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Article

Do Consumers’ Perceived Attributes and Normative Factors Affect Acceptance Behavior Towards Eco-Friendly Self-Driving Food Delivery Services? The Moderating Role of Country Development Status

by
Kyuhyeon Joo
1,
Heather Markham Kim
2 and
Jinsoo Hwang
1,*
1
The College of Hospitality and Tourism Management, Sejong University, Seoul 05006, Republic of Korea
2
Department of Tourism Management, Dong-A University, Busan 49315, Republic of Korea
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(22), 9918; https://doi.org/10.3390/su16229918
Submission received: 17 October 2024 / Revised: 8 November 2024 / Accepted: 13 November 2024 / Published: 14 November 2024
(This article belongs to the Special Issue Sustainable Consumption and Circular Economy)
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Abstract

:
The advent of self-driving technology marks a significant milestone in the evolution of modern transportation and logistics services. More importantly, self-driving food delivery services are expected to play a significant role in environmental protection by operating on batteries instead of the traditional gasoline. The current study examines the relationship between perceived attributes, image, normative factors, and behavioral intentions in the context of eco-friendly self-driving food delivery services. The study deepens the framework by identifying the moderating role of country development status. The study gathered samples from 313 panels in South Korea, a developed country, and 315 respondents in Mongolia, a developing country. The results of the South Korean dataset showed that two types of perceived attributes, perceived innovativeness and perceived risk significantly affect image, which in turn leads to the formation of behavioral intentions. Normative factors, such as subjective norms and personal norms, also positively affect behavioral intentions, and subjective norms increase personal norms. The results of the Mongolian dataset indicated that all paths are statistically supported. Lastly, the moderating role of the country development status was found in the relationship between (1) perceived innovativeness and perceived risk, (2) subjective norms and personal norms, and (3) subjective norms and behavioral intentions.

1. Introduction

The advent of self-driving technology marks a significant milestone in the evolution of modern transportation and logistics services [1,2,3]. Before COVID-19, the global market size of autonomous driving technology was only USD 54 billion [4]. According to Fortune Business Insights [5], the market size is projected to grow from USD 1921.1 billion in 2023 to USD 13,632.4 billion by 2030. Among the various types of self-driving technology, self-driving food delivery services (SFDSs) provide a novel and transformative shift in how consumers receive their meals at home or the office [6]. These services promise increased efficiency, reduced delivery times, and the potential to minimize human error and labor costs [7,8]. They also have the potential to implement sustainable practices by being more environmentally friendly compared to traditional food delivery systems. The adoption of autonomous electric vehicles could reduce transportation-related CO2 emissions and traffic congestion, significantly lowering emissions from idling and stop-and-go driving in urban food delivery [9,10]. The introduction of SFDS thus demonstrates greater environmental sustainability compared to human-operated deliveries using gasoline-powered vehicles. However, the successful establishment of a new technology-based service depends not only on how advanced the technology is but also on how consumers perceive the attributes of the new technology-based service [11,12,13]. That is, while the technology of the service itself is important, it is also crucial to understand how consumers who actually use these services perceive them.
Previous studies have commonly identified perceived innovativeness as a key attribute from the perspective of technology acceptance behavior because consumers with a high level of perceived innovativeness are more likely to adopt and diffuse new technologies [14,15,16]. In addition, it is widely acknowledged that it is necessary to consider both the positive and negative attributes when evaluating new technology-based services [13,17,18]. Hence, the current study also focuses on perceived risk, defined as potential negative consequences or uncertainties associated with a product or service [19,20], in the field of SFDS. Perceived risk can negatively affect consumers’ apprehensions about the potential negative consequences of using the technology, which can hinder their acceptance and adoption [21,22].
More importantly, normative factors also are crucial elements in fostering behavioral intentions toward new technology-based services [23,24]. These studies are grounded on the theory of reasoned action (TRA) by Ajzen and Fishbein [25]. The TRA explains that individuals’ subjective norms form their favorable attitudes toward a specific object and also influence behavioral intentions. The current study more specifically focuses on the two types of normative factors, which are subjective norms and personal norms. Subjective norms are the perceived social pressures to perform or not perform a particular behavior [25], while personal norms drive intrinsic motivation to specific behavior based on personal beliefs and values [26]. Both these norms can shape individuals’ intentions toward adopting new technologies [27,28,29]. This study therefore considers the roles of these normative factors, in addition to perceived attributes, to examine consumer acceptance behavior toward SFDS.
Lastly, distinct from previous research, this study focuses on how different consumption behaviors depend on the development status of a particular country [30,31,32]. In developed countries, advanced technological infrastructure and higher consumer familiarity with autonomous technologies may lead to more favorable initial perceptions and quicker adoption of new technology-based services [33,34]. In developing countries, in contrast, challenges involving inadequate infrastructure, lower levels of technological literacy, and varying regulatory standards may impact consumer perceptions differently [35,36]. For this reason, cultural differences between countries can shape different consumer perceptions on a national level [13,37,38]. Understanding the moderating role of a country’s development status can aid in tailoring strategies that address specific consumer concerns, leverage local strengths, and enhance the overall acceptance and success of SFDS globally.
This study aims to investigate how to form consumers’ acceptance behavior toward SFDS with the moderating role of country development status. The detailed research objectives are (1) exploring the relationships between perceived attributes, image, and behavioral intentions, (2) examining how subjective norms and personal norms form behavioral intentions, and (3) deepening the conceptual framework by identifying the moderating role of country development status. The study consequently presents valuable insights for service providers and researchers interested in successfully implementing and scaling SFDS across diverse socio-economic landscapes.

2. Literature Review and Hypothesis Development

2.1. Self-Driving Food Delivery Services (SFDS)

SFDS refers to the automated transportation of food products from restaurants to customers using autonomous vehicles [6,39]. It operates independently on designated routes, leveraging technologies such as Light Detection and Ranging (LiDAR), global positioning system (GPS), computer vision, and artificial intelligence (AI) for navigation and obstacle avoidance [39,40,41]. SFDSs are novel services using autonomous driving robots that alter traditional human delivery [7]. The introduction of autonomous driving robots has accelerated since the COVID-19 pandemic due to the need for contactless delivery [42]. Autonomous driving robots can successfully deliver products to a set destination with GPS and also check whether consumers picked up products via cameras and weight sensors [40,41]. SFDS also offers valuable consumer experience showing cutting-edge technology and contributes to reduce restaurants’ labor costs [8]. SFDS is spreading and becoming integrated into everyday life, primarily in developed countries with advanced technologies, such as Japan, South Korea, and the United States [43,44,45]. SFDS also has the potential to implement sustainable practices by being more environmentally friendly compared to traditional delivery services. The shift to autonomous electric vehicles could reduce transportation-related CO2 emissions by up to 60%, depending on the energy sources used for electricity generation instead of traditional gasoline [10]. Autonomous vehicle deployment also could reduce traffic congestion by 30%, leading to lower emissions from idling and stop-and-go driving, a common issue in urban food delivery [9]. Their integration in food delivery can further amplify these eco-friendly benefits.
Some scholars have focused on consumer acceptance behavior toward SFDS. Yuen et al. [42] successfully identified the predictors of the perceived value of SFDS, which are perceived ease of use, perceived usefulness, perceived susceptibility, perceived severity, and self-efficacy. However, they observed only the limited stimuli that shape perceived value within the stimulus-organism-response framework. Despite the importance of consumer-perceived innovativeness and risk regarding new technology acceptance behavior as explained earlier, they overlooked such attributes. Martinez et al. [46], on the other hand, found that people are significantly influenced by the members of their group when ordering food through SFDS. Group members influence each other’s perceptions, making them more positive or negative. This implies the role of normative factors in SFDS acceptance behavior. The current paper, therefore, examines the roles of perceived attributes and normative factors in shaping technology acceptance behavior related to SFDS.

2.2. Perceived Attributes

This study focuses on the two perceived attributes, perceived innovativeness and perceived risk, following the aforementioned research gap. Perceived innovativeness refers to the degree to which a particular object is perceived as being capable of producing or implementing new and novel ideas, products, or processes [15,47,48]. The perception of innovativeness towards products or services can influence a range of outcomes, including consumer behavior and market positioning [8,49]. Consumers often use innovativeness as a heuristic to evaluate new products [48]. The perception that a product is innovative can enhance its appeal, leading to increased adoption rates and higher consumer evaluation [50]. The concept of perceived innovativeness is crucial for predicting consumer behavior, including in the restaurant context [15,16,51]. For instance, Hwang et al. [15] found that perceived innovativeness is a key factor in forming attitudes and acceptance behavior regarding drone-based delivery services.
Perceived risk refers to the subjective judgment that individuals make about the probability of a risk. It involves an assessment of the potential negative outcomes associated with a particular decision or action and the likelihood of these outcomes occurring [19,20,21]. Bauer [19] introduced the concept for the first time, highlighting how consumers’ uncertainty and potential negative consequences affect their purchasing decisions. Perceived risk is also a critical barrier to technology adoption. Consumer concerns about the potential negative outcomes of using new technology, such as performance failures, can hinder adoption rates [52]. Venkatesh et al. [53] also emphasized the importance of addressing these concerns to facilitate technology acceptance. The negative impact of perceived risk on consumer behavior is also widely identified in the restaurant context [22,54,55]. As an example, Joo and Hwang [22] demonstrated that consumers’ risk perceptions negatively affect attitudes regarding a smart farm-based restaurant.
The current study postulated a mitigative relationship between these two perceived attributes. According to the signaling theory, a product’s attribute can signal quality and reduce information asymmetry [56]. In this regard, perceived innovativeness can act as a positive signal, and the positive signals of products/services can mitigate their perceived risk [57,58]. Product and service providers’ commitment to innovation also can alleviate concerns about performance and reliability, thereby fostering widespread adoption [59,60]. Jeon et al. [55] also identified that perceived innovativeness mitigates consumers’ hesitation to adopt self-service technology fostered by perceived potential risks. Grounded on the discussions above, this study postulated the first hypothesis as follows:
Hypothesis 1 (H1).
Perceived innovativeness decreases perceived risk.

2.3. Antecedents and Consequences of the Image of SFDS

The concept of image in the marketing context is the set of beliefs, ideas, and impressions that a person holds regarding an object [61,62,63]. It also refers to the overall perception or impression of a specific object [64]. Image towards some products or services pertains to the specific perceptions about their attributes, such as quality, design, features, and performance [65,66]. For instance, Han, Yu et al. [67] found that in-flight core-product quality and service encounter quality significantly form the image of an airline brand. Erkmen and Hancer [68] also found that service quality plays a significant role in forming restaurant image.
The two perceived attributes adopted earlier, perceived innovativeness and perceived risk, also can influence the image of SFDS. There is sufficient empirical evidence that the perceived innovativeness of new technology-based services can foster a positive image. For example, Hwang et al. [69] found that the perceived innovativeness of robotic restaurants leads consumers to evaluate them favorably and increases the desire to visit. Kim et al. [16] also found that the perceived innovativeness of drone food delivery services leads consumers to positively perceive the services and fosters technology acceptance behavior. Based on the discussions above, the current study postulated the causal nexus between perceived innovativeness and image.
Hypothesis 2 (H2).
Perceived innovativeness increases image.
On the other hand, the perceived risk of new technology-based services can drive a negative image. For instance, Joo et al. [13] found that the perceived risk of facial recognition payment systems leads consumers to evaluate the system unfavorably. Koh et al. [70] also found that the perceived risk of urban last-mile delivery drones leads consumers to perceive them negatively. Therefore, the negative effect of perceived innovativeness on image in the technology acceptance behavior context can be inferred as follows.
Hypothesis 3 (H3).
Perceived risk decreases image.
Prior research has also shown that the image of new technology-based services also plays a critical role in shaping behavioral intentions, e.g., [16,71,72,73]. As an example, Hwang and Choe [72] empirically demonstrated that consumers’ positive image increases their behavioral intentions regarding drone-based delivery services. Kim et al. [16] also demonstrated this nexus in the robotic restaurant context. Such empirical studies present sufficient evidence that the image of SFDS can drive consumers’ behavioral intentions.
Hypothesis 4 (H4).
Image increases behavioral intentions.

2.4. Normative Factors

Normative factors are crucial in shaping and influencing human behavior through the lens of societal norms, personal beliefs, and the perceptions of others [74,75]. These factors guide individuals in their decision-making and actions by aligning them with what is considered acceptable or appropriate within a given social context [75,76]. This study focuses on the two normative factors grounded in the TRA by Ajzen and Fishbein [25] and the norm activation model (NAM) by Schwartz [77].
Subjective norms are the perceived social pressure to perform or not to perform a particular behavior [78]. Personal norms drive intrinsic motivation to specific behavior based on personal beliefs and values [26]. More importantly, personal norms can be affected by subjective norms because subjective norms justify specific behaviors in society [23,79]. For instance, Kim and Hwang [29] found a causal relationship between subjective norms and personal norms in the drone delivery service context. Joo et al. [28] also studied consumer technology adoption behavior and identified this link in the context of indoor smart farm restaurants. Based on the discussion above, the nexus between the two norms was postulated as follows.
Hypothesis 5 (H5).
Subjective norms increase personal norms.
According to the TRA [25] and the theory of planned behavior (TPB) [78], subjective norms as volitional triggers foster an individual’s intent to behave. As an example, Joo and Hwang [22] empirically identified the causal nexus between subjective norms and behavioral intentions in the new technology-based restaurant context. Kaushik et al. [80] also postulated the crucial role of subjective norms in forming adoption behavior toward self-service hotel technologies grounded on TPB and statistically proved this significant nexus. The current study thus hypothesized the effect of subjective norms on behavioral intentions in the SFDS context.
Hypothesis 6 (H6).
Subjective norms increase behavioral intentions.
Following the NAM by Schwartz [77], individuals’ norms are activated due to the expectation that they behave pro-socially, and the key driver of behavioral intentions is the concept of personal norm. Ho and Wu [81] applied NAM to study adoption behavior toward using an electric scooter, and they found that personal norms significantly affect behavioral intentions. Joo et al. [28] applied the TPB to examine technology acceptance behavior in the new technology-based restaurant context and also found that subjective norms have positive effects on behavioral intentions. Hence, the effect of personal norms on behavioral intentions in the SFDS context was also hypothesized.
Hypothesis 7 (H7).
Personal norms increase behavioral intentions.

2.5. Moderating Role of the Development Status of Countries

The United Nations [82] divides countries into two major categories based on their development status. Those that are independent and prosperous are known as ‘developed countries’, and they include the United States, France, Germany, Italy, Japan, and South Korea. Those that are at the early stages of industrialization are called ‘developing countries’, with examples including Colombia, India, Kenya, Pakistan, Sri Lanka, and Mongolia. The presence of advanced technological infrastructure and greater consumer familiarity with autonomous technologies can result in a more positive initial reception and faster uptake of new technology-based services [33,34]. Conversely, obstacles such as insufficient infrastructure, lower levels of technological literacy, and diverse regulatory standards may influence consumer perceptions differently [35,36]. That is, it can be interpreted that the development status of countries fosters a technological divide. This technological divide refers to the significant disparity in access to, adoption of, and proficiency with technology [83,84,85]. It can be interpreted that there are differences in the consumer decision-making process of technology adoption in accordance with the development status of countries due to the technological divide.
Moreover, beneficial factors such as the usefulness of technology also enable the fostering of technology acceptance behavior compared to developing countries because developed countries have a high level of individualism [86,87]. Individualist societies tend to prioritize factors and benefits that are advantageous to the individual [88]. On the other hand, concerns about potential risks pose a greater barrier to consumer behavior in low development-status countries due to their risk-averse tendencies [30,89]. Uncertainty avoidance pertains to how comfortable or uncomfortable a society is with ambiguity and uncertainty [88]. It can be inferred that these cultural differences between developed and developing countries moderate the impact of perceived attributes. While individualist societies tend to prioritize factors and benefits that are advantageous to the individual, collectivist societies emphasize group cohesion, pro-sociality, and normativeness [88]. Existing research also supports that normative factors play a more important role in shaping consumer behavior in developing countries due to their collectivist characteristics, compared to developed countries [90,91]. Grounded on the aforementioned technological divide and cultural differences, the current study deepened the postulated conceptual framework by postulating the moderating role of country development status as follows:
Hypothesis 8a (H8a).
The relationship between perceived innovativeness and perceived risk is moderated by country development status.
Hypothesis 8b (H8b).
The relationship between perceived innovativeness and image is moderated by country development status.
Hypothesis 8c (H8c).
The relationship between perceived risk and image is moderated by country development status.
Hypothesis 8d (H8d).
The relationship between image and behavioral intention is moderated by country development status.
Hypothesis 8e (H8e).
The relationship between subjective norms and personal norms is moderated by country development status.
Hypothesis 8f (H8f).
The relationship between subjective norms and behavioral intention is moderated by country development status.
Hypothesis 8g (H8g).
The relationship between personal norms and behavioral intention is moderated by country development status.

2.6. Research Model

This study proposes the research model depicted in Figure 1 based on the postulated hypotheses.

3. Methodology

3.1. Measures

The constructs in this study were assessed using items validated in existing works. Firstly, the three measures of perceived innovativeness were drawn from Hwang et al. [15] and Hwang et al. [69]. Second, perceived risk was assessed using three items from Hwang and Choe [72] and Martins et al. [92]. Third, the three measures of image were drawn from Han, Hsu et al. [66] and Han, Lee et al. [93]. Fourth, subjective norms were gauged using three items from Ajzen [25] and Joo et al. [28]. Fifth, the three measures of personal norms were drawn from De Groot and Steg [26] and Kim and Hwang [29]. Lastly, intentions to use were measured with three items from Zeithaml et al. [94] and Hwang et al. [15]. These items were used as the basis for the survey in this study, evaluated on a seven-point Likert scale (1: strongly disagree—7: strongly agree). Detailed measurement items for each construct are presented in Table 1.

3.2. Sampling

Developing countries are typically less economically and industrially established, with more reliance on labor than assets, while developed countries are economically and industrially advanced [95]. The United Nations classifies countries using the Human Development Index (HDI), with scores below 0.80 indicating developing countries [96,97]. South Korea, with an HDI of 0.925, is considered developed, whereas Mongolia, with an HDI of 0.739, is classified as developing [98]. Mongolia is also undergoing modernization via cooperation with South Korea, and a variety of emerging South Korean services are being introduced in Mongolia [99,100]. Therefore, the current study focused on these two countries. Two different data collection companies were utilized to collect data in each country through an online survey. Before beginning the survey, participants watched a 3-min video explaining the use of self-driving food delivery services (see Appendix A). A total of 320 surveys were collected for the South Korean sample, with seven outlier surveys excluded after Mahalanobis distance test. Similarly, 320 surveys were collected for the Mongolian sample, with five outliers removed following the same checks. Consequently, 313 surveys from the South Korean sample and 315 surveys from the Mongolian sample were included in the hypothesis tests.

4. Data Analysis

4.1. Frequency Analysis

Respondent demographics are shown in Table 1. Among the South Korean samples (n = 313), 51.8% (n = 162) were male, and 48.2% (n = 151) were female. Respondents in their thirties comprised the largest age group, at 31.0% (n = 97), followed by those in their twenties, at 30.4% (n = 95). Over half of the respondents (54.3%) had a bachelor’s degree (n = 170). In terms of marital status, 166 respondents (53.0%) were single, and 143 respondents (45.7%) were married. The average monthly income for South Korean respondents was USD 2795.08.
In the Mongolian sample (n = 315), 60.3% (n = 190) were female, and 39.7% (n = 125) were male. The largest age group comprised respondents in their twenties, making up 33.0% (n = 104). Most respondents (63.2%) had a bachelor’s degree (n = 199). The average monthly income for Mongolian respondents was USD 473.14.

4.2. Confirmatory Factor Analysis

A confirmatory factor analysis (CFA) was conducted to verify the reliability and validity of the data. The results of the CFA for both sets of data are presented in Table 2. The appropriate model fit indexes were satisfied (South Korean: χ2 = 185.872, df = 120, χ2/df = 1.549, p < 0.001, NFI = 0.970, CFI = 0.989, TLI = 0.986, and RMSEA = 0.042; Mongolian: χ2 = 254.717, df = 120, χ2/df = 2.123, p < 0.001, NFI = 0.946, CFI = 0.971, TLI = 0.962, and RMSEA = 0.060) [101]. Convergent validity was confirmed because the average variance extracted (AVE) values for both the South Korean and Mongolian datasets were above 50, as presented in Table 3 [102]. In addition, the composite reliabilities of the variables were above 70, which confirmed internal consistency [103]. Lastly, AVE values were higher than correlations between each pair of constructs, so discriminant validity was satisfied [102].

4.3. Structural Equation Modeling

Structural equation modeling (SEM) analysis was conducted to test the proposed hypotheses regarding causal links. The SEM determined a proper fit for the data to the model (South Korean data: χ2 = 327.098, df = 127, χ2/df = 2.576, p < 0.001, NFI = 0.947, CFI = 0.967, TLI = 0.960, and RMSEA = 0.071; Mongolian data: χ2 = 399.069, df = 127, χ2/df = 3.142, p < 0.001, NFI = 0.916, CFI = 0.941, TLI = 0.928, and RMSEA = 0.083) [101]. The hypothesis tests were evaluated after the model fit was assessed. First, perceived innovativeness does statistically affect perceived risk in the South Korean dataset (β = −0.015 and t = −0.245), but this path is statistically supported in the Mongolian dataset (β = −0.229 and t = −3.851), so H1 is partly supported. Second, perceived innovativeness significantly affects image in both datasets (South Korea: β = 0.691 and t = 11.709; Mongolia: β = 0.719 and t = 11.132), so H2 is supported. Third, perceived risk significantly affects image (South Korea: β = −0.168 and t = −3.678; Mongolia: β = −0.112 and t = −2.252), so H3 is supported. Fourth, image significantly affects behavioral intention (South Korea: β = 0.425 and t = 8.438; Mongolia: β = 0.350 and t = 5.994), so H4 is supported. Fifth, subjective norms significantly affect personal norms (South Korea: β = 0.742 and t = 14.901; Mongolia: β = 0.671 and t = 11.813), so H5 is supported. Sixth, subjective norms significantly affect behavioral intention (South Korea: β = 0.160 and t = 2.325; Mongolia: β = 0.352 and t = 4.822, so H6 is supported. Lastly, personal norms significantly affect behavioral intention (South Korea: β = 0.324 and t = 4.514; Mongolia: β = 0.210 and t = 3.195, so H7 is supported. The results of SEM are summarized in Figure 2.

4.4. Multi-Group Analysis

A multiple-group analysis was conducted to evaluate the moderating effect of development status. First, the moderating effect of development status is partly significant in the paths between perceived innovativeness and perceived risk (Δχ2(1) = 7.880), subjective norms and personal norms (Δχ2(1) = 4.987), and subjective norms and behavioral intentions (Δχ2(1) = 3.881), so H8a, H8e, and H8f are supported. However, the moderating effect of development status in the paths between perceived innovativeness and image (Δχ2(1) = 0.073), perceived risk and image (Δχ2(1) = 1.916), image and behavioral intention (Δχ2(1) = 0.863), and personal norms and behavioral intention (Δχ2(1) = 0.464) are not significant, so H8b, H8c, H8d, and H8g are not supported. The results of the multi-group analysis are presented in Table 4.

5. Conclusions

5.1. Discussion

This study examines consumer technology acceptance behavior regarding SFDS, focusing on perceived attributes, image, and normative factors. In particular, the study examined for the first time the moderating role of country development status (i.e., South Korea vs. Mongolia) grounded in cultural differences and the technological divide. As a result, all proposed causal hypotheses were accepted, excluding the nexus between perceived innovativeness and perceived risk in the South Korean dataset. In this pathway, the moderating impact of the development status was found. In South Korea, where new technology-based services are already widely adopted, the perceived innovativeness of SFDS may not effectively mitigate perceived risk. However, in a developing country like Mongolia, perceived innovativeness is likely an important factor in reducing the perceived risk of SFDS. Perceived innovativeness was identified as a perceived attribute that shapes the positive image of SFDS. Conversely, perceived risk is an important technology acceptance barrier that promotes the negative image of SFDS. These perceived attributes, in turn, influence consumers’ behavioral intentions toward SFDS. This study successfully identified the antecedents and consequences of the image of SFDS, identifying the important role of perceived attributes in technology acceptance behavior.
The study also found a significant relationship between normative factors, highlighting the crucial role of normative factors in forming behavioral intentions. In particular, country development status was found to moderate the nexus between subjective norms and personal norms and the nexus between subjective norms and behavioral intentions. Mongolia, as a developing country with a strong tendency towards collectivism, exhibited a stronger effect of subjective norms on behavioral intentions compared to South Korea. As expected, subjective norms were found to underpin personal norms. This relationship was more pronounced in South Korea, a developed country. This implies that despite South Korea’s status as a developed country, remnants of Eastern collectivist tendencies still persist. However, these tendencies do not directly influence behavioral intentions but instead serve as a stronger basis for personal norms. The findings reveal differences in norm-based behavior between developed and developing countries within the same Eastern region. Lastly, these norms were identified as playing an important role in the context of technology acceptance behavior by shaping behavioral intentions.

5.2. Theoretical Contributions

The current study constructs a theoretical conceptual model to investigate consumer technology adoption behavior toward SFDS. The proposed conceptual model is based on the TRA [25], signaling theory [56], NAM [77], and TPB [78], as well as empirical evidence related to technology adoption behavior, e.g., [55,56,57,58,59,60,69,70,79,80,81,83,84]. Unlike previous empirical studies that merged existing theories [27,28,29], this study introduces a new theoretical framework by adopting the concept of perceived attributes considered crucial in the research context. More importantly, the current study deepens the proposed model by identifying a moderating role of country development status. More specifically, the findings of this study provide the below theoretical contributions.
Firstly, this study has empirically identified the mitigated impact of perceived innovativeness on perceived risk in the SFDS context. The two perceived attributes are critical factors in the context of technology acceptance behavior. Past works on technology adoption behavior have also focused on the nexus between innovativeness and perceived risk, but they primarily emphasized the concept of consumer innovativeness [104,105]. The concept of consumer innovativeness in those studies refers to personal characteristics, such as an individual’s tendency to seek out novel products. Unlike previous works, this study focuses on the perceived attribute of perceived innovativeness in technology-based services. The study formulated hypotheses grounded on the signaling theory and a systematic literature review [55,56,58,59,60], and this causal relationship was successfully demonstrated. The current study consequently contributes to the theoretical extension of the nexus between innovativeness and risk in the technology acceptance behavior field.
Secondly, the study successfully investigated the antecedents and consequences of the image SFDS. Previous studies on technology acceptance behavior have limited their focus to the concept of attitude [13,16,69,70]. The current study presents theoretical extensions through these empirical results.
Thirdly, this study demonstrated the crucial roles of normative factors in the SFDS context. Although the importance of normative factors in the technology acceptance behavior perspective has been widely acknowledged [28,79,80], prior studies in the SFDS context have overlooked them [42,70]. The current study empirically identified the role of normative factors in technology adoption behavior. This study consequently addresses the acknowledged research gap.
Lastly, the current research discovered the moderating impact of country development status from the perspective of technology acceptance behavior. This role was postulated based on the technological divide and cultural differences [38,83,84]. The results imply that the development status of the countries moderates technology acceptance behavior formed by innovativeness and norms, as discussed earlier. This study consequently provides a novel theoretical contribution to the field of cross-country research.

5.3. Managerial Implications

Firstly, promoting the perceived innovativeness of SFDS is crucial for its adoption. For example, distributing short-form promotional videos showcasing the self-driving capabilities of delivery robots, obstacle collision avoidance, and weight detection for pickup confirmation can help consumers recognize the innovativeness of SFDS. Marketers can collaborate with electronics or technology experts to promote these innovations and use testimonials to create a trendsetting perception among early adopters. They also can develop targeted campaigns aimed at tech-savvy consumers who are more likely to appreciate and adopt innovative technologies. It can effectively position SFDS as groundbreaking, driving consumer interest and adoption by implementing such suggestions.
Secondly, it is also important to mitigate the perceived risk associated with SFDS. Marketers can create comprehensive content to address perceived risks associated with SFDS by explaining how self-driving technology works, its safety protocols, and the benefits it offers. They can use a mix of blogs, videos, infographics, and social media posts to reach broad consumers. In developing countries like Mongolia, emphasizing perceived innovativeness alone can sufficiently reduce perceived risk. Thus, marketing strategies can be tailored according to the characteristics of each country.
Thirdly, it is necessary to activate consumer norms to encourage the use of SFDS. Special attention should be given to subjective norms, which influence both personal norms and behavioral intentions. Marketers can plan promotions to encourage users to share their SFDS experiences on social media, thereby spreading authentic and relatable content that can influence others. Collaborating with social media influencers in these promotions can further enhance marketing outcomes.
Lastly, marketers can devise multifaceted strategies tailored to the development status of countries adopting SFDS. In developing countries like Mongolia, it is crucial to decrease perceived risks. For instance, marketers can plan SFDS demonstrations and customer engagement events to ease concerns about using SFDS. Strategies to activate norms can also vary depending on the target countries’ development status. In developed countries like South Korea, it is necessary to enhance personal norms by activating subjective norms through strategies that share and spread related postings on social media. In developing countries like Mongolia, it is important to directly enhance behavioral intention by activating subjective norms through strategies that encourage participation in technology-based service changes in collaboration with social media influencers.

6. Limitations and Further Research Suggestions

The current study focused on developing and developed countries in the East Asian region (i.e., South Korea and Mongolia). The findings from this research have limitations when generalizing to both developing and developed countries in other regions, such as in Europe or America. Future research could investigate the moderating role of the development status of the countries in the context of technology adoption behavior in other regions. Data collected from each country also may have some measurement bias. The data were collected through an online survey of specialized companies in each country. To ensure the data quality obtained from external providers, the online survey page constructed a system that prevented data entry errors and omissions. However, online surveys may introduce common method bias, as they simultaneously measure perceived/normative attributes and behavioral intentions. Future research could design data collection methods or experimental studies to prevent such measurement bias. In addition, this study adopted a single-dimensional approach to perceived risk. It is recommended that future studies explore multidimensional aspects by focusing on the theory of perceived risk and investigating risk-taking behavior. Lastly, SFDS is environmentally friendly, emitting less carbon compared to traditional motorcycles and vehicle-based delivery services, so future research can consider psychological factors associated with its eco-friendly role. For instance, existing works studied green finance availability and consumer consciousness [106] and the impact mechanism and the spatial spillover effect of green technology innovation [107]. Therefore, future research could investigate consumers’ pro-environmental behavior within the context of SFDS based on these findings.

Author Contributions

Conceptualization, K.J. and J.H.; methodology, H.M.K. and J.H.; writing—original draft preparation, K.J.; writing—review and editing, H.M.K. and J.H.; supervision, J.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Culture, Sports, and Tourism R&D Program through the Korea Creative Content Agency (KOCCA) grant funded by the Ministry of Culture, Sports and Tourism (MCST) in 2024 (Project Name: Cultivating masters and doctoral experts to lead digital-tech tourism, Project Number: RS-2024-00442006, Contribution Rate: 100%).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Sustainability 16 09918 g0a1
Figure A1. Screenshots from the video. Source: Lucchetti [108].
Figure A1. Screenshots from the video. Source: Lucchetti [108].
Sustainability 16 09918 g0a1

References

  1. Chen, C.; Demir, E.; Huang, Y.; Qiu, R. The Adoption of Self-Driving Delivery Robots in Last Mile Logistics. Transp. Res. Part E Logist. Transp. Rev. 2021, 146, 102214. [Google Scholar] [CrossRef] [PubMed]
  2. Chy, M.K.A.; Masum, A.K.M.; Sayeed, K.A.M.; Uddin, M.Z. Delicar: A Smart Deep Learning Based Self-Driving Product Delivery Car in Perspective of Bangladesh. Sensors 2022, 22, 126. [Google Scholar] [CrossRef]
  3. Ryan, M. The Future of Transportation: Ethical, Legal, Social and Economic Impacts of Self-Driving Vehicles in the Year 2025. Sci. Eng. Ethics 2020, 26, 1185–1208. [Google Scholar] [CrossRef]
  4. Kimmel, A. State of the Art: Autonomous Driving Technology as 2019 Comes to a Close. Available online: https://techpubs.info/state-of-the-art-autonomous-driving-technology-as-2019-comes-to-a-close/ (accessed on 15 October 2024).
  5. Fortune Business Insights. Autonomous Vehicle Market Size, Share, and Trends. Available online: https://www.fortunebusinessinsights.com/autonomous-vehicle-market-109045 (accessed on 15 October 2024).
  6. Malcein, L.A.; Lee, Y.C. First-Time Use of an Autonomous Food Delivery Robot: Effects of Affect and Delivery Scenarios. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting; SAGE Publications: Los Angeles, CA, USA, 2023; Volume 67, pp. 471–476. [Google Scholar]
  7. Hong, C.; Choi, E.K.; Joung, H.W.; Kim, H.S. The Impact of Customer Perceived Value on Customer Satisfaction and Loyalty Toward the Food Delivery Robot Service. J. Hosp. Tour. Technol. 2023, 14, 908–924. [Google Scholar] [CrossRef]
  8. Sun, Y.; Weng, C.; Liao, Z. Product Innovation and Sustainable Marketing: Effects on Consumer Innovativeness. Technol. Anal. Strateg. Manag. 2019, 31, 765–775. [Google Scholar] [CrossRef]
  9. Fagnant, D.J.; Kockelman, K. Preparing a Nation for Autonomous Vehicles: Opportunities, Barriers, and Policy Recommendations. Transp. Res. Part A Policy Pract. 2015, 77, 167–181. [Google Scholar] [CrossRef]
  10. Taiebat, M.; Brown, A.L.; Safford, H.R.; Qu, S.; Xu, M. A Review on Energy, Environmental, and Sustainability Implications of Connected and Automated Vehicles. Environ. Sci. Technol. 2018, 52, 11449–11465. [Google Scholar] [CrossRef] [PubMed]
  11. Davis, F.D. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef]
  12. Goodhue, D.L.; Thompson, R.L. Task-Technology Fit and Individual Performance. MIS Q. 1995, 19, 213–236. [Google Scholar] [CrossRef]
  13. Joo, K.; Kim, J.; Hwang, J. Effects of Foodservice Consumers’ Perceptions of Face Recognition Payment on Attitude, Desire, and Behavioral Intentions: A Cross-Cultural Study. J. Travel Tour. Mark. 2024, 41, 359–376. [Google Scholar] [CrossRef]
  14. Boisvert, J.; Khan, M.S. Toward a Better Understanding of the Main Antecedents and Outcomes of Consumer-Based Perceived Product Innovativeness. J. Strateg. Mark. 2022, 30, 296–319. [Google Scholar] [CrossRef]
  15. Hwang, J.; Lee, J.S.; Kim, H. Perceived Innovativeness of Drone Food Delivery Services and Its Impacts on Attitude and Behavioral Intentions: The Moderating Role of Gender and Age. Int. J. Hosp. Manag. 2019, 81, 94–103. [Google Scholar] [CrossRef]
  16. Kim, J.J.; Kim, I.; Hwang, J. A Change of Perceived Innovativeness for Contactless Food Delivery Services Using Drones After the Outbreak of COVID-19. Int. J. Hosp. Manag. 2021, 93, 102758. [Google Scholar] [CrossRef]
  17. Kim, H.W.; Chan, H.C.; Gupta, S. Value-Based Adoption of Mobile Internet: An Empirical Investigation. Decis. Support Syst. 2007, 43, 111–126. [Google Scholar] [CrossRef]
  18. Vishwakarma, P.; Mukherjee, S.; Datta, B. Travelers’ Intention to Adopt Virtual Reality: A Consumer Value Perspective. J. Destin. Mark. Manag. 2020, 17, 100456. [Google Scholar] [CrossRef]
  19. Bauer, R.A. Consumer Behavior as Risk-Taking. In Dynamic Marketing for a Changing World; Hancock, R.S., Ed.; American Marketing Association: Chicago, IL, USA, 1960; pp. 389–398. [Google Scholar]
  20. Mitchell, V.W. Consumer Perceived Risk: Conceptualizations and Models. Eur. J. Mark. 1999, 33, 163–195. [Google Scholar] [CrossRef]
  21. Featherman, M.; Jia, S.J.; Califf, C.B.; Hajli, N. The Impact of New Technologies on Consumers’ Beliefs: Reducing the Perceived Risks of Electric Vehicle Adoption. Technol. Forecast. Soc. Chang. 2021, 169, 120847. [Google Scholar] [CrossRef]
  22. Joo, K.; Hwang, J. Acceptance of Green Technology-Based Service: Consumers’ Risk-Taking Behavior in the Context of Indoor Smart Farm Restaurants. Appl. Sci. 2023, 13, 11433. [Google Scholar] [CrossRef]
  23. Han, H.; Hyun, S.S. Drivers of Customer Decision to Visit an Environmentally Responsible Museum: Merging the Theory of Planned Behavior and Norm Activation Theory. J. Travel Tour. Mark. 2017, 34, 1155–1168. [Google Scholar] [CrossRef]
  24. Han, H.; Kim, S.S.; Badu-Baiden, F.; Al-Ansi, A.; Kim, J.J. Drivers of hotel guests’ choice of smart products: Applying a complexity theory involving TAM, technology readiness, TPB, and emotion factors. Int. J. Hosp. Manag. 2024, 120, 103755. [Google Scholar] [CrossRef]
  25. Ajzen, I.; Fishbein, M. Theory of Reasoned Action-Theory of Planned Behavior; Nomos. Ph.D. Thesis, University of South Florida, Tampa, FL, USA, 1988. Volume 2007. pp. 67–98. [Google Scholar]
  26. De Groot, J.I.; Steg, L. Morality and Prosocial Behavior: The Role of Awareness, Responsibility, and Norms in the Norm Activation Model. J. Soc. Psychol. 2009, 149, 425–449. [Google Scholar] [CrossRef]
  27. Chen, T.; Wu, C.; Lu, X.; Xiao, H. Analysis of Factors Influencing Family Farms’ Adoption of Green Prevention and Control Techniques on an Integrative Framework of the TPB and NAM. Acta Psychol. 2024, 247, 104314. [Google Scholar] [CrossRef]
  28. Joo, K.; Lee, J.; Hwang, J. NAM and TPB Approach to Consumers’ Decision-Making Framework in the Context of Indoor Smart Farm Restaurants. Int. J. Environ. Res. Public Health 2022, 19, 14604. [Google Scholar] [CrossRef]
  29. Kim, J.J.; Hwang, J. Merging the Norm Activation Model and the Theory of Planned Behavior in the Context of Drone Food Delivery Services: Does the Level of Product Knowledge Really Matter? J. Hosp. Tour. Manag. 2020, 42, 1–11. [Google Scholar] [CrossRef]
  30. Chopdar, P.K.; Korfiatis, N.; Sivakumar, V.J.; Lytras, M.D. Mobile Shopping Apps Adoption and Perceived Risks: A Cross-Country Perspective Utilizing the Unified Theory of Acceptance and Use of Technology. Comput. Hum. Behav. 2018, 86, 109–128. [Google Scholar] [CrossRef]
  31. Jin, Z.; Lynch, R.; Attia, S.; Chansarkar, B.; Gülsoy, T.; Lapoule, P.; Ungerer, M. The Relationship Between Consumer Ethnocentrism, Cosmopolitanism and Product Country Image Among Younger Generation Consumers: The Moderating Role of Country Development Status. Int. Bus. Rev. 2015, 24, 380–393. [Google Scholar] [CrossRef]
  32. Karim, M.S.; Nahar, S.; Demirbag, M. Resource-Based Perspective on ICT Use and Firm Performance: A Meta-Analysis Investigating the Moderating Role of Cross-Country ICT Development Status. Technol. Forecast. Soc. Chang. 2022, 179, 121626. [Google Scholar] [CrossRef]
  33. Ejiaku, S.A. Technology Adoption: Issues and Challenges in Information Technology Adoption in Emerging Economies. J. Int. Technol. Inf. Manag. 2014, 23, 5. [Google Scholar] [CrossRef]
  34. Liu, C.; Van Wart, M.; Kim, S.; Wang, X.; McCarthy, A.; Ready, D. The Effects of National Cultures on Two Technologically Advanced Countries: The Case of E-Leadership in South Korea and the United States. Aust. J. Public Adm. 2020, 79, 298–329. [Google Scholar] [CrossRef]
  35. Huerta, E.; Sandoval-Almazán, R. Digital Literacy: Problems Faced by Telecenter Users in Mexico. Inf. Technol. Dev. 2007, 13, 217–232. [Google Scholar] [CrossRef]
  36. Miller, J.; Khera, O. Digital Library Adoption and the Technology Acceptance Model: A Cross-Country Analysis. Electron. J. Inf. Syst. Dev. Ctries. 2010, 40, 1–19. [Google Scholar] [CrossRef]
  37. De Mooij, M.; Hofstede, G. Convergence and Divergence in Consumer Behavior: Implications for International Retailing. J. Retail. 2002, 78, 61–69. [Google Scholar] [CrossRef]
  38. Hofstede, G. Culture and Organizations. Int. Stud. Manag. Organ. 1980, 10, 15–41. [Google Scholar] [CrossRef]
  39. Han, J.J.; Seo, S.; Kim, H.J. Autonomous Delivery Robots on the Rise: How Can They Cut Carbon Footprint for Restaurant Food Deliveries? Int. J. Hosp. Manag. 2024, 121, 103804. [Google Scholar] [CrossRef]
  40. Alverhed, E.; Hellgren, S.; Isaksson, H.; Olsson, L.; Palmqvist, H.; Flodén, J. Autonomous Last-Mile Delivery Robots: A Literature Review. Eur. Transp. Res. Rev. 2024, 16, 4. [Google Scholar] [CrossRef]
  41. Abrar, M.M.; Islam, R.; Shanto, M.A.H. An Autonomous Delivery Robot to Prevent the Spread of Coronavirus in Product Delivery System. In Proceedings of the 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, New York, NY, USA, 28–31 October 2020; pp. 461–466. [Google Scholar]
  42. Yuen, K.F.; Cai, L.; Lim, Y.G.; Wang, X. Consumer Acceptance of Autonomous Delivery Robots for Last-Mile Delivery: Technological and Health Perspectives. Front. Psychol. 2022, 13, 953370. [Google Scholar] [CrossRef]
  43. Baek, B.; Robots Slowly Integrating into Everyday Life in Korea. Korea Times. 2023. Available online: https://www.koreatimes.co.kr/www/tech/2024/05/129_363843.html (accessed on 15 October 2024).
  44. Koller, A.; Waymo Self-Driving Cars Are Delivering Uber Eats Orders for the First Time. CNBC. 2024. Available online: https://www.cnbc.com/2024/04/03/waymo-self-driving-cars-are-delivering-uber-eats-orders-for-first-time.html (accessed on 15 October 2024).
  45. Inoue, Y.; Uber Eats Japan Begins Deliveries with Self-Driving Robots. Jpn. Times. 2024. Available online: https://www.japantimes.co.jp/business/2024/03/06/tech/inoue-uber-eats-robot/ (accessed on 15 October 2024).
  46. Martinez, J.E.; VanLeeuwen, D.; Stringam, B.B.; Fraune, M.R. Hey?! What Did You Think About That Robot? Groups Polarize Users’ Acceptance and Trust of Food Delivery Robots. In Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, Stockholm, Sweden, 13–16 March 2023; ACM: New York, NY, USA, 2023; pp. 417–427. [Google Scholar]
  47. Kaplan, M.D. The Relationship Between Perceived Innovativeness and Emotional Product Responses: A Brand-Oriented Approach. Innov. Mark. 2009, 5, 39–47. [Google Scholar]
  48. Rogers, E.M. Diffusion of Innovations, 5th ed.; Free Press: New York, NY, USA, 2003. [Google Scholar]
  49. Kleinschmidt, E.J.; Cooper, R.G. The Impact of Product Innovativeness on Performance. J. Prod. Innov. Manag. 1991, 8, 240–251. [Google Scholar] [CrossRef]
  50. Henard, D.H.; Szymanski, D.M. Why Some New Products Are More Successful Than Others. J. Mark. Res. 2001, 38, 362–375. [Google Scholar] [CrossRef]
  51. Hussain, K.; Afzaal, A.; Al Balushi, M.K.; Junaid, M. Breaking the Mold: How Customer Perceived Innovativeness Sets Restaurants Apart. Kybernetes 2023. [Google Scholar] [CrossRef]
  52. Featherman, M.S.; Pavlou, P.A. Predicting E-Services Adoption: A Perceived Risk Facets Perspective. Int. J. Hum.-Comput. Stud. 2003, 59, 451–474. [Google Scholar] [CrossRef]
  53. Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D. User Acceptance of Information Technology: Toward a Unified View. MIS Q. 2003, 27, 425–478. [Google Scholar] [CrossRef]
  54. Choe, J.Y.; Kim, J.J.; Hwang, J. Perceived Risks from Drone Food Delivery Services Before and After COVID-19. Int. J. Contemp. Hosp. Manag. 2021, 33, 1276–1296. [Google Scholar] [CrossRef]
  55. Jeon, H.M.; Sung, H.J.; Kim, H.Y. Customers’ Acceptance Intention of Self-Service Technology in the Restaurant Industry: Expanding UTAUT with Perceived Risk and Innovativeness. Serv. Bus. 2020, 14, 533–551. [Google Scholar] [CrossRef]
  56. Spence, M. Job Market Signaling. Q. J. Econ. 1973, 87, 355–374. [Google Scholar] [CrossRef]
  57. Meents, S.; Verhagen, T. An Exploration of Trust and Perceived Risk in the Adoption of Electronic Services in Consumers’ Digital Environment. J. Retail. Consum. Serv. 2018, 20, 69–83. [Google Scholar]
  58. Ngo, L.V.; O’Cass, A. Innovation and Business Success: The Mediating Role of Customer Participation. J. Bus. Res. 2013, 66, 1134–1142. [Google Scholar] [CrossRef]
  59. Egbue, O.; Long, S. Barriers to Widespread Adoption of Electric Vehicles: An Analysis of Consumer Attitudes and Perceptions. Energy Policy 2012, 48, 717–729. [Google Scholar] [CrossRef]
  60. Meyer, C.; Schwager, A. Understanding Customer Experience. Harv. Bus. Rev. 2007, 85, 116. [Google Scholar]
  61. Kotler, P. Marketing Management: Millennium Edition; Prentice Hall: Upper Saddle River, NJ, USA, 2001. [Google Scholar]
  62. Parameswaran, R.; Pisharodi, R.M. Facets of Country of Origin Image: An Empirical Assessment. J. Advert. 1994, 23, 43–56. [Google Scholar] [CrossRef]
  63. Poiesz, T.B. The Image Concept: Its Place in Consumer Psychology. J. Econ. Psychol. 1989, 10, 457–472. [Google Scholar] [CrossRef]
  64. Crompton, J.L. An Assessment of the Image of Mexico as a Vacation Destination and the Influence of Geographical Location upon That Image. J. Travel Res. 1979, 17, 18–23. [Google Scholar] [CrossRef]
  65. Bloemer, J.; De Ruyter, K. On the Relationship Between Store Image, Store Satisfaction, and Store Loyalty. Eur. J. Mark. 1998, 32, 499–513. [Google Scholar] [CrossRef]
  66. Han, H.; Hsu, L.T.J.; Lee, J.S. Empirical Investigation of the Roles of Attitudes Toward Green Behaviors, Overall Image, Gender, and Age in Hotel Customers’ Eco-Friendly Decision-Making Process. Int. J. Hosp. Manag. 2009, 28, 519–528. [Google Scholar] [CrossRef]
  67. Han, H.; Yu, J.; Chua, B.L.; Lee, S.; Kim, W. Impact of Core-Product and Service-Encounter Quality, Attitude, Image, Trust, and Love on Repurchase: Full-Service vs Low-Cost Carriers in South Korea. Int. J. Contemp. Hosp. Manag. 2019, 31, 1588–1608. [Google Scholar] [CrossRef]
  68. Erkmen, E.; Hancer, M. Building Brand Relationship for Restaurants: An Examination of Other Customers, Brand Image, Trust, and Restaurant Attributes. Int. J. Contemp. Hosp. Manag. 2019, 31, 1469–1487. [Google Scholar] [CrossRef]
  69. Hwang, J.; Park, S.; Kim, I. Understanding Motivated Consumer Innovativeness in the Context of a Robotic Restaurant: The Moderating Role of Product Knowledge. J. Hosp. Tour. Manag. 2020, 44, 272–282. [Google Scholar] [CrossRef]
  70. Koh, L.Y.; Xia, Z.; Yuen, K.F. Consumer Acceptance of the Autonomous Robot in Last-Mile Delivery: A Combined Perspective of Resource-Matching, Perceived Risk, and Value Theories. Transp. Res. Part A Policy Pract. 2024, 182, 104008. [Google Scholar] [CrossRef]
  71. Alqasa, K.M.A. Impact of Artificial Intelligence-Based Marketing on Banking Customer Satisfaction: Examining Moderating Role of Ease of Use and Mediating Role of Brand Image. Transnatl. Mark. J. 2023, 11, 167–180. [Google Scholar]
  72. Hwang, J.; Choe, J.Y. Exploring Perceived Risk in Building Successful Drone Food Delivery Services. Int. J. Contemp. Hosp. Manag. 2019, 31, 3249–3269. [Google Scholar] [CrossRef]
  73. Singh, J.; Kaur, P. Customers’ Attitude Towards Technology-Based Services Provided by Select Indian Banks: Empirical Analysis. Int. J. Commer. Manag. 2013, 23, 56–68. [Google Scholar] [CrossRef]
  74. Park, S.Y.; Sohn, S.H. Exploring the Normative Influences of Social Norms on Individual Environmental Behavior. J. Glob. Sch. Mark. Sci. 2012, 22, 183–194. [Google Scholar] [CrossRef]
  75. Ru, X.; Wang, S.; Yan, S. Exploring the Effects of Normative Factors and Perceived Behavioral Control on Individual’s Energy-Saving Intention: An Empirical Study in Eastern China. Resour. Conserv. Recycl. 2018, 134, 91–99. [Google Scholar] [CrossRef]
  76. Liu, Y.; Li, X. Pro-Environmental Behavior Predicted by Media Exposure, SNS Involvement, and Cognitive and Normative Factors. Environ. Commun. 2021, 15, 954–968. [Google Scholar] [CrossRef]
  77. Schwartz, S.H. Normative Influences on Altruism. In Advances in Experimental Social Psychology; Academic Press: Cambridge, MA, USA, 1977; Volume 10, pp. 221–279. [Google Scholar]
  78. Ajzen, I. The Theory of Planned Behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar] [CrossRef]
  79. Rezaei, R.; Safa, L.; Damalas, C.A.; Ganjkhanloo, M.M. Drivers of Farmers’ Intention to Use Integrated Pest Management: Integrating Theory of Planned Behavior and Norm Activation Model. J. Environ. Manag. 2019, 236, 328–339. [Google Scholar] [CrossRef]
  80. Kaushik, A.K.; Agrawal, A.K.; Rahman, Z. Tourist Behaviour Towards Self-Service Hotel Technology Adoption: Trust and Subjective Norm as Key Antecedents. Tour. Manag. Perspect. 2015, 16, 278–289. [Google Scholar] [CrossRef]
  81. Ho, C.W.; Wu, C.C. Exploring Intention Toward Using an Electric Scooter: Integrating the Technology Readiness and Acceptance into the Norm Activation Model (TRA-NAM). Energies 2021, 14, 6895. [Google Scholar] [CrossRef]
  82. United Nations. World Economic Situation and Prospects. Available online: https://www.un.org/en/development/desa/policy/wesp/wesp_current/2014wesp_country_classification.pdf (accessed on 15 October 2024).
  83. Cirera, X.; Comin, D.; Cruz, M. Bridging the Technological Divide: Technology Adoption by Firms in Developing Countries; World Bank Publications: Washington, DC, USA, 2022. [Google Scholar]
  84. Murthy, K.B.; Kalsie, A.; Shankar, R. Digital Economy in a Global Perspective: Is There a Digital Divide? Transnatl. Corp. Rev. 2021, 13, 1–15. [Google Scholar] [CrossRef]
  85. Norris, P. Digital Divide: Civic Engagement, Information Poverty, and the Internet Worldwide; Cambridge University Press: Cambridge, UK, 2001. [Google Scholar]
  86. Ashraf, A.R.; Thongpapanl, N.; Auh, S. The Application of the Technology Acceptance Model Under Different Cultural Contexts: The Case of Online Shopping Adoption. J. Int. Mark. 2014, 22, 68–93. [Google Scholar] [CrossRef]
  87. Dutot, V.; Bhatiasevi, V.; Bellallahom, N. Applying the Technology Acceptance Model in a Three-Countries Study of Smartwatch Adoption. J. High Technol. Manag. Res. 2019, 30, 1–14. [Google Scholar] [CrossRef]
  88. Hofstede, G.; Hofstede, G.J. Cultures and Organizations: Software of the Mind, 2nd ed.; McGraw-Hill: New York, NY, USA, 2005. [Google Scholar]
  89. Alcántara-Pilar, J.M.; Del Barrio-García, S.; Crespo-Almendros, E. Cross-Cultural Comparison of the Relationships Among Perceived Risk Online, Perceived Usability, and Satisfaction During Browsing of a Tourist Website. Tour. Manag. Stud. 2015, 11, 15–24. [Google Scholar]
  90. Dai, H.; Palvi, P.C. Mobile Commerce Adoption in China and the United States: A Cross-Cultural Study. ACM SIGMIS Database 2009, 40, 43–61. [Google Scholar] [CrossRef]
  91. Malhotra, N.K.; Ulgado, F.M.; Agarwal, J.; Shainesh, G.; Wu, L. Dimensions of Service Quality in Developed and Developing Economies: Multi-Country Cross-Cultural Comparisons. Int. Mark. Rev. 2005, 22, 256–278. [Google Scholar] [CrossRef]
  92. Martins, C.; Oliveira, T.; Popovič, A. Understanding the Internet Banking Adoption: A Unified Theory of Acceptance and Use of Technology and Perceived Risk Application. Int. J. Inf. Manag. 2014, 34, 1–13. [Google Scholar] [CrossRef]
  93. Han, H.; Lee, K.S.; Chua, B.L.; Lee, S.; Kim, W. Role of Airline Food Quality, Price Reasonableness, Image, Satisfaction, and Attachment in Building Re-Flying Intention. Int. J. Hosp. Manag. 2019, 80, 91–100. [Google Scholar] [CrossRef]
  94. Zeithaml, V.A.; Berry, L.L.; Parasuraman, A. The Behavioral Consequences of Service Quality. J. Mark. 1996, 60, 31–46. [Google Scholar] [CrossRef]
  95. Milner, J.; Kubota, Y. Multinational Corporations and Transnational Knowledge Diffusion: A Theoretical Model and Empirical Analysis. J. Int. Econ. 2005, 73, 74–93. [Google Scholar]
  96. Vargas-Montoya, L.; Gimenez, G.; Fernández-Gutiérrez, M. ICT Use for Learning and Students’ Outcomes: Does the Country’s Development Level Matter? Socio-Econ. Plan. Sci. 2023, 87, 101550. [Google Scholar] [CrossRef]
  97. World Population Review. Human Development Index (HDI) by Country 2023. Available online: https://worldpopulationreview.com/country-rankings/hdi-by-country (accessed on 15 October 2024).
  98. United Nations Development Programme. Human Development Report 2021–2022: Uncertain Times, Unsettled Lives: Shaping Our Future in a Transforming World. Available online: https://hdr.undp.org/content/human-development-report-2021-22 (accessed on 15 October 2024).
  99. Lee, S. With CU Operating More Than 400 Convenience Stores Throughout Mongolia, Including Ulaanbaatar, Korea. Maeil Bus. Newsp. 2024. Available online: https://www.mk.co.kr/en/business/10991520 (accessed on 15 October 2024).
  100. Lkhaajav, B. How Mongolia’s Nomadic Culture is Navigating Modernization. Dipl. 2023. Available online: https://thediplomat.com/2023/09/how-mongolias-nomadic-culture-is-navigating-modernization (accessed on 15 October 2024).
  101. Byrne, B.M. Structural Equation Modeling with AMOS, EQS, and LISREL: Comparative Approaches to Testing for the Factorial Validity of a Measuring Instrument. Int. J. Test. 2001, 1, 55–86. [Google Scholar] [CrossRef]
  102. Fornell, C.; Larcker, D.F. Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. J. Mark. Res. 1981, 18, 382–388. [Google Scholar] [CrossRef]
  103. Hair, J.F.; Anderson, R.E.; Tatham, R.L.; Black, W.C. Multivariate Data Analysis, 6th ed.; Prentice-Hall: Upper Saddle River, NJ, USA, 2006. [Google Scholar]
  104. Hirunyawipada, T.; Paswan, A.K. Consumer innovativeness and perceived risk: Implications for high technology product adoption. J. Consum. Mark. 2006, 23, 182–198. [Google Scholar] [CrossRef]
  105. Aldás-Manzano, J.; Lassala-Navarré, C.; Ruiz-Mafé, C.; Sanz-Blas, S. The role of consumer innovativeness and perceived risk in online banking usage. Int. J. Bank Mark. 2009, 27, 53–75. [Google Scholar] [CrossRef]
  106. Gu, X.; Firdousi, S.F.; Obrenovic, B.; Afzal, A.; Amir, B.; Wu, T. The influence of green finance availability to retailers on purchase intention: A consumer perspective with the moderating role of consciousness. Environ. Sci. Pollut. Res. 2023, 30, 71209–71225. [Google Scholar] [CrossRef]
  107. Yu, Y.; Hu, W.; Dong, C.; Gu, X.; Obrenovic, B. E-commerce development and green technology innovation: Impact mechanism and the spatial spillover effect. Sustainability 2023, 15, 12988. [Google Scholar] [CrossRef]
  108. Lucchetti, R. Hold the Tip, Meet Domino’s Autonomous Pizza Delivery Robot. YouTube. 2021. Available online: https://www.youtube.com/watch?v=pX4MJLoeXH8 (accessed on 15 October 2024).
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Figure 1. Proposed conceptual model. Notes: H = Hypothesis, the normal arrows present the hypotheses regarding causal relationships, and the bold arrows present the hypotheses regarding moderating effects.
Figure 1. Proposed conceptual model. Notes: H = Hypothesis, the normal arrows present the hypotheses regarding causal relationships, and the bold arrows present the hypotheses regarding moderating effects.
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Figure 2. Standardized theoretical path coefficients. Notes: NFI = Normed fit index, CFI = Comparative fit index, TLI = Tucker–Lewis index, RMSEA = Root mean square error of approximation. Unmarked values are for Korean consumers, underlined values are for Mongolian consumers, and * p < 0.05.
Figure 2. Standardized theoretical path coefficients. Notes: NFI = Normed fit index, CFI = Comparative fit index, TLI = Tucker–Lewis index, RMSEA = Root mean square error of approximation. Unmarked values are for Korean consumers, underlined values are for Mongolian consumers, and * p < 0.05.
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Table 1. Profile of the survey respondents.
Table 1. Profile of the survey respondents.
DemographicsSubcategoriesKorean
(n = 313)		Mongolian
(n = 315)
GenderMale162 (51.8%)125 (39.7%)
Female151 (48.2%)190 (60.3%)
Age20s95 (30.4%)104 (33.0%)
30s97 (31.0%)93 (29.5%)
40s68 (21.7%)91 (28.9%)
50s and older53 (16.9%)27 (8.6%)
Education levelLess than high school diploma43 (13.7%)80 (25.4%)
Associate’s degree64 (20.4%)30 (9.5%)
Bachelor’s degree170 (54.3%)199 (63.2%)
Graduate degree36 (11.5%)6 (1.9%)
Marital statusSingle166 (53.0%)68 (21.6%)
Married143 (45.7%)211 (67.0%)
Others4 (1.3%)36 (11.4%)
Monthly income levelMean (USD)2795.08473.14
Table 2. Confirmatory Factor Analysis: Items and Loadings.
Table 2. Confirmatory Factor Analysis: Items and Loadings.
ConstructsScale Itemsλa
KRMN
Perceived innovativenessSelf-driving food delivery services seem to be an original idea for better services.0.9340.791
Self-driving food delivery services are likely to be creative.0.9290.923
Self-driving food delivery services seem to be an advanced, forward-looking service.0.7920.880
Perceived riskUsing self-driving food delivery services makes me feel anxiety.0.9190.913
Using self-driving food delivery services makes me feel nervous.0.9560.957
The usage of self-driving food delivery services would lead me to a psychological loss.0.9260.900
ImageOverall image of self-driving food delivery services is great.0.8530.744
Overall, I have a good image about self-driving food delivery services.0.9140.821
Overall image for using self-driving food delivery services is good.0.8900.779
Subjective normsMost people who are important to me think I should use self-driving food delivery services.0.9390.909
Most people who are important to me would want me to use self-driving food delivery services.0.9700.915
People whose opinions I value would prefer that I use self-driving food delivery services.0.9480.722
Personal normsI feel an obligation to choose technology-based services, such as self-driving food delivery services.0.8920.845
Regardless of what other people do, because of my own values/principles, I feel that I should use technology-based services, such as self-driving food delivery services.0.8750.829
I feel it is important that consumers use technology-based services, such as self-driving food delivery services.0.8850.856
Intentions to useI will use self-driving food delivery services when ordering food.0.9410.882
I am willing to use self-driving food delivery services when ordering food.0.9340.910
I am likely to use self-driving food delivery services when ordering food.0.9380.934
Notes: KR = Korean, MN = Mongolian. a All factors loadings are significant at p < 0.001.
Table 3. Descriptive statistics and associated measures.
Table 3. Descriptive statistics and associated measures.
ConstructsMean (Std Dev.)AVE(1)(2)(3)(4)(5)(6)
(1) Perceived
innovativeness		5.30 (1.05)
5.72 (0.86)		0.788
0.751		0.917
0.900		−0.018 a
−0.236		0.685
0.718		0.482
0.609		0.622
0.660		0.476
0.236
(2) Perceived risk3.87 (1.49)
3.65 (1.39)		0.872
0.853		0.001 b
0.056		0.953
0.946		−0.168
−0.276		−0.070
−0.030		−0.100
−0.344		−0.233
−0.185
(3) Image5.10 (1.02)
5.37 (1.00)		0.785
0.611		0.469
0.516		0.028
0.076		0.916
0.825		0.562
0.643		0.690
0.764		0.686
0.700
(4) Subjective norms3.97 (1.42)
5.01 (1.17)		0.907
0.728		0.232
0.371		0.005
0.001		0.316
0.413		0.967
0.888		0.735
0.640		0.590
0.664
(5) Personal norms4.60 (1.32)
5.47 (1.01)		0.782
0.711		0.387
0.436		0.010
0.118		0.476
0.584		0.540
0.410		0.915
0.881		0.681
0.764
(6) Intentions to use4.90 (1.29)
5.41 (1.11)		0.879
0.826		0.227
0.056		0.054
0.034		0.471
0.490		0.348
0.441		0.464
0.584		0.956
0.934
Notes: Unmarked values are for a Korean sample, and underlined values are for Mongolian sample. AVE = average variance extracted. Shades are composite reliability values, a correlations are above the diagonal, and b squared correlations are below the diagonal.
Table 4. Moderating role of economic status.
Table 4. Moderating role of economic status.
PathUnconstrained ModelConstrained ModelTests of Moderator
KoreanMongolian
βt-Valueβt-Valueχ2(254) = 726.167χ2 DifferenceHypothesis
H8a PI → PR−0.015−0.245−0.229−3.851 *χ2(255) = 734.047Δχ2(1) = 7.880Supported
H8b PI → I0.69111.709 *0.71911.132 *χ2(255) = 726.240Δχ2(1) = 0.073Rejected
H8c PR → I−0.168−3.678 *−0.112−2.252 *χ2(255) = 728.083Δχ2(1) = 1.916Rejected
H8d I → BI0.4258.438 *0.3505.994 *χ2(255) = 727.030Δχ2(1) = 0.863Rejected
H8e SN → PN0.74214.901 *0.67111.813 *χ2(255) = 731.154Δχ2(1) = 4.987Supported
H8f SN → BI0.1602.235 *0.3524.822 *χ2(255) = 730.048Δχ2(1) = 3.881Supported
H8g PN → BI0.3244.514 *0.2103.195 *χ2(255) = 726.631Δχ2(1) = 0.464Rejected
Notes: NFI = Normed fit index, CFI = Comparative fit index, TLI = Tucker–Lewis index, RMSEA = Root mean square error of approximation. Unmarked values are for Korean consumers, underlined values are for Mongolian consumers, * p < 0.05.
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Joo, K.; Kim, H.M.; Hwang, J. Do Consumers’ Perceived Attributes and Normative Factors Affect Acceptance Behavior Towards Eco-Friendly Self-Driving Food Delivery Services? The Moderating Role of Country Development Status. Sustainability 2024, 16, 9918. https://doi.org/10.3390/su16229918

AMA Style

Joo K, Kim HM, Hwang J. Do Consumers’ Perceived Attributes and Normative Factors Affect Acceptance Behavior Towards Eco-Friendly Self-Driving Food Delivery Services? The Moderating Role of Country Development Status. Sustainability. 2024; 16(22):9918. https://doi.org/10.3390/su16229918

Chicago/Turabian Style

Joo, Kyuhyeon, Heather Markham Kim, and Jinsoo Hwang. 2024. "Do Consumers’ Perceived Attributes and Normative Factors Affect Acceptance Behavior Towards Eco-Friendly Self-Driving Food Delivery Services? The Moderating Role of Country Development Status" Sustainability 16, no. 22: 9918. https://doi.org/10.3390/su16229918

APA Style

Joo, K., Kim, H. M., & Hwang, J. (2024). Do Consumers’ Perceived Attributes and Normative Factors Affect Acceptance Behavior Towards Eco-Friendly Self-Driving Food Delivery Services? The Moderating Role of Country Development Status. Sustainability, 16(22), 9918. https://doi.org/10.3390/su16229918

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