search, experience and credence influence customer loyalty; b and through which mechanism customer participation influence the relationships between attributes of bank choice behavior an
INTRODUCTION
Background to the research
The Vietnamese banking sector is experiencing rapid transformation and is projected to outperform many neighboring Asian countries in growth in the coming years This development has been largely driven by state-led economic reforms since the late 1980s and the establishment of several State-Owned Commercial Banks (SOCBs) The sector’s modernization accelerated significantly after Vietnam’s accession to the World Trade Organization in 2007, promoting a proactive liberalization process These reforms, coupled with Vietnam’s impressive economic growth over the past two decades, create substantial opportunities for both domestic and international banks, especially in the retail banking market.
Since early 2014, although enterprise interest rates declined to their lowest levels since the 2009 government stimulus, credit growth remained stagnant as many businesses hesitated to take out loans Consequently, banks shifted their focus towards expanding personal loan services, which attracted individual customers due to their simple and convenient procedures The success of retail banking largely depends on maintaining strong customer relationships and understanding their evolving needs In the commercial banking sector, there is increasing recognition that individual customers are a valuable market segment with diverse and changing financial requirements.
Customer loyalty is a vital strategic tool for banks aiming to attract new clients, retain existing customers, and strengthen long-term relationships It remains a key focus for researchers and marketers interested in understanding its predictors and causal factors Prior studies highlight that customer participation significantly drives loyalty, emphasizing its importance for marketers and managers seeking to foster stronger customer relationships.
Research highlights that bank choice behavior attributes—search, experience, and credence—are of significant interest alongside customer loyalty, attracting scholarly attention (Mitra, Reiss, & Capella, 1999; Babakus, Eroglu, & Yavas, 2004) However, there is limited empirical research exploring how bank choice behavior influences customer participation and loyalty This study develops a model to examine the role of bank choice behavior and customer participation in shaping customer loyalty Specifically, it analyzes the impact of bank choice behavior attributes on customer participation and investigates how these factors, along with customer participation, contribute to enhancing customer loyalty The subsequent section details the research problem.
Statement of the problem
The soaring demand for personal loan products highlights the need for banks to develop more effective strategies to attract new customers and enhance retention A study on customer choice behavior and participation can provide valuable insights for bank managers to craft targeted marketing approaches, ultimately boosting customer loyalty and fostering long-term relationships By understanding the connection between customer participation and loyalty, banks can identify strategies to encourage greater involvement in transactions, which in turn strengthens customer retention Implementing these findings enables banks to increase customer satisfaction, retain existing clients, and maximize service sales through improved engagement.
Research questions
This research aims to empirically examine how bank choice behavior and customer participation influence customer loyalty within Vietnam's retail banking sector In the transitional economy of Vietnam, customers now frequently utilize personal banking services and have access to a wide variety of banking products Understanding the relationship between bank selection, customer engagement, and loyalty is crucial in this evolving financial landscape The study seeks to address key questions regarding the impact of these factors on customer retention and satisfaction in retail banking.
To what extent, do attributes of bank choice behavior (e.g search, experience and credence) and customer participation influence customer loyalty?
Between bank choice behavior and customer participation, which factor will most influence to customer loyalty?
Significance of the Study
This study has important implications for both practitioners (leaders of commercial banks, marketing managers) and academic (researchers, students of the business administration department) as follow:
This study provides valuable insights into bank choice behavior and customer participation, highlighting how both factors significantly contribute to strengthening customer loyalty By understanding these dynamics, commercial bank managers can develop targeted strategies to enhance customer retention and improve operational efficiency The findings support evidence-based decision-making aimed at fostering long-term customer relationships and competitive advantage in the banking sector.
Marketing managers should analyze the factors influencing bank choice behavior that impact customer loyalty in the personal loan market, enabling them to develop targeted communication strategies This study provides valuable insights for researchers and students to build upon for future research and practical applications in banking and customer retention.
Research scope and delimitation
This study was conducted in Ho Chi Minh City, Vietnam’s largest economic hub, focusing on customers utilizing personal loan services at major domestic banks By surveying approximately 500 bank customers, the research provides valuable insights within constraints of time and budget The findings from Ho Chi Minh City can somewhat represent broader trends across Vietnam and serve as a useful reference for future research and strategic decision-making.
This research examines how bank choice behavior attributes and customer participation influence customer loyalty Data was collected using quota sampling, a non-probability sampling method that allows for targeted selection based on specific characteristics Although quota sampling does not allow for generalization to the entire population, selecting appropriate controlling attributes can make the sample representative in practice.
Organization of the thesis
This thesis is structured into five chapters, beginning with an introduction that provides an overview of the research background, clearly defining the research problem and objectives It also outlines the scope of the study, highlights its potential implications, and details the overall structure of the thesis for comprehensive understanding.
Chapter 2 reviews previous research on customer loyalty and customer participation Specifically, the chapter reviews the mediating role of customer participation model and theory of bank choice behavior and, how they are used in various contexts This chapter concentrates on explaining each variable in the model, and reasons for choosing them to be included in the research model
Chapter 3 introduces research methodology used to validate the research model in previous session It presents the research design, development of survey questionnaire, qualitative study, and main survey This chapter also presents how to collect data and analyze the data collected to test the research hypotheses proposed in chapter 2
Chapter 4 analyses data as well as discusses the result finding in connection with research model This chapter explains the empirical part of the study This part discusses the analyses of the data received, psychometric properties of focal constructs, and hypothesis testing
The last chapter, chapter 5 discusses implications and research limitations Finally, this thesis makes suggestions for further research on the topic area
References and appendixes are included in the end of thesis.
Bank choice behavior
The bank choice behavior model is rooted in the economics of information literature, initially developed by Darby and Karni (1973) and Nelson (1970), and subsequently refined within the services context by Bloom and Pailin (1995) and Lynch and Schuler (1990) It hypothesizes that consumer decision-making is influenced by three sets of attributes: search, experience, and credence, as illustrated in Figure 1 (Mitra et al., 1999) Search attributes are those that consumers can accurately evaluate before making a decision, as explained by Srinivasan and Till (2002) According to Babakus et al (2004), these attributes provide critical insights into consumer preferences and influence bank selection behavior.
Search attributes are crucial for making informed banking decisions, as they can be accurately assessed beforehand In the banking sector, these attributes encompass interest rates on deposits and loans, overdraft privileges, fees and charges, banking hours, and the availability of new bank services Evaluating these factors ensures consumers select the most suitable financial institution to meet their needs.
Experience attributes are aspects of banking services that can only be accurately assessed after purchase and use These include the helpfulness and friendliness of tellers, the timeliness of service delivery, the attentiveness and professionalism of bank employees, and the speed at which decisions are made by authorized personnel.
Credence attributes in banking refer to qualities that clients may find difficult to evaluate before making a decision, often due to limited technical knowledge or immediate access to relevant indicators These attributes include maintaining the confidentiality of clients’ banking information, the integrity and trustworthiness of the bank, and the bank’s adoption of the latest technology Additionally, a bank’s effective management and overall competence within the banking industry are key credence attributes that influence customer confidence and trust.
Figure 2.1 - Totally disaggregated second-order model of bank choice behavior
Customer participation
Increasing customer engagement in service production and distribution is a key focus of many theories (Auh et al., 2007; Yi et al., 2011) Customer participation can lead to the creation of new services tailored to their needs (Firat et al., 1995), fostering long-term relationships between companies and customers (Bendapudi & Leone, 2003; Payne et al., 2009) Companies also encourage customers to share positive word-of-mouth, which turns them into brand evangelists and enhances overall brand reputation.
Effective customer engagement is essential for business success, especially for banks that benefit from actively encouraging client participation to gather valuable feedback and ideas for improving products and services While many organizations recognize the importance of listening to customers, some only perform superficial efforts rather than genuine engagement The true value of customer involvement often exceeds managers' expectations, leading to higher returns and enhanced business growth Emphasizing authentic communication with customers can significantly influence product development, customer satisfaction, and overall profitability.
Customer participation, as defined by Dabholkar (1990), involves customers actively engaging in the production and delivery of services Incorporating customers into the innovation process enables companies to enhance service quality and achieve greater market success Dagger & Sweeney (2007) emphasize that improving service quality is the top priority in the service industry, as high-quality services foster positive customer behaviors, increase market share, boost revenue, and enhance profitability.
Customer participation, as defined by Lengnick-Hall et al (2000), involves actively engaging customers in an organization’s work, offering several key benefits It helps organizations reduce costs by enabling customers to identify transaction inefficiencies, leading to process improvements that cut staff expenses and transaction time, ultimately boosting customer satisfaction Additionally, customer involvement can lower the prices of products and services by improving profitability and allowing organizations to offer competitive discounts Mills et al (1983) also highlighted that customer participation increases organizational productivity Moreover, it enables banks to tailor their offerings to meet customer preferences through direct interaction, which is particularly vital in high-contact services like banking, legal, and medical sectors—characterized by high coupling, interdependence, and knowledge exchange (Kellogg and Chase, 1995) Importantly, customer participation benefits are not limited to high-involvement services; even in low- and moderate-involvement contexts, customers value increased perceived control over service delivery and greater opportunities for customization, enhancing their overall service experience (Bateson, 1985; Schneider and Bowen, 1995).
Customer participation involves co-creating value through active collaboration between companies and clients, as highlighted by Bendapudi & Leone (2003) and Ravald & Gronroos (1996) Customers can explore the benefits of their involvement, influencing various aspects of the system to personalize their shopping and consumer experience (Prahalad & Ramaswamy, 2004) Beyond benefiting individual consumers, customer participation enables companies to enhance and refine their products and services, fostering continuous improvement Consequently, businesses are increasingly encouraging customers to actively cooperate, creating mutual value and strengthening the relationship between companies and consumers.
While customer involvement in the service process can enhance perceived value and lead to benefits such as higher quality, faster delivery, greater flexibility, and potentially lower prices, it also presents significant challenges According to Krajewski et al (2010), active customer participation may complicate service management, increase operational costs, and reduce efficiency by making it harder to manage time and customer needs effectively Additionally, measuring service quality becomes more difficult when customers are more involved Despite these challenges, some clients prefer active participation to save time and costs, especially in self-service environments like supermarkets, gas stations, and banking services, where customer control can deliver added convenience and satisfaction.
Encouraging customer participation is essential to maximize their contributions during innovation activities, as it transforms customers into active partners in creating innovative offerings To foster ongoing engagement, businesses should establish continuous relationships and collaborate closely with customers to meet their needs effectively Developing new strategies, such as social control and promotion of innovations, is crucial for motivating customers to participate and ensuring high service quality Additionally, implementing methods to monitor and manage the participation process helps companies achieve successful innovation outcomes.
Customer Loyalty
Customer loyalty is defined as the tendency to hold favorable attitudes towards products or services and to consistently choose or use them, as explained by Bowen and Chen (2001) Marketing experts agree that, in most cases, customer loyalty is more crucial for a brand's success than customer satisfaction (Cooil et al., 2007; Pleshco and Baqer, 2008) Additionally, loyal customers are more likely to forgive small disappointments related to a particular brand or store, highlighting the importance of fostering strong customer loyalty for long-term business growth (Shankar et al., 2003).
According to Bendapudi & Berry (1997), customer loyalty can be understood through two main perspectives: behavior and attitudes The behavioral view defines loyalty as repeat purchase behavior, though customers may sometimes exhibit habitual or obligation-based loyalty without genuine positive feelings, due to barriers preventing switching or lack of attractive alternatives Conversely, the attitudinal approach views loyalty as a psychological state—an emotional or cognitive connection fostered by relational strategies from suppliers—which leads customers to voluntarily maintain relationships based on perceived benefits (Caruana, 2004) This perspective emphasizes that customer loyalty is rooted in a positive attitude toward the supplier, driven by the benefits received, which ultimately encourages repeat purchases and long-term commitment (Oliver, 1999).
Loyalty to a specific supplier is a multidimensional concept encompassing future purchase intentions (behavioral loyalty), positive attitudes toward the supplier (affective loyalty), and perceiving the vendor as a unique option for future needs (cognitive loyalty) (Gremler et al., 2001) To enhance customer loyalty, managers should focus on two main strategies: first, satisfying customers through products and services that foster satisfaction and encourage continued patronage; second, creating barriers to prevent customers from switching to competitors by highlighting differences, thereby reducing the likelihood of defection and increasing loyalty (Balabanis, Reynolds, and Simintiras, 2006) When customers perceive market offerings as homogeneous, the incentive to change providers diminishes, strengthening customer retention and loyalty.
Research model and hypotheses development
Building upon prior research, Figure 2.2 integrates the focal constructs of the thesis with hypothesized relationships.
2.4.1 Bank choice behavior and customer loyalty
Nelson (1970, 1974) and Darby and Karni (1973) expanded the economic information theory of Stigler (1961) by examining how various attribute types interact with customer search and experience Search attributes are aspects customers can verify before purchase through direct checks or available information sources, while experience attributes are only discoverable after using the product (Ford et al., 1990) Credence attributes are difficult for customers to verify even after consumption, making them more challenging to assess For example, in personal loan services, interest rates represent search attributes, professionalism in service is an experience attribute, and staff integrity constitutes a credence attribute These three attribute types are crucial for addressing customer doubts, evaluating marketing information, and building trust and confidence in products and services (Ford et al., 1990; Smith, 1990; Norton and Norton, 1988; Wright and Lynch).
1995), and the important role of price in making choice services (Ostrom and Iacobucci, 1995)
Customer loyalty is typically built after customers feel satisfied with the services provided by banks, as loyalty is not established before experiencing the product Search attributes like interest rates and overdraft privileges allow consumers to inspect services and infer their quality prior to purchase, making them less skeptical of search claims (Ford et al., 1990) According to Hoch and Ha (1986), search attributes are relatively clear and less ambiguous, enabling customers to compare service quality across banks effectively This process influences customers' perceptions of whether a bank’s service quality is better or worse than competitors, impacting their overall evaluation Consequently, changes in search attributes can significantly affect customer loyalty toward banking services.
Hypothesis 1: There is a positive effect of search attributes on customer loyalty
Experience and credence attributes are inherently difficult for customers to confirm before using a product or service, as they require direct use to assess quality (Darby and Karni, 1973; Nelson, 1974; Kirmani and Wright, 1989; Shapiro, 1983; Wernerfelt, 1988) Prior to use, customers often rely on perceptual cues to evaluate these attributes, but even after consumption, credence attributes remain challenging to evaluate accurately Consequently, customers tend to base their initial perceptions on their overall impression of the service, which can influence their subsequent judgments When a service is identical but differs only in branding, trial experiences can shape attitudes, potentially increasing loyalty toward higher-quality services and decreasing it for lower-quality ones Based on this, the study hypothesizes that both experience and credence attributes positively influence customer loyalty by shaping perceptions and trust over time.
Hypothesis 2: There is a positive effect of experience attributes on customer loyalty Hypothesis 3: There is a positive effect of credence attributes on customer loyalty
2.4.2 Bank choice behavior and customer participation
Experience reflects a customer's accumulated knowledge of how a service should perform and their understanding of the typical value offered by similar providers Increased experience often leads to greater customer involvement, as familiar clients can more effectively assess where their contributions are most valuable According to Moorthy et al (1997), as customers gain experience with a product or service, they become better at evaluating its various attributes, enhancing their overall engagement and decision-making.
Experienced customers are more capable of making valuable contributions to service production, increasing the likelihood of their involvement In contrast, inexperienced customers often perceive higher decision-making risks and tend to avoid participation due to fear of producing suboptimal outcomes (Heilman et al., 2000) Additionally, experienced customers have a greater need for control over the service delivery process, which further motivates their involvement in shaping the service outcome (O’Connor and Siomkos, 1994).
In the bank selection process, customer behavior encompasses search, experience, and credence stages, with communication serving as a vital interactive component between clients and bank staff Sharma and Patterson (1999) define communication as the formal and informal exchange of meaningful and timely information conducted empathetically, which in the financial context encourages clients to actively participate in the loan process, seek service information, and respond to bank staff queries Effective communication fosters trust, resolves client concerns, and manages expectations, thereby strengthening customer relationships This enhanced communication flow is expected to increase customer participation and willingness to share sensitive information, ultimately influencing bank choice behavior through improved dialogue between clients and advisors.
Hypothesis 4: There is a positive effect of search attributes on customer participation Hypothesis 5: There is a positive effect of experience attributes on customer participation
Hypothesis 6: There is a positive effect of credence attributes on customer participation
2.4.3 Customer participation and customer loyalty
Customer participation involves meaningful, cooperative engagement in the service creation and delivery process, especially in financial services It includes clients preparing in advance for meetings with advisors, providing valuable input to decision-making, and responding promptly and accurately to information requests from advisors Such active involvement enhances service quality and customer satisfaction.
Customer participation offers numerous benefits for customers, including lower prices, increased purchasing opportunities, and greater control over product customization, leading to shorter wait times and enhanced personalization However, the impact of customer involvement on perceived value is complex, as it can introduce uncertainty and require effort that some customers may find burdensome, possibly causing them to withdraw from active participation Despite these challenges, customers often view involvement in service creation as valuable, especially in the modern era of empowered and informed consumers Research shows that customer compliance enhances satisfaction and fosters mutual understanding between customers and organizations, which elicits positive emotional responses and strengthens loyalty As satisfaction derives from both cognitive and emotional experiences, consistent positive interactions build loyalty, encouraging customers to remain committed to the brand.
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Hypothesis 7: There is a positive effect of customer participation on customer loyalty
Figure 2.2 - The proposed research model with hypotheses
Research design
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Research process includes the steps as illustrated in Figure 3 (Steiger, 1990; Albright, 2006; Schumacker at all, 2006)
Development of questionnaire
The measurement scales used in this study is multi-item five point Likert scales, which were well-established in the extant literature
3.2.1.1 Measure of search, experience and credence
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Table 3.1 - Measurement scales of bank choice behavior
Construct Coding of variables Items Short describe
SA1 1 I always search interest rates on loans information of banks before I make decision of using services Interest rate
SA2 2 I usually ask banks what is the type of collateral the bank will accept to secure my loan? Collateral requirements
SA3 3 I usually ask banks whether they willing offer me an overdraft privileges account (unsecured) Overdraft privileges
SA4 4 I usually compare fee structure between banks when I use their services Fees structure
SA5 5 I always consider bank transaction hour whether it fixes with my times to do transaction at banks Banking hour
SA6 6 I usually make a research if banks have got new services which have advantages for me New bank services SA7
7 I consider whether branch location near my house or not Branch location
8 I consider the bank that I use personal loan services is a well-known or little-known Image/reputation
EA1 1 Bank X’s staffs serve me with friendliness and attentiveness friendliness and attentiveness
EA2 2 Bank X provides services in a timely manner Timely manner of service provision EA3 3 Bank X’s staffsare always willing to listen to me Willingness to listen
EA4 4 Bank X’s staffs present their professional in all services expertise
EA5 5 Bank X gives me a specific period for my application approval period for application
EA6 6 Bank X serves me quickly and efficiently Speed of dicision
EA7 7 Bank X does not require me so much un-need documents Absence of bureaucratic procedures
Credence attributes CRE1 1 I think that bank staff always has up to date knowledge of bank products
Up to date knowledge of bank products
CRE2 2 I feel that bank staff always show their integrity when
I take transaction at bank Integrity of loan officer
CRE3 3 I see that bank staff has got knowledge of the market when they converse with me Knowledge about the market
CRE4 4 I believe that bank staff always keeping my private information in safely status Information confidential
CRE5 5 When I use services at my bank, bank staff usually introduces to me new technological advancements
Help customer up date new technological advancements
CR6 6 I believe that my bank has enough competence in the business
Competence in the business of banking
Customer participation consists of six items These items refer to the application of knowledge in customer participation from the literature (Ngo & O'cass, 2013; Auh at al., 2007;
Chan at al., 2010) This construct will be measured on a five-point Likert scale with scale poles ranging from strong disagree to strong agree
Table 3.2 - Measurement scales of customer participation
Construct Coding of variables Items Short describe
1 I spent a lot of time sharing information about my needs and opinions with the staff of bank X during the service process
2 I put a lot of effort into expressing my personal needs to the staff of bank X during the service process
CP3 3 I always provide suggestions to the staff of Bank
X for improving the service outcome Suggestions for improving
CP4 4 I have a high level of participation in the service using process of bank X Level of participation
5 I am very much involved in deciding how the services should be provided when dealing with bank X
Customer loyalty is comprised of four key components derived from established literature (Auh et al., 2007) These elements collectively represent the application of knowledge in fostering customer loyalty The construct is measured using a five-point Likert scale, ranging from "strongly disagree" to "strongly agree," to accurately assess customer perceptions and commitment levels.
Table 3.3 - Measurement scales of customer loyalty
Construct Coding of variables Items Short describe
CL1 1 I will apply more loan amount at bank Xin the future Willing to use more
CL2 2 I will continue to use bank X for my needs in the future
Willing to continue using services
I am highly likely to try other services from Bank X, demonstrating a strong willingness to explore new banking options Additionally, I predominantly use Bank X for most of my transaction needs, showing a high level of satisfaction and trust in their services.
In summary, based on the previous research and after refinement, 30 variables are select and group in fours indenpent variables and a dependent variable
A five-point Likert scale questionnaire was used to collect data on the key factors of the research model, ensuring reliable measurement The items were primarily adapted from previous studies to maintain content validity Specifically, the measures for search, experience, credence, customer participation, and customer loyalty were derived from established research, as detailed in section 3.2.1.
Structure of questionnaire consists of three main parts:
Part 1: General information of customers who have used personal loan services This information helps select the target respondent to study
Part 2 details the primary information, highlighting that the statements and questions were structured around a 5-point Likert scale Participants rated their responses from 1 (strongly disagree) to 5 (strongly agree), providing a standardized measure of their attitudes and perceptions for the research.
Part 3: Questions on demographics characteristics are included at the end of the questionnaire
The survey questions were translate from English to Vietnamese by the researcher and asked to edit by others.
Pilot study
This step clarifies key concepts and explains items within the customer opinion measurement scale, enabling essential adjustments to enhance its effectiveness Additionally, it provides an opportunity to introduce new ideas and items, improving the overall research model and strengthening the measurement scale's reliability.
This study adapted research scales from previous studies and conducted a pilot qualitative research to gather insights and refine the variables The scales were translated into Vietnamese to ensure respondents clearly understand the questions, minimizing potential confusion and enhancing the accuracy of the data collected.
The pilot test was conducted at Shinhan Bank's Ho Chi Minh branch, involving a questionnaire translated into Vietnamese Ten questionnaires were distributed to three banking experts and seven customers with extensive retail banking experience After receiving their responses, minor adjustments were made to improve clarity and better align the questions with respondents’ understanding This process ensured the questionnaire's effectiveness and relevance for the target audience.
After revising the initial questionnaire and adjusting the table, a small sample of fifteen convenient respondents was surveyed to identify any unclear or confusing sections All participants understood the questionnaire clearly and knew how to respond appropriately Based on this feedback, the final version of the questionnaire was developed for large-scale distribution Subsequently, a main survey was conducted across extensive areas to gather comprehensive data.
Sample method
This study focuses on banking customers in Ho Chi Minh City, Vietnam's largest city, who utilize personal loans from various banks To ensure efficiency and cost-effectiveness, the research targets a sample of approximately 500 customers The findings aim to provide insights into customer behavior and preferences related to personal loan services in this urban banking market.
Quota sampling is a non-probability sampling method where the selected sample is not necessarily representative of the entire population However, by choosing appropriate controlled characteristics, the sample may approximate the population due to high homogeneity within groups This method is widely used in scientific research for testing theories and in applied settings such as business decision-making, especially when the sampling frame is unknown (Nguyen Dinh Tho, 2012).
This research focused on control characteristics such as location (Ho Chi Minh City), usage of personal loan services, gender, and age The location was controlled by emailing individuals with bank accounts in Ho Chi Minh City The usage of personal loan services was determined through yes/no questions to establish whether participants had used such services Gender and age data were used to accurately calculate the sample size, based on the 2009 population census of Ho Chi Minh City and the quota sampling method These measures ensured a representative sample of the target population.
Table 3.4 - calculating sample with quota sampling method
The sample comprised 14 groups, with participants aged between 25 and 59 years, including both men and women Data analysis employed advanced techniques such as Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM) These methods are widely recognized in research for their robustness in validating complex models; however, many researchers agree that a large sample size is essential for accurate and reliable results due to the statistical demands of these techniques.
Age Total Sample Man Women Sample % Man % Women
No Age Gender % in sample
Determining the optimal sample size for women aged 55-59, which is estimated at 4% based on a large sample distribution theory (Raykov & Widaman, 1995), remains unclear The appropriate sample size depends on the specific estimation method employed, such as Maximum Likelihood (ML), Generalized Least Squares (GLS), or Asymptotically Distribution-Free (ADF) methods, highlighting the need for tailored sample calculations based on the chosen analytical approach.
Researchers often focus on determining the sample size (N) needed for specific analytical models by considering the number of variables, constructs, and degrees of freedom, along with the desired statistical power (Kim, 2005; MacCallum, Browne, & Cai, 2006) Additionally, studies have shown that using three or more indicators per factor can improve reliability, with a sample size of 100 respondents generally sufficient for establishing convergent validity, and 150 respondents recommended for achieving both convergent validity and a proper solution (Anderson & Gerbing, 1984).
In EFA analysis, the sample size is typically determined based on the minimum number of observations and the number of variables, with Hair et al (2006) recommending a minimum of 50, preferably 100, and a ratio of observations to items of 5:1 This means that for 30 observed variables measuring customer loyalty, a minimum sample size of 150 is required (30 variables x 5 observations) When using the Maximum Likelihood estimation method, the minimum sample size is generally between 100 and 150 (Hair et al., 1998) Therefore, a sample size of at least 150 is necessary for valid EFA results, and using a sample size of 466 in this study exceeds these recommended thresholds.
Questionnaire administration
Respondents will provide their input through an online survey and face-to-face interviews The researcher distributed 100,000 targeted email invitations to account holders at Vietcombank and Shinhanbank residing in Ho Chi Minh City Additionally, a direct survey was conducted with 50 individuals at Vietcombank branches to gather comprehensive insights.
Between August 10th and August 21st, 2014, the Ho Chi Minh Branch collected survey responses from 581 customers, including 550 online respondents and 31 through direct surveys To ensure representative data based on gender and age quotas, the researcher selected a final sample of 466 respondents for analysis, enhancing the accuracy and reliability of the study’s findings.
Data analysis methods
All completed questionnaires were carefully reviewed, coded, and entered into IBM SPSS Statistics for analysis The reliability and validity of the measurement scales were assessed through Cronbach’s alpha for internal consistency, Exploratory Factor Analysis (EFA) to identify underlying factors, and Confirmatory Factor Analysis (CFA) to confirm the measurement model Structural Equation Modeling (SEM) was employed to interpret the results from both managerial and statistical perspectives A comprehensive list of the coded variables is provided in Appendix 1.
Descriptive analysis
The study sample consisted of 466 respondents, including 48 men aged 25 to 29 years old, with additional demographic details outlined in the accompanying table The sample percentages align with the quota sampling method described in Section 3.4, ensuring representative and reliable data collection for accurate analysis.
Table 4.1 - Descriptive statistic of respondent’s characteristics
No Age Gender Frequency Valid Percent
According to income variable, there were 70 persons have income equal or higher VND
Approximately 15% of individuals earn around 20 million VND per month, while 10.9% have incomes between 15 million VND and less than 20 million VND monthly Additionally, 22.3% of people earn between 10 million VND and less than 15 million VND each month, and a significant portion, totaling 241 persons, have monthly incomes ranging from 5 million VND to less than 10 million VND.
Approximately 27.5% of respondents, or 128 individuals, held postgraduate degrees, indicating a highly educated sample The majority, 69.5% or 324 persons, had a bachelor's degree, demonstrating a strong educational background Additionally, 3.0% or 14 individuals had completed high school The high level of education among respondents suggests they are capable of understanding and accurately responding to the questionnaires, ensuring reliable data collection.
Of 466 respondents, 136 persons in single status, counted 29.2%; 317 persons in married status, counted highest 68% and 13 persons in other status, counted 2.8%
Table 4.2 - Descriptive statistic of respondent’s characteristics
Reliability analysis
According to George and Malley (2003), Cronbach‟s alpha is used as criterion for judging instruments or scales It only indicates if the items “hang together”; it does not
Name of variable Descriptions Frequency Percent Valid Percent
15 - 0.3 are considered to reach a minimum, Factor loading
>0.4 are considered important, ≥0.5 were considered to have practical significance Hair at all
In 1998, it was advised that the minimum factor loading thresholds vary based on sample size: for a sample size of approximately 350, the factor loading should be greater than 0.3; for a sample size of around 100, it should be above 0.55; and for a sample size of about 50, the factor loading must exceed 0.75 These guidelines ensure the reliability and validity of factor analysis results, emphasizing the importance of adjusting factor loadings according to sample size in research studies.
Factors with eigenvalues of 1 or greater are considered significant in factor analysis, while those with eigenvalues less than 1 are discounted (Lee & Hooley, 2005, p 376) Based on these criteria, only factors with eigenvalues exceeding 1 are retained, ensuring the most relevant factors are included Additionally, factor loadings of 0.3 or higher are deemed meaningful, indicating a strong association between variables and their respective factors.
The Kaiser-Meyer-Olkin (KMO) measure evaluates the suitability of data for Exploratory Factor Analysis (EFA), with values between 0.5 and 1 indicating appropriateness A KMO value within this range suggests that factor analysis is appropriate for the dataset Bartlett's Test of Sphericity assesses whether observed variables are sufficiently correlated in the population; a significant result (Sig < 0.05) confirms these variables are correlated, supporting the use of factor analysis.
So, researcher should be EFA with the following condistions:
- Using Principal Axis Factoring method with Promax rotation
- Factor Loading of every item must be > = 0.5
- In each item, the difference between | Factor Loading |'s largest and | Factor Loading | any should be > = 0.3 (Jabnoun & Al-Tamimi, 2003)
- Cumulative % of Extraction Sums of Squared Loadings > = 50% (Gerbing & Anderson,
- KMO> = 0.5, Bartlett testing had statistically significant (Sig