Last-mile delivery is the quintessential example of a logistics sector that is ripe for innovation, owing to a powerful confluence in the advancement of technologies and consumption models. Compared to the conventional delivery mode, the delivery robot offers countless advantages, including maintaining social distancing, improving health safety, protecting the environment, and reducing shipping costs and delivery times. Considering the high investments associated with developing and introducing delivery robots, a better understanding is needed of the consumers’ perceptions and willingness to guide practitioners on whether it is worth starting/continuing investing in such innovation. For this reason, this study addresses the following research questions: 1) What are the relevant characteristics of robot delivery and how do consumers cognitively and affectively respond to these characteristics? 2) What are the processes that form consumers’ willingness to reuse the delivery robot and spread the word about it (i.e., WOM)? Drawing on the technology-task fit model, the present study provides three interesting theoretical insights in relation to the post-adoption behaviour on delivery robots. In the first part, the findings enrich the literature by revealing that consumers’ evaluation of the perceived fit of using delivery robots are based on a combination of internal, external and conditional factors. This study found that hedonic motivations, perceived anthropomorphism and robotic service quality exhibited a positive influence on perceived task-technology fit. This indicates that, being able to experience a sense of fun or enjoyment when using novel technologies (e.g., robot services) will encourage future behaviour and push individuals to continue exploring the benefits of this innovation. Also, consumers tend to attribute the human-like robots as more capable and reliable as it gives the feeling that they are interacting with a social entity. The findings also value to current scarcity research and demonstrate the positive influence of service quality in matching the performance and functionality of delivery robots. In the second part, the empirical findings confirm the positive influence of perceived task-technology fit on trust and value-in-use in the emerging context of delivery robots. In the last part, this research goes further, revealing the indirect link between perceived task-technology fit and post-adoption behaviour. In line with consumer behaviour literature that suggests the individual will undergo a psychological state (or mental evaluation) in goal-oriented behavior, this research deepens our understanding of the mechanisms involved in this relationship. It reveals that trust and value-in-use acted as the missing links in bridging the associations through which perceived task-technology fit influences service reuse likelihood and positive word-of-mouth.

This study focuses on the development of a Last-Mile delivery service using unmanned vehicles to deliver goods directly to the end consumer utilizing drones to perform autonomous delivery missions and an image-based precision landing algorithm for handoff to a robot in an intermediate facility. As the logistics market continues to grow rapidly, parcel volumes increase exponentially each year. However, due to low delivery fees, the workload of delivery personnel is increasing, resulting in a decrease in the quality of delivery services. To address this issue, the research team conducted a study on a Last-Mile delivery service using unmanned vehicles and conducted research on the necessary technologies for drone-based goods transportation in this paper. The flight scenario begins with the drone carrying the goods from a pickup location to the rooftop of a building where the final delivery destination is located. There is a handoff facility on the rooftop of the building, and a marker on the roof must be accurately landed upon. The mission is complete once the goods are delivered and the drone returns to its original location. The research team developed a mission planning algorithm to perform the above scenario automatically and constructed an algorithm to recognize the marker through a camera sensor and achieve a precision landing. The performance of the developed system has been verified through multiple trial operations within ETRI.

Recently, unmanned logistics delivery systems, such as UAV (Unmanned Aerial Vehicle, written as drone below) and autonomous robot delivery systems, have been implemented in many countries due to the rapid development of autonomous driving technology. The development of these new types of advanced unmanned logistics delivery systems is essential not only to become a leading logistics company but also to secure national competitiveness. In this paper, the application of the unmanned logistics delivery system was investigated in terms of market trends, overall technology level of last mile delivery drone and autonomous delivery robot. The direction of response to changes in the last mile delivery service market was checked through a comparison of the technological level between domestic companies that produce last mile devices and advanced foreign companies. As a result of this technology level analysis, the difference between domestic companies and advanced companies was shown using tables and figures to show their relative levels. The results of this analysis reflect the opinions of experts in the field of last-mile delivery technology. In addition, the technology level of unmanned logistics delivery systems for each country was analyzed based on the number of related technology patents. Lastly, insights for the technology level analysis of unmanned last mile delivery systems were proposed as a conclusion.

Purpose – The purpose of this paper is to improve last mile delivery capability and ensure customers’ satisfaction by approaching an analytic hierarchy process(AHP) and identifying criteria framework to determine locations of last mile delivery centre(LMDC). Research design, data, and methodology – Traffic congestion and emission policy in cities are barriers of last mile delivery in dense areas. The urban consolidation centre(UCC) cannot increase last mile delivery efficiency in dense cities because of their space and traffic limitation. In this paper, we develop a case to improve last mile delivery efficiency and to ensure customers’ satisfaction by concentrating on LMDC. In addition, AHP has been applied to identify criteria framework and determine LMDC locations. The weighted priorities are derived from parcel delivery industry experts and have been calculated using Expert Choice software. Results – The framework criteria have assisted decision makers to place LMDC in a dense area to enhance customer’s satisfaction with last mile delivery service. Conclusions – AHP has provided ranking framework criteria of LMDC potential for parcel delivery industry. The LMDC helps by improving last mile delivery efficiency to final destination amids conditions of CO2 emissions, traffic congestion, and pollution problems. It especially concerns delivery service activities when delivering parcels to customers rather than UCC.