Technovation最新文献_第5页

Leveraging AI and generative AI in urban design and planning: Unveiling advantages and challenges through problem structuring methods 在城市设计和规划中利用人工智能和生成式人工智能：通过问题结构化方法揭示优势和挑战

IF 10.9 1区管理学 Q1 ENGINEERING, INDUSTRIAL

Technovation

Pub Date : 2026-03-01 Epub Date: 2025-12-27 DOI: 10.1016/j.technovation.2025.103465

Amali Çipi , Neuza C.M.Q.F. Ferreira , Fernando A.F. Ferreira , João J.M. Ferreira , Florentin Smarandache

The integration of Artificial Intelligence (AI) in general—and its subfield Generative AI (GenAI) in particular—into urban design and planning is revolutionizing traditional methodologies, providing innovative solutions to complex challenges in city development. Despite their transformative potential, existing research underscores a critical need to better understand the multifaceted advantages and challenges associated with these technologies. This study addresses this gap by investigating the causal relationships between the advantages and challenges of AI and GenAI integration in urban design and planning. Leveraging a novel combination of cognitive mapping and neutrosophic DEcision-MAking Trial and Evaluation Laboratory (DEMATEL), the research identifies and evaluates key factors shaping this integration. The findings reveal that dynamic digital city simulations and scenario modeling emerge as the most significant advantages, underscoring their capacity to drive data-informed innovation in urban development. Conversely, ethical concerns surface as the most critical challenge, exhibiting strong interdependencies with other issues, including the “black box” nature of AI systems and the biases embedded in training data. This study provides a comprehensive framework for understanding the interplay between these factors, offering actionable insights to guide both academic research and practical implementation. By addressing a pressing need in the field, the research paves the way for more responsible and effective applications of AI and GenAI in creating smarter, more sustainable urban environments.

将人工智能（AI）——尤其是其子领域生成人工智能（GenAI）——整合到城市设计和规划中，正在彻底改变传统的方法，为城市发展中的复杂挑战提供创新的解决方案。尽管它们具有变革潜力，但现有研究强调，迫切需要更好地了解与这些技术相关的多方面优势和挑战。本研究通过调查人工智能和GenAI集成在城市设计和规划中的优势和挑战之间的因果关系来解决这一差距。利用认知映射和中性决策试验与评估实验室（DEMATEL）的新组合，该研究确定并评估了形成这种整合的关键因素。研究结果显示，动态数字城市模拟和场景建模是最显著的优势，强调了它们在推动城市发展中基于数据的创新方面的能力。相反，道德问题是最关键的挑战，与其他问题表现出强烈的相互依赖性，包括人工智能系统的“黑匣子”性质和训练数据中嵌入的偏见。本研究为理解这些因素之间的相互作用提供了一个全面的框架，为指导学术研究和实际实施提供了可操作的见解。通过解决该领域的迫切需求，该研究为更负责任和更有效地应用人工智能和GenAI铺平了道路，以创造更智能、更可持续的城市环境。

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引用次数: 0

Manifoldness of services firms: Did R&D lead to better performance after the global financial crisis? 服务业多元性：全球金融危机后研发是否带来更好的绩效？

IF 10.9 1区管理学 Q1 ENGINEERING, INDUSTRIAL

Technovation

Pub Date : 2026-03-01 Epub Date: 2025-11-17 DOI: 10.1016/j.technovation.2025.103426

Sihong Wu , Rekha Rao-Nicholson , Yiyi Su , Di Fan

While prior research on service innovation has primarily emphasized R&D drivers and the characteristics of novel service offerings, limited attention has been given to how R&D impacts firm performance across distinct service sub-sectors—particularly during and after periods of economic disruption such as the global financial crisis (GFC). Adopting a service systems framework, this study classifies service firms according to two dimensions: the degree of service intangibility and the extent of customer involvement. Leveraging an unbalanced panel dataset spanning 2003 to 2013, the analysis investigates how returns on R&D investments vary across different service industry categories and how these patterns shift in the aftermath of the GFC. Results indicate that R&D yields positive returns across both classification criteria, though the effect is more pronounced for firms delivering intangible services and those with intensive customer participation. Post-GFC, however, the advantage of R&D investment appears greater among firms offering more tangible services. Moreover, service providers characterized by high customer engagement continued to derive superior R&D benefits in the post-crisis era. These insights contribute to both theoretical advancement and managerial practice, while also suggesting several avenues for future inquiry.

虽然先前对服务创新的研究主要强调研发驱动因素和新服务产品的特征，但很少关注研发如何影响不同服务子行业的公司绩效，特别是在全球金融危机等经济中断期间和之后。本研究采用服务系统架构，从服务无形化程度和顾客参与程度两个维度对服务企业进行分类。利用2003年至2013年的不平衡面板数据集，该分析调查了不同服务行业类别的研发投资回报如何变化，以及这些模式在全球金融危机之后如何变化。结果表明，研发在两种分类标准下都能产生正回报，尽管对于提供无形服务的公司和那些有大量客户参与的公司来说，这种影响更为明显。然而，全球金融危机后，研发投资的优势在提供更多有形服务的公司中显得更大。此外，在后危机时代，以高客户参与度为特征的服务提供商继续获得卓越的研发效益。这些见解有助于理论进步和管理实践，同时也为未来的研究提供了一些途径。

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引用次数: 0

Harnessing artificial intelligence for ambidextrous innovation: Contingent roles of complementary investments 利用人工智能进行双灵巧创新：互补投资的偶然作用

IF 10.9 1区管理学 Q1 ENGINEERING, INDUSTRIAL

Technovation

Pub Date : 2026-03-01 Epub Date: 2026-01-08 DOI: 10.1016/j.technovation.2026.103475

Yang (Eric) Zhou , Jingjun (David) Xu , Zhiying Liu

While a growing body of literature has explored the role of artificial intelligence (AI) in innovation management, current research lacks understanding of how AI adoption affects firms' ambidextrous innovation and what contextual factors may influence this relationship. Drawing on the innovation search perspective and upper echelons theory, this study theorizes the impact of AI adoption on ambidextrous innovation and evaluates the contingent roles of complementary investments. Using panel data from Chinese listed firms between 2011 and 2022, we find strong evidence that AI adoption significantly increases exploratory innovation (ERI) and exploitative innovation (EII). The mechanisms by which AI adoption affect ERI and EII involve broadening and heightening the scope and depth of knowledge search, respectively. Moderating analyses reveal that executives’ features (e.g., educational level and tenure) exert varying contingent effects. Specifically, executive educational level amplifies the positive impact of AI on EII but has no significant role in the AI–ERI relationship. Furthermore, executives with longer tenure tend to diminish the positive effect of AI on ERI and EII. These findings contribute to the existing literature at the intersection of AI and innovation and provide insights into how firms should make complementary investments to fully capture the benefits of AI.

虽然越来越多的文献探讨了人工智能（AI）在创新管理中的作用，但目前的研究缺乏对人工智能的采用如何影响公司的双灵巧创新以及哪些背景因素可能影响这种关系的理解。利用创新搜索视角和上层梯队理论，研究了人工智能应用对双灵巧创新的影响，并评估了互补投资的偶然性作用。利用2011年至2022年中国上市公司的面板数据，我们发现人工智能的采用显著提高了探索性创新（ERI）和剥削性创新（EII）。人工智能的采用影响ERI和EII的机制分别涉及拓宽和提高知识搜索的范围和深度。调节分析表明，高管的特征（如教育水平和任期）会产生不同的偶然效应。具体而言，高管教育水平放大了人工智能对EII的积极影响，但在AI - eri关系中没有显著作用。此外，任期较长的高管往往会削弱人工智能对ERI和EII的积极影响。这些发现为人工智能与创新交叉领域的现有文献做出了贡献，并为企业应如何进行互补投资以充分利用人工智能的好处提供了见解。

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引用次数: 0

Artificial intelligence in technology networks: A catalyst for achieving the SDGs 技术网络中的人工智能：实现可持续发展目标的催化剂

IF 10.9 1区管理学 Q1 ENGINEERING, INDUSTRIAL

Technovation

Pub Date : 2026-03-01 Epub Date: 2025-10-25 DOI: 10.1016/j.technovation.2025.103398

Vincenzo Varriale, Antonello Cammarano, Francesca Michelino, Mauro Caputo

The rapid adoption of artificial intelligence (AI) and other digital technologies is profoundly transforming business processes and activities, enhancing various aspects of sustainability. These technological developments offer new opportunities to address complex sustainability challenges and contribute to the achievement of the United Nations Sustainable Development Goals (SDGs). This study aims to investigate the networked value of AI within an ecosystem of digital technologies for achieving the SDGs. While extensive literature is based on the individual contributions of AI and other digital technologies to enhance sustainable development, there is a gap in understanding the networked value of AI when integrated with other technologies for sustainability. Based on a comprehensive dataset of 2161 sustainable business practices drawn from scientific literature, social network analysis was applied to map the relationships between AI within a technology ecosystem for achieving the SDGs. The results reveal that AI plays a central role, particularly in supporting SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production). Moreover, AI is closely connected to key digital technologies such as the Internet of Things, computing, and digital applications. These findings reveal the importance of strategically leveraging AI in conjunction with other technologies to enhance sustainability performance. In contrast to much of the existing literature, which is largely concentrated on environmental aspects, this research demonstrates that AI holds untapped potential to address socially oriented SDGs. It also identifies current gaps in the integration of AI with immersive environments and proximity technologies.

人工智能（AI）和其他数字技术的迅速采用正在深刻地改变业务流程和活动，增强可持续性的各个方面。这些技术发展为解决复杂的可持续性挑战提供了新的机遇，并有助于实现联合国可持续发展目标（sdg）。本研究旨在调查人工智能在数字技术生态系统中实现可持续发展目标的网络价值。虽然广泛的文献是基于人工智能和其他数字技术对促进可持续发展的个人贡献，但在理解人工智能与其他可持续性技术相结合时的网络价值方面存在差距。基于从科学文献中提取的2161个可持续商业实践的综合数据集，应用社会网络分析来绘制实现可持续发展目标的技术生态系统中人工智能之间的关系。结果显示，人工智能发挥着核心作用，特别是在支持可持续发展目标7（可负担和清洁能源）、可持续发展目标9（工业、创新和基础设施）和可持续发展目标12（负责任的消费和生产）方面。此外，人工智能与物联网、计算和数字应用等关键数字技术密切相关。这些发现揭示了战略性地利用人工智能与其他技术相结合以提高可持续性绩效的重要性。现有的许多文献主要集中在环境方面，与之相反，这项研究表明，人工智能在解决面向社会的可持续发展目标方面具有未开发的潜力。它还指出了目前人工智能与沉浸式环境和接近技术集成方面的差距。

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引用次数: 0

Exploring the directions of artificial intelligence in good health and well-being (SDG3) using big data and LDA topic modeling 利用大数据和LDA主题建模，探索健康福祉领域人工智能（SDG3）的发展方向

IF 10.9 1区管理学 Q1 ENGINEERING, INDUSTRIAL

Technovation

Pub Date : 2026-03-01 Epub Date: 2025-11-03 DOI: 10.1016/j.technovation.2025.103404

Peter Madzík , Lukáš Falát , Raja Jayaraman , Michael Sony , Jiju Antony , Dominik Zimon , Renata Skýpalová

Artificial Intelligence (AI) holds significant potential for advancing Sustainable Development Goal 3 (SDG3)—Good Health and Well-being—yet the field remains fragmented across numerous topics and disciplines. In this study, we apply Latent Dirichlet Allocation (LDA) to a final corpus of 60,010 Scopus abstracts after filtering, extracting k = 160 latent topics (selected via metric-based tuning; see Appendix A) and organizing them into a process-oriented, Health Technology Assessment–inspired framework that links Drivers, AI Infrastructure and Methods, Implementation, and Results. Key findings include dominant research streams in disease diagnostics (e.g., breast cancer, cardiovascular disease), personalized treatment, and automation, alongside the emergence of large language models (LLMs) like ChatGPT. Geographical mapping highlights Asia, North America, and Europe as research hubs, while underexplored areas such as AI in social media and student education are identified. We also introduce a quadrant-based trend analysis to distinguish “niche excellence” from “leading research areas” and chart short-versus medium-term dynamics. This methodological contribution not only offers a comprehensive “scientific map” of AI–SDG3 research but also provides a scalable blueprint for mapping AI's role across other SDGs and guiding future theory-driven and policy-relevant investigations.

人工智能（AI）在推进可持续发展目标3 (SDG3) -良好健康和福祉方面具有巨大潜力，但该领域仍然分散在众多主题和学科中。在本研究中，我们将潜在狄利let分配（LDA）应用于过滤后的60,010个Scopus摘要的最终语料库，提取k = 160个潜在主题（通过基于指标的调优选择；见附录a），并将它们组织成一个面向过程的健康技术评估启发框架，该框架将驱动程序、人工智能基础设施和方法、实施和结果联系起来。主要发现包括疾病诊断（例如乳腺癌、心血管疾病）、个性化治疗和自动化方面的主导研究流，以及ChatGPT等大型语言模型（llm）的出现。地理地图强调亚洲、北美和欧洲是研究中心，而未被开发的领域，如社交媒体和学生教育中的人工智能。我们还引入了基于象限的趋势分析，以区分“利基卓越”和“领先研究领域”，并绘制了短期与中期动态图。这一方法论贡献不仅提供了AI - sdg3研究的全面“科学地图”，还提供了一个可扩展的蓝图，用于绘制AI在其他可持续发展目标中的作用，并指导未来理论驱动和政策相关的调查。

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引用次数: 0

When do intrafirm networks accelerate follow-on invention? Evidence from biotechnology firms 企业内部网络何时加速后续发明？来自生物技术公司的证据

IF 10.9 1区管理学 Q1 ENGINEERING, INDUSTRIAL

Technovation

Pub Date : 2026-03-01 Epub Date: 2025-10-30 DOI: 10.1016/j.technovation.2025.103403

Ding Nan , Arjan Markus , Liukai Wang , Yu Xiong , Yali Zhang

This study examines how the structure of intrafirm inventor networks influences the speed at which biotechnology firms generate follow-on inventions. We conceptualize follow-on invention speed as how quickly a firm recombines and builds on its own prior knowledge. Drawing on social network theory, we focus on two structural dimensions: network clustering and average path length. We theorize that their effects depend on the firm's knowledge environment and tie characteristics—specifically, team knowledge diversity, tie strength, and invention radicalness. Using longitudinal data from 223 U.S. public biotechnology firms (2004–2013), we find that clustering slows invention speed, while longer average path length accelerates it—but only under specific conditions. Team knowledge diversity and radicalness mitigate the downsides of clustering but dampen the benefits of longer path lengths. Tie strength intensifies the negative effects of clustering while enhancing the value of path length. These findings underscore the need to align intrafirm network structure with the firm's internal knowledge context, offering new insights into the microfoundations underlying the speed of internal knowledge reuse and demonstrating that the value of intrafirm networks is contingent rather than universal. For managers, the results highlight that there is no one-size-fits-all optimal network structure: firms can accelerate follow-on invention only by aligning network features with the diversity, strength, and radicalness of their internal knowledge base and relational context.

本研究探讨了企业内部发明人网络的结构如何影响生物技术公司产生后续发明的速度。我们将后续发明速度定义为公司重组和建立其原有知识的速度。利用社会网络理论，我们关注两个结构维度：网络聚类和平均路径长度。我们的理论是，它们的影响取决于公司的知识环境和联系特征——具体来说，是团队知识多样性、联系强度和发明激进性。利用来自223家美国上市生物技术公司（2004-2013）的纵向数据，我们发现集群减缓了发明速度，而更长的平均路径长度则加速了发明速度——但这只是在特定条件下。团队知识的多样性和激进性减轻了聚类的缺点，但却削弱了更长的路径长度带来的好处。连接强度增强了聚类的负面效应，同时提高了路径长度的值。这些发现强调了将企业内部网络结构与企业内部知识背景结合起来的必要性，为内部知识重用速度背后的微观基础提供了新的见解，并证明了企业内部网络的价值是偶然的，而不是普遍的。对于管理者来说，研究结果强调了不存在放之四海而皆准的最优网络结构：企业只有将网络特征与其内部知识库和关系环境的多样性、强度和激进性相匹配，才能加速后续发明。

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引用次数: 0

Artificial intelligence in humanitarian aid: A review and future research agenda 人道主义援助中的人工智能：回顾与未来研究议程

IF 10.9 1区管理学 Q1 ENGINEERING, INDUSTRIAL

Technovation

Pub Date : 2026-03-01 Epub Date: 2025-11-03 DOI: 10.1016/j.technovation.2025.103415

Sophie Lythreatis , Fulya Acikgoz , Noura Yassine

As crises, both natural and man-made, continue to escalate in frequency and complexity, the need for effective and timely humanitarian interventions has become increasingly critical. Artificial intelligence (AI) has emerged as a transformative tool in enhancing humanitarian aid, addressing all stages of the crisis management cycle. Despite growing interest in AI's application within the humanitarian field, the existing literature remains fragmented, with limited synthesis of its overall impact. This study adopts a systematic literature review approach to provide a comprehensive analysis of AI's utilization in humanitarian aid across the crisis cycle, as well as its role in broader humanitarian settings outside of immediate crisis contexts. Based on 60 selected studies, the findings reveal that AI applications in both the pre- and post-crisis phases can be grouped into four specific categories, and that AI's role in broader humanitarian contexts can similarly be divided into four focus areas. Specifically, the categories in the pre-crisis phase include site selection, medical services enhancement, early warning, and information flow, and the categories in the post-crisis phase include distribution and delivery, damage assessment, online and textual insights, and routing optimization. The review highlights AI's significant potential to enhance the effectiveness and efficiency of humanitarian efforts, offering valuable insights for organizations seeking to harness AI's transformative power.

随着自然和人为危机的频率和复杂性不断升级，对有效和及时的人道主义干预的需求变得越来越迫切。人工智能（AI）已成为加强人道主义援助、应对危机管理周期各个阶段的变革性工具。尽管人们对人工智能在人道主义领域的应用越来越感兴趣，但现有文献仍然支离破碎，对其整体影响的综合有限。本研究采用系统的文献综述方法，全面分析了人工智能在危机周期内人道主义援助中的应用，以及它在直接危机背景之外更广泛的人道主义环境中的作用。基于60项选定的研究，研究结果表明，人工智能在危机前和危机后阶段的应用可以分为四个具体类别，人工智能在更广泛的人道主义背景下的作用也可以同样分为四个重点领域。具体而言，危机前阶段的类别包括选址、医疗服务增强、预警和信息流，危机后阶段的类别包括分发和交付、损害评估、在线和文本洞察以及路由优化。该评估强调了人工智能在提高人道主义工作的有效性和效率方面的巨大潜力，为寻求利用人工智能变革力量的组织提供了宝贵的见解。

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引用次数: 0

The impact of income inequality on green innovation: Based on the perspective of institutional environment 收入不平等对绿色创新的影响：基于制度环境的视角

IF 10.9 1区管理学 Q1 ENGINEERING, INDUSTRIAL

Technovation

Pub Date : 2026-03-01 Epub Date: 2025-11-05 DOI: 10.1016/j.technovation.2025.103401

Wenjing Luo , Dali Tao , Tianqi Liu

Based on the perspective of institutional environment, we adopt a fixed effect model and quantile regression to explore the impact of income inequality on green innovation by employing the panel data of 30 provinces from 2006 to 2019. The results show that income inequality impedes green innovation. More specifically, income inequality only has a significant impact on green innovation in the 75th quantiles, while in other quantiles, the estimated coefficients of income inequality are not significant. Furthermore, income inequality has a stronger negative impact on green product innovation than on green process innovation. Market systems, environmental regulation systems and intellectual property protection systems can mitigate the negative effect of income inequality on green innovation. More strikingly, in the central and western regions of China, institutional environment effectively alleviates the negative impact of income inequality on green innovation; this mitigation effect is not observed in eastern region.

基于制度环境视角，采用固定效应模型和分位数回归，利用2006 - 2019年30个省份的面板数据，探讨收入不平等对绿色创新的影响。结果表明，收入不平等阻碍了绿色创新。更具体地说，收入不平等仅在第75分位数对绿色创新有显著影响，而在其他分位数中，收入不平等的估计系数不显著。收入不平等对绿色产品创新的负向影响大于对绿色工艺创新的负向影响。市场制度、环境监管制度和知识产权保护制度可以缓解收入不平等对绿色创新的负面影响。更为显著的是，在中西部地区，制度环境有效地缓解了收入不平等对绿色创新的负面影响；东部地区没有观察到这种缓解效果。

引用次数: 0

Corrigendum to “What “V” of the big data support firms' radical and incremental innovation?” [Technovation volume 146 (2025) 103295] “大数据支持企业激进创新和渐进式创新的V是什么？”[科技创新146 (2025)103295]

IF 10.9 1区管理学 Q1 ENGINEERING, INDUSTRIAL

Technovation

Pub Date : 2026-03-01 Epub Date: 2025-11-04 DOI: 10.1016/j.technovation.2025.103418

Giulio Ferrigno, Saverio Barabuffi, Enrico Marcazzan, Andrea Piccaluga

引用次数: 0

Gender-related aspects of invention networks: A firm-level analysis 发明网络的性别相关方面：一个公司层面的分析

IF 10.9 1区管理学 Q1 ENGINEERING, INDUSTRIAL

Technovation

Pub Date : 2026-03-01 Epub Date: 2025-10-25 DOI: 10.1016/j.technovation.2025.103389

Leila Tahmooresnejad , Ekaterina Turkina

This paper integrates insights from the literature on invention networks, gender, and the sociological literature to analyze differences in how firms participate in man-led and woman-led invention networks. We contribute to the current debate on whether clustering or boundary-spanning network properties are more important for invention by introducing gender as an important factor. We empirically test our hypotheses on a sample of more than 30,000 firms from around the world over time using OECD REGPAT global patent data. Our findings indicate that different network properties are important for firm invention in woman-led and man-led innovation networks. In man-led invention networks, firms strongly benefit from being in a boundary-spanning position and are negatively affected by clustering, whereas in woman-led invention networks, boundary spanning has a less pronounced positive effect, and clustering has a positive rather than negative effect. Our findings have substantial implications for firms and policymakers interested in invention and contribute to the studies of gender and invention networks.

本文综合了有关发明网络、性别和社会学文献的见解，分析了企业参与男性领导和女性领导的发明网络的差异。我们通过引入性别作为一个重要因素，为当前关于集群或跨边界网络属性对发明是否更重要的争论做出了贡献。我们使用OECD REGPAT全球专利数据对来自世界各地的30,000多家公司的样本进行了实证检验。研究结果表明，在女性领导和男性领导的创新网络中，不同的网络属性对企业发明具有重要影响。在男性主导的发明网络中，企业从跨越边界的地位中获得了强烈的利益，并受到集群的负面影响；而在女性主导的发明网络中，跨越边界的积极影响不太明显，集群的积极影响大于消极影响。我们的研究结果对对发明感兴趣的公司和政策制定者具有重大意义，并有助于性别和发明网络的研究。

引用次数: 0