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Article

The Effects of Expected Benefits on Image, Desire, and Behavioral Intentions in the Field of Drone Food Delivery Services after the Outbreak of COVID-19

by
Jinsoo Hwang
1 and
Hyunjoon Kim
2,*
1
The College of Hospitality and Tourism Management, Sejong University, Seoul 143-747, Korea
2
The Department of Tourism Management, The College of Business Administration, Dong-A University, Busan 49236, Korea
*
Author to whom correspondence should be addressed.
Sustainability 2021, 13(1), 117; https://doi.org/10.3390/su13010117
Submission received: 30 November 2020 / Revised: 18 December 2020 / Accepted: 22 December 2020 / Published: 24 December 2020
(This article belongs to the Special Issue Impact of the Covid-19 Pandemic on the Hospitality and Tourism Industry)
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Abstract

:
Food delivery services using drones have emerged, but there is not much research on it. Thus, this study was designed to examine how to form behavioral intentions based on the concept of expected benefits in the field of drone food delivery services. More specifically, this study proposed the five dimensions of expected benefits, such as compatibility, social influence, convenience, function, and emotion, have a positive influence on image. In addition, it was proposed that image has a positive influence on desire, which in turn positively affects behavioral intentions. Lastly, this study hypothesized the moderating role of gender in this process. To achieve this purpose, 343 responses were collected in South Korea. The data analysis results showed that five dimensions of expected benefits, such as compatibility, social influence, convenience, function, and emotion have a positive influence on image. In addition, the image aids to enhance desire, which in turn positively affects behavioral intentions. Lastly, gender moderated the relationship between desire and behavioral intentions. The important theoretical and practical implications of this study are discussed later in the paper.

1. Introduction

Coronavirus Disease 2019 is a viral respiratory disease, and it is also known as COVID-19. COVID-19 first occurred in Wuhan, China in December 2019 [1]. As of November 2020, about 54,900,000 people have been infected with COVID-19, and unfortunately 1,300,000 have died from the disease [2]. According to WHO [1], COVID-19 is “an infectious disease caused by a newly discovered coronavirus.” The organization also suggests that COVID-19 is spread mainly through drops of saliva or secretions from the nose when an infected person coughs or sneezes. That is, COVID-19 can be transmitted through person-to-person contact, so there is a phenomenon that socially attempts to reduce human contact to a minimum [3]. COVID-19 has had a major impact on all industries economically and has also brought about many changes socially and culturally. The foodservice industry is no exception. For example, in terms of economics, approximately 8 million employees, accounting for two-thirds of the food service industry, lost their jobs due to COVID-19. Furthermore, approximately US $240 billion losses are predicted [4]. In terms of social and cultural aspects, people often order food at home rather than going out to restaurants [5]. This shows the importance of contactless services in the foodservice industry.
In this situation, drone food delivery services are in the spotlight in the foodservice industry. A drone refers to an unmanned aerial vehicle that does not require humans to board and manipulate it and was originally developed for military purposes [6]. Drones have been used in many industries including agriculture, broadcasting, distribution, firefighting, and rescue activity. For example, Patrikar, Moon, and Scherer showed the important role of drones as a wind sensor [7]. In addition, many studies indicated that drones play a crucial role in video shooting [8,9,10], suggesting that since drones are not restricted by location, video shooting is possible from various angles. Recently, drones have been used as a means of food delivery in the foodservice industry [11]. According to Hwang, Lee, and Kim [12], drone food delivery services can be defined as “services that use drones to deliver food to customers as the role of services” (p. 94). The important role of drone food delivery services has emerged after COVID-19 in that the services deliver food without customer contact. Furthermore, drone food delivery services have various advantages. For example, since the drone food delivery service is delivered through the air, it can shorten the delivery time and does not depend on the location [13]. In addition, unlike conventional food delivery methods such as cars or motorcycles, the drone food delivery service is expected to play an important role in environmental protection as it operates on batteries [14]. In fact, there have been previous attempts in the foodservice industry to deliver food using drones. For instance, in New Zealand, Domino’s Pizza succeeded in delivering pizza in five minutes to customers about 30 km away using drones [15]. In South Korea, it took 10 minutes to deliver food to customers three kilometers away by drone [16]. Although drone food delivery services is in the beginning stage, it is expected to play an important role in the foodservice industry in the future.
The current study focused on expected benefits of drone food delivery services because they are widely known as the essential factors affecting customers’ technology adoption [17]. That is, understanding the expected benefits of drone food delivery services will show why consumers use the services. In the field of drone food delivery services, prior studies have focused on perceived innovativeness [13], psychological benefits [18], perceived risk [19], and internal environmental locus [20]. However, unlike previous studies, this study tried to identify expected benefits of drone food delivery services after COVID-19 for the first time. Thus, the findings of this study are expected to provide important marketing implications for companies preparing drone food delivery services.
Lastly, it is widely known that the perception of a product/service varies according to gender [21], so many previous studies have tried to show the effect of gender as a moderator in consumer behavior and suggested that developing different strategies based on gender can lead positive customer behavioral intentions [22]. For this reason, this study examined the role of gender in the relationship (1) image and desire, (2) image and behavioral intentions, and (3) desire behavioral intentions for the first time.
In summary, this study investigated how to form the behavioral intentions using the concept of expected benefits in the field of drone food delivery services after COVID-19. More specifically, first, this study identified the influence of expected benefits, such as compatibility, social influence, convenience, function, and emotion on image. Second, the current study examined the effect of image on desire and behavioral intentions. Third, this study investigated the relationship between desire and behavioral intentions. Fourth, this study explored the moderating role of gender in this process.

2. Literature Review

2.1. Expected Benefits

The concept of expected benefits refers to benefits consumers seek from the product/service before being released [23]. Because expected benefits play a very important role in understanding customers’ needs, much research has been conducted on it in consumer behavior [24,25]. Understanding expected benefits before launching new products/services is a great way for companies to provide products/services that consumers really want [26]. In particular, expected benefits are a key factor in predicting consumer behavioral intentions, so many companies make a lot of effort to identify expected benefits [25].
Empirical studies also examined the necessity and importance of expected benefits in diverse industries. In the case of the hospitality industry, Oh, Jeong, and Baloglu [27] investigated the expected benefits of self-service technologies at resort hotels using 1690 customers. They suggested four important sub-dimensions of expected benefits including ease of use, privacy, autonomy, and effectiveness. Kucukusta, Heung, and Hui [28] examined expected benefits in using self-service technology in the luxury hotel industry. They analyzed data collected from 200 hotel customers and showed that relative advantage, ease of use, and communicability have a positive influence on choice of luxury hotel with self-service technology. Kang et al. [24] developed a theoretical model in order to explore the effect of expected benefits of restaurant Facebook fan pages on active participation using 331 restaurant patrons. The results of the data analysis indicated that the social psychological benefit and the hedonic benefit positively affect active participation.
In terms of the tourism industry, Kim et al. [25] examined the important role of expected benefits in using mobile tourism shopping based on data collected from 405 mobile users. The data analysis results showed that value and enjoyment play a significant role in the formation of satisfaction. In addition, the results indicated that time saving and mobility have a positive effect on use context, which in turn positively affects satisfaction. Lai [29] studied expected benefits in using an app-based mobile tour guide. The author analyzed data from 205 travelers visiting Macau. The results revealed that information and entertainment are critical factors affecting traveler acceptance of an app-based mobile tour guide. Lu et al. [30] tried to explore expected benefits of travel apps using 613 participants. They suggested that the following seven types of expected benefits are critical factors in selecting travel apps: information, navigation, marketing, socialization, safety, entertainment, and transaction.
With regard to other industries, Chang, Hajiyev, and Su [31] collected data from 714 undergraduate and master’s degree students in order to identify expected benefits in taking e-learning course. They suggested that five sub-dimensions (i.e., subjective norm, experience, enjoyment, computer anxiety, and self-efficacy) are the key factors of expected benefits. Kumar et al. [26] investigated expected benefits of mobile banking using 144 customers. The results of the study revealed that four types of expected benefits such as perceived usefulness, perceived ease of use, social influence, and trust propensity are critical predictors of intention to use mobile banking.
To sum up, existing studies have investigated the benefits that consumers want from a product/service when a new technology applies to the product/service. In particular, the results of previous studies are used as important marketing information for companies [30,31], so it is very necessary and important to study expected benefits in the field of drone food delivery services.

2.2. Effect of Expected Benefits on Image

The current study hypothesized the relationship between expected benefits and image. The important role of image has been studied in diverse fields, such as airline [32,33] MICE [34,35], hotel [36,37], restaurant [38,39], and tourism [40,41]. The image refers to the perception of a product that consumers usually have [42]. More importantly, such an image can be created by the values or advantages of the product [43,44], which suggested the effect of expected benefits on image.
Empirical studies also supported the relationship between expected benefits and image. Han et al. [45] examined how benefits affect image using 258 samples in the context of virtual golf leisure, and their data analysis results indicated that benefits, such as easier access to golfing opportunities, convenience, healthy environment, social responsibility, and engagement in eco-friendly practices aid to enhance image about virtual golf leisure. In addition, Hwang and Choi [46] also tried to identify the influence on benefits on image using 322 samples in the airline industry. They showed that benefits including warm, self-expressive benefits, and nature experiences play an important role in the formation of image. More recently, Hwang and Cho et al. [18] argued that benefits are a significant factor affecting positive emotions. Therefore, the following hypotheses can be developed based on the discussions.
Hypothesis 1 (H1).
Compatibility has a positive influence on image.
Hypothesis 2 (H2).
Social influence has a positive influence on image.
Hypothesis 3 (H3).
Convenience has a positive influence on image.
Hypothesis 4 (H4).
Function has a positive influence on image.
Hypothesis 5 (H5).
Emotion has a positive influence on image.

2.3. Effect of Image on Desire and Behavioral Intentions

Next, this study proposed the effect of image on desire. According to Perugini and Bagozzi [47] (p. 71), desire refers to “a state of mind whereby an agent has a personal motivation to perform an action or to achieve a goal.” More importantly, Hudson, Wang, and Gil [48] argued that an image of a certain product/service is a key factor affecting customer’s desire to use the product/service. In addition, prior studies tried to identify the relationship between image and desire. For example, Han et al. [45] developed a theoretical model in order to explain the effect of image on desire using 258 samples in the golf industry, and they indicated that image helps to enhance desire. Hwang and Choe [19] also examined how image affects desire using 331 samples in the context of drone food delivery services, and they suggested that when consumers have high levels of desire, they would show positive behavioral intentions.
In addition, it is widely accepted that the image of a certain product has an important impact on the process of purchasing the product, so if a consumer has a positive image of a product, he/she is more likely to have a high level of behavioral intentions [49]. Previous research has also confirmed the effect of image on behavioral intentions. For instance, Prayag et al. [50] examined the role of image on behavioral intentions using 350 tourists. They showed that image is a significant antecedent of behavioral intentions. In addition, Han et al. [42] tried to identify the relationship between image and behavioral intentions using 310 samples in the airline industry. They revealed that when passengers have a good image of an airline, they tend to use the airline in the future. In this regard, the following hypotheses are proposed:
Hypothesis 6 (H6).
Image has a positive influence on desire.
Hypothesis 7 (H7).
Image has a positive influence on behavioral intentions.

2.4. Effect of Desire on Behavioral Intentions

The model of goal-directed behavior (MGB) suggested that a consumer’s desire is a significant factor in forming his/her behavioral intention [51]. Empirical studies also supported the argument. For example, Han, Lee, and Kim [52] collected data from 276 tourists to identify the relationship between desire and loyalty, and they suggested that when tourists have high levels of desire, they are more likely to show positive behavioral intentions. Hwang and Kim et al. [11] also tried to examine the relationship between desire and behavioral intentions using 320 samples in the context of drone food delivery services. They found that desire plays an important role in the formation of behavioral intentions. Based on the theoretical and empirical backgrounds, this study proposed hat desire positively affects behavioral intentions.
Hypothesis 8 (H8).
Desire has a positive influence on behavioral intentions.

2.5. The Moderating Role of Gender

This study hypothesized a moderating role of gender in the relationships of (1) image and desire, (2) image and behavioral intentions, and (3) desire and behavioral intentions based on the following theoretical backgrounds. Gender is generally used as a standard to distinguish between man and woman [53]. Many previous studies in the marketing field have investigated the moderating role of gender [21,54,55], suggesting that gender is regarded as a social construct which is significantly related to human behavior. For this reason, there are differences in the decision process according to gender [56].
Prior research has also examined gender differences in consumer behavior. For example, Joiner et al. [57] tried to find gender differences based on 501 samples in the use of the Internet, and they showed that female users tend to use the Internet due to communication and social network rather than male users. In addition, Chen et al. [58] investigated how gender plays a moderating role in the relationship perceived benefit and re-purchase intention using 484 samples in the online shopping industry. Their data analysis results revealed that male customers tend to have high levels of re-purchase intentions than female customers when they feel that online shopping has great benefits. More recently, Hwang and Lee et al. [13] collected data from 324 samples in order to identify the moderating role of gender in the context of drone food delivery services. They showed that female consumers are more likely to say positive things about drone food delivery services than male consumers when they have a positive attitude toward using the services. Based on the theoretical and empirical backgrounds, the current study proposed the following hypotheses:
Hypothesis 9 (H9a).
Gender moderates the relationship between image and desire.
Hypothesis 9 (H9b).
Gender moderates the relationship between image and behavioral intentions.
Hypothesis 9 (H9c).
Gender moderates the relationship between desire and behavioral intentions.

3. Methodology

3.1. Measures

Each concept in the proposed model (Figure 1) was measured using the items that were verified to be reliable and valid in the prior research. First, the five sub-dimensions of expected benefits including compatibility, social influence, convenience, function, and emotion were measured using 15 measurement items borrowed from Chang et al. [31], Kang et al. [24], and Lu et al. [30]. Second, the three measurement items for image were citied from Han et al. [59] and Jani and Han [60]. Third, the concept of desire was measured using three items adapted from Han and Yoon [61] and Perugini and Bagozzi [51]. Lastly, behavioral intentions were measured with three items used by Hennig-Thurau, Gwinner, and Gremler [62] and Zeithaml, Berry, and Parasuraman [63]. All the measurement items used in this study were revised to fit the background of drone food delivery services, and they were measured based on a seven-point Likert-type scale (1 = strongly disagree; 7 = strongly agree).

3.2. Data Collection

In order to test the eight hypotheses, data collection was performed using an online company’s survey system after the outbreak of COVID-19 in South Korea. Drone food delivery services are not commercialized in South Korea, so respondents have a low understanding of the services. To overcome this problem, we provided a video about 2 min and 30 s long, which clearly explained the system of drone food delivery services (see Appendix A). For example, the video showed consumers ordering food using a smartphone and then how the food was delivered to consumers using a drone. The company sent an e-mail invitation to their 1479 panels, and among them 343 participants completed the survey. Consequently, 343 samples were employed for further statistical analysis.

4. Data Analysis and Results

4.1. Demographic Profile of Respondents

Table 1 shows the demographic profile of the respondents. First, there were more male respondents (n = 177 and 51.6%) than female respondents (n = 166 and 48.4%). Regarding age, the 30s age group (a person aged between 30 and 39 years) (n = 107 and 31.3%) were the most. In terms of the education level, 65.9% of the respondents (n = 226) indicated holding a bachelor’s degree. In addition, the highest percentage of respondents showed that they earned between US $2001~US $3000 (n = 97 and 28.3%) per month. Lastly, 56.6% of respondents (n = 198) were single.

4.2. Confirmatory Factor Analysis

This study employed the confirmatory factor analysis (CFA) in order to check the unidimensionality of the measurement scales and (2) assess the overall measurement model. The results of CFA are presented in Table 2 and showed that the overall fit of the measurement model was statistically acceptable (χ2 = 581.546, df = 224, χ2/df = 2.596, p < 0.001, NFI = 0.937, CFI = 0.960, TLI = 0.951, and RMSEA = 0.068) [64]. The values of all the factor loadings were equal to or higher than 0.702.
As shown in Table 3, the values of all the average variance extracted (AVE) exceeded 0.50, which suggests high levels of convergent validity [65]. The values of all the composite reliabilities were greater than 0.70, which indicates satisfactory levels of internal consistency [66]. Lastly, the data analysis results revealed that the values of all the AVE were higher than the values of all the squared correlations (R2) between any pair of constructs, which means adequate levels of discriminant validity [67].

4.3. Structural Equation Modeling

The results of structural equation modeling analysis are shown in Table 4. The overall evaluation of the model fit indicated an acceptable fit of the model to the data (χ2 = 734.162, df = 234, χ2/df = 3.137, p < 0.001, NFI = 0.921, CFI = 0.944, TLI = 0.934, and RMSEA = 0.079). All the eight proposed hypotheses were statistically supported at p < 0.05. More specifically, image was affected by compatibility (β = 0.373 and t = 6.854 *), social influence (β = 0.161 and t = 3.116 *), convenience (β = 0.163 and t = 3.130 *), function (β = 0.183 and t = 3.304 *), and emotion (β = 0.150 and t = 2.599 *), so hypotheses 1, 2, 3, 4, and 5 were accepted. In addition, the image positively had a positive influence on desire (β = 0.885 and t = 23.877 *) and behavioral intentions (β = 0.145 and t = 2.403 *), which supported Hypotheses 6 and 7. Lastly, there is a positive relationship between desire and behavioral intentions (β = 0.804 and t = 12.013 *). Hence, Hypothesis 8 was statistically supported.

4.4. The Moderating Role of Gender

This study performed the multiple group analyses suggested by Byrne [62]. The respondents were divided into two groups based on gender (male = 177 and female =166). The results of the multiple-group analyses showed that gender plays a moderating role in the relationship between desire and behavioral intentions (Δχ2 = 6.044 > χ2 = 0.05(1) = 3.84, and df = 1), which supports Hypothesis 9c. More specifically, the path coefficient for the male group (β = 0.955) was higher than the path coefficient for the female group (β = 0.696). However, unlike our expectations, Hypotheses 9a (Δχ2 = 0.170 < χ2 = 0.05(1) = 3.84, and df = 1) and 9b (Δχ2 = 3.423 < χ2 = 0.05(1) = 3.84, and df = 1) were not statistically supported.

5. Discussions and Implications

The current research attempts to apply the concept of expected benefits to the context of drone food delivery services. More specifically, this study proposed that the five dimensions of expected benefits (i.e., compatibility, social influence, convenience, function, and emotion) positively affect image. In addition, it was hypothesized that the image has a positive influence on desire, which positively affects behavioral intentions. Lastly, the moderating role of gender was proposed the relationship between (1) image and desire, (2) image and behavioral intentions, and (3) desire and behavioral intentions. The 11 hypotheses were checked using data collected from 343 samples in South Korea. The data analysis results include the following theoretical and practical implications.
First, the five dimensions of expected benefits, such as compatibility (β = 0.373, p < 0.05), social influence (β = 0.161, p < 0.05), convenience (β = 0.163, p < 0.05), function (β = 0.183, p < 0.05), and emotion (β = 0.150, p < 0.05) were shown to significantly enhance image. These findings can be interpreted that when consumers have high levels of expected benefits of drone food delivery services, they would have a good image for using the services. The results of this research accords with the results of prior studies (e.g., Han et al., 2014; Hwang and Choi, 2018; Hwang, Cho et al., 2019) [18,45,46], which suggested that expected benefits are an important factor affecting image. In this respect, the current study expanded the existing literature by showing empirical evidence of the significance of expected benefits in creating image in the context of drone food delivery services. These findings also have managerial implications. For example, the results of data analysis showed that convenience has a positive influence on image, so foodservice companies need to emphasize this when promoting drone food delivery services. In fact, since drone food delivery services deliver food through the sky, food can be delivered faster than conventional delivery services such as cars or motorcycles. Thus, foodservice companies need to emphasize this aspect when promoting drone food delivery services. Furthermore, drones can deliver food to areas where existing delivery services are difficult to access, such as mountains or seas, foodservice companies are required to stress this advantage when promoting the drone food delivery services. The data analysis results also indicated that emotion helps to increase image, so foodservice companies should make ordering food enjoyable for the customer. For instance, it is recommended installing a GPS signal on the drone so that consumers can see where the food they ordered is in real time. By doing so, consumers could have a fun time following their order, so they are more likely to have a positive image using drone food delivery services.
Second, this paper found the important role of image. More specifically, the data analysis results revealed that the image of drone food delivery services helps to enhance desire (β = 0.885, p < 0.05), which in turn positively affects behavioral intentions (β = 0.804, p < 0.05). In other words, if people think that the overall image for using drone food delivery services is good, their desire of using the services when ordering food is strong. Furthermore, they would use drone food delivery services when ordering food in the future. As we suggested in the literature review section, many previous studies have confirmed that the importance of image [19,32,50]. The important theoretical implication of this study is that the importance of image was confirmed in field of drone food delivery services again.
Third, this study found the moderating role of gender in the relationship between desire and behavioral intentions. In particular, the path coefficient for the male group was greater than the path coefficient for the female group. That is, when males perceive a good image for using drone food delivery services, they are more likely to have high levels of behavioral intentions than females. These findings are similar with the previous studies [58,68]. In this regard, this study verified and extended the moderating role of gender in the context of drone food delivery services. Thus, food service companies need to focus more on female consumers in order to enhance the effectiveness of advertising.

6. Limitations and Future Research

This study included important theoretical and practical implications; however, the study also has the following limitations. First, it is somewhat difficult to apply the results of data analysis to other industries and regions because this study focused on the drone food delivery services in South Krorea. Second, the current drone food delivery service has not been commercialized in South Korea, so future research would be more meaningful if data is collected for people who have actually used the drone food delivery service. Third, since COVID-19 has not yet ended, it is difficult to accurately predict changes in consumer behavior after COVID-19. For example, if a vaccine that can completely cure COVID-19 is available, the risk perceived by consumers from COVID-19 may be low in the future. Thus, it is also important to conduct research on drone food delivery services after COVID-19 is completely ended. Lastly, drone food delivery services are currently in the initial stage, but the services will be activated worldwide in the near future. For this reason, some governments around the world are enacting legal procedures for using drones in everyday life. For example, the Korean government has created and supported a test system for licenses to control drones [69]. Although some countries currently do not have drone-related laws, legal requirements are required for the safe operation of drones.

Author Contributions

Conceptualization, J.H. and H.K.; methodology, J.H.; software, J.H.; validation, J.H.; formal analysis, J.H.; investigation, H.K.; resources, H.K.; data curation, J.H.; writing—original draft preparation, J.H. and H.K.; writing—review and editing, J.H. and H.K. All authors have read and agreed to the published version of the manuscript.

Funding

This study is supported by a research grant from Dong-A University.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Sustainability 13 00117 g0a1 550
Figure A1. Screenshots from the Videos. Source from Yogiyo [70].
Figure A1. Screenshots from the Videos. Source from Yogiyo [70].
Sustainability 13 00117 g0a1

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Sustainability 13 00117 g001 550
Figure 1. The proposed conceptual model.
Figure 1. The proposed conceptual model.
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Table
Table 1. Sample characteristics (n = 343).
Table 1. Sample characteristics (n = 343).
VariablenPercentage
Gender
Male17751.6
Female16648.4
Age
20s (a person aged between 20 and 29 years)10330.0
30s (a person aged between 30 and 39 years)10731.3
40s (a person aged between 40 and 49 years)10229.7
50s (a person aged between 50 and 59 years)319.0
Education level
Less than a high school diploma308.7
Associate’s degree4312.5
Bachelor’s degree22665.9
Graduate degree4412.8
Monthly household income
6001 $ US and over 216.1
5001 $ US–6000 $ US102.9
4001 $ US–5000 $ US308.7
3001 $ US–4000 $ US4914.3
2001 $ US–3000 $ US9728.3
1001 $ US–2000 $ US6719.5
Under 1000 $ US6920.1
Marital status
Single19857.7
Married 14141.1
Widowed/Divorced41.2
Table
Table 2. Confirmatory factor analysis: Items and loadings.
Table 2. Confirmatory factor analysis: Items and loadings.
Construct and Scale ItemStandardized Loading a
Compatibility
Using drone food delivery services seems to fit my lifestyle.0.897
Using drone food delivery services would fit well with how I like to order0.938
Drone food delivery services are likely to be suitable when ordering food.0.878
Social influence
If I use drone food delivery services, I could impress others.0.822
If I use drone food delivery services, I could show that I am an early adopter.0.950
If I use drone food delivery services, I could show my image that seems to lead the trend to others.0.843
Convenience
Using drone food delivery services seems to be easy to use.0.702
Using drone food delivery services seems to order food anywhere.0.719
Using drone food delivery services seems to save time.0.852
Function
Drone food delivery services do not seem to lead to food delivery problems.0.846
Drone food delivery services seem to perform satisfactorily.0.855
Using drone food delivery services to order food would be safe.0.919
Emotion
Using drone food delivery services seems to be fun.0.848
Using drone food delivery services seems to bring enjoyment.0.966
Using drone food delivery services seems to make me happy.0.757
Image
Overall image for using drone food delivery services is good.0.935
Overall image I have about drone food delivery services is great.0.953
Overall, I have a good image about drone food delivery services.0.947
Desire
I desire to use drone food delivery services when ordering food.0.946
My desire of using drone food delivery services when ordering food is strong.0.962
I want to use drone food delivery services when ordering food.0.956
Behavioral intentions
I will use drone food delivery services when ordering food.0.846
I am willing to use drone food delivery services when ordering food.0.939
I am likely to use drone food delivery services when ordering food.0.938
Goodness-of-fit statistics: χ2 = 581.546, df = 224, χ2/df = 2.596, p < 0.001, NFI = 0.937, CFI = 0.960, TLI = 0.951, and RMSEA = 0.068
a All factors loadings are significant at p < 0.001. NFI = normed fit index, CFI = comparative fit index, TLI = Tucker-Lewis index, and RMSEA = root mean square error of approximation.
Table
Table 3. Descriptive statistics and associated measures.
Table 3. Descriptive statistics and associated measures.
Mean (SD)AVE(1)(2)(3)(4)(5)(6)(7)(8)
(1) Compatibility3.86 (1.27)0.8180.931 a0.491 b0.5530.6440.5510.7100.7230.776
(2) Social influence5.01 (1.28)0.7630.241 c0.9060.5900.3890.6920.6030.6450.612
(3) Convenience4.73 (1.15)0.5790.3060.3480.8030.5000.6450.6470.6570.639
(4) Function3.66 (1.24)0.7640.4150.1510.2500.9060.3640.5880.6550.642
(5) Emotion4.72 (1.23)0.7420.3040.4790.4160.1320.8950.6240.6620.678
(6) Image4.45 (1.35)0.8930.5040.3640.4190.3460.3890.9620.7630.735
(7) Desire4.15 (1.38)0.9110.5230.4160.4320.4290.4380.5820.9690.732
(8) Behavioral intentions4.19 (1.22)0.8260.6020.3750.4080.4120.4600.5400.5360.934
SD = Standard Deviation and AVE = Average Variance Extracted. a composite reliabilities are along the diagonal, b correlations are above the diagonal and c squared correlations are below the diagonal.
Table
Table 4. Standardized parameter estimates for the structural model.
Table 4. Standardized parameter estimates for the structural model.
Standardized Estimatet-ValueHypothesis
H1Compatibility→Image0.3736.854 *Supported
H2Social influence→Image0.1613.116 *Supported
H3Convenience→Image0.1633.130 *Supported
H4Function→Image0.1833.304 *Supported
H5Emotion→Image0.1502.599 *Supported
H6Image→Desire0.88523.877 *Supported
H7Image→Behavioral intentions0.1452.403 *Supported
H8Desire→Behavioral intentions0.80412.013 *Supported
H9aThe moderating role of gender in the relationship between image and desireNot supported
H9bThe moderating role of gender in the relationship between image and behavioral intentionsNot supported
H9cThe moderating role of gender in the relationship between desire and behavioral intentionsSupported
Goodness-of-fit statistics: χ2 = 734.162, df = 234, χ2/df = 3.137, p < 0.001, NFI = 0.921, CFI = 0.944, TLI = 0.934, and RMSEA = 0.079
* p < 0.05. NFI = Normed Fit Index, CFI = Comparative Fit Index, TLI = Tucker-Lewis Index, and RMSEA = Root Mean Square Error of Approximation.
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Hwang, J.; Kim, H. The Effects of Expected Benefits on Image, Desire, and Behavioral Intentions in the Field of Drone Food Delivery Services after the Outbreak of COVID-19. Sustainability 2021, 13, 117. https://doi.org/10.3390/su13010117

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Hwang J, Kim H. The Effects of Expected Benefits on Image, Desire, and Behavioral Intentions in the Field of Drone Food Delivery Services after the Outbreak of COVID-19. Sustainability. 2021; 13(1):117. https://doi.org/10.3390/su13010117

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Hwang, Jinsoo, and Hyunjoon Kim. 2021. "The Effects of Expected Benefits on Image, Desire, and Behavioral Intentions in the Field of Drone Food Delivery Services after the Outbreak of COVID-19" Sustainability 13, no. 1: 117. https://doi.org/10.3390/su13010117

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