European policymakers chose the systematic funding of smart city initiatives to incentivize and accelerate innovation and sustainability transitions. To ensure these initiatives’ broader effectiveness, smart city replication has been incorporated in funding calls for research projects as a policy instrument for innovation diffusion, information dissemination and mutual learning. With a growing theoretical and empirical base for these replication activities, there is an increased awareness that integrating and transferring new ideas and solutions into the urban context requires a holistic perspective and includes various endogenous and exogenous influencing factors. This article proposes a systemic view by presenting a method for analysing the replication environment for autonomous delivery robots based on a causal loop diagram. The method is applied conceptually to a district in Munich. The developed approach, which is called Replication Causal Loop Diagram, serves as an analytical tool to generate and provide relevant contextual knowledge and information about the replication environment to facilitate the operational planning and implementation of replicable initiatives, solutions, and practices. In further development steps and in particular settings, the approach can also be a valuable addition to the replication portfolio for stakeholder engagement and consensus building.

Urban delivery demand continues to rise, intensifying last-mile logistics challenges and accelerating the transition from manual delivery to autonomous delivery robots (ADRs). This study investigates the behavioral mechanisms underlying consumers’ migration toward ADRs. Grounded in the socio-technical systems perspective, we integrate the Push–Pull–Mooring (PPM) model with Social Cognitive Theory (SCT) to explain how technological and social stimuli shape switching and continuance intentions through cognitive and emotional pathways. Survey data from 786 Chinese consumers, analyzed using second-order structural equation modeling, support the proposed framework. The results indicate that dissatisfaction with manual delivery (push) and perceived benefits of ADRs (pull) significantly enhance both switching and continuance intentions. Outcome expectancy positively predicts switching intention but negatively predicts continuance intention. Technophobia reduces switching intention but does not significantly influence continuance. Moreover, social norms moderate key relationships, highlighting the role of external social influence in technology transition. This study extends PPM research into the smart logistics context, introduces socio-cognitive mechanisms into technology switching analysis, and conceptually distinguishes switching and continuance intentions as separate constructs. The findings offer practical guidance for ADR developers and policymakers by emphasizing strategies to reduce emotional resistance, enhance social endorsement, and promote the sustainable adoption of autonomous delivery technologies.

The convergence of persistent Augmented Reality (AR), robotics, and biometric analytics is creating a new technological paradigm where the digital and physical worlds are inextricably intertwined. This integration, while promising, introduces profound ethical risks that traditional governance models fail to address. This paper examines the complex challenges of data governance in this new era. Using a multi-method approach that integrates a systematic literature review, four purposive case studies (algorithmic recruitment, AR law enforcement, consumer wearables, and autonomous delivery robots), and thematic analysis, this research investigates four primary risk domains: (1) deep inferential threats from biometric and behavioural data, (2) cognitive manipulation and pervasive surveillance, (3) the "bystander problem" of non-consensual data capture, and (4) the diffusion of accountability in complex autonomous systems. Findings reveal systemic vulnerabilities, including "ambient biometric surveillance," "bystander invisibility," and "distributed responsibility," demonstrating the inadequacy of existing individualistic consent frameworks. The paper concludes by proposing a dual-pronged governance framework. This framework combines technical safeguards, such as dynamic consent architectures and mandatory AI Impact Assessments (AI-IAs), with policy innovations, including new legal categories for bystander data and multi-stakeholder co-regulatory oversight, to steer technological development toward a human-centric, rights-respecting future.

The aim of this study was to evaluate the impact of introducing an autonomous courier in a clinical laboratory, focusing on specimen turnaround time (TAT). We assessed whether high-frequency robotic transport from the central accessioning area to analytical sections could reduce delays and variability compared with manual delivery. We retrospectively analyzed routine lithium-heparin glucose specimens processed during the initial days of robot deployment (13-23 May 2025, weekdays, 08:00-16:00) and compared them with the same period in 2024 under manual transport. TAT was defined as the interval from check-in at accessioning to technical validation in the Laboratory Information System (LIS). Data were assessed globally and stratified by time of day, examining changes in central tendency, dispersion, and extreme delays. In total, 6,299 samples in 2024 and 5,759 in 2025 were analyzed, showing a leftward shift of the distribution with fewer delay. In 2025, the mean TAT decreased from 122.64 to 106.72 min, the median from 112 to 101 min, and dispersion tightened. Stratification by time of day also demonstrated consistent improvements. Even in its earliest days of operation, the delivery robot reduced TAT and variability, converting specimen transport from batch runs into near-continuous flow. These findings highlight ease of adoption and the potential of robotic transport to improve speed, predictability, and safety in intralaboratory logistics. Further validation across longer periods and additional laboratory sections is warranted.

Urban logistics faces increasing pressure from rising population densities, escalating delivery demands, and constrained urban resources. Traditional logistics systems struggle to adapt to real-time urban dynamics, leading to inefficiencies, congestion, and environmental concerns. A key challenge lies in mobilizing underutilized assets, such as off-hour freight parking, and adopting multimodal solutions to navigate diverse and increasingly strict regulations, thereby enhancing both sustainability and operational efficiency. However, effective management and utilization of these assets require real-time visibility, cross-stakeholder collaboration, and intelligent decision-making. This study proposes a federated digital twin platform to enhance logistics operations efficiency by integrating asset management and knowledge-driven operations management, relying on real-time asset visibility and delivery knowledge, such as destination characteristics and preferred logistics modalities. Unlike traditional logistics planning, which relies on static assumptions, our approach adapts to urban constraints by continuously querying real-time asset information and integrating logistics-related knowledge into operations management. To assess the effectiveness of this approach, an optimization-based simulation framework with decision-making tools is developed. The study evaluates multi-echelon logistics networks, incorporating micro-hubs, dynamic transshipment points, and multimodal logistics options, including on-foot porters, E-cargo bikes, and Road Autonomous Delivery Robots (RADRs). Findings demonstrate that integrating federated digital twins with knowledge-driven approaches, such as destination-based clustering and modality selection, reduces costs by over 50% and emissions by more than 30%. This study underscores the transformative potential of digital twins in enabling real-time, knowledge-driven operations management, and fostering more sustainable and efficient urban logistics systems. • Federated Digital Twins enhance resource visibility and knowledge-driven logistics. • Dynamic asset management minimizes waste, emissions, and inefficiencies. • Constrained K-Means optimizes modality selection using destination-based clustering. • Simulation evaluates rule-based decision-making for adaptive urban logistics. • Findings guide LSPs and policymakers in optimizing logistics operations and policies.

This study provides a comprehensive bibliometric analysis of 1,226 Scopus-indexed publications on social commerce, direct shopping, digital consumer behavior, and Fear of Missing Out (FoMO) between 2005 and 2025. Using Bibliometrix (R) and VOSviewer, the research maps the intellectual development of the field through co-authorship, co-citation, keyword co-occurrence, and international collaboration networks. The findings reveal that electronic commerce and online shopping remain the conceptual core of the literature, while emerging themes include live streaming commerce, chatbot-assisted shopping, augmented reality, impulsive buying, and social presence. China, the United States, and India are the most influential contributors, reflecting global academic engagement with digital consumption trends. Citation analysis shows that studies on live streaming trust formation, chatbot adoption, autonomous delivery robots, and m-commerce drive the field’s recent evolution. The results highlight a clear shift from early research focused on website quality and online trust toward interactive, technology-driven, and psychologically informed models of consumer behavior. This study contributes to a deeper understanding of how digital technologies and socio-psychological mechanisms shape contemporary shopping practices and provides direction for future research in immersive and socially embedded digital commerce ecosystems.

Purpose This study investigates the factors influencing delivery workers’ willingness to collaborate with autonomous delivery robots (ADRs). As ADRs become more prevalent in Logistics 4.0 environments, understanding human–technology collaboration is critical for supporting both operational efficiency and decent work. Design/methodology/approach We draw from the technology acceptance model (TAM) and service robot acceptance model (sRAM) to develop a model and examine the impact of functional, social and relational factors on delivery workers’ willingness to collaborate with ADRs. A field survey with a sample size of 483 and an online survey with a sample size of 292 were conducted to test the relationships of interest. Findings The results indicate that perceived usefulness, social influence and anthropomorphism have a positive influence on willingness to collaborate with ADRs, with procedural fairness acting as a significant mediator. While the overall model holds across both samples, differences in the strength of relationships suggest that cultural context shapes how employees perceive and respond to ADRs. Originality/value This study contributes to the literature by extending TAM and sRAM to the logistics sector and providing a cross-cultural perspective on employee–ADR collaboration. It addresses a critical gap in logistics and supply chain research, providing practical approaches to technology integration that support decent work in the Logistics 4.0 era.

Sidewalk Autonomous Delivery Robots (SADRs) are vehicles that utilise pedestrian and cycle pathways to carry goods over the last mile. To overcome the typically short-range of SADRs, Mothership vans (MSs) that can carry SADRs from logistics centres to service areas have been proposed. In this paper, we develop analytical models to evaluate two routing strategies of MS and SADRs to service non-urgent, non-medical package delivery operations. The strategies introduced are: the ‘Series’ strategy, where each SADR is dropped off by the MS to service their own sub-region while being resupplied by repeated tours of the MS; and the ‘Parallel’ strategy, where SADRs are deployed simultaneously in one sub-region and the MS waits for their return before moving to the next deployment location. We undertake a cost and travel demand comparison between the MS strategies and conventional truck delivery. To minimise sidewalk travel, the ‘Series’ strategy should be used, rather than the ‘Parallel’ strategy, when there are more SADRs per MS than reloads per MS, and when there is only one SADR per MS. We determine closed-form solutions for the mean demand density range where an MS strategy may be both feasible and profitable, for a given SADR battery capacity and when compared to conventional truck delivery. Finally, we show in a numerical example that the optimal design of MS and SADRs is sensitive to the service area parameters of logistics sprawl and mean demand density, and relatively less sensitive to the unit moving and capital costs of the vehicles.

The importance of Last-Mile Delivery (LMD) in the current economy cannot be overstated, as it is the final and most crucial step in the supply chain between retailers and consumers. In major cities, absent intervention, urban LMD emissions are projected to rise by >30% by 2030 as e-commerce grows (top-100-city “do-nothing” baseline). Sustainable, innovative ground-based solutions for LMD, such as Electric Vehicles, autonomous delivery robots, parcel lockers, pick-up points, crowdsourcing, and freight-on-transit, can revolutionize urban logistics by reducing congestion and pollution while improving efficiency. However, developing these solutions presents challenges in Operations Research (OR), including problem modeling, optimization, and computations. This systematic review aims to provide an OR-centric synthesis of sustainable, ground-based LMD by (i) classifying these innovative solutions across problem types and methods, (ii) linking technique classes to sustainability goals (cost, emissions/energy, service, resilience, and equity), and (iii) identifying research gaps and promising hybrid designs. We support this synthesis by systematically screening 283 records (2010–2025) and analyzing 265 eligible studies. After the gap analysis, the researchers and practitioners are recommended to explore new combinations of innovative solutions for ground-based LMD. While they offer benefits, their complexity requires advanced solution algorithms and decision-making frameworks.

Innovation in the logistics industry has been steadily drawing attention to meet customer and company needs. Delivery services using unmanned aerial vehicles and delivery robots, as next generation delivery vehicles, have been actively investigated in both academia and industry. This paper introduces the new last-mile delivery station model that considers two practical issues. First, to hedge business risks (i.e. delivery suspension or inefficient routing) that can arise when employing homogeneous vehicles, companies are likely to adopt heterogeneous vehicles for their delivery service models. Only homogeneous vehicles are used in existing station models. Second, this study sets the weighted sum of costs and makespan as the objective function and explores the trade-off between the two measures. No previous studies consider the costs and makespan at the same time. A mathematical formulation is developed and a tailored heuristic algorithm is proposed to solve large-scale problems. To observe the proposed model in depth and verify the performance of the heuristic algorithm, numerous computational experiments are conducted for randomly generated instances. Sensitivity analyses are performed to derive managerial implications by manipulating key factors that may affect the performance of the model.

Individual-, task-, and technology-fit perspective of autonomous delivery robots confirmation and adoption in smart cities

Urban logistics are facing growing sustainability challenges, particularly in last-mile delivery operations, which contribute significantly to traffic congestion, emissions and operational inefficiencies. The COVID-19 pandemic further exposed the vulnerabilities in traditional logistics systems, accelerating interest in innovative solutions such as electric vehicles (EVs) and autonomous vehicles (AVs) for last-mile delivery. This study investigates the potential of EV and AV technologies to enhance sustainable urban logistics by integrating cleaner, smarter transportation into delivery networks. Drawing on survey data from logistics professionals and consumers in Italy, the findings highlight the key benefits of EV and AV adoption, including reduced emissions, improved delivery efficiency and increased resilience during global disruptions. Autonomous delivery robots and EV fleets can reduce labor costs, traffic congestion and carbon footprints while meeting evolving consumer demands. However, barriers such as limited charging infrastructure, range constraints, and technological readiness remain critical challenges. By addressing these issues and aligning EV and AV strategies with urban mobility policies, last-mile delivery systems can play a crucial role in advancing cleaner, more efficient and sustainable urban logistics. This research emphasizes the need for continued investment, policy support and public–private collaboration to fully realize the potential of EVs and AVs in reshaping future urban delivery systems.

Abstract Autonomous robots have the potential to assist us in various everyday tasks, including driving and package delivery. In the case of autonomous delivery robots, efficiency and safety need to be guaranteed in order for them to be deployed in real-world settings. The speed of the robot and its signaling distance emerged as crucial research focus points, greatly influencing the perceived efficiency and safety of delivery robots. This study therefore investigates the impact of different robot speeds on participants’ preferred signaling distance, perceived comfort, and safety during encounters. It also explores whether participants’ prior robot experience or pet ownership influences these factors based on the literature. We conducted an online study with 48 participants who watched videos of encounters with delivery robots at different speeds. Participants indicated when they would step aside from the robot, defining the signaling distance. An additional real-life interaction study involved 11 participants. As expected, results indicated that as robot speed increased, participants preferred a larger signaling distance and felt progressively less comfortable and safe with higher robot speeds. However, there were no significant findings related to pet ownership or robot experience. We provide a formula to calculate the most adequate distance for signaling depending on robot speed. In conclusion, careful consideration must be given to robot speed and signaling distance to ensure that participants can react in time and have comfortable, safe interactions with delivery robots in various contexts.

Autonomous delivery robots: differences in consumer´s acceptance in the USA, Europe, and Asia

Corrigendum to “Autonomous delivery robots: differences in consumer´s acceptance in the USA, Europe, and Asia” [Journal of Urban Mobility, Volume 7 (2025), 100110

This project introduces a solar-based wireless charging system for electric vehicles (EVs), designed to provide a contactless, automated, and sustainable energy transfer method using inductive coupling technology.The primary objective is to eliminate the need for physical connectors, reduce human intervention, and enable environmentally friendly EV charging using renewable energy sources.The system harnesses solar energy via a 12V, 10W polycrystalline solar panel, which charges a Li-Ion battery through a solar charge controller.The stored energy is then used to power a high-frequency wireless transmitter coil.When an EV is detected at the station using an IR sensor, the system automatically activates a servo motor that aligns the transmitter coil to maintain an optimal 10mm distance from the vehicle's receiver coil, ensuring maximum energy transfer efficiency.The receiver side of the EV includes a coil that captures the magnetic field, a rectifier to convert the received AC into DC, a 7805 voltage regulator, and a charging module for safe battery management.The energy is then stored in the EV's onboard battery and used to drive a DC motor, simulating EV movement.A voltage sensor continuously monitors the battery, and the status is displayed on a 16x2 I2C LCD screen, indicating states such as "Full", "Half", or "Not Charging".This system not only demonstrates wireless power transmission but also integrates automation, IoT-based monitoring potential, and renewable energy for a fully self-sustaining setup.Its compact and modular design makes it ideal for small-scale applications like autonomous delivery robots or prototype electric cars and opens the door to future scalability for full-size EVs.By eliminating wear-prone connectors and supporting real-time battery monitoring, the project addresses key challenges in modern EV charging.Furthermore, the implementation of smart alignment using servo motors, and the potential to extend the system with IoT platforms like Firebase/Blynk, makes it a next-generation charging model.

Combining autonomous delivery robots and traditional vehicles with public transportation infrastructure in last-mile distribution

This article examines the transformative impact of automation technologies on healthcare supply chains. It explores the technological infrastructure supporting these advancements, including RFID systems, IoT sensors, and blockchain implementations, and their contributions to inventory accuracy, product traceability, and operational efficiency. The article analyzes operational applications such as automated inventory management, robotic order fulfillment, autonomous delivery robots, and temperature monitoring systems that have demonstrated substantial benefits in healthcare settings. Data-driven supply chain optimization through predictive analytics, AI, e-procurement platforms, and automated financial processes is presented with evidence of significant improvements in forecast accuracy, inventory reduction, and cost savings. The article addresses implementation challenges including initial investment barriers, workforce adaptation, regulatory compliance, system integration, and risk management, while offering strategic considerations for successful deployment. Finally, future directions are explored, including emerging technologies, blockchain applications, sustainability considerations, and implications for healthcare policy and standardization.

The increasing demand for contactless urban logistics has driven the integration of autonomous delivery robots into real world operations. This study investigates the application of Deep Reinforcement Learning (DRL) to enhance robot navigation in complex urban environments, focusing on three advanced models: MODSRL, SOAR RL, and NavDP. MODSRL employs a multi objective framework to balance safety, efficiency, and success rate. SOAR RL is designed to handle high obstacle densities using anticipatory decision making. NavDP addresses the sim to real gap through domain adaptation and few shot learning. The models were trained and evaluated in simulation environments (CARLA, nuScenes, Argoverse) and validated using real world deployment data. Evaluation metrics included success rate, collision frequency, and energy efficiency. MODSRL achieved a 91.3% success rate with only 4.2% collision, outperforming baseline methods. SOAR RL showed robust performance in obstacle rich scenarios but highlighted a safety efficiency trade off. NavDP improved real world success rates from 50% to 80% with minimal adaptation data, demonstrating the feasibility of sim to real transfer. The results confirm the effectiveness of DRL in advancing autonomous delivery navigation. Integrating domain generalization, hybrid learning, and real time adaptation strategies will be essential to support large scale urban deployment. Future research should prioritize explainability, continual learning, and user centric navigation policies.

ABSTRACT The integration of robots into daily life is becoming increasingly common, with Autonomous Delivery Robots (ADR) gaining prominence as an eco-friendly delivery option. Understanding how individuals perceive ADR when encountered unintentionally is crucial, especially given the important role the public has concerning sharing public space which form the research objective of this study. Study outcome becomes relevant during planning and integration of ADR, facilitating seamless operation. Employing a mixed-method approach, including questionnaires and interviews, the analysis identified four themes related to pre-conception, attitude, robot design and behaviour, and traffic system integration. Study results highlight the perceived usefulness and generally accepting attitude toward the idea of ADR. While the majority of the public perceives sharing space with ADR positive, special attention is needed concerning the area of operation and considering the factors speed and size. Different perceptions of the ADR behaviour could be noted with the ADR adapting to participants being considered a necessity.