INTRODUCTION
BACKGROUND
The Internet has revolutionized social life over the past few decades, enabling virtual communication and online shopping without the need to visit physical stores E-commerce has provided businesses with effective distribution channels beyond traditional methods, while also allowing customers to save time Consequently, online shopping has experienced exponential growth, evidenced by rapidly increasing e-business revenues and the rising number of Internet transactions.
In Vietnam, the government actively supports e-commerce, with the Ministry of Industry and Trade overseeing an E-commerce Development Centre This centre monitors and analyzes the growth of e-commerce, seeking innovative solutions to enhance its development Additionally, they publish annual reports detailing the state of e-commerce in Vietnam.
Between 2006 and 2009, the number of enterprises engaging in e-business in Vietnam rose from 8% to 12%, as reported by the E-commerce Development Centre in 2009 This shift indicates a growing trend where Vietnamese consumers can purchase products online without the need to visit physical stores However, traditional shopping habits, characterized by close buyer-seller relationships that allow for direct communication and product introductions in convenience stores, pose challenges to the expansion of online business Additionally, barriers such as inadequate electronic payment systems further hinder the growth of online shopping in Vietnam.
PURPOSES AND RESEARCH QUESTIONS
E-commerce in Vietnam has government and commercial enterprise attention but the number of customers shopping online is still limited People are not yet ready to trade online Moreover, with the limited number of current customers, what should companies do to keep them and develop close relationships with them? How can companies ensure customer product repurchase or recommend others people to use them?
Research in Vietnam predominantly examines e-commerce from the perspective of companies, with limited focus on attracting and retaining online customers This study aims to investigate the interplay between trust, commitment, loyalty, and retention—key elements highlighted in previous customer relationship marketing studies By analyzing these relationships, the research will identify factors that influence customer loyalty and retention, providing recommendations for enhancing online shopping experiences in Vietnam Consequently, two primary research questions have been formulated to guide this exploration.
1 What are the antecedents of trust and commitment?
2 How do trust and commitment influence customer loyalty and customer retention in online shopping in Vietnam?
METHODOLOGY
The research aims to test a hypothesis regarding the relationship between variables in online shopping in Vietnam, utilizing a deductive research approach with an explanatory purpose.
The research will begin with a literature review to explore theories related to customer relationship marketing, identifying frequently studied areas and formulating hypotheses Following the recommended research methods, a survey strategy utilizing a questionnaire will be employed to address the research questions effectively.
A questionnaire comprising 28 questions, adapted from various previous studies, is developed for data collection to assess both independent and dependent variables To ensure clarity and comprehension for both participants and researchers, a pilot test is conducted prior to the main study.
The data collected will be analyzed using the SPSS program, starting with a check for normal distribution Subsequently, correlations and multiple regressions will be conducted to assess whether the research hypotheses are supported or rejected, reflecting the relationships among four key areas in comparison to current studies.
DEFINITIONS
This research aims to examine the connections between customer trust, commitment, loyalty, and retention, which are essential elements in customer relationship management theories The study will focus specifically on the e-commerce sector, particularly online shopping, to analyze these relationships Key concepts relevant to the research will be clearly defined to ensure a comprehensive understanding of the findings.
Customer relationship management (CRM) is defined as a strategic approach aimed at enhancing shareholder value by fostering meaningful relationships with key customers and segments By combining relationship marketing strategies with information technology, CRM facilitates the development of profitable, long-term connections with customers and stakeholders It leverages data and information to better understand customers and collaboratively create value, necessitating cross-functional integration of processes, personnel, operations, and marketing capabilities through the use of technology and applications.
Turban and King (2003) defined E-commerce (EC) as “the process of buying, selling, or exchanging products, services, and information via computer networks, including the Internet”
According to Mosuwe et al (2004), online shopping encompasses customers' intentions to purchase products via the Internet from businesses engaged in e-commerce This practice falls under the business-to-consumer (B2C) model within the broader framework of e-commerce.
Morgan and Hunt (1994) defined Trust as “the perception of confidence in the exchange partner‟s reliability and integrity”
Morgan and Hunt (1994) describe commitment as a lasting desire to uphold a valued relationship, emphasizing the importance of a customer's commitment to a company and the ongoing maintenance of that relationship.
Customer loyalty, as defined by Oliver (1999), is a strong commitment to consistently repurchase a preferred product or service in the future This loyalty leads to repeated buying of the same brand, even in the face of external influences and marketing strategies that might encourage switching to other options.
Gerpott (2001) defined Customer retention as “maintaining the business relationship established between a supplier and a customer”
LITERATURE REVIEW
CUSTOMER RELATIONSHIP MANAGEMENT
Customer Relationship Management (CRM) is a strategic approach aimed at enhancing shareholder value by fostering strong relationships with key customers and segments By integrating relationship marketing strategies with information technology, CRM facilitates the development of profitable, long-term connections with customers and stakeholders It leverages data and information to better understand customers and co-create value, necessitating cross-functional integration of processes, people, operations, and marketing capabilities, all supported by advanced technology and applications This comprehensive definition highlights the essential activities of CRM and its implementation within organizations.
Since its emergence in the mid-1990s, Customer Relationship Management (CRM) has evolved through three distinct generations According to Kumar and Reinartz (2006), the first generation focused on functional CRM, aimed at boosting sales and enhancing services through activities like sales force automation and customer support The subsequent generation introduced a customer-facing front-end approach, addressing gaps in enterprise resource planning (ERP) systems to meet business needs However, during the 1990s, effective customer relationship management, encompassing interactions from pre-sales to post-sales via communication channels like telephone and internet, was not fully realized.
By the end of 2002, companies began adopting a strategic approach to third-generation Customer Relationship Management (CRM) by learning from the shortcomings of previous versions This new focus expanded beyond just customer-facing front-end systems to include back-end integrations with partners and suppliers, utilizing Internet technology As a result, CRM evolved into not only a technological solution but also a vital component of overall business strategy, significantly contributing to revenue growth.
Trust plays a crucial role in the success of e-commerce, particularly in online transactions where uncertainties are prevalent Researchers emphasize that trust significantly influences social and economic interactions that involve risk and dependency Two key elements shaped by trust in these transactions are security and privacy, highlighting the importance of clearly defining the concept of trust in the digital marketplace.
E-commerce offers convenience by connecting buyers and sellers, but it also has limitations, such as the absence of direct communication between them and with the products To overcome these challenges, suppliers must cultivate trustworthy relationships with customers, which is essential for enhancing customer loyalty.
Teo & Liu (2007) emphasize that consumer trust is crucial in e-commerce, highlighting the need to understand its antecedents and consequences Identifying the factors that influence trust is essential for developing effective strategies to enhance it Additionally, recognizing the impact of trust on online buying behavior underscores its significance in the e-commerce landscape.
In the realm of organizational trust, Mayer et al (1995) introduced a model outlining the relationship between a trusting party and the party being trusted In the context of e-commerce, Jarvenpaa et al (2000) explored how customers' perceptions of an online store's reputation and size influence their trust in that store Their findings indicate that trust significantly impacts consumers' attitudes, intentions, and behaviors.
Trust is a crucial element in successful marketing relationships, defined by Morgan and Hunt (1994) as the perception of confidence in an exchange partner's reliability and integrity Mayer et al (1995) further elaborate that trust involves a willingness to be vulnerable to another party's actions, based on the expectation that they will perform important actions for the trustor, regardless of the ability to monitor them These definitions highlight that confidence and reliability are fundamental components of trust.
The concept of trust is multifaceted and cannot be fully captured by a single definition To understand trust more comprehensively, it is essential to classify it based on various factors such as attitudes, beliefs, behaviors, and tendencies Different definitions of trust lead to distinct classifications, which may focus on trust in individuals, specific traits like honesty, or broader beliefs about relationships and interactions.
In summary, this study adopts the definition of trust provided by Morgan and Hunt (1994), which describes trust as the perception of confidence in the reliability and integrity of an exchange partner.
This research addresses the antecedents of trust, identifying two key factors: e-retailer reputation and privacy concerns Previous studies, including those by Bennett & Gabriel (2001), Josang et al (2007), and Eastlick (2006), have explored the relationship between these factors and trust This study will further examine these antecedents and their interconnections, contributing to a deeper understanding of trust dynamics in online environments.
The decision to engage in electronic commerce is influenced by various factors, with the retailer's reputation being a key element According to Bennett & Gabriel (2001), e-retailer reputation is synonymous with brand reputation, encompassing the name, term, symbol, sign, or design that distinguishes a retailer's goods and services from competitors This reputation is not solely based on the retailer's image but also reflects external perceptions of the organization's qualities, particularly regarding its past performance.
Research by Van and Leunis (1999) indicates that brand reputation significantly alleviates customers' risk concerns when engaging in online transactions The reputation of e-retailers plays a crucial role in influencing customer participation in e-commerce Furthermore, studies by Bennett and Gabriel (2000) and Josang et al (2007) demonstrate a positive correlation between reputation and trust, suggesting that a strong reputation enhances customer trust This theoretical evidence supports the development and testing of hypotheses related to the importance of brand reputation in e-commerce.
A company's reputation is a delicate and valuable asset that can be easily damaged, making it far more challenging to build than to lose Organizations must prioritize the protection of their reputation, especially suppliers, who need to be particularly vigilant against potential negative impacts.
Research indicates that reputation plays a crucial role in fostering trust between buyers and sellers, particularly in online shopping Teo & Liu (2007) highlight that a vendor's perceived reputation is significantly linked to consumer trust, providing theoretical support for the relationship between e-retailer reputation and customer trust, as outlined in hypothesis H1a.
METHOD OF STATISCAL ANALYSIS
Research data analysis begins with a correlation analysis, which explores the relationships between the study's variables This analysis calculates the relationship based on the standardization of covariance, specifically utilizing Pearson's correlation coefficient (r).
The correlation coefficient has to lie between -1 and +1
A coefficient of +1 indicates a perfectly positive relationship; a coefficient of -1 indicates a perfectly negative relationship, while a coefficient of 0 indicates no linear relationship at all
The correlation coefficient is a commonly used measure of the size of an effect: values of ±0.1 present a small effect, ±0.3 a medium effect and ±0.5 a large effect
Multiple regression is a statistical method utilized to examine the relationship between a dependent variable and multiple independent variables In this study, all regression analyses involved a dependent variable alongside several independent variables, necessitating the use of multiple regressions The key procedures for analyzing multiple regression are outlined in the following sections.
In social science, three primary regression methods are utilized: standard (forced entry) regression, sequential (hierarchical) regression, and statistical (stepwise) regression Standard regression involves entering all predictors into the model at once, while hierarchical regression allows the researcher to determine the order of predictor entry Stepwise regression, on the other hand, relies on mathematical criteria to decide the sequence of predictor inclusion This study aims to examine the mediating relationships among variables, employing hierarchical regression to enter predictors individually, thereby enabling the researcher to control the process rather than using simultaneous entry.
Analysis of regression result in this study was based on major statistics such as sums of squares (R 2 , adjusted R 2 , and R 2 Change) and regression coefficients (Bi & òi)
The sum of squares (R²) quantifies the proportion of variability in the dependent variable that is explained by the independent variables, effectively representing the percentage of variation in the outcome attributable to the model It is calculated by dividing the residual sum of squares (SSR) by the model sum of squares (SSM) The significance of R² is evaluated using the p-value of the F-ratio, with R² considered significant if the p-value is less than 0.05 at an alpha level of 0.05.
Adjusted R 2 is used to calculate how much of the variability in the dependent variable is accounted for by the independent variables if the model close to R 2
R² Change is utilized to assess the variation in R² when one or more independent variables are added to the equation This change is considered significant if the p-value for the R² Change ratio is less than 0.05, with an alpha level set at 0.05.
Assessing regression diagnostics is essential for determining how well a model fits the observed data and identifying any influence from a small number of cases in the sample This evaluation involves examining outliers and influential cases to ensure the reliability of the regression analysis.
An outlier is a data point that significantly deviates from the overall trend, potentially skewing the estimated regression coefficients and introducing bias into the model To minimize this bias, it is crucial to identify outliers by examining the residuals, which are the differences between the predicted values and the observed values in the dataset Small residuals indicate a good model fit, while large residuals suggest a poor fit Standardized residuals are commonly used to detect these outliers effectively.
According to Field (2005), one general rule for residuals:
(1) “ standard residuals with an absolute value greater than 3.29 are cause for concern because in an average sample a value high like this is unlikely to happen by chance”;
(2) “ if more than 1% of a sample has standardized residuals with an absolute value greater than 2.58 there is evidence that the level of error within our model is unacceptable”; and
(3) “ if more than 5% of cases have standardized residuals with an absolute value greater than 1.96 then there is also evidence that the model is a poor representation of the actual data”
Residuals help identify outliers by analyzing errors in a model, while influential cases assess whether specific instances disproportionately affect the model's parameters Key statistical measures for identifying influential cases include Cook's distance, leverage, Mahalanobis distance, DFBeta, and the covariance ratio (CVR).
Cook’s distance is a statistic that considers the effect of a single case on the model as a whole The values of Cook’s distance greater than 1 is may be cause for concern
Mahalanobis distance is closely linked to leverage values, as it quantifies the distance of data points from the mean of the predictor variables In large samples, specifically with 500 observations and five predictors, values exceeding 25 indicate potential issues that warrant attention.
In smaller samplers (N = 100) and with fewer predictors (namely three) values greater than 15 are problematic, and in very small sample (N0) with two predictors values greater than 11 should be examined
DFBeta measures the impact of individual cases on the estimated parameters of a regression model by comparing the parameter values calculated with and without each case By analyzing the DFBetas for each case and parameter, researchers can identify influential cases that significantly affect the model's coefficients Specifically, a standardized DFBeta with an absolute value greater than 2 indicates a considerable influence on the regression coefficients.
Regression analysis accurately reflects the sample of observed values, but it may not be applicable to a broader population To effectively generalize the model, certain underlying assumptions must be satisfied, including independent residuals, normality of residuals, homoscedasticity of residuals, linearity, and the absence of multicollinearity.
(1) Independent residuals (Durbin-Watson test)
The first assumption for generalizing a model is that the residuals from observations must be uncorrelated This can be evaluated using the Durbin-Watson test, which assesses serial correlations among errors The test statistic ranges from 0 to 4, with a value of 2 indicating uncorrelated residuals Values above 2 suggest a negative correlation between adjacent residuals, while values below 2 indicate a positive correlation It's important to note that the Durbin-Watson statistic is influenced by the number of predictors in the model and the total number of observations.
Values below 1 or above 3 are generally alarming, while values around 2 can also indicate potential issues, depending on the sample and model used.
In statistical modeling, it is assumed that the residuals are random and normally distributed with a mean of zero, indicating that discrepancies between the model and observed data are typically minimal, with larger differences occurring infrequently To test this assumption, two graphical methods are employed: histograms and normal probability plots The histogram of the residuals should resemble a normal curve with matching mean and standard deviation, while in a normal probability plot, if the residuals are normally distributed, all points should align along the normal distribution line.
The mediating relationship was assessed using a two-step hierarchical multiple regression approach Initially, all independent variables (Xi) were included to evaluate their total effect (βtxi) on the dependent variable (Y).
METHODOLOGY
RESEARCH STRATEGY
Deduction and induction are two distinct approaches utilized in research, each with unique characteristics that guide researchers in selecting the appropriate method for addressing their research questions Understanding the differences between these approaches is crucial for effective study design and analysis.
Research employing a deductive approach is characterized by its focus on explaining causal relationships between variables, necessitating quantitative measurement of all concepts involved It also requires sufficient sample sizes for generalization In contrast, the inductive approach involves collecting data to develop theories based on analysis, emphasizing the understanding of the meanings humans assign to events This method prioritizes qualitative data and is less focused on generalization.
This study employs a quantitative research approach, focusing on numerical observations to generalize a phenomenon through formalized data analysis, with statistical indicators being pivotal The quantitative method utilized involves a survey conducted via a questionnaire, specifically designed to assess whether the collected data effectively addresses the research questions.
This study employs a quantitative cross-sectional survey methodology, focusing on a specific phenomenon at a single point in time A longitudinal or experimental approach was not feasible due to the extensive time and resources required Given the large total population, a sample was selected to test the theoretical model effectively Data collection occurred once over a brief period, encompassing various contexts within the population.
Survey methodology includes four primary methods: self-administered questionnaires, interviews, structured record reviews, and structured observations This study aims to gather data on individual attitudes and orientations at a single point in time, particularly under limited resources Self-administered questionnaires are advantageous for identifying and describing respondent attitudes and variations in different phenomena In contrast, interviews demand significant time and resources, especially with larger samples Additionally, structured record reviews and structured observations are not suitable for collecting data on attitudes, as they focus on visual and recorded information Given these considerations, self-administered questionnaires emerge as the most effective method for data collection in this social science research study.
DATA COLLECTION METHOD
This research investigates the impact of trust and commitment on customer loyalty and retention in Vietnam's online shopping sector The study focuses on respondents with online shopping experience, as online shopping in Vietnam is characterized by straightforward processes such as purchasing goods via websites and options for online payment or cash on delivery This unique aspect of e-commerce development in Vietnam stems from consumers' limited cash availability and a foundational lack of information technology infrastructure.
Given Vietnam's large population and the lack of precise data on online shoppers, utilizing a representative sample is essential for estimating the characteristics of the entire population By analyzing data from this sample, researchers can draw conclusions that apply to the broader population, effectively addressing the research questions Furthermore, sampling proves to be time-efficient, which is crucial for meeting the strict deadlines associated with the research project.
The research uses probability samples which is most popular in the survey- based research strategy Because the research questions is concerned with online customer
This research utilizes multi-regression analysis to test hypotheses, following Green's (1991) guideline of the 50+8k rule for determining sample size In this formula, 'k' represents the number of model predictors, which in this study includes nine factors: e-retailer reputation, privacy concerns, alternative attractiveness, switching cost, customer satisfaction, trust, commitment, customer loyalty, and customer retention Consequently, the minimum sample size required is calculated as 50 + (9 x 8), totaling 122 participants Anticipating a response rate of 10%, the study plans to distribute 1,220 questionnaires to achieve the desired sample size.
The key to effective research lies in ensuring population representativeness and reliability of the sample size In this study, a sample of 1,000 online shoppers in Vietnam, primarily university students from the National Economics University in Hanoi and working professionals, was analyzed The anticipated response rate is between 10% and 15% To validate the sample's representativeness, a multi-regression analysis will be conducted, followed by additional statistical checks using Cook’s distance, Mahalanobis distances, and DFBeta to identify any potential biases Ultimately, these analyses will determine whether the findings can be generalized to the entire population.
To accommodate time and resource constraints, a self-administered questionnaire was selected for a sample size of 1,000 participants To achieve the anticipated response rate of 10% to 15%, the research employed a delivery and collection method, where questionnaires were distributed to participants and collected upon completion.
The content of the questions focuses on customers‟ assessments expressed by
The survey assessed participants' levels of agreement, ranging from "extremely disagree" to "extremely agree," concerning their recent shopping experiences and online shopping behaviors Questions 1 to 14 focused on the factors influencing trust and commitment, while questions 15 to 28 examined customer loyalty and retention This comprehensive analysis aimed to uncover the interconnections among the key areas of trust, commitment, customer loyalty, and customer retention in the context of both in-store and online shopping.
The questionnaire aimed to collect insights from Vietnamese online shoppers to identify factors affecting their purchasing decisions and to examine the relationships among nine key variables: e-retailer reputation, privacy concerns, alternative attractiveness, switching costs, customer satisfaction, customer trust, customer commitment, customer loyalty, and customer retention Each variable is assessed through multiple questions derived from previous research, ensuring clarity and comprehensibility for participants.
The research utilized a questionnaire comprising 28 questions divided into two sections The first section, featuring 14 questions, explores customers' recent online shopping experiences and identifies factors influencing trust and commitment The second section also contains 14 questions, focusing on customers' perceptions of e-retailers concerning trust, commitment, loyalty, and retention This study aims to uncover the relationships among these variables and determine the key factors affecting customer loyalty and retention in the Vietnamese online shopping market, while also testing specific hypotheses related to these influences.
H1a E-retailer reputation positively affects customer trust
Questions 1-3 measure e-retailer reputation, questions 15-19 measure customer trust
H1b Privacy concerns negatively affect customer trust
Questions 4-6 measure privacy concern, questions 15-19 measure customer trust
H2a Alternative attractiveness negatively affects customer commitment
Questions 7-9 measure alternative attractiveness, questions 20-22 measure customer commitment
H2b Switching cost positively affects customer commitment
Questions 10-12 measure switching cost, Questions 20-22 measure customer commitment
H2c Customer satisfaction positively affects customer commitment
Questions 13,14 measure customer satisfaction, Questions 20-22 measure customer commitment
H3a Increasing customer trust leads to higher customer loyalty
Questions 15-19 measure customer trust, Questions 23-25 measure customer loyalty
H3b Increasing customer trust leads to higher customer retention
Questions 15-19 measure customer trust, Questions 26-28 measure customer retention
H4a Increasing customer commitment leads to higher customer loyalty
Questions 20-22 measure customer commitment, Questions 23-25 measure customer loyalty
H4b Increasing customer commitment leads to higher customer retention
Questions 20-22 measure customer commitment, Questions 26-28 measures customer retention
The questionnaire uses a Likert-scale of seven levels from Extremely Disagree (1) to Extremely Agree (7) to measure different variables in the model of the research and their relationships
This research investigates the connections between trust, commitment, loyalty, and customer retention in the context of e-commerce and online shopping By building on previous studies, the questions aim to uncover how these factors influence consumer behavior and contribute to a successful online retail environment.
Probability sampling techniques, such as simple random, systematic, and stratified random sampling, offer various methods for selecting participants, with random sampling ensuring that each individual in the population has an equal chance of being chosen However, accessing information about online shoppers poses challenges, making it difficult to identify the entire population for a study As a result, convenience sampling is often employed, where participants are selected based on their availability In this study, questionnaires were distributed to friends over eighteen years old in Vietnam who have at least one online shopping experience, with the initial screening question, "Have you ever shopped online?" ensuring that all participants had relevant experience.
The questionnaire underwent a thorough review by experts experienced in customer assessment surveys to eliminate ethical concerns and ensure clarity for participants Although the questions were adapted from existing research, conducting a pilot test was essential to validate the questionnaire's effectiveness for the target population Consequently, this pilot was evaluated by additional experts specializing in online shopping in Vietnam.
DATA ANALYSIS
Variables measured on a Likert scale are considered discrete variables, as they provide specific values within a defined range, such as a 5-point or 7-point scale This limitation can be a disadvantage, as the actual values that these variables can assume are restricted to certain integers, such as 1, 2, and so forth.
3, 4, 5, 6, or 7 and cannot be 3.15 or 5.18
As previously stated in the Literature Review chapter, and in relations to the questions in the questionnaire, the independent variables are determined as following table 3.1
(*): all questions displayed in the questionnaire are attached in the Appendix 3.3.3 Dependent Variables
The independent variables are listed and explained in the table 3.2
(*): all questions displayed in the questionnaires are attached in the Appendix
In this study, data from 219 printed paper responses were entered into SPSS for primary data storage Descriptive statistics, including means, minimums, and maximums, were calculated to assess normal distribution The analysis focused on exploring the relationships between variables to address the research questions regarding the impact of trust and commitment on customer loyalty and retention Various results were subsequently calculated to support these findings.
Based on the mentioned hypotheses, a multivariate regression model is then built, as stated in following equation:
5 LOYALTY = ò 1 ER + ò 2 PC + ò 3 AA + ò 4 SC + ò 5 CS
6 LOYALTY = ò 1 ER + ò 2 PC + ò 3 AA + ò 4 SC + ò 5 CS + ò 6 TR + ò 7 CO
7 RETENTION = ò 1 ER + ò 2 PC + ò 3 AA + ò 4 SC + ò 5 CS
8 RETENTION = ò 1 ER + ò 2 PC + ò 3 AA + ò 4 SC + ò 5 CS + ò 6 TR + ò 7 CO
Where ER: E-retailer reputation; PC: Privacy concerns; AA: Alternative attractiveness; SC: Switching costs; CS: Customer satisfaction; TR: Trust;
CO: Commitment; CL: Customer loyalty and CR: Customer retention.
FINDINGS AND CONCLUSION
DESCRIPTIVE STATISTICS
The initial phase of data analysis involves examining the distribution of the collected data through descriptive statistics, as summarized in Table 4.1 The variables exhibit scores ranging from 1 to 7, indicating no constraints on variability Their means hover around an average of 4.0, with a maximum of 4.4581 and a minimum of 3.1689 The standard deviation averages approximately 1.2, with values ranging from a maximum of 1.51073 to a minimum of 0.94741 Additionally, since all absolute values of Skewness and Kurtosis are below the thresholds of 3 and 5, respectively, it confirms that all variables are normally distributed.
Table 4.1 Descriptive Statistics of Scales
Scales N Min Max Mean Std Skewness Kurtosis
CORRELATIONS
Table 4.2 illustrates the correlation matrix among the variables, indicating that all relationships are within an acceptable range of zero to a maximum of 0.8 This suggests meaningful connections between the variables Notably, the positive correlation between e-retailer reputation and customer trust implies that a higher reputation leads to increased trust from customers Furthermore, as customer trust in e-retailers grows, customer loyalty also strengthens.
ER PC AA SC CS TR CO CL CR
* Correlation is significant at the 0.05 level (2-tailed)
** Correlation is significant at the 0.01 level (2-tailed).
HYPOTHESIS TESTING
This article analyzes the relationships among trust, commitment, customer loyalty, and customer retention, utilizing multi-regression analysis in SPSS Initially, it explores the correlation between trust and its determinants Subsequently, the focus shifts to the relationship between commitment and its associated variables The final sections detail both the direct and indirect effects of these factors on customer retention and loyalty.
This section explores how e-retailer reputation and privacy concerns influence customer trust, positing that a positive e-retailer reputation enhances customer trust (Hypothesis 1a), while heightened privacy concerns diminish it (Hypothesis 1b) The results of the regression analysis, detailed in Table 4.3, illustrate the relationships between these variables.
Table 4.3 The Determinants of Trust
95% Confidence Interval for B Collinearity Statistics
Durbin-Watson 1.807 a Dependent Variable: Trust (TR)
The findings support Hypothesis H1a, indicating that e-retailer reputation (ER) has a positive impact on customer trust (TR) Additionally, Hypothesis H1b is confirmed, as there is a significant negative correlation between privacy concern (PC) and customer trust Overall, both e-retailer reputation and privacy concern significantly influence customer trust.
TR at p= 000 & and 000 with coefficient B = 156 & (-.152) respectively
After conducting multiple regression analysis, it is crucial to perform regression diagnostics using Cook's distance, Mahalanobis distances, and DFBetas to identify any influential cases that may skew the results A standard guideline indicates that if the value of standard residuals exceeds 3.29, it raises concerns about the reliability of the model Additionally, if more than 1% of the sample exhibits standard residuals greater than 2.58, the model's error level becomes unacceptable Lastly, if over 5% of the sample has standard residuals exceeding 1.96, it suggests that the model fails to accurately represent the entire population.
Table 4.4 indicates that 11 cases (5%) fall within ±2, two cases (0.91%) within ±2.5, and none within ±3, highlighting the necessity for regression diagnostics The analysis includes Cook's distance, Mahalanobis distances, and DFBetas Notably, the Cook's distance values for the seven cases are significantly below 1, suggesting that these cases do not exert undue influence on the regression model Additionally, all cases exhibit Mahalanobis distance values below the established threshold.
15 and the absolute values of all DFBetas are far below the threshold of 2 indicating that no case influences the regression parameters
Another important thing need to consider after running multiple-regression is to ensure the result from the sample can be generalized to the whole population
The Durbin-Watson value in the regression analysis is 1.807, which falls within the acceptable range of 1 to 3, indicating that the assumption of independent residuals is satisfied and there are no concerns regarding this sample.
The results presented in Table 4.3 indicate that all VIF values are significantly below the threshold of 10, and all tolerance statistics exceed the minimum requirement of 0.2 This suggests that multicollinearity does not adversely affect the regression model.
The regression estimates in Table 4.3 are likely representative of the true population values, as indicated by the significant coefficients' tight confidence intervals that do not cross zero This suggests that the model's findings can be reliably generalized to the entire population, providing a robust representation of the population's characteristics.
This section explores the relationship between commitment and its determinants, highlighting three key hypotheses: Hypothesis 2a suggests that alternative attractiveness negatively impacts customer commitment, while Hypothesis 2b indicates that switching costs have a positive effect on customer commitment Additionally, Hypothesis 2c posits that customer satisfaction positively influences customer commitment The findings from the regression analysis of these relationships are detailed in Table 4.5.
Table 4.5 The Determinants of Commitment
Upper Bound Tolerance VIF (Constant) 1.098 2.091 038 063 2.134
Durbin-Watson 1.461 a Dependent Variable: Commitment (CO)
The output in Table 4.5 shows that there is a significant relationship between
The study reveals that alternative attractiveness (AA) negatively impacts customer commitment (CO), with a significant p-value of 020 and a coefficient of B = (-.180), supporting hypothesis H2a Additionally, hypotheses H2b and H2c are confirmed, indicating that both switching costs (SC) and customer satisfaction (CS) positively influence customer commitment, as evidenced by their significant correlations with CO (p-values of 000 for both) Consequently, increased levels of alternative attractiveness, switching costs, and customer satisfaction lead to higher customer commitment.
In Table 4.6, there are 10 cases (4.56%) within ±2, 4 cases (1.8%) within ±2.5, and no cases within ±3, indicating the necessity for regression diagnostics The Cook's distance values for these cases are all significantly below 1, suggesting that none exert undue influence on the regression analysis Additionally, all cases exhibit Mahalanobis distance values that remain below the established threshold, reinforcing the reliability of the regression results.
15 Finally, the absolute values of all DFBetas which are far below the threshold of 2 indicate that there are no case influences the regression parameters
The value of Durbin-Watson in the regression is 1.461 (see Table 4.5)
This value is in the range between 1 and 3, close to 2, so the assumption independent residuals are thus met or no cause for concern in this sample
The analysis indicates that all Variance Inflation Factor (VIF) values are significantly below the threshold of 10, while tolerance statistics exceed the minimum threshold of 0.2 Additionally, the significant coefficients exhibit narrow confidence intervals that do not include zero, suggesting that the regression estimates accurately reflect the true population values Therefore, this model can be confidently generalized to the entire population.
4.3.3 The determinants of Customer Loyalty
4.3.3.1 The direct determinants of Customer Loyalty
This section examines how customer trust and commitment impact customer loyalty, proposing that higher levels of trust (Hypothesis 3a) and increased commitment (Hypothesis 4a) both contribute to greater loyalty among customers.
3.7 shows the output of regression predicting the relationships between these variables Both hypotheses H3a and H4a are accepted because the relationships between trust (TR) and customer loyalty (CL) as well as between customer commitment (CO) and customer loyalty (CL) are significant (.002 and 000 < 05 respectively)
Table 4.7 The Direct Determinants of Customer Loyalty
Upper Bound Tolerance VIF (Constant) 1.196 3.797 000 575 1.817
Durbin-Watson 1.999 a Dependent Variable: Customer Loyalty (CL)
Table 4.8 reveals that 12 cases (5.47%) fall within ±2, while 6 cases (2.7%) are within ±2.5, and more than ±3, highlighting the necessity for regression diagnostics Cook's distance values for these cases are significantly below 1, indicating that none exert undue influence on the regression analysis Additionally, all cases exhibit Mahalanobis distance values lower than the critical threshold of 15 Lastly, the absolute values of all DFBetas are well below the threshold of 2, confirming that no individual case affects the regression parameters.
