Technovation最新文献_第6页

Getting a deal: A study on innovation and acquisition value 达成交易：创新与收购价值研究

IF 10.9 1区管理学 Q1 ENGINEERING, INDUSTRIAL

Technovation

Pub Date : 2025-11-19 DOI: 10.1016/j.technovation.2025.103423

Joshua B. Sears

The value of technological acquisitions has continued to climb, with an increasing volume of megadeals exceeding $5 billion. While there has been a plethora of research focused on either post-acquisition innovative productivity or value creation, there has been a lack of research that accounts for the post-acquisition innovative productivity of the target employees relative to the cost to acquire the target. We utilize social identity theory to explicate how technological overlap and similarity in technological prestige affect the antecedents of organizational identification (i.e., distinctiveness, prestige, and continuity) and thus, post-acquisition innovative productivity, while we utilize a strategic factor market perspective to examine their effect on the price paid for the target. In doing so, we develop a measure of innovation return on innovation (i.e., innovative productivity/target price). We submit and find evidence that technological overlap and similarity in prestige facilitate identification as the acquirer receives a greater innovation ROI with target and acquirer collaboration; however, we find that the target having higher prestige negatively influences collaborative innovation ROI. Finally, we find that the acquisition value moderates the effects that technological overlap, similarity in prestige, and the target being more prestigious have on innovation ROI from target and acquirer collaboration.

技术收购的价值继续攀升，大型交易的数量不断增加，超过50亿美元。虽然有大量的研究集中在收购后的创新生产力或价值创造上，但相对于收购目标的成本，目标员工的收购后创新生产力的研究一直缺乏。我们利用社会认同理论来解释技术声望中的技术重叠和相似性如何影响组织认同的前因（即独特性、声望和连续性），从而影响收购后的创新生产率，同时我们利用战略因素市场视角来研究它们对收购目标所支付价格的影响。在此过程中，我们开发了一种衡量创新回报的方法（即创新生产率/目标价格）。我们提交并发现证据表明，技术重叠和声誉相似性有助于识别，因为收购方在目标和收购方合作下获得更大的创新投资回报率；然而，我们发现拥有更高声望的目标对协同创新的投资回报率有负向影响。最后，我们发现收购价值调节了技术重叠、声望相似和目标更有声望对并购双方合作创新投资回报率的影响。

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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 : 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

Business-to-business ecosystem smartification for manufacturing: A definition, an integrative framework, and future directions 面向制造业的企业对企业生态系统智能化：定义、整合框架和未来方向

IF 10.9 1区管理学 Q1 ENGINEERING, INDUSTRIAL

Technovation

Pub Date : 2025-11-15 DOI: 10.1016/j.technovation.2025.103425

Mauro Estefano Kowalski , Leonardo Augusto de Vasconcelos Gomes , Felipe Mendes Borini , Roberto Carlos Bernardes

This study investigates how firms reconfigure their ecosystems in response to the growing integration of artificial intelligence (AI). While many B2B platforms have embraced digital transformation (DT), few studies examine how ecosystems evolve post-DT, as AI becomes embedded in platforms to enable adaptive processes and offerings. Drawing on a systematic literature review, we develop an integrative framework that identifies three interrelated phases, reconfiguring, experimenting, and expanding, through which smartification unfolds. Each phase presents specific governance mechanisms, coordination challenges, and strategic tensions among ecosystem actors. We define smartification as the integration of AI to create autonomous, context-aware, and self-improving solutions that support the development of business models and strategies. Our findings contribute to the literature on B2B platform ecosystems by clarifying the structural, organizational, and governance transformations that underpin AI adoption.

本研究探讨了企业如何重新配置其生态系统，以应对人工智能（AI）的日益整合。虽然许多B2B平台已经接受了数字化转型（DT），但很少有研究考察生态系统在数字化转型后是如何演变的，因为人工智能被嵌入到平台中，以实现自适应流程和产品。在系统文献综述的基础上，我们开发了一个综合框架，确定了三个相互关联的阶段，即重新配置、实验和扩展，通过这些阶段，智能化得以展开。每个阶段都提出了特定的治理机制、协调挑战和生态系统参与者之间的战略紧张关系。我们将智能定义为人工智能的集成，以创建自主的、上下文感知的、自我改进的解决方案，支持商业模式和战略的发展。我们的发现通过阐明支撑人工智能采用的结构、组织和治理转型，为B2B平台生态系统的文献做出了贡献。

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引用次数: 0

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