Why do people use mobile food ordering apps?: Extended UTAUT2 Текст научной статьи по специальности «Экономика и бизнес»

DOI: 10.29141/2218-5003-2024-15-3-7 EDN: COEGNA

JEL dassification: O10, O31, O32

Why do people use mobile food ordering apps?: Extended UTAUT2

I.K. Erdem, K. gelik

Bartin University, Bartin, Turkey

Abstract. With the advancement of technology, many habits of people have changed. One of these is the habit of ordering food. While people used to place food orders through traditional means such as phone or email, nowadays, they can do so more quickly and easily through mobile food ordering applications (MFOAs). The increasing use of MFOAs has necessitated the exploration of factors influencing individuals' intention to reuse these applications. The aim of this study is to investigate the factors influencing individuals' intention to reuse MFOAs. The study is based on marketing theory and the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. In addition to the seven variables proposed by UTAUT2, variables related to the characteristic features of MFOAs, such as online reviews, online ratings, and online order tracking, have been added to the same model. The research data were obtained from surveys conducted with 441 MFOA users between December 2021 and March 2022. The collected data were analyzed using structural equation modeling in the R Software. As a result, it was observed that online ratings, effort expectation, online order tracking, price value, habit, and hedonic motivation variables have a significant and positive impact on the intention to reuse MFOAs. Social influence, facilitating conditions, performance expectation, and online reviews were found to have no effect on customers' reuse intentions toward MFOAs. The findings of the study provide insights into understanding consumer preferences and purchase intentions, offering a prediction for MFOA service providers competing for a larger market share.

Keywords: consumer behaviour; customer retention; continuing use intent; mobile apps; mobile food ordering apps; UTAUT2; Turkey.

Article info: received November 6, 2023; received in revised form December 29, 2023; accepted January 16, 2024

For citation: Erdem I.K., C^elik K. (2024). Why do people use mobile food ordering apps?: Extended UTAUT2. Upravlenets/The Manager, vol. 15, no. 3, pp. 99-119. DOI: 10.29141/2218-5003-2024-15-3-7. EDN: COEGNA.

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Мобильные приложения по доставке еды:

факторы удержания потребителей в рамках модели UTAUT2

И.К. Эрдем, К. Челик

Бартынский университет, г. Бартын, Турция

Аннотация. Новые технологии существенно трансформируют привычки потребителей, в том числе в сфере доставки продуктов питания и готовых блюд. Традиционно заказы оформлялись по телефону или электронной почте, однако развитие средств коммуникации привело к созданию более эффективного и быстрого способа размещения заказов - через мобильные приложения по доставке еды (MFOA). С ростом их популярности вопрос изучения поведения потребителей становится еще более актуальным. Статья направлена на изучение факторов, влияющих на намерения потребителей длительно использовать мобильные приложения по доставке еды. Методологическая основа работы представлена классической теорией маркетинга. Методы исследования - моделирование структурными уравнениями, факторный анализ. Информационную базу работы составили данные опроса 441 пользователя MFOA-приложений Турции, проведенного в декабре 2021 г. - марте 2022 г. Для сбора данных использовалась анкета на основе адаптированной модели UTAUT2: семь исходных переменных дополнены тремя параметрами, специфичными для мобильных приложений, - наличием отзывов, рейтингов и возможностью отслеживания заказа онлайн. Полученные данные анализировались в статистической программе R Software. Результаты исследования показывают, что на намерение потребителей размещать заказы через MFOA-приложения существенно влияют шесть факторов: доступность рейтингов, простота использования, онлайн-тре-кинг, ценовая политика, привычка и стремление к гедонизму. В то же время такие факторы, как влияние социальной среды, технические характеристики приложений, их общая полезность и онлайн-отзывы, не оказывают значимого влияния на стремление к дальнейшему использованию приложений. Выводы исследования вносят вклад в изучение потребительских предпочтений и могут быть полезны компаниям, использующим MFOA-приложения для увеличения их доли рынка. Ключевые слова: потребительское поведение; удержание клиентов; постоянные пользователи; мобильные приложения; доставка еды; UTAUT2; Турция.

Информация о статье: поступила 6 ноября 2023 г.; доработана 29 декабря 2023 г.; одобрена 16 января 2024 г. Ссылка для цитирования: Erdem I.K., ^elik K. (2024). Why do people use mobile food ordering apps?: Extended UTAUT2 // Управленец. Т. 15, № 3. С. 99-119. DOI: 10.29141/2218-5003-2024-15-3-7. EDN: COEGNA.

g INTRODUCTION

3 The modern lifestyle, with its fast pace and heavy reliance

I on technology, is driving consumers to shop via mobile

g applications [£elik, Ózkóse, 2023]. Individuals are always

a looking for new ways to simplify their daily duties and fit

U

5 them into their schedules. Innovative solutions enable a < variety of possibilities, from home deliveries to logistics H service providers depositing e-commerce goods at authorized collection places, particularly in the last-mile logistics services. Urban logistics services include home delivery services, which are a convenient choice for online customers. Additionally, home delivery services are becoming much more important since customers want their purchases to arrive at the appropriate time, location, amount, and condition owing to mobile phone applications [Belanche, Flavian, ve Pérez-Rueda, 2020].

In the digital era, digital transformation has become a means to gain a competitive advantage in countries or industries. Traditional platforms are continuously digitizing, and businesses are creating more value for themselves by utilizing digital technologies [£elik, Ayaz, 2022; Singh, Sharma, Dhir, 2021; Verhoef et al., 2021]. In recent years, one of the businesses undergoing active digitaliza-tion is restaurant business. It receives orders and reaches customers through mobile food ordering applications (MFOAs). MFOAs are rapidly spreading worldwide [Su et al., 2022], especially since the outbreak of COVID-19 in 2020, when there was a significant increase in demand for food ordering and delivery services [Hobbs, 2020]. Major food delivery application providers reported a notable rise in orders and deliveries as a result of the global spread of COVID-19 and the years that followed1. Furthermore, in response to the pandemic, a growing number of catering companies are shifting from offline to online services2. One factor driving this increase was the introduction of food options that could be consumed at home and save time [Raza, Asif, Akram, 2022].

The substantial rise in the smartphone user base has greatly accelerated the expansion of mobile applications by encouraging their adoption and use. Mobile applications have now become central and integral to daily life, facilitating various needs, and changing the entire business ecosystem to adapt to changing consumer habits. They are increasingly being used to promote businesses and have become a crucial part of people's daily lives. Mobile applications are developed to meet various needs that can be easily accessed from smartphones, making them user-friendly and downloadable [Cheng et al., 2020].

The proliferation of mobile devices and the development of the Internet have transitioned food ordering and delivery services from telephone or online services to be-

1 Curry D. (2023). Food delivery app revenue and usage statistics 2023. Business of Apps. https://www.businessofapps.com/ data/food-delivery-app-market/#4

2 ReportLinker. (2021). Online Food Delivery Services Global

Market Report 2021: COVID-19 Growth And Change To 2030.

ing primarily used through mobile applications, thanks to platform service integration and artificial intelligence technology [Song, Ruan, Jeon, 2021]. It is estimated that the revenue in the online food delivery market will reach 1 trillion US dollars by 2023, with an annual growth rate of 12.78%, and the projected market volume by 2027 is expected to be 2 trillion US dollars. In the Food Delivery segment, the number of users is expected to reach 2.5 billion users globally by 2027. The user penetration rate in the Food Delivery segment was 25.2% in 20233.

Today, businesses strive to enhance their competitive advantage in an increasingly complex and evolving business world. For companies operating in a competitive market, the importance of gaining a competitive advantage is growing exponentially [Singh, Sharma, Dhir, 2021]. MFOAs providers aim to increase their competitive advantages and adapt to ever-changing customer demands by offering their customers integrated platforms with new technologies [Su et al., 2022]. MFOAs have given restaurant owners significant technological capabilities to attract customers, transforming the restaurant industry [Singh, Singh, Dhir, 2022]. While these applications revolutionize how people shop for food, understanding how consumers perceive these services is essential. Scholars have shown increased interest in examining various aspects of these applications. Our comprehensive literature review suggests a need to enrich the accumulated knowledge about consumer behaviors regarding MFOAs. More academic research will serve as a valuable guide in this growing field worldwide.

One notable aspect of MFOAs is the availability of user-posted online reviews and ratings. As a result, before making a purchase, users of meal-ordering applications consult these and contrast their experiences with those posted online [Gupta, 2019]. Users are psychologically impacted by reviews and ratings, which may lead them to adopt other people's advice, trends, or styles. Additionally, customers can utilize the online order tracking feature in applications to monitor the whereabouts and status of their orders [Agarwal, Sahu, 2022].

There is general agreement from earlier research that cutting-edge mobile features - like online ordering monitoring, online rating, and online reviews - have a big impact on mobile ordering apps. Alalwan [2020] addressed these topics in a study. Research is required to determine how these factors can affect customer happiness and the likelihood that apps will be reused, as well as how consumers will perceive their utility and worth.

The aim of this study is to investigate the perceptions, behavioral intentions, and reuse intentions of MFOA users. The central question addressed in the research is the identification of factors influencing the reuse intentions

3 Statista. (2023). Online food delivery-worldwide. https://www. statista.com/outlook/dmo/online-food-delivery/worldwide? currency=usd.

of users who have experienced these applications. The objectives of the study include contributing to the existing literature and providing information to developers and businesses dealing with these applications about the precursors of customers' intentions to continue using MFOAs. Additionally, the study aims to determine which variables are determinants and offers practical insights to businesses and application developers on areas to focus on in order to maintain, sustain, and gain a competitive advantage.

CONCEPTUAL FRAMEWORK

The service sector is currently experiencing significant growth. Much of this growth is attributed to the increasing use of the internet and widespread adoption of smart devices [^elik, 2021). One of the business models in the service sector is the use of MFOAs. This apps enable individuals to order food online. These apps provide users with the ability to easily view lists of restaurants and menus, read customer reviews, make online payments, and track their orders, all without the need for physical or telephone communication with restaurants. Online food ordering services operate on a business model that does not involve food preparation but focuses on order placement, payment, and tracking. The face-to-face interaction in traditional commerce has been transformed by online interactions on the internet. Consumers can shop online through various apps, and businesses engage with them by allowing them to search and place orders online. Companies aim to deliver their services and products profitably by implementing new methods of delivery and providing increased value to customers [Kaur et al., 2021].

Providers of food delivery services can be broadly divided into two categories: third-party platforms and direct delivery from restaurant to customer. Customers submit orders through the restaurant's internet platform under the direct-from-restaurant-to-consumer delivery approach. Particularly in small restaurants, the business frequently uses its current workforce, such as waitstaff, to make and deliver food to customers. However, for eateries without their delivery service, third-party networks offer online delivery options. In this case, restaurants utilize independent contractors from platforms like Uber Eats and Rappi. This model offers an efficient and cost-effective approach to food delivery [Li, Mirosa, Bremer, 2020].

MFOAs are essentially internet-based services facilitated through mobile applications. They allow customers to order food and have it delivered to their doorsteps [Ray et al., 2019]. MFOAs provide customers with a wide range of choices and convenience, enabling them to place orders from various restaurants using their mobile phones. In this basic model, customers place orders with local businesses and expect the restaurant to deliver the food to their doorsteps. Clients accustomed to online shopping through mobile apps come to expect a similar experience when ordering food and beverages [Ramos, 2022].

Food is usually delivered to customers' doorsteps without ° requiring any direct physical interaction with restaurant 3 staff [Chotigo, Kadono, 2021]. I

While there are several factors contributing to the g popularity of online food delivery services, the most 2 prominent one is the convenience they offer [Cho, Bonn, < Li, 2019]. Through an app, consumers can access a variety | of food options from different restaurants anytime and x anywhere [Chai, Yat, 2019]. Additionally, consumers save £ time (no need for cooking or going out to get food), can 5 choose based on the reviews of other people using a res- | taurant review system, and compare prices [Zanetta et al., S 2021].

Different theoretical models have been employed in studies investigating customers' intention, perception, and actual behavior regarding the use of mobile food ordering applications. These include the Expectancy Confirmation Model (ECM), Task-Technology Fit, the Technology Acceptance Model (TAM), the Theory of Reasoned Action (TRA), and the Unified Theory of Acceptance and Use of Technology (UTAUT). The latter, particularly its extended version (UTAUT2), has become a popular theory for predicting consumers' behavioral intentions towards adopting new technologies [Vinerean et al., 2022]. UTAUT and its expanded version, UTAUT2, are currently among the most popular models used to assess the intention to use information technology [Venkatesh et al., 2003]. UTAUT comprises four core variables, namely facilitating conditions, effort expectation, performance expectation, and social influence. In an effort to broaden the scope of UTAUT, Venkatesh, Thong and Xu [2012] extended the model by introducing the variables of price value, habit, and hedonic motivation, naming this extended version UTAUT2. Previous studies have empirically analyzed how the seven variables of the UTAUT2 model (habit, effort expectation, performance expectation, facilitating conditions, social influence, price value, and hedonic motivation) are related to usage intention [Shaw, Sergueeva, 2019]. When reviewing previous research related to mobile food ordering applications, it has become evident that features supported by MFOAs, such as online reviews, online order tracking, and online ratings, need to be examined [Kapoor, Vij, 2018; Cho, Bonn, Li, 2019; Saumya, Singh, Dwivedi, 2019; Wang et al., 2019].

The existing literature and practitioner studies were reviewed to understand the key antecedents of reuse intention to use food delivery apps [Guo et al., 2018; Correa et al., 2019; Das, 2018; Chai, Yat, 2019; Chandrasekhar, Gupta, Nanda, 2019; Daud, Yoong, 2019; Kang, Namkung, 2019; Lee, 2019; Hwang, Kim, Kim, 2019; Lee, Sung, Jeon, 2019; Ray et al., 2019; Roh, Park, 2019; Vinaik et al., 2019; Alalwan, 2020; Belanche, Flavian, ve Pérez-Rueda, 2020; Gunden, Morosan, DeFranco, 2020; Hwang, Kim, Lee, 2021; Koay, Cheah, Chang, 2022]. There has been significant interest among researchers in identifying what motivates consumers to use food ordering apps. Studies

have looked at various factors such as purchase intention, loyalty, satisfaction and dissatisfaction, and intention to continue purchasing.

This study identifies the ten antecedents of reuse intention to use food delivery apps: performance expectation, social impact, price value, facilitating conditions, hedonic motivation, online evaluation, habit, effort expectation, online order tracking, and online rating. The research model is depicted in Figure.

Research model Модель исследования

ANTECEDENTS OF REUSE INTENTION

Performance expectation. Performance expectation is a concept drawn from the perceived usefulness framework of the Technology Acceptance Model, as introduced by Davis in 1989 [Davis, 1989]. It reflects the notion that individuals hold a belief that an information system will enable them to achieve benefits and enhance their job performance [Venkatesh et al., 2003; Afacan Adanir, Qnar, 2021; Yilmaz, Kavanoz, 2017]. According to the performance expectation, a user's expectation of the benefit that a new technology will add to their life is one of the motivations of this user to utilize a new technology [Karli, 2019]. If users perceive that a system will save more time and effort than traditional systems, the likelihood of their positive intention to use this system increases [Tamilmani et al., 2019]. Afacan Adanir and Qnar [2021] showed that performance expectation positively affects individuals' intention to use. Okumus et al. [2018] demonstrated that performance expectation plays a critical role in customers' intention to use mobile food applications. According to previous studies, performance expectation is one of the factors that measure reuse intention [Abushanab, Pearson, 2007; Al-Okaily, 2020; Eckhardt, Laumer, Weitzel, 2009; Kijsanayotin, Pannarunothai., Speedie, 2009; Okumus et al., 2018; Wang, Wu, Wang, 2009; Zhou, Lu, Wang, 2010; Slade et al., 2015; Ali, Nair, Hussain, 2016]. MFOAs are designed to enable users to order food directly on a platform instead of traditional systems (for example, web ordering or direct phone). Compared to phone ordering

and website research, consumers can check information on more food alternatives and order food with less performance through MFOAs. Hence, we hypothesize that:

H1: Performance expectation positively affects the reuse intentions of individuals using MFOAs.

Effort expectation. Effort expectation is the degree to which an individual perceives that using a technological innovation requires minimal effort [Chiu, Wang, 2008]. In other words, when system users think it is easy to use, their intention to use it will be more robust [Al-Okaily et al., 2020]. Previous studies show that effort expectation is an essential factor in the intention to use a new technology [AbuShanab, Pearson, 2007; Okumus et al., 2018; San Martin, Herrero, 2012; Venkatesh, Thong, Xu, 2012; Van Raaij, Schepers, 2008]. Hence, we hypothesize that:

H2: Effort expectation positively affects the reuse intentions of individuals using MFOAs.

Social impact. Social impact refers to the strength of normative beliefs and an individual's motivation to adhere to these beliefs, as described by Ajzen [1991]. It also encompasses the social pressure perceived by an individual regarding specific issues. Social influence plays a significant role in shaping an individual's intentions and behaviors, as established by Ajzen and Fishbein [1980]. Another definition of social impact posits that it involves the perception of whether people who hold significance for an individual should or should not engage in a particular behavior [Fishbein, Ajzen, 1975]. Social impact is often recognized to have a direct and substantial correlation with an individual's intention to use a system, as noted by Schepers and Wetzels [2006]. This is because when an individual believes that those around them should adopt a particular system, they tend to conform to these prevailing opinions and subsequently adopt the system themselves. When a person perceives that significant individuals in their life endorse the use of a system, they incorporate these endorsements into their own belief system. This is often driven by the general belief that the judgment of people in one's social circle is reliable, which further motivates the individual to adopt the system [Van Raaij, Schepers, 2008]. The positive word of mouth of individuals using a system or technology through various channels (electronic and traditional) can increase the intention of less innovative users to use such applications [Okumus et al., 2018]. Customers generally show several types of post-purchase reflexes: positive and negative word of mouth, warnings, and recommendations for other people, and complaints and suggestions. In this context, whether an application can please a person or not, concerns many people [£elik, Sokmen, 2018]. Today, many people use MFOAs. It is a fact that the intention to reuse cannot be explained without considering the sharing of information about this application, which is used by so many people [Hwang, Kim, Kim, 2019]. Based on these factors, people may adopt the ideas of individuals they value, such as their colleagues, that an app is useful

[Liao, Chen, Yen, 2007]. Previous studies show that social impact is among the factors that affect the intention to use a new technology [AbuShanab, Pearson, 2007; Oku-mus et al., 2018; Al-Okaily, 2020]. Hence, we hypothesize that:

H3: Social impact positively affects the reuse intentions of individuals using MFOAs.

Facilitating conditions. Technically, food ordering applications are software used on smartphones and cannot be used and adopted effectively by users without the Internet. Customers attach great importance to the quality of the application and the ability to use it without interruptions or technical problems. In addition, human support's role in customer service, call centers, and delivery is critical to ensure high-quality service is provided to customers [Alalwan, 2020]. Therefore, digital marketing and information technology researchers have widely identified facilitating conditions as having a significant impact on customer intention and use [Verkijika, 2018; Khal-ilzadeh, Ozturk, Bilgihan, 2017]. Verkijika [2018] found that customers' intention to adopt mobile commerce is influenced by facilitating conditions. According to Alalwan, Dwivedi and Rana [2017], there is a clear correlation between the adoption of mobile banking in Jordan and the facilitating conditions. According to Baabdullah et al. [2019], enabling factors affect Saudi Arabian consumers' real usage patterns and levels of satisfaction with mobile banking. Hence, we hypothesize that:

H4: Facilitating conditions positively affect the reuse intentions of individuals using MFOAs.

Hedonic motivation. When individuals are looking for a particular product or service, their primary aim is to achieve emotional contentment, derive enjoyment, and experience satisfaction throughout the entire process of acquiring and using it (as emphasized by [Holbrook, Hirschman, 1982]). It is important to note that, according to Holbrook and Hirschman [1982], the hedonic perspective complements traditional consumption theories rather than replaces them, thereby enhancing the application of these theories. Furthermore, hedonism can encompass the aesthetic and experiential pleasure derived from the entire journey, starting from recognizing a need and extending to post-purchase activities involving the consumption of the product or service (as discussed by [Mort, Rose, 2004]). Studies show that emotional stimulation derived from hedonic motivation is an essential motivation for shopping and consumption [Miranda, 2009; Escobar-Rodriguez, Carvajal-Trujillo, 2013]. Frequent visits to an application indicate that the user enjoys this process in terms of hedonic motivation. If this application can meet the user's expectations, they enjoy using it [Alavi et al., 2016; Tsang, Tse, 2005]. Hence, we hypothesize that:

H5: Hedonic motivation positively affects the reuse intentions of individuals using MFOAs.

Price value. Due to their many benefits, including food delivery to consumers' doorsteps, a variety of payment

options, alluring discounts, rewards, and cashback offers, °

MFOAs are growing in popularity [Saad, 2021]. Customers 3

can search for the best deal by using meal-ordering appli- I

cations. The findings of Morganti et al. [2014], Andaleeb g

and Conway [2006], and Parasuraman, Zeithaml and Ber- £

ry [1994] indicate that consumer decisions are influenced <

by factors such as service quality, product quality, and |

pricing. Jin and Gu Suh [2005] classify customers as value- x

and price-conscious. Yeo, Goh and Rezaei [2017] discov- £

u

ered that consumers' perceptions of the benefits and their 5 intention to stick with online meal ordering services are | influenced by how affordable the sites are. Price is a cru- £ cial factor in determining an online food order's intention, according to several studies [Alalwan, 2020; Cho, Bonn, Li, 2019; Al-Okaily et al., 2020]. Hence, we hypothesize that:

H6: Price value positively affects the reuse intentions of individuals using MFOAs.

Habit. Habit reflects the relationship between a person's past and future behavior [Kim, Malhotra, 2005]. Habit is a precursor to user behavior, mainly when repetitive behaviors occur in the use of information systems [Limay-em, Cheung, 2008]. If consumers are satisfied with their previous purchasing experience, using this system continuously is an act of habit [Khalifa, Liu, 2007]. The habit formed when using a system may develop the intention to use that system in the future. Therefore, consumers' habits are expected to influence their intention to reuse MFOAs. Hence, we hypothesize that:

H7: Habit positively affects the reuse intentions of individuals using MFOAs.

Online evaluation. Online evaluation for food ordering applications is to provide feedback about the purchasing experience for restaurants where customers order food and to share this feedback with users who order food from this application [Bert et al., 2014]. Online customer reviews are regarded as a form of digital word-of-mouth communication that is shared through online platforms [Mudambi, Schuff, 2010; Filieri, 2015; Wei, Lu, 2013]. Such evaluations are becoming an increasingly important and valuable source of information for customers in purchasing products or evaluating alternatives [Simonson, Rosen, 2014; Filieri, 2015; Huang, Baptista, Newell, 2015]. According to the research of Elwalda, Lu and Ali [2016], there is a strong and positive correlation between the aspects of perceived usefulness, perceived ease of use, and enjoyment discussed in online reviews, and a customer's intention to shop online. Additionally, Alalwan [2020] discovered a positive association between online evaluation and the intention to reuse a product or service. Hence, we hypothesize that:

H8: Online evaluation positively affects the reuse intentions of individuals using MFOAs.

Online order tracking. E-commerce research has often focused on on-time delivery and shipping rather than price to predict customer satisfaction [Reibstein, 2002]. The issues encountered by online shoppers are often re-

2 lated to delivery rather than the product itself. Delivery

3 services play a crucial role in shaping a consumer's pur-I chasing decision [Saad, 2021].

g Location-based services that are enabled by smartS phone technology allow both customers and vendors to 35 share their specific location and perform location-based < calculations [Shugan, 2004]. Online order tracking is the I ability to know the delivery progress and location after ordering food [Cheng, Chang, Chen, 2021]. An online tracking system allows consumers to stay informed about the expected delivery time and contact the deliverers during the delivery process if any problems arise [Gutierrez et al., 2019]. One of the reasons why consumers prefer online shopping is that it saves time. However, if the product is not delivered within the promised time frame, it may lead to dissatisfaction [Roy Dholakia, Zhao, 2010].

Adjusting delivery times based on traffic conditions can boost customer satisfaction and increase the number of customers, as providers are able to guarantee on-time delivery [Correa et al., 2019]. Saad [2021] found that delivery time positively affects consumers' online food ordering preferences. Furthermore, Alalwan [2020] discovered that online order tracking has a positive impact on customer satisfaction and the intention to use mobile food delivery applications. Hence, we hypothesize that:

H9: Online order tracking positively affects the reuse intentions of individuals using MFOAs.

Online rating. Online rating refers to users expressing their opinion about a product they purchased. This rating contributes to the decision of a user who plans to shop from the same seller later on. Online rating is another type of audience opinion that shows the average reviewer's evaluation of the different product or service features. Customer ratings can help consumers learn about a product's quality by summarizing reviewers' evaluations of its key features. This way, consumers can determine a product's or seller's performance, including its strengths and weaknesses. That can help consumers at the decision-making stage [Filieri, 2015]. Consumers who purchased the same product or shopped from the same seller can rate this experience from 1 to 5, 1 to 7, or 1 to 100. These ratings assist customers in viewing the overall rating of product or service providers based on various attributes such as delivery time, accuracy, price, quality, that have been rated numerically by other customers who have previously used such products and services [King, Racherla, Bush, 2014]. The scores given to a business by customers are considered the voice of customers so that businesses can see their performance through the eyes of customers and have the opportunity to analyze themselves [Philips et al., 2017]. Philips et al. [2017] found that positive reviews are critical to customer demand. Alalwan [2020] concluded that online rating positively affects the intention to reuse. Hence, we hypothesize that:

H10: Online rating positively affects the reuse intentions of individuals using MFOAs.

METHOD

Procedure for collecting samples and data. The data collection tool is comprised of an electronic survey that includes multiple-choice questions and statements adapted from measurement instruments already validated in the literature. Responses are rated on a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). The data for the study were collected from individuals who utilized the YemekSepeti application operating in Turkey. The selection of individuals using this application in the study is due to its status as the most widely used mobile food ordering application in Turkey1. In this context, a survey was administered to individuals who had used the Yemek Sepeti application between December, 2021 and March, 2022. As a result, 441 usable data points were obtained.

Table 1 - Respondentsocio-demographicprofiles Таблица 1 - Социально-демографический профиль

респондентов

Characteristics Measurements Quantity Percentage (%)

Gender Male 225 51.0

Female 216 49.0

Age 18-23 165 37.4

24-29 158 35.8

30-35 59 13.4

36-41 23 5.2

>41 36 8.2

Marital status Married 121 27.2

Single 320 72.8

Education status High school 126 28.6

Associate degree 74 16.8

Bachelor's degree 191 43.3

Postgraduate 50 11.3

Monthly income, Turkish liras (k) 0-1,000 125 28.3

1,001-3,000 73 16.6

3,001-5,000 73 16.6

5,001-7,000 68 15.4

More than 7,000 102 23.1

MFOAs usage time Less than 1 year 164 37.2

1-2 years 91 20.6

2-3 years 50 11.3

>3 years 136 30.8

Average number of orders from MFOAs per month Less than 3 orders 266 60.3

4-6 orders 107 24.3

7-10 orders 31 7.0

>10 orders 37 8.4

Total 441 100

1 Yemek Sepeti. (n.d.). https://kurumsal.yemeksepeti.com/en/ about-us/

Table 1 illustrates the socio-demographic characteristics of the individuals who participated in the study. Among the participants, 51% (n=225) are male, while 49% (n=216) are female. In terms of age distribution, 37.4% (n=165) fall within the 18-33 age group, 35.8% (n=158) are aged 24-29, 13.4% (n=59) are between 30-35, 5.2% (n=23) are in the 36-41 age range, and 8.2% (n=36) are 42 years or older. Marital status reveals that 27.2% (n=121) of participants are married, while the majority, 72.8% (n=320), are single. In terms of educational attainment, 28.6% (n=126) have completed high school, 16.8% (n=74) have earned an associate degree, 43.3% (n=191) hold undergraduate degrees, and 11.3% (n=50) have post-graduate qualifications. Monthly income of 28.3% of the participants (n=125) is less than 1,000k, 16.6% (n=73) is between 1,001 -3,000k, 16.6% (n=73) is between 3,001-5,000k, 15.4% (n=68) are between 5,001-7,000k, and 23.1% (n=102) are 7,001k and above. Participants were analyzed for the duration of use of MFOAs. Accordingly, 37.2% (n=164) stated that they have been using MFAOs for less than 1 year old, 20.6% (n=91) for 1-2 years, 11.3% (n=50) for 2-3 years, 30.8% (n=136). Respondents were asked how many times per month they ordered food, on average, through MFOAs. Based on the data, it can be observed that 60.3% (n=266) of the participants order food through MFAOs less than 3 times. In contrast, 24.3% (n=107) order food 4-6 times, 7% (n=31) order food 7-10 times, and 8.4% (n=37) order food 11 times or more.

The scales and preparation of the questionnaire. The scales used to test the research model were applied to individuals using MFOAs by creating an online questionnaire. The introduction part of the survey informed the participants about the purpose of the study and the researchers. The questions in the first part of the questionnaire aimed to determine the socio-demographic characteristics of the participants. The second part consisted of a comprehensive questionnaire consisting of effort expectation, hedonic motivation, performance expectation, habit, social impact, facilitating conditions, price value, reuse intention, online evaluation, online rating, and online order tracking scales.

The scales for performance expectation, effort expectation, social impact, facilitating conditions, price value, hedonic motivation, habit, and reuse intention were adapted from Venkatesh et al. [2012]. The online evaluation scale was adapted from Filieri [2015], Park, Lee and Han [2007], Jiang and Benbasat [2004], the online rating scale from Filieri [2015], and the online order tracking scale from Alalwan [2020]. The online order tracking scale developed by Alalwan [2020] consists of 5 items. In this study, the item "MFOAs provide map tracking" was removed from the scale, and the scale was reduced to 4 items. This is because there was no online tracking feature on the MFOAs map at the time of the research. The measurement items are shown in Appendix.

Data analysis technique. The R program was used to °

generate demographic data, conduct CFA, and perform й

SEM analysis. SPSS 21 program was utilized to calculate I

the scales' reliability and create the correlation matrix. g

<4 tt

RESULTS §

Common method variance. As suggested by Podsakoff | et al. [2003], we used Harman's one-factor test to inves- x tigate common method variance. As a result, for every £ variable, an unrotated exploratory factor analysis was 5 performed. There was no dominant component in the re- | search, as seen by the one-factor solution's 47% variance S explanation, which is less than the 50% requirement. According to the results of Harman's one-factor test, common method variance was not a cause for worry.

Confirmatory factor analysis. The fit index values of the study are displayed in Table 2. According to this, all fit indices demonstrate good or acceptable fit [Gefen et al., 2002; Zhou, Lu, 2011].

Table 2 - Confirmatory factor analysis Таблица 2 - Результаты подтверждающего факторного

анализа

Fit index Recommended Actual

value value

Chi-square/ degree of freedom <3 1999.2GG/847=2.36

CFI >G.9G G.932

RMSEA G.G56

NNFI >G.9G G.924

TLI >G.9G G.924

RNI >G.9G G.932

IFI >G.9G G.933

Note: CFI is Comparative Fit Index; RMSEA is Root Mean Square Error of Approximation; NNFI is Non-Normed Fit Index; TLI is TuckerLewis Index; RNI is Relative Noncentrality Index; IFI is Incremental Fit Index.

Validity and reliability analysis. Table 3 displays the correlation matrix, AVE, CR, and Cronbach's Alpha Confidence Coefficient for each scale. The results demonstrate that all scales' Cronbach Alpha coefficients took values between 0.781 to 0.955. The Cronbach Alpha reliability coefficient needs to be higher than 0.7 in order to guarantee the scales' reliability [Nunnally, 1978; Iacobucci, Du-hachek, 2003]. The results demonstrate the scales' high level of reliability.

CR must exceed AVE, and AVE must be larger than 0.5 for convergent validity [Hair et al., 2010; Fornell, Larcker, 1981]. Table 3 shows that all AVE values are higher than 0.5. This finding implies that the constructs' validity has been verified. Furthermore, all scales' CR values exceed their AVE values, proving the validity of the scales. The scales' discriminant validity was looked at. The square root

ю ■н s

Table 3 - Validity and reliability analysis Таблица 3 - Результаты анализа на валидность и надежность

а AVE CR 1 2 3 4 5 6 7 8 9 10 11

1-PE .836 .597 .853 (.772)

2-EE .884 .660 .886 .657** (.812)

3-SI .929 .817 .930 .519** .299** (.903)

4-FC .808 .598 .850 .540** .655** .209** (.773)

5-HM .955 .877 .955 .636** .538** .599** .434 (.936)

6-PV .781 .603 .814 .514** .631** .237** .746** .443** (.776)

7-HA .886 .665 .888 .587** .387** .653** 232** .590** .230** (.815)

8-RI .922 .708 .924 .671** .657** .543** .547** .702** .575** .608** (.841)

9-OE .941 .698 .942 .590** .562** .530** .530** .595** .496** .613** .731** (.835)

10-TR .912 .646 .880 .519** .567** .358** .593** .517** .548** .382** .692** .660** (.803)

11-RA .877 .775 .912 .567** .544** .457** .523** .607** .494** .515** .756** .761** .672** (.880)

Note: a is Cronbach's Alpha Confidence Coefficient; CR is Construct Reliability; AVE is Average Variance Extracted; PE is Performance Expectation; EE is Effort Expectation; SI is Social Impact; FC is Facilitating Conditions; HM is Hedonic Motivation; PV is Price Value; HA is Habit; RI is Reuse Intention; OE is Online Evaluation; TR is Online Order Tracking; RA is Online Rating, Values in parentheses are the square root of AVE values, **p<0.01.

of the AVE values must be larger than the correlation coefficient between the relevant variable and the other variables to guarantee discriminant validity [Hair et al., 2010]. Table 3 demonstrates that for every scale, the square root of the AVE value is higher than the correlation between the components.

To show how the variables related to one another, a correlation analysis was done. All of the variables had a substantial (p<0.01) positive connection, according to the research. Online evaluation and online rating had the most correlation between the constructs (r=0.761, p<0.01), whereas social impact and facilitating conditions had the least correlation (r=0.209, p<0.01).

Structural equation modeling (SEM) and path analysis. Table 4 shows the support and rejection of the hypotheses derived from the structural equation model analysis, along with the standardized p, standard error,

Z-value, and p-value. As a result, HI, H3, H4, and H8 were rejected, whereas H2, H5, H6, H7, H9, and H10 were accepted. Additionally, the explained variance of reuse intention has been determined to be 0.84.

DISCUSSION

The purpose of this study was to look at what aspects affect people's intentions to use mobile applications for ordering food again. Finding the elements that influence MFOA reuse and support its success was the aim. To do this, in addition to the elements from the UTAUT2 model, the research model suggested in this study considers elements like online evaluation, online purchase monitoring, and online rating. The results show that MFOA reuse intention is considerably and positively influenced by effort anticipation, hedonic incentive, price value, habit, online order tracking, and online rating factors. The reuse inten-

Table 4 - Structural equation modeling (SEM) analysis Таблица 4 - Результаты моделирования структурными уравнениями

Hypotheses Std. ß Std. Error z P Supported/Rejected

H1: Performance expectation ^ Reuse intention 0.017 0.153 0.284 0.776 Rejected

H2: Effort expectation ^ Reuse intention 0.236 0.163 3.621 **** Supported

H3: Social impact ^ Reuse intention 0.042 0.109 0.970 0.332 Rejected

H4: Facilitating conditions ^ Reuse intention -0.159 0.216 -1.84 0.066 Rejected

H5: Hedonic motivation ^ Reuse intention 0.136 0.113 2.991 0.003 Supported

H6: Price value ^ Reuse intention 0.151 0.183 2.074 0.038 Supported

H7: Habit ^ Reuse intention 0.146 0.140 2.608 0.009 Supported

H8: Online evaluation ^ Reuse intention 0.027 0.154 0.442 0.658 Rejected

H9: Online order tracking ^ Reuse intention 0.229 0.138 4.151 **** Supported

H10: Online rating ^ Reuse intention 0.291 0.164 4.428 **** Supported

tion of MFOAs is not significantly impacted by performance expectations, social impact, facilitating conditions, or online evaluation elements.

According to the analysis, performance expectation does not significantly affect the reuse intentions of individuals using MFOAs. Many studies in the literature have stated that performance expectation has a significant effect on reuse intention [Kim et al., 2016; Morosan, DeFranco, 2016; Alalwan, Dwivedi, Rana, 2017; Kabra et al., 2017; Salloum et al., 2018; Alalwan, 2020; Gunden, Morosan, DeFranco, 2020]. Performance expectation does not affect MFOAs users' reuse intentions for this application. If the actual value of the application for a user is higher than the expected value, the probability of reuse of the user increases. In these research data, it is thought that the real value received by MFOAs users for the system meets or exceeds their expectations. In addition, this conclusion shows that other factors are more important than performance expectations on users' reuse intentions for MFOAs.

The analysis showed that effort expectation is one of the most crucial factors impacting reuse intention, which is consistent with the findings of some prior studies [Tosun-tas, Karadag, Orhan, 2015; Ayaz, Yanarta§, 2020]. In other words, MFOAs need to be easy to use and understand and not to be confusing. It has been concluded that giving importance to features such as "Frequently Asked Questions-FAQ" and explanatory videos in the application can contribute to meeting the effort expectations of the users.

The results indicated that social impact does not have a significant impact on the reuse intention of mobile food ordering applications, which was not anticipated. Although some studies support this finding [Lu, 2014; Alalwan et al., 2017; Shaw, Sergueeva, 2019; Alalwan, 2020], some studies do not [Salloum et al., 2018; Yildiz Durak, 2018; Hwang, Kim, Kim, 2019; Ayaz, Yanarta§, 2020; Be-lanche, Flavian, ve Pérez-Rueda, 2020]. This finding shows that thoughts and comments of people close to individuals, such as their families, friends, and colleagues, about MFOAs do not affect them. People can rely heavily on the opinions and suggestions of others when using new systems for the first time. Nevertheless, gaining usage experience can largely counteract the influence of other people's opinions. In this context, considering that the participants of this study are active users of MFOAs, it is understood that this finding is appropriate.

Facilitating conditions have no significant effect on the reuse intentions of individuals using MFOAs. Although this situation is similar to the findings of some studies [Okumu§ et al., 2018; Shaw, Sergueeva, 2019; Alalwan, 2020], it is not in line with the results of many studies [Ali, Nair, Hussain, 2016; San Martin, Herrero, 2012; Ven-katesh, Thong, Xu, 2012; Wang, Wu, Wang, 2009]. Most of the participants in this study were young people (73.2% were between 18 and 29). Given that young people tend to stay abreast of technological advancements, it can be assumed that they are less likely to be affected by facili-

tating conditions when forming their reuse intention for ° new systems. Facilitating conditions in MFOAs include 3 finding, installing, and using these applications. It can I be accepted that this finding is related to the increase g in smartphone users today. Many people possess smart- 2 phones, so mobile applications such as MFOAs are easily < accessible. Users are accustomed to using smartphone | apps and similar platforms, so they may not consider fa- x cilitating conditions when downloading and using such £ applications as a crucial factor. 5

Hedonic motivation positively influences the reuse | intention of MFOAs. The expectation of enjoying shop- S ping is accepted as a factor affecting online shopping. In this context, users should enjoy ordering through MFOAs. When users perceive that MFOAs can provide them with more entertainment and pleasure, they will have a positive attitude and use the app more. For this reason, the entertainment dimension should not be neglected in the content created. Thus, the resulting hedonic motivation will increase users' reuse intentions of MFOAs.

The price value positively affects the reuse intentions of individuals who use MFOAs. This is consistent with the findings of previous studies [Gupta, Dogra, George, 2018; Singh, Srivastava, 2018; Cho, Bonn, Li, 2019; Al-Okaily et al., 2020]. This finding shows that buying a better product at a lower price or buying a higher quality product at the same price positively affects the reuse intention of MFOAs. In this context, it can be predicted that offering special discounts and promotions to individuals using MFOAs for the first time will significantly increase their use of the application. In addition, attention should be paid to rewarding loyal customers. Shareef et al. [2014] suggest additional financial incentives (for example, price reductions, more products for the same price, points, coupons, and rewards) and a loyalty program for customers who use the app frequently.

Habit positively affects the reuse intentions of individuals using MFOAs. This finding is similar to the findings of Sun and Chi [2018] and Amoroso and Lim [2017]. Consumers' habits are essential in shaping their reuse intentions for MFOAs. An individual who has a habit of using a system will continue to use that system in the future. Marketing communication strategies should be developed to create the habit of purchasing food through the application and improve the reuse intentions of the users. In this context, it is recommended that MFOAs advertise on the Internet and/or traditional media to encourage consumers to purchase food through the app. The messages in these advertisements may include discounts, campaigns, and promotions. In addition, discounts should be offered with loyalty programs on the next purchase. These promotions can be communicated to users via notifications, e-mails, and text messages via mobile applications. In addition, some campaigns or tasks that can be controlled by entering the application can be organized so that users can develop a habit of using the application.

2 According to the findings, the online evaluation has

3 no significant effect on the reuse intentions of individual als who use MFOAs. MFOAs provide users who purchase g from a business with the opportunity to evaluate that S business. This opportunity simplifies evaluating alterna-

U

35 tives and making decisions when ordering food for us-< ers who plan to purchase from the same business later. I It also allows businesses to see themselves through their customers' eyes and take precautionary measures accordingly. This finding obtained in this study shows that different factors are more important than the online evaluation for the reuse intention of the users participating in the study.

Online order tracking is the third most decisive factor in reuse intention (p=0.229). The development of location-based technologies allows order tracking. In addition, thanks to online order tracking, customers can follow the progress of their orders. Location-based technologies allow users to track the location of their orders instantly on the map. However, only the order stages are meant when this study refers to the online order tracking feature. This is because MFOAs did not allow location-based tracking all over Turkey at the time of the study. Since it is an essential predictor of reuse intention, location-based tracking of orders should be enabled all over Turkey. In addition, the order stages must reflect the truth and be instantaneous. It is crucial to ensure that the online order tracking system provides accurate and reliable information. Otherwise, users may lose their trust in the online order tracking system and MFOAs in general. This feature makes learning about the order stages simpler and more efficient by allowing customers to learn the current stage of their order without the need for human factors. It eliminates the problem of the customer calling the business to find out the current stage of their order. It saves the business from telephone traffic, a possible workload, and the customer from the telephone call fee. In addition, online order tracking, a relatively new technology, is thought to increase the pleasure and enjoyment of customers. It also makes food ordering more efficient by providing customers with real-time information about their order's estimated arrival time.

Online rating is the most decisive factor in the reuse intention (p=0.291). Online rating refers to the average ratings given by all users who have purchased from a business and rated that business. These scores can be perceived as a safer evaluation by customers and can be seen as more valuable information than other information since they are an average value. Since online ratings provide feedback from previous customers, they can make it easier for other customers to compare options before placing an order, thus enhancing their purchase experience. The fact that this study determined online rating as the most critical factor affecting reuse intention shows that businesses should pay attention to this issue and try to get the best score from their customers. They

also need to motivate their customers to rate themselves after shopping. The application should allow customers to perform this process easily and quickly technically. In addition, they can increase the number of points given to them with motivations such as giving a discount on the next purchase to the customers who make a rating. Thus, they can get higher rankings in search results by getting high scores. The visual design of the online rating system should also be crafted to make it easy for customers to find the highest-rated restaurants. Displaying the number of users who have rated targeted restaurants can help establish trust among other users and quicken the decision-making process.

MFOAs are applications where users can order food whenever they want and it is necessary to ensure the security of these applications. It should be noted that applications used by millions of people, such as MFOAs, are the target of many hackers. In this context, regular maintenance and backups are required. In addition, users should be able to reach technical support quickly and easily in case of a technical problem. In addition, a customer service system that can be accessed easily and quickly should exist so that when users encounter a problem with their order, they can find a solution to their problems. Both restaurants and MFOAs must have an adequately trained and solution-qualified workforce to be highly responsive to customers. In addition, it is recommended that MFOAs make entertaining and attention-grabbing advertisements that using this application is a part of modern life, both through traditional methods such as radio, television, billboards, and newspapers and through social media platforms such as Instagram and Facebook.

CONCLUSION

This study aimed to investigate users' intentions to reuse MFOAs. A literature review was conducted, revealing a limited number of studies specifically addressing the intention to reuse MFOAs. Various research models proposed in studies exploring the reuse intention of such applications were examined, leading to the decision to utilize UTAUT2 in the present study. Subsequently, the UTAUT2 model was extended by incorporating variables unique to MFOAs, namely online evaluation, online order tracking, and online rating. The data for this study were collected from individuals using the Yemek Sepeti application, which holds a leading position in this field in Turkey. Following a pilot study, data were gathered through a survey of 441 participants. The collected data underwent CFA to assess the compatibility of the obtained results with the proposed model. Subsequently, validity and reliability analyses were conducted. Finally, SEM was employed to test the proposed hypotheses. The results indicated support for six out of the ten proposed hypotheses, while four were rejected. This outcome largely proved the validity of the proposed model. Lastly, factors influencing the intention to reuse MFOAs were discussed.

Limitations and suggestions for future research. This study has several limitations. It was conducted within a specific time frame, and the results are only valid for that period. As such, this study does not provide an understanding of how customers' perceptions may evolve over time. For instance, a data breach or hacking incident on mobile food ordering applications, leading to the unauthorized access of user information, may alter the factors that influence users' reuse intentions in the future.

Since this study uses a convenience sampling technique, the generalizability of the findings is a limitation. Different forms of simple random sampling techniques can be applied in future studies if the customer database can be accessed with the support of mobile food ordering applications.

The study consists of 43% undergraduate students. °

In future studies, research can be conducted exclusively 3

with undergraduate students or individuals who are not I

students. g

This research did not take into account the impact of 2

' u

cultural factors such as dietary habits, health conscious- <

ness, family size, and lifestyle. Further studies that ex- |

amine these cultural elements will deepen our current x

knowledge of the factors that impede or promote the £

success of mobile food ordering apps. 5

In order to investigate users' reuse intentions for |

MFOAs, future studies can expand the research model S provided in the study by adding different variables such as personalization, perceived risk, perceived quality, perceived privacy, perceived trust, mobile interaction, product variety, and personalization.

Appendix - Measurement items Приложение - Исследуемые переменные

Constructs Items Statements

Performance expectation PE1 I find mobile food ordering apps useful in my daily life

PE2 Using mobile food ordering apps increases my chances of achieving tasks that are important to me

PE3 Mobile food ordering apps help me accomplish tasks more quickly

PE4 Using mobile food ordering apps increases my productivity

Effort expectation EE1 Learning how to use mobile food ordering apps is easy for me

EE2 My interaction with mobile food ordering apps is clear and understandable

EE3 I find Internet mobile food ordering apps easy to use

EE4 It is easy for me to become skilful at using mobile food ordering apps

Social influence SI1 People who are important to me think that I should use mobile food ordering apps

SI2 People who influence my behaviour think that I should use mobile food ordering apps

SI3 People whose opinions that I value prefer that I use mobile food ordering apps

Facilitating conditions FC1 I have the resources necessary to use mobile food ordering apps

FC2 I have the knowledge necessary to use mobile food ordering apps

FC3 Mobile food ordering apps are compatible with other technologies I use

FC4 I can get help from others when I have difficulties using mobile food ordering apps

Hedonic motivation HM1 Using mobile food ordering apps is fun

HM2 Using mobile food ordering apps is enjoyable

HM3 Using mobile food ordering apps is entertaining

Price value PV1 Mobile food ordering apps are reasonably priced

PV2 Mobile food ordering apps are good value for the money

PV3 At the current price, mobile food ordering apps provide good value

Habit HA1 The use of mobile food ordering apps has become a habit for me

HA2 I am addicted to using mobile food ordering apps

HA3 I must use mobile food ordering apps

HA4 Using mobile food ordering apps has become natural to me

Reuse intention RI1 I intend to continue using mobile food ordering apps in the future

RI2 I will always try to use mobile food ordering apps in my daily life

RI3 I plan to continue to use mobile food ordering apps frequently

Appendix (concluded) Окончание приложения

Constructs Items Statements

Online evaluation OE1 The information from online reviews provided in mobile food ordering apps was credible

OE2 The information from online reviews provided in mobile food ordering apps was relevant to my needs

OE3 The information from online reviews provided in mobile food ordering apps was based on facts

OE4 The information from online reviews provided in mobile food ordering apps was of sufficient depth (sufficiently detailed)

OE5 The information from online reviews provided in mobile food ordering apps was of sufficient breadth (spanning different subject areas)

OE6 The quantity of information provided in mobile food ordering apps was sufficient to satisfy my needs

OE7 The information provided in online reviews of mobile food ordering apps was helpful for me to evaluate the product

Online order tracking TR1 Tracking system is available on mobile food ordering apps that I have used

TR2 Tracking system is very important feature on the mobile food ordering apps that I use

TR3 Tracking system provided in mobile food ordering apps help me to save my time as I can use a tracking number to help find out when my order will arrive

TR4 The mobile food app I use provides map tracking

TR5 Tracking system available on the food apps that I use reduce costly calls with restaurant

Online rating RA1 Customer ratings provided in mobile food ordering apps have helped me to learn about the product

RA2 Customer ratings provided in mobile food ordering apps have improved my understanding of the quality of the product's features

RA3 Customer ratings provided in mobile food ordering apps were useful in order to evaluate the quality of product specifications/features

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Information about the authors Информация об авторах

Irem Kaymakci Erdem

MSc, Researcher of Business Dept. Bartin University, Bartin, Turkey. E-mail: iremkaymakciii@gmail.com

Kamil Çelik

PhD, Assistant Professor of Management Information Systems Dept. Bartin University, Bartin, Turkey. E-mail: kcelik@bartin.edu.tr

Эрдем Ирем Каймакси

MSc, научный сотрудник кафедры бизнеса. Бартынский университет, г. Бартын, Турция. E-mail: iremkaymakciii@gmail.com

Челик Камил

PhD, доцент кафедры информационных систем управления. Бартынский университет, г. Бартым, Турция. E-mail: kcelik@bartin. edu.tr