The recent rapid growth of e-commerce has essentially revolutionized the way people learn about products, conduct their analysis, and buy them. In the modern digital-linked market, customers are no longer limited to the normal advertising channel, they have gone out of their way and tried to find user-generated information like online reviews and ratings prior to making final purchases (Kabir et al., 2025). E-marketplaces, such as Amazon, Flipkart, and others, have built-in review systems that act as a decisive factor, influencing the perception of buyers at a global level (Novela et al., 2023).

Online reviews are a type of electronic word of mouth (e-WOM), which depicts genuine consumption experiences of actual users (Khalid et al., 2020; Siregar et al., 2024). It has been established that these reviews are very weighty whenever it comes to influencing consumer attitudes and intentions to purchase. As Magnani (2020) noted, online user reviews are both economically and behaviorally consequential and directly affect the sales of products and consumer preferences. On the same note, Watson and Wu (2022) observed that reviews change the information flow in the marketing systems, which allows making better and quicker purchasing decisions. The key to this relationship is the position of customer trust. Trust acts as a psychological link between what is conveyed in online reviews and the purchase behavior of consumers (Aziz and Ali, 2023). Whenever consumers believe that reviews are credible, authentic, and relevant, their confidence in the product and platform grows, which consequently leads to a purchase intent (Setiawati and Rani, 2025). The credibility, which depends on the knowledge of the reviewer, the quality of the review, and the transparency of the platform, was stated by Pooja and Upadhyaya (2024) as one of the main factors of the effectiveness of a review in influencing consumer behavior.

Reviews also influence purchasing outcomes due to their valence and volume of review. A systematic review of the literature by Rolando and Chondro (2025) concluded that positive reviews of customers play a significant role in increasing trust and online purchases, whereas negative ones increase perceived risk. In addition, Gooljar et al. (2024) have shown that the sentiment-based trends in online reviewing are more likely to predict consumer buying behavior in the 5.0 era of marketing. Although this dynamic is gaining increasing academic interest, little empirical studies are done on this dynamic in the Indian urban consumer case. The research, thus, seeks to fill that gap by examining the mediating effect of customer trust on the relationship between online reviews, ratings, and purchase decision with the consumer population in Bengaluru, India. Online reviews and ratings are now an essential component in the online marketplace and they have played a crucial role in determining how consumers buy products online. Although there is increasing reliance on e-commerce, the authenticity of such reviews is doubtful, and this can have an impact on the degree of customer trust. This research paper seeks to discuss the influence of online reviews and ratings to the buyer with regard to their trust levels and subsequent decisions to purchase. Knowledge of this relationship assists the e-commerce sites and marketers in their effort to make their communications more transparent, reliable, and engaging to customers to increase the overall sales performance and satisfaction levels among the customers.

LITERATURE REVIEW

Although there is abundant literature on the role of online review in influencing trust and purchase decisions in the West and globally (Rolando and Chondro, 2025; Magnani, 2020), there is limited empirical research on the impact of online review on the purchase decision of Indian urban consumers, such as Bengaluru. The main target of most reviews is the developed markets or hospitality without taking into consideration the local e-commerce dynamics and cultural peculiarities in the emerging economies.

OBJECTIVES

• To study the relationship between online reviews, ratings, and customer trust.
• To assess the influence of customer trust on online purchase decisions.
• To find out the level of dependence consumers place on online reviews before purchase.

RESEARCH METHODOLOGY

 This research is descriptive because it will establish an understanding and description of the effect of online reviews and ratings on the purchase choice of consumers in relation to forming trust by relying on opinions and perceptions obtained through the respondents. Gathered primary data from Online shoppers in Bengaluru through interviewing of the respondents in form of questionnaires that were structured specifically to determine how they view online reviews, ratings, and trust through Convenience sampling method a sample of 100 respondents were selected for the study. To analyze the data, the descriptive statistics will be used to describe the trends in data, correlation analysis, and regression analysis to define the connection between online reviews, customer trust, and purchase decisions.

CONCEPTUAL FRAMEWORK

RESULTS

• H1: There is a significant positive relationship between online reviews/ratings and customer trust.
• H2: There is no significant positive relationship between online reviews/ratings and customer trust.

REGRESSION ANALYSIS

Interpretation of Regression Analysis

 The multiple regression model is a significant predictor of customer trust based on the characteristics of the online reviews (F (4, 95) = 285.29, p =.000), which is an excellent fit with a R =.944, R 2=.892, and adjusted R 2=.888-explaining 89.2 percent of it. Detailed reviews with photos/videos became the best predictor (= .521, t = 5.34, p <.001) and then verified star ratings (=.358, t = 5.12, p <.001). The trust level is boosted considerably by positive reviews (p <.002). The negative effect of negative reviews has a significant negative influence (0 = -.306, t = -3.65, p <.001), thereby proving their eroding effect on trust. H1 is completely upheld and the association of the quality of review/ratings and customer trust formation is proved to be very strong.

7.2 CORRELATION

• H1: Customer trust significantly influences online purchase decisions, explaining substantial variance
• H0: Customer Trust has no significant predictive relationship with online purchase decisions

Interpretation of Correlation Analysis

 The Pearson correlation result indicates that all trust variables have significant relationships that are strong and significant (p <.001, N= 100). A positive correlation exists between the positive and detailed photo/video reviews (r =.932) and verified star ratings (r =.910) in positive reviews, as positive reviews have a very strong positive correlation with all other types of reviews. It is interesting to note that, negative reviews exhibit strong inverse relationships with all positive trust indicators (-.915 to -.930), which implies that they greatly undermine trust. All these findings endorse H1 that online review characteristics have a strong and significant effect on customer trust.

DESCRIPTIVE STATISTICS

Interpretation of Descriptive Statistics

 The review of 100 respondents shows some major patterns of purchase behavior based on the reviews. The average of the Positive Reviews Increase Trust (M = 4.01, SD = 1.040) and If I trust the reviews, I am more likely to buy the product (M = 4.12, SD = 0.956) mean a strong agreement, and very many consumers rely on reviews to make the purchase decision. The rating of the Detailed reviews with photos/videos build higher trust was moderately high (M = 3.99, SD = 0.969). The consistent review-reliance behavior is proved by the higher score of "Trusted reviews reduce hesitation of new sellers" (M = 3.55) and "I delay purchases until adequate reviews obtained" (M = 3.42). The negative review was rated at a smaller level (M = 2.31), which was selective skepticism among the respondents.

DISCUSSIONS

• The positive online reviews have a great effect as this has a mean score equal to 4.01 and a high regression coefficient which proves that the customers are highly dependent on positive reviews when assessing the credibility of products prior to the decisions made during purchases.
• Detailed reviews, including photos and videos, proved to be the best predictor of trust ( =.521, p <.001), which means that when reviews were made in the form of multimedia content, perceived authenticity was greater than when the reviews were made in the form of text only.
• Checked star ratings by the buyers have high degree of trustworthiness (β = -.358, p =.001), which suggests the platform verification systems are effective at creating confidence in the consumer over rating systems.
• Negative reviews are very damaging to trust (= -.306, p =. 001) in spite of great aggregate ratings, showing that consumers are most delicate to negative remarks in associations with product quality.
• The regression model demonstrates that it has 89.2% variance of trust formation (R2 =.892) and hence, supports the fact that there is a strong association between various review attributes and the level of customer confidence.
• Correlation analysis: There are very strong interrelationships between indicators of trust (r =.910 -.932, p <.001) with positive elements of reviews always supporting one another.
• There is strong customer trust in purchase decisions, and 80% of the respondents concurred that when they trust reviews, they are more likely to buy, which is in line with the trust-purchase behavioral pathway.
• Consumers are high-reliance on reviews as 67% of them will read them regularly or always prior to buying a product with a significant number of 6-10 reviews per product purchase.
• The preferred shopping platform is Amazon (47%), in which review systems most significantly affect electronics purchases, which is due to platform-specific trust dynamics.
• There is a moderate difference in income levels in attitudes to review reliance: however, higher-income groups (more than 1L) have a little less dependence on reviews as validation of purchases.

CONCLUSION

 The paper confirms that online reviews and ratings are essential antecedents that trigger customer trust that then induces online purchase intention among the customers in Bengaluru. Regression analysis showed that an excellent model (R 2 =.892) was found in which the detailed multimedia reviews and validated star ratings were the most significant predictors of trust, whereas negative feedbacks had a significant impact on reducing trust. Patterns of correlation also confirmed the interrelationship that existed between review attributes in creating consumer trust.

The main results illustrate that 67% of the respondents make a regular habit of reading 6-10 reviews prior to making a purchase, mostly on Amazon when buying electronics, which highlights the dependence of the reviews on the platform. The role of trust in the review-purchase process is mediated and 80 percent of the respondents concurred that trusted feedback enhances purchasing intention and lowers uncertainty toward new vendors.

These findings confirm the two hypotheses and address the research gap identified in the Indian urban setting in which the literature on the same mainly concerned Western markets. The e-commerce platforms are supposed to focus on verified and multimedia review systems/ authenticity to improve the rate of trust and conversion. Future studies may address longitudinal consequences and cross-regional analyses across India in order to be able to generalize them. The role of marketers is to understand genuine customer feedback as an asset to develop sustainable purchase behavior in competitive online markets.

SUGGESTIONS

• Introduce photo/video verification of reviews, which has highest trust effect (=.521) compared to text-only content.
• Show positive feedback badges with star ratings ( bek. =.358) to identify genuine feedback.
• Apply the algorithms to rank down the fake negative reviews ( = -.306) to safeguard the sellers against the malicious attacks.
• Filter by relevance/recency 67% of customers look at 6-10 reviews prior to buying.
• Electronics sellers: use detailed specification together with review (39 per cent high dependence) to sell technical products.
• New sellers: encourage early approved reviews to decrease 58% buyer hesitation.
• Combine sentiment analysis of campaigns with high review-trust correlations (r =.910-932).
• Create multimedia, verification and detail predictor based review quality scores.

REFERENCES

1. Novela, S., Sihombing, Y. O., Hansopaheluwakan, S., & Aurellia, C. (2023, November). How Online Customer Review and Online Customer Rating Influence Customer Purchase Decision on E-Marketplace with Customer Trust as A Mediator. In 2023 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD)(pp. 1-6). IEEE.
2. Faliha, N., Siti, A. E., Kusdi, R., & Andriani, K. (2021). Online reservation system and online customer review: Its impact on brand image, trust and hotel booking decision. International Journal of Economics, Business and Accounting Research (IJEBAR), 5(4).
3. Azis, E., & Ali, H. (2023). Literature review purchasing decisions: Price analysis, online consumer reviews and consumer trust. International Journal of Advanced Multidisciplinary, 2(3), 772-781.
4. Magnani, M. (2020). The economic and behavioral consequences of online user reviews. Journal of Economic Surveys, 34(2), 263-292.
5. Rolando, B., & Chondro, J. (2025). THE INFLUENCE OF CUSTOMER REVIEWS ON TRUST AND ONLINE PURCHASE DECISIONS: A SYSTEMATIC LITERATURE REVIEW. International Journal of Economics and Business Studies, 2(1), 33-59.
6. Watson, F., & Wu, Y. (2022). The impact of online reviews on the information flows and outcomes of marketing systems. Journal of Macromarketing, 42(1), 146-164.
7. Pooja, K., & Upadhyaya, P. (2024). What makes an online review credible? A systematic review of the literature and future research directions. Management Review Quarterly, 74(2), 627-659.
8. Pushparaj, P., & Kushwaha, B. P. (2024). Social media influencer marketing: a systematic literature review using TCM and ADO framework. International Journal of Consumer Studies, 48(6), e13098.
9. Khalid, J., Abbas, A., Akbar, R., Mahmood, M. Q., Tariq, A., Khatoon, M., ... & Din, M. J. U. (2020). Significance of electronic word of mouth (e-WOM) in opinion formation. International Journal of Advanced Computer Science and Applications, 11(2).
10. Setiawati, L., & Rani, N. S. A. (2025). From trust to purchase: a systematic review of online review valence, credibility, and consumer intentions. PaperASIA, 41(4b), 393-412.
11. Siregar, A. I., Johannes, J., Yacob, S., & Octavia, A. (2024). Electronic word of mouth and its effects on consumer decision-making: insights from an extensive literature review. Ekonomis: Journal of Economics and Business, 8(2), 1561-1567.
12. Kabir, M. H., Sultana, S., Hossain, M. M., & Islam, S. M. A. (2025). The impact of digital marketing strategies on consumer behavior: A comprehensive review. Business and Social Sciences, 3(1), 1-8.
13. Di Domenico, G., & Ding, Y. (2023). Between brand attacks and broader narratives: How direct and indirect misinformation erode consumer trust. Current Opinion in Psychology, 54, 101716.
14. Lopez-Lopez, D., & Iniesta, M. B. (2025). The impact of conversational AI on consumer decision-making: A systematic review and cluster analysis. International Journal of Engineering Business Management, 17, 18479790251351889.
15. Gooljar, V., Issa, T., Hardin-Ramanan, S., & Abu-Salih, B. (2024). Sentiment-based predictive models for online purchases in the era of marketing 5.0: a systematic review. Journal of Big Data, 11(1), 107.