International Journal of Information Management最新文献_第4页

Moderation analysis in business and management research: Common issues, solutions, and guidelines for future research 商业和管理研究中的适度分析：常见问题、解决方案和未来研究的指导方针

IF 27 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal of Information Management

Pub Date : 2025-11-11 DOI: 10.1016/j.ijinfomgt.2025.102995

Yujing Xu , Wen-Lung Shiau

Moderation analysis is a critical part in business and management research, particularly within the information systems (IS) domain, yet it continues to face persistent methodological issues. These issues not only threaten the reliability of results but also hinder theoretical advancements. To address these challenges, our study undertakes a comprehensive examination of moderation analysis. We commence with a concise synthesis of its conceptual evolution by reviewing 30 foundational publications that have shaped its development. Subsequently, we categorize and clarify the core moderation models, including two-way, three-way interactions, and moderated mediation, highlighting their appropriate application contexts and corresponding analytical techniques. Building upon this foundational knowledge, we identify and detail 15 prevalent methodological issues in moderation research, assessing their contemporary prevalence through an empirical investigation of 274 articles published in top-tier IS journals over the past three years. To equip researchers with actionable guidance, we propose a state-of-the-art, stage-based framework that encompasses the entire research lifecycle—from initial preparation and hypothesis development through design planning, data collection, sophisticated analysis, rigorous interpretation, to transparent reporting. Our contributions are fourfold. First, we present contemporary empirical evidence on the persistence of historical issues and identify emerging trends in moderation research. Second, we offer a comprehensive, stage-based framework that transcends existing piecemeal recommendations, providing actionable support across the research lifecycle. Third, we consolidate theoretical insights by tracing the conceptual evolution of moderation analysis and systematically classifying major moderation models. Finally, we address the identified critical issues throughout the research process, equipping researchers with empirically validated status assessments and evidence-based solutions. Overall, our study enriches the understanding of moderation analysis and equips researchers, journal editors, and practitioners with a robust methodological roadmap for conducting rigorous and theoretically informed moderation research.

适度分析是商业和管理研究的关键部分，特别是在信息系统（is）领域，然而它仍然面临着持续的方法问题。这些问题不仅威胁到结果的可靠性，而且阻碍了理论的发展。为了应对这些挑战，我们的研究对适度分析进行了全面的检查。我们首先通过审查影响其发展的30个基础出版物，对其概念演变进行简要综合。随后，我们对核心调节模型进行了分类和澄清，包括双向、三方交互和有调节的中介，并强调了它们的适用背景和相应的分析技术。在此基础知识的基础上，我们确定并详细说明了适度研究中15个普遍的方法问题，通过对过去三年发表在顶级IS期刊上的274篇文章的实证调查，评估了它们在当代的普遍性。为了给研究人员提供可行的指导，我们提出了一个最先进的、基于阶段的框架，涵盖了整个研究生命周期——从最初的准备和假设发展到设计规划、数据收集、复杂的分析、严格的解释，到透明的报告。我们的贡献是四倍的。首先，我们提出了关于历史问题持续性的当代经验证据，并确定了适度研究的新趋势。其次，我们提供了一个全面的、基于阶段的框架，超越了现有的零碎建议，在整个研究生命周期中提供可操作的支持。第三，通过追溯适度分析的概念演变，并对主要的适度模型进行系统分类，巩固理论见解。最后，我们解决了整个研究过程中确定的关键问题，为研究人员提供了经验验证的状态评估和基于证据的解决方案。总的来说，我们的研究丰富了对适度分析的理解，并为研究人员、期刊编辑和从业者提供了一个强有力的方法路线图，以进行严格的、理论上知情的适度研究。

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引用次数: 0

Algorithmic management in the workplace: A systematic review and topic modeling integration using BERTopic 工作场所的算法管理：使用BERTopic进行系统回顾和主题建模集成

IF 27 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal of Information Management

Pub Date : 2025-11-08 DOI: 10.1016/j.ijinfomgt.2025.102994

Wansi Chen , Anya Li , Chengkai Pan , Ting Yu , Aamir Ali , Yuanyuan Sun

As algorithmic systems become increasingly embedded in organizational processes, algorithmic management in the workplace has emerged as a central mechanism for guiding, evaluating, and coordinating employee attitudes and behaviors. While existing research has extensively examined the application of algorithmic management in gig platforms, there remains a lack of systematic review and theoretical integration concerning its diverse configurations, deployment conditions, and employee response mechanisms within standardized workplace settings. To address this gap, we conducted a systematic review of 167 peer-reviewed articles on workplace algorithmic management using the BERTopic topic modeling method. Guided by socio-technical systems (STS) theory and a bi-dimensional framework of algorithmic and employee autonomy, we identify four archetypal configurations: surveillance, supervision, supplementary, and complementary. These archetypes reflect distinct employee-algorithm interaction logics across role allocation, task interdependence, and goal alignment. We further examine the technological and organizational conditions required for each configuration and synthesize employee responses across cognitive, emotional, and behavioral domains. By constructing a configuration-based taxonomy rooted in the (in)consistencies of employee-algorithm autonomy, this study explicates the socio-technical deployment mechanisms underlying each archetype and illustrates how employees adapt to algorithmic systems through complex and dynamic engagement trajectories. Our findings offer an integrative framework linking configuration logics, deployment demands, and response patterns, contributing to a more nuanced understanding of how intelligent systems reshape organizational structures and employee experiences.

随着算法系统越来越多地嵌入到组织过程中，工作场所的算法管理已经成为指导、评估和协调员工态度和行为的核心机制。虽然已有研究广泛考察了算法管理在零工平台中的应用，但对于零工平台在标准化工作场所环境下的多样化配置、部署条件和员工响应机制，仍然缺乏系统的回顾和理论整合。为了解决这一差距，我们使用BERTopic主题建模方法对167篇关于工作场所算法管理的同行评议文章进行了系统综述。在社会技术系统（STS）理论和算法和员工自治的二维框架的指导下，我们确定了四种原型配置：监督、监督、补充和互补。这些原型反映了跨角色分配、任务相互依赖和目标一致的不同的员工-算法交互逻辑。我们进一步研究了每种配置所需的技术和组织条件，并综合了员工在认知、情感和行为领域的反应。通过构建基于员工-算法自治一致性的基于配置的分类法，本研究阐明了每个原型背后的社会技术部署机制，并说明了员工如何通过复杂和动态的参与轨迹适应算法系统。我们的发现提供了一个连接配置逻辑、部署需求和响应模式的综合框架，有助于更细致地理解智能系统如何重塑组织结构和员工体验。

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引用次数: 0

Sorry, it's my fault: Politeness, attribution, and anthropomorphism in managing generative AI hallucinations 对不起，是我的错：礼貌、归因和拟人论在管理生成型人工智能幻觉中的作用

IF 27 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal of Information Management

Pub Date : 2025-11-07 DOI: 10.1016/j.ijinfomgt.2025.102996

Hayeon Kim, Sang Woo Lee

While generative artificial intelligence (AI) has revolutionized various fields, it also presents a significant challenge: 'hallucinations'—plausible but inaccurate information generated by AI systems. Because hallucinations are difficult to prevent entirely, it is essential for generative AI systems to address these inaccuracies effectively. This study investigates how generative AI response strategies to hallucinations affect user satisfaction and tolerance. We examined the impact of politeness (Gratitude vs. Apology) and attribution (Internal vs. External) strategies, as well as AI anthropomorphism, on user reactions. In a 2 × 2 online experiment with 369 ChatGPT users, participants were randomly assigned to one of four response strategy conditions. Results show that users reported the highest satisfaction when the AI apologized and accepted internal responsibility for the error. This effect was particularly pronounced among users who perceived the AI as less human-like, though positive reactions were also observed among users who anthropomorphized the AI. Moreover, user satisfaction mediated the relationship between the AI’s apology/internal attribution and tolerance for hallucinations. This indirect effect was strongest among those who perceived the AI as less human-like. These findings offer theoretical insights into how social response strategies shape user tolerance of AI errors and provide practical guidance for designing more trustworthy and human-centered AI.

虽然生成式人工智能（AI）已经彻底改变了各个领域，但它也带来了一个重大挑战：“幻觉”——人工智能系统产生的看似合理但不准确的信息。因为幻觉很难完全预防，所以生成人工智能系统必须有效地解决这些不准确的问题。本研究探讨了生成式人工智能对幻觉的反应策略如何影响用户满意度和容忍度。我们研究了礼貌（感激与道歉）、归因（内部与外部）策略以及人工智能拟人化对用户反应的影响。在对369名ChatGPT用户进行的2 × 2在线实验中，参与者被随机分配到四种响应策略条件中的一种。结果显示，当人工智能道歉并为错误承担内部责任时，用户的满意度最高。这种影响在那些认为AI不太像人类的用户中尤为明显，尽管在那些将AI拟人化的用户中也观察到积极的反应。此外，用户满意度在AI道歉/内部归因与幻觉容忍度之间起中介作用。这种间接影响在那些认为人工智能不太像人类的人身上表现得最为强烈。这些发现为社会反应策略如何塑造用户对人工智能错误的容忍度提供了理论见解，并为设计更值得信赖和以人为本的人工智能提供了实践指导。

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引用次数: 0

Corrigendum to “Factors influencing the adoption intent of quantum computing in enterprises: An innovation adoption process perspective”, [International Journal of Information Management 86 (2026) 102978] “影响企业采用量子计算意图的因素：创新采用过程视角”，[International Journal of Information Management] 86 (2026) 102978]

IF 27 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal of Information Management

Pub Date : 2025-11-06 DOI: 10.1016/j.ijinfomgt.2025.102993

Ohbyung Kwon , Seongjun Kwon , Timothy Jung , Saifeddin Alimamy

引用次数: 0

Organizational learning with artificial intelligence: Balancing new tensions between explorative and exploitative learning through hybridization 人工智能的组织学习：通过杂交平衡探索性学习和利用性学习之间的新紧张关系

IF 27 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal of Information Management

Pub Date : 2025-11-06 DOI: 10.1016/j.ijinfomgt.2025.102997

Jitao Yan , Kenneth Husted , Benjamin Fath

To realize the potential of artificial intelligence (AI), organizations must manage the paradoxical tensions that arise when using it in pursuit of innovation. Studies suggest that the adoption of AI introduces new avenues for explorative and exploitative learning in organizations, prompting a reassessment of how these competing learning modes interact. Drawing on interview data, we identify a set of new tensions that arise when AI is used to support organizational learning. We conceptualize these tensions as paradoxes of autonomy (autonomy vs. control), rationality (formal vs. substantive), transparency (opacity vs. accountability), and opportunity (internal vs. external learning). We contribute to the emerging literature on the role of AI in organizational learning by showing why organizational learning with AI benefits from a hybridization approach that integrates the distinctive yet complementary learning capacities of humans and AI. Hybridization creates conditions to enact coping solutions for the paradoxes identified.

为了实现人工智能（AI）的潜力，组织必须管理在使用人工智能追求创新时出现的矛盾紧张关系。研究表明，人工智能的采用为组织中的探索性和剥削性学习引入了新的途径，促使人们重新评估这些相互竞争的学习模式是如何相互作用的。根据采访数据，我们确定了当人工智能用于支持组织学习时出现的一系列新的紧张关系。我们将这些紧张关系概念化为自主性（自主vs控制）、合理性（形式vs实质）、透明度（不透明vs责任）和机会（内部vs外部学习）的悖论。我们通过展示为什么人工智能的组织学习受益于一种融合了人类和人工智能独特但互补的学习能力的混合方法，为人工智能在组织学习中的作用的新兴文献做出了贡献。杂交创造了条件来制定解决所发现的矛盾的解决方案。

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引用次数: 0

The art of attraction: Decoding dynamics of entrepreneurs presentation experience in reward-based crowdfunding 吸引力的艺术：解读创业者在基于奖励的众筹中呈现体验的动态

IF 27 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal of Information Management

Pub Date : 2025-11-06 DOI: 10.1016/j.ijinfomgt.2025.102998

Mohammadreza Meigounpoory , Pouria Rad , Mohsen Jozani , Gianluca Zanella

Crowdfunding outcomes depend not only on projects but on how creators present themselves. We study creator-level factors in the “About the Creator” section on Kickstarter using a large longitudinal dataset, combining speech act analysis of biographies, automated classification of avatar type and quality, and measures of on-platform engagement. Expressive wording that signals ownership and inclusion is positively associated with funding, whereas several commissive framings that emphasize need, process, or persistence are negatively associated, and casual interjections are also detrimental. Higher quality avatars are beneficial, while substituting a logo for a face is associated with poorer outcomes. Engagement matters, as replying to comments and a richer history of backing other projects are positively related to funding, while a passive social media link shows no reliable relationship. An integrated creator-level model that pools these cues improves explanatory power beyond standard campaign-level factors, and a comparison of the highest and lowest scoring biographies by our speech acts index indicates materially better funding performance for the former. The study foregrounds the entrepreneur as the locus of trust formation and offers a portable toolkit and practical guidance for improving creator self-presentation.

众筹的结果不仅取决于项目，还取决于创作者如何展示自己。我们使用大型纵向数据集研究了Kickstarter上“关于创造者”部分的创造者层面因素，结合了传记的言语行为分析、角色类型和质量的自动分类以及平台粘性的测量。表明所有权和包容性的表达性措辞与资金正相关，而强调需求、过程或持久性的一些委托性框架则与资金负相关，随意的叹词也是有害的。高质量的头像是有益的，而用一个标志代替一个脸则与较差的结果相关。参与很重要，因为回复评论和更丰富的支持其他项目的历史与融资正相关，而被动的社交媒体链接则没有可靠的关系。整合了这些线索的创作者级别模型提高了标准活动级别因素之外的解释力，通过我们的言语行为指数对得分最高和最低的传记进行比较，可以看出前者的融资表现更好。本研究将企业家作为信任形成的场所，并为改善创业者自我呈现提供了一个可携带的工具包和实践指导。

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引用次数: 0

Who Am I with AI? Occupational identity crafting in human-AI workplaces 与人工智能在一起，我是谁？人工智能工作场所的职业身份塑造

IF 27 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal of Information Management

Pub Date : 2025-11-04 DOI: 10.1016/j.ijinfomgt.2025.102990

Rui Zhao , Lixia Niu , Shiquan Wang , Zhen Chen

This study introduces the concept of occupational identity crafting, delineating its core dimensions—role, task, and skill crafting—as a proactive response to artificial intelligence (AI)-induced changes in the workplace. Using two pre-registered studies, we examine the impact of AI identity (assistant, partner, and leader) on employees’ occupational identity crafting. Our findings show that with AI framed as a leader, employees experience enhanced role, task, and skill crafting, although the rate of role crafting tends to decelerate (Study 1). Additionally, drawing on the threat–opportunity framework, we explored the moderating role of human–AI relationship cognition (Study 2). The results indicate that the positive effect of AI framed as a leader on occupational identity crafting is stronger among participants with augmentation cognition, whereas substitution cognition does not exert a significant moderating effect. By conceptualizing and empirically validating occupational identity crafting, this study advances the theoretical understanding of employee adaptation to AI identities and provides practical guidance for managing human–AI collaboration.

本研究引入了职业身份塑造的概念，描述了其核心维度——角色、任务和技能塑造——作为对人工智能（AI）引发的工作场所变化的主动反应。通过两项预先注册的研究，我们研究了人工智能身份（助手、合作伙伴和领导者）对员工职业身份塑造的影响。我们的研究结果表明，当人工智能被设定为领导者时，员工会体验到角色、任务和技能锻造的增强，尽管角色锻造的速度往往会减慢（研究1）。此外，利用威胁-机会框架，我们探讨了人类-人工智能关系认知的调节作用（研究2）。结果表明，人工智能作为领导者对职业认同塑造的正向影响在增强认知的参与者中更强，而替代认知不发挥显著的调节作用。通过概念化和实证验证职业身份制作，本研究推进了员工对人工智能身份适应的理论理解，并为管理人类与人工智能的协作提供了实践指导。

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引用次数: 0

E-SF2I: Dynamics modeling and multi-level verification of Online Social Network rumor propagation driven by emotional differentiation and dual-immunity-state coupling E-SF2I：情绪分化和双免疫-状态耦合驱动的在线社交网络谣言传播动力学建模及多层次验证

IF 27 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal of Information Management

Pub Date : 2025-11-03 DOI: 10.1016/j.ijinfomgt.2025.102989

Chenquan Gan , Hongming Chen , Qingyi Zhu , Rui Tang , Deepak Kumar Jain , Liping Feng , Salvador García

The open architecture of Online Social Networks (OSNs) facilitates information sharing but also exacerbates the risk of rumor propagation. In response to the challenge that existing propagation models struggle to quantify the synergistic effects of user emotional polarization and temporary neutral behavior, this paper proposes a novel E-SF2I (Emotion-Susceptible-Forwarding-Temporary Immunity-Permanent Immunity) rumor propagation dynamics model. In terms of theoretical modeling, a differential dynamic system with time-varying characteristics is constructed by coupling the emotion-driven forwarding mechanism with dual immunity state transitions. Based on the Lyapunov stability theory, the system equilibrium and its global asymptotic stability are rigorously demonstrated. A rumor propagation simulation algorithm based on the E-SF2I framework and a multi-level verification are further developed to verify the practical application value of the proposed model. Initially, the theoretical results derived from the model are validated using real network structure datasets (Facebook, Twitter, P2P). Subsequently, the validity of the proposed model is tested using 13 real rumor events on the Twitter dataset. Finally, the proposed model is compared with traditional and new models for three representative real rumor events. The results show that the proposed model achieves higher accuracy in predicting the propagation trends of rumor events.

在线社交网络（Online Social Networks, OSNs）的开放式架构为信息共享提供了便利，但也加剧了谣言传播的风险。针对现有传播模型难以量化用户情绪极化与暂时中立行为的协同效应的挑战，本文提出了一种新的E-SF2I（情绪-敏感-转发-暂时免疫-永久免疫）谣言传播动力学模型。在理论建模方面，通过将情绪驱动的转发机制与双免疫状态转换耦合，构建了具有时变特征的差分动态系统。基于Lyapunov稳定性理论，严格证明了系统的平衡点及其全局渐近稳定性。进一步开发了基于E-SF2I框架的谣言传播仿真算法和多级验证，验证了所提模型的实际应用价值。首先，利用真实的网络结构数据集（Facebook, Twitter, P2P）验证了模型的理论结果。随后，使用Twitter数据集上的13个真实谣言事件来测试所提出模型的有效性。最后，对三个具有代表性的真实谣言事件与传统模型和新模型进行了比较。结果表明，该模型对谣言事件的传播趋势有较高的预测精度。

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引用次数: 0

The literacy paradox: How AI literacy amplifies biases in evaluating AI-generated news articles 识字悖论：人工智能识字如何放大评估人工智能生成的新闻文章的偏见

IF 27 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal of Information Management

Pub Date : 2025-11-01 DOI: 10.1016/j.ijinfomgt.2025.102992

WooJin Kim , Yuhosua Ryoo

As Artificial Intelligence (AI) increasingly generates online news, AI literacy is promoted as essential for recognizing misinformation and bias. However, little is known about how AI literacy itself shapes biased processing. Across scenario-based and interactive AI news chatbot studies, we show that higher AI literacy can intensify opposing biases. When individuals better understand the use of AI in creating data-driven articles, they exhibit automation bias, perceiving AI as more objective and accurate, which in turn leads them to evaluate such articles as more credible. Conversely, when AI generates opinion- or emotion-based articles, high literacy fosters algorithmic aversion, perceiving AI as emotionless and context-blind, thereby evaluating these articles as less credible. These findings reveal a paradox: AI literacy, while intended to foster critical thinking, can instead heighten heuristic judgments, amplifying polarized perceptions of AI-generated news depending on content type.

随着人工智能（AI）越来越多地产生在线新闻，人工智能素养被认为是识别错误信息和偏见的关键。然而，人们对人工智能读写能力本身如何影响偏见处理知之甚少。通过基于场景和交互式人工智能新闻聊天机器人的研究，我们表明，更高的人工智能素养可能会加剧对立的偏见。当个人更好地理解人工智能在创建数据驱动文章中的使用时，他们会表现出自动化偏见，认为人工智能更客观、更准确，这反过来又会导致他们评估这些文章更可信。相反，当人工智能生成基于观点或情感的文章时，高识字率会培养对算法的厌恶，认为人工智能没有情感和上下文盲，从而评估这些文章的可信度较低。这些发现揭示了一个悖论：人工智能素养虽然旨在培养批判性思维，但却可以增强启发式判断，放大对人工智能生成的新闻的两极分化看法，这取决于内容类型。

引用次数: 0

Play the game then forever can begin: Designing games to extend gameplay 玩完游戏就永远可以开始了：设计游戏来扩展游戏玩法

IF 27 1区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal of Information Management

Pub Date : 2025-10-28 DOI: 10.1016/j.ijinfomgt.2025.102991

Gen-Yih Liao , Tzu-Ling Huang , Hsin-Yu Hung , Alan R. Dennis , Ching-I. Teng

Online games are among the most popular digital information systems for entertainment. Game providers frequently encounter the problem of retaining players. Players’ expressed interest in gameplay may not always be transformed into persistent gameplay behavior, posing challenges for game providers to extend players’ actual gameplay. To address this problem, we introduce a new concept, gameplay extension (measured in terms of the number of months a player actually continues playing a game), and explore how game elements can be designed to enhance gameplay extension. Drawing on goal gradient theory, we also examined the possible interplay of game design with gaming skills to formulate gameplay extension. We tested our model using multiwave and multisource data, including psychological and system-captured gameplay data from 379 players. We found that the mechanism affordance positively affects goal progress and concentration. Interestingly, concentration has a stronger effect than does goal progress on gameplay extension. Moreover, our findings uniquely clarify the pathway through which role-playing affordance extends gameplay, particularly among low-skilled players. Overall, our model explained 41% of the variance in gameplay extension, demonstrating its practical relevance. We argue that the findings apply to player-concentrated games or gamified systems.

网络游戏是最受欢迎的娱乐数字信息系统之一。游戏供应商经常会遇到留住玩家的问题。玩家对游戏玩法表达的兴趣可能并不总是转化为持久的游戏行为，这给游戏提供商扩展玩家的实际游戏玩法带来了挑战。为了解决这个问题，我们引入了一个新概念，即玩法扩展（游戏邦注：以玩家持续玩游戏的月数为衡量标准），并探索了如何设计游戏元素来增强玩法扩展。利用目标梯度理论，我们还研究了游戏设计与游戏技能之间可能的相互作用，以形成游戏玩法扩展。我们使用多波和多源数据来测试我们的模型，包括来自379名玩家的心理和系统捕获的游戏玩法数据。研究发现，机制亲和性对目标进展和注意力集中有正向影响。有趣的是，专注比目标进程对玩法扩展的影响更大。此外，我们的发现独特地阐明了角色扮演功能扩展游戏玩法的途径，特别是在低技能玩家中。总的来说，我们的模型解释了游戏玩法扩展中41%的差异，证明了它的实际相关性。我们认为这些发现适用于以玩家为中心的游戏或游戏化系统。

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引用次数: 0