The effects of online credible review on brand trust dimensions and willingness to buy: Evidence from Vietnam consumers

Abstract

This study investigated the relationship between source, receiver, review quality, review sidedness, review consistency, online credible review, reliability, intentionality, and willingness to buy the electric consumers in Vietnam. This study performed structural equation modeling (SEM). A total of 427 valid respondents were used in this research. The findings indicated that sources, receiver review, review quality, review sidedness, and review consistency have positive effects on online credible review. Moreover, it found that online credible reviews had a significantly positive influence on the intentionality and reliability of brand trust. This research also illustrated that intentionality and reliability have a significant impact on willingness to buy. From academic contributions in this research, it has a variety of important indicators of online review credibility. Therefore, marketers should be mindful of the leading position played by periphery signals and focus on taking advantage of the latter to keep improving the credibility of the assessment process.

Keywords:

• source
• receiver
• message
• online credible review
• brand trust
• willingness to buy

JEL Classifications:

• M30
• M31
• M37

PUBLIC INTEREST STATEMENT

Online product reviews are useful in providing information for customer decision-making. Besides, consumers are influenced by different online reviews of products on virtual platforms that make their mark on the minds of them. Organizations need to be knowledgeable about the value that online reviews deliver that helps organizations make more informed decisions. On the other hand, the relationships between trust and other e-service factors, especially the behavioral intention in the online environment, have yet to be deeply discussed. This study, therefore, provided a persistent finding, implying that receiver, review quality, sidedness, and consistency are all directly correlated to both the reliability and intentionality of online reviews. It also considers a new relationship between the credibility of online reviews and the intention among willingness to buy from customer’s side.

1. Introduction

The world has many changes rapidly, which exists many new elements affecting consumer behavior in making purchasing decisions. In these elements, WOM (word of mouth) is mentioned, which has increased dramatically through the global and pervasive usage of the internet. It is also one of the most successful factors contributing to the definition of electronic WOM (eWOM). As what we know, it is possible to classify word of mouth (WOM) as informal contact between two or more individuals. In the context of globalization, online customer reviews seem to be the most popular type of eWOM and it has been shown that these online reviews have a major effect on the purchasing decision making of customers (Chen & Xie, 2008; Nguyen et al., 2020; Tran & Vu, 2019). Customers are likely to use WOM via the online mode known as electronic word of mouth (EWOM) in the new digital age. According to Prendergast et al. (2010), individuals also search for authentic sources with technical experience when choosing products, called word of mouth (WOM) or virtual word-of-mouth (eWOM) while online. Chang et al. (2013) pointed out that female target groups are attracted to the activities of e-WOM. In addition, online reviews not only exist in the specific field but they also appears very widely. Previous studies indicated that consumer reviews seem to be very important for consumer purchasing behavior and selling products (Chen & Xie, 2008). For example, in the hospitality field, online reviews seem to be a significant source of data influencing the decision-making of hospitality purchases by customers (Sparks et al., 2013). In addition, Cheng et al. (2013) also note that some previous studies on online evaluation (OR) only focused on short-term results such as purchase intent or willingness to purchase and “brand trust” (Schlosser et al., 2006) which essentially mediates the online assessments and short-term indicators (Chaudhuri & Holbrook, 2001). According to Xun (2014), consumers are influenced by different online reviews of products on virtual platforms that make their mark on their minds. Organizations need to be knowledgeable about the value that online reviews deliver that helps organizations make more informed decisions.

Many previous studies have found factors that influence the reliability of online reviews. However, very little research has explored the impact of online reviews on brands and consumer buying behavior. Therefore, in this study, not only that credible online reviews affect intention to buy, but it uses a multi-level model in which variables such as source (SO), receiver (RE), review quality (RQ), review sidedness (RS), and review consistency (RC) affect credibility of online reviews. In addition, it will identify how the variables online credibility reviews (OCRs) affect reliability (BTR) and intentionality (BTI). From those primary influences whether it will affect the higher ranking of the consumer purchase decision or not.

2. Literature review and hypothesis

Organizational branding initiatives are centered on the goal of ensuring that brands have a strong influence on potential buyers (Ahmad & Guzmán, 2020). It is common knowledge that brands with more brand equity enjoy greater customer loyalty (Pappu & Quester, 2016). The research of Farzin and Fattahi (2018) illustrated that consumer trust, informational influence, feeling of belonging, empathy, moral imperative, and knowledge self-efficacy were all found to be important for customer participation in eWOM. It went on to say that eWOM, which played a big part in moulding customers’ perceptions of brands and their willingness to buy. As a result, consumer eWOM behavior, in turn, had a positive and significant impact on brand image and consumer purchase intention. Meanwhile, customer purchase intent was positively influenced by brand image. While Chakraborty and Bhat (2018b) argued that the impact of source and review quality on credibility judgment of online reviews is more important than the impact of review consistency and receiver. It was also discovered that the respondents viewed the reviews as a whole. As a result, we can deduce that buyers do not evaluate the sidedness of a particular review, but rather the aggregate of reviews (Baker et al., 2016). Moreover, Abedi et al. (2020) indicated that the quality and reliability of eWOM information has a favorable direct effect on perceived information usefulness. Shoppers found feedback from customers posted by their friends on social media to be reliable and valuable. People in mobile social networks rely on information provided by people they trust since they are familiarized with who generated WOM information (Erkan & Evans, 2016). The effect of perceived information usefulness on information adoption is mediated by attitude toward eWOM information.

2.1. Source

In the research of Li et al. (2013), they found that source credibility was related to the perceived helpfulness of online shopping and relational partners (Cline, 1999). Wathen and Burkell (2002) found that source credibility is positively related to information credibility evaluation and related to information credibility (Luo et al., 2013). Chakraborty and Bhat (2018a) found that the source has statistically significant positive effects on online reviews’ credibility. Cheung and Thadani (2012) showed that there were two dimensions of source credibility including expertise and trustworthiness. Prior research studies on perceived homophily (Wright 2000) indicated that the relationship between opinion providers and recipients is very important. In addition, the component of homophily including perception-based similarity and demography similarity was developed by De Bruyn and Lilien (2008). Thus, the hypothesis is formulated as:

H1: Source is positively related to online credible review

2.2. Receiver

Li (2015) indicated that information quality was evaluated on the content, timeliness, and format. Ma and Agarwal (2007) indicated that the source identity of local communities determined their satisfaction. In addition, Tran and Can (2020) investigated that review consistency is related to review credibility. Uribe et al. (2016) found that credibility has affected behavior intentions. In addition, Hsu and Liao (2014) showed that customer received information two-sided recommendations are better than one-sided information. Similarly, Winter et al. (2015) indicated that receivers perceived two-sided information as more credible than one-sided information recommendations. Thus, the next hypothesis is formulated as:

H2: Receiver is positively related to online credible review

2.3. Message

Buyers generally seek the reliability of online reviews before they consider reviews (Shan, 2016). Besides, the review quality impacted on the online credibility reviews, which is in line with the research’s finding of Chakraborty and Bhat (2018a). In the research of Shankar et al. (2011), they found that online reviews with a clear source are considered to be more reliable than reviews without high-quality sources. Fan et al. (2013) showed that review quality is positively related to online credible reviews. Thus, the next hypothesis is formulated as:

H3: Review quality is positively related to online credible review

Forman et al. (2008) claimed that one-sided reviews were observed to be much more beneficial than two-sided reviews. However, Chakraborty (2019) does not support the theory that review sidedness clearly affects the reliability of online reviews. Moreover, Uribe et al. (2016) indicated that two-sided reviews improve credible information. Besides, they found that two-sided reviews affected behavioral intentions. Winter et al. (2015) believed that good-sided reviews could have more effects on the success of online reviews than one-sided reviews. Sidedness of the review as a seldom-used aspect of the quality of the knowledge was also considered to have a strong influence on the perceptions (Garg & Pandey, 2020). Finally, Baker et al. (2016) indicated that consumers did not consider the sidedness of a given review, but they consider reviews as an aggregate. Thus, the next hypothesis is formulated as:

H4: Review sidedness is positively related to online credible review

According to Chang et al. (2013), online customer reviews showed the establishment of a specific brand trust for all prospective customers via the idiotic comments of former customers. Based on the aforementioned outcomes of this report, there are many consequences for marketing strategies in the context of e-commerce and social networking sites. By the various online reviews about products on the virtual world help to leave a deep impression on the customer’s mind (Xun, 2014). Luo et al. (2015) indicated that review consistency positively affected review credibility. On the flipside, if the particular comment is inconsistent with the other comments, then the receiverfeels confused and may not consider that particular comment as credible (Zhao el, 2017) . Thus, the next hypothesis is formulated as:

H5: Review consistency is positively related to online credible review.

2.4. Brand trust

The relationships between trust and other e-service factors, especially the behavioral intention in the online environment, have yet to be deeply discussed. This study follows the understanding of the effects of online reviews on brand trust. Trust is so important for making the relationship between brand and customer. Morgan and Hunt (1994) developed a brand trust scale with reliability and integrity dimensions, reliability, and intentionality (Delgado-Ballester, 2004). Moreover, Mayer et al. (1995) showed that trust affected risk-taking in a moderated by perceived risk. In addition, Deighton (1992) found that reliability dimension of brand trust affected customer and satisfaction (Kau & Loh, 2006). Meanwhile, another research from Chakraborty and Bhat (2018a) suggested that online credible review affected the hedonic brand image. Cheng et al. (2013) also noted that several previous online review studies (ORs) have only concentrated on short-term assessments and results such as purchasing intent or willingness to purchase and “brand confidence” (Schlosser et al., 2006) frequently serve as a mediator between online reviews. This leads to the following hypotheses:

H6a: Online credible review is positively related to reliability

H6b: Online credible review is positively related to intentionality

2.5. Willing to buy

According to Chakraborty (2019), he pointed out the existence of online reviews on social media that significantly impacts brand trust and intentionality leading to the influence on willingness to buy. In addition, Grewal et al. (1994) showed that perceived risk was a critical determinant of consumer’s willingness to buy a product. Teo and Yeong (2003) found that perceived risk negatively affected customer purchases and had a positive effect on willingness to buy online. Moreover, Tran and Can (2020) examined that perceived risk affects online shopping behaviors and prevents perceived risk from increasing. Besides, Gambetta (1988) supposed that the relationship between the trustor’s beliefs about the trustee’s capabilities and about the context in which the relation occurs. It was revealed in Verhagen et al. (2006) study that e-trust has a positive effect on customer’s online shopping intentions. Another research from Chang et al. (2013) suggested that users focus on trust when they buy a product for the first time. They believe that intentionality is beyond the user’s control and that it is not critical when deciding to purchase. Finally, Gefen (2002) found that online customer trust positively related to shopping intentions. This leads to the following hypotheses:

H7a: Reliability is positively related to willingness to buy

H7b: Intentionality is positively related to willingness to buy

3. Methodology

3.1. Research framework

The purpose of this study is to access the relationship between the source, receiver, message, online credible review, brand trust, and willingness to buy. Thus, based on the literature review and hypothesis formulation. The following is the research framework (Figure Figure 1):

Figure 1. Research framework.

3.2. Questionnaire design

Questionnaires were used in this study. Credible online reviews and sources were measured with items based on Cheung et al. (2008). In order to measure message and receiver, modified items of scale were developed by Cheung et al. (2009). The study by Delgado-Ballester (2004) was followed to confirm the items to measure reliability and intentionality. Finally, to measure willingness to buy, we adopt items from Grewal et al. (1998). All the items were measured through a five-point Likert scale, where 1 = strongly disagree and 5 = strongly agree (Table 1)

Table 1. Response rate of groups

Category	Number of respondents	Percentage
Gender
Male	197	46%
Female	230	54%
Age
Less than 20 years old	58	14%
20–30 years old	115	27%
30–40 years old	105	25%
40–50 years old	71	17%
50–60 years old	58	13%
Above 60 years old	20	4%
Occupation
Student	175	41%
Banking/financial/insurance	102	24%
Education/culture	59	14%
Government	31	7%
Media/publishing	37	8%
Others	23	6%
Education
Senior High Diploma or Below	27	6%
Associate Bachelor Degree	165	39%
Bachelor Degree	146	34%
Master Degree	64	15%
PhD Degree	25	6%

3.3. Demographic statistics

The questionnaires were distributed to 450 respondents on a Google form. Of which, there have been 427 responses that are valid. There have been 23 invalid responses because the respondents have not answered correctly the reversed scale questions.

3.4. Confirmatory factor analysis (CFA)

According to Bagozzi and Foxall (1996), confirmatory factor analysis (CFA) is a method to assess reliability and validity. CFA was applied with the following indexes: Chi-square/df (cmin/df) = 1.224, Goodness-of-fit index (GFI) = 0.929, adjusted goodness-of-fit index (AGFI) = 0.912, comparative fit index (CFI) = 0.992, root mean squared error of approximation (RMSEM) = 0.0224, and Tucker Lewis Index (TLI) = 0.990. Therefore, all the factors in this research were within the accepted level (Table 2)

Table 2. Confirmatory factor analysis

Measure	Threshold	Present study results	Source
Chi-square/df (cmin/df)	≤2 good; ≤sometimes permissible	1.224	Hair et al. (2009)
Goodness-of-fit index (GFI)	≥0.9: acceptable; ≥0.8: marginal	0.929	Hair et al. (2009)
Adjusted goodness-of-fit index (AGFI)	≥0.8	0.912	Hair et al. (2009)
Comparative fit index (CFI)	≥0.95 great; ≥0.90 traditional; ≥0.80 sometimes permissible	0.992	Hair et al. (2009)
Root mean squared error of approximation (RMSEM)	≤0.05 good; ≤0.08 moderate	0.0224	Hair et al. (2009)
Tucker Lewis Index (TLI)	≥0.90	0.990	Hair et al. (2009)

Construct Validity: The reliability of all the variables including source, message, receiver, review quality, review sidedness, review consistency, online credible review, reliability, intentionality, and willingness to buy were in the range of 0.885–0.941 (Table 3). Reliability results were acceptable.

Table 3. Confirmatory factor analysis (CFA) fitting indices

	Estimate	t-value	Cronbach	CR	AVE
SO1	0.853	–	0.908	0.909	0.769
SO2	0.907	24.079
SO3	0.870	22.801
RE1	0.845	–	0.902	0.903	0.756
RE2	0.895	22.953
RE3	0.868	22.076
RQ1	0.844	–	0.907	0.909	0.768
RQ2	0.909	23.865
RQ3	0.876	22.726
RS1	0.839	–	0.902	0.904	0.76
RS2	0.911	23.679
RS3	0.863	22.013
RC1	0.861	–	0.888	0.889	0.727
RC2	0.872	22.386
RC3	0.825	20.709
OCR1	0.858	–	0.885	0.885	0.719
OCR2	0.845	21.970
OCR3	0.841	21.797
BTR1	0.858	–	0.931	0.931	0.731
BTR2	0.871	23.774
BTR3	0.864	23.456
BTR4	0.842	22.395
BTR5	0.840	22.307
BTI1	0.851	–	0.941	0.941	0.728
BTI2	0.871	23.647
BTI3	0.843	22.298
BTI4	0.846	22.430
BTI5	0.873	23.754
BTI6	0.834	21.924
WB1	0.870	–	0.911	0.911	0.774
WB2	0.908	25.064
WB3	0.860	23.164

According to Hair et al. (2009), the factor loading of all items was more than 0.5. In addition, the average variance extracted (AVE) for each construct is greater than 0.5 and the construct reliability (CR) of all the latent variables was more than 0.7. All the indicators had significant loading into the respective latent constructs with values between 0.719 and 0.774. Therefore, results were acceptable (Table 3)

Discriminant validity was tested by comparing the square root of AVE of a latent construct is higher than all the constructed correlations. The results showed that square of AVE values of all the variables, source, message, receiver, review quality, review sidedness, review consistency, online credible review, reliability, intentionality, and willingness to buy are higher than the inter-construct correlations (Table 4). Therefore, the results were acceptable (Hair et al., 2009)

Table 4. Discriminant validity and correlations among the constructs

	SO	RE	RQ	RS	RC	OCR	BTR	BTI	WB
SO	0.877
RE	0.531	0.869
RQ	0.562	0.524	0.876
RS	0.547	0.58	0.622	0.872
RC	0.567	0.574	0.606	0.673	0.853
OCR	0.665	0.684	0.724	0.782	0.754	0.848
BTR	0.531	0.531	0.533	0.525	0.536	0.634	0.855
BTI	0.432	0.424	0.431	0.488	0.454	0.568	0.365	0.853
WB	0.413	0.397	0.389	0.441	0.414	0.537	0.526	0.596	0.880

3.5. Hypothesis testing

After acceptable reliability and validity results (Figure 2), we formulated a structural equation model to examine the hypothesis. Various indices of the structural model normed, chi-square/df (cmin/df) = 1.233, goodness-of-fit index (GFI) = 0.926, adjusted goodness-of-fit index (AGFI) = 0.912, comparative fit index (CFI) = 0.991, root mean squared error of approximation (RMSEM) = 0.023, and Tucker Lewis Index (TLI) = 0.990. All the results were accepted (Hair et al., 2009).

Figure 2. Model tests.

Data analysis indicated that the source has significant effects to online credible review (β = 0.168 and p < 0.001). Thus, H1 is supported. Besides, the receiver has significant positive effects on online credible review (β = 0.195 and p < 0.001). In addition, the review quality (β = 0.216 and p < 0.001, review sidedness (β = 0.299 and p < 0.001), and the review consistency (β = 0.225 and p < 0.001) have a significantly positive impact on online credible review. Thus, H3, H4, and H5 are supported. Moreover, online credibility review is significantly positive relating to reliability (β = 0.665 and p < 0.001) and intentionality (β = 0.583 and p < 0.001). Thus, H6a and H6b are supported. Finally, reliability (β = 0.357and p < 0.001) and intentionality (β = 0.465 and p < 0.001) have a significant positive effect on the willingness to buy (Table 5).

Table 5. Results of significance test for paths of the model

Hypothesis	Path	Standardized path coefficient	Results
H1	Source ➔ Online credible review	0.168***	Supported
H2	Receiver ➔ Online credible review	0.195***	Supported
H3	Review quality ➔ Online credible review	0.216***	Supported
H4	Review sidedness ➔ Online credible review	0.299***	Supported
H5	Review consistency ➔ Online credible review	0.225***	Supported
H6a	Online credible review ➔ Credibility	0.665***	Supported
H6b	Online credible review ➔ Intentionality	0.583***	Supported
H7a	Credibility ➔ Willingness to buy	0.357***	Supported
H7b	Intentionality ➔ Willingness to buy	0.465***	Supported

4. Discussion

Despite the substantial consumer doubts about the credibility of online reviews and the little scientific information about what influences the associated perception of consumers. The objectives of this paper would have been to evaluate and understand the determinants of the credibility of online reviews as well as their impact also on the buying intentions of consumers. The critical outcome of this research is the empirical affirmation of coexistence and the separate but interrelated impacts of these features. Based on previous research findings such as Djafarova and Rushworth (2017), this research also found that the source has statistically significant positive effects on online reviews’ credibility assessment through accepting the H1. To prove this hypothesis strongly, Ma and Agarwal (2007) also said that the source identity of local communities determined their satisfaction. Xie et al. (2011) also evaluated the effect of the online reviewer’s source identification on the legitimacy of evaluation and purchase intention under periodically re-decisional structures. Shanka et al. (2011) also confirmed that an online review with an identified source was deemed more reliable and contributed to improved initial trust. If they regard the source of the reviews as reliable, customers are likely to trust the reviews. In addition, the hypothesis of the effect of the receiver on credible online review was supported by the author, which is in line with the research of Chakraborty (2019). He showed that if the receiver understands that the reviews contain compelling points, then the reviews would be followed. Since they want to believe the reviews that complement his/her preconceived evidence and experience, then individual tends to endorse and believe the reviews (Cheung et al., 2009). Upon the effect of other factors, the quality of reviews also becomes statistically meaningful, with beneficial outcomes on reliable online reviews. Therefore, this research supposed that the review quality impacted on the online credibility reviews, which is the same finding as the research of Chakraborty and Bhat (2018a). Based on the research of Yang et al. (2016), they assessed that customers are not only looking for just a review but also looking for a review that contains justifications behind that review. As a consequence, Chakraborty and Bhat (2018a) gave a conclusion that the quality of the review is also statistically meaningful, with potential benefits for credible online reviews. It is expression-oriented and transparent in consumer views, positively affecting attitude change as comparison towards any it has a greater impact on attitude change than an irrational and personal message (Petty & John, 1983). Further, other findings in this research are review-sidedness which impacts credibility reviews being supported. This comes is not the same as the supported hypothesis of Forman et al. (2008). He claimed that one-sided reviews were observed to be much more beneficial than two-sided reviews. Chakraborty and Bhat (2018a) also said that consumers do not consider the sidedness of a given review, but they consider reviews as aggregate. However, some findings are in contrast to this finding. Winter et al. (2015) believed that good-sided reviews could have more effect on the success of online reviews than one-sided reviews. These outcomes also confirm the impact of review consistency on review credibility. According to Luo et al. (2015), the author proved this relationship by empirical evidence. Thomas et al. (2019) also put a remark that it is easy to compare related reviews for customers. When comparing reviews online, people are more likely to have these reviews as much more trustworthy, providing greater quality with many other relevant reviews.

In addition, this research has found that reliable online reviews have a significantly significant and positive influence on intentionality and reliability. If a customer reads an online review written by an unknown person from an unknown website, the reliability of the reviews is judged after the consumer evaluates the reviewer’s trustworthiness and the critic’s information. Besides, reviews with reliable source prestige seem more trustworthy than ones with bad quality source prestige, which have a strong impact on the intention to buy (Shan, 2014). However, there are not many previous research studies that found the significant influence of credible online reviews on customer’s intention. In particular, in line with the research of Grewal et al. (1998) which classified “willingness to purchase” as “the probability which the customer intends to consume the product”. This research also illustrated that intentionality and reliability have significant impacts on willingness to buy, which was not the same finding of Chang et al. (2013). They demonstrated that a hierarchy level of trust in the specific elements would not result in a higher hierarchy of willingness to buy.

5. Conclusion

In today’s evolving digital landscape, online reviews become a reliable source of information before buyers are willing to buy anything. The purpose of this study is to find out what factors influence online reviews reliability. Numerous online reviews also influence the purchasing decisions of shoppers (Lee & Hong, 2016). Buyers generally seek the reliability of online reviews before they consider reviews (Shan, 2016). Therefore, this research developed more arguments that seek the influence of online reviews on intentionality and brand trust. Finally, these two factors affect the user’s willingness to buy. The present study also provides a piece of empirical evidence to support the elements that affect credibility online. A brief glance at the findings of the research demonstrates that four of the five suggested variables, namely, receiver, source, review quality, review sidedness, and review consistency, greatly affect the credibility of the review. Finally, the contribution of this research found that online credible review is positively related to reliability and intentionality. Besides, this research found the relationship between intentionality, reliability, and willingness to buy. These results are most consistent with the earlier studies that investigated such variables throughout the background of online reviews (Chih et al., 2013). This outcome is not parallel with the research by Chakraborty and Bhat (2018b) and does not support the theory that review sidedness clearly affects the reliability of online reviews. However, this paper is similar to the results of previous studies on some elements. In the research of Shanka et al. (2011), they found that an online review with a clear source is considered to be more reliable than reviews without high-quality sources, which has the same finding as this study. This research demonstrates that users not only focus on brand trust but also consider online review credibility. Attribution theory acts as a theoretical bridge that connects credible online reviews and two mentioned dimensions. Via the integrated theoretical prism, the current study claims that online reviews generate an image of the brand in the consumer’s mind that obviously impacts intention and willingness to buy.

6. Managerial management

From our comprehensive views in this research, there are a variety of important indicators of online review credibility. Hence, a wide range of tangible points of departure that marketing experts should identify. In particular, to perform the task of diminishing the effectiveness of online reviews, authoritative management must identify why customers interpret as well as how to evaluate the credibility of online reviews and, especially, to know what factors impact the credibility of customer reviews. Therefore, marketing departments should be mindful of the leading position played by periphery signals as well as focus on taking advantage of the latter to keep improving the credibility of the assessment process. For example, they should encourage the credibility of the website through obtaining as well as showcasing their quality-price seals highly on their website. Another approach is to highlight expert feedback by using obvious symbols or icons. Companies can offer free goods or services to bloggers or industry leaders who have much influence as an effective way to enhance the trustworthiness of online reviews. Judgments on this topic would be more authentic if they show clarity. Moreover, marketers may encourage their current customers to review not only about specific products or quality but also express their opinions, which means that the review should have been a combination of customer experiences and product-related facts. From that, they will use consumer ratings as website content to increase trustworthiness. It builds confidence among consumers, which in turn creates consumer reputation in the digital community. Further, advertising agencies would include their consumers in advertising their goods (Chakraborty, 2019). The advertising could rely on customer perceptions or emotions about services or goods. Besides, this research is a standard for future research. It could define more deeply about other elements such as social, perceived of customers, and so on impacting on credible online reviews and if these dimensions whether help customer willing to buy or not. Further studies also might validate this concept in many other social contexts.

7. Limitation

Although this research has many practical and academic contributions, it still has some limitations from the objective and subjective factors that need to be considered for further development in the future research. It especially refers to such results that are in contrast to earlier studies as well as the consequentially established preliminary hypotheses of this report. In addition, the study’s data collection considers selected e-commerce brand pages in a small region. In the future, researchers would be able to use this framework in many other market segment scenarios. Moreover, as the results of this study established crucial impacts on consumer trust in online reviews that used a quantitative research approach, more qualitative methods, including in-depth interviews or rigorous focus groups, are necessary for future solutions to improve the influence of consumer trust in user reviews. Such potential methods can help to get a valuable understanding and knowledge into the findings of the standard laboratory conditions in this research. Despite the inhibition, the findings of this study provide valuable insights for understanding the factors that influence purchasing willingness through the reliability of reviews online.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors received no direct funding for this research.

Notes on contributors

Van Dat Tran

Van Dat Tran is a lecturer in marketing and currently heads the Department of Marketing, Faculty of Business Administration, Banking University of Hochiminh City, Vietnam. Presently, he teaches subjects such as consumer behaviors, consumer psychology, brand management, marketing management, and digital marketing.

Minh Dung Nguyen

Minh Dung Nguyen is currently a PhD scholar at College of Management, National Kaohsiung University of Science and Technology, Taiwan

Lan Anh Lương

Lan Anh Lương is currently a Master student in Business Administration, Banking University, Ho Chi Minh city, Vietnam