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As food service industry customers are notoriously fickle, the industry has to keep up with changes in taste, fashion, and ease of access. Technology assists in this process, and with the dramatic growth of wireless communication technology and the high penetration rate of the Internet, food service businesses now rely on technology as a major information resource and marketing tool (Bickerton, 2015). The proliferation of smartphones has exacerbated this trend, as they provide for the real-time connectivity of mobile apps, making food delivery apps popular with busy diners in pursuit of speed and convenience. As food delivery apps have increased in popularity, the competitive dynamics of the food delivery market have increased (S.-W. Jeong, 2016), particularly in Korea, because delivery service is deeply embedded in Korean society (S.-W. Jeong, 2016). Demand for this service exploded from the 1990s onwards, resulting in the market reaching 12 trillion won (US$10.3 billion; Bae, 2015). When food delivery apps appeared in early 2010, their market size was estimated to be 1.7 trillion won (US$1.4 billion), in 2015 and this extended to an estimated 2 trillion won (US$1.7 billion) in 2015 (Bae, 2015).

With accelerated competition in the food service industry and the popularity of food delivery apps, it is useful to have an understanding of the factors that entice consumers to use these apps. Therefore, we used the extended technology acceptance model (TAM) to investigate the determinants that either drive or impede user intention to use food delivery apps. It is indicated in this model that system use will be based on perceived ease of use and perceived usefulness.

Literature Review and Development of Hypotheses


In an environment dominated by the Internet, customers' purchasing decisions may be determined by the perceived quality of information (M. Jeong & Lambert, 2001). Ahn, Ryu, and Han (2004) stated that information quality, system quality, and service quality are variables that directly affect the perceived ease of use and perceived usefulness of a technology. Rese, Schreiber, and Baier (2014) found that, of these, information quality is the primary concern for online customers. They surveyed IKEA's mobile app users and showed that when participants were provided with an augmented reality app, the perceived informative nature of the app influenced its perceived usefulness.

Previous researchers have categorized information that influences consumers into user-generated (Pavlou & Dimoka, 2006) and firm-generated (Cheung, Lee, & Rabjohn, 2008; Dellarocas, Zhang, & Awad, 2007; Z. Liu & Park, 2015) types. When a product is purchased online, asymmetric information possessed by the buyer and seller will eventually lead to the exposure of additional risk to the customer (Pavlou & Dimoka, 2006). This happens when the buyer cannot physically check the product and must, thus, rely on possibly inaccurate or insufficient information provided by the seller (H. G. Lee, 1998). Because of the uncertainty of the quality of a product in an online environment, consumers obtain trust and credibility from consumer reviews (Z. Liu & Park, 2015). This reduces the asymmetry of the information (Cheung et al., 2008). Higher ratings and more reviews of products lead to more sales and encourage the buyer's decision (Dellarocas, Zhang, & Awad, 2007). However, regardless of whether the reviews are positive or negative, consumers perceive them as providing useful information (Purnawirawan, De Pelsmacker, & Dens, 2012). Buyers' negative information is read more carefully than positive information and buyers perceive these posts to be more useful (Ito, Larsen, Smith, & Cacioppo, 1998). Therefore, we proposed the following hypotheses:

Hypothesis 1: User-generated information will have a positive effect on the perceived usefulness of a food delivery app.

Hypothesis 2: Firm-generated information will have a positive effect on the perceived usefulness of a food delivery app.

System Quality

Several researchers have noted that system quality is an external variable in the TAM. Ahn et al. (2004) showed that the system quality provided by online stores strongly influenced customers' perceived usefulness and perceived ease of use. Celik and Yilmaz (2011) used the TAM to analyze the e-commerce of online stores in Turkey and found that of five external variables (information quality, service quality, system quality, trust, and enjoyment), system quality affected both perceived ease of use and perceived usefulness. Using the TAM, Sternad and Bobek (2013) carried out an analysis of a company's enterprise resource planning (ERP) system. They concluded that system quality and its technological characteristics improved the perceived ease of use, which had a further positive effect on the attitude toward the ERP system and its perceived usefulness. This demonstrates that system quality is an important external variable in the TAM. Thus, we proposed the following hypotheses:

Hypothesis 3a: System quality will have a positive effect on the perceived usefulness of a food delivery app.

Hypothesis 3b: System quality will have a positive effect on the perceived ease of use of a food delivery app.

Design Quality

Design quality is another exogenous variable that has a strong effect in the application of the TAM. Pei, Zhenxiang, and Chunping (2007), when targeting Chinese business-to-consumer websites, extended the TAM to measure website design effectiveness. They showed that design quality had a positive effect on both perceived usefulness and perceived ease of use. In their study on e-shopping purchase and intention, Ha and Stoel (2009) integrated e-shopping quality (website design, customer service, privacy/security, and atmospheric/ experiential), enjoyment, and trust into the TAM to examine consumer acceptance of e-shopping. They showed that e-shopping quality, particularly website design, had a direct effect on perceived ease of use. This indicates that design quality plays a prominent role as an antecedent of perceived ease of use.

I.-F. Liu, Chen, Sun, Wible, and Kuo (2010) emphasized the importance of design when they extended the TAM to explore the factors that affect intention to use an online learning community. Because online education is web-based, the design of the course has a significant effect on students' success or failure. I.-F. Liu et al. categorized design into online course and user interface design, and found that both categories directly influenced perceived ease of use. Therefore, we proposed the following hypothesis:

Hypothesis 4: Design quality will have a positive effect on the perceived ease of use of a food delivery app.

Technology Acceptance Model

Davis (1986) developed the TAM, which is the most widely applied model of consumer acceptance and use of information technology (Venkatesh, 2000). It is posited in the TAM that behavioral intention determines actual system use, which is then influenced by the user's attitude toward using the system. Users' beliefs about the system affect their attitude, such as its perceived usefulness and perceived ease of use.

Several researchers have extended the applicability of the TAM by adding external variables, so as to influence the external features of users' attitude, behavioral intention, and actual use of technology (Gefen & Straub, 2000; Pikkarainen, Pikkarainen, Karjaluoto, & Pahnila, 2004). Using Master of Business Administration students at a U.S. university as their participants, Gefen and Straub (2000) explored the effect of perceived ease of use and perceived usefulness on e-commerce adoption. They found that perceived usefulness affected intended use when a website was used for a purchasing task, yet perceived ease of use had only an indirect effect on online shopping behavior by directly influencing perceived usefulness. Pikkarainen et al. (2004) found that perceived usefulness and information on online banking on a banking website were the key features influencing online banking acceptance.

Because e-commerce has been expanded to mobile devices, the TAM has been adapted for this new category of mobile commerce. For example, Lopez-Nicolas, Molina-Castillo, and Bouwman (2008) used the extended TAM and 3G mobile technology to examine the demand for mobile services. They found that attitude toward mobile innovation and its perceived flexibility benefits directly influenced perceived usefulness, and social influence factors directly influenced perceived usefulness and perceived ease of use. Jayasingh and Eze (2010) likewise presented a theoretical extension of the extended TAM to explore consumer adoption of mobile coupons and found that perceived usefulness and perceived ease of use influenced attitude, which in turn influenced intention to use mobile coupons.

In their study of American college students, Yang and Zhou (2011) applied the theory of planned behavior (TPB) and the TAM to examine mobile viral marketing attitude, intention, and behavior. They suggested that subjective norm, behavioral control, and perceived cost for the young American consumers strongly influenced their attitude toward viral marketing. Likewise, Abadi, Ranjbarian, and Zade (2012) combined the TPB and TAM in their mobile banking study. They showed that perceived risk excluded behavioral intention, thus leading to a meaningful result concerning the extension of the TAM to a mobile. Choi and Totten (2012) examined the effect of cultural variance in mobile television acceptance, by adding factors to the TAM, such as individual-level cultural orientation, interdependence, and independence. Choi and Totten concluded that adding external variables to the TAM could create a more practical model of mobile television technology. Therefore, we proposed the following hypotheses:

Hypothesis 5a: Perceived ease of use will have a positive effect on the perceived usefulness of mobile delivery apps.

Hypothesis 5b: Perceived ease of use will have a positive effect on attitude toward the use of mobile delivery apps.

Hypothesis 6: Perceived usefulness will have a positive effect on attitude toward the use of mobile delivery apps.

Hypothesis 7: Attitude toward the use of mobile delivery apps will have a positive effect on intention to use mobile delivery apps.


Participants and Procedure

As Internet-based surveys provide benefits such as access to a specific population (Wright, 2005), we used a qualified online research firm in Korea to conduct an online survey to collect data. With the help of this firm, we distributed an electronic self-report questionnaire from March 3-28, 2015 to 395 individuals who had purchased food through food delivery apps. After we had eliminated those that showed unusual patterns in the reply (such as repeating the same numbers throughout the questionnaire), 350 valid questionnaires (88.6%) remained.

Of the participants, 54.3% (n = 190) were men and 45.7% were women (n = 160), and they ranged in age from 20 to 59 years. In Korea, as few teens have sufficient economic status to use food delivery apps and few aged over 60 years choose to use them, we excluded these groups. In terms of app use per month to order food, 47.8% (n = 167) used them 1-2 times, 25.1% (n = 88) used them 3-4 times, and 27.1% (n = 95) used them 5 or more times.


Valid and reliable measurement variables were adapted from prior studies, with modifications made to fit the context of our conceptual model. All measurement items were rated on a 5-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree. We developed the questionnaire in English, and translated it into Korean. To verify the accuracy of the translation, we asked native Koreans who were fluent in English to back-translate the Korean questionnaire into English. We then compared the two versions of the original questionnaire and verified the absence of errors.

User-generated information. User-generated information refers to review information uploaded by delivery app users after using the service. We measured this information with a scale developed by D. H. Park, Lee, and Han (2007) that consisted of five items to assess usefulness, sufficiency, understandability, and reliability of information. Cronbach's [alpha] coefficient was .81 in this study.

Firm-generated information. We assessed firm-generated information through restaurant and menu information provided by the delivery app. We measured this information using a scale adapted from Al-Qeisi, Dennis, Alamanos, and Jayawardhena (2014) and Mohammadi (2015) that consisted of five items: usefulness, accuracy, latest menu, understandability of information, and information on the desired menu. Cronbach's [alpha] coefficient was .83 in this study.

System quality. We measured the quality of the application system using four items from Al-Qeisi et al. (2014), Hsu, Chang, and Chen (2012), and Mohammadi (2015): prompt response, easy access, prompt page change, and stable drive. Cronbach's [alpha] coefficient was .80 in this study.

Design quality. We measured the quality of the application design using five items from Al-hawari and Mouakket (2010) and Al-Qeisi et al. (2014): organization, clear design, user friendliness, appropriate colors, and attractiveness. Cronbach's [alpha] coefficient was .85 in this study.

Technology Acceptance Model. We used the four items of perceived usefulness, perceived ease of use, attitude, and intention to use, in the extended TAM.

(a) Perceived usefulness: We adapted five items from Lopez-Nicolas et al. (2008), Choi and Totten (2012), and Yilmaz (2014). A sample item is "The food delivery app is worth using." Cronbach's [alpha] coefficient was .86 in this study.

(b) Perceived ease of use: We used four items generated from Lopez-Nicolas et al. (2008), Al-hawari and Mouakket (2010), and Choi and Totten (2012). A sample item is "The food delivery app is easy to use." Cronbach's [alpha] coefficient was .81 in this study.

(c) Attitude: We used five items adapted from Yu, Ha, Choi, and Rho (2005) and Choi and Totten (2012). A sample item is "The food delivery app brings profit for me." Cronbach's [alpha] coefficient was .87 in this study.

(d)Intention to use: We adapted five items from Yu et al. (2005), Lopez-Nicolas et al. (2008), and Choi and Totten (2012). A sample item is "I am willing to purchase food through the food delivery app." Cronbach's [alpha] coefficient was .90 in this study.

Data Analysis

Prior to conducting structural equation modeling to test the hypotheses, we used AMOS 18.0 to conduct a confirmatory factor analysis (CFA) to evaluate the unidimensionality of the measures. To examine the relationships among the variables, we conducted a correlation analysis with SPSS 18.0.


Identification of Underlying Characteristics

The CFA results showed that all the estimated loadings exceeded .80 and each indicator t value exceeded 9.38 (p < .05). The chi-square ([chi square]) value was 503.52 with the value of Q at 1.60 ([chi square]/df). The goodness-of-fit index (GFI) was .906, the normed fit index (NFI) was .910, the comparative fit index (CFI) was .964, the root mean square residual (RMR) was .023, and the root mean square error of approximation (RMSEA) was .041, indicating acceptable model fit.

The average variance extracted (AVE) exceeding the cut-off threshold level of .50 for constructs (Hair, Black, Babin, Anderson, & Tatham, 2006) indicates sufficient convergent validity, and the composite reliability (CR) exceeding the threshold of .70 indicates sufficient internal consistency and reliability of constructs (Hair et al., 2006). In this study, the AVE of each construct was larger than .50, and the CR value ranged from .81 to .90, indicating sufficient convergent validity.

AVE is the common test of discriminant validity. Hatcher (1994) suggested that the square root of AVE should be greater than the shared variance between the latent constructs. In this study, the square root of AVE of construct pairs exceeded the correlation between the two constructs, demonstrating acceptable discriminant validity (see Table 1).

Hypothesis Testing

We examined the interrelationship between the constructs using a structural model with a covariance matrix. According to the goodness-of-fit indices, the result for the overall fit indices indicated that the proposed structural model provided an acceptable fit to the data, [chi square] = 94.496, df = 8, p < .01, RMSEA = .172, GFI = .938, CFI = .956, IFI = .953, NFI = .949, and RMR = .019.

A summary of the hypothesis testing results is shown in Table 2. The relationships between both user-generated information and perceived ease of use ([beta] = .190, t = 4.08) and firm-generated information and perceived ease of use ([beta] = .204, t = 3.65) were significantly positive. Therefore, Hypotheses 1 and 2 were supported. These results indicate that not only the information the firm provided, but also user-provided information (such as reviews of a product) increased the perceived ease of use of an app.

The result for the link between system quality and perceived usefulness indicates that the system quality of the app was a significantly positive antecedent of perceived usefulness ([beta] = .297, t = 4.82), thus supporting Hypothesis 3a. Further, the relationship between system quality and perceived ease of use ([beta] = .498, t = 8.70) was strongly associated with perceived usefulness, supporting Hypothesis 3b.

The effect of design quality on perceived ease of use was significantly positive ([beta] = .378, t = 6.46), indicating that an increase in design quality may increase perceived ease of use. Hypothesis 4 was, thus, supported.

In regard to the configuration variable of the TAM model, perceived ease of use had a positive effect on both perceived usefulness ([beta] = .355, t = 6.99) and attitude ([beta] = .121, t = 2.15). Hypotheses 5a and 5b were, thus, supported. Perceived usefulness had a positive effect on attitude ([beta] = .712, t = 13.71) and attitude was a strong predictor of intention to use ([beta] = .869, t = 26.82). Therefore, Hypotheses 6 and 7 were supported.


Our results have enhanced understanding of the relational factors that either drive or impede user intention to use food delivery apps, and provide insight for food service industry management to develop strategies for their businesses to remain competitive. First, user-generated information increased perceived usefulness. This result extends the literature on perceived informativeness and perceived usefulness of mobile apps (Rese, Schreiber, & Baier, 2014). When shopping online, consumers have perceived risk concerns about the attributes, exchanges/refunds, and delivery of a product. Consumers, therefore, search for a variety of information to reduce risk, and, thus, consumers' reviews can be a strong indicator of perception of level of risk (H. Lee & Choi, 2003). S. Park and Nicolau (2015) found that consumers judged extreme Further, when the ratings were negative, consumers perceived the information to be more useful. Therefore, administrators of food delivery apps should be aware that negative reviews mean that consumers perceive the apps as more useful, and that, counter-intuitively, negative reviews should not be removed. Further, when the identity of the reviewer is revealed, it strongly influences the usefulness of online reviews (Z. Liu & Park, 2015). This suggests the reviewer's real name, rather than a registered form of identification, such as a username, should be used in online reviews.

Second, information provided by restaurants had a positive effect on an app's perceived usefulness, making it imperative for food delivery apps to have a framework providing precise information to consumers. As food that can be ordered through the app were viewed is mostly popular food consumed daily by Koreans, detailed descriptions of the food are often absent. When selecting apps, however, consumers consider accuracy of information and the number of registered restaurants (S.-K. Park, 2014). The restaurant generally provides information on current delivery apps. Registered restaurant numbers of the three most used mobile delivery apps in Korea are 200,000 (Baedaltong), 40,000 (Yogiyo), and 13,000-14,000 (Baedal Ui Minjok). It may, thus, be challenging to differentiate between information given by each restaurant. However, because our results show that information provided by the app has a direct effect on its perceived usefulness, administrators of delivery apps should make multipronged efforts to deliver accurate information.

Third, system quality also influenced perceived usefulness. Stability of the program is vital because users make their payments online and are more attentive to risk. As consumers must provide personal information there is a risk of transactions being incomplete or inaccurate, which influences their purchase decision (Kim, Ferrin, & Rao, 2008). Therefore, it is important that the app is stable so that the consumer can make a safe payment. The factors that we used to measure system quality contributed to the ease with which the consumer can use the app. Our results show that system quality has a direct effect on perceived ease of use. When a system can be used easily and without concern, consumers perceive the app to be easier to use (Celik & Yilmaz, 2011).

Fourth, design quality had a positive effect on perceived ease of use. Unlike websites, mobile apps are accessed on a small screen, so there needs to be minimal data in the server and data communication process. Factors such as easily perceived font, composition, and color are required. When the app is designed to be user friendly, users find it easier and more comfortable to use (I.-F, Liu et al., 2010).

Finally, perceived ease of use, which was affected by these exogenous variables, had a positive effect on perceived usefulness and attitude toward the use of mobile apps. Perceived usefulness also had a positive effect on attitude toward the use of mobile apps, and its influence was greater than that of perceived ease of use. These results are comparable to previous findings (Ahn et al., 2004; Ha & Stoel, 2009; Rese et al., 2014). When consumers use online shopping malls, their main interest is usefulness (Ahn et al., 2004). These exogenous variables had a similar effect as previously observed on configuration variables of the TAM. This shows that our finding, which is focused on Korea's food delivery apps, can be applied to the TAM in other contexts.

Although our findings have important theoretical and practical implications, there are several limitations to the study. First, although other factors may influence use of the food delivery app, we used four determinants only. Given that apps used in the food delivery market in Korea are getting larger, future researchers need to consider other variables that may affect the key components of the TAM. Second, we examined system quality in regard to how well the app runs on mobile devices. As credit card companies once leaked personal information of 75% of the economically active population in Korea (The Kyunghyang Shinmun, 2014), personal information security is an important issue. Therefore, future researchers of the importance of system quality need to include personal information security. Finally, as the sample was limited to food delivery app users in Korea, the results may not apply to international markets. Future researchers are advised to examine different settings further afield to make a useful contribution to the food service industry literature. 10.2224/sbp.6185


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Kyung Hee Cyber University


Kyung Hee University


Far East University

Eun-Yong Lee, Department of Hotel Management, Kyung Hee Cyber University; Soo-Bum Lee, College of Hotel and Tourism Management, Kyung Hee University; Yu Jung Jennifer Jeon, Department of Hotel and Tourism Management, Far East University.

Correspondence concerning this article should be addressed to Yu Jung Jennifer Jeon, Department of Hotel and Tourism Management, Far East University, 76-32 Daehak-gil, Gamgok-myeon, Eumseong-gun, Chungcheongbuk-do 27601, Republic of Korea. Email:
Table 1. Discriminant Validity, Correlation Coefficient Matrix, and
Square Roots of Average Variance Extracted

                                  M      SD     1       2        3

1. User-generated information    3.42   0.65   1      .704 *   .549 *
2. Firm-generated information    3.59   0.61   .496   1        .666 *
3. System quality                3.68   0.56   .301    .443    1
4. Design quality                3.61   0.55   .328    .496     .474
5. Perceived usefulness          3.66   0.66   .358    .464     .504
6. Perceived ease of use         3.80   0.60   .208    .375     .519
7. Attitude                      3.54   0.70   .298    .378     .348
8. Intention to use              3.57   0.74   .299    .359     .350

                                   4        5        6        7

1. User-generated information    .573 *   .598 *   .456 *   .546 *
2. Firm-generated information    .704 *   .681 *   .612 *   .615 *
3. System quality                .652 *   .710 *   .721 *   .590 *
4. Design quality                1        .621 *   .691 *   .580 *
5. Perceived usefulness           .386    1        .704 *   .715 *
6. Perceived ease of use          .477     .495    1        .580 *
7. Attitude                       .336     .511     .336    1
8. Intention to use               .293     .516     .294     .514


1. User-generated information    .547 *
2. Firm-generated information    .599 *
3. System quality                .592 *
4. Design quality                .541 *
5. Perceived usefulness          .719 *
6. Perceived ease of use         .543 *
7. Attitude                      .717 *
8. Intention to use              1

Note. Numbers under the diagonal represent the squared correlation
coefficient. * p < .05.

Table 2. Estimation and Testing of Hypotheses

Hypotheses   Paths

1            User-generated information
              [right arrow] Perceived usefulness
2            Firm-generated information
              [right arrow] Perceived usefulness
3a           System quality [right arrow] Perceived usefulness
3b           System quality [right arrow] Perceived ease of use
4            Design quality [right arrow] Perceived ease of use
5a           Perceived ease of use
               [right arrow] Perceived usefulness
5b           Perceived usefulness [right arrow] Attitude
6            Perceived ease of use [right arrow] Attitude

Hypotheses   Standardized     t      Result

1                .179       3.92    Supported

2                .188       3.65    Supported

3a               .254       4.82    Supported
3b               .463       8.70    Supported
4                .344       6.46    Supported
5a               .327       6.99    Supported

5b               .105       2.15    Supported
6                .674       13.71   Supported
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Author:Lee, Eun-Yong; Lee, Soo-Bum; Jeon, Yu Jung Jennifer
Publication:Social Behavior and Personality: An International Journal
Article Type:Report
Date:Oct 1, 2017

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