Knowledge that Transforms

How does online trading intensity influence investors’ gambling preferences? How do financial advisors shape the relationship between online trading intensity and gambling preference? This study answers these important questions in response to concerns about whether digital technologies such as the Internet exacerbate the tendency of retail investors to gamble in the stock market. Leveraging transactional trading data of approximately 20,000 investors and more than 450,000 monthly observations, we find a U-shaped relationship between online trading intensity and investors’ holdings of lottery-like securities. Interestingly, we show that financial advisors flatten this relationship between online trading intensity and investors’ holdings of lottery-like securities. In other words, financial advisors can reduce the undesirable costs associated with online trading. These findings, which draw on literature at the intersection of information systems and behavioral finance, provide valuable insights on how the Internet influences stock trading. Our work suggests the continued relevance and importance of human capital (e.g., financial advisors) in the digital era increasingly shaped by artificial intelligence, and provides guidance as to when financial advisors are more effective in reducing investors’ gambling preferences.

In the sharing economy, screening can mitigate information asymmetry between service providers and consumers, but excessive screening may cause market inefficiency. We adopt a multimethod approach to examine how forgoing screening affects competition in the sharing economy. We first develop a game-theoretic model to analyze how a focal service provider’s decision to forgo screening impacts its own and a rival provider’s prices, market demands, and profits. Using Airbnb as the investigation context, we then empirically validate our analytical results. We find that our key analytical and empirical findings are consistent. Specifically, due to aggressive responses from rival providers, a low-rating focal provider may lower its service price after forgoing screening despite its increased attractiveness to consumers. Intriguingly, forgoing screening cannot increase a provider’s demand if its rating is high, whereas the demand for rival providers will increase. Moreover, our analysis reveals that in markets with relatively high competition intensity, forgoing screening benefits all three parties—the focal provider, rival providers, and the sharing platform—thereby improving the total social welfare. Based on these findings, we suggest that sharing platforms should encourage high-rating providers to forgo screening as a strategic approach to enhance the overall market performance. Furthermore, special attention is needed to prevent a decline in social welfare when the provider forgoing screening has a low rating, while its rivals have high ratings. Our findings reveal that platform governance through feature-level design can directly enhance platform profits at the expense of social welfare, thereby creating misaligned incentives and calling for external regulation.

Previous research on recommender systems primarily focuses on their short-term effects on customer search and purchase behaviors. This study investigates the effect of a recommender system on customer loyalty and long-run retail sales using a randomized field experiment. We manipulated the presence vs. absence of product recommendations from an item-based collaborative filtering recommender system at an online retailer. The results reveal that displaying recommendations increases consumers’ purchases of recommended products but at the cost of reduced sales of non-recommended ones, which may not increase total sales in the short run. In spite of this, consumers’ shift of focus (to recommended products) due to the presence of recommendations plays an important role in enhancing customer loyalty. When recommendations are disabled, long-term sales decrease significantly due to reduced customer loyalty. The results show that the loyalty effect is primarily driven by a preference effect, in which displaying recommendations induces consumers to view more recommended products, enhancing their shopping experience and increasing customer returns. For returning visitors, who may deem product recommendations as built-in elements of a desirable store environment, a one-time disablement of recommendations can directly lead to defection. The findings provide insights on the true value of recommender systems.

Doxing, the malicious exposure of personal information online to threaten or intimidate individuals, is an escalating global threat. A particularly consequential form is politically motivated doxing (PMD), where perpetrators— known as doxers—target individuals for their viewpoints and beliefs, shaping sociopolitical discourse in the process. Despite PMD’s profound impact, research on the perspective of victims (referred to as doxees) remains scarce. Drawing on affective events theory, this study argues that doxees perceive PMD as a breach of their fundamental civil rights. We theorize that PMD triggers an affective duality—fear and anger—in doxees, which drives divergent behavioral shifts in their civic engagement across online and offline domains. Additionally, we examine how doxees’ strength of conviction regarding the PMD issue moderates their affective reaction, revealing distinct responses among individuals with moderate versus extreme views. Using a monostrand mixed-methods approach with a dominant quantitative and a complementary qualitative component, we conduct a personalized, immersive online vignette study in the context of U.S. school boards to empirically test our model. Our findings contribute to the IS literature by: (1) advancing theoretical and empirical understanding of doxees’ perceptions of PMD, (2) linking these perceptions to their affective and behavioral responses, and (3) demonstrating that PMD has a particularly suppressive effect on civic engagement among doxees with less firmly held convictions, highlighting PMD as a mechanism through which malicious actors exacerbate sociopolitical polarization. Our work offers timely implications for policymakers and online platforms.

This paper examines the influence of artificial intelligence (AI) on research and development (R&D) practices, proposing that AI enables firms to discover, evaluate, and make sense of information from their partners, thereby reducing information asymmetry and allowing alliances to flourish. Empirically, we approximate a firm’s AI resources using patents and job postings to analyze the relationship amongst AI, alliances, and drug innovation in the pharmaceutical industry. Our findings show that firms with greater AI resources shift their locus of innovation toward alliances and develop more drugs from their alliances. Furthermore, AI can be particularly useful in exploiting information held by counterparties within an alliance. Taken together, our study highlights the complementary role of AI and alliances, shedding light on how modern organizational and technological advancements jointly shape the production of innovation.

This paper explores how different types of fact-checkers (i.e., AI or human) impact users’ perception of the believability of news that is flagged as false. Building on source credibility theory, we evaluate how the reputation of the news source and the user's political orientation (i.e., progressive or conservative) moderate the impact of AI versus human fact-checkers. We examined this interaction in two separate 3×2×2 online quasi-experiments conducted in the United States and the United Kingdom. In both studies, we found differences in the impact of fact-checker type and in the moderating effects of poster reputation and user political orientation. Our results show that AI fact-checkers are more effective than human fact-checkers in reducing users’ perceptions of news believability, particularly among progressives. We also found that if the news poster has a high reputation, this can further enhance this impact. By investigating the interplay among fact-checker type, the poster, and users’ political orientation, and by comparing results across two countries, we extend understanding of how different fact-checker types affect news believability regarding false news on social media platforms (and user engagement in robustness checks). Finally, we derive managerial implications for mitigating the spread of false news on social media platforms.

This study examines how the extent of business intelligence (BI) systems within business enterprises is associated with compliance risk. BI tools promote a risk-aware culture by providing real-time data and analytics, helping establish objectives aligned with compliance, and ensuring transparent communication of compliance information across the organization. Using BI, organizations can better address compliance challenges, strengthen risk management practices, and adhere to regulatory requirements more effectively. Using a BI implementation dataset, we find that firms with more widespread BI implementation in the organization have lower compliance risk, reporting fewer regulatory violations and lawsuits. We also provide evidence that some measures of strategic IT focus moderate the association between BI implementation and compliance risk outcomes. Delving into the particularities of the association between BI implementation and compliance risk, we find that greater BI implementation is associated with lower lawsuit settlement amounts, fewer lawsuit types, and shorter lawsuits. Further, the beneficial BI implication is associated mainly with operational compliance risk rather than financial accounting compliance risk. Our findings have crucial implications for the value of BI in corporate compliance risk management.

Ride-sharing platforms that match riders with drivers have become one of the most celebrated examples of disruptions caused by information technology (IT). A rider’s willingness-to-pay (WTP) for the platform’s service is affected by the expected waiting time, which in turn depends on the match made by the platform (“match quality”). While some riders may have low sensitivity to match quality (“low segment”), others may be highly sensitive (“high segment”). Without knowing an individual rider’s sensitivity, ride-sharing platforms often use priority matching to offer differentiated services, allowing riders to self-select the service that best suits them. While such a strategy is akin to quality-differentiated products in conventional markets, the ride-sharing context exhibits unique features. For instance, one-to-one matching of riders and drivers imposes constraints on the match qualities the platform can offer. Moreover, any differentiation in driver wage that accompanies service differentiation on the rider side can induce drivers to anticipate a possibly higher future wage (”forward-looking behavior”), which can lead to supply-side cannibalization analogous to well-known demand-side cannibalization resulting from rider self-selection. As a consequence of these features, service differentiation generates novel implications for ride-sharing platforms. We find that a platform would enhance the match quality intended for the high segment and degrade the match quality intended for the low segment to mitigate demand-side cannibalization, whereas a conventional firm would degrade only the quality intended for the low segment. If drivers are not forward-looking, the platform would also differentiate wages when it offers differentiated services. However, if forward-looking drivers wait to obtain the higher wage, the platform may find it optimal to offer a uniform wage to drivers even though it may offer differentiated services to riders. Moreover, the platform could degrade the match quality intended for the high segment and enhance the match quality intended for the low segment to mitigate supply-side cannibalization. We further find that the demand-side and supply-side cannibalization have qualitatively similar same-side effects, but they have asymmetric cross-side effects.

Sensors, actuators, and controllers are becoming deeply embedded across nearly every aspect of life, from transportation and manufacturing to energy systems, consumer technologies, and healthcare. This expansion of cybernetic systems carries two important implications. First, the category of “computing machines” is broadening in both form and scope, now including a diverse array of computational architectures that sense and actuate across increasingly complex physical environments. Second, the growing integration of cybernetics is driving higher degrees of computational autonomy, enabling systems to operate with greater independence—both augmenting and, in some cases, replacing human agency. At the core of cybernetic systems are translational actions (TAs) that facilitate the detection and transformation of the energies of the physical world, across the strata of technology artifacts, to ultimately become virtualized representations in the computational world. However, the concept of TA remains underspecified, offering scant insight into its concrete forms, the value and costs these different forms create, and ultimately, how physical-digital TAs function in real-world systems. In response, we conducted an inductive study of 188 detection and computational technologies originating from leading scientific research institutions to develop a more nuanced vocabulary of how translational actions govern the relationship between digital representations and their physical referents. Our analysis identifies six distinct TA forms and three hierarchical levels at which they operate, each with specific trade-offs that we characterize as fidelity costs. These insights enable us to theorize how TAs shape the technical construction of data as inputs to digital representations, and, in turn, how such representations attain performative value in the physical world.

Cyber breaches pose an increasing concern for executives and boards of directors, as they involve the exposure, damage, or loss of critical organizational data. The risk of a cyber breach is particularly acute for large corporations, which are prime targets for cybercriminals due to their vast data reserves. Prior literature has examined the role of CEOs and boards in cybersecurity management. However, their cybersecurity effectiveness may depend on firm size, which can provide more resources but also create challenges such as bureaucracy and resistance to change. To test these theoretical perspectives, this study develops a contingency theory by exploring the moderating role of firm size in the relationship between CEOs with IT functional experience, board interlock ties with technological firms, and the likelihood of cyber breaches. Analyzing a sample of U.S. Fortune 500 public firms from 2009 to 2018, our findings reveal that compared with smaller firms, CEO IT functional experience and board technological ties in larger firms reduce the likelihood of a cyber breach. These effects are also complementary, representing the potential for collaborative IT governance. Our findings provide important insights into IT governance and cybersecurity.1