Factors affecting students adoption of using grabfood application during covid 19 pandemic in ho chi minh city 2022

MINISTRY OF EDUCATION AND TRAINING THE STATE BANK OF VIET NAM HO CHI MINH CITY UNIVERSITY OF BANKING NGUYEN THIEN LUAN FACTORS AFFECTING STUDENTS’ ADOPTION OF USING GRABFOOD APPLICATION DURING COVID 1.

Research Statement

Internet-based sales and purchases have grown significantly and heterogeneously across a wide range of goods, nations, and sectors, with the bio-economy sector standing out in particular The expansion of e-commerce websites, mobile-commerce apps, instant payment systems, and mobile payments has cemented the internet as a dominant platform for exchanging goods and services Online-to-offline (O2O) models, where customers view products online, express interest, and complete the transaction offline, illustrate the blurring line between online and offline commerce Among the sectors shaped by e-commerce and digital payments, online food delivery platforms have been especially affected, reshaping how meals are ordered and delivered.

The rapid growth of food delivery platforms and apps has created a new link between meal producers and consumers Food delivery applications (FDAs) are mobile services that enable quick online ordering and offline delivery of goods and services As FDA usage has risen, so have the profits, with global revenue increasing from 95.4 billion USD in 2018 to 107.4 billion USD in 2019 (Statista Reports, 2021), and forecasts suggesting it will reach 164.5 billion USD by 2024 (IMARC Group, 2020).

As a result of the growth and global pandemic caused by the coronavirus (COVID-

By May 2020, the World Health Organization (WHO) reported about 5 million confirmed COVID-19 cases worldwide By February 2021, the global tally had risen to over 106 million confirmed cases and more than 2.32 million deaths In response to the pandemic, governments across numerous nations implemented a range of precautionary measures to curb transmission and protect public health.

During the COVID-19 situation, to avoid direct contact, essential non-pharmaceutical interventions such as donning masks, maintaining social distance, and self-isolation were adopted to achieve the greatest possible reduction in transmission These measures, aimed at reducing the spread of COVID-19, are supported by Wilder-Smith et al (2020), Kuc-Czarnecka (2020), and Korzeb & Niedziółka (2020).

The shift toward cashless transactions to minimize direct person-to-person contact has spurred a surge in online transactions and the use of FDAs (Yang et al., 2021; Dabija et al., 2018; Pop et al., 2021) Online payment platforms and applications, such as FDAs, have made it easier for consumers and businesses to transition from traditional cash and in-store services to online-to-offline (O2O) experiences.

Smart technologies and mobile applications have become an integral part of everyday life, driven by rapid advances in information and communication technology and smartphones This shift is supported by extensive research across multiple studies, underscoring the growing importance of mobile apps (Baabdullah et al., 2019; Dwivedi, 2019; Dwivedi et al., 2016; Lal & Dwivedi, 2009; Lu et al., 2019; Malaquias & Hwang, 2019; Shareef et al., 2012; Shareef et al., 2016; Ismagilova et al., 2019) Apps designed for smartphones and other mobile platforms are developed to be downloaded and used on various devices, including tablets such as iPads.

Rapid urbanization and a fast-paced lifestyle have transformed eating habits, pushing people to seek convenient alternatives to on-site dining Driven by Millennials (born 1980–2000) and students, demand for on-demand meals has surged, boosting the growth of food delivery apps (FDAs) in Vietnam These apps are particularly active in major cities such as Ho Chi Minh City and Hanoi, meeting urban residents’ need for quick and reliable delivery services.

Online food delivery market share is currently divided among several brands, with GrabFood, Now, and Baemin as the leading players, and GrabFood being the most popular app It is therefore essential to identify and measure the factors affecting students’ adoption of GrabFood in Ho Chi Minh City and to propose practical solutions that help managers implement policies to improve service quality and attract more customers.

In 2020, Vietnam saw strong growth in online food delivery driven by the Covid-19 pandemic According to Euromonitor International, the online food delivery market in Vietnam was worth USD 33 million in 2018 and is expected to reach USD 38 million by 2020, with an average annual growth rate of about 11% projected over the next five years.

According to the 2020 Vietnamese Online Food Delivery Market Report, analyzed by the Refuta-Social Listening Platform, GrabFood led the Vietnamese online food delivery market with 33.38% of social discussions, followed by Now at 23.16% and Baemin at 21.95%, while Loship and GoFood accounted for 15.14% and 6.37%, respectively.

On December 16, 2020, Q&Me released a study of Vietnam's food delivery market based on surveys conducted in Hanoi and Ho Chi Minh City with 1,046 respondents aged 18 to 45, finding GrabFood to be the most-used app, followed by Now in second place, then Baemin and GoFood in third and fourth, and Loship in fifth.

This study investigates why GrabFood has sustained and expanded its position during the Covid-19 pandemic by examining the factors that influence students in Ho Chi Minh City to adopt and use the GrabFood application It identifies key drivers of adoption such as convenience, perceived safety, price sensitivity, delivery speed, and digital literacy, and explains how these factors help GrabFood maintain demand among university and college students during health crises The findings aim to offer actionable insights for GrabFood's marketing and service strategies in Ho Chi Minh City and similar urban markets facing ongoing Covid-19 challenges.

The objective of research

This study focuses on four main goals:

Objective 1: Determine the factors affecting adoption of using GrabFood application of students during Covid-19 pandemic in Ho Chi Minh City

Objective 2: Examine the impact of these factors on students’ satisfaction of using Grabfood app during Covid-19 pandemic in Ho Chi Minh City

Objective 3: Examine students’ satisfaction on continuance intention of using Grabfood app

Objective 4: From the research results, propose solutions that businesses can apply to improve service quality, maintain anddevelop GrabFood in Ho Chi Minh City.

Research Subjects and Scope

The research focuses on the factors affecting students’factors affecting satisfaction, thereby affecting intention to continue using Grabfood application during Covid-19 pandemic in Ho Chi Minh City

The study will be conducted from March 2022 to June 2022.

Research Question

Food delivery apps for mobile devices with real-time connectivity bring speed and convenience to busy users

Before the pandemic, Vietnamese consumers did not widely embrace food delivery services via third-party apps However, the landscape changed in 2020 when the share of regular users reached 80%, signaling a major shift in consumer behavior and the rapid adoption of on-demand food delivery during the health crisis.

2016 only 20% of the number of users according to the survey

Q&Me's 2020 survey reveals that ordering food through third-party apps reached 82%, up from 58% in 2018 Direct phone orders declined significantly, from 71% to 23%, indicating a major shift toward app-based food ordering (Q&Me, 2020).

Therefore, the research questions are:

RQ1: What factors determine adoption of using GrabFood app?

RQ2: Do these factors influence satisfaction of using GrabFood app?

RQ3: Does the satisfaction influence continuance intention of using GrabFood app?

Thesis outline

This thesis comprises five chapters, with the front matter—cover, declaration, acknowledgments, table of contents, acronyms, lists of figures and tables, references, and appendices—excluded from the main content; the body begins with Chapter 1: Introduction, which outlines the study’s objectives, scope, and context.

Chapter 1 outlines the urgency of studying the factors affecting students’ adoption of the GrabFood application during the COVID-19 pandemic in Ho Chi Minh City, detailing the research objectives, tasks, problems and questions, research methods, data sources, and the anticipated contributions It shows that this topic is both scientifically rigorous and practically relevant, providing a solid basis for further research steps By establishing a strong theoretical framework, outlining rigorous research methods, and describing data collection processes, the study aims to produce meaningful results and actionable solution implications for students, educators, policymakers, and service providers navigating pandemic conditions in Ho Chi Minh City.

LITERATURE REVIEW

The concept of Mobile Food Delivery Applications

2.1.1 Overview of Mobile Food Delivery Applications (MFDA)

A food-ordering app offers a convenient and efficient way for mobile users to browse menus, place orders, and securely pay for meals from a wide range of restaurants, all within a single platform Orders are often delivered directly to customers’ doorsteps with little to no face-to-face interaction, enabling faster service through contactless delivery (Chotigo, J.; Kadono, Y.)

Taylor notes that food service organizations are experimenting with new ways to reach customers, and mobile food-ordering apps have grown in popularity in recent years These smartphone-based apps let customers connect with restaurants, browse menus, place delivery orders, and pay bills without interacting with restaurant staff.

A Rakuten Insight survey (Minh Ngoc Nguyen, 2022) shows that in Vietnam, more than 27% of respondents aged 45–54 used food delivery apps once or twice a week in 2020, while 9% of those aged 16–24 ordered food through apps several times daily.

Online meal delivery apps provide a comprehensive suite of services, giving users access to a wide range of food options while efficiently collecting orders and routing them to restaurants, processing payments, scheduling deliveries, and offering real-time tracking They also offer benefits such as region-specific information about delivered meals and the convenience of ordering and paying for food through a mobile app once it is installed (Ko, H.S.A., 2016).

Food delivery apps are widely praised for positively influencing consumer behavior by letting people enjoy their favorite cuisines while providing precise nutritional information, a combination that promotes sustained use of these platforms (Chotigo, J.; Kadono, Y., 2021).

2.1.2 The need to order food online during the Covid-19 pandemic

Safety rules that forbade direct contact and enforced social distancing disrupted the catering industry during the COVID-19 pandemic, yet consumer dining behavior shifted toward online meal delivery services to lower transmission risk and meet changing needs (Cepel, M et al., 2020) In China, there was a surge in FDA usage, with an average 71 percent increase from February to March 2020, as many participants turned to these services to reduce outings and minimize the risk of COVID-19 transmission (Kuzmenko, O et al., 2020).

During the COVID-19 pandemic, the surge in demand for online food delivery and the rise of platforms partnering with catering companies created major opportunities for the online delivery industry With few alternatives available, customers were willing to pay higher delivery fees, while delivery riders accepted lower earnings Simultaneously, online middlemen exploited the situation by charging excessive commissions to expand their sales Online platforms facilitated online-to-offline (O2O) food delivery services during the epidemic, meeting consumer preferences for convenience and safety.

With global internet access and the widespread use of mobile devices, buying and selling activities have become easier, and brands increasingly rely on apps for marketing communication and online brand building Today, most companies maintain brand and product apps—enterprise apps and e-commerce platforms—that support digital marketing in a globalized economy, using online media and the internet to attract consumer attention and drive purchase impulses In Vietnam, the COVID-19 pandemic reshaped everyday life, forcing people to study and work from home and expanding demand for convenient services As a result, the food delivery model emerged as a rapid-growth service, achieving surprising success Nielsen Vietnam reports that during the pandemic, up to 62% of Vietnamese consumers preferred ordering food to eat at home, underscoring a shift toward online food ordering and home-based consumption.

To reduce on-site dining during Covid-19, the demand for online food ordering has surged in Ho Chi Minh City and Hanoi, with online and takeaway orders rising about 10-30% from before A 2020 Kantar market survey on consumer demand for Vietnamese cuisine found that up to 43% of people in Ho Chi Minh City and 34% in Hanoi order food online at least once per week Driven by epidemic fears, consumers pursue two purchasing options—takeaway and home delivery—but home delivery accounts for a much higher share, roughly twice that of take-away.

Grab started as a ride-hailing app in 2014, and Grab Food officially entered the fast-food delivery market in June 2018 With strong financial resources and extensive coverage, Grab Food has emerged as the leader in the food delivery market In early October 2019, Grab further expanded its ecosystem by launching Grab Kitchen, operating under the Cloud Kitchen model.

Grab has expanded its service to 15 provinces and cities across Vietnam, with Hanoi and Ho Chi Minh City serving as its primary markets The Grab app is downloaded on more than 130 million mobile devices, giving users access to over 175 thousand restaurants and partner establishments In the first half of 2019, Grab Food saw a 400% increase in total transaction value, averaging 300,000 daily orders (Vietdata, 2021)

Grab is one of the most frequently used online-to-offline (O2O) mobile platforms in Southeast Asia, delivering essential daily services through a single app The Grab app has been downloaded on more than 130 million devices and connects users to over 8.5 million driver partners, business partners, and agents, offering a diverse range of ride-hailing services and delivery options Operating across 336 cities in eight Southeast Asian countries, Grab also runs GrabFood, the region’s food delivery service, with operations in Malaysia, Singapore, Vietnam, Indonesia, the Philippines, and Thailand Grab Financial expands access to cashless payments for millions of customers and serves those without a bank account or full banking access by connecting them to financial services.

Unified theory of acceptance and use of technology 2 (UTAUT2)

The Technology Acceptance Model (TAM), rooted in the Theory of Reasoned Action (TRA) from social psychology, has been widely used to explain technological acceptance across information systems research (Pavlou & Fygenson, 2006) It has been applied to mobile commerce, e-commerce, and social networks, among other domains, to investigate how users come to accept and adopt new technologies However, TAM's ability to reveal the relationships among different elements in IT environments is limited because it cannot account for the influence of numerous exogenous variables on its core constructs (Agarwal & Karahanna, 2000).

The technology acceptance model (TAM) has been extensively applied to information systems research across mobile commerce, e-commerce, and social networks, among other domains However, TAM’s ability to uncover correlations among IT components is limited because it does not adequately account for the effects of numerous exogenous variables on its constructs (Agarwal & Karahanna, 2000) Additionally, TAM has been criticized for failing to provide a thorough description of work–technology contexts (Morosan & De Franco, 2016).

Venkatesh, Morris, Davis, and Davis proposed the Unified Theory of Acceptance and Use of Technology (UTAUT), a comprehensive model that integrates multiple theories of technology acceptance, including the Technology Acceptance Model (TAM), to explain information system users’ adoption and use behavior According to UTAUT, performance expectancy, effort expectancy, social influence, and facilitating conditions have direct effects on behavioral intention and actual usage The model also includes moderator variables such as gender, age, experience, and voluntariness of use, which shape the strength of these relationships By combining these elements, UTAUT provides enhanced explanatory power for information systems adoption and usage in organizational and consumer contexts.

In the Technology Acceptance Model (TAM), performance expectancy reflects perceived usefulness and gauges the extent to which using a system will improve task or job performance According to Lai, this construct is a crucial predictor of users' behavioral intentions, underscoring its significance for technology adoption.

On multiple instances, it has been validated in studies on the factors that influence the adoption and use of new products and technology

Gender, age, experience, and voluntariness, according to UTAUT, may have mediation effects when these four exogenous variables influence users' behavioral or use intentions (Nair, P K., Ali, F., & Leong, L C, 2015)

UTAUT has been applied in a number of recent hotel industry studies to examine technology adoption The model treats performance expectancy as the perceived usefulness of a technology and effort expectancy as its perceived ease of use, but these constructs largely capture service and the functional aspects of technology within organizations Consequently, the framework may overlook cognitive and psychological factors that influence technology acceptance, as noted by Chen and Holsapple (2013).

To address the shortcomings of the original model, Venkatesh, Thong, and Xu extended UTAUT2 by integrating psychological and cognitive factors—hedonic motivation, price value, and habit—into the framework Hedonic motivation, defined as the pleasure derived from using technology, significantly influences technology acceptance and ongoing utilization (Venkatesh et al., 2012) Price value represents the perceived benefits of using an app relative to its monetary costs, a concept described by Dodds, Monroe, and Grewal (1991) Together, these components enhance the predictive power of UTAUT2 for understanding user adoption and continued use of technology.

Price value has a positive impact on adoption of using when the benefits of adopting technology are assessed to outweigh the monetary costs (Venkatesh, V., Thong, J Y., & Xu, X., 2012)

Habits are learned, automatic behaviors that are performed without conscious effort (Limayem, M., Hirt, S G., & Cheung, C M., 2007) They can be influenced by current environmental conditions or past experiences and may operate at either a conscious or unconscious level (Hsu, M H., Chang, C M., & Chuang, L W., 2015).

According to Venkatesh et al., prior technology experience is essential for habit to influence technology use, and habit is a crucial predictor of future technology acceptance The seven factors of the UTAUT2 model—performance expectancy, effort expectancy, social influence, enabling conditions, hedonic motivation, price value, and habit—have been examined for their impact on past technology adoption The study identifies these seven variables as likely to shape the long-term use of delivery app services Accordingly, the factors listed are proposed as determinants of sustained engagement with delivery apps.

Satisfaction

When an individual's previous feeling is blended with surrounding unjustified expectations, satisfaction (SAT) is described as a mixture of experiences (Oliver,

Within the Expectancy Confirmation Model (ECM), satisfaction is an emotion-based, overall evaluation of an information system (IS) When the service meets or exceeds users' expectations, satisfaction leads to a positive intention toward ongoing FDA use For instance, Gao et al (2015) showed that satisfaction significantly affects customers’ propensity to continue mobile purchasing Additionally, satisfaction acts as an extra component of the UTAUT model and positively shapes users’ willingness to continue using information technology (Alghamdi et al., 2018) Other research on continuance intention across mobile technologies includes mobile banking (Liébana-Cabanillas et al., 2017; Susanto et al., 2016; Yuan et al., 2016), mobile apps (Hsiao et al., 2016; Tam et al., 2018), mobile payment (Cao et al., 2018; Dlodlo, 2014), and mobile commerce (Marinkovi et al.).

2020) are just a few examples As a result, the following hypothesis proposes satisfaction as a complimentary variable of UTAUT and ECM:

H1: The satisfaction significantly influences continuance intention of mobile food delivery applications.

Performance expectancy

Performance expectancy (PE) is the extent to which new technology and applications help users perform tasks more productively and conveniently PE is a key determinant of a user’s willingness to try new technology and to continue using it Research indicates that users perceive FDAs as more useful and are more likely to sustain their use (Mun et al., 2017; Yeo et al., 2017; Roh & Park, 2019) Moreover, PE has a significant positive impact on the continued use of mobile technologies, including mobile internet (Zhou, 2011), mobile instant messaging and social networking apps (Lai & Shi, 2015), mobile banking (Yuan et al., 2016), and mobile shopping apps (Yuan et al., 2016; Chopdar & Sivakumar).

Perceived enjoyment (PE) significantly influences customer satisfaction and the intention to adopt mobile technology in the future (Tam et al., 2018) Within the UTAUT framework, studies by Marinkovi et al (2020) and Chong (2013) indicate that PE is a strong predictor of users' mobile commerce usage satisfaction Under the ECM model, PE also has a significant impact on happiness and the intention to continue using mobile devices, as shown by Yuan et al (2016) and Susanto et al (2016) Consequently, PE is a crucial variable in both UTAUT and ECM, yielding beneficial effects on users' intention to return and overall satisfaction Therefore, the following hypotheses are proposed:

H2: The performance expectancy significantly influences satisfaction of using Grabfood app.

Effort expactancy

Effort expectancy (EE) is a core component of the Unified Theory of Acceptance and Use of Technology (UTAUT) and measures how easy users find a technology to use (Venkatesh et al., 2003) In the mobile app domain, higher EE strengthens users' intention to continue using the app, signaling that ease of use promotes ongoing engagement Evidence suggests that perceived ease of use can enhance satisfaction, reduce effort, and support sustained adoption of mobile applications Consequently, EE is a key predictor of continued mobile app use, alongside other UTAUT factors such as performance expectancy, social influence, and facilitating conditions.

Ease of Use (EE) refers to users’ impressions of how easy it is to operate FDAs in this study, and these perceptions drive higher long‑term intentions to use FDAs, especially during the COVID‑19 pandemic EE has been widely examined in prior research, including its application within the UTAUT framework to explain why people continue using computers (Venkatesh et al., 2011), with supportive evidence from Kang (2014), Fang and Fang (2016), and Ray et al (2019) on FDAs.

Electronic engagement (EE) significantly influences satisfaction with mobile commerce usage, as shown by Marinkovi et al (2020) in a UTAUT-based study This pattern is also reported by Yeh and Li (2009) and Agrebi and Jallais (2015), among others, highlighting the positive impact of EE on mobile shopping satisfaction.

Shang and Wu (2015) As a result, the following hypothesis is proposed in this study:

H3: The effort expectancy significantly influences satisfaction of using Grabfood app.

Social influence

Social influence is the primary predictor of technological acceptance in both the UTAUT and UTAUT2 models It is defined as the extent to which individuals perceive approval of a specific behavior by important referents (Venkatesh et al., 2003, p.159) Opinion leaders, including family, friends, and celebrities, play a key role in teaching consumers how to use new technologies and in guiding those who are trying something new for the first time, particularly early adopters A growing body of research indicates that social influence positively impacts consumer intentions across various sectors, including mobile diet apps (Okumus et al., 2018) and mobile commerce (Macedo et al.).

Research by Hsiao et al (2016) and Gallarza and Gil Saura (2006) identified a link between social influence and customer happiness, showing that high trust in a group or individual makes customers more easily influenced by that source; Chotigo and Kadono (2021) further highlight that social influence can drive action A study on food delivery apps during the COVID-19 pandemic found that executing targeted campaigns or activities via social media can encourage more users to adopt a meal delivery app and enhance their experiences, with social influence exerting a considerable impact on customer satisfaction Based on these findings, this investigation proposes H4: The social influence significantly influences satisfaction of using Grabfood app.

Facilitating condition

Enabling conditions, as described by Venkatesh et al (2003), refer to the degree to which individuals believe there is sufficient organizational and technological infrastructure to support the use of a given technology This construct encompasses factors that increase a consumer’s readiness to adopt technology, including monetary resources, time, internet connectivity, and an individual’s cognitive and physical abilities, all of which can shape future willingness to use technology (Lu et al.).

Facilitating conditions, defined as the ease of access to infrastructure and technical support for technology users, influence how customers experience digital payments When adequate facilities are available, customers feel more comfortable using digital payments, which increases their happiness with these services Prior studies have confirmed the link between facilitating conditions and customer happiness (Alalwan 2020; Maillet et al 2015) Therefore, this study proposes the following hypothesis: facilitating conditions positively affect customer happiness with digital payments.

H5: The facilitating condition significantly influences satisfaction of using Grabfood app.

Price value

The UTAUT2 model includes a price value construct to identify how financial constraints influence customer technology use, defined as the cognitive trade-off between perceived benefits and monetary cost (Venkatesh et al., 2012) Consumers consider how much they are willing to pay to meet their needs or obtain goods and services Price adjustments and pricing strategies significantly shape marketing strategy and often alter demand and customer satisfaction Prior research shows price value as a key factor influencing customers’ willingness to try new technology (Shaw et al., 2019; Alalwan et al., 2017; Venkatash et al., 2012) Moreover, increasing perceived benefits or reducing perceived costs of using a food delivery service can boost consumer satisfaction Iyer et al (2018) found that customer happiness with a retailer app is influenced by perceived value, which leads to the formulation of hypothesis H6.

H6: The price value significantly influences satisfaction of using Grabfood app.

Habit

Within the UTAUT2 framework, habit is defined as an automatic behavior that develops through repeated experience and perceived benefits from a technology, leading users to adopt it more regularly When customers perceive positive outcomes and satisfaction from a technology, their usage becomes habitual, reinforcing ongoing use and potentially influencing others to follow suit Previous work highlights habit’s hidden yet powerful role in sustaining current users and persuading peers to adopt technology (Alalwan et al., 2020; Macedo et al., 2017; Alalwan et al., 2018; Venkatesh et al., 2012) Habit also affects customer satisfaction by distinguishing genuine needs from pleasing elements, shaping whether individuals form durable usage routines (Limayem et al., 2003) A study conducted during the COVID-19 pandemic in Thailand found that habit predicted customer happiness with food delivery apps, underscoring the importance of habitual use for satisfaction in digital services.

2021) As a result, the following hypothesis is offered:

H7: Habit regarding delivery apps significantly influences satisfaction of using Grabfood app

Hedonic Motivation (HM)

This refers to the amount of enjoyment derived from using a product or information technology service, such as e-banking (Pikkarainen et al., 2004; Venkatesh et al.,

Hedonic motivation is often viewed as an intrinsic driver that pushes individuals to use a service, and empirical evidence shows a positive association with online banking use frequency (Pikkarainen et al., 2004) Prior studies have used 'fun' across contexts; for example, Igbaria, Schiffman, and Wieckowski (1994) describe pleasure as acts performed voluntarily without the expectation of a reward, while Moon and Kim (2001) define perceived playfulness as comprising curiosity, concentration, and pleasure that positively influence internet use intention and adoption We propose two types of hedonic motivation based on distinct aspects of activity completion pleasure, and the findings suggest that hedonic satisfaction is grounded in positive emotion linked with engagement, as well as a feeling of having the means and opportunities to meet basic and psychological needs (Kraut, 1979) Accordingly, hedonic motivation can be defined as the pleasurable satisfaction of deficiency needs resulting from participation in an athletic event and the consumption of services provided during the event.

H8: Hedonic motivation regarding delivery apps significantly influences satisfaction of using Grabfood app

Eudaimonic Motivation (EM)

Eudaimonic motivation is the willingness to act in pursuit of personal excellence, driving people to realize distinctive potentials (such as pursuing an athletic career) and universal human potentials (like cultivating wisdom, kindness, or gratitude) The ultimate goal is thriving—the realization of one’s highest potentials This stands in contrast to hedonic motivation, defined as the pursuit of pleasure while avoiding pain, and it marks the division between eudaimonists and hedonicists: eudaimonists strive to become a certain kind of person rather than merely seek pleasure The source of eudaimonic motivation lies in the pleasure derived from fulfilling one’s full potential and advancing toward self-actualization As a result of participating in athletic events and the services provided during them, eudaimonic motivation can be described as the satisfaction of increasing needs, and it is associated with a positive influence on happiness.

H9: Eudaimonic motivation regarding delivery apps significantly influences satisfaction of using Grabfood app

Chapter 2 of this chapter has systematized the theoretical bases related to the research topic, including the theoretical basis of the mobile food delivery applications (MFDA), unified theory of acceptance and use of technology 2 (UTAUT 2) In addition, the author has also proposed a model of factors affecting adoption of using mobile food devivery applications and research hypotheses.

METHODOLOGY

Research Design and Data collection

The COVID-19 pandemic and related government measures have spurred an explosion in online meal delivery services in Vietnam (Al Amin et al., 2021) Market data from Kantar TNS show that Vietnam's online food delivery market generated $148 million in revenue in 2018 and grew at an average annual rate of 28.5%, with projections suggesting it could reach about $449 million by 2023 Earlier research has identified the factors that influence behavioral intention and continued use of food delivery apps, highlighting the importance of understanding both initial adoption and ongoing engagement in this fast-growing sector (Bich Thao, 2021).

The goal of this study is to use the UTAUT model to look at the factors that influence the adoption of food delivery mobile applications during the COVID-19 epidemic A quantitative research technique was applied, with Structural Equation Modeling (SEM) being used to test the conceptual framework and examine the validity of the hypotheses

An online questionnaire was used as the study instrument, and a convenience sample strategy was used

This quantitative study distributes questionnaires to students who use the GrabFood service in Ho Chi Minh City to collect data The analysis employs Structural Equation Modeling (SEM) to test the proposed hypotheses, as SEM enables the simultaneous estimation of all constructs in the model and the assessment of causal relationships among variables Latent constructs are identified through the integration of measurement and structural models, and only valid surveys—determined after data collection and screening—are retained for analysis.

Procedure

This study investigates the factors influencing students' adoption of the GrabFood application in Ho Chi Minh City during the Covid-19 pandemic, drawing on theoretical concepts and a comprehensive review of prior research on food-delivery adoption Building on this evidence, the author defines clear research objectives, formulates hypotheses, and outlines the study scope A theoretical framework is applied to develop a research model that identifies the key drivers of GrabFood adoption among students in Ho Chi Minh City amid the pandemic Based on the proposed model, a data collection plan is designed to evaluate the relationships and test the hypotheses, ensuring rigorous empirical analysis The author constructs a measurement scale to serve as the data-gathering and analysis tool and to support robust conclusions and managerial implications The experimental investigation is guided by a formal study design, acting as a blueprint for data collection and analysis in line with established methodological guidance (Hair et al., 2003; Malhotra, 2006).

Questionaire Design

The second section of this study adopts the Unified Theory of Acceptance and Use of Technology (UTAUT) model and comprises measurement items designed within this framework The UTAUT-based measurements used in the current research were adapted from Venkatesh et al (2003), Palau-Saumell et al (2019), and Zhao and Bacao (2020).

The study uses a two-section questionnaire comprising demographic characteristics and measurement items, with the measurement part assessing six constructs—performance expectancy, effort expectancy, social influence, facilitating conditions, price value, and habit—based on prior literature to determine the magnitude of the model’s variables Developing a scale for the model’s constructs is crucial for linking theory to empirical testing (Anderson and Gerbing, 1988) In quantitative research, the focus is on economic and social phenomena, which require precise and reliable scales for evaluation The Likert scale is the most widely used in quantitative studies, with options including the 7-point Likert scale (often called the Likert-7), as well as 3- and 5-point formats; researchers can also use verbal anchors such as "agree" to simplify responses.

Objective of reserach Review the literature

Collecting data Analysis of the

The result, the solution implication

This study employs a Likert-type attitude measurement with evenly spaced response levels on 3-, 5-, or 7-point scales Each item is a statement about the attitude in question, and respondents select one of the provided responses Respondents can rate using a five-point scale, ranging from "Strongly disagree" to "Strongly agree," while the instrument also uses a 7-point scale to assess agreement for each observed variable Four items represent each of the seven components of the independent-variable constructs The dependent variables are satisfaction (SAT) and continuance intention (CI), each measured with two items Overall, the Likert-scale methodology quantifies attitudes, perceptions, and behavioral intentions in the survey.

To ensure objectivity in the research, the measured variables are coded into symbols, specifically as follows:

Table 1 Measurement scale of observed variables

During the COVID - 19 pandemic, Grabfood application is useful for me to order and receive delivery food

During the COVID - 19 pandemic, Grabfood application is useful for me to order and receive delivery food

PE3 Using Grabfood application streamlines the ordering and delivery of food

During the COVID - 19 epidemic, using Grabfood application enhances the efficiency of ordering and receiving delivery food

EE1 It's simple for me to figure out how to use Grabfood application

EE2 All phases of Grabfood application are simple for me to follow

It's simple for me to master the use of Grabfood application on a mobile device

The interaction with Grabfood application is simple and straightforward

During the COVID - 19 epidemic, people who matter to me (such as family members, close friends, and coworkers) advise that I use food delivery mobile apps

People close to me believe that food delivery mobile apps is useful during the COVID - 19 epidemic

SI3 People close to me believe that using food delivery mobile apps during the

COVID - 19 pandemic is a fantastic idea

People that are significant to me help me by using Grabfood application on their phones

FC1 I have the essential smartphone to use

FC2 I have the essential skills to use

Grabfood application on my phone

FC3 I'm comfortable utilizing Grabfood delivery on my phone

FC4 Grabfood application, is compatible with the other technologies I have used

P1 Good price P2 Price according to quality P3 Attractive discount program P4 Prices are clearly presented

HT1 I've developed a habit of ordering food through food delivery apps

HT2 Food delivery applications have become an addiction for me.

HT3 I must use food delivery apps for purchasing foods

HT4 I've become accustomed to using meal delivery apps to make food purchases Venkatesh, Hedonic HM1 Using Grabfood app is fun

(2012) motivation HM2 Using Grabfood app is enjoyable

HM3 Using Grabfood app is very entertaining

HM4 Using Grabfood app give me pleasure HM5 Using Grabfood app is exciting

HM6 Using Grabfood app is thrilling

HM7 Using Grabfood app is delightful

I use Grabfood app in my food buying during the COVID - 19 pandemic when

I am seeking to develop a skill, learn, or gain insight into something

I use Grabfood app in my food buying during the COVID - 19 pandemic when

I am seeking to do what I believe in

I use Grabfood app in my food buying during the COVID - 19 pandemic when

I am seeking to pursue excellence or a personal ideal

I use Grabfood app in my food buying during the COVID - 19 pandemic when

I am seeking to use the best in myself

I use Grabfood app in my food buying during the COVID - 19 pandemic when

I am seeking to contribute to others or the surrounding world

Oliver, R.L., Satisfaction SAT1 I am satisfied with my decision to use

SAT2 I am satisfied with my previous experiences with this Grabfood app

SAT3 My choice to use this Grabfood app is a wise one

Continue intention CI1 I intend to continue using this Grabfood app rather than discontinue its use

My intentions are to continue using this Grabfood app than use others similar apps

CI3 I will recommend others to use the

CI4 If I could, I would like to continue my use of this Grabfood app.

Formal research

Achieving correctness and precision in research requires a suitable sample size The expected reliability of results, the data analysis technique, the estimation method used, and the parameters to be estimated are all essential factors that influence the required sample size in a research model By understanding how these elements interact, researchers can determine an appropriate, efficient sample size and enhance the validity of their study.

Hair et al (1998) state that Exploratory Factor Analysis (EFA) requires a minimum of five observations per variable Given 35 variables in this study, the recommended 5:1 ratio translates to a total sample size of n = 35 × 5 = 175.

According to Tabachnick and Fidell (2007), the minimum sample size should be determined using the rule n ≥ 50 + 8m, where m represents the number of variables in the study Since this investigation includes seven independent variables, the required sample size is n ≥ 50 + 8 × 7 = 106.

Raykov and Widaman (1995) explain that structural equation modeling (SEM) based on large-sample distribution theory requires a high sample size, making large samples essential for SEM analyses Anderson et al (1985) illustrate the large-scale approach by applying parsing on the Statue of Liberty for their wide-scale study For structural linear models, the sample size is crucial: a sample of 200 is appropriate for unstable relationships, while 300 is preferable for more stable and reliable estimates.

The author, however, selects the size of this study model to be n = 325 to assure the dependability of the scale investigations

This study uses a non-probability sampling approach for its time-saving, low-cost efficiency and purported objectivity The target population includes residents and workers in Ho Chi Minh City who are familiar with or have used the GrabFood delivery app.

To develop a thorough survey questionnaire, the author built it using the specified scales and the observed factors described earlier Before commencing official data collection, the questionnaire was tested and tweaked to improve its validity, reliability, and clarity.

The discussion questionnaire identifies key factors influencing the adoption of GrabFood delivery apps, including performance expectancy, effort expectancy, facilitating conditions, social influence, price value, and habit Its comprehensive structure is presented in the form of a survey, organized into two main parts, with Appendix 1 detailing the framework and items used.

Part 1: The survey's introduction and instructions for responding many key questions of the study are quantitative, with a 7-level Likert scale used to evaluate the level of agreement for each observable characteristic

Part 2: The personal information of the questioned persons.

Data analysis

To ensure the scale's reliability, we first remove variables with Cronbach's alpha below a suitable threshold and then analyze the remaining items using exploratory factor analysis (EFA) Guided by a factor-analytic approach, any factor with poor structure—where the total correlation and factor loadings are less than 0.5—will be deleted, and the scale will be re-evaluated using confirmatory factor analysis (CFA) In some studies, it may be necessary to retain items with the smallest loadings to achieve an acceptable error level (greater than 50%), while variables with low CFA values are discarded at this stage Concurrently, the model structure is computed to determine the final model form.

Cronbach's Alpha is used to determine a scale's reliability by assessing the internal consistency among its measures It has been used historically to exclude inappropriate variables that could produce artificial factors (Nguyen Dinh Tho and Nguyen Thi Mai Trang, 2009) However, Cronbach's Alpha only indicates whether the measures are related; it does not specify which variables should be retained or deleted To refine the construct, the correlation between each item and the total score (item-total correlation) is examined, and items that do not contribute meaningfully to the concept are excluded, ensuring the total variables improve the construct definition.

During reliability assessment, any item whose correlation with the total score is below 0.3 should be removed to improve Cronbach's Alpha If the resulting Cronbach's Alpha is above 0.6, the scale is considered reliable, and among candidate scales you should choose the one with the higher Alpha, since a higher Cronbach's Alpha indicates stronger internal consistency and dependability, as noted by Nunnally (1978).

Most researchers regard Cronbach's Alpha of 0.8 or higher as acceptable, with 0.7–0.8 also considered suitable for establishing internal consistency When the measured construct is new or unfamiliar to respondents, a Cronbach's Alpha of 0.6 or higher may be acceptable (Nunnally, 1978) Consequently, items with a Cronbach's Alpha below 0.6 and a corrected item-total correlation below 0.3 are typically omitted from the model to improve reliability.

The next stage is to examine the EFA exploratory factor after determining the scale's reliability using Cronbach's Alpha of the factors

EFA is a strategy for reducing observable variables into more generic and relevant components based on their connection Convergent and discriminant validity were determined using the EFA method

The criteria for the EFA analysis are as follows:

1 To guarantee that the scale achieves the convergent value, factor loading must be used to determine whether to maintain or delete the observed variable It will be eliminated from the model if the loading factor is less than 0.5 Loading factor 0.5 (Hair et al., 2006)

2 The Eigenvalue index is used to calculate the number of factors The factor will be eliminated from the research model if the Eigenvalue index is less than one

3 Total Variance Explained to evaluate the scale: When the extracted variance is more than 50%, the EFA model is considered appropriate (Hair et al., 2006) Total Variance Explained demonstrates how the components explain the variance of the variation when the variation is set to 100%

4 The Barlett test and the Kaiser - Meyer - Olkin coefficient is used to assess whether the EFA factor analysis is appropriate (KMO) The KMO coefficient must be more than 0.5 (0,5 KMO 1) The model is not approved for EFA factor analysis if the KMO coefficient is less than 0.5

5 To see if the population's observed variables are connected The Barlett test exhibits statistical significance (Sig.0.05), indicating that the observed variables in the population are associated

Structural Equation Modeling (SEM) is one of the most powerful and flexible techniques for analyzing complex relationships in causal modeling, enabling researchers to test comprehensive models with multiple variables, directions of influence, and latent constructs This approach has been widely applied across disciplines, particularly in psychology (Anderson & Gerbing, 1988; Hansell & White, 1991), sociology (Lavee, 1988; Lorence & Mortimer, 1985), and child development research (Anderson, 1987; Biddle & Marlin).

Originating in 1987 and further developed in management research by Tharenou, Latimer and Conroy (1994), this model has become a widely applied framework in customer satisfaction studies In particular, it has been used to analyze the mobile information service industry in Korea, as demonstrated by M.K Kim et al in Telecommunications Policy.

28, 2004), Research model of customer loyalty Mobile information services in Vietnam (Pham Duc Ky, Bui Nguyen Hung, 2007)

SEM is used to estimate measurement models (Mesurement Models) and structural models (Structure Models) of multivariable theory problems

Within structural equation modeling, the measurement model defines how latent variables relate to their observed indicators, revealing the reliability and validity of the measurements The structural model specifies the relationships among latent variables, allowing researchers to test theoretical predictions and hypotheses about how constructs influence one another.

The suitability of the entire model is in fact assessed through the following criteria of relevance:

 Chi-Square test (χ2): Expresses the overall goodness of fit of the whole model at the p-value = 0.05 level of significance (Joserkog & Sorbom, 1989)

Chi-Square ratio to degrees of freedom (χ2/df) is used to assess the overall goodness-of-fit of a model, offering a more detailed evaluation of fit Some authors recommend a fit range of 1 < χ2/df < 3 (Hair et al., 1998), while others argue that χ2 should be as small as possible and propose χ2/df < 3:1 (Chin & Todd, 1995) In practical studies, two common conventions are cited: χ2/df < 5 when the sample size is greater than 200 (N > 200), or χ2/df < 3 when the sample size is smaller (N < 200), with Kettinger and Lee (1995) noting that these thresholds indicate a good fit.

- CFI: value > 0.9 is considered as a good fit model If these values are equal to 1, the model is perfect (Segar, Grover, 1993) & (Chin & Todd, 1995)

RMSEA is a key fit criterion used to evaluate how well a proposed model matches the data In information systems (IS) research, a RMSEA value below 0.05 indicates a good model fit, and in some cases values up to 0.08 are considered acceptable, as noted by Taylor, Sharland, Cronin, and Bullard (1993).

This chapter details the research design, data collection procedures, questionnaire development, and data analysis for a quantitative study It explains how the predicted scale was refined and constructed from observed factors, using a seven-point Likert scale for the variables, based on prior research reported in Chapter 2 The central focus is identifying the factors that influence the adoption of the GrabFood application The chapter also describes the quantitative data analysis approaches used in this study A practical survey was conducted in accordance with the methodologies outlined in Chapter 3, and the comprehensive results are presented in Chapter 4.

RESULT AND DISCUSSION

Construct Reliability

Cronbach's alpha is a statistic commonly referenced to show that tests and scales used in research projects are reliable and fit for purpose (Taber, 2018) A reliability value above 0.7 typically indicates that a scale is trustworthy (Nunnally, 1978) In this study, Cronbach’s alpha indexed the following variables: Performance expectancy = 0.956, effort expectancy = 0.953, social influence = 0.944, facilitating conditions = 0.744, price value = 0.8847, habit.

Cronbach's alpha values indicate strong internal consistency for the measurement instruments, with the full-scale alpha at 0.937 and subscales for hedonic motivation 0.949, eudaimonic motivation 0.963, satisfaction 0.866, and continuance intention 0.941 Since all Cronbach's alpha values exceed 0.7 and the corrected item-total correlations are above 0.4, the subjective variables scale demonstrates reliable measurement.

Confirmatory Factor Analysis (CFA)

Following exploratory factor analysis, confirmatory factor analysis (CFA) was performed to identify the number of underlying latent variables (factors or constructs) and to assess the pattern of observed variable–factor relationships This step serves to validate the factor structure indicated by the exploratory analysis and to establish the construct validity of the measurement model, as described by Mustafa, M.B et al.

A loading factor of 0.50 (positive values only) is treated as the primary criterion for assessing model compatibility, not a loading of 1.00 (Hair et al., 2007) To evaluate each model’s goodness-of-fit, the chi-square statistic, the comparative fit index (CFI), and the root mean square error of approximation (RMSEA) were used The CFI and TLI indices exceeded 0.90, and RMSEA was at or below 0.08 (0.05 is ideal), with chi-square/df at or below 2 (3 may be acceptable in rare circumstances) The results align with these thresholds, indicating excellent model fit, as all fit indices meet the required levels: chi-square/df = 1.827, CFI = 0.956, TLI = 0.952, RMSEA = 0.051, and SRMR = 0.058.

RMSEA = root mean square error of approximation

SRMR = standardized root mean square residual

Table 3 Convergent Validity and Reliability

M = Mean, Loading = Factor Loading, CR = Composite Reliability

Model dependability was evaluated using Cronbach's alpha and composite reliability (CR) to assess internal consistency Cronbach's alpha, the standard measure of internal consistency, should be 0.7 or greater (Barclay, Higgins, and Thompson, 1995) In this study, the minimum Cronbach's alpha and CR values were 0.866 and 0.921, respectively, both well above the 0.7 threshold The high Cronbach's alpha and CR values indicate that the measurement approach is highly reliable and demonstrates strong internal consistency for the model.

Discriminant Validity and Correlations

Within the measurement model, the maximum shared variance (MSV) of latent variables should be lower than the average variance extracted (AVE) to confirm discriminant validity (Hair et al., 2010) In this study, all MSVs were less than the AVEs, indicating there is no cross-loading among components Accordingly, discriminant validity, convergent validity, and reliability passed for the dataset, as demonstrated by the values in Tables 2 and 3.

A discriminant validity assessment was conducted using the Fornell-Larcker criterion, which requires that the square root of AVE for each construct exceed its inter-construct correlations with other constructs The lowest AVE observed was 0.7446 for HT, and Table 5 shows a maximum inter-construct correlation of 0.96 (for PE); despite these values, the analysis concluded that the measurement model's discriminant validity was satisfactory, allowing structural equation modeling to proceed.

Table 4 Correlation Matrix and Discriminant Validity

AVE CI EE EM FC HM HT PE PV SAT SI

SI 0.882 0.242 0.380 0.438 0.349 0.402 0.360 0.410 0.429 0.455 0.939 AVE = Average Variance Extracted

Hypothesis Testing

The validity of the model and the interactions among the constructs were investigated using structural equation modeling (SEM) This data analysis was carried out using the

Using the AMOS program, the model demonstrated excellent to high-quality fit, as reflected in the path diagram: chi-square = 1504.347 with 823 degrees of freedom (p-value = 0.00), chi-square/df = 1.828, CFI = 0.956, TLI = 0.952, and SRMR = 0.058.

RMSEA = 0.051 The study model's Normed X2/df was 1.828 (Bagozzi, 1988), suggesting that it was effective

Data analysis indicated that the satisfaction had a significantly positive impact on continuance intention of mobile food delivery applications (β = 0.665, p < 0.001)

The results indicate that H1 was supported, with effort expectancy having a significantly positive effect on satisfaction with using the GrabFood app (β = 0.159, p < 0.01), thereby supporting H3 The study also found direct positive relationships between facilitating conditions (β = 0.136, p < 0.01) and satisfaction, as well as between perceived value (β = 0.127, p < 0.05) and satisfaction, confirming H5 and H6 Moreover, hedonic motivation (β = 0.243, p < 0.050) and eudaimonic motivation (β = 0.010, p < 0.001) significantly influenced continued behavior, supporting H8 and H9 On the other hand, the study did not find any effects of performance expectancy (β = 0.065) and social influence.

(β = 0.087) and habit (β = -0.010) on satisfaction of using Grabfood, thus H2, H4 and

Hypotheses Path Beta t-value P Decision

H1 The satisfaction (SAT) significantly influences continuance intention

(CI) of mobile food delivery applications

H2 The performance expectancy (PE) significantly influences satisfaction (SAT) of using Grabfood app

(SAT) of using Grabfood app

H4 The social influence (SI) significantly influences satisfaction (SAT) of using Grabfood app

(SAT) of using Grabfood app

H6 The price value (PV) significantly influences satisfaction (SAT) of using Grabfood app

H7 Habit (HT) regarding delivery apps significantly influences satisfaction (SAT) of using Grabfood app

(HM) regarding delivery apps significantly influences satisfaction

(SAT) of using Grabfood app

(EM) regarding delivery apps significantly influences satisfaction

(SAT) of using Grabfood app

Samples were collected using a convenient survey method via Google Forms, and after removing invalid responses, 325 valid responses remained for quantitative analysis The results describe the characteristics of the sample.

53.8% of respondents were female, indicating that women are more frequent smartphone users and more interested in discounts, cashback, and other Grabfood benefits than men, who accounted for 46.2% The participants were divided into three age groups, with the largest share concentrated in the youngest group.

Among the respondents, the overwhelming majority are aged 25 years old, comprising 80.3% of the sample The oldest age group—those over 40—represents the smallest share at 0.3%, while the 26–29 age group accounts for 19.4% In terms of occupation, the largest group are students, making up 67.7% of respondents, followed by full-time employees at 23.4% Part-time workers and those looking for work account for 8% and 0.9%, respectively.

CONCLUSION AND IMPLICATIONS

Discussion

Analysis of the data identified six validated hypotheses and three that were not, indicating that price value, social influence, effort expectancy, hedonic motivation, eudaimonic motivation, facilitating conditions, and satisfaction significantly shape consumers' behavioral intentions to use food delivery apps (FDAs) during the COVID-19 pandemic These findings contrast with earlier research in the same area, such as Ray et al (2019), which used satisfaction theory to show that performance expectations, customer experience, restaurant search, and ease of use drive FDA usage intentions From this, it follows that FDAs must be designed to deliver tangible benefits to users, creating demand that can mature into a lifestyle and drive financial success for restaurant and delivery service operators Providing as much information as possible satisfies consumer curiosity and strengthens the likelihood of positive word-of-mouth recommendations based on past experiences In addition, there should be simplicity in the installation and deployment of these apps to support rapid adoption and ongoing use.

Compared with the global COVID-19 pandemic, which drove many businesses to rely on online channels to comply with safety regulations that limit human-to-human contact and to offset revenue losses from extended lockdowns and restrictions, online ordering and food delivery platforms became essential The study by Kuzmenko et al (2020) shows how these platforms made it easier for people to access prepared meals, helped restaurant owners stay in business during lockdown hardships, and mitigated the impact of income restrictions; without such platforms, the majority of restaurant businesses would have faced bankruptcy and liquidation.

The technical component of FDA platforms is essential to the ongoing success of the online meal delivery enterprise Study H5 shows a positive association between perceived task-technology fit (TT) and behavioral intention to use (BIU) For users to return to an app repeatedly, they must trust the technology and feel secure, making data protection a central priority for restaurants aiming to stay involved in the online meal delivery market Research by Li et al (2020) and Riana et al (2021) addresses integrity and the need for user confidence in technology The safety and hygiene benefits of adopting FDA platforms—versus personally visiting restaurants—were highlighted in Riana et al (2021) and Teo et al (2014) Elvandari et al (2018) identified the technical prerequisites needed to raise the quality of regional online food delivery services Overall, findings point to key technical needs for improving local online meal delivery, including perceived technology-task fit, order conditions, user-friendliness, and civility Cho et al (2019) further show that single-person and multi-person households perceive FDAs differently, with task-technology fit, perceived value, attitudes, and reuse desire varying across household types.

Online customers expect top-tier performance in both information delivery and the speed of customer support To build trust, ensure security, and guarantee ongoing use and patronage, businesses must be readily available to respond to inquiries and concerns as quickly as possible Adopting inclusive, accessible technology helps reach a broad audience and the business’s target demographic, ensuring information and support are available across multiple channels and devices.

During the COVID-19 pandemic, this study emphasizes leveraging existing knowledge to guide the deployment of food delivery apps (FDAs) For managers driving FDA sales and marketing, the findings show that users’ behavioral intention to use FDAs is shaped by several factors, notably price value, effort expectancy, facilitating conditions, satisfaction, hedonic motivation, and eudaimonic motivation The report also identifies the key considerations for FDA users and owners to align the technology with performance expectancy, habit, and social influence Overall, the advancement of FDA adoption should incorporate all these factors, especially in the COVID-19 context The study offers important insights for startups planning to enter the FDA market and other on-demand delivery apps in the future.

Conclusion

Respondents in Bangkok who had used FDAs contributed data for the study Of 410 questionnaires distributed, 325 valid responses were identified and analyzed using AMOS 26 to perform structural equation modeling The study's results demonstrated that there is a behavioral intention to use FDAs during the COVID-19 period.

The COVID-19 pandemic is significantly influenced by effort expectancy, facilitating conditions, perceived price value, satisfaction, hedonic motivation, and eudaimonic motivation The study identifies issues and actionable areas for managers and owners of online food delivery platforms, highlighting factors to consider when moving from brick-and-mortar to online operations To help potential customers quickly locate items and place orders, the online food display interface must be intuitive and easy to use Equally important is protecting users’ sensitive data, as strong online security parallels physical barriers in a restaurant; safeguarding credit card details and other personal information is essential to prevent data breaches and the associated financial and reputational losses.

An enhanced UTAUT2 model augmented with information quality was applied to identify the key factors driving continuance intention to use food delivery apps, with Grabfood as the focal service The model includes satisfaction, performance expectancy, effort expectancy, social influence, enabling conditions, price value, habit, hedonic motivation, and eudaimonic motivation as determinants of delivery app use Data analysis shows that effort expectancy, facilitating conditions, price value, hedonic motivation, and eudaimonic motivation positively affect customer satisfaction, and satisfaction acts as a mediator that enhances the desire to continue using the service Consequently, effort expectancy, enabling conditions, price value, hedonic motivation, and eudaimonic motivation influence the willingness to order meals via an app The results partly align with prior studies (Lee et al., 2017; Okumus et al., 2003; Prasanna et al., 2016), suggesting that Grabfood satisfaction boosts continued use by highlighting benefits such as time and cost savings, faster transactions, and diverse options; however, respondents were not significantly influenced by social ties and habit, contradicting Singh (2017) and Shaw & Sergueva (2019) Notably, hedonic motivation exerts the strongest positive impact on continuance intention, while eudaimonic motivation enhances satisfaction’s benefits to further drive Grabfood use, whereas effort expectancy, facilitating conditions, and price value were not significant direct determinants—likely due to high smartphone/ICT maturity reducing perceived effort and the lack of a distinct price-value advantage in mobile ordering Overall, the extended UTAUT2 model with information quality can explain customers’ repeated meal ordering through delivery apps.

Implications

Using the UTAUT2 model, this study investigates e-commerce customer behavior in the ongoing use of mobile delivery app services The study argues that UTAUT2 offers greater explanatory power than the TAM model, which long dominated technology adoption research, and the UTAUT model, which complemented TAM This research is notable for applying UTAUT2 to the food services industry, where it has been seldom used It also demonstrates that information quality is a key determinant of consumer acceptance of new information systems in food service, by specifying information quality as an antecedent in the UTAUT2 framework and examining its relationships with performance expectancy, effort expectancy, and continuous use intention By integrating trends from food services and information technology, the study makes a meaningful contribution to academic discourse Consequently, the UTAUT2-based theoretical framework may serve as a basis for future investigations into e-commerce behavior of food service customers.

To motivate continued use of delivery apps, it is essential to understand the significance of performance expectancy (PE), habit (HT), and social influence (SI) Increasing customers' performance expectations depends on controlling the information provided on the platform, with operators delivering comprehensive, reliable data that is accurate and timely Delivery app providers must continually update menus and pricing information to avoid confusion and dissatisfaction, while also verifying credibility by offering detailed restaurant information such as hours, location, menus, ratings, and contact details By prioritizing high-quality information and leveraging PE, HT, and SI, these apps can boost user trust, reinforce habitual use, and enhance overall customer satisfaction.

Social influence on the use of delivery apps like GrabFood is constrained when advertising and word-of-mouth approaches fail to deliver contentment, and even recommendations from family or friends can fall short if user expectations aren’t met Peers have a strong impact on which apps people choose, so distributed app developers should actively push word-of-mouth marketing and harness social media to reach the core user base, especially younger users under 25 and those under 30 who are prolific delivery app users and active on social platforms GrabFood must prioritize customer feedback and ratings, and the app design should present information in a simple, user-friendly way to ensure a seamless experience and drive sustained engagement.

On Grabfood, the habit element (HT) does not enhance customer satisfaction, signaling that habit alone does not drive loyalty Managing users of delivery platforms is a major challenge because use history is essential for forming habits that influence technology adoption Pre-customer management is more important than acquiring new clients, just as in traditional restaurants To prevent customers from switching to competing delivery apps, the service must offer multiple incentives to retain them.

Limitations and Future Work

Several limitations emerged in this study First, data were collected in Vietnam during the COVID-19 pandemic, which restricts the generalizability of the findings to other periods and settings; to broaden applicability, future research should replicate the study across multiple countries Second, a provider’s potentially biased view of product quality underscores the value of including candid customer reviews on websites, while surveys may be completed quickly, potentially compromising data quality Third, the study’s predominantly quantitative approach narrowed the scope of the results To strengthen understanding, future studies should prioritize qualitative and longitudinal data collection Moreover, subjective standards are shaped by various external factors, so research in the food delivery sector should incorporate belief structure as a predictor of subjective norms, a dimension this study did not examine.

1 Aarts, H., Verplanken, B., & Van Knippenberg, A (1998) Predicting behavior from actions in the past: Repeated decision making or a matter of habit? Journal of applied social psychology, 28(15), 1355-1374

2 Agarwal, R., & Karahanna, E (2000) Time flies when you're having fun:

Cognitive absorption and beliefs about information technology usage MIS quarterly, 665-694

3 Al Amin, M.; Arefin, M.S.; Alam, M.R.; Ahammad, T.; Hoque, M.R Using mobile food delivery applications during COVID-19 pandemic: An extended model of planned behavior J Food Prod Mark 2021, 27, 105–126

4 Anderson, D R., Choi, H P., & Lorch, E P (1987) Attentional inertia reduces distractibility during young children's TV viewing Child Development, 798-

5 Anderson, J C., & Gerbing, D W (1988) Structural equation modeling in practice: A review and recommended two-step approach Psychological bulletin, 103(3), 411

6 Androniceanu, A (2020) Major structural changes in the EU policies due to the problems and risks caused by COVID-19 Adm Manag 34, 137–149

7 Androniceanu, A (2020) Social responsibility, an essential strategic option for a sustainable development in the field of bio-economy Amfiteatru Econ 52, 503–519

8 Baabdullah, A M., Alalwan, A A., Rana, N P., Patil, P., & Dwivedi, Y K

(2019) An integrated model for m-banking adoption in Saudi Arabia International Journal of Bank Marketing

9 Bagozzi, R.P.; Yi, Y On the evaluation of structural equation models J Acad

10 Barclay, D., Higgins, C., & Thompson, R (1995) The partial least squares

(PLS) approach to casual modeling: personal computer adoption ans use as an Illustration

11 Biddle, B J., & Marlin, M M (1987) Causality, confirmation, credulity, and structural equation modeling Child development, 4-17

12 Brown, T.A Confirmatory Factor Analysis for Applied Research; Guilford

Publications: New York, NY, USA, 2015

13 Cepel, M.; Gavurova, B.; Dvorsky, J.; Belas, J (2020) The impact of the

COVID-19 crisis on the perception of business risk in the SME segment J Int

14 Cepel, M.; Gavurova, B.; Dvorsky, J.; Belas, J (2020) The impact of the

COVID-19 crisis on the perception of business risk in the SME segment J Int

15 Chen, L., & Holsapple, C W (2013) E-business adoption research: State of the art Journal of Electronic Commerce Research, 14(3), 261

16 Chin, W W., & Todd, P A (1995) On the use, usefulness, and ease of use of structural equation modeling in MIS research: A note of caution MIS quarterly, 237-246

17 Chiu, C M., Hsu, M H., Sun, S Y., Lin, T C., & Sun, P C (2005) Usability, quality, value and e-learning continuance decisions Computers & education, 45(4), 399-416

18 Chiu, C.-M.; Wang, E.T Understanding Web-based learning continuance intention: The role of subjective task value Inf Manag 2008, 45, 194–201

19 Cho, M.; Bonn, M.A.; Li, J.J (2019) Differences in perceptions about food delivery apps between single-person and multi-person households Int J Hosp Manag 77, 108–116

20 Cho, M.H.; Bonn, M.A.; Li, J (2019) Differences in perceptions about food delivery apps between single-person and

21 Cho, M.H.; Bonn, M.A.; Li, J Differences in perceptions about food delivery apps between single-person and multi-person households Int J Hosp Manag

22 Chopdar, P K., & Sivakumar, V J (2019) Understanding continuance usage of mobile shopping applications in India: the role of espoused cultural values and perceived risk Behaviour & Information Technology, 38(1), 42-64

23 Chtourou, M S., & Souiden, N (2010) Rethinking the TAM model: time to consider fun Journal of Consumer Marketing

24 Dabija, D.-C.; Bejan, B.M.; Tipi, N (2018) Generation X Versus Millennials

Communication Beahviour on Social Media when Purchasing Food Versus Tourist Services Mark 21, 191–205

25 Delone, W.H.; McLean, E.R (2003) The DeLone and McLean model of information systems success: A ten-yearupdate J Manag Inf Syst.,19, 9–30

26 Demoulin, N.T.M.; Coussement, K (2018) Acceptance of text-mining systems: The signaling role of informationquality Inf Manag

27 Dodds, W B., Monroe, K B., & Grewal, D (1991) Effects of price, brand, and store information on buyers’ product evaluations Journal of marketing research, 28(3), 307-319

28 Dwivedi, Y K., Shareef, M A., Simintiras, A C., Lal, B., & Weerakkody, V

(2016) A generalised adoption model for services: A cross-country comparison of mobile health (m-health) Government Information Quarterly, 33(1), 174-

29 Elvandari, C.D.R.; Sukartiko, A.C.; Nugrahini, A.D (2018) Identification of

Technical Requirement for Improving Quality of Local Online Food Delivery Service in Yogyakarta J Ind Inf Technol Agric 1, 1

30 Fang, I C., & Fang, S C (2016) Factors affecting consumer stickiness to continue using mobile applications International Journal of Mobile

31 Fornell, C.D.; Lacker, D.F Evaluating Structural Equation models with

Unobservable Variables and Measurement Error J Mark Res 1981, 18, 39–50

32 Hair, J.F.; Money, A.H.; Samouel, P.; Page, M Research Methods for Business;

John Wiley and Sons: Chichester, UK, 2007

33 Hansell, S., & White, H R (1991) Adolescent drug use, psychological distress, and physical symptoms Journal of Health and Social Behavior, 288-301

34 Hsu, M H., Chang, C M., & Chuang, L W (2015) Understanding the determinants of online repeat purchase intention and moderating role of habit: The case of online group-buying in Taiwan International Journal of

35 Huta, V., & Waterman, A S (2014) Eudaimonia and its distinction from hedonia: Developing a classification and terminology for understanding conceptual and operational definitions Journal of happiness studies, 15(6), 1425-1456

36 Igbaria, M., Schiffman, S J., & Wieckowski, T J (1994) The respective roles of perceived usefulness and perceived fun in the acceptance of microcomputer technology Behaviour & information technology, 13(6), 349-361

37 Jeon, H.M.; Ali, F.; Lee, S.W Determinants of consumers’ intentions to use smartphones apps for flight ticket bookings Serv Ind J 2019, 39, 385–402

38 Jeon, H.M.; Choi, H.M (2017) Consumer’s acceptance on mobile delivery app service: Focused on UTATU2 FoodServ Ind J.,13, 67–82

39 Ji, S.W.; Sun, X.Y.; Liu, D (2014) Research on core competitiveness of

Chinese retail industry based on O2O.Adv Mater Res, 834–836, 2017–2020

40 Joreskog, K.D and Sorbom, D (1989), LISREL 7: A Guide to the Program and

Applications, SASS Inc., Chicago, IL

41 Kayes, K Ordering Food during COVID-19: Crave or Cave? 2020 Available online: https://www.dhakatribune.com/magazine/avenue-t/2020/07/02/ordering- food-during-covid-19-crave-or-cave (accessed on 11 September 2021) Waterman, A S (1993) Two conceptions of happiness: Contrasts of personal expressiveness (eudaimonia) and hedonic enjoyment Journal of Personality and Social Psychology, 64(4), 678–691

42 Kettinger, W J., & Lee, C C (1995) Exploring a ―gap‖ model of information services quality Information Resources Management Journal (IRMJ), 8(3), 5-

43 Kim, M K., Park, M C., & Jeong, D H (2004) The effects of customer satisfaction and switching barrier on customer loyalty in Korean mobile telecommunication services Telecommunications policy, 28(2), 145-159

44 Kim, S.; Park, H (2013) Effects of various characteristics of social commerce

(s-commerce) on consumers’ trust andtrust performance Int J Inf Manag.,33, 318–332

45 Kim, S.H.; Bae, J.H.; Jeon, H.M (2019) Continuous intention on accommodation apps: Integrated value-based adoption and expectation— Confirmation model analysis Sustainability, 11, 1578

46 Korzeb, Z.; Niedziółka, P (2020) Resistance of commercial banks to the crisis caused by the COVID-19 pandemic: The case of Poland Equilib Q J Econ Econ Policy 15, 205–234

47 Kraut, R (1979) Two conceptions of happiness The philosophical review, 88(2), 167-197

48 Kuc-Czarnecka, M (2020) COVID-19 and digital deprivation in Poland

49 Kuzmenko, O.; Vasylieva, T.; Vojtovič, S.; Chygryn, O.; Snieška, V (2020).z

Why do regions differ in vulnerability to COVID-19? Spatial nonlinear modeling of social and economic patterns Econ Sociol 13, 318–340

50 Kuzmenko, O.; Vasylieva, T.; Vojtovič, S.; Chygryn, O.; Snieška, V

(2020).Why do regions differ in vulnerability to COVID-19? Spatial nonlinear modeling of social and economic patterns Econ Sociol., 13, 318–340

51 Kuzmenko, O.; Vasylieva, T.; Vojtovič, S.; Chygryn, O.; Snieška, V (2020)

Why do regions differ in vulnerability to COVID-19? Spatial nonlinear modeling of social and economic patterns Econ Sociol 13, 318–340

52 Lai, I K W., & Shi, G (2015) The impact of privacy concerns on the intention for continued use of an integrated mobile instant messaging and social network platform International Journal of Mobile Communications, 13(6), 641-669

53 Lavee, S (1988, July) Involvement of plant growth regulators and endogenous growth substances in the control of alternate bearing In VI International Symposium on Growth Regulators in Fruit Production 239 (pp 311-322)

54 Lee, E.Y.; Lee, S.B.; Jeon, Y.J.J Factors influencing the behavioral intention to use food delivery apps Soc Behav Pers 2017, 45, 1461–1474

55 Lee, E.Y.; Lee, S.B.; Jeon, Y.J.J Factors influencing the behavioral intention to use food delivery apps Soc Behav Pers 2017, 45, 1461–1474

56 Li, C.; Mirosa, M.; Bremer, P (2020) Review of Online Food Delivery

Platforms and their Impacts on Sustainability Sustainability 12, 5528

57 Limayem, M., Hirt, S G., & Cheung, C M (2007) How habit limits the predictive power of intention: The case of information systems continuance MIS quarterly, 705-737

58 Liu, W.; Batra, R.; Wang, H (2017) Product touch and consumers’ online and offline buying: The role of mental representation J Retail, 93, 369–381

59 Lorence, J., & Mortimer, J T (1985) Job involvement through the life course:

A panel study of three age groups American Sociological Review, 618-638

60 Lustig, C., Konkel, A., & Jacoby, L L (2004) Which route to recovery?

Controlled retrieval and accessibility bias in retroactive interference Psychological Science, 15(11), 729-735

61 Lyu, S.O.; Hwang, J (2015) Are the days of tourist information centers gone?

Effects of the ubiquitous informationenvironment Tour Manag., 48, 54–63

62 Minh Ngoc Nguyen (2022) Frequency of using food delivery apps in Vietnam

2020 by age group Available at: https://www.statista.com/statistics/1154081/vietnam-reasons-using-food- delivery-apps-less-during-covid-19-by-age-group/#statisticContainer

63 multi-person households Int J Hosp Manag, 77, 108–116

64 Nair, P K., Ali, F., & Leong, L C (2015) Factors affecting acceptance & use of ReWIND: Validating the extended unified theory of acceptance and use of technology Interactive Technology and Smart Education

65 Nascimento, B., Oliveira, T., & Tam, C (2018) Wearable technology: What explains continuance intention in smartwatches? Journal of Retailing and

66 Nguyen, V T., Ngo, T X B., & Nguyen, T K P (2018) Factors Affecting to

Brand Equity: An Empirical Study in Vietnam Banking Sector Studies in Computational Intelligence, 982–998

67 Nunnally, J C (1978) Psychometric Theory New York: McGraw-Hill

68 Nunnally, J.C Psychometric Theory, 2nd ed.; McGraw-Hill: New York, NY,

69 Nysveen, H., Pedersen, P E., & Thorbjứrnsen, H (2005) Explaining intention to use mobile chat services: moderating effects of gender Journal of consumer Marketing

70 O'Hair, D., Friedrich, G W., & Dixon, L D (1998) Strategic communication in business and the professions

71 Okumus, B.; Ali, F.; Bilgihan, A.; Ozturk, A.B (2018) Psychological factors influencing customers’ acceptance ofsmartphone diet apps when ordering food at restaurants Int J Hosp Manag.,72, 67–77

72 Oulasvirta, A., Rattenbury, T., Ma, L., & Raita, E (2012) Habits make smartphone use more pervasive Personal and Ubiquitous computing, 16(1), 105-114

73 Pagani, M (2004) Determinants of adoption of third generation mobile multimedia services Journal of interactive marketing, 18(3), 46-59

74 Philip Kotler (2001) Quản trị Marketing Nhà xuất bản Thống Kê

Consumer acceptance of online banking: an extension of the technology acceptance model Internet research

76 Pop, R.-A.; Săplăcan, Z.; Dabija, D.-C.; Alt, M.-A (2021) The impact of social media influencers on travel decisions: The role of trust in consumer decision journey Curr Issues Tour 1–21

77 Prasanna, R.; Huggins, T.J Factors affecting the acceptance of information systems supporting emergency operations centres Comput Hum Behav 2016,

Faslah, R (2020) Application of UTAUT (Unified Theory of Acceptance and Use of Technology) to understand the acceptance and use of the e-learning system International Journal of Advanced Science and Technology, 29(4), 5051-5060

79 Ray, A.; Dhir, A.; Bala, P.K.; Kaur, P (2019) Why do people use food delivery apps (FDA)? A uses and gratification theory perspective J Retail Consum

80 Riana, D.; Hidayanto, A.N.; Hadianti, S.; Napitupulu, D (2021) Integrative

Factors of E-Health Laboratory Adoption: A Case of Indonesia Future Internet

81 Roh, M., Park, K., 2018 Adoption of O2O food delivery services in South

Korea: the moderating role of moral obligation in meal preparation Int J Inf Manage

82 San, S S., & Dastane, O (2021) Key Factors Affecting Intention to Order

Online Food Delivery Journal of Industrial Distribution & Business, 12(2), 19-

83 Segar, H., & Grover, V (1993) Re-examining perceived ease of use measurements and perceived usefulness Decision sciences

84 Shao, X.; Siponen, M Consumer acceptance and use of information technology:

Adding consumption theory to UTAUT2 Work Pap Inf Syst 2011, 11, 1–8

85 Shaw, N.; Sergueeva, K The non-monetary benefits of mobile commerce:

Extending UTAUT2 with perceived value Int J Inf Manag 2019, 45, 44–55 [

86 Singh, M.; Matsui, Y How long tail and trust affect online shopping behavior:

An extension to UTAUT2 framework Pac Asia J Assoc Inf Syst 2017, 9, 1–

87 Singh, M.; Matsui, Y How long tail and trust affect online shopping behavior:

An extension to UTAUT2 framework Pac Asia J Assoc Inf Syst 2017, 9, 1–

88 Tabachnick, B G., Fidell, L S., & Ullman, J B (2007) Using multivariate statistics (Vol 5, pp 481-498) Boston, MA: pearson

89 Taber, K.S The Use of Cronbach’s Alpha When Developing and Reporting

Research Instruments in Science Education Res Sci Educ 2018, 48, 1273–

90 Taylor, S A., Sharland, A., Cronin, J J., & Bullard, W (1993) Recreational service quality in the international setting International journal of service industry management

91 Taylor, S., Jr Campus dining goes mobile: Intentions of college students to adopt a mobile food-ordering app J Foodserv Bus Res 2020, 24, 121–139

92 Teo, T.; Zhou, M (2014) Explaining the intention to use technology among university students: A structural equation modeling approach J Comput High Educ 26, 124–142

93 Tharenou, P., Latimer, S., & Conroy, D (1994) How do you make it to the top?

An examination of influences on women's and men's managerial advancement Academy of Management journal, 37(4), 899-931

94 Thong, J.Y.L.; Hong, S.J.; Tam, K.Y The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance Int J Hum Comput Stud 2006, 64, 799–810

95 Troise, C.; O’Driscoll, A.; Tani, M.; Prisco, A Online food delivery services and behavioural intention—A test of an integrated TAM and TPB framework

96 Van der Heijden, H User Acceptance of hedonic information system MIS Q

97 Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D User acceptance of information technology, toward a unified view MIS Q 2003, 27, 425–478

98 Venkatesh, V.L.; Thong, J.Y.; Xu, X Consumer acceptance and use of information technology: Extending theunified theory of acceptance and use of technology MIS Q 2012,36, 157–178

99 Venkatesh, V.L.; Thong, J.Y.; Xu, X Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology MIS Q 2012, 36, 157–178

100 Wang, C., J Harris, and P Patterson 2013 The roles of habit, selfefcacy, and satisfaction in driving continued use of self-service technologies: A longitudinal study Journal of Service Research 16(3): 400–414

Wang, Ou, and Chen (2019) examine how inertia and user satisfaction influence the continuance intentions to use mobile communication applications, viewed through a mobile service quality perspective Published in the International Journal of Information Management (Volume 44, October 2018, pages 178–193), the study highlights the central role of perceived mobile service quality in shaping user satisfaction and inertia, which in turn affect ongoing usage intentions for mobile apps.

102 Waterman, A S (1990) Personal expressiveness: Philosophical and psychological foundations The Journal of Mind and Behavior, 47-73

103 Weniger, S., & Loebbecke, C (2011) Cognitive absorption: Literature review and suitability in the context of hedonic IS usage Department of business, media and technology management, University of Cologne, Germany, 1-19

104 Wilder-Smith, A.; Freedman, D.O (2020) Isolation, quarantine, social distancing and community containment: Pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak J Travel Med 27, 1–

105 Xu, C., Peak, D., & Prybutok, V (2015) A customer value, satisfaction, and loyalty perspective of mobile application recommendations Decision Support

106 Yan, M., Filieri, R., & Gorton, M (2021) Continuance intention of online technologies: A systematic literature review International Journal of

107 Yang, M.; Mamun, A.A.; Mohiuddin, M.; Nawi, N.C.; Zainol, N.R (2021)

Cashless transactions: A study on intention and adoption of e-Wallets 13, 831

108 Yuan, S., Liu, Y., Yao, R., & Liu, J (2016) An investigation of users’ continuance intention towards mobile banking in China Information

109 Zanetta, L D A., Hakim, M P., Gastaldi, G B., Seabra, L M A J., Rolim, P

M., Nascimento, L G P., & da Cunha, D T (2021) The use of food delivery apps during the COVID-19 pandemic in Brazil: The role of solidarity, perceived risk, and regional aspects Food Research International, 149, 110671

110 Zhang, D.; Huang, G.; Yin, X.; Gong, Q Residents’ Waste Separation

Behaviors at the Source: Using SEM with the Theory of Planned Behavior in Guangzhou, China Int J Environ Res Public Health 2015, 12, 9475–9491

111 Zhou, T (2011) Understanding mobile Internet continuance usage from the perspectives of UTAUT and flow Inf Dev 27, 207–218

1 Bich Thao (2020) Đón đầu cơ hội kinh doanh thực phẩm online Available at: https://vietnamfinance.vn/don-dau-co-hoi-kinh-doanh-thuc-pham-online- 20180504224240146.htm

2 Grab VN (2019) GrabFood mở rộng thêm 12 tỉnh, thành phố, trở thành dịch vụ giao nhận thức ăn phát triển nhanh nhất Việt Nam Available at: https://www.grab.com/vn/press/business/grabfood-mo-rong-them-12-tinh- thanh-pho-tro-thanh-dich-vu-giao-nhan-thuc-an-phat-trien-nhanh-nhat-viet- nam/

3 Kỳ, P Đ., & Hùng, B N (2007) Nghiên cứu mô hình sự trung thành của khách hàng trong lĩnh vực dịch vụ TTDĐ tại Việt Nam Tạp chí Bưu chính viễn thông và công nghệ thông tin, Kỳ, 1

4 Luu Ha (2021) Dịch vụ giao nhận đồ ăn trực tuyến tăng cao trong mùa dịch Covid-19 Available at: https://vneconomy.vn/dich-vu-giao-nhan-do-an-truc- tuyen-tang-cao-trong-mua-dich-covid-19.htm

5 Q&Me Vietnam Market Research (2020) Xu hướng sử dụng Ứng dụng giao hàng thức ăn tại Việt Nam 2020 Available at: https://qandme- net.translate.goog/en/report/vietnam-food-delivery-trend-

2020.html?_x_tr_sl=en&_x_tr_tl=vi&_x_tr_hl=vi&_x_tr_pto=op,sc.