International Journal of Information Management最新文献_第2页

Generative AI in academic research activities: The hidden side of self-detrimental consumption 学术研究活动中的生成式人工智能：自我有害消费的隐藏一面

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

International Journal of Information Management

Pub Date : 2025-12-23 DOI: 10.1016/j.ijinfomgt.2025.103024

Mai Nguyen , Yunen Zhang , Yi Bu , Russell Belk

Generative AI (GenAI) is increasingly embedded in academic research activities undertaken by researchers (including research-active educators) and research students. While GenAI can raise efficiency, it may also foster self-detrimental consumption for short-term convenience that erodes long-term research integrity and capability. To map this “hidden side”, we conducted a netnography of discussions on X-platform (formerly Twitter) by self-identified researchers, research-active educators and research students (between October and November 2024; Study 1), alongside semi-structured interviews with 19 Australia-based researchers (aged 19–45; Study 2). Across the data, we identified five key themes: user misuse, environmental facilitators, usage barriers, GenAI limitations, and challenges, along with related sub-themes. Integrating both studies, we propose the GenAI Self-Detrimental Consumption (GAI-SDC) framework, which explicates how these factors interrelate within academic research contexts. The framework offers a focused lens for analyzing GenAI-related behaviors by examining how factors interact in academic research activities. The practical contribution includes actionable strategies from the framework, providing tangible measures for institutions, researchers, and developers to mitigate self-detrimental use and promote responsible GenAI integration in academic research activities.

生成人工智能（GenAI）越来越多地嵌入到研究人员（包括积极从事研究的教育工作者）和研究学生的学术研究活动中。虽然GenAI可以提高效率，但它也可能助长为了短期便利而自我损害的消费，从而侵蚀长期的研究完整性和能力。为了描绘这一“隐藏的一面”，我们在x平台（以前的Twitter）上进行了一项由自我认同的研究人员、研究活跃的教育工作者和研究生（2024年10月至11月；研究1）进行的讨论网络图，同时对19名澳大利亚研究人员（年龄19 - 45岁；研究2）进行了半结构化访谈。通过这些数据，我们确定了五个关键主题：用户滥用、环境促进因素、使用障碍、GenAI限制和挑战，以及相关的子主题。结合这两项研究，我们提出了基因自我有害消耗（GAI-SDC）框架，该框架解释了这些因素在学术研究背景下如何相互关联。该框架通过考察各种因素在学术研究活动中的相互作用，为分析基因相关行为提供了一个聚焦的视角。实际贡献包括来自框架的可操作策略，为机构、研究人员和开发人员提供切实的措施，以减轻对自身有害的使用，并促进学术研究活动中负责任的GenAI集成。

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引用次数: 0

How does artificial intelligence capacity enhance the production system resilience and operational performance? A human-organization-technology fit perspective 人工智能能力如何提高生产系统的弹性和运行性能？人-组织-技术契合的视角

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

International Journal of Information Management

Pub Date : 2025-12-23 DOI: 10.1016/j.ijinfomgt.2025.103023

Junbin Wang , Yangyan Shi , Xinyu Jiang , V.G. Venkatesh

Artificial Intelligence (AI) capabilities are increasingly pivotal for enhancing production system resilience in today's volatile business environments. However, the integration of AI technologies with established organizational information processing and decision-making frameworks remains inadequately understood. Grounded in the Human-Organization-Technology (HOT) fit theory, this study investigates how AI capacity positively influences a firm’s operational performance. Using multi-wave survey data collected from 305 manufacturing firms via a professional online platform during the COVID-19 pandemic, we identify critical factors that reinforce this positive effect and elucidate its underlying mechanisms, with particular emphasis on how AI reconfigures organizational information flows and knowledge practices. Partial least squares-based structural equation modeling was employed to test the hypothesized model. The findings reveal a significant positive impact of AI capacity on production system resilience. Furthermore, production system resilience itself exerts a strong positive influence on operational performance. Crucially, production system resilience serves as a key mediating mechanism, through which AI capacity indirectly enhances operational performance. Finally, the degree of fit, conceptualized across task-tool, human-tool, and data-tool dimensions, moderates the positive effect of AI capacity on production system resilience. This research is contextualized within the Chinese manufacturing sector, a major global production hub, and enriches the theoretical discourse on AI capacity and production system resilience from an information management perspective, highlighting its transformative role in organizational information flows, knowledge creation, and data-driven decision processes.

在当今多变的商业环境中，人工智能（AI）能力对于增强生产系统的弹性越来越重要。然而，人工智能技术与已建立的组织信息处理和决策框架的集成仍然没有得到充分的理解。基于人-组织-技术（HOT）契合理论，本研究探讨了人工智能能力如何积极影响企业的运营绩效。利用2019冠状病毒病大流行期间通过专业在线平台从305家制造企业收集的多波调查数据，我们确定了加强这种积极影响的关键因素，并阐明了其潜在机制，特别强调了人工智能如何重新配置组织信息流和知识实践。采用偏最小二乘结构方程模型对假设模型进行检验。研究结果揭示了人工智能能力对生产系统弹性的显著积极影响。此外，生产系统弹性本身对运营绩效有很强的正向影响。至关重要的是，生产系统弹性是关键的中介机制，通过该机制，人工智能能力间接提高了运营绩效。最后，跨任务-工具、人-工具和数据-工具维度概念化的契合度调节了人工智能能力对生产系统弹性的积极影响。本研究以全球主要生产中心中国制造业为背景，从信息管理的角度丰富了人工智能能力和生产系统弹性的理论论述，突出了其在组织信息流、知识创造和数据驱动决策过程中的变革作用。

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引用次数: 0

B&S2Vec: Mapping market structure in two-sided platform based on consumers’ purchase trajectories B&S2Vec：基于消费者购买轨迹的双边平台市场结构映射

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

International Journal of Information Management

Pub Date : 2025-12-23 DOI: 10.1016/j.ijinfomgt.2025.103025

Peng Wu , Shansen Wei , Xian Cheng , Runshi Liu

Platform companies must identify their market structure to develop effective growth strategies. This study introduces a method to vector buyers and sellers (B&S2Vec), using network representation learning to automatically extract latent buyer and seller attributes derived from the buyer’s purchase trajectories among thousands of sellers on a two-sided platform. We first construct a large-scale bipartite buyer-seller network by purchase trajectories; and then we compress the network into a low-dimensional representation space to learn complex patterns from the bipartite network by using B&S2Vec; we use t-SNE to obtain market structure visualization by reducing the learned representation vector to obtain the associated 2-dimensional visualization map. Our theoretical and simulation studies show that B&S2Vec effectively identifies market structures. In addition, we demonstrate its efficiency in optimizing marketing campaigns with budget constraints on a real platform. This study contributes to the advancement of research in two-sided platform marketing and market structure analysis.

平台公司必须确定自己的市场结构，以制定有效的增长战略。本研究引入了一种向量买家和卖家的方法（B&S2Vec），利用网络表示学习在双边平台上的数千个卖家中自动提取买家购买轨迹中衍生的潜在买家和卖家属性。首先利用购买轨迹构造了一个大规模的二部买卖网络；然后利用B&；S2Vec算法将网络压缩到低维表示空间，从二部网络中学习复杂模式；我们使用t-SNE通过减少学习到的表示向量来获得相关的二维可视化图，从而获得市场结构可视化。我们的理论和模拟研究表明，B&；S2Vec有效地识别了市场结构。此外，我们在真实平台上展示了它在预算约束下优化营销活动的效率。本研究有助于推进双边平台营销和市场结构分析的研究。

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引用次数: 0

The impact of creativity on the attitude toward and intention to adopt metaverse social media among youth in developing countries 创造力对发展中国家青年使用虚拟社会媒体的态度和意向的影响

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

International Journal of Information Management

Pub Date : 2025-12-22 DOI: 10.1016/j.ijinfomgt.2025.103020

Jean Robert Kala Kamdjoug , Serge-Lopez Wamba-Taguimdje , Philippe Jefferson Guessele

Metaverse social media (MSM) is a transformative space for consumers, organizations, and society that fosters creative expression, collaborative value creation, and socio-economic interactions beyond traditional digital platforms. Although the metaverse has garnered increasing attention from scholars and practitioners, few studies have empirically explored how creativity-related beliefs influence youth attitudes and adoption intentions regarding MSM, especially in developing countries, where contextual barriers such as poor internet quality (QIC) exist. Drawing on the technological learning and usage theory and creativity support systems literature, this study conceptualizes attitude toward MSM for creativity (ATMC) as a user’s evaluative belief that metaverse platforms offer rich opportunities for creative exploration, innovation, and self-expression. We focus on three attitudinal dimensions (attitude toward success [ATS], attitude toward failure [ATF], and attitude toward the learning process [ATL]) and find that all three influence ATMC, which is moderated by QIC, which, in turn, drives the intention to adopt MSM (IMSM). Using Cameroon as the context and adopting a cross-sectional field research design, we employ a multi-analytical hybrid technique that combines structural equation modeling and artificial neural networks to evaluate our research model using a sample of 144 users. The results show that ATS and ATL are critical factors influencing ATMC; these can effectively influence consumer IMSM. QIC moderates the relationships between ATF, ATL, and ATMC. We contribute to the theoretical understanding of active youth attitudes and intentions toward metaverse technology in developing countries and offer practical guidance on how to encourage the active adoption of this technology to foster creativity.

虚拟世界社交媒体（MSM）是一个面向消费者、组织和社会的变革空间，它促进了传统数字平台之外的创造性表达、协作价值创造和社会经济互动。虽然虚拟世界已经引起了学者和实践者越来越多的关注，但很少有研究从经验上探讨与创造力相关的信念如何影响年轻人对男同性恋者的态度和采用意图，特别是在存在诸如互联网质量差（QIC）等背景障碍的发展中国家。利用技术学习与使用理论和创造力支持系统文献，本研究将MSM对创造力的态度（ATMC）定义为用户对虚拟世界平台为创造性探索、创新和自我表达提供丰富机会的评价信念。我们重点研究了三个态度维度（对成功的态度[ATS]、对失败的态度[ATF]和对学习过程的态度[ATL]），发现这三个维度都影响ATMC，而ATMC受QIC的调节，而QIC反过来又推动了采用MSM （IMSM）的意愿。以喀麦隆为背景，采用横断面实地研究设计，我们采用多分析混合技术，结合结构方程建模和人工神经网络，使用144个用户样本评估我们的研究模型。结果表明，ATS和ATL是影响ATMC的关键因素；这些可以有效地影响消费者的IMSM。QIC调节ATF、ATL和ATMC之间的关系。我们有助于从理论上理解发展中国家的年轻人对虚拟技术的积极态度和意图，并就如何鼓励积极采用这种技术来培养创造力提供实践指导。

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引用次数: 0

Presence by design: A multi-method examination of design considerations for immersive virtual reality in corporate training 通过设计呈现：对企业培训中沉浸式虚拟现实设计考虑的多方法考察

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

International Journal of Information Management

Pub Date : 2025-12-20 DOI: 10.1016/j.ijinfomgt.2025.102999

Ersin Dincelli

Workforce training is a cornerstone of organizational success, as the value of intellectual capital increasingly rivals that of physical and financial assets in today’s knowledge-driven economy. The emergence of consumer-grade head-mounted display (HMD)-based virtual reality (VR) technology offers organizations innovative opportunities to meet evolving workforce training needs. This study employs a multi-method investigation to systematically examine HMD-based VR training and education, with a focus on presence as a core experiential quality of VR technology and an important determinant of learning effectiveness. First, we synthesize the literature on HMD-based VR in training and education through the Community of Inquiry (CoI) framework’s four interdependent components: cognitive, teaching, social, and emotional presence. The findings highlight the dynamic interplay among different dimensions of presence and their collective impact on learning outcomes, providing an integrated framework to inform the design of HMD-based VR training and education programs. To validate the real-world relevance of the CoI components and inform design practices, we conduct semi-structured interviews with a diverse group of stakeholders, including executives and managers from select Fortune 500 companies that have integrated HMDs into their workflows, professionals from companies specializing in VR training, pedagogical experts, VR application developers, and content creators. We identify 80 key design factors linked to different dimensions of presence. By bridging theory with practical insights, this study underscores the central role of presence in shaping immersive learning experiences and provides a foundation for designing impactful HMD-based VR training and education programs.

劳动力培训是组织成功的基石，因为在当今知识驱动的经济中，智力资本的价值日益与物质和金融资产的价值相媲美。基于消费级头戴式显示器（HMD）的虚拟现实（VR）技术的出现为组织提供了创新的机会，以满足不断变化的劳动力培训需求。本研究采用多方法调查，系统地考察了基于hmd的VR培训和教育，重点关注存在作为VR技术的核心体验质量和学习效果的重要决定因素。首先，我们通过探究社区（CoI）框架的四个相互依存的组成部分：认知、教学、社会和情感存在，综合了关于基于hmd的VR在培训和教育中的文献。研究结果强调了不同维度的存在之间的动态相互作用及其对学习成果的集体影响，为基于hmd的VR培训和教育计划的设计提供了一个综合框架。为了验证CoI组件与现实世界的相关性并为设计实践提供信息，我们对不同的利益相关者进行了半结构化访谈，其中包括来自将头戴式显示器集成到其工作流程中的财富500强公司的高管和经理，专门从事VR培训的公司的专业人员，教学专家，VR应用程序开发人员和内容创作者。我们确定了80个关键设计因素与不同维度的存在。通过将理论与实践相结合，本研究强调了在场在塑造沉浸式学习体验中的核心作用，并为设计有影响力的基于hmd的VR培训和教育项目提供了基础。

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引用次数: 0

Why ride-hailing platform firms are reluctant to share data with governments: Evidence from China 为什么网约车平台公司不愿与政府分享数据：来自中国的证据

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

International Journal of Information Management

Pub Date : 2025-12-19 DOI: 10.1016/j.ijinfomgt.2025.103019

Guoyin Jiang, Wanqiang Yang, Xingshun Cai

Data sharing between the public and private sectors, such as ride-hailing platform (RHP) firms and the government, aims to generate value. However, the reasons behind the intentions of RHP firms to share data with public entities remain unclear. In this research, a business-to-government (B2G) information-sharing framework is employed, and a mixed study combining structural equation modeling (SEM), with a sample size of 426 and fuzzy-set qualitative comparative analysis (fsQCA), with a sample size of 82 is conducted. The same variables are adopted and assessed through different methods, providing complementary insights into how information and technology, organizational and managerial dynamics, and political and policy considerations affect the intentions of RHP firms to share data with the government. The results of SEM analysis show government-led initiatives related to data infrastructure, data management improvement, robust systems for data security, administrative penalties, and strong government–business political connections collectively decrease the reluctance to share data (RSD) among RHP firms. The platform power (PP) level of RHP firms influences B2G data sharing to varying degrees. The fsQCA analysis identifies four configurations linked to the RSD of RHP firms, and their combinations result in the same outcome. Heterogeneity analysis further yields variations in configurations of reluctance across different PP levels. This research has important implications for governments seeking to address firm reluctance and promote sustainable B2G data-sharing practices.

公共和私营部门之间的数据共享，如叫车平台（RHP）公司和政府之间的数据共享，旨在创造价值。然而，RHP公司与公共实体共享数据的意图背后的原因尚不清楚。本研究采用企业对政府（B2G）信息共享框架，采用结构方程模型（SEM）和模糊集定性比较分析（fsQCA）相结合的混合研究，样本量为426个，样本量为82个。采用相同的变量，并通过不同的方法进行评估，从而对信息和技术、组织和管理动态以及政治和政策考虑因素如何影响RHP公司与政府共享数据的意图提供互补的见解。SEM分析的结果显示，政府主导的与数据基础设施、数据管理改进、强健的数据安全系统、行政处罚以及强大的政府-企业政治关系相关的举措，共同降低了RHP公司之间共享数据的意愿（RSD）。RHP企业的平台权力水平对B2G数据共享有不同程度的影响。fsQCA分析确定了与RHP公司的RSD相关的四种配置，它们的组合导致相同的结果。异质性分析进一步得出了不同PP水平的磁阻构型的变化。这项研究对寻求解决企业不情愿和促进可持续B2G数据共享实践的政府具有重要意义。

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引用次数: 0

Managing Intellectual Property leakage in the digital era: An integrated process model 管理数字时代的知识产权泄漏：一个集成过程模型

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

International Journal of Information Management

Pub Date : 2025-12-17 DOI: 10.1016/j.ijinfomgt.2025.103021

Rens Scheepers , Lars Mathiassen , Atif Ahmad , Rachelle Bosua , Richard Baskerville

As a result of the ongoing digitalization of business, Intellectual Property (IP) increasingly manifests in digital forms and as digital footprints, causing escalating exposure to IP leakage. This calls for a paradigm shift from a relatively static view of IP management focused on protection against and recovering from IP leakage to an emerging view focused on dynamically adapting approaches to IP management. Accordingly, we present a model of IP leakage management for the digital era that includes technological innovations to monitor digital forms and footprints of IP, proactive and reactive measures to mitigate escalating IP leakage risks, and adaptive strategizing in response to constantly changing internal and external business landscapes. Ultimately, such adaptive strategizing may include disclosing IP for the benefit of open innovation with external partners. The model offers a generative platform IS researchers can use to engage in interdisciplinary discourse on IP management as an important practical and theoretical concern in the digital era.

随着业务的持续数字化，知识产权（IP）越来越多地以数字形式和数字足迹表现出来，导致知识产权泄漏的风险不断上升。这就要求从专注于保护和恢复IP泄漏的相对静态的IP管理观点转变为专注于动态适应IP管理方法的新兴观点。因此，我们提出了一个数字时代的知识产权泄漏管理模型，其中包括监测知识产权数字形式和足迹的技术创新，主动和被动措施以减轻不断升级的知识产权泄漏风险，以及应对不断变化的内部和外部业务格局的适应性战略。最终，这种适应性战略可能包括披露知识产权，以便与外部合作伙伴进行开放式创新。该模型提供了一个生成平台，研究人员可以使用它来参与知识产权管理的跨学科讨论，这是数字时代一个重要的实践和理论问题。

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引用次数: 0

Reinforcement or deterioration？Unraveling how employee and AI collaboration impacts service innovation 加固还是恶化？揭示员工和人工智能协作如何影响服务创新

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

International Journal of Information Management

Pub Date : 2025-12-09 DOI: 10.1016/j.ijinfomgt.2025.103018

Jiaoyang Li , Dan Ding

Integrating Artificial Intelligence (AI) into service sectors is increasingly prevalent, yet the effects of employee-AI collaboration on service innovation fail to reach a consensus. To bridge this research gap, we conducted two complementary studies by delineating three distinct types of AI in service: mechanical AI for standardization, thinking AI for personalization, and feeling AI for relationalization. The first study, an exploratory experiment with 214 credit card salespeople, examined the impact of employee-AI collaboration on employee innovation. Compared to a no-AI control condition, mechanical AI was found to significantly hinder employee innovation, while thinking AI and feeling AI significantly enhanced innovation. The second study, a confirmatory survey of 246 employees across business and service sectors, integrated role identity theory and social cognitive theory to further uncover the mechanisms and boundary conditions underlying the discovered effects from the first study. Results revealed that mechanical AI undermines innovation through identity deterioration, whereas thinking and feeling AI promote innovation via identity reinforcement. Furthermore, employees’ occupational self-efficacy was shown to significantly strengthen the link between mechanical AI and identity deterioration, and weaken the relationship between thinking AI and identity reinforcement. This study advances research on employee-AI collaboration by elucidating the nuanced effects of distinct types of AI on employee innovation. It also offers practical suggestions for human-centered AI implementation by prioritizing thinking and feeling AI for innovation-driven tasks while limiting mechanical AI to standardized operations, and tailoring AI implementation strategies based on employees’ self-efficacy levels.

将人工智能（AI）整合到服务领域越来越普遍，但员工-AI协作对服务创新的影响尚未达成共识。为了弥补这一研究差距，我们通过描述服务中的三种不同类型的人工智能进行了两项互补研究：标准化的机械人工智能，个性化的思考人工智能和关系化的感觉人工智能。第一项研究对214名信用卡销售人员进行了探索性实验，考察了员工与人工智能协作对员工创新的影响。与无AI控制条件相比，机械AI显著阻碍员工创新，而思考AI和感受AI显著促进员工创新。第二项研究通过对246名商业和服务行业的员工进行验证性调查，整合角色认同理论和社会认知理论，进一步揭示了第一项研究发现的效应的机制和边界条件。结果表明，机械人工智能通过身份退化破坏创新，而思考和感觉人工智能通过身份强化促进创新。此外，员工的职业自我效能显著强化了机械性人工智能与身份退化之间的联系，削弱了思维性人工智能与身份强化之间的关系。本研究通过阐明不同类型的人工智能对员工创新的细微影响，推进了员工与人工智能协作的研究。它还提出了以人为中心的人工智能实施的实用建议，包括将思考和感觉人工智能优先用于创新驱动型任务，将机械人工智能限制在标准化操作中，以及根据员工的自我效能水平定制人工智能实施策略。

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引用次数: 0

Unveiling AI data security: How employee awareness evolves in smart manufacturing 揭示人工智能数据安全：智能制造中员工意识的演变

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

International Journal of Information Management

Pub Date : 2025-12-03 DOI: 10.1016/j.ijinfomgt.2025.103011

Juan Yu , Weihong Xie , Diwen Zheng , Liang Guo

In the era of Industry 4.0, smart manufacturing leverages artificial intelligence (AI) to enhance operational efficiency, yet heightened data security risks underscore the critical role of employee data security awareness (DSA). This study pioneers a Cellular Automata (CA) model, grounded in Social Cognitive Theory (SCT), to investigate the emergent dynamics of employee AI DSA in smart manufacturing enterprises. By integrating local security climates and dynamic threshold mechanisms, the model simulates collective awareness evolution under three scenarios: no intervention, mild publicity, and mandatory training, using an initial distribution of 30% low, 40% intermediate, and 30% high-awareness employees. Findings reveal that without intervention, awareness fluctuates unstably, with low-awareness employees rising to 50% and high-awareness declining to 20%, driven by intermediate-state volatility. Mild publicity boosts high-awareness to 45% and reduces low-awareness to 25% (13.3% overall increase), while mandatory training elevates high-awareness to nearly 80% and suppresses low-awareness below 5% (37.8% overall increase). Sensitivity analysis validates model robustness, highlighting intermediate-state employees as pivotal drivers of awareness dynamics. This study advances SCT by quantifying triadic interactions in AI-driven contexts and offers actionable insights for optimizing data security through targeted interventions, demonstrating that hybrid strategies combining publicity and training yield superior outcomes.

在工业4.0时代，智能制造利用人工智能（AI）来提高运营效率，但数据安全风险的加剧凸显了员工数据安全意识（DSA）的关键作用。本研究开创了基于社会认知理论（SCT）的元胞自动机（CA）模型，以研究智能制造企业中员工AI DSA的涌现动态。通过整合当地安全气候和动态阈值机制，该模型模拟了不干预、温和宣传和强制培训三种情景下的集体意识演变，初始分布为30%低意识、40%中级意识和30%高意识员工。研究结果表明，在不进行干预的情况下，意识波动不稳定，在中间状态波动的驱动下，低意识员工上升到50%，高意识员工下降到20%。温和的宣传将高知晓率提高到45%，将低知晓率降低到25%（总体增长13.3%），而强制性培训将高知晓率提高到近80%，将低知晓率抑制在5%以下（总体增长37.8%）。敏感性分析验证了模型的稳健性，强调了中间状态员工作为意识动态的关键驱动因素。本研究通过量化人工智能驱动环境中的三元交互作用来推进SCT，并为通过有针对性的干预优化数据安全提供了可操作的见解，表明宣传和培训相结合的混合策略产生了更好的结果。

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引用次数: 0

High interest but low adoption: Navigating organizations’ journey towards generative artificial intelligence implementation 高兴趣但低采用：引导组织走向生成式人工智能实现的旅程

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.2025.103009

Xiaoqing Wang , Wanle Zhong , Keman Huang , Bin Liang

The rapid development of generative artificial intelligence (aka, LLMs) provides high potential to transform organizational operations, yet a pronounced high interest but low adoption gap persists. Hence, moving beyond individual-level studies to examine organization-wide implementation, we draw on Rogers’ innovation decision process and Engeström’s activity theory, and conduct in-depth interviews with 27 front-line experts, including LLM providers, adopters, and advisors. Our analysis uncovers ten key contradictions and corresponding practice-driven solutions that emerge across five implementation stages (agenda-setting, matching, redefining and restructuring, clarifying, and routinizing). These insights illuminate not only the multi-stage, socio-technical complexity of LLM deployment but also shifting priorities among activity subsystems and the collaborative mechanisms essential for success. Building on these findings, we offer actionable recommendations for practitioners: a tiered rollout strategy; the technical capability building including decision-support and trial platforms, agile modular architectures and multi-layer update pipelines; as well as an accountable governance framework that integrates internal controls with external accountability. By synthesizing theoretical and practical perspectives, our study intends to guide researchers and business leaders navigate the challenges of organizational LLM implementation and realize their transformative potential at scale.

生成式人工智能（又名法学硕士）的快速发展为改变组织运营提供了巨大的潜力，然而，人们对它的兴趣很高，但采用程度却很低。因此，我们超越了个人层面的研究，考察了组织范围内的实施情况，借鉴了罗杰斯的创新决策过程和Engeström的活动理论，并对27位一线专家进行了深入采访，包括法学硕士提供者、采用者和顾问。我们的分析揭示了在五个实施阶段（议程设置、匹配、重新定义和重组、澄清和常规化）中出现的十个关键矛盾和相应的实践驱动解决方案。这些见解不仅说明了LLM部署的多阶段、社会技术复杂性，而且还说明了活动子系统之间的优先级转移和成功所必需的协作机制。基于这些发现，我们为从业者提供了可操作的建议：分层推出策略；技术能力建设包括决策支持和试验平台、敏捷模块化架构和多层更新管道；以及一个负责任的治理框架，将内部控制与外部问责制相结合。通过综合理论和实践观点，我们的研究旨在指导研究人员和商业领袖应对组织法学硕士实施的挑战，并在规模上实现其变革潜力。

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引用次数: 0