INTRODUCTION
Reason for choosing the topic
The rise of online ordering and delivery services has been driven by factors such as busy lifestyles, work pressure, adverse weather, and the Covid-19 pandemic, which have made customers hesitant to dine out Major players like GrabFood, GoFood, and Now.vn have emerged in this growing market, catering to modern consumer needs According to a report by Bamboo, 80% of surveyed individuals have utilized food delivery services, indicating significant market demand This trend has roots in countries like the US, Australia, and Germany, but has gained unprecedented popularity due to internet advancements A 2020 survey by Q&me revealed that 73% of respondents in Hanoi and Ho Chi Minh City used GrabFood and Now, while BAEMIN and GoFood had a usage rate of about 46%.
In today's competitive market, BAEMIN must identify the key factors that influence customer satisfaction with food ordering services to effectively compete with larger players and attract more customers Understanding these factors will enable BAEMIN to enhance its service offerings and better meet the needs of its clientele.
2 using BAEMIN services in the current market” with the desire to contribute to solving the above problems.
Research objectives and questions
This article explores the key factors affecting customer satisfaction with the BAEMIN online ordering service among users aged 18 to 25 in Ho Chi Minh City By identifying these factors, the study aims to provide actionable recommendations for enhancing the service experience offered by BAEMIN.
This research aims to identify the key factors influencing customer satisfaction with BAEMIN services It also seeks to measure and analyze how these factors impact overall customer satisfaction Additionally, the study will propose actionable solutions to enhance service quality, ensuring a more satisfying experience for BAEMIN users.
The research aims to address three key questions: Firstly, it seeks to identify the primary factors that affect customer satisfaction with online food ordering services Secondly, it will evaluate the extent to which each of these factors influences customer satisfaction Lastly, the study will propose actionable solutions for Baemin to enhance its customers’ satisfaction levels.
Research scopes and subjects
The subjects of the research are factors affecting the decision to use BAEMIN About the survey subject, the research aims at customer who have used and are using BAEMIN
This study analyzes data from students aged 18 to 25 at four universities: Banking University, University of Economics, University of Finance - Marketing, and International University The focus is on examining the dependent variable in relation to the collected data.
3 defined as the satisfaction of those students on using Baemin Data were collected by a questionnaire survey method Time of conducting the survey was from July to September
2021 in Ho Chi Minh City, Vietnam.
Research methods
The author employs both qualitative and quantitative research methods to explore factors influencing customer satisfaction with BAEMIN services Initially, a synthesis of relevant theories and prior research is conducted to identify key factors Subsequently, a theoretical model is developed based on these findings, utilizing exploratory survey techniques, online interviews with students and residents of Ho Chi Minh City, and consultations with expert lecturers to refine and finalize the research scale.
Quantitative research is utilized to preliminarily assess the scale's reliability and value following calibration Data collected in this study will be processed using SPSS software for encryption and cleaning, allowing the author to evaluate reliability and eliminate unsatisfactory variables Subsequently, the author will adjust the observed variables and research concepts to establish an official scale, which serves as the foundation for evaluating scale value and testing the proposed theoretical model and hypotheses To determine the factors influencing customer satisfaction, data were gathered through online interviews, distributing questionnaires to 200 students using the Baemin service in Ho Chi Minh City A random sampling method was employed to select participants, facilitating data processing and analysis through descriptive statistics and exploratory factor analysis (EFA).
To evaluate the factors influencing student satisfaction in Ho Chi Minh City, a comprehensive analysis was performed using data gathered from survey questionnaires The study employed a non-probability sampling design, utilizing a convenient sampling method Data analysis was conducted using SPSS 20 software to ensure accurate results.
The research employs various data analysis techniques, including descriptive statistics, exploratory factor analysis (EFA), and scale testing using Cronbach's Alpha Additionally, it utilizes multiple regression analysis and ANOVA to compare differences between qualitative attributes, as well as T-Test for further analysis.
Contribution
This article explores the concept of customer satisfaction, summarizing essential theories and highlighting key findings from previous research It aims to provide a comprehensive understanding of customer satisfaction specifically related to Baemin's online food delivery service in Ho Chi Minh City.
The study provides practical information and scientific arguments as the basis for managers to orient, build, perfect and improve Baemin service quality.
Thesis structure
This chapter introduces the research topic, including: reason for forming the topic, research objectives, research scope, practical significance, research structure and research plan.
LITERATURE REVIEW
Important Concepts
Satisfaction is a multifaceted emotion shaped by various factors including service quality, product quality, pricing, and personal influences (Zeithaml & Bitner, 2000) It represents an overall emotional response that arises from comparing pre- and post-consumption expectations (Oliver, 1999) The literature distinguishes between two types of satisfaction: transaction-specific satisfaction, which focuses on individual experiences, and cumulative satisfaction, which encompasses the totality of a customer's consumption experiences (Johnson et al., 1995) According to Phillip Kotler (2001), satisfaction reflects the extent to which a person's sensory experience aligns with their expectations These expectations are influenced by personal needs, past experiences, and external sources such as advertising and recommendations from friends and family, highlighting the importance of individual perceptions in the pursuit of satisfaction in various aspects of life.
Previous experience significantly shapes consumer expectations, as individuals learn and grow from their past encounters As experience accumulates, expectations tend to rise correspondingly Additionally, external information plays a crucial role in influencing these expectations Ultimately, consumer satisfaction is determined by the perceived gap between prior expectations and the actual performance of a product, reflecting the overall acceptance of its use (Tse & Wilton).
1988) Or satisfaction is the consumer's response to the fulfillment of wants (Oliver,
Customer satisfaction is defined as the response a customer has after using a product or service, particularly when it aligns with or exceeds their expectations It reflects the degree to which a customer's sensory experience matches the outcomes they anticipated prior to usage, highlighting the importance of meeting and surpassing customer expectations for optimal satisfaction.
We can understand customer satisfaction as a process as follows: Customers form in their minds expectations about the elements that make up the quality of service that the
Customer satisfaction is influenced by the comparison between expected and actual service experiences, with three possible outcomes: confirmation of expectations, frustration due to unmet expectations, or satisfaction when experiences exceed expectations According to Parasuraman, satisfaction results from a combination of service quality, product quality, and price While service quality and customer satisfaction are often used interchangeably, literature distinguishes them; service quality focuses on specific components, whereas customer satisfaction encompasses a broader assessment Parasuraman et al (1994) identify two components of customer satisfaction: the perceived value of quality goods or services at reasonable prices and the trust and relationships developed over time with service providers Zeithaml and Bitner (2000) emphasize that service quality pertains to service components, while customer satisfaction is a more general concept Although researchers have not reached a consensus on these definitions, many agree on the relationship between service quality and customer satisfaction, highlighting the need for further research on how specific service quality components affect satisfaction in various service industries.
8 quality and the level of customer satisfaction in specific areas Giese and Cote (2000) define customers as end-product consumers
According to Philip Kotler (2000), a service is an intangible activity or benefit provided by one entity to another, which does not result in ownership Zeithaml & Bitner (2000) describe services as behaviors and processes aimed at creating value for customers and fulfilling their needs and expectations Kotler & Armstrong (2004) further emphasize that services are activities that one party offers to another, predominantly intangible, and do not confer ownership Ultimately, a service is the outcome of interactions between suppliers and customers, as well as the internal processes of the supplier (General Department of Vietnam, 1999).
Service involves a combination of behind-the-scenes and front-end activities that facilitate interaction between the customer and the service provider This interaction aims to meet the customer's needs and expectations while also creating value for them (Bui Nguyen Hung, 2004).
Since the 1930s, quality has been a key competitive factor in manufacturing, while service quality has gained prominence only in recent decades The principles for defining, evaluating, and managing quality in services largely stem from those established in manufacturing According to the International Organization for Standardization (ISO), quality is defined as the ability of a product, system, or process to meet customer and stakeholder requirements This definition highlights that a product deemed unacceptable by customers is considered low quality, regardless of its advanced manufacturing technology In contrast, measuring the quality of tangible products often presents more advantages compared to assessing service quality.
9 is no complete answer to the measurement of service quality, this difficulty and complexity is based on the following distinguishing characteristics of services and products:
Intangibility is a fundamental characteristic of services, indicating that customers cannot perceive them through sight, touch, taste, or smell before making a purchase decision This inherent quality poses significant challenges for managing production and service delivery.
- Inseparability, means that products and services are associated with their production and distribution, the process of providing services is also the consumption of services;
Heterogeneity in services refers to the variability influenced by numerous factors that are challenging to manage This variability arises primarily from supply activities, as service providers often cannot deliver the same service consistently at different times Consequently, achieving an identical service experience is nearly impossible.
Perishability refers to the inherent nature of services that prevents them from being stored, transported, or pre-tested for quality prior to delivery To maintain high standards, service providers must consistently execute each service correctly from beginning to end, ensuring quality is upheld every time a service is offered.
2.1.4 The concepts of competition and pricing
Economic competition involves various actors such as manufacturers, distributors, retailers, consumers, and traders striving to secure advantageous positions in the market This competition aims to enhance their production capabilities, optimize consumption of goods and services, and ultimately achieve greater economic benefits for themselves.
Competition arises between producers and distributors, as well as between producers and consumers, particularly when producers aim to sell goods and services at higher prices while consumers seek lower prices In this context, competition represents a business's strategic approach to navigating rivalries within the same industry.
There are many methods of competition: price competition (discounts) or non-price competition (promotions, advertising) Or the competition of an enterprise, an industry,
The effectiveness of a country’s economy is determined by its ability to produce goods and services that satisfy industry and market demands under free and fair market conditions This capability not only fosters job creation but also enhances real incomes for its citizens.
Competition is a prevalent concept across various sectors, including economics, commerce, law, politics, ecology, and sports Renowned economist Michael Porter defines competition as the pursuit of market share, driven by the objective of achieving profits that exceed the industry average This competitive dynamic ultimately leads to the equalization of profits within the industry, fostering significant improvements and potentially resulting in lower prices for consumers.
Competition in the marketplace involves businesses and industries striving for customer approval and satisfaction The free enterprise system empowers these industries to independently determine their production choices, methods, and pricing strategies for their products or services.
Theoretical models on customer satisfaction
Customer satisfaction is described through the Teboul model as follows:
Figure 2.1 Teboul’s customer satisfaction model
(Source: James Teboul, Managing Quality Dynamics Englewood Cliffs, N.J: Pretice
Hall, 1991) For this model, the author shows customer satisfaction at the intersection, between business capabilities and customer needs Thereby, customers can feel at 3 different levels of satisfaction:
- First, if the actual results are worse than the expectations, the customer will not be satisfied
- Second, if actual results match expectations, customers will be satisfied The larger the intersection, the greater the customer satisfaction
- And finally, customers will be delighted if the actual results exceed their expectations
However, this model is still quite simple, so it has not shown any factors affecting customer satisfaction
2.2.2 Zeithaml & Bitner's model of customer perception of quality and satisfaction
According to Zeithaml & Bitner (2000), customer satisfaction is influenced by the following factors:
Figure 2.2 Relationship between service quality and customer satisfaction
(Source: Zeithaml & Bitner (2000), Service Marketing, Mc Graw-Hill)
According to the Zeithaml & Bitner (2000) model of customer perception, both service quality and pricing significantly influence customer satisfaction Service quality encompasses various elements, including reliability, responsiveness, service capacity, empathy, and tangibles, which shape customers' perceptions and experiences.
Empirical research on customer satisfaction
Ye-Eun Song's 2017 study examines how various factors related to delivery application usage affect customer satisfaction and the intention to reuse these services The findings reveal that delivery application usage factors account for 43.8% of customer satisfaction, with information, payment and safety, usefulness, and convenience significantly enhancing satisfaction levels Additionally, these factors influence customers' reuse intention by 37.3%, highlighting that information, payment, and safety are the primary contributors to encouraging repeat usage.
Sihipman (2019) identifies key factors influencing customer satisfaction and future behavioral intentions in online food ordering, including website design, information quality, security, payment options, variety of food choices, and food delivery services.
Product qualityPricePersonal factors
Consumer satisfaction significantly influences consumer behaviors, particularly in online food ordering This study reveals that factors such as website quality, delivery efficiency, payment options, and a variety of food choices have a positive impact on consumer satisfaction Additionally, higher satisfaction levels are linked to increased repurchase intentions and a greater likelihood of recommending online food ordering services.
Azizul, Albattat, and Shahriman (2019) explore how food delivery application attributes influence customer perceived value among young working adults in Shah Alam, Selangor, Malaysia The study examines key variables, including convenience, application design, trustworthiness, price, and food variety Findings reveal that price has the strongest impact on customer perceived value, while factors such as convenience, application design, trustworthiness, and diverse food options also contribute positively, albeit to a lesser extent.
Chaudhary (2018) investigates the key factors influencing consumers to order food online and their preferences for online food service providers The study identifies several critical factors, including delivery time, application design, accessibility, flexibility, payment options, and promotional types Results indicate that product pricing, promotional types, special offers, and user-friendly application interfaces positively impact online food ordering Notably, the research highlights that timely delivery is the most significant factor affecting consumers' online food ordering decisions.
Zulkarnain Kedah (2015) explored the factors influencing the consumer ordering experience, highlighting the importance of website trust, consumer satisfaction, and loyalty The study revealed a significant positive correlation between website quality and website trust, as well as between customer service and consumer satisfaction Additionally, strong positive relationships were identified between website trust and consumer satisfaction, and between consumer satisfaction and loyalty Interestingly, the research also uncovered an unexpected direct link among these variables.
15 between service quality and loyalty Overall, the study provides valuable insights for operating online food ordering services successfully
In a study conducted by Hoang Nguyen Bao Long in 2018, the key factors affecting the quality of Grabfood service in Ho Chi Minh City were identified as perceived price, responsiveness, empathy, customer service, reliability, and tangible means The findings revealed that perceived price, responsiveness, empathy, customer service, and reliability positively influence the overall quality of Grabfood services in the city.
Synthesis of factors influencing customer’s satisfaction
The table below is a summary of results of previous studies
Table 2.2 Summary of previous results
Factors affecting customer satisfaction when using online food ordering service
Information, payment and safety, usefulness, convenience are positively correlated (+) (positive impact) on customer satisfaction when using online food ordering service
Website quality, delivery, various food choice, payment are positively correlated (+) (positive impact) on customer satisfaction when using online food ordering service
Convenience, application design, trustworthiness, price, various food choice are positively correlated (+) (positive impact) on customer satisfaction when using online food ordering service
Price of the product, type of promotion, special offer, application design are positively correlated (+) (positive impact) on customer satisfaction when using online food ordering service
Customer service, website trust are positively correlated (+) (positive impact) on customer satisfaction when using online food ordering service
Price, responsiveness, empathy, customer service, reliability are positively correlated (+) (positive impact) on customer satisfaction when using online food ordering service
Source: Author's compilation After reviewing the research works, the author plans to include the following factors in the research model:
Table 2.3 Factors expected to be included in the proposed research model
Customer service (CS) Zulkarnain Kedah (2015), Hoang Nguyen Bao Long (2018)
(TPA) Ye - Eun Song (2017), Sihipman (2019)
Azizul, Albattat, & Shahriman (2019), Chaudhary (2018), Hoang Nguyen Bao Long (2018)
Research hypothesis
Based on a comprehensive review of existing research models, we identify key factors that significantly influence customer satisfaction in online food ordering services These factors form the basis of our hypotheses regarding customer experience and service quality in this growing industry.
Food delivery applications offer a diverse range of options that attract consumers, making them prefer ordering food online instead of dining out To ensure customer satisfaction, these applications must prioritize food variety and quality Food quality encompasses both external factors, such as the visual appeal of the dishes, and internal factors, including the quality of raw materials used in food preparation Research by Azizul, Albattat, and Shahriman (2019) and Sihipman (2019) indicates that product appearance significantly influences consumer purchasing decisions, highlighting the importance of visual presentation in attracting buyers.
H1: Variety of food has positive relationship with customer satisfaction when using online food ordering service
Customer service is crucial for retaining customers and fostering business growth, as it encompasses the interactions between sellers and consumers In today's digital age, customer service extends beyond phone calls to include email, web chat, text messaging, and social media platforms Research indicates that effective customer service distinguishes online businesses from their physical counterparts and significantly influences consumer satisfaction According to studies by Zulkarnain Kedah (2015) and Hoang Nguyen Bao Long (2018), timely customer service is essential for addressing consumer concerns regarding real-time inquiries, as well as the return and replacement of purchased goods or services.
H2: Customer service has positive relationship with customer satisfaction when using online food ordering service
Promotions are activities undertaken by sellers to enhance consumer awareness of their products One key type of promotion is sales promotion, which encompasses various consumer incentives such as coupons, cash refunds, discounts, premiums, and prizes (Chaudhary, 2018) Research indicates that offers and discounts are particularly effective in attracting consumers to engage in online food ordering consistently.
H3: Type of promotion has positive relationship with customer satisfaction when using online food ordering service
Payment refers to the exchange for goods or services received, with consumers utilizing various methods to complete transactions According to Ye-Eun Song (2017) and Sihipman (2019), the most preferred payment option is cash on delivery (C.O.D.), followed by digital methods such as credit cards and online banking Cash on delivery allows consumers to make payments after receiving their goods or services, while online banking facilitates money transactions over the internet.
H4: Convenient type of payment has positive relationship with customer satisfaction when using online food ordering service
Application design encompasses the user interface and the functionality of each feature, influencing how users interact with the application Research by Azizul, Albattat, & Shahriman (2019) and Chaudhary (2018) indicates that a more interactive and user-friendly application design significantly enhances customer satisfaction in online food ordering services.
H5: Application design has positive relationship with customer satisfaction when using online food ordering service
Perceived price refers to the amount paid to acquire a product or service When BAEMIN ensures high service quality at a reasonable price that aligns with the income and living standards of Ho Chi Minh City residents, customer satisfaction with BAEMIN's services rises significantly.
H6: Perceived price has positive relationship with customer satisfaction when using online food ordering service.
Proposed research model
Research highlighted in Chapter 2 indicates that various time periods, situations, and markets exhibit unique characteristics, leading to distinct consumer behaviors To ensure objectivity in the study, a diverse range of scales from multiple research models will be selected as a foundation for analysis A summary of these scales is provided in Chapter 2.
H1: Variety of food has positive relationship with customer satisfaction when using online food ordering service
H2: Customer service has positive relationship with customer satisfaction when using online food ordering service
H3: Type of promotion has positive relationship with customer satisfaction when using online food ordering service
H4: Type of payment has positive relationship with customer satisfaction when using online food ordering service
H5: Application design has positive relationship with customer satisfaction when using online food ordering service
H6: Perceived price has positive relationship with customer satisfaction when using online food ordering service
The author employs SPSS software to conduct regression analysis for testing hypotheses, focusing on factors that influence customer satisfaction Utilizing the one-pass selection method, known as Enter, the analysis adheres to key principles Initially, the Adjusted R Square coefficient is examined to assess model fit, followed by the evaluation of Sig values below 0.05 and the F coefficient in the ANOVA table to confirm the regression model's compatibility with the sample population Additionally, the variance inflation factor (VIF) is checked to ensure it falls within the acceptable range of 1 to 10, indicating no multicollinearity issues Finally, the impact of the variables on customer satisfaction is analyzed through the Beta coefficients in the Coefficient table.
RESEARCH METHODOLOGY
Research process
This article explores the factors influencing customer satisfaction with BAEMIN services in Ho Chi Minh City through survey data analysis By evaluating these factors, the research aims to provide insights into customer behavior in the online food delivery market The findings will offer actionable solutions for businesses in the sector, enabling them to enhance their sales strategies and better meet customer needs.
After identifying the research problem, the author created a comprehensive outline for the research paper Utilizing this framework, the author explored relevant documents and studies to determine the variables necessary for developing an effective survey questionnaire Following the survey, the collected data will be screened and analyzed using SPSS software to yield results for evaluation Once these steps are completed, the author will present their insights and suggest appropriate solutions Finally, the instructor will review and assess the research before the final project is completed A diagram summarizing the research process is provided below.
Scale formulation
Through the process of selection and research, the author has selected a research model that includes factors affecting customer satisfaction as follows:
Table 3.1 Summary of research models and observed variables
Number Code Observable variables Source
Variety of food Azizul, Albattat &
1 VF1 I am satisfied with the variety of restaurants BAEMIN offer
Correct and complete the research
2 VF2 I am satisfied with the variety of food choices BAEMIN offer
3 VF3 Based on the price range in the application,
4 CS1 I am satisfied using customer service
5 CS2 I find that it is easy to contact customer service
6 CS3 I am satisfied with friendliness of customer service
7 TPR1 I am satisfied with the promotion code provided by BAEMIN
8 TPR2 I am satisfied with free delivery fee provided by BAEMIN
9 TPR3 The promotions help me save my money
10 TPR4 I am satisfied with the limitation of using voucher per day
Type of payment Ye - Eun Song
11 TPA1 I am satisfied with the option of payment in BAEMIN application
12 TPA2 I am satisfied with the efficiency of payment gateway system
13 TPA3 I am satisfied when certain restaurant only accept cash on delivery for the payment
14 AD1 I like the interface of the BAEMIN app
15 AD2 BAEMIN app has more functions than other food delivery apps
16 AD3 Ordering food through the BAEMIN app is faster and easier than other apps
17 AD4 BAEMIN application is well compatible with many devices
Perceived price Hoang Nguyen Bao
18 PP1 The price of Baemin's service is reasonable with the quality of the service
19 PP2 Prices during peak hours are acceptable
20 PP3 Prices are not too high compared to other services during off-peak hours
21 PP4 Prices are not high and competitively cheap compared to other services
Customer satisfaction Hoang Nguyen Bao
22 CSA1 In general, you are very satisfied with
23 CSA2 In general, you are very satisfied with the rate provided by BAEMIN
24 CSA3 In general, you are very satisfied with the experience of using BAEMIN
25 CSA4 In general, you are very satisfied with
In the data collection process, the author utilized the Likert scale, developed in 1932, which is a five or seven-point scale designed to gauge individual levels of agreement or disagreement with specific statements This method provides a nuanced understanding of respondents' opinions, allowing for varying degrees of agreement rather than a simple yes or no answer, and also accommodates those who may have no opinion at all.
Methods of data collection
Secondary information is gathered from various sources, including theoretical documents such as books and textbooks focused on E-Commerce Marketing and Sales Additionally, the author collects data from scientific research and graduation theses related to the subject.
To ensure that research findings are logical, reliable, and accurate, authors must gather primary information in addition to collecting secondary data from various sources.
On the basis of the proposed model and preliminary research, the author build and measure the influencing factors including 07 main components, with 25 observed variables, included:
- Variety of food: 3 observed variables
- Type of promotion: 4 observed variables
- Type of payment: 3 observed variables
The questionnaire include questions that are a combination of scales such as nominal and Likert scales including 5 levels: (1) Completely disagree, (2) Disagree, (3) Normal,
(4) agree, (5) Totally agree to measure the values
The questionnaire consists of three parts:
The author introduces the researcher, outlining the study's purpose and the pressing nature of the issue at hand This approach not only establishes credibility but also fosters cooperation among participants, ultimately leading to the collection of the most accurate data possible.
This section outlines the objective of gathering additional demographic information about respondents, including their gender, school year, occupation, and place of residence The data collection utilizes nominal scales to effectively categorize these characteristics.
Part 3: Information about online food ordering services
This article explores key factors influencing customer satisfaction with online food ordering services in Ho Chi Minh City By investigating these elements, the research aims to enhance understanding of consumer preferences and improve service quality in the online food delivery market.
For the EFA discovery factor, the minimum sample size ensures the formula: n ≥ 5*x (n: sample size and x: total observed variables) (Hair et al, 2014)
The official study's survey questionnaire comprises 25 observed variables, necessitating a minimum sample size of 125 participants (5 times the number of variables) to ensure statistical validity The author aimed for a sample size of 200 to enhance the research's robustness The target demographic consists of BAEMIN customers aged 18 to 25 in Ho Chi Minh City A total of 206 responses were collected through an online survey using Google Forms However, six responses were deemed invalid due to incomplete answers, excessive blank responses, or consistent repetition of the same answer throughout the questionnaire.
27 author rejects to use these 6 observations Therefore, the number of observations used for the study is 200
The research focuses on students from four universities: Banking University, University of Economics, University of Finance – Marketing, and International University The author's close relationship with student groups at these institutions facilitates easier access for surveys, encouraging honest responses Data collection is conducted through an online questionnaire designed using Google Docs, which is distributed to respondents via email.
Upon clicking the "Submit" button, the responses to the questionnaire will be sent to the researcher's email Once the required sample size is achieved, the survey will be closed, marking the end of data collection The survey employs a nominal scale to categorize participants' choices and assesses their interest levels using a hierarchical scale Ultimately, the author will analyze and quantify the factors using a 5-point Likert scale.
Data processing methods
The statistical method is designed to identify the key characteristics of a research sample, utilizing both quantitative and qualitative variables to assess their effects on the research model Descriptive statistics are employed to illustrate graphs that depict the percentage of a variable in relation to the overall sample.
Testing Cronbach's Alpha is a crucial method for assessing the reliability of a measurement scale This test aims to determine whether the observed variables consistently measure the same underlying concept By analyzing the Corrected Item-Total Correlation coefficient, researchers can identify and eliminate unsuitable variables from the research model Specifically, observable variables with an item-total correlation of less than 0.3 are considered inadequate for the analysis.
28 excluded and the scale selection criteria will be when Cronbach's Alpha is 0.6 or higher According to Hoang Trong et al (2008)
EFA factor analysis is a statistical technique that simplifies a large number of observed variables into a smaller, more meaningful set while retaining the majority of the original information.
Hair et al (1998) state that in Exploratory Factor Analysis (EFA), a Factor Loading index value exceeding 0.5 indicates practical significance The Kaiser-Meyer-Olkin (KMO) measure assesses the suitability of the EFA method; a KMO coefficient between 0.5 and 1 suggests that factor analysis is appropriate According to Trong & Ngoc, these metrics are essential for ensuring the validity of the analysis.
Bartlett's test, established in 2005, assesses the hypothesis that the correlation among observed variables in a population is zero A statistically significant result, indicated by a p-value (Sig.) less than 0.05, suggests that there is a correlation among the observations within the population.
Gerbing and Anderson (1988) established that for factor analysis to be deemed satisfactory, the stopping criterion should be when the Eigenvalue exceeds 1, and the total variance explained by each factor is greater than 50%.
Regression analysis is a statistical technique used to identify the relationship between a dependent variable and one or more independent variables This method enables the formulation of a final regression equation that highlights the factors directly influencing customer satisfaction For this analysis, the one-pass selection method, known as Enter, has been chosen.
When conducting regression analysis, it is essential for the author to adhere to several key principles Firstly, the Adjusted R Square coefficient should be examined to assess the model's fit Secondly, the significance values (Sig) should be checked to ensure they are less than 0.05, along with the F coefficient in the ANOVA table, to validate the regression model's compatibility with the sample population Lastly, it is crucial to evaluate whether the variance inflation factor (VIF) falls within an acceptable range.
(1, 10) to consider multicollinearity Finally, the author evaluate the strong or weak
29 impact of the variables on the satisfaction level through the Beta coefficients in the Coefficient table
ANOVA, or analysis of variance, is a statistical method used to compare the means of three or more populations This technique is particularly valuable for analyzing survey and experimental data Researchers utilize One-Way ANOVA to assess differences between groups concerning qualitative and dependent variables.
In the Test of Homogeneity of Variances, a significance level (Sig.) greater than 0.05 suggests that there is no significant difference in variance among the groups Conversely, a Sig value less than 0.05 indicates a significant difference in mean values between groups for qualitative variables.
RESEARCH RESULTS
Introduction to BAEMIN
E-hailing services are known to be on-demand vehicle acquisition that relies on network dependency and use of a specific digital application through the Internet (Ahmad Sahir Jais, 2020) In the previous study, Nur A thirah Nabila Mohd Idros (2019) define the e- hailing is online transportation service that involves the interaction between consumer with seller
E-hailing is a process of ordering a car, taxi, or any other form of transportation pick up via virtual devices: computer or mobile device E-hailing is not only vehicle booking services such as Grab or Uber, but e-hailing is also food delivery services such as GoFood, GrabFood, Now and Baemin In August 2020, The Human Resources Ministry has categorized food delivery riders as selfemployed under e-hailing to ensure their welfare was well taken care of by employers, meaning that e-hailing has official to enter this category
BAEMIN, a food delivery service operated by Woowa Brothers Corp and based in Seoul, made its official debut in the Vietnamese market in early 2019 After successfully acquiring the online food ordering app, BAEMIN launched its services in Ho Chi Minh City following a month of testing in key central areas.
BAEMIN is committed to its mission of "Helping people eat delicious food anytime, anywhere" in Vietnam, operating similarly to other food delivery apps like Now and Grabfood Users can easily select their desired food, choose the nearest restaurant, and place an order for quick delivery at reasonable prices, complemented by daily promotions The brand's catchy slogan, "BAEMIN, it's hot and crispy!", highlights its swift delivery service while prioritizing driver safety, building on the success of its parent company.
Launched in Korea in June 2010, the Baedal Minjok app, now known as BAEMIN, has become the leading food ordering app with over 10 million active users and 30 million monthly orders Expanding to Vietnam, BAEMIN has made a strong impression by tailoring its brand identity to local preferences, incorporating unique design elements such as specific brand colors, fonts, and the iconic Fat Cat symbol Additionally, the app actively promotes Korean cuisine and cultural trends, further enhancing its appeal in the Vietnamese market.
Baemin is a food delivery application that allows users to conveniently order food online through its mobile app By partnering with various restaurants and eateries, Baemin enables sellers to upload their menus, offering customers a wide selection of dishes to choose from With its dedicated food delivery team, Baemin ensures that orders are delivered directly to customers without relying on intermediary services.
Survey data analysis results
Descriptive statistic for norminal variables
This study provides an overview of the research sample by examining key demographic factors, including student gender, student level, university affiliation, monthly usage level, and monthly income The results of the descriptive statistics for these nominal variables are presented below.
Table 4.1 Sample characteristics of gender
Table 4.1 reveals that a total of 200 students participated in this study, with females representing 61% of the respondents and males accounting for 39%.
According to Table 4.2, the study reveals that 18 second-year students represent 9%, while 32 junior students account for 16% In contrast, senior students make up the majority with 150 participants, comprising 75% of the total Therefore, this research primarily targets senior students.
Based on Table 4.3, the author finds that there are 105 students of Banking university held the lion’s share, with 52.5%, followed by that of University of Finance - Marketing,
University of Economics and International University, 53 students (26.5%), 28 students (14%) and 14 students (7%) respectively Thus, this research mostly focuses on students of Banking university and University of Economics
Table 4.4 Sample app installed statement
According to Table 4.4, a total of 200 students participated in the study, with 167 students (83.5%) indicating that they have installed the BAEMIN application, while 33 students (16.5%) reported that they have not This indicates that the majority of university students have adopted the BAEMIN app.
Table 4.5 Sample used level per month
Taking a closer look at the table 4.5, we find that the level of used BAEMIN per month of Universities’ students with “1-2 times” answer held the lion’s share, with 112 people
(56%), far exceeding that of 3-5 times, 6-10 times and over 10 times with 35 students (17.5%), 30 students (15%) and 33 students (16.5%) respectively
Table 4.6 Sample income per month
A survey conducted among 200 students revealed that 25.5% earn below 3 million VND per month, while 39.5% have an income ranging from 3.1 million to 5 million VND Additionally, 21% of students earn between 5.1 million and 7 million VND, and 14% earn above 7 million VND The findings indicate that there is little variation in the income levels among the student population.
Descriptive statistics for ordinal variables
Descriptive statistics for ordinal variables help identify the minimum and maximum values of the scales, while also calculating the mean and standard deviation.
Table 4.7 Descriptive statistic of VF
N Minimum Maximum Mean Std Deviation
The results in Table 4.7 show a great variation in the views of 200 participants about Variety of food with the smallest score of 1 (Strongly disagree) and the highest score of
The survey results indicate a strong consensus, with a significant number of respondents expressing positive sentiments, reflected in a standard deviation that confirms the reliability of the findings The average values of the VF1 to VF3 factors consistently range from 3 to 5, suggesting a shift from normal to strong agreement This reinforces the initial hypothesis that positive indicators are present in the model, as the standard deviation remains within acceptable limits, validating the appropriateness of the observed data.
Table 4.8 Descriptive statistic of CS
N Minimum Maximum Mean Std Deviation
The findings presented in Table 4.8 reveal a significant variation in the perspectives of 200 participants regarding customer service, with scores ranging from 1 (Strongly disagree) to 5 (Strongly agree) The survey aimed for a "positive sign," and this variability resulted in a standard deviation Nevertheless, the average scores for the CS1 to CS3 factors indicate that participants generally rated their experiences between 3 and 5, reflecting a range from Normal to Strongly agree Consequently, the initial hypothesis suggesting positive indicators in the model remains validated.
In other words, "standard deviation" is within the allowable limits Therefore, the observed data is "appropriate"
Table 4.9 Descriptive statistic of TPR
N Minimum Maximum Mean Std Deviation
The results presented in Table 4.9 indicate significant variability in the opinions of 200 participants regarding the type of promotion, with scores ranging from 1 (Strongly disagree) to 5 (Strongly agree) Despite this oscillation leading to a standard deviation, the average scores for the TPR1 to TPR4 factors consistently fall between 3 and 5, suggesting a general consensus from normal to strong agreement Consequently, the initial hypothesis indicating positive signs in the model remains validated.
In other words, "standard deviation" is within the allowable limits Therefore, the observed data is "appropriate"
Table 4.10 Descriptive statistic of TPA
N Minimum Maximum Mean Std Deviation
The results in Table 4.10 show a great variation in the views of 200 participants about Type of payment with the smallest score of 1 (Strongly disagree) and the highest score
The survey results indicate a strong positive response, with an average rating between 3 and 5, suggesting agreement with the TPA1 to TPA3 factors This consistency around the average value reinforces the initial hypothesis of positive indicators in the model, despite the standard deviation observed in the data.
In other words, "standard deviation" is within the allowable limits Therefore, the observed data is "appropriate"
Table 4.11 Descriptive statistic of AD
N Minimum Maximum Mean Std Deviation
The findings in Table 4.11 reveal significant variability in the opinions of 200 participants regarding application design, with scores ranging from 1 (Strongly disagree) to 5 (Strongly agree) Although the survey team anticipated a "positive sign," this variation has led to a standard deviation Nevertheless, the average scores for factors AD1 to AD4 consistently fall between 3 and 5, indicating a general consensus from "Normal" to "Strongly agree." Thus, the initial hypothesis suggesting positive indicators in the model remains validated.
In other words, "standard deviation" is within the allowable limits Therefore, the observed data is "appropriate"
Table 4.12 Descriptive statistic of PP
N Minimum Maximum Mean Std Deviation
The results in Table 4.12 show a great variation in the views of 200 participants about Perceived price with the smallest score of 1 (Strongly disagree) and the highest score of
The survey team perceives a "positive sign," leading to the creation of a standard deviation due to oscillation The average values of the PP1 to PP4 factors consistently range from 3 to 5, indicating a shift from Normal to Strongly Agree Consequently, the initial hypothesis suggesting positive indicators in the model remains validated.
In other words, "standard deviation" is within the allowable limits Therefore, the observed data is "appropriate"
The Cronbach’s Alpha test serves as an initial assessment of the scale by examining the Cronbach’s Alpha coefficient and the corrected item-total correlation for each observed variable Variables are selected based on having an item-total correlation exceeding 0.3 and a Cronbach’s Alpha coefficient greater than 0.7 The subsequent analysis will focus on factors such as Security Risk, Operational Risk, Finance Risk, Fraud Risk, and Intention to use credit cards, as detailed in the following tables, to conduct an exploratory factor analysis (EFA).
The results of the Cronbach’s Alpha analysis for the independent variables reveal key components: the Variety of Food, which includes three observed variables (VF1, VF2, VF3); Customer Service, also comprising three observed variables (CS1, CS2, CS3); Type of Promotion, consisting of four observed variables (TPR1, TPR2, TPR3, TPR4); and Type of Payment, which includes three observed variables.
39 observed variables: TPA1, TPA2 and TPA3 The component of Application design has
4 observed variables: AD1, AD2, AD3 and AD4 The component of Perceived price has
4 observed variables: PP1, PP2, PP3 and PP4
Table 4.13 Cronbach ’Alpha coefficient Observed variables
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
VF scale with Cronbach’s Alpha = 0.866
CS scale with Cronbach’s Alpha = 0.939
TPR scale with Cronbach’s Alpha = 0.930
TPA scale with Cronbach’s Alpha = 0.976
AD scale with Cronbach’s Alpha = 0.895
PP scale with Cronbach’s Alpha = 0.858
CSA scale with Cronbach’s Alpha = 0.909
The VF scale demonstrates strong reliability, with a total Cronbach’s alpha coefficient of 0.866, falling within the acceptable range of 0.8 to 0.9 Furthermore, all component scales exhibit correlation coefficients exceeding the minimum threshold of 0.4 Consequently, the VF variable comprises three reliable component scales—VF 1, VF 2, and VF 3—making it suitable for subsequent analyses.
