INTRODUCTION
Introduction
This study explores the factors influencing internet users' loyalty and their intention to remain with their current Internet Service Providers (ISPs) within Vietnam's telecommunications sector It presents an overview of the research problem and background, outlines the objectives and questions of the study, and provides a brief structure of the research framework.
Research background
High-speed internet services are increasingly popular, significantly impacting various aspects of our lives due to rapid advancements in information technology While basic internet services were available in Vietnam since the early nineties, the first commercial ISP, Vietnam Data Communication Company (VDC), began operations in 1997, establishing connections through gateways in Hanoi and Ho Chi Minh City Despite historically low internet usage compared to other Asian nations, Vietnam has seen a surge in connectivity, with over 31 million internet users reported by June 2012, representing 35.4 percent of the population The growth of internet services has led to a remarkable increase in the number of ISPs, fostering intense competition among more than ten providers, including VDC/VNPT, Viettel, FPT, Netnam, and SPT.
In Vietnam's competitive internet service market, three major ISPs—VDC/VNPT, Viettel, and FPT—dominate with over 85% market share, necessitating robust marketing strategies and enhanced business performance among all providers As the industry approaches saturation, focusing on customer satisfaction and loyalty emerges as a crucial competitive advantage Research indicates that satisfied customers are more likely to remain loyal, leading to increased market share and profitability Brand loyalty not only reduces marketing costs but also attracts new customers, underscoring its importance in the brand equity process Consequently, understanding the factors that foster customer loyalty is vital for ISPs This study aims to analyze internet service users in Ho Chi Minh City, identifying key factors that influence their perceptions and loyalty towards their current providers in the telecommunications sector.
This research offers ISPs essential insights into the key factors necessary for achieving success and fostering customer loyalty, ultimately enhancing profitability and strengthening competitiveness for sustainable growth in the future.
The research objectives
This research investigates the key factors affecting customer loyalty in internet service provision within the Vietnamese telecommunications market The primary goal is to identify the attributes that influence customer loyalty in internet services in Vietnam.
We also examine the affecting level of each factor on Vietnamese internet users’ loyalty.
The research questions
Based on the research objectives, we accordingly give two main questions as follow:
- What are the factors that influence customer loyalty in Internet services in Vietnam?
- How do these factors influence on loyalty of Internet customers?
The research model
This study proposes a research model consisting of four hypotheses aimed at investigating customer loyalty towards Internet Service Providers (ISPs) in Vietnam Illustrated in Figure 1 of Chapter 2, the model examines the connections between network quality, price perception, perceived value, and trust, ultimately influencing customers' intentions to remain with their current ISP.
The research methodology
This research utilized a survey with a questionnaire to collect data, which was initially created in English and then translated into Vietnamese for the respondents To minimize bias, random sampling was employed The collected data was coded and analyzed using SPSS software Further details on the research methodology can be found in Chapter 3, while the results are presented in Chapter 4.
The research structure
Chapter 1 of this research presents an introduction that outlines the general landscape of internet services, with a specific focus on Vietnam It includes the research background, objectives, questions, methodology, and the overall structure of the study.
Chapter 2 – Literature review - screens the literature of customer loyalty from previous researches Basing on these literature reviews, a model with four hypotheses is developed We examine the relationship between customer loyalty and its affecting factors including network quality, price perception, perceived value and trust Besides, demographic factors such as gender, income, and experience of service using are also tested their impact on customer loyalty
Chapter 3 – Research methodology - presents methods to conduct this research, consisting of three sections Firstly, measuring scale with items for each factor is provided Next, sample and data collection are explained in detail The last one describes the process and analytical techniques used for data analysis
Chapter 4 – Data analysis - reports the results from data analysis process and confirms the value of hypotheses mentioned in Chapter 2 and relationship among factors
Chapter 5 – Conclusion, implications and limitations – repeats the result derived from this research Moreover, this chapter also discusses limitations of the research and suggestions for future researches.
Conclusion
This chapter outlines the research background, highlighting the key research problem, objectives, questions, model, and methodology The study aims to explore the relationship between customer loyalty and its influencing factors within the Vietnamese Internet service market It posits that four specific factors positively impact Internet customer loyalty Data is gathered through questionnaires and analyzed using SPSS software.
LITERATURE REVIEW
Introduction
This chapter reviews the literature on customer retention, focusing on the factors that influence customer loyalty within the internet service provider sector, particularly in the context of Vietnam's telecommunications industry.
The study investigates the relationship between customer loyalty and key factors such as network quality, price perception, perceived value, and trust, leading to the formulation of four hypotheses (H1, H2, H3, H4) Additionally, it examines the impact of controlling variables, including gender, income, and service usage experience, on customer loyalty.
Customer loyalty with internet service provider
Customer loyalty is the result of a customer's evaluation of the rewards and costs associated with their purchases, influenced by emotional factors (Churchill & Surprenant 1982; Jacoby 1975) It can be measured through various metrics such as purchase frequency and likelihood of repeat buying (Dick & Basu 1994) Essentially, customer loyalty reflects a customer's intention to remain with their current service provider while consistently choosing their preferred brands Businesses prioritize fostering customer loyalty, as it leads to increased revenue and profit, while a lack of loyalty can jeopardize their competitive edge Loyal customers are typically easier to serve, as they are familiar with the company's processes, and they tend to exhibit more repeat purchases without seeking alternatives (Chow & Holden 1997; Bowen & Chen 2001) The longer customers remain loyal, the greater the profitability for the business (Reichheld & Teal 1996).
Businesses benefit significantly from having loyal customers, as it leads to enhanced profitability and the ability to charge premium prices Additionally, loyal customers foster long-term relationships and commitment, boost purchase intentions, and generate positive word-of-mouth referrals This loyalty also helps reduce marketing and operational costs while creating higher switching barriers, making it less likely for customers to leave their current service provider.
Customer loyalty is essential for businesses, as it fosters long-term relationships and ensures repeat patronage Companies must prioritize customer satisfaction to cultivate this loyalty and maintain a competitive edge in the market.
2.2.1 Network quality and customer loyalty
Network quality plays a crucial role in determining overall service quality and customer satisfaction in telecommunications Key attributes of network quality include stability, transmission speed, and coverage Users prioritize stability and speed, often considering switching providers if these factors are lacking Additionally, service uptime significantly influences both customer satisfaction and loyalty To remain competitive, service providers must focus on enhancing service availability.
H1: Network quality has a positive impact on customer loyalty
2.2.2 Price perception and customer loyalty
Internet broadband users often exhibit a willingness to pay more for enhanced service; however, price remains a critical factor that may prompt them to switch providers (Mohd R.B Yaacob, 2011) This indicates that customers are sensitive to pricing, and elevated price levels can lead to decreased demand Research by Ranaweera & Neely (2003) supports the notion of a direct linear relationship between price perception and customer loyalty within the telecommunications sector Given the intense price competition in markets like Vietnam, we propose that this relationship is even more pronounced Therefore, we hypothesize that price perception significantly influences customer loyalty in the Vietnamese telecommunications market.
H2: Service price is positively related to customer loyalty
2.2.3 Perceived value and customer loyalty
Perceived value is the customer's overall evaluation of a product or service's utility, which is determined by the balance between benefits received and sacrifices made (Monroe 1991, Parasuraman, Zeithaml & Berry 1988) This trade-off encompasses perceived sacrifices, including purchase price, acquisition costs, installation, repair, maintenance, and the risks associated with failure or subpar performance Conversely, perceived benefits consist of physical attributes, service quality, technical support, and other factors that contribute to the overall service experience.
Perceived value is subjective and varies among customers, as noted by Berry (1988) Research indicates that customers who feel they receive good value for their money are generally more satisfied and more likely to become loyal, compared to those who do not perceive such value (Zeithaml, Berry & Parasuraman).
Long-term customer retention is influenced by the perceived quality and value of services, leading to increased purchases of additional services, positive word-of-mouth, and a higher likelihood of recommendations Service providers should recognize perceived value as a crucial competitive advantage that can enhance their sustainability in the market By enhancing perceived value, businesses can foster greater customer loyalty Therefore, we hypothesize that increasing perceived value will significantly boost customer retention and loyalty.
H3: Perceived value has a positive effect on customer loyalty
Trust is a key factor in fostering customer loyalty, as highlighted by Dick and Basu (1994) Establishing long-term relationships with customers is essential for cultivating trust and, in turn, enhancing brand loyalty, according to Morgan and Hunt.
When customers trust their preferred brand, they are more inclined to make purchases without extensive persuasion, leading to reduced advertising costs Trust complements customer satisfaction by fostering long-term relationships and enhancing loyalty Furthermore, trust involves a readiness to depend on others, and when buyers have confidence in sellers, it positively influences their purchasing behavior This establishes a foundation for the hypothesis that trust plays a crucial role in consumer decision-making.
H4: Trust positively influences customer loyalty.
Controlling factors
Research by Choudrie (2005) highlights the significant impact of demographic factors, particularly gender and income, on customer loyalty The study also investigates the interplay between these demographic variables and customer loyalty Additionally, it emphasizes the importance of evaluating customer experiences with services to understand their effect on loyalty.
The research model
The research model serves as the cornerstone of the study, aiding in the identification and labeling of key variables related to the research problem (Sekaran, 2000) This section synthesizes existing literature to propose a model that illustrates the relationships between customer loyalty and various influencing factors To effectively address the research questions, the model incorporates five primary variables: network quality, price perception, perceived value, and trust, all of which are considered dependent variables impacting customer loyalty Additionally, the influence of demographic factors as a controlling variable on the relationships among these variables is also examined.
The proposed model and all hypotheses tested in the context of Vietnam telecommunication market are outlined as follow:
The following table provides a summary of the research hypotheses and questions to be tested in the research:
1 What are the factors that influence customer loyalty in Internet
H1: Network quality has a positive impact on customer satisfaction
Controlling variables (Gender, Experience, Income)
H2: Price perception is positively related to customer loyalty
H3: Perceived value has a positive effect on customer loyalty services in Vietnam?
2 How do the factors influence customer loyalty in Internet services in Vietnam?
H4: Trust positively influences customer loyalty
Table 1 - Research questions and hypotheses
Conclusion
In the context of Vietnam's telecommunication industry, our theoretical model presents four hypotheses indicating that customer loyalty is positively influenced by factors such as network quality, price perception, perceived value, and trust.
In addition, we also examine the effect of demographic factors on customer loyalty.
RESEARCH METHODOLOGY
Introduction
This chapter aims to outline the research methods and approaches employed to explore customer perceptions of Internet service users in Ho Chi Minh City It provides an overview of the research process, detailing the research approach, measurement techniques, sampling methods, data collection strategies, and data analysis procedures used in the study.
The research approach
The quantitative approach focuses on statistical analysis and utilizes numerical data to achieve objective and precise results for drawing conclusions or testing hypotheses (Ticehurst & Veal, 2000) This research method quantifies the relationships between variables and employs quantitative techniques to evaluate research assumptions and hypotheses, addressing the research problem effectively By relying on established hypotheses derived from prior literature, this study exemplifies a quantitative research methodology rather than aiming to create new theories.
Measurement
This section presents the items for measurement of all relating factors Items of each factor are developed from previous literature review mentioned in Chapter 2
Measuring scale of network quality from Mohd R.B Yaacob (2011) and Wang, Lo
- The upload and download speed of network is always strong
- The uptime of network is always available without interruption
- The connection quality is always reliable
- I can access the network at anytime without delay
Items for price perception from Ranaweera & Neely (2003):
- The price charged by X is reasonable
- The service’s price of X is cheaper than others
- Service supplied by X is equivalent to its price
- Service offered by X is better value for money than what I would pay for the same service of others
- I’m willing to pay more for better service quality
Based on Wang, Lo & Yang (1988), five items for measuring the effect of perceived value on customer loyalty are adopted:
- The customer service staff of my ISP gives me adequate support
- Whenever I have problem with services, X takes corrective action without delay
- X keeps me informed of things that I need to get the best use of the service
- I feel comfortable with the willingness of assistance and support from X
- Overall, the chosen offerings are worth for my money, effort and time
Items for measuring trust from Dick & Basu (1994)
- I believe that X will be ready and willing to offer me assistance and support
- I believe that X always fulfills the promises that it makes to its customers
- I believe that X has necessary ability and knowledge to fulfill its tasks
- I believe that X always puts interests of its customers first of all
Items for customer loyalty from Nguyen and Leblanc (1991)
- I will recommend X as the best ISP in HCMC
- I will continue to do business with X
- X is my first choice in my future internet service needs
- I will purchase other services offered by X in the future
- I will encourage friends and relatives to do business with X
Items for demographic of respondents:
- Income Measuring scales for variables are summarized as table below:
No Variables Literature review Measuring items
1 The upload and download speed of network is always strong
2 The uptime of network is always available without interruption
3 The connection quality is always reliable
4 I can access the network at anytime without delay
1 The price charged by X is reasonable
2 The service’s price of X is cheaper than others
3 Service supplied by X is equivalent to its price
4 Service offered by X is better value for money than what I would pay for the same service of others
5 I’m willing to pay more for better service quality
1 The customer service staff of my ISP gives me adequate support
2 Whenever I have problem with services, X takes corrective action without delay
3 X keeps me informed of things that I need to get the best use of the service
4 I feel comfortable with the willingness of assistance and support from X
5 Overall, the chosen offerings are worth for my money, effort and time
4 Trust Dick & Basu 1 I believe that X will be ready and willing to
2 I believe that X always fulfills the promises that it makes to its customers
3 I believe that X has necessary ability and knowledge to fulfill its tasks
4 I believe that X always puts interests of its customers first of all
1 I will recommend X as the best ISP in HCMC
2 I will continue to do business with X
3 X is my first choice in my future internet service needs
4 I will purchase other services offered by X in the future
5 I will encourage friends and relatives to do business with X
Table 2: Measuring scale for variables
Sample and Data collection
Sampling involves selecting a representative subset of individuals from a larger population (Sekaran, 2000) To minimize bias among Vietnamese Internet users, random sampling was employed DeCoster (2004) asserts that the minimum sample size for statistical analysis should be at least five times the number of independent variables, with a minimum of 100 participants: n ≥ 100 and n ≥ 5k (where k represents the number of items) Given that this research includes 23 items, the minimum sample size for conducting Exploratory Factor Analysis (EFA) is calculated as n = 5 x 23 = 115 Additionally, Tabachnick & Fidell (2001) recommend a minimum sample size for multiple regression as n = 50 + 8m (where m is the number of independent variables) Applying this formula to the four independent variables in this study yields a minimum sample size of n = 50 + 8 x 4 = 82 Thus, this research necessitates a sample size of at least 115 for EFA and 82 for multiple regression analysis.
To ensure reliability and validity in our research, a quantitative approach was employed, requiring a sample size of approximately 300 internet service users The survey was conducted from August to October among users in Ho Chi Minh City To achieve this target sample size, questionnaires were distributed to over 500 potential respondents.
Data was collected through a comprehensive questionnaire survey divided into three sections The first section focused on customers' experiences with Internet Service Providers (ISPs), while the second section gathered respondents' opinions on service quality and their future decisions regarding their current ISP The final section included demographic questions The questionnaire featured both ranking statements and open-ended questions, allowing respondents to provide insights into their ISP experiences and identify their current providers Additionally, a seven-point Likert scale was utilized in the second section to measure opinions more precisely.
A seven-point Likert scale ranging from "Strongly disagree" (1) to "Strongly agree" (7) was utilized to assess respondents' perceptions of their current Internet Service Provider's (ISP) service quality and their intention to either remain with or switch to a different provider The initial questionnaire was developed in English and subsequently translated into Vietnamese to accommodate the Vietnamese respondents.
Data analysis
After collecting data, the subsequent step is data analysis to address the research questions This involves transforming raw data into meaningful information through various processes such as editing, coding, data entry, and analysis (Zikmund, 2000) In this study, SPSS software was utilized for analyzing data using descriptive statistics The following section outlines the data processing procedures and analytical techniques employed in this research.
The reliability of the measuring scale was assessed using Cronbach's alpha, a statistical test that evaluates the correlation between items within the scale (Hair, 2006) This analysis method effectively identifies and eliminates inappropriate items, ensuring the scale's reliability According to Pallant (2005), a scale is considered reliable when its Cronbach's alpha coefficient is 0.7 or higher.
Effecting Factor Analysis (EFA) was performed to identify the number of factors influencing customer loyalty towards Internet services This multivariate statistical technique, as defined by Hair (2006), reveals the underlying structure among numerous variables The primary objectives of factor analysis are to summarize extensive variable information and reduce data into fewer factors In this study, five key factors were identified that significantly impact customer loyalty Additionally, factor analysis facilitated the identification of highly correlated factors related to customer loyalty, with results deemed acceptable when specific conditions are met (Pallant, 2005).
The sample size should be equal to or greater than five cases for each variable
Factor analysis is appropriate to data if:
The Kaiser-Meyer-Olkin value (KMO) is 0.6 or greater
The Bartlett’s test of sphericity is statistically significant: p < 0.05
The number of factors is determined when:
The total variance explained by these components should be above 50%
Factor loading criteria should be 0.5 or more to ensure a practical significance
Pearson’s correlation coefficient is a statistical tool used to analyze the relationships between two or more research variables (Veal, 2005) This coefficient can indicate different types of relationships: a value of 1.0 signifies a perfect positive correlation, while a value of -1.0 indicates a perfect negative correlation Additionally, a correlation coefficient of zero suggests that there is no relationship between the variables.
Multiple regression analysis was employed to investigate the relationships among various variables, focusing on the interaction between multiple independent variables and a single dependent variable (Hair et al., 2006) This statistical technique allows for the examination of the simultaneous effects of these independent variables on the dependent variable In this study, the Multiple Linear Regression method was utilized to evaluate the research model and hypotheses, with Pallant (2005) outlining the necessary conditions for accepting the results.
The sample size is: n > 50 + 8m (where m is the number of independent variables)
Normality and linearity should exist
We also use R-square value to express how much of the variance in the dependent variable was explained by the model.
Conclusion
This chapter outlines the research methods employed in our study, detailing the research approach, measurement techniques, sampling strategies, data collection processes, and data analysis methods A comprehensive discussion of the analytic techniques and findings will be presented in Chapter 4.
DATA ANALYSIS AND RESULT
Introduction
This chapter presents a detailed analysis of the data, offering interpretations of the research findings to address the research questions and test the study hypotheses It is organized into four key sections: (1) preliminary data analysis, (2) evaluation of the measuring scales, (3) hypothesis testing, and (4) assessment of the research model.
Preliminarily data analysis
A quantitative research study was conducted using a survey questionnaire among Internet service users in Ho Chi Minh City Initially, a pilot test was performed with 100 samples, followed by the collection and analysis of 286 completed questionnaires for the main study The preliminary data analysis involved cleaning and screening the data, as well as summarizing the profiles of the respondents.
This step is crucial for identifying inappropriate samples completed by careless respondents, such as those who select the same response for all questionnaire statements Additionally, while missing data can negatively impact results, it was not an issue in this research as the online survey design prevented submissions with incomplete answers Consequently, we focused solely on detecting inappropriate responses After cleaning and screening the data, we found that 11 samples were distorted due to out-of-range values.
To ensure comprehensive data coverage, a sufficiently large sample size is crucial, with a recommended ratio of observations to independent variables of at least five to one, ideally between 15 and 20 observations per variable (Hair et al., 1998) This research includes five variables with 23 measuring items, necessitating a minimum sample size of approximately 194 However, to achieve proportional representation across Internet Service Providers (ISPs), the target sample size was set at around 300 An online questionnaire was distributed to over 500 Internet service users, resulting in 297 recorded responses via Google tools After data cleaning and screening, 286 samples were finalized for analysis, comprising 148 samples (52%) from VNPT, 72 samples (25%) from Viettel, 58 samples (20%) from FPT, and the remaining 3% from other providers such as SCTV and SPT The profiles of the respondents are detailed in the accompanying table.
No Items Scale No of samples Percentage
From above 35 to 50 years old
4 Experience of using internet service 286
Table 3: The profile of respondents
The respondents in the survey were evenly split between males and females, with the majority aged between 25 and 35 years Most participants reported a monthly income ranging from 10 million to 15 million Notably, 47% of respondents had been with their current Internet Service Provider (ISP) for over three years.
Evaluating the measuring scales
The reliability and validity of the measuring scales were initially assessed using Cronbach's alpha and Exploratory Factor Analysis (EFA) Cronbach's alpha is a widely recognized method for evaluating the reliability of multipoint-scaled items, indicating the consistency and dependability of measurements (Neuman, 2006) An alpha coefficient above 0.7 is deemed acceptable (Hair et al., 1998), and in this study, items with coefficients below 0.6 and corrected item-total correlations lower than 0.30 were excluded The results revealed that all factors had acceptable reliability, with Cronbach's alpha coefficients ranging from 0.882 to 0.901, as summarized in Table 4.
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
The network quality was assessed using four items, achieving a strong overall alpha coefficient of 901 The Cronbach alpha values for each item, when deleted, remained below the overall coefficient, indicating that removing any item would not enhance the reliability of the factor Additionally, the Corrected Item-Total correlations for all items exceeded the threshold of 0.3, confirming their strong association Consequently, we concluded that all items related to the network quality variable are reliable.
The price perception variable demonstrated a strong reliability with a Cronbach's alpha of 888 Each item's alpha coefficient significantly contributed to the overall reliability, as reflected in the high values of the Corrected Item-Total Correlation Consequently, the price perception variable and its five measuring scales were deemed acceptable for further analysis.
The alpha coefficient for perceived value was 879, indicating acceptable reliability Additionally, the analysis of Cronbach’s alpha if Item Deleted revealed that removing any questions did not enhance the overall reliability of the scale, with all items exhibiting a Corrected Item-Total Correlation above 0.3 Consequently, all items were retained for further analysis.
The trust variable demonstrated a strong reliability with a general alpha coefficient of 879, exceeding the acceptable standard of 70 Each item positively contributed to the overall reliability, as indicated by their Cronbach’s alpha if Item Deleted values being lower than the total alpha Additionally, the scale items showed high corrected Item-Total correlations, all surpassing 3, with values such as perval10 at 721, perval11 at 746, perval12 at 622, perval13 at 749, and perval14 at 732 Therefore, this factor is deemed reliable, and all measuring scales will be utilized in the upcoming exploratory factor analysis (EFA) test.
The alpha coefficient for customer loyalty was notably high at 882, indicating strong reliability While most items contributed positively to this alpha, the item cusloy23 revealed that removing it could enhance the overall alpha to 909 Despite this, the item-total correlation for cusloy23 was 515, which is considered acceptable as it exceeds the 03 threshold Consequently, five items related to customer loyalty were accepted for further analysis.
The Cronbach’s alpha test confirmed the reliability of all measuring scales, with no items needing deletion To assess the validity of the items, exploratory factor analysis (EFA) was employed, a multivariate statistical technique that identifies the underlying structure among numerous variables, thereby summarizing and reducing them into fewer factors (Hair et al., 2006) Utilizing promax rotation, which produces correlated factors, allowed for a more accurate representation of human behavior and facilitated easier interpretation of results As noted by Gerbing & Anderson (1998), principal axis factoring with promax rotation offers a more precise reflection of data structure compared to principal components with varimax rotation This research focused on the telecommunication sector in Thailand, relying on a theoretical model built from distinct factors with specific measuring items, allowing for the predetermined number of factors to be established.
Factor analysis is deemed appropriate when the KMO (Kaiser-Meyer-Olkin) value exceeds 0.7, while a value below 0.5 indicates inadequacy (Morgan, 2005) In this study, a KMO value of 957 demonstrated that the sampling was adequate Additionally, the Bartlett’s test of sphericity must yield a significant value of less than 0.05; this research achieved a significance level of 000 (p