Last-mile deliveries using drones and robots have recently emerged as alternatives to deliveries using conventional trucks. Because of the newness of the topic, there has been no synthesized summary of the acceptance of the emerging delivery modes, although the importance of the topic has increased with the growth of home deliveries. To fill the knowledge gap, this research systematically reviewed the literature on the willingness to accept drone and robot deliveries using the two largest publication databases: Web of Science and SCOPUS. A total of 46 academic studies were reviewed with respect to their trends, study scope (i.e., type of mode, type of products, modeling framework, and background theories), and factors that positively and negatively affect the willingness. Based on the review, knowledge gaps and the corresponding future research directions were derived: (a) robot-related investigations have been understudied compared to drones; (b) research from the shipper perspective is necessary; (c) transportation and logistics planning-related factors (e.g., socio-economic factors, trip and shipment characteristics) should be investigated; (d) an investigation of users’ behaviors based on actual usage (rather than relying solely on hypothetical scenarios) is necessary; (e) regulatory aspects should be taken into account; and (f) the heterogeneity of products needs to be taken into consideration.

This paper examines behavioral change patterns of food consumption through three channels—dine-in at restaurant, food delivery, and food take-out—during and after the recent COVID-19 pandemic. The unique feature of the behaviors during the COVID-19 pandemic that set them apart from those during previous pandemics was the rise of online shopping. Thus, it is important to understand the new consumer behaviors for the three food consumption channels over time (before, during, and after the pandemic), for example, how they were affected by the pandemic, what dominant change patterns were, and how the intertwined relationship between the channels evolved. This study develops a latent class model using survey data collected by the Korea Rural Economic Institute in 2021 to jointly investigate the behavioral change patterns of dine-in at restaurant, food delivery, and food take-out. The results showed that there existed six latent groups, namely, 1) “status quo maintainers,” 2) “dine-in minimizers,” 3) “mild online adopters,” 4) “temporary shifters,” 5) “food expenditure cutters,” and 6) “strong online adopters.” The results highlighted that the behavioral change pattern varied by the persistence of the changes and the consumption channel choice. The socio-economic and demographic characteristics of each group were also discussed.

Driven by the green and low-carbon transformation of urban logistics, the integration of crowdsourced delivery and green transportation is considered an important pathway to achieving sustainable last-mile delivery. This study focuses on urban crowdsourced delivery using cargo bikes and develops an extended behavioral model based on the Theory of Planned Behavior (TPB). The model systematically examines the key factors influencing the public’s behavioral intention (BI) to participate as crowdshippers. While retaining the core structure of TPB, the model incorporates external variables—perceived risk (PR), policy support (PS), and infrastructure conditions (IC)—to improve its explanatory power and applicability to real-world delivery scenarios. A questionnaire survey was conducted in South Korea, yielding 600 valid responses. The results indicate that usage attitude and perceived behavioral control exert significant positive effects on BI. PR has a significant negative effect on both attitude and BI. PS indirectly enhances BI by improving attitudes, whereas IC primarily influences BI by strengthening the public’s sense of control. This study not only expands the theoretical explanatory power of the TPB model in the context of green crowdsourced delivery but also provides empirical evidence for policymakers and platform operators.

Crowdsourced deliveries have expanded globally, driven in part by an increase in online shopping demand. This study examines behavioral heterogeneity in crowdsourced delivery participation in South Korea using latent class analysis (LCA), with a particular focus on the differences between (a) individuals who are willing to participate but have no prior experience and (b) those who have actual experience working as crowdshippers. While prior studies have largely focused on stated willingness or intention to participate in crowdshipping, little is known about how these stated expectations differ from the actual participation patterns of experienced workers. The analysis identified three classes among prospective crowdshippers—Work–Life Balancers, Occasional Part-Timers, and Ambitious Crowdshippers—distinguished primarily by expected earnings and intended work intensity. Two classes emerged among experienced crowdshippers—Intermittent Gig Practitioners and Dedicated Crowdshippers—differentiated by realized earnings, delivery frequency, and vehicle utilization. The experienced sample was more male-dominated and younger, while the prospective sample was more gender-balanced and older. The findings offer practical implications for gig platforms and policymakers, including segment-specific scheduling, compensation design, and policy frameworks addressing worker safety and public-transport integration.