Knowledge that Transforms

Generative AI (GenAI) promises to revolutionize creative work, but its value is not universal. Using controlled lab settings with students and real-world tests with professional designers, our research shows that GenAI is a double-edged tool. In the initial brainstorming (ideation) stage, GenAI reliably boosts creativity for all users. However, in the execution (implementation) stage, whereas novice designers continue to benefit from GenAI’s assistance, expert designers encounter inefficiencies—spending significantly more time without improving creativity, because GenAI’s methods conflict with experts’ well-established routines. For firms, this means adoption strategies must be nuanced. GenAI delivers the greatest value when applied to brainstorming, early concept development, and work by less-experienced employees. In contrast, deploying GenAI in later-stage production tasks, especially with seasoned professionals, may reduce efficiency. Managers and tool designers should avoid blanket promotion of GenAI across all tasks and instead develop targeted adoption strategies that align with employees’ expertise and the stage of the creative process. By tailoring GenAI use, organizations can harness its creative potential while minimizing risks of counterproductive outcomes.

This paper proposes a novel algorithmic market mechanism to address key challenges in individual data markets. Current data collection practices lack transparency and proper compensation, leading privacy-conscious users to opt out and creating biased data sets. Our proposed mechanism enables an intermediary platform to obtain unbiased samples of individual-level data while appropriately compensating users for privacy loss. Through theoretical analysis and simulations using both synthetic and real-world data sets, the authors demonstrate that their mechanism provides unbiased data samples at near-optimal cost compared with benchmark approaches. The mechanism outperforms both fixed-compensation methods and centralized-optimization approaches, even when platforms have partial information about user privacy preferences. Surprisingly, platforms achieve better outcomes by using this market mechanism rather than relying on estimated privacy preferences from user behavior. The approach is practical to implement, using straightforward sampling and conventional compensation mechanisms rather than complex techniques, like differential privacy. The mechanism enables creation of effective data markets that benefit both data subjects and buyers while ensuring compliance with regulations requiring transparency and consent. The findings are particularly relevant as new privacy regulations emerge globally and third-party tracking faces increased constraints, providing a viable solution for improving data quality and fairness in digital markets.

Resale royalties, first introduced in the 1920s to support artists through a share of future resales, have now adopted by nonfungible token (NFT) marketplaces for digital art trading. Although these royalties are often viewed as beneficial for creators, our research reveals unexpected consequences. Using data from a major NFT marketplace, we find that NFTs with higher royalty rates sell for significantly lower prices and take longer to sell. Surprisingly, creators do not recoup these initial losses through royalty payments within four years. We discover that higher up-front minting costs lead creators to set higher royalty rates. We reveal a delayed gratification effect where creators with higher royalties accept lower up-front prices in hopes of future royalty income. We also find an overconfidence effect where confident creators, measured by their past sales and follower count, are more likely to lower initial prices. Our research contributes to the ongoing debate about royalty enforcement in NFT marketplaces and offers empirical evidence to inform platforms and creators. Platform managers should carefully consider both reducing up-front minting costs and implementing royalty rate limits to improve market liquidity. Creators should be cautious about setting high royalty rates as they may not provide the expected financial benefits.

Given the widespread adoption of online learning, it is crucial to examine how these platforms can effectively maintain learners’ motivation and engagement. This study suggests that the prevalent hands-off, self-directed design approach, which relies heavily on learners’ intrinsic motivation, can potentially neglect crucial aspects like achievement emotions and lead to disengagement. Our findings show that online learning providers can address this issue through motivational interventions based on situational achievement goals. Specifically, our results indicate that among three achievement goals, learning, performance-prove and performance-avoidance, the performance-prove goal is the most effective one in enhancing learner engagement and performance. Additionally, we discuss that a one-size-fits-all course design may not be the most effective strategy for maintaining high engagement. Customizing interventions based on learners’ motivational behavior, prior performance, and social activity can significantly improve engagement. Learners with stronger prior performance benefit more from the performance-prove goal, whereas those with moderate performance levels gain from the performance-avoidance goal, and those with lower prior performance are positively influenced by the learning goal. Socially isolated learners respond best to performance goals.

Generative artificial intelligence (AI) is not merely changing how information systems (IS) research gets done—it is reshaping what research can be. We stand at a pivotal moment where machines can help generate hypotheses, synthesize vast literatures, and identify patterns that would take human researchers months to uncover. Yet, this unprecedented capability presents equally unprecedented risks to scholarly integrity. Because the field is uniquely positioned to understand sociotechnical transformations, IS research faces an extraordinary opportunity to pioneer “inventing with machines” while preserving the human insight and oversight that gives scholarship, as currently defined, its meaning. This transformation demands more than tool adoption. It requires a reimagination of scholarly infrastructure, norms, and practice. However, this transformation of research tooling creates a dangerous paradox: Powerful AI tools are now accessible to researchers who lack the technical literacy to understand and use them responsibly, threatening everything from citation accuracy to theoretical validity. Yet within this paradox lies the potential for revolutionary advances in how we craft our future as scholars. Informed by the sociotechnical perspective, we argue that the path forward requires coordinated community action that goes far beyond individual skill development. The IS community must lead the development of specialized AI tools that consider our theoretical traditions, create educational frameworks that preserve scholarly values while embracing computational capabilities, and pioneer review processes that harness AI’s analytical power without ceding human control, at least, in the short run. Success will determine not only the future of IS scholarship but our field’s capacity to guide other disciplines through this fundamental transformation of academic practice. The era of human-AI collaboration in research has already begun. How we govern and guide it will define the next generation of scholarly discovery.

Practice- and Policy-Oriented Abstract Mobile gaming is a prominent component of the entertainment industry, yet high attrition rates and low engagement are notorious problems faced by most mobile game publishers. This paper investigates the effects of in-game challenge-related factors and reward ads on player engagement in mobile games. Using a Hidden Markov Model, we analyze how perceived challenge, fluctuation of perceived challenge, and reward ads impact players’ engagement states. Our findings indicate that whereas moderate levels of perceived challenge enhance engagement, excessive challenge level has diminishing returns. Furthermore, fluctuations in perceived challenge help sustain engagement by providing a balance between stimulation and cognitive restoration. Third, reward ads provide access to scaffolds, offering temporary support that helps players move to a higher engagement state, especially during challenging phases of the game. For game developers, incorporating dynamic challenge adjustments can improve player experience and keep players engaged. Additionally, reward ads offer a strategic opportunity for game publishers to monetize their games while maintaining engagement, with a stronger impact when players perceive a higher level of challenge. These insights provide valuable practices for designing mobile games that effectively balance user engagement and monetization strategies, ensuring sustained growth in a competitive landscape.

Practice-Oriented Abstract This paper proposes a novel approach for enhanced sales forecasting by leveraging multifaceted product relations, disentangled on the ground of the cross-category choice dependence theory. With superior forecasting performance over state-of-the-art alternatives and a deep understanding of product relations, the proposed approach has significant practical implications for various stakeholders (e.g., retail store managers, inventory department, purchasing department, operational staff, marketers, and retail platforms). On the one hand, improved forecasting could provide solid data-driven decision support for supply chain management, resource planning, inventory control, and purchasing planning. The semblance of predictive power in sales forecasting demonstrates operational utility. On the other hand, derived insights on product relations could facilitate reasonable pricing and promotion strategies, enhance the relevance and diversity of recommendation systems, and provide benefits for assortment planning, cross-selling, and shelf space allocation.

Practice- and policy-oriented abstract: Companies are increasingly deploying highly realistic digital human agents (DHAs) controlled by advanced AI for online customer service, tasks typically handled by chatbots. We conducted four experiments to assess users’ perceptions (trustworthiness, affinity, and willingness to work with) and behaviors while using DHAs, utilizing quantitative surveys, qualitative interviews, direct observations, and neurophysiological measurements. Our studies involved four DHAs, including two commercial products (found to be immature) and two future-focused ones (where participants believed the AI-controlled DHAs were human-controlled). In the first study, comparing perceptions of a DHA, chatbot, and human agent from descriptions revealed few differences between the DHA and chatbot. The second study, involving actual use of a commercial DHA, showed participants found it uncanny, robotic, or difficult to converse with. The third and fourth studies used a “Wizard of Oz” design, with participants believing a human-controlled DHA was AI-driven. Results showed a preference for human agents via video conferencing, but no significant differences between DHAs and human agents when visual fidelity was controlled. Current DHAs, despite communication issues, trigger more affinity than chatbots. When DHAs match human communication abilities, they are perceived similarly to human agents for simple tasks. This research also suggests DHAs may alleviate algorithm aversion.

For managers, we show that opportunities for skill development strongly influence matching in online information technology (IT) markets. Employers cannot easily circumvent labor scarcity by adopting older technologies, as workers avoid projects with declining future skill value absent substantial wage premiums. However, older workers, having shorter career horizons, are less sensitive to declining skill value, suggesting potential benefits in matching them with legacy technologies. For policy makers, our research demonstrates that labor market tightness persists across both new and old technologies in online IT markets. This challenges the notion that employers can engage in labor arbitrage by avoiding cutting-edge technologies. Policy frameworks therefore need flexibility to address skill shortages wherever they emerge. Additionally, our findings highlight the need for more granular data collection on technical skill evolution beyond broad occupational categories. Our online context provides unique insights into how corporate decisions about technology standards cascade into labor markets. The findings underscore the importance of policies promoting continuous learning and adaptability, while suggesting that age-diverse hiring practices could help address both skill shortages and age discrimination concerns in technical fields.

An integral component of user participation in online communities is giving “likes” to content posted by others. Meanwhile, online users are often allowed to create a virtual identity unrelated to their real-world identity. The objective of this study is to identify the motivations behind users’ giving “likes” when their virtual identity (i.e., username) is hidden or shown. Specifically, we examine the impact of an exogenous policy change in an online community that made usernames publicly visible. Our results show that users “liked” fewer but higher-quality articles after the policy change, consistent with their protective self-presentation motivation. This study emphasizes the significance of virtual identity, arguing that a virtual identity devoid of real-world information should not be equated with anonymity. It also identifies “liking” as a key channel of self-presentation and underscores the importance of protective self-presentation. For platforms, understanding users’ motivations to give “likes” and the effects of virtual identity disclosure can help refine community policies to encourage quality content engagement. For content creators, our findings suggest they can enhance content engagement by aligning their offerings with the self-presentation goals of their audience.