Evidence for a creative dilemma posed by repeated collaborations

We focused on how repeat collaborations in projects for inventions affect performance. Repeat collaborations have two contradictory aspects. A positive aspect is team development or experience, and a negative aspect is team degeneration or decline. Since both contradicting phenomena are observed, inventors have a dilemma as to whether they should keep collaborating in a team or not. The dilemma has not previously been quantitatively analyzed. We provide quantitative and extensive analyses of the dilemma in creative projects by using patent data from Japan and the United States. We confirm three predictions to quantitatively validate the existence of the dilemma. The first prediction is that the greater the patent a team achieves, the longer the team will work together. The second prediction is that the impact of consecutive patents decreases after a team makes a remarkable invention, which is measured by the impact of patents. The third prediction is that the expectation of impact with new teams is greater than that with the same teams successful in the past. We find these predictions are validated in patents published in Japan and the United States. On the basis of these three predictions, we can quantitatively validate the dilemma in creative projects. We also propose preventive strategies for degeneration. One is developing technological diversity, and another is developing inventor diversity in teams.We find the two strategies are both effective by validating with the data

A two-layer team-assembly model for invention networks

Companies are exposed to rigid competition, so they seek how best to improve the capabilities of their innovations. One strategy is to collaborate with other companies in order to speed up their own innovations. Such inter-company collaborations are conducted by inventors belonging to the companies. At the same time, the inventors also seem to be affected by past collaborations between companies. Therefore, interdependency of two networks, namely inventor and company networks, exists. This paper discusses a model that replicates two-layer networks extracted from patent data of Japan and the United States in terms of degree distributions. The model replicates two-layer networks with the interdependency. Moreover it is the only model that uses local information, while other models have to use overall information, which is unrealistic. In addition, the proposed model replicates empirical data better than other models

The Coexistence of Infection Spread Patterns in the Global Dynamics of COVID-19 Dissemination

The novel coronavirus SARS-CoV-2, commonly referred to as COVID-19, triggered the global pandemic. Although the nature of the international spread of infection is an important issue, extracting diffusion networks from observations is challenging because of its inherent complexity. In this paper, we investigate the process of infection worldwide, including time delays, based on global infection case data collected from January 3, 2020 to December 31, 2022. We approach the data with a complex Hilbert principal component analysis, which can consider not only the concurrent relationships between elements, but also the leading and lagging relationships. Then, we examine the interactions among countries by considering six factors: geography, population, GDP, stringency of countermeasures, vaccination rates, and government type. The results show two primary trends occurring in 2020 and in 2021-2022 and they interchange with each other. Specifically, European, highly populated, and democratic countries, i.e., countries with high mobility rates, show leading trends in 2020. In contrast, African and nondemocratic countries show leading trends in 2021-2022, followed by countries with high vaccination rates and advanced countermeasures. The results reveal that, although factors that increase infection risk lead to certain trends at the beginning of the pandemic, these trends dynamically changes over time due to socioeconomic factors, especially the introduction of countermeasures. The findings suggest that international efforts to promote countermeasures in developing countries can contribute to pandemic containment

The strength of long ties and the weakness of strong ties: Knowledge diffusion through supply chain networks

AbstractUsing a large firm-level panel dataset for Japan, this paper examines the effects of the structure of supply chain networks on productivity and innovation capability through knowledge diffusion. We find that ties with distant suppliers improve productivity (as measured by sales per worker) more than ties with neighboring suppliers, which is likely because distant firms’ intermediates embody more diversified knowledge than those from neighboring firms. Ties with neighboring clients improve productivity more than ties with distant clients, which is likely because neighboring clients more effectively diffuse disembodied knowledge than distant clients. By contrast, ties with distant suppliers and clients improve innovative capability (as measured by the number of registered patents), whereas ties with neighboring suppliers or clients do not affect innovative capability. In addition, the density of a firm's ego network (as measured by how densely its supply chain partners transact with one another) has a negative effect on productivity and innovative capability, implying knowledge redundancy in dense networks. These results suggest that access to diversified ties is important for improving productivity and innovation capability through knowledge diffusion