Examining the impact of online customer review attributes on purchase intention: an integrated EFA and PLS-SEM analysis

The digitised world has been overtaken by online reviews (Smith, 2020). It is only a matter of time, with everyone using the internet today, that people will base their decisions to research before they buy something on online reviews (Johnson and Lee, 2018). The manner in which consumers leave feedback has evolved significantly over the years, thanks to social media and review websites and online shopping (Chen et al., 2019). Today, a person has the opportunity to express their likes and dislikes, which actually provides consumers with much authority to appraise products and services in a single location (Garcia and Martinez, 2021). This transformation has solely transformed the way brands and consumers interact with each other since the brands receive immediate feedback, which can corrupt or acquit their reputation (Johnson and Lee, 2018). Online reviews are quite fateful as they enable us to understand how our consumers are satisfied or dissatisfied with products and services (Smith, 2020). The eminence of positive brand perception has been emphasised in influencing consumer buying behaviour (Burmann et al., 2008). Online reviews, too, have a momentous influence on what is purchased. This emphasises the role of online reviews in modelling consumer opinions and buying decisions. Moreover, reviews that can be left online are also important to individual perceptions of a brand. They provide information about the values, customer service and quality of products of a company. By reading reviews, people get to know about the reputation and reliability of a company. This is the information that affects their thought and purchases. Zhu and Zhang (2010) observed that online opinions play a vital role in the perceptions that makepeople view a brand and buy it. Online reviews can impact people in several ways. A few of them include social proof, trust and numerous others. Cui et al. (2012) believe online reviews are social proof to consumers, as they are likely to follow and trust other consumers. Hence, they get to countercheck the products and services provided by the company when they meet positive reviews, and they also develop their confidence and reinforce their purchasing decision. However, negative reviews have the reverse effect, as they would not want to buy the products and services. Online reviews are another aspect that persuades people because they will give the customer a positive feeling. Online positive comments, aimed at promoting the positive sides of the products and the services, produce a positive emotional impact on the consumers, thereby affecting their perception and purchase behaviour in a positive way (Cheng and Ho, 2015). The online reviews are a source of information to prospective buyers. With this information-proliferating age, consumers are constantly demanding greater information and views of other like-minded shoppers in a bid to get a better understanding (Dellarocas, 2003). As such, online reviews serve as a referential point where would-be buyers can read comments, opinions and reviews of other buyers who have tried the products and services of the business. This assists potential buyers in making better purchases because they are now aware of what they want to make purchases, hence creating a more transparent playing field that is level between the customer and the seller. Floyd et al. (2014) underline the conspicuousness of the number of online reviews. The higher the number of reviews, the higher the trust the consumers will have. Online reviews can also affect the opinions and actions of consumers based on the tone, attitudes and opinions expressed in online reviews. Cheng and Ho (2015) discovered that the responsibility of matching two products, based on the positive aspects on the website, could translate into buying intentions. Conversely, discouraging purchases may be caused by negative reviews aimed at the disadvantages of a product. The nature of reviews may also influence the reception of online reviews due to the attributes of the reviewers. Consumers might believe reviews of individuals whose background or experience is similar to theirs more than others. Cui et al. (2012) observed that the traits of reviewers had the potential to affect the attitude and purchase decision of consumers. Due to the power of reviews, companies should take good care of their online reputation. Chen et al. (2017) recommend that firms ought to pay attention to customer satisfaction and react swiftly and frankly to consumer complaints to have a favourable image of their brands. By establishing a presence, seeking feedback and interacting with customers, businesses can build a better image and create trust and loyalty. Meanwhile, players are encouraged to exercise caution in reviews, as there are chances of bias or false reviews. Luca and Zervas (2016) advised those companies that want to have a balanced overview of the view should consider factors such as credibility and diversity to conduct reviews.

Although online customer reviews (OCRs) and purchase intention have been extensively studied in prior research, most studies have concentrated on only a few distinct aspects, such as brand reputation, brand engagement, trust and favourable reviews. Very inadequate attention has been given to examining these dimensions combined in a single framework to recognise how they jointly influence consumer purchase intention in today’s digital marketplace. Furthermore, many earlier studies have relied on conventional statistical approaches, while limited research has pooled exploratory factor analysis (EFA) and PLS-SEM to both recognise the underlying magnitudes of online reviews and inspect their causal associations with purchase intention.

At the same time, consumer behaviour in online surroundings is constantly changing. Growing exposure to sponsored content, fake reviews and manipulated ratings has made consumers more cautious while interpreting user-generated content. As a result, discoveries from earlier studies about the influence of favourable reviews on purchase intention remain inconsistent, especially in the context of digitally active consumers in evolving economies.

Considering these gaps, the current study endeavours to deliver a broader indulgence of OCRs by incorporating multiple review-related dimensions within a single model. The study further applies both EFA and PLS-SEM techniques to examine the factor structure of OCRs and inspect their influence on consumer purchase intention in the contemporary digital environment.

The following research objectives guide the present study:

To achieve the research objectives, EFA was performed to identify the latest dimensions of OCRs that meaningfully affect customer purchase intention, and in total, five factors were acknowledged. To achieve the second research objective, the study adopted the PLS-SEM technique to observe the association between dimensions of OCRs and purchase intention. The outcome divulges that trust, reliability, brand reputation and brand engagement have a momentous positive impact on consumer decision-making in a digital environment.

Overall, the discoveries of the current study are consistent with prior research indicating that OCRs are an acute driver of consumer purchase intention. While previous studies have observed this association from different viewpoints, they jointly strengthen the prominence of review-related information in modelling consumer decision-making. For instance, Macheka et al. (2024) and Akbar et al. (2023) both stated that OCRs positively influence purchase intention, but they acknowledged diverse underlying mechanisms. Macheka et al. (2024) accentuated the complementary role of consumer attitude and brand awareness, whereas Akbar et al. (2023) underlined brand awareness as the primary mediating pathway linking reviews to purchase intention. Similarly, Iqbal et al. (2022) and Beyari and Garamoun (2024) established the positive sway of review-related information, but in altered contexts. Iqbal et al. (2022) found that the quantity and credibility of electronic word-of-mouth (e-WOM) knowingly improve consumers’ purchase intentions for smartphones, whereas Beyari and Garamoun (2024) confirmed that online reviews are predominantly leading in the hospitality sector, signifying that the impact of reviews extends across product and service settings. Additionally, Sudaryanto et al. (2025) exhibited that consumer trust mediates the association between online reviews and purchase intention for electronic products, while Ahn and Lee (2024) underlined the role of social influence and group similarity, demonstrating that consumers are directed not only by review content but also by social cues. Correspondingly, Sembiring and Nisa (2023) acknowledged perceived value as a key element of purchase intention, whereas Asadi Damavandi and Ha (2024) found that online reviews work alongside technological features such as augmented reality to boost mobile app adoption. Collectively, these studies recommend that although the mechanisms through which online reviews influence purchase intention vary across contexts, they steadily validate that review quality, credibility, trust, social influence and perceived value jointly shape consumers’ purchase decisions. These findings support the current study and further underscore the multifaceted role of OCRs in digital marketplaces.

Sussman and Siegel advanced the information acceptance model in 2003. Information Adoption Model (IAM) describes how consumers are persuaded by information (Li et al. 2016; Zhang et al. 2014). Developed by integrating the Elaboration Likelihood Model with information systems research, IAM suggests that consumers adopt information when they perceive it as useful and credible. In online shopping environments, customer reviews provide an important source of product-related information that reduces uncertainty and facilitates purchase decisions. The IAM model is used to know people’s buying intentions through social media networks. It is the most suitable model to discover the effect of online content on customer purchase intention. The IAM model delivers a robust understanding of the process and factors that have a noteworthy impact on customer purchase intention. User-generated content (UGC) is also a form of computer-mediated communication; this IAM can deliver robust insight regarding the adoption of UGC and its magnitude. Most of the studies in the UGC domain, in the context of IAM, elucidate that the factors of IAM have a momentous impact on purchase intention.

In this study, IAM serves as the primary theoretical foundation because it explains how consumers assess OCRs based on characteristics such as trust, reliability, favourable reviews, brand reputation and brand engagement. These characteristics shape consumers’ evaluation of review information, which subsequently influences purchase intention. Accordingly, IAM provides a comprehensive explanation of the proposed relationships and is therefore adopted as the principal theoretical lens.

The present study is grounded in the wider background of the IAM and consumer behaviour literature associated with online user-generated content. IAM claims that consumers adopt and use online information when they recognise it as credible, useful, reliable and trustworthy. In digital markets, OCRs act as a vital source of information that outlines consumer attitudes and purchase decisions. Also, Signalling Theory proposes that affirmative brand-related indicators such as favourable reviews and robust brand reputation diminish ambiguity and influence consumer decision-making.

Based on these theoretical viewpoints, the study inspects trust, reliability, favourable reviews, brand reputation and brand engagement as significant antecedents of OCRs and their subsequent influence on purchase intention.

Trust is viewed as a belief (Hampton-Sosa and Koufaris, 2005). Trust has been studied for a long time from various perspectives, including economics, psychology and sociology (Kim and Park, 2013). In general, trust is when one party (the customer) believes the actions of the other party (the seller) and hopes they will do something particular that won’t damage that trust (Mayer et al., 1995). Trusting others has become an enormous issue in online shopping because there is no direct interaction. Think about having to purchase something without being able to see, touch, or try it out from a stranger. The internet can be a little weird when it comes to privacy and security. Hoffman et al. (1999) stated that without trust between customers and e-vendors, no one would be willing to share their credit card or personal details. Trust is a major factor in the world of e-vendor websites, and it actually affects what customers do. Consumer trust has a significant effect on purchase intention (Wu and Huang, 2023; He et al., 2021). Trust is a psychological factor, and it has a momentous impact on consumer behaviour. A higher level of trust leads to higher purchase intention (Mashatan et al., 2022). If consumers find a review trustworthy, it positively influences their buying intention (Tan and Chen, 2021):

Reliability is an acute factor influencing the effectiveness of OCRs in shaping consumer decision-making. Reliable reviews are perceived as accurate, credible and trustworthy, thereby reducing ambiguity in online purchasing environments (Cheung et al., 2009). Prior research designates that review reliability expressively improves consumers’ trust in products and sellers, which in turn absolutely affects purchase intention (Kim et al., 2008). The perceived reliability of OCRs depends on factors such as review consistency, reviewer expertise and argument quality (Filieri, 2016). Generally speaking, the sources highlight how OCRs’ credibility contributes to information adoption, customer trust and perception of trust, making it a unique feature that determines the success of online reviews on e-commerce platforms. Reliability refers to providing a correct measurement of any issue under consideration (Wu and Liao, 2022). The reliability of reviews has been a main focus of studies in the context of online reviews (Bilal and Almazroi, 2023; Xu et al., 2020):

Favourable OCRs are dramatic in determining consumer perceptions and purchase decisions in the context of e-commerce. Similar to e-WOM, reviews are helpful strategies that reduce information asymmetry by indicating the dependability and quality of products (Chevalier and Mayzlin, 2006). According to the previous literature, positive OCRs contribute to consumer trust (more so perceived risk), which positively relates to the intention to purchase (Kim et al., 2008). Negative reviews are especially dominant in situations where consumers do not know the product, as they will act as good, credible sources of external information (Park and Kim, 2008). Moreover, Mudambi and Schuff (2010) emphasise that positive reviews are more convincing when elaborate and informative, which leads to their perceived greater helpfulness. Overall, the literature consistently exhibits that favourable OCRs positively shape consumer attitudes, build trust and stimulate buying behaviour, making them a crucial determinant of success for online retailers:

Before the creation and usage of the internet, consumers’ views of cost were impacted by brand image (Biswas, 1992). This was corroborated by another study (Anselmsson et al., 2014), which discovered that consumers were more worried about a product’s price when they were unaware of its brand. According to Chiang and Jang (2007), consumer trust in the modern hotel industry is developed in hotels that have established brands. From a managerial perspective, these discoveries can be considered significant because online purchasing always relies on how much trust a consumer has in their supplier. A hotel with a good brand reputation has a momentous impact on customers’ trust in the hotel’s products and services, as well as their perceived value of the hotel (Lien et al., 2015).

Social media platforms allow customers to share their experiences with brands; the information shared on these platforms helps customers to raise their voice, and it leads to brand reputation (Arma, 2022; Setiawan, 2022; Wijaya, 2022). Data shared on these platforms also helps brands to increase their reputation and market share (Montaser et al., 2025). Brand reputation has a significant role in OCRs and has a positive influence on purchase intention (Ingriana et al., 2024; Rolando, 2024). The prominence of brand reputation rises as consumers actively seek brands with favourable online sentiments (Venkateswaran et al., 2024):

The concept of experiential marketing was announced by Schmitt (1999) by considering it from the customer’s perspective. It is all about being involved in activities, experiencing things, being recognised and purchasing products. Experiential marketing is not just about products or services. It is all about the entire process that the customers undergo. Addis and Holbrook (2001) presented a cool distinction between two types of value from the customer’s perspective. The first one is utilitarian value, which emphasises the functional aspects of the product. The other one is hedonic value, which centres on the experience itself. It is all about how the customer engages with the service organisation, its employees, facilities and technology (Johnston and Clark, 2008). The brand experience differs from previous theories. Brands progressively identify that merely being present in consumers’ online search results is inadequate. Sustained and meaningful connections with consumers have become indispensable for raising repeat purchase behaviour. These continuous interactions are conceptualised in the marketing literature as consumer brand engagement. Today’s consumers perceive, contribute to and interact with brands and also with other consumers (Labrecque et al., 2013). Studies show that brand engagement has a significant impact on purchase intention (Naumann et al., 2020):

OCRs refer to the feedback and opinions that customers share on company or third-party websites (Mudambi and Schuff, 2010). They are one of the most vital types of consumer feedback (Sen and Lerman, 2007) and are the most popular form of e-WOM (Purnawirawan et al., 2012). In recent years, research has focused more on understanding how e-WOM, including OCRs, affects consumer behaviour (Chen and Xie, 2008; Li et al., 2011).

Online reviews provide information about the experiences of past users with the product and have a significant impact on purchase intention (Jain et al., 2021; Dash et al., 2021; Al-Hawary and Obiadat, 2021). However, there are some key differences between OCRs and e-WOM. Firstly, e-WOM can come from both consumers and businesses, while OCRs are only created by consumers (Cheong and Morrison, 2008). Secondly, e-WOM is often posted on social networking sites like Facebook and Twitter, where the information’s accessibility can be managed. In contrast, OCRs are typically found on e-retailer websites like eBay and Amazon, where there are no such limits, and anyone can view them.

Consumers trust reviews to obtain more evidence about product quality and features (Song et al., 2019). Online reviews have become a more powerful marketing tool for businesses because it is related to social media platforms (Mayrhofer et al., 2020). Online reviews are becoming an important source of information about products and services (Xiao and Tang, 2021). Content on social media is considered by people to be more credible, authentic and up-to-date (Arif, 2019). Consumers generally take these reviews as a trustworthy source of information (Hong et al., 2020):

Openly, purchase intention is the extent to which a consumer wants and plans on purchasing a specific product or service (Yusuf and Busalim, 2018). Purchase intention is said to occur when a product or brand is included in the buyer’s consideration set (Yusuf and Busalim, 2018). On the buyer side, acquisition intention shows that users are willing to buy a certain item (Grewal et al., 1998). One of the conditions for a customer buying is sales. Sales, as a signal (if there is a sale), attract consumers’ attention. The availability of social media platforms leads to influencing consumers’ purchase decisions. The recommendation article works as an instrument of marketing communication and a point of reference for consumers making purchasing decisions (Chen and Xie, 2008). e-WOM has given customers a new means to learn more about the product or service they need to purchase, but they could not purchase. This provided an additional chance for leaders to endorse products or brands for companies. (Erkan and Evans, 2016; Knoll, 2016).

This research study investigating the influence of OCRs on consumer purchase intention uses the methodology to outline its approach, data collection and analysis. The author uses a quantitative approach that incorporates quantitative data collected through a structured questionnaire. The primary data were collected between 20 August 2025 and 15 November 2025. The numerical aspect involves a standard questionnaire administered to 200 respondents. The measurement items were self-developed by the authors, and the constructs were validated through EFA and CFA. The study applied 200 valid responses for observed analysis. The sample size meets the minimum recommendations for multivariate analysis and EFA. Hair et al. (2021) propose that a sample above 200 is generally adequate for SEM-based research. According to Comrey and Lee (1992), a sample size of 200 is considered “fair” and acceptable for factor analytical studies. The demographic characteristics of respondents are offered in Table 1. A pilot survey was also conducted before collecting primary data from 40 respondents to assess the questionnaire’s accuracy. The sampling method was purposive since it involved selecting participants who were representative of different age groups, sexes and differing economic levels. Quantitative data obtained from the survey were analysed using SPSS, a statistical analysis software. After identifying the factors through EFA, to confirm the factors and model, confirmatory factor analysis was performed using SmartPLS. The model was confirmed, and the relationship between factors and purchase intention was uncovered by using PLS-SEM in SmartPLS. Figure 1 represents the conceptual framework for the present research.

This section describes the statistical methods applied to analyse and interpret the primary data. Specifically, the data set features, assumptions and different factors that could have an impact were taken into account before the tools and techniques were employed. Primary data was collected from respondents through a questionnaire. Table 1 shows the demographic profile of respondents, which represents the demographic characteristics of the 200 respondents. The majority of respondents (32%) belonged to the 20–40 years age group, followed by the 40–60 years age group (27.5%). Male respondents constituted 61% of the sample, while females accounted for 39%. In terms of education, most respondents (68%) were graduates, followed by those educated up to the 12th standard (20%) and postgraduates (12%). Regarding annual income, the largest proportion of respondents (56%) reported an income between ₹2.5 and ₹500000, indicating that the sample was predominantly composed of middle-income individuals. Overall, the sample represents a diverse demographic profile, providing an appropriate basis for the study.

The reliability of the constructed procedure was evaluated using Cronbach’s alpha coefficient, a widely used metric for assessing the accuracy of collected data. Generally, a Cronbach’s alpha value of 0.6 or higher is considered acceptable for exploratory purposes, and in this case, the original value exceeds this threshold (Hair et al., 2010). In the present study, the value of Cronbach’s alpha for 20 items is 0.837, which indicates that the data are suitable for factor analysis. It validates that the scale shows high reliability, with an inclusive reliability well exceeding 0.8. This indicates a satisfactory level of reliability for the scale.

The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy (MSA) and Bartlett’s Test of Sphericity were used to decide whether the data are suitable for factor analysis. KMO and the Bartlett Test of Sphericity, which are the tools used in the past for assessing investor behaviour, are shown in Table 2. The KMO MSA for investor behaviour is 0.738, signifying that factor analysis is appropriate for this data set. Also, the Bartlett test of sphericity result is highly noteworthy (0.000), which means there is a momentous correlation among the variable factors. These findings together show that the set of variables is fit for factor analysis (Chitra and Jayashree, 2015).

The result from SPSS software shows that KMO = 0.738 > 0.5 and the value Sig. < 0.05. According to the results, five factors are applicable for Factor Analysis. Based on the results of Factor Analysis, five factors were extracted, and in total, 15 items were taken as the items of five constructs. A superior understanding of the consequences of varimax rotation, which restructures the components among the factors. Varimax changes the percentage of variation coming from each factor, but at the same time, upholds the total variance, which is 66.191% in the unrotated matrix. For example, the variance of the first element falls from 26.509% to 15.958%, thus pointing to the shift in the factor’s importance. As a result, it is a matter of elucidation so that researchers can better appreciate the findings. The loading significance for the six components of the 15-factor analysis of the OCR settings is shown in the table following varimax rotation.

All the communality values presented in this table are high, specifically between 0.820 and 0.574, and thus, we can conclude that the extracted components yield the best results in terms of signifying the variables (Table 3). Values closer to 1 mean that the variable’s variance is highly prejudiced by the extracted factors, but values closer to 0 show that the variance of the variable is mostly unexplained by the factors.

High communalities in practice show a bright side for characteristics such as strong factor structure.

The factors that are vital and were concluded again with five are marked as trust, reliability, Favourable reviews, brand reputation and brand engagement. When put communally, these five aspects are collectively called seriously defined and illustrated by the name “Factors Influencing Online Reviews’ Impact on Purchase Intention.” The initial factor derived from the rotated component matrix involves the resulting statements, which are shown in Table 4.

The factors of the “Trust” element that regulate genuineness, consistency and diversity of reviews, and the company’s capability to manage criticism, have a substantial impact on OCRs. The variety of different standpoints, which can be both favourable and unsympathetic, besides positive comments, adds integrity to the subject. The very responsive and mindful attitude of the companies to customer interaction – as shown by the follow-up to problem-solving – shows the instance of devotion and caring they provide the customers with, which strengthens trust.

The second factor is reliability, in which the prominence of customer feedback from online reviews in creating trust is explained (Table 4). In this vein, reviews are seen as a source of a brand’s publicity, potentially shaping trust with consumers, as a positive review indicates that the brand is trustworthy. Trustworthiness, a brand’s reliability through the evaluations of its online reviews, is of paramount importance.

The third factor that arises from the rotated component matrix is named a favourable review. The given factors are the ones that talk about how online reviews can either make or break a brand’s identity. They speculate the role of the customers’ sentiments in judgments, with one of the factors pointing out that reviews of this kind boost the brand’s name so that it becomes a selling point for the brand, and the other one, which constructs a link between what is articulated online and what the brand is known for. These considerations evidence that in our scenario, where technology is the main tool and where customers have an effect in any situation, online reviews are moving away from the hands of managers because they directly relate to managing the brand’s image.

The fourth factor that emerges from the rotated component matrix is brand reputation. Our findings display that online reviews have a great deal to do with a brand’s positive reputation. A person’s trust in a brand is predicated on what they see and read, and the loadings in the model suggest a strong relationship. Thus, we can conclude that as long as we can manage online reviews and ensure our brand has a positive online image, we should be able to maintain a trustworthy reputation.

The fifth factor that is the result of the rotated component matrix is brand engagement. In this case, there is an expression that brand engagement is cautiously connected with OCRs. Before people make any purchases, they kind of rely on these analyses, which is evidence of their relevance in influencing consumer behaviour. The role of opinions in the evaluation of the brand is emphasised by the fact that faith in customer reviews is higher than faith in conventional advertising methods. Overall, good online reviews are one of the key determinants in establishing brand interaction on different websites.

The data were analysed using PLS-SEM with the SmartPLS 4 software. Two main factors led to the selection of PLS-SEM. Firstly, it is particularly well-suited for studies with small sample sizes. Secondly, it does not require the assumption of multivariate normality, making it suitable for the characteristics of the present data set. Within this framework, the evaluation involves two phases: assessment of the measurement (outer) model and the structural (inner) model. The measurement model ensures the reliability and validity of the constructs, while the structural model assesses the hypothesised relationships among variables using path coefficients and explanatory power.

The measurement model was first assessed to determine the reliability and validity of the constructs (Figure 2). As part of confirmatory analysis, this study conducted tests of reliability, convergent validity and discriminant validity. This method is used to evaluate the constructs: Trust, Reliability, Favourable reviews, Brand reputation, brand engagement and purchase intention.

Tables 5 and 7 represent the results from the measurement model. As shown in Table 5, all item loading values were above 0.60, indicating a good explanation of each factor and suggesting good internal consistency and construct validity (Hair, 2014). The internal consistency of the variables was assessed using Cronbach’s α, which was 0.851 for Trust, 0.851 for reliability, 0.808 for favourable reviews, 0.829 for brand reputation, 0.902 for brand engagement, 0.951 for OCR and 0.913 for purchase intention. These findings confirm the reliability and convergent validity of the measurement model. The heterotrait–monotrait ratio (HTMT) criteria were used to assess the discriminant validity (Henseler et al., 2015). All HTMT values are ≤ 0.85 (see Table 7), fulfilling the threshold limit. For further validation, the Fornell–Larcker criterion (Table 8) was also applied, and the square root of average variance extracted (AVE) for each construct is higher than its correlation with other constructs (Fornell and Larcker, 1981). These findings are compelling evidence of discriminant validity.

All HTMT values were below the recommended threshold of 0.85, with the highest value being 0.585, as seen in Table 7. This suggests that discriminant validity was achieved (Hair et al., 2019a, 2019b).

The bootstrapping procedure was employed using SmartPLS4 to examine the hypotheses. The Bootstrapping technique is a non-parametric procedure that enables the evaluation of the significance of structural models, such as model fit, path analysis and R-square. The Bootstrapping technique evaluates the importance of the root mean square residual (SRMR) value to determine the model fit. A perfect fit is indicated by an SRMR value of less than 0.08, as shown in Table 6, which varies from 0 to 1.

The result of the structural model identifies the fact that all the hypotheses are statistically endorsed. All of the factors observed possess a monumental positive influence on online customer views and purchase intention. Trust (H1) is a crucial factor. It has a positive and noteworthy effect on OCRs (β = 0.155, t = 2.833, p = 0.005). This implies that consumers will have increased purchase intentions when they feel a sense of increased trust. It has a moderate effect, yet trust is a valuable psychological parameter of the empathetic consumer buying process. The strongest predictor of OCRs of all variables is reliability (H2) with a 0.283 value and t = 5.543 and p = 0.001. This shows that consumers attach a lot of importance to the reliability of the information or sources. The greater the likelihood of perceived reliability, the better the purchase intentions of consumers are. Favourable reviews (H3) have no momentous influence on OCRs (β = −0.009, t = 0.178, p = 0.859). Brand reputation (H4) has a positive and significant impact on OCRs (β = 0.209, t = 4.506, p = 0.001). This implies that it is more likely that brands with a good reputation and positive perceptions will appeal to consumers. Good brand recognition will lower perceived risk and the level of trust in the product or service. OCRs are influenced by Brand Engagement (H5) absolutely and expressly (β = 0.149, t = 2.217, p = 0.027). This advises that when consumers are emotionally or interactively involved with a brand, they have higher chances of considering buying the products. Finally, the OCRs also positively and significantly affect purchase intention (β = 0.366, t = 6.198, p = 0.000). This highlights the importance of forging long-term relationships with customers.

The structural model was subsequently tested to investigate the proposed relationships between the constructs (Hair et al., 2019a, 2019b). The validity of the structural paths was assessed using a bootstrapping procedure with 5,000 samples using SmartPLS 4.0. The outcome of the hypothesis testing is shown in Table 9.

Table 10 specifies the specific indirect effect of the research model. The table shows that the mediating effect of OCRs between factors of review and purchase intention is significant for trust (p < 0.05), reliability (p < 0.05), brand reputation (p < 0.05) and brand engagement (p < 0.05). For a favourable review, the mediation of OCR is not significant (p > 0.05).

Figure 3 embodies a PLS-SEM model that elucidates how different antecedents sway purchase intention through OCRs. In the model, Trust, Reliability, Favourable review, Brand reputation and Brand engagement are indicated as exogenous (independent) latent constructs. Each construct is measured using multiple observed indicators. These constructs are hypothesised to influence OCRs, which acts as a mediating endogenous construct. The arrows from the five antecedents to OCR designate the structural relationships, and the values shown on these paths signify the significance levels (p-values), demonstrating that most relationships are statistically significant. The R² value of 0.352 for OCR indicates that approximately 35.2% of the variance in OCR is elucidated jointly by Trust, Reliability, Favourable review, Brand reputation and Brand engagement, suggesting a moderate explanatory power. OCR is measured using four indicators (OCR1–OCR4), all of which show statistically significant loadings.

Further, OCRs have a direct effect on purchase intention, which is measured using four indicators (PI1–PI4). The path from OCR to purchase intention is statistically significant, specifying that OCRs play a vital role in shaping consumers’ purchase intentions. The R² value of 0.134 for purchase intention suggests that OCR explains 13.4% of the variance in purchase intention, which is acceptable in consumer behaviour research, especially when a key mediator is involved. Overall, the model exhibits that brand- and review-related factors indirectly influence purchase intention through OCRs, emphasising the central mediating role of OCR in consumer decision-making. This supports the theoretical premise that positive perceptions related to trust, reliability, engagement and reputation boost online reviews, which in turn drive consumers’ intention to purchase.

OCRs are informal data communication channels in which people can express their comments on their experiences with goods and services and their performance (Litvin et al., 2008). Consumers tend to trust web comments more than personal comments before purchasing items for their needs, hence the conventional recommendation system (Nieto et al., 2014). This research investigates the influence of OCR on shoppers’ purchase attitudes and verifies its noteworthy impact on shoppers’ purchase decisions (Prasad et al., 2017; Jalilvand and Samiei, 2012; Torlak et al., 2014). The outcome shows that trust and reliability have a noteworthy impact on purchase intention. Customers feel more confident when making purchasing decisions and perceive less risk when they recognise a product, supplier, or online platform as trustworthy and reliable. Customers are more confident about the calibre of goods, the security of transactions and the legitimacy of sellers when they have trust, which totally persuades them to make purchases. Secondly, high levels of trust and reliability usually result in strong purchase intentions and higher levels of customer engagement. The findings are consistent with previous studies (Kim and Park, 2013; Mayer et al., 1995; Cheung et al., 2009; Filieri, 2016). The current study supports earlier research on customer reliability and confidence in online reviews.

One of the prominent findings of the study is that favourable reviews did not display a noteworthy optimistic sway on purchase intention. This finding is contrary to several previous studies, which recognised positive online reviews as a strong force of consumer purchasing behaviour (Kim et al., 2008; Mudambi and Schuff, 2010). The most likely explanation is that contemporary consumers are increasingly becoming doubtful of excessively positive online comments owing to the growing number of fraudulent reviews, sponsored advertisements and doctored scores on online platforms. Consumers can thus have better trust in the authenticity of the reviews, the credibility of the reviewers and the usefulness of the information provided by the reviews, instead of the tone of the reviews being encouraging. The study contradicts the previous studies, which show that favourable reviews have a significant and positive impact on purchase intention (Kim et al., 2008; Mudambi and Schuff, 2010). The other explanation can be connected with information work within the digital sphere, as consumers are exposed to redundant commercials and various conflicting viewpoints. Consequently, the favourable reviews will no longer suffice to expressively influence the purchase decision. This finding supports the idea that buyers take a while to evaluate the reliability and quality of user-generated material before deciding to make a purchase. The results also indicate that marketers should concentrate on developing genuine, thorough and experience-based consumer feedback in addition to creating favourable evaluations, as this increases perceived utility and legitimacy. The current study shows that customers’ purchasing intentions and OCRs are significantly impacted by brand reputation.

Once a brand is well-known and well-trusted, customers will have greater confidence in reading the reviews and products, and it makes them more confident about their decision to purchase a product. A solid brand image also reduces uncertainties and creates an impression of trust in consumers. This way, customers will tend to believe the reviewed information and progress on a more difficult agenda to buy the product or service that is corroborated by other studies as well (Anselmsson et al., 2014; Lien et al., 2015). The study also advocates that brand engagement has an optimistic and substantial influence on OCRs and purchase intentions. When shoppers actively interact with a brand through social media or brand communities, they are inclined to advance robust emotional influences and larger trust towards the brand. This engagement inspires customers to share encouraging reviews and experiences, which sway the purchase decisions of other consumers. As customers feel more associated and entangled with the brand, their likelihood of purchasing the product or service also increases, which is also supported by previous studies (Johnston and Clark, 2008; Brakus et al., 2009).

Finally, the research divulges that OCRs have an extensive effect on consumers’ purchase intentions. Before making a purchase, many consumers rely on the experiences and opinions pooled by other customers to assess the quality and reliability of a product or service. Encouraging reviews support building confidence, diminishing uncertainty and boosting consumers to move forward with their purchase decisions, emphasising the dominant role of online reviews in shaping buying behaviour.

The current study offers vital insights into OCRs and their effects on the purchase decision. The study recognises five significant factors that affect the consumer purchase decision using EFA. These factors include trust, reliability, favourable reviews, brand reputation and brand engagement. The study divulges that the factors of OCRs affect the consumer’s purchase intention. To validate the factors and the model, confirmatory factor analysis was conducted using SmartPLS. The research model and its factors were found to be substantial in confirmatory factor analysis. The study also offers valuable insights into the association and connotation of factors in influencing the consumer purchase decision via OCRs. The reliability factor is acknowledged as the most significant factor among the others. This implies that the greater perceived reliability pointedly increases their purchase intention. Trust has a positive and momentous impact on OCRs and purchase intention. Similarly, brand reputation and brand engagement also expressively affect purchase intention. This indicates that if consumers feel emotionally or interactively engaged with the brand, they are more likely to be motivated to purchase the product. Overall, the findings support the idea that brand-related factors (trust, reputation and engagement) and dependability work together to shape consumers’ purchase intentions, with reliability and brand reputation having a comparatively greater impact on purchase behaviour. In summary, the current study provides a clear picture of e-commerce consumer pleasure and the key components of OCR utility. The discoveries demonstrate the vital role of companies in the processes that happen and the advantage of benefiting from their marketing activities, positioning and enhancing consumer purchase intention. Since internet evaluations should be thorough, accurate and reliable, businesses should place a high priority on producing succinct and correct content. Priorities should also be set to increase the quantity of reviews because the more reviews there are, the more reliable and favourable the brand’s reputation will be.

This study contributes to the literature on OCRs by scrutinising the collective impact of trust, reliability, favourable reviews, brand reputation and brand engagement on purchase intention within a single framework. Although previous studies have mostly inspected these factors individually, the present study associates their relative prominence and displays how they mutually influence consumer decision-making. The use of EFA followed by PLS-SEM also reinforces the theoretical model by validating the key review dimensions and testing their associations in a cohesive model.

The discoveries extend the IAM by displaying that consumers do not rely on positive reviews alone when making purchase decisions. As an alternative, they place more significance on whether the review is trustworthy, reliable and reinforced by a credible brand. This suggests that, in today’s digital atmosphere, the quality and credibility of review information have become more dominant than the optimistic tone of the message itself. The results also reinforce Source Credibility Theory by authorising that trust and reliability are key elements of the effectiveness of OCRs. Consumers appear to assess the genuineness and trustworthiness of review content before using it to make purchase decisions. Similarly, the noteworthy effects of brand reputation and brand engagement sustenance extend Signalling Theory by presenting that a reputable brand and robust customer engagement act as reliable signals that increase consumers’ confidence in online review information.

The most vital theoretical input of this study is the non-significant effect of favourable reviews on purchase intention. Conventionally, positive reviews have been viewed as a significant driver of consumer behaviour. Conversely, the present verdicts recommend that this assumption may no longer hold in digital marketplaces. A possible explanation can be drawn from the Persuasion Knowledge Theory. As consumers become more knowledgeable about online shopping, they are more able to identify promotional or exaggeratedly positive content and are less likely to rely on review positivity alone. The rising existence of fake reviews, sponsored endorsements and exaggerated ratings has reduced the diagnostic value of favourable reviews. As a result, consumers are more likely to base their decisions on whether the review appears authentic, credible and useful rather than simply positive.

Overall, this study recommends that the effectiveness of OCRs depends more on their credibility than on their positivity. By emphasising this shift in consumer evaluation, the study encompasses prevailing theories of online information processing and delivers a more appropriate elucidation of consumer behaviour in today’s digital marketplace.

The findings in the present research present valuable insights to marketers, brand managers and online platforms that deal with the digital markets. The study highlights that trust and reliability are among the most significant factors persuading online customers to leave reviews and influence consumer purchase intention. Businesses should prioritise authentic and credible reviews by implementing verified purchase badges, eliminating fake reviews and encouraging customers to share genuine purchase experiences. These measures can reinforce consumer confidence in online reviews. The momentous role of brand reputation specifies that organisations should steadily work on preserving an optimistic brand image across digital platforms. Consumers are more likely to trust reviews related to reputable brands, which eventually impacts their buying behaviour. Similarly, the discoveries associated with brand engagement propose that corporations should actively interact with consumers through social media, online communities and customer engagement activities to support consumer interactions and inspire meaningful online interactions. The momentous effects of brand reputation and brand engagement recommend that firms should focus on retaining a positive brand image and actively engaging with customers through social media, prompt responses to feedback and post-purchase interactions. Such efforts can inspire consumers to pay more attention to online reviews. A notable finding is that favourable reviews alone do not expressively influence review adoption. This suggests that consumers are not influenced merely by positive comments; instead, they seek reviews that are detailed, balanced and trustworthy. Consequently, managers should encourage customers to deliver honest and informative reviews rather than simply increasing the number of positive ratings.

Finally, as OCRs positively impact purchase intention, organisations should invest in enlightening the quality, visibility and credibility of their review systems. Exhibiting the most helpful reviews and endorsing transparent customer feedback can boost consumer confidence and eventually increase purchase intentions.

The present research has some limitations that should be acknowledged. Firstly, the study was conducted using a comparatively modest sample size of 200 respondents, which may limit the generalizability of the findings. Future research can represent greater and more heterogeneous samples that cut across geographical areas and demographic groups to enhance external validity. Secondly, the research proposed a cross-sectional study design, in which data were collected at one point in time. Increased online behaviour and technology advancements might alter consumer perceptions and online behaviour over time. To better understand how OCR behaviour and purchase intention change over time, future researchers can perform longitudinal studies.

Thirdly, the research focused on a few selected magnitudes, including trust, reliability, favourable reviews, brand reputation and brand engagement. Other crucial variables were not included in the current model, such as the quality of reviews, the number of reviews, the credibility of the person who conducted the review, perceived risk and the type of platform. These other variables can be incorporated in future research to progress a broader understanding of online consumer behaviour. Finally, the fact that the positive reviews did not have an expressive influence on the OCRs also provides some new research opportunities. Future researchers could progress by testing the impact of counterfeit reviews and consumer mistrust on purchase intention in online markets.

The authors thank the editor and anonymous reviewers for their time and insightful comments.

“The authors declare that no funds, grants, or other support were received during the preparation of this manuscript”.

All authors contributed to the study conception and design. All the authors read and approved the final manuscript.

Primary data collected through structured questionnaires from online consumers/shoppers.

20 August 2025 to 15 November 2025.

Quantitative survey-based research using convenience/snowball sampling techniques. Data were analysed using EFA and PLS-SEM.

The data used in this study are original and collected by the authors solely for academic research purposes. No copyright permission was required.