Adoption of Incremental and Radical Innovations in E-Commerce Delivery: Evidence from Smart Lockers and Autonomous Drones Using the UTAUT Model

Abstract

With the rapid advancement of e-commerce delivery technologies, understanding consumer responses to different types of innovations has become increasingly important. This study examines how consumers react to incremental innovations (e.g., smart lockers) versus radical innovations (e.g., autonomous drones) by integrating personal innovativeness into the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Based on 300 valid survey responses from Chinese consumers and analyzed using structural equation modeling (SEM), the findings demonstrate that personal innovativeness significantly influences key adoption determinants—performance expectancy, effort expectancy, social influence, and facilitating conditions. The adoption of smart lockers is primarily driven by perceived performance and convenience, whereas the adoption of autonomous drones is more strongly shaped by social influence. The proposed model provides both theoretical and practical implications for firms seeking to promote diverse e-commerce delivery technologies.

1. Introduction

With the rapid advancement of technology and the growth of e-commerce, retailers and e-commerce delivery service providers are actively exploring new delivery methods to address the challenges of low efficiency, high costs, and high failure rates in the ‘last mile’ delivery segment [1]. Traditional door-to-door delivery models are gradually being replaced by more efficient and flexible solutions, particularly as consumer demand for delivery convenience and autonomy rises [2]. In recent years, smart lockers and autonomous drones have emerged as two representative technologies, embodying incremental and radical logistics innovation pathways, respectively. The former enhances terminal flexibility through time-slotted pickup, while the latter optimizes the overall delivery structure through automation and aerial transportation [3,4].

Despite the continuous evolution of technology, whether consumers are willing to adopt and continue using these new e-commerce delivery services remains the key factor determining the success of their commercialization [5]. Existing studies have primarily focused on how technology enhances the operational efficiency of e-commerce delivery [6,7], while research on the psychological mechanisms underlying consumer adoption behavior, particularly individual differences, remains limited. Some studies have employed the UTAUT model, the Theory of Planned Behavior (TPB), or the Innovation Diffusion Theory to explore factors influencing technology adoption, but these often emphasize rational decision-making pathways while neglecting consumers’ intrinsic motivations and trait differences [8,9].

Against this backdrop, individual innovation as a stable psychological trait has been shown to significantly impact technology adoption behavior [10,11]. Highly innovative consumers are more willing to actively try new technologies, and when faced with radical innovations like autonomous drones, they demonstrate more positive adoption intentions. In the current context of the diversified and differentiated evolution of logistics technologies, identifying individual behavior patterns in various technological settings holds important practical value for enhancing corporate technology deployment efficiency and user acceptance.

Therefore, this study combines the UTAUT model with individual innovation variables to systematically analyze the behavioral differences of consumers in adopting e-commerce delivery services under progressive and radical delivery technology contexts. The aim is to fill the theoretical gap in consumer behavior research regarding the interactive mechanism between individual characteristics and technology types and to provide more behaviorally insightful theoretical and practical support for the implementation of e-commerce delivery technology innovation.

In summary, this study examines the theoretical and practical needs of the current e-commerce delivery sector regarding the adoption of emerging technologies. It further investigates two core research questions: (1) How does the integration of personal innovativeness into the UTAUT model enhance the understanding of consumers’ intentions to adopt new technologies in e-commerce delivery systems? This question seeks to improve the traditional UTAUT model by incorporating psychological characteristic variables at the individual consumer level and exploring the impact mechanism on the core pathways of performance expectancy, effort expectancy, social influence, and facilitating conditions, thereby revealing how individual innovativeness plays a crucial role in the technology adoption process. (2) How do consumers’ adoption intentions differ between incremental and radical innovation contexts when applying the UTAUT model to e-commerce delivery technologies? This question further differentiates between two forms of technological innovation (incremental vs. radical) and, by comparing the adoption paths of smart lockers and autonomous drones, examines the variations in the role of UTAUT constructs on behavioral intention and usage behavior under different innovation types, thereby illustrating how the type of technology moderate’s consumer decision-making logic.

The rest of this study is summarized as follows: Section 2 reviews the related literature. Section 3 constructs a conceptual model based on the theoretical foundations of this study and the proposed research hypotheses. Section 4 elaborates on describing the specific variables and the process of data collection in this study. Section 5 analyzes the collected data using the structural equation modeling research methodology. Finally, Section 6 discusses and summarizes this study’s theoretical contributions, practical implications, and research limitations.

2. Literature Review

2.1. The UTAUT Model

The Unified Theory of Acceptance and Use of Technology (UTAUT) was proposed by Venkatesh et al. [12]. It integrates multiple classic technology adoption models (such as TAM, TPB, and IDT) to provide a structurally complete theoretical foundation for understanding individuals’ behavioral intentions and actual usage behavior in new technology usage contexts [12,13,14]. The UTAUT model proposes four core variables: Performance expectancy, effort expectancy, social influence, and facilitating conditions, which collectively determine the extent to which users adopt the technology. Performance expectancy refers to the extent to which individuals believe that using the technology will improve efficiency and performance; effort expectancy emphasizes the ease of use of the technology itself; social influence reflects the impact of essential others (such as family, friends, and colleagues) on adoption decisions; and facilitating conditions reflect the role of external resources and support systems in promoting usage behavior. Compared to other models, UTAUT incorporates both individual cognitive factors and social and technological environmental factors, making it particularly suitable for explaining the adoption of emerging technologies in e-commerce delivery [15,16]. In this study, consumers’ acceptance of smart lockers (an incremental innovation) and autonomous drones (a radical innovation) is driven by the interaction of multiple cognitive and social influence pathways, and the UTAUT framework is well-suited to capture this multidimensional decision-making mechanism. A comparative analysis of UTAUT and other theoretical models in E-commerce delivery contexts is presented in

Table 1. Comparative analysis of theoretical models in E-commerce delivery contexts.

Model Name	Core Variables	Strengths	Limitations	Representative Studies
UTAUT (Recommended)	Performance expectancy, effort expectancy, social influence, facilitating conditions	Comprehensive, explains adoption behavior holistically, high predictive accuracy	Requires further cultural adaptation	Venkatesh et al. (2003) [12], Pinyanitikorn et al. (2024) [17]
TAM	Perceived usefulness, perceived ease of use	Simple, easy to apply	Ignores social factors and external conditions	Davis (1989) [18], Min et al. (2008) [19]
TPB	Attitude, subjective norms, perceived behavioral control	Emphasizes psychosocial factors	Lacks focus on specific technology attributes	Ajzen (1991) [20]
DOI	Relative advantage, compatibility, complexity, trialability, observability	Focuses on technology diffusion process	Neglects individual psychological differences and detailed perception analysis	Rogers et al. (2014) [21]
S-O-R	Stimulus, organism (emotion, cognition), response	Considers emotional and psychological factors	Complex quantitative analysis, empirical challenges	Mehrabian & Russell (1974) [22], Bu et al. (2021) [23]

.

The UTAUT model has been widely applied to study technology adoption in various contexts [8,24,25]. In the logistics field, this includes consumer use of e-commerce delivery technologies such as smart lockers and autonomous drones, logistics and supply chain management companies’ intentions for sustainable use of blockchain, and resource integration of e-commerce delivery platforms. These studies have generally confirmed the predictive ability of the model while emphasizing the need to adjust it according to specific technological and industry contexts. The UTAUT model has been used extensively to study technology adoption in a variety of contexts and has shown good applicability in the e-commerce delivery and supply chain sectors. For example, Pinyanitikorn et al. [17] used the model to analyze companies’ intentions to continue using blockchain technology, highlighting the need to adapt the model appropriately to the specific characteristics of the industry and technology. However, it is important to note that the UTAUT model was originally developed in a Western cultural context [12]. Its core variables may be influenced by different cultural contexts. The research suggests that in collectivist cultures, users are more influenced by social groups or authoritative organizations, which may amplify the impact of social influence (SI) on behavioral intentions [16,26]. However, many empirical studies have shown that the UTAUT model is still very applicable in China. For example, Venkatesh and Zhang [27] compared the technology adoption behaviors of users in China and the United States. They found that the UTAUT model was effective in explaining user intentions in both cultures. Bu et al. [23] confirmed the model’s applicability in the Chinese culture by examining the acceptance of the concept of ‘privacy by design’ by information systems engineers, confirmed the applicability of the model in the Chinese cultural context and found that the UTAUT model can effectively explain user intentions in both cultural contexts.

In user adoption studies of e-commerce delivery technologies, although different theoretical models (e.g., technology acceptance model (TAM), Theory of Planned Behavior TPB, Diffusion of Innovations Theory (DOI), and Stimulus–Organism–Response Model (S-O-R)) are widely used, comparative analyses have revealed that the Unified Theory of Acceptance and Use of Technology (UTAUT) has a distinctive advantage [12,17]. Specifically, TAM, although concise and effective, lacks consideration of social influences and external factors [18,19]; TPB emphasizes psychosocial factors but neglects the role of technological characteristics [20]; and DOI models, although focusing on innovation characteristics and the process of technological diffusion, lack in-depth analyses of individual psycho-cognitive mechanisms [21]. The S-O-R model focuses on affective factors but lacks a clear quantitative structure [22,23]. In contrast, the UTAUT model, by integrating multiple dimensions such as performance expectancy, effort expectancy, social influence, and convenience conditions, can more comprehensively and systematically capture users’ multidimensional perceived factors of emerging delivery technologies (e.g., smart lockers, autonomous drones), and is more suitable for explaining complex e-commerce delivery technology adoption behaviors.

2.2. Personal Innovativeness

Previous studies have shown that innovation can be defined as ideas, practices, or technologies that individuals or groups perceive as novel [28]. In studies of technology adoption behavior, consumers’ personal innovativeness has been widely regarded as an important indicator of their willingness to adopt new technologies, as it is a stable individual psychological characteristic [25,29,30]. According to Maslow’s cognitive needs theory, consumers exhibit high innovativeness because of their continuous pursuit of novel experiences and technological progress [31]. This characteristic makes them more open and willing to try new technologies.

Personal innovativeness is not only reflected in active attention to new technologies but also in its critical influence on actual usage decisions. Existing research has pointed out that consumers with higher innovation tendencies often possess stronger learning abilities and adaptability and are, therefore, more willing to invest time and effort in understanding and trying new e-commerce delivery technologies [32]. Even without relying on the opinions or experience of others, such consumers can independently judge the value of a technology and make adoption decisions. For example, in the context of smart lockers and autonomous drones, highly innovative individuals demonstrate higher acceptance rates and maintain a positive attitude toward the potential risks associated with e-commerce delivery technologies [26,33]. This ability enables them to serve as ‘innovation leaders’ during the initial stages of technology diffusion, thereby exerting an effect on surrounding consumers.

Further, innovation diffusion theory also emphasizes that highly innovative individuals are often active adopters of new technological information. They can form higher adoption intentions and practical behaviors in uncertain situations. Smart lockers and autonomous drones, as emerging technologies in e-commerce delivery, have developed rapidly in highly uncertain and changing environments, particularly during the COVID-19 pandemic [8,34,35]. They have become important tools for improving logistics efficiency and service quality and are gradually becoming mainstream delivery methods. Therefore, when understanding the acceptance mechanism of consumers towards e-commerce delivery technologies, the variable of personal innovativeness has important theoretical value and practical significance.

Therefore, this study introduces personal innovativeness into the UTAUT model, which not only enriches the original model’s explanatory dimensions of individual differences but also provides a more comprehensive analytical framework for exploring the psychological mechanisms underlying consumer adoption of different types of innovative technologies.

2.3. Incremental vs. Radical Innovation

Previous studies have generally categorized innovation into two broad types: incremental innovation and radical innovation. Henderson and Clark [36] noted that the boundary between the two is not always clear-cut. Most technological updates typically involve optimizing and expanding existing technologies and processes, which fall under incremental innovation. Radical innovation, on the other hand, may completely overhaul existing systems, disrupting established technological pathways and business logic and exerting a fundamental impact on industry structures [36]. Specifically, incremental innovation typically relies on established technological frameworks, focusing on the continuous optimization of product performance or service processes. It is relatively easy to implement, with manageable costs and risks, and is therefore widely adopted in routine business operations and efficiency improvements. However, it has a limited impact on consumer perceptions and behavior patterns, and the adoption process often exhibits path dependence [37]. In contrast, radical innovation is characterized by higher technical novelty and market uncertainty, often accompanied by significant changes in business models. While such innovation holds great potential to enable companies to achieve leapfrog development, it also imposes higher demands on users’ cognitive acceptance and behavioral decision-making. Successful technology diffusion requires overcoming significant psychological and social barriers [38,39].

In service-oriented industries, particularly in the e-commerce delivery sector, technological innovation is emerging as a key driver of the industry’s intelligent transformation. Traditional delivery services heavily rely on human resources and standardized routes, which struggle to meet the growing demands of contemporary consumers for flexibility and efficiency. As a result, companies in the e-commerce delivery sector are pursuing two primary paths to upgrade their services: on the one hand, they are leveraging incremental technological improvements, such as optimizing last-mile delivery and enhancing user autonomy; on the other hand, they are actively exploring breakthrough innovations, such as autonomous delivery systems and automatic recognition technologies, to redefine service models and enhance user experience.

This study focuses on two representative technologies—smart lockers and autonomous drones, which, respectively, represent mature and widely adopted technological optimizations in current e-commerce delivery services and cutting-edge breakthrough applications under the backdrop of emerging high-tech developments. Smart lockers alleviate the waiting and failure issues associated with traditional ‘last mile’ delivery by establishing a self-service pickup system and integrating end-to-end delivery resources, thereby significantly enhancing service convenience [3]. In contrast, autonomous drones overcome traditional transportation path constraints through highly maneuverable flight platforms, particularly demonstrating unique advantages in remote or traffic-congested areas [40].

3. Theoretical Framework and Hypotheses

3.1. Theoretical Premise

All the theoretical assumptions of this study are grounded in the UTAUT theoretical model discussed in the previous chapters of the literature, and the original model has been optimized and expanded. In addition to retaining the four key elements of the original model—performance expectancy, effort expectancy, social influence, and facilitating conditions—this study specifically introduces “personal innovativeness” as a significant factor influencing consumers’ adoption of new e-commerce delivery technologies. These factors comprehensively capture consumers’ perceptions of the technology’s intrinsic characteristics, external support conditions, and social dimensions. The research process is divided into three phases: the first phase analyzes consumers’ personal traits and the internal and external attributes of the technology; the second phase examines the impact of the new e-commerce delivery technology on consumers’ behavioral intentions; and the third phase provides an in-depth analysis of how consumers’ behavioral intentions can be transformed into actual usage behaviors. Through these three closely interrelated stages, we systematically reveal the role of various factors in consumers’ adoption of new e-commerce delivery technologies.

Additionally, this study selects incremental versus radical innovation as the background and foundation for proposing theoretical hypotheses. In the context of ongoing service innovations within the e-commerce delivery industry, the public tends to adopt favorable technologies. Furthermore, due to the influence of the social environment, consumers’ adoption of new technologies is a complex process. By enhancing the integration capabilities of e-commerce delivery providers, continually improving existing technologies, and exploring the market to identify consumer needs and technological development opportunities, companies have recognized and created innovation opportunities, choosing the right type of innovation to adapt to environmental changes and consumer preferences.

3.2. Conceptual Framework

A technology’s performance expectancy (PE), also known as perceived usefulness, refers to the perception of the benefits an individual will gain when adopting new technology. Additionally, performance expectancy implies that the technology must be useful to motivate consumers’ behavioral intentions. Individual innovativeness can influence consumer evaluations of new technologies across various contexts. Prior research has indicated that innovative individuals play a crucial role in the diffusion of new technologies, as they develop positive attitudes toward them by gathering adequate information [41]. Personal innovativeness signifies a person’s potential risk-taking ability to try new things and innovate while simultaneously reflecting their willingness to change [42]. Consequently, individuals with higher levels of personal innovativeness tend to be more proactive in understanding and experiencing new e-commerce delivery technologies. In the process of gathering and analyzing information, they are more likely to recognize the benefits of new e-commerce delivery technologies in terms of improving efficiency, reducing costs, and enhancing the service experience; thus, they hold higher expectations for the performance of such technologies, meaning they believe that the technology can provide them with greater practical value.

Therefore, this study proposes the following hypotheses:

H1: 

Personal innovativeness positively influences consumers’ performance expectancy of using e-commerce delivery technology.

Distribution options are feasible in real life [43]. The study by Kuo and Yen [44] concluded a positive correlation between personal innovation and technology adoption; consumers with higher levels of personal innovation are more likely to adopt new technologies [44]. Many researchers have determined that personal innovativeness can influence interactions with new technologies [45]. Therefore, consumers with high personal innovativeness typically possess strong learning abilities and adaptability, making them more willing to proactively invest time and effort to understand and become familiar with the operational processes of new e-commerce delivery technology. Due to their tendency to actively explore new ideas, this group of consumers can master the use of e-commerce delivery technology more swiftly and is more likely to overcome the challenges encountered during the learning process.

Therefore, this study proposes the following hypotheses:

H2: 

Personal innovativeness positively influences consumers’ effort expectancy to use e-commerce delivery technology.

It is argued that social influence (SI) may affect an individual’s assessment of their confidence or ability to use new technologies and systems [46]. At the same time, social influence has been recognized as a key factor in the literature on innovation diffusion. The support from influential others significantly impacts the actions that potential adopters choose to take as individuals adjust their behavior to their social environment [47]. It has been suggested that people with high personal innovativeness (PI) require a less positive attitude toward new technologies than those with lower personal innovativeness. In other words, individuals with greater personal innovativeness may generally have a more favorable acceptance of new technologies, while their perceptions of these technologies may not be as demanding [48]. Personal innovativeness can determine whether consumers are more willing or likely to adopt new technologies, as they tend to be more proactive in sharing their own experiences and perceptions of using new technology or in accepting recommendations from others regarding new technology. Due to their own positive acceptance and promotion of new technologies, they can influence the perceptions and attitudes of those around them toward new e-commerce delivery technologies.

Therefore, this study proposes the following hypotheses:

H3: 

Personal innovativeness positively influences the social influence of consumer use of e-commerce delivery technology.

Facilitating conditions (FC) refer to the organizational support provided to technology users, which can influence their system usage [49]. An individual’s personal innovativeness significantly shapes their subjective perception of new information technologies and reflects their willingness to embrace these innovations. Consequently, the level of individual innovativeness directly impacts the relationship between technology perception and acceptance. Furthermore, throughout the innovation diffusion process, consumers’ perceptions are not merely a matter of individual understanding; rather, they are shaped by an interactive process that is profoundly influenced by the social environment and technological conveniences [50]. Consumers who exhibit higher personal innovativeness, driven by their strong interest in new technologies and proactive exploration, tend to be more engaged in seeking out relevant information regarding new e-commerce delivery technologies. They also actively communicate with e-commerce delivery providers, expressing their needs and expectations for these new technologies. To address the needs of consumers with high personal innovativeness, enterprises are likely to allocate more resources and energy to enhance the technical support system, ensuring that these consumers can utilize new e-commerce delivery technology efficiently.

Therefore, this study proposes the following hypotheses:

H4: 

Personal innovativeness positively influences consumers’ facilitating conditions to use e-commerce delivery technology.

In the context of incremental and radical technological innovation discussed in this study, the new delivery technologies deployed by e-commerce platforms (such as smart lockers and autonomous drones) are widely regarded as important means of enhancing consumer experience and enterprise service quality. Innovative technologies not only optimize the last-mile delivery process but also reshape consumers’ overall perception of logistics services. The introduction of such technologies has been proven to provide users with greater convenience, efficiency, and autonomy, thereby enhancing their overall perceived value [51]. The research indicates that significant improvements in service quality often positively influence consumers’ willingness to adopt technology, with “performance expectancy” identified as one of the most critical predictive factors [12]. When consumers perceive that e-commerce delivery technologies can effectively reduce delivery times, enhance delivery flexibility, and lower package loss rates, they are more likely to actively adopt such technologies [24]. For example, smart lockers enhance users’ sense of time control through time-slot notifications and flexible pickup mechanisms. Autonomous drones improve perceived safety and efficiency by shortening transportation routes and offering contactless services. If consumers perceive these experiences as “useful”, they are more likely to develop positive adoption intentions [18].

Therefore, this study proposes the following hypotheses:

H5: 

Performance expectancy positively influences consumers’ behavioral intentions to use e-commerce delivery technology.

In technology adoption research, effort expectancy (EE) refers to an individual’s subjective judgment of the amount of effort required to use a particular technology [12]. The existing literature generally indicates that when users perceive new technology as easy to learn and operate, they are more likely to form positive adoption intentions [18,52]. Technology usability not only influences cognitive costs but also affects users’ time expectations, psychological stress, and behavioral motivation [53]. This perspective is particularly important in the promotion of innovative technologies in service industries, especially in situations where consumers must bear some learning costs. In e-commerce delivery scenarios, if new delivery technologies (such as smart lockers or autonomous drones) are perceived as easy to operate, have clear interfaces, and provide user-friendly guidance, consumers are more likely to develop confidence and motivation to use them [54]. For example, smart lockers provide commuters or busy workers with a self-service solution for ‘flexible parcel collection’, reducing the psychological burden and time wasted from missing deliveries. The contactless and high-efficiency characteristics of autonomous drones also make them highly attractive in situations with low cognitive load [55]. Especially in the context of a pandemic or labor shortages at the last mile, technological ease of use becomes a key barrier to adoption for consumers.

Therefore, this study proposes the following hypotheses:

H6: 

Effort expectancy positively influences consumers’ behavioral intentions to use e-commerce delivery technology.

In the contemporary context of highly integrated intelligent technologies and social media, consumers’ attitudes toward and adoption of new technologies are often driven not only by their personal perceptions but also significantly influenced by the opinions and behaviors of those around them [56]. Especially when ‘significant others’ such as friends, colleagues, or family members express support or actively recommend a particular technology, consumers are more likely to reduce their uncertainty about the technology, enhance their confidence in using it, and increase their willingness to adopt it [12]. In the field of e-commerce delivery, the pace of technological innovation is accelerating, and consumers often find themselves in a state of information asymmetry or lack of experience when faced with new services such as smart lockers or autonomous drones. In such situations, social influence, as an indirect source of experience, can reduce consumers’ psychological resistance and trial costs. For example, when friends or family members give positive evaluations of the convenience of smart lockers or showcase the quick delivery experience of drones on social media, audiences are more likely to feel positive social recognition, thereby increasing their adoption intentions [5]. Furthermore, social influence plays a particularly critical role in the early stages of high-tech diffusion, as it not only provides external behavioral references but also creates a kind of ‘group norm’ pressure, prompting individuals to develop motives to ‘assimilate’ or ‘conform to the mainstream’ [20,46]. This is particularly evident in radical innovations such as drones, as their high novelty typically comes with greater uncertainty and higher acceptance barriers, leading individuals to rely more on social evaluations to form their judgments.

Therefore, this study proposes the following hypotheses:

H7: 

Social influence positively affects consumers’ behavioral intentions to use e-commerce delivery technology.

In the context of e-commerce delivery, convenience is manifested in the following specific ways: whether consumers can obtain sufficient guidance and assistance, whether they possess the basic knowledge required to use new technologies, and whether they trust the infrastructure systems on which these technologies rely [57]. If consumers believe they possess the necessary operational capabilities or can obtain effective support from logistics service providers, platform systems, or nearby personnel when encountering difficulties, they are more likely to form positive behavioral intentions. Additionally, as an extremely dependent service chain, consumers’ expectations regarding the stability, safety, and reliability of e-commerce delivery also constitute an important component of convenience condition perception. For example, if the operating interface of smart lockers is sufficiently simple and intuitive, or if the autonomous drone system can guarantee real-time tracking and anti-loss mechanisms for parcels, consumers will be more confident in adopting the technology. Especially in non-face-to-face delivery scenarios where face-to-face services are lacking, the reliability of system technology and the availability of external support become key sources of users’ perceived convenience [24]. From the perspective of behavioral pathways, the role of convenience conditions extends beyond whether the technology is ‘usable’ and further influences whether consumers are ‘willing to use’ it. When external barriers to technology use are minimized, consumers’ psychological resistance naturally decreases, and behavioral intent increases accordingly. Therefore, in the context of the widespread deployment of intelligent logistics and delivery technologies, the influence of convenience conditions on consumer adoption intent cannot be overlooked.

Therefore, this study proposes the following hypotheses:

H8: 

Facilitating condition positively influences consumers’ behavioral intentions to use e-commerce delivery technology.

Behavioral intention is regarded as an individual’s subjective judgment and psychological inclination regarding their future use of a new technology, reflecting the strength of their willingness to adopt it [9]. According to the theory of technology adoption and planned behavior, behavioral intention is typically regarded as the most direct antecedent of actual usage behavior. If consumers form positive attitudes toward a particular e-commerce delivery technology during the cognitive process, they are more likely to convert this intention into actual usage behavior. In the context of e-commerce delivery technology applications, factors influencing behavioral intention include the technology’s ease of use, usefulness, social influence, trust, and perceived security. When e-commerce delivery service providers enhance consumer experience by optimizing user interfaces, improving system stability and delivery efficiency, and strengthening security measures, they often effectively stimulate consumers’ positive behavioral intentions and further promote their actual adoption behavior [12]. Therefore, once consumers psychologically recognize a delivery technology, their adoption behavior will also be more stable and sustainable.

Therefore, this study proposes the following hypotheses:

H9: 

Behavioral intentions positively influence consumers’ use behavior of e-commerce delivery technology.

Adoption studies of e-commerce delivery technologies cover a wide range of technologies, such as autonomous drones, automated robots, and smart lockers. Valencia-Arias et al. [58] studied autonomous drones in Colombia based on DOI and TAM and found that performance risk, compatibility, and environmental friendliness are the key drivers, but they did not consider social influence, and the study is regionally limited. Osakwe et al. [59] similarly applied DOI and TAM to analyze autonomous drones cross-country, noting privacy risk and complexity as key barriers, ignoring social and facilitating conditions. Tang et al. [60] studied smart lockers in China based on service quality theory, emphasizing the impact of convenience and reliability on adoption but lacking technology acceptance theory support. Jazairy et al. [61] provided an overview of automated robot delivery from a logistics perspective, identifying technological advances and legislation as key factors with descriptive analyses and a lack of theoretical depth. Koh and Yuen [62] studied automated robots using HBM and TTF, highlighting the importance of task-technology fit, but the HBM application is not sufficiently relevant and does not fully explore social factors.

Table 2. Studies on E-commerce delivery technology adoption.

Source	Theory	Main Factors	Technology
Valencia-Arias et al. (2022) [58]	DOI, TAM	Performance risk, compatibility, personal innovativeness, environmental friendliness relative advantage	Autonomous drones
Osakwe et al. (2022) [59]	DOI, TAM	Delivery risk, privacy risk, performance risk, speed relative advantage, complexity, compatibility, personal innovativeness	Autonomous drones
Tang et al. (2021) [60]	Service quality, customer satisfaction	Service price, service reliability, convenience, failure handling capability, service diversity	Smart lockers
Jazairy et al. (2024) [61]	Logistics management perspective	Technological advancement, legislation, user acceptance, social intelligence	Autonomous robots
Koh & Yuen (2023) [62]	HBM, TTF	Outcome expectation, task-technology fit, perceived usefulness, perceived ease of use	Autonomous robots

provides a summary of these studies on e-commerce delivery technology adoption.

Taken together, UTAUT is better suited to explain the adoption of e-commerce delivery technologies due to the integration of performance expectancy, effort expectancy, social influence, and facilitating conditions. For example, Osakwe et al.’s [59] privacy risk can be analyzed by the social impact of UTAUT, Tang et al.’s [60] convenience fits with performance expectancy, and Nguyen et al.’s [54] task-technology fit can be mapped to facilitating condition. The comprehensiveness of UTAUT compensates for the limitations of DOI, TAM, and HBM and provides future research with a more comprehensive framework.

This study proposes an empirical research model based on the above theoretical premises and conceptual framework within the research hypotheses, referring to Figure 1.

4. Methodology

4.1. Measurement Items

To empirically test the proposed theoretical model, a structured questionnaire survey was administered to collect data from residents in China. The questionnaire was carefully designed and divided into three main sections to ensure clarity and alignment with the research objectives. Part A introduced the research background and purpose, emphasizing the significance of the study to encourage participants’ engagement and understanding. Part B gathered demographic information, including gender, age, education level, and monthly income, to provide a comprehensive profile of the respondents and facilitate subgroup analysis. Part C focused on measuring seven theoretical constructs central to the study: performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), personal innovativeness (PI), behavioral intention (BI), and use behavior (UB) related to the two technologies. Most measurement items for PE, EE, SI, FC, BI, and UB were adapted from Venkatesh et al. [12], which provides a well-validated framework for examining technology acceptance and usage. Meanwhile, the items for personal innovativeness were adopted from Thakur and Srivastava [63] to capture individual differences in openness to new technologies. All constructs were measured using a seven-point Likert scale, ranging from “strongly disagree” (1) to “strongly agree” (7). This scale is commonly employed in technology adoption research as it provides sufficient granularity for capturing respondents’ attitudes [12].

To ensure data reliability, an attention-check question was embedded in Part C. This required participants to select “strongly disagree” for a specific statement, ensuring that respondents carefully read and answered each question. Responses failing this check were automatically excluded from further analysis, following established practices for improving data quality in survey research. Recognizing the potential for cognitive fatigue and reduced data quality when asking respondents to evaluate multiple technologies simultaneously [64], the questionnaire was divided into two versions. Each version focused exclusively on one of the two technologies under study: (a) smart lockers (incremental innovation) or (b) autonomous drones (radical innovation). Respondents were randomly assigned to complete one version of the questionnaire, ensuring an equal distribution of participants across both technology contexts. This approach not only reduced respondent burden but also enhanced the comparability and reliability of responses for each technology. The constructs and measurement items are summarized in

Table 3. Constructs and measurement items.

Construct	ID	Measurement Item	Source
Performance Expectancy (PE)		From 1 = strongly disagree to 7 = strongly agree	(Venkatesh et al., 2003) [12]
	PE1	Using smart lockers/autonomous drones increases my chances of achieving things that are important to me.
	PE2	Using smart lockers/autonomous drones helps me accomplish things more quickly
	PE3	Using smart lockers/autonomous drones increases my productivity.
Effort Expectancy (EE)		From 1 = strongly disagree to 7 = strongly agree	(Escobar-Rodríguez & Carvajal-Trujillo, 2014; Venkatesh et al., 2003) [12,65]
	EE1	My interaction with smart lockers/autonomous drones is clear and understandable.
	EE2	It is easy for me to become skillful at using smart lockers/autonomous drones.
	EE3	I think picking up a package from smart locker/autonomous drones is simple.
Social Influence (SI)		From 1 = strongly disagree to 7 = strongly agree	(Venkatesh et al., 2003) [12]
	SI1	People who are important to me think that I should use smart lockers/autonomous drones.
	SI2	People who influence my behavior think that I should use smart lockers/autonomous drones.
	SI3.	People whose opinions I value prefer that I use smart lockers/autonomous drones.
Facilitating Conditions (FC)		From 1 = strongly disagree to 7 = strongly agree	(San Martín & Herrero, 2012; Venkatesh et al., 2003) [12,66]
	FC1	I have the resources necessary to use smart lockers/autonomous drones
	FC2	I have the knowledge necessary to use smart lockers/autonomous drones.
	FC3	Smart lockers/autonomous drones are compatible with other technologies I use.
Personal Innovativeness (PI)		From 1 = strongly disagree to 7 = strongly agree	(Thakur & Srivastava, 2014) [63]
	PI1	I heard about smart lockers/autonomous drones; I would look for ways to experiment with them.
	PI2	Among my peers, I am the first one to try out smart lockers/autonomous drones.
	PI3	In general, I am not hesitant to try out smart lockers/autonomous drones.
Behavioral Intention (BI)		From 1 = strongly disagree to 7 = strongly agree	(Venkatesh et al., 2003) [12]
	BI1	I intend to continue using smart lockers/autonomous drones in the future.
	BI2	I will always try to use smart lockers/autonomous drones.
	BI3	I plan to continue to use smart lockers/autonomous drones frequently.
Use Behavior (UB)		From 1 = strongly disagree to 7 = strongly agree	(Patil et al., 2020; Venkatesh et al., 2003) [12,67]
	UB1	I use smart lockers/autonomous drones.
	UB2	I use smart lockers/autonomous drones during the delivery process.
	UB3	When shopping online, I choose to use smart lockers/autonomous drones for the delivery process.

.

4.2. Data Collection and Bias Tests

We employed a questionnaire-based survey to collect primary data using Credamo (www.credamo.com), a professional online research platform commonly utilized in academic research. At the beginning of the questionnaire, screening questions were included to verify whether the participants met the predefined criteria, such as having recent experience with e-commerce delivery services and being familiar with at least one of the two technologies under study. Respondents who failed to meet the screening requirements were automatically disqualified by the survey platform. To reduce self-selection bias and encourage truthful responses, the introduction to the questionnaire did not disclose the desired traits or target characteristics. To ensure respondent attentiveness and filter out careless responses, attention-check questions were embedded within the survey. Respondents who failed either of these checks were excluded from the final dataset.

A total of 386 questionnaires were collected for this study. After excluding 86 responses that failed to pass the attention check questions, 300 valid responses were retained for analysis, with 150 responses corresponding to each technological context: smart lockers (incremental innovation) and autonomous drones (radical innovation). A service fee was paid to the platform to ensure participant incentives and data reliability. We conducted several bias tests to examine the data quality. First, to evaluate non-response bias, we divided the sample based on response time and compared key demographic characteristics between early and late respondents using independent samples t-tests. The results revealed no significant differences in age (p = 0.31), education level (p = 0.24), or monthly income (p = 0.40), indicating that non-response bias is unlikely to pose a concern [68]. Second, we examined the potential for common method bias by performing Harman’s one-factor test [69]. An unrotated exploratory factor analysis showed that the first factor accounted for 48.26% of the total variance, which is below the commonly accepted threshold of 50%, suggesting that common method bias is not a serious issue in our data. Lastly, we tested for multicollinearity by constructing a linear regression model with UB as the dependent variable and the key predictors as independent variables. The variance inflation factor (VIF) values for all predictors were below 3.3, indicating no multicollinearity problem. Lastly, before formal analysis, we created a one-factor model and compared the model fit indices with our theoretical model. The results did not provide justification for the one-factor model, given the unacceptable model fit indices (CFI = 0.71; IFI = 0.72; RMSEA = 0.14).

As presented in

Table 4. Respondents’ profile.

Characteristics	Items	Frequency (n = 300)	Percentage (%)
Gender	Male	138	46
	Female	162	54
Age (years)	<20	17	5.7
	21–30	187	62.3
	31–40	93	31
	>40	3	1
Education	High school or below	16	5.3
	Associate’s degree	35	11.7
	Bachelor’s degree	222	74
	Master’s degree or above	27	9
Monthly income (RMB)	0	44	9.8
	<5000	85	18.9
	5000–9999	220	48.9
	10,000–14,999	80	17.8
	15,000–19,999	14	3.1
	>20,000	7	1.6

, the final sample consisted of 46% male (138) and 54% female (162) respondents. In terms of age, 5.7% of the participants were under 20 years old, 62.3% were aged 21–30, 31% were between 31 and 40 years old, and 1% were over 40 years old. In terms of education level, 5.3% of the respondents had a high school education or below, 11.7% had an associate’s degree, 74% had a bachelor’s degree, and 9% had a master’s degree or above. In terms of monthly income, 9.8% of participants indicated that their income was 0 RMB, 18.9% earned less than 5000 RMB, 48.9% earned 5000–9999 RMB, 17.8% earned 10,000–14,999 RMB, 3.1% earned 15,000–19,999 RMB, and 1.6% earned over 20,000 RMB.

5. Results

5.1. Measurement Model Analysis

We conduct a confirmatory factor analysis to ensure that the data fit the measurement model. Then, the structural equation modeling is used to verify the hypothesis proposed in this study. The data analysis is processed by the software AMOS.23.

The results of the confirmatory factor analysis are shown in Table 3. The model fit indices of the measurement model are all within the threshold range (χ²/df = 3.3; CFI = 0.90; IFI = 0.90; RMSEA = 0.09), which indicates that the measurement model fits with the data [70].

As shown in

Table 5. Confirmatory factor analysis results.

Construct	Item	λ	AVE	CR
Performance expectation (PE)	PE1 PE2 PE3	0.81 0.84 0.82	0.68	0.86
Effort expectancy (EE)	EE1 EE2 EE3	0.79 0.79 0.86	0.66	0.86
Social influence (SI)	SI1 SI2 SI3	0.85 0.84 0.76	0.67	0.86
Facilitating condition (FC)	FC1 FC2 FC3	0.84 0.79 0.73	0.62	0.83
Personal Innovativeness (PI)	PI1 PI2 PI3	0.79 0.85 0.88	0.71	0.88
Behavioral Intention (BI)	BI1 BI2 BI3	0.84 0.87 0.89	0.75	0.90
Use Behavior (UB)	UB1 UB2 UB3	0.78 0.77 0.83	0.63	0.84

Note: Model fit indices: χ²/df = 3.295, (p < 0.001, df = 180); CFI = 0.90; IFI = 0.90; RMSEA = 0.09. CR: composite reliability; AVE: average variance extracted.

all constructs’ composite reliability (CR) values are higher than 0.8, above the recommended level of 0.7 [71]. Thus, the reliability of the measurement items is verified.

According to Henseler et al.’s [72] suggestion, the standardized factor loadings and average variance extracted (AVE) are used to evaluate convergence validity. It is found that the factor loading of each measurement item exceeds 0.5. Additionally, the values of all construct’s AVE are higher than the acceptable value of 0.5. Therefore, the convergence validity of the measurement model is also verified.

5.2. Structural Model Analysis

Survey data regarding the two e-commerce delivery technologies are aggregated to analyze consumers’ overall acceptance by way of structural equation modeling (SEM). Figure 2 shows the results of model fit indices and hypothesis tests. The model fit indices demonstrated an acceptable overall fit to the data: χ²/df = 3.3; CFI = 0.90; IFI = 0.90; RMSEA = 0.09. These indices suggest that the model provides an adequate representation of the relationships among the constructs. Except for hypothesis 6, all other hypotheses are supported.

The influence of personal innovativeness on PE (0.68 ***), EE (0.57 ***), SI (0.66 ***), and FC (0.58 ***) are all positive and statistically significant. Therefore, hypotheses 1, 2, 3, and 4 are accepted.

PE, SI, and FC have positive and significant impacts on behavioral intention, explaining 60% of its variance (R² = 0.60). However, EE is not a significant factor that affects behavioral intention. Thus, hypotheses 5, 7, and 8 are accepted, and hypothesis 6 is rejected. The non-significant effect of EE on behavioral intentions may be due to contextual factors specific to the technology under study. For example, respondents may have been familiar enough with the use of smart lockers and autonomous drones that the EE has less of a determining effect on their behavioral intentions. The influence of behavioral intention on use behavior highlights its role in the technology acceptance process. This result suggests that increasing consumers’ intention to adopt these e-commerce delivery technologies is essential for driving their actual usage. Furthermore, the substantial explanatory power of the model, reflected in the R² values for both behavioral intention and use behavior, highlights the robustness of the theoretical framework in predicting technology acceptance.

To further investigate whether the proposed model holds under two different innovation contexts, we analyze the survey data corresponding to each technology. The results are shown in

Table 6. Path coefficient of different e-commerce delivery technologies.

Path	Coefficient of Smart Locker	Coefficient of Drone
H1 PI→PE	0.655 ***	0.703 ***
H2 PI→EE	0.438 ***	0.651 ***
H3 PI→SI	0.635 ***	0.683 ***
H4 PI→FC	0.564 ***	0.596 ***
H5 PE→BI	0.399 ***	0.449 ***
H6 EE→BI	0.116 ns	−0.240 ns
H7 SI→BI	0.088 ns	0.244 **
H8 FC→BI	0.425 ***	0.323 ***
H9 BI→UB	0.601 ***	0.724 ***

Note: ** p < 0.01; *** p < 0.001; ns p > 0.05. Smart lockers’ model fit indices: χ²/df = 2.041, (p < 0.001, df = 180); CFI = 0.89; IFI = 0.89; RMSEA = 0.08. Autonomous drones’ model fit indices: χ²/df = 2.708, (p < 0.001, df = 180); CFI = 0.88; IFI = 0.88; RMSEA = 0.10.

.

A comparison of the path coefficients for smart lockers and autonomous drones reveals significant differences in how various factors influence consumer behavior across the two technologies. For both technologies, personal innovativeness significantly influences PE, EE, SI, and FC. However, the coefficients for automated drones are consistently higher across all paths, suggesting that consumers with higher personal innovativeness perceive stronger benefits and adaptability in adopting this more novel and more radical technology. This reflects the greater influence of innovativeness in shaping perceptions of radical innovation.

Regarding behavioral intention, the key drivers differ between the two technologies. For smart lockers, PE (β = 0.399, p < 0.001) and FC (β = 0.425, p < 0.001) significantly predicted behavioral intention, while SI (β = 0.088, p > 0.05) and EE (β = 0.116, p > 0.05) are not significant. This indicates that consumers’ adoption of smart lockers is largely dependent on perceived performance benefits and availability of external support rather than social or effort-related considerations. In contrast, for autonomous drones, behavioral intention was significantly influenced by PE (β = 0.449, p < 0.001), FC (β = 0.323, p < 0.001), and SI (β = 0.244, p < 0.05), while EE has a negative but not significant effect (β = −0.240, p > 0.05). These findings highlight the key role that SI plays in fostering trust and reducing uncertainty in the adoption of radical innovations such as autonomous drones, where social approval and peer recommendation become more influential.

Finally, the impact of behavioral intention on use behavior is stronger for autonomous drones (β = 0.724, p < 0.001) than for smart lockers (β = 0.601, p < 0.001). This suggests that once consumers form a behavioral intention, they are more likely to move to using autonomous drones, possibly due to the novelty and excitement associated with the technology. In contrast, the more familiar and routine nature of smart lockers may lead to a relatively weaker translation of behavioral intention into use behavior.

We also conducted two additional measurement invariance tests under the SEM framework to ensure the robustness of our findings. These analyses were conducted using the “Lavaan” tool in R. Specifically, we estimated a set of nested multi-group models across gender and education level groups. First, we utilized gender (1 = male, 2 = female) as the grouping variable and estimated the configural model (the baseline model without any equality constraints), metric invariance model (equality constraints on factor loadings were imposed), and scalar invariance model (equality constraints on intercepts were further imposed). The results of Chi-square difference tests showed no significant differences between configural and metric models (($\Delta {\chi }^2$ = 15.17, p = 0.37), nor between the metric and scalar models ($\Delta {\chi }^2$ = 11.44, p = 0.65). These results indicate that our model is sufficiently invariant across gender.

Further, respondents’ education levels were employed as another grouping variable (1 = associate’s degree or below, 2 = bachelor’s degree or above). The results indicate that differences between configural and metric models ($\Delta {\chi }^2$ = 20.67, p = 0.11), as well as metric and scalar models ($\Delta {\chi }^2$ = 11.34, p = 0.66) are insignificant. Thus, our results are sufficiently invariant across respondents’ education levels.

We also conducted bootstrap analysis with 95% bias-corrected confidence intervals (sample size = 5000) to investigate the mediating effects in our model. The results confirmed an indirect effect of PI on BI (b = 0.68, p < 0.01) and UB (b = 0.52, p < 0.01). Moreover, the mediated influence of FC on UB via BI was significantly positive (b = 0.22, p < 0.05), whereas the effects of PE, EE, and SI were insignificant.

5.3. Discussion

The current paper, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) complemented with the personal innovativeness (PI) variable, created a technology adoption model for various kinds of e-commerce delivery innovations, its validity was tested empirically in two cases of context prototypes (smart lockers—incremental innovation and autonomous drones—radical innovation). Through the model paths and relationships with variables, this study can, on the one hand, demonstrate the mediating effect of innovation characteristics on the paths of technology adoption and, on the other hand, also show how personal characteristics affect users’ generation of cognition and start-off in behavior.

5.3.1. Key Findings and Mechanism Interpretation

The findings imply that users’ personal innovativeness has a statistically significant influence on core perceptual variables—PE, EE, SI, and FC—which subsequently impact behavioral intention and also users’ actual use behavior. More precisely, PI users have stronger mental involvement and learning motivation upon their first contact with new technologies and thus make quicker and more favorable appraisals of their potential value [16,73,74]. This phenomenon has been further validated by research on the role of personal innovativeness in technology adoption [75,76,77]. Moreover, this resonates with recent insights by Corti [78], who emphasized that consumer receptivity to innovative service models, such as alternative last-mile delivery solutions, is often mediated by their willingness to engage with novel technologies—underscoring the broader applicability of personal innovativeness in digitally driven consumption contexts.

Moreover, EE did not significantly influence behavioral intention in our model. This pattern may be explained by the characteristics of our sample, which predominantly comprises young adults aged 21 to 30, who are typically more digitally fluent and experienced in using similar technologies [76,79]. For such users, the perceived effort required to interact with e-commerce delivery innovations is likely uniformly low, resulting in limited variance in EE ratings and a reduced capacity for EE to explain differences in behavioral intention or use behavior.

From the analysis of the path structure, we found that, again, there are important differences between the different innovation contexts. For smart lockers (incremental innovation), PE and FC were drivers of behavioral intention, but EE was not. This means that users who are used to the system see its usability as no longer being of any importance. On the contrary, for radical innovations such as autonomous drones, the SI showed a higher impact, which indicates that the higher the uncertainty users have, the more they need social cues to build cognition [80]. Several studies have identified social influence as a key factor influencing consumer decision-making [26,33,81].

5.3.2. Theoretical Comparison and Model Extension

This study extends the original UTAUT model both contextually and structurally in two primary ways. First, it introduces innovation type (incremental vs. radical) as a contextual variable, addressing the under-emphasis on technology characteristics in prior adoption models [16,25]. Second, it models PI as an exogenous variable that influences all four core perceptual constructs. This treatment of PI is grounded in the view that innovativeness is a stable personality trait, as widely recognized in the prior literature [29,30,82].

The comparative empirical validation of these two technological scenarios has confirmed the robustness and broad applicability of the UTAUT model in the field of e-commerce delivery technology. However, the research contributions of this study extend far beyond model validation. By systematically examining the differences in the strength of variable relationships between incremental innovation and radical innovation scenarios, this study has revealed the underlying mechanisms through which technological characteristics dynamically reshape user cognition and behavioral pathways. More importantly, the research results indicate that personal innovativeness, as a key factor, reshapes the order of influence and the impact of core decision variables in the technology adoption process. This dynamic adaptability significantly enriches the explanatory power of traditional technology acceptance models and provides a more detailed theoretical perspective for understanding consumer behavior changes under different levels of technological novelty and uncertainty. Therefore, this study not only further consolidates the foundational assumptions of the UTAUT model but also extends it into a more context-sensitive and user-individuality-adaptive application framework, thereby enhancing the explanatory power and practical guidance of the model for understanding user behavior toward e-commerce delivery technology.

6. Conclusions

6.1. Research Summary

This research built a technology adoption model by incorporating UTAUT into the new construct of personal innovativeness to explain the consumer’s behavior of adopting technologies of different levels of innovations in e-commerce delivery. Following the evidence of empirical findings for the e-commerce delivery technologies of smart lockers and drones, the innovation type significantly influences the structure of user perception and the path of decision-making. As an important individual-level variable, personal innovativeness directly affects behavioral intention and use behavior through users’ cognitive appraisal. These conclusions can improve the explanatory ability of the model and provide practical guidance for the application of e-commerce delivery technology under various circumstances.

6.2. Theoretical Contributions

Our research makes three main theoretical contributions. First, we incorporate the incremental and radical innovations theory into the framework of UTAUT, which provides a fresh perspective on adoption differences between innovation types and expands the applicability of technology adoption models concerning the context. Second, incorporating personal innovativeness as an intervening variable is expected to enhance the modeling power of heterogeneity in users and provide a more comprehensive set of behaviors. Third, we build a multidimensional model of adoption behavior combining innovation type and personality factors, positioning users in an active role in the process of cognitive construction and regulation of behavior, contrary to the idea of user passivity. This allows us to better ground users’ behavioral decisions in stochastic settings and strengthens our model’s utility under technological uncertainty. Additionally, cross-context validation among two unique technology contexts certifies the strength and generality of the model, thus adding to the premise for its theory generalization to other fields (e.g., healthcare/education, smart city services) of services.

6.3. Practical Implications

In practice, e-commerce delivery firms should use differential approaches corresponding to specific innovation types. When it comes to mature incremental technological solutions, the focus should be on achieving performance enhancement and accessibility of services. Conversely, for radically innovative novel technologies, the focus should be on introducing trust and enabling social pathways of influence. Furthermore, we argue that detecting and supporting high-PI user groups could be a viable means to drive widespread take-up via social influence and normative modeling.

According to the results of this study, we suggest the following management suggestions:

(1)

As far as incremental technologies are concerned, the firm’s efforts need to be directed at fine-tuning the system performance and facilitating conditions that enhance the perception of usefulness and accessibility ease.

(2)

In terms of radical innovations, positive social influence may attract people to the new form of production via pilot programs and social endorsement and activity by influencers, as this would lower the uncertainty about the new opportunities the firm hopes to enjoy and enhance the confidence of users in taking part in it.

(3)

Firms should proactively identify high-PI users and leverage their enthusiasm, social capital, and diffusion potential to drive broader technology adoption across user networks.

6.4. Limitations and Future Research

Although this study makes a few contributions, its development still has limitations. One limitation is that its data sample derives from Chinese respondents only, and the current results can be less generalizable. In the future, datasets from samples of different cultures can be included to examine the model’s generalizability among various cultural or institutional settings. A second limitation of this paper is that the sample is dominated by participants under a certain age, whose impact on the model may be over-emphasized, and in future studies, the overall age distribution should be balanced more so. Third, this study mainly emphasizes psychological level variables and future work can bring more situational variables (e.g., urban–rural divide, the maturity of infrastructure) as a new layer to enrich the model’s explanation in more complicated areas.

In addition, although the extended UTAUT model proposed in this study demonstrates strong explanatory potential, it still faces several structural constraints. The current model places primary emphasis on cognitive and perceptual factors, while emotional or affective components such as trust, perceived risk, and technology-related anxiety are not fully addressed. These factors may have a significant influence on user responses, especially when encountering radical innovations like autonomous drones. Furthermore, while personal innovativeness is included as a key individual trait, the model does not capture potential dynamic interactions between users’ prior experiences and their adoption behaviors. Future research may benefit from longitudinal study designs and the inclusion of multidimensional variables, such as perceived risk, hedonic motivation, and habitual usage, to develop a more comprehensive framework for understanding user behavior under conditions of technological uncertainty or digital saturation.

In summary, this research provides not only a theoretical perspective but also real case evidence to explain the adoption of different e-commerce delivery technologies by users, and it provides useful insights for industrial practice and an excellent basis for further research on this topic.