This article examines how product and consumer characteristics moderate the influence of online consumer reviews on product sales using data from the video game industry. The findings indicate that online reviews are more influential for less popular games and games whose players have greater Internet experience. The article shows differential impact of consumer reviews across products in the same product category and suggests that firms’ online marketing strategies should be contingent on product and consumer characteristics. The authors discuss the implications of these results in light of the increased share of niche products in recent years.

Conformity toward the majority choice among a user’s friends on a social-networking site first decreases and then increases with the adoption rate of that choice.

The literature on the private provision of public goods suggests an inverse relationship between incentives to contribute and group size. We find, however, that after an exogenous reduction of group size at Chinese Wikipedia, the nonblocked contributors decrease their contributions by 42.8 percent on average. We attribute the cause to social effects: contributors receive social benefits that increase with both the amount of their contributions and group size, and the shrinking group size weakens these social benefits. Consistent with our explanation, we find that the more contributors value social benefits, the more they reduce their contributions after the block. (JEL H41, L17, L82)

Keyword advertising, or sponsored search, is one of the most successful advertising models on the Internet. One distinctive feature of keyword auctions is that they enable advertisers to adjust their bids and rankings dynamically, and the payoffs are realized in real time. We capture this unique feature with a dynamic model and identify an equilibrium bidding strategy. We find that under certain conditions, advertisers may engage in cyclical bid adjustments, and equilibrium bidding prices may follow a cyclical pattern: price-escalating phases interrupted by price-collapsing phases, similar to an “Edgeworth cycle” in the context of dynamic price competitions. Such cyclical bidding patterns can take place in both first- and second-price auctions. We obtain two data sets containing detailed bidding records of all advertisers for a sample of keywords in two leading search engines. Our empirical framework, based on a Markov switching regression model, suggests the existence of such cyclical bidding strategies. The cyclical bid-updating behavior we find cannot be easily explained with static models. This paper emphasizes the importance of adopting a dynamic perspective in studying equilibrium outcomes of keyword auctions. This paper was accepted by Pradeep Chintagunta and Preyas Desai, special issue editors. This paper was accepted by Pradeep Chintagunta and Preyas Desai, special issue editors.

Our research examines the impact of online food delivery platforms on female employment in South Korea, highlighting the often-overlooked positive externalities that digital platforms generate on the labor market and economics. We find that these platforms lead to an immediate and lasting increase in the female employment rate, with a notable increase in the female employment rate by 6.5%. The economic benefits derived from this rise in female employment account for 0.27% to the country’s GDP, or 17 times the revenue of the online food delivery platform. Our analysis shows that such digital platforms offer a pathway for women to break free from traditional household roles, thus granting them more time and the opportunity to decide whether to join the labor market or stay at home. The freedom to be able to join the labor force promotes gender equality and fosters economic engagement. Policymakers need to recognize such indirect effects of technology-driven business models because they shape labor markets and drive economic outcomes. This research has vital implications for practice and policy, offering fresh perspectives on fostering female labor force participation and boosting economic growth. Empowering individual households and granting women greater agency can have profound effects, supplementing organizational measures. The study highlights the underestimated economic and societal value created by innovative technology platforms, emphasizing the need for policymakers to consider these spillover effects when assessing and regulating such platforms. Leveraging digital platforms can lead countries toward a more inclusive and prosperous future, dismantling barriers and promoting gender equality.

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.

In view of the significant role project success plays for all parties in the crowdfunding market, a wealth of research has extensively explored its vastly diverse antecedents. Drawing on the elaboration likelihood model as an overarching theoretical basis, our meta-analysis examines the aggregated effects of widely investigated antecedents (as central and peripheral cues) and moderating roles of research contexts (referring to the elaboration likelihood). It reveals a stronger link with soft information–related factors, weaker ties with backer-related factors, and varied effects of project factors. Crowdfunding success measure, crowdfunding model, platform popularity, and project location are important reasons for the inconsistencies in findings across individual studies. Our research offers valuable insights for stakeholders in the crowdfunding ecosystem. For backers, it empowers them to select projects with greater potential for success among the vast number of available options, ensuring more informed investment decisions. Fundraisers can leverage our findings to refine their fundraising strategies, thereby boosting their chances of securing funds effectively. Crowdfunding platforms can harness our findings to refine their system architecture, enrich service offerings, and improve user satisfaction. Furthermore, regulators and policymakers can draw from our study to devise regulations that nurture a robust and favorable crowdfunding environment.

Knowledge-sharing platforms such as Wikipedia are widely used by investors, yet much of the content they circulate is secondhand: factually accurate information that has already been disclosed and is later reshared without substantive updates. Under the efficient market hypothesis (EMH), such information should already be reflected in prices. However, qualitative evidence suggests that retail investors may react to it even when they recognize it as old, implying a behavioral mis-reaction. If systematic, such responses can amplify noise trading and misallocate capital. To provide causal evidence on whether recognized secondhand information influences investor behavior when reshared, we conducted a randomized field experiment on Wikipedia. We focused on salient negative secondhand information, operationalized as litigation news. Between June 15 and July 13, 2013, we introduced such content into the Wikipedia pages of selected public firms and compared their outcomes with those of matched control firms whose pages were left unedited. We also included a placebo group whose pages were edited with operations news. The results show that retail investors respond to recognized secondhand litigation news. Relative to controls, treated firms experienced significant increases in Wikipedia pageviews, a 15.7% rise in retail trading volume, and a 6.9 basis point reduction in bid–ask spreads. These effects were stronger when the litigation was more severe or recent, when firms operated in more visible and positive informational environments, and when the underlying litigation signaled governance weaknesses—a pattern consistent with representativeness-based judgment and therefore difficult to reconcile with the EMH. Mediation analysis further indicates that heightened attention is one channel through which secondhand information shapes trading behavior and market outcomes. We contribute to the information systems and behavioral finance literatures by identifying salient negative secondhand information as a distinct, behaviorally potent content category. Our findings carry implications for regulators, firms, and digital platforms, and point to mitigation strategies such as interface-level cues, investor education, and novelty-detection tools. Overall, we underscore the need to reconsider how platform-mediated salient negative secondhand information can shape market outcomes.

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.

Existing research on word-of-mouth considers various descriptive statistics of rating distributions, such as the mean, variance, skewness, kurtosis, and even entropy and the Herfindahl-Hirschman index. But real-world consumer decisions are often derived from visual assessment of displayed rating distributions in the form of histograms. In this study, we argue that such distribution charts may inadvertently lead to a consumer-choice bias that we call the histogram distortion bias (HDB). We propose that salient features of distributions in visual decision making may mislead consumers and result in inferior decision making. In an illustrative model, we derive a measure of the HDB. We show that with the HDB, consumers may make choices that violate well-accepted decision rules. In a series of experiments, subjects are observed to prefer products with a higher HDB despite a lower average rating. They could also violate widely accepted modeling assumptions, such as branch independence and first-order stochastic dominance. This paper was accepted by Chris Forman, information systems. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72131001 and 92146003] and the Research Grants Council, University Grants Committee [GRF 14500521, GRF 14501320, GRF 14503818, and TRS:T31-604/18-N]. Supplemental Material: Data and the online appendices are available at https://doi.org/10.1287/mnsc.2022.4306 .