Smart healthcare is a rapidly growing field with significant market potential. Although previous studies have explored diverse methods for identifying innovation opportunities in smart healthcare, most offer fragmented approaches as they fail to incorporate both biological and technological perspectives. In this study, we proposed a novel framework to identify repurposed smart healthcare innovation opportunities for cross-disease applications, along with country-level policy considerations to facilitate their effective translation. Our framework employs temporal dynamic link prediction and community detection in a multiplex disease network. This network was constructed based on genomic, phenotypic, and technological similarities between diseases to consider both biological and technological aspects while identifying repurposed innovation opportunities in smart healthcare. Various combinations of network embedding models, windowing strategies, and community detection algorithms were compared to identify the optimal combination, which was subsequently employed to predict potential repurposed innovation opportunities for cross-disease applications. Finally, country-level bibliometric analysis was conducted to derive policy directions that can facilitate the sustained translation of these predicted innovation opportunities. This study is expected to contribute to public health by facilitating the effective identification and translation of feasible and actionable innovations in smart healthcare, thereby enhancing the efficiency of technological innovation and multi-domain technology mining strategies.

This study examines the regional patterns of technological convergence, which are significantly associated with the spatial spillover of innovation. The regional patterns of technological convergence are embedded in both association rule mining and Doc2Vec based on all triadic patents. Spatial spillovers are measured using the leverage centrality of the patent citation network among regions. Subsequently, a linear regression analysis is performed to examine spatial spillover effects against embedded convergence patterns. Findings suggest that specific technological convergences in the medical or chemical sectors are significantly associated with spatial spillovers. These findings could contribute to fostering technological convergence for the regional innovation system.

Following the COVID-19 pandemic, the cosmetics industry has faced increasing challenges in adapting to a new wave of technological convergence that simultaneously addresses both distinct regional demands and global trends. However, this area has received limited research attention. In this study, we propose a novel multiplex network-based framework that simultaneously incorporates both inter- and intra-regional characteristics to prioritize firm-level technology development strategies in the cosmetics industry. We construct a three-layered multiplex network using the co-occurrence patterns of International Patent Classification (IPC) codes from post-pandemic cosmetics patents filed in the United States, Europe, and Japan. After comparing various monoplex- and multiplex-based network embedding models, we identify communities with newly predicted combinations of IPC codes to highlight emerging technology convergence opportunities for each region characterized by high convergence scores. Furthermore, we integrate association rule analysis with exploitation and exploration concepts to generate feasible and implementable recommendations for firm-level technology development strategies. An illustrative example is provided for the French cosmetics company L’Oréal. This study offers practical and effective firm-level strategies to support adaptive and implementable post-pandemic technological innovation in the cosmetics industry. The framework’s generalizability across diverse firm scales, industries, and regions highlights its practicality for post-pandemic technological innovation and strategy development.

Although AI has been widely adopted by researchers in non-AI disciplines, the path to fully realizing its benefits through adoption remains unclear. A comprehensive understanding of AI adoption patterns can reveal who is able to leverage this emerging technology and in what ways, providing insights into the future direction of AI applications and research collaboration. This study leverages the Microsoft Academic Graph, a massive bibliographic dataset with detailed subfield information, to investigate AI adoption patterns among researchers in various disciplines (18 non-AI disciplines ranging from the humanities and social sciences to STEM), career stages (early, mid, and senior), and the interactions between these two aspects from 2006 onwards. Our findings indicate that researchers in economics and business can play an important bridging role in AI-related collaborations between STEM and social science researchers, who currently exhibit substantial disparities in AI adoption patterns. Late early-career to early mid-career researchers tend to adopt AI more actively than others, although this pattern varies across disciplines. In some fields, such as materials science, chemistry, and physics, early-career and senior researchers share a considerable level of common understanding and interest in AI, implying the potential for fruitful cross-seniority collaboration.

The transition of oil-producing developing countries to low-carbon energy industry is an important issue in the era of climate change. However, their transition is challenging due to economic and technological barriers. This study employs data envelopment analysis and topic modeling to help establish effective low-carbon energy transition strategies to promote sustainable technological development. First, we compare the technological innovation efficiency for transition in 22 oil-producing developing countries using data envelopment analysis drawing on patent data from 2015 to 2019. The countries are clustered based on the DEA peer weights. Then, technical areas to be benchmarked by the followers per group are identified using topic modeling, and technological strategies are suggested to improve the efficiency of transition. Our research suggests that patent trends in frontier countries exhibit a strong relationship with relevant policies and standards, leading to the introduction of technologies that support these standards. The findings of this study can contribute to establishing national low-carbon energy policies in oil-producing developing countries.

Despite huge amount of R&D cost, the efficiency of drug development has not significantly improved, which highlights the importance of effective R&D collaboration. Current methods for selecting collaboration partners tend to focus on knowledge characteristics such as technology similarity, complementarity, and spillover risk, yet they often overlook the integration of critical protein interaction data. Our study proposes a framework for selecting collaborative organizations suitable for drug development by integrating protein interaction information, along with knowledge similarity, complementarity, spillover risk, and expertise. First, A heterogeneous network of diseases, proteins, and drugs is constructed, and a knowledge graph embedding model is employed to predict protein interaction probabilities. Among several models tested (DistMult, COMPLEX, HolE, TransR, and R-GCN), the model with the best performance is applied to predict protein interaction probabilities. These probabilities, combined with patent data, are then applied in a collaborative filtering analysis to identify proteins that can reinforce or expand the focal organization's knowledge. Appropriate collaboration partners are recommended by evaluating the knowledge similarity, complementarity, spillover risks, and expertise of the candidates. An empirical analysis in the lung cancer research field demonstrates the framework's effectiveness. By integrating implicit protein interactions into the collaboration selection process, this framework offers a more informed approach to enhancing collaboration outcomes and increasing the potential for successful drug development. We believe that the proposed framework can increase the possibility of drug development.

As car original equipment manufacturers (OEMs) announce the electrification of their product line-ups, assessing the technological adaptability of their suppliers in the electric vehicle (EV) era is necessary. The technological adaptability of a supplier can vary significantly in terms of accumulated technologies. Herein, we propose a framework to assess adaptability from two aspects—EV-related and pivoting—based on patent portfolio analysis. First, adaptability to EV-related technologies is evaluated in terms of a supplier's patents applied individually and copatents with OEMs for parts to be modified. The degree of similarity of the patents related to internal combustion engine vehicle (ICEV) components with the patents related to EVs is calculated using PatentSBERTa, which reflects the modification degree required for ICEV components to be suitable for EVs. Second, we utilize automotive industry-related and unrelated diversity based on the entropy of International Patent Classification (IPC) codes to measure the supplier's technological adaptability to pivot within the automotive industry or other industries. The framework is empirically applied to suppliers in the drivetrain and climate control sectors to assess their technological adaptability to supply EV parts associated with the parts they are delivering. Our framework is expected to provide transition strategies for automotive suppliers in the EV era.

Globally increasing concerns on climate change have garnered considerable attention toward green investments. Although the effects of green investments have been investigated, their productivity remains largely unexplored. This study proposes an initial discussion of green efficiency based on the cyclical relationship among green investments, technology, and national-level performance. This study applies a three-stage data envelopment analysis to compare green efficiency of 19 countries based on carbon dioxide emissions, green patents, and green-bond issuance data of 12 years starting from 2010 when national green-bond issuance was initiated. We use a logistic regression to derive policy implications by analyzing major factors affecting green efficiency. The results reveal that no country is efficient across all stages. Overall, the finding reveals which stage each country should focus on to improve green efficiency; suggests benchmarks and green efficiency strategies for inefficient countries; and highlights the appropriate level of research and development (R&D) expenditure and adoption support for solar/wind energy as part of the technology support policy. As the national green efficiency level is derived in stages, our approach contributes to forming green efficiency strategies at the national level in three stages. Moreover, this study presents the policy implications related to R&D expenditures and technology support to address the limited understanding of green efficiency management.

The particulate matter (PM)2.5 forecasting has been being advanced with the development of deep learning methods. However, most of them do not consider the active population exposed to air pollution. We propose to apply a population-based centrality weight to the cost function of the forecasting model, reflecting both of residential and changes in active populations. This weight gives higher penalties for prediction errors in more and densely populated areas in terms of residential populations. Also, higher penalties are applied to areas with more active population. The proposed weight was applied to two types of deep learning models, the long-and-short term temporal neural network (LSTNet) and temporal-graph convolutional network (T-GCN) to forecast the PM2.5 in 25 districts of Seoul Metropolitan City in Korea for empirical experiments. The experimental results show that forecasting utilizing the population-based weight enhances not only accuracies in terms of the centrality-based evaluation metrics by around 2 – 7

To meet the continuously increasing demand for batteries in electrical devices, solutions in terms of theoretical capacity, safety, and reusability are required. Therefore, it is important to investigate the development trends of sustainable battery technologies by forecasting technological convergence concerning these three aspects. However, no lithium-based battery-related studies have analyzed the fusion of technologies regarding multiple aspects. In this article, we proposed a multiplex network to identify the sustainable convergence of lithium-based battery technologies. We utilized the patents filed at the United States Patent and Trademark Office. The proposed multiplex network consists of three layers representing co-occurrence networks of international patent classification codes for storage, safety, and recycling technologies of batteries, respectively. Specifically, we focused on the technological convergence of the lithium-ion battery, the most influential energy storage solution for electronic products, and the lithium-based solid-state battery, which can be applied to recently emerging robust safety and energy-intensive products. The result of the article suggests future battery technology areas that consider three aspects. This article is expected to contribute to presenting guidelines for sustainable technology development to stakeholders in the complexly characterized battery industry.

Most countries provide veterans with various benefits to reward their sacrifice. Unfortunately, many veterans have failed to prove their status due to loss of military records. Thus, some governments allow the verification of those veterans through "buddy statements" obtained from the people who can vouch for the buddy's participation in the war. However, it is still challenging for veterans to find guarantors directly. With this background, we suggest to utilizing historical war records of combined operations to increase the pool of potential guarantors for the buddy statements. However, a combined operation network among troops can have missing edges and perturbations on attributes of the troop due to inaccurate information. In this study, we learn from some recorded interactions which might be incomplete and noisy, and predict missing linkages among the troops that might have interacted together in the war, by proposing Robust-SEAL (learning from Subgraphs, Embeddings, and Attributes for Link prediction). It combines two Graph Neural Network (GNN) architectures: robust Graph Convolutional Network which considers the uncertainty of node attributes with a probabilistic approach, and SEAL which improves the expressive power of the GNN with a labeling trick. Our proposed approach was applied to Korean War data with perturbations. For experimentations, we hid some actual interactions and found that Robust-SEAL restores missing interactions better than other GNN-based baselines.

In our modern, technologically advanced society, 84% of the world's population holds some form of religious faith. Our history has also witnessed religion and technology having an impact on each other. Assuming the underlying correlation between them, we question whether there are distinguished technological features among countries with strong religions. This study examines the technological trends in countries with a majority religion according to the dominant religion of each - namely, Judaism, Islam, and Hinduism. We applied structural topic modelling (STM) and co-patent network analysis to patents from those countries registered with the United States Patent and Trademark Office between 1965 and 2015. According to the STM results, technologies appear to reflect geographical environments, industrial concentrations, and investments led by governments and private sectors rather than religion. Moreover, countries with a majority religion in our study had mainly collaborated with the United States and European countries, regardless of religious tendency.

Technology convergence, as a key driving force of innovation, has brought a burgeoning of research attention. Although numerous studies on technology convergence have been carried out, there were limitations in consideration of a firm's capability in technology convergence. This article proposes a framework for "Convergence Technology Opportunity Discovery" (CTOD) based on firms' technical convergence competence manifested in their patent portfolios, market competition, and technological growth potential. The present research, by employing a stacked denoising autoencoder, a deep neural network-based collaborative filtering method, provides reliable latent preference toward convergence technology for individual firms. Our CTOD framework is applied to three information technology and biotechnology firms to elaborately demonstrate its validity. Ultimately, the proposed framework is expected to provide practical assistance to organizations seeking technology convergence opportunities in various fields.

Majority customers of cosmetics are female. Would this imply a high proportion of inventors of cosmetics technology is female? Would the inventor’s gender be related to the characteristics and quality of corresponding patent? This study tries to identify manifestation of gender equity in cosmetics technology in terms of patent application and grant, technical characteristics, and its performance. We apply topic modeling, zero-inflated Poisson regression, and survival analysis to patents related to cosmetics that were applied to the United States Patent and Trademark Office from 1970 to 2016. The results show that women’s participation in cosmetic inventions is becoming active and has experienced many changes in technical characteristics, but in terms of performance, it is still sluggish. This study is expected to contribute to deepening our understanding about gender issues in technology development.

This study proposes a principal alpha-style factor integrated risk parity strategy that can diversify style risk factors and the stock selection risk of external managers in Fund-of-Funds (FoFs) portfolios. First, we separated the style risk factors and stock-specific sources held by each individual fund. Stock-specific sources, referred to as principal alpha portfolios, are extracted through principal component analysis, where the sources are utilized for risk parity in the alpha division. As the parity portfolio was integrated into both the alpha and style factor divisions, we used a Basin-Hopping two-phase optimization technique, which can mitigate the local optimal trap by exploring the surroundings of the sequential quadratic programming solution secondarily. Through this, a more stable integrated risk parity portfolio can be realized. Finally, the suggested integrated risk parity portfolios were simulated with a global fund dataset. The simulation results from 2006 through June 2022 show a more stable risk-return profile than an independently constructed strategy using style risk factors or principal alpha sources, especially in high volatility and down-market periods, such as a global financial crisis or unexpected events like COVID-19. This study can be applied to various areas covering other FoFs and asset allocation strategies by integrating alpha and factor divisions.

The assistive technology industry has been growing rapidly due to increasing innovation and rising demand to satisfy the needs of the disabled population. Assistive technology accepts knowledge of mainstream technology and is expected to contribute to the latter's redevelopment. However, a paucity of research supporting this potential exists. In this article, we monitor the directional knowledge flows between the assistive and mainstream technologies. We identify dynamic brokerage patterns over seven segmented periods from 1981 to 2015 via a citation network analysis of the nonrestorative visual assistive patents applied to the United States Patent and Trademark Office. We find intermediary roles played by assistive technology and major mainstream technology fields that are affected by assistive technology. The results show that assistive technology for the visually impaired plays an active role as a technology transfer consultant between the mainstream technologies. Assistive technologies affect communication, mobility, and environmental technology subfields. The findings suggest that assistive technology is impactful beyond being a mere knowledge receiver and it provides information as a knowledge broker for firms to partner with assistive technology manufacturers or enter such markets.

This study investigates the mechanism by which knowledge spilled over from a firm’s research publication consequently spills into the focal firm as a form of proprietary knowledge when it is engaged in an emerging science-related technology. We define the knowledge spillover pool (KSP) as an evolving group of papers citing a paper published by a firm. Focusing on the recent development of artificial intelligence, on which firms have published actively, we compare the KSP conditions related to the increase in patents created by the focal firm with those created by external actors. Using a Cox regression and subsequent contrast test, we find that both an increasing KSP and an increasing similarity between the idea published by the focal firm and KSP are positively related to the proprietary knowledge creation of both the focal firm and external actors, with such relations being significantly stronger for the focal firm than for external actors. On the contrary, an increasing proportion of industry papers in the KSP are positively associated with the proprietary knowledge creation not only by the focal firm but also by external actors to a similar degree. We contribute to the literature on selective revealing and to the firms’ publishing strategies.

Processing-in-Memory (PiM), which combines a memory device with a Processing Unit (PU) into an integrated chip, has drawn special attention in the field of Artificial Intelligence semiconductors. Currently, in the development and commercialization of PiM’s technology, there are challenges in the hegemony competition between the PU and memory device industries. In addition, there are challenges in finding strategic partnerships rather than independent development due to the complexity of technological development caused by heterogeneous chips. In this study, patent Main Path Analysis (MPA) is used to identify the majority and complementary groups between PU and memory devices for PiM. Subsequently, Document-to-Vector (Doc2Vec) and similarity-scoring analyses are used to determine the potential partners for technical cooperation required for PiM technology development for the majority group identified. According to the empirical results, PiM core technology is evolving from PU to memory device with an ‘architecture-operation-architecture’ design pattern. The ten ASIC candidates are identified for strategic partnerships with memory device suppliers. Those partnership candidates include several mobile AP firms, implying PiM’s opportunities in the field of mobile applications. It suggests that memory device suppliers should prepare for different technology strategies for PiM technology development. This study contributes to the literature and high-tech industry via the proposed quantitative technology partnership model.

One of the recent developments in safety systems is an external airbag installed on the front bumper of a vehicle and autonomous emergency braking system. In this paper, we propose a framework for a cost-benefit analysis of the external airbag and autonomous emergency braking system in order to validate its commercialization. Road traffic crash data obtained from the National Automotive Sampling System/Crashworthiness Data System (NASS/CDS) was used, and three different crash types related to frontal damage in vehicles were extracted to estimate the safety performance of an external airbag. An ordinal logistic regression model was applied to estimate the safety performance in terms of the reduced maximum abbreviated injury scale (MAIS) based on a reduction in the total delta-v following the installation of an external airbag. Given the estimated safety performance of the external airbag, a cost-benefit analysis is conducted. According to the results, the external airbag system saves 46% of occupants with MAIS 3+ injuries and prevents 40% of fatalities. Moreover, the benefit/cost ratios of the external airbag system range from 0.496 to 0.509 depending on the scenario. Lastly, sensitivity analyses were performed with important parameters, including the initial and maximum market penetration ratio and the price of the system. This study aims to evaluate the technology of safety devices by analyzing the effectiveness of a new safety device using real-world vehicle accident data. We also statistically estimated its effectiveness and analyzed its societal value. We expect that our comprehensive findings will be helpful in evaluating the effectiveness of safety devices.