Robots at your doorstep: acceptance of near-future technologies for automated parcel delivery
Maher Said,
Spencer Aeschliman &
Amanda Stathopoulos
Scientific Reports volume 13, Article number: 18556 (2023) Cite this article
Article
Open access
Published: 29 October 2023
details
Abstract
The logistics and delivery industry is undergoing a technology-driven transformation, with robotics, drones, and autonomous vehicles expected to play a key role in meeting the growing challenges of last-mile delivery. To understand the public acceptability of automated parcel delivery options, this U.S. study explores customer preferences for four innovations: autonomous vehicles, aerial drones, sidewalk robots, and bipedal robots. We use an Integrated Nested Choice and Correlated Latent Variable (INCLV) model to reveal substitution effects among automated delivery modes in a sample of U.S. respondents. The study finds that acceptance of automated delivery modes is strongly tied to shipment price and time, underscoring the importance of careful planning and incentives to maximize the trialability of innovative logistics options. Older individuals and those with concerns about package handling exhibit a lower preference for automated modes, while individuals with higher education and technology affinity exhibit greater acceptance. These findings provide valuable insights for logistics companies and retailers looking to introduce automation technologies in their last-mile delivery operations, emphasizing the need to tailor marketing and communication strategies to meet customer preferences. Additionally, providing information about appropriate package handling by automated technologies may alleviate concerns and increase the acceptance of these modes among all customer groups.
Introduction
The last-mile logistics of freight distribution is a critical and challenging aspect of the supply chain. It is often the least efficient and most expensive stage1,2,3, accounting for up to 28% of delivery transportation costs, and is a considerable source of congestion and other externalities, such as pollution and noise2,3,4,5. These inefficiencies and negative impacts on citizen well-being make improving last-mile logistics a priority for businesses and policymakers alike2. Last-mile delivery challenges are likely to become more pressing due to factors such as increased e-commerce, rising consumer expectations, and the growth of ride-hailing competing for scarce curbspace6. Increased e-commerce and expectations for speed by consumers have been tied to the growing volume of parcels, smaller shipment sizes, and higher frequency of delivery trips7. Additionally, the rapid proliferation of ridehailing services presents a series of novel challenges to the effective management of curbside operations, including reduced available space for loading and parking for conventional urban delivery vehicles8,9,10. The global COVID-19 pandemic, the transformation of work, and a corresponding surge in e-commerce and delivery demands11,12 have also brought to light critical vulnerabilities in the supply chain, particularly related to cascading disruptions, lasting reliance on residential home-deliveries, and labor shortages13,14.
To address these challenges, firms such as Amazon, Walmart, Einride, Eliport, and UPS are exploring the deployment of autonomous freight delivery options15,16. The market size for automated delivery technologies, including autonomous vehicles (AVs), drones, and robots, is projected to reach $665 billion (about $2,000 per person in the US) by 2030, representing up to 20% of the package delivery industry17. These technologies offer numerous benefits, including greater efficiency, safety, and sustainability, and can help reduce human error. Smaller-scale automation technologies, such as drones and delivery robots, can offer more efficient, safer, cheaper, and sustainable solutions than traditional truck deliveries by, for example, bypassing congested streets and curbsides15,16. Yet, the shift towards automation can be fraught with negative effects on employment, safety, and security, and open questions about shipping performance, operational needs, and regulatory support18. However, by combining various automated delivery technologies, such as launching drones from autonomous vehicles, it is possible to mitigate these drawbacks and enhance efficiency compared to single technology operations15,16. Combined systems can address obstacles for ground delivery in urban environments and expand service in suburban and rural areas, where higher delivery costs are a significant challenge19,20,21. Thus, the (combined) deployment of robots, autonomous vehicles, and drones holds promise for addressing reliance on human drivers, managing curbside challenges, meeting growing demand and customer expectations, and preparing for future disruptions22,23,24.
The materialization of these benefits, however, is dependent on the adoption and public acceptability of these technologies25. While most acceptability research to date has focused on self-driving vehicle use among passengers19,20,21,26,27, limited insight exists on customer attitudes toward near-future automated delivery modes25,28,29,30,31,32,33. Both delivery performance attributes and attitudes toward automated services will play a role in their acceptance. Existing studies find that acceptance is linked to the perceived usefulness, convenience, and flexibility of automated parcel delivery28,31,32,33,34,35,36, all constructs related to cost and delivery speed. Studies also identify several adoption barriers, namely concerns over package handling, security, and privacy, as well as a lack of trust and familiarity25,28,29,30,31,32,33. The role of environmental concerns is not yet clear. Research is still needed to determine the impact of automation on supply chain sustainability. For example, recent simulation work suggests that while drones offer some energy efficiency improvements over traditional delivery, their per-day energy consumption would be comparable to battery electric vehicles on normal days and as high as diesel trucks on windy days37. Other work suggests that drone delivery leads to lowered CO2 emissions for logistics38. Additionally, the link between customers' perceptions of the environmental impact of delivery automation and their acceptance of these technologies remains ambiguous39. A general takeaway is that the acceptability of automated delivery options depends on a complex set of attitudinal, demographic, and market-based factors. A current challenge is that studies generally focus on a single technology (e.g. Figliozzi and Jennings40 and Hwang et al.41), while in reality customers will likely be faced with a portfolio of innovative options, and make trade-offs between several delivery attributes at once. A notable exception is Polydoropoulou et al.42, who study a multi-option decision context in a choice experiment in Greece, finding that respondents were generally unwilling to opt for innovative delivery modes over traditional ones due to cost, lack of familiarity, and infrastructure barriers. More research is needed to understand the relative importance of these different factors and how they interact, especially in light of multiple competing or complementary automation technologies, and different urban delivery contexts.
This paper aims to understand the potential acceptability of near-future automated delivery technologies among U.S. customers. The paper makes three main contributions to the literature. First, we study the adoption likelihood of a portfolio of multiple innovative delivery automation technologies in tandem. We design a Bayesian efficient choice experiment including traditional delivery along with four automated delivery innovations: (a) autonomous vehicles, (b) drones, (c) sidewalk robots, and (d) bipedal robots. This multi-technology setting enables us to examine patterns in customer acceptability and relationships among similar technologies. Second, we examine the role of shipping attributes, shipped item type, and socio-demographic and behavioral variables, thereby revealing the role of personal and shipping characteristics on the preference over future technologies. Third, we explore the impact of attitudes on delivery mode preferences. Specifically, we study the role of packaging preferences, environmental awareness, and affinity towards technology on the acceptability of automated delivery technologies.
Using an Integrated Nested Choice and Correlated Latent Variable (INCLV) model, we account simultaneously for dependence across alternative technologies and for correlated attitudinal latent variables, uncovering critical factors that influence customer acceptance of automated parcel delivery innovation. The model reveals the importance of attributes like shipment price, time performance, customer-specific attributes like age and education, and attitudes such as concerns about package handling and affinity towards technology. Scenario simulation is applied to examine business strategies and contextual events on the preference for automation. Our findings provide valuable insight and recommendations for analysts, policymakers, and practitioners, including retailers and couriers, seeking to successfully introduce these technologies to the market while mitigating their negative impacts on customers, society, and the environment.
Spencer Aeschliman &
Amanda Stathopoulos
Scientific Reports volume 13, Article number: 18556 (2023) Cite this article
Article
Open access
Published: 29 October 2023
details
Abstract
The logistics and delivery industry is undergoing a technology-driven transformation, with robotics, drones, and autonomous vehicles expected to play a key role in meeting the growing challenges of last-mile delivery. To understand the public acceptability of automated parcel delivery options, this U.S. study explores customer preferences for four innovations: autonomous vehicles, aerial drones, sidewalk robots, and bipedal robots. We use an Integrated Nested Choice and Correlated Latent Variable (INCLV) model to reveal substitution effects among automated delivery modes in a sample of U.S. respondents. The study finds that acceptance of automated delivery modes is strongly tied to shipment price and time, underscoring the importance of careful planning and incentives to maximize the trialability of innovative logistics options. Older individuals and those with concerns about package handling exhibit a lower preference for automated modes, while individuals with higher education and technology affinity exhibit greater acceptance. These findings provide valuable insights for logistics companies and retailers looking to introduce automation technologies in their last-mile delivery operations, emphasizing the need to tailor marketing and communication strategies to meet customer preferences. Additionally, providing information about appropriate package handling by automated technologies may alleviate concerns and increase the acceptance of these modes among all customer groups.
Introduction
The last-mile logistics of freight distribution is a critical and challenging aspect of the supply chain. It is often the least efficient and most expensive stage1,2,3, accounting for up to 28% of delivery transportation costs, and is a considerable source of congestion and other externalities, such as pollution and noise2,3,4,5. These inefficiencies and negative impacts on citizen well-being make improving last-mile logistics a priority for businesses and policymakers alike2. Last-mile delivery challenges are likely to become more pressing due to factors such as increased e-commerce, rising consumer expectations, and the growth of ride-hailing competing for scarce curbspace6. Increased e-commerce and expectations for speed by consumers have been tied to the growing volume of parcels, smaller shipment sizes, and higher frequency of delivery trips7. Additionally, the rapid proliferation of ridehailing services presents a series of novel challenges to the effective management of curbside operations, including reduced available space for loading and parking for conventional urban delivery vehicles8,9,10. The global COVID-19 pandemic, the transformation of work, and a corresponding surge in e-commerce and delivery demands11,12 have also brought to light critical vulnerabilities in the supply chain, particularly related to cascading disruptions, lasting reliance on residential home-deliveries, and labor shortages13,14.
To address these challenges, firms such as Amazon, Walmart, Einride, Eliport, and UPS are exploring the deployment of autonomous freight delivery options15,16. The market size for automated delivery technologies, including autonomous vehicles (AVs), drones, and robots, is projected to reach $665 billion (about $2,000 per person in the US) by 2030, representing up to 20% of the package delivery industry17. These technologies offer numerous benefits, including greater efficiency, safety, and sustainability, and can help reduce human error. Smaller-scale automation technologies, such as drones and delivery robots, can offer more efficient, safer, cheaper, and sustainable solutions than traditional truck deliveries by, for example, bypassing congested streets and curbsides15,16. Yet, the shift towards automation can be fraught with negative effects on employment, safety, and security, and open questions about shipping performance, operational needs, and regulatory support18. However, by combining various automated delivery technologies, such as launching drones from autonomous vehicles, it is possible to mitigate these drawbacks and enhance efficiency compared to single technology operations15,16. Combined systems can address obstacles for ground delivery in urban environments and expand service in suburban and rural areas, where higher delivery costs are a significant challenge19,20,21. Thus, the (combined) deployment of robots, autonomous vehicles, and drones holds promise for addressing reliance on human drivers, managing curbside challenges, meeting growing demand and customer expectations, and preparing for future disruptions22,23,24.
The materialization of these benefits, however, is dependent on the adoption and public acceptability of these technologies25. While most acceptability research to date has focused on self-driving vehicle use among passengers19,20,21,26,27, limited insight exists on customer attitudes toward near-future automated delivery modes25,28,29,30,31,32,33. Both delivery performance attributes and attitudes toward automated services will play a role in their acceptance. Existing studies find that acceptance is linked to the perceived usefulness, convenience, and flexibility of automated parcel delivery28,31,32,33,34,35,36, all constructs related to cost and delivery speed. Studies also identify several adoption barriers, namely concerns over package handling, security, and privacy, as well as a lack of trust and familiarity25,28,29,30,31,32,33. The role of environmental concerns is not yet clear. Research is still needed to determine the impact of automation on supply chain sustainability. For example, recent simulation work suggests that while drones offer some energy efficiency improvements over traditional delivery, their per-day energy consumption would be comparable to battery electric vehicles on normal days and as high as diesel trucks on windy days37. Other work suggests that drone delivery leads to lowered CO2 emissions for logistics38. Additionally, the link between customers' perceptions of the environmental impact of delivery automation and their acceptance of these technologies remains ambiguous39. A general takeaway is that the acceptability of automated delivery options depends on a complex set of attitudinal, demographic, and market-based factors. A current challenge is that studies generally focus on a single technology (e.g. Figliozzi and Jennings40 and Hwang et al.41), while in reality customers will likely be faced with a portfolio of innovative options, and make trade-offs between several delivery attributes at once. A notable exception is Polydoropoulou et al.42, who study a multi-option decision context in a choice experiment in Greece, finding that respondents were generally unwilling to opt for innovative delivery modes over traditional ones due to cost, lack of familiarity, and infrastructure barriers. More research is needed to understand the relative importance of these different factors and how they interact, especially in light of multiple competing or complementary automation technologies, and different urban delivery contexts.
This paper aims to understand the potential acceptability of near-future automated delivery technologies among U.S. customers. The paper makes three main contributions to the literature. First, we study the adoption likelihood of a portfolio of multiple innovative delivery automation technologies in tandem. We design a Bayesian efficient choice experiment including traditional delivery along with four automated delivery innovations: (a) autonomous vehicles, (b) drones, (c) sidewalk robots, and (d) bipedal robots. This multi-technology setting enables us to examine patterns in customer acceptability and relationships among similar technologies. Second, we examine the role of shipping attributes, shipped item type, and socio-demographic and behavioral variables, thereby revealing the role of personal and shipping characteristics on the preference over future technologies. Third, we explore the impact of attitudes on delivery mode preferences. Specifically, we study the role of packaging preferences, environmental awareness, and affinity towards technology on the acceptability of automated delivery technologies.
Using an Integrated Nested Choice and Correlated Latent Variable (INCLV) model, we account simultaneously for dependence across alternative technologies and for correlated attitudinal latent variables, uncovering critical factors that influence customer acceptance of automated parcel delivery innovation. The model reveals the importance of attributes like shipment price, time performance, customer-specific attributes like age and education, and attitudes such as concerns about package handling and affinity towards technology. Scenario simulation is applied to examine business strategies and contextual events on the preference for automation. Our findings provide valuable insight and recommendations for analysts, policymakers, and practitioners, including retailers and couriers, seeking to successfully introduce these technologies to the market while mitigating their negative impacts on customers, society, and the environment.
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