INTRODUCTION
Research Background
In today's economy, the significance of bank deposits is crucial, as they provide businesses with access to necessary funds (Ritter, Silber & Udell, 2008) Banks serve as a vital channel for collecting savings from surplus sectors and redistributing these funds as loans, preventing economic capital from remaining idle (Rosly, 2005) In Vietnam, despite the challenges posed by investments in gold, the stock market, or real estate for novice investors (Vietnam News, 2014), bank deposits remain a popular and favorable option, even amid low interest rates as of February 2014 (The State Bank of Viet Nam, 2014).
In 2014, Ho Chi Minh City saw total capital mobilization reach VND1.16 trillion (US$55.18 million), marking a 15.1% year-on-year increase Commercial banks contributed significantly, holding 56.8% of the city's total deposits and experiencing an 18.4% annual growth (Ho Chi Minh City Statistic Office, 2014) This underscores the crucial role of raising capital through commercial bank deposits in fostering economic growth and supporting struggling businesses during economic downturns, while also generating profits for both the banks and depositors through interest rates (Ritter, Silber & Udell, 2008) Consequently, attracting customers to deposit is essential for commercial banks, necessitating thorough research for both theoretical and managerial insights.
In recent decades, the study of bank selection criteria has garnered significant attention from bank marketing researchers (Abduh, 2012) While many studies have examined various aspects of financial services, including deposits and online banking, they often utilized statistical methods such as T-tests, Probit, or Multiple Linear Regression to analyze factors influencing customers' bank choices In Vietnam, however, recent research has been limited to specific branches, such as Hue's investigation into retail customers' deposit intentions at BIDV Hue Branch in 2012 and Ha's 2013 study on An Binh Bank Hue Branch This study aims to delve deeper into the factors influencing bank selection criteria for deposits in a specific region of Vietnam, employing Logistic Regression for analysis.
This study emphasizes the importance of researching the "Factors Affecting Customers’ Choice of Banks for Deposits." It aims to identify the key factors that influence retail consumers' decisions when selecting banks for their deposits, focusing on customers in a specific region of Vietnam.
Research Problem and Research Objective
According to State Bank of Vietnam (2014), recently, there is a quite severe credit shortage since credit growth has been fallen from 51.4% in 2007 to 10% in the Fall of
In contrast to developed economies, where businesses often access capital through equity and fixed income markets, Vietnamese enterprises heavily depend on bank credit for their financial needs (VinaCapital, 2014) Many domestic companies struggle with limited access to affordable credit, prompting bankers and economists in Vietnam to prioritize attracting surplus capital into the banking system This initiative aims to stimulate investment and business expansion, ultimately contributing to the overall growth of the national economy Additionally, the government's decision to lower the ceiling interest rate on deposits from 6% to 5.5% as of October 29, 2014, has made saving less appealing (State Bank of Vietnam).
2014) As to the bankers, the main problem raised is how to identify and measure the main factors influencing depositing customers’ choice of banks to have right strategies in
In the current economic climate, banks can attract more deposits by addressing key challenges faced by businesses This, in turn, creates greater opportunities for enterprises to access capital, ultimately supporting the economy as it navigates through a period of regression.
This study aims to identify and assess the key factors that influence customers' decisions when selecting banks for deposits To achieve this, the research is guided by two primary questions.
- What are factors affecting customers’ choice of banks to deposit?
- How these factors affect customers’ choice of banks to deposit?
Scope of Research
The study was conducted in Ho Chi Minh City, focusing on respondents from diverse professions who hold bank deposits The research period spanned from mid-June to the end of August 2014.
Significance of Research
Depositing serves as the primary method for capital accumulation in Vietnam's economy, which is currently experiencing a regression due to insufficient funds Understanding the key factors that influence customers' bank selection for deposits is crucial for enhancing capital-raising efforts in this critical context.
Research Structure
The research is structured into five chapters: the first chapter outlines the background, research problems, objectives, questions, scope, and structure; the second chapter reviews relevant literature, presenting hypotheses and the conceptual framework; the third chapter details the research process, including sampling size, measurement scales, surveys, and data analysis methods; the fourth chapter focuses on data preparation, descriptive analysis, measurement assessments, and hypothesis testing; finally, the fifth chapter summarizes the research overview, findings, managerial implications, limitations, and future research directions.
LITERATURE REVIEW
Review on Customer choice of banks
The financial industry, particularly banking, is increasingly competitive as the distinctions between institutions diminish In addition to traditional competitive factors like interest rates and product features, banks are now vying for service differentiation, accessibility, and advanced technology in service delivery To effectively attract customers, banks must understand the selection criteria that influence customer decisions when choosing where to deposit their money.
The study of bank selection criteria and bank patronize behavior has been done in a large number of studies In some developed countries, Kennington et al., (1996), Almossawi
Research by Şafakli (2007) and others in 2001 indicates that a bank's reputation significantly influences customer decisions regarding banking services In contrast, studies conducted in various developing countries, such as those by Owusu-Frimpong (1999), Ta and Har (2000), and Kaynak and Harcar, suggest that other factors may also play a crucial role in shaping customer preferences.
(2005) and Safakli (2007) found that profitability factors and service quality, such as low service charges and high interest rates, were the major reasons why customers chose a particular bank
The other factors which are also reported to be significant affecting customers’ decision are convenience, competence, recommendation by peers, and free banking charges
Research by Saunders et al (2007) highlights that banks in South Africa have a significant opportunity to attract customers from low-income groups by offering more advantageous financial products This finding aligns with previous studies (Hinson et al., 2009; Blankson et al., 2007; Laroche et al., 1993; Ardic & Yuzereroglu, 2009; Yavas & Kaynak, 1993; Babakus et al., 2004), which emphasize the importance of tailored banking solutions for underserved populations.
A study by Abduh et al (2012) examined the attitudes of Indonesian customers regarding bank selection criteria and patronizing behavior, specifically focusing on Islamic bank customers The research revealed that a majority of these customers favored state-owned banks, with interest rates being the primary factor influencing their choice of bank.
During the financial crisis, customers prioritized the safety of their funds when choosing to patronize Islamic banks in Indonesia Additionally, effective marketing and advertising strategies played a significant role in attracting individuals to these banks.
A study by Khaled et al (2013) examined the unique factors affecting Yemeni consumers' use of banking services, highlighting differences from consumers in developed countries The research revealed that service quality, the banking legal framework, and bank advertising positively influenced the intention to utilize banking services in Yemen However, cultural beliefs were found to have a significant negative impact on the adoption of these services.
A study by Hue (2012) in Vietnam identified five key factors influencing retail customers' intention to deposit at BIDV Hue Branch These factors include high interest rates, recommendations from relatives, brand awareness, the population's perception, quality of service, and promotional offerings.
Most recently, Ha (2013), in her study in Vietnam obtained information about the factors determining the decision of retail customers in depositing in An Binh Bank Hue Branch
She found that promotion, employees’ skills, recommendation from relatives, location,
7 and convenience were key determinants in the decision of retail customers in depositing in An Binh Bank Hue Branch.
Hypothesis development
From the above literature review, the paper chose to investigate the following constructs which might be suitable to Vietnamese context
The banking industry is often perceived as unstable, making safety a top priority for customers (Khaled, 2013) Key factors contributing to this sense of safety include a bank's reputation, government ownership, and its establishment duration (Goiteom, 2011) Research by Kennington (1996) suggests that while reputation wasn't directly addressed, it plays a crucial role in fostering trust and safety, indicating a strong link between a bank's reputation and the protection of customer funds.
In developing countries, customers prefer to deposit their funds in banks that offer protection and guarantees due to the evolving nature of the banking system, which remains vulnerable to crises The fear of losing money in the event of bank liquidation or bankruptcy has led customers to seek safer alternatives, such as Islamic banks (Abduh, 2012) A study conducted in Malaysia revealed that the reputation of banks ranks third among the key factors influencing customers' banking choices (Abduh, 2012).
The paper, therefore, hypothesizes that:
H1: There is a positive relationship between banks’ security and customers’ choice of banks to deposit
Financial benefits, such as high interest rates on savings and new account premiums, play a crucial role in customers' decisions when selecting a bank (Goiteom, 2011; Anderson, 1976) Research by Erol and El-Bdour (1989), Metawa and Almossawi (1998), and Dusuki and Abdullah (2007) further emphasizes that profitability is a key factor influencing customers' choices to open accounts with financial institutions.
In a study by Abduh (2012) conducted in Malaysia, financial benefits were identified as the second most important factor influencing customers' choice of banks Similarly, Erol & El-Bdour (1989) found that in Jordan, a developing country, the rate of return was a key determinant for individual customers selecting a bank, overshadowing the influence of religiosity.
In USA, Javalgi et al (1989) found out that high interest rate on savings was among the most important factors affecting the determinants of customers’ bank selection decision
Similarly are the results of Boyd et al (1994) research run in USA, and Ta and Har
The paper, therefore, hypothesizes that:
H2: There is a positive relationship between financial benefits and customers’ choice of banks to deposit
As economies increasingly shift towards service-oriented models, the banking sector faces intensifying competition, making high-quality service essential for survival (Sharma & Mehta, 2005) The customer interaction skills of bank staff significantly influence service quality, extending beyond mere greetings to encompass effective customer relations and telephone etiquette (Jantan, 1998) Furthermore, service efficiency is characterized by quick transaction processes and reduced waiting times, which are crucial for customer satisfaction (Muhamad et al., 1998).
9 service, speed of transaction and staff friendliness were frequently stated as the most important attributes in purchase decision process (Sudin & Norafifah 1992; Kaynak, Kucukemifoglu & Odabasi 1991; Erol & El-Bdour 1989; Tumbull 1989)
Another prerequisite issue is that professionalism and politeness of staff are of the essence of making service faster and more efficient (Stafford, 1994) According to Jantan
In 1998, the inefficiency and low quality of service from bank employees diminished the credibility of banks, leading to customer dissatisfaction and potentially influencing their choice of banking institutions Addressing issues related to technical skill accuracy and timely service is crucial for attracting new customers and retaining existing ones.
A study by Haron et al (1994) in Malaysia examined the selection criteria for both Muslim and non-Muslim customers when choosing a bank The findings revealed that key factors influencing their decisions include the "fast and efficient service" provided by the bank and the "friendliness of bank personnel."
The paper, therefore, hypothesizes that:
H3: There is a positive relationship between banks’ service quality and customers’ choice of banks to deposit
In today's fast-paced world, convenience has become a highly sought-after commodity that drives profitability (LoBello, 1985) Convenience encompasses comprehensive service offerings and extended hours of operation, providing customers with optimal comfort in managing their deposits (Anderson, 1976) Consequently, convenience plays a crucial role in influencing customers' decisions when selecting a bank (Anderson, 1976).
Offering extended banking hours enhances customer flexibility, allowing them to conduct transactions at their convenience rather than being constrained by standard banking hours (Jantan, 1998).
Between 2000 and 2005, Bank Indonesia (BI) collaborated with the Department of Statistics at Bogor Agricultural University (IPB) to conduct an extensive survey across West, Central, and East Java, as well as West Sumatera and South Kalimantan The findings revealed that convenience emerged as a key factor influencing customers' choice of banks.
A study by Khaled (2013) in Yemen identified convenience as the primary factor influencing bank selection Similarly, a 1975 Wall Street Journal/NBC survey revealed that 75% of U.S households earning over $100,000 prioritized time management over financial management (Mesister, 1996) Thus, convenience plays a crucial role in customers' purchasing decisions.
The paper, therefore, hypothesizes that:
H4: There is a positive relationship between convenience and customers’ choice of banks to deposit
Bank image includes bank’s facilities, transaction space, internal or external decoration…
A bank with well-equipped facilities, attractive and professional internal or external decoration would increase the customers’ belief in depositing
A study by Muhamad et al (1998) in Malaysia utilized the Analytic Hierarchy Process (AHP) to assess the significance of five key attributes: efficiency, physical facilities, range of service, terms of payment, and media and social influence, in relation to foreign and local banks The results indicated that customers' preferences for local banks are primarily shaped by factors such as physical facilities, service range, payment terms, and the influence of media and social factors.
In developed countries, Erol and El-Bdour (1989), Erol et al (1990), Kennington et al
Research by Almossawi (2001), Şafakli (2007), and Dusuki and Abdullah (2007) highlights that key factors influencing depositors' bank selection include the quality of services and facilities, the bank's reputation, profitability, and the friendliness of the staff.
In 2012, Abduh M and Omar M conducted a study on the criteria for selecting Islamic banks in Malaysia, gathering data from 279 respondents in the Klang Valley Utilizing the Analytic Hierarchy Process, they ranked the selection criteria based on respondents' preferences, revealing that profitability, the bank's reputation, status, facilities and services, and friendly personnel were the most important factors.
The paper, therefore, hypothesizes that:
H5: There is a positive relationship between bank image and customers’ choice of banks to deposit
Demographic characteristics, including gender, age, education, income level, marital status, and family size, play a crucial role in customer decision-making (Mateja & Irena, 2009) These factors significantly influence purchasing decisions, particularly in the banking sector, where gender has been shown to affect customer behavior in selecting banks (Goiteom, 2011) This study focuses on gender, age, income, and deposit experience as control variables to examine their impact on customers' choices regarding bank deposits.
Research Model
The theoretical model developed from the literature review outlines five hypotheses, indicating that customers' choices of banks for deposits are positively influenced by factors such as security, financial benefits, service quality, convenience, and bank image Additionally, the model explores how demographic factors impact customers' banking decisions.
Security Financialbenefits Service quality Convenience Bank image
Demographic attributes (gender, income, and experience of depositing
RESEARCH METHODOLOGY
Data Sources
Primary data: The author designed a questionnaire based on bank choosing criteria review
This research utilized secondary data sources, including books, academic papers, journals, and magazines, to explore customer behavior theories and the factors influencing bank selection and deposit decisions Key attributes of banks and the quality of their services were examined through resources obtained from Google Scholar, Proquest Search, and university libraries.
Specially, annual reports of Government departments concerning bank depositing analysis in Vietnam were quoted in this research
Through extensive research, insights from banking specialists have offered valuable references for understanding customer preferences in bank deposits This knowledge aims to enhance the factors influencing these choices and develop strategies to boost deposit levels for banks.
Research process
Step 1: Review the literature background to find out factors that influence in customers’ choice of banks to deposit The findings in this step were used to be the foundation for later quantitative research to confirm the intensity of the explored results
Step 2: Theory Model was built based on the literature review above The dependable variable was identified as “Customers’ choice” The five independable variables were defined as “Reputation”, “Financial benefits”, “Employers’ efficiency”, “Variety of services” and “Convenience” with Demographic attributes as Control variables
Step 3: Focus group interview was chosen as an in-depth interview In this research, the author has chosen group of 10 customers who were depositing or used to deposit in banks and ask some open-ended questions based on the previous research, for example:
- What are the key factors for you to choose to deposit in a bank?
- What makes you think that these factors are important?
- What are determinants affecting this key factor in your choice of banks to deposit?
- Why you choose theses factors but not the others?
A draft questionnaire with the measurement scales was then set up
Step 4: Next, the draft questionnaire was delivered to 02 management officers who involving in depositing department and 03 specialists in depositing in banks to respond, and a discussion about the draft questionnaire was carried out later The aim of the pilot phase was to modify and clear the measure scale
The revised questionnaires were distributed to a small group of five individuals to ensure clarity and understanding Subsequently, a main survey was developed incorporating the necessary adjustments based on their feedback.
Step 5: The main survey was adjusted based on feedbacks received from the customers and specialists The dependable variable was still “Customers’ choice” The five independable variables were re-defined as “Security”, “Financial benefits”, “Service quality” “Convenience” and “Bank image” The main survey was conducted with 254 respondents
Step 6: The sample size consisted 254 observations Questionnaire was sent directly to the customers who visited banks for transactions The rest amount of questionnaire was sent via email to the author’s friends, colleagues or relatives who live in Ho Chi Minh City By using filter question, the respondents were defined as people who were depositing or used to deposit in Banks
Step 7: After finishing the survey, the data was collected and prepared to analysis Data was edited, coded and adjusted for missing data Next, reliability of measuring instrument was analyzed by calculation Cronbach’s alpha which was required above 0.7 (Hair et al., 2010)
Step 8: The validity of measuring instrument was also evaluated to define the number extracted factors based on the Eigenvalue value over than 1 and changing of the slope in the Scree plot (Hair et al., 1998; Tabachnick & Fidell, 2001)
Step 9: The coded and adjusted data, then, was analyzed through five hypothesizes to find out whether five dimensions above have positive relationship with customers’ choice of banks to deposit The Logistic regression analysis was applied to evaluate the relationship between five independent variables, including “Security”, “Convenience”,
The study examines the impact of service quality, financial benefits, and bank image on customers' choice of banks for deposits Additionally, it analyzes how control variables such as gender, income, and deposit experience significantly contribute to this decision-making process, utilizing logistic regression for a comprehensive evaluation.
Step 10: Based on the findings from previous step, conclusion was drawn and managerial implication was suggested
The research process is illustrated by the following Figure 3.1
In-depth Interview Design First Draft Questionnaire
Sample Size
For a valid Exploratory Factor Analysis (EFA), the sample size must be at least five times the number of variables and exceed 100 participants (Hair et al., 1998) In this study, with 23 variables, the minimum required sample size was calculated to be 115, following the formula n = 5k (where k represents the number of variables).
According to Peduzzi et al (1996), the minimum sample size for logistic regression should follow the formula N = 10 k / p, where p represents the smaller proportion of negative or positive cases in the population and k denotes the number of independent variables In this study, with five independent variables and a negative case proportion of 39%, the required minimum sample size is calculated as N = 10 x 5 / 0.39, resulting in 128 cases If the calculated sample size falls below 100, it is recommended to adjust it to at least 100, as advised by Long S J (1997).
Testing Hypotheses by Logistic Regression
To evaluate the reliability of measurement
To evaluate the validity of measurement
As a result, the minimum sample size in this study was 128 which would be satisfied both EFA and logistic regression analysis In this paper, the author collected a sample of 254 observations.
Measurement Scale
To effectively operate concepts, it is essential to measure them using various variables that help select an appropriate scale In this study, independent variables were measured using a five-point Likert scale, ranging from "totally disagree" to "totally agree."
The study utilized a dichotomous dependent variable with options for "Yes" or "No" responses, while the control variables were simplified into two categories for testing purposes Gender was classified as either Male or Female, income levels were divided into two groups: Less than 10 million and Above 10 million, and the experience of depositing was categorized as Less than 3 years and Above 3 years Participants expressed their opinions on a scale ranging from disagree to totally agree.
A summary table of main factors affecting customers’ choice of banks to deposit is presented as following Table 3.1
Table 3.1: Main factors affecting customers’ choice of banks to deposit
No Factor Variables Code Reference
Banks with reputable brand names stimulates confidence
(2012) Banks which relatives recommend stimulates confidence
Banks relatives work at stimulates confidence
Banks with high reputation of information security stimulates confidence
Interest rate on savings is competitive Fin1 Mikhail K (2005),
Ha (2013), Goiteom (2011), Aderson (1976), Javalgi et al
Interest payment schedule is flexible Fin2 Transparent interest rate is quoted Fin3
High interest rate with short schedule is offered
Low service fee is offered Fin5
Banks with modern facilities provide good impression
Professional documents provide good impression
Comfortable transactional space provide good impression
Attractive external/internal design provide good impression
Hospitality of employees provide good impression
Stafford (1994), Jantan (1998), Muhamad et al
Efficency of employees provide good impression
Support of employees provide good impression
Consultancy of employees provide good impression
Promotion gifts provide good Ser5
Convenient location stimulate customers’ transaction
Convenient operating hours stimulate customers’ transaction
Simple procedures stimulate customers’ transaction
Bank employees’ support stimulate customers’ transaction
At-home services stimulate customers’ transaction
Gender affects the customers’ decision Sex1 Mateja & Irena
Income affects the customers’ decision Inc1
Experience of depositing affects the customers’ decision
Whether the Customers decide to deposit or not
Yuzereroglu (2009); Hinson et al (2009); Abduh
Data Analysis Method
Following data collection, the initial step involved data preparation, which included editing, coding, and data entry to guarantee the accuracy of the raw data while identifying and correcting any errors or omissions Subsequently, the data was classified into groups or categories based on common demographic characteristics.
Finally, variables were tested reliability by Cronbach’s alpha, validity by EFA, and hypothesis and model was tested by Logistic regression of SPSS
To assess the reliability of each scale with a specific sample and evaluate their internal consistency, Cronbach’s Alpha coefficient should be utilized, with a recommended value exceeding 0.7 (Devellis, 2003).
Also, the corrected item - total correlation values should be at least 0.3 to ensure each of items was measuring the same from the scale as a whole (Pallant, 2011)
3.5.2 Validity measure by EFA (Exploratory Factor Analysis)
To assess the validity and correlation between variables while identifying underlying factors, Exploratory Factor Analysis (EFA) is utilized with an oblique approach, specifically the Promax method It is essential to meet certain prerequisites for EFA, as outlined by Pallant (2011).
- The minimum of sample size should be at least 100 and rate of observations per items of models should be five cases for each of the items
- The correlations of r of the correlation matrix should show at least 0.3
- Kaiser-Meyor-Olkin (KMO) test must be equal or above 0.6 (Tabachnick & Fidell,
- Barllett’s test of sphericity should have significant less than 5%
- In order to extract factors, the eigenvalue of factors must be greater than 1 (Kaiser,
To explore the relationship between independent variables, consisting of “Security”,
The study utilized logistic regression analysis to assess the impact of key independent variables, including "bank image," "service quality," "convenience," and "financial benefits," on the dichotomous dependent variable of "customer's choice." This approach allowed for a comprehensive evaluation of how these factors influence customer decision-making within the banking sector.
The Logistic regression analysis required that following conditions should be satisfied:
- The minimum sample size required
- The multicollinearity does not exist, so r value, the correlated score is less than 0.9
- The collinearity test on variables is through two values “tolerance” and “VIF”, particularly the VIF should not be less than 0.1, or above 10
Logistic regression is a statistical method utilized to test hypotheses and examine the relationship between five independent variables and a single dependent variable It also assesses the influence of control variables on the dependent variable, providing a comprehensive analysis of their interactions.
Where: p = the probability that a case is in a particular category; a = the constant of the equation and, b = the coefficient of the predictor variables
This chapter outlines the research methods utilized in the study, detailing the research approach, measurement techniques, sampling strategies, data collection processes, and data analysis methods A comprehensive discussion of the analytical techniques employed and the findings will be presented in Chapter 4.
DATA ANALYSIS AND RESULTS
Descriptive stastistics survey results
A quantitative research study was conducted in Ho Chi Minh City using a questionnaire divided into two parts: the first part gathered general information about customers, while the second assessed their perceptions of banks where they deposit or have deposited funds The initial section served as control variables, and the latter provided data for analyzing dependent and independent variables The survey design was developed in consultation with banking professionals and experienced depositors A total of 368 surveys were distributed to deposit customers both in person and via email, resulting in 311 responses and an impressive response rate of 84.5%.
After collecting 311 feedback responses through paper and electronic surveys, we initially screened all cases There were six blank sheets, no instances of half-filled Part I, 19 responses missing some general information or bank judgment questions, five lacking a decision on depositing, and seven that predominantly selected numbers 3, 4, or 5 Out of the remaining 274 cases, we identified those where respondents chose the same scale for all statements due to negligence, which distorted the data Ultimately, 254 samples, free from missing data and contributing effectively to the research, were selected for official analysis.
In this study, customer information was coded by assigning numerical symbols to responses, allowing for organization into a limited set of categories Customer evaluations of banks were assessed using a 5-point Likert scale, ranging from 1 to 5, as detailed in Table 4.1.
No Factors Code Description Variable name
1 Security Brandname Secu1 Record numbers
3 Banks relatives work at Secu3
5 Financial benefits Interest rate on savings Fin1 Record numbers
8 High interest rate with short schedule
10 Convenience Location Con1 Record numbers
15 Service quality Hospitality Ser1 Record numbers
20 Bank image Modern facilities Ima1 Record numbers
24 Choice Yes/No Choice Creating dummy variable 1= Yes 0= No
25 Demography Gender Gender Creating dummy variable 1= Female 0= Male
26 Income Income Creating dummy variable 1= “Less than 40”
27 Experience of depositing Experience Creating dummy variable
4.1.3 Characteristics of the sample population
The study targeted customers aged 18 and over in Ho Chi Minh City who had experience with bank deposits Participants were asked to evaluate their current or previous banks and decide whether to continue or discontinue their services To gather demographic information, five questions were posed regarding gender, age, education, income, and deposit experience A total of 254 samples were analyzed, with findings summarized in the accompanying table.
No Items Scale No of samples Percentage
The research population comprised 47% males and 53% females, demonstrating gender balance Income distribution was also equal, with 50% of participants earning less than 10 million and 50% earning over 10 million Additionally, the study highlighted a greater focus on individuals with less than 3 years of deposit experience, accounting for 61% of the sample, compared to 39% with over 3 years of experience.
Reliability of measurement instruments
Cronbach’s alpha was calculated to evaluate the reliability of the researched items A scale is deemed reliable and internally consistent when it achieves a Cronbach’s alpha coefficient greater than 7 (Devellis).
Also, to ensure each of the items is measuring the same from the scales as a whole, the corrected item-total correlation values should be at least 3 (Pallant, 2011)
To ensure the reliability of each concept, Cronbach’s Alpha was conducted, resulting in the removal of items with a coefficient below 7 and a corrected item-total correlation lower than 3 The findings of this analysis are presented in Table 4.3 below.
Table 4.3 Cronbach’s Alpha test results
The security measurement consisted of four items, achieving an overall alpha coefficient of 838 Analysis revealed that the Cronbach alpha values, when any item was deleted, remained below the overall coefficient, indicating that removing any item would not enhance the reliability of the factor.
Besides, Corrected Item-Total correlation of all items was much more than 0.3
Therefore, all items for Security were reliable for next analysis
The Cronbach’s alpha coefficient for the Financial Benefits variable was 888, indicating good reliability Additionally, the alpha coefficients for individual items were all lower than 888, suggesting that no items should be removed from this variable The Corrected Item-Total Correlation for this factor also showed high values, exceeding 3.
Financial benefits variable and five of its measuring scales were accepted and used for further analysis
The alpha coefficient for Convenience was 877, indicating strong reliability Additionally, the analysis of Cronbach’s alpha if Item Deleted revealed that removing any questions did not enhance the scale's overall reliability, with all items exhibiting a Corrected Item-Total Correlation above 0.3 Consequently, all items were retained for further analyses.
The overall alpha coefficient for service quality was 871, exceeding the acceptable standard of 70, indicating strong reliability The Cronbach’s alpha if Item Deleted values for all items were lower than the overall alpha, demonstrating their positive contribution to the factors Additionally, the Corrected Item-Total Correlation for all items was significantly above 3, confirming the reliability of this factor and validating the use of all measuring scales for the subsequent Exploratory Factor Analysis (EFA) test.
The Bank Image's Alpha coefficient reached an impressive value of 872, indicating strong internal consistency Each item contributed positively to the overall alpha, with the Cronbach’s alpha remaining below 872 when any item was removed Additionally, the Corrected Item-Total Correlation for all items exceeded 03, further confirming their relevance and reliability.
Therefore, 5 items of customer loyalty were accepted and used for further analyses.
Validity of measurement instruments
Construct validity was examined by analyzing both convergent and discriminant validity
Convergent validity evaluates the extent to which measures of the same construct are highly correlated, while discriminant validity assesses whether these measures do not correlate too strongly with other constructs (Sekaran, 2000) Various methods, including factor analysis and correlation, have been proposed to assess both types of validity In this study, exploratory factor analysis (EFA) was utilized to evaluate convergent and discriminant validity For a measure to be considered valid in terms of convergent validity, the final items must exhibit a high loading on a single factor, specifically with a factor loading of 0.50 or greater (Anderson and Gerbing, 1988).
Discriminant validity requires that the correlations between factors be assessed, ensuring that items within a single construct correlate more strongly with each other than with items from different constructs (Hair et al., 1995; Kline, 2005).
The Cronbach’s alpha test confirmed that all measurement scales were reliable, with no items needing deletion Subsequently, exploratory factor analysis (EFA) was conducted to assess the validity of the items.
Factor analysis is a statistical method used to identify and explain the relationships between variables by reducing them to a smaller number of underlying factors Its primary objectives include determining the essential influences within a set of variables, measuring the degree of association between each variable and these factors, and gaining insights into the characteristics of these factors based on their impact on variable performance (Cudeck, 2000) Essentially, factor analysis simplifies complex data by summarizing numerous variables into key factors.
Principal axis factoring is a method of factor analysis aimed at identifying the minimum number of factors that can explain the shared variance among a group of variables Essentially, it examines how each variable is influenced by underlying common factors.
Principal axis factoring was employed in this research due to the incomplete variance accounted for by individual items Varimax rotation, an orthogonal method, enhances the variance of squared loadings in a factor matrix, effectively distinguishing original variables by their extracted factors This approach results in each factor exhibiting either high or low loadings for specific variables, facilitating the identification of variables associated with a single factor Varimax rotation is the preferred choice as it maximizes the sum of squared loadings, resulting in a greater number of items aligning with the extracted factors (Russell, 2002).
We performed exploratory factor analysis (EFA) using Varimax rotation to evaluate the observed variables, while employing the KMO (Kaiser-Meyer-Olkin) and Bartlett’s test methods to assess the survey's compatibility and outcomes.
There are two main conditions necessary for the suitability of data for factor analysis
For effective analysis, a relationship between the variables is essential A larger sample size enhances the reliability of the resulting factors, particularly when compared to the number of variables (Leech, Barrett, & Morgan, 2005) In this study, the research population of 254 met the minimum sample size requirement Furthermore, as shown in Table 1 of Appendix 3, all items demonstrated strong correlations with Pearson correlation values exceeding 4 Consequently, all conditions necessary for conducting Exploratory Factor Analysis (EFA) were fulfilled.
Factor analysis is deemed appropriate when the KMO (Kaiser-Meyer-Olkin) value exceeds 0.7, while a value below 0.5 indicates inadequacy (Leech et al., 2005) The KMO test assesses the sufficiency of items predicted by each factor In this study, a KMO value of 931 was achieved, indicating that the research sample was adequate.
The Bartlett’s test of sphericity yielded a significant value of 000 (p