Smartphones and mobile apps have reshaped human behaviour, and the social, cultural, and economic patterns. The food service sector has particularly evolved with platforms like Zomato, Swiggy, and UberEats, which have revolutionised how customer order and consume food. These digital aggregators reflect changing shopping habits by enabling users to browse food menus, compare prices, refer peer reviews and place orders. Experts estimate that India’s online shopping market will reach around $200 billion by 2026, with food delivery contributing substantially to this growth in future. 

The rapid rise of India’s food aggregator market highlights its growing relevance in the digital economy. Estimates indicate that the online food delivery industry, valued at $31.77 billion in 2024, may exceed $140 billion by 2030, increasing at a CAGR of 28.17% (Research and Markets, 2025). Similarly, IMARC Group (2024) projects $45.15 billion in 2024 and $320.31 billion by 2033. Collectively, these estimates affirms that smartphone penetration, rising incomes, urbanisation, and demand for convenience are propelling e-commerce through food aggregator platforms.

Within this context, e-WOM has become increasingly crucial. Online reviews, ratings, and peer recommendations reduce perceived risk and guide purchase decisions. Credible e-WOM enhances e-satisfaction and fosters e-trust by narrowing information gaps between customers and food aggregators. Positive reviews can strengthen brand credibility and encourage repeat purchases, whereas, negative feedback can quickly damage reputation of a brand.

Although prior studies have explored e-commerce broadly, limited research examines the combined influence of e-satisfaction, e-WOM credibility and e-trust in the food aggregator context. This study has identified this gap of knowledge and it investigates how e-satisfaction and e-WOM credibility interact to build trust and affect repurchase intentions, with e-WOM credibility moderating the link between satisfaction and trust. Findings will offer practical insights to help platform managers to enhance user satisfaction, leverage trustworthy peer reviews and strengthen consumer loyalty.

e-satisfaction and e-trust

Kim et al. (2009) defined e-satisfaction as the overall satisfaction experienced by customers following a purchase and repeated interactions with products or services on an online platform. Moreover, Zeithaml et al. (1996) argue that dissatisfied customers are prone to switch to other brands, while satisfied customers are more inclined to endorse and repurchase goods and services (Anderson & Srinivasan, 2003). Kim et al. (2009) conducted a study that found that e-trust and service quality are influential factors in e-satisfaction. Kim et al. (2009) studied the relationship between e-satisfaction and e-trust was found to be significant as well. More recent studies such as (Martínez-Navalón et al., 2019), show how in tourism companies, user satisfaction on social networks influences trust in these companies. (Lai, 2014) demonstrated the relationship between satisfaction with a travel agency and improved trust in the travel agency. Another study elaborated by Liang et al. (2018), explores the relationship between satisfaction, trust and repurchase intention. Gelashvili et al. (2021) demonstrated that the satisfaction of users who make restaurant reservations via a mobile Apps has a direct impact on trust in those restaurants.

H1: e-satisfaction on online shopping websites has a positive impact on e-trust

E-WOM credibility as moderator

Electronic word-of-mouth (E-WOM) refers to online shoppers' real, previous, or potential product reviews (Hennig-Thurau et al., 2004). Customers' views of the dependability, validity, and explanatory value of easily available e-WOM data are called "e-WOM credibility" (Daowd et al., 2021). E-WOM credibility is consumers' perception of e-retailer websites' claims, reviews, and recommendations in relation to real-world conditions (Mannan et al., 2019). E-commerce shoppers often base their purchases on e-WOM. Positive word-of-mouth (WOM) boosts e-customer pleasure, perceived value, and trust, increasing their likelihood of buying again. It influences e-commerce consumer behaviour and provides important information and recommendations. Marketers often use expert reviewers or offer incentives to boost product e-WOM (D. Shin et al., 2014). J.-K. Shin & Lee (2018) argue that e-WOM's number and quality boost e-Trust, which influences customers' online shopping intentions. According to (Miao et al., 2022), internet shoppers repurchase intentions are strongly influenced by word-of-mouth (WOM). Positive word-of-mouth (WOM) boosts e-customer pleasure, perceived value, and trust, increasing their likelihood of buying again. Bulut & Karabulut (2018) studied the amount and quality of e-WOM affects consumers' trust in online retailers, which affects their chance of making additional purchases online. The moderation role of WOM has been explored regarding green purchase intention by (Mansoor & Noor, 2019), the study claimed that positive. This is because customers tend to forget the negative aspects of the information with time, and their familiarity with the brand is mistakenly equated to like, as explained by (Berger et al., 2010). While the impact of word-of-mouth (WOM) on purchase intention and behaviour has been extensively studied in several marketing scenarios, there is a lack of research on the influence of WOM credibility in relation to repurchase intentions in online purchasing. Subsequently, the following hypotheses arise:

H2: Word-of-mouth (WOM) significantly moderates the relationship between e-satisfaction and e-trust in online shopping websites.

Figure 1: conceptual Model

Population and Sample

The proposed model assesses the direct and indirect relationships of e-customer satisfaction on repurchase intentions, and the study has utilized quantitative techniques to support these claims. People who have shopped online before are the focus of this research. A total of 409 samples were collected using a purposive sampling strategy, taking into consideration the large size of the population. According to Hair et. Al (2010), the “10-times rule” suggest that the sample size should be at least ten times the number of links to a latent variable. Based on this, 90 responses (9x10) were required for conducting qualitative analysis. Primary data were collected through a Google Forms survey distributed via email and social media platforms, and were analysed using PLS-SEM 4.0 to examine complex relationship between the variables in the study.

Measures

E-satisfaction was measured employing a three-item scale taken from Wang et al. (2019) which has a reliability score of 0.84.  One example of the scale is “My experience with the food aggregator apps/websites is highly satisfactory.”  E-trust was assessed with a three-item scale developed by Kim and Yum (2024), where the scale generated a reliability score of 0.865, the example of a statement, “I consider the platform highly reliable.”  The third construct “E-WOM credibility” was assessed utilizing a three-item scale adapted from Siddiqui et al. (2021) which had a reliability score (α) of 0.799.  Each item was measured using a five-point Likert scale ranging from 1= “strongly disagree” to 5= “strongly agree.”

Common method biasness

The post-hoc procedure known as the Harman one-factor analysis is performed subsequent to data collection in order to ascertain whether the observed variance can be attributed to a single factor (Podsakoff et al., 2003). The research here used Harman's single-factor test, which yielded a one-factor variance extraction of 45.746% (less than half, or 50%). So, there was no evidence of CMV.

Data Analysis

Data analysis includes both descriptive statistics and SEM analysis. The frequency distribution table is used to present the sample profile. Whereas, the SEM model was to establish the hypothetical relationship between the latent variables.

Table 1. Sample Profile

It can be viewed from the table 1 that among the 409 respondents, Male constitutes the significant proportion of (51.8%) and the female represent (48.2%). Out of 409 respondents, most of the respondents 35.0% have completed their Graduation followed by 27.4% are under graduates, 20.8% have completed their matriculation and only16.9 % of respondents are having an educational qualification ever post-graduation or above. Regarding occupation it can be observed that majority of respondents were students (35.0%) then service holder (30.0%), followed by Businessman (18.6%) and housewives (11.0%). Majority of the respondents belongs to the age group of 25-35 years (37.9%) indicating younger respondents.

Measurement Model

This analysis assesses the reliability of constructs, the significance of factor loadings, convergence, and discriminant validity (J. F. Hair et al., 2011). The findings are presented in Table 2. According to the recommendation by (J. F. Hair et al., 2011), to achieve an acceptable level of convergent validity, the factor loadings should be more than 0.5. One item RE3, was excluded from the model because its factor loading was less than 0.5. According to George & Mallery (1999), internal reliability was assessed using Cronbach's Alpha.

Table 2. Assessment of measurement model and multicollinearity

A value of Cronbach's alpha greater than 0.7 is considered acceptable. All items or constructs in the study satisfied the reliability criteria since their computed value exceeded 0.7. According to (J. F. Hair et al., 2020), an internal composite reliability value greater than 0.7 indicates internal consistency and a convergent validity value greater than 5.0 is considered acceptable. Each construct's convergent validity value is higher than 0.5 what is considered acceptable in social science studies.  Table 2 shows that the variance inflation factor (VIF) values for each construct are less than 5, indicating that there is no multicollinearity problem. This assessment was done to rule out the possibility of multicollinearity. According to Fornell & Larcker (1981), it was ensured that the correlation between groups of constructs wasn't greater than the diagonal values, which are supposed to be the square root of the average variance retrieved for each construct.  Therefore, we confirm the discriminant validity and present the results in Table 3.

Table 3. Discriminant Validity

Structural Model and testing of hypothesis

Furthermore, a bootstrapping method involving 5,000 resamples was used to compute beta coefficients, t-values, and p-values (J. F. Hair et al., 2011). The hypotheses H1  that "Customer e-satisfaction has a positive effect on customer e-trust in online shopping" was supported by the data in Table 4 and picture 2, which show that e-customer satisfaction has a positive and significant impact on e-trust (β = 0.669 and p<0.05). Following this, we examined how the credibility of electronic word of mouth (e-WOM) mediated the connection between e-trust and e-satisfaction. There is a strong correlation between e-satisfaction and e-trust, and this finding suggests that the credibility of e-WOM moderates this relationship. The positive correlation between e-satisfaction and e-trust is strengthened when the credibility of e-WOM grows, according to the positive coefficient (B = 0.220, p<0.000). So, we agree with H2. That is to say, when consumers have faith in the evaluations and recommendations they see online, they are more likely to trust the platform or seller after a positive experience with the service or product.

Figure 2. SEM model

Table 4. Path and Hypotheses Analysis

The explanatory power of the model was evaluated using the coefficient of determination (R²) and predictive relevance (Q²) of the endogenous construct i.e. e-trust. According to J. Hair et al. (2014), R² values between 0.25 and 0.50 indicate weak explanatory power, values between 0.50 and 0.75 suggest moderate explanatory power, and values above 0.75 reflect substantial explanatory power. The findings indicate that e-trust achieved an R² value of 0.623, signifying a moderate level of explanatory power. In addition, the predictive relevance of the model was assessed using the Q² statistic. J. Hair et al. (2014) state that when Q² is greater than zero, the model demonstrates predictive relevance, meaning the exogenous constructs have strong predictive ability for the endogenous construct. In this study, the Q²-value for the construct e-trust was found to be 0.573, confirming that the model has high predictive accuracy.

The research illustrates that e-satisfaction substantially affects user behavior on food aggregator websites. The findings indicate a positive relationship between e-trust and e-satisfaction. People trust Zomato, Swiggy, and Uber Eats when they have had favorable experiences with them in the past, like when their orders were delivered on time, correctly, and without hassle. They trust online transactions because they know they will always provide reliable services. E-WOM credibility was identified as a strong moderator in the relationship between e-satisfaction and e-trust towards food aggregator service providers in India. Reviews on the internet can really change how much people trust or like your brand. People might stop trusting the service if the reviews are not true and accurate, even if the food aggregator provides a committed service. The study also shows that just making customers happy is not sufficient to build trust, whereas word of mouth is necessary to keep customers' trust towards the food aggregator brands.

Implications

It is essential for food aggregator platforms to focus on enhancing service quality and ensuring that online reviews are authentic and reliable. Doing so helps strengthen customer trust and satisfaction. Managers should implement robust review versification systems that assess and validate customer feedback, making the overall service experience more consistent and trustworthy. In the digital marketplace, offering secure payment option, transparent refund processes, and dependable service operations can significantly reduce customer anxiety (Kim & Yum, 2024). Platforms should also verify review scores, flag suspicious programs, referral bonuses, and promotional rewards can enhance customer satisfaction, encouraging repeat purchases and long-term loyalty. As highlighted by Jain et al. (2023), genuine positive reviews build customer trust and motivate continuous engagement with brand. In an increasingly competitive digital landscape, business can gain an advantage by delivering superior services and managing e-WOM effectively.

The study highlights that customer satisfaction with e-services significantly influences both loyalty and trust toward food aggregator platforms. The findings further affirm that e-satisfaction is a key determinant of users’ trust in such systems. Moreover, e-WOM credibility plays a crucial moderating role, strengthening the impact of e-satisfaction on e-trust through dependable online reviews. The results also indicate that customers are more inclined to male repeat purchases when they encounter positive feedback about a brand on digital platforms. Regarding scope for future research, scholar may examine the limited impact of privacy concerns on e-satisfaction in the food delivery sector. Additional investigations could explore how privacy perceptions and customer attitudes differ across demographic groups, thereby identifying unexplored dimensions within the field. The insights derived from this study can help managers design effective strategies to enhance customer satisfaction, build trust, and strengthen customer confidence in India’s rapidly expanding e-commerce industry.

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