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Article

Exploring the Existence of Moderated Mediation of Attitudes Between Privacy Risk and the Intention to Use Drone Delivery Services

by
Kyung-A Sun
1 and
Joonho Moon
2,*
1
Department of Tourism Management, Gachon University, Sungnam-si 13120, Republic of Korea
2
Department of Tourism Administration, Kangwon National University, Chuncheon 24341, Republic of Korea
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(6), 2585; https://doi.org/10.3390/su17062585
Submission received: 27 January 2025 / Revised: 7 March 2025 / Accepted: 12 March 2025 / Published: 14 March 2025
(This article belongs to the Special Issue Managing Sustainable Development: Technology, Modelling & Applications)
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Abstract

:
Drone delivery services have attracted increasing interest in the retail business market. Drone delivery services have both positive and negative aspects considering privacy risk and eco-friendliness. Given these points, this work investigates the relationships between privacy risk, attitudes, and the intention to use. This work also explores the moderating effects of gender and the eco-friendliness of drone delivery services using stakeholder theory as a theoretical underpinning. This research thus used a survey as an instrument. This work recruited survey participants through the Clickworker platform service. The number of observations was 409. To test the research hypotheses, this study used Hayes Process Macro Model 7. The results revealed that privacy risk negatively affects attitudes. Additionally, the results revealed that attitude is positively associated with the intention to use. Plus, this research revealed the significant moderating effects of gender and eco-friendliness on the impact of privacy risk on attitude. This research contributes to the literature by documenting market information for drone delivery services. Additionally, the managerial implications of this work are presented.

1. Introduction

Fortune Business Insight [1] reported that the global market size of the drone delivery service market is projected to grow from approximately 472 million US dollars in 2024 to 57 billion US dollars in 2032. According to Grand View Research [2], the market size of drone delivery services in the US is approximately 530 million dollars. Grand View Research [2] noted that the size of the market is expected to grow; the market size will increase by approximately 42% by 2030. This finding indicates that drone delivery services are likely to become an important market in the US. Scholars also claim that drone delivery services are likely to become more popular in the market [3,4,5]. Previous studies also have highlighted that the market’s focus has increasingly shifted towards drone delivery services, largely driven by advancements in retail technology. This shift is primarily motivated by the potential for cost savings in delivery logistics and the reduction in environmental impact [6,7,8]. However, the adoption of drone delivery services remains controversial, particularly due to concerns over privacy risks and their environmental sustainability [5,9,10]. Specifically, some scholars argue that drone delivery is environmentally beneficial due to its low carbon emissions, while others raise concerns about privacy issues, particularly in relation to personal data security [10,11,12]. Numerous works have investigated consumer perspectives on drone delivery within the retail sector, underscoring the need for further research to explore these concerns within the retail industry [4,8,13]. This condition leads this work to perform this research to inform drone delivery service providers, which is likely to result in better service for customers.
This research adopted the concept of “intention to use” as the dependent variable because numerous studies have employed the intention to use as the dependent variable to appraise consumer reactions in the context of technology-related businesses [14,15,16]. Additionally, previous studies have shown that attitudes are likely to become a mediator variable for investigating the perception of the market [14,17]. In many prior works, attitude was used as the determinant of intention to use and as an explained attribute of independent variables [17,18,19]. Next, scholars have contended that a drawback of drone delivery services is privacy risk, as drones can intrude on the personal lives of others and gather information without permission [7,19]. Therefore, privacy risk might be the downside of drone delivery services. Considering prior works, this work selects three attributes: privacy risk (independent variable), attitude (mediator), and intention to use (dependent variable).
Previous studies have shown that female individuals are more vulnerable to threats because they tend to have less social, financial, and physical power [20,21]. Such vulnerability might lead female consumers to become more sensitive to the privacy risk of drone delivery services. This is because female individuals might need to allocate more time and effort to address the undesired conditions caused by drone delivery services. This research attempts to determine the moderating effect of gender in the domain of drone delivery services. The literature indicates that eco-friendliness, which protects the natural environment, is a merit of drone delivery services, and this merit might become a critical point for consumers because interest in the natural environment has become increasingly prominent globally [22,23,24]. Researchers also have stated that socially responsible implementation of business might improve market reputation and that such a reputation could become a defensive instrument for stakeholders [25,26]. Thus, it could be reasonably assumed that eco-friendliness might dilute the negative influence of the privacy risk of drone delivery service in the perceptions of consumers. Hence, this work aims to examine the moderating effect of the eco-friendliness of drone delivery services. This might be worthwhile because eco-friendliness is related to environmental sustainability [5,10,27]. Researchers have documented that the merit of drone delivery services is environmental, which is imperative because the market has become interested in increased protection of the natural environment [28,29]. Stakeholder theory provides a theoretical framework for examining the moderating effects of gender and eco-friendliness in the context of drone delivery services. According to stakeholder theory, a key to achieving business sustainability lies in fostering positive relationships with stakeholders [13,30,31]. Prior research has suggested that, in the realm of drone delivery services, protecting vulnerable customers and minimizing environmental impact are central to building strong relationships with stakeholders [13,19,30]. Despite extensive studies on drones, there is a noticeable gap in research linking stakeholder theory to drone delivery services. To address this gap, the present study applies stakeholder theory to explore the dynamics of drone delivery services [7,8,13].
Although privacy risk is a critical issue identified in the literature on drone delivery services, few studies have empirically examined its influence on consumer perceptions. To minimize this gap, the first objective of this research is to investigate the relationships between privacy risk, consumer attitudes, and intentions to use drone delivery services. A secondary goal of this study is to explore the moderating effects of gender and eco-friendliness within the framework of stakeholder theory. By appraising the roles of gender and eco-friendliness, this research seeks to contribute to the literature by highlighting the impact of gender and the influence of eco-friendly marketing in the context of drone delivery services. Through this approach, the study attempts to inspect the explanatory power of stakeholder theory within the domain of consumer behavior and technology, ultimately contributing to the literature on sustainability in these fields. Such efforts might shed light on the literature by clarifying the relationships among attributes in the context of the consumer behavior of drone delivery services. By scrutinizing consumer perception and eco-friendliness, this work might be able to contribute to the literature on sustainability in both natural environment and business areas. Moreover, this study is essential because it presents implications that can be used to improve drone delivery services by offering consumer information.

2. Review of the Literature and Hypothesis Development

2.1. Intention to Use

The intention to use is defined as how likely individuals are to use certain goods and services [14,16]. Previous research highlighted that the intention to use a technology is a psychological state that significantly influences decision-making and can drive business sales [14,32]. Many studies have explored users’ intention to adopt various technologies. For example, An et al. [14] used intention to use as a key variable to examine food delivery application users, while Salloum et al. [16] focused on intention to use in their study of metaverse technology users. Foroughi et al. [32] also employed intention to use as an outcome variable to explore user behavior regarding ChatGPT 3.5 version for educational purposes. Similarly, Higueras-Castillo et al. [15] adopted intention to use in the context of online business. Alrawad et al. [33] investigated the antecedents of the intention to use mobile payment systems, and Foroughi et al. [34] examined the influence of various attributes on the intention to use autonomous vehicles. In the context of drone delivery services, Hwang et al. [8] similarly utilized intention to use to explore consumer adoption. In summary, a significant body of literature has employed intention to use as a dependent variable to understand user behaviors across a range of technology systems.

2.2. Attitude

Attitude refers to an individual’s position relative to a certain target based on long-term evaluation [14,35]. Researchers noted that attitude plays a critical role in consumer behavior, as it significantly influences consumer decision-making and intentions [36,37]. For instance, An et al. [14] examined user behavior in the context of food delivery applications, with attitude serving as a central variable in their analysis. Kasilingam et al. [38] used attitude as a main variable to investigate chatbot systems for shopping. Moon et al. [17] adopted attitude as a main attribute to scrutinize the behavior of mobile transportation service system users. Cha [36] also investigated the characteristics of attitude in the case of robot-restaurant service systems. Ansong-Gyimah [18] employed attitude as the main element to explore the behavior of the Google classroom system. Similarly, Mailizar et al. [39] utilized attitude as a key attribute to understand teachers’ behavioral characteristics toward e-learning systems. Additionally, Shahzad et al. [40] and Li et al. [41] explored user behavior in the context of drone delivery services, with attitude serving as a central variable in both studies. Overall, numerous studies have adopted attitude as an important attribute in diverse technology domains.

2.3. Privacy Risk

Privacy risk is defined as the degree of discomfort experienced by individuals from threats against private life and information [42,43,44]. Previous studies claimed that privacy risks cause consumers to hesitate in decision-making due to concerns about the security of their personal information [11,45]. Milne and Culnan [46] argue that the protection of personal privacy and information is increasingly critical in society, particularly with the rapid acceleration of digitalization. Toch et al. [47] and Bornschein et al. [45] alleged that consumers consider encroachments on privacy through online user behavior as a concern. Cheng and Jiang [48] found that privacy risk deters a better experience of service in the area of chatbot systems. Wang et al. [49] also alleged that privacy risk becomes an obstacle to adopting smart home systems. Tseng [50] contended that the mitigation of privacy risk is a critical piece in the area of online commerce business. Similarly, Beltrán and Calvo [51] argued that the success of facial recognition systems is significantly influenced by solving privacy risk concerns. Miyazaki and Fernandez [52] and Yang [53] argued that privacy is an important issue in the case of online transactions. In the case of drone delivery service, researchers stated that privacy risk is a drawback for marketing [5,9,10]. Berke et al. [19] reported that offering a solution for privacy risk in drone delivery services plays a significant role in increasing the likelihood of usage. Leon et al. [7] and Dukkanci et al. [11] argued that privacy risk is a downside of drone delivery systems because drones have cameras and controllers’ identification is not ensured from the perspective of consumers. Berke et al. [19] found that privacy risks associated with drone delivery services negatively impact consumer decision-making, as demonstrated through choice experiments. Similarly, Fang et al. [54] investigated Malaysian consumers and found that privacy risk is a significant concern from the consumer perspective. Xie et al. [12] also highlighted privacy risk as a key cost factor in the adoption of drone delivery services within the context of e-commerce. Thus, privacy risk has been explored by numerous scholars, and it is likely to become a critical attribute in the case of drone delivery service.

2.4. Hypothesis Development

Choi et al. [55] demonstrated the negative association between privacy concerns and the intention to adopt in the domain of travel products. Tseng [50] also revealed the negative impact of privacy risk on attitude in the context of online commerce. Zhang et al. [56] demonstrated the negative impact of privacy risk on consumer decision-making regarding purchasing smartphones. Zhu and Kanjanamekanant [57] employed consumers targeted by social media marketing, and the findings indicated that privacy risk influences both attitudes and intentions to use. Maseeh et al. [58] performed a meta-analysis, and the results revealed that privacy risk influences consumer assessment and decision-making processes. In the case of drone delivery services, Leon et al. [7] argued that privacy concerns cause the hesitation of consumers to adopt the service. Zhu et al. [59] and Knobloch and Schaarschmidt [60] also alleged that privacy concerns play a significant role in building consumers’ negative perceptions. Hence, it can be inferred that privacy risk is likely to be the determinant of attitudes toward and intentions to use drone delivery services from the perspective of consumers. An et al. [14] adopted users of food delivery applications, and their findings indicated that intention to use is positively influenced by attitude. Moon et al. [17] also demonstrated the significant association between attitude and intention to use in the context of the Uber taxi service system. Ansong-Gyimah [18] demonstrated a significant positive influence of attitude on the intention to use Google Classroom services. Similarly, Waris et al. [61] found a positive association between attitude and the intention to use drone delivery services. This research thus proposes the following research hypotheses:
Hypothesis 1.
Privacy risk negatively affects attitudes toward drone delivery services.
Hypothesis 2.
Privacy risk negatively affects the intention to use drone delivery services.
Hypothesis 3.
Attitude positively affects attitudes toward drone delivery services.

2.5. Moderating Effects of Gender and Eco-Friendliness

Prior studies have shown that female individuals are generally more vulnerable to external threats [20,21,61]. Moreover, scholars have noted that women have less power than men socially, financially, and physically [62,63,64]. This finding indicates that women might experience more distress than men if they experience undesirable situations. Therefore, women tend to be more sensitive to safety and prevention against undesired outcomes. Because the drawback of drone delivery services relates to privacy, which threatens individuals’ personal information [7,12], such vulnerability is likely to cause discomfort in women. Stakeholder theory states that businesses foster positive relationships with stakeholders by offering safe services to customers, which contributes to achieving sustainability [13,30,31]. Due to their increased vulnerability to external threats, women are more likely to be sensitive to privacy risks. Thus, it could be presumed that the sensitivity of privacy risk to the attitude toward drone delivery service is likely to vary. Therefore, this research proposes the following research hypothesis:
Hypothesis 4.
Gender significantly moderates the effect of privacy risk on attitudes toward drone delivery services.
Eco-friendliness refers to the ability to minimize harmful effects on the natural environment [28,29,65]. Eco-friendliness refers to practices that aim to increase the welfare of consumers from the side of business by minimizing pollution to the natural environment [23,65,66]. Eco-friendliness has become an increasingly important element globally because people are considering global warming and weather changes as more serious phenomena [22,24,67]. Prior studies contended that socially responsible business implementation has a negative influence on the reputation of businesses [25,26,68]. Similarly, previous works noted that stakeholder management plays an essential role in insurance for businesses by building friendly relationships with important subjects [26,27,69]. In the domain of drone delivery services, scholars have highlighted that a key advantage is their pro-environmental aspect, particularly regarding global warming, as the market increasingly values environmental issues [7,70,71]. Furthermore, researchers have suggested that maintaining a strong environmental reputation contributes to business sustainability, as environmental concerns are a significant consideration for stakeholders, in line with stakeholder theory [30,31,40]. Therefore, it is reasonable to expect that eco-friendliness will influence market evaluations by offering solutions to social and environmental challenges. By integrating a literature review, this study proposes the following research hypothesis:
Hypothesis 5.
Eco-friendliness significantly moderates the effect of privacy risk on attitudes toward drone delivery services.

3. Method

3.1. Research Model

Figure 1 describes the research model. This study has five elements: privacy risk, attitude, intention to use, eco-friendliness, and gender. Privacy risk has negative effects on both attitudes and intentions to use. Moreover, attitude positively affects intention to use, considering the link with the technology acceptance model [39,61]. Eco-friendliness and gender significantly moderate the effects of privacy risk on attitude. Hence, five hypotheses are proposed in this work. Privacy risk is the independent variable, and attitude is the mediator. Lastly, intention to use is the dependent variable, and gender and eco-friendliness are moderators.

3.2. Measurement Items

Table 1 shows the description of the measurement items. This study used a five-point Likert scale (1 = strongly disagree; 5 = strongly agree) to measure privacy risk, intention to use, and eco-friendliness. Additionally, this study adopted a semantic differential scale (e.g., 1 = bad and 5 = good) to measure attitude. All the items were derived from the extant literature, and this work adjusted the measurement items to make them more suitable for the aim of the current work. The four attributes are privacy risk [55,56,57], attitude [14,17,18], intention to use [14,16], and eco-friendliness [23,65,66]. This research also measured gender as a binary variable (0 = male; 1 = female). The operational definition of privacy risk is the degree to which individuals feel uncomfortable with the likelihood of encroachments on privacy via drone delivery services. Attitude is defined as the appraisal of drone delivery for a long period, either positively or negatively. This study defined intention to use as the degree to which individuals are likely to employ drone delivery services in their future shopping. Eco-friendliness is defined as the degree to which individuals perceive that drone delivery is beneficial to the natural environment.

3.3. Data Collection and Analytic Instruments

This study used an online survey as an instrument for data collection. This survey was uploaded to the Google survey program, after which the survey participants were recruited through Clickworker (https://www.clickworker.com/, accessed on 20 June 2024). Online surveys offer the advantage of being less constrained by time and location; however, the sampling process may be limited, which can affect the level of concentration among survey participants [72,73]. The Clickworker platform has been widely used by researchers for data collection [74,75,76], and it can be inferred that the data quality obtained from Clickworker is reliable for statistical inference. This study initially presented short videos to the participants to help them understand the drone delivery service. Figure 2 shows an example video. The running time of the video was 30 s because videos that are too long are likely to result in the decreased concentration of survey participants. After watching the video, the survey participants responded to the survey. This research employed random sampling to reflect the broader market response to drone delivery services, as these services are accessible to everyone in the market. Scholars have noted that a sample size between 200 and 400 is typically required to perform maximum likelihood-based confirmatory factor analysis [77,78]. Following these guidelines, this study aimed for a sample size exceeding 400 to ensure reliable estimation. Consequently, 409 observations were collected for data analysis. This sample size is deemed adequate for statistical analysis, in line with the rule that the number of observations should be at least ten times the number of measurement items [77]. Data collection took place between 20 and 28 June 2024. Figure 2 displays the video content shown to participants. All survey respondents were American, as the video featured Walmart, a representative retail business in the U.S. market. Therefore, this study targeted Americans, given the rapid growth of drone delivery services in the U.S. market [2].
Table 2 shows the profile of the survey participants. The number of observations was 409. The number of male and female participants was 128 and 281, respectively. Table 2 also displays information on age (20s: 77 participants; 30s: 153 participants; 40s: 128 participants; 50s: 39 participants; and older than 60: 12 participants). The proportion of employed individuals was 67.7 percent. Finally, monthly household income information is presented in Table 2 (less than USD 2500: 125 participants; USD 2500–4999: 141 participants; USD 5000–7499: 60 participants; USD 7500–9999: 26 participants; and more than USD 10,000: 57 participants).
This work analyzed survey participants’ demographic information via frequency analysis. Confirmatory factor analysis was carried out to inspect convergent validity via multiple criteria, which included a loading value of >0.5, an average variance extracted (AVE) of >0.5, and a construct reliability (CR) of >0.7 [77,78]. Scholars have also reported that the goodness of fit for confirmatory factor analysis is acceptable according to the following criteria: χ2/df < 3, goodness-of-fit index (GFI), root mean square error of approximation (RMSEA) < 0.05, and root mean square residual < 0.05 [77,78]. Researchers have also contended that the baseline comparison index can be used to assess the goodness of fit for confirmatory factor analysis, which includes the normed fit index (NFI), relative fit index (RFI), incremental fit index (IFI), Tucker–Lewis index (TLI), and comparative fit index (CFI) > 0.8 [77,78]. This study also calculated the mean values and standard deviations (SDs) for the variables. Correlation matrix analysis was employed to explore discriminant validity by following the rule that the square root of the AVE is greater than the correlation coefficient for the detection of problems in discriminant validity [77,78].
To test the research hypotheses, this research chose the Process Macro Model 7 to assess the relationships between variables. Process Macro Model 7 with bootstrapping at 5000 times was selected. Process Macro Model 7 is the path analytic model used to analyze the conditional indirect effects of the mediated moderation model based on ordinary least squares, which minimizes the errors in the estimation of the regression line [79]. Additionally, Hayes Process Macro Model 7 is less constrained by the normality assumption issue of the sample for estimation [79]. Also, Hayes Process Macro Model 7 enables researchers to assess the mediated moderating effect using multiple criteria: significance of paths and index for moderated mediation [79]. The Process Macro Model 7 requires four attributes: an independent variable (X: privacy risk), a mediator variable (M: attitude), a dependent variable (Y: intention to use), and a moderating variable (W: gender and eco-friendliness). The interaction variable was produced to explore the moderating effect (gender × privacy risk and eco-friendliness × privacy risk). Additionally, this study used age as a covariate based on the argument that technology-related behavior is influenced by age [80,81]. Therefore, this study tested the model’s sensitivity by considering both cases with and without the inclusion of the covariate. Furthermore, a median split analysis was conducted to examine the moderating effect, with the mean value calculated for the four groups to provide a more detailed analysis. The median values of privacy risk and eco-friendliness are 3 and 4, respectively. Given the results of the median split analysis, this study generated four groups for testing the moderating effect of gender: (1) male × high privacy risk, (2) male × low privacy risk, (3) female × high privacy risk, and (4) female × low privacy risk. Additionally, this research generated four groups for testing the moderating effect of eco-friendliness: (1) high eco-friendliness × high privacy risk, (2) high eco-friendliness × low high privacy risk, (3) low eco-friendliness × high privacy risk, and (4) low eco-friendliness × low high privacy risk. Figure 3 presents the method algorithm.

4. Results

4.1. Results of Testing the Validity of the Measurement Items

Table 3 shows the results of the confirmatory factor analysis. All the factor loadings and CRs are greater than the threshold values. Additionally, the AVE is greater than 0.5. For the goodness of fit indices, the results of the confirmatory factor analysis are statistically significant (goodness of fit indices: χ2 = 193.436; df = 98; χ2/df = 1.974; RMR = 0.024; GFI = 0.945; NFI = 0.977; RFI = 0.972; IFI = 0.989; TLI = 0.986; CFI = 0.989; and RMSEA = 0.049). By integrating the results, the convergent validity of the measurement items can be ensured. Moreover, the results presented the mean values and SDs of privacy risk (mean = 2.89, SD = 1.06), attitude (mean = 3.80, SD = 1.09), intention to use (mean = 3.33, SD = 1.28), and eco-friendliness (mean = 3.84, SD = 0.96).
Table 4 describes the results of the correlation matrix. Privacy risk is negatively correlated with attitude (r = −0.471, p < 0.05), intention to use (r = −0.386, p < 0.05), and eco-friendliness (r = −0.360, p < 0.05). Attitude positively correlates with intention to use (r = 0.830, p < 0.05) and eco-friendliness (r = 0.597, p < 0.05). Additionally, intention to use positively correlates with eco-friendliness (r = 0.544, p < 0.05). Additionally, the values of the diagonals are greater than those of the diagonal correlation coefficients. This finding suggests that the discriminant validity of the measurement items is likely to be confirmed.

4.2. Results of Hypothesis Testing

Table 5 shows the results of hypothesis testing in the case of the moderating effect of gender. All models are statistically significant according to the F values (p < 0.05). Privacy risk negatively affected attitude (β = −0.346, p < 0.05). Privacy × gender (β = −0.206, p < 0.05) negatively affected attitudes. Moreover, attitude positively affected the intention to use (β = 0.973, p < 0.05).
Table 6 displays the results of the hypothesis testing in terms of the moderating role of eco-friendliness. All models are statistically significant according to the F values (p < 0.05). Privacy risk negatively affected attitudes (β = −0.718, p < 0.05). The results demonstrated a significant moderating effect of eco-friendliness on the relationship between privacy risk and attitude: privacy × eco-friendliness (β = 0.104, p < 0.05). Moreover, attitude positively affected the intention to use (β = 0.973, p < 0.05).
Figure 4 displays the moderating effect of gender on the relationship between privacy risk and attitudes. For women, the mean values of the high- and low-privacy-risk groups are 3.08 and 4.16, respectively. Moreover, the mean values of men for the low-privacy-risk and high-privacy-risk groups are 4.26 and 3.74, respectively.
Figure 5 presents the results of the median split analysis in the case of eco-friendliness. For the high eco-friendliness group, Figure 4 presents the information of the low (mean = 4.60)- and high (mean = 4.21)-privacy-risk groups. Moreover, the mean values of the low- and high-privacy-risk groups are 3.67 and 2.72, respectively, in the case of the low-eco-friendliness group.

4.3. Discussion of the Empirical Findings

This work examined the relationships between privacy risk, attitudes, the intention to use, gender, and the eco-friendliness of drone delivery services. Using the Clickworker platform service, this work recruited American survey participants. Concerning the means and SDs, the results indicated that consumers may have little concern for privacy risks in terms of drone delivery services (mean = 2.89) compared with other attributes. In contrast, consumers had a quite positive perception of the eco-friendliness of drone delivery services (mean = 3.84). This can be interpreted as meaning that drone delivery services are positively perceived in terms of environmental protection. Additionally, the results revealed that the intention to use drone delivery services is controversial (SD = 1.28). This might be because the technology has not been popular with the public.
The results of the hypothesis testing presented in Figure 6 provide important insights. All hypotheses were supported except for H2. In detail, the findings indicate that attitudes toward drone delivery services are negatively affected by privacy risk. In particular, consumers who express concerns about privacy were more likely to perceive drone delivery services unfavorably. This outcome is aligned with prior research, indicating that privacy concerns can deter consumers from adopting technologies that promise greater utility [48,49]. Furthermore, the present study demonstrated that attitudes toward drone delivery services significantly influence consumers’ intention to use. Such results imply that fostering a positive attitude toward drone delivery could enhance its adoption.
The results revealed a moderating effect of gender on the relationship between privacy risk and attitudes. Specifically, female consumers appear to be more sensitive to privacy risks when considering drone delivery services, likely due to a heightened vulnerability to privacy infringements. This finding aligns with previous studies, indicating the stronger impact of privacy risk on attitudes towards drones for women compared to men [7,12]. Moreover, this research highlights the significant moderating role of eco-friendliness in mitigating the negative effect of privacy risk on attitudes. This supports the arguments put forth by scholars who contend that eco-friendliness reduces the adverse impact of privacy concerns on attitudes toward drone delivery services [26,27]. However, the research also found that privacy risk did not significantly influence consumers’ intention to use drone delivery services. It can be inferred that the effect of privacy risk may be limited to its influence on attitudes, rather than on the intention to use. One possible explanation for this finding is that the numerous benefits of drone delivery services might outweigh concerns about privacy risk, making it less likely to deter consumers’ intentions to adopt the technology.

5. Conclusions

5.1. Theoretical and Managerial Implications

This research has several theoretical contributions. First, it advances the understanding of the relationships among three critical attributes: privacy risk, attitude, and intention to use. A distinctive finding of this study is that while privacy risk significantly influences attitudes, it does not directly affect the intention to use the drone delivery service. This highlights the importance of attitudes in shaping consumer behavior toward drone delivery services, even when privacy concerns are present.
Next, this work contributes to the existing literature by demonstrating the moderating effects of gender and eco-friendliness on the relationship between privacy risk and attitudes. Prior studies have documented that women are more sensitive to threats [7,12], and this research confirms the significant moderating role of gender in the context of drone delivery services. In particular, this study demonstrates the explanatory power of stakeholder theory within the domain of drone delivery services in the retail sector, emphasizing the roles of both gender and eco-friendliness. Stakeholder theory, which has been used to address environmental and consumer protection concerns in the sustainability research domain [30,31,71], is underutilized in prior research on drone delivery services. This study addresses this gap by exploring the characteristics of drone delivery service users through the lens of stakeholder theory by demonstrating the importance of female consumers and natural environment. Furthermore, scholars have increasingly emphasized the importance of environmental concerns in shaping business strategy [26,69]. This study corroborates the above argument within the context of drone delivery services, demonstrating the significant moderating effect of eco-friendliness on consumer attitudes. The findings underscore the critical role of environmental attributes in influencing consumer behavior, aligning with the recent literature on the topic [4,13,82]. These insights contribute to a deeper and more nuanced understanding of the relationships among key variables in the rapidly evolving field of drone delivery services.
This study provides several practical implications for the management of drone delivery services. First, it suggests that managers of drone delivery services might be able to prioritize addressing privacy concerns to enhance the perceived safety and security of these services. This could be achieved by developing more secure systems and ensuring that employees who operate drones are adequately trained to handle privacy issues. Because female consumers tend to be more concerned about privacy risks than male consumers, it would be beneficial for managers to tailor communications specifically to this group, emphasizing that drone delivery services are designed to safeguard privacy. By focusing on targeted messaging, companies can allocate their resources more efficiently and address the concerns of a key consumer segment. In addition to addressing privacy concerns, this research showed the importance of shaping positive consumer attitudes toward drone delivery services, as attitudes significantly influence the intention to use these services. Managers of drone delivery service might be able to invest in initiatives that foster positive perceptions, such as offering reliable and timely service and promoting the environmental benefits of drone delivery. Demonstrating a commitment to sustainability could help mitigate negative perceptions associated with privacy risks. Furthermore, developing eco-friendlier systems and incorporating sustainability messages into marketing communications can enhance market appraisal, making drone delivery services more attractive.
As the market for drone delivery services is still emerging, it is expected that consumers may have hesitations and reservations due to concerns over privacy and reliability. To overcome these barriers, it is crucial for service providers to not only offer more reliable systems but also engage in clear, effective messaging that addresses consumer concerns. To be specific, marketing efforts might be able to focus on assuring consumers that privacy risks are being actively managed and mitigated. In parallel, highlighting the eco-friendly aspects of drone delivery can help improve brand reputation and consumer perception, ultimately creating greater value in the market. By integrating these strategies, drone delivery service providers can improve customer acceptance and achieve business sustainability.

5.2. Suggestion for Future Research

This study has several limitations. First, this work was limited by sample selection, in that it utilized only a US sample. The results of studies in non-US locations might vary depending on the geographical market conditions because consumers’ familiarity with the technology could vary. Future research should thus consider survey participants from different geographic areas. Additionally, this research used a video to provide information to survey participants; however, survey participants might have insufficient information about drone delivery services. Therefore, future research could collect data from consumers with actual drone delivery service experience because statistical inference may reflect the more vivid experience of consumers. Moreover, this research concentrated on only five key attributes to examine consumer behavior in the context of drone delivery services. Future studies could expand the scope by incorporating a broader range of attributes, which would provide a more comprehensive understanding of the factors that influence consumer behavior in this domain. By including additional variables, future research could offer deeper insights into the diverse elements that shape consumer decisions and perceptions in terms of drone delivery services. Lastly, future research could benefit from utilizing face-to-face surveys for data collection. This method has the potential to enhance participant engagement and focus, which could result in higher-quality data and more accurate findings. Direct interaction with participants might also allow researchers to clarify any misunderstandings and ensure more precise responses, further improving the overall reliability of the study’s conclusions.

Author Contributions

K.-A.S., writing—original draft; J.M., writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

According to the exemption standard of the Kangwon National University, ethical review and approval requirements for this study were waived because this research did not collect any personal information (https://irb.kangwon.ac.kr:461/02_board/board03.htm?Item=board3&mode=view&No=103, accessed on 16 February in 2025).

Informed Consent Statement

Informed consent was obtained from all the subjects involved in the study.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy concerns.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Research model.
Figure 1. Research model.
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Figure 2. Description of the videos shown to the survey participants. Source: https://www.youtube.com/shorts/3OQnL0q0Ut0 (accessed on 28 June 2024).
Figure 2. Description of the videos shown to the survey participants. Source: https://www.youtube.com/shorts/3OQnL0q0Ut0 (accessed on 28 June 2024).
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Figure 3. Method algorithm.
Figure 3. Method algorithm.
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Figure 4. Moderating effect of gender.
Figure 4. Moderating effect of gender.
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Figure 5. Moderating effects of eco-friendliness.
Figure 5. Moderating effects of eco-friendliness.
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Figure 6. Results of hypotheses testing.
Figure 6. Results of hypotheses testing.
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Table 1. Depiction of measurements.
Table 1. Depiction of measurements.
ConstructCodeItem
Privacy riskPR1Drone delivery creates privacy risks.
PR2Drone delivery infringes on privacy.
PR3Drone delivery creates privacy problems.
PR4Drone delivery brings about privacy encroachment.
AttitudeAT1For me, drone delivery is (negative/positive).
AT2For me, drone delivery is (bad/good).
AT3For me, drone delivery is (unfavorable/favorable).
AT4For me, drone delivery is (useless/useful).
Intention to useIU1I intend to use a drone delivery service.
IU2I am going to use a drone delivery service.
IU3I will use a drone delivery service.
IU4I have an intention to use a drone delivery service.
Eco-friendlinessEF1Drone delivery causes less air pollution.
EF2Drone delivery service is environmentally friendly.
EF3Drone delivery service protects the environment.
EF4Drone delivery service is eco-friendly.
Table 2. Demographic information (N = 409).
Table 2. Demographic information (N = 409).
ItemFrequencyPercentage
Male12831.3
Female28168.7
20–29 years old7718.8
30–39 years old15337.4
40–49 years old12831.3
50–59 years old399.5
Older than 60 years old122.9
Unemployed12430.3
Employed28567.7
Monthly household income
Less than USD 250012530.6
Between USD 2500 and USD 499914134.5
Between USD 5000 and USD 74996014.7
Between USD 7500 and USD 9999266.4
More than USD 10,0005713.9
Table 3. Confirmatory factor analysis.
Table 3. Confirmatory factor analysis.
ConstructCodeLoadingMean (SD)CRAVE
Privacy riskPR10.8762.89
(1.06)		0.9450.811
PR20.928
PR30.959
PR40.836
AttitudeAT10.9573.80
(1.09)		0.9630.868
AT20.955
AT30.936
AT40.877
Intention to useIU10.9223.33
(1.28)		0.9730.901
IU20.967
IU30.968
IU40.941
Eco-friendlinessEF10.7773.84
(0.96)		0.9340.783
EF20.901
EF30.932
EF40.921
Note: SD stands for standard deviation. Goodness-of-fit indices: χ2 = 193.436; df = 98; χ2/df = 1.974; RMR = 0.024; GFI = 0.945; NFI = 0.977; RFI = 0.972; IFI = 0.989; TLI = 0.986; CFI = 0.989; RMSEA = 0.049. CR stands for construct reliability. AVE is the average variance extracted.
Table 4. Correlation matrix and results of discriminant validity.
Table 4. Correlation matrix and results of discriminant validity.
Variable1234
1. Privacy risk0.900
2. Attitude−0.471 *0.931
3. Intention to use−0.386 *0.830 *0.949
4. Eco-friendliness−0.360 *0.597 *0.544 *0.884
Note: * p < 0.05. Diagonal values represent the square root of the average variance extracted.
Table 5. Results of hypothesis testing: moderating effect of gender.
Table 5. Results of hypothesis testing: moderating effect of gender.
Model 1a
Attitude		Model 1b
Attitude		Model 2a
Intention to Use		Model 2b
Intention to Use
β (t value)β (t value)β (t value)β (t value)
Constant4.986 (22.25) *4.834 (18.23) *−0.393 (−1.81)−0.403 (−1.71)
Privacy risk−0.346 (−4.56) *−0.337 (−4.51) *0.008 (0.21)0.008 (0.22)
Gender0.339 (3.30) *0.349 (1.23) *
Interaction−0.206 (−2.26)−0.209 (−2.25)
Attitude 0.973 (26.51)*0.973 (26.44) *
Age 0.052 (1.07) 0.003 (0.10)
F value43.17 *32.68 *448.43 *298.23 *
R20.24230.24450.68840.6884
Conditional effect of the focal predictor
Gender
Male−0.346 (−4.66) *−0.337 (−4.66) *
Female−0.553 (−9.90) *−0.547 (−9.74) *
Index of mediated moderationIndexIndex
−0.2013 *−0.2040 *
Note: * p < 0.05; interaction in Model 1a: privacy × gender (test of interaction: F = 4.94 *) and interaction in Model 1b: privacy × gender (test of interaction: F = 5.07 *).
Table 6. Results of hypothesis testing: the moderating effect of eco-friendliness.
Table 6. Results of hypothesis testing: the moderating effect of eco-friendliness.
Model 3a
Attitude		Model 3b
Attitude		Model 4a
Intention to Use		Model 4b
Intention to Use
β (t value)β (t value)β (t value)β (t value)
Constant3.872 (7.22) *3.753 (6.90) *−0.393 (−1.81)−0.403 (−1.71)
Privacy risk−0.718 (−4.72) *−0.719 (−4.73) *0.008 (0.21)0.008 (0.22)
Eco-friendliness0.229 (1.82)0.221 (1.76)
Interaction0.104 (2.83) *0.107 (2.89) *
Attitude 0.973 (26.51) *0.973 (26.44) *
Age 0.053 (1.27) 0.003 (0.10)
F value107.13 *80.88 *448.43 *298.23 *
R20.44250.44470.68840.6884
Conditional effect of the focal predictor
Eco-friendliness
3.00−0.404 (−7.45) *−0.398 (−7.34) *
4.00−0.299 (−7.29) *−0.291 (−7.05) *
5.00−0.194 (−3.45)−0.185 (−3.26) *
Index of mediated moderationIndexIndex
0.1021 *0.1041) *
Note: * p < 0.05; interaction in Model 3a: privacy × eco-friendliness (test of interaction: F = 8.03 *) and interaction in Model 3b: privacy × gender (test of interaction: F = 4.94 *).
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Sun, K.-A.; Moon, J. Exploring the Existence of Moderated Mediation of Attitudes Between Privacy Risk and the Intention to Use Drone Delivery Services. Sustainability 2025, 17, 2585. https://doi.org/10.3390/su17062585

AMA Style

Sun K-A, Moon J. Exploring the Existence of Moderated Mediation of Attitudes Between Privacy Risk and the Intention to Use Drone Delivery Services. Sustainability. 2025; 17(6):2585. https://doi.org/10.3390/su17062585

Chicago/Turabian Style

Sun, Kyung-A, and Joonho Moon. 2025. "Exploring the Existence of Moderated Mediation of Attitudes Between Privacy Risk and the Intention to Use Drone Delivery Services" Sustainability 17, no. 6: 2585. https://doi.org/10.3390/su17062585

APA Style

Sun, K.-A., & Moon, J. (2025). Exploring the Existence of Moderated Mediation of Attitudes Between Privacy Risk and the Intention to Use Drone Delivery Services. Sustainability, 17(6), 2585. https://doi.org/10.3390/su17062585

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