Relationship between seller attributes, trust and repurchase intention in e-marketplaces

Relação entre atributos do vendedor, confiança e intenção de recompra em e-marketplaces

Abstract

Purpose:  This research aims to analyze antecedents of consumer trust and repurchase intention in sellers who operate on e-marketplaces.

Originality/value:  E-marketplaces are expanding worldwide, acting as intermediaries in buying and selling products and services on the Internet. In this context, few studies have sought to identify factors that lead consumers to buy on e-marketplaces, with trust being identified as the main influencer. However, the factors associated with third-party sellers in e-marketplaces and their impact on consumers’ purchasing intentions is relatively unknown in the literature.

Design/methodology/approach:  We study four important attributes associated with third-party sellers in e-marketplaces identified in the literature, namely, customer-generated evaluation, perceived price, product delivery, and information quality. The proposed model was tested with 470 Brazilian consumers from different e-marketplaces through structural equation modeling based on the partial least squares method.

Findings:  The study concluded that the four tested antecedents influence consumers’ trust in sellers, with information quality being the main predictor. We also confirmed that trust remains a fundamental antecedent of repurchase intention in third-party sellers in e-marketplaces, as well as the frequency of consumers’ purchases on these online platforms and their previous experience with the third-party store.

Keywords:
e-marketplace; e-commerce; trust; continuity of use; repurchase intention

Resumo

Objetivo:  Essa pesquisa busca analisar antecedentes da confiança e da intenção de recompra de consumidores em vendedores que atuam em e-marketplaces.

Originalidade/valor:  Os e-marketplaces se encontram em plena expansão no mundo, atuando como intermediários no processo de compra e venda de produtos e serviços pela Internet. Nesse contexto, poucos estudos têm buscado identificar os fatores que levam os consumidores a comprar em e-marketplaces, sendo a confiança apontada como o principal influenciador. No entanto, os fatores associados aos vendedores presentes nos e-marketplaces e sua relação com a intenção de compra dos consumidores são pouco conhecidos na literatura.

Design/metodologia/abordagem:  Foram identificados na literatura quatro importantes atributos associados aos vendedores que atuam em e-marketplaces, sendo eles: avaliação gerada pelo cliente, preço percebido, entrega do produto e qualidade da informação. O modelo proposto foi testado junto a 470 consumidores brasileiros de diferentes e-marketplaces, por meio da técnica de modelagem de equações estruturais, baseada no método PLS.

Resultados:  O estudo permitiu concluir que os quatro antecedentes testados influenciam a confiança dos consumidores nos vendedores presentes em e-marketplaces, sendo a qualidade da informação disponibilizada pelo vendedor o principal preditor. Confirmou-se, ainda, que a confiança continua um antecedente fundamental da intenção de recompra nos vendedores que atuam em e-marketplaces, assim como a frequência de compra dos consumidores em e-marketplaces e a sua experiência prévia com a loja terceira.

Palavras-chave:
e-marketplace; comércio eletrônico; confiança; continuidade de uso; intenção de recompra

INTRODUCTION

Online commerce has experienced impressive growth in recent years. Global sales in 2024 amounted to over US$7 trillion and are expected to exceed US$10.4 trillion by 2028 (Coppola, 2025). In emerging countries such as Brazil, the largest and most populous country in Latin America, Internet sales reached almost US$ 40 billion in 2024, and it is estimated that this revenue will surpass US$60 billion by 2027 – representing a growth of 50% in the period (Statista, 2025a). This expansion has encouraged the emergence of large e-marketplaces – digital platforms that act as intermediaries in the buying and selling of products or services over the Internet, connecting different sellers and consumers (Cano et al., 2022).

Furthermore, social distancing resulting from the COVID-19 pandemic became another driver for this market, especially because many traditional stores were forced to migrate to the online environment, and e-marketplaces were a path followed by thousands of them (Cano et al., 2022). As of 2024, e-marketplace platforms have accounted for the largest share of online purchases worldwide (Statista, 2025b); in Brazil, these online platforms became the leaders in sales and monthly access to online commerce, occupying the top ten positions (Rodrigues, 2022). Among the reasons given by online consumers, there is their perception that it is more advantageous to buy from e-marketplaces than from online stores, as they can simultaneously compare prices and products from different sellers (Kuviatkoski, 2022). Likewise, consumers feel more confident when purchasing products from better-known stores or platforms (Maia et al., 2022), such as Amazon and Mercado Livre, the leader e-marketplaces worldwide and in Latin America, respectively. The main motivations that have influenced traditional and e-commerce companies to modify their business models for e-marketplaces are: the possibility of expanding their sales and delivery range, reducing inventory, logistics time, and transaction costs (Cano et al., 2022).

On the other hand, some problems related to e-marketplaces have driven away specific consumers (Malak et al., 2021), whether due to negative experiences caused by sellers, the online platform, or the logistics operators responsible for delivering the products. Additionally, some consumers perceive risks associated with technology, such as concerns about the privacy of their data and the security of transactions with third-party sellers. These risks include the possibility of not receiving the product, receiving it damaged or different from the advertisement (Inoue et al., 2019; Soleimani, 2022).

Research on e-marketplaces is still recent, and the majority was conducted using the B2B model. Regarding B2C e-marketplaces, little research has been conducted to study the success of this type of enterprise, especially analyzing the factors associated with third-party sellers and their relationship with consumers’ purchasing intentions (Cano et al., 2022). In many cases, consumers do not even realize they are buying from a third-party seller, which explains why around 30% of them do not remember the name of the seller from whom they purchased in the e-marketplace (Oliveira, 2023). Therefore, conducting new research that identifies factors and characteristics that influence consumer behavior on these platforms becomes relevant for researchers and practitioners interested in this topic and can contribute to identifying more efficient ways of reaching and retaining customers and providing better services to consumers.

The following question was proposed to fill this research gap: What factors related to the seller operating on e-marketplace platforms influence consumers’ purchasing behavior? Through a survey conducted with 470 Brazilian consumers from different e-marketplaces, we aimed to analyze antecedents of consumers’ trust and repurchase intention in sellers operating in e-marketplaces. The paper is structured as follows: section 2 presents a literature review on e-marketplaces, highlighting their characteristics, advantages, and disadvantages. The third section presents the research model and the proposed hypotheses, followed by the methodology in the fourth section. The results and their discussion are presented in section 5, with the main conclusions, contributions, limitations, and suggestions for future research in section 6.

E-MARKETPLACES

With technological advances, new business models based on digital technology platforms have emerged, including e-marketplaces or virtual markets, which have changed several processes in marketing and supply chain (Täuscher & Laudien, 2018). Among the several types of e-commerce associated with e-marketplaces, there is the Business-to-Business (B2B) model, in which the online platform mediates between companies and suppliers, regardless of the product or service. This means these companies can take advantage of benefits provided by the platform, such as advertising and cost reduction (Farias et al., 2022; Hong & Cho, 2011). In the Consumer-to-Consumer (C2C) model, the transaction occurs between consumers through the online platform, which favors the direct sale of products between these consumers and allows payment through the platform itself, facilitating the transaction and increasing the participants’ trust in the transaction. Finally, the Business-to-Consumer (B2C) model enables companies to sell their products directly to consumers, who, by using the platform structure as an intermediary, have made their purchases easier, in addition to being able to use it as a price comparison tool. Hossain et al. (2021) highlight that smaller companies often lack the technical capacity and infrastructure to maintain an online store. However, it becomes easier to enter this market through an intermediary, taking advantage of the benefits that e-marketplaces provide to their sellers.

Among the several types of e-marketplaces, B2C stands out for having the most successful platforms in different sectors. Amazon, for example, was a pioneer in the sector, starting its activities in the 1990s, and is currently considered one of the largest companies operating in e-commerce (Kiniulis, 2025). Still regarding the types of e-marketplace, Turban et al. (2008) classify them into four different categories: (i) generic stores, which sell any types of products, operating similarly to large department stores, (ii) specialized (or niche) stores, which sell a single product or a few types of products, (iii) regional stores, operating in a single location or region – or global stores, present in several countries, and (iv) pure online stores, which do not have a physical store, and “click-and-mortar” (or “bricks-and-clicks”) stores, which sell products online and also in physical stores.

Recognizing the possibility of increasing their profits by making any product sought by their customers available on their platforms, e-commerce giants began to integrate sellers into their platforms. They transformed their traditional e-commerce models into large e-marketplaces, aiming to diversify their sources of revenue and increase customer traffic on their platforms (Farias et al., 2022). In general, e-marketplaces tend to be widely recognized in the market and, as a result, attract a vast number of visitors, providing greater visibility to sellers who advertise their products on the platform (Francisco, 2021). In some cases, they are also responsible for delivery logistics and product guarantees (Farias et al., 2022). Many of these retailers have website layouts that frequently leave consumers unaware that they are purchasing from a third-party seller.

Cano et al. (2022) and Farias et al. (2022) highlighted several advantages and disadvantages from both the sellers’ and buyers’ points of view. When using an e-marketplace as an online sales channel, the seller benefits from a smaller initial investment compared to creating their own store. Furthermore, the e-marketplace offers significant potential to leverage the business, as these platforms are widely known and act as virtual showcases with great reach. Another crucial benefit is the high sales conversion rate observed in e-marketplaces compared to proprietary e-commerce sites, resulting from the trust in the intermediary platform and the high number of visitors they attract (Francisco, 2021; Hong & Cho, 2011). On the other hand, one disadvantage of sellers using e-marketplaces is the fact that the brand of the intermediary platform stands out in relation to the third-party store. An associated disadvantage identified in the literature is the difficulty in strengthening brand positioning. This occurs because sellers lose their identity within the platform since the layouts of e-marketplaces highlight the products offered to the detriment of third-party stores. Another disadvantage is the high fee to the intermediary for each product sold, which is not generally the case on their own websites (Farias et al., 2022).

Considering the buyer’s side, what makes the use of e-marketplaces more attractive is the convenience of searching for a specific product on a single channel, where it is possible to check the supply of the same item by different sellers and simultaneously compare prices between several stores. Another advantage of e-marketplaces when using mobile devices is that instead of having several applications (apps) from various retailers on the device to search and choose the product, only the e-marketplace app is needed (Hong & Cho, 2011). These advantages are not limited to consumer convenience but also concern storage capacity and data processing of mobile devices, which often hinder the use of many applications, especially simultaneously (Lucas et al., 2023). The use of e-marketplaces also has some disadvantages to consumers, such as the risk of hackers invading personal data and credit card numbers, purchasing products from multiple sellers with separate shipping costs, products from a single purchase being delivered at different times, the time spent in case of needing to exchange the product is generally longer, and a possible asymmetry between the advertised product and the product delivered, given the impossibility of trying or testing the product before purchasing (Cano et al., 2022; Farias et al., 2022).

We can realize that e-marketplaces offer a set of advantages and disadvantages to consumers, several of which are associated with the third-party sellers operating on these platforms. Below, we highlight some factors identified in the literature as potential influencers of consumer participation in e-marketplaces concerning third-party sellers. The section also presents the proposed hypotheses, followed by the theoretical model defined as the basis for the study.

DEVELOPMENT OF THE RESEARCH MODEL AND HYPOTHESES

Studies have addressed different aspects associated with e-marketplaces. Some of them aimed to identify factors that influence consumers to participate in e-marketplaces. In this search, we found two systematic reviews, one conducted by Prihastomo et al. (2018) and the other by Cano et al. (2022). The first aimed to identify factors related to the success of e-marketplaces. Among the several factors found in the 37 studies analyzed by Prihastomo et al. (2018), trust stood out as the most influential construct in the e-marketplace literature, followed by other factors associated with the platform, the seller, and the buyer. The review by Cano et al. (2022) identified, among the analyzed documents, factors such as consumer trust in the platform, product delivery, perceived risk, information quality, and communication as essential elements for the sustainability of e-marketplaces.

Trust is the factor that has received the most attention in the literature on the subject, including consumer trust in the third-party seller. Therefore, the following hypothesis is proposed:

• H1: Trust in the seller has a positive impact on consumers’ repurchase intention in e-marketplaces.

Other studies analyzed several factors associated with the e-marketplace platform and the third-party seller, finding significant effects to increase buyers’ level of trust in e-marketplaces (Malak et al., 2021; Sfenrianto et al., 2018). Thus, a set of potential antecedents of trust in the seller were selected from the literature, considering only the seller’s performance in the e-marketplace, which comprises the other research hypotheses.

The seller’s experience can also be a crucial predictor of consumer trust in e-marketplaces, as considered by several studies in e-commerce. Web-based textual information about sellers and their previous sales experiences, elements such as the total number of transactions the seller has made or even evaluations made by consumers and reported by the platforms (based on the number of stars, ratings, averages, recommendations), can help consumers identify signs of reliability in the seller, contributing to the purchase decision process (Ghose et al., 2009). Customer-generated evaluation can include quantitative aspects, such as star ratings and volume of reviews per product or seller, and qualitative aspects, such as the comments made by other consumers in the seller’s review (Kim & Kim, 2022). Based on these statements, the following hypothesis is proposed:

• H2: Customer-generated evaluation has a positive impact on trust in third-party sellers operating in e-marketplaces.

Consumers are also concerned about the quality of the information provided by the e-commerce store (Kim & Park, 2013), considering it in their purchasing decision process (Malak, 2021). Therefore, the information the sellers offer on the website must be useful and relevant so that the consumer can predict the quality and usefulness of the products and services offered. The more quality of information provided to online customers, the more interested customers will be in purchasing those products (Fadhillah et al., 2021). Information quality is also essential in e-marketplaces as a way of differentiating the various sellers on these platforms, in which several of them offer very similar products and prices. So, information quality can be an essential guide for the consumer when deciding between one store or another to complete the purchase. Thus, the following hypothesis is proposed:

• H3: Information quality has a positive impact on trust in third-party sellers operating in e-marketplaces.

According to Ilmiyah and Krishernawan (2020), price is a measure or monetary unit of a good or service paid to obtain ownership/use rights of goods or services. As for e-marketplaces, the ability to easily compare prices from different suppliers gives consumers the advantage of getting products cheaper than in traditional stores (Hong & Cho, 2011). However, the perceptions of high versus low prices are subjective and based on the perceived value the consumer has from the product relative to the price they paid for it. Therefore, offering the lowest price does not necessarily guarantee that consumers will buy from a particular store (Ba et al., 2007) since price also signals the product quality (Zeithaml, 1988). In this sense, a competitive price represents the consumer’s perception of the price charged by a seller as a lower price than that offered by other suppliers or a price, although higher, considered more attractive due to other incentives, such as payment method, delivery time or lower perceived risk (Maia et al., 2019). Therefore, the following hypothesis is proposed:

• H4: The perceived price of the product has a positive impact on trust in third-party sellers operating in e-marketplaces.

Another aspect that can affect consumer trust in the seller is the delivery of the product, as this relationship has already been confirmed in other e-commerce contexts (Maia et al., 2018) as a crucial variable in the consumer’s purchasing decision. The study by Susanti and Yosefly (2021) was conducted with Shopee consumers and identified an overvaluation of shipping prices – so that high shipping costs make consumers rethink purchasing on the platform. In addition to the shipping cost, comments and reviews from other consumers regarding their experiences with product delivery also influence the perception of the seller’s performance (Maia et al., 2019). Therefore, companies with attractive delivery dates and freight rates, which guarantee the delivery of the product, corresponding to the advertisement and arriving in good condition, have a greater chance of gaining the consumer’s trust (Maia et al., 2018). Accordingly, the following hypothesis is proposed:

• H5: Product delivery has a positive impact on trust in third-party sellers operating in e-marketplaces.

Some control variables were incorporated into the model, such as gender, age, frequency of purchases in e-marketplaces, and the consumer’s previous experience with the seller, aiming to verify whether these characteristics also influence consumers’ intention to repurchase from third-party sellers in different e-marketplaces. The frequency of purchases in e-marketplaces and the consumer’s previous experience with the seller’s store are variables that can influence the individual’s behavior regarding their intention to purchase from a given e-marketplace or even online seller, since greater familiarity or experience with the online platform used has already been shown to be significant in other studies regarding the intention to continue using different online platforms, in the same way as the consumer’s profile (Venkatesh et al., 2012; Lucas et al., 2023), showing itself to be potentially relevant in the study. Thus, we expect that the consumer will be influenced by different factors associated with the third-party seller, such as the review generated by the customer, the quality of the information in their posts, the price perceived by the consumer, and the product delivery characteristics, as well as their profile and previous purchase experience, which will influence their trust in the seller and their intention to repurchase (Figure 1).

Figure 1
Conceptual Model

METHODOLOGY

The study is characterized as a descriptive research, conducted through a survey applied to 470 Brazilian consumers. Potential study participants were defined as consumers over 18 years old who had purchased products in e-marketplaces in the last 12 months. We used a non-probabilistic sampling technique (purposive sampling), where participants were chosen deliberately due to their characteristics (Etikan et al., 2016). To do so, we invited members of different consumer groups on social networks to participate in a survey on e-marketplace, responding to an online questionnaire. Users who agreed to participate in the study were asked to evaluate one of their last shopping experiences on any product e-marketplace, such as Amazon, Americanas, AliExpress, Mercado Livre, Magalu, Netshoes, Shein, Shopee, Submarino, among others – responses regarding service e-marketplaces, such as Uber, Airbnb, and IFood, for example, were not accepted.

We developed the questionnaire from constructs previously identified in the literature, adapting the items to the research context. Constructs, items, and their references are available in Appendix A. The questions were operationalized with a five-point Likert scale, ranging from (1) strongly disagree to (5) strongly agree. We added some sociodemographic questions to the instrument to characterize the respondent – such as gender, age, marital status, income, education, and some information regarding his/her experience with e-marketplaces. Among these questions, we asked the frequency of purchase on e-marketplaces, the name of the e-marketplace evaluated, the responsible for the sale – third-party seller or e-marketplace -, the responsible for delivering the product, and whether he/she has already purchased more than once from the seller evaluated). The control variables “age” and “frequency of purchase in e-marketplaces” are interval variables; “gender” (female/male) and “previous experience with the seller” were treated as dichotomous, with the value “0” being assigned in cases where the respondent had no previous experience with the seller and “1” for consumers who had previous experience with the same seller before the evaluated purchase.

Following, three IT experts – familiar with electronic commerce research – analyzed the instrument. They suggested some changes regarding the wording of specific questions and the structure of the questionnaire. Once the modifications were made, the questionnaire was finalized using Google Forms, and its pre-test was conducted. Firstly, ten MBA students with theoretical and practical knowledge in research checked the questionnaire. The pre-test suggested eliminating some items (as they were very similar to each other), in addition to minor adjustments to the formatting. The questionnaire was then applied to a group of 30 respondents, of which the profile was like the intended sample, with no further suggestion of changes to the instrument.

Furthermore, strategies were adopted to avoid the negative effects of common method bias (CMB). We randomize the questions about the seller, not maintaining a logical order between them. Also, questions regarding the independent variables were separated from dependent variables, which helps reduce the likelihood of CMB occurring (Podsakoff et al., 2003). To alleviate any feeling of pressure on participants, some information about the research and instructions for completing the questionnaire were presented, emphasizing that there were no right or wrong answers and that the analysis of the questionnaires would be conducted in a group, not individually, to ensure the anonymity of the participants. The structural equation modeling technique, based on the Partial Least Squares (PLS) method, was used to test the hypotheses and validate the proposed model and its constructs. The results are presented below.

RESULTS

Data collection occurred between February and March 2023 and obtained 470 valid responses. The sample is predominantly composed of women (83.0%), with the majority (70.9%) of respondents aged between 18 and 30 (mean of 27.5 (±8.5)). Regarding the level of education, respondents with completed secondary education (52.6%), followed by people with completed higher education (35.1%), are the majority. Regarding marital status, the sample is predominantly single (73.6%). Regarding income, 49.6% of participants received between 1-3 Brazilian minimum wages, and 30.9% receive up to 1 minimum wage.

Respondents have a high frequency of purchases on e-marketplaces: 49% said they buy at least once a month on this type of platform, 26% buy more than once a month, and 21% rarely buy (1 or 2 times a year). Regarding the reported shopping experience, Amazon, Shopee, Shein, and Magalu stood out as the most cited e-marketplaces (in that order), accounting for around 80% of the responses. Third-party sellers were responsible for 49% of the products purchased, of which 31.5% of consumers did not remember the seller they had purchased from, 40.6% were sold through the e-marketplace itself, and 10% did not know or did not remember the seller. Furthermore, 60.4% of participants had purchased more than once from the same seller, whether it was a third-party seller or the platform.

The analysis of the conceptual research model was conducted using the SmartPLS 4.0 software. Firstly, the measurement model was evaluated, verifying the discriminant validity, convergent validity, and reliability of the scales. Discriminant validity was verified through confirmatory factor analysis, in which the factor loadings of the indicators of each construct must be greater than all their cross-loadings, reaching values greater than .707 in their respective factors (Hair Jr. et al., 2017). Based on this criterion, item DELIV1 was eliminated because it presented high factor loadings (above .70) in more than one factor. A second criterion used to assess discriminant validity was the Heterotrait-Monotrait ratio of correlations (HTMT), in which the relationship between the constructs is expected to be less than .90, which was also tested in this research (Hair Jr. et al., 2017). As a result, we identified that the relationship between two constructs (Information Quality and Perceived Price) had exceeded the limit of .90, and question QUAL4 was excluded because it presented high factor loadings in both factors. A new confirmatory factor analysis was generated (Table 1), which met the different criteria suggested by the literature.

Table 1
Confirmatory Factor Analysis (CFA)

	Customer Review	Product Delivery	Repurchase Intention	Perceived Price	Information Quality	Trust
CGEI1	.793	.397	.235	.424	.383	.404
CGEI2	.878	.486	.305	.542	.505	.489
CGEI3	.806	.543	.339	.554	.526	.506
CGEI4	.853	.495	.317	.560	.554	.535
DELIV2	.461	.732	.309	.518	.527	.44
DELIV3	.404	.750	.269	.439	.434	.445
DELIV4	.484	.838	.330	.488	.539	.544
REPURCH1	.264	.306	.835	.342	.310	.389
REPURCH2	.256	.294	.816	.340	.308	.385
REPURCH3	.384	.387	.912	.466	.411	.568
PRICE1	.500	.504	.420	.869	.612	.539
PRICE2	.567	.543	.454	.735	.684	.545
PRICE3	.468	.443	.234	.799	.477	.404
PRICE4	.476	.484	.325	.804	.510	.587
QUAL1	.455	.484	.341	.542	.829	.512
QUAL2	.576	.620	.352	.665	.911	.602
QUAL3	.541	.594	.386	.680	.902	.666
TRUST1	.570	.490	.516	.577	.589	.922
TRUST2	.579	.572	.517	.649	.648	.944
TRUST3	.438	.624	.427	.564	.609	.853

• Note: CGEI – customer review, DELIV – product delivery, REPURCH – repurchase intention, PRICE – perceived price, QUAL – information quality, TRUST – trust.

The Fornell-Larcker criterion, shown in Table 2, was also used to assess discriminant validity, while convergent validity was assessed using the Average Variance Expected (AVE) criterion and the factor loadings. Regarding the first criterion, in which the square root of the AVE values is compared with the correlations of the other latent variables, the square root of the AVE of each construct (Table 2, in bold) is higher than its highest correlation with the other constructs. Likewise, AVE values exceeded the minimum limit of .50 in all constructs, ensuring their convergent validity. Finally, the reliability of the scales was assessed through Composite Reliability (CR), in which all constructs reached the minimum limit suggested by the literature.

Table 2
Shared variance, correlations, and reliability of constructs

	CR	AVE	Customer review	Trust	Product delivery	Information quality	Perceived price	Repurch intention
Customer review	.901	.694	.833
Trust	.933	.823	.585	.907
Product delivery	.818	.600	.581	.618	.775
Information quality	.912	.777	.598	.679	.646	.881
Perceived price	.879	.645	.629	.659	.620	.718	.803
Repurchase intention	.891	.731	.363	.537	.391	.409	.457	.855

After ensuring the quality of the model, the bootstrapping technique was used with 5,000 samples to evaluate the general adherence of the model, as well as its parameters. Regarding hypothesis H1, we identified that trust in the seller significantly affects the consumer’s intention to buy again in the same store (β = .54; ρ < .000), corroborating other studies, such as that of Malak et al. (2021) – conducted in a large Brazilian e-marketplace – and Maia et al. (2022) – conducted in a travel e-marketplace, which identified trust as the main predictor of the consumer’s purchase intention in the companies evaluated. In the Malak et al. (2021)’s study, trust in third-party sellers replaced trust in intermediaries in terms of the magnitude of its influence on purchase intention. Therefore, once the consumer has gained trust in the seller, there is a high chance that they will buy again from the same online store on the e-marketplace platform.

Regarding the antecedents of trust in the seller, we found that the four independent variables impact directly the consumer’s trust in the seller – information quality (β = .30; ρ < .000), perceived price (β = .23; ρ < .001), product delivery (β = .20; ρ < .000), and customer review (β = .15; ρ = .03), confirming hypotheses H2, H3, H4, and H5 (Figure 2). The quality of the information provided by the seller on the platform proved to be the main antecedent of the trust the consumer places in the online store, suggesting that the more complete the information provided by the seller – in terms of quantity, quality, diversity, usefulness, and accuracy – the greater their credibility will be with the consumer market and, consequently, the interest in their products (Wandoko & Panggati, 2022).

Figure 2
Structural Model Results

Another essential aspect that affects consumer trust in online stores is the perception of the price. For Mandira et al. (2018), a positive price perception will result in positive consumer responses and, consequently, positive behavior. On the other hand, a price perceived as “unfair” will lead to negative behavior, which may cause the consumer to give up on the purchase or even returning to the online store in the future. Additionally, Othman et al. (2008) identified price as an important mechanism that can generate trust in companies operating in e-commerce, a relationship also tested in this study, which concluded that perceived price is an essential antecedent of consumer trust in sellers offering products in e-marketplaces, being identified as the second greatest influencer of trust in the third-party seller.

Furthermore, product delivery also proved to be a relevant factor since companies with attractive delivery times and freight rates, which guarantee delivery of the product or that it corresponds to what was advertised, have a higher chance of gaining consumer trust. Therefore, it is crucial to understand that product delivery involves several elements and cannot be limited to the deadline but also to the product delivered (Yu et al., 2015). Similarly, Phan Tan and Le (2023) identified that delivery and product price affect the perceived value of the online transaction (the difference between the expense made on the purchase and the product purchased and delivered), and this directly affects the consumer’s intention to repurchase.

The customer-generated review was also observed, which uses elements such as the total number of transactions of the seller and evaluations made by consumers, influencing the consumer’s trust in the seller, corroborating previous studies such as that of Li et al. (2015), conducted with participants of the main e-marketplace in China. This variable was identified as a strong influence on the sales performance of the companies present in the e-marketplace studied, which this study also confirmed.

Subsequently, the control variables in the model were analyzed, identifying the frequency of purchases in e-marketplaces (β = .12; p < .01) and the consumer’s previous purchase experience with the third-party seller (β = .46; p < .000) as significant variables positively impacting the intention to repurchase from the same seller – especially the later variable, which demonstrated a high impact on the dependent variable. Thus, we can say that the more frequently the consumer purchases on e-marketplaces and remembers the seller from whom they purchased, the higher the likelihood that the consumer will buy from the same seller on a new occasion through the platform. Table 3 summarizes the results of the structural model.

Table 3
Structural Model Results

H	Relationship	β	p-value	f2	VIF	Result
H1	Trust in the seller → Repurchase intention	.50	.000	.38	1,026	Confirmed
H2	Customer-Generated Review → Trust in the seller	.15	.030	.03	1,893	Confirmed
H3	Information quality → Trust in the seller	.30	.000	.08	2,465	Confirmed
H4	Perceived price → Trust in the seller	.23	.001	.05	2,473	Confirmed
H5	Product delivery → Trust in the seller	.20	.000	.05	1,991	Confirmed

• Note. The effect size (f²) of the structural coefficients considered values higher than .02, .15, and .35 for small, medium, and large effects, respectively (Hair Jr. et al., 2017).

Analyzing the R² values (Figure 2), we can identify that Information Quality, Product Delivery, Perceived Price, and Customer-Generated Review explain 56.2% of the variance of consumer trust, which can be considered a high degree of prediction (Cohen, 2013). Trust in the seller, the frequency of purchases in e-marketplaces, and the consumer’s previous purchase experience with the third-party seller (the latter two used as control) explain 35.7% of the variance in the consumer’s intention to make a new purchase in the same online store, which, according to Cohen (2013), can also be considered to have a high impact. In addition to R², the effect size (f²) of the structural coefficients was also evaluated, observing a large effect in the relationship between trust in the seller and repurchase intention (f² = .38) and small effects in the other confirmed relationships, including purchase frequency (f² = .02) and previous experience with the store (f² = .06).

Finally, we analyze the Variance Inflation Factor (VIF) to assess the level of multicollinearity. The results presented values between 1,026 and 2,465, not indicating a potential collinearity problem (Hair Jr. et al., 2017). Also, the Standardized Root Mean Residual (SRMR) (Hu & Bentler, 1999) was examined to verify the model’s fit, which met the parameters suggested by the literature, both in the saturated model (.06) and the estimated model (.069) (Hair Jr. et al., 2017; Hu & Bentler, 1999), showing a good model fit.

CONCLUSIONS

This study aimed to analyze different antecedents of consumers’ trust and repurchase intention in third-party sellers operating in e-marketplaces. Trust was found to be an important antecedent of repurchase intention on this type of platform. Thus, as consumers gain confidence in the seller on the platform, the higher the chances that they will return to the same store in the future, using the platform as an intermediary – also confirming the frequency of purchases by consumers in e-marketplaces and their previous experience with the third-party seller as significant aspects. As antecedents of trust in the seller operating on the platform, Information Quality, Perceived Price, Product Delivery, and Customer-generated Review were assessed, and all were identified as significant. Information Quality, however, proved to be the variable with the highest impact.

The research contributes to a better understanding of different gaps highlighted by the existing literature on e-marketplaces. When the third-party seller performs well in aspects related to customer reviews, the quality of the information on its page or advertisement, the characteristics related to product delivery, and the price perceived by the customer, the seller gains the consumer’s trust, increasing the chances of them returning to the company to a new purchase. Therefore, it would be up to sellers who operate in e-marketplaces to provide clear and useful information to customers about their products or services, including photos and even videos about them, making the consumer aware of different characteristics related to the products and also the seller, in order to make the online shopping experience as pleasant as possible for consumers. In addition, it would be up to the sellers to answer questions asked by customers quickly and effectively, whether they are about products, delivery times, shipping costs, and shipping methods, among others.

Along with the information quality, aspects related to product delivery (clarifying shipping methods, fees, and deadlines) and its prices must be prioritized. In this regard, sellers must ensure that their prices are attractive and fair when compared to other sellers on the platform, whether by emphasizing the origin, shipping time, product delivery deadline, or shipping costs – since in the process of choosing a product, the consumer is often aware of the price charged by the competition, especially within the e-marketplace, where the same product, offered by different sellers, is positioned side-by-side. Therefore, very different prices between one seller and another can signal a risk (especially when the price charged for a given product is much lower than that of the competition) – affecting not only the trust in the seller who advertises but also the e-marketplace where the seller’s store is hosted. On the other hand, very high prices – when compared to competitors – can make the offer less attractive and, consequently, reduce sales on this type of platform, even though they may try to reinforce the main attributes of the product or store to differentiate it from competitors and charge a premium price.

Furthermore, it would be up to e-marketplace platforms to establish stricter entry controls capable of identifying and monitoring sellers qualified to meet the sales flow that stores receive through the platform, in addition to constantly verifying whether they are genuine and reputable companies. Efforts could also be made to control the history of reviews/sales and the prices charged by sellers by type and/or segment of product, aiming to recognize and identify disloyal or unfit sellers who are not complying with the legislation or the guidelines necessary to participate in the e-marketplace platform, thus being suspended, disconnected or even reported.

This study differs from previous research, especially because it identified a group of variables as crucial antecedents of consumer trust in sellers operating in e-marketplaces, being the only one identified so far to assess isolated variables impacting trust in the third-party seller. Another difference in this research is its simultaneous evaluation of several e-marketplaces (Amazon, Americanas, Shopee, and Schein, among others). Also, the study was conducted in the post-pandemic period, after a long period of changes in traditional and online commerce, which provides more recent data on differences in the profile and behavior of consumers who purchase in e-marketplaces. We expect to contribute to third-party sellers and platforms that operate – or intend to operate – in the different types of e-marketplaces currently available.

As limitations of the study, it should be considered that the participants evaluated one of their shopping experiences based on one or more products purchased from the same seller, which may reduce the realism of the research, given that this type of platform allows the consumer to acquire one or more products in the same purchase, from different sellers. Finally, it is suggested that future research analyze the consumer’s behavior, considering different types of e-marketplaces, whether generic, niche, national, or cross-border, to verify possible differences regarding the antecedents of consumer trust and purchase intention on such platforms.

• RAM does not have information about open data regarding this manuscript.

• RAM does not have permission from the authors or evaluators to publish this article’s review.

ACKNOWLEDGMENTS

This research was partially supported by the National Council for the Improvement of Higher Education – CAPES and the State Research Support Foundation of Rio Grande do Sul – FAPERGS, both Brazilian governmental agencies. We gratefully acknowledge their support. We are also thankful to the Editor and the anonymous reviewers for their valuable and insightful feedback on earlier versions of this paper.

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Appendix A

Instrument Items

Customer-Generated Evaluation – adapted from Maia et al. (2019)

CGEI1. I noticed positive online reviews and comments for the seller and their products.

CGEI2. The online reviews about the seller I purchased the product from were positive.

CGEI3. The online comments and reviews available on the marketplace about the seller I purchased the product from are very favorable to the company and its products.

CGEI4. The seller I purchased the product from had positive online reviews and ratings about the company and its products.

Information Quality – adapted from Kim and Park (2013)

QUAL1. The seller I purchased the product from provided accurate information about the item(s) I wished to purchase.

QUAL2. Overall, I think the seller I purchased the product from provided useful information.

QUAL3. The seller I purchased the product from provided reliable information.

QUAL4. The seller I purchased the product from provided sufficient information to complete the transaction.*

Product Delivery – adapted from Maia et al. (2018)

DELIV1. The delivery time of the product was attractive.*

DELIV2. The shipping (when) charged for delivery of the product was fair.

DELIV3. The delivery method of the product was satisfactory.

DELIV4. The purchased product arrived as shown in the advertisement.

Perceived Price – adapted from Dodds et al. (1991)

PRICE1. The product offered by the seller had a good cost-benefit ratio.

PRICE2. The product offered by the seller can be considered a good purchase.

PRICE3. The price charged for the product offered by the seller was very acceptable.

PRICE4. The seller offers products at affordable prices.

Trust in the Seller – adapted from Gefen (2000)

TRUST1. I can say that I trust the seller from whom I purchased the product on this marketplace.

TRUST2. I believe the seller I purchased the product from is trustworthy.

TRUST3. The seller I purchased the product from kept his promises and commitments.

Repurchase Intention – Fang et al. (2014)

Please indicate your likelihood of purchasing again from this store/seller you purchased from.

REPURCH1. In the medium term?

REPURCH2. In the long term?

REPURCH3. Overall, the likelihood that I will purchase from this same seller again is high.

* Items eliminated after validation procedures.

Edited by

• Editor-in-chief
  Almir Martins Vieira

• Associated editor
  Helena Belintani Shigaki

Edited by

• Publishing coordination
  Andreia Cominetti

Data availability

RAM does not have information about open data regarding this manuscript.

Publication Dates

• Publication in this collection
  24 Oct 2025
• Date of issue
  2025

History

• Received
  26 Sept 2024
• Accepted
  20 Mar 2025