Impact of Review Quantity, Review Quality, Reviewer Expertise, Product/Service Rating on Purchase Intention: The Moderating role of Consumer Trust

This study examines how review quantity, quality, expertise, and product/service rating affect consumer purchase intention. The research examines the moderating impact of consumer trust to better understand how internet reviews affect consumer decision-making. Online reviews significantly influence consumer perceptions and choices in the digital age. However, the literature does not examine how review quantity, quality, reviewer expertise, and product/service ratings affect purchase intention. Effective e-commerce strategies need understanding review credibility and customer trust’s moderating effects. This study uses a quantitative approach to analyze survey data from a broad sample of internet customers. Data was collected from 385 internet users in Pakistan. The study found complex correlations between review quantity, quality, reviewer competence, product/service ratings, and purchase intention. Consumer trust modifies these interactions, emphasizing its importance in review influence. The study provides a complete examination of the complex relationship between review-related criteria and purchase intention, adding to the current knowledge. The moderating impact of customer trust helps us comprehend digital consumer decision-making mechanisms. Businesses, marketers, and politicians can use the study's findings to improve online reviews and e-commerce platform


Introduction
Social media marketing is an essential component of the marketing mix.Marketers use social media platforms as a medium for promoting and marketing their products.Social media acts as an essential instrument that empowers marketers to actively interact with their customers.Online social media is a digital platform that allows consumers to evaluate brands (Kang et al., 2016;Phua et al., 2017;Santos et al., 2022).With the significant growth of the internet and online social media platforms, people have started sharing their opinions through online reviews as well.Consumers commonly perceive online reviews as a more dependable and trustworthy source of information when compared to other conventional sources (Fang et al., 2016).Online reviews play a pivotal role in shaping consumer behavior within the dynamic realm of e-commerce (Ullal et al., 2021).As consumers navigate through an extensive online marketplace, scholars and marketers are more focused on comprehending the influence of many elements on their purchase intention.Online reviews now play a important role in consumer purchase decisions in the age of digital commerce.Online review system factors like the quantity and quality of reviews, reviewer expertise, and product or service rating all contribute to the complex network of information customers need to make judgments (Shariffuddin et al., 2023).Given consumers' rising reliance on user-generated reviews, firms in the intensely competitive digital marketplace must understand how these variables affect purchase intention (Thomas et al., 2019).Firms need this expertise to obtain strategic advantages.
The quantity and quality of reviews now determine a product's appeal and credibility.The number of reviews shows its popularity, while validity and relevancy determine its quality.However, the relationship between these two traits and customer's purchase intention has not been widely studied (Kim et al., 2021).Does a lot of reviews enhance trust and purchase intention?Can review quality reduce or increase the impact of quantity?
The reviewer's expertise and product grade complicate consumer decision-making.Assessing the reviewer's reliability and knowledge may affect how prospective buyers view and trust the material.The numerical ranking of a product or service may also help consumers make decisions.However, further research is needed to determine how reviewer expertise and product/service rating affect purchase intent (Sebastianelli & Tamimi, 2018).
Customer trust is vital yet understudied in the context of these issues.Consumer trust, which is directly tied to online review dependability, is expected to regulate the impact of reviewrelated factors on purchase intention.Understanding the relationship between trust and elements like the amount and quality of reviews, the competence of the reviewers, and the product or service rating is essential to understanding digital consumer decision-making (Zahara et al., 2021).
Today's e-commerce market makes internet reviews increasingly important to consumer choices.The increasing reliance on user-generated evaluations needs a careful examination of their effects on customer purchasing intentions.This research paper analyses how the amount of reviews, quality of reviews, skill of reviewers, and product or service rating affect the complex process of influencing consumer choices.Our study also examines how digital consumer trust affects reviewrelated metrics and purchase intention, taking into account complicated contextual factors (Md Altab et al., 2022).
Internet reviews are crucial to consumer purchase decisions in today's e-commerce world.Online reviews have been studied for their number, quality, reviewers' expertise, and product or service ratings.How these qualities jointly and interactively affect customers' buying intention is still poorly understood.The complex dynamics of customer trust, which is vital to the digital buying process, have not been thoroughly explored in terms of its moderating effect on other review-related elements (Zheng, 2021).To fill this gap, a comprehensive study must expose the complex interaction of these components and their overall impact on digital economy consumer behaviour.
The volume, quality, and ranking of internet reviews have been analyzed.However, how these characteristics affect consumer behaviour is still unclear.A more complete investigation is needed due to the intricate relationship between review reliability and consumer trust regulation (Bilgies et al., 2023).Our goal with this thorough research is to provide a deeper theoretical understanding and practical insights for enterprises navigating the digital economy.Current study has thoroughly explored certain components of internet evaluations, often scattered (Shaheen et al., 2020).The amount and quality of reviews, the competence of reviewers, and the rating of the product or service all affect purchasing intention, but there is no thorough analysis.The lack of a comprehensive framework makes it difficult to understand how customers use these complicated signs to make decisions.Customer trust is important in online purchases, but its moderating effect on review-related variables has not been well studied (Shukla & Mishra, 2023).Our research addresses this gap by giving a detailed perspective that improves theoretical understanding and provides practical advice for enterprises trying to navigate the digital marketplace and create trust with their target audience (Majali et al., 2022).
This study analyses how review qualities affect consumers' purchase intentions to fill gaps in the literature.It will also study how customer trust mitigates these consequences (Tran 2020).This study uses a variety of research methods to provide theoretical insight and practical advice for organizations seeking to manage online consumer reviews and develop trust with their target audience.

Theory of Planned Behavior
According to planned behavior theory, purchase intention is an attitude based on perceived behavioral control (Ajzen, 1991).Attitudes lead to intention and behavior, according to Ajzen's theory of planned behavior.Despite this, Nobel Prize winner Richard Thaler's research show that humans are not rational beings and that behavior change is influenced by both attitude and context.Governments, motivated by Nudge, frequently address the external circumstances to change behavior rather than Ajzen's attitude and attitudinal changes.The theory predicts consumer behavior and emphasizes that intentions drive behavior.

Review Quantity and Review Credibility
The use of review quantity as a peripheral cue in relation to online reviews is a widely recognized and significant factor in both e-commerce and social media literature (Fan et al., 2013;Fang et al., 2013;Filieri and McLeay, 2013;Obiedat, 2013;Park et al., 2007;Zhang et al., 2014;Zhou et al., 2013;Thomas et al., 2019).This aspect pertains to the quantity of online reviews accessible for a certain product or service on a review website (Filieri and McLeay, 2013).A higher volume of online reviews enhances their visibility (Cheung and Thadani, 2010) and contributes to the authentication of individual online evaluations, which is a significant factor in the context of peripheral cues (Zhang et al., 2014).Prior studies have also shown empirical data indicating that the number of reviews has a favourable influence on the perceived trustworthiness of online reviews (Fan et al., 2013).Based on this, consumers consider a large number of reviews to be a prominent information signal that captures their attention (Radiansyah et al., 2023).Furthermore, the quantity of reviews appears to be linked to a legitimizing function, wherein the abundance of evaluations enhances their trustworthiness and, thus, their legitimacy.Thus, we consider the number of reviews as an external clue and investigate its impact on the legitimacy of reviews, putting up the following hypothesis:

Review Quality Review Credibility
The relationship between the quality of reviews and the credibility of reviews is crucial for the effectiveness of online reviews as a persuasive instrument for consumers.The quality of reviews is commonly linked to the informativeness, relevancy, and authenticity of the content, which in turn affects consumers' perception of the reliability of the information provided.The studies conducted by Dellarocas (2003) and Mudambi and Schuff (2010) highlight the beneficial relationship between high-quality reviews and perceived credibility.They argue that such reviews boost credibility by offering important and detailed information, while also showcasing the expertise of the reviewer.Nevertheless, it is essential to acknowledge the subjective nature of quality evaluation, as customers may differ in their criteria for assessment.Moreover, the presence of biassed or fraudulent reviews undermines the notion of a direct positive correlation, highlighting the need for careful discernment in identifying genuine, top-notch reviews amidst misleading material (Chevalier & Mayzlin, 2006).

Reviewer Expertise Review Credibility
Several studies, especially in the field of social media research, have examined the role of reviewer competence as a factor or peripheral cue in relation to online reviews (e.g., Cheng & Ho, 2015;Fang, 2014;Jamil & Hasnu, 2013;Racherla & Friske, 2012;Lo et al., 2019).Reviewer expertise pertains to the level of knowledge had by reviewers regarding a certain product or service, along with their capability and inclination to furnish accurate and honest information (Guo and Zhou, 2016;Racherla and Friske, 2012).The recipients of an online review find it crucial, especially when the information they are looking for would assist them in making decisions.(Gilly et al., 1998;Liu & Park, 2015).Prior research suggests that individuals with expertise are perceived as more credible than individuals without specialized knowledge.Furthermore, the reviewers' proficiency is not only a crucial factor in determining their credibility, but also has a beneficial influence on the credibility of an online review (Cheung & Thadani, 2012;Fang, 2014;Thomas et al., 2019).Put simply, if customers see the source (reviewer) of an online review as trustworthy, they are also likely to view the review itself (product) from that source as trustworthy (Zhang et al., 2023).Consistent with this line of reasoning, we define reviewer expertise as a peripheral cue in our research model, leading to the formulation of the following hypothesis:

Product/Service Rating Review Credibility
Both e-commerce and social media research have examined product and service ratings.Most of these techniques conceptualized this factor using individual indicators rather than a hidden construct.Star ratings can be considered final product or service reviews.Cho (2022) aggregates these star ratings from all internet product or service reviews.Therefore, consumers can see an average rating of all relevant online reviews (Cho, 2022).Since a product or service rating is a pictogram, such as a star icon next to a written online review, it is largely a visual signal provided to the consumer as an information short cut (Filieri & McLeay, 2013).Consumer cognitive involvement or elaboration intensity is minimal since processing this visual information is easy.Given the proposed link between low elaboration intensity and the peripheral pathway, "Product or Service Rating" is a peripheral cue.According to Cheung et al. (2009) and Fang (2014), such evaluations may affect consumers' perceptions of online review reliability.The aggregated star rating shows the majority opinion, legitimizing an online review.In our research paradigm, we use product or service rating as a peripheral cue and propose the following hypothesis.

Review Credibility and Purchase Intention
Trust and kindness in online reviews and opinions are called perceived credibility.Review credibility can boost purchasing intent and reliability.Credibility is a key indicator of shared information quality (Bae & Lee, 2011).Credibility is "the attitude towards a source of communication held at a given time by a receive.Communication credibility is based on perceived traits (Perloff, 2013).These days Companies are also giving e-WOM, especially customer reviews, great importance because it dramatically affects consumers' purchase decisions.Consumers examine review credibility (Kulmala et al., 2013;Lee et al., 2015;Shukla & Mishra 2023) since they are aware that advertisements may produce phoney reviews.More good recommendation ratings help buyers to trust material, according to Lis (2013).Price and Hersh (1999:912) also expect high recommendation ratings to increase eWOM review trustworthiness.Positive evaluations from several users of eWOM communication increase consumers' acceptance and trust in the material.Thus, comments gain credibility.(Fang, 2014:75).Information credibility strongly influences consumer purchasing intention.Most consumers look online for products and product information, either to buy online or offline.Consumers trust other consumers more than specialists during investigation.Thus, people seek reliable information before buying.Purchase intention is the willingness to buy a product in the future (Sher & Lee, 2009: Xia & Bechwati, 2008).Credible information is valuable to consumers and positively influences their buying intention.

Moderating role of Consumer Trust
Review Credibility pertains to the perceived integrity and dependability of evaluations for a product or service.Consumers frequently depend on reviews to make well-informed decisions regarding their purchases.Purchase intention refers to the probability or predisposition of a consumer to make a purchase of a product or service.It quantifies their intention to make a purchase based on multiple parameters.Consumer trust refers to the level of confidence and belief that a consumer has in the information they get, whether it comes from reviews, the brand itself, or other sources.In this context, moderation refers to the influence of consumer trust on the link between review credibility and purchase intention.The statement suggests that the relationship between review credibility and buy intention is not linear and simple; rather, it is contingent upon the level of trust that the consumer possesses.Furthermore, a consumer's buying decision depends on how much they trust review credibility.Even if a review is credible, a consumer may not buy if they question the information source or platform on which it is published.Conversely, consumer trust can boost the influence of reliable reviews on purchase intent.This stresses the importance of credible reviews and consumer confidence, which can dramatically impact purchasing decisions.

Methodology
The study employed quantitative cross-sectional design to examine how review-related factors affect purchase intention in social media users.Review quantity, quality, reviewer expertise, product/service rating, and their combined effects on purchase intention are examined, with moderating role of customer trust.Data was collected form 385 social media users in Pakistan using purposive sampling.Power analysis will establish the sample size for SmartPLS analyses to ensure statistical power.Participants will be screened based on online review involvement and participation.Online surveys with standardized questionnaires will collect data.Established scales will measure review number, quality, reviewer expertise, product/service rating, purchase intention, review credibility, and consumer trust in the survey.The participants will recall and score their recent online review experiences.
Common method bias was eliminated utilizing the Harman single factor method to ensure data accuracy.Counterbalancing question order and anonymizing replies will also be used during survey design and administration.Participants' replies will be kept anonymous to reduce social desirability bias.For data analysis and structural equation modeling, SmartPLS will be used.The study will examine review-related elements' direct and indirect effects on purchase intention using route analysis.To get robust estimates and confidence intervals, bootstrapping will evaluate the mediating effect of review credibility and the moderating effect of consumer trust.Data will be analyzed using the Harman single factor approach to determine common method bias.These constructs are Consumer Trust (CT), Moderating Effect (ME), Purchase Intention (PI).

Scale and Measurements
All construct items were adapted from the literature.Using a five-point Likert scale, we rated responses from strongly agree (1) to strongly disagree (5).Scale was adopted Review Quantity is measured with 4 items (Fan et al., 2013), Reviewer Expertise is measured with 4 items (Cheng and Ho, 2015) is measured with 4 items, Product service rating (Cheung et al., 2009), review quality, (Park et al., 2007), consumer trust is measured with 4 items (Lorenzo-Romero and Gómez 2010) and Purchase intention is measured with 4 items (Thananuraksakul, 2007).In Table 3, the reliability and validity metrics for each structural equation model latent construct are detailed.Cronbach's Alpha, Composite Reliability, and Average Variance Extracted (AVE) reflect the measurement model's internal consistency, reliability, and convergent validity.Internal consistency is excellent for all structures, with Cronbach's Alpha values from 0.974 to 0.981.High scores indicate that items within each construct measure the same notion, demonstrating reliability.Composite Reliability, reliability metric, supports Cronbach's Alpha and reinforces build robustness.The constructs have strong composite reliability (0.981-0.984), supporting the measurement model's consistency and stability.Average Variance Extracted (AVE), a convergent validity measure, compares concept variance to measurement error variance.From 0.778 to 0.938, all constructs in this study have AVE values over 0.5, which is acceptable.These values suggest good convergent validity, meaning each construct's variance is largely due to its latent idea.Table 3 shows that the measurement model is reliable and valid.Convergent validity, internal consistency, and reliability of the constructs give trust in the measurement devices' accuracy and precision.These substantial measures support future analyses and interpretations, bolstering the study's trustworthiness.4 shows the Fornell-Larcker Criterion for discriminant validity, revealing structural equation model latent concept distinctiveness.The diagonal numbers are the square root of the Average Variance Extracted (AVE) for each construct, whereas the off-diagonal parts are construct correlations.When the square root of the AVE for each construct exceeds its correlations with other components, discriminant validity is verified.In this study, diagonal values (square roots of AVE) for each construct-Consumer Trust (CT), Moderating Effect (ME), Purchase Intention (PI), Product/Service Rating (PSR), Review Credibility (RC), Review Expertise (RE), Review Quality (RQ), and Review Quantity and Length (RQL)-are consistently higher than off-diagonal correlations This suggests that each latent construct measures a different notion and has excellent discriminant validity.Consumer Trust (CT) has a square root of AVE of 0.969, which is greater than its correlations with other constructs (0.874 to 0.962).All constructs show similar patterns, proving discriminant validity.In conclusion, the Fornell-Larcker Criterion results demonstrate measurement model discriminant validity.The uniqueness of each latent construct is wellestablished, boosting confidence in the model's capacity to capture each concept's variance.

Discriminant Validity
Table 5 shows the Heterotrait-Monotrait Ratio (HTMT), which compares construct correlations to a threshold value to measure discriminant validity.This table shows how much  Table 6 summarizes the hypothesis testing results for structural equation model latent construct relationships.First, Consumer Trust (CT) is positively correlated with Purchase Intention (PI) (T = 11.45,P = 0.001), supporting the hypothesis that trust positively affects consumer purchasing decisions.Moderation Effect 1 on the relationship between Consumer Trust and Purchase Intention does not exhibit statistical significance (T = 0.39, P = 0.697), suggesting that the model does not significantly alter the relationship.Higher Product/Service Ratings (PSR) increase review credibility (RC) (T = 3.657, P = 0.005).It is also statistically significant (T = 6.513,P = 0.042) that higher perceived Review Credibility positively increases Purchase Intention.According to a statistically significant connection (T = 8.909, P = 0.003), higher perceived Review Expertise improves review trustworthiness.However, the link between Review Quality (RQ) and Review Credibility (RC) is not statistically significant (T = 0.859, P = 0.391), suggesting that review quality may not affect credibility.Finally, the connection between Review Quantity and Length (RQL) and Review Credibility (RC) is marginally significant (T = 6.001,P = 0.051), showing that more reviews may somewhat affect credibility.In conclusion, the hypothesis testing results illuminate the major and marginal interactions across components, revealing consumer behavior patterns in online reviews.

Discussion and Conclusion
Given consumers' growing skepticism of online reviews and the lack of research on what influences their perception, this study sought to identify and examine online review credibility determinants and their effects on purchase intentions.The study advances e-commerce and social media research and practice.It contributes to theory in e-commerce by presenting a comprehensive causal model for online review credibility based on argument quality (accuracy, completeness, and timeliness of online reviews) and peripheral cues (review quantity, review consistency, reviewer expertise, product/service.The study shows that peripheral cues are crucial to customers' assessments of online review reliability, based on argument quality and other characteristics.This investigation must prove the presence and complementing effects of various dimensions.In its comprehensiveness, the causal model and the major determinants identified and confirmed have great explanatory power for understanding what drives consumers' perceptions of online review credibility and subsequent purchase intentions, allowing for accurate consumer judgments and behavior prediction.The valid and reliable multi-item measures created may be useful for future e-commerce research.Looked examined closely, six of eight potential determinants significantly affect consumers' review credibility impression.In this regard, website reputation, product/service rating, and reviewer experience have the greatest beneficial effects on consumers' credibility perceptions.When people trust a website, they are more likely to trust an online review.Online review studies on these factors (Chih et al., 2013;Fang, 2014) agree most with these findings.These data show that consumers process online reviews peripherally.Most intriguingly, review number negatively affects review credibility, contrary to original hypotheses and past research (Fan et al., 2013).Accordingly, people see a product or service with more internet reviews as less reputable.Due to the recent public debate about fake online reviews and increased media coverage, consumer interest in online reviews and awareness of fake reviews and companies' deceptive practices may explain this contradictory finding.Knowing firms want as many favorable ratings as possible may make consumers wary of a lot of internet reviews, since they assume companies deceived them.They may consider more reviews as less credible.This result contradicts past study on online reviews, therefore it warrants more investigation.However, it challenges corporate practice of promoting internet reviews for a product or service, which has fascinating consequences for practitioners.All the peripheral indicators stated above have a big impact, but not on review consistency.Thus, we cannot validate recent studies that show a strong beneficial influence on review credibility (Cheung et al., 2012;Luo, 2015).However, past research has demonstrated that specific situations or customer attributes modify this effect, defining its relevance or insignificance.This effect was minimal whether online review respondents were very knowledgeable or uninvolved (Cheung et al., 2012).Thus, this may apply to a larger portion of our study participants, causing a negligible effect.
Overall, our findings demonstrate that review credibility is mostly peripheral.Beside these most powerful peripheral cue drivers, argument quality determinants affect review believability.The largest influence is accuracy, followed by online review completeness.The importance of accuracy and completeness in argument quality is supported by empirical research (Filieri and McLeay, 2013;Jamil and Hasnu, 2013).To our knowledge, this is the first study to show that they improve review trustworthiness, which is a huge contribution to e-commerce research and practice.We found no significant influence of timeliness on review credibility, but it does affect Internet information credibility (Abdulla et al., 2002).Both findings may indicate that timeliness and completeness are more interconnected than previously thought (Cheung, 2014).Accordingly, consumers may regard timeliness as part of completeness, which may have negated its effect on review credibility.Considering both components as independent constructs is still reasonable and valid, especially because statistical research has shown discriminant validity.Overall, the fact that both argument quality and peripheral cues affect review credibility in different effect sizes supports the ELM's assumption that both information processing routes may be triggered simultaneously and vary in intensity.

Limitations of the Study
Review quantity, quality, reviewer competence, and product/service rating affect buy intention, with customer trust as a moderator.However, the study has limitations.Consumer behavior is heavily influenced by contextual factors, making it difficult to generalize findings across industries, products, and services.The study may also overlook temporal differences in customer trust and purchasing decisions.Survey design and sample methods can potentially limit the reliability and validity of variable measurements.The study may also overlook other moderating variables like brand loyalty or customer differences because it focuses on consumer trust.Cross-cultural analyses, longitudinal studies to track changes, comparative industry studies, experimental designs to improve causal inferences, and studies on how emerging technologies affect consumer trust and online review interpretation are possible research directions.Addressing these limitations and investigating these future research directions will help us grasp the complex relationships in the modern marketplace.

Practical Implications
This study's findings have significant implications for e-commerce and social media marketing.Reliable companies must understand how consumers perceive and assess online review credibility, especially what factors determine review credibility from the consumers' perspective, to address the erosion of online review credibility.These consumer-focused data are crucial for ecommerce enterprises with a strong market and customer focus.Our comprehensive and integrative methodology provides marketing managers with several substantial starting points by identifying a wide range of key factors of online review reliability.First, marketing managers should recognize peripheral cues' dominance and use them to boost review trustworthiness.For instance, they should display quality seals on their website to boost website reputation.To demonstrate reviewers' competence, they should use prominent symbols or iconography.The use or prominent display of product/service ratings may further boost consumer review trustworthiness.
This study's most groundbreaking finding is that review quantity contradicts the corporate practice of boosting online reviews for a product or service.Accordingly, marketing managers should be aware that too many reviews may backfire and arouse consumer distrust.After reaching a critical quantity of reviews, they should no longer actively encourage consumers to submit online reviews, but rather verify quality, particularly accuracy and completeness.Although peripheral indications are more important in determining review trustworthiness, marketing managers can also examine argument quality, such as online review correctness and thoroughness.Marketing managers could reward customers with vouchers, discounts, or points for a customer bonus program for posting an online review that passes accuracy and completeness standards.Additionally, they might install a monitoring system that can detect erroneous or incomplete reviews and respond to them by posting a corrected comment with accurate or complete information.
The study findings can also help businesses minimize the impact of fabricated online reviews on consumers' perceived legitimacy by providing a reference point for planning and implementing efficient forgery and fraud detection methods for online reviews.Our study does not propose explicit strategies to minimize such effect, but it does identify the consumer-perceived drivers of review credibility and the characteristics these approaches should target.Therefore, suppliers should prioritize improving online reviews' accuracy and completeness, reviewer expertise, product/service rating, and website reputation to boost consumer credibility.Overall, understanding the determinants of online review credibility and its impact on consumers' purchase intentions is crucial to e-commerce research and practice because it helps us understand consumers' shopping behavior and boosts companies' competitiveness (Lin et al., 2018).Despite these contributions to research and practice, this study has certain critical limitations that should be considered but may also be good beginning points for future research.This applies especially to discoveries that contradict past research and this study's initial assumptions.In this context, the non-significant effect of review consistency suggests examining moderating variables

Figure
Figure No 1: Conceptual Framework

Table 2 -
Outer Loadings shows the outer loadings for structural equation model structures.Outside loadings show the strength and direction of the association between each observed variable (indicator) and its latent construct.These constructs are Consumer Trust (CT), Moderating Effect (ME), Purchase Intention (PI), Product/Service Rating (PSR), Review Credibility (RC), Review Expertise (RE), Review Quality (RQ), and Review Quantity and Length.Consumer Trust (CT) has high outer loadings for its indicators (CT1, CT2, CT3, CT4), ranging from 0.962 to 0.975, showing a strong connection between the observed variables and the latent construct.Purchase Intention (PI) has outside loadings of 0.964 to 0.973 for its four indicators (PI1, PI2, PI3, PI4), indicating a strong link.Review Credibility (RC) has outer loadings (RC1, RC2, RC3, RC4) from 0.959 to 0.975, indicating a strong correlation between review credibility and observed variables.Similar to Product/Service Rating (PSR), Review Expertise (RE), Review Quality (RQ), and Review Quantity and Length (RQL), large outer loadings indicate reliable measurement of latent variables.The interaction variables between Consumer Trust and Review Credibility (CT * RC1, CT * RC2, CT * RC3, CT * RC4) estimate the moderation impact (ME).The moderating effect on Consumer Trust and Review Credibility is strong, as seen by the moderate outer loadings on these interaction variables (0.648 to 0.687).The measurement model's robust outer loadings across all constructs support the structural equation model's latent construct-observed variable correlations.These findings underpin study analyses and interpretations.

Table No 5: Heterotrait-Monotrait Ratio (HTMT)
to their shared variance using HTMT values.The HTMT values between constructs are consistently below 0.85, indicating strong discriminant validity.Consumer Trust (CT) and Moderating Effect (ME) have HTMT values of 0.694, significantly below the threshold, showing that both variables are unique and share little variance.CT and Purchase Intention (PI), CT and Product/Service Rating (PSR), and CT and Review Credibility (RC) also have HTMT values < 0.85, supporting discriminant validity.These findings support the idea that each model latent construct measures a distinct concept.