Investigating motivated consumer innovativeness in the context of drone food delivery services

Journal of Hospitality and Tourism Management 38 (2019) 102–110

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Journal of Hospitality and Tourism Management journal homepage: www.elsevier.com/locate/jhtm

Investigating motivated consumer innovativeness in the context of drone food delivery services

T

Jinsoo Hwanga, Hyun Kimb, Woohyoung Kimc,∗ a

The College of Hospitality and Tourism Management, Sejong University, 98 Gunja-Dong, Gwanjin-Gu, Seoul 143-747, South Korea Small Enterprise and Market Service, Daejeon 34917, South Korea c The Graduate School of Technology Management, Kyunghee University, Yongin, Gyeonggi-do 17104, South Korea b

ARTICLE INFO

ABSTRACT

Keywords: Drone food delivery services Motivated consumer innovativeness Attitude Desire Behavioral intentions

The objective of this research is to explore the motivated consumer innovativeness in the context of drone food delivery services. More specifically, it was hypothesized that four dimensions of motivated consumer innovativeness, which include functionally, hedonically, cognitively, and socially motivated consumer innovativeness, play an important role in the formation of attitude and behavioral intentions. In addition, this study proposed that the attitude positively affects desire and behavioral intentions. Based on the proposed relationships among the constructs, a conceptual model was developed and evaluated using data collected from 320 respondents in Korea. The data analysis results revealed that three dimensions of motivated consumer innovativeness except for cognitively motivated consumer innovativeness are important predictors of attitude. In addition, functionally motivated consumer innovativeness had a positive influence on behavioral intentions. Lastly, the attitude was shown to significantly increase desire and behavioral intentions.

1. Introduction In the era of the fourth industrial revolution, the interest in drones has been greatly increasing in Korea. The Korean government plans to increase the size of the drone market, which is about US $70.4 million, to US $4.4 billion by 2026 (Dong-A Daily News, 2017). In addition, the government plans to introduce more than 3700 drones (about US $350 billion) into the public sector over the next five years. Currently, 2300 people are trained in the field of piloting drones at 22 educational institutions. The government decided to expand support for training drone pilots from US $1.5 million in 2017 to US $3.7 million in 2018 and expects drone-related workers to reach 164,000 by 2026 (NEWSIS, 2017). One of the reasons why the drone market is growing is its variety of uses. In fact, drones are widely used in various fields, such as agriculture, delivery, firefighting, national defense, and rescue activity. For example, a rescue drone dropped the flotation device that saved two boys in the sea in Australia. As a result, the boys grabbed it and returned safely to the beach (The Straits Times, 2018). As an example in Korea, it took 10 min for a drone to deliver 8 kg of mail to a small island off Jeollanam-do Province, 4 km away from land (Seoul Economy, 2017). When considering that it typically takes about 2 h to deliver mail to the island with ships, the superiority of the drone can be seen. The importance of drones is no exception in the foodservice ∗

industry. In recent years, many companies have tried to provide drone food delivery services, because of its various benefits. For example, in New Zealand, Domino's Pizza succeeded in delivering pizza to a customer about 32 km away in about 5 min using a drone (CNBC, 2016). After this test, the New Zealand government authorized Domino's Pizza to deliver food using drones. In 2017, Costa Coffee, which is located near Kite Beach in Dubai, began using drones to deliver ice coffee to customers on the beach (The National, 2017). Previously, it was difficult to reach customers who ordered ice coffee on the sandy beach. After using the drones, it became much more convenient to deliver ice coffee to customers on the beach. In addition, Yogiyo, one of the most well known food delivery service companies in Korea, succeeded in delivering food using drones (Digital Daily, 2016). More importantly, drones are expected to make the following important contributions to the food service industry. Drone food delivery services above all can save time by avoiding traffic congestion and deliver food anywhere without restrictions. From the viewpoint of the company, the services have the advantage of saving the wages of delivery workers (Business Insider, 2017). In addition, drones in Korea can be a solution to the decline in manpower. In particular, about 1500 people have been injured and about 30 people are killed in the course of delivering food per year, and sadly about 50% of those injured or killed are young people under the age of 29 (Asia Economy, 2017). If drone

Corresponding author. E-mail addresses: [email protected] (J. Hwang), [email protected] (H. Kim), [email protected] (W. Kim).

https://doi.org/10.1016/j.jhtm.2019.01.004 Received 5 September 2018; Received in revised form 16 December 2018; Accepted 10 January 2019 Available online 17 January 2019 1447-6770/ © 2019 CAUTHE - COUNCIL FOR AUSTRALASIAN TOURISM AND HOSPITALITY EDUCATION. Published by Elsevier Ltd All rights reserved.

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food delivery services are introduced, it can reduce the loss of life. Lastly, the current methods of food delivery services are based on gasoline powered vehicles, such as cars or motorcycles, and therefore affect environmental pollution. On the contrary, drones can play a big role in protecting the environment, because they are operated by electricity (Environmental Technology, 2018). For these reasons, drones are significant and important in the food service industry. Although there are legal procedures for the commercialization of drone food delivery services, it has been proven through many tests that there are no problems with drone services from a technical point of view. Furthermore, these services are now available only in certain areas, but it is expected that they will be widely used in the future. After successfully testing drone food delivery services in various countries, such as Japan, Korea, and New Zealand, consumers' expectations for the commercialization of drone food delivery services have become greater. Above all, it is very important to examine consumers' motivations when new technology-based services, such as drone food delivery services, are introduced because the motivations are considered internal and external factors, which create actions to achieve a consumer's goal (Percy & Rossiter, 1997; Vandecasteele & Geuens, 2010). More specifically, the greater motivation for a consumer to purchase a product, the more the consumer actually purchases items. However, there is no research about how to motivate consumers to use drone food delivery services even though the commercialization of such services is not far away. Thus, it will be significant and meaningful to investigate potential consumers' motivations in the context of drone food delivery services for the first time. This study attempted to fill the gaps outlined above by empirically identifying the importance of consumers' motivations in the context of drone food delivery services. To be specific, the purposes of this study were: (1) to investigate the casual relationships between motivated consumer innovativeness (hereafter MCI) and attitude, and (2) to examine the influence of attitude on desire and behavioral intentions. It is expected that the results of this study will provide important clues for the development of effective marketing strategies based on consumers' motivation to use drones for food service companies.

examine how consumers accept and use new technologies (e.g. Amoako-Gyampah & Salam, 2004; Hsu & Lu, 2004; Mathieson, Peacock, & Chin, 2001). The concept of MCI can also be seen as an extended model of TAM theory because it investigates the adoption of new technologies based on more motivations. That is, although the theory of TAM has been validated for a long time in a variety of fields, many previous studies have attempted to extend TAM by including additional constructs (e.g. Kim & Woo, 2016; Melas, Zampetakis, Dimopoulou, & Moustakis, 2011; Shih, 2004). This study also tried to measure consumers' intention formation more precisely by adding hedonic and social motivations based on the MCI model. The term ‘motivated consumer innovativeness’ is a combination of the concepts of motivation and consumer innovativeness. As previously mentioned, motivation is deemed internal and external factors that lead an action to achieve a consumer's goal (Percy & Rossiter, 1997; Vandecasteele & Geuens, 2010). For instance, the more motivated consumers are to use new technology services, the more likely they are to actually use them. In addition, consumer innovativeness is a consumer's tendency to prefer to purchase new products/services (Foxall, Goldsmith, & Brown, 1998). In other words, consumers who have high levels of innovativeness are more likely to adopt a new technology. Consequently, MCI can be defined as internal and external factors that lead to consumers' innovative buying behavior. The taxonomy of human goals provides a theoretical background for the concept of MCI (Ford & Nichols, 1987), which suggests that human behavior is highly relevant to goals, such as task goals, affective goals, cognitive goals, and self-assertive social relationship goals. In other words, people show different behaviors depending on the motivations they pursue. Furthermore, previous research has suggested that MCI has the following four theoretical sub-dimensions in the field of adopting a new technology either individually or collectively (Reinhardt & Gurtner, 2015; Stock, Oliveira, & Hippel, 2015; Vandecasteele & Geuens, 2010), which are: (1) functionally motivated consumer innovativeness (hereafter fMCI), (2) hedonically motivated consumer innovativeness (hereafter hMCI), (3) cognitively motivated consumer innovativeness (hereafter cMCI), and (4) socially motivated consumer innovativeness (hereafter sMCI). First, fMCI is defined as “consumer innovativeness motivated by the functional performance of innovations and focuses on task management and accomplishment improvement” (Vandecasteele & Geuens, 2010, p. 310). The functional dimension is related to instrumental, efficient, task-specific, and practical aspects (Holbrook & Hirschman, 1982), so consumers have high levels of functionally motivated innovativeness regard convenience and time savings as important when selecting a product/service of new technology (Ozturk, Bilgihan, Nusair, & Okumus, 2016; Stock et al., 2015). Second, hMCI refers to “consumer innovativeness motivated by affective or sensory stimulation and gratification” (Vandecasteele & Geuens, 2010, p. 310). When compared with fMCI, hMCI is more subjective, because it is closely related to sensory and emotional arousal (Babin, Darden, & Griffin, 1994; Batra & Ahtola, 1990; Chua, Kim, Lee, & Han, 2018). Thus, consumers motivated by the hedonically innovative dimension are more likely to use the newness of the product for fun and playfulness (Roehrich, 2004). Third, cMCI can be defined as “consumer innovativeness motivated by the need for mental stimulation” (Vandecasteele & Geuens, 2010, p. 310). Consumers' desire for a new technology involves the purpose of stimulating the mind, because the new technology plays an important role in satisfying consumers' cognitive goals, which include exploration, understanding, and intellectual creativity (Ford & Nichols, 1987; Vandecasteele & Geuens, 2010). In addition, consumers with high levels of cognitively innovative motivation to use a new technology are more likely to overcome their own cognitive limits (Vandecasteele & Geuens, 2010). Fourth, sMCI refers to “consumer innovativeness motivated by the self-assertive social need for differentiation” (Vandecasteele & Geuens, 2010, p. 310). Consumers tend to purchase a product in order to enhance their self-image among other people, so sMCI is considered

2. Literature review 2.1. Motivated consumer innovativeness The theory of the technology acceptance model (hereafter TAM) is the fundamental and widely known model to understand how consumers adopt new technologies (Davis, 1989), so the theory has been applied to various fields, such as online education, internet banking, mobile, and online shopping (e.g. Gefen, Karahanna, & Straub, 2003; Lee, 2009; Liu, Chen, Sun, Wible, & Kuo, 2010; Wu & Chen, 2005). According to the TAM theory (Davis, 1989; Davis, Bagozzi, & Warshaw, 1989), there are two important determinants of attitude, which in turn positively affect behavioral intentions and actual use. The first determinant of attitude is perceived usefulness, which is defined as “the degree to which a person believes that using a particular system would enhance his or her job performance” (Davis, 1989, p. 320). Considering the fact that perceived usefulness is related to efficient and practical aspects, it is similar with functionally motivated consumer innovativeness, which is the first dimension of MCI. In addition, the second determinant of attitude is perceived ease of use, which refers to “the degree to which a person believes that using a particular system would be free of effort” (Davis, 1989, p. 320). That is, perceived ease of use means how easy it is to learn new technology-based services. In this regard, perceived ease of use A is highly related to cognitively motivated consumer innovativeness, which is the third dimension of MCI. Although the TAM theory has been used in diverse fields and its reliability and validity have been widely verified, there are many opinions that it is insufficient to explain the adoption of new technologies. For this reason, many scholars have extended the TAM theory to 103

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an important factor when making a purchase decision (Bearden & Netemeyer, 1999; Brown & Venkatesh, 2005). For this reason, previous studies have emphasized the significance of sMCI in consumer innovativeness research (e.g. Reinhardt & Gurtner, 2015; Roberts, Hughes, & Kertbo, 2014; Roehrich, 2004), which suggests that people try to show their wealth, power, and social status through the possession of innovative products.

higher level of desire. Empirical studies also suggested the effect of attitude on desire. For example, Song, Lee, Kang and Boo (2012) examined how attitude affects desire using 400 tourists in the context of the Boryeong Mud Festival, which found that tourists have more likely to desire to visit the festival when they have a favorable attitude. In addition, Han and Yoon (2015) developed a research model in order to test the role of attitude in the formation of desire to visit an environmentally responsible hotel using 384 customers. They confirmed the effect of attitude on desire to visit an environmentally responsible hotel. This implies that when customers have a positive attitude towards staying at an environmentally responsible hotel, they are more likely to desire to stay at the hotel when traveling. Integrating the theoretical and empirical backgrounds, the following hypothesis can be proposed.

2.2. Effect of motivated consumer innovativeness on attitude According to the following theoretical and empirical backgrounds, this study proposed the effect of MCI on attitude. The importance of attitude has been studied in consumer research. Attitude can be defined as “the degree to which a person has a favorable or unfavorable evaluation or appraisal of the behavior” (Ajzen, 1991, p. 188). It means that attitude reflects individual favorable or unfavorable evaluation of a specific behavior (Ajzen & Fishbein, 2000; Ajzen & Madden, 1986; Han, Yu, & Kim, 2018). More importantly, many previous studies have consistently suggested that there is a significant relationship between MCI and attitude. Katz (1960) explained that motivation plays a critical role in forming and changing attitude. Fishbein and Ajzen (1975) proposed the theory of planned behavior (hereafter TPB) in order to explain how to form an individual's volitional behavior. Although there is no statistical test for a casual relationship between motivation and attitude, they suggested that attitude can be different based on individual motivation. Empirical studies have also supported this argument. For instance, Hsu and Lin (2008) suggested that technology acceptance factors, such as perceived ease of use and perceived enjoyment positively affect attitude toward using a blog. In addition, Vandecasteele and Geuens (2010) examined the effect of MCI on attitude in the context of mobile phones. The results of regression analyses showed that MCI plays an important role in the formation of attitude. Lien and Cao (2014) investigated the importance of psychological motivations in the context of mobile applications. They found that three dimensions of psychological motivations including entertainment, sociality, and information aid to increase attitude towards using mobile applications. Based on the theoretical and empirical backgrounds, the following hypotheses were therefore proposed.

This study hypothesized the effect of MCI on behavioral intentions. Many previous studies have focused on behavioral intentions in the hospitality and tourism industry (e.g. Byun & Jang, 2018; Hwang & Hyun, 2017; Hwang & Lyu, 2018; Trang, Lee, & Han, 2018). Behavioral intentions refer to ‘‘a stated likelihood to engage in a behavior’’ (Oliver, 1997, p. 28). Customers who have higher levels of behavioral intentions are more likely to show actual consumption, because the intentions are formed by their evaluations after using the product or service (Han, Meng, & Kim, 2017; Jeon, Ali, & Lee, 2018). There are enough evidence to support the relationship between MCI and behavioral intentions. First, according to the theory of TAM (Venkatesh & Davis 1996), the attributes of new technology-based services, such as perceived ease of use and perceived usefulness have a direct impact on behavioral intentions. In addition, Chenoweth, Minch, and Gattiker (2009) suggested that motivation of using new software positively affects behavioral intentions. Vandecasteele and Geuens (2010) also found that four dimensions of MCI including fMCI, hMCI, cMCI, and sMCI play a critical role in the formation of buying intentions. Therefore, it can be proposed that MCI has a positive influence on behavioral intentions.

H1a. Functionally motivated consumer innovativeness positively affects attitude.

H3a. Functionally motivated consumer innovativeness positively affects behavioral intentions.

H1b. Hedonically motivated consumer innovativeness positively affects attitude.

H3b. Hedonically motivated consumer innovativeness positively affects behavioral intentions.

H1c. Cognitively motivated consumer innovativeness positively affects attitude.

H3c. Cognitively motivated consumer innovativeness positively affects behavioral intentions.

H1d. Socially motivated consumer innovativeness positively affects attitude.

H3d. Socially motivated consumer innovativeness positively affects behavioral intentions.

H2. Attitude positively affects desire. 2.4. Effect of motivated consumer innovativeness on behavioral intentions

2.5. Effect of attitude on behavioral intentions

2.3. Effect of attitude on desire

The relationship between attitude and behavioral intentions has been verified by many existing theories. For example, the TPB suggested that attitude is a crucial factor that explains an individual's behavioral intention (Ajzen, 1991). In addition, the MGB showed that attitude plays an important role in predicting behavioral intentions. Empirical studies have also found the effect of attitude on behavioral intentions. For instance, Song, Lee, Norman and Han (2012) examined the effect of attitude on behavioral intentions in the casino industry. They suggested that attitude is a key predictor of behavioral intentions. Kim and Qu (2014) also investigated the relationship between attitude and behavioral intentions in the context of hotel self-service kiosk. They found that attitude positively affects the behavioral intentions. Based on these arguments, the following hypothesis is proposed.

Desire refers to “a state of mind whereby an agent has a personal motivation to perform an action or to achieve a goal” (Perugini & Bagozzi, 2004, p. 71). Particular behaviors are expressed a through internal stimulation, which is called the state of desire (Perugini & Bagozzi, 2004). In addition, desire is created based on positive or negative evaluations, which play a critical role in the formation of behavioral intentions (Han & Yoon, 2015; Leone, Perugini, & Ercolani, 2004). For instance, if consumers have a positive appraisal of a new technology, they will likely have higher levels of desire to use it more. On the other hand, when consumers have a negative evaluation of a new technology, their desire to use drone the technology would be weak. According to the model of goal-directed behavior (hereafter MGB) (Leone et al., 2004), attitude is an important predictor of desire, which suggests that when consumers have a positive attitude, they have a

H4. Attitude positively affects behavioral intentions. 104

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2.6. Effect of desire on behavioral intentions

on a seven-point Likert-type scale (“Strongly disagree” [1]–“Strongly agree” [7]). In addition, three bipolar semantic-differential scales for attitude were adapted from Bagozzi, Dholakia, and Basuroy (2003) (e.g. “Unfavorable” [1]–“Favorable” [7]). Three measurement items for desire were cited from Han and Yoon (2015) and Perugini and Bagozzi (2001). Lastly, behavioral intentions were measured with three items cited from Zeithaml, Berry, and Parasuraman (1996). Those concepts including desire and behavioral intentions were measured using a seven-point Likert-type scale, which ranged from strongly disagree (1) to strongly agree (7).

According to the MGB, desire to engage in a certain behavior is the most important factor affecting intention/behavior (Perugini & Bagozzi, 2001). Furthermore, the relationship between desire and behavioral intentions has been empirically confirmed in consumer behavior-related research. For instance, Lee, Song, Bendle, Kim, and Han (2012) collected data from 398 tourists in order to find the relationship between desire and behavioral intentions. The results of data analysis indicated that when people have high levels of desire to visit a certain tourist destination, they are more likely to show positive behavioral intentions. In addition, Han, Lee, and Kim (2018) investigated the relationship between desire to take pro-environmental actions and green loyalty using data collected from 276 passengers in the cruise industry. They found that desire to take pro-environmental actions positively affects green loyalty. That is, when customers' desire for traveling on an environmentally responsible cruise is strong, they are willing to travel with an environmentally responsible cruise in the future. Based on this evidence, it can be proposed that desire has a positive influence on behavioral intentions.

3.2. Data collection

3. Methodology

Before the main survey, a pretest was administered using 50 actual restaurant patrons in Korea. In addition, questionnaires were distributed using an online company. Since the context of this study was drone food delivery services, respondents watched a video that was about 2 min 30 s, which helped respondents to understand how drone food delivery services are operated (see the Appendix). The data analysis results revealed that Cronbach's alpha values for all of the constructs were higher than 0.70, indicating a high level of reliability (Nunnally, 1978). In Korea, the main survey also was performed in order to collect samples using an online company in a manner similar to the pretest. Respondents participated in the questionnaire after watching the video related to drone food delivery services. An email invitation was sent to 2794 respondents who have used food delivery services within the last six months, and 346 answered the questionnaire. Of the 346 respondents, 26 respondents were removed because of multivariate outliers. As a result, 320 respondents were used for further analyses.

3.1. Measurement

4. Data analysis

To measure each concept, multi-item scales that had been validated by existing research were used. First, MCI was measured with 12 items borrowed from Vandecasteele and Geuens (2010). These items were anchored

4.1. Sample characteristics

H5. Desire positively affects behavioral intentions. 2.7. Proposed model Based on the eleven research hypotheses presented above, the following research model is presented in Fig. 1.

Table 1 provides the sample characteristics. Among the respondents,

Fig. 1. Proposed conceptual model. 105

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positive influence on desire (β = 0.779, p < .05). Hence, Hypothesis 2 was supported. The results also showed that fMCI positively affects behavioral intentions (β = 0.080, p < .05), which supported Hypothesis 3a. However, hMCI, cMCI, and sMCI did not significantly affect behavioral intentions. Therefore, Hypotheses 3b, 3c, and 3d were not supported. Lastly, behavioral intentions were significantly affected by attitude (β = 0.117, p < .05) and desire (β = 0.797, p < .05), which supports Hypotheses 4 and 5.

Table 1 Sample characteristics (n = 320). Variable Gender Male Female Education level Less than a high school diploma Associate's degree Bachelor's degree Graduate degree Monthly household income $6001 and over $5001-$6000 $4001-$5000 $3001-$4000 $2001-$3000 $1001-$2000 Under $1000 Marital status Single Married Widowed/Divorced Mean age = 35.07 years old

n

Percentage

184 136

57.5 42.5

31 52 188 49

9.7 16.3 58.8 15.3

59 35 50 51 71 43 11

18.4 10.9 15.6 15.9 22.2 13.4 3.4

179 138 3

55.9 43.1 .9

5. Discussion and implications The purpose of this study was to explore the importance of MCI in the context of drone food delivery services. To be specific, a review of the extant literature suggested that the four dimensions of MCI, which are fMCI, hfMCI, cfMCI, and sMCI, positively affect attitude and behavioral intentions. In addition, it was proposed that attitude has a positive influence on desire and behavioral intentions. Lastly, it was hypothesized that desire plays an important role in the formation of behavioral intentions. Based on the proposed relationships among constructs, a conceptual model was developed and evaluated using data collected from 320 respondents in Korea. The data analysis results have the following important theoretical and managerial implications. 5.1. Theoretical implications

57.5% (n = 184) were male, and the average age was 35.07 years. With regard to the respondents' education level, the largest category was university graduates (n = 188, 58.8%), followed by associate's degree (n = 52, 16.3%), graduate degree (n = 49, 15.3%), and less than high school diploma (n = 31, 9.7%). About 22.2% of the participants' monthly household income was between $2,001and $3000. Lastly, the majority showed that they were single (55.9%, n = 179).

First, fMCI was shown to significantly enhance attitude (0.205, p < .05). This means that when consumers feel that drone food delivery services are efficient, they are more likely to have a favorable attitude towards using the services. In technology research, the importance of the functional aspect has been emphasized (e.g. Ozturk et al., 2016; Stock et al., 2015), suggesting that convenience and time savings are important factors when consumers adopt a new technology. The result of this study is similar to those from previous studies, because it showed the importance of fMCI. In particular, this study found that fMCI has a direct influence on behavioral intentions. The data analysis result indicated that fMCI positively affects behavioral intentions (0.080, p < .05). That is, when consumers perceive high levels of fMCI, they would use drone food delivery services when ordering food in the future. Previous studies also argued a direct effect of fMCI on behavioral intentions (Chenoweth et al., 2009; Vandecasteele & Geuens, 2010; Venkatesh & Davis 1996). Consequently, this study verified and extended the extant literature by empirically finding the effect of fMCI on attitude and behavioral intentions in the context of drone food delivery services for the first time. Second, the results of data analysis also indicated that hMCI had the greatest impact on attitude (0.395, p < .05). The existing studies have consistently focused on the significance of hMCI when selecting a new product/service (e.g. Roehrich, 1994; Vandecasteele & Geuens, 2010). However, there is no relationship between hMCI and behavioral intentions. When compared with previous research, the most important theoretical contribution of this finding is to identify the important role of hMCI in the formation of attitude in the context of drone food delivery services. Furthermore, this can be interpreted as showing that when consumers perceive drone food delivery services offer excitement and stimulation, they are more likely to have a positive attitude towards using the services. Third, contrary to expectations, the effect of cMCI on attitude (Hypothesis 1c) and behavioral intentions (Hypothesis 3c) was not statistically significant. The result is somehow different from the results of existing studies (e.g. Reinhardt & Gurtner, 2015; Vandecasteele & Geuens, 2010), which suggests the importance of cMCI in adopting a new technology. Even though the test of drone food delivery services was successfully completed in Korea, such services are not commercialized yet, so it is difficult for respondents to logically evaluate the services. Also, because people do not have enough information about drone food delivery services, the measurement of cMCI used in this

4.2. Confirmatory factor analysis The results of the CFA indicated an acceptable model fit (χ2 = 402.778, df = 168, χ2/df = 2.397, p < .001, NFI = 0.951, IFI = 0.971, CFI = 0.971, TLI = 0.964, RMSEA = 0.066). Table 2 shows the specific items of theoretical constructs used in the proposed model, in conjunction with their standardized factor loadings. All values of factor loading were higher than 0.776, and all were significant at a level of 0.001. Table 3 shows the descriptive statistics and associated measures for the proposed concepts. The convergent and discriminant validity of the proposed concepts were statistically supported, because the average variance extracted (hereafter AVE) was greater than the 0.50 standard (Bagozzi & Yi, 1988) for all of the constructs proposed. Also, all of the squared correlations (R2) between a pair of constructs were lower than the AVE for each construct (Fornell & Larcker, 1981). In addition, the values of all composite reliabilities were higher than 0.7, indicating high levels of internal consistency (Hair, Black, Babin, Anderson, & Tatham, 2006). 4.3. Structural model The proposed model with eight constructs was tested using a structural equation modeling (hereafter SEM) analysis. As shown in Table 4, the model had a suitable fit (χ2 = 442.760, df = 172, χ2/ df = 2.574, p < .001, NFI = 0.946, IFI = 0.967, CFI = 0.966, TLI = 0.959, RMSEA = 0.070). Fig. 2 provides the SEM results with standardized regression weights. In addition, Table 4 presents detailed results from the hypotheses testing. The results supported seven out of the eleven hypotheses. More specifically, attitude was significantly affected by fMCI (β = 0.205, p < .05), hMCI (β = 0.395, p < .05), and sMCI (β = 0.191, p < .05). Thus, Hypotheses 1a, 1b, and 1d were supported. However, Hypothesis 1c, which proposed the effect of cMCI on attitude, was not supported (β = 0.030, p > .05). In addition, attitude had a 106

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Table 2 Confirmatory factor analysis: Items and loadings. Standardized loadinga

Construct and scale item Functionally motivated consumer innovativeness Drone food delivery services seem to be efficient. Drone food delivery services seem to be convenient. Drone food delivery services are likely to shorten the delivery time. Hedonically motivated consumer innovativeness Drone food delivery services seem to make my life exciting and stimulating. It seems to give me a good feeling to use drone food delivery services. Using drone food delivery services seems to give me a sense of personal enjoyment. Cognitively motivated consumer innovativeness I am likely to think logically when using drone food delivery services. I am likely to use drone food delivery services after considering various aspects of drone food delivery services. I am likely to use drone food delivery services after comparing its advantages and disadvantages. Socially motivated consumer innovativeness Using drone food delivery services could impress others. Using drone food delivery services could show that I am an early adopter. Using drone food delivery services could distinguish me from others. Attitude Unfavorable – Favorable Bad – Good Negative – Positive Desire I desire to use drone food delivery services when ordering food. My desire to use drone food delivery services when ordering food is strong. I want to use drone food delivery services when ordering food. Behavioral intentions I will use drone food delivery services when ordering food. I am willing to use drone food delivery services when ordering food. I am likely to use drone food delivery services when ordering food. Goodness-of-fit statistics: χ2 = 402.778, df = 168, χ2/df = 2.397, p < .001, NFI = .951, IFI = .971, CFI = .971, TLI = .964, RMSEA = .066

.964 .937 .891 .973 .964 .911 .907 .904 .776 .791 .906 .835 .888 .929 .930 .947 .956 .956 .951 .882 .953

Notes 1. Notes 2: NFI = Normed Fit Index, IFI = Incremental Fit Index, CFI = Comparative Fit Index, TLI = Tucker-Lewis Index, RMSEA = Root Mean Square Error of Approximation. a All factors loadings are significant at p < .001.

study can confuse the respondents. We inferred that such reasons can lead to the result of an insignificant relationship between cMCI and behavioral intentions. Furthermore, it is widely accepted that perceived risks have a negative influence on the adoption of a new technology because consumers tend to worry about unexpected results when using new technology-based services (Martins, Oliveira, & Popovič, 2014; Yang, Liu, Li, & Yu, 2015). For example, if there is not enough information about drone food delivery services, consumers may have a time risk because they need to spend time learning to use drone food delivery services. In addition, consumers may also have a performance risk because they worry about how well drone food delivery services perform. Thus, it can be inferred that cMCI did not affect attitude and behavioral intentions due to consumers' perceived risks. Fourth, sMCI was found to exert an important impact on attitude (0.191, p < .05), while sMCI did not significantly affect behavioral intentions (0.022, p > .05). This implies that when consumers feel that

using drone food delivery services could impress others, they are more likely to have a good attitude towards using the services. As previously explained, sMCI is a crucial part of MCI (e.g. Reinhardt & Gurtner, 2015; Roberts et al., 2014; Roehrich, 2004). This finding also supports that assertion. In particular, this research is the first attempt to examine the influence of sMCI on attitude, which makes a significant theoretical contribution to the current literature. Another important theoretical contribution of this paper is to check the important role of attitude in the context of drone food delivery services. The SEM results revealed that attitude had a positive influence on desire (0.779, p < .05) and behavioral intentions (0.117, p < .05). In addition, the data analysis results indicated that desire helps to increase behavioral intentions (0.797, p < .05). That is, when consumers have a favorable attitude towards using drone food delivery services, their desire to use drone food delivery services is strong. Furthermore, they are more likely to use drone food delivery services when ordering

Table 3 Descriptive statistics and associated measures. No. of items (1) (2) (3) (4) (5) (6) (7)

Functionally motivated consumer innovativeness Hedonically motivated consumer innovativeness Cognitively motivated consumer innovativeness Socially motivated consumer innovativeness Attitude Desire Behavioral intentions

3 3 3 3 3 3 3

Mean (Std dev.) 5.22 5.73 4.94 5.65 4.88 4.40 4.61

(1.26) (1.17) (1.43) (1.11) (1.31) (1.36) (1.32)

Notes 1: AVE = Average Variance Extracted. Notes 2. a Composite reliabilities are along the diagonal. b Correlations are above the diagonal. c Squared correlations are below the diagonal. 107

AVE .867 .902 .747 .715 .839 .905 .864

(1)

(2) a

.951 .352c .401 .347 .309 .295 .327

b

.593 .965 .225 .514 .417 .445 .412

(3)

(4)

(5)

(6)

(7)

.633 .474 .898 .187 .179 .138 .154

.589 .717 .433 .882 .359 .270 .286

.556 .646 .423 .599 .940 .584 .588

.543 .667 .371 .520 .764 .977 .527

.572 .642 .392 .535 .767 .726 .950

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Table 4 Standardized parameter estimates for the structural model. Standardized Estimate H1a Functionally motivated consumer innovativeness → Attitude .205 H1b Hedonically motivated consumer innovativeness → Attitude .395 H1c Cognitively motivated consumer innovativeness → Attitude .030 H1d Socially motivated consumer innovativeness → Attitude .191 H2 Attitude → Desire .779 H3a Functionally motivated consumer innovativeness → Behavioral intentions .080 H3b Hedonically motivated consumer innovativeness → Behavioral intentions .019 H3c Cognitively motivated consumer innovativeness → Behavioral intentions .001 H3d Socially motivated consumer innovativeness → Behavioral intentions .022 H4 Attitude → Behavioral intentions .117 H5 Desire → Behavioral intentions .797 Goodness-of-fit statistics: χ2 = 442.760, df = 172, χ2/df = 2.574, p < .001, NFI = .946, IFI = .967, CFI = .966, TLI = .959, RMSEA = .070

t-value

Hypothesis

3.079 5.710 .496 2.660 16.938 1.996 .435 .003 .521 2.194 .17.026

Supported Supported Not supported Supported Supported Supported Not supported Not supported Not supported Supported Supported

Notes 1: *p < .05. Notes 2: NFI = Normed Fit Index, IFI = Incremental Fit Index, CFI = Comparative Fit Index, TLI = Tucker-Lewis Index, RMSEA = Root Mean Square Error of Approximation.

food. The extant theories have also supported the significance of attitude. For instance, the MGB suggested that a favorable attitude leads to a greater desire to do the behavior (Leone et al., 2004). In addition, the TPB explained that attitude aids to predict an individual's behavioral intention (Ajzen, 1991). In this regard, this study verified and expanded the existing literature by empirically finding the important role of attitude in the formation of desire and behavioral intentions in the context of drone food delivery services.

Another functional advantage of drone food delivery services is that customers can pre-order food while on the move outdoors. For example, in Japan, drone food delivery services are currently commercialized for golf courses. During golf games, customers order food on their smartphones and select the location where they want to receive the food (MBN, 2016). Thus, if the functional aspects of drone food delivery services are highlighted for potential customers, they will likely be highly motivated to use the services when ordering food. Second, the result of data analysis indicated the important role of hMCI in the formation of attitude (Hypothesis 1b). This finding has the following important managerial implications. Most of all, it should be exciting and stimulating for customers who want to use drone food delivery services. For instance, a lot of consumers recently have tried to order food delivery through mobile apps, so if a foodservice company provides a video that makes it easy and fun for anyone to use drone food delivery services, they are highly motivated to use them. It is also recommended that customers be able to use their smartphones to see

5.2. Managerial implications First, this study found the effect of fMCI on attitude (Hypothesis 1a) and behavioral intentions (Hypothesis 3a). Thus, food service companies are required to emphasize that drone food delivery services are superior to the existing delivery services (e.g. cars or motorcycles). In fact, the existing delivery services are often delayed due to traffic congestion. However, drones can shorten delivery times in this regard.

Fig. 2. Standardized theoretical path coefficients. 108

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where their food is at any particular moment. Furthermore, using the drones' camera enables customers to see the actual images being delivered on their smartphones, which would give them a sense of enjoyment. Third, Hypothesis 1d, which proposed the effect of sMCI on attitude, was supported, so food service companies should emphasize that using drone food delivery services can give customers an image that can differentiate them from others. For instance, it is widely known that current methods of delivery, such as cars and motorcycles, are a major cause of air pollution. Instead, drone food delivery services can reduce environmental pollution (Environmental Technology, 2018), so if food service companies stress the environmentally friendly aspect of drone food delivery services, it will help to induce consumers' socially motivated innovativeness. In addition, as previously explained, 1500 people get injured in car accidents during food delivery every year in Korea, and 30 of them are killed (Asia Economy, 2017). Thus, if food service companies emphasize the innovativeness of drone food delivery services that can safely deliver food to their customers without the loss of life, they are highly motivated to use the services when ordering food. Fourth, the results of data analysis revealed that attitude is an important factor affecting desire and behavioral intentions (hypotheses 2, 4, and 5). Thus, foodservice companies are necessary to enhance consumer attitude towards using drone food delivery services. For instance, improving the three dimensions of MCI, such as fMCI, hMCI, and sMCI, would make consumers have a favorable attitude towards the services. Additionally, as described earlier, perceived risks negatively affect the adoption of a new technology (Martins et al., 2014; Yang et al., 2015), so food service companies are required to reduce perceived risks of using drone food delivery services before launching the services.

6. Limitations and future research This study provides meaningful theoretical and practical implications, but it has the following limitations that need careful consideration. First, data were collected only in Korea, so the generalizability of the findings can be limited. In other words, applying the findings of this study to other areas may not be appropriate. Second, this study focused only on food delivery services using drones. In particular, MCI has the disadvantage that external validity is not high because it is not as widely used in previous studies as TAM theory, so future research is necessary to apply the proposed model in this study to other contexts, which helps to enhance external validity of MCI. Third, although an online survey based on the convenience sampling technique is widely used in consumer research, it can cause selection biases (Wright, 2005). Therefore, future research needs to use different types of data collection methods in order to reduce biases. Fourth, all variables including the independent and dependent variables used in this study were measured at the same time. This can lead to a common method bias, so a Harmon one-factor test was conducted in order to check whether the bias was critical or not. The results showed that there is no single factor, which explained a majority of the variance (Podsakoff & Organ, 1986). However, to overcome a common method bias, future research is required in order to collect data at different times (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Fifth, it is widely accepted that the TAM theory is a representative information system theory in order to identify how consumers accept and use a new technology (Davis, 1989; Davis et al., 1989). Therefore, it would be meaningful to examine the important role of the TAM theory in the context of drone food delivery services. Lastly, although the lower RMSEA values the better, the values of RMSEA in this study (i.e. 0.066, 0.070) are an acceptable fit to the model (Browne & Cudeck, 1993; Neale, Boker, Xie, & Maes, 1999).

Appendix

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