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This paper examines how the integration of mobile touchpoints with existing technologies affects the efficiency and equity of crowd-sourcing of non-emergency complaints for cities. We analyze the City of Boston’s effort to integrate multiple technological touchpoints for residents to report non-emergency complaints and request city services. In 2015, Boston integrated its existing citizen app with its phone and website services into a single unified ‘311’ service, where complaints would be handled consistently regardless of submission channel. This was among the first efforts in the U.S. to bring an omnichannel approach to public services. We document that this change had an unexpected effect. Before the omnichannel integration, though the app performed less well than other channels, it actually appeared to provide more equitable service for higher- and lower-income neighborhoods compared to other channels. After the integration, the app’s expanded complaint options led residents to use it more often for code-enforcement complaints against other residents. The mobile app enabled faster resolution of enforcement complaints, especially in wealthier areas with signs of gentrification. We find that this effect can be explained by the fact that residents in richer neighborhoods were more likely to submit multiple enforcement requests on the same day. Complaints that required citations were issued more quickly when the relevant complaints originated from a geographically concentrated area rather than a more diffuse one.

Open source projects involve two strategic decisions: when to release new versions, and how actively to engage community contributors. Both compete for the same scarce team resources. This study examines how core teams jointly manage these decisions over time using detailed data from GitHub. We find that both release and co-creation positively influence community interest, but the relative emphasis placed on them depends on a project’s operational conditions and cost structure, with team capacity playing a particularly important role in enabling co-creation. Projects that push either activity beyond what their team can sustain face diminishing rather than increasing returns. The adoption of AI and automation tools may reshape how these trade-offs play out. The effects depend critically on which channel dominates: when AI expands internal team capacity, both release and co-creation activity increase; when AI primarily attracts more outside contributors, co-creation rises but release frequency does not. This contrast reflects a key asymmetry: AI tools reduce the cost of generating contributions far more than the cost of reviewing them, making internal capacity a prerequisite for translating participation into broader project gains.

This study examines how influencer marketplaces reshape sponsorship opportunities and content strategies on social media platforms. Using the launch of RedNote’s influencer marketplace as a natural experiment, we analyze more than 152,000 posts from 1,161 influencers before and after the platform introduced centralized search and matching tools. The findings show that reducing search costs and increasing transparency disproportionately benefit medium-tier influencers, who receive significantly more sponsorship opportunities than top-tier creators after the marketplace launch. The study also finds that influencers strategically adapt their content in response to increased sponsorships. Medium-tier influencers post more organic and diverse content to maintain audience trust and reduce the reputational risks of appearing overly commercialized, whereas top-tier influencers improve the quality of sponsored posts. The findings offer important policy and managerial implications. For platforms, transparent creator marketplaces can broaden economic opportunities for smaller creators and support a healthier, more competitive digital ecosystem. For policymakers, the results highlight how platform design and transparency policies influence competition, market access, and consumer trust in digital labor markets. The study also underscores the importance of governance mechanisms that balance monetization, disclosure, and authenticity in influencer marketing.

Practice and Policy-Oriented Abstract Social media has accelerated the growth of influencer marketing, for which authenticity signals are central to shaping consumer purchase behavior, yet empirical evidence on their effects remains limited. Through a partnership with an influencer-affiliated e-commerce platform, we analyze influencer endorsement posts and develop a methodological framework to extract transparency cues from content, providing a practical tool for measuring and evaluating authenticity at scale. We show that these cues have distinct effects on consumer purchase decisions: unedited content and objectivity increase purchase likelihood, whereas openness about brand affiliations can reduce it, especially when consumers feel strongly connected to influencers. In contrast, consumers who engage with a broader set of influencers rely more heavily on these cues when making decisions. These findings offer actionable guidance for influencers and brands in designing content and partnership strategies that align with audience relationships and expectations. From a policy perspective, the results highlight the need for more nuanced disclosure guidelines as uniform transparency requirements may produce unintended effects across different relationship contexts, underscoring the importance of evidence-based regulation in digital advertising environments.

Long before new technologies are adopted, organizations already anticipate and shape what those technologies will become. Through a comparative ethnography of two municipal water agencies pursuing “smart meter” systems, this study shows how past innovation experience steers digital innovation’s earliest initiation period. Both agencies faced similar conditions but pursued the technologies in very different ways. The agency with decades of digital metering experience approached smart meters as a limited hardware replacement, while the second agency pursued them as a foundation for data-driven organizational transformation. To explain this divergence, this study introduces interpretive debt: the accumulated assumptions, obligations, and habits of attention from past innovation that shape what organizations recognize as possible in new technologies. It also develops layered architectural framing, showing how organizations focus on different layers of the digital stack. These aspects become consequential through anticipatory practices: organizational actions enacting projected futures before adoption. Through procurement, these practices shape what technologies can do; through reorganizing departments and workflows, they shape what the organization becomes. For organizations confronting new technologies, this study shows how digital futures take shape well before procurement, when organizations define both the technologies they adopt and the pathways they pursue.

Does AI democratize knowledge production or amplify existing disparities? We investigate this tension by studying the deployment of neural machine translation across more than 100 Wikipedia language communities. Leveraging rich, fine-grained data and exogenous variation from a natural experiment, we uncover the “AI democratization paradox,” where the technology simultaneously drives democratizing and concentrating forces. AI lowered barriers, leading to a substantial increase in content creation across diverse target languages without sacrificing quality or readership. However, the benefits were concentrated: well-resourced communities captured disproportionate gains—three to four times larger than mid-tier editions. Whereas editors actively leveraged AI to address representation gaps, translating female biographies at twice the expected rate, structural constraints still limited the impact in high-need areas. We conclude that technological solutions alone cannot overcome structural inequalities; AI’s distributional impact is contingent on the interplay between technological capabilities and existing social structures. This paper was accepted by Anindya Ghose, information systems. Funding: This work was supported by the Wikimedia Research & Technology Fund and European Union – NextGenerationEU funds, Component M4.C2, Investment 1.1, PRIN 2022 PNRR, CUP: J53D23015440001. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.04717 .