Consumer trust in E-commerce
Author : Ninad Malaji Rawool
Literature Review :
1.Voice Chatbots and Consumer Trust in E-Commerce
Gharib et al. (2025) studied the role of conversational AI and voice-based chatbots in improving consumer engagement and trust in e-commerce. The authors explained that AI-powered chatbots help firms communicate with customers more easily and efficiently. The study compared voice mode and text mode chatbots to understand consumer preferences. Results showed that consumers prefer voice-based chatbots when they seek service assistance on e-commerce websites. Voice interaction gives users a sense of control and makes communication feel more natural. The researchers highlighted perceived technological optimism (PTO) as an important factor influencing chatbot acceptance. The study also found that cognition-based trust (CBT) develops when consumers experience smooth and reliable chatbot interactions. This trust further improves perceived customer care (PCC). Higher trust and customer care lead to stronger continuance intention toward e-commerce platforms. Overall, the literature suggests that voice-based chatbots help build trust, engagement, and long-term relationships in e-commerce (Gharib et al., 2025).
2. AI Personalization, Trust, and Buying Intention
Jith et al. (2025) examined the impact of AI-driven personalization on consumer trust and purchase intention in e-commerce platforms. The authors explained that artificial intelligence helps online retailers customize content, recommendations, and offers for users. The study found that AI personalization significantly increases purchase intention by making shopping more relevant and convenient. However, the research also highlighted complex trust issues linked to data privacy and perceived intrusiveness. Transparency in data usage was identified as a key factor in building consumer trust. When consumers clearly understand how their data is used, their trust in e-commerce platforms improves. The study also showed that perceived control over personalization positively influences trust. Using Technology Acceptance Model (TAM) and Privacy Calculus Theory, the authors explained how consumers balance benefits and risks. While personalization improves conversion rates, excessive personalization can reduce trust. Overall, the literature suggests that e-commerce firms must balance AI personalization with privacy protection to maintain consumer trust and encourage purchase intention (Jith et al., 2025).
3. Online Reviews, Trust, and Buying Decisions in E-Commerce
Kiruthika and Arif (2025) studied the factors influencing consumer trust and purchase intention in e-commerce, with special focus on online reviews and star ratings. The authors explained that online shoppers often depend on reviews and ratings when they cannot physically check products. Positive review valence and higher average star ratings were found to strongly increase consumer trust. The study showed that trust plays a central role in shaping online purchase intention. When consumers trust an e-commerce platform, they are more likely to make buying decisions. The findings also revealed that trust acts as a mediating factor between reviews, ratings, and purchase intention. This means reviews and ratings influence buying behavior mainly through building trust. Kiruthika and Arif highlighted that helpful and recent reviews improve consumers’ confidence in online platforms. The study further emphasized that visible security, return policies, and customer support strengthen trust in e-commerce. Overall, the literature suggests that online reviews and ratings are powerful tools for increasing trust and encouraging purchases in e-commerce settings (Kiruthika & Arif, 2025).
4. AI Personalization and Consumer Trust in E-Commerce
Koneti (2025) studied the impact of AI-powered personalization on consumer trust in e-commerce and digital marketing strategies. The author explained that artificial intelligence helps online platforms offer personalized experiences based on consumer behavior and preferences. Such personalization increases user engagement and improves conversion rates. However, the study highlighted growing concerns related to data privacy, algorithmic transparency, and perceived manipulation. These concerns can negatively affect consumer trust in AI-driven systems. The findings showed that while consumers enjoy relevant and customized content, their trust depends on how ethically data is used. The study emphasized that transparency and consumer control are key factors in maintaining trust. When users feel they have control over their data, trust in personalization increases. The research also noted that excessive personalization may create discomfort among consumers. Overall, the literature suggests that a balanced approach between personalization and privacy protection is essential for sustaining trust in e-commerce (Koneti, 2025).
5. Transparency and Trust in E-Commerce Recommendations
Li et al. (2024) examined how recommendation system transparency (RST) affects consumer trust in recommendation systems (CTRS) in e-commerce. The authors explained that recommendation systems are useful but often suffer from low consumer trust. Using a three-layered trust model, the study analyzed how transparency shapes trust outcomes. The findings showed that transparent systems increase perceived effectiveness, which positively influences consumer trust. At the same time, transparency reduces consumer discomfort, which otherwise lowers trust. Both perceived effectiveness and discomfort were identified as parallel mediators between transparency and trust. The study also found that domain knowledge acts as a moderator, strengthening the positive effect of transparency on perceived effectiveness. Consumers with higher product knowledge trusted transparent systems more than those with low knowledge. Gender differences were observed, as trust varied for experience products but not for search products. Overall, the literature suggests that transparent recommendation systems enhance trust by improving effectiveness and reducing discomfort in e-commerce environments (Li et al., 2024).
6. Dropshipping, Trust, and Purchase Intention
Ong et al. (2025) examined the relationship between dropshipping, consumer trust, and purchase intention in the Philippine e-commerce market. The study explained that dropshipping allows online sellers to sell products without holding inventory. Consumers form opinions about dropshipping based on reliability, delivery efficiency, and seller transparency. Positive perceptions of dropshipping were found to increase purchase intentions among online shoppers. However, negative perceptions reduced purchase intention, especially when trust in the platform was low. The research highlighted consumer trust as a key mediating factor between dropshipping practices and buying behavior. When consumers trust the e-commerce platform, they are more willing to purchase from dropshipping sellers. The study used regression analysis and mediation analysis to support its findings. It also emphasized the importance of trust-building strategies for online platforms using dropshipping models. Overall, the literature suggests that improving trust and transparency is essential for the success of dropshipping in e-commerce (Ong et al., 2025).
7. AI Streamers, Trust, and Purchase Intention
Shui et al. (2025) examined how AI virtual streamers influence consumer trust and purchase intention in live-streaming e-commerce. The study explained that AI streamers are a new form of digital sellers used in online live-streaming platforms. Guided by stimulus–organism–response (SOR) theory, the authors analyzed how streamer features affect consumer behavior. The results showed that image characteristics such as cuteness and vitality positively influence purchase intention. The research also found that scenario fit between the product and live-streaming environment improves consumer responses. However, ability characteristics like professionalism and responsiveness did not significantly increase purchase intention. Consumer trust was identified as a key mediating variable between AI streamer characteristics and purchase intention. When consumers trust AI streamers, they are more willing to buy products promoted in live streams. The study further revealed that consumer innovativeness negatively moderates the trust-building effect of AI streamers. Overall, the literature suggests that building emotional appeal and trust is crucial for AI streamers to succeed in live-streaming e-commerce (Shui et al., 2025).
8. Impact of Data Breaches on Consumer Trust and Privacy in E-Commerce
Singh et al. (2025) examined the impact of personal data breaches on consumer trust and privacy protection behavior in e-commerce. The authors explained that the rapid growth of online shopping has increased companies’ dependence on personal consumer data to enhance user experience. However, frequent data security failures have raised serious concerns about data privacy among online shoppers. According to the study, personal data breaches do not only cause financial loss but also strongly damage consumer trust, which is a key factor in long-term e-commerce success. The researchers found that when consumers lose trust after a data breach, they become more cautious in sharing personal information online. This reduced trust leads consumers to adopt stronger privacy protection behaviors, such as limiting data sharing and avoiding certain platforms. The study also highlighted that trust acts as a mediator between data breaches and consumer behavioral changes. Singh et al. emphasized that consumers now evaluate e-commerce platforms based on their ability to protect personal information. The findings suggest that businesses must strengthen data protection systems to rebuild trust after breach incidents. Overall, the literature shows that protecting consumer data is essential for maintaining trust and sustaining growth in the e-commerce environment (Singh et al., 2025).
9. AI Personalization, Trust, and Buying Decisions
Singhal et al. (2025) examined how AI-powered personalization influences consumer perceptions, trust, and purchase decision-making in e-commerce. The authors explained that artificial intelligence helps online platforms offer personalized product recommendations using machine learning and recommender systems. Such personalization improves convenience, relevance, and overall shopping experience for consumers. The study found that personalization has a positive effect on purchase intention when consumers trust the AI system. However, excessive or over-personalization can create feelings of discomfort and reduce trust. The research highlighted consumer trust as a key mediating factor between personalization and buying decisions. It also emphasized concerns related to data privacy, algorithm transparency, and ethical AI use. When consumers feel their data is handled responsibly, their trust in e-commerce platforms increases. The study further showed that privacy concerns can weaken the positive impact of personalization on trust. Overall, the literature suggests that balancing AI personalization with transparency and privacy protection is essential for long-term trust and success in e-commerce (Singhal et al., 2025).
10. Trust Factors in Livestream E-Commerce
Zhang et al. (2025) investigated the trust entities that influence consumer purchase intention in livestream e-commerce. The study explained that livestream shopping depends heavily on different types of trust, including trust in the streamer, product, platform, brand, and co-viewers. Using trust transfer theory (TTT) and social cognitive theory (SCT), the authors analyzed how trust moves between these entities. The findings showed that trust in streamers, products, brands, platforms, and co-viewers significantly affects purchase intention. Trust in the streamer and product was found to mediate the influence of co-viewers on buying decisions. The research also revealed that trust in the platform transfers indirectly through trust in streamers, products, and brands. Results from SEM and fsQCA highlighted that trust in streamers, co-viewers, and brands are the core drivers of purchase intention. Trust in the product played a secondary but supportive role in the buying process. The study emphasized that trust interactions are not equal and vary across different livestream contexts. Overall, the literature suggests that strengthening multi-level trust relationships is essential for improving purchase intention in livestream e-commerce (Zhang et al., 2025).
Conclusion :
The overall literature clearly shows that consumer trust is the central factor influencing purchase decisions in e-commerce. Across studies, technologies such as AI personalization, voice chatbots, recommendation systems, and AI streamers improve customer experience only when users feel safe and confident. Features like transparency, perceived control, ethical data use, and system reliability strongly help in building trust. Online reviews, star ratings, and livestream interactions also play an important role by reducing uncertainty and increasing confidence. However, the studies consistently warn that over-personalization, privacy invasion, and data breaches can quickly damage trust. Trust often acts as a mediator, meaning technology influences buying behavior mainly through trust. In livestream and social commerce, trust is shared between streamers, platforms, brands, and co-viewers, making it multi-level in nature. Consumers prefer technologies that feel human, transparent, and emotionally engaging, such as voice chatbots and friendly AI streamers. At the same time, strong data protection and security measures are essential to prevent loss of trust. Overall, the literature suggests that e-commerce success depends not just on advanced technology, but on using technology responsibly to build long-term consumer trust and loyalty.
References :
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Jith, M., Dhanalakshmi, M., Dhinakaran, D. P., Bambuwala, S., Buvaneswari, R., & Kumar, R. (2025). AI-driven personalization in e-commerce: Impact on consumer purchase intention and trust. European Economics Letters, 15(3), 3697–3710.
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