Search results

This study aims to analyse the relationship between a patent’s network position in a knowledge search network and the likelihood and speed of patent transactions. Additionally, it explores whether patent scope moderates these relationships.

In this empirical study, the authors collected a sample of patents in the artificial intelligence industry over the period of 1985–2018. Then, the authors examined the direct roles of degree centrality, betweenness centrality and closeness centrality on the likelihood and speed of patent transactions and the moderating role of patent scope in the knowledge search network using the logit and accelerated failure time models.

The findings reveal that degree centrality positively affects both the likelihood and speed of patent transactions, while betweenness centrality enhances the likelihood, and closeness centrality significantly boosts both. However, regarding the speed of patent transactions, closeness centrality is the most impactful, followed by degree centrality, with no significant influence of betweenness centrality. Additionally, the patent scope moderates how betweenness centrality affects the likelihood of transactions.

This study has limitations owing to its exclusive use of data from the Chinese Intellectual Property Office, lack of visibility of the confidential terms of most patent transactions, omission of transaction directionality and focus on a single industry, potentially restricting the breadth and applicability of the findings. In the future, expanding the data set and industries and combining qualitative research methods may be considered to further explore the content of this study.

This study has practical implications for developing a better understanding of how network structure in the knowledge search network affects the likelihood and speed of patent transactions as well as the identification of high-value patents. These findings suggest future directions for patent holders and policymakers to manage and optimise patent portfolios.

This study expands the application boundaries of social network theory and the knowledge-based view by conducting an in-depth analysis of how the position characteristics of patents within the knowledge search network influence their potential and speed of transactions in the technology market. Moreover, it provides a theoretical reference for evaluating patent value and identifying high-quality patents by quantifying network positions. Furthermore, the authors construct three centrality measures and explore the development of patent transactions, particularly within the context of the developing country.

The purpose of this paper is to investigate the friction and wear mechanisms in lubricated sliding conditions of additively manufactured SS316L parts. The different viscous oils 5W30, 15W40, 20W50 and SAE140 are used. These investigations provide a theoretical basis for the high performance of printed and postheattreated SS316L.

Tribological tests were carried out on selective laser melting-made SS316L printed specimens and heat-treated specimens. The parameters in 15 min of test duration are 20 N of load, 200 rpm, 8 mm of pin diameter, 25 mm length, 80 mm of track diameter and EN31 counter disc body. This work presented the phenomena of lubrication regimes and their characterization, as identified by the Stribeck curve, and these regimes affect the tribological properties of additively manufactured SS316L under the influence of industrial viscous lubricants. The results are observed using Scanning electron microscope (SEM), X-ray diffraction (XRD) and wear tests.

The observations indicate that additively manufactured SS316L shows a reduced coefficient of friction (COF) and specific wear rate (SWR). This is credited to the utilization of different viscous lubricants.

This exclusive research demonstrates how various viscous lubricants affect the COF and SWR of printed and post-heat-treated SS316L parts. Lambda (λ), lubricant film thickness (h₀), surface roughness and wear mechanisms are studied and reported.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0110/

This paper represents the first attempt to examine investor behavior for green stocks through the lens of return co-movement, and provides evidence indicating that green investment practices have gained traction after 2012.

We empirically test the hypotheses that the stock returns of firms with similar carbon dioxide emissions levels move together and, if so, whether this co-movement has increased over time as people become more “carbon-conscious.” Our baseline sample, based on carbon emissions data from public company disclosures, suffers from limited coverage, particularly before 2016, leading to low statistical power and sample selection bias. To address this, we employ machine learning methodologies to forecast the carbon emissions of firms that do not disclose such information, nearly quadrupling the sample size. Our findings indicate that stocks with similar carbon emissions exhibit higher co-movement in stock returns in both the baseline and augmented data samples. Furthermore, this co-movement has increased during the 2012–2020 period compared to the 2004–2011 period, suggesting that green investment has gained traction over time.

We find that stocks with similar carbon emissions exhibit higher co-movement in stock returns in both the baseline sample and the augmented data sample, and the co-movement has increased in the 2012–2020 period compared to the 2004–2011 years, suggesting that green investment has gained traction over time.

(1) We use machine learning methodology to augment carbon emissions sample which goes back to 2004. Our approach almost quadruples the original data, enabling large-sample testing. (2) We are the first paper to examine how green companies' stock returns co-move and thus provide complementary results on the research on expected returns and carbon emissions.

Despite significant research attention to top management team diversity, the potential influence of other employees’ diversity on venture growth at different maturity stages such as middle management teams and nonmanagerial employees at the base of the organizational hierarchy remains largely overlooked. This study explores this relationship in new technology venture development, with a focus on the influence of functional skills diversity across different organizational levels and maturity stages.

This study uses a linked employer–employee dataset covering new technology ventures in the digital industry that spans from 2010 to 2020. The sample includes 296 VC-backed new ventures located in the greater Paris (France) area. Through a problem-solving lens, the study examines functional skills at various organizational levels, including 5,243 top managers, 10,274 middle managers and 29,306 nonmanagerial employees.

Results indicate that placing exclusive emphasis on top-level managers could lead to incorrect assignment of diversity effects as such effects are likely shared with lower organizational levels. In addition, the findings demonstrate that the diversity–venture growth relationship varies across new ventures’ funding stages, with a notably stronger correlation in the late stages of financing.

This study demonstrates that promoting functional diversity across all organizational levels, not just top management, may boost firms’ growth. Furthermore, the effectiveness of functional diversity varies depending on the firm’s financing stage.

This study constructs a multilayered organizational framework to explore the relationship between diversity and new technology venture growth, using a fully reproducible skills diversity classification methodology that is applicable for future investigations.

This study aims to explore the association among financial resources, renewable energy, environmental degradation and technological innovation in BRICS economies.

To estimate the long-run impacts between these variables, the AMG method of estimation, which incorporates cross-sectional reliance and slope homogeneity, is adopted in this research.

According to the empirical findings, the long-run coefficients of environmental degradation and technological innovation show a statistically significant and negative impact on renewable sources of energy. Furthermore, a 1% increase in environmental degradation reduces 0.32% of renewable sources of energy in BRICS economies. Whereas only the coefficient of GDP shows a positive and statistically significant impact on renewable sources of energy, which demonstrates that a 1% increase in economic growth causes a 0.02% incline in renewable sources of energy. Therefore, strong policy recommendations are provided to encourage green energy utilization in these economies.

The majority of the participating nations have inexpensive labor and an abundance of resources from nature, which strengthens their appeal. Given that population growth is still quite conservative, this presents a chance for GDP per capita to expand significantly.