【2026-1期】

Digital Resilience for the Climate Crisis: A Multi-Perspective Analysis

https://doi.org/10.25300/MISQ/2025/18779

This commentary explores multiple perspectives on the potential use of digital technologies to improve organizational resilience in the context of climate change. Such an approach is needed to address this complex problem space, especially since it encompasses a wide variety of phenomena, including floods and landslides, disruptions to global supply chains, heat waves, biodiversity loss, greenhouse gas emissions, and food insecurity. We assembled a diverse set of five scholarly teams specializing in multiple problem topics, research approaches, and theoretical perspectives on this project. Each team identified and problematized a specific facet of digital resilience for the climate crisis. The perspectives cover a range of rich narratives, including digital resilience in the context of floods and landslides in Brazil and Indonesia, conceptual development efforts incorporating the natural environment with people and technology, reconceptualization of the problem space in terms of time and type, and two applications of digital resilience in the domains of global supply chains and carbon emissions tracking. This research commentary thus presents a multi-perspective examination and interrogation of digital resilience for addressing the climate crisis, out of which four transcending themes emerge: the need to integrate nature into sociotechnical thinking, the need to examine actions at both micro and macro levels, the need to include both reactive and proactive strategies, and the need to view climate crisis as a process rather than a series of events. This commentary aims to motivate other scholars who take diverse theoretical perspectives to join us in developing fundamental knowledge and practical solutions needed to achieve digital resilience for the climate crisis.

Keywords: Digital resilience, climate crisis, mitigation, adaptation, sociotechnical systems, ecology, nature, complex system

[!小结]
问题：如何通过多元理论和问题视角，系统性地理解并利用数字技术来增强组织在气候危机背景下的韧性？

Understanding and Improving Data Repurposing

https://doi.org/10.25300/MISQ/2025/18361

We live in an age of unprecedented opportunities to use existing data for tasks not anticipated when those data were collected, resulting in widespread data repurposing. This commentary defines and maps the scope of data repurposing to highlight its importance for organizations and society and the need to study data repurposing as a frontier of data management. We explain how repurposing differs from original data use and data reuse and then develop a framework for data repurposing consisting of concepts and activities for adapting existing data to new tasks. The framework and its implications are illustrated using two examples of repurposing, one in healthcare and one in citizen science. We conclude by suggesting opportunities for research to better understand data repurposing and enable more effective data repurposing practices.

Keywords: Data repurposing, data management for repurposing, data management, data analytics, data science, artificial intelligence, data reuse, secondary data, data integration, schema matching

[!小结]
问题：如何系统地定义、理解并有效实践“数据再利用”，以把握其在组织和社会中的巨大潜力？

AI-Augmented Content Validation in Behavioral Research: Development and Evaluation of the RATER System

https://doi.org/10.25300/MISQ/2025/18946

Content validation is an essential aspect of the scale development process that ensures that measurement instruments capture their intended constructs. However, researchers rarely undertake this core step in behavioral research because it requires costly data collection and specialized expertise. We present RATER (replicable approach to expert ratings), a free web-based system (www.contval.org) that can help the broader research community (scientists, reviewers, students) gain quick and reliable insights into the content validity of measurement instruments. Guided by psychometric measurement theory, RATER evaluates whether a scale’s items correspond to their intended construct, remain distinct from other constructs, and adequately represent all aspects of the construct’s content domain. The system employs two unique artificial intelligence models, RATERC and RATERD, which leverage psychometric scales from 2,443 journal articles spanning eight disciplines and two state-of-the-art large language model architectures (i.e., BERT and GPT). A set of six complementary studies confirms the RATER system’s accuracy, reliability, and usefulness. We find that RATER can augment the scale development and validation process, increasing the validity of findings in behavioral research.

Keywords: Content validity, psychometrics, scale development, large language models (LLMs), machine learning, design science research, behavioral research, research rigor

[!小结]
问题：如何利用人工智能技术，构建一个易于使用且可靠的工具，以辅助研究者高效地进行量表开发中的内容效度验证？
本文介绍了一个名为RATER的免费网络系统，该系统基于心理测量理论，利用从2443篇跨学科文献中提取的量表数据及BERT和GPT两种大语言模型，通过两个AI模型（RATER_C和RATER_D）协助研究者快速、可靠地评估测量工具的内容效度，包括条目与构念的对应性、构念间的区分性以及内容域的充分覆盖。六项补充研究验证了其准确性、可靠性和实用性，表明它能有效增强行为研究中的量表开发与验证过程。

Extending the IT Risk Control Framework: Incorporating the Role of Team Personality

https://doi.org/10.25300/MISQ/2025/17925

Many information technology (IT) projects fail to deliver the promised value within the initial budget and the estimated schedule. These shortfalls materialize in part due to unaddressed project risks. The IT project risk and control literature has demonstrated that process controls can alleviate the adverse effects of technical IT project risks on project outcomes. However, the literature has yet to investigate why certain IT project teams respond better than others to process controls. To fill this gap, we extend the IT risk control framework of Venkatesh et al. (2018) by integrating the contingent role of team personality, which includes two meta-traits based on the Big Five personality traits: team stability (conscientiousness, agreeableness, emotional stability) and team plasticity (extroversion, openness to experience). We conducted a field study of 424 offshore unique IT projects, comprising 4,516 unique project team members, to test our model. We found that, in general, teams with a higher level of stability respond better to internal process control, and teams with a higher level of plasticity respond better to external process control. We discuss further nuances of the three-way interactions of technology (technical risks), process (process controls), and people (team personality), and their effects on product and process performance. Our work thus contributes to the IT project management literature by extending the nomological network of IT project risk and control and incorporating the people aspect into this framework. In addition, it outlines how the consideration of team personality can assist managers in better deploying process controls to achieve IT project success.

Keywords: IT projects, IT implementations, project management, technical risks, process control, team personality, IT project performance, IT project success

[!小结]
问题：IT项目团队的人格特质（稳定性与可塑性）如何调节过程控制对项目风险与绩效的影响，从而解释为何不同团队对相同控制措施的响应存在差异？

Digital Futures and Cultivating Imagined Ecosystems: The Rise and Fall of the First Digital Pill Venture

https://doi.org/10.25300/MISQ/2025/17972

Existing literature has primarily focused on how established firms engage in orchestration through deliberate, purposeful actions to promote existing digital innovation ecosystems. However, many such digital innovation ecosystems start as digital ventures aspiring to build ecosystems around their digital product innovations. Less is known about how these ventures initiate and engage in ecosystem dynamics before established ecosystem structures are in place. In this study, we address this shortcoming by examining how a digital venture enacts a digital innovation ecosystem de novo. To do so, we conducted a three-year inductive field study of an ingestible biosensor venture that sought to develop a platform-enabled ecosystem around the first digital pill. Our findings suggest that digital venturing unfolds within temporal tensions that create dissonance between digitization and digitalization. We develop the concept of cultivating an imagined ecosystem, which comprises three core mechanisms, namely entrepreneurial trajectory dynamics, digital future-making, and sociotechnical enactment. Our findings highlight three cultivation practices—perceptive response to emergent futures, prefigurative action in relation to desired distant futures, and inattentive inaction to alternative futures that become consequential in enacting an ecosystem de novo. Finally, we present a model of cultivating an imagined ecosystem, which offers a future-oriented perspective of the mutually reinforcing dynamics of the social and the technical in digital venturing.

Keywords: Digital innovation ecosystems, de novo digital categories, digital entrepreneurship, digital venturing, digital futures, cultivation

[!小结]
问题：数字创业公司如何从零开始发起、实践并逐步构建起一个围绕其数字产品创新的生态系统？
本文针对现有文献多关注成熟企业促进既有数字创新生态系统，而对数字创业公司如何从零开始构建生态系统知之甚少这一空白，通过对一个“数字药丸”生物传感器创业项目开展为期三年的归纳式实地研究，提出了“培育想象生态系统”概念。该过程包含创业轨迹动态、数字未来制造和社会技术实践三大机制，并通过三种具体实践（对新出现未来的感知响应、对期望远期未来的预演行动、对替代性未来的疏忽性不作为）来动态协调社会与技术的互构，最终形成了一个面向未来的生态系统创建模型。

Shapley Value-Based Feature Attribution for Data Masking

https://doi.org/10.25300/MISQ/2025/18502

Despite its many benefits, widespread access to individuals’ personal data also causes severe privacy concerns for consumers, companies, and policymakers. This study proposes a novel framework that adapts the Shapley value-based feature attribution approach to the problem domain of data privacy by capturing the two crucial dimensions of data privacy—disclosure risk and data utility. Our proposed framework takes a holistic view of data masking through a fair feature attribution approach based on Shapley values. Different from the existing literature that mostly focuses on the risk-utility trade-off at the dataset level, the proposed framework addresses the trade-off at the feature level. Furthermore, the proposed framework is agnostic to data masking methods, statistical and machine learning methods, and data utility and disclosure risk evaluation metrics. Experimental results show that our proposed method can effectively reduce disclosure risk while preserving data utility.

Keywords: Data masking, data privacy, feature attribution, risk-utility trade-off, Shapley value

[!小结]
问题：如何在数据隐私保护中，从特征层面而非数据集层面，更精细地平衡披露风险与数据效用，并设计一种具有通用性的评估与掩码指导框架？
本文针对数据隐私保护中的核心矛盾（披露风险与数据效用权衡），提出了一种基于Shapley值的特征归因新框架。该框架在特征层面而非传统的数据集层面进行权衡，能够公平地评估每个特征对风险与效用的贡献，并且与具体的数据掩码方法、分析模型及评估指标无关。实验结果表明，该框架能在保持数据效用的同时有效降低披露风险。

Data Valuation for Vertical Federated Learning: A Model-Free and Privacy-Preserving Method

https://doi.org/10.25300/MISQ/2025/19161

Vertical federated learning (VFL) is a promising paradigm for predictive analytics, empowering an organization (i.e., task party) to enhance its predictive models through collaborations with multiple data suppliers (i.e., data parties) in a decentralized and privacy-preserving way. Despite the fast-growing interest in VFL, the lack of effective and secure tools for assessing the value of data owned by data parties hinders the application of VFL in business contexts. In response, we propose FedValue, a privacy-preserving, task-specific but model-free data valuation method for VFL, which consists of a data valuation metric and a federated computation method. Specifically, we first introduce a novel data valuation metric, namely MShapley-CMI. The metric evaluates a data party’s contribution to a predictive analytics task without the need of executing a machine learning model, making it well-suited for real-world applications of VFL. Next, we develop an innovative federated computation method that calculates the MShapley-CMI value for each data party in a privacy-preserving manner. Extensive experiments conducted on synthetic and realistic datasets validate the efficacy of FedValue for data valuation in the context of VFL. In addition, we illustrate the practical utility of FedValue with case studies involving federated recommendations and financial default prediction.

Keywords: Data valuation, predictive analytics, privacy, vertical federated learning, federated recommendation

[!小结]
问题：如何在纵向联邦学习的协作预测场景下，设计一种既能保护数据隐私、又能有效评估不同数据提供方价值的通用估值方法？
本文针对纵向联邦学习（VFL）中缺乏有效且安全的数据价值评估工具这一关键障碍，提出了名为FedValue的隐私保护、任务特定且无需模型执行的数据估值方法。该方法包含一个新的数据估值指标MShapley-CMI（用于评估数据方对预测任务的贡献）和一套创新的联邦计算方法（以隐私保护方式计算该指标）。通过在合成和真实数据集上的大量实验以及联邦推荐和金融违约预测的案例研究，验证了FedValue的有效性和实用性。

【key】Social Media Moderation and Content Generation: Evidence From User Bans

https://doi.org/10.25300/MISQ/2025/18108

The rise of inappropriate content (e.g., misinformation, spam, hate speech, etc.) has become a major concern for social media platforms. To deal with such challenges, platforms adopt various strategies to moderate the content on their websites. This study focuses on user bans, a common but controversial moderation strategy that suspends rule-violating users from further participation on a platform for a predetermined period. Specifically, we investigated the impacts of user bans on banned users’ content-generating behavior (both quantity and quality). Leveraging reactance theory, we formalized our hypotheses relating users’ behavioral reactions to this content moderation strategy. We implemented multiple empirical designs to analyze data from a major social media platform. Our results show that users provided more answers, on average, after bans were lifted. In contrast, we found that the quality of the content (measured by linguistic features and content appropriateness) decreased after user bans. Furthermore, we found that platform recognitions, such as badges and recommendations, alleviated individuals’ reactance toward bans. Specifically, users who have received platform recognitions reduced inappropriate postings and improved the quality of their content after bans. Lastly, we explored the heterogeneous effects of user bans for different banning causes and repeated bans. Our research is among the first to evaluate the effectiveness of user bans and has important implications for content moderation on social media.

Keywords: Social media, platform moderation, user bans, reactance theory

[!小结]
问题：社交媒体平台对违规用户的暂时封禁策略，究竟如何影响这些用户在解封后的内容生成数量与质量，以及哪些因素能缓解该策略可能带来的负面影响？
本文聚焦社交媒体平台常用的“用户封禁”这一内容审核策略，基于心理抗拒理论，通过多项实证设计分析某大型社交媒体平台的数据，考察了封禁对违规用户解封后内容生成行为的影响。研究发现，解封后用户提供的内容数量平均增加，但质量（以语言特征和内容适当性衡量）下降；平台给予的认可（如徽章、推荐）能缓解用户的抗拒心理，促使获得认可的用户减少不当发帖并提升内容质量；此外，封禁原因和重复封禁存在异质性效应。

The Pleasant Visual Path to Review Helpfulness: Picture-Evoked Emotional Valence and Picture-Text Alignment

https://doi.org/10.25300/MISQ/2025/17965

Viewing pictures evokes pleasant or unpleasant feelings (valence) and influences perceptions. How valence evoked by pictures in online reviews impacts reader perceptions of review helpfulness remains understudied. Based on affect-as-information theory, we propose that both picture-evoked emotional valence (PEvoV) and its alignment with text-expressed emotional valence (TExpV) exhibit a positive effect on perceived review helpfulness. A large-scale field test and a series of laboratory experiments support our hypotheses. The positive effects are partially mediated by conceptual processing fluency. Additionally, PEvoV is associated with various interpretable picture features. Our empirical strategy involves techniques of computer vision, deep learning, and econometrics. From an emotion-focused perspective, our work deepens the understanding of helpful reviews, contributes to the literature on picture-text interaction in reviews, and derives theoretical insight into underlying mechanisms. It offers practical implications for online review platform design and online reputation management.

Keywords: Review helpfulness, picture-evoked emotions, valence, picture-text alignment, conceptual processing fluency

[!小结]
问题：在线评论中图片所引发的情绪效价及其与文本情绪效价的一致性，如何影响用户对评论有用性的感知，以及其背后的心理机制是什么？
本文基于“情绪即信息”理论，探讨了在线评论中图片引发的情绪效价（PEvoV）及其与文本表达的情绪效价（TExpV）之间的一致性对评论感知有用性的正向影响。通过大规模实地测试和系列实验室实验，研究发现这一正向效应部分由概念处理流畅性所中介，并且PEvoV与多种可解释的图片特征相关。研究采用了计算机视觉、深度学习与计量经济学相结合的方法，深化了对评论有用性的情绪视角理解。

Seizing Growth Opportunities: A Risky Business? Effects of Cloud Sourcing on Mergers and Acquisitions

https://doi.org/10.25300/MISQ/2025/18085

Recent research has shown that enterprise information technology (IT) can drive strategic growth through mergers and acquisitions (M&As). An implicit assumption underlying this research is that firms own their IT infrastructure. Challenging this assumption, however, the emerging trend of cloud sourcing suggests that IT may be owned by third-party vendors. Since third-party ownership of IT can introduce significant transaction costs and operational inefficiencies, cloud sourcing, unlike in-house enterprise IT, may be considered unlikely to drive M&A growth. However, the unique combination of IT infrastructural and service flexibilities that cloud sourcing provides could help in reducing the risk of integration failure posed by M&As, thereby driving M&A growth. Grounded in the transaction cost economics and resource-based views of the firm, respectively, these arguments illustrate the conflicting theoretical viewpoints offered by prior literature. This study seeks to improve our theoretical understanding of the relationship between cloud sourcing and M&A growth by addressing the theoretical conflict. Analyzing a dataset of cloud sourcing deals and M&As comprising 4,075 observations from 673 firms, our research finds that highly standardized cloud services, i.e., SaaS (software as a service), public, and globalized clouds, have a positive impact on M&As, particularly in information industries. Overall, these results indicate that it is only under conditions of enhanced flexibility afforded by a standardized platform and informationally rich operating environments that cloud sourcing positively affects M&A growth. Support for the theoretical propositions is further established through interviews with industry experts and mechanism tests, which reveal that cloud sourcing has a positive impact, specifically on M&As requiring intensive integration, and is associated with reduced disclosure of M&A risks in annual reports. Finally, consistent with our view that cloud sourcing smooths post-M&A integration, this research also finds that firms with cloud sourcing have relatively stronger post-M&A performance.

Keywords: Cloud sourcing, mergers and acquisitions, integration risk, flexibility, standardization

[!小结]
问题：第三方拥有的云计算服务（特别是标准化的云服务）能否以及如何促进企业的并购增长，其作用机制和边界条件是什么？
本文针对企业信息技术外包（即云服务）能否像自有的企业IT一样驱动并购增长这一理论冲突，基于交易成本经济学与资源基础观，通过分析673家企业的4075条云服务与并购交易数据，并结合专家访谈与机制检验，发现高度标准化的云服务（如SaaS、公有云、全球化云）能通过增强基础与服务灵活性、降低整合失败风险，从而促进并购增长，尤其体现在需要深度整合的并购及信息行业中，且能改善并购后绩效并减少年报中并购风险的披露。

Opening the Network of Trust: How Domain Experts in Triadic Relationships Build Trust in AI-Based Counterparts

https://doi.org/10.25300/MISQ/2025/18041

The growing integration of AI into professional domains amplifies the epistemic and relational stakes for domain experts—ranging from financial advisors to medical and legal professionals—who must entrust their clients to emerging AI-based counterparts. For these domain experts in triadic relationships, trusting AI is challenging, as AI’s inscrutability and autonomy provoke fundamental trust tensions of opacity vs. performance and replacement vs. complementarity. Drawing on an in-depth longitudinal case study at a large European bank that established a robo-advisor alongside domain experts and their clients, we investigated how domain experts navigate these trust tensions. We found that they interpret the AI-based counterpart with regard to their relational networks (relational interpretation) and subsequently adapt these networks (relational adaptation) to cope with emerging vulnerabilities. Domain experts develop trust through three stages that engender specific sequences of relational interpretation and adaptation, leading to avoided, safeguarded, and, ultimately, accepted vulnerabilities. We contribute to research on trust in AI by overcoming the focus on dyadic perspectives involving AI and users to uncover complex interrelationships between different trustors and specific trust tensions. Going beyond technology-centric perspectives on trust-building processes, our longitudinal analysis reveals relational perspectives on trusting beliefs and behaviors in a multistage process where influences of the social context precede and shape trust in AI.

Keywords: Artificial intelligence, trust, triadic relationships, domain experts, robo-advisory

[!小结]
问题：在包含领域专家、客户和AI的三边关系中，领域专家如何通过社会关系动态来建立和发展对AI的信任，以应对AI带来的核心信任张力？
本文通过对一家欧洲大型银行引入机器人投顾的纵向案例研究，揭示了三边关系（客户、人类专家、AI）中领域专家如何应对AI带来的核心信任张力（不透明性 vs. 绩效，替代 vs. 互补）。研究发现，专家通过“关系性解读”（根据关系网络理解AI）和“关系性适应”（相应调整关系网络）的三阶段过程，逐步从回避、保障到最终接受脆弱性，从而发展对AI的信任。该研究超越了用户-AI二元视角，揭示了信任的社会情境根源和关系动态。

Exploring Online Help-Seeking Tendencies: The Influence of Experience Type and Help Provider Type

https://doi.org/10.25300/MISQ/2025/19133

Businesses are increasingly providing online assistance through help features to enhance the user experience in the digital era. Understanding factors influencing users’ tendencies to seek online help is crucial for optimizing resources and improving support and the overall user experience. Drawing on utilitarian- and hedonic-motivation systems theory, this paper examines how the type of experience and the type of help provider impact users’ online help-seeking behavior. Across three studies—including secondary data analysis, an online experiment, and an observational study of actual user behavior—we found that users are more (vs. less) inclined to seek help when encountering difficulties in utilitarian (vs. hedonic) experiences. This pattern was driven by users’ greater focus on achieving specific outcomes in utilitarian contexts, in contrast to their emphasis on experiential enjoyment in hedonic contexts. Importantly, users showed a stronger preference for seeking assistance from human agents over service robots when facing challenges in utilitarian experiences. Nevertheless, during hedonic experiences, no notable difference between humans and robots emerged in users’ inclination to seek help. These findings highlight the importance of considering the type of experience and the type of help provider when planning online support services, contributing valuable insights to the literature on hedonic vs. utilitarian motivation systems, users’ online help-seeking behavior, and user experience.

Keywords: Online support, online help-seeking tendencies, human customer service representative, service robot, utilitarian experience, hedonic experience, process-outcome focus

[!小结]
问题：用户在使用数字服务时，其面临的体验类型（功利性 vs. 享乐性）和帮助提供者类型（人类 vs. 机器人）如何共同影响其在线帮助寻求行为？
本文基于功利与享乐动机系统理论，通过三项研究（二手数据分析、在线实验和实际用户行为观察），考察了体验类型与帮助提供者类型对用户在线帮助寻求行为的影响。研究发现，在功利性体验中，用户更倾向于寻求帮助，且更偏好人类助手而非服务机器人；而在享乐性体验中，这种对帮助提供者的偏好差异不显著，其内在机制在于功利情境下用户更聚焦于结果达成，享乐情境下则更注重体验享受。

The Curse of Experience in Creative Crowdsourcing Contests and Contest Searches as a Remedy

https://doi.org/10.25300/MISQ/2025/17751

In creative crowdsourcing contests that seek novel ideas and solutions, experience can help solvers use relevant knowledge to generate solutions, but may also cause solvers to fixate on familiar solution paths, which we call the “curse of experience.” This study examines how experience affects solvers’ performance in such contests and how contest searches (searching descriptive information about other contests), as a type of external information-seeking activity, moderate the effects of experience. Our analyses show that both the benefit and the curse of experience coexist. Solvers’ experience contributes to a higher likelihood of generating acceptable submissions (solutions meeting basic quality standards) but reduces the chance of creating winning solutions (extreme-value solutions selected as contest winners). Contest searches prior to participation in a contest provide solvers with contest choices, strengthening the beneficial effects of experience on creating both acceptable and winning solutions. Contest searches that occur during participation in a contest, although potentially triggering an overload effect that decreases the beneficial effect of experience on solution acceptance, also bring new ideas and inspiration to solvers’ solution search processes, reducing the impedimental effect of experience on creating winning solutions. Searches of contests that differ in skill or context from the focal contest are beneficial for both prior and parallel contest search processes but are not always fully utilized by solvers. These findings delineate a comprehensive picture of how contest search activities can remedy the curse of experience and inform the effective design and use of search features for crowdsourcing platforms and solution seekers to help improve solvers’ performance.

Keywords: Crowdsourcing contest, creative innovation, solver performance, solver experience, information search

[!小结]
问题：在创意众包竞赛中，参与者的既有经验如何同时产生“益处”与“诅咒”，而赛前与赛中的外部信息搜索活动又如何分别调节这两种效应？
本文探讨了创意众包竞赛中参与者“经验”的双刃剑效应，发现经验虽有助于产出合格作品，却会降低产出获胜作品的几率（即“经验诅咒”）。研究进一步揭示，竞赛前的搜索活动能强化经验的正面效应，而竞赛中的搜索活动虽可能削弱对合格作品的影响，却能引入新灵感以破除经验诅咒。此外，搜索与目标竞赛技能或背景差异大的信息虽有益，但并未被参与者充分利用。

The Impact of Digital Profile Enrichment on Charitable Giving on Social Networking Sites

https://doi.org/10.25300/MISQ/2025/17632

In the last decade, social networking platform designers have made notable efforts to harness the power of networks for social good by elevating the prominence of individual donation information. This study investigates how digital profile enrichment that exhibits users’ charitable giving activities could influence users’ decisions about whether to give, how much to give, and whether to disclose contribution amounts. Our analyses are based on a profile enrichment intervention on Weibo, China’s largest social networking site. We found that the profile enrichment to exhibit users’ historical donation counts on social profiles decreased an average user’s odds of donating by 15.5% but increased the contribution amount by 2.69%. Strikingly, it increased the odds of revealing contribution amounts by 162%. Clustering analyses further revealed three patterns in response to the profile enrichment: “presenters” (9.7%), who reduced donation frequency but increased contribution amounts and the disclosure of contribution amounts; “restrainers” (47.3%), who reduced their giving; and “conformers” (43%), who increased giving after the profile enrichment. We discuss potential mechanisms by comparing the characteristics of different user clusters to underscore donor heterogeneity and uncover the nuanced impact of such digital profile enrichment.

Keywords: Digital profile, interrupted time series design, charitable giving, social networking sites

[!小结]
问题：在社交网络上展示用户的慈善捐赠历史，会如何系统性且异质地影响用户“是否捐赠”、“捐赠多少”及“是否公开捐赠金额”这三方面的行为？
本文基于中国社交平台微博的一项个人资料丰富化干预（展示用户历史捐赠次数），研究该设计对用户捐赠行为的影响。研究发现，这一干预措施平均使用户的捐赠几率下降15.5%，但捐赠金额增加2.69%，并大幅提高披露捐赠金额的几率达162%。通过聚类分析，识别出三类反应模式：“展示者”（减少频率但增加金额和披露）、“克制者”（全面减少捐赠）和“顺应者”（捐赠增加），揭示了捐赠者异质性及数字个人资料丰富化的微妙影响。

A Field Experiment in Local Personalization for Charitable Crowdfunding

https://doi.org/10.25300/MISQ/2025/19044

Charitable crowdfunding platforms use personalized outreach to engage donors and increase contributions, often leveraging cognitive heuristics such as home bias (an inclination towards local projects) and social influence (an inclination towards personal connections). While both heuristics can shape giving behavior, they may not always align, and prior literature offers limited insight into how they interact or which dominates in practice. We address this gap through a large-scale randomized field experiment with nearly 160,000 donors on a major charitable crowdfunding platform. In this experiment, donors who had previously supported non-local projects were randomly assigned to receive one of two types of personalized emails: (1) local personalization, which highlighted projects in the donor’s billing location, or (2) default personalization, which highlighted projects from the same community as the donor’s most recently supported project. Results show that local personalization increased engagement and donations overall, even among donors without prior evidence of home bias, suggesting that geographic cues can activate latent heuristics. However, among donors with a history of giving via social influence, local personalization increased engagement but not the likelihood of a donation. Moreover, local personalization disproportionately directed funds toward affluent communities demographically similar to the donor’s own, reinforcing a rich-get-richer dynamic. These findings highlight the behavioral power and equity risks of simple personalization strategies, offering insights for platform design and the responsible use of heuristics in charitable crowdfunding.

Keywords: Crowdfunding, charitable crowdfunding, field experiment, geographic preferences, home bias, social influence, donation-based crowdfunding, personalization, rich-get-richer

[!小结]
目的：在慈善众筹平台的个性化外展中，“本地偏好”与“社会影响”两种认知启发式如何互动，以及基于地理位置的本地个性化策略对捐赠行为的影响及其公平性后果是什么？
本文通过一项在大型慈善众筹平台上对近16万名捐赠者开展的随机实地实验，比较了两种个性化邮件策略（基于地理位置的“本地个性化”与基于捐赠历史的“默认个性化”）的效果。结果显示，本地个性化策略显著提高了捐赠者的整体参与度和捐赠金额，甚至能激活捐赠者潜在的地理偏好，但其效果在受社会影响驱动的捐赠者中表现为“参与增加但捐赠转化未提升”，并且该策略导致资金不成比例地流向与捐赠者人口统计特征相似的富裕社区，加剧了资源分配的“富者愈富”不平等现象。