查看更多>>摘要：This study relaxes the efficient market hypothesis by introducing a model that accounts for initial mispricing and explores the effects of algorithmic trading. The research finds that algorithmic strategies can cause significant market volatility and affect financial stability, particularly when they amplify overpricing, leading to bubbles and crashes. Key insights include: 1. Initial mispricing is crucial for algorithmic trading to impact market prices. 2. Market reactions vary with the direction of the trading strategy relative to the asset's true value. 3. Informed traders can benefit from mispricing, whereas noise traders typically incur losses. Policy implications suggest that algorithmic trading is not universally harmful; its effects depend on the alignment of trading strategies with accurate pricing. The study advises regulators to differentiate between stabilizing and destabilizing trading practices. For traders, the research highlights the importance of adaptive strategies that help correct mispricing to ensure long-term profitability and market health. This research advances our understanding of algorithmic trading's dual potential and informs the development of more nuanced financial regulations and trading strategies.
查看更多>>摘要：Over the past decades, information systems (IS) scholars have explored a wide variety of healthcare research topics involving emergent challenges and the technologies to address them. The enduring characteristics of healthcare, such as its complexity, stakeholder groups with competing interests, and diversity of healthcare systems worldwide, continue to provide a rich and challenging research landscape. Being at the intersection of two dynamic industry sectors-information and communication technology (ICT) and healthcare-digital health has played a pivotal role in addressing challenges within the healthcare sector. Its importance to society's economic prosperity, human wellness, dignity, and prosperity further underscores the significance of healthcare IS research. The rapidly evolving healthcare landscape calls for sustained attention from IS scholars as emerging technologies create unprecedented opportunities for impactful research.
查看更多>>摘要：The widespread adoption of information technology has fundamentally transformed the way information is processed in the financial market. One such technological advancement is algorithm trading, which allows traders to develop sophisticated strategies based on historical price data. This raises important questions: Do these algorithm trading strategies contribute to market instability? When do they yield profits for different market participants? To address these questions, we must move beyond the efficient market hypothesis, as this theory would suggest that such strategies yield no profit due to market efficiency. Instead, we explicitly incorporate initial market mispricing into our analysis and develop a stylized continuous-time model of algorithm feedback trading to investigate market outcomes. Our model yields closed-form solutions, enabling us to assess the degree to which the price diverges from the efficient level. We discover that algorithmic trading, when combined with initial market mispricing, can lead to significant market volatility, resulting in financial bubbles and crashes. However, this scenario only occurs when there is overpricing and the algorithm traders collectively employ a strategy that enlarges the mispricing. Depending on the initial mispricing in the form of underpri-cing or overpricing, different algorithm trading strategies (positive or negative) have different market impact, profitability, and policy implications.
Haoyuan LiuWen WenAnitesh BaruaAndrew B. Whinston...
41-60页
查看更多>>摘要：In modern enterprise computing environments, multiple information technology (IT) services from first and third parties are often integrated to form coherent solutions for business customers. Using transaction cost economics (TCE) as a theoretical foundation, we seek to understand how uncertainties introduced by third-party services shape enterprise customers' use of various IT services in these multivendor service settings. Specifically, we analyze a case of service disruption caused by a third party that affects the multivendor service but does not directly affect the first-party services. In line with the tenets of TCE, we find a temporary increase in the use of first-party services that can serve as a similar-goal substitute to fulfill the organization's needs during the disruption; however, on average, we observe a net decline in the total use of services in the long run. We empirically analyze the role of first-party technical support during the disruption. Based on textual data from the first party's technical support log, we use deep learning to assess what actions the first party can take during such disruptions to turn the challenge into an opportunity. We find that if the first party offers high-quality technical support that specifically addresses issues related to its product, it may be able to make lemonade out of lemons. Such technical support effectively boosts customers' use of first-party services in the long run. Curiously, however, similar efforts by the first party in the predisruption period are ineffective in achieving the same effect.
查看更多>>摘要：Financial social media platforms, which rely on social media analysts (SMAs) to contribute content to investors, have emerged as a crucial channel for investors to gain access to financial information and for SMAs to monetize their content. However, we still have a limited understanding of the factors that affect how content is generated and monetized in financial social media platforms. This study focuses on the novel role of investors' preferences for free/paid content and its sentiment and investigates the extent to which SMAs exhibit strategic content generation and monetization behaviors by catering to and trading off the investors' preferences. We also evaluate the underlying mechanisms and implications of such strategic behaviors. Utilizing a data set from a financial social media platform based in China, we propose a Bayesian empirical model to jointly analyze the investor's demand and SMAs' strategic supply of financial social media content. The model estimation results show that SMAs cater to investors', especially paid subscribers', preferences in their content generation such that their strategic behaviors account for 46.20% (24.50%) of the variation in SMAs' generation decision for free (paid) content sentiment. In addition, an SMA is more likely to produce paid content when the expected free readership increases and is less likely to do so when the expected paid subscriptions increase, evidence that SMAs do balance the preferences of different investors when monetizing content. We find that SMAs are strategic in acquiring readers via their content monetization decisions and retaining subscribers via their content generation decisions. Importantly, we uncover that the orientation of an SMA's strategic catering behavior is driven by the audience composition effect. Our study provides new empirical evidence, associated theoretical explanations for the results, and a practical illustration of an approach to reduce the potential confirmation bias of investors who may favor information from some SMAs that are prone to strategic catering behaviors.
查看更多>>摘要：Product recommendation and search are two technology-mediated channels through which e-commerce platforms can help customers find products. However, the relationship between the two channels and the underlying mechanisms and implications for platform design are not well understood. We leverage a randomized field experiment with 555,800 customers on a large e-commerce platform to investigate how product recommendation affects customer search. We vary the relevance of the recommendation that users experience upon arriving at the home page of the platform and find that a decrease in recommendation relevance leads to a significant increase in consumers' use of the search channel, indicating a (partial) substitution effect between the two at the aggregate level. We find substantial heterogeneity across product categories, propose a conceptual framework, and theorize how different states of customer demand-demand fulfillment and demand formation-may drive such heterogeneity. The results are aligned with our framework and provide evidence that both demand formation and fulfillment are at work in the channel interactions between recommendation and search. Specifically, when customers receive more product recommendations in a category, they search more in that category with generic query words, which indicates complementarity between recommendation and search. However, when customers receive fewer product recommendations in a category of interest, they compensate for this reduction by searching more in that category with long-tail query words, which indicates a substitution between recommendation and search. This experimental study is among the first to examine the causal relationship between the recommendation channel and search channel and offers implications for the design of e-commerce platforms.
查看更多>>摘要：Crowdsourcing is about leveraging information technologies to outsource tasks to a large group of people, who can either be paid workers or nonpaid workers. Differing from monetarily incentivized workers, nonpaid workers are more likely to be affected by coworking relationships. To explore the link between the network and volunteering behavior, we construct dynamic collaboration networks from 827,260 unique volunteers' participation in 183,445 projects initiated by 74,556 nonprofit organizations over nine years in the capital city of China. Following a multiplex perspective, we allow each type of organization (i.e., school-based, community-based, other-based) to represent a separate network layer. We construct the measures of multiplex ties (i.e., social connections that are linked through multiple layers) and relational pluralism (i.e., involvement diversity in different layers). We find that volunteers with more multiplex ties and a lower level of relational pluralism have higher volunteering continuity and intensity of engagement, guaranteeing the supply of the volunteer labor force. However, they are less likely to explore unfamiliar organizations through interorganization movements. At a macro level, we show how the reduced interorganization movements impeded the development of small and newly established organizations. In addition, we show that incorporating these multiplexity measures improves the prediction of volunteer behavior by 5.390%, 1.624%, and 8.792% for continuity, movement, and engagement, respectively.
查看更多>>摘要：Platform giants typically possess strong power over other participants on the platforms. Such power asymmetry gives platform owners the edge on setting platform fees to capture the surplus created on their platforms. Although there is a heated debate on regulating these powerful platforms, the lack of empirical studies hinders the progress toward evidence-based policymaking. This research empirically investigates this regulatory issue in the context of on-demand delivery. Delivery platforms (e.g., DoorDash) charge restaurants a commission fee, which can be as high as 30% per order. To support small businesses, recent regulatory scrutiny has started to cap the commission fees for independent restaurants. This research empirically evaluates the effectiveness of platform fee regulation, by investigating recent regulations across 14 cities and states in the United States. Our analyses show that independent restaurants in regulated cities (i.e., those paying reduced commission fees) experience a decline in orders and revenue, whereas chain restaurants (i.e., those paying the original fees) see an increase in orders and revenue. This intriguing finding suggests that chain restaurants, not independent restaurants, benefit from the regulations mat were intended to support independent restaurants. We find that platforms' discriminative responses to the regulation may explain the negative effects on independent restaurants. That is, after cities enact commission fee caps, delivery platforms become less likely to recommend independent restaurants to consumers, and instead turn to promoting chain restaurants. Moreover, delivery platforms increase their delivery fees for consumers in regulated cities, suggesting that these platforms attempt to cover the loss of commission revenue by charging customers more.
查看更多>>摘要：Online peer-to-peer lending (i.e., P2P lending) has grown rapidly in recent years and is a new source of fixed income for investors. However, there is limited understanding of factors affecting individual lenders' decision making in this context, which is characterized as highly risky. Drawing on construal level theory, we theorize how bidding amounts lenders submit are affected by interest rates and psychological distance caused by the borrower's demographic attributes (i.e., geographic location, age, educational degree, and marital status) relative to those of the lender. Specifically, we study how psychological distance directly influences and shapes the effects of the duality of interest rates (i.e., as rates of return and as signals of potential risk) on bidding amounts. Using a rich data set from a popular Chinese online P2P lending platform, we apply multiple identification strategies and estimation methods to conduct our analyses. We find that geographic distance decreases the lenders' bidding amounts (i.e., home bias effect), whereas social distance increases the bidding amounts (i.e., social distance effect). In addition, the positive effects of interest rates on bidding amounts are strengthened by the geographic and social distance between the lender and the borrower. Furthermore, we conduct four controlled experiments to explore the causality and mechanisms behind these relationships. Theoretical contributions and practical implications are discussed.
查看更多>>摘要：Users on online dating platforms tend to encounter a cold-start problem, with limited user engagement in the initial stages of the matching process; this is partially due to privacy concerns. In this study, we propose ephemeral sharing as a privacy-enhancing design to strike a balance between users' privacy concerns and the need for voluntary information disclosure. Ephemeral sharing refers to a digital design in which the information shared (e.g., a personal photo) becomes invisible and irretraceable to the receiver shortly after the receipt of such information. In partnership with an online dating platform, we report a large-scale randomized field experiment with more than 70,000 users to understand how ephemeral sharing influences users' disclosure of personal photos, match outcome, and receiver engagement. The experiment features a treatment group in which subjects can upload an ephemeral photo along with their matching request and a control group in which subjects can instead upload a persistent photo. We find that users in the treatment group send more personal photos (and ones with human faces) compared with users in the control group. Additionally, the ephemeral sharing treatment leads to a higher number of matches and a higher level of receiver engagement. Further analyses suggest that the treatment effects are more salient for privacy-sensitive senders. Moreover, we find that the treatment effects on match outcome and receiver engagement can be explained by increases in the disclosure of personal photos. Last, through an online experiment, we show that ephemeral sharing increases disclosure intention by reducing privacy concerns related to data collection, dissemination, and identity abuse. Our study contributes to the literature and practice on privacy-enhancing designs for online matching platforms.
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