An integral component of user participation in online communities is giving “likes” to content posted by others. Meanwhile, online users are often allowed to create a virtual identity unrelated to their real-world identity. The objective of this study is to identify the motivations behind users’ giving “likes” when their virtual identity (i.e., username) is hidden or shown. Specifically, we examine the impact of an exogenous policy change in an online community that made usernames publicly visible. Our results show that users “liked” fewer but higher-quality articles after the policy change, consistent with their protective self-presentation motivation. This study emphasizes the significance of virtual identity, arguing that a virtual identity devoid of real-world information should not be equated with anonymity. It also identifies “liking” as a key channel of self-presentation and underscores the importance of protective self-presentation. For platforms, understanding users’ motivations to give “likes” and the effects of virtual identity disclosure can help refine community policies to encourage quality content engagement. For content creators, our findings suggest they can enhance content engagement by aligning their offerings with the self-presentation goals of their audience.

Artificial intelligence (AI) has been increasingly deployed in business operations over the past decade, whereas direct evidence of its effectiveness in uncertain contexts is limited. Our work examines the contribution of AI to corporate resilience under natural disaster shocks, particularly concentrating on AI-using and goods-producing firms. We measure firm AI investment by the cumulative AI-relevant skills extracted from a comprehensive job posting database and firm resilience by the changes in corporate valuation in response to operational shocks. Evidence suggests that AI generates resilience: An average firm that equips 2.4% of total jobs to be AI-related could approximately recover the full damage of disasters reflected in corporate valuation over a short event window. From the product function test, we find that resilience is attributable to the moderating effect of AI on the damaged input responsiveness under the volatile production environment. Our analyses further reveal a pressing phenomenon: Although underperforming firms could benefit more from an additional unit of AI investment, the realized productivity is notably restrained due to a lack of complementary organizational designs. Our findings provide managerial implications regarding the interplay between environmental conditions and firm investments in both AI technology and complementary infrastructures.

The resale of used products presents the challenge of cannibalization, particularly pronounced in digital goods markets where perfect substitutes are easily replicable. In this article, we assert that, rather than a threat, resale can serve as an effective pricing tool for managing heterogeneous demand. We consider a seller of digital goods/services who offers a contract to a heterogeneous group of customers at a fixed price for a specified amount of usage allowance. Rather than imposing restrictive sharing barriers, the seller allows subscribers to share their allowances with others in a secondary market. Our analysis reveals that the seller’s optimal strategy involves facilitating resale by eliminating transaction costs. The sharing contract effectively achieves the same outcome as a two-part tariff, wherein subscribers pay an entry fee along with a marginal usage rate. Both approaches generate equivalent revenue and market coverage, and result in idential demand and individual surplus for customers of the same type. Consequently, the sharing contract acts as a mechanism for price discrimination. Our finding provides a new perspective on peer-to-peer resales and also challenges the conventional belief that successful price discrimination hinges on preventing resale.