“Meet Me Halfway”: The Costs and Benefits of Bargaining

Bargaining is an important pricing mechanism, prevalent in both online and offline markets. However, there is little empirical work documenting the costs and benefits of bargaining, primarily because of the lack of real-world bargaining data. We leverage rich, transaction-level bargaining data from a major online platform and supplement it with primary data to quantify the costs and benefits of bargaining for sellers, buyers, and the platform. We do this by building a structural model of buyer demand and seller pricing decisions while allowing for the existence of bargaining initiation cost, loss-of-face cost, and price discrimination. Using our results, we perform three policy simulations to quantify the importance of not distinguishing between no-bargain and failed-bargain transactions, ignoring the loss-of-face cost, and not allowing for bargaining. These simulations provide rich details on how the various costs of bargaining impact our understanding of buyer and seller behavior and transaction outcomes. Banning bargaining, in particular, benefits the buyer and the platform greatly but only has a modest benefit for sellers. Finally, we show that our results are robust to our assumptions and replicate in another product category.

PREVALENSI ASPERGILLUS FUMIGATUS PADA PENJUAL KACANG TANAH DAN JAGUNG KOTA BENGKULU

Aspergillus fumigates infection in peanut and corn sellers may arise due to the behavior of sellers of raw food materials which is not good. The behavior of how the storage and use of raw food materials and Personal Protective Equipment (PPE), is a behavior that must be considered. PPE is a self-protection tool that can be used for work in order to protect themselves and others, while the storage method is the treatment of raw food materials. The purpose of this study was to determine the Aspergillus fumigates infection associated with peanut and corn seller behavior in market town of Bengkulu.This research is an analytic survey research using cross-sectional design. In this study, the population was the whole peanut and corn seller in the market of Bengkulu were 30 people. Samples in Panorama Market were 10 sellers of peanuts and 5 corn sellers. The data was collected using primary data on the presence or absence of the fungus Aspergillus fumigates in seller’s sputum which obtained from laboratory tests. Then performed a collection of data about how to storage and use of masks by observation. Data analysis was done analytically using the chi-square test.The results showed the majority (53.3%) prevalence of the fungus Aspergillus fumigates. There was a significant correlation between the behavior of the storage of peanuts and corn and the use of masks by the behavior of sellers in the market of Bengkulu. There is no determinant highly influential on the cause of the two variables.For the merchant of peanut and corn should further enhance self-protection such as the use of appropriate PPE in order to avoid infection fungal pathogens such as Aspergillus fumigates.

Cyber-Shilling in Automobile Auctions: Evidence from a Field Experiment

We run a large field experiment with an online company specializing in selling used automobiles via ascending auctions. We manipulate experimentally the “price grid,” or the possible amounts that bidders can bid above the current standing price. Using two diverse auction sites, one in New York and one in Texas, we find that buyer and seller behavior differs strikingly across the two sites. Specifically, in Texas we find peculiar patterns of bidding among a small but prominent group of buyers suggesting that they are “cyber-shills” working on behalf of sellers. These patterns do not appear in the New York auctions. (JEL C93, D12, D44, L62, L81)

A Case-Based Reasoning Framework to Choose Trust Models for Different E-Marketplace Environments

The performance of trust models highly depend on the characteristics of the environments where they are applied. Thus, it becomes challenging to choose a suitable trust model for a given e-marketplace environment, especially when ground truth about the agent (buyer and seller) behavior is unknown (called unknown environment). We propose a case-based reasoning framework to choose suitable trust models for unknown environments, based on the intuition that if a trust model performs well in one environment, it will do so in another similar environment. Firstly, we build a case base with a number of simulated environments (with known ground truth) along with the trust models most suitable for each of them. Given an unknown environment, case-based retrieval algorithms retrieve the most similar case(s), and the trust model of the most similar case(s) is chosen as the most suitable model for the unknown environment. Evaluation results confirm the effectiveness of our framework in choosing suitable trust models for different e-marketplace environments.