Knowledge that Transforms

Practice- and Policy-Oriented Abstract How does the human capital of chief executive officers (CEOs) drive digital product innovation in manufacturing firms? Analyzing data from 216 U.S. firms and over 8,000 new product announcements, this study shows that technological and business knowledge can both enable and inhibit digital product innovation, depending on the external environment. In stable settings, tech-savvy CEOs drive digital product innovation, whereas business-savvy CEOs focus elsewhere. In dynamic environments, these effects reverse. These findings offer a contextual view on the ongoing debate about the value of technological versus business expertise in top management, suggesting that neither type of knowledge is universally beneficial. We also find that CEOs serve as distinct innovation catalysts beyond their top management teams. Follow-up interviews reveal the diverse strategies they use to initiate, develop, and implement digital product innovation in established firms. Based on these findings, the study offers guidance for boards of directors on aligning CEO selection with environmental demands and for CEOs seeking to expand their knowledge base to more effectively foster digital product innovation under varying conditions.

Housing discrimination has been recognized as an important societal issue for decades. While this issue can manifest in multiple ways, one of the most observed avenues is price discrimination, where houses in white-dominant neighborhoods are worth more than houses in minority-dominant neighborhoods that are otherwise similar. Prior studies have empirically documented such pricing discrimination and attributed it to human biases. In addition, recent studies have shown that issues of this kind are unlikely to be addressed by traditional AI models, even those specifically designed to address discrimination. In this paper, we first compare AI-generated versus human-generated housing prices using a sample of 284,749 U.S. properties. We then study the impact of generative AI in the context of price discrimination in the housing market and find that it can help alleviate this issue. Our mechanism exploration provides empirical evidence regarding underlying mechanisms that drive such a counter-intuitive result. Practical and policy implications are also discussed.

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.

In 2018, the Supreme Court of the United States struck down the Professional and Amateur Sports Protection Act (PASPA), ending a nearly 30-year federal ban on sports betting and paving the way for dozens of states to legalize such operations. The sports betting market has experienced triple- and double-digit growth every year since the PASPA decision. Yet the downstream consequences of the legalization of sports betting remain understudied. In this paper, we examine the impact of the legalization of both off-line and online sports betting on the well-being of individuals in those jurisdictions. We focus on two outcomes: the number of calls to the National Problem Gambling Hotline and the number of suicides reported per state as a result of legalization of sports betting. Our results indicate that, whereas the number of calls associated with problem gambling is uncorrelated with the legalization of physical sportsbooks, it is strongly correlated with the legalization of online sports betting. Further, results suggest that the legalization of online betting is correlated with an increase in suicides, an outcome historically associated with problem gambling. Finally, we observe that these deleterious effects are more pronounced for certain groups, in particular, for young, unmarried, and less educated men.

As brick-and-mortar retail rebounds, congestion in fitting rooms has reemerged as a critical operational challenge. Augmented reality (AR) fitting applications offer a scalable solution by enabling rapid virtual trials and reducing in-store delays, yet imperfect assessments may increase product mismatches. This study provides actionable guidance for retailers on AR adoption and pricing strategies by clarifying the economic roles of the substitution and complementarity effects. Our findings underscore the importance of a market-contingent approach to AR adoption and system upgrades. Rather than presuming that improvements in accuracy or usability will uniformly enhance performance, retailers and IT managers should rigorously evaluate how technological capabilities interact with market size, price fluctuations, and congestion dynamics. We also inform targeted marketing by demonstrating how heterogeneity in consumer technology preferences shapes channel selection and effective transaction outcomes. More broadly, our results caution policymakers that emerging digital technologies—while intended to reduce friction—may generate unintended welfare losses if mismatch risks and strategic responses are overlooked. These insights provide actionable guidance for digital transformation initiatives in retail and other capacity-constrained service systems characterized by heterogeneous consumers.

As artificial intelligence (AI)-powered digital streamers gain popularity in live commerce, online retailers face critical questions about the actual business value of their operations. This study offers timely, evidence-based insights into the economic impact and optimal design of digital streamers. Although current designs do not significantly improve sales over no live streaming, incorporating behavioral realism—especially enhanced real-time question and answer (Q&A)—can boost sales by 25%, making digital streamers as effective as human hosts. Visual upgrades and human-like voices also help but to a lesser degree. Importantly, not all AI-driven enhancements deliver immediate returns, and imitating human scripts does not guarantee success. Retailers should focus on dynamic human-AI interaction features that drive engagement and trust, such as real-time Q&A and interactive giveaways. Designers are encouraged to integrate multiple realism features to maximize effectiveness while managing cost and scalability. These findings offer actionable guidance for retailers and platform designers seeking to leverage AI effectively and cost efficiently in live streaming commerce.

Many online platforms now use complex recommender systems to decide which products, restaurants, or services people see. These systems can improve matching, but their recommendations are often hard for users and managers to understand. This article introduces MG-GAT, a recommender system framework that uses multiple networks and attribute data while keeping track of the neighbors and features that influence each recommendation. The same evidence is then used to generate explanations, so the reason shown to a user is tied to the model’s internal decision process rather than added afterward. Across Yelp data from Ontario and Pennsylvania, the method performs competitively with strong deep-learning baselines. In a randomized experiment, explanations based on MG-GAT increased users’ trust, persuasiveness, satisfaction, and engagement relative to similarity-based, social, and SHAP-based explanations. For practice, the results show that platforms do not have to choose between predictive performance and accountable explanations. Recommendation teams can design systems that expose the signals behind predictions, audit generated explanations for unsupported claims, and give users clearer reasons for accepting or questioning automated recommendations.

Digital Content Platforms (DCPs), such as Netflix and Spotify, rely on subscriptions and advertising as their primary revenue sources. Beyond pure subscription-only and ad-only revenue models, DCPs increasingly blend these models by offering a two-tier menu: a free, ad-supported tier for price-sensitive users and a paid, ad-free tier for ad-sensitive users. In this paper, we introduce the Generalized Ad-Supported Subscription (GAS) mechanism – a broad class of subscription-fee and ad-intensity combinations that spans the continuum from ad-only to subscription-only – and nests the traditional mechanisms as special cases. Using a mechanism-design framework, we characterize the revenue-maximizing GAS mechanism and compare its performance with the optimal ad-only, subscription-only, and two-tier mechanisms. While GAS is optimal within this broad class, the simpler two-tier mechanism can achieve near-optimal revenue. We then characterize conditions under which the GAS mechanism delivers a material revenue advantage over the two-tier mechanism. Finally, we estimate our model parameters and empirically validate the theoretical results in the context of Video-on-Demand platforms.

As electric vehicles (EVs) become more widespread, cities face the growing challenge of managing charging demand without overloading the grid. This study presents a novel information systems (IS) solution that supports smart and sustainable EV integration. The authors develop a capacity-based pricing model that adjusts in real time based on charging rates and grid capacity. Unlike many existing approaches, it avoids “avalanche effects” where synchronized charging behavior creates new demand peaks. The presented solution is also computationally efficient, making it practical for real-world use. Evaluated through simulations based on realistic urban scenarios, the model reduces demand volatility, aligns EV charging with renewable energy availability, and maintains overall charging costs for users. This work offers policy makers and energy providers a concrete tool to balance environmental goals with energy system reliability. For urban mobility planners, it provides a scalable, adaptive method to support the transition to cleaner urban mobility.

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.