Influence on young people's decision making to order fast food in hanoi Influence on young people's decision making to order fast food in hanoi
Research background, statement of discussion topic
In today's fast-paced society, young people often juggle demanding schedules that include both studying and working The need to wake up early for breakfast or to pack lunch can be inconvenient and exhausting, leading to a desire for more convenient solutions As a result, ordering fast food through delivery apps has emerged as a popular and time-saving option for many.
The global online food delivery market is experiencing significant growth, with projections indicating it will reach a revenue of US $1.40 trillion, highlighting a worldwide trend in this sector.
The online food delivery industry is projected to experience significant growth, with a compound annual growth rate (CAGR) of 7.83% from 2025 to 2029 In Asia, market revenue is anticipated to reach US $630.30 billion in 2024, continuing to grow at a CAGR of 9.02% during the period from 2024 to 2029 This indicates a robust growth potential for the industry in the coming years.
Vietnam's online food delivery market is experiencing significant growth, with revenue projected to reach $2.37 billion by 2024, reflecting a compound annual growth rate (CAGR) of 11.05%, surpassing the Asian average A report by Momentum Works highlights that Vietnam is among the fastest-growing countries in Southeast Asia for food delivery services, anticipating a remarkable growth rate of 26% by 2024, which will increase the total transaction value from $1.4 billion in 2023 to $1.8 billion in 2024.
The usage of online food delivery services in Vietnam has surged, with 83% of the population utilizing these services in 2024, up from 62% the previous year Grab's 2023 commodity trend report highlights that the primary users of food delivery apps are predominantly in the 18-24 age group, indicating a strong preference among young people for these convenient options.
The online food delivery market is poised for significant growth in the coming years, driven by the fast-paced lifestyle, technological advancements, and an expanding young user demographic As food ordering platforms continue to evolve and attract more users, the demand for online food delivery services is expected to surge.
Figure 1.1: Revenue per user of the ODF market in Vietnam from 2019-2028
Source: Statista 2025 1.1.2 Rationale of the study
The rapid advancement of the Internet and technology has transformed consumer behavior in the food service sector, particularly among students who now prioritize convenience, speed, and variety over home-cooked meals This shift, accelerated by the Covid-19 pandemic, has led to a surge in online food delivery, prompting F&B businesses to invest in digital platforms and enhance delivery services to cater to young customers Understanding the fast-food purchasing behavior of young people in Hanoi is essential for businesses to identify the factors influencing their ordering decisions, ultimately improving service quality and increasing order rates Research in this area is crucial for clarifying these factors, enabling businesses to enhance user experience and develop effective strategies that boost customer satisfaction This study not only provides practical insights for the F&B industry but also aids in optimizing online food delivery platforms, fostering competitive advantages and sustainable growth in the future.
This study investigates the Hanoi market, particularly targeting young consumers who have a strong demand for online fast food ordering services This dynamic demographic frequently engages with technology, exhibits rapid consumption habits, and prioritizes convenience Despite the rising demand for food delivery services among young people, existing research has yet to analyze the specific factors influencing their ordering decisions in Hanoi.
This study investigates the impact of changing consumer habits and the growth of online food delivery services in Hanoi It seeks to answer two key questions: first, what factors influence young people's intention to order food via these platforms, and second, how do their attitudes shape their behavioral intentions towards ordering fast food online?
This research paper explores the psychology of young consumers in Hanoi, focusing on the factors that influence their user experience By conducting surveys and analyses, the study aims to offer targeted recommendations for food and beverage businesses utilizing online food delivery platforms.
This study examines the factors affecting fast food ordering behavior via food delivery apps, specifically targeting young consumers in Hanoi, a rapidly evolving market Building on Jun's (2021) model, the research has been tailored to the Vietnamese context by omitting the hypothesis linking Social Influence (SI) to Attitude (ATT), as prior studies and preliminary data indicate this factor lacks a consistent impact in Vietnam By focusing exclusively on young individuals in Hanoi, the study aims to gather more relevant and actionable insights for businesses in the online food delivery industry, thereby identifying key elements that shape contemporary consumer behavior in this market.
This study enhances the dialogue surrounding online shopping behavior, particularly in food ordering through applications, while offering valuable insights into the consumption habits and viewpoints of young consumers The findings not only benefit food delivery platforms but also equip restaurants and eateries partnering with these services to refine their business strategies, enhance customer service experiences, and attract more customers.
The study enhances businesses' understanding of customer needs and expectations for online food delivery services, enabling them to adapt their business models effectively It identifies key factors influencing young people's intention to order food through apps in Hanoi, offering a valuable database for advertising and market research This insight allows companies to develop more effective marketing, advertising, and incentive strategies, ultimately boosting revenue and brand value.
- Spatial scope: The study focuses on young people living, studying and working in Hanoi, where the online food delivery ecosystem is developed
- Time scope: The study focuses on fast food ordering behavior of young people from December 2024 to January 2025
The research subjects are young people, including students and young people aged 18-29, who have a need to use food delivery services
This study employs an online questionnaire survey using Google Forms to efficiently gather data from young individuals living, studying, and working in Hanoi The questionnaire will be distributed through social media platforms, enhancing response rates and broadening the study's reach This method ensures that the collected data is highly representative of the target demographic.
1.7 Research methods and data processing
This research employs quantitative techniques to investigate the influences on the fast food ordering habits of young individuals using online food delivery services in Hanoi Data was gathered through a survey and analyzed with Smart PLS software, utilizing the PLS-SEM method to explore the interrelationships among the study's variables.
Theoretical framework
Technological advancements in Internet-based transactions have significantly transformed consumer and business behaviors (Sjahroeddin, 2018) The Internet has created new opportunities for online food delivery (OFD) services, enhancing communication, promotion, and product/service delivery to potential customers (Sidharta et al., 2021) Online food delivery is now characterized as the process where customers place orders online, and partnered food service providers prepare and deliver the food via mobile applications (Ray et al., 2021).
The rise of online food delivery (OFD) services has enabled restaurants and fast food chains to broaden their customer base cost-effectively, while providing consumers with the convenience of ordering food on demand (Shankar et al., 2022) These services keep customers informed throughout the ordering and delivery process via mobile applications, allowing them to easily select their preferred items from the menu and input their delivery details (Pignato et al., 2017) Additionally, OFD services save time for users, eliminating the need for them to switch between different resolutions to fulfill their dining needs (Novita & Husna, 2020).
The rapid growth of online food delivery (OFD) services has sparked significant interest among customers, leading them to explore e-delivery systems Research indicates that "action intention" reflects customers' willingness to register for OFD services in the future (Venkatesh et al., 2008; Chai & Yat, 2019) Positive attitudes towards new products or technologies enhance acceptance, while behavioral intention is closely linked to customer experience (Olorunniwo et al., 2006) Satisfied customers are more likely to continue using OFD, particularly those who appreciate the convenience and ease of online ordering, such as younger urban consumers who prioritize speed and autonomy in their food choices These digital natives are inclined to experiment with new platforms, utilize app-based promotions, and integrate OFD services into their busy lifestyles.
Early theories suggest that an individual's attitude toward a behavior (BI) is directly related to the beliefs about the expected outcomes of that behavior (Fishbein & Ajzen, 1977) This attitude plays a crucial role in predicting the intention to engage in specific actions (Yeo et al., 2017) Defined as the extent of a person's favorable or unfavorable evaluation of a behavior, attitude (ATT) can influence the likelihood of adopting that behavior (Ajzen & Fishbein, 1975; Rezaei et al., 2016) For instance, positive attitudes can increase the likelihood of behaviors such as ordering food online.
Perceived usefulness (PU) refers to the extent to which an individual believes that utilizing a specific system can enhance their performance (Davis, 1989) As noted by Davis, Bagozzi, and Warshaw (1989), this concept highlights how potential users perceive the ability of a system to improve their work efficiency within an organizational context.
Perceived ease of use (PEOU) is the extent to which individuals believe that utilizing a system is effortless (Davis, 1989) In the context of food purchasing applications, ease of use (EOU) encompasses the perceived benefits and value users gain from the experience (Piroth et al., 2020) User-friendly mobile devices and web interfaces that are easily accessible and require minimal effort significantly enhance the likelihood of customers adopting online shopping (Ramayah & Ignatius, 2005).
Enjoyment is a crucial factor influencing users' acceptance of new technology, as highlighted by Davis et al (1989) In the realm of technology, enjoyment encompasses the satisfaction, comfort, and intrinsic attraction users experience while interacting with a system Prashar et al (2019) note that enjoyment signifies the positive experiences and "flow state" customers achieve during information searches or online shopping When users find the process engaging, they are likely to invest more time in the activity, enhancing their willingness to adopt and continue using the technology Furthermore, as Ramayah & Ignatius (2005) emphasize, enjoyment plays a significant role in shopping orientation, encompassing both the interaction with technology and the satisfaction of receiving products conveniently at home This satisfaction is characterized by the "joy, delight, or pleasure" associated with online shopping.
Trust (TR) is defined as the confidence consumers have in a service provider's ability to meet commitments, deliver quality on time, and address issues transparently (Pavlou, 2003) It encompasses a sense of safety and security during online transactions, particularly in online food shopping, where product quality cannot be directly verified (McCloskey & Leppel, 2010) Furthermore, trust is a balance between supplier reliability and transaction security, incorporating technological aspects like payment security and user-friendly interfaces, as well as human factors such as prompt responses, clear return policies, and a professional service attitude (Wen et al., 2011).
Social influence significantly affects an individual's decision to adopt new technology, as highlighted by Mazman et al (2009) It encompasses the impact of one's environment, particularly the opinions of influential figures like friends, colleagues, and superiors, on the individual's willingness to embrace a technology This social pressure is crucial in the technology adoption process, as users frequently depend on social cues to determine whether to engage with a service or platform.
Bhukya et al (2023) define social influence as the alteration of an individual's thoughts, feelings, attitudes, or behaviors due to interactions with others In online food delivery, this influence manifests through recommendations from friends, social media trends, and the perceived popularity of a platform among peers Observing others actively using a service increases the likelihood that users will develop positive intentions to engage with that service themselves (Dickinger et al., 2008).
Literature Review
Table 2.1 Overview of previous studies
2.2.2 Summary of research table findings
Online food ordering behavior is shaped by several key factors, including Attitude, Behavioral Intention, Perceived Usefulness, Perceived Ease of Use, Enjoyment, Trust, and Social Influence Research highlights that these elements significantly affect the choice to utilize online food ordering applications Notably, Attitude serves as a crucial mediating variable that links the initial influencing factors to Behavioral Intention.
Theoretical Framework
This study explores the factors that shape young consumers' online fast food ordering behavior, utilizing established theories in technology adoption and consumer behavior The research framework is based on the Technology Acceptance Model (TAM) by Davis (1989) and the Theory of Reasoned Action (TRA) by Fishbein and Ajzen (1975) These models are instrumental in understanding how attitudes and cognitive processes affect behavioral intentions, particularly in technology-driven environments.
Figure 1.1 The Technology Acceptance Mode (TAM) theory model
The TAM model, developed by Davis in 1989, identifies Perceived Usefulness (PU) and Perceived Ease of Use (EOU) as key factors influencing human behavior In contrast, the Theory of Reasoned Action (TRA) highlights the importance of Attitude (ATT) and Subjective Norm in determining Behavioral Intention (BI) Together, TAM and TRA create a robust framework that elucidates the impact of rational beliefs and social influences on consumer behavior.
Figure 2.2 Theory of Reasoned Action (TRA) model
Traditional models like TAM and TRA fall short in addressing emotional factors, user experience, and social influence in the current digital landscape, particularly among young people in Vietnam To enhance relevance to real-world scenarios, this study expands the model by incorporating three additional variables: Enjoyment (EJM), Trust (TR), and Social Influence (SI).
Enjoyment emphasizes the significance of a straightforward and pleasurable app experience Trust is highlighted by over 60% of Vietnamese consumers prioritizing safety and reliability in online services (Deloitte Vietnam, 2022) Additionally, social influence is evident, with 68% of consumers consulting reviews prior to making purchase decisions (Decision Lab, 2022) Expanding the model in this manner provides a comprehensive understanding of the factors affecting the online food ordering behavior of young people in Hanoi.
The selected variables are grounded in empirical evidence highlighting their significance in digital consumer behavior Enjoyment captures the hedonic motivation linked to food ordering, often viewed as a source of fun and relaxation, particularly among younger users Trust is crucial in addressing concerns about data privacy, delivery accuracy, and platform reliability, all of which significantly impact online purchasing decisions Additionally, social influence is incorporated as an essential operational component.
Subjective Norm in TRA, according to Venkatesh et al (2003) Social influence is defined as
In the realm of online food delivery services, consumers' decisions are significantly shaped by the perceptions of key individuals in their lives, including friends, family, and social media influencers This influence highlights the importance of social validation in the adoption of new systems, as individuals often look to those they trust for guidance on whether to embrace these services.
Hypothesis development
In fundamental behavioral theories, an individual's attitude toward a behavior is an overall evaluation, either positive or negative, influenced by beliefs about the expected outcomes of that behavior The Theory of Reasoned Action (TRA) posits that these attitudes are shaped by the cumulative beliefs and evaluations an individual holds regarding the behavior.
Numerous studies highlight the critical role of attitude in predicting behavioral intention, particularly in online grocery shopping, where Hansen et al (2004) and Thompson et al (1994) found that consumer attitudes are the strongest predictors of behavioral intention Recent research on food ordering applications further supports this, demonstrating that a positive attitude significantly influences users' behavioral intentions (Cho et al., 2019; Lee et al., 2017; Yeo et al., 2017).
This study aims to explore the attitudes of young people in Hanoi towards fast food delivery apps, highlighting their significant influence on the intention to continue using these services Based on theoretical and empirical foundations, the research proposes a key hypothesis regarding this relationship.
Hypothesis 1 (H1): Attitude positively influences behavioral intention
In the TAM model, perceived usefulness (PU) significantly impacts attitudes (ATT) and behavior intention (BI) PU is defined as the degree to which an individual believes that utilizing a system enhances work efficiency or offers tangible advantages (Davis, 1989) Specifically, in online food ordering, PU encompasses the perceived benefits users gain from the application, including time savings, the ability to easily compare restaurants, and selecting the ideal dish (Piroth et al.).
Research indicates that perceived usefulness (PU) significantly influences consumer attitudes towards food delivery apps and online retail environments Studies by O'Cass & Fenech (2003), Hong et al (2021), and Karahanna et al (2006) demonstrate that PU positively affects behavioral intention (BI) Additionally, Roh & Park (2019) highlight that users' willingness to adopt technology is contingent upon the anticipated benefits Therefore, the following hypothesis is proposed:
Hypothesis 2a (H2a) PU positively influences attitude
Hypothesis 2b (H2b) PU positively influences behavioral intentions
2.4.3 Perceived Ease of use (EOU)
In the Technology Acceptance Model (TAM), perceived ease of use (EOU) is described as the degree to which an individual feels that utilizing a specific system requires minimal effort (Davis, 1989) Within the realm of online food delivery services, EOU highlights the convenience offered by the application, including features like swift ordering, an intuitive interface, and straightforward order tracking (Ray et al.).
2019) When an application is easy to use, users tend to evaluate it more positively and form a favorable attitude toward the service
Research by Troise et al (2021) indicates that perceived ease of use (EOU) significantly enhances users' attitudes (ATT) towards online food delivery platforms Consumers favor applications with user-friendly interfaces and straightforward operations that save time and effort Choi et al (2019) also emphasize that ease of use is crucial for fostering a positive attitude towards technology platforms, particularly as users increasingly prefer online services over traditional options Additionally, Murat & Haluk (2012) found that user-friendly and easily accessible systems lead to long-term positive attitudes, encouraging continued use In Hanoi, young people, who are the primary users of food delivery services, particularly value convenient and easy-to-use applications, which fundamentally shapes their attitudes towards ordering fast food online Therefore, the following hypothesis is proposed:
Hypothesis 3 (H3) EOU positively influences attitude
Many researchers have frequently hypothesized the role of enjoyment in user behavioral expectations (Chen et al., 2014; Davidson, 2018; Pietro et al., 2012) Enjoyment is considered
(EJM) as an important factor influencing users' acceptance of new technology (Davis et al.,
1989) In the Technology Acceptance Model (TAM), enjoyment is considered an intrinsic motivational factor that directly influences users' attitudes and behavioral intentions toward using technology
Numerous studies highlight the significant impact of enjoyment on the intention to use technology Henderson et al (1998) identified enjoyment as the primary factor influencing behavioral intention in electronic supermarkets Similarly, Childers et al (2001) noted that enjoyment is crucial in shaping positive consumer attitudes towards online shopping Lee et al (2005) found that enjoyment not only directly influences usage but also affects users' information states on online learning platforms In the realm of online food delivery services, enjoyment stems from user experiences such as a friendly interface, easy order setup, and engaging interactive features For young individuals in Hanoi, these elements enhance satisfaction and foster a desire to continue using the service Based on these insights, the following hypothesis is proposed:
Hypothesis 4a (H4a) Enjoyment positively influences attitude
Hypothesis 4b (H4b) Enjoyment positively influences behavioral intention
Numerous studies indicate that trust is crucial in shaping behavioral intention (BI) in online settings (Alagoz & Hekimoglu, 2012; Ashraf et al., 2019) Pavlou (2003) defines trust as the confidence customers have in the safety and reliability of both retailers and Internet technology.
Trust in technology platforms is crucial for influencing consumers' online shopping decisions, as highlighted by Nilashi et al (2015) In the realm of online food delivery (OFD) services, Alagoz & Hekimoglu (2012) noted that trust not only improves consumers' attitudes towards the service but also fosters initial satisfaction and goodwill, which in turn enhances consumer behavior Additionally, research by Cho et al (2019) and Ciro Troise (2020) supports the notion that trust plays a significant role in shaping both attitude and behavioral intention in the OFD sector, indicating that consumer confidence is essential for positive engagement.
In the Vietnamese market, the trust factor is crucial as consumers express concerns regarding product quality, shipping processes, and data security in online transactions Practical surveys conducted in Vietnam further highlight the significance of trust, as noted by Deloitte Vietnam.
In 2022, consumer trust in digital platforms emerged as a crucial element influencing online shopping choices, particularly in food delivery services Key factors such as transparency, reliability, and service safety significantly affect consumers' attitudes and intentions towards food delivery applications in Hanoi Consequently, the authors put forth two hypotheses to explore this relationship further.
Hypothesis 5a (H5a) Trust positively influences attitude
Hypothesis 5b (H5b) Trust positively influences behavioral intention
Social influence plays a crucial role in shaping consumer behavioral intentions in the online marketplace Research by Ajzen & Fishbein (1977) highlights that pressure from friends, family, and peers can significantly guide individual behavior, as people often act with the awareness that their actions may be evaluated by others This concept is central to the Theory of Reasoned Action (TRA), which underscores the impact of social factors on consumer behavior.
In the context of online consumer behavior, many studies have shown that social influence positively impacts users' behavioral adoption of mobile social networking platforms (Zhou &
Research indicates that social influence significantly affects consumer decisions regarding online food delivery services in Vietnam, as demonstrated by Duc et al (2024) and Le et al (2022) Additionally, Dung et al (2024) confirm that this influence positively impacts students' intentions to use food delivery applications in Ho Chi Minh City Therefore, this study hypothesizes that social influence plays a crucial role in the adoption of online food delivery services.
Hypothesis 6 (H6) Social influence positively influences behavioral intention.
Proposed research model
This research model builds upon Jun's (2021) study to analyze factors influencing attitude (ATT) and behavioral intention (BI) in online food delivery services, with adjustments tailored to the Vietnamese market Notably, the hypothesis linking social influence (SI) to ATT is excluded, as empirical evidence in Vietnam shows a limited and inconsistent impact of SI on ATT Instead, studies by Dung et al (2024) and Lee et al (2022) indicate that SI primarily affects BI, while research by Hong et al (2023) suggests SI may not significantly influence consumer behavior Consequently, this study focuses on the hypothesis that SI directly impacts BI, aligning more closely with the actual consumer behavior of young individuals in Hanoi regarding online food delivery.
Research Approach
This study employs a questionnaire survey method to gather quantitative data, aiming to analyze the fast food ordering intentions of young people in Hanoi This approach is commonly utilized in health service research and social sciences (Mathers et al., 1998) As noted by Sekaran and Bougie (2016), the questionnaire survey is the most effective means for collecting extensive data through structured questions Consequently, this research method was selected to ensure high reliability and alignment with the research objectives.
Sampling, Data Collection, and Data Processing Methods
3.2.1 Sampling Method for Quantitative Research
In this study, non-probability sampling methods, such as convenience and snowball sampling, were utilized to gather data from friends, acquaintances, and students within the same learning environment The survey was then widely distributed through the personal networks of initial participants, allowing for a diverse research group of subjects aged 18-29 This approach not only optimized the time and cost of data collection but also effectively captured the fast food ordering behavior of young people in Hanoi.
3.2.2 Data collection and data processing methods
Data was gathered through a widely distributed Google survey shared on Facebook Initially, it was sent via text messages to relatives, friends, and classmates, as well as directly to relevant participants, who were encouraged to share the survey with others that met the research criteria.
Data collected from the questionnaire will be coded and cleaned to ensure accuracy and optimization Data analysis will be performed using Partial Least Squares software (Smart PLS version 4.0).
Research Process
The research process of "Influence on young people's decision-making to order fast food in Hanoi" through the following steps:
Step 1: Review and analyze relevant research literature, refine the scope of the study, and define the research topic
Step 2: Examine theoretical frameworks to identify existing research gaps in food ordering behavior
Step 3: Establish research objectives, define the scope of the study, and develop and identify appropriate research methods
Step 4: Develop a hypothetical model illustrating the factors influencing fast food ordering behavior
Step 5: Design a survey plan and construct a questionnaire based on previous studies
Step 6: Distribute the survey and collect responses from participants
Step 7: Monetary analysis to assess the reliability of the scale and perform analytical definition
Step 9: Interpret the research results to draw meaningful insights
Step 10: Develop recommendations and solutions based on the results to improve understanding of fast food restaurant establishment actions.
Sample Method
To test the published hypotheses, this study selected a sample that could represent the population with a confidence level of 90% and 95%, applying the method of Yates (Box et al.,
In 2024, Hanoi's population is projected to reach 8.5 million, with 77% expected to utilize online food delivery services, translating to approximately 6.545 million users This data underscores the importance of ensuring a sufficient sample size to accurately represent the population's characteristics.
The study determined that a minimum sample size of 196 is required, based on a total population of 'N' in Hanoi and a significance level of 'e' at 0.07 (7%) or 0.1 (10%) To enhance the quality of customer data, the research collected a total of 302 survey samples.
Sampling and Data Collection
This study investigates the factors that affect young people's decisions to order fast food via online food delivery (OFD) in Hanoi To gather reliable data, a quantitative survey method was employed, with data collection taking place in November.
2024 to January 2025 with a sample size of 250 participants
In a recent survey conducted in Hanoi, 302 respondents were initially screened to determine their experience with online food delivery services Out of these, 22 individuals did not complete the survey, resulting in a total of 280 valid responses available for analysis.
Questionnaire Design
A questionnaire was developed to explore the factors influencing young people's intention to order fast food in Hanoi This research model aimed to assess the impact of various elements, including Perceived Usefulness (PU), Perceived Ease of Use (EOU), Enjoyment (EJM), Trust (TR), Social Influence (SI), Attitude (ATT), and Behavioral Intention (BI).
The questionnaire, adapted from Jun et al (2021), was tailored to the Vietnamese research context and utilized a 5-point Likert scale to assess various factors, with responses ranging from 1 ("Strongly disagree") to 5 ("Strongly agree") In addition to the core research questions, the survey included two sections: one for demographic information (age, gender, income, education level) and another to classify participants based on their habits of using food delivery applications, specifically targeting those who have previously utilized such services.
Operational Definitions and Measurement Design
This study utilizes structural equation modeling (SEM), a second-generation statistical analysis method, to explore the complex relationships among multiple variables SEM distinguishes between two types of variables: exogenous variables, which remain unaffected by others in the model, and endogenous variables, which are influenced by one or more other variables.
The endogenous variables used in this study include attitude (ATT) and behavioral intention (BI) Both variables are measured using a 5-point Likert scale and are presented as follows:
Table 3.1 Dimensions and indicators of Attitude
1 ATT1 I rate the price high for the convenience and efficiency the platform brings to online food
(Jun et al., 2021) solution for busy lives
3 ATT3 Using an online delivery platform was a good decision for me
Table 3.2 Dimensions and indicators of Behavior Intention
1 BI1 I believe that online food delivery platforms will help me improve my experience
2 BI2 I intend to order food through an online delivery service in the near future
3 BI3 I found the online delivery platform to be a solid choice and I plan to continue using it in the future
4 BI4 Using the online food delivery platform has made me intend to recommend it to friends and family
This study examines the impact of several exogenous variables, including Perceived Usefulness (PU), Perceived Ease of Use (EOU), Enjoyment (EJM), Trust (TR), and Social Influence (SI), all of which are evaluated using a 5-point Likert scale.
No Variables Items Indicators Sources
PU1 Online food delivery platforms help me set up food faster and more conveniently
PU2 Online food delivery on the platform helps me set up a more efficient way to eat food
PU3 Online food delivery platforms help set up and receive food conveniently
EOU1 Learning how to operate an online food delivery platform was easy for me
EOU2 The online food delivery platform is well-structured, and its advertisements are easy to comprehend
EOU3 I found the online food delivery platform very easy to navigate and use
Using online food delivery services brings me satisfaction and happiness, allowing me to discover new foods and enjoy my meals with joy.
EJM4 The deals and offers on the platform make me happy to order
4 Trust TR1 I believe the food I ordered I received was exactly as advertised
TR2 I believe that I will share information on online food delivery apps (OFDS) will not be leaked outside
TR3 I trust that the online platform will keep my information secure people around me using and highly appreciate them
SI2 I tend to use online food delivery platforms because it is popular in my community
SI3 Your frequent use of online food delivery platforms influenced my decision
Note: Perceived usefulness (PU), Perceived ease of use (EOU), Enjoyment (EJM), Trust (TR), Social Influence (SI).
Scale of study
This study utilizes a Nominal scale, incorporating the following questions
Table 3.4 General Information of Respondents
1 Have you ever ordered fast food through an online food ordering platform?
Yes / No Select only “Yes” votes
4 Academic level Undergraduate, Bachelor of university (college); Master or higher
5 Where do you live? Ha Noi / Other Select only “Ha Noi” votes
6 What is your average monthly income?
Data Gathering Procedure
The research was conducted by Vietnamese Research The data collection work was processed through the following stages:
Step 1: The main research tool for this study is the survey questionnaire This questionnaire is based on previous research papers and was revised by trying to provide high to suit the questions from Vietnamese people, helping to clarify to improve the reliability and effectiveness of the data collection tool
Step 2: The survey questionnaire is distributed with informational answers via social platforms to expand the next scope
Step 3: After distributing the online survey questionnaire, the data will be collected and screened to remove invalid responses.
Data Analysis Techniques
This research utilized Descriptive Statistics Analysis to effectively summarize and visually present data from an online survey, ensuring clarity and comprehensibility This analysis provides an overview of the sample's characteristics, setting the stage for a more detailed investigation using Partial Least Squares Structural Equation Modeling (PLS-SEM) with Smart PLS software.
The survey data will be analyzed using statistics that include gender ratio, speed, and frequency of food delivery application usage, along with other relevant factors The findings will be presented through tables, bar charts, and pie charts, providing essential insights for initial assessments and serving as a foundation for more detailed analysis with Smart-PLS software.
3.10.2 Reliability and Content Validity Analysis scale The goal is to examine each question in the survey to determine whether they ensure consistency or not If Cronbach’s Alpha is greater than 0.7, it indicates that the scale has high reliability If it is between 0.5 - 0.7, the reliability level is acceptable However, if this value is less than 0.3, the variant surveys should be removed from the scale
Scale validity testing, as outlined by Malhotra (2002), assesses the accuracy of a measurement instrument in reflecting differences among subjects while minimizing random errors This process is essential to ensure that the survey instrument effectively measures the intended research concept.
Content validity is essential for ensuring that a survey accurately reflects the theoretical aspects it aims to measure This involves constructing questions that effectively capture the nature of each factor To guarantee the accuracy and reliability of the scale, both reliability and content validity analyses will be conducted across all research models related to this topic.
In structural equation modeling (SEM), evaluating the reliability and validity of the measurement model is crucial Composite reliability (CR), as noted by Alarcón et al (2015), is a key metric that indicates the internal consistency of observed variables within a scale A CR value between 0.70 and 0.90 is deemed suitable for comprehensive research, while values exceeding 0.95 may suggest excessive similarity among variation measures, potentially diminishing the scale's value Conversely, a CR below 0.60 signals inadequate reliability of the scale.
The study emphasizes the importance of reliability alongside two key aspects of validity: convergent validity and discriminant validity It highlights that the validity of measurement variables should accurately represent a latent concept A critical evaluation criterion is the Average Variance Extracted (AVE), which must have a minimum value of 0.50 to confirm that the latent concept accounts for over 50% of the variance in the observed variables.
Outer Loadings are crucial for assessing a measurement model, as they indicate the strength of the relationship between observed variables and the latent concepts they represent (Ab Hamid et al., 2017) Ideally, Outer Loadings should be 0.70 or above If the values fall between 0.40 and 0.70, it may be necessary to consider removing the observed variable based on its effect on the overall scale Observed variables with Outer Loadings below 0.40 should be eliminated.
To ensure clear distinction among latent concepts in the model, the study employs validity analysis, specifically the Fornell-Larcker criterion This method requires that the Average Variance Extracted (AVE) of a latent variable exceeds its correlation with other latent variables By utilizing this assessment, the study confirms that the variables accurately measure the intended concepts while preserving their distinctiveness.
Descriptive Statistics
Figure 4.1 Descriptive statistics on the frequency of use of online food delivery services
After analyzing responses from 302 survey participants, the author focused on those who had ordered food from online delivery platforms while living and studying in Hanoi The findings revealed that 280 participants (92.7%) had utilized online food delivery services, whereas 22 participants (7.3%) had not All respondents who used these services resided in Hanoi The research specifically targeted individuals with experience in online food ordering, leading to a detailed analysis of the 280 respondents, representing 92.7% of the total sample.
Figure 4.2 Descriptive statistics about gender
The research results collected from 280 people who have used food delivery services include
The study reveals that 155 women (55.4%) participated compared to 125 men (44.6%), highlighting a greater interest among women in food delivery services.
Figure 4.3 Descriptive statistics about age
A survey of 280 food delivery service users revealed that the majority, 83.6%, belong to the young demographic aged 18-29, highlighting this age group as the primary target audience In contrast, only 10.7% of respondents were over 50 years old, and a mere 5.7% fell within the 29-50 age range, indicating a lower popularity of food delivery services among older consumers.
Figure 4.4 Descriptive statistics about income
The survey also showed that the monthly income of participants is mainly in the range of
5,000,000 - 10,000,000 VND/month, using 79.7% (44.3% in the range of 5,000,000 -
7,000,000 VND and 35.4% in the range of 7,000,000 - 10,000,000 VND) The group with income under 5,000,000 VND uses 12.5%, while those with income over 10,000,000 VND use the lowest rate, only 7.9%
Most young people in Hanoi have an average income that allows them to use food delivery services while being price-conscious This tendency influences their food choices, prompting them to prefer affordable options, appealing promotions, and services with low delivery fees.
Measurement Model Assessment
ATT BI EJM EOU PU SI TR
Note: Perceived usefulness (PU), Perceived ease of use (EOU), Enjoyment (EJM), Trust (TR), Social Influence (SI)
The Outer Loading table indicates that the observed variables significantly contribute to the latent concepts in the model, with the majority of external loading coefficient values exceeding 0.7, thereby confirming the scale's reliability.
The high loading coefficients of PU (Perceived Usefulness), BI (Behavioral Intention), and EOU (Ease of Use) demonstrate that these observed variables effectively measure their respective concepts While variables like EJM1 (0.739), EJM4 (0.758), and SI1 (0.740) are within an acceptable range, they may require adjustments to enhance reliability Furthermore, the absence of strong cross-loading confirms the clear distinction between the concepts.
In general, the observed variables through Outer Loading show that the scale has good
4.2.1.2 Cronbach’s Alpha, Composite Reliability & Average Variance Extracted
Table 4.2 Cronbach’s Alpha, Composite Reliability & Average Variance Extracted
Cronbach's alpha Composite reliability (rho_a)
Note: Perceived usefulness (PU), Perceived ease of use (EOU), Enjoyment (EJM), Trust (TR), Social Influence (SI), Attitude (ATT), Behavior Intention (BI)
The reliability and convergence assessment of the urban scales, evaluated through Cronbach's Alpha, Composite Reliability, and Average Variance Extracted (AVE), indicates strong reliability The Cronbach's Alpha values range from 0.710 to 0.843, surpassing the acceptable threshold of 0.7, which confirms the internal consistency among the observations.
In particular, the PU index (0.808), BI (0.843), EJM (0.794) have the highest reliability, showing the strong stability of the scale
The Composite Reliability index (CR - rho_c) for all concepts exceeds 0.8, indicating high reliability Notably, the variables Perceived Usefulness (PU) at 0.886, Behavioral Intention (BI) at 0.895, and Attitude (ATT) at 0.856 demonstrate the highest CR values, confirming their effectiveness in accurately measuring the latent concepts they represent.
The Average Variance Extracted (AVE) exceeds 0.5, satisfying the convergence criteria of the scale High AVE values, including PU (0.721), BI (0.680), and ATT (0.666), indicate that the majority of observed variables align well with the latent concept, thereby ensuring strong convergence.
The study's scales have been found to meet the necessary standards for reliability and convergence Subsequently, structural equation modeling (SEM) analyses were conducted to test the research hypotheses.
ATT BI EJM EOU PU SI TR
Note: Perceived usefulness (PU), Perceived ease of use (EOU), Enjoyment (EJM), Trust (TR), Social Influence (SI), Attitude (ATT), Behavior Intention (BI)
The assessment of parsimony is crucial in studies involving latent variables, as it helps avoid multicollinearity issues (Ab Hamid et al., 2017) One of the most widely used methods for this assessment is the Fornell-Larcker Criterion (Ab Hamid et al., 2017).
The survey results indicate that the diagonal values for ATT (0.816), BI (0.824), EJM (0.785), EOU (0.806), PU (0.849), SI (0.795), and TR (0.801) are all higher than any other values in their respective rows or columns, demonstrating strong discriminative ability among the latent concepts Notably, the ATT value (0.816) surpasses the correlation values with other variables, such as BI (0.575) and EJM (0.364), highlighting the distinctiveness of ATT These findings confirm that the Fornell-Larcker Criterion is satisfied, ensuring that the latent variables represent unique concepts without overlap.
ATT BI EJM EOU PU SI TR
Note: Perceived usefulness (PU), Perceived ease of use (EOU), Enjoyment (EJM), Trust (TR), Social Influence (SI), Attitude (ATT), Behavior Intention (BI)
The HTMT (Heterosexual-Monosexual Ratio) is an advanced technique for evaluating the discriminability of latent variables within a measurement model (Dirgiatmo, 2023) By utilizing the Fornell-Larcker criterion, the HTMT offers a more accurate evaluation of the discriminability between different concepts.
Teo et al (2008) indicate that a model demonstrates discrimination when all HTMT values are below 0.90 In contrast, Kline (2011) recommends a stricter threshold of less than 0.85 for HTMT values to guarantee distinct discrimination among latent variables.
The HTMT values range from 0.155 to 0.719, all below the threshold of 0.85, indicating that the latent variables exhibit distinct discrimination features This finding establishes a strong foundation for the subsequent testing of the hypotheses.
Assessment of structural model
Figure 4.6 below illustrates the structural model with the relationships between the latent variables and the number of each observed scale
Structural Model Assessment
Note: Perceived usefulness (PU), Perceived ease of use (EOU), Enjoyment (EJM), Trust (TR), Social Influence (SI), Attitude (ATT), Behavior Intention (BI)
The Variance Inflation Factor (VIF) is utilized in the structural model to assess multicollinearity among independent variables As noted by Hair Jr et al (2019), a VIF value under 3.3 is deemed acceptable, suggesting no significant multicollinearity issues within the model The VIF indices in the data table range from 1.308 to 1.989, with all variables below 2, indicating a very low level of multicollinearity that does not substantially impact the analysis results.
Note: Attitude (ATT), Behavior Intention (BI)
R² values, which range from 0 to 1, indicate the predictive ability of a model, with higher values signifying better performance Sarstedt et al (2021) categorize R² values of 0.75, 0.50, and 0.25 as strong, medium, and weak predictive power, respectively The data reveals a moderate influence of independent variables on the dependent variable, with the ATT variable showing an R² of 0.352, accounting for 35.2% of its variation In contrast, the BI variable has an R² of 0.518, indicating that 51.8% of its variation is explained by the model According to Cohen (1988), these R² values for ATT and BI are deemed acceptable (Cohen, 2013).
Note: Perceived usefulness (PU), Perceived ease of use (EOU), Enjoyment (EJM), Trust (TR), Social Influence (SI), Attitude (ATT), Behavior Intention (BI)
Table 4.7 illustrates the impact of the independent variables ATT and BI, as indicated by the f² values Cohen (1988) categorizes f² values into three classifications: small (0.02), medium (0.15), and large (0.35) (Cohen, 2013).
The analysis reveals that the perceived usefulness (PU) variable significantly influences attitude toward technology (ATT) with the highest f² value of 0.186, while the effort justification model (EJM) and ease of use (EOU) show weak impacts with f² values of 0.059 and 0.051, respectively The trust (TR) variable has a negligible effect on ATT, indicated by its low f² value of 0.004 In terms of behavioral intention (BI), EJM again demonstrates a significant impact with an f² value of 0.177, while PU and social influence (SI) have minor effects with values of 0.082 and 0.060, respectively TR's influence on BI is minimal, reflected in its very low f² value of 0.009.
The findings indicate that perceived usefulness (PU) significantly affects attitude (ATT), while electronic journal management (EJM) strongly influences behavioral intention (BI) In contrast, ease of use (EOU) and social influence (SI) have minimal effects on both ATT and BI Additionally, the trust (TR) variable exhibits very low f² values of 0.004 for ATT and 0.009 for BI, suggesting that it does not significantly impact either dependent variable in the research model.
4.4.4 Path coefficient and Hypothesis Testing
To assess the statistical significance of the list, it is essential to analyze the t-value, p-value, and bootstrapping reliability using the Bootstrapping method This approach is particularly important in complex structural models that include mediating variables, as it provides direct and nuanced insights into the data.
Table 4.8 Results for Structural equation model Original sample (O)
Note: Perceived usefulness (PU), Perceived ease of use (EOU), Enjoyment (EJM), Trust (TR), Social Influence (SI), Attitude (ATT), Behavior Intention (BI)
The structural equation model (SEM) testing results, as shown in Table 4.9, indicate that most hypotheses are statistically significant at the 5% level (p < 0.05) Notably, attitude (ATT) significantly influences behavioral intention (BI) with a coefficient of (β = 0.279, t = 4.155, p = 0.000), confirming the validity of hypothesis H1 Additionally, extrinsic motivation (EJM) positively affects both attitude (ATT) (β = 0.202, t = 3.730, p = 0.000) and behavioral intention (BI) (β = 0.308, t = 5.463, p = 0.000), supporting hypotheses H4a and H4b.
In addition, the variables ease of use (EOU) and perceived usefulness (PU) both have positive effects on attitude (ATT) and behavioral intention (BI), with t values greater than 1.96 and p
< 0.05 In which, PU has the strongest effect on attitude (β = 0.381, t = 6.540, p = 0.000) and behavioral intention (β = 0.236, t = 3.929 p = 0.000) That is, H3, H2a, H2b and H6 are all supported by the model
Trust (TR) did not significantly influence attitude (β = 0.075, t = 1.444, p = 0.149) or behavioral intention (β = 0.107, t = 1.612, p = 0.107), leading to the conclusion that hypotheses H5a and H5b were not supported This indicates that trust may not be a crucial factor in this study.
The study reveals that attitude, extrinsic motivation, perceived usefulness, perceived ease of use, and social influence significantly impact behavioral intention, whereas trust does not play a significant role in the published model.
Discussion
Research indicates that perceived usefulness (PU), enjoyment (EJM), ease of use (EOU), and social influence (SI) significantly affect young people's decisions to order fast food through online platforms Data analysis reveals that these factors directly influence behavioral intention (BI) and also impact BI through attitude (ATT), with SI serving as a crucial mediator The model analysis confirms that the proposed framework effectively addresses the factors influencing fast food ordering decisions among Hanoi's youth, highlighting the significant roles of PU, EOU, EJM, and SI.
Perceived usefulness (PU) significantly influences attitudes toward technology (ATT) and is a key factor in shaping behavioral intentions (BI) for ordering food via online platforms This aligns with earlier research on the acceptance of new technologies and online services (Ingham et al., 2015; Troise et al., 2021).
The study revealed that enjoyment (EJM) significantly influences young people's behavioral intention (BI) to order fast food and impacts their attitude (ATT) This aligns with previous research (Jun et al., 2021), indicating that a fun and engaging online food delivery experience encourages continued use of the service In the service sector, hedonic motivation is crucial for assessing service quality, confirming that enjoyment (EJM) is a key factor in young people's decisions to utilize online fast food delivery services (Childers et al.).
Ease of use (EOU) plays a crucial role in shaping attitudes (ATT), although its influence is less pronounced than that of perceived usefulness (PU) and enjoyment (EJM) The significant effect of EOU on ATT aligns with the findings of King.
Research by He (2006) and Yousafzai et al (2007) indicates that ease of use (EOU) positively influences customer attitudes (ATT) towards food delivery platforms However, this finding contrasts with the conclusions of Troise et al (2021), suggesting that while customers value the ease of use, it is not the primary factor influencing their decision to utilize these services.
Recent findings indicate that trust (TR) does not significantly influence attitude (ATT) or behavioral intention (BI) towards online food delivery services, contradicting earlier studies that highlighted its importance in technology adoption for online shopping This shift may stem from the evolving market where food delivery platforms implement stringent security measures to safeguard personal information and enhance transaction transparency Consequently, customers may prioritize practical benefits (PU) and enjoyable experiences (EJM) over trust when choosing to use these services.
Social influence (SI) significantly affects young people's behavioral intention (BI) to order fast food through online food delivery platforms, aligning with findings from Duc et al (2024) and Le et al (2022) This supports previous research in e-commerce, which highlights the crucial role of opinions from friends, family, and influencers in shaping attitudes and encouraging the use of these services (Al Amin et al., 2021; Kim, 2012).
Summary of the Findings
RQ1: What factors affect the intention of young people in Hanoi to order food through online food delivery platforms?
This study reveals that among five factors—perceived usefulness (PU), enjoyment (EJM), ease of use (EOU), social influence (SI), and trust (TR)—four significantly influence young people's decisions to order fast food online in Hanoi Notably, perceived usefulness (PU) has the strongest impact, as customers are more likely to use food delivery services that save them time and effort Enjoyment (EJM) also plays a crucial role, with a fun and engaging ordering experience encouraging continued use While ease of use (EOU) affects attitudes towards the service, its influence is less significant than that of PU and EJM Additionally, social influence (SI) moderately impacts online ordering decisions, as recommendations from friends, family, or influencers can sway users.
Trust (TR) does not significantly influence attitude (ATT) and behavioral intention (BI) in the context of online food delivery This suggests that customers have developed a certain level of trust in these platforms due to their security policies, service quality, and transaction transparency In a more advanced market, trust has become less critical in customers' food ordering decisions, with a greater focus on perceived usefulness (PU) and enjoyable experiences (EJM) offered by the service.
RQ2: How do the attitudes of young people in Hanoi affect the behavior intention of ordering fast food through online food delivery platforms?
The study reveals that young people in Hanoi significantly mediate the relationship between influencing factors—perceived usefulness (PU), enjoyment of media (EJM), and ease of use (EOU)—and their intention to order fast food online A positive attitude towards online food delivery services leads to increased ordering frequency, particularly when users appreciate the convenience, enjoyable experience, and efficiency offered by the platform Notably, EJM and PU exert the strongest influence on attitude (ATT), indicating that when customers perceive food ordering as both useful and enjoyable, they are more likely to develop a positive attitude and a long-term intention to use the service Additionally, EOU is crucial in shaping ATT; a user-friendly platform with simple navigation and flexible payment options enhances user comfort and encourages ordering.
Limitations and Future Research
While the study offers insights into the key factors affecting online food ordering decisions among young people in Hanoi, it has notable limitations It primarily examines five main factors—Perceived Usefulness (PU), Enjoyment (EJM), Ease of Use (EOU), Social Influence (SI), and Trust (TR)—without delving into other potential influences on consumer behavior, such as personal habits, economic conditions, or additional psychological factors.
The research findings are based on a limited survey scope and a relatively short research duration, which means they only represent a specific group of young people in Hanoi Consequently, while the results are objective, they do not comprehensively reflect the entire population of young individuals in the area.
The study has not thoroughly identified all potential factors influencing the decision to order food online, resulting in a primarily theoretical analysis with limited research depth Consequently, the findings may not completely capture the precise relationship between these factors and their significant impact on consumers' intentions to order food online.
This study concludes its analysis by focusing solely on the factors influencing ordering decisions, without delving into customer psychology or the interactions among these factors This limitation paves the way for future research aimed at exploring customers' psychological motivations and the interplay between the various factors identified in the study.
Recommendations
To effectively penetrate the online food delivery market, businesses must prioritize enhancing customer experience and increasing the perceived value of their services Key strategies include improving delivery speed and ensuring food freshness to elevate the perception of usefulness (PU) Additionally, implementing attractive incentive programs can boost economic benefits for customers Enjoyment (EJM) is also crucial; thus, enhancing the app interface, incorporating gamification elements, and partnering with renowned brands to offer exclusive dishes can create excitement Furthermore, optimizing ease of use (EOU) by simplifying the setup process, providing flexible payment options, and enhancing customer service quality is essential for attracting and retaining customers.
Social influence (SI) significantly affects young people's ordering intentions, prompting businesses to leverage KOLs/KOCs for marketing, encourage customer reviews on social networks, and foster active user communities Trust (TR) is not a primary factor, as customers already possess a baseline trust in food delivery platforms Therefore, businesses should emphasize practical benefits like delivery speed, attractive products, and quality rather than solely focusing on credibility By adopting this strategy, F&B businesses in Hanoi can capitalize on young consumers' trends, enhance competitiveness, and expand their market share in the online food delivery industry.
Ab Hamid, M R., Sami, W., & Sidek, M M (2017) Discriminant validity assessment: Use of Fornell & Larcker criterion versus HTMT criterion Journal of physics: Conference series,
Adams, D A., Nelson, R R., & Todd, P A (1992) Perceived usefulness, ease of use, and usage of information technology: A replication MIS quarterly, 227-247
Ajzen, I (1991) The theory of planned behavior Organizational behavior and human decision processes, 50(2), 179-211
Ajzen, I., & Fishbein, M (1977) Attitude-behavior relations: A theoretical analysis and review of empirical research Psychological bulletin, 84(5), 888
Alagoz, S M., & Hekimoglu, H (2012) A study on tam: analysis of customer attitudes in online food ordering system Procedia-Social and Behavioral Sciences, 62, 1138-
Alarcón, D., Sánchez, J A., and De Olavide, U (2015) conducted a study on the ADHD-R IV rating scale, focusing on the assessment of convergent and discriminant validity They developed user-written commands for calculating Average Variance Extracted (AVE), Composite Reliability (CR), and the Heterotrait-Monotrait ratio of correlations (HTMT) This research was presented at the Spanish STATA meeting.
Ashraf, M A., Joarder, M H R., & Ratan, S R A (2019) Consumers’ anti-consumption behavior toward organic food purchase: an analysis using SEM British Food Journal,
Al Amin, M., Arefin, M S., Alam, M R., Ahammad, T., & Hoque, M R (2021) Using mobile food delivery applications during COVID-19 pandemic: An extended model of planned behavior Journal of Food Products Marketing, 27(2), 105-126
An, S., Eck, T., & Yim, H (2023) Understanding consumers’ acceptance intention to use mobile food delivery applications through an extended technology acceptance model
Baba, S N., Dada, Z A., & Qureshi, R A (2024) Ethnic food tasting and gastronomy online reviews: incorporating enjoyment in the behavioral intention model Journal of Hospitality and Tourism Insights, 7(2), 743-762
Barkhi, R., & Wallace, L (2007) The impact of personality type on purchasing decisions in virtual stores Information Technology and Management, 8, 313-330
Bhukya, R., & Paul, J (2023) Social influence research in consumer behavior: What we learned and what we need to learn?–A hybrid systematic literature review Journal of
The study by Bonn et al (2016) explores the impact of social influence, perceived usefulness, perceived ease of use, and wine involvement on online wine purchasing behavior Additionally, Bowen and Guo (2011) provide insights into structural equation modeling, a crucial statistical method for analyzing complex relationships Furthermore, Box et al (1978) offer foundational knowledge in experimental statistics, which is essential for researchers in the field.
Wiley and sons New York
Cohen, J (2013) Statistical power analysis for the behavioral sciences routledge
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 https://doi.org/10.1080/0144929X.2018.1515989
Chai, L T., & Yat, D N C (2019) Online food delivery services: Making food delivery the new normal Journal of Marketing advances and Practices, 1(1), 62-77
Childers, T L., Carr, C L., Peck, J., & Carson, S (2001) Hedonic and utilitarian motivations for online retail shopping behavior Journal of retailing, 77(4), 511-535
Cho, M., Bonn, M A., & Li, J J (2019) Differences in perceptions about food delivery apps information systems: Theory and results Massachusetts Institute of Technology]
Davis, F D (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology MIS quarterly, 319-340
Davis, F D., Bagozzi, R P., & Warshaw, P R (1989) User acceptance of computer technology: A comparison of two theoretical models Management science, 35(8),
Davis, F D., Bagozzi, R P., & Warshaw, P R (1992) Extrinsic and intrinsic motivation to use computers in the workplace 1 Journal of applied social psychology, 22(14), 1111-
Deloitte Vietnam (2022) The Vietnam consumer survey: Rebound, rebalance, and renewal
Retrieved from https://www.slideshare.net/slideshow/deloitte-report-102022-the- vietnam-consumer-survey-rebound-rebalance-and-renewal/253827686
I don't know!
Dirgiatmo (2023) examines the discriminant validity and the heterotrait–monotrait ratio of correlation (HTMT) within the context of Indonesian SMEs This study is featured in the book "Macroeconomic Risk and Growth in the Southeast Asian Countries: Insight from Indonesia," highlighting the importance of robust measurement techniques in understanding the dynamics of small and medium enterprises in Indonesia The findings contribute valuable insights into the methodological approaches used in evaluating business performance and relationships in the region.
Dickinger, A., Arami, M., & Meyer, D (2008) The role of perceived enjoyment and social norm in the adoption of technology with network externalities European Journal of Information Systems, 17(1), 4-11
Fishbein, M (1963) An investigation of the relationships between beliefs about an object and the attitude toward that object Human relations, 16(3), 233-239
Fishbein, M., & Ajzen, I (1975) Chapter 2 Theories of Attitude Belief, Attitude, Intention, and Behavior, An Introduction to Theory and Research
Fishbein, M., & Ajzen, I (1977) Belief, attitude, intention, and behavior: An introduction to theory and research
Haenlein, M., & Kaplan, A M (2004) A beginner's guide to partial least squares analysis
Hair, J F., Black, W C., Babin, B J., & Anderson, R E (2019) Multivariate data analysis
Hansen, T., Jensen, J M., & Solgaard, H S (2004) Predicting online grocery buying intention: a comparison of the theory of reasoned action and the theory of planned behavior International journal of information management, 24(6), 539-550
Henderson, R., Rickwood, D., & Roberts, P (1998) The beta test of an electronic supermarket
Hong, C., Choi, H H., Choi, E.-K C., & Joung, H.-W D (2021) Factors affecting customer intention to use online food delivery services before and during the COVID-19 pandemic Journal of Hospitality and Tourism Management, 48, 509-518
Jarvenpaa, S L., Tractinsky, N., & Vitale, M (2000) Consumer trust in an Internet store
The COVID-19 pandemic has significantly influenced customer decisions to utilize online food delivery services, as highlighted by Jun et al (2021) Their research identifies key factors that drive consumer behavior in this context Additionally, Kim et al (2008) emphasize the importance of trust in electronic commerce, illustrating how trust and perceived risk shape consumer decision-making processes Together, these studies underscore the critical role of trust and external factors in the evolving landscape of online food delivery.
Lee, E.-Y., Lee, S.-B., & Jeon, Y J J (2017) Factors influencing the behavioral intention to use food delivery apps Social Behavior and Personality: an international journal, 45(9), 1461-1473
Lee, M K., Cheung, C M., & Chen, Z (2005) Acceptance of Internet-based learning medium: the role of extrinsic and intrinsic motivation Information & management, 42(8), 1095-1104
Mathers, N J., Fox, N J., & Hunn, A (1998) Surveys and questionnaires (Vol 1998) NHS
Mazman, S G., Usluel, Y K., & Çevik, V (2009) Social influence in the adoption process and usage of innovation: Gender differences International Journal of Behavioral,
Cognitive, Educational and Psychological Sciences, 1(4), 229-232
McCloskey, D W., & Leppel, K (2010) The impact of age on electronic commerce participation: An exploratory model Journal of Electronic Commerce in Organizations (JECO), 8(1), 41-60
Mishra, P., Pandey, C M., Singh, U., Gupta, A., Sahu, C., & Keshri, A (2019) Descriptive statistics and normality tests for statistical data Annals of cardiac anaesthesia, 22(1), 67-72
Nguyen, T T., Thi Thu Truong, H., & Le-Anh, T (2023) Online purchase intention under the integration of theory of planned behavior and technology acceptance model Sage Open, 13(4), 21582440231218814
Novita, D., & Husna, N (2020) The influence factors of consumer behavioral intention towards online food delivery services TECHNOBIZ: International Journal of Business, 3(2), 40-42
O’cass, A., & Fenech, T (2003) Web retailing adoption: exploring the nature of internet users
Web retailing behaviour Journal of retailing and consumer services, 10(2), 81-94 Olorunniwo, F., Hsu, M K., & Udo, G J (2006) Service quality, customer satisfaction, and behavioral intentions in the service factory Journal of services marketing, 20(1), 59-
Pavlou, P A (2003) Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model International journal of electronic commerce,
Pigatto, G., Machado, J G d C F., Negreti, A d S., & Machado, L M (2017) Have you chosen your request? Analysis of online food delivery companies in Brazil British Food Journal, 119(3), 639-657 https://doi.org/10.1108/BFJ-05-2016-0207
Piroth, P., Ritter, M S., & Rueger-Muck, E (2020) Online grocery shopping adoption: do personality traits matter? British Food Journal, 122(3), 957-975
Ray, A., Dhir, A., Bala, P K., & Kaur, P (2019) Why do people use food delivery apps
(FDA)? A uses and gratification theory perspective Journal of retailing and consumer services, 51, 221-230
Ramayah, T., & Ignatius, J (2005) Impact of perceived usefulness, perceived ease of use and perceived enjoyment on intention to shop online ICFAI Journal of Systems Management (IJSM), 3(3), 36-51
Roh, M., & Park, K (2019) Adoption of O2O food delivery services in South Korea: The moderating role of moral obligation in meal preparation International journal of information management, 47, 262-273
Sarstedt, M., Ringle, C M., & Hair, J F (2021) Partial least squares structural equation modeling In Handbook of market research (pp 587-632) Springer
Sekaran, U., & Bougie, R (2016) Research methods for business: A skill building approach john wiley & sons
Shankar, A., Jebarajakirthy, C., Nayal, P., Maseeh, H I., Kumar, A., & Sivapalan, A (2022)
This article presents a systematic synthesis of literature on online food delivery, culminating in the development of a comprehensive framework It highlights the significance of social influence on the perceived usefulness and ease of use of course delivery systems, as discussed by Shen et al (2006) in the Journal of Interactive Online Learning The findings contribute to the understanding of consumer behavior in the online food delivery sector, emphasizing the importance of user experience and social factors in enhancing service effectiveness.
Sidharta, S M., Adityo, D B., Iqbal, P M., & Gunadi, W (2021) Customer loyalty analysis on online food delivery services Turkish Journal of Computer and Mathematics
Smilde, A K., Jansen, J J., Hoefsloot, H C J., Lamers, R.-J A N., van der Greef, J., &
Timmerman, M E (2005) ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data Bioinformatics, 21(13), 3043-
3048 https://doi.org/10.1093/bioinformatics/bti476
Smith, S M., Roster, C A., Golden, L L., & Albaum, G S (2016) A multi-group analysis of online survey respondent data quality: Comparing a regular USA consumer panel to MTurk samples Journal of Business Research, 69(8), 3139-3148
Thompson, K E., Haziris, N., & Alekos, P J (1994) Attitudes and food choice behaviour
Troise, C., O'Driscoll, A., Tani, M., & Prisco, A (2021) Online food delivery services and behavioural intention–a test of an integrated TAM and TPB framework British Food
Venkatesh et al (2008) explore the various conceptualizations of system use, emphasizing the interplay between behavioral intention, facilitating conditions, and behavioral expectations Additionally, the Deloitte Report (2022) highlights insights from the Vietnam Consumer Survey, focusing on themes of rebound, rebalance, and renewal in consumer behavior However, the link to the report is currently inaccessible, resulting in a 404 Client Error.
Wen, Prybutok, and Xu (2011) developed an integrated model to analyze customer online repurchase intentions, highlighting key factors influencing repeat purchases Similarly, Yeo, Goh, and Rezaei (2017) examined consumer experiences, attitudes, and behavioral intentions regarding online food delivery services, emphasizing the importance of customer satisfaction in driving service usage Both studies contribute valuable insights into consumer behavior in the digital marketplace, underscoring the significance of understanding customer motivations for enhancing online service offerings.
Zhou, T., & Li, H (2014) Understanding mobile SNS continuance usage in China from the perspectives of social influence and privacy concerns Computers in Human Behavior,