A Supervised Learning-Based Approach to Anticipating Potential Technology Convergence

Choi, Sungchul; Afifuddin, Mokhammad; Seo, Wonchul

doi:10.1109/access.2022.3151870

Cited by 7 publications

(4 citation statements)

References 78 publications

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“…Another stream of research applies ensemble approaches to link prediction, which employs supervised classification models to learn combinations of several individual link predictors [7,15,16]. A novel research paradigm is to develop machine learning algorithms that predict missing links based on a combination of multi-modal information, such as topology, bibliometric, and semantic information [17][18][19].…”

Section: Technology Convergence and Its Anticipationmentioning

confidence: 99%

“…Two main objectives of the literature for anticipation of technology convergence are developing new methods and their practical applications [8]. From the perspective of network science, these anticipation approaches could be categorized into three categories: (1) topology-based [9,[11][12][13][14], (2) ensemble-learningbased [7,15,16], and (3) multi-modal-based [17][18][19]. These methods are successfully applied in a wide range of industries, such as power systems [15], smartphones [11], electric vehicles [13], manufacturing [14], and telecommunications [17].…”

Section: Introductionmentioning

confidence: 99%

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Self-Similar Growth and Synergistic Link Prediction in Technology-Convergence Networks: The Case of Intelligent Transportation Systems

et al. 2023

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Self-similar growth and fractality are important properties found in many real-world networks, which could guide the modeling of network evolution and the anticipation of new links. However, in technology-convergence networks, such characteristics have not yet received much attention. This study provides empirical evidence for self-similar growth and fractality of the technology-convergence network in the field of intelligent transportation systems. This study further investigates the implications of such fractal properties for link prediction via partial information decomposition. It is discovered that two different scales of the network (i.e., the micro-scale structure measured by local similarity indices and the scaled-down structure measured by community-based indices) have significant synergistic effects on link prediction. Finally, we design a synergistic link prediction (SLP) approach which enhances local similarity indices by considering the probability of link existence conditional on the joint distribution of two scales. Experimental results show that SLP outperforms the benchmark local similarity indices in most cases, which could further validate the existence and usefulness of the synergistic effect between two scales on link prediction.

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Section: Technology Convergence and Its Anticipationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Self-Similar Growth and Synergistic Link Prediction in Technology-Convergence Networks: The Case of Intelligent Transportation Systems

et al. 2023

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“…Previous studies have examined various aspects of knowledge flow in the context of research and development, including technology knowledge flow across different organizations, innovation parameters across organizations, highly central organizations in the innovation landscape, and more. [13][14][15][16][17][18]. by analyzing the patent data and patent citations.…”

Section: Related Workmentioning

confidence: 99%

Identifying Firm Significance and Positions in the Patent Innovation Based on Centrality Measures’ Clustering Approach

Bhatt

2023

IEEE Access

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In the current era of inevitable technological progress, organizations strive to achieve technological competence. One way to measure the adaptability of firms to huge technological shifts is through various parameters, including patenting activities. This study presents a method for identifying the significance of firms in an innovation network using patent citation analysis and centrality measures. Specifically, the study employs k-means clustering to classify firms into similar clusters based on Networkbased centrality measures such as betweenness, closeness, and eigenvector centrality. The study then develops a cluster relational network by establishing a cluster adjacency network and identifying firm positions within and between clusters. By examining the relation between clusters, the cluster network identifies the significance of firms. The study identifies four positions, namely, leader, follower, knowledge inertia, and significantly emerging, that align with the status of firms in patenting innovation capability. The method is implemented using blockchain technology as a case study. The novelty of the study lies in the structured approach to identifying firm significance by adding another layer of adjacency network to existing patent citation analysis techniques.

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“…Knowledge, serving as a crucial foundational resource for sustainable innovation, facilitates the foundational elements of technology convergence through the reconstruction and reengineering of knowledge across disparate domains [21]. Concurrently, the interactions derived between core knowledge and ancillary knowledge offer a network topology perspective, enabling the characterization of technology convergence amid intricate network relationships [22,23]. Scholars have begun to focus on technology convergence characteristics and knowledge flow, integration, and creation to analyze the impact of knowledge flow in heterogeneous technology domains on technology convergence opportunities, dual innovation capabilities, and enterprise performance through different intra-organizational and extra-organizational knowledge integration strategies [16,24].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Technology Convergence on the Sustainable Innovation of China’s Modern Manufacturing Enterprises: The Mediating Role of the Knowledge Base

Li,

Gong,

Luo

et al. 2024

Sustainability

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Against the backdrop of swiftly changing industrial environments, this study aims to investigate the influence of technology convergence on the sustainable innovation of manufacturing enterprises. The purpose of this research is to determine the future competitiveness and expansion potential of industries by evaluating the impact of technological convergence on innovation performance, which serves as a significant metric for assessing the sustainability of corporate innovation practices. Specifically, the relationships among three characteristics of technological convergence and enterprise innovation performance—betweenness, closeness, and clustering—are analyzed. Using the financial, property, and patent data of listed companies in China’s automotive manufacturing industry, an empirical study is conducted using a negative binomial regression model. Enhancing all three technology convergence characteristics is found to be conducive to enhancing sustainable innovation. Meanwhile, the corporate knowledge base plays a mediating role in which the effect of knowledge base width on clustering technology integration is more strongly mediated by the effect of knowledge base depth on approaching technology integration. The results of this study are useful for policymakers, corporate strategists, and innovation managers who are looking to enhance sustainable innovation practices within their organizations. By understanding the critical roles of betweenness, closeness, and clustering in technological convergence, stakeholders can better position their firms to leverage these attributes for improved innovation performance and competitive advantage.

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A Supervised Learning-Based Approach to Anticipating Potential Technology Convergence

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 7 publications

References 78 publications

Self-Similar Growth and Synergistic Link Prediction in Technology-Convergence Networks: The Case of Intelligent Transportation Systems

Self-Similar Growth and Synergistic Link Prediction in Technology-Convergence Networks: The Case of Intelligent Transportation Systems

Identifying Firm Significance and Positions in the Patent Innovation Based on Centrality Measures’ Clustering Approach

The Impact of Technology Convergence on the Sustainable Innovation of China’s Modern Manufacturing Enterprises: The Mediating Role of the Knowledge Base

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