Technovation最新文献

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Digital divide and artificial intelligence for health 数字鸿沟和人工智能促进健康

IF 10.9 1区管理学 Q1 ENGINEERING, INDUSTRIAL

Technovation

Pub Date : 2025-11-08 DOI: 10.1016/j.technovation.2025.103392

Jean Clara , Bussotti Jean-Flavien , Cecere Grazia , Omrani Nessrine , Papotti Paolo

Social media platforms have become key intermediaries for ad campaigns, but concerns persist regarding the veracity of information presented in ads. In the health sector, false or unsupported claims in ad content can have real-world public health consequences. On these platforms, the display of ads is managed by recommendation systems that match the content of the ad to the interests of the user. This paper investigates whether the use of AI algorithms to recommend ads on social media platforms may help progress toward the Sustainable Development Goals (SDGs). We collected ads across all US states on Meta and Instagram during a period marked by increased public health concerns. Using a fine-tuned deep learning model, we fact-checked the content of these ads. The results of the fact-check show that only 0.2 % of the ads were classified as misinformation, and 15.41 % of the ads were classified as ambiguous. Both types of ads are less likely to be recommended to users located in wealthier states especially when health-related. Also, health-related ads classified as misinformation are more likely to be recommended to users in states with high percentage of people without health insurance. We argue that the use of recommendation systems contributes to widening the digital divide, which can hinder the achievement of SDGs.

社交媒体平台已成为广告活动的关键中介，但人们对广告中所提供信息的真实性仍然存在担忧。在卫生部门，广告内容中的虚假或未经证实的说法可能对现实世界的公共卫生造成影响。在这些平台上，广告的显示由推荐系统管理，该系统将广告内容与用户的兴趣相匹配。本文研究了使用人工智能算法在社交媒体平台上推荐广告是否有助于实现可持续发展目标（sdg）。在公共卫生问题日益严重的时期，我们在Meta和Instagram上收集了美国所有州的广告。使用微调的深度学习模型，我们对这些广告的内容进行了事实检查。事实检查的结果显示，只有0.2%的广告被归类为错误信息，15.41%的广告被归类为模棱两可。这两种类型的广告都不太可能被推荐给富裕州的用户，尤其是与健康相关的广告。此外，被归类为错误信息的健康相关广告更有可能被推荐给那些没有医疗保险的人比例很高的州的用户。我们认为，推荐系统的使用会导致数字鸿沟的扩大，从而阻碍可持续发展目标的实现。

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引用次数: 0

Market, R&D and multi-technology Co-evolution: An explorative study on metaverse 市场、研发与多技术协同进化：基于元宇宙的探索性研究

IF 10.9 1区管理学 Q1 ENGINEERING, INDUSTRIAL

Technovation

Pub Date : 2025-11-07 DOI: 10.1016/j.technovation.2025.103406

Dong Huo, Xinyuan Cui, Xiaolin Huang

Innovation is inherently characterized by significant uncertainty, particularly in emerging industries centered on complex technologies. A profound understanding of the inherent nature of complex technologies and their interplay with firm R&D strategy and market environment is paramount for achieving technology leadership. From an evolutionary perspective, we model and simulate the multi-technology co-evolution process across different scenarios. Meanwhile, we conduct empirical analyses on both simulation data (1,072,500 observations) and patent data (17,532 US patents), which confirm the robustness and applicability of the model. Further, we focus on metaverse as a typical case of emerging complex technologies. Specifically, we identify metaverse-relevant technologies and utilize approximately three million US patents from 1926 to 2020 to parameterize the model. This allows us to perform simulations to analyze the process and performance of the metaverse system. The results from the above analyses demonstrate that, first, the effects of internal and external coupling on average fitness are quite complex and jointly depend on their interaction, while stronger internal coupling or weaker external coupling consistently enhances efficacy. Second, a balanced R&D strategy generally leads to higher average fitness and efficacy, while an aggressive strategy, despite early gains, prolongs the time to equilibrium except in the high external coupling state. Third, a stable market environment improves average fitness and efficacy of the system. Fourth, the metaverse system is currently in a state of strong internal and external coupling, which necessitates a long time to reach equilibrium; in the current turbulent market environment, a balanced R&D strategy emerges as the optimal choice.

创新本质上具有显著的不确定性，特别是在以复杂技术为中心的新兴产业中。对复杂技术的内在本质及其与公司研发战略和市场环境的相互作用的深刻理解对于实现技术领先至关重要。从进化的角度，我们对不同场景下的多技术协同进化过程进行了建模和模拟。同时，我们对模拟数据（1,072,500个观测值）和专利数据（17,532项美国专利）进行了实证分析，验证了模型的稳健性和适用性。此外，我们将把元宇宙作为新兴复杂技术的典型案例来关注。具体来说，我们确定了与元宇宙相关的技术，并利用1926年至2020年的大约300万项美国专利来参数化模型。这允许我们执行模拟来分析元系统的流程和性能。以上分析结果表明，首先，内外耦合对平均适应度的影响相当复杂，并共同依赖于它们之间的相互作用，内耦合越强或外耦合越弱，效力越强。其次，均衡的研发策略通常会带来更高的平均适应度和效率，而积极的研发策略虽然会提前获得收益，但除了在高外部耦合状态下，达到均衡的时间会延长。第三，稳定的市场环境提高了系统的平均适应度和有效性。第四，元宇宙系统目前处于内外强耦合状态，需要较长时间才能达到平衡；在当前动荡的市场环境下，平衡的研发战略成为企业的最佳选择。

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引用次数: 0

Mapping the determinants influencing the adoption of blockchain innovations in SMEs: A multi-stage pythagorean fuzzy decision-making framework 影响中小企业采用区块链创新的决定因素：一个多阶段毕达哥拉斯模糊决策框架

IF 10.9 1区管理学 Q1 ENGINEERING, INDUSTRIAL

Technovation

Pub Date : 2025-11-07 DOI: 10.1016/j.technovation.2025.103402

Hannan Amoozad Mahdiraji , Aliasghar Abbasi-Kamardi , Fatemeh Yaftiyan , Demetris Vrontis , Qingyu Zhang

Expanding blockchain applications is a novel issue in emerging countries and developed economies. Therefore, studying the effective adoption of this technology is a fundamental requirement for its successful implementation. The dimensions that should be considered in these studies are those that lead to the effective adoption of blockchain innovations, which have not been deeply investigated. Hence, the current research employs an embedded mixed method to identify and analyse these factors. First, a systematic literature review (SLR) and thematic analysis (TA) are conducted using the SPAR-4-SLR protocol to identify the key factors in blockchain adoption. In addition, the extracted factors are screened and finalised in the next step using a Pythagorean fuzzy Delphi (PFD) method. Afterwards, a Pythagorean fuzzy (PF)-interpretive structural modelling (ISM)-cross-impact matrix multiplication applied to classification (MICMAC) investigates the cause and effect of the screened factors and provides a level-based conceptual framework. As a result of implementing the SLR-TA, 15 factors/themes are extracted, nine of which are selected as the most determinant factors based on the PFD method. Three drivers, one dependent, and five linkage factors are identified using the PF-ISM-MICMAC method. Based on these findings, a four-level conceptual framework is proposed to map the key determinants influencing the adoption of blockchain innovations in SMEs within an emerging economy.

在新兴国家和发达经济体中，扩大区块链应用是一个新问题。因此，研究该技术的有效采用是其成功实施的基本要求。在这些研究中应该考虑的维度是那些导致有效采用区块链创新的维度，这一点尚未得到深入研究。因此，目前的研究采用嵌入式混合方法来识别和分析这些因素。首先，使用SPAR-4-SLR协议进行系统文献综述（SLR）和专题分析（TA），以确定区块链采用的关键因素。此外，提取的因素进行筛选，并在下一步使用毕达哥拉斯模糊德尔菲（PFD）方法确定。然后，毕达哥拉斯模糊(PF)-解释结构模型(ISM)-交叉影响矩阵乘法应用于分类（MICMAC）调查筛选因素的因果关系，并提供了一个基于层次的概念框架。作为实施SLR-TA的结果，提取了15个因素/主题，其中9个是基于PFD方法选出的最具决定性的因素。使用PF-ISM-MICMAC方法确定了三个驱动因素，一个依赖因素和五个联动因素。基于这些发现，本文提出了一个四级概念框架，以绘制影响新兴经济体中小企业采用区块链创新的关键决定因素。

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引用次数: 0

Identifying firm-specific technology opportunities: Heterogeneous graph neural network-based link prediction 识别公司特定的技术机会：基于异构图神经网络的链接预测

IF 10.9 1区管理学 Q1 ENGINEERING, INDUSTRIAL

Technovation

Pub Date : 2025-11-06 DOI: 10.1016/j.technovation.2025.103405

Yingwen Wu , Zhouzhou Lin , Yangjian Ji , Fu Gu

A firm’s technological innovation is influenced by both its internal capabilities and external technological trends. However, previous firm-specific technology opportunity discovery (TOD) studies have predominantly focused on structural associations between technologies within a firm’s internal and external contexts, with limited exploration of deeper semantic relationships. This paper proposes a novel firm-specific TOD approach that considers both structural and semantic associations. Our methodology consists of four modules: (1) collecting patent data; (2) constructing a technological innovation heterogeneous graph; (3) identifying the target firm’s technology opportunities using Multi-Attention Graph Link Prediction (MAG-LP), which captures both structural and semantic information from the graph; and (4) evaluating technology opportunities using indicators of technology competitiveness, technology growth, and technology maturity. The efficiency and effectiveness of our proposed approach are demonstrated through its application to Honda Motor Company. This work contributes to a comprehensive understanding of potential R&D directions for the target firm.

企业的技术创新受到企业内部能力和外部技术趋势的双重影响。然而，之前的企业特定技术机会发现（TOD）研究主要集中在企业内部和外部环境中技术之间的结构关联上，对更深层次的语义关系的探索有限。本文提出了一种新的企业特定TOD方法，该方法同时考虑了结构和语义关联。我们的方法包括四个模块：(1)收集专利数据；(2)构建技术创新异构图；(3)利用多注意图链接预测（MAG-LP）识别目标公司的技术机会，该预测从图中捕获结构和语义信息；(4)利用技术竞争力、技术成长性和技术成熟度指标评价技术机会。通过对本田汽车公司的应用，证明了该方法的效率和有效性。这项工作有助于全面了解目标公司潜在的研发方向。

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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 : 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 : 2025-11-04 DOI: 10.1016/j.technovation.2025.103418

Giulio Ferrigno, Saverio Barabuffi, Enrico Marcazzan, Andrea Piccaluga

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 : 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

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 : 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

Takers and givers: Exploring the drivers of peer support in intra-incubator networks 索取者和给予者：探索孵化器内部网络中同伴支持的驱动因素

IF 10.9 1区管理学 Q1 ENGINEERING, INDUSTRIAL

Technovation

Pub Date : 2025-11-01 DOI: 10.1016/j.technovation.2025.103399

Joris Ebbers , Wouter Stam , Tom Elfring

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 : 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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