Knowledge that Transforms

A decentralized autonomous organization (DAO) is a novel form of blockchain-based organization designed for collective decision-making. As DAOs emphasize a decentralized, democratic decision-making approach, participation serves as the foundation for their sustainable operation and development. Unfortunately, many DAOs struggle with low participation rates, often falling short of the required quorum. To address this critical issue, an increasing number of DAOs have adopted delegated voting, which allows members to transfer their voting rights to others. However, the impact of delegated voting within the DAO context remains unknown. By leveraging variation in the adoption of delegated voting across DAOs, we find that delegated voting increases members’ participation in proposal voting and enhances decision quality. Our results further show that delegated voting stimulates greater participation in proposals with higher participation costs, including those that are more complex, urgent, or operational in nature. However, in the long term, delegated voting also leads to greater voting power concentration and reduces engagement from both new and active voters, potentially harming sustained participation and the growth of the DAO community. Overall, our findings highlight the need for DAOs to balance the short-term gains from higher participation with the potential long-term risks to decentralization.

As data privacy regulations expand consumers’ control over personal data, e-commerce platforms and sellers increasingly face users whose data cannot be used for targeting or pricing. This paper studies how such information withholding reshapes competition in digital marketplaces. We develop a two-stage model of platform segmentation and pricing competition and introduce “fuzzy segmentation,” whereby the platform strategically pools privacy-preserving users with selectively grouped data-sharing consumers. Our analysis yields three main insights. First, fuzzy segmentation softens competition and increases seller profits, contrasting with the canonical intuition that horizontal segmentation tends to intensify price competition. Second, contrary to the intuition that privacy protects consumers from price discrimination, privacy-preserving consumers may face higher prices. Third, some data-sharing consumers may experience negative spillovers due to their inclusion in the mixed segment with privacy-preserving users. These results reveal unintended consequences of data privacy regulation: although privacy rules may enhance consumers’ control over personal information, they can also alter market segmentation in ways that raise prices for both privacy-preserving users and some data-sharing consumers. From a strategic perspective, the results show how privacy-driven incomplete information can be leveraged as a profit-enhancing force in e-commerce platforms.

Generative artificial intelligence (AI) facilitates content production and enhances ideation, with potentially important implications for developer productivity and participation in software development. To explore its impact on collaborative open-source software (OSS) development, we investigate the role of GitHub Copilot, a generative AI pair programmer, in OSS development where multiple distributed developers voluntarily collaborate. Using GitHub's proprietary Copilot usage data, combined with public OSS project data obtained from GitHub, we find that Copilot use increases project-level code contributions by 5.9%. This gain is accompanied by a 3.4% increase in developer coding participation and a 2.1% increase in individual code contributions. However, Copilot use is also associated with an 8% increase in coordination time and more code discussions. This reveals an important tradeoff: While AI expands who can contribute and how much they contribute, it slows coordination in collective development efforts. Despite this tension, the overall effect remains positive, resulting in a net increase in the timely merge of code contributions at the project level. Interestingly, we also find heterogeneous effects across developer roles. Peripheral developers exhibit relatively smaller increases in project-level code contributions and larger increases in coordination time than core developers. Together, our findings highlight the dual effects of AI pair programmers on code contributions and coordination in OSS development and provide implications for how generative AI may reshape the structure of OSS communities over time.

Algorithmic management (AM) has become a defining feature of online labor platforms (OLPs), profoundly shaping platform workers’ control over their working conditions. Prior research has documented diverse forms of worker resistance to AM—or algoactivism—yet existing studies rest on two problematic assumptions. First, that algoactivistic practices are uniformly accessible and arise directly from workers’ perceptions of structural constraints. Second, that such practices are primarily reactive resistance broadly targeted at the OLP’s AM system. These assumptions obscure heterogeneity in workers’ motivations and resources, as well as variation in how algoactivistic practices unfold. This study develops a more textured understanding of platform workers’ algoactivism by tracing how corresponding practices emerge through situated, reflective, and resource-dependent processes. Drawing on the contested terrain lens from labor process theory, we conceptualize the interplay between OLPs and workers as an ongoing struggle over control of working conditions. We examine this struggle in the context of Uber, a widely recognized extreme case of AM. Using a computer-assisted grounded theory approach that integrates topic modeling and qualitative coding procedures across multiple data sources, we develop a process-theoretical model of how platform workers contest AM. Our model centers on three recurring dynamics—reassessing terrain, exploring opportunities for contestation, and contesting terrain through algoactivistic practices—and yields two core theoretical contributions. First, we show that worker algoactivism depends on continual terrain reassessments and uneven capacities to engage in three forms of resourcing—algorithm, market, and voice resourcing. Second, we theorize algoactivism as a heterogeneous and multi-arena phenomenon comprising self-optimizing, distancing, and confronting practices that vary in logics, targets, and durability. Together, these contributions advance a more dynamic and agentic understanding of worker algoactivism and provide actionable insights for the design and governance of platform-mediated work.

Generative Artificial Intelligence (AI) technologies have emerged as a transformative force in the content creator economy. This paper examines how the AI stances signaled by specific platform policy decisions shape creator behavior on visual arts platforms. We leverage two independent natural experiments on leading Chinese platforms: Lofter’s launch of an AI image generator, which signaled a pro-AI stance, and Graffiti Kingdom’s prohibition of AI-generated artwork, which signaled an anti-AI stance. Our analysis shows that creators decreased their activity on Lofter following the AI generator launch, while activity increased on Graffiti Kingdom after the AI prohibition. Multiple lines of evidence show these effects stem from policy-communicated stance signaling: creators respond to what the focal AI policy communicates about AI’s role, the platform’s commitment to human creators, and the future competitive environment for human-created work, rather than merely to specific tool features or enforcement actions. Heterogeneity analysis reveals that higher-popularity, multi-homing, and AI-averse creators show larger activity reductions on Lofter. Through analysis of creator posts, we identify three primary concerns driving resistance: replacement risk, perceived low quality, and copyright infringement. To our knowledge, this is the first paper to causally identify how the AI stance signaled by a platform policy decision affects creator behavior. Our findings have implications for how platforms communicate AI-related policy decisions, and for policymakers on fostering a constructive relationship between AI and human creators.

In the current era of digital business, firms continuously experiment to enhance the online experience of individuals visiting their websites and platforms. The possible changes range from minor tweaks to large product or user experience updates, which are tested before the full rollout to estimate performance and minimize unintended negative outcomes. Because of the clear benefits of digital experimentation, increases in the number of experiments have strained the resource of online participants/customers. In tension with this scarcity, many digital experiments are not carefully powered for their objectives, leading to either over-sampling that wastes resources or under-sampling that weakens inference. These issues warrant methods that can help experimenters balance power and efficient resource use — that is, to sample enough for the proper power without over-sampling. To address this issue, we propose a sequential hypothesis testing method for selecting the best treatment among multiple alternatives for experimenter-specified levels of statistical power and false positive rate. Critically, the method not only samples for no more than the necessary level of statistical power, it also has low sample size variance relative to other methods, meaning that the resulting sample size is a precise estimate of the required sample size, and low bias in the treatment effect estimates, reducing a common problem for adaptive sampling methods. We also demonstrate our method on a dataset from a real multi-armed online experiment which demonstrates the method's efficacy in a realistic scenario. Our method can be implemented in an experimentation pipeline, facilitated with an R package we provide.

Generative AI content-generation (GCG) platforms enable users to co-create personalized content with remarkable speed. Yet recent research suggests that such immediacy may undermine early engagement: when content appears instantly, users may not realize sufficient value to register on the platform. We address this challenge by introducing fulfillment, i.e., the extent to which co-created content is revealed prior to registration on GCG platforms, as an experiential design lever that shapes value realization in initial interactions. Drawing on value co-creation literature, we suggest that fulfillment operates through two motivational pathways: value-in-use, reflecting users’ recognition that their input meaningfully shaped the output, and curiosity, reflecting anticipatory motivation when the experience remains perceptually open. Using a randomized field experiment on a GCG platform, complemented by a follow-up online experiment, we show that partial fulfillment, which reveals some but not all generated output, outperforms both full and no fulfillment in driving registration. This effect is also conditioned by the framing of the registration message. While loss-framed messages that emphasize the cost of inaction increase registration on average, this effect attenuates under full fulfillment, suggesting a substitution relationship. Formal mediation analyses indicate that although both full and partial fulfillment enhance value-in-use, only partial fulfillment sustains curiosity, and this dual activation explains its effectiveness. Additional analyses delineate the scope of these effects, which persist beyond registration to shape subsequent engagement and return behavior, but arise only when users meaningfully co-produce content and are enhanced by better quality outputs. Together, these findings suggest that registration on GCG platforms depends not on maximizing disclosure or curiosity alone, but on structuring interactions to preserve users’ involvement in shaping generated outputs. In doing so, they highlight how effective design on GCG platforms supports engagement that emerges from complementary human and GenAI contributions, rather than from automation alone.

For-profit firms increasingly adopt open-source product development by engaging external community members alongside internal employees on social coding platforms such as GitHub. Yet whether and through what mechanisms this engagement affects firm profitability remains an open question. Drawing on the knowledge-based view of the firm, we conceptualize open-source product development as a form of distributed knowledge integration that enhances labor productivity by expanding the specialized expertise available for product development beyond the firm’s internal boundaries. We posit that improvements in labor productivity translate into higher profitability, as labor constitutes a primary input in software product development. However, the labor productivity effect depends on the extent of participation by external contributors, and the resulting profitability gains are shaped by equifinal configurations of firm resource allocation. We examine our theoretical framework using a longitudinal dataset of 977 U.S. high-tech firms from 2001 to 2025 and find that firms adopting open-source product development via GitHub realized, on average, a 4%–5% increase in gross margin. These results are robust across staggered difference-in-differences, generalized synthetic control, instrumental variable, and dynamic panel specifications. A moderated mediation analysis decomposing over 323,000 project-level contributions across more than 44,000 repositories into internal employee and external volunteer sources reveals that labor productivity partially mediates the profitability effect and that this mediation is amplified by external contributor engagement. The indirect effect of open-source development intensity on profitability through labor productivity becomes discernibly positive only beyond a threshold of external volunteer contributions (approximately 35% in our sample). Configurational analysis further reveals that research and development intensity is present across all high-profitability configurations, consistent with the absorptive capacity required to integrate externally sourced knowledge. These findings extend the knowledge-based view to the open-source context and provide managerial guidance for aligning open-source strategies with firms’ resource configurations.

Physical retailers have long lacked the real-time behavioral visibility that online platforms enjoy through clickstream data. This research addresses that gap by introducing a video analytics framework that transforms in-store security camera footage into a rich, structured behavioral record: an "offline clickstream." Using computer vision and deep learning techniques, including person re-identification, trajectory reconstruction, pose estimation, and vision-language models, the system extracts moment-by-moment signals of shopper intent: how customers move through the store, how they interact with products, and how their body language evolves during a visit. A transformer-based prediction model trained on these signals achieves dramatically better purchase prediction accuracy than conventional demographic or contextual benchmarks alone: improving predictive performance by up to 79% on key metrics. Beyond prediction, the framework supports five real-time targeting policies; simulations show that a persuadability-based policy yields a 13.1% profit lift over no targeting. For retailers and policymakers, this research offers a scalable, privacy-conscious blueprint for bridging the capability gap between physical and digital commerce, enabling timely, personalized interventions that improve customer experience and store profitability.

Regulators increasingly mandate transparency regarding generative AI (GenAI) use in creative work, aiming to protect audiences from deception while preserving creators' self-expression. One way of achieving this transparency is through disclosure labels that directly inform audiences about GenAI use. Yet, prior research focused almost exclusively on how such labels affect audience evaluations and paid surprisingly little attention to whether mandatory disclosure affects creators, too. We refer to this as the indirect disclosure effect. Drawing on Goffman's account of impression management, we theorize that creators who anticipate disclosure fear that audiences will not recognize their human creative agency, threatening their validation as a creative self, which leads them to adjust their collaboration with GenAI. To investigate this mechanism, we employ two nested mixed-methods experiments in which participants collaborate with a text-to-image GenAI tool under different disclosure conditions. We empirically establish the indirect disclosure effect: when disclosure is anticipated, the majority of creators withdraw from the creative process, leaving image generation to GenAI. We provide evidence that this withdrawal is driven by creators’ fears that audiences will not recognize their creative agency. Hence, the produced artifacts predominantly reflect computational rather than human creativity, which is also recognized and evaluated by the audience regardless of the direct disclosure label. Overall, our study reveals a fundamental tension at the heart of transparency regulation: by disclosing GenAI use through a simple label, regulators may inadvertently diminish the very human creative agency they aim to protect, and they do so before audiences ever see the label.