Establish Trust With Electronic Word-of-Mouth to Improve Brand Equity

Data Collection

A qualitative method was used to find out how e-WOM affects the value of a brand for online companies and how consumers feel about the things that affect how they feel about e-WOM. Corbin (1998) says that qualitative research is mostly used to figure out how people act, not to project their actions or opinions. The research used a semi-structured interview, a planned sampling method, and a theoretical sample of participants, most of whom were Ho Chi Minh City students who used internet brands and knew a lot about this type of business. Five individual respondents and two interview groups, all between the ages of 19 and 22, were interviewed. The interviews lasted between 10 and 15 minutes and took place in September 2022. We chose to employ an online interview form with video recording due to the complexity of the COVID-19 outbreak and to secure the safety of both interviewers and respondents, as well as interviewee confidentiality and consent to its use.

Five respondents participated in in-depth interviews for the purpose of gaining customer insights. Nielsen and Landauer (1993) developed a mathematical model demonstrating that 85% of interface problems can be identified by conducting a qualitative evaluation with five participants. After that, we conducted group interviews with two groups of five individuals each in order to obtain the respondents’ perspectives on multiple dimensions. Each research group should consist of 4 to 12 individuals, but in order to conduct the interview more efficiently, we have created two groups so that the focus group has ten participants (Carson et al., 2001). We randomly divided the respondents into two interview groups and arranged the interviews based on their availability.

Question Design

The offered questions served as a guide for the interview. However, because this research used a semi-structured interview format, the participants were free to say what they thought and could change how they answered. At the beginning of the interview, theoretical information about e-WOM and the definition of brand equity, according to Keller (2003), was given to make sure our respondents understood the idea and cognitive stimulus. Next, the interviewer will ask nine open-ended questions related to the research goal for the article. This study will explore the subject and compile informants’ viewpoints and ideas.

In order to explore characteristics of brand equity and how e-WOM can enhance them to anwer “What role does e-WOM play in the development of brand equity?,” we designed the followwing questions:

Data Analysis and Results

According to the suggested coding and transition analysis standards by Bhattacherjee and Sanford (2006), the replies were translated using the content analysis approach. The first two researchers looked at each interview sample individually, putting them into groups based on their common characteristics. The third researcher will read the final material for each phase again, make changes, and sign off on them. This procedure lessens coding bias and supports the researcher’s analysis.

Trust

Trust, as defined by Moorman et al. (1993), is the capacity to put one’s faith in, depend upon, and connect with others. Even in the worst circumstances, trust may be an important element in achieving long-term success. Therefore, trust will always be an element that has to be taken into account and respected in the interaction between brands and consumers. When confidence in a brand would reduce perceived brand awareness, brand trust is defined as the readiness to believe in the company’s capacity to deliver on its promises (Chaudhuri & Holbrook, 2001). As the cornerstone for creating a sustained connection between the brand and the client, customer risk to the brand (Matzler et al., 2008). A considerable impact of trust on brand equity was found by S. Kim and Manoli (2022) in research on company reputation and brand equity via corporate social responsibility initiatives. More precisely, research on brand equity by Zehir et al. (2011) found that trust increases brand recognition. Brand equity may be built via customer contentment and trust, and it has a favorable effect on customer satisfaction (Kahraman & Kazançoğlu, 2019). The brand image will be positively impacted by improving the hospital’s total reputation, or, to put it another way, customer satisfaction and trust. The results of an earlier study by Rialti et al. (2017) demonstrate that trust has a substantial impact on the brand image of airlines. The researchers also found a connection between brand loyalty and consumer trust. Brand loyalty benefits from increased consumer trust (Lau & Lee, 1999). Customers who trust a brand tend to show more brand loyalty (Chaudhuri & Holbrook, 2001). Thus, we proposed the following hypotheses:

Electronic Word-of-Mouth (e-WOM)

Trust is one of the most essential business attributes, especially for internet firms (Grabner-Kraeuter, 2002). Trust minimizes the fear, ambiguity, and pain involved with purchase choices, resulting in improved customer satisfaction and thus affecting e-WOM, especially in a complicated online business environment (Ismagilova et al., 2021). Therefore, prospective buyers typically seek the counsel of family members, friends, or coworkers with past experience. The influence of e-WOM on customer behavior may be much greater than that of conventional word of mouth (Jeong & Jang, 2011). Since e-WOM information is seen as more credible than WOM owing to its anonymity and absence of other incentives (Gerrath et al., 2023; Shi et al., 2019), e-WOM information is deemed more dependable than WOM (Leong et al., 2022). Zhao et al. (2020) assert that there is a connection between e-WOM, customer trust, and purchase intent, and that the direction of e-WOM will determine whether or not consumers’ faith in the brand improves or declines. Therefore, it shows that consumers’ desire to test a brand is correlated with their faith in the brand, and this trust is significantly impacted by industry evaluations (Kassim & Asiah Abdullah, 2010). Thus, we proposed the following hypothesis:

Content Quality

To lessen confusion and ambiguity before making a purchasing decision in the wide internet market, shoppers must research reviews and past brand experiences (Walsh et al., 2007). Customers trust reviews less when they think the reviews are impacted by sponsorship (Golmohammadi et al., 2020; S. J. Kim et al., 2019). The caliber of an information source has a big impact on e-WOM. Both the content of the message and the reviewer’s reputation have an impact on that reviewer’s influence (Jeong & Jang, 2011; Verma et al., 2023) Consumers carefully consider the post’s real quality before determining whether or not to accept the viewpoint (Oh, 1999). Few papers have included qualitative factors like content quality, reputation, or source type, whereas previous research has typically concentrated on quantitative elements of e-WOM like volume and value. It’s important to take into account bogus reviews and anonymity (Malbon, 2013). Thus, we proposed the following hypotheses:

Volume

The amount or count of online brand information is referred to as volume (Park et al., 2007). The number of posted reviews a user has discovered regarding a certain item or company is referred to as the review count (Tastan et al., 2014). Popularity and the quantity of reviews are essential metrics for measuring the effectiveness of electronic word of mouth (e-WOM), which has an impact on purchase choices (Z. Zhang et al., 2010). Each brand’s volume is said to be a sign of how popular it is (Chevalier & Mayzlin, 2006). As the number of reviews rises, the brand’s online visibility will also rise and more consumers will be able to access various brand-related materials. As a result, the brand becomes more well-known to consumers and they tend to view brands as less risky, which increases their intention to use the brand (Seo & Jang, 2013). The quantity of positive online word-of-mouth grows as the number of reviews does (Litvin et al., 2018). Thus, we proposed the following hypotheses:

Norm Conflict

Subjective norms are the social rules that a person feels they have to follow and think about when deciding if they want to do something (Ajzen, 1991). Subjective standards are set based on regional cultural elements. Even though these standards may not be the same as those in other cultures, they are still okay in one culture (Cho, 2000). For instance, Asian women would have a different societal definition of beauty than Western women. When social media users express opinions that are commonly accepted by that culture, it might impact other people’s purchase choices (Godey et al., 2016). Depending on the notions and criteria of each person, these concepts or remarks may sometimes contradict one another. Eventually, purchasers may find it challenging to make decisions when there are several streams of internet comments, since they are unsure which ones to believe (AL-Nawafleh et al., 2019). As a result of the interview we performed to get respondents’ opinions on conflict, we discovered that respondents tended to be reluctant and had been researching companies more (Shi et al., 2019). Customers are also led to believe that reviews are getting more truthful and reliable due to the formation of standards conflicts and that most responses will adhere to more widely accepted standards (Ahmad & Guzmán, 2021). Thus, we proposed the following hypotheses:

Sponsored Recommendations

Customers perceive sponsored items as though the brand were marketing them. This advertising may be conducted via new channels such as review, livestream, affiliate marketing, and information exchange, which is regarded as e-WOM (Lu et al., 2014). This makes it easier for clients to assume that this is a type of e-WOM, rather than advertising. Therefore, certain rules mandate that if a company is advertising a piece of content, the implementer must explicitly indicate that the material is sponsored so that consumers may avoid confusion (De Jans et al., 2018). Customers often mistrust sponsored content and see it as a deliberate attempt to improve the brand’s image and acquire the confidence of newcomers through the use of money. If clients believe the article information to be somewhat skewed toward anything other than recommending a great user experience, the information will no longer be dependable, hence reducing the persuasiveness of the suggestion (De Jans et al., 2020). We thus suggest the following hypothesis:

Source Expertise

Customers are more likely to have faith in a product, service, or brand if they are provided with information from experts expressing their thoughts on the product, service, or brand (Ki & Kim, 2019). Customers are often more swayed by the suggestions and evaluations of industry professionals than by the advice of non-specialists (Kang & Namkung, 2019). However, they will depend on the person’s popularity, search for more credentials, workplace, or biography to determine whether they are a genuine expert (Harris & Dennis, 2011). A customer or professional may be successful in that sector, such as someone with great cosmetics, beautiful skin, or a gorgeous figure (Chan et al., 2020; Gannon & Prothero, 2018). A consumer expert is a broader idea than an academic expert. This is a fantastic opportunity for companies to boost their brand’s reputation by selecting authoritative information sources (Yu & Yuan, 2019). For new items, difficult-to-evaluate products, or products with too many alternatives, buyers often depend on expert evaluations to decide whether to purchase a trial. Therefore, the following hypothesis is made:

This research employs Aaker and Keller’s definition of Brand Equity, and in this study, we combine both concepts, focusing more on image, brand awareness, and brand loyalty (as a feature of brand resonance) than on the tangible characteristics (Keller, 2003).

Data Collection

The team gathered survey data using Google Form’s convenience sampling. This is a wonderful tool for sending and distributing surveys, updating and saving data immediately online, and monitoring sample collection progress. To disseminate the survey, we publish a Google Form link on student Facebook pages and groups, comment with links beneath articles in public groups, and send links through Messenger andandand Zalo; study groups of data collection is October 2022. Respondents spent around 3 or 5 minutes completing the questionnaire. The response is entirely voluntary, and we have the respondents’ permission to use the questionnaires for research. Convenience sampling was used to select the non-probability sample, as it does not prioritize equal selection for all groups and the respondents are primarily students (Hair, 2011). Before the main questionnaire, we introduced the study and asked a selective question at the start of the panel to remove respondents who do not utilize internet brands. Choosing “No” ends the survey. If the answer is “Yes,” the respondent will be asked some demographic information and a multiple-choice question: “What online brands do you typically use?” This question prompts survey respondents to think about internet brands they use while answering subsequent questions. All inquiries are set to the internetan answer, preventing respondents from omitting values. 440 respondents responded to the questionnaire, but only 410 responded affirmatively to the screening questions and completed the survey. The total number of respondents utilized for the study was 410, with a 93% validity rate.

Measurements

This study adopted the scales from previous studies, and modified to fit with the current study context. Brand image items were adapted from Lin et al. (2021); brand awareness were adapted from (Godey et al., 2016) and Langaro et al. (2018); brand loyalty were adapted from Zehir et al. (2011) trust were adapted from Hasan et al. (2021); trust were adapted from H. Zhang et al. (2021) and Fatma et al. (2020); content quality were adapted from Magno (2017); norm conflict were adapted from McDonald et al. (2013); volume were adapted from Abd-Elaziz et al. (2015); sponsored recommendation were adapted from Lu et al. (2014); source expertise were adapted from Wiener and Mowen (1986) and Ecker and Antonio (2021). All variables are measured using a 5-point Likert scale, from 1 to 5 represent increasing agreement from “Strongly disagree” to “Strongly agree.” According to Hair (2011), each item requires a minimum of five respondents; this study contains 43 items; therefore, a minimum of 215 respondents are required for statistically significant results. The total number of respondents is 410, thereby making the sample representative.

Methodology

Descriptive Statistics

There were 410 responses, and 40.2% of them were men and 59.8% were women. Young people (18–24 years old) make up the majority of responses (90.2%); those under 18 years old make up just 5.1%; those between 25 and 30 years old make up 4.1%; and those beyond 30 years old make up the lowest percentage (0.5%). Since data is mainly Gen Z and students, the vast majority are 18 to 24 years old. Due to the fact that the majority of survey participants are young adults in the age bracket of students and recent graduates, the low income level under $5 million accounts for the majority of respondents (78.5%), followed by the income level of $5 million to $10 million (16.3%), and the remaining income level group of $10 million to $15 million ($20 million and over) only account for a small portion of respondents (1.5%, 2.0%, and 1.5%, respectively) (see Table 1). Moreover, when customers were asked which digital businesses they regularly use or think about, 63.9% thought of Grab (moving), 13.7% thought of Gojek, 10.7% thought of Shopee, 6.8% thought of Spotify, and below 2% thought of Bee, Lazada, and Moo respectively (see Table 2).

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Table 1.

Demographic Information of the Respondents.

Items		Amount	Percentage (%)
Gender	Male	165	40.2
	Female	245	59.8
	Total	410	100.0
Age	Under 18 years old	21	5.1
	25–30 years old	17	4.1
	18–24 years old	370	90.2
	Over 30 years old	2	0.5
	Total	410	100.0
Income (VND)	Under 5 million VND	322	78.5
	5−10 million VND	67	16.3
	10−15 million VND	6	1.5
	15−20 million VND	8	2.0
	Over 20 million VND	6	1.5
	Total	410	100.0

Note. Vietnam Dong (VND).

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Table 2.

Frequency of Digital Brands.

Digital brands	Frequency	Percentage (%)
Grab	262	63.9
Gojek	56	13.7
Shopee	44	10.7
Spotify	28	6.8
Bee	8	2.0
Netflix	7	1.7
Lazada	4	1.0
Momo	1	0.2
Total	410	100.0

Reliability and Validity Check

Table 3 presents the factor loadings values of each item, all are greater than .5, crobach’s alpha values are all greater than 0.7, CR (>.7); and AVE (>.5), ensuring the reliability of the scale. Table 4 shows the correlation between the variables in the model. There are some variables CQ, BI have no correlation, but CQ predicts the impact on e-WOM in the model, so it does not affect the research model in terms of overall. Table 5 evaluates Discriminant validity through the HTMT value, all values in the table do not exceed .85, so the condition is satisfied that the variables are different from each other. Table 6 predicts the phenomenon of multicollinearity, the results show that all values are less than 3, so this phenomenon does not occur in the research model. Thus, all variables, and items meet the requirements of reliability and accuracy to perform SEM evaluation.

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Table 3.

Test Results of Reliability.

Factors	BA	α	CR	AVE
Brand Awareness (BA)		.832	0.882	0.599
BA1: I am always aware of this brand.	0.720
BA2: Characteristics of this brand come to my mine quickly.	0.720
BA3: I can quickly recall the symbol or logo of this brand.	0.746
BA4: I can remember this brand often.	0.817
BA5: I can recognize the characteristics of this brand.	0.857
Brand Image (BI)		.861	0.896	0.589
BI1: The products/services have a high quality.	0.753
BI2: The products/services have better characteristics than competitors.	0.780
BI3: The brand has a personality that distinguishes it from competitors.	0.768
BI4: It is a brand that does not disappoint its customers.	0.759
BI5: It is one of the best brands in the sector.	0.774
BI6: This online brand is very consolidated in the market.	0.770
Brand Loyalty (BL)		.844	0.895	0.682
BL1: I intend to buy this brand in the near future.	0.863
BL2: The next time I need that product, I will buy the same brand.	0.821
BL3: Commercials regarding competing brands are not able to reduce my interest in buying this brand.	0.805
BL4: I intend to recommend this brand to other people.	0.812
Content Quality (CQ)		.775	0.869	0.689
CQ1: The information provided by this e-review is accurate.	0.814
CQ2: The information and suggestions published in this e-review are reliable.	0.850
CQ3: The content of this e-review is updated.	0.827
eWOM (EW)		.797	0.868	0.622
EW1: Online discussions had a significant influence on me.	0.799
EW2: I followed the suggestion given in online discussions.	0.791
EW3: I agree with the online discussions about the brand.	0.791
EW4: I frequently gather information from e-reviews about the brand before using it.	0.773
Norm Conflict (NC)		.859	0.904	0.703
NC1: I feel confused when I read many e-reviews with mixed opinions.	0.800
NC2: I feel the e-review’s conflict of opinions makes it more believable.	0.853
NC3: I will want to learn more about the brand when I encounter many mixed e-reviews	0.867
NC4: When there is a conflict between the opinions of the e-review, I will trust the opinion with more people agreeing.	0.832
Source Expertise (SE)		.838	0.880	0.596
SE1: I believe popular review sites or forums provide me with helpful information.	0.779
SE2: I believe the number of reviews written by the reviewer represents knowledge in evaluating the experience.	0.757
SE3: I believe the longer a reviewer has registered on a review site.	0.737
SE4: I rely on the reviews which other readers believe useful.	0.823
SE5: I believe in the review from the real customer experiencing the brand.	0.760
Sponsored Recommendations (SR)		.855	0.902	0.697
SR1: I think the sponsored recommendations and e-reviews tells the truth.	0.847
SR2: I don’t believe in what the sponsored reviewer wrote in this article.	0.801
SR3: I can learn the real product information from the sponsored review.	0.857
SR4: After reading the sponsored e-review, I have been accurately informed about the product information.	0.833
Trust (TS)		.890	0.924	0.753
TS1: I expect this brand to be sincere and genuine.	0.887
TS2: I belive this brand performs its roles very well.	0.865
TS3: This brand works hard to satisfy me.	0.868
TS4: This brand fulfills its commitments to customers.	0.849
Volume (VOL)		.821	0.881	0.650
VOL1: The number of online review/comment is large, inferring that the brand is popular.	0.811
VOL2: Highly ranking and recommendation, inferring that the brand has good reputations.	0.760
VOL3: The more the brand is mentioned in front of me the more am aware of it.	0.833
VOL4: The more the brand is discussed in front of me the more it influences my purchasing decision.	0.819

Note. α = Cronbach’s alpha; CR = composite reliability; AVE = average variance extracted.

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Table 4.

Correlations Analysis Result.

	BA	BI	BL	CQ	EW	NC	SE	SR	TS	VOL
BA	1.000
BI	0.392	1.000
BL	0.203	0.322	1.000
CQ	0.066	−0.063	−0.079	1.000
EW	0.187	0.160	0.206	0.386	1.000
NC	0.140	0.046	0.127	0.148	0.321	1.000
SE	0.079	0.054	0.051	0.143	0.188	0.215	1.000
SR	0.049	0.005	0.040	0.336	0.477	0.492	0.367	1.000
TS	0.390	0.410	0.538	−0.088	0.381	0.178	0.130	0.147	1.000
VOL	0.071	0.013	0.056	0.212	0.380	0.376	0.397	0.553	0.126	1.000

Note. TS = trust; BI = brand image; BA = brand awareness; BL = brand loyalty; EW = eWOM; CQ = content quality; VOL = volume; NC = norm conflict; SR = sponsored recommendations; SE = source expertise.

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Table 5.

Discriminant Validity—Heterotrait-Monotrait Ratio (HTMT).

	BA	BI	BL	CQ	EW	NC	SE	SR	TS	VOL
BA
BI	0.456
BL	0.233	0.377
CQ	0.096	0.107	0.104
EW	0.225	0.190	0.252	0.490
NC	0.167	0.075	0.151	0.185	0.386
SE	0.113	0.081	0.072	0.158	0.195	0.229
SR	0.090	0.070	0.058	0.408	0.575	0.575	0.407
TS	0.447	0.463	0.620	0.109	0.450	0.202	0.136	0.167
VOL	0.094	0.066	0.097	0.268	0.468	0.449	0.465	0.660	0.152

Note. TS = trust; BI = brand image; BA = brand awareness; BL = brand loyalty; EW = eWOM; CQ = content quality; VOL = volume; NC = norm conflict; SR = sponsored recommendations; SE = source expertise.

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Table 6.

Collinearity Statistics (VIF).

	BA	BI	BL	CQ	EW	NC	SE	SR	TS	VOL
BA
BI
BL
CQ					1.129
EW									1.000
NC					1.347
SE					1.233
SR					1.838
TS	1.000	1.000	1.000
VOL					1.568

Note. TS = trust; BI = brand image; BA = brand awareness; BL = brand loyalty; EW = eWOM; CQ = content quality; VOL = volume; NC = norm conflict; SR = sponsored recommendations; SE = source expertise.

Structural Equation Model Assessment

Table 7 shows the explanatory power of the variables in the model. Specifically, the variables that explain 30.2% e-WOM build ability, e-WOM can generate 14.3% trust for the brand, and to increase brand equity, brand loyalty explains 28.9%, brand image explains 16.8%, and brand awareness explains 15.2%.

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Table 7.

R-Squared Values.

	R-square	R-square adjusted
BA	0.152	0.150
BI	0.168	0.166
BL	0.289	0.288
EW	0.311	0.302
TS	0.145	0.143

Note. TS = trust; BI = brand image; BA = brand awareness; BL = brand loyalty; EW = eWOM.

Table 8 shows the results of the research model, all hypotheses were approved, except norm conflict (H2) and source expertise (H5). In which, content quality (β = .253, p-value < .0001), volume (β = .151, p-value = .0087), and sponsored recommendation (β = .270, p-value < .0001) all increase the likelihood of creating a positive e-WOM. E-WOM moderately increases trust in the brand (β = .381, p-value < .0001). Finally, trust moderately increases brand awareness (β = .390, p-value < .0001) and brand image (β = .410, p-value < .0001), and strongly increases brand loyalty (β = .538, p-value < .0001).

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Table 8.

Hypotheses Results.

Ho	Relationships	β	S.E.	t-Value	p-Value	Results
H1	CQ → EW	.253	0.053	4.763	.000	Approved
H2	NC →EW	.100	0.059	1.710	.087	Rejected
H3	VOL → EW	.151	0.062	2.451	.014	Approved
H4	SR → EW	.270	0.065	4.165	.000	Approved
H5	SE → EW	−0.029	0.038	0.760	.447	Rejected
H6	EW → TS	.381	0.039	9.695	.000	Approved
H7	TS → BI	.410	0.043	9.440	.000	Approved
H8	TS → BA	.390	0.051	7.643	.000	Approved
H9	TS → BL	.538	0.043	12.573	.000	Approved

Note. β = standardized regression weights; S.E = standard error; TS = trust; BI = brand image; BA = brand awareness; BL = brand loyalty; EW = eWOM; CQ = content quality; VOL = volume; NC = norm conflict; SR = sponsored recommendations; SE = source expert.

Discussion

The results of the data analysis confirmed the original research hypotheses, confirming that consumers care about a variety of factors when reading eWOM, including content, number of reviews, controversies, referrers, and source expertise. The research model is built based on the combination of two models of brand equity by Aaker (1991) and Keller (1993), WOM (Bei et al., 2004), and eWOM (Verma et al., 2023). Customers first examine the review’s content to determine whether the product or service warrants further consideration. Then, they view the number of evaluations as an implicit confirmation of the product’s credibility since there are so many people interested in it and providing feedback.

Thus, the quality of review content as well as having a famous, reputable recommender will have a great impact (Filieri, 2015).

Sponsored reviews substantially influence brand equity and e-WOM. In other words, when they know it’s an open and public sponsorship, individuals have greater faith in a recommendation. In fact, a significant number of influencers have received sponsorships from firms in return for writing product reviews. This is a persuasive marketing strategy that businesses frequently use today, according to Hsu and McDonald (2002), a representative group has a substantial role in the effectiveness of the brand marketing campaign (del Mar Garcia de Los Salmones et al., 2013). If brands choose the best candidate for their target audience and provide high-quality content, their sales and brand equity will improve dramatically. Moreover, doing the opposite will reverse the impact and temporarily damage the brand’s trust (Min et al., 2013).

Customers peruse low-rated reviews to determine whether they make sense. If it is insignificant, the risk will be categorized as permissible. Consumers believe that no brand is ideal, but brands with more positive than negative reviews should be considered for purchase. Moreover, contradictory opinions create a strong impression of the brand image in consumers’ perceptions and arouse their interest and need for personal authenticity (Baumeister et al., 2001).

It is also essential to have individuals endorse goods and services since, if they are well-known, customers are more inclined to believe them because of their reputation and professional accomplishments. A blue check mark or the number of followers on a social media account indicates how highly consumers regard knowledge.

The person who introduces the product or service is also essential, because if it is a well-known individual, consumers will be more likely to believe it based on that individual’s reputation and career. A blue checkmark or the quantity of followers on a social media account both indicate how highly consumers value expertise. Contrary to predictions, the study’s results indicate that e-WOM and the source expertise of the review had no effect on one another (confirmed H. As specialists, they would be less affected by e-WOM than less experienced individuals, consistent with the results of the previous. In spite of this, it would be unwise to dismiss this idea outright, since alternative events might still occur. Savvy shoppers can determine whether evaluations are genuine and give high-quality information (Johnson et al., 2023). Therefore, people should regard evaluations obtained from respected websites as especially reliable and persuasive.

Theoretical Implications

Previous research has demonstrated that e-WOM increases brand equity, and because there are two categories of e-WOM impact, including positive and negative e-WOM, the manner of impact also varies. Prior research has also examined e-WOM from the perspective of reviewers. However, there is no research that explains e-WOM consumer concerns and how customers evaluate e-WOM holistically. Nonetheless, prior research has not explained why eWOM can increase brand equity.

This research contributes to the existing literature on e-WOM and brand equity. This study first identifies the factors in which a consumer is interested when reading online reviews, including content quality, volume of online reviews, norm conflict, sponsored recommendations, and source expertise. This is a novel and distinctive aspect of this study compared to previous research that concentrated solely on reviewers. Customers care about a variety of factors, including the online review content, the number of controversies, the referrer’s reputation, and the expert’s ideas. An effective e-WOM incorporates multiple components.

This study expands on the reasons why e-WOM can increase brand equity through trust. It is the components of eWOM that instill customer trust in the brand, thereby enhancing the brand’s image, customer perception, and customer loyalty. This research adds to the customer-based brand equity model proposed by Aaker (1991) and Keller (1993) and completes earlier investigations into the variables influencing e-WOM. Brand loyalty has the highest standardized regression coefficient when compared to brand image and brand awareness in terms of its effect on consumer trust. Given that trust has a beneficial impact on brand image and brand awareness, it seems logical to include brand loyalty in Keller's (1993) customer-based brand equity model. In contrast to previous studies, this one explains in greater detail why positive eWOM increases brand equity.

Finally, this study expands on the reasons why e-WOM can increase brand equity through trust. It is the components of eWOM that instill customer trust in the brand, thereby enhancing the brand’s image, customer perception, and customer loyalty. In contrast to previous studies, this one explains in greater detail why positive eWOM increases brand equity.

Practical Implications

This research has various ramifications for brand managers using e-WOM to develop online companies’ brand equity. Managers can use the study’s findings to decide how to build trust in e-WOM across a variety of platforms. Consequently, e-WOM builds positive brand equity. First, according to the study’s findings, e-WOM increases customer trust (TS). Furthermore, positive e-WOM messages increase trust by removing ambiguity (Duman & Das, 2021). If brand managers are ready to invest in, manage, and perceive e-WOM as a vehicle to increase brand trust among consumers, we contend that the link between e-WOM and brand equity will offer a significant source of income in the long run. In this approach, the brand will be promoted more via inexpensive web advertising.

Second, we make the observation that the factor that has the most impact is the variable sponsored recommendations, which has an effect on e-WOM. This indicates that sponsored e-reviews to be published on forums will have more credibility than voluntary, discretionary e-reviews because online consumer evaluations are frequently supplied anonymously or in a general sense. Even though this will be made abundantly evident in sponsored e-reviews, the blog article may nevertheless contain photos of the product being utilized or links to its trademarks (Ismagilova et al., 2021). Brand managers have the opportunity to encourage affiliate marketing through the use of e-WOM by giving enticing commissions and awards. These incentives will help individuals boost their earnings while also assisting businesses in attracting new clients.

Thirdly, in terms of their influence on e-WOM, content quality (CQ) and volume (VOL), information quality and reliability had a favorable impact on e-WOM in research analyzing the impact of e-WOM information quality and reliability (Sijoria et al., 2018). A high degree of familiarity may also elicit a favorable emotional reaction, and studies in the past have shown that good feeling often enhances performance (Jeong & Jang, 2011). We may infer from this that e-reviews provide reliable, public, thorough, and often updated information, and that the more businesses that are mentioned or discussed, the more customers will learn about them. more familiar with and confident in the brand. Promos in the form of codes, discounts, and vouchers may be used more often to entice customers to submit high-quality e-reviews. The assessment must also be published on the brand’s application or website in a clear and transparent manner and be adaptable to social networking sites, blogs, etc.

Limitations and Future Researchs

Several of the study’s drawbacks will be useful for further research. The majority of survey participants are between the ages of 18 and 24, have monthly incomes under $5 million, and reside in Ho Chi Minh City, which poses the first constraint. For respondents of various ages, socioeconomic statuses, and geographic locations, the survey’s findings could no longer be very accurate. Future research should include data from a wider variety of geographic locations, ages, and economic levels.

Due to time and research constraints, we chose factors that affect e-WOM and certain components of brand equity from prior studies for this study. We anticipate that future studies will collect e-WOM and brand equity impacts that are functional enough to undertake a thorough investigation. A future study has the potential to become in-depth research and draw the interest of higher management. The form is more successful at creating brand equity when compared to the effects of e-WOM and conventional business marketing. Contrary to expectations, the study’s findings showed that the source expert for the review and the e-WOM did not interact to impact one another. This finding necessitates the revision of a subsequent investigation. Why aren’t consumers as interested as you’d think in the comments on online channels, and do they originate from experts?

The norm conflict (NC) has no impact on the e-WOM of all the variables. According to the findings of the interviews with the respondents, they have a range of responses to the e-reviews of the brand. The majority of them believe that the conflicting viewpoints of the e-review make the brand more credible and pique customers’ interest in it. Future research may recheck the impact of norm conflict on e-WOM.