SPSS 25 software is used for data analysis, involving descriptive statistics, Cronbach’s alpha, and exploratory factor analysis, followed by Multiple Linear Regression to assess how inde
Research Overview
The global fashion industry has experienced significant growth and influence over the decades, evolving from a focus solely on clothing and accessories to a major economic and cultural force Key factors driving this transformation include the rise of e-commerce, changing consumer interests, and technological advancements According to Statista, the global apparel market revenue rose from 2015 to 2020, reaching approximately $1.53 trillion in 2022, with projections exceeding $1.7 trillion by 2023 In Vietnam, fashion spending averages 13.9%, ranking just behind food and savings However, this rapid growth has negative consequences, as the fashion industry contributes 8-10% of global carbon emissions and nearly 20% of wastewater It consumes more energy than both aviation and transportation combined, promoting overconsumption and resource waste A McKinsey report highlights that most new fashion items are used only 7-10 times before being discarded, underscoring the industry's environmental impact.
Fashion companies can enhance brand awareness and customer loyalty by integrating Corporate Social Responsibility (CSR) into their practices As consumers increasingly prioritize social responsibility, businesses are encouraged to address social issues responsibly Implementing CSR is vital for fashion brands, encompassing the use of recycled materials, sustainable supply chain management, and ensuring fair working conditions A notable example is Patagonia, a leader in environmental sustainability, which exemplifies how commitment to social and environmental responsibility can foster customer engagement and loyalty.
The "Worn Wear" campaign by Patagonia promotes the repair and reuse of products instead of purchasing new ones, highlighting their commitment to environmental causes by allocating a portion of sales to sustainability and utilizing recycled materials Additionally, fashion companies bear social responsibility, engaging in charitable activities that foster community cohesion and promote diversity and inclusion In Vietnam, several fashion companies, including Vinatex, exemplify Corporate Social Responsibility (CSR) by focusing on sustainable production, environmental protection, and social welfare Vinatex has invested in wastewater treatment systems and initiatives aimed at enhancing the living standards of workers and local communities.
Consumers are increasingly socially conscious, seeking brands that reflect their values and engage in responsible business practices A Nielsen survey reveals that 73% of global consumers are willing to alter their consumption habits to lessen their environmental impact, highlighting the expectation for brands to commit to social and environmental responsibility alongside offering quality products The research topic, “The Influence of Implementing Corporate Social Responsibility (CSR) on Customer Loyalty in the Fashion Industry,” aims to explore how CSR practices affect customer loyalty within the fashion sector As preferences shift towards socially responsible brands, understanding the link between CSR and customer loyalty is essential for fashion companies This research will analyze successful CSR initiatives and their influence on customer loyalty, providing insights and strategies for businesses to enhance brand reputation and foster long-term customer relationships.
Research Objectives
In this report, we mainly analyze the power of factors affecting customer loyalty with businesses in the fashion sector The research objectives include:
- To identify aspects of CSR in the fashion industry, develop and test a scale of factors that influence customer loyalty.
Evaluating the influencing factors on the fashion industry requires an understanding of customer behavior and the elements that impact it This includes assessing how customer loyalty is affected when businesses engage in Corporate Social Responsibility (CSR) initiatives By measuring these factors, companies can better navigate the dynamics of consumer preferences and enhance their strategies for sustainable growth in the fashion sector.
To effectively attract customers and drive sales in the fashion industry, businesses must develop targeted marketing and corporate social responsibility (CSR) strategies By prioritizing CSR initiatives, companies can enhance their market share and position themselves as trend leaders in sustainable practices within the sector.
By addressing these research objectives, the study aims to contribute to the existing literature on CSR and its impact on customer loyalty in the fashion industry.
Research Scope
This study investigates Corporate Social Responsibility (CSR) within the Vietnamese fashion industry, specifically targeting consumers who have purchased clothing in Ho Chi Minh City An online survey conducted via Google Form will gather data from participants, running from June 15, 2023, to July 15, 2023.
Research Method
The research utilized a quantitative approach, gathering data through an online survey via Google Forms Additionally, the team incorporated secondary data sources, including relevant reports and media, to enhance the effectiveness of the research model and optimize results.
The research team will analyze collected samples using SPSS 25 software, following a three-step process: first, they will conduct descriptive statistics; second, they will test the reliability of the data using Cronbach’s alpha; and third, they will perform Exploratory Factor Analysis (EFA) To assess the influence of independent variables on the dependent variable in the research model, Multiple Linear Regression (MLR) will be employed Lastly, the Bootstrapping method will be utilized to evaluate the moderating effects within the framework.
LITERATURE REVIEW II 1.1 Definitions
CSR
Researchers widely agree that Corporate Social Responsibility (CSR) encompasses essential economic, legal, ethical, and philanthropic dimensions (Carroll, 1991; Lee et al., 2013; Lo, 2020; McWilliams & Siegel, 2001; Rashid, Khalid, & Rahman, 2015; Wang & Han, 2017) Notably, Carroll (1979), a leading figure in CSR scholarship, defined it as the expectations society holds regarding businesses in these four areas at any given time He also introduced the CSR pyramid, a theoretical framework that categorizes these responsibilities.
Corporate Social Responsibility (CSR) encompasses a company's voluntary commitment to address the social and environmental effects of its operations Since its inception in the 1950s, the concept of CSR has evolved significantly, now widely recognized as a company's role in society It emphasizes the importance of fulfilling moral obligations that enhance positive impacts while reducing negative consequences on the community and environment.
Corporate Social Responsibility (CSR) is defined as an organization's tailored actions and policies designed to improve stakeholder welfare by focusing on the triple bottom line: economic, social, and environmental performance The primary objective of CSR is to generate shared value for both the company and society by addressing pressing social and environmental issues through business initiatives.
Customer Loyalty
A widely accepted meaning of customer loyalty comes from Jacoby and Chesnut
Customer loyalty, as defined by Moisescu (2017), arises from psychological processes that influence consumer behavior towards specific brands over time, distinguishing them from alternative options in the market Researchers commonly adopt a dual perspective on customer loyalty, encompassing both behavioral and attitudinal dimensions (Cossio-Silva et al., 2016) This duality highlights that repeat purchasing alone does not suffice to identify true loyalty versus superficial loyalty (Dick & Basu, 1994; Moisescu, 2017) Genuine customer loyalty requires psychological commitment and a positive brand attitude, with attitudinal loyalty integrating cognitive, affective, and conative factors, while behavioral loyalty is characterized by consistent purchasing patterns Ultimately, attitudinal loyalty acts as a driving force behind repeat patronage (Cossio-Silva et al., 2016).
Brand Image
Brand image has been a significant topic in marketing since the 1950s, defined as the perceptions and associations consumers hold about a brand (Keller, 1993) It plays a crucial role in differentiating brands from competitors (Aaker, 1996; Kapferer, 1997) Companies that maintain a positive public image are likely to achieve a favorable market position, gain sustainable competitive advantages, and enhance their market share and performance (C w Park et al., 1986; Sondoh Jr et al., 2007) Consequently, fostering a positive brand image is a top priority for many organizations (Cho et al., 2015).
Literature review
The research model is developed based on the following research topics.
1.2.1 Min-Hsin Huang, Zhao-Hong Cheng, 1-Chun Chen (2017), “The importance of CSR in forming customer company identification and long-term loyalty”.
This study employs a latent growth curve model to explore the impact of customer perceptions of service quality and corporate social responsibility (CSR) on customer-company identification (CCI) over time A comparative analysis reveals the long-term effectiveness of service quality versus CSR in shaping CCI Data collected from 213 surveys in Taiwanese restaurants shows that while both service quality and CSR perceptions influence customer-company trust (CCT), prioritizing CSR investments over time enhances long-term relationship effectiveness The research highlights the importance of shifting focus from merely achieving high service quality to fostering consumer belief in a firm's commitment to CSR.
1.2.2 Chen-Ying Lee, Wei-Chen Chang, Hsin-Ching Lee, (2017), “An investigation of the effects of corporate social responsibility on corporate reputation and customer loyalty-Evidence from the Taiwan Non-life insurance industry”
This study employs a survey method to evaluate consumers' perceptions of CSR activities in the non-life insurance industry, focusing on their effects on corporate reputation and customer loyalty Data was gathered from 362 consumers who have purchased insurance in Taiwan The findings reveal that CSR activities significantly enhance corporate reputation, customer loyalty, and brand image Additionally, the research highlights the mediating role of brand image in the relationship between CSR, corporate reputation, and customer loyalty.
1.2.3 Ali Raza, Amer Saeed, Muhammad Khalid Iqbal, Umair Saeed, Imran
Sadiq and Naveed Ahmad Faraz (2020), “Linking corporate social responsibility to customer loyalty through co-creation and customer company identification: Exploring sequential mediation mechanism”.
The study utilized partial least square-based structural equation modeling (PLS-SEM) to examine the conceptual framework, focusing on how banks' corporate social responsibility (CSR) initiatives foster positive customer responses, including co-creation (CoCreat), customer-company identification (CCI), and customer loyalty (CL) A survey was conducted to gather relevant data for this analysis.
A study involving 280 banking customers in Pakistan reveals that Corporate Social Responsibility (CSR) is a complex construct that influences customer loyalty both directly and indirectly The findings indicate that CSR has a sequential partial effect on co-creation and Customer Citizenship Intention (CCI) Additionally, CCI significantly impacts both co-creation and customer loyalty This research proposes a model that encapsulates these relationships.
1.2.4 Abdulalem Mohammed, Basri Rashid (2018), “A conceptual model of corporate social responsibility dimensions, brand image, and customer satisfaction in Malaysian hotel industry”.
This study introduces a conceptual model that illustrates how the dimensions of Corporate Social Responsibility (CSR) are positively linked to customer satisfaction, with brand image serving as a mediating factor in this relationship.
Hypothesis development
Economic responsibilities compel companies to generate profits through effective economic activities, with key indicators of success including earnings per share maximization and maintaining a competitive advantage (Carroll, 1991) A robust economic foundation enables firms to invest in brand image development, which significantly impacts profitability (Akhtar, Xicang & Iqbal, 2017) By fulfilling their economic responsibilities, companies can enhance the effectiveness of their marketing programs, thereby improving brand image and allowing both the company and its stakeholders to benefit from increased profit margins (Erdem et al 2002; Bendixen et al 2004).
Hl: Economic CSR positively influences Brand image
The legal CSR is related to conducting a fair business and meeting or exceeding all applicable legal requirements at local, state, and federal levels (Carroll, 1991; Lee et al.,
Corporate Social Responsibility (CSR) activities significantly impact a company's goals and enhance its brand image (Beckwith & Lehman, 1975) Companies that align with government expectations and comply with legal standards are more likely to foster a positive brand perception (Koh et al., 2022) In today's socioeconomic landscape, demonstrating accountability to societal and legal commitments is crucial for improving brand image (Balmer, 2001; Ind, 1997) Furthermore, companies that maintain honesty, diversity, legal responsibility, and respectability garner greater societal attention, thereby boosting their brand image (Fan, 2005).
The use of fur and animal skin in luxury fashion negatively affects customer perception, raising concerns about animal welfare and ethical sourcing For instance, Timberland encountered backlash after Greenpeace revealed potential links to suppliers harming wildlife, highlighting the necessity for brands to comply with business regulations to maintain a positive image among consumers.
H3: Legal CSR positively influences Brand Image
Carroll and Shabana (2010) define the ethical dimension of a firm as its voluntary efforts to advance social goals that surpass mere legal obligations These ethical responsibilities encompass strategies aimed at environmental sustainability, the promotion of civil rights, and adherence to the moral norms and values recognized within society.
Businesses that generate profits from the public must uphold ethical standards to benefit society, as doing so can provide a competitive advantage This ethical approach not only enhances the firm's reputation but also leads to increased profitability.
A strong brand identity requires the integration of ethical responsibilities, as highlighted by Iglesias and Ind (2016), and should effectively communicate this commitment to customers (Balmer 2001; Ind 1997; Rindell et al 2011) Research by Zhang and Cui (2018) suggests that when fashion companies implement economic and ethical corporate social responsibility (CSR) initiatives, it positively influences customers' perceptions of the brand image Thus, we propose the following hypothesis.
H5: Ethical CSR positively influences Brand Image
Philanthropic responsibility involves enhancing societal quality of life by offering a company's resources, facilities, and employee time at no cost (Carroll, 1991; Wang & Han, 2017) It reflects how well a company's core values align with societal philanthropic expectations, including employee volunteering, educational sponsorship, and community engagement Engaging in philanthropy can foster brand loyalty, enhance recognition, and build a positive reputation, positioning the company as a socially responsible entity (Sanchez, 2000).
Corporate philanthropy serves as an effective marketing strategy for brand development and global expansion Governments often encourage businesses to donate through tax incentives aimed at enhancing public goods By engaging in social corporate social responsibility (CSR) initiatives, such as supporting charitable organizations and funding programs, companies can significantly enhance their brand image.
H7: Philanthropic CSR positively influences Brand Image
Customer-company identification, rooted in social identity theory, reflects consumers' psychological connections to a company This theory posits that individuals categorize themselves into social groups based on perceived similarities, leading to a defined identity Consequently, customer-company identification emerges as a distinct social categorization, fostering strong emotional bonds between customers and companies.
When customers identify with their target groups, they experience a sense of belonging, especially towards socially responsible and distinct organizations (Chu and Chen, 2019) In professional settings, customers are more inclined to support products and services from companies recognized for their social responsibility Research has shown a positive correlation between Corporate Social Responsibility (CSR) and customer connection A strong reputation for social responsibility can satisfy customers' self-definitional needs, enhancing their identification with the organization Additionally, CSR initiatives can reinforce customers' perceptions of an organization's values Therefore, we hypothesize that:
H2: Economic CSR positively influences Customer - Company Identification H4: Legal CSR positively influences Customer - Company Identification
H6: Ethical CSR positively influences Customer - Company Identification
H8: Philanthropic CSR positively influences Customer - Company Identification
A strong brand image fosters customer goodwill and trust, leading to repeat purchases and loyalty to the brand (Armstrong & Kotler, 2000; Lee et al., 2017) Previous studies have established a clear link between brand image and customer loyalty (Anwar et al., 2019; Hsieh et al., 2018; Tu et al., 2012) Additionally, empirical evidence suggests that a favorable brand or retail image significantly influences customer loyalty (Anwar et al., 2019; Hsieh et al., 2018; Tu et al., 2012) Thus, we propose the following hypothesis:
H9: Brand Image positively influences Customer Loyalty
Bhattacharya and Sen (2019) highlight that customer identification with a company leads to favorable responses, including positive in-role and extra-role behaviors This emotional and psychological connection fosters customer loyalty (Bhattacharya and Sen, 2003; Marin et al., 2009) and cultivates a stable, long-term relationship (Bhattacharya and Sen, 2003) Identified customers are more likely to exhibit repurchase intentions and loyalty (All Raza et al., 2020) Furthermore, since customers identify with the company as a whole, their loyalty can be maintained even amid minor changes in products or services (Bhattacharya and Sen, 2019).
2003) Previous research has also provided empirical evidence of a positive relationship between CC1 and customer loyalty (Haumann et al., 2014; Homburg et al., 2009). Therefore, we hypothesize that:
H10: Customer - Company Identification positively influences Customer Loyalty
Proposed research model
Based on all the hypotheses and evidence from various studies, the team has proposed the following research model for the topic:
HI: Economic CSR positively influences Brand Image
H2: Economic CSR positively influences Customer - Company Identification
H3: Legal CSR positively influences Brand Image
H4: Legal CSR positively influences Customer - Company Identification
LI5: Ethical CSR positively influences Brand Image
H6: Ethical CSR positively influences Customer - Company Identification
H7: Philanthropic CSR positively influences Brand Image
HR: Philanthropic CSR positively influences Customer - Company Identification H9: Brand Image positively influences CustomerLoyalty
LI 10: Customer - Company identification positively influences Customer Loyalty
RESEARCH METHODOLOGY
Research process
This study employs a quantitative research approach using non-experimental methods, primarily through online survey questionnaires to collect direct opinions from relevant subjects Additionally, complementary methods such as documentation research and statistical analysis are utilized The research process is structured around eight key steps outlined below.
Step 1: Identify and select the research subject:
- Identify a research topic that aligns with your goals and area of interest.
- Select a specific research subject, including its characteristics and scope.
Step 2: Overview of the research subject and model:
- Conduct a literature review and create an overview of the research subject,including key aspects related to the topic.
- Build a detailed research model, including factors, variables, and their relationships.
- Research and examine relevant studies, theories, and concepts related to the research topic.
- Establish a theoretical framework for the research by applying appropriate theories and models.
Step 4: Identify the research methods:
- Literature review on CSR in the fashion industry to understand key aspects and standards.
- Analyzing the relationship between CSR implementation, customer loyalty, and related variables.
- Consulting experts in fashion and CSR through interviews and sessions.
- Evaluating and adjusting key variables based on expert feedback.
- Analyzing qualitative survey data and synthesizing findings.
- Conducting a qualitative survey with fashion industry professionals and stakeholders.
- Evaluating and analyzing survey results to draw conclusions on CSR’s influence on customer loyalty.
Quantitative research was carried out to test scale and model through a small number ofsamples to then evaluate the necessary parameters Specific process as follows: -
A preliminary survey was conducted with a sample size of 385 participants The collected data was analyzed using SPSS, which facilitated the evaluation of descriptive statistics, the reliability coefficient (Cronbach's alpha), and exploratory factor analysis (EFA) to assess the model and refine the questionnaire.
After summarizing the evaluated results, we will consider the reliability and exploratory factor analysis, along with the observed variables and satisfactory scale, ensuring they are preserved and prepared for the official final study section.
The formal research will involve surveying individuals from two distinct groups, with a minimum sample size of 385 participants A key focus of this study is to employ multiple regression analysis to assess the diverse effects of Corporate Social Responsibility (CSR) on the fashion industry.
Step 5: Preliminary scales and questionnaire design:
- Develop appropriate scales and questionnaires to gather data from the research subject.
- Ensure the accuracy, completeness, and reliability of the questions and scales.
- Collect data from the research subject using the designed scales and questionnaires.
- Ensure adherence to the proper process and ensure the security and reliability of the collected data.
Step 7: Data processing and analysis:
- Process the collected data, including checking, filtering, and encoding the data.
- Use appropriate data analysis methods to understand, summarize, and analyze the results.
Step 8: Conclusion and managerial implications:
- Draw conclusions from the data analysis results and compare them with the initial research objectives.
Sample selection process
Yamane Taro (1967) outlines two scenarios for determining sample size: when the population is known and when it is unknown In our research, we focus on individuals residing and working in Ho Chi Minh City who are interested in the fashion industry Due to insufficient data on the population size, we will adopt the method applicable for cases where the population size is not predetermined.
Determine the sample size based on the following formula:
With: n: Sample size to be determined.
P: The estimated sample size ratio N is successful We choose p = 0.5 so that the product P( 1 - P) is the largest, this ensures safety for the sample n estimates.
Z: The standardized z-value associated with the level ofconfidence The study used a conventional 95% confidence conesponding to z = 1.96.
E: Acceptable tolerance level of error The error ratio is used as ± 0.05 (5%)
=> In conclusion, our group decided to choose the total number of participants as 385 participants.
Our group chose a sample using a non-probability sampling method, namely selecting a convenience sampling and germination development.
To achieve the target sample size of n = 385, the team utilized an indirect approach through the Internet, employing a questionnaire designed via Google Forms The group structured the survey in a specific manner to facilitate data collection.
We distribute a survey link through Facebook and Messenger, employing a non-probability sampling technique known as germination development Initially, the researcher randomly selects clue respondents from a student class list or personal connections After completing the online questionnaire, these respondents will then choose additional participants based on their own relationships.
Our group chooses an online survey in Google Forms.
Quantitative research
Quantitative research emphasizes the design of measurable observations for variables, utilizing statistical analysis grounded in mathematical principles to explore and explain relationships between these variables This approach aids in hypothesis testing and ensures the objectivity of the research model, though it comes with its own strengths and limitations.
Strengths of this method inelude:
- Replication: Repeating the study is achievable because of defined data collection techniques and realistic definitions of abstract notions.
- Direct comparisons of outcomes: The study can be repeated in other cultural situations, times or with different sets of volunteers Results can be compared statistically.
- Large samples: Data from large samples can be processed and evaluated utilizing reliable and consistent techniques through quantitative data analysis.
Hypothesis testing involves a systematic approach that requires a comprehensive analysis and reporting of study variables, predictions, data collection methods, and testing methodologies prior to drawing conclusions.
Despite the benefits of quantitative research, it is occasionally inadequate in discussing difficult study issues Its limitations include:
Superficial definitions can fail to capture the complexity of concepts, such as mood, which may be oversimplified to a mere numerical value in quantitative research, while qualitative research provides a more nuanced discussion.
- Narrow focus: Predetermined variables and measurement methodologies can mean that you ignore other pertinent observations.
- Structural bias: Despite established techniques, structural biases can nevertheless impair quantitative research Missing data, inaccurate measurements, or incorrect sample procedures are biases that can lead to wrong findings.
- Lack of context: Quantitative research often exploits artificial settings like laboratories or fails to address historical and cultural circumstances that may affect data gathering and results.
The Cronbach’s Alpha test evaluates the reliability of observed variables used to measure specific characteristics of a representative factor A high Cronbach's Alpha value signifies that these variables effectively reflect the factor's characteristics, indicating that the study has developed a reliable scale for the representative factor For accurate results, the Cronbach’s Alpha test must meet certain conditions.
(1) The Cronbach’s Alpha coefficient is greater than 0.6 (Hoang and Chu, 2008, P-24)
(2) The correlation coefficient with the total variable of the observed variables is greater than 0.3 (Nunnally, 1978)
Exploratory Factor Analysis (EFA) aims to condense interrelated observed variables into a smaller set of factors that effectively measure underlying concepts To begin, the reliability of the data is assessed using Cronbach’s Alpha scale, followed by an analysis of EFA factors through the Principal Component method with Varimax rotation Successful EFA requires adherence to specific conditions to ensure accurate results.
(1) KMO coefficient: to indicate the appropriateness of EFA, when 0.5 < KMO < 1.
If Bartlett's test indicates a significant correlation (p < 0.05) among the observed variables, it suggests that these variables are interrelated within the population (Hoang & Chu, 2005) Additionally, observed variables with a correlation coefficient of less than 0.3 with the total variable are excluded to ensure clear differentiation between factors.
In this study, a sample size of 340 is utilized, and the research team considers a factor loading greater than 0.5 for observed variables as optimal for ensuring convergence, as suggested by Hair et al (1998) However, the acceptability of factor loading may vary depending on the sample size.
(4) The scale is accepted when the total extracted variance > 50% (Hair et al., 1998; Gerbing & Anderson, 1988).
Multiple Linear Regression is utilized to examine hypotheses concerning the relationships among variables and to assess the influence of independent variables on the dependent variable The equation for multiple linear regression is structured as follows:
2 3.4 Evaluating the intermediate impact Bootstrapping
An intervening variable, often referred to as a mediator, plays a crucial role in transforming the relationship between cause and effect from independence to dependence Evaluating this variable involves assessing its true impact on the relationship and understanding how it influences the transition from independent to dependent variables.
Bootstrapping is a resampling method employed to estimate the indirect effect (a*b) within each resampled dataset derived from a sample dataset By repeating this process, a distribution of the product ab is generated, enabling the calculation of confidence intervals for the indirect effects (Preacher and Hayes, 2004).
Figure 2.2: Basic Intermediate Variable Model
Research indicates that Bootstrapping is the most effective technique for evaluating intermediate relationships (Williams & MacKinnon, 2008; Preacher & Hayes, 2008; Zhao, Lynch & Chen, 2010), leading to its widespread adoption in modern studies involving intermediate variables.
For an intermediate effect to exist between variables X and Y, the bootstrap product's confidence interval (a*b) must exclude the value 0, indicating a significant indirect effect from X to Y.
RESEARCH RESULTS
Sample Descriptive Statistics
This study targeted respondents aged 14 to 37 residing in Ho Chi Minh City with an interest in fashion Out of 420 questionnaires distributed, 392 were returned, yielding a response rate of 93.33% However, 7 questionnaires were deemed invalid due to uniform responses or significant omissions, resulting in 385 valid questionnaires for data analysis The data was encrypted, imported, cleaned, processed, and analyzed using SPSS 25.0 software.
Table 1: Age distribution of respondents
Descriptive statistics reveal that 57.7% of respondents are aged between 26 and 37, while those aged 14 to 25 make up 42.3% Recent research on social media usage by age highlights these trends.
In 2023, adults aged 27 to 42 are the primary users of social media in the US, while in Vietnam, this demographic represents the largest population segment, as highlighted in the Digital 2023: Vietnam report on datareportal This indicates that the most valuable insights for the study come from individuals aged 26 to 35, who are more engaged with social media, fashion trends, and corporate social responsibility (CSR) research than those aged 14 to 25 Nevertheless, the gap is minimal, as teenagers aged 14 to 25 also demonstrate significant internet usage and shopping activity.
Table 2: Gender distribution of respondents
The survey revealed a notable gender disparity among participants, with 135 males (35.1%) and 250 females (64.9%) out of 385 valid respondents This trend aligns with the common observation that women typically show greater interest in shopping, particularly within the fashion industry.
Table 3: Educational level distribution of respondents
Descriptive statistics reveal that a significant portion of respondents, over two-thirds, possess bachelor's degrees or higher, with 34.8% holding undergraduate degrees and 45.2% having postgraduate degrees In contrast, only 14.8% completed high school, and 5.2% attended secondary school The education level of participants correlates with their age group, indicating that higher education attainment aligns with the main surveyed age demographics This relationship underscores that education influences respondents' comprehension of Corporate Social Responsibility (CSR) and their concern for related issues, equipping them with the necessary knowledge to engage with these topics effectively.
Total 385 100.0 able lo receive information and analyze problems from scientific research, reports, newspapers,
Table 4: Income distribution of respondents
In terms of respondents' income, the income group from 5 to 10 million VND accounts for the largest percentage (41.5%) Besides, the proportion of income groups from
Income levels significantly influence fashion shopping behavior, with 10 to 15 million VND representing over a quarter of total respondents, followed by those earning less than 5 million VND (17.2%) and over 15 million VND (12.8%) Lower income levels correlate with reduced demand for fashion items, as individuals prioritize essential goods for daily living, leading to diminished interest in the fashion industry Notably, nearly half of the respondents belong to the upper middle-income bracket, suggesting their insights will provide valuable perspectives for this study.
Table 5: Purchase Frequency distribution of respondents
The purchase frequency of fashion items among respondents indicates that 41.3% buy these items 5 to 10 times per month, while 33.7% purchase less than 5 times monthly Additionally, 19.22% of respondents buy between 10 to 15 times, and only 5.71% purchase more than 15 times per month Notably, nearly 60% of respondents with lower middle income tend to shop less frequently, primarily reflected in the groups buying less than 5 times and 5 to 10 times per month.
Table 6: Frequency distribution ofrespondents'purchase suggestions
HOW THEY CHOOSE A BRAND FOR PURCHASING
Recommend by friends and relatives 263 22.85
Descriptive statistics reveal that factors motivating respondents to choose fashion brands are relatively balanced, with the most significant influence stemming from recommendations by friends and relatives at 22.85% Famous brands follow closely at 20.24%, while advertising, suggestions from social networks, and KOL/KOC influence account for 19.9%, 19.64%, and 17.38%, respectively This illustrates a shift in word-of-mouth marketing, which now extends beyond traditional methods to include online platforms, termed amplified word-of-mouth marketing According to SocialHeat, 78% of internet users are swayed by trusted advice, with 49% of consumers making purchasing decisions based on influencer recommendations, thereby reinforcing the reliability of these findings.
Table 7: Frequency distribution offactors affecting respondents' purchase intention
FACTORS AFFECTING FASHION PURCHASING DECISIONS
Based on the collected data, price and attractive design significantly influence purchase intentions, with approximately 19% of respondents (240 people) citing these factors Additionally, 16.47% (207 respondents) identified the number of purchases and positive reviews as key influences on their fashion buying decisions Fabric quality and trend adherence were noted by 15.27% and 13.84% of respondents, respectively The study indicates that consumers assess the reasonableness of a product's price and often compare it with competitors' pricing (Chen et al., 1994; Gauzente & Roy, 2012; Han & Ryu, 2009; Jayasingh & Eze, 2012; Kukar-Kinney et al., 2007; Palazon & Delgado, 2009; Watchravesringkan et al.).
In 2008, research indicated that consumers' interest in clothing positively influences their purchase intentions However, this relationship is influenced by the degree of price consciousness among consumers, particularly within Generation Y.
Despite a strong interest in a specific clothing brand, consumers may not intend to make a purchase, highlighting the significant role of price in influencing fashion buying intentions (Tat Huei Cham et al., 2017).
Thus, it can be seen that the size and structure of the research sample are consistent with the requirements of the research design and ensure representativeness of the population.
Scale reliability test - Cronbach's Alpha
The validation of the Cronbach's Alpha coefficient for all measurement variables confirmed that each observed variable exceeded the acceptable threshold of 0.6, indicating a highly reliable scale Additionally, the correlation coefficients, ranging from 0.3 and above, further demonstrated the robustness of the scale's construction, fulfilling the necessary reliability criteria.
Table 8: Cronbach's Alpha test results
Scale Mean if Item Deleted
Scale Variance if Item Deleted
1 ECONOMIC CSR (EcC): Cronbach’s Alpha = 0.810
2 LEGAL CSR (LC): Cronbach’s Alpha = 0.856
3 ETHICAL CSR (EtC): Cronbach’s Alpha = 0.881
4 PHILANTHROPIC CSR (PC): Cronbach’s Alpha = 0.874
5 BRAND IMAGE (BI): Cronbach’s Alpha = 0.830
6 CUSTOMER - COMPANY IDENTIFICATION (CCI): Cronbach’s Alpha = 0.913
7 CUSTOMER LOYALTY (CL): Cronbach’s Alpha = 0.836
No observed variables are removed and continue to be included in the ExploratoryFactor Analysis.
Exploratory factor analysis (EFA)
3.3.1 Exploratory Factor Analysis (EFA) for independent variables
The study employs Cronbach’s Alpha reliability test, retaining 28 observed variables across 7 factor groups To assess the compatibility of the survey sample, exploratory factor analysis (EFA) is utilized EFA uncovers conceptual research structures, measures the convergence of observed variables for each factor, eliminates unsatisfactory measurement variables, and ensures scale uniformity The analysis focuses on examining the relationships among observed variables to group them effectively and explain the underlying factors.
Table 9: Evaluation results of the independent factor measurement scale
Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.764
Bartlett's Test of Sphericity Approx Chi-Square 2400.975 df 78
The KMO value of 0.764 indicates that the data is suitable for factor analysis, as it falls within the acceptable range of 0.5 to 1 Additionally, Bartlett's Test of Sphericity shows a significance level of 0.000, which is below the 0.05 threshold, confirming that the observed variables are significantly correlated and meet the necessary conditions for effective factor analysis.
The fourth factor in the analysis has an eigenvalue of 1.865, indicating that the four factors extracted from the Exploratory Factor Analysis (EFA) provide the most comprehensive summary of the observed variables Additionally, the total variance extracted, with a cumulative percentage of 76.087%, significantly exceeds the 50% threshold, demonstrating that these four factors account for 76.087% of the data variation.
Table JO: The results ofEFA analysis
All variables exhibit a load factor exceeding 0.7, confirming the validity of the analysis Additionally, no observed variable within the two factor groups displays a load factor difference of less than 0.3 The exploratory factor analysis indicates that the scales demonstrate convergent validity and practical significance, warranting their inclusion in future testing.
3.3.2 Exploratory Factor Analysis (EFA) for mediating variable
Table ỉ Ỉ: Evaluation results of the mediatingfactor measurement scale
Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.863
Bartlett's Test of Sphericity Approx Chi-Square 2047.386 df 45
The KMO value of 0.863 indicates that the data is suitable for factor analysis, as it falls within the acceptable range of 0.5 to 1 Additionally, Bartlett’s Test of Sphericity yielded a significance coefficient of 0.000, which is below the 0.05 threshold, confirming the statistical significance of the test and the correlation among the observed variables within the factor.
The analysis reveals an eigenvalue of 2.633 for the second factor, indicating that the two factors extracted from the Exploratory Factor Analysis (EFA) effectively summarize the observed variables Additionally, the cumulative variance explained by these factors is 68.642%, significantly exceeding the 50% threshold, demonstrating that they account for a substantial portion of the data's variation.
Table Ỉ2: The results ofEFA analysis
All variables demonstrate a load factor exceeding 0.7, confirming the validity of the analysis, with no observed variable in either factor group showing a load factor difference of less than 0.3 The exploratory factor analysis indicates that the scales maintain convergent validity and possess practical significance, allowing for their inclusion in subsequent testing.
3.3.3 Exploratory Factor Analysis (EFA) for dependent variables
Table 13: Evaluation results of the dependent factor measurement scale
Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.849
Bartlett’s Test of Sphericity Approx Chi-Square 681.207 df 10
The KMO value of 0.849 indicates that the data is suitable for factor analysis, as it falls within the acceptable range of 0.5 to 1 Additionally, Bartlett’s Test of Sphericity shows a significance coefficient of 0.000, which is less than 0.05, confirming that the observed variables are correlated and meet the necessary conditions for effective factor analysis.
The eigenvalue of 3.034 indicates that the factor derived from the Exploratory Factor Analysis (EFA) effectively summarizes the observed variables, as it exceeds the threshold of 1 Additionally, the cumulative total variance extracted is 60.687%, which surpasses the 50% benchmark, demonstrating that this factor accounts for a significant portion of the data's variation.
Table 14: The results ofEFA analysis
All variables exhibit a load factor exceeding 0.7, confirming the analysis's validity, and there are no observed variables in the two factor groups with a load factor difference of less than 0.3 The exploratory factor analysis demonstrates that the scales maintain convergent validity and hold practical significance.
Multiple Linear Regression
The research model employs multiple linear regression to analyze the relationship and correlation between variables, utilizing the average values of observed variables associated with each factor in the regression analysis.
Before assessing the study model, it is essential to examine the correlation between the variables The correlation matrix analysis employs the Pearson Correlation coefficient (r) to evaluate the relationships between the dependent variable and each independent variable, as well as among the independent variables themselves.
A strong correlation between two independent variables may indicate they are essentially the same variable When the significance level (sig) is greater than 0.05, it suggests that the two variables are independent and unlikely to exhibit collinearity Conversely, if the significance level is less than 0.05 and the absolute value of the correlation coefficient exceeds 0.7, the likelihood of collinearity increases significantly To effectively avoid multicollinearity, it is crucial that the correlation coefficient (r) remains above 0.7.
With a model consisting of 4 independent variables, 2 intermediate variables and 1 dependent variable, the researcher decides to test the coiTelation 3 times.
Independent variables and the intermediate variable (BI)
Table Ỉ5: Examine the correlation between independent variables and intermediate variable (Bl)
EcC LC Etc PC BI
Independent variables and the intermediate variable (CCI)
EcC LC Etc PC CCI
Table Ỉ6: Examine the correlation between independent variables and intermediate variable (CCI)
Independent variables and the intermediate variable (CL)
Table 17: Examine the correlation between intermediate variables and the dependent variable (CL)
All Sig values, are less than 0.05 and the correlation coefficient between the variables is less than 0.7 Therefore, there is no multicollinearity between the variables, suitable for regression analysis.
Similar to the correlation test, the regression analysis will be divided into 3 parts: Independent variables affect the intermediate variable (BI)
Table 18: Regression analysis for independent variables and intermediate variables (BỈ)
EcC -> BI Positive Positive 0.297 0.000 Accepted
LC -> BI Positive Positive 0.140 0.004 Accepted
Etc -> BI Positive Positive 0.159 0.000 Accepted
PC -> BI Positive Positive 0.203 0.000 Accepted
The regression analysis reveals an Adjusted R Square coefficient of 0.538, indicating that the income dataset accounts for 53.8% of the variance in the dependent variable This suggests that the variables EcC, LC, Etc, and PC collectively explain a significant portion of the variability in the model.
BI The remaining 46.2% is explained by out-of-model variables and random error.
BI = 0.998 + 0.297 EcC + 0.203 PC + 0.159 Etc + 0.140 LC
The analysis reveals that all independent variables positively influence Brand Image (Bl), with regression coefficients indicating their respective impact: EcC at 0.297, PC at 0.203, Etc at 0.159, and LC at 0.140.
Independent variables affect the intermediate variable (CCI)
Table 19: Regression analysis for independent variables and intermediate variable (CCĨ)
EcC -> BI Positive Positive 284 0.000 Accepted
LC -> BI Positive Positive 180 0.000 Accepted
Etc -> BI Positive Positive 148 0.000 Accepted
PC -> BI Positive Positive 184 0.000 Accepted
The regression analysis indicates an Adjusted R Square coefficient of 0.571, signifying that the income dataset accounts for 57.1% of the variability in the dependent variable CCL This suggests that the variables EcC, LC, Etc, and PC collectively explain a significant portion of the model, while the remaining 42.9% is attributed to external variables and random errors.
CCI = 0.953 + 0.284 EcC + 0.184 PC + 0.180 LC + 0.148 Etc
The analysis reveals that all independent variables positively influence Customer-Company Identification (CCI), with regression coefficients indicating the order of impact as follows: EcC (0.284), PC (0.184), LC (0.180), and Etc (0.148).
Intermediate variables affect the dependent variable (CL) Research hypothesis Expected results
BI -> CL Positive Positive 0.300 0.000 Accepted
CCI -> Cl Positive Positive 0.294 0.000 Accepted
Table 20: Regression analysisfor intermediate variables and dependent variables (CL)
The regression results show that the Adjusted R Square coefficient is 0.478 This means that the income dataset fits 47.8% to the built regression model, in other words,the variables
BI, CC1 explain 47.8% of the variability of the variable dependent on CCI The remaining 52.2% is explained by out-of-model variables and random error.
Both intermediate variables have a positive impact on customer loyalty (CL).Although there is not too much difference between the two variables, BI is the factor that has a greater impact.
Evaluating the intermediate impact
Table 21: Evaluating the intermediate impact
According to the table’s findings, the bootstrapping confidence intervals do not encompass the value 0, thus indicating the acceptance of the intermediate effects.
Discussion of the research results
The research model was validated, confirming that both economic CSR and philanthropic CSR significantly influence a company's brand image and perception of corporate social responsibility While legal CSR and ethical CSR received lower rankings, they nonetheless captured consumer interest, showing no significant difference in their impact coefficients.
Businesses that thrive in economic activities, including job creation and fair wage distribution, significantly contribute to building a strong brand reputation By prioritizing sustainable economic growth and supporting local suppliers, these companies improve their visibility and foster positive community relationships.
Despite achieving only a second-place ranking, which fell short of the team's expectations, this outcome can be attributed to the observation that Vietnamese consumers tend to overlook companies that focus primarily on community-oriented activities.
Legal compliance in corporate social responsibility (CSR) is essential for Vietnamese consumers, significantly influencing their trust and purchasing decisions Adhering to legal regulations in business operations is as important as other key factors that affect customer loyalty and choice.
The ethical issue in the fashion industry in Vietnam has not had a significant impact on the brand image, customer loyalty, and purchasing intentions.
This study evaluates the mediation effects of corporate social responsibility (CSR) factors on consumer loyalty, highlighting their significant influence through brand image (BI) and customer-company identification (CCI) Our innovative approach distinctly illustrates the individual impact of each CSR factor on BI, CCI, and customer loyalty These insights equip businesses to develop more effective strategies for their social initiatives.
To improve brand image, corporate identity, and consumer loyalty in Vietnam's fashion industry, companies must develop targeted strategies for each CSR indicator, emphasizing the economic dimensions of their social initiatives.
This study thoroughly examines the complex relationship between Corporate Social Responsibility (CSR) and customer loyalty in the fashion industry The research highlights the critical role of CSR practices in shaping brand perception and Consumer Company Identification (CCI), ultimately fostering customer loyalty Analyzing a diverse group of participants across various age groups, genders, educational backgrounds, and income levels reveals their attitudes towards CSR in fashion Notably, the impressive response rate of 93.33% indicates a strong public interest in the intersection of CSR and the fashion sector.
A survey conducted in Ho Chi Minh City from August 15 to August 20, 2023, gathered quantitative data through questionnaires, with a sample size of 385 respondents, including students, employees, and high school students aged 14 to 37 The research model's scales were evaluated using Cronbach's Alpha reliability, Exploratory Factor Analysis (EFA), and Multiple Linear Regression analysis.
The survey revealed notable gender differences among participants, with 250 females (64.9%) and 135 males (35.1%) Additionally, there is a significant age distribution, as 222 respondents belong to Generation Y (ages 26 to 37), making up 57.7% of the total.
163 respondents from Generation z (ages 14 to 25), amounting to 42.3%.
The reliability of the measuring scales was evaluated using the Cronbach's Alpha coefficient, with results ranging from 0.810 to 0.913, all exceeding the acceptable threshold of 0.7 Additionally, the overall variable's correlation coefficient surpassed 0.3, confirming the scales' robust construction and reliability As a result, all observed variables were retained for further analysis without any eliminations.
The Exploratory Factor Analysis (EFA) demonstrated that all observed variables met the necessary criteria, with KMO values of 0.764 for independent variables, 0.863 for intermediate variables, and 0.849 for dependent variables, all significant at p < 0.001 The analysis revealed eigenvalues exceeding 1 and load factors above 0.7, indicating strong factor structures that account for 50-76% of the data variance This confirms the reliability and practical relevance of the factors for further testing, leading to the retention of the independent factors without modifications.
The research model was validated through multiple linear regression analysis, incorporating 4 independent variables, 2 intermediate variables, and 1 dependent variable, with three correlation tests conducted The consistently low Sig values, below 0.05, and correlation coefficients under 0.7 indicate no multicollinearity among the variables, making them suitable for regression analysis Similar to the correlation test, the regression analysis was divided into three segments Ultimately, all proposed hypotheses were validated, confirming the effectiveness of the research model.
The regression analysis reveals an Adjusted R Square coefficient of 0.478, indicating that the income dataset accounts for 47.8% of the variability in the dependent variable CCI This suggests that the independent variables BI and CCI explain nearly half of the variations observed, while the remaining 52.2% is attributed to external factors and random errors.
Both intermediate variables have a positive impact on customer loyally (CL). Although there is not too much difference between the two variables, BI is the factor that has a greater impact.
The intermediate analysis results indicate that the impacts are uniformly accepted and there is not a significant difference between each CSR factor in influencing loyalty through 2 intermediate factors.
Based on the findings of our research, we offer insightful recommendations for both scientific advancements and effective management strategies in the subsequent section.
Research implication
Our study reveals key managerial insights, emphasizing that managers should prioritize economic and philanthropic corporate social responsibility (CSR) over legal and ethical CSR.
Businesses can meet their economic responsibilities by launching charitable programs, selling products to benefit the underprivileged, and participating in initiatives that improve society and the environment For instance, Nike is committing $125 million over the next five years to support small businesses that aim to "level the playing field" and tackle racial inequalities.
Secondly, the fashion firm should employ a wide range of communication techniques to further promote their philanthropic CSR campaigns The campaign, like
Employee Giving Programs not only enhance community support but also foster a sense of responsibility among employees For instance, Gap Inc.'s “Pledge 1%” initiative encourages staff to contribute 1% of their time, talent, or resources to charities they care about Similarly, H&M collaborates with UNICEF to provide essential education and healthcare to underprivileged children These philanthropic efforts enable companies to positively impact society In Vietnam, children in highland regions, often affected by natural disasters, face significant hardships, including a lack of clothing during winter Fashion companies can play a vital role by organizing clothing donation programs, encouraging customers to donate warm clothes to support these vulnerable children.
Customer loyalty in the fashion industry is enhanced when companies are seen as meeting their legal obligations To foster this loyalty, businesses must ensure they pay taxes, adhere to labor laws, and comply with environmental regulations.
Although ethical CSR has less impact on customer loyalty than other factors, the analysis shows that loyalty still increases when businesses implement ethical responsibility.
To enhance their corporate social responsibility (CSR), businesses must prioritize fair treatment of employees, responsible sourcing of materials, and waste reduction A prime example of ethical CSR in action is Patagonia, a fashion company dedicated to minimizing its environmental footprint Patagonia's commitment includes using only organic cotton in its products and launching a recycling program for used clothing, showcasing its initiatives aimed at sustainability.
The analysis indicates that managers must prioritize the mediating effects of brand image and customer-company identification (CCT) to enhance customer loyalty through corporate social responsibility (CSR) initiatives These elements serve as crucial mediators and catalysts, emphasizing the significance of CSR activities within organizations.
Corporate Social Responsibility (CSR) is an emerging and captivating concept, particularly within the fashion industry, which has seen limited research focus This study provides valuable insights for managers by highlighting the significant influence of CSR on consumer loyalty among Vietnamese shoppers, an area that has been largely unexplored in existing literature.
Research limitations and further research directions
Despite efforts in carrying out the research, due to limited professional knowledge and limited research capacity, it is inevitable that the research paper will have certain shortcomings:
The study's findings reveal a significant bias towards female participants, which, while common in fashion shopping research (Koca & Koc, 2016), limits the objectivity and representativeness of the results Additionally, all respondents were aged between 14 and 37, raising the question of whether older consumers aged 40 and above might exhibit different decision-making styles.
The study's reliance on surveys raises concerns about mono-method bias, as data collection may be incomplete due to constraints like time, budget, and participant numbers The absence of face-to-face interviews and focus groups could result in some respondents being disengaged, leading to inaccuracies and an incomplete representation of the data sample Additionally, subjectivity in data evaluation and processing poses a risk, as differing approaches among individuals can introduce errors, ultimately affecting the reliability of the research findings.
This review focused exclusively on English-language academic papers from two databases, concentrating on corporate social responsibility (CSR), sustainability, and fashion This narrow selection criteria may have resulted in the exclusion of relevant studies not written in English Consequently, the study faced limitations due to its specific focus on areas such as social responsibility, environmental and economic sustainability, stakeholder relationships, regulations, ethics, strategies, consumer behavior, technology, marketing, and supply chain management.
The research paper utilizes a quantitative real-time survey method to examine the impact of Corporate Social Responsibility (CSR) on customer loyalty within the fashion industry However, this approach assumes that individuals' behaviors and attitudes remain constant across different contexts, which limits the ability to establish a long-term causal relationship due to the cross-sectional nature of the study Additionally, user perceptions of the research elements may evolve over time as they acquire new knowledge and experiences.
Finally, limitations remain in their social responsibility activities The authors' group encountered shortcomings in proposing solutions related to the process of implementing social responsibility for businesses.
The potential limitations above create opportunities for future research:
Future research should focus on expanding the user sample by specifically examining male participants in online contexts Additionally, increasing the sample size and incorporating diverse geographical areas and age groups will improve accuracy and generalizability It is also essential to segment single consumers more effectively, as not all single adults behave similarly In summary, future studies should prioritize diverse consumer groups and utilize selective sampling methods to ensure a highly representative sample.
Future research should focus on enhancing the understanding of how Corporate Social Responsibility (CSR) influences consumer loyalty by utilizing qualitative methods such as group discussions and in-depth interviews Additionally, it is essential to incorporate various factors and models to study the adoption of CSR more comprehensively.
It would be better to invest in applying some other modern analytical methods to the data analysis process to achieve higher efficiency.
The identified limitations offer valuable insights for future research, establishing a comprehensive foundation of primary information This groundwork is essential for developing a theoretical framework that explores the intricate relationships between Corporate Social Responsibility (CSR), sustainability, and the fashion industry.
Future research should consider utilizing longitudinal data to investigate the effects on the performance variable identified in our study, which necessitates a sustained investment in tracking changes over time.
Future research should focus on developing actionable solutions to encourage companies in the fashion industry to engage in social responsibility activities This involves creating a long-term social responsibility strategy aligned with each company's development goals Companies should implement these activities annually and regularly review and assess their effectiveness Additionally, when a company's development direction shifts, it is essential to adjust its long-term social responsibility strategy accordingly.
Future research should examine the proposed relationship model with various composite loyalty measurements to determine if the variable strengths and directions align with our findings Additionally, it would be beneficial to investigate the mediating effects of constructs such as trust, satisfaction, and commitment.
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