INTRODUCTION
Introduction
This research investigates the factors influencing internet users' loyalty and their intention to remain with their current Internet Service Provider (ISP) within Vietnam's telecommunications sector It presents an overview of the research problem and background, outlines the objectives and questions guiding the study, and provides a brief structure of the research.
Research background
High-speed internet services are increasingly popular, significantly impacting various aspects of our lives due to advancements in information technology The essential role of information and communication technology is evident in its application across multiple sectors to enhance services Although basic internet services were available in Vietnam since the early 1990s, the first commercial Internet Service Provider, Vietnam Data Communication Company (VDC), began operations in 1997 under Vietnam Posts & Telecommunication Groups (VNPT) This marked the start of Vietnam's connectivity through two gateways: one in Hanoi linking to Hong Kong and Australia, and another in Ho Chi Minh City connecting to the United States via Sprint While internet usage in Vietnam has historically lagged behind other Asian countries, recent initiatives by the Ministry of Post and Telecommunications have led to a rapid increase in connectivity.
As of June 2012, Vietnam boasts over 31 million internet users, representing approximately 35.4% of the population Over the past fifteen years, the country's internet landscape has evolved significantly, with a notable rise in internet service providers (ISPs) This growth has intensified competition among ISPs, particularly in the context of a developing nation.
In Vietnam, the Internet service market is dominated by three major players: VDC/VNPT, Viettel, and FPT, which collectively hold over 85% of the market share This intense competition necessitates that Internet Service Providers (ISPs) enhance their marketing strategies and business performance to boost competitiveness As the telecommunications industry approaches saturation, focusing on customer satisfaction and loyalty has become essential Research indicates that satisfied customers contribute to higher market shares and profitability, as retaining existing customers is more cost-effective than acquiring new ones Brand loyalty not only reduces marketing costs but also increases trade leverage and attracts new customers Consequently, understanding the factors that foster customer loyalty is vital for ISPs This study aims to assess Internet service users in Ho Chi Minh City to identify the elements influencing their perceptions of ISPs and the factors that drive customer loyalty within the telecommunications sector.
This research offers ISPs essential insights into the key factors that drive customer loyalty and success, ultimately enhancing profitability and fostering competitive strength for sustainable growth in the future.
The research objectives
This research investigates the key factors affecting customer loyalty in internet service provision within Vietnam's telecommunications market The primary objective is to identify the attributes that influence customer loyalty towards 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
The literature review establishes a model with four hypotheses aimed at investigating customer intentions to remain with their current Internet Service Provider (ISP) in Vietnam Illustrated in Figure 1 of Chapter 2, this research model examines the connections between network quality, price perception, perceived value, trust, and customer loyalty The four hypotheses derived from this model are tested to reveal their interrelationships.
The research methodology
This research utilized a survey with a questionnaire to collect data, which was initially created in English and translated into Vietnamese for the respondents To minimize bias, random sampling was employed The data gathered from the survey was coded and analyzed using SPSS software Further details on the research methodology can be found in Chapter 3, while the research results are presented in Chapter 4.
The research structure
Chapter 1 – Introduction outlines the general context of internet services, focusing on Vietnam's specific landscape It details 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 It aims to investigate the relationship between customer loyalty and its influencing factors in the Vietnamese Internet service market The study identifies four factors believed to positively impact Internet customer loyalty Data is gathered through questionnaires and analyzed using SPSS software.
LITERATURE REVIEW
Introduction
This chapter reviews literature on customer retention, focusing on the factors that influence customer loyalty among internet service providers, particularly within the Vietnamese telecommunications industry.
This study explores the relationship between customer loyalty and several influencing factors, including network quality, price perception, perceived value, and trust, leading to the formulation of four hypotheses (H1, H2, H3, H4) Additionally, the research examines the impact of controlling variables such as gender, income, and service usage experience on customer loyalty.
Customer loyalty with internet service provider
Customer loyalty emerges from a customer's evaluation of the rewards and costs associated with their purchases, influenced by emotional processes (Churchill & Surprenant 1982; Jacoby 1975) It can be quantified through various metrics such as purchase frequency, sequence, and likelihood (Dick & Basu 1994) Essentially, customer loyalty reflects a customer's commitment to remain with a service provider while consistently choosing their preferred brands, which is vital for businesses aiming to enhance income and profitability Retaining loyal customers is crucial for maintaining a competitive edge, as they are typically easier to serve and demand less from staff, having a better understanding of the company's operations (Chow & Holden 1997) Additionally, loyal customers tend to engage in more repeat purchases and are less inclined to seek alternatives among competitors (Bowen & Chen 2001).
Companies generate profit through long-term customer relationships, as the longer loyal and profitable customers remain with a business, the greater the potential for increased profits (Reichheld & Teal, 1996).
Having loyal customers offers numerous benefits for businesses, such as enhanced profitability and the ability to charge premium prices These loyal relationships foster long-term commitment and boost customers' purchase intentions, leading to positive word-of-mouth referrals Additionally, businesses can reduce marketing and operational costs while creating higher switching barriers that discourage customers from leaving 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 effectively.
2.2.1 Network quality and customer loyalty
Network quality is a critical factor influencing overall service quality and customer satisfaction in telecommunications Key attributes of network quality include stability, transmission speed, and coverage Users prioritize the stability and speed of internet services, often considering switching providers if these factors are lacking Additionally, service uptime significantly affects 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 are willing to pay a premium for superior service; however, price remains a significant 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) demonstrates a direct linear relationship between price perception and customer loyalty within the telecommunications sector Given the intense price competition in markets like Vietnam, this relationship is expected to be even more pronounced Thus, we propose the following hypothesis.
H2: Service price is positively related to customer loyalty.
2.2.3 Perceived value and customer loyalty
Perceived value refers to a customer's overall evaluation of a product or service based on the balance of benefits received versus sacrifices made, including costs like purchase price, installation, and maintenance (Monroe, 1991; Parasuraman, Zeithaml & Berry, 1988) Essentially, it is a trade-off where perceived sacrifices encompass all costs incurred during the purchasing process, while perceived benefits include product attributes, service quality, and technical support.
Perceived value is subjective and varies among customers, as noted by Berry (1988) Research indicates that customers who feel they receive value for money are generally more satisfied and more likely to become loyal, compared to those who do not share this perception (Zeithaml, Berry & Parasuraman).
Long-term customer retention is driven by satisfaction with perceived quality and value, leading to increased purchases of additional services and positive word-of-mouth recommendations (Boulding 1993, Zeithaml & Berry 1988) Service providers should leverage perceived value as a key competitive advantage to enhance customer loyalty and sustain their market position (Khatibi, Ismail & Thyagarajan 2002) Thus, we propose that increasing perceived value will significantly boost customer loyalty.
H3: Perceived value has a positive effect on customer loyalty
Trust is a fundamental precursor to customer loyalty, as highlighted by Dick and Basu (1994) Establishing long-term relationships with customers is essential for fostering trust and, consequently, enhancing brand loyalty, according to Morgan and Hunt.
Trust in a brand significantly influences customer purchasing behavior, as consumers are more inclined to buy from brands they trust without needing extensive persuasion, ultimately reducing advertising costs Furthermore, trust enhances customer satisfaction, fostering long-term relationships and increasing loyalty When buyers have confidence in sellers, it positively impacts their purchasing decisions, highlighting the critical role of trust in the consumer-brand relationship.
H4: Trust positively influences customer loyalty.
Controlling factors
Choudrie (2005) identifies significant effects of demographic factors, including gender and income, on customer loyalty This study also explores the interplay between these demographic variables and emphasizes the importance of evaluating customer service experiences to understand their influence on loyalty.
The research model
The research model serves as the cornerstone of the study, facilitating the identification and categorization of key variables associated with the research problem (Sekaran, 2000) This section draws on existing literature to propose a model that illustrates the relationships between customer loyalty and its related variables, ultimately aiming to provide comprehensive answers to the research questions.
This study investigates the impact of gender, experience, and income on customer loyalty by developing a model that includes five key variables: network quality, price perception, perceived value, and trust These variables are identified as dependent factors that influence customer loyalty Additionally, the research examines how demographic factors act as controlling variables and their effect on the relationships among the primary variables.
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. services in Vietnam?
2 How do the factors influence customer loyalty in Internet services in Vietnam?
H2: Price perception is positively related to customer loyalty.
H3: Perceived value has a positive effect on customer loyalty H4: Trust positively influences customer loyalty.
Table 1 - Research questions and hypotheses
Conclusion
In the context of the Vietnam telecommunication industry, our theoretical model proposes four key hypotheses, suggesting 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 utilized 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 procedures, and data analysis strategies employed in the study.
The research approach
The quantitative approach focuses on statistical analysis and numerical data to achieve objective and accurate results for testing hypotheses and drawing conclusions (Ticehurst & Veal, 2000) This type of research quantifies relationships between variables and utilizes existing literature to address specific research assumptions, 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:
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.
1 I believe that X will be ready and willing to offer me assistance and support.
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 is the process of selecting a representative group from a larger population (Sekaran, 2000), and random sampling was employed to minimize bias among Vietnamese Internet users DeCoster (2004) suggests that the minimum sample size for statistical analysis should be at least five times the number of independent variables and no less than 100 for reliable outcomes, resulting in a required sample size of n = 115 for this research with 23 items Additionally, according to Tabachnick & Fidell (2001), the minimum sample size for multiple regression is calculated as n = 50 + 8m, where m represents the number of independent variables; thus, for four independent variables, the minimum sample size is n = 82 Overall, this research necessitates a sample size that meets these criteria for valid analysis.
To ensure the reliability and validity of the research, a sample size of approximately 300 internet service users was targeted for an exploratory factor analysis (EFA) and multiple regression analysis The quantitative study was conducted between August and October among internet service users in Ho Chi Minh City A questionnaire was distributed to over 500 potential respondents to achieve the desired sample size.
The data for this study was gathered through a comprehensive questionnaire divided into three sections The first section explored customers' experiences with Internet Service Providers (ISPs), while the second section sought respondents' opinions on the service quality and their future choices regarding their current ISP The final section included demographic questions The questionnaire featured both ranking statements and open-ended questions, allowing respondents to detail their experiences with ISPs and identify their current providers Additionally, a seven-point Likert scale was utilized in the second section to assess service opinions.
A 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, as well as their intentions to either remain with or switch to a different provider The initial questionnaire was crafted in English and subsequently translated into Vietnamese to accommodate the Vietnamese respondents.
Data analysis
After collecting data, the next crucial step is data analysis to answer the research questions This process involves transforming raw data into meaningful information through various stages, including editing, coding, data entry, and analysis (Zikmund 2000) For this research, SPSS software was selected to perform descriptive statistical analysis The following sections outline the data processing procedures and analytical techniques employed in this study.
The Cronbach alpha was utilized to evaluate the reliability of the measurement scale, serving as a statistical test to analyze the correlation among the items within the scale (Hair, 2006) This analytical method effectively eliminates unsuitable variables and minimizes extraneous factors in the research process, thereby ensuring the reliability of the results.
Loyalty of Internet customers in Vietnam - 25
- - the scale through the Cronbach alpha coefficient (Hair 2006) The scales are reliable when Cronbach’s alpha coefficient of each scale is equal to or bigger than 0.7 (Pallant 2005).
Effecting Factor Analysis (EFA) was utilized to identify the number of factors influencing customer loyalty in Internet services This multivariate statistical technique, as outlined by Hair (2006), helps summarize extensive data by condensing numerous variables into fewer factors In this study, five key factors were identified that significantly impact customer loyalty Additionally, factor analysis enabled the researcher to ascertain which of these factors exhibited strong correlations with customer loyalty The results are deemed acceptable when specific conditions, as noted by Pallant (2005), are satisfied.
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 If the correlation coefficient equals zero, it suggests that there is no relationship between the variables.
Multiple regression analysis was employed to explore the relationships among various variables, specifically examining how multiple independent variables impact a single dependent variable (Hair et al., 2006) This statistical technique allowed for the assessment 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, adhering to the conditions outlined by Pallant (2005) 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, 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 evaluate the study hypotheses It is structured into four key sections: (1) preliminary data analysis, (2) assessment of the measurement scales, (3) hypothesis testing, and (4) evaluation of the research model.
Preliminarily data analysis
A quantitative research study was carried out using a survey questionnaire targeted at Internet service users in Ho Chi Minh City Initially, a pilot test was conducted with 100 samples, followed by the collection of 286 completed questionnaires for the main analysis 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 scale range for all questionnaire statements Additionally, missing data can negatively impact results; however, in this research, it was not an issue since the online questionnaire was designed to prevent submissions with incomplete answers Consequently, we focused solely on identifying inappropriate samples 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 essential, with a recommended ratio of observations to independent variables not falling below five to one, ideally between 15 to 20 observations per variable (Hair et al., 1998) This research includes five variables with 23 measurement items, necessitating a minimum sample size of approximately 194 However, to achieve proportional representation across different 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 utilized for official 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.
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 survey revealed an equal distribution of male and female respondents, primarily aged between 25 and 35 years Most participants reported a monthly income ranging from 10M to 15M, and notably, 47% 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, a widely recognized method for evaluating the reliability of multipoint-scaled items, measures the consistency of variable measurements, indicating the degree to which results are free from error (Neuman, 2006) An alpha coefficient above 0.7 is deemed acceptable (Hair et al., 1998), while items with coefficients below 0.6 or corrected item-total correlations under 0.30 were excluded from the analysis In this study, all factors demonstrated strong 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 measurement of network quality was based on four items, demonstrating a strong overall reliability with a Cronbach's alpha coefficient of 901 The analysis indicated that removing any item would not enhance the reliability, as the Cronbach's alpha if item deleted remained below 901 Additionally, the Corrected Item-Total correlations for all items were significantly above the acceptable threshold of 0.3 Consequently, we concluded that the items used to assess network quality are reliable.
The Cronbach’s alpha for the price perception variable demonstrated a strong reliability coefficient of 888 Additionally, each item's alpha coefficient contributed positively to the overall alpha, with the Corrected Item-Total Correlation showing high values Therefore, the price perception variable and its five measuring scales were deemed valid for further analysis.
The alpha coefficient for perceived value was 879, indicating acceptable reliability Additionally, the Cronbach's alpha if Item Deleted analysis revealed no improvement in overall reliability when any questions were removed, with all items exhibiting a Corrected Item-Total Correlation above 0.3 Consequently, all items were retained for further analysis.
The overall alpha coefficient for the trust variable was 879, exceeding the acceptable threshold of 70, indicating strong reliability Each item demonstrated a positive contribution to the overall factor, as evidenced by Cronbach’s alpha if Item Deleted values being lower than the total alpha Additionally, the scale items exhibited high corrected Item-Total correlations, all surpassing 3, with specific values of perval10: 721, perval11: 746, perval12: 622, perval13: 749, and perval14: 732 Therefore, this factor is deemed reliable, and all measuring scales will be utilized in the subsequent Exploratory Factor Analysis (EFA) test.
The Alpha coefficient for customer loyalty demonstrated a strong reliability score of 882 While most items contributed positively to this score, the item cusloy23 indicated that the overall alpha could be enhanced to 909 if it were removed Despite this, the item-total correlation for cusloy23 was 515, which is considered acceptable as it exceeds the 03 threshold Consequently, five customer loyalty items were retained for further analysis.
The Cronbach’s alpha test confirmed the reliability of all measurement scales without the need to delete any items Subsequently, exploratory factor analysis (EFA) was employed to assess the validity of the items, summarizing a large number of variables into fewer factors, as described by Hair et al (2006) Utilizing promax rotation allowed for the examination of internal consistency among composite variables, yielding correlated factors that enhance the accuracy of research related to human behaviors Gerbing & Anderson (1998) noted that Principal Axis Factoring with promax rotation provides a more precise reflection of data structure compared to Principal Components with varimax rotation In this study, the extraction of Principal Axis Factoring was implemented to evaluate convergent and discriminant validity, accepting items with extraction sums of squared loadings of 50% or more (Gerbing & Anderson, 1988) This research focused on the telecommunications sector in Thailand, utilizing a theoretical model built from distinct factors with specific measuring items, leading to the decision to use a fixed number of factors for analysis.
Factor analysis is 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 confirmed that the sampling was adequate Additionally, Bartlett’s test of sphericity must yield a significance level below 0.05; this research achieved a significant value of 000 (p