Information Systems Research

Gu, Grace; Yin, Zhitao; Rai, Arun

doi: 10.1287/isre.2022.0446pmid: N/A

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.

Sabzehzar, Amin; Burtch, Gordon; Hong, Yili; Raghu, T. S.

doi: 10.1287/isre.2024.1190pmid: N/A

Equity appeals are increasingly used by digital fundraising platforms, nonprofits, and public institutions to direct attention and resources toward disadvantaged communities. However, it remains unclear whether and when equity appeals actually increase giving. We examine this question in the context of educational crowdfunding, where platforms explicitly focus on reducing funding disparities across schools, particularly for students from racial or ethnic minority and low-income backgrounds. Leveraging large-scale data from DonorsChoose, one of the largest educational crowdfunding platforms in the United States, and exploiting arbitrary cutoffs in the platform’s deployment of equity appeals based on the student composition of benefitting schools, we show that equity appeals increase fundraising when they highlight student disadvantage in terms of poverty while providing little to no measurable benefit when they highlight student disadvantage in terms of race. These differential effects reflect how donors interpret disadvantage. Many donors appear to view poverty as a legitimate and actionable barrier to learning, making poverty-based appeals effective. In contrast, perceptions of race as a structural barrier to educational opportunity are more heterogeneous and politically sensitive, limiting the impact of race-based appeals. For platform designers and policymakers seeking to reduce educational fundraising disparities, our findings highlight the importance of how equity appeals are framed. More broadly, our results contribute to understanding under what conditions behavioral nudges can meaningfully reduce inequality versus when alternative approaches may be necessary to achieve equitable outcomes.

Wang, Qiu-Hong; Luo, Kai; Geng, Ruibin; Chen, Xi; Teo, Hock-Hai

doi: 10.1287/isre.2022.0136pmid: N/A

Digital platforms are reshaping professional service delivery, yet how online engagement feeds back into offline clinical practice remains unclear. This study examines whether physicians’ information provision on healthcare question-and-answer platforms influences treatment decisions in hospital care. Drawing on cognitive consistency and professional identity theories, the study proposes a mechanism of identity-based digital commitment whereby publicly articulated medical advice functions as a “cognitive anchor,” creating pressure for physicians to align subsequent bedside decisions with the online standards, particularly in discretionary domains that are vulnerable to economic or institutional pressures. Using a physician-level matched data set that links a leading online health platform to hospital electronic health records, the analysis shows that physicians with higher levels of online advisory activity are associated with reduced overall medication costs, lower uncovered medication cost ratios, and lengths of stay that more closely adhere to guideline benchmarks. Mediation and stratified analyses mitigate concerns that these patterns are driven by patient selection or improved patient adherence alone, whereas moderation by dimensions of professional commitment and content relevance provides supportive evidence for a cognitive consistency mechanism. The results are robust to alternative model specifications and controls for peer spillovers, digital reputation, and local market conditions, indicating no deterioration in clinical quality as readmission and mortality rates remain unchanged. Overall, the findings position digital platforms as a form of “soft governance” that complements formal oversight by activating intrinsic professional motives for consistency between online identity and offline practice.History: Rajiv Kohli, Senior Editor; Wenjing Duan, Associate Editor.Funding: This work was supported by the Key Program of National Natural Science Foundation of China [Grant 72231009], the General Program of National Natural Science Foundation of China [Grant 72472128], the Science Fund for Creative Research Groups of the National Natural Science Foundation of China [Grant 71821002], the National University of Singapore, the Provost’s Chair Grant [Grant E-253-00-0021-01], and the Fundamental Research Funds for the Central Universities [Grant D5000240057].Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2022.0136.

Zhang, Bin; Hao, Haijing; Zhan, Yongcheng; Wu, Jiang

doi: 10.1287/isre.2024.1183pmid: N/A

This study introduces an efficient specialized artificial intelligence (AI) tool and the SEPTE model—a comprehensive framework for evaluating healthcare service quality—to help healthcare platforms and hospitals better understand what drives patient demand for online consultations. By analyzing physician reviews from one of China’s largest telehealth platforms, our small language model (Doc-BERT) uses the SEPTE framework to accurately identify key aspects of service quality, such as medical effectiveness and empathy, that matter most to patients. Unlike traditional large language models, our approach is cost-effective and can be readily implemented in real-world healthcare settings. We find that higher service-quality scores, especially in effectiveness and patient-centeredness, lead to greater patient demand for online consultations. These insights offer actionable guidance for healthcare providers and administrators seeking to improve patient experiences, optimize physician performance, and inform platform design and policy. Our work demonstrates that targeted, domain-specific AI—guided by the SEPTE model—can deliver both efficiency and impact for digital health services.

Wang, Wen; Pei, Siqi; Sun, Tianshu

doi: 10.1287/isre.2023.0487pmid: N/A

This study introduces a novel, human-centered framework for evaluating the holistic intelligence of large language models (LLMs), using behavioral theory and experimental benchmarks drawn from human intelligence. Through extensive online experiments, the framework reveals that GPT-4 outperforms humans in cognitive, emotional, and creative intelligence, but falls short in social intelligence, especially in social interest, self-efficacy, and understanding mental states. Beyond theoretical insight, the study validates this framework by assessing GPT-4’s impact across diverse job roles, finding results consistent with established labor market research. It also offers a reusable tool for firms and policymakers to evaluate LLM intelligence and forecast job-level impacts. This enables informed decisions about where and how to integrate LLMs, match models to specific job requirements, and identify risks in socially intensive roles. The framework provides a foundation for responsible LLM deployment, ensuring alignment with human-centered structures and supporting strategic workforce planning.

Vial, Gregory; Rivard, Suzanne

doi: 10.1287/isre.2020.0604pmid: N/A

While agile software development (ASD) promises rapid, iterative delivery, agile teams often face temporal demands—such as organizational reporting schedules, quality requirements, and resource availability—that challenge their ability to meet this promise. These conflicting temporal demands create what we call temporal misfits.Based on an in-depth study of five software development projects, we found that a temporal misfit disturbs an agile team’s work by creating delays and undermining software quality every time it occurs. Because a given temporal misfit reoccurs at each sprint until resolved, work disturbance escalates over successive sprints. Teams respond in different ways. They may sacrifice the speed of delivery and comply with demands that jeopardize it. They may preserve the agile rhythm, sometimes shielding the team from external temporal requirements. Finally, they may mobilize people or tools—digital or not—to play the role of a differential gear, therefore allowing conflicting temporal demands to be met simultaneously.Our work invites ASD teams to consider both the immediate and the longer-term effects of temporal misfits and their responses, highlighting how decisions made within each agile sprint can impact the entire project.

Zhang, Maggie Mengqing; Gao, Yang; Li, Jingjing; Johnson, Steven L.

doi: 10.1287/isre.2025.2270pmid: N/A

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.

Ai, Kexin; Feng, Juan; Sun, Xinyu

doi: 10.1287/isre.2022.0433pmid: N/A

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.