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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.

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.

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.

In online exchange platforms' sponsored search advertising, the array of product quality signals within a keyword search results list plays a crucial role in shaping buyers' purchasing decisions. This research seeks to explore the impact of various quality signals – namely, ranking position, seller reputation and product price – on ad clicks. Additionally, it examines the role of keyword attributes, such as specificity and popularity, in modulating the effects of these quality signals on advertising clicks.

A total of 5,763 effective data points were collected from a leading B2B electronic platform company, and we employed negative binomial regression with Heckman correction methods to test the hypotheses.

The results indicate that in online exchange platforms, search ad clicks are significantly and positively affected by displayed signals such as ranking position, seller reputation and product price information. Notably, a U-shaped relationship emerges between product price and ad clicks. Furthermore, keyword specificity and popularity distinctly moderate the impact of these displayed signals on ad clicks within online exchange platforms.

This paper addresses the gap in existing research on search advertising by methodically analyzing the impact of various signals displayed in search results and how keyword attributes moderate ad clicks, all through a signaling theory lens.

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.

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.

As a type of electronic commerce, online C2C markets have experienced a rapid growth in both sales volume and user numbers. Buy‐it‐now (BIN) auction is a mechanism to facilitate online auctions, and is adopted by the major online C2C marketplaces. This study aims to investigate consumers' purchase and adoption of risk relief service in BIN auctions in the online C2C market from the perspective of perceived risk.

A research model is developed based on relevant theory. Data are collected from American and Chinese consumers through a web‐based experiment system that simulates the transaction process on online C2C markets. The proposed hypotheses are tested with logistic and multiple linear regressions.

The results show that buyer's purchase behavior and usage of risk relief service for a transaction in BIN auctions are determined by the perceived risk, which is influenced by the buyer's risk attitude, seller online reputation, and the product price and type.

This study provides an in‐depth understanding of consumers' behaviors on online C2C markets at transaction level, and makes implications for online marketplace operation and business strategy making.

The primary value of this paper lies in providing a better understanding of consumers' behaviors on online C2C market, and investigating the factors that influence the consumers' purchase and adoption of risk relief service in online BIN auctions from the perceived risk perspective.

This study seeks to investigate the signals on which consumers may rely to reduce the problems of information asymmetry on an online auction site. The research aims to develop and test based on information signaling theory. It classifies signals from auction web pages into three types: seller reputation, product condition, and argument quality. To understand how the signals affect consumers' online buying decisions, the study intends to test the impacts of these signals on the auction outcome variables: number of bids, auction success, and willingness to pay.

The paper employs an empirical test with real observation data comprised of 5,013 samples coded from the eBay auction site in the USA. Ordinary least squares (OLS) regression is used to predict the effect of web page signals on the number of bids, logistic regression to determine which web page signals contribute to auction success, and Tobit maximum likelihood estimation to estimate the impact of web page signals on willingness to pay.

Results show that, in addition to the seller's reputation, signals like product condition and the quality of the sellers' arguments on the web page are significantly related to the three auction outcomes. Buyers tend to rely on these signals to resolve information asymmetry in online auction transactions.

Past studies have found that the seller's feedback score is central to a positive online auction outcome. This paper is the first to classify web page signals comprehensively and to investigate their impacts on online auction outcomes using real transaction data. The findings provide substantial evidence and implications for both academic research and practitioners in online auctions. A dynamic strategy for success in online auctions is offered in the conclusion section.

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.

The purpose of this paper is to identify the practices that owners of public profiles in social networking sites can leverage to actively build online reputation and to evaluate the impact of the adoption of such practices on the initial formation of trust toward these individuals when they are presented as new virtual work partners.

A theoretical model was developed and an experiment with 233 participants was utilized to assess the model using partial least squares structural equation modeling.

The results suggest that individuals can build their online reputations in public profiles of social networking sites via a series of practices of self-disclosure of information and that the adoption of these practices has significant effects on the initial formation of trust toward the profile owner in virtual work contexts. Categorization mechanisms such as stereotyping, unit grouping and reputation categorization have been found to contribute to the initial formation of trust, both from an affect and cognition-based perspectives.

Little is known about the information disclosure practices in public profiles of social networking sites that new work partners can adopt to facilitate the formation of trust between them before they start working together. This study has contributed to the existing body of literature by clarifying these practices and the relative importance of online reputation to the initial formation of trust during the outset of a new virtual work relationship.