International Journal of Information Management最新文献_第3页

Can AI streamers' perseverance and exceptional intelligence overcome KOLs' viewership moat? 人工智能主播的毅力和过人的智慧能否克服kol的收视率护城河？

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

International Journal of Information Management

Pub Date : 2025-12-01 DOI: 10.1016/j.ijinfomgt.2025.103005

Hao Xiong , Chen Zhang , Yong Lin , Huili Yan , Yumiao Xu

To examine the optimal live-streaming model for manufacturers and its influencing factors under artificial intelligence (AI) conditions, this study introduces two variables: the intelligence level of AI streamers and livestream duration sensitivity. Two models are constructed, solved using optimization theory, and evaluated through comparative and sensitivity analyses. The findings are as follows: (1) Compared with Key Opinion Leader (KOL) live streaming, AI live streaming does not always lead to higher profits for manufacturers. AI streamers with greater endurance and intelligence are preferred only under specific conditions. (2) Manufacturers will opt for AI live streaming under four scenarios: high livestream duration sensitivity, moderate livestream duration sensitivity with high-intelligence AI streamers, high commission rates with low livestream duration sensitivity, high commission rates combined with moderate livestream duration sensitivity and low-intelligence AI streamers. (3) Although AI live streaming can effectively lower live streaming costs, manufacturers do not necessarily implement price reduction strategies accordingly. Only when both livestream duration sensitivity and the intelligence level of AI streamers are low do manufacturers potentially reduce prices to compensate for any negative experiences resulting from suboptimal live-streaming effects. Additionally, the study further explores four different scenarios, including KOLs' fixed fees, AI streamers with higher intelligence than KOLs, one-time investment costs for AI live-streaming, and AI streamers without popularity, thereby validating the robustness of the constructed model.

为了考察人工智能（AI）条件下制造商的最优直播模式及其影响因素，本研究引入了人工智能主播的智能水平和直播时长敏感性两个变量。构建了两个模型，运用最优化理论求解，并通过对比分析和敏感性分析对模型进行评价。研究发现：(1)与关键意见领袖（KOL）直播相比，AI直播并不总是为制造商带来更高的利润。只有在特定条件下，才会优先选择具有更大耐力和智能的AI主播。(2)厂商将在四种场景下选择AI直播：高直播时长敏感性、中度直播时长敏感性与高智能AI主播、高佣金率与低直播时长敏感性、高佣金率与中度直播时长敏感性与低智能AI主播相结合。(3)虽然AI直播可以有效降低直播成本，但厂商并不一定会实施相应的降价策略。只有当直播时长敏感性和人工智能主播的智能水平都很低时，制造商才有可能降低价格，以弥补因直播效果不佳而导致的负面体验。此外，该研究进一步探讨了四种不同的场景，包括kol的固定费用，AI主播的智力高于kol， AI直播的一次性投资成本，以及AI主播没有人气，从而验证了构建模型的鲁棒性。

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引用次数: 0

Optimizing crowdfunding campaigns: A stage-based analysis of key success factors 优化众筹活动：基于阶段的关键成功因素分析

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

International Journal of Information Management

Pub Date : 2025-11-29 DOI: 10.1016/j.ijinfomgt.2025.103007

Bih-Huang Jin , Yung-Ming Li , Zho-Wei Li

In recent years, crowdfunding has emerged as a key mechanism for raising capital for innovative projects. This study examines the funding patterns and critical success factors of 1294 Kickstarter campaigns by dividing each campaign into three phases: initial, middle, and final. Our analysis reveals an S-shaped funding pattern, with the majority of contributions occurring in the initial and final phases. Phase-specific fixed-effects regression shows that prior backers consistently drive subsequent funding, while the effects of updates and comments vary by phase—significantly boosting funding in the initial and final phases but sometimes negatively impacting unsuccessful projects. Moreover, our findings indicate that product-reward campaigns rely on a strong early surge, whereas non-product-reward campaigns benefit from sustained engagement throughout the campaign. Based on these insights, we propose a phase-adaptive strategy matrix to optimize communication strategies, thereby offering actionable guidance for improving crowdfunding success rates and challenging traditional static models.

近年来，众筹已成为创新项目融资的关键机制。本文研究了1294个Kickstarter活动的融资模式和关键成功因素，将每个活动分为三个阶段：初始、中期和最终阶段。我们的分析揭示了一个s形的资助模式，大部分捐款发生在初始和最后阶段。特定阶段的固定效应回归显示，先前的支持者持续推动后续的融资，而更新和评论的影响因阶段而异——显著提高了初始和最终阶段的融资，但有时会对不成功的项目产生负面影响。此外，我们的研究结果表明，产品奖励活动依赖于早期的强劲增长，而非产品奖励活动则受益于整个活动的持续参与。基于这些见解，我们提出了一个阶段自适应策略矩阵来优化传播策略，从而为提高众筹成功率和挑战传统的静态模型提供可操作的指导。

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引用次数: 0

To stream or not: A mixed-methods study of merchants’ willingness to join live-streaming E-commerce platforms 流还是不流：商家加入电商直播平台意愿的混合方法研究

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

International Journal of Information Management

Pub Date : 2025-11-29 DOI: 10.1016/j.ijinfomgt.2025.103008

Jiaxuan Li , Feng Ding , Zhongyuan Yuan , Yang Yi , Ruohan Li , Qinjian Yuan

Live streaming e-commerce platforms (LSEPs) have emerged as a mainstream sales channel, offering consumer’s real-time interaction and immersive shopping experiences. While consumer behaviour and decision-making on LSEPs have been extensively studied, little is known about the motivations behind merchants’ participation in LSEPs. As core participants in LSEPs, understanding merchants’ participation motivations is crucial for the sustainable development of the platform. To address this research gap, this study constructs a dual-layer model explaining merchant participation in LSEPs based on the Technology-Organization-Environment (TOE) framework and the Perceived Value Theory (PVT). A mixed-methods research approach was employed, involving semi-structured interviews with 30 merchants with LSEPs experience in China, identifying 12 driving factors. Based on these factors, the study utilized structural equation modeling to analyse 405 cross-sectional data points, examining the complex relationships among the factors and their impact on merchant participation intentions. The findings reveal that external factors such as market conditions, policy support, platform service quality, and peer influence significantly influence merchants’ perceived value and risk of joining LSEPs, thereby affecting their participation intentions. Based on these findings, this study deepens the theoretical understanding of merchants’ entry into LSEPs and provides actionable insights for LSEPs’ managers aiming to enhance merchant participation and platform sustainability.

直播电商平台（lsep）已经成为主流的销售渠道，为消费者提供实时互动和沉浸式购物体验。虽然消费者的行为和对低成本服务的决策已经得到了广泛的研究，但人们对商家参与低成本服务背后的动机知之甚少。作为lsep的核心参与者，了解商家的参与动机对平台的可持续发展至关重要。为了弥补这一研究空白，本研究基于技术-组织-环境（TOE）框架和感知价值理论（PVT）构建了一个解释商人参与lsep的双层模型。采用混合研究方法，对30位在中国有lsep经验的商家进行了半结构化访谈，确定了12个驱动因素。基于这些因素，本研究利用结构方程模型对405个截面数据点进行分析，考察了各因素之间的复杂关系及其对商家参与意愿的影响。研究发现，市场条件、政策支持、平台服务质量、同行影响力等外部因素显著影响商家的感知价值和加入lsep的风险，从而影响商家的参与意愿。基于这些发现，本研究深化了商家进入LSEPs的理论认识，并为LSEPs管理者提供了可操作的见解，旨在提高商家参与度和平台可持续性。

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引用次数: 0

The impact of public data openness on firm narrative R&D disclosure 公共数据开放对企业叙述性研发信息披露的影响

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

International Journal of Information Management

Pub Date : 2025-11-28 DOI: 10.1016/j.ijinfomgt.2025.103006

Zhengfu Wang, Weiwei Wu

This paper examines the impact of public data openness on firm narrative R&D disclosure by leveraging the launch of public data platforms as a policy shift. Using a Difference-in-Differences (DID) approach, our findings reveal that firms significantly reduce their narrative R&D disclosures following the implementation of public data openness. This effect is stronger for firms with higher R&D intensity and those operating in more competitive industries. Our study contributes to the literature on R&D information flows by highlighting the unintended consequences of public data openness. We also discuss practical recommendations to mitigate the potential negative effects on the R&D information dissemination.

本文通过利用公共数据平台的启动作为政策转变，考察了公共数据开放对企业叙事研发披露的影响。使用差异中的差异（DID）方法，我们的研究结果显示，在实施公共数据开放后，公司显著减少了叙事研发披露。这种效应对于研发强度较高的企业和处于竞争激烈行业的企业更为明显。我们的研究通过强调公共数据开放的意外后果，对研发信息流的文献做出了贡献。本文还讨论了减轻对研发信息传播的潜在负面影响的实用建议。

引用次数: 0

Artificial intelligence in healthcare IT: Enhancing work productivity through techno-eustress 医疗保健IT中的人工智能：通过技术压力提高工作效率

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

International Journal of Information Management

Pub Date : 2025-11-26 DOI: 10.1016/j.ijinfomgt.2025.103004

Shameem Shagirbasha , Naman Agarwal , Angelin Vilma G.

In a labor-intensive sector such as healthcare, the work productivity of frontline healthcare workers (FHWs) is crucial to reducing costs and managing patient volume. This study explores the affordances of Gen AI HITs that enhance FHWs’ work productivity and examines the mechanisms underlying this effect. A sequential mixed-methods design was employed for this study: qualitative interviews with 32 FHWs to identify the affordances that positively influence work productivity, followed by quantitative analyses using the PROCESS macro and structural equation modeling (SEM) to assess mediation by techno-eustress and moderation by job self-efficacy. The qualitative findings indicate that Gen AI HITs’ information, navigation, and interactivity affordances foster work productivity among FHWs, among other affordances identified. The quantitative results highlight that techno-eustress mediates the positive impact of Gen AI HITs’ interactivity and information affordances on FHWs’ work productivity, but not navigation affordance. However, when accounting for FHWs’ job self-efficacy, the mediation effect of techno-eustress becomes significant for all three affordances of Gen AI HIT – information, navigation, and interactivity. Specifically, the indirect positive impact of these affordances on productivity is stronger among FHWs with higher job self-efficacy. These results offer significant contributions to understanding the human–technology interaction in healthcare and provide practical insights for designing Gen AI HITs and training programs that improve adoption while enhancing work performance.

在医疗保健等劳动密集型行业，一线医疗工作者（FHWs）的工作效率对于降低成本和管理患者数量至关重要。本研究探讨了新一代人工智能HITs在提高fhw工作效率方面的优势，并研究了这种影响的潜在机制。本研究采用顺序混合方法设计：对32名外籍家庭佣工进行定性访谈，以确定对工作效率产生积极影响的支持，然后使用PROCESS宏观和结构方程模型（SEM）进行定量分析，以评估技术压力的中介作用和工作自我效能的调节作用。定性研究结果表明，Gen AI HITs的信息、导航和交互性能力提高了fhw的工作效率，以及其他已确定的能力。定量结果强调，技术压力介导了Gen AI HITs的交互性和信息能力对FHWs工作效率的积极影响，但不影响导航能力。然而，当考虑到FHWs的工作自我效能感时，技术压力对Gen AI HIT的信息、导航和交互性三种能力的中介作用都是显著的。具体而言，这些能力支持对工作效率的间接积极影响在工作自我效能感较高的外籍佣工中更为明显。这些结果为理解医疗保健领域的人机交互做出了重大贡献，并为设计Gen AI hit和培训计划提供了实际见解，从而在提高工作绩效的同时提高采用率。

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引用次数: 0

Fatigued by uncertainties: Exploring the cognitive and emotional costs of generative AI usage 被不确定性所累：探索生成人工智能使用的认知和情感成本

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

International Journal of Information Management

Pub Date : 2025-11-26 DOI: 10.1016/j.ijinfomgt.2025.103010

Hui Yang , Yu Zeng , Huizi Xing , Peng Hu

Generative AI (GenAI) systems like ChatGPT offer immense potential but also introduce unique challenges, particularly for users navigating uncertainty in GenAI interactions. This study focuses on two distinct uncertainties: prompt uncertainty (uncertainty about how to phrase effective prompts) and response uncertainty (uncertainty about how GenAI will respond even for the same prompt). We examine how these uncertainties contribute to user fatigue and influence feedback behavior. Using data collected from 832 GenAI users, we find that prompt uncertainty induces emotional fatigue, whereas response uncertainty triggers cognitive fatigue. Furthermore, both types of fatigue can reduce users' willingness to provide feedback to GenAI (e.g., rating GenAI outputs or reporting GenAI errors), which can hinder the iterative refinement of GenAI performance. By disentangling the distinct impacts of these uncertainties, this study contributes to a deeper understanding of GenAI-induced fatigue and its implications for user behavior. The findings also offer insights for GenAI developers to address uncertainty and mitigate user fatigue, ultimately fostering sustained user engagement and improving feedback mechanisms.

像ChatGPT这样的生成式人工智能（GenAI）系统提供了巨大的潜力，但也带来了独特的挑战，特别是对于在GenAI交互中导航不确定性的用户。本研究主要关注两种不同的不确定性：提示不确定性（关于如何表达有效提示的不确定性）和响应不确定性（关于GenAI如何响应相同提示的不确定性）。我们研究了这些不确定性如何导致用户疲劳和影响反馈行为。使用从832位GenAI用户收集的数据，我们发现即时不确定性导致情绪疲劳，而反应不确定性引发认知疲劳。此外，这两种类型的疲劳都会降低用户向GenAI提供反馈的意愿（例如，评价GenAI输出或报告GenAI错误），这可能会阻碍GenAI性能的迭代改进。通过解开这些不确定性的不同影响，本研究有助于更深入地了解基因诱导的疲劳及其对用户行为的影响。这些发现还为GenAI开发人员提供了解决不确定性和减轻用户疲劳的见解，最终促进持续的用户参与和改进反馈机制。

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引用次数: 0

Artificial intelligence and career development: Concerns and insights from first-generation college students 人工智能与职业发展：来自第一代大学生的关注和见解

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

International Journal of Information Management

Pub Date : 2025-11-21 DOI: 10.1016/j.ijinfomgt.2025.103003

Xuefei (Nancy) Deng , Rui Sun

Artificial intelligence (AI) is disrupting workforce and posing an unprecedented threat of job displacement. However, our understanding of AI's role in shaping individual career development is limited. This study provides insights into AI and career development within the context of first-generation college students (FGCSs), a marginalized group that is arguably among the most vulnerable to the career disruption of AI. Employing mixed methods, this exploratory study examines the effects of FGCS status and career anchor on individual concerns about AI’s career impact and the perceptions of FGCSs and non-FGCSs regarding their career development. Using survey data from 70 students at a minority-serving public university in the United States, the quantitative analysis shows that FGCS status is positively associated with individual concern about AI’s career impact, whereas prior ChatGPT experience is negatively associated with this concern. However, we did not find evidence that a student’s career anchor affects their concerns about AI’s career impact. Meanwhile, the qualitative analysis revealed four themes that highlight employed FGCSs’ reliance on college education to change to a professional career or prepare for entrepreneurship. Our follow-up study revealed four types of individual attitudes toward AI’s career impact and suggested that the attitudes are influenced by generational status and career stage. We compare FGCSs and their peers in terms of career stage, career development and attitude toward AI’s impact and propose intervention strategies to help FGCSs mitigate AI-related job replacement risks. The study contributes to research on the AI impact on career development of a marginalized population.

人工智能（AI）正在颠覆劳动力市场，并带来前所未有的工作岗位流失威胁。然而，我们对人工智能在塑造个人职业发展中的作用的理解是有限的。这项研究提供了第一代大学生（FGCSs）背景下的人工智能和职业发展的见解，这是一个边缘化群体，可以说是最容易受到人工智能职业中断的影响。采用混合方法，本探索性研究考察了FGCS地位和职业锚对个人对人工智能职业影响的影响，以及FGCS和非FGCS对其职业发展的看法。通过对美国一所少数族裔公立大学70名学生的调查数据，定量分析表明，FGCS状态与个人对人工智能职业影响的担忧呈正相关，而之前的ChatGPT经历与这种担忧呈负相关。然而，我们没有发现证据表明学生的职业锚会影响他们对人工智能职业影响的担忧。与此同时，定性分析揭示了四个主题，突出了在职fgcs对大学教育的依赖，以转向专业职业或为创业做准备。我们的后续研究揭示了个人对人工智能职业影响的四种态度，并表明这种态度受到代际地位和职业阶段的影响。我们比较了fgcs和他们的同龄人在职业阶段、职业发展和对人工智能影响的态度方面的差异，并提出了干预策略来帮助fgcs减轻人工智能相关的工作替代风险。这项研究有助于研究人工智能对边缘人群职业发展的影响。

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引用次数: 0

Gain or loss? The dual effects of dependence on AI on employee’s creativity 得还是失？依赖人工智能对员工创造力的双重影响

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

International Journal of Information Management

Pub Date : 2025-11-19 DOI: 10.1016/j.ijinfomgt.2025.103001

Su Cui , Longdong Wang , Weihang Cao , Tongqing Zhu

The distinct advantages of artificial intelligence (AI) in cognitive and creative projects have driven organizations to advocate for and implement AI, which has contributed to a deep and widespread dependence on AI among employees in creative generation. However, why, how, and when the dependence on AI influences employees’ creativity remains understudied. To figure out these issues, this research explored the double-edged effect of employee dependence on AI on their creativity, drawing on the job demands-resources model. Our mixed methods reveal that employee dependence on AI positively and indirectly affects their creativity via creative process engagement, while the indirect effect is stronger when employee cognitive flexibility is higher than lower. In contrast, employee dependence on AI negatively affects their creativity via information overload when cognitive flexibility is low. These findings have several theoretical and managerial implications related to AI-creativity research and practice.

人工智能（AI）在认知和创造性项目中的独特优势促使组织倡导和实施人工智能，这导致了创造性一代员工对人工智能的深刻而广泛的依赖。然而，对人工智能的依赖为何、如何以及何时影响员工的创造力，仍未得到充分研究。为了解决这些问题，本研究利用工作需求-资源模型，探讨了员工对人工智能的依赖对其创造力的双刃剑效应。我们的混合方法发现，员工对人工智能的依赖通过创造性过程投入正向和间接影响其创造力，而当员工认知灵活性高时，间接影响更强。相反，当认知灵活性较低时，员工对人工智能的依赖会通过信息过载对其创造力产生负面影响。这些发现对人工智能创造力的研究和实践具有若干理论和管理意义。

引用次数: 0

Deskilling, reskilling, or upskilling? Unpacking the pathways of student adaptation to generative artificial intelligence 去技能化，再技能化，还是提升技能？解开学生适应生成式人工智能的途径

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

International Journal of Information Management

Pub Date : 2025-11-19 DOI: 10.1016/j.ijinfomgt.2025.103002

Bo Yang, Yongqiang Sun, Zihan Zeng, Qinwei Li

The proliferation of generative AI (GAI) like ChatGPT is transforming how students engage with information and knowledge-focused activities in higher education, sparking debate about its dual impact on learning. While GAI offers potential benefits like enhanced efficiency, concerns about risks such as skill erosion persist. To address this tension, we investigate how students’ dependence on GAI shapes their learning outcomes through skill adaptation processes and under what conditions these effects occur. We conducted a three-phase mixed-methods study (survey N = 306; interviews N = 16; experiment N = 397). Our findings reveal that GAI dependence, influenced by individual learning goals (performance-avoidance/-approach), drives three distinct skill adaptation processes: deskilling (skill erosion), reskilling (acquiring new GAI-related competencies), and upskilling (enhancing existing skills). These adaptations, in turn, differentially impact routine and innovative performance. Qualitative results corroborate and complement these findings, indicating that task characteristics shape GAI use patterns into substitutive and augmentative use. Finally, a scenario-based experiment provides causal evidence for this emergent insight, demonstrating how task characteristics drive the adoption of substitutive vs. augmentative use, which in turn leads to divergent skill adaptation pathways. By combining diverse methodologies, this study clarifies the lights and shadows of GAI dependence, demonstrating how its effects are contingent on individual agency, technological appropriation (substitutive vs. augmentative), and task context. Our findings advance theory on human-AI adaptation and provide practical guidance for practitioners to optimize GAI’s role in learning and knowledge-focused activities.

像ChatGPT这样的生成式人工智能（GAI）正在改变学生在高等教育中参与信息和知识活动的方式，引发了关于其对学习的双重影响的辩论。虽然GAI提供了提高效率等潜在好处，但对技能流失等风险的担忧仍然存在。为了解决这种紧张关系，我们研究了学生对GAI的依赖如何通过技能适应过程塑造他们的学习成果，以及这些影响在什么条件下发生。我们进行了一项三相混合方法研究（调查N = 306；访谈N = 16；实验N = 397）。我们的研究结果表明，GAI依赖受到个人学习目标（表现回避/接近）的影响，推动了三种不同的技能适应过程：去技能化（技能侵蚀）、再技能化（获得新的GAI相关能力）和技能提升（提高现有技能）。这些调整反过来又对常规性能和创新性能产生不同的影响。定性结果证实并补充了这些发现，表明任务特征将GAI使用模式塑造为替代和增强使用。最后，基于场景的实验为这一新兴见解提供了因果证据，展示了任务特征如何驱动替代使用与增强使用的采用，这反过来又导致了不同的技能适应途径。通过结合多种方法，本研究阐明了GAI依赖的光和影，展示了其影响如何取决于个人代理、技术挪用（替代与增强）和任务上下文。我们的研究结果推进了人类-人工智能适应的理论，并为实践者优化人工智能在学习和知识活动中的作用提供了实践指导。

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引用次数: 0

Building on the legacy of the International Journal of Information Management 建立在国际信息管理杂志的遗产

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

International Journal of Information Management

Pub Date : 2025-11-15 DOI: 10.1016/j.ijinfomgt.2025.103000

Michael Chau, Andrew Schwarz

引用次数: 0