Live streaming commerce (LSC) is characterized by extreme concentration and event-driven competition, where a small set of star streamers commands disproportionate attention through episodic broadcasts. Despite LSC’s rapid growth, we still know little about how such event-driven competition reshapes peer performance across the attention-conversion funnel, defined as a viewer journey from audience presence to downstream follower accumulation and sales. We leverage the unexpected re-entry of a former top streamer on a leading LSC platform as an exogenous competitive shock. Using a difference-in-differences design, we trace its impact on peer streamers across multiple funnel stages, including viewership, followership, and sales. Our results reveal stage-contingent externalities. The competitive shock generates positive spillovers in early-stage attention, increasing peers’ viewership. However, these gains do not translate materially into downstream performance. Peers experience declines in follower counts and sales, indicating substitution in viewer loyalty and conversion. These effects are time-sensitive: viewership spillovers intensify with a lag, while follower losses accumulate over time. On the supply side, peers adjust broadcasting frequency, expanding activity after the star’s broadcasts while scaling back during weeks of star presence. Together, we conceptualize event-driven externalities across the attention-conversion funnel in creator platforms, extending research on digital platform competition and governance.

This research investigates whether and how predecessors’ usernames—as evaluated from a perspective of perceived anonymity—affect successors’ herding momentum through the varying extent of perceived source credibility. Using a unique data set collected from a leading debt-based crowdfunding platform, we classify lenders’ usernames as either anonymous or real-seeming, with the latter referring to usernames that seem to reveal one’s legal name. We find that successors demonstrate weaker herding momentum toward predecessors who are presented with real-seeming usernames than anonymous ones. This finding, which we attribute to a lower extent of perceived credibility resulting from a nonconforming behavior, challenges the conventional wisdom that considers anonymity a negative factor for source credibility. Further, we demonstrate the importance of risk-related factors, in that the uncovered positive effect of perceived anonymity on herding is accentuated in the early stage of the fundraising period. Our findings provide actionable insights for platform owners to utilize the user heterogeneity with respect to perceived anonymity and hence perceived credibility in herding. These findings are also informative for borrowers who desire to exert effort to encourage participation from the crowd.

In online crowdfunding markets, backers face high uncertainty about the quality of a campaign. To mitigate such uncertainty, crowdfunding platforms often allow campaign creators to post communicative messages-that is, campaign updates and creator comments-to dynamically disclose further information about the campaigns. In addition, previous funding transactions of ongoing campaigns are made publicly available, giving rise to herding among backers. In this research, we aim to understand how communicative messages and herding interactively shape the behavior of backers contributing to crowdfunding campaigns. Our results show that the frequency of communicative messages has a positive effect on backer contributions; however, it attenuates successors' herding momentum toward predecessors, perhaps because the information disclosed in those messages lowers the informational value of previous funding transactions. To investigate the role of message contents, we extract topics addressed in update and comment messages using a Latent Dirichlet Allocation model. The results reveal that distinct messages have different impacts on backers' contribution and herding behavior, and such discrepancies are found to be topic specific. This study not only contributes to operations management literature on crowdfunding but also offers implications for campaign creators and platform managers.

Online platforms, such as App Store and Kindle, are facing a common dilemma: while the implementation of technology-based protection impedes piracy and hence boosts demand from legal users (positive effect), the resulting restriction meanwhile imposes some level of disutility on the same due to inconvenience (negative effect). This paper investigates a monopolistic platform's optimal protection level and pricing strategy under an agency business model (content agency model or advertising agency model). We find that the platform's protection strategy hinges on the relative magnitude of the two opposing effects of protection. When the negative effect dominates, the minimal protection is optimal. However, as the positive effect becomes more salient, the platform has an incentive to increase the protection level. We also find that although the demand of non-pure platform users always increases as the level of content substitutability rises under the minimal and maximal protection regions, it is not necessarily the case under the medium protection region. The study advances our understanding of the content protection against piracy from a platform's perspective. Our findings also provide insights into business model decision when platform protection is endogenously determined.

Inspired by the evaluation mode theory, we show online vendors mispredict consumers’ responses to different types of sales displays. While vendors predict that consumers evaluate a featured product more positively in a cooperative sales promotion (CSP; i.e., multiple stores promoting synchronously) than in an independent sales promotion (ISP; i.e., a single store promoting independently), consumers actually do the opposite. The reason is that vendors compare CSP with ISP and are able to evaluate depending on perceived acquisition utility; consumers, however, see either CSP or ISP, resulting in difficulty in accessing acquisition utility. Thus, they evaluate according to perceived transaction utility.

Online peer-to-peer (P2P) lending is a two-sided market that enables direct interactions between borrowers and investors. The network effects that arise in P2P lending markets can make the decision-making process of both participant groups interdependent. However, extant research on P2P lending primarily focuses on decisions of one specific participant group without considering the interactive behaviors from the other side of the market. We attempt to fill this gap by developing a utility-based structural model that simultaneously governs both borrowers’ and investors’ platform choice decisions. Our results show that a platform’s short-term liquidity, cross-network effect (CNE), and direct-network effect (DNE) are the top three factors that positively drive investors’ platform choice. In contrast, a platform’s background, tenure, membership fee, long-term debt risk, and the average loan duration have a negative effect on their participation decision. On the borrower side, we find that a platform’s short-term liquidity, tenure, CNE, and DNE are the top four factors that attract their platform selection, while a platform’s background and membership fee will reduce their utility of choosing the platform. Overall, borrowers play a more important role than investors in the growth of a P2P lending platform. The counterfactual analyses suggest that a handful of interventions can be implemented to influence both investors’ and borrowers’ platform choice. The new information-transparency regulations issued in China are estimated to save investors more than $1.36 million. Our findings offer important managerial implications for platform managers and policy makers in the P2P lending market.

In online crowdfunding markets, backers face high uncertainty about the quality of a campaign. To mitigate such uncertainty, crowdfunding platforms often allow campaign creators to post communicative messages—i.e., campaign updates and creator comments—to dynamically disclose further information about the campaigns. In addition, previous funding transactions of ongoing campaigns are made publicly available, giving rise to herding among backers. In this research, we aim to understand how communicative messages and herding interactively shape the behavior of backers contributing to crowdfunding campaigns. Our results show that the frequency of communicative messages has a positive effect on backer contributions; however, it attenuate successors’ herding momentum towards predecessors, perhaps because the information disclosed in those messages lowers the informational value of previous funding transactions. To investigate the role of message contents, we extract topics addressed in update and comment messages using a Latent Dirichlet Allocation model. The results reveal that distinct messages have different impacts on backers’ contribution and herding behavior, and such discrepancies are found to be topic-specific. This study not only contributes to operations management literature on crowdfunding but offers implications for campaign creators and platform managers.

Problem definition: Crowdfunded Supply Chain Finance (SCF) is an innovative Fintech service that transforms financial flows, allowing individual investors to serve as funders under the SCF paradigm. As required by crowdfunded SCF platforms, the unique presence of loan guarantors in the financing process alters how fundraisers and investors interact, which gives rise to investor learning. Academic/practical relevance: Understanding how such learning behavior impacts investors’ decision-making leads to actionable recommendations for platform managers who desire to encourage investor participation as well as for capital seekers who wish to stimulate fundraising performance. Methodology: We develop a Bayesian learning model, wherein we conceptualize individual perception of guarantor reliability as a subjective attitude underlying the perceived risk of a loan listing. Given that a guarantor may be involved in multiple loans in this unique market, we consider that individuals can learn about a guarantor’s true reliability and dynamically update their perception as they receive more repayments, or lack thereof, over time. We model investor behavior as two separate yet interdependent outcomes: (1) the incidence decision of whether to invest and (2) the amount decision of how much to invest. Results: Our estimation results confirm the existence of investor learning: an individual’s incidence decision and amount decision are both driven by her perception of guarantor reliability. In addition, we observe that this latent perception has different moderating effects on investor responses to listing attributes, such as interest rate and loan duration. Managerial implications: Our counterfactual simulations generate useful implications. For platform managers, enabling investor learning from correlated investment experience can help mitigate adverse selection and improve overall market efficiency. For supply chain members, optimizing the structure of loan listings could accelerate investor learning, which in turn can help simulate fundraising performance as a desirable outcome of reputation building.

This research studies the effect of disconfirmation-the discrepancy between the expected and experienced assessment of the same product-on the behavior of consumers leaving online product reviews. We propose a modeling framework in which an individual's prepurchase expectation is shaped by 1 the product ratings she observes and 2 the perception of the review system she has at the time of the purchase. Upon product consumption, the individual obtains the postpurchase evaluation and encounters a certain level of disconfirmation. Drawing on the Bayesian learning framework, we model individual perception of the review system as a subjective attitude underlying how well the aggregate ratings match one's own usage experience. A hierarchical Bayesian model is developed and estimated using a rich data set comprising complete purchasing and rating activities on an e-commerce website. Our results suggest that an individual's decisions of whether to post a rating and what rating to post are affected by disconfirmation in two distinct manners. Specifically, an individual is more likely to leave a review when the magnitude of disconfirmation she encounters is larger. In addition, when the individual decides to review a product, the rating she chooses may not neutrally reflect her postpurchase evaluation; the direction of such a bias is in accordance with the sign of disconfirmation. We also observe several moderating effects: the disconfirmation effect on posting is attenuated by the time gap between purchase and receipt of the same product but accentuated by the dissension in product evaluations among peer consumers. A more granular examination reveals that infrequent raters are systematically more susceptible to disconfirmation than frequent posters. The insights from this research lead to actionable strategies for marketers and designers of recommender systems.

Through reimbursing a portion of the transactional amount to some consumers in a form of cash back, merchants are able to exercise third-degree price discrimination by offering two asymmetric prices via an online dual channel. To better understand such a novel pricing mechanism, we develop a game theoretical model and start our analyses with a market consisting of one merchant, one affiliate site, and consumers heterogeneous in their product valuation. From a price point of view, cash-back shopping appears to provide site users with a saving opportunity since the effective post-cash-back price they pay is perceived to be lower than the regular price targeted at nonusers. However, we find that under some conditions, this seemingly lower price could be actually higher, compared with the optimal uniform price when the merchant does not price discriminate. An important implication is that all consumers may end up suffering from higher prices in the presence of the cash-back mechanism. This surprising result, referred to as the cash-back paradox, defies a common intuition that a price-discriminating firm must raise the price for one segment of consumers but decrease it for the other. We also develop two extensions to seek explanations behind various industry practices. We find that it is in a merchant's best interest to affiliate with multiple sites, and the resulting competition improves overall market efficiency. Moreover, merchants who are disadvantageous in brand valuation should target price-sensitive consumers by strategically offering cash-back deals. Our results, consistent with several real-world observations, have useful implications for marketers.The online appendix is available at https://doi.org/10.1287/isre.2017.0693

Online peer-to-peer (P2P) lending, one of the most successful technology-enabled initiatives in the fintech revolution, has drastically changed the way individual investors and borrowers meet and transact. While prior research has found herding among investors at the listing level, such social behavior has been underexplored at a macro, platform level. In this study, we attempt to fill this gap by examining whether subsequent investors follow their predecessors’ actions when choosing which platform to invest, and if so, how various platform attributes and regulations moderate herding behavior. We collected a novel data set from leading platforms in a large P2P lending market. Our baseline analysis reveals that herding exists at the platform level. Using a multilevel model, we further identify several interesting moderators: the investor’s herding behavior is accentuated by platforms’ market share and the cumulative amount funded, but attenuated by their time in operation. Finally, we find that government regulatory events dampen the magnitude of the herding effect, suggesting that more information disclosure and stricter operation standards reduce the value of observational learning. The results from our analysis provide implications for P2P lending investors, platform designers, and policymakers.