# Customer’s Adoption Intentions toward Autonomous Delivery Vehicle Services: Extending DOI Theory with Social Awkwardness and Use Experience

Authors: Miaojia Lu; Chengyuan Huang; Ran Wang; Hao Li
Journal: Journal of Advanced Transportation
 (2023)

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## Abstract

The high demand and acute timeliness that characterizes instant delivery entail the challenges of high labor costs and an increase in courier traffic accidents. Autonomous delivery vehicles (ADVs) may serve as a key solution, with their attendant reduced labor input and higher efficiency. Customers play a key role in the successful implementation of ADVs on a large scale. However, understanding the factors that affect customers’ intentions to use ADVs is still limited. Compared to autonomous driving, ADV customers are ultimately not the real users, who only are served by ADVs during the last leg of a trip. On account of this, the Technology Acceptance Model (TAM) may not be well-fitted for explaining the dynamics involved in ADV adoption. Within the context of ADVs, our study identified influencing factors that have not been captured by prior studies. This study incorporates infection risk, use experience, and social awkwardness into the Diffusion of Innovation (DOI) theory to explore customers’ intentions to use ADVs. Data from 691 survey respondents were collected to validate the research design. The results demonstrate that compatibility, social influence, infection risk, green image, social awkwardness, and use experience all have a significantly positive impact on customers’ intentions to adopt ADV services, while complexity and perceived risk both exhibited a negative impact. But no effect could be found for relative advantage, which may be because of the fact that customers only need ADVs to meet their delivery demand. This study contributes to understanding customers’ adoption intentions toward ADVs, informing policymakers in formulating ADV regulations and standards, and promoting the large-scale application of ADVs in instant delivery services.


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## Body

## 1. Introduction

With the acceleration of the pace of life, combined with the ease and convenience of online shopping and increases in income, more and more people have chosen to use instant delivery services [1]. Instant delivery orders include not only meals but also fresh food, flowers, and other products such as medicine and important documents. The scale of instant delivery reached 24.37 billion orders in China in the year 2020, a ten-fold increase since 2015 [2]. In particular, the frequency of customers shopping online for fresh food grew 71.2% during the COVID-19 pandemic [3]. The increasing order volume, the importance of freshness, and the demand for timeliness have consequently led to more courier traffic accidents related to instant delivery. In August 2020, approximately 12 thousand traffic violations related to instant delivery occurred in Shenzhen, China [4]. In addition, the working-age population in China has been declining since 2013 and nearly 25% of couriers work more than 12 hours a day [5]. The concomitant rising human labor costs account for 50 to 60% of the total cost of last-mile delivery [6]. Therefore, many startup companies and Internet companies such as Amazon, Nuro, Jingdong, and Meituan [7] have begun to explore innovative delivery technologies such as Autonomous Delivery Vehicles (ADVs), which are self-driving vehicles that travel on roads (as distinct from other autonomous robots that travel on sidewalks) to deliver goods. ADVs can reduce labor costs and accident rates, as well as increase delivery efficiency, as one ADV can replace two couriers [8]. In the United States, Europe, and China, many regulations have been enacted to support the public road operation of ADVs, with corresponding operation requirements [7, 9, 10]. Due to the often intensely challenging conditions that have arisen from the COVID-19 pandemic, customers, businesses, and governments have switched from being cautious beta testers to becoming eager early adopters [11]. The novelty, convenience, efficiency, and contactless delivery associated with ADVs have led customers to develop high expectations for this technology [12].However, many customers are still skeptical and cautious about the viability of ADVs. Accidents involving autonomous vehicles are worrisome to the public. In October 2021, a traffic accident involving an ADV and a private car took place in Beijing, with the ADV found to be primarily responsible for the accident [13]. On August 14, 2021, an entrepreneur died while driving NIO’s ES8 SUV when the automatic cruise system was turned on. In addition, ADVs may break down on the way or lose customers’ goods [1, 14]. Moreover, the massive amount of data required for operating autonomous vehicles has raised concerns about privacy. Some have expressed alarm about the risk of hackers infiltrating ADV systems and controlling ADVs to attack pedestrians [15]. It has been argued that the biggest barrier to widespread adoption of ADVs is not technological capability but social acceptance. Customers play a key role in the successful implementation of ADVs on a large scale [16]. Therefore, factors that may have significant influences on customers’ adoption intentions need to be explored to expand ADV acceptance as well as to integrate their consideration in the development of company logistics and government regulations. The aim of this study is to investigate factors that significantly influence customers’ intentions to use ADV, drawing upon behavior theories.Diffusion of Innovation (DOI) theory is applied in this study because of the following reasons: (1) it can be used to explore the characteristics of personal innovativeness and (2) this theory is suitable for examining the customers, who are not the real users of ADVs [17] but are served by ADVs during the last leg of a trip. Our study also identifies factors affecting adoption that have not been captured in previous studies. ADVs have played an important role in the delivery of daily necessities and medicine during the COVID-19 pandemic because of their ability to facilitate contactless delivery. Given this key characteristic of ADVs and the ongoing public health context in which ADVs operate, infection risk is considered in this study. Because ADVs can bypass person-to-person contact, another factor, i.e., social awkwardness, is also considered. Use experience of ADVs is also incorporated into the research design. This study is the first to analyze the adoption intention of customers with regard to ADVs based on DOI theory. This study also contributes to existing research by considering infection risk, use experience, and social awkwardness in relation to ADV adoption intentions. Gaining a more comprehensive understanding of the elements that influence adoption intentions can aid in advancing large-scale application of ADV technology. In the meantime, legislators are tackling how to properly regulate the use of ADVs. The findings of this study as they relate to customer behaviors can also assist in the formulation of sound legislation.The remainder of the study is organized as follows: Section2 reviews previous studies about the acceptance of autonomous vehicles (AVs), ADVs, unmanned aerial vehicles (UAVs), and autonomous robots, as well as studies using DOI theory. Section 3 details the constructs and hypotheses in the proposed theoretical model. This section also describes the questionnaire designed to investigate potential customers’ intentions and the methods used to analyze the collected data. Section 4 presents the descriptive statistics and discusses the results of the model assessments. Section 5 recaps some key contributions of this study, indicates limitations of the study, and suggests directions for future research.


## 2. Literature Review

### 2.1. Factors and Theories Related to Autonomous Vehicles Acceptance and Intentions

Here, we review previous studies related to acceptance and intentions toward autonomous vehicles, with particular attention to autonomous driving and autonomous delivery. Theoretical models used in previous research are shown in Table1.Table 1
Theory models applied in previous studies of autonomous driving and autonomous delivery.
TheoryTheory descriptionReferencesTechnology acceptance model (TAM)TAM is used to explain how perceived usefulness and perceived ease of use affect personal behaviors [18]AVs: Wu et al. [18]; Liu et al. [19]; Dirsehan and Can [20]; Chan and Lee [21]; Li et al. [22]UAVs: Leon et al. [23]; Michels et al. [24]; Choe et al. [25]Autonomous robots: Abrams et al. [26]Unified theory of acceptance and use of technology model (UTAUT)UTAUT was developed from TAM, TPB, and other models to explore user acceptance and includes four constructs (i.e., performance expectancy, effort expectancy, social influence, and facilitating conditions) [21]AVs: Chan and Lee [21]; Adnan et al. [27]; Hewitt et al. [28]; Meyer-Waaeden and Cloarec [29]; Zhang et al. [30]UAVs: Kim and Chung [31]; Ganjipour and Edrisi [32]ADVs: Kapser and Abdelrahman [16]; Kapser et al. [33]Cognitive appraisal theory (CAT)CAT holds that emotions are aroused due to the individual’s evaluation of the stimulus, which further determines the behavioral response [34]AVs: Ribeiro et al. [34]Motivated consumer innovativeness (MCI)MCI emphasizes the effect of motivation on customers’ behaviors. Motivation is classified into four main types, namely, functional motivation, hedonical motivation, cognitive motivation, and social motivation [1]AVs: Hewitt et al. [28]UAVs: Mathew et al. [1]Theory of planned behavior (TPB)TPB is designed to predict customers’ behavioral intentions according to three main components, namely, attitude, subjective norms, and perceived behavioral control [35]UAVs: Ramadan et al. [35]Diffusion of innovation theory (DOI)DOI is used to explain why customers adopt new technologies and includes five constructs (i.e., relative advantage, complexity, compatibility, observability, and trialability) [14]UAVs: Yoo et al. [14]


#### 2.1.1. Autonomous Driving

Autonomous driving refers to cars capable of sensing its environment and operating without human involvement. Many researchers have investigated the acceptance of autonomous driving. Some studies have been conducted based on behavioral theories [18, 21, 28, 34], while other studies have investigated particular factors to explain user acceptance [36–41].Perceived usefulness and perceived ease of use are both important factors proven to affect user acceptance and intention [18, 19, 42]. Many researchers add other factors such as green image [18] and perceived trust [19, 22] into the Technology Acceptance Model (TAM) for further analytical refinement. Using the Unified Theory of Acceptance and Use of Technology (UTAUT), Adnan et al. [27], Hewitt et al. [28], and Meyer_Waaeden and Cloarec [29] all investigated the importance of performance expectancy, effort expectancy, trust, and social influence in AV adoption. Liu et al. [36] studied customers’ willingness to pay for self-driving vehicles and they found that more than 50% of respondents were willing to pay extra, although the amounts are different. In addition, Berrada et al. [43] divided customers into five clusters including conservatives, skeptics, late adopters, early adopters, and explorers and found more than 60% of respondents were willing to use autonomous vehicles. Similar to this study, Bernhard et al. [41] conducted two subsurveys to investigate the effects of autonomous minibus using experience and found that respondents who already had experience using autonomous minibuses were more willing to accept the technology because respondent confidence levels often increase with such direct experience.


#### 2.1.2. Autonomous Delivery

Autonomous delivery includes not only ADVs but also UAVs and autonomous robots. UAVs, also commonly referred to as drones, are flying robots that can be remotely controlled or autonomously flown to deliver more light-weight, compact goods. Autonomous robots refer to robots that also operate and travel upon sidewalks, usually complemented by a mothership van that can transport the robots close to the delivery zone [7].(1) UAVs. Combining TAM and DOI theory, Yoo et al. [14] proposed a model to investigate the factors that affect customer perceptions of UAVs and analyze the effect of delivery area. The researchers found that two major perceived advantages of UAVs are their speed and environmental friendliness. In their study, perceived risk was also incorporated and defined to include performance risk, delivery risk, and privacy risk. Based on customer motivated innovativeness, Hwang et al. [44] applied consumer innovativeness theory to study customer attitudes and adoption intentions toward UAV food delivery. The results showed that attitude played an important role in intention to use and intention to pay more. Mathew et al. [1] explored customer intention to adopt UAVs for food delivery in India. Perceived risk and green image were found to be significant predictors of customer attitude and intention. Aydin [45] investigated public knowledge and acceptance of UAVs in specific application areas, for example, commercial and public safety applications. However, the results showed that UAVs were not generally accepted except for in the arenas of public safety and scientific research. Need for human interaction was added into the UTAUT2 model in Ganjipour and Edrisi’s study and was found to have a negative effect on adoption intentions toward delivery drones [32]. Other studies used the Knowledge, Attitude, and Practice Model or other methods to explore customer acceptance [45–47].(2) ADVs. Kapser and Abdelrahman [16] studied customer intention toward ADVs and analyzed various key behavioral components (e.g., performance expectancy, effort expectancy, perceived risk, and price sensitivity) based on UTAUT theory. The results showed that respondents were neutral on the use of ADVs as a delivery alternative while price sensitivity exerted a great influence on customer intention. Building on this research, Kapser et al. [33] integrated trust and innovativeness into their acceptance model while factors shown to be insignificant (i.e., effort expectancy) were removed. In addition, a moderating factor, i.e., gender, was applied and they found that social influence, perceived risk, and hedonic motivation had particularly significant effects on the use intentions of women customers.(3) Autonomous robots. Pani et al. [11] analyzed customers’ willingness to pay more for autonomous robots by dividing customer groups based on latent class analysis. 61.28% of respondents showed a willingness to pay more, with one group willing to pay up to $2.92 (average) or between 0-$6 for the service. Abrams et al. [26] proposed a new concept, “existence acceptance” to extend TAM. Existence acceptance refers to pedestrians’ or other traffic respondents’ acceptance of sharing the same roads with autonomous robots.


#### 2.1.3. Theoretical Background

In previous studies of autonomous driving and autonomous delivery, TAM and UTAUT are the most widely used behavioral theories for exploring new technology adoption (see Table1). However, TAM and UTAUT are not suitable for explaining customer adoption intention toward ADV services because customers are not the real users of ADVs. The logistics practitioners are the direct users of ADV technology, not the customers, who do not need to be familiar with the technology [17]. The perceived usefulness and perceived ease of use are not the main concerns of customers when deciding whether to adopt ADVs in instant delivery. Thus, TAM and UTAUT may be not well suited for examining and characterizing ADV adoption intention. Another approach that could be considered to investigate technology adoption is Cognitive Appraisal Theory (CAT). This theory holds that emotion is a kind of individual mental state that is generated when evaluating relevant information and that influences individual behavioral motivation. One advantage of CAT is that it can analyze customer behavior through a wide range of emotional experiences. But at the same time, too many cognitive evaluation dimensions make it difficult for researchers to capture quick and unconscious evaluation results. This theory also does not answer the question that whether any kind of emotion can be acquired without an evaluation system [48]. Motivated consumer innovativeness (MCI) theory models motivation according to four main dimensions, namely, (1) functional, (2) hedonical, (3) cognitive, and (4) social [1]. In MCI, social motivation refers to customers wanting to improve their image by purchasing innovative products/services (rather than referring to customers imitating the purchasing decisions of those in their social networks). Theory of Planned Behavior (TPB), which assumes that individuals consciously control their actions, has been used to study a wide range of health-related behaviors and learning behaviors. It includes three main variables, i.e., attitude, subjective norms, and perceived behavior control. Although this theory has been applied by many researchers, there is a lack of agreement about its main variables, raising doubts about the accuracy of results from research based on this theory. In addition, there are many behaviors that may not be explained by the main variables proffered for this theory [49]. As an alternative, DOI theory explains how innovative technologies and ideas spread through society over time and is also applied in communication and information systems research to help explain why customers adopt innovative technologies [14, 50]. Given its particular focus on innovative technology adoption, DOI theory is selected as the behavior theory of this study of ADVs.


### 2.2. DOI Theory Models of User Perception toward a New Technology

DOI theory as presented by Rogers [51] employs more specific innovation characteristics, which are not typically found in behavioral theories in order to explain the adoption of new technologies or products. According to Rogers, “the innovation is an idea, practice, or object that is perceived as new by an individual or other unit of adoption.” Rogers also contends that innovations are diffused through various channels and within a particular social system. Rogers [52] presented five innovation characteristics, namely, (1) relative advantage, (2) complexity, (3) compatibility, (4) observability, and (5) trialability. Relative advantage is defined as “the degree to which an innovation is perceived as better than the idea it supersedes.” Complexity is defined as “the degree to which an innovation is perceived as difficult to understand and use.” Compatibility involves “the degree to which a service is perceived as consistent with users’ existing values, beliefs, habits, and present and previous experiences.” Observability is defined as “the degree to which the innovation can be visible to others.” Finally, trialability is defined as “the extent to which the innovation can be trialed, modified, and experienced before the adoption” [53]. However, previous studies found that only three characteristics—relative advantage, complexity, and compatibility—impact innovation adoption [14, 50, 54–62]. DOI theory has proven to be an effective theory for exploring customers’ acceptance of a new technology. Constructs used in the original formulation of DOI theory and added in other subsequent studies are reviewed in Table 2. As indicated in Table 2, relative advantage, complexity and compatibility are the most frequently applied constructs of DOI. Social influence has moreover been found to promote the adoption of new technologies [60]. Green image and perceived risk have also exhibited positive effects on adoption intention [1, 14, 18].Table 2
Constructs used in DOI.
ConstructMain findingsReferencesRelative advantageRelative advantage can promote adoption intentionDuan et al. [62]; Ooi et al. [55]; Wang et al. [56]; Chung [57]; Kang et al. [58]; Hamad et al. [63]; Yoo et al. [14]; Min et al. [60]ComplexityComplexity is a barrier to adoption intentionDuan et al. [62]; Wang et al. [56]; Chung [57]; Yoo et al. [14]; Min et al. [60]CompatibilityCompatibility can promote adoption intentionDuan et al. [62]; Wang et al. [56]; Chung [57]; Kang et al. [58]; Hamad et al. [63] Min et al. [60]; Yoo et al. [14]ObservabilityObservability can promote adoption intentionDuan et al. [62]; Chung [57]TrialabilityTrialability can promote adoption intentionDuan et al. [62]; Chung [57]; Hamad et al. [63]Social influenceSocial influence can promote adoption intentionMin et al. [60]Green image/environmental concernGreen image/environmental concern can promote adoption intentionYoo et al. [14]Perceived riskPerceived risk is a barrier to adoption intentionYoo et al. [14]


### 2.3. Research Gap

A review of the literature in the autonomous driving and autonomous delivery fields revealed only two studies [16, 33] that appear to explicitly concentrate on the acceptance of ADVs. In contrast with these two studies, which were based on UTAUT theory, the DOI theory employed in this study focuses more on how innovative technologies spread in society rather than on the use of those technologies. We also incorporate some new constructs that may affect the ADV adoption intention of end customers, such as use experience and infection risk. As ADVs have become regularly available as a delivery option in Anting, Shanghai, some respondents to our survey have already used ADVs, with their use experiences increasing their confidence in ADV services. The risk of COVID-19 infection caused by person-to-person contact is likely to encourage customers to more readily adopt ADV supported services due to the contactless delivery that ADVs enables. This study also considers the impacts of social awkwardness on ADV adoption intention as ADVs can bypass direct communications with couriers and may become more popular among customers who are not so adept at socializing. To the best of our knowledge, this study is the first to investigate customer adoption intentions toward ADVs in China by adding the influences of use experience, infection risk, and social awkwardness to DOI theory.


## 2.1. Factors and Theories Related to Autonomous Vehicles Acceptance and Intentions

Here, we review previous studies related to acceptance and intentions toward autonomous vehicles, with particular attention to autonomous driving and autonomous delivery. Theoretical models used in previous research are shown in Table1.Table 1
Theory models applied in previous studies of autonomous driving and autonomous delivery.
TheoryTheory descriptionReferencesTechnology acceptance model (TAM)TAM is used to explain how perceived usefulness and perceived ease of use affect personal behaviors [18]AVs: Wu et al. [18]; Liu et al. [19]; Dirsehan and Can [20]; Chan and Lee [21]; Li et al. [22]UAVs: Leon et al. [23]; Michels et al. [24]; Choe et al. [25]Autonomous robots: Abrams et al. [26]Unified theory of acceptance and use of technology model (UTAUT)UTAUT was developed from TAM, TPB, and other models to explore user acceptance and includes four constructs (i.e., performance expectancy, effort expectancy, social influence, and facilitating conditions) [21]AVs: Chan and Lee [21]; Adnan et al. [27]; Hewitt et al. [28]; Meyer-Waaeden and Cloarec [29]; Zhang et al. [30]UAVs: Kim and Chung [31]; Ganjipour and Edrisi [32]ADVs: Kapser and Abdelrahman [16]; Kapser et al. [33]Cognitive appraisal theory (CAT)CAT holds that emotions are aroused due to the individual’s evaluation of the stimulus, which further determines the behavioral response [34]AVs: Ribeiro et al. [34]Motivated consumer innovativeness (MCI)MCI emphasizes the effect of motivation on customers’ behaviors. Motivation is classified into four main types, namely, functional motivation, hedonical motivation, cognitive motivation, and social motivation [1]AVs: Hewitt et al. [28]UAVs: Mathew et al. [1]Theory of planned behavior (TPB)TPB is designed to predict customers’ behavioral intentions according to three main components, namely, attitude, subjective norms, and perceived behavioral control [35]UAVs: Ramadan et al. [35]Diffusion of innovation theory (DOI)DOI is used to explain why customers adopt new technologies and includes five constructs (i.e., relative advantage, complexity, compatibility, observability, and trialability) [14]UAVs: Yoo et al. [14]


### 2.1.1. Autonomous Driving

Autonomous driving refers to cars capable of sensing its environment and operating without human involvement. Many researchers have investigated the acceptance of autonomous driving. Some studies have been conducted based on behavioral theories [18, 21, 28, 34], while other studies have investigated particular factors to explain user acceptance [36–41].Perceived usefulness and perceived ease of use are both important factors proven to affect user acceptance and intention [18, 19, 42]. Many researchers add other factors such as green image [18] and perceived trust [19, 22] into the Technology Acceptance Model (TAM) for further analytical refinement. Using the Unified Theory of Acceptance and Use of Technology (UTAUT), Adnan et al. [27], Hewitt et al. [28], and Meyer_Waaeden and Cloarec [29] all investigated the importance of performance expectancy, effort expectancy, trust, and social influence in AV adoption. Liu et al. [36] studied customers’ willingness to pay for self-driving vehicles and they found that more than 50% of respondents were willing to pay extra, although the amounts are different. In addition, Berrada et al. [43] divided customers into five clusters including conservatives, skeptics, late adopters, early adopters, and explorers and found more than 60% of respondents were willing to use autonomous vehicles. Similar to this study, Bernhard et al. [41] conducted two subsurveys to investigate the effects of autonomous minibus using experience and found that respondents who already had experience using autonomous minibuses were more willing to accept the technology because respondent confidence levels often increase with such direct experience.


### 2.1.2. Autonomous Delivery

Autonomous delivery includes not only ADVs but also UAVs and autonomous robots. UAVs, also commonly referred to as drones, are flying robots that can be remotely controlled or autonomously flown to deliver more light-weight, compact goods. Autonomous robots refer to robots that also operate and travel upon sidewalks, usually complemented by a mothership van that can transport the robots close to the delivery zone [7].(1) UAVs. Combining TAM and DOI theory, Yoo et al. [14] proposed a model to investigate the factors that affect customer perceptions of UAVs and analyze the effect of delivery area. The researchers found that two major perceived advantages of UAVs are their speed and environmental friendliness. In their study, perceived risk was also incorporated and defined to include performance risk, delivery risk, and privacy risk. Based on customer motivated innovativeness, Hwang et al. [44] applied consumer innovativeness theory to study customer attitudes and adoption intentions toward UAV food delivery. The results showed that attitude played an important role in intention to use and intention to pay more. Mathew et al. [1] explored customer intention to adopt UAVs for food delivery in India. Perceived risk and green image were found to be significant predictors of customer attitude and intention. Aydin [45] investigated public knowledge and acceptance of UAVs in specific application areas, for example, commercial and public safety applications. However, the results showed that UAVs were not generally accepted except for in the arenas of public safety and scientific research. Need for human interaction was added into the UTAUT2 model in Ganjipour and Edrisi’s study and was found to have a negative effect on adoption intentions toward delivery drones [32]. Other studies used the Knowledge, Attitude, and Practice Model or other methods to explore customer acceptance [45–47].(2) ADVs. Kapser and Abdelrahman [16] studied customer intention toward ADVs and analyzed various key behavioral components (e.g., performance expectancy, effort expectancy, perceived risk, and price sensitivity) based on UTAUT theory. The results showed that respondents were neutral on the use of ADVs as a delivery alternative while price sensitivity exerted a great influence on customer intention. Building on this research, Kapser et al. [33] integrated trust and innovativeness into their acceptance model while factors shown to be insignificant (i.e., effort expectancy) were removed. In addition, a moderating factor, i.e., gender, was applied and they found that social influence, perceived risk, and hedonic motivation had particularly significant effects on the use intentions of women customers.(3) Autonomous robots. Pani et al. [11] analyzed customers’ willingness to pay more for autonomous robots by dividing customer groups based on latent class analysis. 61.28% of respondents showed a willingness to pay more, with one group willing to pay up to $2.92 (average) or between 0-$6 for the service. Abrams et al. [26] proposed a new concept, “existence acceptance” to extend TAM. Existence acceptance refers to pedestrians’ or other traffic respondents’ acceptance of sharing the same roads with autonomous robots.


### 2.1.3. Theoretical Background

In previous studies of autonomous driving and autonomous delivery, TAM and UTAUT are the most widely used behavioral theories for exploring new technology adoption (see Table1). However, TAM and UTAUT are not suitable for explaining customer adoption intention toward ADV services because customers are not the real users of ADVs. The logistics practitioners are the direct users of ADV technology, not the customers, who do not need to be familiar with the technology [17]. The perceived usefulness and perceived ease of use are not the main concerns of customers when deciding whether to adopt ADVs in instant delivery. Thus, TAM and UTAUT may be not well suited for examining and characterizing ADV adoption intention. Another approach that could be considered to investigate technology adoption is Cognitive Appraisal Theory (CAT). This theory holds that emotion is a kind of individual mental state that is generated when evaluating relevant information and that influences individual behavioral motivation. One advantage of CAT is that it can analyze customer behavior through a wide range of emotional experiences. But at the same time, too many cognitive evaluation dimensions make it difficult for researchers to capture quick and unconscious evaluation results. This theory also does not answer the question that whether any kind of emotion can be acquired without an evaluation system [48]. Motivated consumer innovativeness (MCI) theory models motivation according to four main dimensions, namely, (1) functional, (2) hedonical, (3) cognitive, and (4) social [1]. In MCI, social motivation refers to customers wanting to improve their image by purchasing innovative products/services (rather than referring to customers imitating the purchasing decisions of those in their social networks). Theory of Planned Behavior (TPB), which assumes that individuals consciously control their actions, has been used to study a wide range of health-related behaviors and learning behaviors. It includes three main variables, i.e., attitude, subjective norms, and perceived behavior control. Although this theory has been applied by many researchers, there is a lack of agreement about its main variables, raising doubts about the accuracy of results from research based on this theory. In addition, there are many behaviors that may not be explained by the main variables proffered for this theory [49]. As an alternative, DOI theory explains how innovative technologies and ideas spread through society over time and is also applied in communication and information systems research to help explain why customers adopt innovative technologies [14, 50]. Given its particular focus on innovative technology adoption, DOI theory is selected as the behavior theory of this study of ADVs.


## 2.1.1. Autonomous Driving

Autonomous driving refers to cars capable of sensing its environment and operating without human involvement. Many researchers have investigated the acceptance of autonomous driving. Some studies have been conducted based on behavioral theories [18, 21, 28, 34], while other studies have investigated particular factors to explain user acceptance [36–41].Perceived usefulness and perceived ease of use are both important factors proven to affect user acceptance and intention [18, 19, 42]. Many researchers add other factors such as green image [18] and perceived trust [19, 22] into the Technology Acceptance Model (TAM) for further analytical refinement. Using the Unified Theory of Acceptance and Use of Technology (UTAUT), Adnan et al. [27], Hewitt et al. [28], and Meyer_Waaeden and Cloarec [29] all investigated the importance of performance expectancy, effort expectancy, trust, and social influence in AV adoption. Liu et al. [36] studied customers’ willingness to pay for self-driving vehicles and they found that more than 50% of respondents were willing to pay extra, although the amounts are different. In addition, Berrada et al. [43] divided customers into five clusters including conservatives, skeptics, late adopters, early adopters, and explorers and found more than 60% of respondents were willing to use autonomous vehicles. Similar to this study, Bernhard et al. [41] conducted two subsurveys to investigate the effects of autonomous minibus using experience and found that respondents who already had experience using autonomous minibuses were more willing to accept the technology because respondent confidence levels often increase with such direct experience.


## 2.1.2. Autonomous Delivery

Autonomous delivery includes not only ADVs but also UAVs and autonomous robots. UAVs, also commonly referred to as drones, are flying robots that can be remotely controlled or autonomously flown to deliver more light-weight, compact goods. Autonomous robots refer to robots that also operate and travel upon sidewalks, usually complemented by a mothership van that can transport the robots close to the delivery zone [7].(1) UAVs. Combining TAM and DOI theory, Yoo et al. [14] proposed a model to investigate the factors that affect customer perceptions of UAVs and analyze the effect of delivery area. The researchers found that two major perceived advantages of UAVs are their speed and environmental friendliness. In their study, perceived risk was also incorporated and defined to include performance risk, delivery risk, and privacy risk. Based on customer motivated innovativeness, Hwang et al. [44] applied consumer innovativeness theory to study customer attitudes and adoption intentions toward UAV food delivery. The results showed that attitude played an important role in intention to use and intention to pay more. Mathew et al. [1] explored customer intention to adopt UAVs for food delivery in India. Perceived risk and green image were found to be significant predictors of customer attitude and intention. Aydin [45] investigated public knowledge and acceptance of UAVs in specific application areas, for example, commercial and public safety applications. However, the results showed that UAVs were not generally accepted except for in the arenas of public safety and scientific research. Need for human interaction was added into the UTAUT2 model in Ganjipour and Edrisi’s study and was found to have a negative effect on adoption intentions toward delivery drones [32]. Other studies used the Knowledge, Attitude, and Practice Model or other methods to explore customer acceptance [45–47].(2) ADVs. Kapser and Abdelrahman [16] studied customer intention toward ADVs and analyzed various key behavioral components (e.g., performance expectancy, effort expectancy, perceived risk, and price sensitivity) based on UTAUT theory. The results showed that respondents were neutral on the use of ADVs as a delivery alternative while price sensitivity exerted a great influence on customer intention. Building on this research, Kapser et al. [33] integrated trust and innovativeness into their acceptance model while factors shown to be insignificant (i.e., effort expectancy) were removed. In addition, a moderating factor, i.e., gender, was applied and they found that social influence, perceived risk, and hedonic motivation had particularly significant effects on the use intentions of women customers.(3) Autonomous robots. Pani et al. [11] analyzed customers’ willingness to pay more for autonomous robots by dividing customer groups based on latent class analysis. 61.28% of respondents showed a willingness to pay more, with one group willing to pay up to $2.92 (average) or between 0-$6 for the service. Abrams et al. [26] proposed a new concept, “existence acceptance” to extend TAM. Existence acceptance refers to pedestrians’ or other traffic respondents’ acceptance of sharing the same roads with autonomous robots.


## 2.1.3. Theoretical Background

In previous studies of autonomous driving and autonomous delivery, TAM and UTAUT are the most widely used behavioral theories for exploring new technology adoption (see Table1). However, TAM and UTAUT are not suitable for explaining customer adoption intention toward ADV services because customers are not the real users of ADVs. The logistics practitioners are the direct users of ADV technology, not the customers, who do not need to be familiar with the technology [17]. The perceived usefulness and perceived ease of use are not the main concerns of customers when deciding whether to adopt ADVs in instant delivery. Thus, TAM and UTAUT may be not well suited for examining and characterizing ADV adoption intention. Another approach that could be considered to investigate technology adoption is Cognitive Appraisal Theory (CAT). This theory holds that emotion is a kind of individual mental state that is generated when evaluating relevant information and that influences individual behavioral motivation. One advantage of CAT is that it can analyze customer behavior through a wide range of emotional experiences. But at the same time, too many cognitive evaluation dimensions make it difficult for researchers to capture quick and unconscious evaluation results. This theory also does not answer the question that whether any kind of emotion can be acquired without an evaluation system [48]. Motivated consumer innovativeness (MCI) theory models motivation according to four main dimensions, namely, (1) functional, (2) hedonical, (3) cognitive, and (4) social [1]. In MCI, social motivation refers to customers wanting to improve their image by purchasing innovative products/services (rather than referring to customers imitating the purchasing decisions of those in their social networks). Theory of Planned Behavior (TPB), which assumes that individuals consciously control their actions, has been used to study a wide range of health-related behaviors and learning behaviors. It includes three main variables, i.e., attitude, subjective norms, and perceived behavior control. Although this theory has been applied by many researchers, there is a lack of agreement about its main variables, raising doubts about the accuracy of results from research based on this theory. In addition, there are many behaviors that may not be explained by the main variables proffered for this theory [49]. As an alternative, DOI theory explains how innovative technologies and ideas spread through society over time and is also applied in communication and information systems research to help explain why customers adopt innovative technologies [14, 50]. Given its particular focus on innovative technology adoption, DOI theory is selected as the behavior theory of this study of ADVs.


## 2.2. DOI Theory Models of User Perception toward a New Technology

DOI theory as presented by Rogers [51] employs more specific innovation characteristics, which are not typically found in behavioral theories in order to explain the adoption of new technologies or products. According to Rogers, “the innovation is an idea, practice, or object that is perceived as new by an individual or other unit of adoption.” Rogers also contends that innovations are diffused through various channels and within a particular social system. Rogers [52] presented five innovation characteristics, namely, (1) relative advantage, (2) complexity, (3) compatibility, (4) observability, and (5) trialability. Relative advantage is defined as “the degree to which an innovation is perceived as better than the idea it supersedes.” Complexity is defined as “the degree to which an innovation is perceived as difficult to understand and use.” Compatibility involves “the degree to which a service is perceived as consistent with users’ existing values, beliefs, habits, and present and previous experiences.” Observability is defined as “the degree to which the innovation can be visible to others.” Finally, trialability is defined as “the extent to which the innovation can be trialed, modified, and experienced before the adoption” [53]. However, previous studies found that only three characteristics—relative advantage, complexity, and compatibility—impact innovation adoption [14, 50, 54–62]. DOI theory has proven to be an effective theory for exploring customers’ acceptance of a new technology. Constructs used in the original formulation of DOI theory and added in other subsequent studies are reviewed in Table 2. As indicated in Table 2, relative advantage, complexity and compatibility are the most frequently applied constructs of DOI. Social influence has moreover been found to promote the adoption of new technologies [60]. Green image and perceived risk have also exhibited positive effects on adoption intention [1, 14, 18].Table 2
Constructs used in DOI.
ConstructMain findingsReferencesRelative advantageRelative advantage can promote adoption intentionDuan et al. [62]; Ooi et al. [55]; Wang et al. [56]; Chung [57]; Kang et al. [58]; Hamad et al. [63]; Yoo et al. [14]; Min et al. [60]ComplexityComplexity is a barrier to adoption intentionDuan et al. [62]; Wang et al. [56]; Chung [57]; Yoo et al. [14]; Min et al. [60]CompatibilityCompatibility can promote adoption intentionDuan et al. [62]; Wang et al. [56]; Chung [57]; Kang et al. [58]; Hamad et al. [63] Min et al. [60]; Yoo et al. [14]ObservabilityObservability can promote adoption intentionDuan et al. [62]; Chung [57]TrialabilityTrialability can promote adoption intentionDuan et al. [62]; Chung [57]; Hamad et al. [63]Social influenceSocial influence can promote adoption intentionMin et al. [60]Green image/environmental concernGreen image/environmental concern can promote adoption intentionYoo et al. [14]Perceived riskPerceived risk is a barrier to adoption intentionYoo et al. [14]


## 2.3. Research Gap

A review of the literature in the autonomous driving and autonomous delivery fields revealed only two studies [16, 33] that appear to explicitly concentrate on the acceptance of ADVs. In contrast with these two studies, which were based on UTAUT theory, the DOI theory employed in this study focuses more on how innovative technologies spread in society rather than on the use of those technologies. We also incorporate some new constructs that may affect the ADV adoption intention of end customers, such as use experience and infection risk. As ADVs have become regularly available as a delivery option in Anting, Shanghai, some respondents to our survey have already used ADVs, with their use experiences increasing their confidence in ADV services. The risk of COVID-19 infection caused by person-to-person contact is likely to encourage customers to more readily adopt ADV supported services due to the contactless delivery that ADVs enables. This study also considers the impacts of social awkwardness on ADV adoption intention as ADVs can bypass direct communications with couriers and may become more popular among customers who are not so adept at socializing. To the best of our knowledge, this study is the first to investigate customer adoption intentions toward ADVs in China by adding the influences of use experience, infection risk, and social awkwardness to DOI theory.


## 3. Methodology

In this section the constructs that are used in this study’s customer adoption intention model are identified and the model structure is described. Customer adoption intention here refers to the degree of willingness to use ADVs (i.e., intention to use) and the willingness to pay more for the use of ADVs (i.e., intention to pay more). First, the conceptual research model, which includes DOI theory supplemented with three new constructs, is presented. The proposed “Customer Adoption Intention toward Autonomous Delivery Vehicle (CAI-ADV)” model is depicted in Figure1. Next, the survey design is delineated, including the time, place, and scale of the questionnaire, as well as the questionnaire’s content. Finally, the data analysis process is presented. In addition to the constructs, demographic factors as control variables are considered in our analyses.Figure 1
Customer adoption intention toward autonomous delivery vehicle model (CAI-ADV).


### 3.1. Constructs in the Conceptual Research Model and Proposed Hypotheses

The constructs are divided into two parts, namely, the original constructs from DOI and constructs that we added. The former includes relative advantage, complexity, and compatibility. The latter encompass social influence, perceived risk, green image, infection risk, social awkwardness, and use experience. Definitions, related research, and our hypotheses for each construct are introduced as follows:Relative advantage (RA) is defined as “the degree to which an innovation is perceived as better than the idea it supersedes” [53]. Previous research has found RA to be a positive predictor of behavioral/use intention [14, 55–58, 60, 62, 63]. In this study, relative advantage is defined as a customer’s subjective evaluation of the benefits afforded by ADVs against those by courier delivery. Autonomous delivery typically enables customers to schedule delivery of their orders at any a time of their choice [64]. Customers can place orders at midnight and be served by ADVs without worrying about order rejection. Time convenience is an important characteristic of ADVs, making it a particularly attractive delivery option for customers. Moreover, without the personal preferences of couriers hampering options, orders from remote areas would not be rejected and wait times may be greatly reduced during inclement weather. Time convenience [58] and zero rejection rates in harsh conditions may result in more adoption. In addition, there are reports of couriers contaminating customer food orders [65]. In contrast to courier delivery, delivery by ADVs could better protect the cleanliness and integrity of goods. Therefore, the following hypothesis is proposed:H1: Relative advantage is positively related to customer intentions toward using ADV services.Complexity is defined as “the degree to which an innovation is perceived as difficult to understand and use” [53]. Duan et al. [62], Wang et al. [56], Chung [57], Yoo et al. [14], and Min et al. [60] found that complexity had a negative influence on adoption of a new technology. In our study, complexity refers to the problems that may arise in relation to a customer getting a notification when an order has arrived and/or difficulties possibly experienced by a customer in retrieving the food from the vehicle. For example, the operation interface is challenging for some elderly people to use, especially for picking up goods through a verification code. Without person-to-person contact with a courier, it can be more difficult to return and exchange goods. Therefore, the following hypothesis is proposed:H2: Complexity is negatively related to customer intention toward using ADV services.Compatibility is defined as “the degree to which a service is perceived as consistent with users” existing values, beliefs, habits, and present and previous experiences” [53]. Studies from Duan et al. [62], Wang et al. [56], Chung [57], Kang et al. [58], Hamad et al. [63], and Min et al. [60] show that compatibility can positively affect customers’ intentions. Whether ADV service is compatible with customers’ usual delivery demands will affect their intentions toward using ADVs. In this study, compatibility refers to the consistency between the new ADV service and some previous experience with or more habitual use of instant delivery by customers. For example, ADVs cannot provide home delivery services in some residential areas or office buildings. A customer that used to enjoy door-to-door delivery service may then need to pick up goods delivered by ADVs in a certain area such as a parcel locker that is three hundred meters away from home. Such an alteration engendered by the new delivery mode demonstrates a gap in compatibility as compared to the customer’s usual delivery demand. As a result, the customer’s adoption intention is lower. On the other hand, if ADV service is not much different from courier delivery experiences, such as the ADVs delivery price being the same as the previous delivery price, customers’ ADV adoption intention would be higher. Therefore, the following hypothesis is proposed:H3: Compatibility is positively related to customer intentions toward using ADV services.Social influence can be described as the phenomenon in which “people adopt when enough other people in the group have adopted, that is, innovations spread by a conformity motive” [66]. In other words, people will consciously imitate behaviors as exhibited in their social network. Consistent with Higgins et al. [67], Min et al. [60] also found that social influence had a positive effect on the acceptance of a new technology. Social influence is also considered in this study in order to explore the positive feedback and recommendations communicated between members of close social groups as they relate to intentions toward ADV adoption. Therefore, the following hypothesis is proposed:H4: Social influence is positively related to customer intentions toward using ADV services.Perceived risk has been generally found to be a useful addition to DOI in studying customer intentions toward a new technology. Yoo et al. [14] and Mathew et al. [1] both used three dimensions to characterize perceived risk, performance risk, delivery risk, and privacy risk. ADV technology has the potential to alleviate the problem of courier accidents. Whether ADVs can complete the delivery process and avoid traffic accidents (i.e., delivery risk) is a thought-provoking problem. ADVs have not been implemented extensively enough to accurately evaluate their ability to reduce accidents. Due to the need to draw on a large amount of sensitive data online such as names, addresses, and phone numbers, protecting privacy is another important concern that may affect customer intentions. Furthermore, network instability can also affect the operation of ADVs. An additional technical risk is hacking (e.g., breach of firewalls). Considering these risks, the more they are reduced, the higher the adoption intentions of customers. Previous studies found that perceived risk had a negative effect on acceptance of a new technology [1, 14]. Therefore, the perceived risk discussed in this study refers to the delivery risk, privacy risk, and technical risk. In light of these considerations, the following hypothesis is proposed:H5: Perceived risk is negatively related to customer intentions toward using ADV services.Because of global warming and increasing air pollution, the environmental impacts of any given transportation system has become a factor worth considering [68]. The emergence of autonomous vehicles powered by electricity enables cleaner energy consumption [64]. The implementation of ADVs is expected to be a valuable step towards zero-emission logistics in the United States [69]. Many studies show that customers’ environmental concerns could affect their adoption intentions toward green products such as Avs and UAVs [1, 14, 18]. In this study, green image is defined as “customers” perceptions of ADVs having a salutary environmental performance.” Therefore, the following hypothesis is proposed:H6: Green image is positively related to customer intentions toward using ADV services.Most countries around the world have been greatly affected by COVID-19. The disease spreads primarily through contact with an infected person (even through a cough or sneeze) or touching a surface that has the virus on it. Due to the fear of infection, contactless delivery (as enabled through ADVs and UAVs) has become increasingly popular, especially in areas experiencing high infection risk. The chance of human-to-human transmission during the COVID-19 pandemic is reduced through the implementation of ADVs. Therefore, the following hypothesis is proposed:H7: Infection risk is positively related to customer intentions toward using ADV services.The results of Xi et al.’s study [70] show that same-day-delivery has a negative effect on greeting/chatting with neighbors but has a positive association with social gatherings that often occur between acquaintances. ADV technology can make our lives more convenient but can also further alienate us from our neighbors or other casual acquaintances. Similar to autonomous taxis [22], ADVs circumvent the person-to-person contact that comes with using couriers. One study has found that the greater the need for human interaction, the less intention there is to try drone delivery [32]. Drawing upon this finding, customers who are socially awkward, meaning that they are less adept at or inclined to communicate with other people, may find ADVs to be an attractive option. Therefore, the following hypothesis is proposed:H8: Social awkwardness is positively related to customer intentions toward using ADV services.Studies from Bernhard et al. [41] show that use experience can affect customers’ intentions because customers respond on the basis of real experience, not imagination. However, few studies have taken this construct into consideration. Customers who have used ADVs are better able to understand ADV delivery service, including the process, its advantages, and disadvantages. Customer confidence in the service could then increase as a result of direct experience. Based on previous experiences, customers may be more likely to choose ADVs as a delivery option. Therefore, the following hypothesis is proposed:H9: Use experience is positively related to customer intentions toward using ADV services


### 3.2. Survey Design

Based on the research model proposed in Section3.1, we designed and conducted an online and offline survey to test the proposed hypotheses. Our online survey was conducted through the Sojump website, and the offline survey was conducted in Anting, Shanghai. In Anting, the Yonghui supermarket began implementing ADV service in 2020, covering a delivery area of 5 square kilometers with more than six thousand households [71].The size of a sample from a given population necessary to achieve a good or very good degree of adequate representativeness has been suggested to be 300 or 500, respectively [72] or alternatively achieved through a minimum 20 : 1 ratio between the sample size and the number of model parameters to be statistically estimated [73]. Thus, the 691 valid samples collected for our survey data analysis satisfy both of these recommendations. The data collection took place in October and November in 2021.An introduction to ADVs is presented at the beginning of the survey, including history, delivery process, equipment parameters, advantages, and disadvantages, in order to provide a clearer understanding of ADVs for respondents. The second part of the survey includes questions about respondents’ intention to adopt ADV services. These questions are developed with reference to previous relevant literature. Consistent with previous surveys [1, 16, 26, 44], constructs except for use experience are measured based on a seven-point Likert-type scale, ranging from strongly disagree (1) to strongly agree (7). Based on the hypotheses proposed in 3.1, the related observational variables and corresponding references are presented in Table 3. The third part of the survey includes basic individual information (e.g., age, gender, monthly household disposable income, education, job, and instant delivery experience). To improve the validity of the questionnaire, reverse worded questions are included in this survey. Responses containing what are functionally contradictory “strongly agree” answers to such questions, along with responses that were completed in less than 50 seconds, are excluded from analysis.Table 3
Constructs, observational variables, and supporting references.
ConstructItemSourceRelative advantage (RA)RA1: Using autonomous delivery vehicles would help me receive orders more quicklyMathew et al. [1]RA2: Autonomous delivery vehicles can operate in inclement weatherRA3: Autonomous delivery vehicles can deliver at any time I wantRA4: Using autonomous delivery vehicles would be coolComplexity (COL)COL1: Interacting with autonomous delivery vehicles is clear and understandableYoo et al. [14]; Wu et al. [18]COL2: I believe that it is easy to make autonomous delivery vehicles to do what I wantCOL3: I believe that retrieving goods from autonomous delivery vehicles is easyCompatibility (COA)COA1: Using autonomous delivery vehicles is compatible with my past experience and habitual use of delivery serviceYoo et al. [14]COA2: Using autonomous delivery vehicles is comparable to the pickup distance and delivery price of my previous experienceSocial influence (SI)SI1: I can be influenced by my peers’ preferences or decisions in choosing whether to use autonomous delivery vehiclesHamad et al. [63]SI2: The more people who have used autonomous delivery vehicles, the more willingness to use I havePerceived risk (PR)PR1: The package the vehicle is carrying might be damaged or stolenYoo et al. [14]; Mathew et al. [1]PR2: The delivery process may take too longPR3: Autonomous delivery vehicles will cause disclosure of private information such as address, name, and telephone numberPR4: The remote-control center and the operating system of autonomous delivery vehicles may break downPR5: Hackers may attack the system and steal personal data, even operate autonomous delivery vehicles to attack pedestriansGreen image (GI)GI1: Using autonomous delivery vehicle services can reduce environmental pollutionMathew et al. [1]; Li et al. [22]GI2: By using autonomous delivery vehicle services, I can demonstrate that I care about environmental conservationInfection risk (IR)IR: Using autonomous delivery vehicles can reduce the infection riskThis studySocial awkwardness (SA)SA1: When communicating with strangers, I tend to feel uncomfortableThis studySA2: Using an autonomous delivery vehicle can remove the need for communication with couriersUse experience (UE)UE: Have you used autonomous delivery vehicles before?This studyAdoption intention (AI)AI1: I Intend to use autonomous delivery vehicles as a delivery option in the futureKapser and Abdelrahman [16]; Hwang et al. [44]AI2: I am willing to pay more for autonomous delivery vehicle services


### 3.3. Data Analysis

Our analysis includes two approaches, namely, demographic analysis and structural equation modeling (SEM). The demographic analysis includes a basic profile of customers and the degree of infection risk for the location under study. These demographic characteristics such as age, gender, income, and education are analyzed as control variables to capture their effects on customers’ adoption intentions. The demographic analysis is followed by SEM, which can be used to study the relationships between observed variables and latent variables [74]. SEM includes two stages-measurement model assessment and structural model assessment.The measurement model assessment is performed to test the reliability and validity of the questionnaire. To validate the model construct, four steps are implemented. The first step is to test the internal construct reliability with Cronbach’s alpha and the composite reliability (CR), the outputs of which should have thresholds higher than 0.70 [75, 76]. Second, convergent and discriminant validity should both be examined. Convergent validity is assessed in terms of average variance extracted (AVE) with a threshold of 0.50. In addition, factor loadings should be higher than 0.5 [76]. Third, discriminant validity should be examined to measure how a construct is different from other constructs in the SEM model by calculating the degree to which it correlates with other constructs and how distinctly it exists as a unique construct [76]. In order to verify good discriminative validity, the square root of AVE should be greater than the correlation coefficient between the construct and other constructs [76]. Finally, confirmatory factor analysis is used to assess the measurement model [73]. The parameters in confirmatory factor analysis and their standards are drawn from Cheung et al. [77].The structural model assessment is employed for path analysis. The results include path coefficients andp-values. Only when the p-value <0.05 is the construct deemed to have a significant influence on intention, thus supporting the corresponding hypothesis. AMOS version 24 (Asymptotically distribution-free) is used for data analysis.


## 3.1. Constructs in the Conceptual Research Model and Proposed Hypotheses

The constructs are divided into two parts, namely, the original constructs from DOI and constructs that we added. The former includes relative advantage, complexity, and compatibility. The latter encompass social influence, perceived risk, green image, infection risk, social awkwardness, and use experience. Definitions, related research, and our hypotheses for each construct are introduced as follows:Relative advantage (RA) is defined as “the degree to which an innovation is perceived as better than the idea it supersedes” [53]. Previous research has found RA to be a positive predictor of behavioral/use intention [14, 55–58, 60, 62, 63]. In this study, relative advantage is defined as a customer’s subjective evaluation of the benefits afforded by ADVs against those by courier delivery. Autonomous delivery typically enables customers to schedule delivery of their orders at any a time of their choice [64]. Customers can place orders at midnight and be served by ADVs without worrying about order rejection. Time convenience is an important characteristic of ADVs, making it a particularly attractive delivery option for customers. Moreover, without the personal preferences of couriers hampering options, orders from remote areas would not be rejected and wait times may be greatly reduced during inclement weather. Time convenience [58] and zero rejection rates in harsh conditions may result in more adoption. In addition, there are reports of couriers contaminating customer food orders [65]. In contrast to courier delivery, delivery by ADVs could better protect the cleanliness and integrity of goods. Therefore, the following hypothesis is proposed:H1: Relative advantage is positively related to customer intentions toward using ADV services.Complexity is defined as “the degree to which an innovation is perceived as difficult to understand and use” [53]. Duan et al. [62], Wang et al. [56], Chung [57], Yoo et al. [14], and Min et al. [60] found that complexity had a negative influence on adoption of a new technology. In our study, complexity refers to the problems that may arise in relation to a customer getting a notification when an order has arrived and/or difficulties possibly experienced by a customer in retrieving the food from the vehicle. For example, the operation interface is challenging for some elderly people to use, especially for picking up goods through a verification code. Without person-to-person contact with a courier, it can be more difficult to return and exchange goods. Therefore, the following hypothesis is proposed:H2: Complexity is negatively related to customer intention toward using ADV services.Compatibility is defined as “the degree to which a service is perceived as consistent with users” existing values, beliefs, habits, and present and previous experiences” [53]. Studies from Duan et al. [62], Wang et al. [56], Chung [57], Kang et al. [58], Hamad et al. [63], and Min et al. [60] show that compatibility can positively affect customers’ intentions. Whether ADV service is compatible with customers’ usual delivery demands will affect their intentions toward using ADVs. In this study, compatibility refers to the consistency between the new ADV service and some previous experience with or more habitual use of instant delivery by customers. For example, ADVs cannot provide home delivery services in some residential areas or office buildings. A customer that used to enjoy door-to-door delivery service may then need to pick up goods delivered by ADVs in a certain area such as a parcel locker that is three hundred meters away from home. Such an alteration engendered by the new delivery mode demonstrates a gap in compatibility as compared to the customer’s usual delivery demand. As a result, the customer’s adoption intention is lower. On the other hand, if ADV service is not much different from courier delivery experiences, such as the ADVs delivery price being the same as the previous delivery price, customers’ ADV adoption intention would be higher. Therefore, the following hypothesis is proposed:H3: Compatibility is positively related to customer intentions toward using ADV services.Social influence can be described as the phenomenon in which “people adopt when enough other people in the group have adopted, that is, innovations spread by a conformity motive” [66]. In other words, people will consciously imitate behaviors as exhibited in their social network. Consistent with Higgins et al. [67], Min et al. [60] also found that social influence had a positive effect on the acceptance of a new technology. Social influence is also considered in this study in order to explore the positive feedback and recommendations communicated between members of close social groups as they relate to intentions toward ADV adoption. Therefore, the following hypothesis is proposed:H4: Social influence is positively related to customer intentions toward using ADV services.Perceived risk has been generally found to be a useful addition to DOI in studying customer intentions toward a new technology. Yoo et al. [14] and Mathew et al. [1] both used three dimensions to characterize perceived risk, performance risk, delivery risk, and privacy risk. ADV technology has the potential to alleviate the problem of courier accidents. Whether ADVs can complete the delivery process and avoid traffic accidents (i.e., delivery risk) is a thought-provoking problem. ADVs have not been implemented extensively enough to accurately evaluate their ability to reduce accidents. Due to the need to draw on a large amount of sensitive data online such as names, addresses, and phone numbers, protecting privacy is another important concern that may affect customer intentions. Furthermore, network instability can also affect the operation of ADVs. An additional technical risk is hacking (e.g., breach of firewalls). Considering these risks, the more they are reduced, the higher the adoption intentions of customers. Previous studies found that perceived risk had a negative effect on acceptance of a new technology [1, 14]. Therefore, the perceived risk discussed in this study refers to the delivery risk, privacy risk, and technical risk. In light of these considerations, the following hypothesis is proposed:H5: Perceived risk is negatively related to customer intentions toward using ADV services.Because of global warming and increasing air pollution, the environmental impacts of any given transportation system has become a factor worth considering [68]. The emergence of autonomous vehicles powered by electricity enables cleaner energy consumption [64]. The implementation of ADVs is expected to be a valuable step towards zero-emission logistics in the United States [69]. Many studies show that customers’ environmental concerns could affect their adoption intentions toward green products such as Avs and UAVs [1, 14, 18]. In this study, green image is defined as “customers” perceptions of ADVs having a salutary environmental performance.” Therefore, the following hypothesis is proposed:H6: Green image is positively related to customer intentions toward using ADV services.Most countries around the world have been greatly affected by COVID-19. The disease spreads primarily through contact with an infected person (even through a cough or sneeze) or touching a surface that has the virus on it. Due to the fear of infection, contactless delivery (as enabled through ADVs and UAVs) has become increasingly popular, especially in areas experiencing high infection risk. The chance of human-to-human transmission during the COVID-19 pandemic is reduced through the implementation of ADVs. Therefore, the following hypothesis is proposed:H7: Infection risk is positively related to customer intentions toward using ADV services.The results of Xi et al.’s study [70] show that same-day-delivery has a negative effect on greeting/chatting with neighbors but has a positive association with social gatherings that often occur between acquaintances. ADV technology can make our lives more convenient but can also further alienate us from our neighbors or other casual acquaintances. Similar to autonomous taxis [22], ADVs circumvent the person-to-person contact that comes with using couriers. One study has found that the greater the need for human interaction, the less intention there is to try drone delivery [32]. Drawing upon this finding, customers who are socially awkward, meaning that they are less adept at or inclined to communicate with other people, may find ADVs to be an attractive option. Therefore, the following hypothesis is proposed:H8: Social awkwardness is positively related to customer intentions toward using ADV services.Studies from Bernhard et al. [41] show that use experience can affect customers’ intentions because customers respond on the basis of real experience, not imagination. However, few studies have taken this construct into consideration. Customers who have used ADVs are better able to understand ADV delivery service, including the process, its advantages, and disadvantages. Customer confidence in the service could then increase as a result of direct experience. Based on previous experiences, customers may be more likely to choose ADVs as a delivery option. Therefore, the following hypothesis is proposed:H9: Use experience is positively related to customer intentions toward using ADV services


## 3.2. Survey Design

Based on the research model proposed in Section3.1, we designed and conducted an online and offline survey to test the proposed hypotheses. Our online survey was conducted through the Sojump website, and the offline survey was conducted in Anting, Shanghai. In Anting, the Yonghui supermarket began implementing ADV service in 2020, covering a delivery area of 5 square kilometers with more than six thousand households [71].The size of a sample from a given population necessary to achieve a good or very good degree of adequate representativeness has been suggested to be 300 or 500, respectively [72] or alternatively achieved through a minimum 20 : 1 ratio between the sample size and the number of model parameters to be statistically estimated [73]. Thus, the 691 valid samples collected for our survey data analysis satisfy both of these recommendations. The data collection took place in October and November in 2021.An introduction to ADVs is presented at the beginning of the survey, including history, delivery process, equipment parameters, advantages, and disadvantages, in order to provide a clearer understanding of ADVs for respondents. The second part of the survey includes questions about respondents’ intention to adopt ADV services. These questions are developed with reference to previous relevant literature. Consistent with previous surveys [1, 16, 26, 44], constructs except for use experience are measured based on a seven-point Likert-type scale, ranging from strongly disagree (1) to strongly agree (7). Based on the hypotheses proposed in 3.1, the related observational variables and corresponding references are presented in Table 3. The third part of the survey includes basic individual information (e.g., age, gender, monthly household disposable income, education, job, and instant delivery experience). To improve the validity of the questionnaire, reverse worded questions are included in this survey. Responses containing what are functionally contradictory “strongly agree” answers to such questions, along with responses that were completed in less than 50 seconds, are excluded from analysis.Table 3
Constructs, observational variables, and supporting references.
ConstructItemSourceRelative advantage (RA)RA1: Using autonomous delivery vehicles would help me receive orders more quicklyMathew et al. [1]RA2: Autonomous delivery vehicles can operate in inclement weatherRA3: Autonomous delivery vehicles can deliver at any time I wantRA4: Using autonomous delivery vehicles would be coolComplexity (COL)COL1: Interacting with autonomous delivery vehicles is clear and understandableYoo et al. [14]; Wu et al. [18]COL2: I believe that it is easy to make autonomous delivery vehicles to do what I wantCOL3: I believe that retrieving goods from autonomous delivery vehicles is easyCompatibility (COA)COA1: Using autonomous delivery vehicles is compatible with my past experience and habitual use of delivery serviceYoo et al. [14]COA2: Using autonomous delivery vehicles is comparable to the pickup distance and delivery price of my previous experienceSocial influence (SI)SI1: I can be influenced by my peers’ preferences or decisions in choosing whether to use autonomous delivery vehiclesHamad et al. [63]SI2: The more people who have used autonomous delivery vehicles, the more willingness to use I havePerceived risk (PR)PR1: The package the vehicle is carrying might be damaged or stolenYoo et al. [14]; Mathew et al. [1]PR2: The delivery process may take too longPR3: Autonomous delivery vehicles will cause disclosure of private information such as address, name, and telephone numberPR4: The remote-control center and the operating system of autonomous delivery vehicles may break downPR5: Hackers may attack the system and steal personal data, even operate autonomous delivery vehicles to attack pedestriansGreen image (GI)GI1: Using autonomous delivery vehicle services can reduce environmental pollutionMathew et al. [1]; Li et al. [22]GI2: By using autonomous delivery vehicle services, I can demonstrate that I care about environmental conservationInfection risk (IR)IR: Using autonomous delivery vehicles can reduce the infection riskThis studySocial awkwardness (SA)SA1: When communicating with strangers, I tend to feel uncomfortableThis studySA2: Using an autonomous delivery vehicle can remove the need for communication with couriersUse experience (UE)UE: Have you used autonomous delivery vehicles before?This studyAdoption intention (AI)AI1: I Intend to use autonomous delivery vehicles as a delivery option in the futureKapser and Abdelrahman [16]; Hwang et al. [44]AI2: I am willing to pay more for autonomous delivery vehicle services


## 3.3. Data Analysis

Our analysis includes two approaches, namely, demographic analysis and structural equation modeling (SEM). The demographic analysis includes a basic profile of customers and the degree of infection risk for the location under study. These demographic characteristics such as age, gender, income, and education are analyzed as control variables to capture their effects on customers’ adoption intentions. The demographic analysis is followed by SEM, which can be used to study the relationships between observed variables and latent variables [74]. SEM includes two stages-measurement model assessment and structural model assessment.The measurement model assessment is performed to test the reliability and validity of the questionnaire. To validate the model construct, four steps are implemented. The first step is to test the internal construct reliability with Cronbach’s alpha and the composite reliability (CR), the outputs of which should have thresholds higher than 0.70 [75, 76]. Second, convergent and discriminant validity should both be examined. Convergent validity is assessed in terms of average variance extracted (AVE) with a threshold of 0.50. In addition, factor loadings should be higher than 0.5 [76]. Third, discriminant validity should be examined to measure how a construct is different from other constructs in the SEM model by calculating the degree to which it correlates with other constructs and how distinctly it exists as a unique construct [76]. In order to verify good discriminative validity, the square root of AVE should be greater than the correlation coefficient between the construct and other constructs [76]. Finally, confirmatory factor analysis is used to assess the measurement model [73]. The parameters in confirmatory factor analysis and their standards are drawn from Cheung et al. [77].The structural model assessment is employed for path analysis. The results include path coefficients andp-values. Only when the p-value <0.05 is the construct deemed to have a significant influence on intention, thus supporting the corresponding hypothesis. AMOS version 24 (Asymptotically distribution-free) is used for data analysis.


## 4. Results and Discussion

### 4.1. Demographic Analysis

The demographic details of respondents are shown in Table4. The percentages of respondents who are male and female are 42.40% and 54.85%, respectively. The percentage of respondents aged between 25–34 and 35–49 years are 23.44% and 36.61%, respectively. The distribution of other age groups generally mirrors that of the population studied, whereas the percentage of elderly over 65 is rather low due to the lower participation of the elderly in using intelligent devices. Respondents with average monthly disposable incomes of 2000–6000 RMB make up the largest proportion of respondents, accounting for 44.86% of them. Respondents with only a high school degree also account for the largest portion of respondents. The demographic analysis reveals that the sample is also representative for the Chinese population in terms of age and gender [78]. Therefore, it is possible to generalize the findings to a larger population in China. The number of respondents who have used instant delivery service and those who have not (50.65% and 49.35% respectively) are almost half and half. Takeaway food delivery is used most, followed by fresh food delivery and medicine delivery. It is worth noting that 56 respondents (8.1% of the survey sample) indicated that they already have experience using ADV services. At the same time, as shown in Figure 2, 58.32% respondents are not willing to pay extra for ADV service. Finally, the results shown in Figure 3 indicate that 58.93% of respondents remain neutral toward ADVs, which is consistent with previous research [16].Table 4
Demographic details of respondents.
VariableCategoryFrequencyPercentage (%)GenderFemale37954.85Male29342.40Third gender192.75AgeBelow 18 years11516.6419–249613.8925–3416223.4435–4923536.6150–647510.85Above 65 years81.16Monthly household disposable incomeBelow 2000 RMB14320.692000–6000 RMB31044.866000–10000 RMB12017.3710000–15000 RMB588.3915000–20000 RMB213.04Above 20000 RMB395.64EducationSecondary school certificate or below284.05High school degree24134.88Bachelor’s degree13319.25Master’s degree21330.82Doctorate588.39No degree182.60Instant delivery use experienceYes35050.65No34149.35Instant delivery services usedFresh food15522.43Take-away food28541.24Medicine8912.88Other426.08ADV use experienceYes568.10No63591.90Epidemic risk levelMedium and high-risk area233.33Low-risk area66896.67Figure 2
Percentage of respondents according to acceptable increase in cost for ADV service.Figure 3
Percentage of respondents according to attitude towards ADVs.


### 4.2. Measurement Model Assessment

Both the Cronbach’s alpha and CR for all the items of this study are found to be above 0.70, except the Cronbach’s alpha for AI (Table5). According to a study by McCrae et al. [79], the reason for this exception may be the low number of questions regarding AI in the survey [80]. The AVE values range from 0.612 to 0.926, which are all higher than 0.5, thus establishing validity. Factor loadings range from 0.669 to 0.972 are all also higher than 0.5 [76] (Table 5). The results show that convergent validity is established, indicating good agreement between two or more items that measure the same structure. Although the Cronbach’s alpha for AI is below 0.70, the rest of parameters of AI including factor loadings, CR, and AVE all meet the standard, thus subsequent analysis can be conducted.Table 5
Results for factor loading, Cronbach’s alpha, CR, AVE, and Sqrt of AVE.
VariableItemFactor loadingCronbach’s alphaCRAVEAVERARA10.9050.8990.9380.7900.889RA20.905RA30.897RA40.848COLCOL10.9190.9280.9590.8850.941COL20.957COL30.946COACOA10.9580.9330.9610.9260.962COA20.966SISI10.8800.7240.9050.8270.909SI20.938PRPR10.9160.9050.9690.8630.929PR20.918PR30.900PR40.959PR50.950GIGI10.9490.9340.9600.9230.961GI20.972SASA10.9510.8700.9600.9230.961SA20.970AIAI10.8810.5730.7590.6120.782AI20.669Notes. RA = relative advantage; COL = complexity; COA = compatibility; SI = social influence; PR = perceived risk; GI = green image; SA = social awkwardness; AI = adoption intention.The value ofAVE is presented on the diagonal in Table 6 and correlation coefficients are presented below the diagonal. Each variable has a greater AVE than the correlation coefficient, which demonstrates discriminant validity, i.e., that each construct is distinct from each other.Table 6
Results for discriminant validity (Fornell-Larcker criterion).
RACOLCOASIPRGISARA0.889COL−0.8300.941COA0.756−0.8160.962SI0.631−0.6980.6720.909PR0.321−0.3340.3160.4740.929GI0.584−0.6510.6560.6750.3480.961SA0.340−0.3630.3880.3940.5360.4240.961Notes. RA = relative advantage; COL = complexity; COA = compatibility; SI = social influence; PR = perceived risk; GI = green image; SA = social awkwardness.The results show that the proposed model has a greater fit to the data based on the standards showed in Table7.Table 7
Results of confirmatory factor analysis.
Indicesχ2dfCMIN/DFRMSEANFIGFIAGFIStandards——<3<0.08>0.7>0.9>0.7Results567.4862532.2430.0370.8250.9340.893


### 4.3. Structural Model Assessment

The measurement model assessment is followed by structural model assessment [73, 76]. The results, including path coefficients and p-values, are shown in Table 8 and Figure 4. Only when the p-value <0.05 is when the construct deemed to have a significant influence on intention, thus supporting the corresponding hypothesis. The analysis from Table 8 shows that eight of nine hypotheses are supported. More specifically, compatibility (β = 0.171, p<0.001), social influence (β = 0.205, p<0.001), infection risk (β = 0.076, p<0.05), green image (β = 0.229, p<0.001), social awkwardness (β = 0.302, p<0.001), and experience (β = 0.040, p<0.05) have a positive influence. Complexity (β = −0.294, p<0.001) and perceived risk (β = −0.149, p<0.001) have a negative influence on customers’ intentions. Hence, hypotheses 2, 3, 4, 5, 6, 7, 8, and 9 are supported. However, contrary to expectation, relative advantage (β = −0.058, p=0.195) does not have a significant positive influence. The practical reason may be that the relative advantage in this context relates to whether ADVs can complete the delivery process and meet the delivery demands of customers, while how goods are delivered is not the main concern of customers. This view was also expressed by many respondents during the offline investigation.Table 8
Results of path analysis.
HypothesisPathProposed effectβSignificanceResultH1RA ⟶ AI+−0.0580.195RejectedH2COL ⟶ AI−−0.294<0.001SupportedH3COA ⟶ AI+0.171<0.001SupportedH4SI ⟶ AI+0.205<0.001SupportedH5PR ⟶ AI−−0.149<0.001SupportedH6GI ⟶ AI+0.229<0.001SupportedH7IR ⟶ AI+0.076<0.05SupportedH8SA ⟶ AI+0.302<0.001SupportedH9UE ⟶ AI+0.040<0.05SupportedNotes. RA = relative advantage; COL = complexity; COA = compatibility; SI = social influence; PR = perceived risk; GI = green image; IR = infection risk; SA = social awkwardness; UE = use experience; AI = adoption intention.Figure 4
Results of path analysis. Notes:∗ means that this attribute is a significant contributor according to the analysis.In addition to the variables related to our hypotheses, the influence of control variables (i.e., age, gender, income, and education) on adoption intention is also studied (Table9). Except for gender, the control variables that were included in our survey are found to have significant effects on customers’ intentions, although the effects sometimes diverge in direction. In addition, in comparison with the previous results without control variables, the inclusion of age, gender, and income does not change the significance of any of the constructs (see Table 8). Thus, it is demonstrated that the model has good robustness.Table 9
Control variable results.
PathβSignificanceAge ⟶ AI−0.058<0.001Gender ⟶ AI−0.0140.475Income ⟶ AI0.058<0.01Education ⟶ AI0.054<0.05The results of path analysis provide evidence supporting DOI in the context of ADVs. The results also indicate that infection risk, use experience, and social awkwardness are important constructs affecting customer adoption intentions.


### 4.4. Discussion

In accordance with findings from previous studies [14, 60], complexity has a negative effect on customers’ intention to adopt ADVs in instant delivery. The processes supporting the pickup and return of goods, which are major components of complexity in the context of ADV service, should be as clear and efficient as possible to maximize customer ease of use of the service. An example process would be “face swiping,” which has become widely applied in check-in and -out services in Chinese railway stations for picking up and returning goods.In line with findings from Yoo et al. [14], Min et al. [60], and Hamad et al. [63], our results show that compatibility has a positive effect on customers adoption intentions toward ADV services. This indicates that when ADV service is more consistent with ’customers’ previous delivery experience and habits, ’customers’ adoption intentions are higher. For example, traditional courier service enables goods to be delivered directly to one’s doorstep, but ADVs may not be allowed to enter a given neighborhood or are not able to climb stairs. In these conditions, customers need to go downstairs to pick up goods. Therefore, some management regulations may need to be established to allow ADVs to operate in particular residential areas, and ADVs may need to be equipped with RFID technology to communicate with the lift or parcel locker. Thus, home delivery can be offered and the convenience of using ADVs is improved. Kapser and Abdelrahman [16] found that when the price is higher than the current price of courier delivery, customers are much less likely to accept ADVs. To increase customer use intentions, it is recommended that the price be equal to or lower than courier delivery. These findings are also consistent with our results as shown in Figure 2, with more than 90% of respondents indicating that they are not willing to pay more than 10% for ADV service compared to courier delivery. Moreover, the ADVs should be available for service late at night and during severe weather like couriers. The ADVs should have the ability to operate in such settings, such as being equipped with specific sensors and waterproof casing.The results also confirm a positive effect of social network in the context of ADVs, congruent with the findings of Higgins et al. [67] and Min et al. [60]. Once one person starts using ADVs, more and more of their friends and family also begin to use ADVs, with such use diffusing along their social networks. Therefore, the influence of social networks can be utilized to promote the expansion of ADV service. For example, ADV service providers can introduce some incentive strategies such as issuing discount coupons for ADV services that can be shared with their friends to encourage them to try ADV services. We suggest that ADV service providers invite individuals who have strong social influence to try ADV services, similar to the effect of celebrity product endorsements in advertisements.Along with the other positive findings of this study, a negative effect from perceived risk was also found, in line with previous studies [1, 14]. In our study, perceived risk, including delivery, privacy, and technical risk, are found to exert a negative effect on customer adoption intentions. Due to the need to upload a large amount of data such as addresses online to successfully provide ADV services, customers are sensitive about protecting their privacy. Because of the usage of the Internet in the process of delivery, system safety and robustness are also important components that should be considered to ensure the smooth operation of the whole process and prevent hacking. To address these concerns, we recommend the following three measures: First, the ADV should be installed with high-precision mapping and detection equipment to improve awareness of the surrounding environment, so that the customer can track the ADVs in real-time via an application. Second, service providers should strengthen the robustness of their respective systems. For example, after an attack perpetrated by hackers, a backup system prepared in advance could be used to ensure uninterrupted service. Third, with reference to the (H.R.3711) SELF DRIVE Act of America [81], ADV manufactures should establish a clear and reasonable data protection plan that encompasses the collection, use, sharing, and storage of information about vehicle owners or customers, in order to enhance customer confidence in data protection.Infection risk was rarely considered in previous studies. In the present study, it has proven to be an important positive predictor of customer intentions. Contactless delivery has become an effective method for reducing the spread of COVID-19 and other diseases while meeting daily needs. ADVs play an important role in epidemic prevention and control. We suggest that more ADVs be deployed in medium-high infection risk areas, including hospital settings.Green image also demonstrated a significant positive effect on customer intentions, in agreement with previous studies [1, 14, 18]. These findings indicate that protecting the environment has become increasingly important to customers. ADVs may be an especially attractive option given that they are powered by electricity and have a lower adverse impact on the urban environment as compared to the use of delivery trucks and motorcycles. We suggest that the adoption of ADVs could be accelerated by promoting their superior environmental performance as compared to the traditional option. Nonetheless, it should be acknowledged that vehicle electrification and the carbon intensity of electricity still have key influences on environmental performances [82].The results of the present study also indicate that social awkwardness is an important positive predictor of customers’ intentions. Perhaps due to the development of online shopping, social activities are decreasing, and people are more accustomed to being alone or just communicating with acquaintances. Direct contact with couriers may make some customers feel awkward or even be perceived as potentially dangerous, especially for women who live alone, thus making interactions with ADVs an acceptable or a desirable alternative. Although social awkwardness promotes the use of ADVs, it may also aggravate the degree of social isolation. To address this concern, ADV developers can develop more humanized interactions between ADVs and humans during delivery services.Use experience is also found to positively affect customer adoption intentions. The results of path analysis confirm that customers who have already used ADVs are more likely to accept ADV services. The reason may be that based on their use experiences, their understanding of ADV services is deeper and trust and confidence in ADVs services are increased by the direct use experience. Based on their previous experience with ADVs, they are more inclined to choose ADVs as a delivery option. If more customers are provided use opportunities, for example through trial activities held in some delimited areas such as industrial parks, then large-scale use of ADV services could be achieved.Lastly, age, income, and education are all found to be significant contributors to customers’ adoption intentions. While age exhibited a negative effect on adoption intentions, income and education both displayed positive effects. Compared to younger customers, older adults are more likely to be conservative and have less propensity to use new technologies. Thus, they are less willing to use driverless vehicles for delivery. For customers with lower incomes, a key reason that may discourage them from choosing ADVs is a concern about the delivery price. They may think that the delivery price of a new technology may be higher than the traditional courier delivery. Thus, they are more likely to choose courier delivery. Finally, customers with higher education may know more about ADVs. Therefore, they trust this new technology more and are more willing to adopt ADVs. Based on the previous analysis, two suggestions are proposed as follows: (1) the use of ADVs could be promoted among young and/or more highly educated customers, so they can act as early adopters in their social networks; with the assistance of such customers, older adults could gain more familiarity and comfort with ADV service processes; (2) a discount on the delivery price of the ADVs could be offered to promote its use among those who can serve as early adopters.


## 4.1. Demographic Analysis

The demographic details of respondents are shown in Table4. The percentages of respondents who are male and female are 42.40% and 54.85%, respectively. The percentage of respondents aged between 25–34 and 35–49 years are 23.44% and 36.61%, respectively. The distribution of other age groups generally mirrors that of the population studied, whereas the percentage of elderly over 65 is rather low due to the lower participation of the elderly in using intelligent devices. Respondents with average monthly disposable incomes of 2000–6000 RMB make up the largest proportion of respondents, accounting for 44.86% of them. Respondents with only a high school degree also account for the largest portion of respondents. The demographic analysis reveals that the sample is also representative for the Chinese population in terms of age and gender [78]. Therefore, it is possible to generalize the findings to a larger population in China. The number of respondents who have used instant delivery service and those who have not (50.65% and 49.35% respectively) are almost half and half. Takeaway food delivery is used most, followed by fresh food delivery and medicine delivery. It is worth noting that 56 respondents (8.1% of the survey sample) indicated that they already have experience using ADV services. At the same time, as shown in Figure 2, 58.32% respondents are not willing to pay extra for ADV service. Finally, the results shown in Figure 3 indicate that 58.93% of respondents remain neutral toward ADVs, which is consistent with previous research [16].Table 4
Demographic details of respondents.
VariableCategoryFrequencyPercentage (%)GenderFemale37954.85Male29342.40Third gender192.75AgeBelow 18 years11516.6419–249613.8925–3416223.4435–4923536.6150–647510.85Above 65 years81.16Monthly household disposable incomeBelow 2000 RMB14320.692000–6000 RMB31044.866000–10000 RMB12017.3710000–15000 RMB588.3915000–20000 RMB213.04Above 20000 RMB395.64EducationSecondary school certificate or below284.05High school degree24134.88Bachelor’s degree13319.25Master’s degree21330.82Doctorate588.39No degree182.60Instant delivery use experienceYes35050.65No34149.35Instant delivery services usedFresh food15522.43Take-away food28541.24Medicine8912.88Other426.08ADV use experienceYes568.10No63591.90Epidemic risk levelMedium and high-risk area233.33Low-risk area66896.67Figure 2
Percentage of respondents according to acceptable increase in cost for ADV service.Figure 3
Percentage of respondents according to attitude towards ADVs.


## 4.2. Measurement Model Assessment

Both the Cronbach’s alpha and CR for all the items of this study are found to be above 0.70, except the Cronbach’s alpha for AI (Table5). According to a study by McCrae et al. [79], the reason for this exception may be the low number of questions regarding AI in the survey [80]. The AVE values range from 0.612 to 0.926, which are all higher than 0.5, thus establishing validity. Factor loadings range from 0.669 to 0.972 are all also higher than 0.5 [76] (Table 5). The results show that convergent validity is established, indicating good agreement between two or more items that measure the same structure. Although the Cronbach’s alpha for AI is below 0.70, the rest of parameters of AI including factor loadings, CR, and AVE all meet the standard, thus subsequent analysis can be conducted.Table 5
Results for factor loading, Cronbach’s alpha, CR, AVE, and Sqrt of AVE.
VariableItemFactor loadingCronbach’s alphaCRAVEAVERARA10.9050.8990.9380.7900.889RA20.905RA30.897RA40.848COLCOL10.9190.9280.9590.8850.941COL20.957COL30.946COACOA10.9580.9330.9610.9260.962COA20.966SISI10.8800.7240.9050.8270.909SI20.938PRPR10.9160.9050.9690.8630.929PR20.918PR30.900PR40.959PR50.950GIGI10.9490.9340.9600.9230.961GI20.972SASA10.9510.8700.9600.9230.961SA20.970AIAI10.8810.5730.7590.6120.782AI20.669Notes. RA = relative advantage; COL = complexity; COA = compatibility; SI = social influence; PR = perceived risk; GI = green image; SA = social awkwardness; AI = adoption intention.The value ofAVE is presented on the diagonal in Table 6 and correlation coefficients are presented below the diagonal. Each variable has a greater AVE than the correlation coefficient, which demonstrates discriminant validity, i.e., that each construct is distinct from each other.Table 6
Results for discriminant validity (Fornell-Larcker criterion).
RACOLCOASIPRGISARA0.889COL−0.8300.941COA0.756−0.8160.962SI0.631−0.6980.6720.909PR0.321−0.3340.3160.4740.929GI0.584−0.6510.6560.6750.3480.961SA0.340−0.3630.3880.3940.5360.4240.961Notes. RA = relative advantage; COL = complexity; COA = compatibility; SI = social influence; PR = perceived risk; GI = green image; SA = social awkwardness.The results show that the proposed model has a greater fit to the data based on the standards showed in Table7.Table 7
Results of confirmatory factor analysis.
Indicesχ2dfCMIN/DFRMSEANFIGFIAGFIStandards——<3<0.08>0.7>0.9>0.7Results567.4862532.2430.0370.8250.9340.893


## 4.3. Structural Model Assessment

The measurement model assessment is followed by structural model assessment [73, 76]. The results, including path coefficients and p-values, are shown in Table 8 and Figure 4. Only when the p-value <0.05 is when the construct deemed to have a significant influence on intention, thus supporting the corresponding hypothesis. The analysis from Table 8 shows that eight of nine hypotheses are supported. More specifically, compatibility (β = 0.171, p<0.001), social influence (β = 0.205, p<0.001), infection risk (β = 0.076, p<0.05), green image (β = 0.229, p<0.001), social awkwardness (β = 0.302, p<0.001), and experience (β = 0.040, p<0.05) have a positive influence. Complexity (β = −0.294, p<0.001) and perceived risk (β = −0.149, p<0.001) have a negative influence on customers’ intentions. Hence, hypotheses 2, 3, 4, 5, 6, 7, 8, and 9 are supported. However, contrary to expectation, relative advantage (β = −0.058, p=0.195) does not have a significant positive influence. The practical reason may be that the relative advantage in this context relates to whether ADVs can complete the delivery process and meet the delivery demands of customers, while how goods are delivered is not the main concern of customers. This view was also expressed by many respondents during the offline investigation.Table 8
Results of path analysis.
HypothesisPathProposed effectβSignificanceResultH1RA ⟶ AI+−0.0580.195RejectedH2COL ⟶ AI−−0.294<0.001SupportedH3COA ⟶ AI+0.171<0.001SupportedH4SI ⟶ AI+0.205<0.001SupportedH5PR ⟶ AI−−0.149<0.001SupportedH6GI ⟶ AI+0.229<0.001SupportedH7IR ⟶ AI+0.076<0.05SupportedH8SA ⟶ AI+0.302<0.001SupportedH9UE ⟶ AI+0.040<0.05SupportedNotes. RA = relative advantage; COL = complexity; COA = compatibility; SI = social influence; PR = perceived risk; GI = green image; IR = infection risk; SA = social awkwardness; UE = use experience; AI = adoption intention.Figure 4
Results of path analysis. Notes:∗ means that this attribute is a significant contributor according to the analysis.In addition to the variables related to our hypotheses, the influence of control variables (i.e., age, gender, income, and education) on adoption intention is also studied (Table9). Except for gender, the control variables that were included in our survey are found to have significant effects on customers’ intentions, although the effects sometimes diverge in direction. In addition, in comparison with the previous results without control variables, the inclusion of age, gender, and income does not change the significance of any of the constructs (see Table 8). Thus, it is demonstrated that the model has good robustness.Table 9
Control variable results.
PathβSignificanceAge ⟶ AI−0.058<0.001Gender ⟶ AI−0.0140.475Income ⟶ AI0.058<0.01Education ⟶ AI0.054<0.05The results of path analysis provide evidence supporting DOI in the context of ADVs. The results also indicate that infection risk, use experience, and social awkwardness are important constructs affecting customer adoption intentions.


## 4.4. Discussion

In accordance with findings from previous studies [14, 60], complexity has a negative effect on customers’ intention to adopt ADVs in instant delivery. The processes supporting the pickup and return of goods, which are major components of complexity in the context of ADV service, should be as clear and efficient as possible to maximize customer ease of use of the service. An example process would be “face swiping,” which has become widely applied in check-in and -out services in Chinese railway stations for picking up and returning goods.In line with findings from Yoo et al. [14], Min et al. [60], and Hamad et al. [63], our results show that compatibility has a positive effect on customers adoption intentions toward ADV services. This indicates that when ADV service is more consistent with ’customers’ previous delivery experience and habits, ’customers’ adoption intentions are higher. For example, traditional courier service enables goods to be delivered directly to one’s doorstep, but ADVs may not be allowed to enter a given neighborhood or are not able to climb stairs. In these conditions, customers need to go downstairs to pick up goods. Therefore, some management regulations may need to be established to allow ADVs to operate in particular residential areas, and ADVs may need to be equipped with RFID technology to communicate with the lift or parcel locker. Thus, home delivery can be offered and the convenience of using ADVs is improved. Kapser and Abdelrahman [16] found that when the price is higher than the current price of courier delivery, customers are much less likely to accept ADVs. To increase customer use intentions, it is recommended that the price be equal to or lower than courier delivery. These findings are also consistent with our results as shown in Figure 2, with more than 90% of respondents indicating that they are not willing to pay more than 10% for ADV service compared to courier delivery. Moreover, the ADVs should be available for service late at night and during severe weather like couriers. The ADVs should have the ability to operate in such settings, such as being equipped with specific sensors and waterproof casing.The results also confirm a positive effect of social network in the context of ADVs, congruent with the findings of Higgins et al. [67] and Min et al. [60]. Once one person starts using ADVs, more and more of their friends and family also begin to use ADVs, with such use diffusing along their social networks. Therefore, the influence of social networks can be utilized to promote the expansion of ADV service. For example, ADV service providers can introduce some incentive strategies such as issuing discount coupons for ADV services that can be shared with their friends to encourage them to try ADV services. We suggest that ADV service providers invite individuals who have strong social influence to try ADV services, similar to the effect of celebrity product endorsements in advertisements.Along with the other positive findings of this study, a negative effect from perceived risk was also found, in line with previous studies [1, 14]. In our study, perceived risk, including delivery, privacy, and technical risk, are found to exert a negative effect on customer adoption intentions. Due to the need to upload a large amount of data such as addresses online to successfully provide ADV services, customers are sensitive about protecting their privacy. Because of the usage of the Internet in the process of delivery, system safety and robustness are also important components that should be considered to ensure the smooth operation of the whole process and prevent hacking. To address these concerns, we recommend the following three measures: First, the ADV should be installed with high-precision mapping and detection equipment to improve awareness of the surrounding environment, so that the customer can track the ADVs in real-time via an application. Second, service providers should strengthen the robustness of their respective systems. For example, after an attack perpetrated by hackers, a backup system prepared in advance could be used to ensure uninterrupted service. Third, with reference to the (H.R.3711) SELF DRIVE Act of America [81], ADV manufactures should establish a clear and reasonable data protection plan that encompasses the collection, use, sharing, and storage of information about vehicle owners or customers, in order to enhance customer confidence in data protection.Infection risk was rarely considered in previous studies. In the present study, it has proven to be an important positive predictor of customer intentions. Contactless delivery has become an effective method for reducing the spread of COVID-19 and other diseases while meeting daily needs. ADVs play an important role in epidemic prevention and control. We suggest that more ADVs be deployed in medium-high infection risk areas, including hospital settings.Green image also demonstrated a significant positive effect on customer intentions, in agreement with previous studies [1, 14, 18]. These findings indicate that protecting the environment has become increasingly important to customers. ADVs may be an especially attractive option given that they are powered by electricity and have a lower adverse impact on the urban environment as compared to the use of delivery trucks and motorcycles. We suggest that the adoption of ADVs could be accelerated by promoting their superior environmental performance as compared to the traditional option. Nonetheless, it should be acknowledged that vehicle electrification and the carbon intensity of electricity still have key influences on environmental performances [82].The results of the present study also indicate that social awkwardness is an important positive predictor of customers’ intentions. Perhaps due to the development of online shopping, social activities are decreasing, and people are more accustomed to being alone or just communicating with acquaintances. Direct contact with couriers may make some customers feel awkward or even be perceived as potentially dangerous, especially for women who live alone, thus making interactions with ADVs an acceptable or a desirable alternative. Although social awkwardness promotes the use of ADVs, it may also aggravate the degree of social isolation. To address this concern, ADV developers can develop more humanized interactions between ADVs and humans during delivery services.Use experience is also found to positively affect customer adoption intentions. The results of path analysis confirm that customers who have already used ADVs are more likely to accept ADV services. The reason may be that based on their use experiences, their understanding of ADV services is deeper and trust and confidence in ADVs services are increased by the direct use experience. Based on their previous experience with ADVs, they are more inclined to choose ADVs as a delivery option. If more customers are provided use opportunities, for example through trial activities held in some delimited areas such as industrial parks, then large-scale use of ADV services could be achieved.Lastly, age, income, and education are all found to be significant contributors to customers’ adoption intentions. While age exhibited a negative effect on adoption intentions, income and education both displayed positive effects. Compared to younger customers, older adults are more likely to be conservative and have less propensity to use new technologies. Thus, they are less willing to use driverless vehicles for delivery. For customers with lower incomes, a key reason that may discourage them from choosing ADVs is a concern about the delivery price. They may think that the delivery price of a new technology may be higher than the traditional courier delivery. Thus, they are more likely to choose courier delivery. Finally, customers with higher education may know more about ADVs. Therefore, they trust this new technology more and are more willing to adopt ADVs. Based on the previous analysis, two suggestions are proposed as follows: (1) the use of ADVs could be promoted among young and/or more highly educated customers, so they can act as early adopters in their social networks; with the assistance of such customers, older adults could gain more familiarity and comfort with ADV service processes; (2) a discount on the delivery price of the ADVs could be offered to promote its use among those who can serve as early adopters.


## 5. Conclusion

In this study, we propose a customer adoption model that applies a DOI theory model with three new constructs and that focuses on customer adoption intention toward ADV services in instant delivery. To our knowledge, this is the first study that considers impacts of use experience, infection risk, and social awkwardness in the context of ADVs. Eight of nine constructs (i.e., complexity, compatibility, social influence, perceived risk, infection risk, green image, social awkwardness, and use experience) are found to be statistically significant contributors to customer intentions toward adopting ADV services. Furthermore, 58.32% of the respondents surveyed in this study expressed a neutral attitude toward ADV adoption, while 47.03% of respondents expressed a willingness to pay more for ADV services compared to courier delivery. This study also examines the effects of age, gender, income, and education on adoption intentions. Income and education are found to have significant positive effects on the intention to adopt ADVs, while age is found to serve as a negative predictor. These results shed light on which socio-demographic factors exert the most influence on adoption intentions towards ADV service.Overall, this study offers some important insights into the influences of various constructs on customers’ intentions to adopt ADVs in instant delivery. This study not only enriches the research by providing a more refined theoretical framework for characterizing customer intentions toward adopting ADV services, but also contributes to promoting the expansion of ADVs in instant delivery. It is the first study to investigate customer adoption intention toward ADVs using DOI theory and broadening its application, rather than utilizing TAM theory, in recognition of the lack of end consumer concern for matters related to the direct use of ADVs, with personal innovativeness and social influence being more prominent factors affecting customer intentions toward ADVs. New constructs are incorporated into the proposed model to adapt to the specific context of ADVs, including infection risk, social awkwardness, and use experience, with each found to be important predictors of customer intention. In terms of practical implications, the findings of this study can contribute to the formulation of relevant regulations (data privacy, security risks, etc.), marketing strategies (such as discount coupons) and technology development (such as RFID and waterproofing) for ADVs, furthering the popularization of their use.The study is not without limitations. The samples involved in this study are all from China, so future research should examine sample data covering different countries in order to validate the model proposed in this study and to differentiate geographical effects. In addition, only 8.1% of respondents in our survey sample indicated prior ADV service use experience. Future research could investigate customers with more experience with ADV service and conduct multigroup analysis to compare adoption intentions between non-users and users, identifying possible differences in key variables such as the willingness to pay more. Another limitation is that it is a cross-sectional study that was conducted during the COVID-19 pandemic. Therefore, future investigation could include a longer investigation period and compare the results to postpandemic periods.



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*Source: 3440691-2023-03-08.xml*