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Abstract

This study investigates the factors influencing restaurant operators’ intention to terminate their relationship with food delivery apps. This research develops a model with key variables based on the loyalty theory and adds variables reflecting the specific context of the food delivery app market. The face-to-face survey of 128 restaurant managers was conducted in South Korea from Oct 1st to Oct 30th, 2020, and the data obtained were analyzed by SEM. The results showed that distrust is a direct antecedent of their intention to terminate. However, there is no significant relationship between dissatisfaction and that intention. Revenue increase is negatively associated with dissatisfaction, whereas exposure opportunities are positively associated with dissatisfaction. The fee paid for food delivery apps, lack of control of delivery service, goal incongruity, and transparency are shown to be the important determinants of distrust. Our findings will enhance the understanding of the food delivery app market in South Korea and the relationship between the food delivery app and the small restaurant. Based on the results, recommendations for prospect research and theoretical and practical implications are presented.

Plain language summary

The number of people using food delivery apps instead of going to a restaurant has increased significantly during Covid-19 pandemic. As food delivery apps become essential to people, partnering with one is not a matter of choice but a requirement for small restaurant operators. However, food delivery apps are not purely advantageous and many small restaurant operators insist that their relationship with these apps is unfair. This study investigates why small restaurant operators intend to terminate their relationship with food delivery apps during Covid-19 pandemic. This research develops a model with key variables based on the loyalty theory and adds variables reflecting the specific context of the food delivery app market. The face-to-face survey of 128 restaurant managers was conducted in South Korea. The results showed that distrust is a direct antecedent of their intention to terminate. However, there is no significant relationship between dissatisfaction and that intention. Revenue increase is negatively associated with dissatisfaction, whereas exposure opportunities are positively associated with it. The fee paid for food delivery apps, lack of control of delivery service, goal incongruity, and transparency are shown to be the important determinants of distrust.

Keywords

food delivery app restaurant operators COVID-19 South Korea mobile platform intention to terminate loyalty theory

Introduction

With the increasing number of one-person households and the development of information and communication technology (ICT), the dining culture is changing. People prefer ordering food to their homes via delivery services to going to a restaurant and are less willing to go outside to eat currently. Instead, food delivery applications (apps) display restaurants that can deliver food home based on location once customers make their choice of food. Orders and payments are also made at once through such apps. According to the Korea Consumer Agency (KCA, 2022), the share of food service transactions on mobile platforms, such as food delivery apps, has grown every year since 2017. Specifically, since the end of January 2020, when COVID-19 cases began to be confirmed in South Korea, the number of people using food delivery services instead of going to a restaurant has increased significantly, and the rate of food delivery apps usage has skyrocketed (J. Kwak, 2020). The monthly payments to major food delivery apps reached $916 million by the end of 2020 in Korea (Yeonhapnews, 2020). As a result of a survey conducted by KCA (2022), 24.4% of 1,950 consumers utilize food delivery apps to order food once a week, while 15.2% use these apps two to three times a week, showing that the use of food delivery apps has become a habitual practice for a significant portion of consumers. In this survey, respondents cited the convenience of a seamless process encompassing searching, ordering, and payment, along with the precautionary measure of avoiding dining out amid the COVID-19 pandemic, as the primary motivations behind their utilization of food delivery apps.

As food delivery apps become essential to people, partnering with one is not a matter of choice but a requirement for restaurant operators; hence, they are increasingly incorporating such apps into their businesses (S. Kim, 2020; Ryu, 2023). However, food delivery apps are not purely advantageous. Many restaurant operators insist that their relationship with these apps is unfair, and thus, as food delivery apps grow, the situation of restaurants worsens (Y. Kim, 2018). For example, as the delivery market has overgrown due to the spread of non-face-to-face culture in the aftermath of the COVID-19, the “era of delivery charge of $10” has become a reality (H. Choi, 2022). As the dependence on delivery has increased, the food delivery fees are soaring. Food delivery fees are more expensive during peak demand times such as lunch and dinner or when the weather turns bad. Due to the sharp rise in food delivery costs, restaurant operators try to share the burden of delivery costs with consumers, and as a result, delivery costs are also a huge burden on consumers (J. Kim, 2022). Restaurant operators are worried as more consumers are complaining of rising delivery prices.

Increasing complaints from restaurants are serious concerns that food delivery apps cannot ignore because they need to retain as many restaurant partners as possible. Additionally, with the expansion of the food delivery app market and the positive outlook on its consistent development, small and medium-sized companies, as well as large ones, are joining this market (Kim et al., 2018). Following this trend, multiple local governments such as Gyeonggi-do Province, Daegu City, and Gunsan City have launched public delivery apps that could reduce the burden on restaurant executives who protest existing apps’ monopolistic market power (K. Choi, 2020; KCA, 2022). This implies that the market for food delivery apps is becoming increasingly competitive, which is unfavorable for existing apps because restaurant operators would then have more choices, possibly leading to termination of the current relationships. With the current operation, it is difficult for food delivery apps to become a sustainable service in the future.

Most studies on the relationship between users and mobile platforms have focused on the relationship between buyers and the platforms. Regarding food delivery apps, past studies are limited to examining the factors affecting customers’ intention to use those apps (Kim & Kim, 2016; E. Y. Lee et al., 2017; X. Li et al., 2016; Muangmee et al., 2021; Yeo et al., 2017), analyzing and determining an appropriate level of commission paid by restaurant operators using the food delivery apps (Shin et al., 2015), examining user perceptions of the quality of a food delivery app (Sun & Park, 2019) and the impact of food delivery apps on restaurants (Gupta, 2019). Research focused on the relationship between restaurant operators and food delivery apps as sellers is insufficient.

Food delivery apps have brought some opportunities and benefits to restaurant owners during the COVID-19 pandemic, but they have also come with significant costs. However, in the food delivery app market, the mutual growth of apps and restaurants is important. They need to have a symbiotic relationship. For the apps, restaurant operators are main customers and source of income. For restaurant executives, food delivery apps provide a brisk marketplace where they can generate revenue. Considering this complementary relationship, developing abiding relationship and growing together is crucial for both the food delivery apps and restaurants. However, the two parties have been in conflict for a few years. Food delivery apps need to understand what problems occur in their relationship and how they should incorporate restaurant management into their organizational strategy. At the same time, restaurants should share their difficulties and requirements with food delivery apps so that both sides can establish an improvement plan.

Thus, the purpose of this paper is to focus on the risks or costs of food delivery apps for restaurant operators and investigate the factors influencing restaurant executives’ intention to terminate their relationship with food delivery apps. To do this, this study focuses on the quality of the relationship and adds variables reflecting the specific context of the food delivery app market. Assuming that restaurants are customers who use the app, this study borrows key concepts, such as satisfaction and trust, from the loyalty theory. This research also introduces the platform specific factors of food delivery apps, including revenue increase, exposure opportunity, fee paid to those apps, lack of control of delivery service, goal incongruity, and transparency, and investigates their relationships with key variables anchoring its theoretical basis on the loyalty theory.

This paper is organized as follows. Section “Literature Review and Research Hypotheses” presents the literature review and research hypotheses. Section “Methodology” explains the research methodology. Section “Results” reports the results. Section “Discussion and Conclusion” describes our conclusions along with some implications and limitations.

Literature Review and Research Hypotheses

Overview of the Food Delivery App Market in South Korea

The first food delivery app service in Korea started in April 2010 when the developer “Stony Kids” created “Baedaltong” app, which enables users to search for and order from restaurants through the telephone based on location and menu (Kim & Kim, 2016). Next, “Baemin” of Woowa Brothers Corp. entered the market in June 2010, and “Yogiyo” of Delivery Hero Korea (DH), a German company, launched the service in August 2012. In 2015, Baedaltong and Yogiyo became a family as DH took over Baedaltong. As Korean Fair Trade Commission (FTC) conditionally approved the merger between DH and Baemin in December 2019, all three food delivery apps are under the management of the same company. In the early stage, food delivery apps served as a link connecting restaurants’ information, which used limited media, such as flyers and pamphlets, to consumers through smartphones (Do, 2015). These apps provided various information about food, order quantity, and reviews of other consumers. Afterwards, food delivery app companies started to operate call centers to connect consumers’ food orders to the delivery company (Do, 2015). As “Yogiyo” utilized a mobile payment system with various discount coupons, cultural gift certificates, mobile payments, and kakao pay in 2013, food delivery app services turned into a form of mobile commerce (Do, 2015).

Currently, three major food delivery apps, Baemin, Yogiyo, and Coupangeats occupy more than 97% of the domestic food delivery app market in South Korea (KCA, 2022), as shown in Table 1. Notably, among them, Baemin dominates the market, holding almost two-thirds of the market share (see Table 1). Relative latecomers, such as Uber eats, Kakaotalk order, and WMPO, have small market shares. It is remarkable that Baemin and Yogiyo have the same ownership, thus DH greatly surpass competitors in market share. The public concern regarding this issue is that the balance would be heavily skewed toward one big foreign company, producing a monopoly in the food delivery app market. Despite several difficulties, the food delivery app market in South Korea has steadily and rapidly grown and expanded. As of 2021, the transaction value of food delivery app services in Korea was approximately KRW 25 trillion (approximately $19.5 billion), accounting for 97.3% of all online food delivery service transactions (KCA, 2022).

Table 1.

Current Situation of the Food Delivery App Market in South Korea.

Food delivery app	Market share	MAU	Net users (duplicate users excluded)	Cumulative downloads
Food delivery app	%	N	Net users (duplicate users excluded)	Cumulative downloads
Baemin	66	20.7 million	15.8 million	Over 54 million
Yogiyo	17.9	8.9 million	3 million	Over 22 million
Coupangeats	13.6	6.6 million	6 million	Over 11.2 million
Others	2.5
Total	100

Note. Statistics are based on Android OS and iOS. The number of MAU (monthly active users) is as of February 2022. Net users are people using only one food delivery app, corresponding to the concept of loyal customers.

The expansion of food delivery apps is related to several temporal social economic changes, such as the spread of smartphones, the increased number of one-person households, and the development of logistics infrastructure. The smartphone use rate by adults in South Korea exceeded 80% in 2014, increasing to more than 97% in 2022 (Gallup Korea, 2022). With the increase of smartphone use, O2O services that connect offline and online have been expanded in a trend that was applied to the food delivery market. According to the survey conducted by Korea Agro-Fisheries and Food Trade Corporation (2016), 84.2% of consumers think that dining life has changed with the spread of smartphones. Moreover, according to Statistics Korea (2020, 2022), one-person households, the primary consumers of food delivery apps, occupied 33.4% of the total population in 2021, and this household structure will be the most common one in 2035. As demand for food delivery apps sharply increases, a new form of logistics that efficiently deploys online platforms and logistical networks appeared to fill the gap between restaurants looking for couriers and consumers looking for delivery food (Ahn, 2019). “Riders” who pick up the food and deliver it to the customer have become an essential workforce in the food delivery app market. To improve the efficiency of delivery, many companies have tried to establish a smart logistics network and platform based on IT technology (Ahn, 2019).

Despite the impressive development of the market, there are also negatives. For example, the profit-making way of food delivery apps is one of the big concerns between them and restaurant operators. This problem is based on the properties of the market structure and the relationships that food delivery apps have with consumers and restaurants. The app service is part of a two-sided market. In a conventional market, which is one-sided, customers may be attracted by lower prices and higher quality of the food provided by restaurants (S. Li et al., 2010). The relationship between these two subjects is direct and straightforward. However, the two-sided market is different. There are at least two different groups of participants, such as sellers and buyers (Roson, 2005). The value of participating for one group is raised by the other group’s participation because of an indirect network externality (Roson, 2005). In the case of food delivery apps, two groups exist in the market. Restaurant managers are the sellers, and consumers are the buyers. In this context, for restaurants who are joining a food delivery app, the greater the number of consumers in the app, the greater the utility (Evans, 2003). Likewise, consumers have more utility as the number of restaurants partnering with such apps increase. Moreover, food delivery apps generate revenues by charging a fee on participating groups for providing a virtual marketplace where the restaurant executives and customers can be connected according to market logic (K. Kwak, 2018).

However, only restaurant operators food delivery apps demand commissions in the real market. When considering the elasticity of demand, consumers are not willing to pay a cost for just searching for information about the food (K. Kwak, 2018). If the app imposes a fee on customers, they will find alternatives and stop using it. Therefore, customers pay only a delivery charge to the delivery company but do not pay any price for using food delivery apps. However, restaurant executives are willing to pay the app (K. Kwak, 2018). In the one-sided market, restaurant management should work hard and spend a lot of money and time to attract customers through activities, such as advertising and promotion. In contrast, using a food delivery app can significantly reduce the effort and cost of those activities. As mentioned above, the fact that an increasing number of consumers prefer delivery service over visiting a restaurant is another reason. Additionally, the rise in rent, the rise of labor costs due to the increase of minimum wage, and fierce competition due to increasing self-employment have challenged restaurant management to endure with existing store-oriented sales alone. This creates a stronger need of maintaining a healthy relationship with the food delivery app.

In this circumstance, food delivery apps profit from charging restaurants with participation fees, such as subscription, advertising, and other fees for orders and direct payment (K. Kwak, 2018). In contrast, the apps claim zero or even negative participation fees from customers who have a high elasticity of demand or low marginal utility to attract them to such platforms (H. S. Cho, 2016). This demonstrates that the relationship between food delivery apps and restaurant executives is intertwined with more substantial interests. However, such market dynamics provide a reason for conflict between the two parties (Y. Kim, 2020). Many restaurant operators insist that their relationship with food delivery apps is unfair, with excessive advertising fees and sales commissions. Restaurants argue that they are not guaranteed sufficient rights in market transactions. For instance, as Baemin, the dominant food delivery app in South Korea, presented a new fee system converting from a flat rate of $80 to a flat rate of 5.8% from April 2020, most restaurant executives who joined the app strongly resisted, protesting that the commission burden is two to three times higher than it was before applying the new fee system (S. Cho, 2020). As the political world and the public’s sentiment worsen, Baemin withdrew the new fee system plan, at last (Lim, 2020).

However, as food delivery apps have implemented speedier one-order-per-delivery systems since 2021, the issue of food delivery fees has reignited. The food delivery fee reached $10 and became socially controversial (H. Choi, 2022). As of the beginning of 2022, food delivery fees have been raised by $ 0.5 to $1 more due to the soaring labor costs for delivery drivers (J. Kim, 2022). In the capital area, the average figure jumped by $5 to $6 during the pandemic (J. Kim, 2022). In the aftermath of the COVID-19, it is still difficult to eat out, and the time spent at home is prolonged. With heavy reliance on food delivery, more people are ordering, but the problem of delivery fees remains unsolved (Lee & Lee, 2021). In this circumstance, a tectonic shift occurs in the cost structure surrounding food delivery apps. Unable to fully absorb rising delivery costs, restaurant operators have begun sharing the burden with customers. Restaurant operators are worried about customer dissatisfaction and separation, but they are also protesting major food delivery apps that are driving up food delivery costs. In March 2023, Baemin introduced a bundled delivery service with a modest delivery fee of approximately $1, intending to alleviate delivery costs for consumers. Unlike the previous delivery service that targeted a 30-min delivery timeframe, the bundled delivery service boasts a more economical price point, despite an average delivery time ranging from 40 to 50 min. However, consumer discontent has arisen due to the extended delivery times, while restaurant managers face financial losses because the basic fee remains unchanged despite the delivery delays (Yoon, 2023). Several restaurant operators who adopt bundled delivery services have encountered a notable issue wherein the expenses associated with paying Baemin, encompassing diverse fees, VAT, and delivery charges, can exceed 20% of the total order value (Yoon, 2023). Other issues regarding communicative methods, operational policy, service quality, and so on, also constitute problems. In this sense, the current food delivery apps’ monopolistic market power and business policy has become a significant public concern (J. Lee, 2023).

Loyalty Theory and Relationship Quality: Dissatisfaction, Distrust, and Intention to Terminate

Loyalty was considered the essential goal in the relationship marketing field (Henning-Thurau et al., 2002), as it represents either future intentions, or some form of current or past behavior (Johnson et al., 2006). In this context, loyalty has been covered extensively in business literature discussing diverse types of relationships. Significant amount of relationship marketing literature identified that loyalty is determined by relationship quality, including satisfaction, commitment and trust, and relationship quality is determined by service-related factors (Sivapalan et al., 2022; Sureshchandar et al., 2002; Yi, & Gong, 2009). From tangible goods to intangible services, the concept of loyalty has been dealt with in various industry field (Ryu et al., 2014). This study applied loyalty theory to the mobile platform industry, in particular food delivery app market, assuming the restaurant executives will join the food delivery app and repeatedly assess the quality of the relationship with the food delivery app.

As a construct of relationship quality, satisfaction is regarded as one of the most relevant antecedents of loyalty by numerous marketing researchers (Hallowell, 1996; Ryu et al., 2014; J. Stauss & Neuhaus, 1997), and the “satisfaction leads to loyalty” paradigm has dominated the loyalty study field (Lee et al., 2001; Wu, 2011). Regarding customer loyalty, customer satisfaction has been widely used as one of its predictors (Caruana, 2002). In loyalty theory, customers’ satisfaction toward products and services has been identified as the primary step for shaping their loyalty (Koo, 2018; Ryu et al., 2014) since the satisfaction leads customers to make another transaction or repurchase the same product or service, and this behavior corresponds to the concept of loyalty (Sashi, 2012). Many studies have discovered that providing higher satisfaction leads to developing higher loyalty (Hallowell, 1996; Stauss & Neuhaus, 1997). Additionally, loyal customers are more likely to maintain a relationship with the same brand, product, or service than other customers (Koo, 2018), and they are more willing to remain in and support for the service provider which they have a relationship with (Niehoff et al., 2001). Considering this, it can be claimed that developing customers’ satisfaction and loyalty is a crucial task for service providers trying to defeat the competition in the market.

However, it is also crucial to understand how customers react when they are dissatisfied with their relationship with service providers (Colgate & Smith, 2005; Kabadayi, 2016). Dissatisfaction refers to negative disconfirmation, where the perceived performance fails to reach expectations (Chebat et al., 2010). Customers’ evaluation of the quality of the relationship with service providers is a key factor for deciding whether to maintain this relationship or not (Henning-Thurau et al., 2002). Previous studies have discovered that poor relationship quality regarding a failure of core services, unfair price increases, inability to deal with the organization professionally, inadequate customer service, failure to deliver promises, service provider’s inability to help customers overcome problems, rude service personnel, and poor responsiveness, can be a reason for dissatisfaction (Felix, 2015; Srijumpa et al., 2007). According to Panther and Farquhar (2004), customers who encounter an unsatisfactory experience can terminate the relationship with the service provider as a reaction. Halinen and Tähtinen (2002) also argued that business relationships could face terminal ending depending on the level of satisfaction. Assuming that restaurant executives are customers of the food delivery app, when they confront dissatisfying experience with the app, restaurant management may decide to terminate the relationship. Therefore, in this study, the following hypothesis is posed:

H1. Restaurant operators’ dissatisfaction is positively related to the intention to terminate the relationship with the food delivery app.

Trust refers to the willingness of a trusting party (trustor) to maintain a relationship with the trusted party (trustee) based on the expectation that the trustee will perform a reciprocative response or action for the trustor with volition (Mayer et al., 1995). Jambulingam et al. (2009) argued that trust is largely categorized by two dimensions. One is the cognitive dimension which is a belief in the capability of the opponent to accomplish tasks, and the other is the emotional dimension which is a belief in the benevolence and favorable relation of the opponent. According to Mayer et al. (1995), the willingness to take risks despite probability of loss also represents one dimension of trust, which has been regarded as one of the most significant predictors of loyalty by many previous studies (Chaudhuri & Holbrook, 2001; Erdem & Swait, 2004; Koo, 2018; Ryu et al., 2014). These studies have suggested that trust determines the quality of the relationship with other constructs depending on the situation. Customer loyalty research have also considered that customer’s trust for service providers is an essential component of relationship’s quality (Dorsch et al., 1998; Garbarino & Johnson, 1999). When customers think that service providers do what they say they will do, they perceive them as trustworthy partners. Then, customers are more likely to be in a long-term relationship with, and make more investments for, those trustful partners (Doney & Cannon, 1997; Erdem & Swait, 2004). These behaviors are equivalent to the concept of loyalty. The argument that trust has a strong positive relationship to loyalty has been supported by considerable studies (Chaudhuri & Holbrook, 2001; Garbarino & Johnson, 1999; Islam et al., 2021). In this sense, it can be claimed that building customers’ trust and loyalty is important for service providers to hold a lead in the market.

However, in this context, it is also crucial to understand customers’ reaction when they distrust service providers. When the latter act contrary to customers’ expectations that service providers will do their best for customers and behave with integrity, it inspires distrust in customers. Distrust leads customers to lower the dedication toward maintaining the relationship that they have with service providers (Bendapudi & Berry, 1997). Numerous studies have also suggested that distrust is a significant variable in customers’ intention to terminate the current relationship (Chaudhuri & Holbrook, 2001; Erdem & Swait, 2004). Assuming that restaurant operators are customers of the food delivery app, when the former do not believe the app is reliable, they may decide to terminate the relationship with it. So, it can be inferred that higher level of distrust with food delivery apps will positively affect restaurants’ intention to terminate the relationship with it. Thus, the following hypothesis is posed:

H2. Restaurant operators’ distrust is positively related to the intention to terminate the relationship with the food delivery app.

In addition to independently examining dissatisfaction and distrust as two separate predictors of intention to terminate the relationship, this study considers the impact of distrust on dissatisfaction. Previous studies have identified that trust is an antecedent of satisfaction (Chiou & Pan, 2009; Islam et al., 2021; Shahin Sharifi & Rahim Esfidani, 2014). Martez and del Bosque (2013) explained that various components comprising trust can impact individual’s evaluation of organization or a service by influencing one’s satisfaction judgment. Considering that trust and satisfaction are closely related, it will be reasonable to assume that the level of distrust toward food delivery apps will positively affect restaurant executives’ evaluation of dissatisfaction, which in turn will show the indirect impact of distrust on intention to terminate based on dissatisfaction. Hence, the following hypothesis is developed:

H3. Restaurant operators’ distrust is positively related to the dissatisfaction toward the food delivery app.

Platform Specific Factors of Food Delivery App

It is important for researchers to develop industry-specific factors to reflect the reality. Investigating food delivery app-specific factors would be a meaningful contribution to properly understand the needs and goals of restaurants in this growing business. Previous research has highlighted industry-specific factors involving unique characteristics of a certain industry. For example, Ryu et al. (2014) introduced software development kit (SDK) to examine developer’s loyalty to platforms. Therefore, this study would discover and test certain food delivery app-specific variables that could affect restaurant operators’ dissatisfaction and distrust.

In this study, two different aspects of food delivery app platforms were examined. The first aspect centered on the perceived benefits and costs, mainly concerning the technical and functional factors that these apps provide and require from restaurants. The second aspect delved into the relationship dynamics between food delivery apps and restaurant operators. These two aspects were ultimately broken down into a total of six independent variables. Consequently, this research proposes the following four perceived benefits and costs factors, and two perceived relationship factors, that reflect the characteristics of food delivery apps: revenue increase, exposure opportunity, fee paid to food delivery apps, lack of control of delivery service, goal incongruity, and transparency.

Perceived Benefits and Costs Factors

Generating profit is crucial for sustainable business management. As the background of food delivery apps is to create a virtual marketplace where restaurants and consumers can make transactions, it is essential that the apps provide sufficient opportunities for generating profit. According to previous studies, revenue increase is the top reason for restaurants to join food delivery apps (See-Kwong et al., 2017; Shin et al., 2015). In these studies, restaurant managers indicated that joining food delivery apps is helpful for sales increase, as well as wider customer reach. For example, the research by Shin et al. (2015) found that more than half of a total of 108 restaurant executives think that food delivery apps have a positive effect on sales, and two-thirds of them said that the apps help to gather huge number of customers faster and cheaper compared to restaurants’ individual efforts to reach out for each consumer. Relying only on existing store-oriented sales limits supplementary profit because stores have a limited capacity for dining-in, therefore constraining stock turnover (See-Kwong et al., 2017). In this circumstance, food delivery apps can be additional lucrative income stream by carrying out delivery services, making it possible to expand market target to potential diners who do not live or work near the restaurant. In other words, food delivery service helps restaurants overcome the limitations of the number of people they can accommodated, leading to an increased number of orders which ultimately increases sales. Since revenue increase can be regarded as the main direct benefit, fundamental for restaurants to maintain business, it can determine the overall quality of dissatisfaction and distrust toward the food delivery app. It is thus hypothesized that revenue increase will negatively affect restaurant operators’ dissatisfaction and distrust of the food delivery app.

H4a. Expected revenue increase is negatively related to restaurant operators’ dissatisfaction toward the food delivery app.

H4b. Expected revenue increase is negatively related to restaurant operators’ distrust toward the food delivery app.

Joining food delivery apps has a positive effect for restaurant executives in promoting their restaurants. These apps offer various exposure opportunities through the platform and marketing campaigns which can effectively increase the number of customers. Such opportunities are an efficient and quick way for managers to get their restaurants’ name seen by customers. For example, food delivery apps let restaurants on their list be shown in various types of online platforms, such as websites, social media, and even printed materials (See-Kwong et al., 2017). Therefore, once restaurants join the food delivery app, the possibility to be noticed by customers using the apps when looking for delivered food is significantly raised (See-Kwong et al., 2017). Additionally, food delivery apps sometimes issue discounting coupons for the restaurants whose menu is relevant to the app’s marketing theme, which also attracts customers to place an order. Since restaurant executives can considerably lower the promotion burden, exposure opportunity is assumed to negatively affect their dissatisfaction and distrust toward the food delivery app. Thus, following hypotheses are proposed.

H5a. Expected exposure opportunity is negatively related to restaurant operators’ dissatisfaction toward the food delivery app.

H5b. Expected exposure opportunity is negatively related to restaurant operators’ distrust toward the food delivery app.

Fee is the major perceived cost associated with the food delivery app and is defined as a sum of monetary value charged to customers being allowed to use the service. Fee plays an important role for customers and is considered reasonable, or vice versa, depending on the perceived rationality and adequacy of rates. When customers perceive that fees are too high compared to the received services or competitive fees, they are considered excessive (Lin et al., 2012). When customers are dissatisfied with the value provided or perceive the fee to be unreasonable, this perceived high fee becomes the antecedent to their intention to terminate the relationship with service providers (Homburg et al., 2005; Lin et al., 2012). In the food delivery app market, when a restaurant management joins an app, they are charged fees for subscription, advertising, delivery service, online order, and payment system (K. J. Kwak, 2018). In this mechanism, the fee level has become a controversial problem for both the restaurant executives and the food delivery apps (Lee, 2023; Yoon, 2023). According to the research of Shin et al. (2015), more than three quarters of a total of 108 restaurant operators mentioned that the perceived level of satisfaction toward the food delivery app is relatively lower compared to the perceived level of fees they paid. Study by See-Kwong et al. (2017) also discovered that fees have a significant and negative relationship with the restaurant managers’ satisfaction. Therefore, the fee can be regarded as the main cost, critical for restaurant operators to maintain business. It is thus hypothesized that the fee paid to food delivery apps will positively affect restaurant executives’ dissatisfaction and distrust of the food delivery app.

H6a. Fee paid to food delivery apps is positively related to restaurant operators’ dissatisfaction toward the app.

H6b. Fee paid to food delivery apps is positively related to restaurant operators’ distrust toward the app.

Perceived control refers to the level of consideration that people believe they voluntary control the performance of a behavior (Trafimow et al., 2002). When the level of perceived control is high, people believe that the outcome of their behavior is more predictable, thus they feel more comfortable (Neves & Caetano, 2006). Prior research supported this argument by discovering that perceived control is positively associated to satisfaction (C. Lee et al., 1990; Rohe & Stegman, 1994), and trust (Gabay, 2015). It is known that restaurant managers think that they lack control over the food delivery-related works, such as delivery process, food quality, delivery time, and customer satisfaction (See-Kwong et al., 2017; Yoon, 2023). This means that the managers do not have confidence in customers’ experience associated to their order and food. When negative comments and complaints about late deliveries, poor food quality, or bad customer service, which are the remit of the food delivery app, are written on the app, the perceived control of restaurant executives is significantly reduced, and they feel it can further cause a decrease in sales. In this context, lack of control of delivery service could be considered as a significant cost making restaurant managers dissatisfied and distrusting toward the food delivery app. It is thus hypothesized that lack of control of delivery service will positively affect restaurant operators’ dissatisfaction and distrust of the food delivery app.

H7a. Lack of control of delivery service is positively related to restaurant operators’ dissatisfaction toward the food delivery app.

H7b. Lack of control of delivery service is positively related to restaurant operators’ distrust toward the food delivery app.

Perceived Relationship Factors

Goal incongruity refers to the basic difference between the firms regarding their goals and values (Song et al., 2000), and its occurrence is natural since each firm has different backgrounds and founders (Yang et al., 2011). According to Halinen and Tähtinen (2002), a change of policy or management style can also increase the level of goal incongruity, a high level of which can cause dissatisfaction and distrust for several reasons: First, high incongruity level in goals is commonly connected to a high level of open hostility (Pinto et al., 1993; Song et al., 2000). If a company believes that a partner firm’s goals defer from its own, or that the partner firm impedes the work progress toward goals to meet its selfish objectives, the incentive to cooperate decrease (Dyer & Song, 1997; Webb & Hogan, 2002). When the incentive to cooperate is low, less collaborating and more avoiding occur (Song et al., 2000; Thomas, 1976, 1992). Second, high level of goal incongruity reduces the intention to make consensus (Song et al., 2000), which requires a great amount of time, effort, and resources in this situation. Therefore, the company tends to delude itself about the situation rather than try to resolve problems. This bitter relationship, where each firm considers the other unreliable, cannot last long (Halinen & Tähtinen, 2002). Firms with different perspectives will repetitively experience distrustful processes and unsatisfactory outcomes when trying to make transactions and interactions (Yang et al., 2011). This may cause dissatisfaction and distrust of each other and further lead to terminating the relationship (Yang et al., 2011). In the context of the food delivery app market, restaurants and food delivery app companies represent each other with different backgrounds. When restaurant managers believe that the food delivery app’s goals differ greatly from theirs, they will show higher levels of dissatisfaction and distrust. Consequently, the following hypotheses are proposed:

H8a. Goal incongruity is positively related to restaurant operators’ dissatisfaction toward the food delivery app.

H8b. Goal incongruity is positively related to restaurant operators’ distrust toward the food delivery app.

Transparency is defined as “the deliberate attempt to make available all legally releasable information whether positive or negative in nature in a manner that is accurate, timely, balanced, and unequivocal, for the purpose of enhancing the reasoning ability of publics and holding organizations accountable for their actions, policies, and practices” (Rawlins, 2008, p. 74). According to Cotterrell (1999), transparency includes not only information accessibility, but also a process of active participation in acquiring, distributing, and creating knowledge. This concept started to receive attention as many companies’ deceptive practices have been repetitively exposed (Rawlins, 2008). Advanced technologies contributed to the development of tools that can deliver the company’s transparency to consumers, as well (Meyer, 2003). Specifically, the development of the Internet allows consumers to access information about the company and share their information and knowledge with others (Meyer, 2003). This shifts the balance of power toward the customer rather than the company (Rawlins, 2008). Transparency makes the company expose its weakness and areas that need to be reformed (Rawlins, 2008), and concealing these from the customers is almost impossible nowadays. Even though the company feels uncomfortable revealing its weakness and receiving feedback for them, this will eventually motivate it to improve and overcome the situation. If the firm only accepts positive feedback and ignores the situations when something goes wrong, it will repeat the debilitating behaviors and customers would perceive the company lacking integrity. This can be then connected to the corporate crisis.

Transparency is a concept closely related to trust, corporate social responsibility (CSR), and ethics. When the companies have a reputation of integrity, honesty, openness, being ethical, and being concerned with society, that reputation generates bottom-line benefits (Rawlins, 2008). More importantly, firm transparency leads key stakeholders, such as employees, customers, and investors, to increase their trust, satisfaction, and loyalty toward the company (Hon & Grunig, 1999; Ledingham & Bruning, 2000; Rawlins, 2008). This is based on the fact that being transparent allows the company to make two-way symmetrical communication (Ledingham & Bruning, 2000), and this communication strategy helps firms to initiate, develop, maintain, and repair the mutually productive, satisfactory, and trustful relationship between them and stakeholders (Bruning & Ledingham, 2000). Ultimately, this relationship raises loyalty toward the company among key stakeholders (Bruning & Ledingham, 2000). Supporting this, Rawlins (2008) noted that company’s efforts for transparency is composed of traits as participation, substantial information, accountability, and secrecy. In other words, satisfying stakeholders’ need to participate, need for information, need to hold the company accountable, and prevention of attempts at secrecy, are key factors for transparency. In the context of the food delivery app market, restaurant executives are key stakeholders for the food delivery apps. When restaurant managers believe that the food delivery app is not transparent to them, the level of dissatisfaction and distrust will increase. Hence, the following hypotheses are proposed:

H9a. Transparency is negatively related to restaurant operators’ dissatisfaction toward the food delivery app.

H9b. Transparency is negatively related to restaurant operators’ distrust toward the food delivery app.

Our research model is proposed in Figure 1.

Figure 1.

Research model.

Methodology

Data Collection and Analysis

This study employed the face-to-face survey method by distributing questionnaires to current restaurant executives who are joining at least one food delivery app in Seongbukgu and Dongdaemungu, Seoul, in South Korea from Oct 1st to Oct 30th, 2020. The reason face-to-face surveys with personal interviews by a researcher were conducted is that specialized research institutes do not have the list of restaurants which joined food delivery apps. We had to manually create a list of restaurants using food delivery apps, visit restaurants, and persuade their executives to respond to the survey individually. The specific areas were selected as survey locations because these districts are around the big universities (Korea University, Sungshin Women’s University, Hansung University, Kyunghee University, Hankuk University of Foreign Studies), thus there are many one-person households in the age of 20s and 30s who are the primary consumers of food delivery apps (H. Kim, 2019) and restaurants which deliver food through food delivery apps. Considering this, it may be appropriate to set those areas as survey location. Prior to the survey, we conducted searches on Naver Maps to identify restaurants situated near each university, compiling a preliminary list. Naver Maps is a popular mapping and navigation app used frequently in South Korea. Subsequently, we cross-referenced these restaurants on three major food delivery apps. The reason for choosing the abovementioned three food delivery apps is that they are the most representative ones, amounting to the majority of the food delivery app market in South Korea. We then retained only those restaurants that were found on at least one of the three food delivery apps and merged any duplicate entries into a single, comprehensive final list. As a result, a total of 150 restaurants were listed. Considering the particularity of the respondent’s profession, that is, having flexible working hours, the researcher and one assistant were assigned to distribute the printed questionnaires during restaurants’ operation hours, from Oct 1st to Oct 30th, 2020. In detail, two interviewers, including the researcher and one assistant, visited listed restaurants and distributed the questionnaire individually. Generally, the survey took about 15 to 20 min, and respondents read and answered the questions by themselves. However, in the case of respondents struggling with the questionnaire, interviewers read the questions and checked the answer instead. After the survey, respondents were paid 5,000 KRW (about 5 US$). Out of 130 responses, 128 were used after deleting incomplete answers (see Table 2). The survey instrument was constructed based on established measures of constructs from marketing and PR literature, adapted to be applicable to the context of our proposed model. But some of the items were developed and used by the researcher when appropriate references were not available. All items were anchored on a 7-point Likert scale ranging from “1 = strongly disagree” to “7 = strongly agree.”Table A1 in Appendix A shows all items used for the survey. This study adopted structural equation modeling (SEM) using AMOS to analyze the theoretical propositions because of its efficiency for simultaneously testing multi-staged causal relationships (Gefen et al., 2000).

Table 2.

Sample Characteristics (N = 128).

Characteristics	Frequency	Valid percent
Restaurant type
Franchise	66	51.6
Private	61	47.7
Others	1	0.8
Position
Owner	102	79.7
Manager	22	17.2
Others	4	3.1
Number of food delivery apps
1	25	19.5
2	40	31.3
3	40	31.3
>4	23	18.0
Food delivery app (multiple responses)
Baemin	124	37.1
Yogiyo	89	26.6
Coupangeats	71	21.3
Baedaltong	6	1.8
Foodfly	11	3.3
WMPO	12	3.6
Kakaotalk order	5	1.5
Lotteeats	3	0.9
Others	13	3.9
Menu (multiple responses)
Korean food	29	21.3
Korean snack	16	11.8
Café & desert	20	14.7
Japanese food	17	12.5
Chicken	6	4.4
Pizza	4	2.9
Asian & western food	15	11.0
Chinese food	7	5.1
Pork dishes	3	2.2
Steamed dishes & soup	4	2.9
Lunchbox	4	2.9
Fast food	7	5.1
Pub & bar	1	0.7
Others	3	2.2

Sample Characteristics

The profile of the responding restaurant operators is presented in Table 2, which shows the following features: restaurant type, position, the number of food delivery apps that the restaurant joined, currently used food delivery app, and menu. Most respondents (79.7%) were owners using about two to three food delivery apps (62.6%), mainly “Baemin,”“Yogiyo,” and “Coupangeats.”

Preliminary Analysis—Test of Measurement Model

To analyze the measurement model, this study employed a reliability and validity test. The reliability test, which examines the internal consistency within a construct, is performed by Cronbach’s alpha and composite reliability (CR). As shown in Table 3, all constructs show a value above the threshold of 0.7 for both Cronbach’s alpha and CR, adopted by Werts et al. (1974). Convergent validity reflects the extent to which the indicators of a construct are stronger correlated to each other than to indicators of other constructs.

Table 3.

Descriptive Statistics of Variables.

Variable name		Code	No. of items	Mean (std. dev.)	Cronbach’s alpha	AVE	Composite reliability
Endo1	Revenue increase	RI	4	5.15 (1.41)	.926	0.762	.927
Endo2	Exposure opportunity	EO	4	3.47 (1.35)	.868	0.621	.867
Endo3	Fee paid to food delivery apps	FEE	3	5.53 (1.27)	.905	0.781	.914
Endo4	Lack of control	LOC	3	4.30 (1.30)	.719	0.491	.732
Endo5	Goal incongruity	GI	3	4.28 (1.21)	.751	0.506	.752
Endo6	Transparency	TR	3	2.99 (1.16)	.716	0.481	.730
Exo1	Dissatisfaction	DISF	3	3.75 (1.33)	.833	0.656	.850
Exo2	Distrust	DIST	4	4.61 (1.16)	.777	0.466	.776
Exo3	Intention to terminate	INT	3	5.74 (1.37)	.830	0.672	.857
Total items			30

To test convergent validity, CR, factor loading, and average variance extracted (AVE) are examined. It is acceptable for an individual item factor loading to be greater than 0.5, CR to exceed 0.7, and AVE to exceed 0.5 (Hair et al., 2006). The factor loadings of all observed variables or items are usually ranging from 0.573 to 0.983. All other values are above the marginal standard, except for several AVE values, as shown in Table 3. However, according to Fornell and Larcker (1981), we can accept 0.4 as a threshold if CR is higher than 0.6. Thus, the convergent validity of the construct is still adequate.

To test discriminant validity, which shows that measurement items load highly on their theoretically assigned constructs and do not load on other factors, this study examines the table correlation of constructs and latent square root of AVE. To satisfy discriminant validity, the square root of AVE should be greater than the correlations between different constructs (Ryu et al., 2014). As Table 4 presents, the square root of AVE for each construct in this study exceeded the correlations between the construct and other constructs.

Table 4.

Results of Discriminant Validity.

Variables	RI	EO	FEE	LoC	GI	TR	DISF	DIST	INT
RI	(0.873)
EO	.241	(0.788)
FEE	−.078	−.213	(0.884)
LoC	−.118	−.406	.433	(0.701)
GI	−.017	.097	.12	.028	(0.712)
TR	.106	.344	−.492	−.485	.153	(0.693)
DISF	−.295	−.105	.516	.432	.348	−.523	(0.810)
DIST	−.153	−.280	.713	.623	.331	−.627	.767	(0.683)
INT	−.084	−.162	.431	.202	.132	−.258	.258	.464	(0.819)

Note. The number in parentheses is the square root of AVE. The numbers not in the parentheses are correlations.

Results

In this section, we attempt to verify our hypotheses using SEM analysis. Our research model could explain 27.2% of the variance in intention to terminate, 67.2% of the variance in dissatisfaction, and 78.1% of the variance in distrust. Table 5 and Figure 2 show our research model with a brief summary of the results following hypotheses testing; a boot-strapping procedure was used to confirm the significance of the path coefficients. Based on the analysis, seven out of the fifteen hypotheses were supported. Regarding intention to terminate, the conventional belief that dissatisfaction has a significant relationship with loyalty was not confirmed in our research settings (H1 was not supported). However, distrust was significantly related to dissatisfaction and intention to terminate (H2 and H3 were supported). Among food delivery app-specific constructs, revenue increase was significantly related to dissatisfaction (H4a was supported), but exposure opportunity showed the opposite direction for dissatisfaction despite a significant relationship (H5a was not supported). Fee, lack of control of delivery service, goal incongruity, and transparency were not significantly related to dissatisfaction (H6a, H7a, H8a, and H9a were not supported) but had a significant relationship with distrust (H6b, H7b, H8b, and H9b were supported), while the relationships with revenue increase and exposure opportunity were not significant (H4b and H5b were not supported).

Table 5.

Direct Impact of Model: Standardized Regression Weights.

Hypothesis	Relations	Std. estimate	SE	CR	p-Value
H1	Dissatisfaction → Intention to terminate	−0.262	0.172	−1.566	.117
H2	Distrust → Intention to terminate	0.695	0.248	3.876	.000
H3	Distrust → Dissatisfaction	0.634	0.332	2.572	.010
H4a	Revenue increase → Dissatisfaction	−0.208	0.083	−2.849	.004
H4b	Revenue increase → Distrust	−0.064	0.059	−0.910	.363
H5a	Exposure opportunity → Dissatisfaction	0.177	0.086	2.080	.038
H5b	Exposure opportunity → Distrust	0.007	0.063	0.087	.931
H6a	Fee → Dissatisfaction	−0.064	0.137	−0.508	.611
H6b	Fee → Distrust	0.416	0.076	4.378	.000
H7a	Lack of control → Dissatisfaction	0.021	0.130	0.183	.855
H7b	Lack of control → Distrust	0.237	0.086	2.311	.021
H8a	Goal incongruity → Dissatisfaction	0.157	0.194	1.297	.195
H8b	Goal incongruity → Distrust	0.326	0.112	3.458	.000
H9a	Transparency → Dissatisfaction	−0.213	0.176	−1.462	.144
H9b	Transparency → Distrust	−0.349	0.104	−3.013	.003
Goodness of fit analysis (N = 128)		CMIN/DF = 1.452, p = .000, CFI = 0.920, TLI = 0.907, RMSEA = 0.060 SRMR = 0.075

Figure 2.

Research model and result.

Discussion and Conclusion

Key Findings and Implication

This study hypothesized and tested key variables reflecting the loyalty theory and platform-specific factors of food delivery apps. Several key findings can be derived from this study.

First, this study confirms the significant role of distrust in developing disloyalty for food delivery apps while extending the scope of the loyalty research to two-sided mobile platform market. More specifically, the paper shows that distrust is a direct antecedent of restaurant operators’ intention to terminate the relationship with food delivery apps. This finding supports the ones of previous research which also concluded that higher levels of customer distrust will lead to greater customer disloyalty (e.g., Bendapudi & Berry, 1997; Chaudhuri & Holbrook, 2001; Erdem & Swait, 2004).

However, dissatisfaction was not an antecedent of intention to terminate the relationship with food delivery apps. This finding does not support the results of previous studies (e.g., Halinen & Tähtinen, 2002; Panther & Farquhar, 2004) which showed that a business relationship could face terminal ending depending on the level of satisfaction. Thus, we may believe that restaurant managers have little intention to terminate their relationship with the apps when feeling dissatisfied. This result can signify that even though restaurant executives feel dissatisfied, they cannot terminate the relationship with food delivery apps because they lack substantive alternatives. This is associated with the current market situation. The big three food delivery app (i.e., Baemin, Yogiyo, and Coupangeats) occupies more than 97% of the South Korean market (KCA, 2022; S. Lee, 2021) and two of these big three food delivery apps, Baemin and Yogiyo, have the same ownership. Even though Coupangeats has stepped up to rake in market share in the capital area recently (D. Lee, 2021), DH still greatly surpasses competitors in market share in the whole market. Moreover, considering that food delivery apps become essential to their customers, partnering with the food delivery app is a restaurant operator’s livelihood. In this circumstance, it is difficult for restaurant operators to terminate the relationship with food delivery apps for the somewhat emotional reason that the food delivery app’s behavior is unsatisfactory. Moreover, it is practically impossible for restaurant executives to shift to a substantive alternative with a significantly different operating policy from the delivery app currently in use. To break through this situation, the restaurant executives may consider actively cooperating with invigorating a public food delivery app with a low fee burden. In contrast to private food delivery apps that impose fees ranging from 6.8% to 27.0%, public food delivery apps levy a maximum fee of 2.0%, substantially alleviating the fee burden for restaurant operators (KCA, 2022). However, the market share of public food delivery apps is less than 3.0%, which is insignificant compared to private food delivery apps (KCA, 2022). Restaurant operators need to improve their strategies, such as more aggressive promotion and incentives to encourage customers to order food through public delivery apps. In addition, restaurant operators themselves need to become more active participants in public food delivery apps. Due to the nature of food delivery apps with indirect network externalities, both restaurant operators and customers both tend to gravitate toward private food delivery apps that already have a large number of users (Evans, 2003). However, if restaurant operators actively participate in the public food delivery app from a long-term perspective and gradually increase the number of restaurants joining, the number of customers will also increase due to the network effect. The number and variety of restaurants on a food delivery app would be attractive factors for customers when deciding which food delivery app to use. Then, there is a possibility that the public food delivery app will grow as an alternative to confronting the big three food delivery apps.

Meanwhile, distrust has an influence on disloyalty. When restaurant operators think that food delivery apps are not trustworthy, thus demonstrating distrust toward the apps, they are likely to terminate the relationship with them. It signifies that restaurant operators recognize satisfaction and trust as different dimensions and they consider that distrust is more lethal for maintaining the relationship than dissatisfaction. Practically, this result implies the possibility that food delivery apps have more incentive to create a trustful relationship with restaurants when they have some advantage in the market. In other words, the apps could intentionally put more weight on one side of the relationship (i.e., goal congruity and transparency for restaurants) than the other (i.e., providing support for selling and delivering food). Moreover, theoretically this result indicates that the loyalty theory may explain why there is no link between dissatisfaction and intention to terminate. For example, the theory may need to consider the absence of substantive alternatives and lock-in effect when addressing the relationship between dissatisfaction and disloyalty.

Second, as hypothesized, the results support that revenue increase is one of the key determinants of decreasing restaurant managers’ dissatisfaction of the app. The findings support the argument that revenue increase is the top reason for restaurant operators to join food delivery apps (Ryu, 2019; See-Kwong et al., 2017; Shin et al., 2015). Meanwhile, this result implies that providing sufficient opportunities for generating profit is the most important responsibility of food delivery apps. For example, many restaurant executives said that because Baemin has the most users, and therefore the most orders and revenue, they have no choice but to join this app by priority, despite other dissatisfying factors. Moreover, the result shows that the market share of Baedaltong, which once had been considered one of the big three food delivery apps, has significantly declined, with Coupangeats taking its place, as it gains an increasing number of users while Baedaltong does not. Given that the number of users is directly connected to the revenue of restaurants due to the characteristics of the two-sided market, maintaining or securing sufficient users is a vital requirement for food delivery apps. Overall, the study finds that restaurant operators put the highest weight on the revenue increase for their satisfaction with the food delivery app. Previously, food delivery apps enticed customers by providing discount coupons, but the cost of events was transferred to the restaurant operators. However, in light of the findings from this study, it appears imperative for food delivery apps to devise a customer acquisition strategy that does not pass on the burden to the restaurant operators. This approach aims to stabilize both restaurant operators, fostering further growth for the platform.

Third, exposure opportunity was shown to be an antecedent of dissatisfaction, contrary to our prior expectation. The more exposure opportunities, the more dissatisfaction the restaurant management felt. Initially, we predicted that more opportunities for exposure would be a significant factor for decreasing the dissatisfaction level of restaurant executives because they can lower the price paid for promotion. However, the result was the opposite. A plausible reason is that even though restaurant managers joined food delivery apps expecting revenue increase, they prefer to be less well-known outside the neighborhood where the restaurant is located. Accepting orders from locations that surpass a restaurant’s capabilities escalates the probability of longer delivery times, cold food, customer dissatisfaction, and other potential issues, which, in turn, results in a surge of negative reviews. This negative feedback can significantly impact a restaurant’s revenue. Thus, restaurant operators might be dissatisfied with the burden of increased orders and delivery fees for long distance due to exposure effects beyond their operational capabilities.

Fourth, the fee paid for food delivery apps, lack of control of delivery service, goal incongruity, and transparency were shown to be antecedents of distrust. Fee paid for delivery apps showed the largest effect size for distrust levels of restaurant operators. Indeed, according to a survey conducted in May 2019 by the Korea Federation of SMEs, which involved 506 restaurant operators utilizing food delivery apps in Seoul, 14.4% of respondents stated that they had encountered “unfair behaviors” from food delivery apps (Ryu, 2019). Additionally, a significant 37% of these restaurant operators reported experiencing issues with excessive fees (Ryu, 2019). Traditionally, autonomy and trustworthiness were considered key antecedents of trust (Robert & You, 2018; Wiener & Mowen, 1986). Our results also confirm the previous arguments regarding fee paid for food delivery apps and lack of control of food delivery as autonomy, and goal incongruity and transparency as trustworthiness. Furthermore, the results may explain the strong resistance for Baemin’s announcement of new fee system in April 2020 in terms of the importance of fee paid to food delivery apps. For example, when Baemin presented the new fee system changing from a fixed quantity to a fixed rate in April 2020, most restaurant managers who joined the app strongly resisted. This resistance is further connected to the political world and the public’s sentiment deterioration. As Baemin faces pressure from various levels of society, the app eventually raised the white flag and withdrew the new fee system plan (Lim, 2020).

However, as food delivery apps have implemented speedier one-order-per-delivery systems since 2021, the issue of food delivery fees has reignited. One-order-per-delivery systems led to a shortage of delivery drivers, and thus the labor costs have soared. Consequently, it caused an explosion in food delivery costs (J. Kim, 2022). In this circumstance, restaurant operators felt challenged to bear the high delivery cost and eventually began to share this burden with customers. Customers are dissatisfied with the restaurant for the high delivery fees, and the restaurant operators are blaming the food delivery app, protesting that they have no choice. It implies that the relationship between delivery apps, restaurant operators, and customers has started to crack. And recently, the complaints about the commissions of restaurant executives, triggered by Baemin’s bundled delivery service, are wriggling again. At first, food delivery apps played a role of a helper to assist the local restaurant businesses in COVID-19. However, now they are giving anger not only to restaurant operators but also to consumers due to excessive delivery fees. In this operation, it is not easy to maintain a healthy relationship between the food delivery app and the restaurant. Ultimately, it will also damage the sustainability of the food delivery app services. Considering that the fee paid for the apps considerably increases the distrust of restaurant managers and eventually acts as a decisive factor for their intention to terminate the relationship, food delivery apps should have a cautious approach to establishing sustainable fee strategies. Designing fee strategies with a short-term focus on profitability can be fatal to the food delivery app’s business model in the long run.

As one of the initial attempts to study the food delivery app market, this research provides empirical evidence examining how food delivery apps work and the main disincentive for restaurant executives. With deliberate consideration of their relationship with restaurants, food delivery apps will be able to maximize the synergy effect with affiliated restaurants and pursue sustainable development. When the food delivery apps adopt sustainable and socially responsible policies to resolve a conflict with restaurant operators and further customers, they will be able to expand and develop businesses.

Limitation and Future Research

Several limitations and suggestions for future research should be noted. First, there may be a sampling bias, and the sample size may be too small. Because of the COVID-19 pandemic where there was a limit to conduct the face-to-face survey with a large sample, this study used a convenient sampling method with a small number of samples. Therefore, considering the characteristic of convenient sampling of respondents and time, our result needs to be interpreted cautiously. Accordingly, future studies can employ more representative and larger sample. In addition, although this research was conducted in several big university areas, there could also be regional differences among restaurant management in different cities or areas; it would be difficult to generalize our results to the whole country, or other countries.

As one of the initial attempts to study the food delivery app market, this research provides a discussion on various topics. Further analysis of the differences among food delivery apps is needed according to their policies, that is, hiring delivery agent company (Baemin, Yogiyo, Baedaltong) and directly employing riders (Coupangeats) could be studied. As restaurant executives’ major interest is food delivery service and fee, the relationship could be different within a more detailed observation. In addition, investigating the specific perception related to delivery agency fee could be an interesting future research topic.

Footnotes

Appendix A

Table A1.

Measurement Items.

Construct	Item	Reference
Revenue increase	1. (The delivery app(s) currently in use) helped increase the number of orders. 2. (The delivery app(s) currently in use) helped increase the number of customers. 3. (The delivery app(s) currently in use) helped increase the sales. 4. (The delivery app(s) currently in use) helped increase overall revenue.	Developed by the researcher
Exposure opportunity	1. (The delivery app(s) currently in use) provides useful and various resources for marketing. 2. (The delivery app(s) currently in use) provides useful and various opportunities for marketing. 3. (The delivery app(s) currently in use) provides useful and various ideas for marketing. 4. (The delivery app(s) currently in use) provides useful and various advice for marketing.	Developed by the researcher
Fee paid to food delivery apps	1. Fee required by (the delivery app(s) currently in use) is reasonable. 2. Fee required by (the delivery app(s) currently in use) is fair. 3. Fee required by (the delivery app(s) currently in use) is acceptable.	Lin et al. (2012)
Lack of control for delivery service	1. I have enough power in (the delivery app(s) currently in use) to control events that might affect my restaurant. 2. In (the delivery app(s) currently in use), I can prevent negative things (i.e., Negative reviews, complaint, etc.) from affecting my restaurant. 3. I understand the process of the food delivery service by (the delivery app(s) currently in use) well enough to be able to control things that affect my restaurant.	C. Lee et al. (1990), Neves and Caetano (2006)
Goal incongruity	1. My restaurant and (the delivery app(s) currently in use) have different short-term goals. 2. My restaurant and (the delivery app(s) currently in use) have different values. 3. My restaurant and (the food delivery app) have different decision-making criteria.	Song et al. (2000)
Transparency	1. (The delivery app(s) currently in use) takes the time with restaurant operators to understand who we and what we need. 2. (The delivery app(s) currently in use) provides information in a timely fashion to restaurant operators. 3. (The delivery app(s) currently in use) is open to criticism by restaurant operators.	Rawlins (2008)
Dissatisfaction	1. It is dissatisfactory for me to make business relation with (the delivery app(s) currently in use). 2. It is not pleasant for me to make business relation with (the delivery app(s) currently in use). 3. I am not enjoying the relation with (the delivery app(s) currently in use).	Ryu et al. (2014)
Distrust	1. (The delivery app(s) currently in use) work for the app’s interest rather than ours. 2. (The delivery app(s) currently in use) is not reliable. 3. It seems that the relationship between (the delivery app(s) currently in use) and my restaurant is not equal. 4. The behavior of (the delivery app(s) currently in use) is inconsistent compared to the initial transaction.	Ryu et al. (2014)
Intention to terminate	1. If there is a better alternative, I would like to terminate the transaction with (the delivery app currently in use). 2. I am looking forward to being launched a new, better food delivery app. 3. If a new food delivery app with better conditions is released, I want to change the transactional app to another app.	Lin et al. (2012), Yang et al. (2011)

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported under the framework of international cooperation program managed by National Research Foundation of Korea (NRF-2023S1A5C2A03095169) and by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2024-2020-0-01749) supervised by the IITP (Institute of Information & Communications Technology Planning & Evaluation).

ORCID iDs

Yumi Jang

Seongcheol Kim

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Why Do Small Restaurant Operators Intend to Terminate Their Relationship with Food Delivery Apps During Covid-19 Pandemic?

Abstract

Plain language summary

Keywords

Introduction

Literature Review and Research Hypotheses

Overview of the Food Delivery App Market in South Korea

Loyalty Theory and Relationship Quality: Dissatisfaction, Distrust, and Intention to Terminate

Platform Specific Factors of Food Delivery App

Perceived Benefits and Costs Factors

Perceived Relationship Factors

Methodology

Data Collection and Analysis

Sample Characteristics

Preliminary Analysis—Test of Measurement Model

Results

Discussion and Conclusion

Key Findings and Implication

Limitation and Future Research

Footnotes

Appendix A

Declaration of Conflicting Interests

Funding

ORCID iDs

Data Availability Statement

References