This study explores how the composition of multinational R&D teams influences knowledge spillovers across countries and industries, focusing on spatial and cultural determinants. By examining patent data from the United States Patent and Trademark Office (USPTO), the research investigates whether inventions developed by geographically and culturally diverse teams sustain cross-country and cross-industry characteristics in subsequent innovations. This inquiry delves into an underexplored area, focusing on the continuity of collaborative efforts beyond initial invention stages. The findings reveal that greater spatial and cultural distances between inventors significantly enhance the cross-country knowledge spillovers, facilitating broader international dissemination of knowledge. However, these same distances hinder cross-industry spillovers. Cross-industry knowledge transfer is more dependent on geographical proximity and cultural cohesion to effectively integrate and apply diverse technological insights. The study contributes to theory by distinguishing the divergent mechanisms through which team diversity affects knowledge spillover in different contexts and by introducing a unified framework of spatial and cultural distance to explain mechanisms in spillover pathways. Practically, the study also offers practical guidance for organizing global R&D teams to optimize innovation reach while managing complexity in international and interdisciplinary collaboration.

This study examines the role of technological convergence in the emergence and evolution of General-Purpose Technologies (GPTs). Utilizing patent data from 2010 to 2019, it investigates how cross-disciplinary integration facilitates knowledge diffusion and innovation across industries, assignees, technological domains, and national boundaries. Patent generality and technological distance are employed as proxies for convergence, capturing the extent to which heterogeneous knowledge inputs contribute to the breadth and adaptability of emerging technologies. The findings indicate that technological convergence significantly enhances both the generality and value of GPT-related patents. These results underscore the critical importance of cross-disciplinary collaboration in fostering global innovation networks and enabling cross-sectoral technological advancement. The study offers policy-relevant insights for optimizing research and development strategies aimed at accelerating broad-based economic and industrial transformation.

Patents are essential for securing market leadership and protecting intellectual property, but traditional valuation methods often fail to fully capture their strategic value. This paper introduces a causality-driven framework for patent valuation that integrates domain expertise with advanced language models in a structured interview-like process. By analyzing semiconductor patents filed between 1997 and 2007-a pivotal period of technological transformation-our approach employs Directed Acyclic Graphs (DAGs) and Structural Equation Models (SEMs) to reveal causal relationships, providing precise value attribution. This integration enhances the analysis of complex patent data, offering a powerful tool for aligning technological innovation with market and legal strategies.

The pharmaceutical sector, characterized by its research intensity and strict regulatory frameworks, depends critically on effective knowledge management and the integration of diverse technologies to sustain innovation. Yet, limited research has examined how knowledge dynamics influence the trajectory of technological convergence in this domain. Addressing this gap, the present study explores the impact of knowledge originality across four dimensions-technological domains, organizational boundaries, industrial sectors, and geographical regions-on innovation outcomes. Leveraging FDA-approved patent data from the USPTO, we employ forward and backward citation analyses and multiple regression models to assess the role of each originality dimension in shaping technology convergence. Our findings reveal that technology originality broadens convergence by increasing both technological distance and speed. Similarly, industry originality expands the convergence scope, although its impact on the rate is less pronounced. In contrast, country originality introduces coordination challenges that hinder both convergence distance and speed, while assignee originality has a limited effect in this highly specialized sector. By introducing technology convergence distance and speed as performance metrics, the study offers a novel framework to understand innovation pathways in pharmaceuticals. These insights advance knowledge-based innovation theory and guide R&D managers in developing effective and adaptive innovation strategies.

Technology fusion is seen as an important driver of emerging industries and economic growth, creating opportunities for enterprises and societies around the world. Yet, asymmetric technological development in different countries or industries leads to disparities in technology fusion, so it is always an issue for policy-makers and scholars to identify and discover the frontier areas where technologies intersect. The objective of this article is to analyze the overall picture of global technology fusion and to explore the differences between fusion and nonfusion technologies through patents. This study utilized patents issued from 1976 to 2017 in the U.S. Patent and Trademark Office (USPTO) database to analyze technology distances through interdisciplinary patent citations for examining a global picture of technology fusion. Both fusion patents and nonfusion patents are characterized by analysis of variance (ANOVA) and logistic regression based on a total of 20 patent indicators, to differentiate the two types of patents and the likelihood for nonfusion patents to become fusion patents. It is found that most characteristics of fusion patents and nonfusion patents are significantly different. Meanwhile, the novelty of industry knowledge, the richness of knowledge resources, and other conditions increase the likelihood of nonfusion technology becoming fusion technology. Findings of this study provide guidance for potential interdisciplinary innovation through patented technologies.

This study examines how the integration of internal expertise with external research partnerships influences the confluence of technologies. Analyzing a decade's patents, it clarifies the efficacy of varied knowledge acquisition strategies on innovation. The research quantifies the contribution of such synergies to innovation across technological, industrial, and geographic realms. Methodologically, it employs patent analytics to dissect originality into technology scope, assignee variety, industry diversity, and country-specific innovation trends from 2010 to 2019. Findings reveal a positive link between comprehensive technological and industrial knowledge and enhanced technological convergence. Significantly, it accentuates the role of global collaboration in unifying new technologies and industries. This interdisciplinary approach and in-depth organizational learning understanding are vital for spurring innovation. The results offer pivotal insights for academia and policy formulation in technology governance, advocating for integrated knowledge strategies to drive sector-spanning technology unification. The study provides a blueprint for fostering innovation through inter-sectoral and interdisciplinary exchange.

In the rapidly evolving landscape of global technology, the designation of Standard-Essential Patents (SEPs) plays a crucial role in shaping societal and technological strategies. This study explores the intricate relationship between technological fusion and its impact on the likelihood of a patent being recognised as an SEP. It further examines the moderating influence of international collaboration, as seen through the involvement of diverse international assignees and inventors. Utilising a comprehensive dataset of declared SEPs from 1977 to 2014, compared against a control group of non-SEPs, the study employs probit regression analysis to test its hypotheses. The findings suggest that an increased degree (i.e. criticality) of technological fusion within patents correlates with a decreased probability of SEP recognition. Additionally, this negative correlation is accentuated by factors associated with transnational knowledge collaboration, such as referencing international assignees and inventors. While rooted in the domain of patent analysis, the study's insights have significant implications for SEP developers, policymakers, and stakeholders at the nexus of technology and international collaboration, offering a novel perspective on the complex interplay between technological innovation, global knowledge sharing, and SEP recognition.

In today's technology-driven landscape, companies face intense competition due to rapid technology application and industry convergence. Upon these phenomena, this study aims to comprehensively understand the factors that influence the success of interdisciplinary knowledge combinations and their impact on initiating technology convergence. Drawing the dynamic capabilities framework, this study explores how three dimensions of dynamic capabilities influence the initiation of technology convergence: the pace of assimilating prior knowledge, the diversity of cross-disciplinary knowledge integrations and refiguration, and the propensity for differentiated knowledge adoption. United States Patent and Trademark Office data from 1976 to 2019 are retrieved to measure these attributes. Findings reveal that firms fostering the initiation of technology convergence assimilate knowledge more rapidly, across diverse fields, and demonstrate a propensity for differentiated knowledge. It operationalizes the dynamic capabilities framework within the context of technology convergence and takes a proactive approach by examining the trajectories preceding the initiation of technology convergence. Executives and the R&D team can leverage these findings to assess their current positioning and determine the direction for future collaboration or exploration efforts.

The unprecedented proliferation of standard-essential patents (SEPs) has exerted a transformative impact on the evolution of standards, fueling global technological innovation. Recognized as a linchpin for enabling rapid and widespread innovation diffusion, SEPs have become pivotal drivers of contemporary business strategies. While the superior value of SEPs vis-à-vis non-standard-essential patents (non-SEPs) is widely acknowledged, the intricate process underlying the transformation of patents into SEPs warrants a comprehensive investigation. Drawing on an extensive analysis of 4859 SEPs and 775 669 non-SEPs granted by the United States Patent and Trademark Office from 1976 to 2014, this study employs a rigorous examination of ten commonly utilized patent indicators, delineating four critical dimensions: 1) collaboration, 2) knowledge, 3) diversity, and 4) legal protection. Through a strategic lens, this study shed light on the multifaceted factors that amplify the likelihood and hasten the speed of SEP declarations. The empirical findings of this study compellingly illustrate the predominantly positive influence exerted by most indicators on SEP declaration likelihood, effectively expediting the declaration process.

In a rapidly evolving technological landscape, firms are strategically diversifying into multiple tech sectors, blurring traditional industry lines through ‘industry convergence’. To comprehend the inception of technology convergence from the roots of knowledge, this study bridges knowledge flow framework with the dynamic capabilities theory, considering the dual roles of firms in knowledge flow and whether they engage in pioneering or follower-type technological innovations. Through this integrated approach, this study classifies technology convergence into four distinct patterns. An analysis of USPTO data spanning 1976–2019 underscores that as knowledge matures, its application in convergence processes intensifies. Pioneers exhibit faster knowledge advancement and maintain focused partnerships, whereas followers diversify across disciplines and pursue extensive collaborations. These results highlight the strategic importance for firms to coordinate their knowledge-based attributes with their technological progression pace. Firms should strategically harmonize their innovation approaches with their market stance to fully leverage the advantages of technology convergence.

In a world of swiftly changing technology and external challenges, predicting the role of core patents in technology systems' strength and power is vital. This research presents a method that combines robustness analysis of patent citation networks with core patent identification, assessing their global industrial technology innovation significance. It aims to identify patents key to network stability and external change adaptation, understanding their impact in dynamic tech environments. Using network robustness, the study examines connectivity, efficiency, and clustering in patent citation networks, assessing patent node importance based on structural feature changes postremoval. The study employs patents from five technological domains as case studies, ranking the importance of nodes and exploring how patent attributes affect these rankings. This research contributes by merging patent network robustness with valuation, supporting IP strategies and tech management policies, and offering insights into tech system complexity and dynamism.

In recent years, there has been a significant surge in commercial demand for citation graph-based tasks, such as patent analysis, social network analysis, and recommendation systems. Graph Neural Networks (GNNs) are widely used for these tasks due to their remarkable performance in capturing topological graph information. However, GNNs’ output results are highly dependent on the composition of local neighbors within the topological structure. To address this issue, we identify two types of neighbors in a citation graph: explicit neighbors based on the topological structure, and implicit neighbors based on node features. Our primary motivation is to clearly define and visualize these neighbors, emphasizing their importance in enhancing graph neural network performance. We propose a Correlation-aware Network (CNet) to re-organize the citation graph and learn more valuable informative representations by leveraging these implicit and explicit neighbors. Our approach aims to improve graph data augmentation and classification performance, with the majority of our focus on stating the importance of using these neighbors, while also introducing a new graph data augmentation method. We compare CNet with state-of-the-art (SOTA) GNNs and other graph data augmentation approaches acting on GNNs. Extensive experiments demonstrate that CNet effectively extracts more valuable informative representations from the citation graph, significantly outperforming baselines. The code is available on public GitHub 1 .

In an era marked by rapid technological advancements and global interconnectedness, understanding how knowledge traverses geographical boundaries is crucial for fostering innovation. This study examines the relationship between knowledge recombination and geographical knowledge diffusion, employing empirical research using patent data from FDA-approved pharmaceuticals. It uncovers that the intensity of knowledge recombination, as denoted by the reference count, and its breadth, as indicated by the knowledge originality index (which captures the 'technological' scope) and the backward citation geographical distance (illustrating the 'geographical' scope), are key factors driving the diffusion of knowledge across geographical regions. These findings are pivotal as they reveal how the depth and range of knowledge recombination significantly influence the spatial spread of technological insights. This research makes a unique contribution to existing literature by shedding light on the impact of knowledge recombination on geographical knowledge diffusion.

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Measuring the value of inventions in the intellectual property landscape is a complex challenge. Experts rely on patent valuation techniques that use resources like the US Patent and Trademark Office (USPTO). This study contributes to patent value research by examining the relationship between the geographic distance among collaborating inventors and patent value indicators. Scholars have suggested that the value of an invention increases as the geographic distance between co-inventors grows. To test this proposition, the study conducted a regression analysis on a dataset of over two million USPTO patents. The analysis controlled for patent and inventor characteristics and consistently found a positive influence of geographic distance on patent value. These findings challenge assumptions and offer insights into collaborative innovation dynamics. They have significant implications for innovation management, urging inventors and organizations to carefully consider spatial factors and recognize the advantages of extended geographic distances when forming collaboration networks. By strategically selecting partners, inventors can optimize patent value, foster innovation, and navigate the evolving intellectual property landscape effectively.

In the rapidly evolving landscape of global technology, the designation of Standard-Essential Patents (SEPs) plays a crucial role in shaping societal and technological strategies. This study explores the intricate relationship between technological fusion and its impact on the likelihood of a patent being recognized as an SEP. It further examines the moderating influence of international collaboration, as seen through the involvement of diverse international assignees and inventors. Utilizing a comprehensive dataset of declared SEPs from 1977 to 2014, compared against a control group of non-SEPs, the study employs probit regression analysis to test its hypotheses. The findings reveal a decreased probability of SEP recognition correlating with increased technological fusion. Additionally, this negative correlation is accentuated by factors associated with transnational knowledge collaboration, such as referencing international assignees and inventors. While rooted in the domain of patent analysis, the study's insights have significant implications for SEP developers, policymakers, and stakeholders at the nexus of technology and international collaboration, offering a novel perspective on the complex interplay between technological innovation, global knowledge sharing, and SEP recognition.