Social Media Influence, Brand Reputation, and Electronic Word-of-Mouth Marketing: A Structural Equation Model on Consumer Purchase Decision Making on Apparel Businesses

Social Media Influence, Brand Reputation, and Electronic Word-of-Mouth Marketing: A Structural Equation Model on Consumer Purchase Decision Making on Apparel Businesses

Ecel J. Juaton; Angelie Enaga; John Mark B. Lazaro; Helaria B. Carmona

Student, Santo Tomas College of Agriculture Sciences and Technology,

DOI: https://dx.doi.org/10.47772/IJRISS.2025.905000121

Received: 30 April 2025; Accepted: 02 May 2025; Published: 03 June 2025

ABSTRACT

In the digital age, consumer purchasing decisions are significantly influenced by social media, brand reputation, and electronic word-of-mouth marketing, with consumers increasingly relying on online platforms and peer reviews for their choices. This study aimed to develop a causal model examining the influence of social media, brand reputation, and electronic word-of-mouth marketing on purchasing decisions and to determine the best-fit model for understanding purchasing decisions in the apparel industry. The researchers utilized a quantitative, non-experimental design and applied Structural Equation Modeling (SEM) to analyze data from 200 consumers in Santo Tomas. Respondents were chosen using convenience sampling techniques. Various data analysis methods were utilized, including the mean, the Pearson Product-Moment Correlation Coefficient, Multiple Regression Analysis, and Structural Equation Modeling (SEM), based on responses gathered from four adapted questionnaires. The findings indicated that the exogenous variables, social media influence, brand reputation, and electronic word-of-mouth marketing, had very high levels of impact on the endogenous variable of consumer purchase decision-making and were consistently evident in the apparel sector. These factors were strongly linked to consumer purchasing decisions, with social media influence being the most significant. The analysis shows that social media and brand reputation were key in predicting purchases, making them the best-fit model. To improve consumer decisions, strategies may leverage social media marketing and build a strong brand reputation through customer orientation and a commitment to social and environmental responsibility. Future researchers may conduct studies investigating factors that might influence consumer choices, such as impulsive buying tendencies and psychological attitudes.

Keywords: Social Media Influence, Brand Reputation, Electronic Word-Of-Mouth Marketing, Consumer Purchase Decision-Making, Structural Equation Modeling, Philippines

INTRODUCTION

Consumer purchase decision-making is a complex task that requires evaluating and comparing different options to make an informed choice (Azis et al., 2024). However, there are problems that consumers face when making purchasing decisions in deciding to buy something, consumers consider various risks, including financial loss, product malfunction, physical harm, social judgment, and personal guilt. These risks influence their decision-making process (Trout, 2024). Additionally, consumer trust is a key factor influencing purchasing decisions. Mistrust in a vendor’s reliability or product quality can discourage purchases (Patro, 2023).  Bilovodska and Poretskova (2023) also indicate that security concerns, especially those related to privacy and financial risks, can significantly influence consumers’ willingness to make purchases.

In Vietnam, consumers have increasingly embraced online shopping, leading consumers to increasingly shift away from traditional retail. Promotions and price perception are key factors driving online shopping behavior and a positive perception of price can lead to increased purchase decisions and customer loyalty (Cuong, 2023). Moreover, in China, personalized products and platform loyalty are key factors influencing consumer purchase decisions. Although price, promotions, the live streamer’s image, and interaction had little effect on purchases, the streamer’s image significantly influenced platform loyalty, affecting sales (Wongsunopparat, 2021). Further, Peña-García et al. (2020) found that impulsive buying significantly influences online shopping decisions in countries like Colombia, where consumers often prioritize short-term thinking. This suggests that despite the growing popularity of online shopping, there are still significant challenges to overcome for its widespread adoption.

Despite the growing popularity of online shopping, Filipinos remain cautious and discerning in their product choices. Filipinos often desire to physically inspect products before purchase, but online shopping restrictions make this difficult, leading them to rely heavily on product information, reviews, and feedback (Dulay et al., 2022). Additionally, concerns about the safety of online transactions persist due to widespread reports of scams by fake online sellers who take money without delivering goods (Atienza & Tabuena, 2021). Furthermore, Manapul et al. (2022) believed that the perceived cost of shipping can also significantly influence online purchasing decisions. Consumers often balance shipping fees against their overall satisfaction, and excessive charges may discourage purchases.

Although there are existing studies that link consumer purchase decision-making to different factors, numerous inquiries have been conducted to advance our understanding of consumer purchase that creates a causal model on consumer purchase decision-making through social media influence, brand reputation, and electronic word-of-mouth marketing within local settings. Hence, the researcher finds the urgency to conduct this study to fill the gap in the literature covering these subjects, especially in the local context. Therefore, this study aims to investigate the complex interplay among brand reputation, social media influence, and electronic word-of-mouth marketing on consumer purchase decisions and determine the best model fit for consumer purchase decision-making in terms of social media influence, brand reputation, and electronic word-of-mouth marketing within apparel businesses. By exploring how online reviews and social media impact consumer perceptions and decisions, this study helps apparel businesses improve their brand reputation and competitiveness by creating effective marketing strategies. The study uses a structural equation model to analyze these relationships and offers valuable insights for apparel business owners looking to grow using digital tools.

Statement of the Problem

This study generates a causal model about consumer purchase decision-making through social media influence, brand reputation, and electronic word-of-mouth marketing. Specifically, this finds answers to the following research questions:

1. What is the level of social media influence in terms of:

1.1 customer relations;

1.2 advertising; and

1.3 marketing?

2.What is the level of brand reputation in terms of:

2.1 customer orientation;

2.2 product quality;

2.3 good employer;

2.4 emotional appeal; and

2.5 environmental and social responsibility?

3. What is the level of electronic word-of-mouth marketing in terms of:

3.1 trust;

3.2 quality;

3.3 quantity;

3.4 consumer expertise; and

3.5 adoption?

4. What is the level of consumer purchase decision-making in terms of:

4.1 price consciousness;

4.2 brand consciousness;

4.3 quality consciousness;

4.4 fashion consciousness;

4.5 confused by over choice; and

4.6 consumer decision-making?

5. Is there a significant interrelationship between:

5.1 social media influence and consumer purchase decision-making;

5.2 brand reputation and consumer purchase decision-making; and

5.3 electronic word-of-mouth marketing and consumer purchase decision-making?

6. Which among the exogenous variables’ best influences consumer purchase decision-making?
7. What model best fits a consumer purchase decision-making?

Hypothesis

The following null hypotheses were tested at 0.05 level of significance:

1. There is no significant relationship between:

1.1 social media influence and consumer purchase decision-making;

1.2 brand reputation and consumer purchase decision-making; and

1.3 electronic word-of-mouth marketing and consumer purchase decision-making.

2. There is no exogenous variable that best influences the consumer purchase decision-making.
3. There is no model that best fits consumer purchase decision-making.

Theoretical Framework

This study is grounded in the Theory of Planned Behavior (TPB), proposed by Ajzen (1991). The Theory of Planned Behavior states that a person’s actions are driven by intentions, which are influenced by attitudes, social norms, and perceived control. Social media, brand reputation, and electronic word-of-mouth can impact these factors, ultimately shaping consumer purchasing decisions.  Moreover, Azad et al. (2023) emphasized that in the Theory of Planned Behavior, social media significantly influences consumer behavior by shaping opinions and social norms in which social media platforms foster consumer interaction and brand visibility, leading to increased purchase intent.

The study by Kustiawan and Pandansari (2023) illustrates that the Theory of Planned Behavior shows how the social and economic benefits of brands can significantly influence consumer behavior. It emphasizes the role of social media in enhancing brand reputation and driving sales. Kurniawati (2023) also states that the Theory of Planned Behavior (TPB) highlights the significant impact of electronic word-of-mouth (eWOM) on consumer intentions. It emphasizes that online opinions and experiences play a crucial role in shaping attitudes and behaviors, which underscores the importance of maintaining a positive brand reputation.

Conceptual Framework

Presented in Figure one is the hypothesized model which is composed of two latent contracts, the endogenous and exogenous variables. Consumer purchase decision-making is the endogenous variable while social media influence, brand reputation, and electronic word-of-mouth marketing are the exogenous variables. Latent variables are constructs that cannot be directly observed or measured. Consequently, researchers use regression models to link these latent variables to observable data, helping to clarify their relationships and influences.

Social media influence is the first exogenous variable. This variable is composed of three (3) indicators which are customer relations, advertising, and marketing. Brand reputation is the second exogenous variable. This latent variable construct includes five (5) indicators: customer orientation, product quality, good employer, emotional appeal, and environmental and social responsibility. The third exogenous variable is electronic word-of-mouth marketing. This variable construct consists of five (5) indicators which are trust, quality, quantity, consumer expertise, and adoption. Lastly, the endogenous latent variable represents consumer purchase decision-making, comprising six (6) indicators: price consciousness, brand consciousness, quality consciousness, fashion consciousness, confusion from over-choice, and consumer decision-making.

METHODOLOGY

This chapter outlines the research design, subject, instrument, and data collection methods used to explore the relationship between brand reputation, social media influence, and electronic word-of-mouth marketing on consumer purchasing decisions. It also aims to identify the best-fit model for understanding consumer decision-making.

Research Design

The researchers employed a quantitative non-experimental research design. The structural equation model (SEM) was utilized to create a model that best fits the endogenous variable. The process had two distinct stages. The initial stage used descriptive-causal research methodology to assess the varying interrelationships between variables. According to Holmes et al. (2024), descriptive-causal research helps researchers understand complex social issues by combining detailed descriptions with explanations of cause-and-effect relationships. It bridged the gap between simply observing phenomena and understanding why they happened.

Additionally, this study employed the structural equation modeling (SEM) method. Structural Equation Modeling (SEM) is an advanced statistical method examining the intricate relationships between observed and unobserved variables. Through SEM, it allowed researchers to examine both direct and indirect connections within a single model by integrating path analysis and confirmatory factor analysis (Jongerling et al., 2024). These methods were utilized to create a best-fit model on the consumer purchase decision-making in research viewing social media influence, brand reputation, and electronic word-of-mouth marketing as exogenous variables.

Research Subject      

The participants of this study were the customers who purchased from the apparel stores in the Municipality of Santo Tomas. Ardikoesoema (2024) suggests the number of respondents for SEM in the order of 200, which often appears in SEM research, in order to achieve sufficient statistical power and produce credible results.  Based on the general rules regarding sample size calculation when using SEM and the quantitative concept of favoring larger sample sizes, the researchers obtained a sample of around 200.

Moreover, convenience sampling was used in this study, a method where researchers select participants who are easily accessible. While this approach may not perfectly represent the entire population of consumers (Andrade, 2021), the researchers actively sought a diverse and representative sample by randomly selecting participants from a pool of readily available individuals. This strategy helped ensure that the study captured a range of perspectives while working to minimize potential biases.

Research Instrument           

The researchers utilized four (4) modified and adapted survey questionnaires from internationally disseminated journal articles were used by the researchers to explore the interrelationship between social media influence, brand reputation, and electronic word-of-mouth marketing towards the consumer purchase decisions of clothing retailing establishments. Quantitative research often uses questionnaires to collect numerical data from people. This data can then be analyzed using statistics to identify trends and connections (Humble, 2020).  The social media influence instrument is adapted from The Influence of Social Media on Business: A Case Study in North Cyprus by Amjad Abu Al Rub (2016). The survey questionnaire used in this study consisted of 15 items, which were categorized into three indicators, namely customer relations with five (5) items, advertising with five (5) items, and marketing with five (5) items. This questionnaire used a 5-point scale to measure data, ranging from 5 (Strongly agree), 4 (Agree), 3 (Neutral), 2 (Disagree), and 1 (Strongly disagree).

Adapted from The Effect of Firm’s Brand Reputation on Customer Loyalty and Customer Word of Mouth: The Mediating Role of Customer Satisfaction and Customer Trust by Mohamed Ali Barakat Ali (2022). A 23-item survey questionnaire was used, encompassing five indicators: customer orientation (10 items), product quality (3 items), good employer (5 items), emotional appeal (3 items), and environmental and social responsibility (2 items). This questionnaire used a 5-point scale to measure data, ranging from 5 (Strongly agree), 4 (Agree), 3 (Neutral), 2 (Disagree), and 1 (Strongly disagree).

The instrument for electronic word-of-mouth marketing is adapted from The Effects of Electronic Word of Mouth (EWOM) on the Adoption of Consumer EWOM Information by Phuong Viet Le-Hoang (2020). This instrument contained a 26-item survey questionnaire comprising four indicators namely: trust with seven (7) items, quality with six (6) items, quantity with three (3) items, consumer expertise with seven (7) items, and adoption with three (3) items. This questionnaire used a 5-point scale to measure data, ranging from 5 (Strongly agree), 4 (Agree), 3 (Neutral), 2 (Disagree), and 1 (Strongly disagree).

The instrument for consumer purchase decision-making is adapted from Executing Marketing through a gender lens: A Consumer Purchase Decision-making Study in an Emerging Economy by Ahsan Siraj and Yongming Zhu (2024). The survey questionnaire used in this study consisted of 21 items, which were categorized into seven indicators namely: price consciousness with three (3) items, brand consciousness with five (5) items, quality consciousness with three (3) items, fashion consciousness with three (3) items, confused by over-choice with four (4) items, and consumer decision-making with three (3) items. This questionnaire used a 5-point scale to measure data, ranging from 5 (Strongly agree), 4 (Agree), 3 (Neutral), 2 (Disagree), and 1 (Strongly disagree).

Statistical Treatment of Data          

The study utilized the following statistical tools for data computation and hypothesis testing at a 0.05 alpha level.

Mean. The mean is the most common method for measuring central tendency. The average is calculated by dividing the total sum of values in a dataset by the number of values. The mean is also known as the average (Triola, 2020). This was used to determine the level of social media influence, brand reputation, electronic word-of-mouth marketing, and consumer purchase decision-making.

Pearson Product-Moment Correlation Coefficient. The Pearson Product-Moment Correlation Coefficient is a statistical measure that quantifies the linear association between two variables. It is a widely used tool in research for analyzing and interpreting data (Gnambs, 2023). This was utilized to determine the interrelationship between social media, brand reputation, electronic word-of-mouth marketing, and consumer purchase decision-making.

Multiple Regression Analysis. Multiple regression analysis is a statistical technique that explores the connection between several independent variables and a single dependent variable. Unlike simple linear regression, it allows researchers to assess the combined impact of various predictors, providing a more comprehensive understanding of real-world data (Ruan, 2024). This was used to determine the significant influence of social media, brand reputation, electronic word-of-mouth marketing, and consumer purchase decision-making.

Structural Equation Modeling (SEM). Structural Equation Modeling (SEM) is a statistical technique employed to analyze the relationships among both variables that are directly measured (observed) and those that are not (unobserved). It involves case studies, explicitly defining latent variables, and assessing model fit (Dong et al., 2020).  To explore the model’s fit, particularly in comparison to an alternative model, Analysis of Moment Structures (AMOS) was employed. Identifying the best-fit model also required the index values to align with predefined criteria.

RESULTS AND DISCUSSIONS         

This chapter presents and discusses the findings from the structural equation model analysis, focusing on how social media influence, brand reputation, and electronic word-of-mouth marketing impact consumer purchase decision-making. The data is presented, analyzed, and interpreted following the order outlined in the research objectives.

Level of Social Media Influence

The following section presents, examines, and interprets the results of social media influence. Table 1 displays the outcome for the social media influence level, revealing a mean range of 4.56 to 4.66. The overall mean was 4.61 with a standard deviation of 0.33. This indicates a “very high” level of social media influence, suggesting it was consistently manifested. The overall finding suggests that it is always manifested that social media empowers businesses to build strong customer relationships and enhance brand influence by facilitating direct engagement and fostering community. In agreement with Febrian et al. (2024), this finding indicates that building strong customer relationships through social media has a positive effect on customer equity, particularly brand equity, and subsequently encourages consumers to make purchases.

Moreover, the data could glean that customer relations is the indicator with the highest mean of 4.66 and is described as very high. The indicator with the lowest mean of 4.56 is also described as very high: marketing. These findings indicate that consumers consistently associate marketing and customer service with consumer loyalty, perceptions, and purchase decisions. Effective brand marketing and customer relationship management (CRM) strategies significantly improve customer satisfaction and engagement. The results also corroborate Zhang’s (2024) study, indicating that social media marketing strongly influences what consumers intend to buy, especially when they have high-quality customer experiences. Trust and brand awareness serve as key mediators, highlighting the importance of effective social media strategies in enhancing customer relations.

Level of Brand Reputation

Table 2 presents the results for brand reputation. The mean scores ranged from 4.60 to 4.66, with an overall mean of 4.62 and a standard deviation of 0.33. This is interpreted as “very high.” The overall findings imply that a good employer brand significantly enhances a company’s reputation. Effective employer branding management helps project a positive corporate image, attract and retain talent, and ensure employees share brand values, driving long-term competitive advantage. High employer branding fosters employee loyalty, benefiting the company’s standing. It conforms to the study of Gurgu and Kuleto (2023), which stated that a strong employer brand enhances reputation, attracts top talent, and motivates employees. Factors like company culture, benefits, and growth opportunities influence this brand, further enhanced by employee advocacy and effective communication. Moreover, the study by Padilla (2019) reveals that a positive company image directly impacts employer branding, which is vital for securing and retaining skilled employees.

Furthermore, the data reveals that “good employer” received the highest mean score at 4.66, categorized as very high. Conversely, “customer orientation” had the lowest mean score of 4.60, though it was also classified as very high. These findings show that consumers’ perceptions of customer orientation and the importance of good employers significantly impact a business. A strong customer-oriented approach among employees fosters a positive work environment and enhances customer satisfaction. Moreover, Widelska and Krot (2021) stated that a strong focus on customers, treating them as collaborators in value creation, is a key driver of positive brand reputation. By emphasizing long-term customer relationships, organizations build a favorable image as both an employer and service provider.

Level of Electronic Word-of-Mouth Marketing

Table 3 presents the results for electronic word-of-mouth (e-WOM) marketing. The mean scores ranged from 4.51 to 4.69, with an overall mean of 4.59 and a standard deviation of 0.31, which is qualitatively described as very high. These overall findings suggest that e-WOM marketing is consistently present among consumers, indicating its strong influence within the apparel industry. It indicates that the volume of consumer communications significantly influences the extent of eWOM marketing. While increasing consumer engagement and potentially influencing purchase decisions, a larger volume of eWOM can sometimes outweigh the quality of these evaluations.

The data analysis shows that the “quantity” indicator received the highest mean score of 4.69, categorized as very high. Conversely, the “consumer expertise” indicator had the lowest mean score at 4.51, although it was also classified as very high. These findings suggest that perceptions of quantity are consistently strong among consumers. Moreover, various factors influence these perceptions, such as discount framing, the perceived monetary value of the product, and individual preferences for quality versus quantity.

This conform to the study of Siregar et al. (2024), which found that in electronic word-of-mouth, the volume of reviews is more important than the quality of the reviews. Additionally, Hung et al. (2024) supported that in e-WOM, more reviews are more effective than higher-quality reviews.

Level of Consumer Purchase Decision-Making

Table 4 presents the results for consumer purchase decision-making. The mean scores ranged from 4.19 to 4.50, with an overall mean of 4.32 and a standard deviation of 0.60, which is described as very high. These overall findings suggest that consumer purchase decision-making is consistently evident. This indicates that consumer purchase decisions are influenced by price consciousness by impacting how consumers evaluate options and make choices. Price-conscious buyers often prioritize price over other factors, leading to distinct decision-making processes that shape their buying behavior.

Furthermore, the data reveals that “price consciousness” had the highest mean score at 4.50, categorized as very high. In contrast, “confused by over-choice” had the lowest mean score of 4.19, which was described as high. These findings imply that price consciousness significantly influences consumer behavior across various product categories, impacting both purchase intentions and decisions.

This conforms to the study of Ardianto (2022), which found that price awareness significantly influences consumer purchasing decisions. Highly price-conscious consumers make informed decisions, which builds trust and positively impacts their overall purchasing behavior. It was also supported by Levrini and Jeffman (2021) that price has a large impact on if someone will buy a product, especially for people who are very aware of price.

Correlation between Social Media Influence and Consumer Purchase Decision-Making

Table 5 presents the findings regarding the relationship between social media influence and consumer purchase decision-making. The calculated correlation coefficient (r-value) was .512, and the p-value was .000. Since this p-value is less than the study’s significance level of .05, the association is statistically significant. This implies that the association of the variables had a positive, high, and significant correlation. The significant p-value also demonstrates an association between social media influence and consumer purchase decision-making. Consequently, the null hypothesis is rejected.

It conforms to the statement of Chu (2024), stating that social media greatly affects purchasing decisions through detailed information, comparisons, and influencing consumer psychology. Moreover, the study of Bandara (2021) study found that engaging content, such as entertainment, familiarity, and social imaging, in social media ads strongly affects consumer behavior. However, the amount spent on advertising does not significantly influence consumers.

Correlation between Brand Reputation and Consumer Purchase Decision-Making

Table 6 presents the findings regarding the relationship between brand reputation and consumer purchase decision-making. The calculated correlation coefficient (r-value) was .460, and the p-value was .000. Since this p-value is less than the study’s significance level of .05, the association is statistically significant. This implies that the association of the variables has a positive, medium, and significant correlation. Also, this shows that the brand reputation of the apparel businesses is associated with consumer purchase decision-making. Thus, the null hypothesis is rejected.

It conforms to the study of Syafe’I (2024), who found brand image to be a key driver of consumer purchases. In the fashion industry’s competitive landscape, a strong brand reputation is crucial for customer satisfaction and loyalty. Furthermore, Mamuaya (2024) also found that perceived product quality has a strong positive influence on consumers’ buying intentions and that brand reputation is a significant factor in predicting their choices.

Correlation between Electronic Word-of-Mouth Marketing and Consumer Purchase Decision-Making

Table 7 presents the findings regarding the relationship between electronic word-of-mouth (e-WOM) marketing and consumer purchase decision-making. The calculated correlation coefficient (r-value) was .462, and the p-value was .000. Since this p-value is less than the study’s significance level of .05, the association is statistically significant. This implies that the association of the variables had a positive, medium, and significant correlation. This significant p-value also demonstrates an association between electronic word-of-mouth (e-WOM) marketing and consumer purchase decision-making, leading to the rejection of the null hypothesis.

This finding supports Kochhar and Bhagat’s (2024) statement that brand image is significantly influenced by e-WOM, which in turn affects consumer purchase intentions. This highlights the crucial role of e-WOM in shaping consumer perceptions and purchase decisions. Furthermore, Anggraeni and Suwito (2020) supported that the perceived persuasiveness, informativeness, and source expertise of online reviews influence their usefulness, which then affects purchase decisions.

Influence of Exogenous Variables on Consumer Purchase Decision-Making

Table 8 illustrates the combined influence of social media influence, brand reputation, and electronic word-of-mouth marketing on consumer purchase decision-making. The analysis yielded a significant f-value of 33.188, a multiple correlation coefficient r-value of .580, an R-squared value of .337, and a p-value of .001, which is below the .05 significance level. These results lead to the rejection of the null hypothesis, indicating that the exogenous variables collectively and significantly influence the endogenous variable, consumer purchase decision-making.

Exogenous variables produced a probability value that was lower than the necessary value established in this investigation, as seen in the results. Social media influence significantly impacts consumer purchase decision-making, as indicated by a beta estimate of .707 and a p-value of .000. Similarly, brand reputation (beta = .522, p = .036) and electronic word-of-mouth marketing (beta = .565, p = .031) also show significant correlations with consumer purchase decision-making. These results collectively demonstrate the significant influence of the exogenous variables on the endogenous variable.

Establishing the Best Structural Model

To find the model that best fits consumer purchase decision-making, five alternative models were investigated. Each model in this study is structured into two sub-models: one addressing the structural relationships and the other focusing on the measurement of variables. The exogenous variables within these models are social media influence, brand reputation, and electronic word-of-mouth marketing, while the endogenous variable is consumer purchase decision-making.

To evaluate the model’s goodness of fit, various indicators were used as benchmarks for acceptance or rejection. The researcher established the causal relationships between the latent variable and its constituent variables. Furthermore, the model specifies how the endogenous and exogenous variables are interconnected. It underlines the consistency of the observed link between the variables assumed by the structured model when it shows an appropriate fit. The model parameters quantify both the strength and the direction of the relationships between the variables in the model.

Five models were framed and put to the test in this investigation. The usefulness of the generated models was assessed using strong theoretical justifications. To ensure the validity of the structural equation modeling analysis, fundamental assumptions, such as the normal distribution of the data, were checked and confirmed to be met beforehand. Additionally, the data’s completeness was confirmed, and no missing cases were found to prevent underfitting and overfitting.

The main research objective is to generate the best model to explain how the variables predict consumer purchase decisions in apparel businesses. The five hypothesized models were revised to meet goodness-of-fit measures and are summarized in Table 9.

To be considered a good fit, the values of all evaluation indices should consistently fall within their acceptable ranges. Specifically, the Chi-square/degrees of freedom (CMIN/DF) ratio should be below two, with a corresponding probability value of 0.05 or higher. The Root Mean Square Error of Approximation (RMSEA) should ideally be less than 0.05, accompanied by a p-close value exceeding 0.05. For the Tucker-Lewis Index (TLI), Comparative Fit Index (CFI), and Normed Fit Index (NFI), values above 0.95 are required.

The modified model six, which met all the acceptable ranges for the fit indices, demonstrates the relationship between social media influence and brand reputation and their direct impact on consumer purchase decision-making. This model exhibited a very good fit, with the following indices: p-value = .082, CMIN/DF = 1.453, p-close = .505, and RMSEA = .048. Furthermore, the NFI (.968), TLI (.982), and CFI (.990) all exceeded the .95 threshold, satisfying the criteria for goodness of fit. Therefore, the null hypothesis was rejected.

The generated structural model six, identified as the best-fit model for consumer purchase decision-making, is displayed in Figure 2. Notably, this model includes two latent variable constructs that remained as significant predictors.

As shown in Table 10, the latent variable of social media influence must be associated with brand reputation. With this combined association, there will be a substantial direct effect on consumer purchase decision-making (Beta = 1.280).

To carefully examine the interactions between latent and measurable variables, regression weights were measured. Table 11 presents the regression weights for the best-fit model, revealing that both social media influence and brand reputation are significant predictors of consumer purchase decisions. Particularly, social media influence had the strongest effect, with a beta estimate of 1.280 and a highly significant p-value of .000.

Table 12 presents the goodness-of-fit measures for the best-fit structural model, indicating a highly acceptable fit. The Chi-square/degrees of freedom (CMIN/DF) ratio is 1.453 with a probability value of .082, suggesting a good fit to the data. This is strongly supported by an RMSEA of .048 (below the .05 threshold) and a corresponding p-close of .505 (above .05). Furthermore, the NFI (.968), TLI (.982), and CFI (.990) all exceed the .95 criterion. Thus, this model is the best fit for understanding consumer purchase decision-making in apparel businesses.

The best-fit model for consumer purchase decision-making includes two remaining exogenous latent variables. Social media influence is indicated by marketing, while brand reputation is indicated by customer orientation and environmental and social responsibility. Lastly, the endogenous variable of consumer purchase decision-making remains to have six indicators: price consciousness, brand consciousness, quality consciousness, fashion consciousness, confused by over-choice, and consumer decision-making. Therefore, the best-fit model indicates a significant relationship between social media influence and brand reputation, and their direct impact on consumer purchase decision-making. This suggests that to enhance consumers’ purchasing decisions, strategies should focus on leveraging social media and cultivating a strong brand reputation.

SUMMARY OF FINDINGS, CONCLUSIONS, AND RECOMMENDATIONS

Summary of Findings

1. The level of social media influence has an overall mean of 4.60 with a descriptive equivalent of very high. It obtained an overall standard deviation of 0.33.
2. The level of brand reputation has an overall mean of 4.62 with a descriptive equivalent of very high. It obtained an overall standard deviation of 0.33.
3. The level of electronic word-of-mouth marketing has an overall mean of 4.59 with a descriptive equivalent of very high. It obtained an overall standard deviation of 0.31.
4. The level of consumer purchase decision-making has an overall mean of 4.32 with a descriptive equivalent of very high. It obtained an overall standard deviation of 0.60.
5. The interrelationship of social media influence, brand reputation, and electronic word-of-mouth marketing on consumer purchase decision-making showed a strong positive correlation (p<0.05). The p-values are at 0.05 level of significance. These results lead to the rejection of the null hypotheses.
6. The exogenous variable that best influences consumer purchase decision-making has an unstandardized beta coefficient of .707 for social media influence, followed by electronic word-of-mouth marketing with an unstandardized beta coefficient of .565, and brand reputation with an unstandardized beta coefficient of .522.
7. The model 6 with latent variable of social media influence and brand reputation best fits for consumer purchase decision-making in apparel businesses found to have indices that displays a very good fit as indicated by p-value = .082, CMIN/DF = 1.453, p-close = .505 and RMSEA = .048, while the indices of the following generated NFI .968, TLI .982, and CFI .990. As can be seen, all of the indices had values more than 0.95, which satisfied the criteria for the goodness of fit measures.

Conclusions

1. The very high level of social media influence observed in this study indicates its significant and consistent role in shaping consumer purchase decisions within the apparel sector. Social media platforms appear to be effective marketing channels that foster emotional connections, spread information, and mold consumer perceptions, thereby directly impacting their purchasing choices through interactions between consumers, influencers, and businesses.
2. The level of brand reputation was very high, considered to always be manifested. Thus, brand reputation is crucial for consumer purchasing decisions in apparel businesses. Research shows that a positive reputation enhances customer loyalty and trust, significantly influencing consumer behavior.
3. The level of electronic word-of-mouth marketing of the consumers was very high, as always manifested. Therefore, consumers were more likely to trust and act upon evaluations from reliable sources; the credibility of e-WOM is essential in influencing consumer purchasing behavior.
4. The very high descriptive level of consumer purchase decision-making in apparel businesses suggests that it is a consistently active process. This indicates that a variety of factors influence how consumers decide what apparel to buy, including information from social media, brand image, and electronic word-of-mouth (eWOM).
5. The study found positive correlations between social media influence, brand reputation, and electronic word-of-mouth marketing and consumer purchase decision-making in apparel businesses. Furthermore, all three of these independent variables significantly influenced consumer purchase decisions. This indicates that variations in what consumers decided to buy were affected by social media influence, brand reputation, and electronic word-of-mouth marketing.
6. The study’s findings indicated that social media influence was the most significant factor influencing consumer purchase decision-making. Therefore, social media influence impacts how a consumer makes purchase decisions and the range of options they consider.
7. The study identified Model 6 as the best fit for explaining consumer purchase decision-making in apparel businesses. This model illustrates a direct impact of both social media influence and brand reputation on these decisions. Therefore, consumers’ purchasing outcomes were significantly shaped by the interplay between social media influence and brand reputation.

Recommendations

1. Apparel businesses may prioritize leveraging social media influence and brand reputation to enhance consumer purchase decision-making. Given that social media influence had the highest impact on purchasing behavior, businesses should focus on effective marketing strategies, including interactive content, influencer partnerships, and targeted advertising. Additionally, maintaining a strong brand reputation through customer-oriented services, environmental responsibility, and high product quality can further boost consumer trust and loyalty. Continuously assessing social media engagement and consumer feedback will help businesses adjust to evolving market trends to secure long-term growth.
2. Clothing business owners may focus on smart, clear marketing and excellent customer service. This means using simple product descriptions, attractive ads, and personalized suggestions to help shoppers decide what to buy. Keeping customers happy with good service, loyalty rewards, and fun shopping experiences will encourage them to come back. Also, staying active in the community and online, offering discounts, and highlighting eco-friendly practices can help boost the brand’s image.
3. Businesses may be honest and open with their customers. Train staff to know products well so they can give useful advice and answer questions. Share stories about how the company works, especially about ethical and sustainable practices, on social media. This transparency builds trust and a better reputation, and quickly addressing customer feedback shows that the company cares about its buyers.
4. Shoppers may look for local clothing brands that are open, ethical, and involved in their communities. When brands are clear about where their products come from and how they are made, it is easier for consumers to trust them. By choosing these brands and sharing good experiences with friends and on social media, consumers can help improve business practices and support high-quality, ethical companies.
5. To attract customers and influence their purchases, business owners may utilize the best-fit model, social media, and brand reputation. This involves actively engaging with their audience online while staying authentic, collaborating with influencers to build trust and reach, and prioritizing honesty and transparency. Promptly addressing customer concerns and encouraging positive reviews are also crucial for enhancing their brand’s image and boosting sales. By implementing these strategies, businesses can strengthen their online presence and grow their customer base.
6. Future studies may explore other factors that affect how people decide to buy clothes, such as impulse buying, fashion trends, and personal values. Researchers can also look at differences among diverse demographics, including age groups, income levels, and cultural backgrounds, alongside comparing online and in-store shopping. Using these insights, future research can give clearer, practical advice for apparel businesses to better tailor their marketing strategies.

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