS spatiotemporal accessibility. Our study contributes to the understanding of OHDS accessibility from a spatiotemporal perspective, and the empirical insights can assist policymakers in creating intervention plans that take into account variations in OHDS spatiotemporal accessibility.

This study presents and discusses the home delivery services in stochastic queuing-inventory modeling (SQIM). This system consists of two servers: one server manages the inventory sales processes, and the other server provides home delivery services at the doorstep of customers. Based on the Bernoulli schedule, a customer served by the first server may opt for a home delivery service. If any customer chooses the home delivery option, he hands over the purchased item for home delivery and leaves the system immediately. Otherwise, he carries the purchased item and leaves the system. When the delivery server returns to the system after the last home delivery service and finds that there are no items available for delivery, he goes on vacation. Such a vacation of a delivery server is to be interrupted compulsorily or voluntarily, according to the prefixed threshold level. The replenishment process is executed due to the (s,Q) reordering policy. The unique solution of the stationary probability vector to the finite generator matrix is found using recursive substitution and the normalizing condition. The necessary and sufficient system performance measures and the expected total cost of the system are computed. The optimal expected total cost is obtained numerically for all the parameters and shown graphically. The influence of parameters on the expected number of items that need to be delivered, the probability that the delivery server is busy, and the expected rate at which the delivery server’s self and compulsory vacation interruptions are also discussed.

The fast and cost-efficient home delivery of goods ordered online is logistically challenging. Many companies are looking for new ways to cross the last mile to their customers. One technology-enabled opportunity that recently has received much attention is the use of drones to support deliveries. An innovative last-mile delivery concept in which a truck collaborates with a drone to make deliveries gives rise to a new variant of the traveling salesman problem (TSP) that we call the TSP with drone. In this paper, we model this problem as an integer program and develop several fast route-first, cluster-second heuristics based on local search and dynamic programming. We prove worst-case approximation ratios for the heuristics and test their performance by comparing the solutions to the optimal solutions for small instances. In addition, we apply our heuristics to several artificial instances with different characteristics and sizes. Our experiments show that substantial savings are possible with this concept compared to truck-only delivery. The online appendix is available at https://doi.org/10.1287/trsc.2017.0791 .

PurposeThis research leverages an integrated framework that uses the technology acceptance model (TAM) and the theory of planned behaviour (TPB) to analyse the main drivers of users' intention to use food delivery apps. The purpose of this paper is to investigate the consumer's willingness to adopt online food delivery (OFD) using the models' constructs and extend them to consider food choices, convenience, trust and the effect of the perceived risks related to the coronavirus disease 2019 (COVID-19) pandemic as contextual factors.Design/methodology/approachThe study adopts the partial least squares approach to structural equation modelling (PLS-SEM) to examine the data. The final sample consists of 425 people in Italy.FindingsThe authors have found that combining the TAM and the TPB provides a valid and significant model that can be used to understand OFD users' behavioural intentions. Moreover, the results show that subjective norms have a stronger effect on behavioural intentions than the personal attitude and that trustworthiness and the perception of risks related to COVID-19 have different effects. Accordingly, the authors derive several theoretical and managerial implications from these results.Originality/valueThis research contributes to the current debate on consumer behaviour in the OFD context. Only a few studies have integrated the TAM and TPB models in this context. This paper sheds light on the factors useful in predicting people's choice to buy food via OFD. Furthermore, it highlights the key role of some contextual factors and subjective norms over more technical ones.

Purpose The purpose of this paper is twofold: first, to review and classify scientific publications dealing with those innovative solutions aimed at increasing the efficiency of last-mile delivery in business to consumer (B2C) e-commerce; and, second, to outline directions for future research in this field. Design/methodology/approach The review is based on 75 papers published between 2001 and 2019 in international peer-reviewed journals or proceedings of conferences, retrieved from bibliographic databases and science search engines. Findings Due to its importance in affecting the overall logistics costs and, as a consequence, the economic sustainability of a B2C e-commerce initiative, last-mile delivery process deserves particular attention in order to be optimised. The review highlights that, among the main factors affecting its cost, there are the probability to have failed deliveries, the customer density in the delivery areas and the degree of automation of the process. Innovative and viable last-mile delivery solutions – which may impact the mentioned drivers – include parcel lockers, crowdsourcing logistics, mapping the consumer presence at home and dynamic pricing policies. Eventually, some gaps and areas for further research activities have been identified (e.g. mapping customer behaviour, crowdsourcing logistics). Originality/value This review offers interesting insights to both academics and practitioners. On the academic side, it analyses and classifies relevant literature about innovative and efficiency-oriented last-mile delivery solutions, proposing directions for future research efforts. On the managerial side, it presents a holistic framework of the main factors affecting last-mile delivery cost and of viable innovative solutions that may be implemented to increase efficiency.

Online food delivery service (OFDS) has been widely utilized during the new normal of the COVID-19 pandemic, especially in a developing country such as Indonesia. The purpose of this study was to determine factors influencing customer satisfaction and loyalty in OFDS during the new normal of the COVID-19 pandemic in Indonesia by utilizing the extended theory of planned behavior (TPB) approach. A total of 253 respondents voluntarily participated and answered 65 questions. Structural equation modeling (SEM) indicated that hedonic motivation (HM) was found to have the highest effect on customer satisfaction, followed by price (P), information quality (IQ), and promotion (PRO). Interestingly, this study found out that usability factors, such as navigational design (ND) and perceived ease of use (PEOU) were not significant to customer satisfaction and loyalty in OFDS during the new normal of COVID-19. This study can be the theoretical foundation that could be very beneficial for OFDS investors, IT engineers, and even academicians. Finally, this study can be applied and extended to determine factors influencing customer satisfaction and loyalty in OFDS during the new normal of COVID-19 in other countries.

PurposeThis paper investigates the economic performances of two business-to-consumer (B2C) e-commerce last-mile delivery options –parcel lockers (PLs) and traditional home delivery (HD) in contexts where e-commerce is still at its early stages. It analyses and compares two different implementation contexts, urban and rural areas.Design/methodology/approachThis study develops an analytical model that estimates delivery costs for both the PL and HD options. The model is applied to two base cases (representative of urban and rural areas in Italy), and sensitivity analyses are subsequently performed on a set of key variables/parameters (i.e. PL density, PL fill rate and PL annual costs). To support the model development and application, interviews with practitioners (Edwards et al., 2011) were performed.FindingsPLs imply lower delivery cost than HD, independently from the implementation area (urban or rural): advantages mainly derive from the higher delivery density and the drastic reduction of failed deliveries. Benefits entailed by PLs are more significant in rural areas due to lower PL investments and annual costs, as well as higher HD costs.Originality/valueThis paper offers insights to both academics and practitioners. On the academic side, it develops a model to compare the delivery cost of PL and HD, which includes the analysis of urban and rural contexts. This could serve as a platform for developing/informing future analytical/optimisation contributions. On the managerial side, it may support practitioners in making decisions about the implementation of PLs and HD, to benchmark their costs and to identify the main variables and parameters at play.

Purpose This paper aims to explore the types of perceived risks involved with using drone food delivery services. Furthermore, this study investigates the relationship between perceived risk and image of drone food delivery services. Lastly, this study examines the effect of image of drone food delivery services on desire, intentions to use and willingness to pay more. Design/methodology/approach This study collected data from 331 respondents in Korea. Before the start of the survey, the respondents were given a video, which made it easier for them to understand drone food delivery services. Findings The three types of perceived risks (i.e. time risk, performance risk and psychological risk) have a negative influence on image of drone food delivery services and, thus, aids in increasing desire, intentions to use and willingness to pay more. Originality/value The concept of perceived risk was applied to the context of drone food delivery services in this study in combination with other understudied concepts, image, desire, intentions to use and willingness to pay more. This study is one of the first studies that applied those significant concepts to the context of drone food delivery services, even though there are a large number of papers in the technology field. Thus, the findings of this study will be important to foodservice companies when building successful drone food delivery services.

PurposeThe purpose of this paper is to re-examine the extant research on last-mile logistics (LML) models and consider LML’s diverse roots in city logistics, home delivery and business-to-consumer distribution, and more recent developments within the e-commerce digital supply chain context. The review offers a structured approach to what is currently a disparate and fractured field in logistics.Design/methodology/approachThe systematic literature review examines the interface between e-commerce and LML. Following a protocol-driven methodology, combined with a “snowballing” technique, a total of 47 articles form the basis of the review.FindingsThe literature analysis conceptualises the relationship between a broad set of contingency variables and operational characteristics of LML configuration (push-centric, pull-centric, and hybrid system) via a set of structural variables, which are captured in the form of a design framework. The authors propose four future research areas reflecting likely digital supply chain evolutions.Research limitations/implicationsTo circumvent subjective selection of articles for inclusion, all papers were assessed independently by two researchers and counterchecked with two independent logistics experts. Resulting classifications inform the development of future LML models.Practical implicationsThe design framework of this study provides practitioners insights on key contingency and structural variables and their interrelationships, as well as viable configuration options within given boundary conditions. The reformulated knowledge allows these prescriptive models to inform practitioners in their design of last-mile distribution.Social implicationsImproved LML performance would have positive societal impacts in terms of service and resource efficiency.Originality/valueThis paper provides the first comprehensive review on LML models in the modern e-commerce context. It synthesises knowledge of LML models and provides insights on current trends and future research directions.

The purpose of this study was to investigate the factors determining the behavioral intention of using food delivery apps (FDAs) during COVID-19 pandemics, under a case study of Bangkok, Thailand. The study was necessitated by the increased use of FDAs during the lockdown; online transactions were considered important in preventing the spread of the virus. The study used quantitative techniques involving structural equation model (SEM) to evaluate the effects of exogenous variables on endogenous variables. Primary data were collected from people who had installed and used FDAs. The findings indicated that performance expectancy, effort expectancy, social influence, timeliness, task technology fit, perceived trust, and perceived safety significantly influence the behavioral intention to use (BIU) to use food delivery apps during the COVID-19 pandemic. To this end, effort should be intensified to study and understand FDAs as it pertains to safety and usage.