INTRODUCTION
Introduction
This study explores the factors influencing internet users' loyalty and their intention to remain with their current Internet Service Provider (ISP) within Vietnam's telecommunications sector The chapter presents an overview of the research problem and background, outlines the research objectives, and poses key research questions Additionally, it provides a brief overview of the research structure.
Research background
The growing popularity of high-speed internet services is significantly influenced by the rapid advancement of information technologies, impacting all facets of our lives The crucial role of information and communication technology is evident as it enhances services across various fields While basic internet services have been available in Vietnam since the early 1990s, the establishment of the first commercial Internet Service Provider, Vietnam Data Communication Company (VDC), in 1997 marked a pivotal moment in the country's digital landscape.
Since its establishment, VNPT has facilitated Vietnam's connectivity through two major gateways: one located in Hanoi, linking the country with Hong Kong and Australia, and another in Ho Chi Minh City, connecting to the United States via Sprint.
Internet usage in Vietnam is still lower than in other Asian countries, but it has seen significant growth in recent years due to initiatives by the Ministry of Post and Telecommunications As of June 2012, Vietnam had over 31 million internet users, representing 35.4% of the population The past fifteen years have witnessed a surge in internet service providers (ISPs), leading to increased competition in the market Currently, more than ten ISPs operate in Vietnam, including major players like VDC/VNPT, Viettel, FPT, Netnam, and SPT.
In Vietnam's Internet service market, VDC/VNPT, Viettel, and FPT dominate with over 85% market share, leading to intense competition among ISPs like Hanoi Telecom, VTC, G-Tel, Vishiped, and CMC TI To enhance competitiveness, these companies must refine their marketing strategies and improve business performance As the market approaches saturation, focusing on service diversification, customer satisfaction, and loyalty becomes crucial Customer loyalty is a vital asset for ISPs, significantly influencing customers' decisions to remain with their current providers (Khatibi, Ismail & Thyagarajan, 2002).
Providers that meet customer satisfaction are likely to achieve higher market shares and increased profitability, fostering customer loyalty (Khatibi, Ismail & Thyagarajan, 2002) Aaker (1991) highlights that brand loyalty contributes to marketing advantages, including lower marketing costs, the attraction of new customers, and enhanced trade leverage Expanding a loyal customer base is vital for profitability, as retaining existing customers is generally less expensive than acquiring new ones (Reichheld, Teal, 1996) Consequently, understanding the development of customer loyalty is a key management challenge today This research, focusing on Internet service users in Ho Chi Minh City, aims to identify the factors influencing Vietnamese users' perceptions of their current ISPs, ultimately deriving elements that affect customer loyalty in the telecommunications sector.
This research offers ISPs critical insights into the key factors necessary for achieving success and fostering customer loyalty, ultimately leading to increased profitability and enhanced competitiveness for sustainable growth in the future.
The research objectives
This research investigates the key factors affecting customer loyalty in the Internet service sector within Vietnam's telecommunications market The primary objective 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 research establishes a model with four hypotheses to investigate customers' intentions to remain with their current Internet Service Provider (ISP) in Vietnam Illustrated in Figure 1 of Chapter 2, the model examines the relationships among network quality, price perception, perceived value, trust, and customer loyalty The findings will test these hypotheses to provide insights into the factors influencing customer retention.
The research methodology
An overview of the research methods used to collect and analyze the data are briefly discussed In this research, a survey with questionnaire in was used to collect data
The questionnaire, initially created in English, was translated into Vietnamese to accommodate all respondents To minimize bias, a random sampling method was employed 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 of this research presents an overview of internet services, focusing on their significance in Vietnam It outlines 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 examine 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 collection is conducted through questionnaires, with analysis performed using SPSS software.
LITERATURE REVIEW
Introduction
This chapter reviews the literature on customer retention, focusing on the factors that influence customer loyalty among internet service providers, particularly within the Vietnamese telecommunications industry.
The study examines the relationship between customer loyalty and various factors, including network quality, price perception, perceived value, and trust, leading to the formulation of four hypotheses (H1, H2, H3, H4) Additionally, it evaluates the impact of controlling variables such as gender, income, and service experience on customer loyalty.
Customer loyalty with internet service provider
Customer loyalty arises when consumers evaluate the rewards and costs associated with their purchases, considering the expected outcomes (Churchill & Surprenant, 1982) According to Jacoby (1975), this loyalty is influenced by emotional processes, leading to a biased decision-making behavior in purchasing.
Customer loyalty can be assessed through various factors such as purchase proportion, sequence, and probability (Dick & Basu, 1994) It reflects a customer's intention to remain with their current service provider while consistently making repeat purchases of their preferred brands Consequently, businesses prioritize retaining valued customers and fostering their loyalty, as this leads to increased income and profits Without customer loyalty, service providers risk losing their competitive edge, as loyal customers typically require less attention from employees due to their familiarity with the company's processes (Chow & ).
Loyal customers contribute significantly to a business's success by ensuring repeat patronage and reducing the likelihood of seeking better deals from competitors The longer these loyal and profitable customers remain with a company, the greater the potential for increased profits.
Businesses benefit significantly from having loyal customers, as it leads to increased profitability and the ability to command higher price premiums Loyal customers foster long-term relationships and commitment, enhance purchase intentions, and generate positive word-of-mouth Additionally, they help reduce marketing and operating costs while creating higher switching barriers, making it less likely for customers to leave their current service provider.
Customer loyalty is crucial for businesses, as it fosters long-term relationships and ensures repeat patronage Companies must prioritize customer satisfaction to cultivate loyalty and maintain a competitive edge in the market.
2.2.1 Network quality and customer loyalty
Network quality is one of the most important drivers of overall service quality which leads to customer satisfaction in the context of telecommunication (Chun &
Research indicates that stability, transmission speed, and network coverage are essential components of network quality (Hahn, 2007; Wang, Lo & Yang, 2004; Woo & Fock, 1999; Mohd R.B Yaacob, 2011) Users prioritize the stability and speed of internet services, often contemplating a switch to different providers if these factors are lacking (Mohd R.B Yaacob, 2011).
The uptime of service significantly affects customer satisfaction and loyalty, as demonstrated by Wang, Lo, and Yang (2004) Their study indicates that service providers aiming for competitive success must prioritize 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 enhanced service quality; however, price remains a significant factor that can drive them to consider alternative providers (Mohd R.B Yaacob, 2011) This indicates that customers are sensitive to pricing, where higher prices may result in decreased demand Research by Ranaweera & Neely (2003) confirms a direct linear relationship between price perception and customer loyalty within the telecommunications sector Given the intense price competition in markets like Vietnam, we hypothesize that this relationship is even more pronounced.
H2: Service price is positively related to customer loyalty
2.2.3 Perceived value and customer loyalty
Perceived value is defined as the overall assessment by customers of the utility of products or services, weighing what they receive against what they sacrifice (Monroe 1991; Parasuraman, Zeithaml & Berry 1988) Essentially, it represents a trade-off between benefits and sacrifices The perceived sacrifices encompass all costs incurred during the purchasing process, including purchase price, acquisition costs, installation, maintenance, and the risks associated with potential failure or poor performance Conversely, perceived benefits consist of physical attributes, service quality, technical support, and other indicators of overall service excellence.
Perceived value is subjective and varies among customers, as noted by Berry (1988) Research indicates that customers who feel they receive good value for money experience higher satisfaction and are more likely to become loyal compared to those who do not perceive such value (Zeithaml, Berry & Parasuraman).
Customers who stay loyal to a service provider due to their satisfaction with the perceived quality and value are more likely to purchase additional services, share positive word-of-mouth, and recommend the company to others.
Perceived value is a crucial competitive tool for service providers, as it plays a significant role in establishing and enhancing sustainable competitive advantages By increasing the perceived value of their services, providers can foster greater customer loyalty Therefore, we propose the following hypothesis:
H3: Perceived value has a positive effect on customer loyalty
Trust is a crucial factor in fostering customer loyalty, as highlighted by Dick and Basu (1994) Establishing long-term relationships with customers is essential for cultivating trust, which ultimately enhances brand loyalty (Morgan & Hunt).
When customers trust a brand's products or services, they are more likely to make additional purchases with minimal persuasion, leading to reduced advertising costs Trust complements customer satisfaction, fostering long-term relationships and enhancing loyalty.
Trust is defined as the willingness to rely on others whom one finds credible When buyers place their trust in sellers, it significantly influences their purchasing decisions This relationship leads to the hypothesis that trust impacts consumer behavior in the marketplace.
H4: Trust positively influences customer loyalty.
Controlling factors
Research by Choudrie (2005) highlights the significant impact of demographic factors, including gender and income, on customer loyalty This study also explores the relationship between these demographic variables and their effect on customer retention Additionally, it emphasizes the importance of assessing customer experience with services to understand its influence on loyalty.
The research model
The research model serves as the foundation for the study, aiding in the identification and labeling of key variables related to the research problem (Sekaran, 2000) This section draws from existing literature to propose a model that explores the correlations between customer loyalty and various related factors To effectively address the research questions, we develop a model featuring five primary variables: network quality, price perception, perceived value, and trust, all of which are dependent variables influencing customer loyalty Additionally, the impact of demographic variables on the relationships among these factors 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 the Vietnam telecommunication industry, our theoretical model posits four key 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 present the research methods and approaches utilized to explore customer perceptions of Internet service users in Ho Chi Minh City It will provide an overview of the research process, detailing the research approach, measurement techniques, sampling methods, data collection strategies, and data analysis techniques employed in the study.
The research approach
The quantitative approach focuses on statistical analysis and utilizes numerical data to achieve objective and precise results, enabling researchers to draw conclusions and test hypotheses (Ticehurst & Veal, 2000) This method quantifies the relationships between variables, employing quantitative research to evaluate research assumptions and hypotheses to effectively address the research problem.
This is a quantitative research because it seeks to answer set hypotheses from reviews of previous literature rather than attempting to develop new theory.
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 segment of individuals from a larger population (Sekaran, 2000) To minimize bias in the study of Vietnamese Internet users, a random sampling method was employed.
According to DeCoster (2004), the minimum sample size for statistical analysis should be at least five times the number of independent variables, with a minimum threshold of 100 participants for reliable results In this study, which includes 23 items, the required minimum sample size for conducting Exploratory Factor Analysis (EFA) is calculated as n = 5 x 23 = 115.
According to Fidell (2001), the minimum sample size required for multiple regression analysis is calculated using the formula n = 50 + 8m, where m represents the number of independent variables For this research, which includes four independent variables, the minimum sample size is determined to be n = 50 + 8 x 4, resulting in a total of 82 participants needed for effective analysis.
To ensure the reliability and validity of the research, a quantitative approach was employed, requiring a sample size of approximately 300 internet service users The survey was conducted between August and October among internet service users in Ho Chi Minh City, utilizing at least 115 samples for exploratory factor analysis (EFA) and multiple regression analysis.
The questionnaire was sent to more than 500 potential respondents to get the target sample size of around 300 respondents
A questionnaire survey was conducted to gather data, comprising three key sections: the first explored customers' experiences with Internet Service Providers (ISPs), the second assessed respondents' opinions on the service and their future decisions regarding their current ISP, and the final section included demographic questions.
The questionnaire included both ranking and open-ended questions to gather insights on respondents' experiences with their Internet Service Providers (ISPs) Participants were asked fundamental questions regarding their current ISPs and satisfaction levels Additionally, the second section utilized a seven-point Likert scale to assess various aspects of their ISP experiences.
The study utilized a seven-point Likert scale ranging from "Strongly disagree" (1) to "Strongly agree" (7) to assess respondents' perceptions of their current Internet Service Provider's (ISP) service quality, as well as their intentions to remain with or switch to a different provider Initially developed in English, the questionnaire was subsequently translated into Vietnamese to accommodate the Vietnamese respondents.
Data analysis
After data collection, the subsequent step is data analysis to address the research questions This process involves transforming raw data into meaningful information through various stages, including editing, coding, data entry, and analysis (Zikmund, 2000) In this study, SPSS software was utilized for analyzing data using descriptive statistics The data processing procedures and analytical techniques employed in this research are outlined below.
Firstly, the Cronbach alpha was used to examine the reliability of measuring scale
Cronbach's alpha is a statistical measure used to assess the correlation between items on a scale, indicating their reliability (Hair, 2006) This analysis helps identify and eliminate unsuitable items from the scale based on the Cronbach alpha coefficient A scale is considered reliable when its Cronbach’s alpha coefficient is 0.7 or higher (Pallant, 2005).
Effecting Factor Analysis (EFA) was performed to determine the number of extracted factors This multivariate statistical technique identifies the underlying structure among numerous variables (Hair, 2006).
Factor analysis serves two main purposes: it summarizes extensive information from numerous variables and condenses this data into a smaller set of factors (Hair, 2006) In this study, five key factors influencing customer loyalty in Internet services were identified By employing factor analysis, the researcher was able to establish distinct factors for each of the five measurement scales and determine the factors that exhibited a strong correlation with customer loyalty The results are deemed acceptable when specific conditions are satisfied (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 utilized to analyze the relationships between two or more research variables (Veal, 2005) The correlation coefficient can indicate various types of relationships; a value of 1.0 signifies a perfect positive correlation, while -1.0 indicates a perfect negative correlation A correlation coefficient of zero suggests no relationship exists between the variables.
Multiple regression analysis was employed to explore the relationships among various variables, utilizing a statistical technique that assesses the impact of multiple independent variables on a single dependent variable (Hair et al., 2006) This approach allowed for the examination of the simultaneous effects of these independent variables on the dependent variable In this study, the Multiple Linear Regression method was implemented 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, detailing the research approach, measurement techniques, sampling strategies, data collection processes, and data analysis methods A comprehensive discussion of the analytical techniques and findings will be presented in Chapter 4.
DATA ANALYSIS AND RESULT
Introduction
This chapter presents a detailed analysis of the data and interpretations of the research findings, aiming 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 measuring scales, (3) hypothesis testing, and (4) evaluation of the research model.
Preliminarily data analysis
A quantitative research study was carried out through a survey questionnaire targeting Internet service users in Ho Chi Minh City Initially, a pilot test was conducted 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 essential for identifying inappropriate samples completed by careless respondents, such as those who selected the same response for all questions Additionally, missing data can negatively impact results; however, in this study, missing data was not an issue due to the online design of the questionnaire, which required respondents to provide complete answers Consequently, we focused solely on detecting inappropriate responses, leading to the identification of 11 samples distorted by out-of-range values after data cleaning and screening.
To ensure comprehensive data coverage, the sample size in research should be sufficiently large, adhering to a recommended ratio of at least five observations for each independent variable, with an ideal target of 15 to 20 observations per variable (Hair et al., 1998) This study involves five variables with 23 measurement items, necessitating a minimum sample size of approximately 194 To achieve a representative distribution among Internet Service Providers (ISPs), a target sample size of around 300 was set 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 like 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 divided between males and females, with the majority aged between 25 and 35 years Most participants reported an average 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, a widely recognized method for evaluating the reliability of multipoint-scaled items, measures the consistency and dependability of variable measurements, indicating the extent to which results are free from error (Neuman, 2006; Sekaran, 2000) An alpha coefficient above 0.7 is deemed acceptable (Hair et al., 1998), and in this study, items with an alpha coefficient below 0.6 and a corrected item-total correlation lower than 0.30 were excluded The findings revealed that all factors had acceptable Cronbach's alpha coefficients, ranging from 0.882 to 0.901, as detailed in Table 4.
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
The network quality was evaluated using four items, achieving a strong overall alpha coefficient of 901 The Cronbach alpha values for each item, when deleted, remained below this coefficient, indicating that removing any item would not enhance the reliability of the factor Additionally, the Corrected Item-Total correlations for all items exceeded 0.3, further confirming their reliability Consequently, we concluded that all items related to the network quality variable are dependable.
Cronbach’s alpha of price perception variable indicated reliable coefficient by 888
The alpha coefficient for each item demonstrated a significant contribution to the overall coefficient alpha, with high values in the Corrected Item-Total Correction column Consequently, the price perception variable, along with its five measuring scales, was accepted 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 deemed representative of this factor and were included in further analyses.
The overall alpha coefficient for the trust variable was 879, surpassing the acceptable threshold of 70, indicating strong reliability Each item demonstrated a positive contribution to the overall factor, as evidenced by the Cronbach’s alpha if Item Deleted values being lower than the overall coefficient Additionally, the scale items exhibited high corrected Item-Total correlations, all exceeding 3, with values of 721 for perval10, 746 for perval11, 622 for perval12, 749 for perval13, and 732 for perval14 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 at 882 While most items contributed positively to this score, the item cusloy23 indicated that removing it could enhance the overall alpha to 909 Despite this, the item-total correlation for cusloy23 was 515, which is acceptable as it exceeds the 03 threshold Consequently, five items related to customer loyalty were retained for further analysis.
The Cronbach’s alpha test confirmed the reliability of all measuring scales, with no items requiring deletion To assess the validity of the items, exploratory factor analysis (EFA) was employed, which is a multivariate statistical technique that identifies the underlying structure among numerous variables This method effectively summarizes extensive variable information into fewer factors (Hair et al., 2006) A promax (oblique) rotation was utilized to evaluate the internal consistency of the composite variables, as it produces correlated factors, yielding more accurate results for research related to human behavior or when data does not meet prior assumptions Additionally, promax rotation facilitates easier interpretation of results and generates a more parsimonious solution.
Gerbing & Anderson (1998) assert that Principal Axis Factoring with promax rotation provides a more accurate reflection of data structure compared to Principal Components with varimax rotation Consequently, this research employed Principal Axis Factoring to evaluate two critical item values: convergent and discriminant validity The study focused on the Thailand telecommunication sector, using a theoretical model constructed from distinct factors, each with its own measurement items, allowing for a precise determination of the number of factors Thus, the method of fixed number of factors was selected to ascertain the number of factors.
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 demonstrated that the sampling was adequate Additionally, the Bartlett’s test of sphericity must be significant with a value less than 0.05; in this research, it was found to be 000 (p