Search results

Innovation quality is a critical component of enterprise innovation. Prior research primarily focuses on company-level and external policy-level factors that affect innovation quality, while ignoring social-level factors. Based on institutional isomorphism theory, this study examines how the innovation quality of three-dimensional institutional equivalence, which is an important and unique reference group for firms to follow the “law of imitation of close preference”, affects the likelihood of firms' innovation quality.

This study conducts firm random effects and industry/year fixed effects models using China's listed companies from 2002 to 2021.

This study finds that compared with the innovation quality of its other industry, community, or network peers, the innovation quality of three-dimensional institutional equivalence has a greater impact on firm innovation quality. Furthermore, technological intensity significantly increases the effect of three-dimensional institutional equivalence on focal company innovation quality, while financing constraints significantly attenuate this effect. Additionally, when there is no institutional equivalent, the innovation quality of network, industry, and community peers has significant positive effects on enterprise innovation quality. Heterogeneity analysis also indicates that, under the conditions of non-state-owned enterprises, a low regional legal environment, or low regional factor market development, three-dimensional institutional equivalence contributes significantly to firm innovation quality.

This study focuses on the effect of three-dimensional institutional equivalence on Chinese enterprises' innovation quality. Nonetheless, research samples from other countries are not considered in this study.

This study explores the impact of three-dimensional institutional equivalence on firm innovation quality within a systematic theoretical framework and incorporates firm attributes into this framework.

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.

This study aims to deliver a new perspective on how the interaction of independent and cooperative innovation affects firm digitization. Based on resource constraint theory, this study argues that the aforementioned interaction negatively affects firm digitization. The moderating role of managerial discretion is also discussed in light of the principles of the awareness-motivation-capability (AMC) framework.

The proposed hypotheses are empirically tested using a negative binomial modeling approach. The data used are from A-share listed companies in China’s Shanghai and Shenzhen stock markets from 2006 to 2020.

This study suggests that the interaction of independent innovation and cooperative innovation negatively impacts digitization. In addition, this study argues that environmental discretion and organizational discretion weaken the negative impact of the mentioned interaction on digitization. However, additional discretion in the Chinese context has no effect on above relationships.

This study explores the impact of the interaction of independent and cooperative innovation on digitization and incorporates managerial discretion into this framework based on the AMC framework.

This study aims to examine how the knowledge breadth and depth of a patent affect its likelihood of being pledged. It also seeks to explore whether these relationships change diversely in different technological environments.

A complementary log-log model with random effects was conducted to test the hypotheses using a unique data set consisting of 348,927 invention patents granted by the China National Intellectual Property Administration from 1985 to 2015 belonging to 74,996 firms.

The findings reveal that both knowledge breadth and depth of a patent positively affect its likelihood of being pledged. Furthermore, the knowledge breadth and depth entail different degrees of superiority in different technological environments.

This study focuses on the effect of an individual patent’s knowledge base on its likelihood of being selected as collateral. It does not consider the influence of the overall knowledge characteristics of the selected patent portfolio.

Managers need to pay attention to patents’ knowledge characteristics and the changes in technological environments to select the most suitable patents as collateral and thus improve the success rate of pledge financing.

This study explores the impact of multidimensional characteristics of knowledge base on patent pledge financing within a systematic theoretical framework and incorporates technological environments into this framework.

Collective reputation cognition is an enterprise's perception of the general rules of reputation evaluation, jointly formed by a network's collective members. It affects the choice of enterprises' innovation behavior and guides enterprises to occupy a dominant position in the innovation network, thus achieving high innovation performance. In this process, it is inseparable from the enterprise's good network competence. This study attempts to bring collective reputation cognition, network competence and innovation performance into the same framework and aims to explore the relationship among them and determine the influential roles of collective reputation perception and network capability on innovation performance.

This study uses 227 Chinese enterprises in the innovation network as samples and applies partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to explore the questions mentioned above.

The results verify the relationship among collective reputation cognition, network competence and innovation performance. Furthermore, the results yield five paths that lead to high innovation performance, such as “putting ability first” and “both fame and competence”, which are different combinations of collective reputation cognition and network competence.

Based on institutional theory, this study considers the network context and identifies “collective reputation cognition” as a key variable. Meanwhile, it opens the “black box” of the mechanism of reputation's influence on innovation performance and finds that the combined paths of collective reputation cognition and network competence achieve high performance in terms of innovation.

This paper aims to review the existing bond-based peridynamic (PD) and state-based PD heat conduction models, and further propose a refined bond-based PD thermal conduction model by using the PD differential operator.

The general refined bond-based PD is established by replacing the local spatial derivatives in the classical heat conduction equations with their corresponding nonlocal integral forms obtained by the PD differential operator. This modeling approach is representative of the state-based PD models, whereas the resulting governing equations appear as the bond-based PD models.

The refined model can be reduced to the existing bond-based PD heat conduction models by specifying particular influence functions. Also, the refined model does not require any calibration procedure unlike the bond-based PD. A systematic explicit dynamic solver is introduced to validate 1 D, 2 D and 3 D heat conduction in domains with and without a crack subjected to a combination of Dirichlet, Neumann and convection boundary conditions. All of the PD predictions are in excellent agreement with the classical solutions and demonstrate the nonlocal feature and advantage of PD in dealing with heat conduction in discontinuous domains.

The existing PD heat conduction models are reviewed. A refined bond-based PD thermal conduction model by using PD differential operator is proposed and 3 D thermal conduction in intact or cracked structures is simulated.

This paper aims to investigate the effect of lubricant viscosity model with improver on friction and lubrication of piston skirt-cylinder liner conjunction.

A dynamic calculation model is established for the piston skirt-cylinder liner conjunction of a heavy-duty commercial diesel engine, to explore the effects of two kinds of lube oil viscosity models named after polyalkyle-metacrylate-1 (PAMA1) and styrene-isoprene-copolymer (SICP) improvers on the maximum oil film viscosity, the minimum oil film thickness, the peak oil film pressure, the maximum shear rate, the friction force and the total friction power loss.

The variation trends with the crank angle of the above parameters are not changed with the difference of improvers, while obvious numerical differences are found except the maximum oil film pressure. The minimum oil film thickness and maximum shear rate of PAMA1 are larger than that of SICP, the maximum oil film viscosity of SICP is larger than that of PAMA1, which indicates that the shear-thinning effect of PAMA1 is greater, the maximum friction force on the piston of SICP is larger than that of PAMA1, and the total friction power consumption is also larger, the average friction power consumptions of SICP and PAMA1 are 385.4 and 262.8 W, respectively, with the relative difference of 31.8 per cent.

The influence of different lubricating oil additive models on the lubrication and friction of piston skirt-cylinder liner conjunction is simulated and analyzed.

Women are, in increasing numbers, participating in the labour market and are an important part of an organisation’s human resource pool. Nevertheless, women still face inappropriate treatment at work. One cause of this is family‐related issues. In particular, pregnancy and child birth present special challenges for working women. Discrimination towards pregnant women is commonplace in work settings. Problems are often related to individual work relationships, for example, the one between the pregnant follower and her manager. It is important to understand problems that impact on women in working life that can disturb their job satisfaction, their performance and willingness to give their best for the organisation. Therefore, for the benefit of both employer and employee, existing practices in leader follower relationships during pregnancy are worth studying in more depth. In leadership studies, the Leader‐Member Exchange (LMX) theory is focused on dyadic leader‐follower relationships and is thus used here to understand this phenomenon. In the present article, the literature on pregnancy and work as well as on LMX is re viewed. On the basis of these reviews, a future research agenda is offered.

The purpose of this research is to investigate how generalisable the public service motivation (PSM) observed in Western society is to China and to examine the effects of public service motivation on job satisfaction.

Exploratory factor analysis and confirmatory factor analysis techniques are applied to survey data of 191 public servants in China to investigate the generalisability of Western PSM. Using hierarchical regression analysis, the paper examines the effects of the dimensions of PSM on job satisfaction.

The results show that the public service motivation observed in the West exists in China, but the generalisability of the construct is limited. Three of the four dimensions of public service motivation (attraction to public policy making, commitment to the public interest, and self‐sacrifice) exist in China, but the fourth dimension (compassion) is unconfirmed.

The paper is the first to examine the generalisability and instrumentality of PSM as observed in Western society to China. The results indicate that the public service motivation observed in the West also exists in China, but that the generalisability is limited. Public service motivation emerges from the results as a positively significant predictor of job satisfaction in the public sector of China. It enhances the applicability and meaningfulness of the concept of public service motivation across political and cultural environments.

The purpose of this study is to propose a precise and standardized strategy for numerically simulating vehicle aerodynamics.

Error sources in computational fluid dynamics were analyzed. Additionally, controllable experiential and discretization errors, which significantly influence the calculated results, are expounded upon. Considering the airflow mechanism around a vehicle, the computational efficiency and accuracy of each solution strategy were compared and analyzed through numerous computational cases. Finally, the most suitable numerical strategy, including the turbulence model, simplified vehicle model, calculation domain, boundary conditions, grids and discretization scheme, was identified. Two simplified vehicle models were introduced, and relevant wind tunnel tests were performed to validate the selected strategy.

Errors in vehicle computational aerodynamics mainly stem from the unreasonable simplification of the vehicle model, calculation domain, definite solution conditions, grid strategy and discretization schemes. Using the proposed standardized numerical strategy, the simulated steady and transient aerodynamic characteristics agreed well with the experimental results.

Building upon the modified Low-Reynolds Number k-e model and Scale Adaptive Simulation model, to the best of the authors’ knowledge, a precise and standardized numerical simulation strategy for vehicle aerodynamics is proposed for the first time, which can be integrated into vehicle research and design.