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Platform certification constitutes an effective mechanism for managing the lemon problem concerning food e-commerce. This work aims to evaluate the market effect of platform certification and analyzes its correction mechanism for lemon problem combined with reputation mechanism.

Utilizing the Gold Seller certification of Taobao.com to serve as an illustration, the authors conducted an empirical study based on the sales data of hairy crabs among 2,239 sample sites over six points in time from October to December 2019, systematically examining the market effect of food e-commerce platform certification along with the interaction between food e-commerce platform certification and reputation mechanisms, followed by a heterogeneity test by product price.

This study finds that sellers with platform certification can significantly increase their sales. The market effect of platform certification is more easily observed in the low-price product market. In addition, platform certification and reputation mechanisms have complementary effects. In a low-price product market, the complementary effect of platform certification and product reputation diminishes, while the complementary effect of platform certification and seller reputation disappears.

This study explores the market effect of food e-commerce platform certification, reveals the market effect of certification mechanism when multiple signaling mechanisms exist simultaneously and conducts an empirical test based on real market data. It provides a better comprehension of how platform certifications work in food e-commerce.

The purpose of this study is to examine the role of customer experience moderating the relationship between reputation (online consumer reviews) and price premium.

This paper collected half-year period transaction of Nokia 5230XM and Kingston SD card from Taobao.com, the largest e-commerce platform in China. This paper combined theoretical analysis and empirical analysis together. Two-stage regression and logistic regression analysis was applied in this empirical analysis. The sensitivity analyses (robustness check) were also conducted in this paper.

Customer experience negatively moderates reputation price premium; thus, the positive effect of the reputation system is weaker for the experienced customer than for the naïve customer. Customers with more experience are less likely to pay the price premium and rely on a reputation system.

The results help sellers to strategize in the online marketplace. Sellers that wish to compete in the e-market must understand the type of customers they are addressing and differentiate the way they treat customers based on the level of customer experience.

This research contributes to the reputation management and customer behavior literature by identifying the effects of customer experience on the relationship between the reputation system and price premium. The results address the conflicts found in previous studies by extending the explanation of the negative reputation price premium.

This paper aims to examine the antecedent of purchase intention: online seller reputation, product category and surcharge.

This paper uses five experimental designs to explore the seller reputation, product category and surcharge effects in Internet shopping. The authors chose one seller of low reputation and one seller of high reputation from Yahoo Mall. ANOVA are used to evaluate the results.

Sellers of high reputation can post higher surcharges to increase the total price paid by the buyer, but sellers of low reputation cannot do so (experiment 1). Moreover, partitioned price will decrease purchase intention for sellers of low reputation more than for sellers of high reputation (experiment 2). Consumers take the longest time to make purchasing decisions when buying credence goods (experiment 3) or buying from sellers of low reputation (experiment 4). The effect of surcharge levied by sellers of low reputation is weakened for consumers with low (vs high) shipping‐charge skepticism (experiment 5).

This study is helpful to online sellers if they can identify their reputation, product category and those consumers who have shipping‐charge skepticism, they can create extra profit through surcharge practice.

The authors’ investigation extends the literature on consumers’ price processing by identifying the important moderators (seller reputation, product category, and elaboration) and probing into the decision process (via the response time). The results suggest prescriptive strategies for online sellers.

The purpose of this paper is to examine consumer reactions to product categories, online seller reputation, and brand name syllables.

This paper uses four experimental designs to explore the seller reputation, product category, and brand name syllable effects in internet shopping. The authors chose sellers of (low/high) repute from Yahoo Mall. ANOVA is used to evaluate the results.

Seller reputation moderates the effect of the brand name syllable level on purchase intention and product category moderates the effect of the brand name syllable level on purchase on internet (experiment 1). Consumers take the longest time to make purchasing decisions when buying credence goods or buying from sellers of low repute and that the response time mediates the moderating role of the product category (experiment 2) or reputation (experiment 3). Moreover, the effect of brand name syllable levels chosen/assigned by sellers of low repute is weakened for consumers with low (vs high) skepticism toward non-store shopping (experiment 4).

This study is helpful to online sellers if they can identify their reputation, product category and those consumers have skepticism, they can create extra profit through brand name syllable practice.

This paper extends the literature on consumers’ brand name syllable processing by identifying important moderators and probing into the decision process. The results allow us to substantiate prior research and suggest prescriptive strategies for internet retailers.

The purpose of this paper is to assess the impact of country-of-origin (COO) cues and pricing perspective based on the third-party seller's name, intermediary, on consumers' purchasing decisions on e-commerce sites. A model was proposed to investigate consumers' perception toward sellers' online reputation, the mediating role of trust between the reputation of third-party sellers and attitude toward e-commerce as an intermediary, and attitude toward third-party sellers. In addition, this study also looks at the pricing threshold of consumers who are willing to buy from a third-party seller that has a negative COO cue, which is an area that has received limited attention in e-commerce studies.

The paper opted for an experimental study using survey data gathered from general American consumers. Two studies were conducted. One hundred seventy surveys were gathered for study 1, and 171 surveys were gathered for study 2. The two studies had two product snippets which showed an Amazon product page with a list of third-party sellers. For study 2, all variables were kept the same – reviews and ratings for both products and sellers, delivery time, descriptions, e-commerce as an intermediary and brand of a bag – except for the price.

The findings showed that consumers' perceived reputation of a third-party seller has a positive impact on their attitude toward the seller and toward the e-commerce intermediary. In addition, the role of a positive COO influences attitudes and intentions. However, this influence is moderated by price when price is noticeably higher when compared to an alternative option provided by a seller from a country with a lower COO evaluation. This study suggests that the benefits of a positive COO diminish when a seller with a lower COO evaluation is able to provide a lower price for the product. In study 1, the results show that positive COO trumps negative COO. In study 2, the result shows that consumers lean toward a lower-price product and disregard their evaluation toward the COO. Furthermore, in study 2, results show that in order for the pricing to offset the negative COO attributes of a third-party seller, the price needs to be within 22–30% lower than the American seller's product pricing.

With the chosen research approach, the research results may lack generalizability for the other markets (e.g. Asian consumer market). Therefore, researchers are encouraged to test the proposed propositions further.

This study highlights the implications of COO cues such as sellers' names and how they impact consumers' willingness to purchase a product. The second study investigates consumers' willingness to purchase when the pricing for a product sold by a negative COO seller versus a positive COO seller is different in an e-commerce environment. In addition, the second study determines that the role of trust has more impact on consumers' attitude toward a third-party seller than it has on their attitude toward the e-commerce intermediary.

Livingston (2002) shows that bidders in Internet auctions are easily convinced of a seller’s trustworthiness: they bid large amounts even if sellers have barely established a reputation for performance, suggesting that they believe that typical sellers usually perform. This study reinforces this conclusion by looking at how bidders choose which auction to bid in when there are several that are selling the same item. The analysis shows that so long as a seller has some history, bidders consider bidding in the seller’s auction. They then choose auctions that offer the best chance to obtain the good at the lowest price.

Cross-border e-commerce in China has been booming in recent years. This paper aims to study pricing in Chinese cross-border e-commerce companies and focuses on the baby food market, which is simply examined as a case study to highlight broader implications. In this intensely competitive sector, the biggest challenge faced by such companies is ensuring that they are in a position to be able set prices in the short-term to maximize their competitive advantage and profitability. The study of pricing will help management to make correct operational decisions.

This study utilizes transaction data, which were obtained from the Taobao e-commerce platform. Taobao is the largest e-commerce retail platform in the world. We analyzed factors, including business models, homogeneity, reputation ratings and sales volumes, which may affect pricing.

This study found that consumers in the baby food sector of Chinese cross-border e-commerce are not price-sensitive. Consumers are reputation-rating-sensitive. The reputation ratings of sellers affect the price dispersion in e-commerce markets. The Core Price Dispersion Rate Model not only considers the prices but also takes sales volumes into account in the calculations. Finally, based on Gaussian processes, a model was developed for price forecasting in the area of cross-border e-commerce. The experimental results show that the proposed method is highly valuable for price forecasting.

This study provides a novel understanding of the baby food sector in the Chinese cross-border e-commerce market by examining the business model, price dispersion, reputation rating and correlation between the reputation of sellers, prices and sales volume. Furthermore, a model for price forecasting is proposed.

Despite the availability of side‐by‐side price comparisons, online retailers often charge very different prices for the same product. The purpose of this paper is to identify the drivers of price differences in an online retail marketplace by examining pricing information from a sample of sellers in the market.

An empirical, quantitative research study of Amazon Marketplace, using 498 observations of online sellers of a variety of electronics products was conducted. A regression analysis is employed to determine the drivers of these sellers' prices.

The results provide a set of factors associated with deviation from the mean price Amazon Marketplace retailers charge for a given product. The authors find that online retailers that charge higher prices post additional channels of customer service, post their return policy, have lower reputation scores, display a retail brand logo, offer more products, and are not electronic specialists.

The paper contributes to the theoretical understanding of the effects of information quality and governance structures on prices. This is the first study to focus on these issues in an online marketplace setting.

Due to the cross-network effect, two-sided users communicate with each other, producing a coupling network. To study the spread of platform self-operation in two-sided users' marketing and purchasing tactics, this paper considers the differences in reputation acquired by platform-owned and third-party operating channels.

This study proposes a two-layer network with cross-network links: one layer represents the social network of consumers, while the other layer represents the competitive network of buyers. A closed system of differential equations, based on the binary dynamics of the stochastic network, is developed to study the trend and stability points of the platform self-operation dissemination. Then the overall benefits of platform are analyzed to unify the platform diffusion and pricing strategies.

The degree of difference in social influence and cross-network effects affect diffusion synergistically. Cross-network effects hinder diffusion when there is a significant difference of social influence between consumers and sellers but promote diffusion when there is little difference of social influence between consumers and sellers. Additionally, the network weights and reputation gap exhibit a nonlinear correlation with diffusion. For pricing strategy of the platform, it can achieve maximum profit when the pricing of self-operated goods and third-party-operated goods is equal.

This study considers the complex network architecture created by bilateral markets and the dynamic influence of group interactions on product. Additionally, this study takes reputation into account when considering the price and dissemination tactics of various operating channels, offering guidelines for platforms to control merchants and mediate disputes between various operating channels.

This paper aims to solve the problem of information overload and reduce search costs. It proposes a social e-commerce online reputation formation model and community state-introduced model. A system dynamics trend simulation has been run to capture the relationship among the sellers, buyers, social e-commerce platforms and external environment to obtain an online reputation.

Empirical research relating to social e-commerce reputation has been used to confirm the influencing factors in social e-commerce, and a conceptual framework is developed for social e-commerce reputation formation. Thereafter, a trend simulation is generated to classify the relationship among the factors based on system dynamics. Also, the improved algorithm for community detection and a state-introduced model based on a Markov network are proposed to achieve better network partition for better online reputation management.

The empirical model captures the interaction effect of social e-commerce reputation and the state-introduced model to guide community public opinion and improve the efficiency of social e-commerce reputation formation. This helps minimize searching cost thereby improving social e-commerce reputation construction and management.

There is no appropriate online reputation system to be constructed to test the relationship proposed in the study for a field experiment. Also, deeper investigation for the nodes’ attributes in social networks should be made in future research. Besides, researchers are advised to explore measurement for the reputation of a given seller by using social media data as from Twitter or micro blogs.

Investigations that study online reputation in the social e-commerce are limited. The empirical research figured out the factors which can influence the formation of online reputation in social e-commerce. An SD model was proposed to explain the factors interaction and trend simulation was run. Also, a state-introduced model was proposed to highlight the effect of nodes’ attributes on communities’ detection to give a deeper investigation for the online reputation management.