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

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 : 2026-04-01 Epub 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

Bound to disclosure: An assessment of secondary data use concerns 约束披露：对二级数据使用问题的评估

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

International Journal of Information Management

Pub Date : 2026-04-01 Epub Date: 2025-12-27 DOI: 10.1016/j.ijinfomgt.2025.103026

Stephen Flowerday , Jake Mead , Rene Moquin

The sudden and complete dominance of social media by a few select companies has often led users to feel at odds with the rapidly changing business strategies in digital environments. One practice, the secondary use of personal information, has received limited attention in privacy behavior research. While many people remain unaware of how much of their data is collected, the secondary use of personal information, using personal information for reasons beyond the original transaction, is an increasing concern among social media users. Grounded in privacy calculus theory, this study aimed to propose and empirically test a research model regarding user concerns about secondary data use and its impact on self-disclosure intentions on Facebook. Privacy calculus research seeks to explain the privacy paradox, which refers to the disconnect between individuals' privacy concerns and their actual behavior. We posit that users are not perfectly rational but rather operate under conditions of bounded rationality, shaped by both real-world and engineered constraints, particularly evident in secondary data use practices. The findings demonstrate that concerns about the secondary use of personal information significantly diminish users' perceived benefits and heighten their perceived risks. Despite this, users continue to perceive that the benefits of information disclosure outweigh the risks. Our findings suggest that the opaque, multilayered nature of secondary data use on social media platforms exemplifies the conditions of bounded rationality under which users operate. Faced with limited information, cognitive constraints, and complex data ecosystems, individuals engage in satisficing behaviors that inadvertently increase their vulnerability to exploitation. Building on this observation, we extend privacy calculus by modeling disclosure decisions under bounded rationality and by centering secondary data use as the key driver of privacy concerns.

少数几家公司在社交媒体上突然完全占据主导地位，这常常让用户感到与数字环境中快速变化的商业战略格格不入。个人信息的二次利用这一行为在隐私行为研究中受到的关注有限。虽然许多人仍然不知道他们的数据被收集了多少，但个人信息的二次使用，即出于原始交易之外的原因使用个人信息，越来越受到社交媒体用户的关注。基于隐私演算理论，本研究旨在提出并实证检验一个关于用户对二级数据使用的关注及其对Facebook自我披露意图的影响的研究模型。隐私微积分研究试图解释隐私悖论，即个人对隐私的关注与实际行为之间的脱节。我们假设用户不是完全理性的，而是在有限理性的条件下操作，这是由现实世界和工程约束形成的，特别是在二次数据使用实践中。研究结果表明，对个人信息二次使用的担忧显著降低了用户的感知利益，并增加了他们的感知风险。尽管如此，用户仍然认为信息披露的好处大于风险。我们的研究结果表明，社交媒体平台上二手数据使用的不透明、多层性质体现了用户操作时的有限理性条件。面对有限的信息、认知约束和复杂的数据生态系统，个体参与的满足行为无意中增加了他们被剥削的脆弱性。在此观察的基础上，我们通过在有限理性下建模披露决策并将二手数据使用作为隐私问题的关键驱动因素来扩展隐私演算。

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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 : 2026-04-01 Epub 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

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 : 2026-04-01 Epub 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

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 : 2026-04-01 Epub 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

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 : 2026-04-01 Epub 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

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 : 2026-04-01 Epub 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

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 : 2026-04-01 Epub 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 : 2026-04-01 Epub 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 : 2026-04-01 Epub 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