Editor’s Introduction 27(2)
International Journal of Electronic Commerce,
Volume 27, Number 2, 2023, pp. 161-162.
There are many faces of e-commerce fraud, all of them ugly. On second thought, with the arrival of “digital humans,” possibly as mimetic avatars playing on the target’s emotions, the faces can even be attractive—which is a part of the problem. The first article in this issue of IJEC deals with a fraud strategy enacted by dishonest online retailers and serving to inflate the actual sales volume in order to enhance the visibility of their firm by moving up its ranking and the listing position. Employing this fraud technique, called brushing, the retailer places fake orders to itself with the intent of being more readily found during a prospective customer’s search. The authors, Yue Liu, Minghui Jiang, and Hang Wu, build a game-theoretic model to show the economic outcomes of brushing in a competition between an honest and a brushing retailer on an e-commerce platform. The advantage of brushing hinges on the commission rate charged by the platform for a sale—and thus the fraudulent brushing strategy can be defeated. The authors’ model surfaces the variety of contingencies and the level of platform-use charges that can help defeat brushing.
Online reviews have brought forth a broad seam of research. These reviews are frequently a dialogue between the reviewing customer and the responding merchant. It has been long established that a skillful response to a service failure can serve the merchant well, even when confronting a significantly negative review. Yes, but do the responding merchants follow up on their promise of seeking redemption by action? Algorithms can determine from the future reviews whether the merchant’s response was made in good faith—or amounted to deception. Here, Xiaolin Li, Li Ma, Benjiang Lu, and Kexin Huang construct and exercise a novel indicator of consistency between a merchant’s words and deeds, as reflected in the follow-up reviews. The authors show both the obvious practical worth of such an index and the contribution to theory they make.
Two subsequent articles in the issue address with formal modeling the always salient problem of pricing that underlies the market-making mechanisms. In the first article, Jianghua Wu and Chenchen Zhao focus on dynamic pricing, where online retailers have a seemingly incontrovertible advantage in the competition with the offline merchants. With randomized dynamic pricing, the consumers with a monetized higher valuation for the product would buy at a higher initial offering price, while others would wait for promotions. The authors present a pricing model comparing a set of outcomes for an online and offline retailers, or for an omni-channel retailer, in which the online sales feature a randomized pricing strategy. The results are directly applicable to the pricing strategies in dual-channel markets and push our knowledge forward.
Co-creation of value by a firm and consumers is ever more exploited in the competitive marketplaces, where the companies are able to benefit from the explicit or inferred advice of its consumers, while strengthening the brand in their mindspace. Here, Siyuan Zhu, Shaofu Du, Tengfei Nie, and Yangguang Zhu present a formal model of collaborative innovation involving three parties: consumers contributing product reviews and design suggestions, a customer-review platform, and a manufacturer who acquires the consumers’ ideas from the platform. At issue are the economic effects of the platform’s and the manufacturer’s overconfidence, a cognitive bias stemming from the possession of the acquired ideas and leading to the perhaps too hasty decisions in product design and pricing. The work surfaces the positive effects of such so-called overconfidence, and is a valuable contribution to our knowledge about collaborative innovation and co-creation in general.
In the concluding article of the issue, Bin Dan, Yu Tian, Xumei Zhang, Molin Liu, and Songxuan Ma analyze the challenging supply chain of fresh products, where the supplier provides an expensive freshness-keeping effort and the selling platform owns the demand information. The supplier can choose to cooperate with the platform in a reselling or an agency selling mode, and the platform can choose whether to share information derived from customers’ interactions. The multistage game modeling reveals the alternative choices and outcomes for both under different cooperation modes. The authors then present an incentive-compatible mechanism to improve the performance of the entire supply chain. The work is generative, as its modeling can be adapted to other challenging supply-chain settings.