Recent regulatory scrutiny has highlighted concerns regarding Amazon’s use of market data in competing with its sellers through its private-label operations, prompting the introduction of new data governance policies across multiple jurisdictions. Motivated by this development, this paper examines the effectiveness of regulations governing platform data use. We study two policies: Policy S, which prohibits the retailer’s use of market data while granting exclusive access to sellers, and Policy RS, which allows shared data access between the retailer and sellers, reflecting emerging industry practices. These policies are evaluated against a baseline case with no data restrictions. Our analysis shows that while Policy S effectively restricts platform data use and protects sellers, it also generates strong incentives for the retailer to engage in strategic responses, including contractual adjustments and policy circumvention, as it represents the least favorable outcome for the platform. Policy RS, in contrast, emerges as a practical compromise that better aligns platform and seller incentives. We further show that the effectiveness of data regulations depends on the contractual environment and the platform’s ability to adjust its organizational structure. A key insight is that the retailer can undermine regulatory intent through subtle but systematic strategies, such as steering sellers across contract forms and exploiting regulatory ambiguities. These effects become more pronounced under the wholesale contract. Our findings highlight that recent regulations may be insufficient if they do not account for strategic platform behavior. We find that recent data regulations face important challenges, as they may not prevent the retailer from exploiting contractual flexibility and regulatory loopholes. We therefore provide guidance for designing more robust regulatory frameworks with clearer conditions and enforcement mechanisms that explicitly incorporate contract structure and limit opportunities for regulatory circumvention, thereby strengthening protection against unfair competitive practices by dominant platforms.
For a collaboration between humans and artificial intelligence (AI) to be fruitful, tasks should be allocated based on their complementary capabilities. Prior research shows that when humans are responsible for allocating tasks between themselves and an AI through delegation, they often delegate too infrequently or delegate the wrong tasks, preventing complementary performance gains.We study how different types of AI system information affect both delegation frequency and delegation effectiveness, which capture the extent to which humans can leverage existing complementarities with AI. Specifically, we study ex-ante AI certainty (the AI’s estimated likelihood of being correct) and ex-post AI outcome information (whether the AI was actually correct on a given task). We show experimentally that presenting either AI certainty before or the AI’s outcome after a delegation decision has no or even negative effects on combined human-AI performance. However, providing both AI certainty and AI outcome information leads to increased delegation frequency as well as more effective delegation, ultimately leading to beneficial performance. We find that ex-ante certainty information calibrates users’ expectations about AI performance on the task-instance level, while ex-post outcome information confirms or disconfirms these expectations. This complementary use of AI system information supports more accurate mental models of the AI’s capabilities, reduces unwarranted algorithm aversion and improves appropriate task allocation. Overall, our results show that the effects of AI system information should not be assessed in isolation. While each signal on its own can be uninformative or even harmful, combining them can reverse the potentially harmful individual effects and facilitate effective human-AI collaboration. Our findings have implications for the design of AI systems in collaborative delegation settings, suggesting that carefully designed system information can help users better leverage complementarities with AI.
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 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.
Digital platforms often share publicly visible behavioral data, such as ratings and reviews, to improve service quality. Yet, these seemingly innocuous data can also reveal hidden social ties among users. We develop a two-stage framework that first diagnoses this leakage risk by inferring latent social interactions from behavioral data and then mitigates the risk while preserving data utility. We characterize when hidden ties are identifiable from observed actions and show, using public Yelp data from Louisiana and Pennsylvania, that an attacker can recover about half of true social ties at a 10% false-positive rate and more than 60% at a 20% false-positive rate. These inferred ties can materially increase cyber risk: when used for spear phishing, the estimated return on attack rises from 109% for a campaign with 500 impersonation attempts to 1,098% for one with 10,000 attempts. To reduce this leakage, we propose two perturbation mechanisms with formal differential privacy guarantees on the released representations: one adds Gaussian noise directly to the released action matrix, and the other adds Laplace noise to the learned representation before resynthesizing the released data. Both mechanisms reduce the link-inference accuracy and substantially lower estimated attacker returns; under our main protection settings, returns become negative for smaller campaigns and remain much lower at larger scales. Overall, the paper provides a practical framework for diagnosing social interaction leakage and evaluating the privacy-utility trade-off when platforms share behavioral data. History: Peiyu Chen, Senior Editor; Heng Xu, Associate Editor. Funding: Y. Leng is supported by the U.S. National Science Foundation (NSF) [Grant IIS-2153468]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2024.1469 .
As digital platforms increasingly rely on automated tools to govern user expression, an important practical question is whether algorithmic moderation can improve content quality without undermining user cooperation. Drawing on Wikipedia’s bot-based enforcement of neutrality rules, we find an unintended consequence: Contributors whose prior edits are moderated often respond with more politically slanted subsequent expression, rather than moving closer to neutrality. This pattern is stronger when moderation targets a contributor’s focal area of attention, among contributors with stronger prior political bias, and after repeated bot intervention. It is weaker when moderation occurs outside the contributor’s focal area and among contributors with greater experience in politically sensitive topics. Together, these findings suggest that effective platform governance requires more than scalable automated enforcement. For platform leaders, the results underscore the value of pairing bots with transparent explanations, context-sensitive messaging, and human oversight. For policymakers, the study indicates that algorithmic content governance should be evaluated not only by its ability to remove problematic content, but also by its downstream effects on user behavior, participation, and polarization. Well-designed governance systems must balance rule enforcement with users’ sense of autonomy.
Information privacy concerns shape consumer behaviors, regulatory choices, and the design of AI-driven products, yet prior research offers contradictory guidance. We synthesize three decades of evidence from 305 empirical studies, mapping the antecedents, consequences, and moderators of privacy concerns. For practitioners, the strongest levers are situational rather than demographic: assurance mechanisms, perceived control, information sensitivity, and perceived vulnerability have stronger effects than age, gender, or personality. Resultantly, systems and regulations that enhance user control and transparency outperform demographics- or personality-based approaches. However, context shapes these relationships. Cultural and regulatory environments, the type of risk (financial, social, physical, or general), and whether a system relies on conventional data flows or AI-driven inference moderate how users respond. Notably, AI-augmented systems invert the classic privacy calculus: concerned users often keep sharing, suggesting that trust-building and assurance mechanisms differ for new analytics and AI systems. For policy, our findings support investments in meaningful transparency, standardized assurance disclosures, and risk-tailored safeguards, particularly for AI governance. For research, we identify underexplored but high-impact variables, including privacy empowerment, psychological ownership, app permission concerns, and regulatory awareness. These variables should anchor the next generation of privacy studies.
Open innovation and crowdsourcing have become standard tools for governments, foundations, and firms seeking solutions to hard problems. The implicit assumption is that more participation and more collaboration produce better outcomes. This research complicates that picture. Using agent-based simulation calibrated against one of the world’s largest crowdsourcing platforms, we show that spontaneous team formation during contests trades off parallel exploration for collaborative depth and that this tradeoff is not always favorable. For simple, well-defined problems, enabling teaming at the right moment improves the odds of finding the best solution. For complex, interdependent problems, precisely the kind that public innovation challenges tend to target, poorly timed or signal-driven teaming can suppress the diversity of search and reduce the likelihood of breakthrough outcomes. Policymakers designing grand challenge competitions, innovation prizes, or open problem-solving initiatives should attend carefully to the structural features of their platforms: what information is made visible, when teaming is permitted, and how incentives interact with collaboration norms. The design of the information environment is not neutral; it shapes who collaborates with whom, and ultimately whether the crowd delivers on its promise.
Practice and Policy Oriented Abstract for “Ecosystem Competition: Platforms, Subsidiary Markets and Multihoming in the Videogames Industry” This study examines how royalty-bearing game sales and multi-platform publishing reshape competition in the videogame console industry. By modeling consoles, publishers, and gamers as part of an interconnected ecosystem rather than a simple two-sided market, the analysis shows that downstream game revenues can intensify, rather than soften, platform competition. When consoles expect to earn royalties from later game purchases, they have stronger incentives to lower hardware prices to attract gamers, while shifting more of the revenue burden to publishers through higher licensing fees under royalty-based contracts. The study also finds that when publishers release games on multiple platforms, gamer subsidies deepen, licensing fees rise, and multihoming may cease to benefit platforms beyond a threshold, overturning a common assumption in platform strategy. For practice, these findings suggest that platform managers should evaluate pricing, exclusivity, and revenue-sharing decisions at the ecosystem level, not in isolation. For policy, the results imply that regulators and industry observers should expect royalty structures and content distribution rules to affect consumer prices, publisher bargaining power, and market competition in digital ecosystems more broadly.
Organizations are increasingly integrating artificial intelligence (AI) into traditionally human-driven evaluation processes, such as recruitment and performance evaluation. Yet the complexity of the underlying AI algorithms often renders these processes opaque, limiting users’ understanding and potentially inducing stress and other behavioral changes. In response to these challenges, transparency is often advocated as a mitigative approach, but its effects remain somewhat ambiguous. While transparency in algorithmic evaluation may mitigate interviewees’ stress in the recruitment context, it may also incentivize opportunistic impression management (IM), thereby engendering a transparency paradox. Focusing on this tension, we theorize that while algorithmic evaluation, relative to human evaluation, may heighten stress and deceptive IM in the recruitment process, transparency has the potential to mitigate these disruptions. We test this across eight studies, including two main experiments and a quasi-field replication. In Experiment I, we found that while algorithmic evaluation increased interviewees’ stress and deceptive IM relative to human evaluation, transparency counteracted these effects, aligning them closely with human evaluation. In Experiment II, we study how these effects caused deviations in downstream interview performance when such performance is assessed in human-only, AI-augmented (wherein human decision-makers are assisted by AI scores), or fully automated decision-making configurations. Although transparency consistently reduced deviations in stress and IM from human evaluation for interviewees, its corrective effect on interview performance was constrained on the evaluative side: when AI scores were present, evaluators anchored on them, discounting their own judgment, even as the AI scores failed to distinguish honest from deceptive behaviors. Together, these findings disentangle the dual-edged effects of transparency in algorithmic evaluation, showing that its benefits depend not only on how users respond, but also on how evaluation is conducted. This work advances research on algorithmic decision-making and socio-technical systems, and offers implications for organizations, policymakers, and AI developers.
As social media platforms deploy large language model (LLM)-powered agents to help influencers manage social relationships with users, it remains unclear how this delegation impacts user engagement. Automating interactions provides scalability and efficiency for influencers, but it may weaken the influencer-user relationship if the agents fail to serve as effective social delegates. To explore this question, we empirically investigate the impact on user engagement when influencers delegate social interaction tasks, such as replying to comments, to a social artificial intelligence (AI) agent, an LLM-powered proxy that responds on behalf of an influencer. Leveraging the rollout of a social AI agent feature on a major social media platform, we use a staggered difference-in-differences design to compare engagement behaviors between users who received an AI reply (i.e., a reply from an influencer’s social AI agent) and those who did not. Our results show that receiving an AI reply significantly increases user commenting on subsequent influencer posts, particularly when AI replies amplify an influencer’s social presence, as reflected in content relevance, stylistic alignment, and reply timeliness. We also find heterogeneous effects based on influencer-user relationships: engagement gains are stronger among loyal followers but weaker for commercialized influencers and those in the technology domain. Additionally, reply scarcity amplifies the effect: engagement increases more when influencers rarely replied previously or when fewer AI replies appear under the focal post. The engagement boost extends to both sponsored and nonsponsored posts, as well as user reposting behavior, whereas influencers themselves also post more frequently after adopting AI agents. This study contributes to the literature on AI delegation and influencer engagement by highlighting when and how delegating social relationship management to social AI agents can enhance user engagement. History: Jeffrey Parsons, Senior Editor; Pallab Sanyal, Associate Editor. Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2025.2270 .
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.
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.
As businesses expand globally, effective website localization becomes crucial, especially in countries where consumers have diverse cultural backgrounds. Traditional website localization strategies focus on tailoring its content and design to the dominant culture of a country. Our research investigates the complexities of localizing websites for countries with multicultural consumers who identify with more than one culture. We found that successful website localization involves priming multicultural consumers with cultural cues (in the forms of language and image) to activate specific cultural identities, for example, individualism or collectivism. The activated culture of multicultural consumers can interact with various website design and content elements to affect multicultural consumers’ trust in the website and crowdfunding support. Our findings offer actionable insights for global businesses. By strategically implementing these insights in website localization, global companies can better navigate diverse consumer landscapes and foster stronger connections with multicultural consumers.
Marketing brand alliances are consumer-facing collaborations in which brands are jointly presented through shared offerings or campaigns. Despite their widespread use, the systematic discovery and evaluation of such opportunities remains challenging because managers must screen vast combinatoric candidates to identify the few truly promising alliance opportunities. This study proposes brand alliance network exploitation (BANE), a sociotechnical framework that leverages information networks derived from digital user traces for market-wide, ex ante alliance opportunity discovery with execution guidance. BANE integrates off-line brand attributes, online consumer comention signals, and network embeddings to predict alliance opportunities; validates predictions through consumer and brand manager surveys; qualifies positive, nonmaterialized opportunities through the BANE matrix; and generates marketing mix execution guidance. Temporal holdout testing demonstrates that BANE predicts future alliances from preannouncement signals, and stakeholder surveys show that BANE-identified opportunities are perceived as significantly more promising than controls. For brand managers, BANE offers a scalable, data-driven alternative to survey-based alliance search for partner selection, portfolio-level collaboration strategy, and campaign execution. For investors, agencies, and consultancies, BANE provides systematic and timely signals of emerging alliances and evolving brand connections across industries.
Innovation depends on generating ideas that are not merely more numerous but meaningfully different. This article offers a practical framework for improving creativity in both people and generative artificial intelligence (AI). It models ideation as movement through a network of concepts, where common associations are easy to reach and more creative ideas lie on less-traveled paths. The framework explains why familiar techniques work and gives managers a systematic way to create new ones: narrow attention through constraints, shift the context, or delay output before selecting an answer. Across experiments with human participants and large language models, simple interventions—such as asking for alliterative responses, introducing an unusual environment, or requesting the fifth idea rather than the first—raised the novelty of responses compared with baseline prompts. For practice, the findings show that creativity can be improved through small changes to prompts, workflows, brainstorming rules, and AI-copilot interfaces. For policy, the study suggests that AI adoption should focus not only on model capability, but also on how human-AI interaction is structured to preserve diversity, reduce fixation, and support innovation across organizations.
This study examines how similarity, social identity, and platform design jointly shape user experiences in two-sided digital platforms. Drawing on theories of power asymmetry, social identity, and digital platforms, we analyze more than 500,000 reviews on Airbnb to evaluate how shared marginalized identities between guests and hosts influence perceived experiences. Contrary to expectations, similarity alone does not consistently yield more positive outcomes; however, platform features that enable power sharing, such as flexible cancellation policies and instant booking, significantly increase the likelihood of positive reviews among marginalized groups. These findings contain practical and policy implications. For platform designers and policymakers, the results highlight the opportunity of structural features that redistribute control and flexibility within transactions to foster inclusive and positive experiences. For platform owners and regulators, these results suggest a shift from representation-focused interventions toward feature-based governance that embeds power sharing, and potentially other shared values, into platform architecture. More broadly, the study underscores the importance of aligning technological features with the values and lived experiences of marginalized users, offering a scalable pathway to promote both inclusion and marketplace performance.
Whereas online reviews have become a primary data source for understanding customer requirements in both research and practice, using such information alone to guide product design can be unreliable. Our research investigates whether and how consumers’ preferences expressed through online reviews (words) align with their actual purchase decisions (deeds). Our analysis shows that features praised in online reviews do not necessarily translate to market success. This inconsistency between what consumers say and what they do poses significant challenges for manufacturers in product development decisions. We empirically identify the existence of word–deed inconsistency in consumer preferences. Some features are silent in online reviews yet significantly drive purchase decisions, whereas others are frequently praised but have limited influence on actual purchases. Building on these insights, we propose an innovative dual-weights model that extends existing two-dimensional customer requirement analysis by integrating both prepurchase choice drivers and postpurchase satisfaction determinants. Using this model, we classify features based on their importance for satisfaction versus purchase decisions and offer actionable product improvement strategies for different types of features.
As the cybersecurity landscape shifts toward more sophisticated, artificial intelligence–enabled, and unpredictable threats, employees are increasingly targeted as the primary entry points for breaches. Modern incidents, such as zero-day exploits and ransomware, often present employees with situations that are technically complex and ambiguous. We show that these situations are often perplexing for employees. Security perplexity is a tense cognitive state experienced by employees in threatening cybersecurity situations, arising from a conflict between perceived pressure to act and a simultaneous state of confusion and uncertainty about how to do so. Through rigorous construct development and multistage testing, we demonstrate that perplexity disrupts how employees cope with threats. Left unmanaged, this tension reaches a tipping point where employees move from proactive protection to maladaptive behaviors like avoidance or emotion coping. Cybersecurity managers must shift their focus from simple threat detection toward action-clarity to better support their teams. Organizations should prioritize employee coping by providing procedural walkthroughs to build confidence. Implementing a "pause-and-assess" principle with decision checklists helps employees manage ambiguity, reduces emotional escalation, and ensures a more resilient organizational response.
