Investigating consumer innovativeness in the context of drone food delivery services: Its impact on attitude and behavioral intentions

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Investigating consumer innovativeness in the context of drone food delivery services: Its impact on attitude and behavioral intentions Jinsoo Hwang a, Jinkyung Jenny Kim b, Kwang-Woo Lee c, * a

College of Hospitality and Tourism Management, Sejong University, 98 Gunja-Dong, Gwanjin-Gu, Seoul 143-747 Republic of Korea School of Hotel and Tourism Management, Youngsan University c Department of Tourism Management, College of Economics and Business Administration, Daegu University, 201, Daegudae-ro, Gyeongsan-si, Gyeongsangbuk-do, 712714 Republic of Korea b

A R T I C L E I N F O

A B S T R A C T

Keywords: Drone food delivery services Consumer innovativeness Attitude Behavioral intentions

This study investigates the importance of consumer innovativeness in the context of drone food delivery services by proposing that eight sub-dimensions of consumer innovativeness (i.e., novelty seeking, eagerness, vigilance, openness, quality experience seeking, hedonic experience seeking, venturesomeness, and social distinctiveness) positively affect attitude toward using drone food delivery services. It was hypothesized that attitude has a positive influence on behavioral intentions, including intentions to use and willingness to pay more. Based on the theoretical relationships, a research model comprising 10 hypotheses are presented and validated by examining a total of 321 samples collected in Korea. Data analysis results indicated that four sub-dimensions of consumer innovativeness, namely, novelty seeking, quality experience seeking, hedonic experience seeking, and social distinctiveness, enhance attitude toward using drone food delivery services. Furthermore, the results showed that attitude plays an important role in the formation of intentions to use and willingness to pay more.

1. Introduction Ongoing technological advances have rolled out immense improve­ ments in the physical configuration of retail supply chains during the last ten years (Vlahovi´c et al., 2015). In order to remain aggressively competitive in globalized markets and to stay aware of new techno­ logical innovations, organizations should persistently furnish customers with seamless and easier services to required services and products (Ramadan et al., 2017). Because of the innovative induced changes, consumers demand ever greater quality, lower cost, and more rapid item delivery, and the degree of services offered during the procedures to fulfill the order (Vlahovi´c et al., 2015). The application of commercial drones in a diverse industry was described as the spillover of military technology, and the use of drones for home deliveries has drawn keen attention in the recent industrial activity (Malik, 2017). Drones will, without a doubt, prompt an expansion in sales, particularly for online business organizations, as they will enable them to deliver their products to their customers, faster, cheaper and easier (Lotz, 2015). The utilization of drones in business could possibly and drastically change several different industries, while also changing customer’s attitudes and practices in regards to their

impact on their everyday lives (Rao et al., 2016). Hence, many experi­ ments and endeavors in academia have been continuously made to activate drones in harmony with the current environment (Andreani et al., 2019; Goodchild, and Toy, 2018; Nakamura, and Kajikawa, 2018). Current research demonstrates that many technological innovations have transformed business operations (Beldona et al., 2012; Couture et al., 2015; Tussyadiah, 2016; Wang, 2015). Ngo and O’Cass (2013) suggested a theoretical framework to look at the function of technical and nontechnical developments and their outcomes for service quality and organizational performance. Despite the fact that technological improvements have progressively influenced the service delivery and encounters in the hospitality and tourism industry, exact research on consumer inclination for adjusting and adapting to new innovations in technology-driven devices is incomplete (Wang, 2015). Very few pub­ lished studies on drones have been impartial and presented a complete argument (Lotz, 2015). Each article appears to spotlight just one facet of the drones, for example, maybe the cost or the technological constraints or regulations (Lotz, 2015). What is more, such a study should recognize and target customers with a high affinity to embrace new products or services to their requirements as “innovative” (Hirschman, 1980) because this idea has generally not been researched in the tourism field

* Corresponding author. E-mail addresses: [email protected] (J. Hwang), [email protected] (J.J. Kim), [email protected] (K.-W. Lee). https://doi.org/10.1016/j.techfore.2020.120433 Received 20 November 2018; Received in revised form 5 October 2020; Accepted 26 October 2020 0040-1625/© 2020 Elsevier Inc. All rights reserved.

Please cite this article as: Jinsoo Hwang, Technological Forecasting & Social Change, https://doi.org/10.1016/j.techfore.2020.120433

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(Fraj et al., 2015; Hwang and Hyun 2016; Sandvik, Duhan, & Sandvik, 2014; Tajeddini, 2010). Considering the absence of either theoretical or experimental work on the consumer– drone relationship, researchers took a cognitive route to better comprehend the consumer-drone adoption choice (Ramadan et al., 2017). The central aspects of the framework were adopted from a simplistic form of the theory of planned behavior (TPB), which in turn, is utilized to propose a conceptual framework for this work. As one of the most prevailing models of the attitude– behavior correlations, TPB provides a good conceptual framework for evaluating attitudes towards delivery service-drone usage and its usage expectation and intention (Fishbein and Ajzen, 1975). At the center of this causal model adoption is the supposition that consumers’ innovativeness to use the delivery-service drones is the direct determinant of that specific activity. The extant studies demonstrated the significant role of the in­ dividuals’ innovativeness toward specific technology-powered products or services, which include mobile-RFID services and service robots in restaurants, in the consumer adoption behavior (Cha, 2020; Jeong et al., 2009). Thus, it would be meaningful to examine what effects of con­ sumer innovativeness exist in the context of drone food delivery services for the successful diffusion of the services. This study therefore in­ vestigates the ongoing phenomenon related with consumer adaptability to new services driven by innovation advances in technologies and proposes consumer innovation as a theoretical cornerstone for depicting consumers’ flexibility to services. Several aspects may influence con­ sumer attitudes towards utilizing this kind of delivery service. There­ fore, the proposed model specifically considers consumers’ attitudes towards delivery-service drone use and consumers’ intentions. Along these lines, this paper examines i) the impact of consumer innovative­ ness, such as novelty seeking, eagerness, vigilance, openness, quality experience seeking, hedonic experience seeking, venturesomeness, and social distinctiveness upon attitudes towards using drone food delivery services, ii) the connection between attitudes toward using drone food delivery services and intention to use, and iii) the relationship between attitudes toward using drone food delivery services and willingness to pay more. This model should, concurrently, offer an organized portrayal of the distinctive levels at which innovativeness has been conceptualized within the hypothetical underlying foundations of the concept. The distinctive measurements of innovativeness are incorporated. This model also offers theoretical underpinnings to the development of innovativeness scales, including things that are particular to the theo­ retical elements of imaginativeness, which to a great extent, affirms that the model that can promptly be used to forecast customers’ attitudes towards drone usage, and subsequently their goal to embrace this service-delivery mode later on. The conceptualization, materialization, and more compelling esti­ mation of consumer innovation with regards to tourism and innovation utilization could thus be researched to better elucidate consumer pat­ terns in connection to these new technology services. Conceptually, it fills an existing research gap on the irregularities of the innovation theory and the dimensions in consumer studies. It moves from the more universally basic consumer study’s methodology into ways that propel our current understandings of innovation in the consumer experience industry. Identifying the fundamental elements of consumer innovation may likewise enable marketers to portray or foresee the attitudes of innovative consumers and predict their needs appropriately.

the use of artificial intelligence in hospitality organizations has received `s some consideration by researchers, although not nearly enough (Borra et al., 2014), studies on service automation and the implementation of robots is exceedingly rare (Murphy et al., 2017). With ever increasing demand for a quick and productive delivery system, drone delivery promises very prompt deliveries. Led by rapid advances in drone innovation, drones are being progressively utilized for delivery services (Snead and Seibler, 2017). The adoption of drones in a delivery network results in quicker delivery and lower cost from energy efficiency (Goodchild and Toy, 2018). Drone delivery may shorten wait times to inside 30 min from online customer requests (Seo et al., 2016). Keeping delivery time guarantees will be a key factor and could be essential to drone delivery’s success (Lotz, 2015). On the other hand, There are a few disadvantages by only utilizing drones for de­ livery, such as distance, payload, and cost/time to recharge (Hill et al., 2018; Lotz, 2015; van der Kaauwen and Van Duin, 2018). Drone de­ livery has definite impediments. For example, they are currently only able to deliver lightweight items and only suitable for certain selected areas (Hepp, 2018). Particularly, it will not be easy to find landing areas for secure performance in urban zones (Hepp, 2018). Likewise, if the customer fails to make the delivery window or the payload is stolen, then they will be incredibly disturbed that they will need to stand by longer for their re-delivery and the organization additionally have to take on the burden of burning through valuable flight time and maybe replacing the lost package (Lotz, 2015). Furthermore, the risk percep­ tions towards drones were addressed in the study conducted by Wright et al. (2014) and in the study by Hwang and Choe (2019). In addition, the drones are operated not directly by humans, but by using a computer program to designate a moving point (Kesteloo, 2018), so accidents due to drone operations are not significant. Drones have caught the intrigue and creative imagination of con­ sumers, despite having been actively studied for quite a long time (D’Andrea, 2014). As drone use for business purposes is right now being conceived, drones are gradually becoming part of our reality and of customers’ lives (Joel, 2013). Drones are fit for delivering items and fit for transporting light loads. These items may incorporate grocery products, food goods, packages and small others. Drones likewise have been utilized broadly by the military and for humanitarian aid. Helpful non-military drone applications in other various enterprises may involve agriculture surveillance and crop dusting, shark reconnaissance along the sea shores, wildlife conservation, wildfire observation, and border patrol surveillance, media coverage like sports and entertainment, and others emergency circumstances (Choi-Fitzpatrick et al., 2016; Clarke, 2014; Esler, 2015). Some other advantages that drones offer are working in challenging topography, potentially taking shorter courses, and diminished ecological effect on the climate as there will be less vehicles required (Lee et al., 2016). Since the drone can bypass customary transportation infrastructure (Scott and Scott, 2017), blocked streets will never again forestall delivery of blood or other healthcare items (Scott and Scott, 2017). For this reasons, the creation of a drone healthcare delivery system will encourage all the more convenient, productive and practically low cost medical delivery services to possibly save lives (Scott and Scott, 2017). Even though the technological ability has been affirmed, social and legislative issues still require continuing adaptation efforts in order to accomplish wide spread commercial feasibility (Vlahovi´c et al., 2015). Drones heavily have been attacked for lacking regulations and having safety concerns with privacy misuse (Clarke, 2014; Welch, 2015). Privacy advocates and celebrities are worried about drones being used to keep an eye on them. Moreover, reckless drone proprietors have been a disturbance in cases identified with capturing accidents or fires and meddling with emergency re­ sponders (Choi-Fitzpatrick et al., 2016). Governmental bodies like the Federal Aviation Administration (FAA), typically do not allow drones to be used commercially because they worry about drones entering into restricted air space (Scott and Scott, 2017). Current FAA guidelines only permit drone flights during the daytime and only within line of sight of

2. Literature review 2.1. Background of drone market Service automation and artificial intelligence promise immense op­ portunities for hospitality organizations to enhance their procedures and profitability, provide dependable product quality and give a portion of the service delivery procedure to the customers (Mathew, 2015). While 2

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its operator and not to be flown over individuals and so forth. Amaz­ ingly, advertising activities have moved from attention on useful high­ lights and showcasing its advantage to focus around marketing experiences (Schmitt et al., 2014). Drones can be autonomous and used to deliver merchandise to cus­ tomers (Valavanis, 2008). For example, in the US there are plans about utilizing drones for pizza delivery (Pepitone, 2013) while in Russia, pizzas are presently being delivered by drones (Vergouw et al., 2016). There are a few technologies that are helping service applications to get easier and more practical. One example of this is that more than 150 visitors at the OppiKoppi music celebration in South Africa received cold lager beer by means of drones (Daily News, 2013), giving them the convenience of delivery while eliminating the labor costs (Mack, 2013). In March 2012, Silicon Valley startup TacoCopter stood out as a truly newsworthy event as it publically reported plans for the delivery service of tacos inside the City of San Francisco by means of unmanned aerial vehicles (UAVs), also called “drones” (Gilbert, 2012). They said that interested customers would have the capacity to submit their request on a smartphone application and quickly wait as a drone delivers their tacos to them from the sky (Goodchild and Toy, 2018). In addition, in China, online internet business giant Alibaba, has been approved to test the delivery of tea (Harrison, 2015). Regarding delivery, there are many ways for a drone docking station to be set up for dropping off items during a drone package deliver, such as a protected yard, porch, rooftop, window or any building mounted box. The drone dock may utilize diverse innovative gadgets to accommodate correspondence between the drone docking station and a drone to give security and protection to the transported items before, during and after delivery. Hence, there is a requirement for a drone friendly secure delivery box that can depend­ ably accept items transported by a drone. Likewise, there is a require­ ment for a protected box to hold things delivered items by a drone (O’toole, 2017). Amazon CEO, Jeff Bezos, first introduced drone delivery (Lawson, 2017). With the advancement of online business and B2C delivery, Bezos had thought of an approach to decrease the delivery time in a developing market (Lawson, 2017). Amazon was developing a substitute method for delivering their products by drones. During the episode, Bezos intro­ duced the world to Amazon’s new Octocopter which will be the essential delivery device behind this new service (Lawson, 2017). Bezos guaran­ teed that Amazon would have the capacity to deliver their products in less than 30 min from its warehouses to the customer’s home (Bam­ burry, 2015). Amazons reputation to ensure satisfaction has given Amazon a major advantage in the retail industry. Drone delivery services have gained buzz in the media because internet businesses have been developing quickly (Lawson, 2017). As indicated by E-Marketer (2016), retail sales worldwide will be around $26.6 trillion with online retail deals to supersede $4 trillion by 2020. The utilization of business drones may turn into a key advantage and a key difference that assists internet business organizations to achieve almost instantaneous deliveries, which will create enormous advantages for web-based business retailers over the globe that can help separate them from each other as the opposition warms up for consumer satis­ faction and retention (Chen, 2016). Subsequently, drones will turn out to be progressively accessible to the general population everywhere and will be utilized for an expanding scope of purposes (Vergouw et al., 2016). Along these lines, drones will become progressively more accessible to the overall population, and they will be utilized for an expanding range of applications (Vergouw et al., 2016). Realizing a future that makes drones a regular part of our daily lives will require answers to the following potential issues and obstacles (Lawson, 2017). Drone delivery business seems to be, by all accounts, a very lucrative business. However, like other delivery services, it is essential for drone operators to understand their duty towards their customers (Nok et al., 2015). Research with respect to impact appraisals of drones is rare because of the recent introduction and minimal operational utilization of drone

technology in the delivery industry. This is an unavoidable development of drone-based organizations which appears to be bound to change consumer behavior and also reshape our thoughts on how it can be embraced, and how it will be utilized by individuals and in business applications. Correct communication is an essential part in guaranteeing customer satisfaction (Datta, 2018).This study will concentrate on consumers’ viewpoints of the future of drone delivery and will examine their readiness to receive the service by utilizing the consumer innova­ tiveness theory. 2.2. Consumer innovativeness Consumer innovativeness refers to the propensity of some consumers to accept innovative technologies more often and more quickly than others (Midgley and Dowling, 1978). Consumer innovativeness studies expect that innovative consumers are constantly both involved and proficient in the product category (Arts et al., 2011; Goldsmith and Newell, 1997). Consumer innovativeness, which prompts imaginative and innovative behavior, has regularly been referred to and analyzed in research on the diffusion of innovation (Roehrich, 2004). Consumers with high levels of innovativeness are attracted by new services that are superior to the existing choices; therefore, advertising directors should monitor levels of consumers’ perceived benefits and value of the hos­ ¨m and Kowalkowski, 2014; Nicolau and pitality innovation (Kindstro Santa-María, 2013; Sandvik et al., 2014). Eventually, it might be por­ trayed as an early buy of a new item (Cestre, 1996) and also as an inclination to be attracted by new items (Steenkamp et al., 1999), while most authors appear to think about innovativeness as an attribute, the nature of which is still under inquiry (Roehrich, 2004). In new technology research, prior research has suggested that the following eight attributes influence the evaluation of consumer inno­ vativeness, either individually or collectively: (1) novelty seeking, (2) eagerness, (3) vigilance, (4) openness, (5) quality experience seeking, (6) hedonic experience seeking, (7) venturesomeness, and (8) social distinctiveness (e.g. Reinhardt and Gurtner, 2015; Roehrich, 2004; Wang, 2014, 2018). Hirschman (1980, p. 284) expressed novelty seeking as “the desire of the individual to seek out novel stimuli” or “the actual behavior by the individual to acquire novel stimuli.” Innovative consumers were concerned with new and remarkable products or ser­ vices. Therefore, innovative consumers were not satisfied with the cur­ rent state in order to acquire for distinct uniqueness (Wang, 2014). Existing investigations have proposed an element of innovation, which has been conceptualized as consumers’ behavior of the requirement for uniqueness (Roehrich, 1994; Simonson and Nowlis, 2000). Fromkin (1971) suggests a connection between innovative behavior and the requirement for uniqueness. Although upheld by only two experimental outcomes, Fromkin’s theoretical proposition recommends that the requirement for uniqueness can be thought to be a believable precursor of innovativeness. Eagerness is related to consumer enthusiasm to attempt new services or products. Innovative consumers are interested to learn something new, pursue information regarding services or products, and look for imaginative services or products innovation (Wang, 2014). However, eagerness can vary according to gender and product category classifi­ cation (Tellis et al., 2009). Regarding vigilance, innovative consumers attempt to look for cautious information before decision making related to purchasing because they have realistic and differential perception. Thus, innovative consumers decide carefully with rational decisions (Wang, 2014). Likewise, two concepts, which included eagerness and vigilance, were illustrated as the opposite sides of the same coin, which seem to be diametrically opposed as a promotion and as a prevention, but both of these explain the characteristics of the consumer innova­ tiveness (Crowe and Higgins, 1997; Wang et al., 2018). Tellis et al. (2009, p. 10) described openness as “general attitudes towards new things.” Openness represents consumers’ tendency and inclination regarding services or product variety. Because they are 3

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open-minded, innovative consumers accept new services and products (Wang, 2014). Innovativeness is the inclination to participate with delight in new encounters that invigorate thinking, which may be from either inner or outer stimuli. Therefore, innovativeness is a propensity to connect with delight in internalized encounters like dreaming or stim­ ulating and risky actions. This innovativeness could be initiated by stimuli, which can be internal or external experiences (Roehrich, 2004). Concerning quality experience seeking, innovative consumers pursue more effective quality and performance regarding services and products (Vandecasteele and Geuens, 2010). It means that they are looking for better quality or greater value more than other contributions (Wang, 2014). Thus, consumers who are driven by the quality experience tend to use novel products that promote functional benefits, such as comfort and time-saving (Wang et al., 2018). Hedonic experience seeking refers to the dedication of enjoyment and pleasure. Fun, delight, and enjoy­ ment could be beneficial outcomes gained by using and purchasing new services or products (Reinhardt and Gurtner, 2015). Hedonic innova­ tiveness focuses on positive feelings that go with new item purchases (Vandecasteele and Geuens, 2010). Roehrich (1993) characterized consumer innovation as the requirement for hedonism. Hedonism as a key sensory segment measures consumers’ desire for change (Wood and Swait, 2002). As to venturesomeness, innovative consumers have an intention to take a risk by trying new services or products. Likewise, Bowden and Corkindale (2005) and Wang et al. (2018) described venturesomeness as an essential attribute of the consumer innovativeness in the acquisition of newly introduced products/services. Because they endure potential risk, innovative consumers are adventurous and withstand uncertainty in order to manage the risks due to latent great results (Wang, 2014). Social distinctiveness describes “consumers’ social needs for uniqueness and the provision of feedback” (Wang, 2014, p. 31). Vandecasteele and Geuens (2010) also describe socially motivated consumer innovative­ ness as consumer innovativeness inspired by the emphatic social desire for individuality. Thus, having leadership, symbolism, or visibility is frequently used in order to predict the individuals’ social distinctiveness (Simonson and Nowlis, 2000; Vandecasteele and Geuens, 2010). As innovative consumers assert their opinions and provide feedback to companies, businesses can enhance and understand consumers’ needs in order to deliver a greater degree of service quality (Wang, 2014). Consequently, because these eight characteristics measure the concept of consumer innovativeness from a wide range of different perspectives, these themes could be utilized to enhance the understanding of con­ sumer attitudes and behaviors and provide a marketing strategy directed at the target group (Wang, 2014).

and by consumers’ attitudes and intentions indirectly (Limayem et al., 2000). Especially, innovativeness within new technology is positively connected with general attitude (Crespo and del Bosque, 2008). Limayem et al. (2000) confirmed the positive influence of personal innovativeness on attitude toward online shopping. General innova­ tiveness also affects attitude regarding online shopping (Crespo and del Bosque, 2008). Likewise, Bartels and Reinders (2011) reviewed the past seventy-nine studies about the consumer innovativeness and its effect on the new product adoption. Also, they denoted the positive relationship between the consumer innovativeness and the attitude. In addition to this, Fort-Rioche and Ackermann (2013) examined the consumers’ response toward a highly technical neoretro product design, and their results revealed that the consumer innovativeness exerted a positive influence on the consumers’ attitude. Frimpong et al. (2017) tested the role of the individuals’ innovativeness with accepting mobile banking, and the analysis results indicated its important role in generating the consumers’ attitude toward this type of digitized platform. Similarly, the significant effect of the consumer innovativeness on the attitude toward new products or services was found in the context of hospitality and tourism. For example, Chang (2017) proposed social, functional, he­ donic, and cognitive facets of the consumer innovativeness and identi­ fied their influence on the consumers’ attitude toward space travel. Cha (2020) attempted to investigate the formation of the consumer behavior toward robot-serviced restaurants, and the author provided empirical evidence of a close link between the consumer innovativeness and the attitude. Eventually, such empirical evidence demonstrated that the impact of innovativeness influences attitude regarding such behavior (Fenech and O’Cass, 2001; Goldsmith, 2002; Limayem et al., 2000). Integrating the theoretical and empirical background, the following hypothesis is proposed: H1. Novelty seeking positively affects attitude towards using drone food delivery services. H2. Eagerness positively affects attitude towards using drone food delivery services. H3. Vigilance positively affects attitude towards using drone food delivery services. H4. Openness positively affects attitude towards using drone food delivery services. H5. Quality experience seeking positively affects attitude towards using drone food delivery services.

2.3. Effect of consumer innovativeness on attitude toward using drone food delivery services

H6. Hedonic experience seeking positively affects attitude towards using drone food delivery services.

First, this study proposed the relationship between consumer inno­ vativeness and attitude toward using drone food delivery services based on the following theoretical and empirical background. Attitudes are conceptualized as steady fundamental disposition used to assess psy­ chological issues (Ajzen and Fishbein, 2000). Attitude toward the behavior, which is the main indicator of intention, can be characterized as one’s general assessment of the particular behavior (Ajzen, 1991). This study embraces this definition, which focuses on the emotional part of attitudes and in which the assessment depends on a measurement of “favor or disfavor, good or bad, like or dislike” (Ajzen and Fishbein, 2000). More importantly, perceptions of consumer innovativeness have a varying effect on attitude across countries regarding service-based technological innovations (Truong, 2013). Jin and Suh (2005) pro­ posed that consumer innovativeness was an accurate overall predictive factor for the shoppers’ private brand attitude and positively influenced attitude and purchase intention for private brand foods. Internet shop­ ping behaviors have been affected by consumer innovativeness directly

H7. Venturesomeness positively affects attitude towards using drone food delivery services. H8. Social distinctiveness positively affects attitude towards using drone food delivery services. 2.4. Effect of attitude toward using drone food delivery services on behavioral intentions Next, this study proposed the effect of attitude toward using drone food delivery services on behavioral intentions based on the following theoretical background and empirical evidence. As indicated by Ajzen (1985, 1991), behavioral intentions are the probability of a person attempting a specific behavior, which is a direct forerunner of behavior. While the connection between intention and actual behavior is not flawless, intention can be utilized as the best in­ dicator of behavior (Ajzen, 1985, 1991; Lam and Hsu, 2004). It is widely 4

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Fig. 1. Proposed conceptual model.

accepted that behavioral intentions include intentions to use and a willingness to pay more (e.g. Ajzen and Driver, 1992; Hwang and Choi, 2017; Hwang and Lyu, 2018; Rekola, 2001). Intentions to use refer to “the degree to which a person has formulated conscious plans to perform or not perform some specified future behavior” (Warshaw and Davis, 1985, p. 214). Intentions to use are formed based on the evaluation of products/services, so consumers have a high level of intentions to use when they make good evaluations of the product/services (Al-Qeisi et al., 2014; Han et al., 2017). The willingness to pay more is the highest price a customer will pay for an item or service (Cameron and James 1987; Krishna, 1991). Subsequently, willingness to pay more is seen as the gage of the value people give to the utilization or experience in fiscal units (Bernath and Roschewitz, 2008). The TPB (Ajzen, 1991) is a broadly utilized framework to clarify how attitude shapes intentions toward specific behaviors (Armitage and Conner, 2001). The effects of attitude on behavior intentions have been broadly analyzed in the literature with regards to technology adoption (Dabholkar, 1996; Farah and Newman, 2010). An individual’s specific behavior is controlled by their intention to play out that behavior. This intention is essentially an element of one’s attitude towards the result of the behavior (Ajzen and Fishbein, 1980). Ajzen (1991) also character­ ized attitude towards one’s behavior as how much execution of the behavior is decidedly or adversely valued. Empirical studies also sup­ ported the argument in the context of new technology. For example, Hung et al. (2006) found that attitude plays an important role in the formation of behavioral intentions in the context of public acceptance of

e-Government services. Fu and Elliott (2013) also suggested that atti­ tude using a new technology product is an important predictor of behavioral intentions. Lastly, Wu and Ke (2015) showed that when consumers have a favorable attitude using online shopping, they are more likely to have high levels of behavioral intentions. Therefore, based on the theoretical and empirical background, this study proposed the following hypotheses. H9. Attitude towards using drone food delivery services positively affects intentions to use. H10. Attitude towards using drone food delivery services positively affects willingness to pay more. Based on the theoretical background discussed above, the following research model is presented (Fig. 1). 3. Methodology 3.1. Measurement Measurement items were generated from validated measurement items, adapted from previous studies. More specifically, consumer innovativeness consisted of eight sub-dimensions, including novelty seeking, eagerness, vigilance, openness, venturesomeness, hedonic experience seeking, quality experience seeking, and social distinctive­ ness, and was measured with 24 items adapted from Vandecasteele and 5

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Table. 1 Profile of survey respondents (n = 321). Variable Gender Male Female Age 20s 30s 40s 50s Mean age = 35.06 years old Monthly household income $6001 and over $5001-$6000 $4001-$5000 $3001-$4000 $2001-$3000 $1001-$2000 Under $1000 Marital status Single Married Widowed/Divorced Education level Less than high school diploma Associate’s degree Bachelor’s degree Graduate degree

Table. 2 Confirmatory factor analysis: Items and loadings. n

Percentage

187 134

58.3 41.7

120 99 67 35

37.4 30.8 20.9 10.9

58 36 47 52 74 44 10

18.1 11.2 14.6 16.2 23.1 13.7 3.1

181 137 3

56.4 42.7 .9

33 49 191 48

10.3 15.3 59.5 15.0

Construct and scale item

Standardizedloadinga

Consumer innovativeness Novelty seeking I like to try new products. .888 I enjoy trying unusual products. .939 I like purchasing novel products. .901 Eagerness I am passionate about trying new products. .910 I am eager to find out about new products. .938 I am enthusiastic about buying new products. .940 Vigilance I make careful decisions about what I want to buy. .864 I do extensive research before acquiring new products. .909 I do not make unplanned decisions when buying new .751 products. Openness I am open to a variety of product options. .817 I prefer to have many alternatives when deciding what to .843 buy. I would like to experience new products of different kinds. .850 Quality experience seeking If a new product is more functional than existing products, .923 I usually buy it. If the product I have does not work well enough, I try to .937 buy a new product. I often consider buying products that are more effective .792 than the current options. Hedonic experience seeking Using new products gives me a sense of personal .912 enjoyment. Acquiring new products makes me happier. .933 I feel good when using new products. .957 Venturesomeness I cope well with risks associated with trying new products. .822 I am fine with the uncertainty of using new products. .764 I anticipate uncertainty when using new products. .882 Social distinctiveness It is necessary to buy new products to impress others. .767 I enjoy using new products that make me a visionary .940 leader. Using new products makes me a trendsetter. .884 Attitude toward using drone food delivery services Unfavorable – Favorable .893 Bad – Good .942 Negative – Positive .938 Intentions to use I will use drone food delivery services when ordering .951 food. I am willing to use drone food delivery services when .913 ordering food. I am likely to use drone food delivery services when .961 ordering food. Willingness to pay more I am likely to pay more for drone food delivery services. .959 It is acceptable to pay more for drone food delivery .968 services. I am likely to spend extra in order to use drone food .976 delivery services. 2 2 Goodness-of-fit statistics: χ = 969.870, df = 440, χ /df = 2.204, p < .001, NFI = 0.917, IFI = 0.953, CFI = 0.953, TLI = 0.943, RMSEA = 0.061

Geuens (2010), Wang (2014), and Wang et al. (2018). Attitude was measured with three items borrowed from Hwang and Hyun (2016) and Han and Hyun (2017). Measurements for intentions to use were adapted from Hwang and Park (2018) and Zeithaml et al. (1996), and those for willingness to pay more were from Hwang and Choi (2017) and Zei­ thaml et al. (1996). In addition their wording was modified to ensure adequateness in the context of drone food delivery services. All items were measured based on a seven-point Likert-type scale, anchored from strongly disagree (1) to strongly agree (7). Although a five-point Likert scale is widely employed in various fields, a seven point Likert scale is more exact evaluation by offering additional scale points. In addition, Nunnally (1978) also suggested that it is better to provide response options to respondents. Therefore, a seven point Likert scale is being used more in field of hospitality and tourism. 3.2. Data collection Before the main survey, a pretest was performed to test the reliability of the measurement items using 50 actual restaurant patrons via online surveys in Korea. Approximately 2 min and 30 s of video was provided to respondents in order to enable them to fully understand the overall system and operation of drone food delivery services before beginning the survey (see the Appendix). As a result, the values of Cronbach’s alpha for all of the constructs were higher than 0.70, indicating a high level of reliability (Nunnally, 1978). In the same way as the pretest, data were collected for the main survey. An online survey company’s system was employed to distribute the questionnaires to the respondents who have used food delivery services within the last six months in Korea. The company sent an invitation email to 2794 panel members. Among them, 346 respondents completed the questionnaire. In addition, after checking multivariate outliers and visual inspection, 25 outliers were excluded from statistical analysis. Consequently, statistical analysis was conducted using 321 samples.

Notes 1: a All factors loadings are significant at p < .001. 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.

4. Data analysis 4.1. Profile of survey respondents Table 1 provides the profile of survey respondents. The 321 re­ spondents consisted of 187 males (58.3%) and 134 females (41.7%). Among the respondents, most were in their 20 s and the average age of the respondents was 35.06 years. With regard to the respondents’ 6

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Table. 3 Descriptive statistics and associated measures. Mean (SD) (1) Novelty seeking (2) Eagerness (3) Vigilance (4) Openness (5) Quality experience seeking (6) Hedonic experience seeking (7) Venturesomeness (8) Social distinctiveness (9) Attitude (10) Intentions to use (11) Willingness to pay more

5.12 (1.14) 4.56 (1.40) 5.51 (1.02) 5.40 (1.01) 4.69 (0.99) 5.35 (1.06) 5.40 (1.02) 3.76 (1.44) 4.79 (1.41) 4.54 (1.40) 3.25 (1.62)

AVE .827 .864 .712 .700 .786 .873 .679 .751 .855 .887 .936

(1)

(2) a

.935 .548c .152 .334 .248 .415 .299 .089 .213 .293 .062

b

.740 .950 .118 .392 .156 .233 .297 .075 .074 .105 .048

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

.390 .344 .881 .501 .100 .116 .258 .002 .010 .033 .013

.578 .626 .708 .875 .173 .312 .421 .007 .056 .097 .004

.498 .395 .316 .416 .916 .246 .077 .138 .188 .235 .101

.644 .483 .341 .559 .496 .954 .299 .110 .248 .307 035

.547 .545 .508 .649 .277 .547 .864 .045 .043 .100 .011

.299 .273 − 0.040 .086 .371 .331 .211 .900 .125 .130 .149

.462 .272 .101 .236 .434 .498 .207 .354 .947 .635 .234

.541 .324 .183 .312 .485 .554 .316 .360 .797 .959 .289

.249 .220 − 0.115 .065 .318 .187 .106 .386 .484 .538 .978

Notes 1: SD = Standard Deviation, 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. Table. 4 Standardized parameter estimates for the structural model. Standardized Estimate

t-value

Hypothesis

H1 Novelty seeking → Attitude .345 3.778* Supported H2 Eagerness → Attitude − 0.105 − 1.771 Not supported H3 Vigilance → Attitude − 0.093 − 1.126 Not supported H4 Openness → Attitude .010 .091 Not supported H5 Quality experience seeking → Attitude .190 2.988* Supported H6 Hedonic experience seeking → Attitude .295 3.936* Supported H7 Venturesomeness → Attitude − 0.084 − 1.088 Not supported H8 Social distinctiveness → Attitude .155 2.706* Supported H9 Attitude → Intentions to use .815 18.579* Supported H10 Attitude → Willingness to pay more .506 9.617* Supported Goodness-of-fit statistics: χ2 = 1080.849, df = 457, χ2/df = 2.365, p < .001, NFI = 0.908, IFI = 0.944, CFI = 0.944, TLI = 0.935, RMSEA = 0.065

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.

monthly household income, the largest category was between $2001 and $3000 (n = 74, 23.1%). Among the participants, 56.4% (n = 181) were single, while 42.17% (n = 137) were married. In terms of educa­ tion level, 59.5% (n = 191) held a bachelor’s degree. Overall, the samples were collected as evenly as possible from the age cohorts, but there were more young people in their 20 s and 30 s. This is believed to be due to the online data collection method and the background of this study.

hypotheses. The structural model was found to have a suitable fit the data (χ2 = 1080.849, df = 457, χ2/df = 2.365, p < .001, NFI = 0.908, IFI = 0.944, CFI = 0.944, TLI = 0.935, RMSEA = 0.065) (Byrne, 2001). The SEM results showed 6 out of 10 hypotheses were statistically supported. More specifically, novelty seeking (β = 0.345, p < .05), quality experience seeking (β = 0.190, p < .05), hedonic experience seeking (β = 0.295, p < .05), and social distinctiveness (β = 0.155, p < .05) had a positive influence on attitude. Hence, Hypotheses 1, 5, 6, and 8 were supported. However, contrary to expectation, eagerness (β = − 0.105, p > .05), vigilance (β = − 0.093, p > .05), openness (β = 0.010, p > .05), and venturesomeness (β = − 0.084, p > .05) had no influence on atti­ tude. Thus, Hypotheses 2, 3, 4, and 7 were not supported. In addition, attitude played an important role in the formation of intentions to use (β = 0.815, p < .05) and willingness to pay more (β = 0.506, p < .05), which supports Hypotheses 9 and 10.

4.2. Confirmatory factor analysis A confirmatory factor analysis (CFA) was conducted in order to evaluate the adequacy of construct measures (see Table 2). The CFA results showed that the measurement structure of the proposed theo­ retical model included a satisfactory fit to the data (χ2 = 969.870, df = 440, χ2/df = 2.204, p < .001, NFI = 0.917, IFI = 0.953, CFI = 0.953, TLI = 0.943, RMSEA = 0.061) (Byrne, 2001). The values of all factor loadings exceeded 0.751 and were all significant at the 0.001 level. All values for composite reliability were higher than the minimum threshold of 0.70 (Hair et al., 2006), suggesting that multiple mea­ surement items used in this study had high levels of internal consistency. Then convergent and discriminant validities were checked. As shown in Table 3, average variance extracted (AVE) values ranged from 0.679 to 0.936. Furthermore, these values were greater than the squared corre­ lations between constructs. Thus, the convergent and discriminant val­ idities were statistically supported.

5. Discussion and implications The purpose of this paper was to examine the importance of con­ sumer innovativeness in the context of drone food delivery services. To be specific, a review of the extant literature proposed that eight subdimensions of consumer innovativeness (i.e. novelty seeking, eager­ ness, vigilance, openness, quality experience seeking, hedonic experi­ ence seeking, venturesomeness, and social distinctiveness) play a critical role in the formation of attitude toward using drone food delivery ser­ vices. In addition, it was hypothesized that the attitude positively affects two types of behavioral intentions, such as intentions to use and will­ ingness to pay more. A conceptual model was developed based on the proposed relationships among constructs and the model was assessed based on empirical data collected from 321 respondents in Korea. The results of the data analysis have the following significant theoretical and managerial implications.

4.3. Structural equation modeling The results of structural equation modeling (SEM) are summarized in Table 4 and Fig. 2. After checking the appropriateness of the measure­ ment structure, the SEM was conducted in order to test the 10 7

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Fig. 2. Standardized theoretical path coefficients.

5.1. Theoretical implications

attitude is influenced by hedonically and socially motivated customer innovativeness. Among the theoretical contributions of this paper is that it found the important role of consumer innovativeness in the formation of attitude as mentioned above. The results of this study are similar to previous studies (e.g. Jin and Suh, 2005; Limayem et al., 2000; Truong, 2013). For example, Jin and Suh (2005) found that consumer innovativeness plays an important role in the formation of consumer attitude toward new technologies. In addition, Truong (2013) also suggested that con­ sumer innovativeness is a critical predictor of consumer attitude. From a theoretical point of view, this study revealed the importance of con­ sumer innovativeness in the field of drone food delivery services for the first time. More importantly, according to Bacharach (1989), it is sig­ nificant to apply and expand the existing theoretical relationship to the new field. Likewise, this study has a significant theoretical implication since it validated the relationship between consumer innovativeness and attitude in the context of drone food delivery services which currently draw an exceptional attention as a possible solution in many aspects. Contrary to our expectations, eagerness, vigilance, openness, and venturesomeness did not statistically affect attitude toward using drone food delivery services. It can be inferred that the findings are related to perceived risks. The concept of perceived risks can be defined as “the nature and amount of risk perceived by a consumer in contemplating a particular purchase decision” (Cox and Rich, 1964, p. 33). That is, if consumers lose more than the benefits of using drone food delivery services, they may be reluctant to use the services. In particular,

First, the data analysis results revealed that four sub-dimensions of consumer innovativeness are important predictors of attitude toward using drone food delivery services. More specifically, these results can be interpreted as follows. Novelty seeking had a positive influence on attitude toward using drone food delivery services (β = 0.345, p < .05), suggesting that if consumers like to try new products, they would have a favorable attitude toward the services. Quality experience seeking also positively affected attitude toward using drone food delivery services (β = 0.190, p < .05). That is, consumers who often consider buying prod­ ucts that are more functional and effective than the current options are more likely to have a good attitude toward using the services. Thus, our result is consistent with previous studies that displayed the close asso­ ciation between the consumer innovativeness that is driven by the practical aspect and the attitude (Frimpong et al., 2017). In addition, the findings showed the positive relationship between hedonic experience seeking and attitude toward using drone food delivery services (β = 0.295, p < .05), indicating that consumers with a tendency to acquire new products because of a sense of personal enjoyment have a positive attitude toward the services. Social distinctiveness was found to bear a significant impact on attitude toward using drone food delivery services (β = 0.155, p < .05), which suggested that when consumers tend to buy new products to impress others, they are more likely to have a favorable attitude toward using the services. These results echoed a prior finding in the study conducted by Cha (2020) that confirmed the individuals’ 8

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delivered. Fourth, Hypothesis 8, which proposed the relationship between so­ cial distinctiveness and attitude toward using drone food delivery ser­ vices, was statistically supported, so it is necessary to food service companies to emphasize a positive image of using drone food delivery services which can impress others. Current delivery services, such as motorcycles and automobiles are known to cause environmental pollu­ tion. On the other hand, drone food delivery services have an ecofriendly image because it is operated by electricity. Therefore, food companies should appeal the eco-friendly image of drone food delivery services. By doing so, consumers are more likely to use drone food de­ livery services. Lastly, this study found that attitude is an important factor affecting behavioral intentions including intentions to use and willingness to pay more. It is widely known that advertising is an important factor in making consumers have positive attitude towards products (Saleem and Abideen, 2011), so food companies preparing for drone food delivery services should emphasize the innovation of the services through advertising.

respondents who participated in this study have greater perceived risks because they have not used drone food delivery services. Thus, restau­ rant companies need to emphasize advertising to consumers that can show the excellence of drone food delivery services. By doing so, con­ sumers would have low levels of perceived risks. The current study paid attention to the facilitating factors of the individuals’ adoption behavior. However, the consumers’ concerns and the risk perceptions toward the emerging technologies have often been discussed, since they affect the technology acceptance behavior (Nakamura, and Kajikawa, 2018; Wright et al., 2014). Also, our results imply the possible risks that in­ fluence the consumers’ response, and it would thereby be meaningful to assess the potential barriers to better predicting the formation of the consumers’ acceptance behavior toward drone food delivery services. Another significant finding of this study was to identify the effect of attitude toward using drone food delivery services on outcome variables in the context of drone food delivery services. More specifically, the results of the data analysis indicated that attitude toward using drone food delivery services had a positive influence on intentions to use (β = 0.815, p < .05) and willingness to pay more (β = 0.506, p < .05). It can be interpreted that when consumers have a favorable attitude toward using drone food delivery services, they are more likely to use the ser­ vices and pay more for the services when ordering food. According to the TPB theory, attitude is a critical factor affecting behavioral intentions (Ajzen, 1991). Furthermore, prior research has also verified the impor­ tant role of attitude in the formation of behavioral intentions (e.g. Fu and Elliott, 2013; Hung et al., 2006; Wu and Ke, 2015). Although this study showed that the results are consistent with existing theories, this study found the important role of attitude in the context of drone food delivery services for the first time.

6. Limitations and future research The results of this study must be considered in light of the following limitations. First, this study focused only on consumer behaviors in the context of drone food delivery services. However, consumer behaviors relating to other industries can be different, so it is somewhat difficult when trying to generalize the results of this study to other industries. Second, this study has limitations in relation to generalizability because data were collected only in Korea. Thus, it is recommended to test the cross-cultural validity of our model. Third, this study used a convenience sampling method using an online survey in order to collect data. Although the method is widely used in consumer behavior research, it can cause selection biases (Bethlehem, 2010). Thus, future research needs to use other forms of data collection to overcome the biases. Fourth, regulations are necessary to operate drone food delivery ser­ vices, such as the weight of food, accident during shopping, and altitude restrictions. Lastly, this study employed 24 measurement items of con­ sumer innovativeness, which were generated from validated measure­ ment items, adapted from other fields. In other words, they are not developed for drone food delivery services, which can lead a relatively low acceptance rate of hypotheses. Thus, future research should be careful in applying the measurement item of the consumer innovation. The current study paid attention on facilitating factors of individuals’ adoption behavior. However, consumers’ concerns and risk perceptions toward emerging technologies have been often discussed since they affect technology acceptance behavior (Nakamura, and Kajikawa, 2018; Wright et al., 2014) and also our results imply the possible risks influ­ encing consumers’ response. And thereby it would be meaningful to assess potential barriers in better predicting the formation of consumers’ acceptance behavior toward drone food delivery services.

5.2. Managerial implications First, this study found that novelty seeking positively affects attitude toward using drone food delivery services (Hypothesis 1). This finding includes the following significant managerial implications. Above all, it is necessary to emphasize that drone food delivery services are a new technology that has not existed before. For example, it would be better if food service companies advertise that consumers can use their smart­ phones to see where their food is in transit. In addition, food delivery is known as a very dangerous job. In fact, about 1500 are killed or injured during delivery each year in Korea (Asia Economy, 2017). Therefore, if food service companies emphasize that drone food delivery services are a new technology that can reduce the number of casualties, then con­ sumers are more likely to have a favorable attitude toward using the services. Second, the data analysis result revealed the effect of quality expe­ rience seeking on attitude toward using drone food delivery services (Hypothesis 5), so it is important to emphasize that drone food delivery services outperform current delivery services. In fact, the current de­ livery services, such as motorcycles or cars, deliver food late to cus­ tomers when traffic is congested. Drone food delivery services are not affected by this problem because they deliver food in the air. In addition, drone food delivery services allow consumers to order food in difficult to access areas such as mountains and valleys, so food service companies need to appeal to this aspect, which will enhance a favorable consumer attitude toward using the services. Third, the result of the data analysis indicated that hedonic experi­ ence seeking plays an important role in the formation of attitude toward using drone food delivery services (Hypothesis 6). Thus, it is important to emphasize that it is fun to use drone food delivery services for con­ sumers. For example, consumers tend to avoid new technologies if learning new technologies are difficult and time consuming. Therefore, food service companies should provide fun videos that make it easy for consumers to use drone food delivery services. In addition, consumers will have a high level of hedonic experience if food service companies use drones’ cameras to provide consumers with a view of the food being

Author statement 1 As the first author, Jinsoo Hwang made the following contributions to this study: development of research model, methodology, data collection and discussions and implications 2 As the second author, Jinkyung Jenny Kim developed to improve this study. 3 As the third (corresponding) author, Lee, Kwang-Woo made the following contributions to this study: introduction and literature review. CRediT authorship contribution statement Jinsoo Hwang: Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Resources, Software, Supervision, 9

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Fig. A.1. Screenshot from videos. Source: Yogiyo (2018).

Validation, Visualization, Writing - original draft, Writing - review & editing. Jinkyung Jenny Kim: Writing - original draft, Writing - review & editing. Kwang-Woo Lee: Data curation, Funding acquisition, Investigation, Project administration, Validation, Visualization, Writing - original draft, Writing - review & editing.

Acknowledgement This publication was supported by Daegu University Research Grants in 2017.

Appendix 1 Fig. A.1 Appendix 2. The questionnaire 1. Please read each item carefully and circle the appropriate number which best reflects your true opinions or feelings.

I like to try new products. I enjoy trying unusual products. I like purchasing novel products. I am passionate about trying new products. I am eager to find out about new products. I am enthusiastic about buying new products. I make careful decisions about what I want to buy. I do extensive research before acquiring new products. I do not make unplanned decisions when buying new products. I am open to a variety of product options. I prefer to have many alternatives when deciding what to buy. I would like to experience new products of different kinds. If a new product is more functional than existing products, I usually buy it. If the product I have does not work well enough, I try to buy a new product. I often consider buying products that are more effective than the current options. Using new products gives me a sense of personal enjoyment. Acquiring new products makes me happier. I feel good when using new products. I cope well with risks associated with trying new products. I am fine with the uncertainty of using new products. I anticipate uncertainty when using new products. It is necessary to buy new products to impress others. I enjoy using new products that make me a visionary leader. Using new products makes me a trendsetter.

Strongly Disagree

→

Neutral

→

Strongly Agree

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3

4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4

5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5

6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6

2. Please read each item carefully and circle the appropriate number which best reflects your true opinions or feelings

Strongly Disagree → Neutral → Strongly Agree Attitude toward using drone food delivery services. I enjoy trying unusual products. I like purchasing novel products. I am passionate about trying new products.

1 1 1

2 2 2

3 3 3

10

4 4 4

5 5 5

6 6 6

7 7 7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

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3. Please read each item carefully and circle the appropriate number which best reflects your true opinions or feelings.

Strongly Disagree → Neutral → Strongly Agree 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.

1 1 1

2 2 2

3 3 3

4 4 4

5 5 5

6 6 6

7 7 7

4. Please read each item carefully and circle the appropriate number which best reflects your true opinions or feelings.

Strongly Disagree → Neutral → Strongly Agree I am likely to pay more for drone food delivery services. It is acceptable to pay more for drone food delivery services. I am likely to spend extra in order to use drone food delivery services.

1 1 1

2 2 2

References

3 3 3

4 4 4

5 5 5

6 6 6

7 7 7

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Psychol. 12 (1), 1–13. ... & Wright, D., Finn, R., Gellert, R., Gutwirth, S., Schütz, P., Friedewald, M., Mordini, E., 2014. Ethical dilemma scenarios and emerging technologies. Technol Forecast Soc Change 87, 325–336. Wu, W.Y., Ke, C.C., 2015. An online shopping behavior model integrating personality traits, perceived risk, and technology acceptance. Soc. Behav. Pers. 43 (1), 85–97. Yogiyo (2018). Korea’s very first official drone food delivery test, Retrieved from https://www.youtube.com/watch?v=-BxAqGSgs1Y. Zeithaml, V.A., Berry, L.L., Parasuraman, A., 1996. The behavioral consequences of service quality. J. Mark. 31–46. Jinsoo Hwang is an associate professor in the College of Hospitality and Tourism Man­ agement at Sejong University, Korea (http://home.sejong.ac.kr/~jhwang/). Dr. Hwang has published in various professional journals. He specializes in hospitality and tourism marketing. Jinkyung Jenny Kim is an assistant professor in the School of Hotel and Tourism Man­ agementat Youngsan University, Korea. Dr. Kim has published in top journals in the hospitality and tourism fields. She specializes in hotel and restaurant management. Kwang-Woo Lee is associate professor in the Department of Tourism Management at the Daegu University in South Korea. His research interests are on the related areas of strategic management; e-marketing, and customer relationship marketing.

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