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This study aims to explore the different effects of inter-community group networks and intra-community group networks on group innovation.

The authors used a pooled panel dataset of 12,111 self-organizing innovation groups in 463 game product creative workshop communities from Steam support to test the hypothesis. The pooled ordinary least squares (OLS) model is used for analyzing the data.

The results show that network constraint is negatively associated with the innovation performance of online groups. The average path length of the inter-community group network negatively moderates the relationship between network constraint and group innovation, while the average path length of the intra-community group network positively moderates the relationship between network constraint and group innovation. In addition, both the network density of inter-community group networks and intra-community group networks can negatively moderate the negative relationship between network constraint and group innovation.

The findings of this study suggest that network structural characteristics of inter-community networks and intra-community networks have different effects on online groups’ product innovation, and therefore, group members should consider their inter- and intra-community connections when choosing other groups to form a collaborative innovation relationship.

The study utilises unique survey data gathered from 12,427 party members. The dependent variable measures party members’ in-party commitment and is based on willingness to donate money, to contribute effort, the feeling of belonging in the party network and social trust in the party network.

In this article, we study how different extra-parliamentary online and offline activities are associated with in-party commitment amongst political party members from the six largest Finnish parties. We especially delve into the differences between members of the Finnish parties.

We found that extra-parliamentary political activity, including connective action through social media networks and collective action through civic organisations, is highly associated with members’ in-party commitment. Additionally, members of the newer identity parties more effectively utilised social media networks, whilst the traditional interest parties were still more linked to traditional forms of extra-parliamentary political action.

By employing the sociological network theory perspective, the study contributes to ongoing discussions surrounding the impact of social media on political participation amongst party members, both within and beyond the confines of political parties.

This study proposes a predictive neural network model reference decoupling control method for the coupling problem between the leg joints of hydraulic quadruped robots, and verifies its decoupling effect..

The machine–hydraulic cross-linking coupling is studied as the coupling behavior of the hydraulically driven quadruped robot, and the mechanical dynamics coupling force of the robot system is controlled as the disturbance force of the hydraulic system through the Jacobian matrix transformation. According to the principle of multivariable decoupling, a prediction-based neural network model reference decoupling control method is proposed; each module of the control algorithm is designed one by one, and the stability of the system is analyzed by the Lyapunov stability theorem.

The simulation and experimental research on the robot joint decoupling control method is carried out, and the prediction-based neural network model reference decoupling control method is compared with the decoupling control method without any decoupling control method. The results show that taking the coupling effect experiment between the hip joint and knee joint as an example, after using the predictive neural network model reference decoupling control method, the phase lag of the hip joint response line was reduced from 20.3° to 14.8°, the amplitude attenuation was reduced from 1.82% to 0.21%, the maximum error of the knee joint coupling line was reduced from 0.67 mm to 0.16 mm and the coupling effect between the hip joint and knee joint was reduced from 1.9% to 0.48%, achieving good decoupling.

The prediction-based neural network model reference decoupling control method proposed in this paper can use the neural network model to predict the next output of the system according to the input and output. Finally, the weights of the neural network are corrected online according to the predicted output and the given reference output, so that the optimization index of the neural network decoupling controller is extremely small, and the purpose of decoupling control is achieved.

An emerging body of literature has pinpointed the role of supply chain structure in influencing the extent to which supply chain members disclose information about their internal practices and performance. Nevertheless, empirical research investigating the effects of firm-level relational embeddedness on network-level transparency still lags. Drawing on social network analysis, this research examines the effect of relational embeddedness on supply chain transparency and the contingent role of digitalization in the context of environmental, social and governance (ESG) information disclosure.

In their empirical analysis, the authors collected secondary data from the Bloomberg database about 2,229 firms and 14,007 ties organized in 107 extended supply chains. The authors employed supplier and customer concentration metrics to measure relational embeddedness and performed multiple econometric models to test the hypothesis.

The authors found a positive effect of supplier concentration on supply chain transparency, but the effect of customer concentration was not significant. Additionally, the digitalization of focal firms reinforced the impact of supplier concentration on supply chain transparency.

The study findings contribute by underscoring the critical effect of relational embeddedness on supply chain transparency, extending prior literature on social network analysis, providing compelling evidence for the intersection of digitalization and supply chain management, and drawing important implications for practices.

High-tech enterprises, as the national innovation powerhouses, have garnered considerable interest, particularly regarding their technological innovation capabilities. Nevertheless, prevalent research tends to spotlight the impact of individual factors on innovative behavior, with only a fraction adopting a comprehensive viewpoint, scrutinizing the causal amalgamations of precursor conditions influencing the overall innovation proficiency of high-tech enterprises.

This paper employs a hybrid approach integrating necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA) to examine the combinatorial effects of antecedent factors on high-tech enterprises' innovation output. Our analysis draws upon data from 46 listed Chinese high-tech enterprises. To promote technological innovation within high-tech enterprises, we introduce a novel perspective that emphasizes technological innovation networks, grounded in a network agents-structure-environment framework. These antecedents are government subsidy, tax benefits, customer concentration, purchase concentration rate, market-oriented index and innovation environment.

The findings delineate four configurational pathways leading to high innovative output and three pathways resulting in low production.

This study thereby enriches the body of knowledge around technological innovation and provides actionable policy recommendations.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

The purpose of this paper is to investigate how complementors’ marketing capability and technology capability affect their performance. Drawing on social capital theory, the authors examine platform network centrality as a mediator and platform reputation as a moderator of the relationships between these two capabilities and complementor performance.

This study collects data by questionnaire from 154 Chinese firms adopting e-commerce platforms. Hierarchical multiple regression is used to test the hypotheses of this study.

This study finds that complementors’ marketing capability and technology capability positively affect performance by increasing their platform network centrality. Moreover, platform reputation positively moderates the relationship between platform network centrality and complementor performance, and it strengthens the mediating role of platform network centrality.

This paper emphasizes the critical role of marketing capability and technology capability on complementor performance. It explores the improvement path of complementor performance from the perspective of network position, which is a key element for complementors to effectively leverage their capabilities to build competitive advantage.

The outbreak of COVID-19 has caused a slowdown of economic activity across the globe, which has resulted in high levels of disruption to labour markets. This study seeks to examine how the outbreak of COVID-19 has impacted the search strategies of students seeking for an internship, and whether these have changed since the start of the pandemic. The study utilises the strength of weak ties hypothesis, social capital theory and status attainment theory to explore the changes in securing a position since the outbreak of COVID-19.

This study draws on data from two cohorts of MBA students seeking to secure internships: one before the outbreak and one during. A multinomial regression is employed to examine how students have used network ties to secure internships and how this has changed since the outbreak of COVID-19.

The multinomial regression results indicate that there was little difference in the strategies employed by students before the crisis compared to those that secured them during, potentially indicating that students are unwilling to deviate from typical job search strategies, especially in times of uncertainty.

This study provides insights into how network ties are used by job seekers during a period of economic and environmental uncertainty.

As sustainable performance has a central role in the small and medium enterprises (SMEs) performance literature, this study aims to examine the influence of networking capabilities in enhancing sustainable performance through knowledge workers’ productivity and digital innovation. It also examines the sequential mediating role of knowledge workers’ productivity and digital innovation on networking capabilities and SMEs’ sustainable performance relationship.

Data were collected from 308 knowledge workers in the information technology sector and analyzed using the Hayes Process Macro bootstrapping method to test the proposed hypotheses.

Results indicate that knowledge workers’ productivity and digital innovation individually and sequentially mediate the relationship between networking capabilities and SME’s sustainable (economic and environmental) performance, surprisingly, they do not act as a mediator between networking capability and SME’s social performance. SMEs should prioritize investments in the professional development of their knowledge workers through training and skill enhancement programs. This investment equips knowledge workers with the tools to effectively use the knowledge and resources acquired through networking. Thus, knowledge workers may improve performance by using these resources to tackle challenges.

Although this research focused on this specific context, it is prudent to acknowledge that additional factors may also exert influence on sustainable performance within SMEs, factors that managers may consider when making decisions. Methodologically, the cross-sectional design of this research poses a potential limitation, as it does not allow for the complete elimination of endogeneity concerns. However, it is worth noting that scholars have endorsed the use of cross-sectional data in cases where management researchers aim to expand beyond well-documented and longitudinal data sets.

This research offers practical recommendations for SMEs to improve their sustainable performance through networking. SMEs should seek partnerships with complementary knowledge to improve operations and for other performance-oriented benefits.

This study adds significantly to the literature on sustainable SME performance by studying the interdependent effects of networking capabilities. It also represents the individual and sequential mediation mechanism that links networking capabilities to SME success through knowledge worker productivity and digital innovation.

This study investigates the extent to which a firm’s centrality and autonomy in its supply network are associated with the intensity and complexity of its competitive actions.

Utilizing social network analysis and dynamic panel data models, this study analyzes a comprehensive panel dataset with 10,802 firm-year observations across various industries between 2011 and 2018 to test the hypotheses.

Our findings show that a firm’s level of centrality in its supply network has an inverted U-shaped relationship with both competitive intensity and competitive complexity. In addition, the turning points of these two inverted U-shaped relationships differ in that firms with a lower level of centrality tend to compete aggressively by launching more actions within fewer categories, while firms with a higher level of centrality tend to compete aggressively by launching fewer actions that cover a larger range of categories. Finally, we find that a firm’s structural autonomy has a positive relationship with competitive complexity.

This study bridges the gap between the supply chain management literature and strategic management literature and investigates how supply networks shape competitive aggressiveness. In particular, this research investigates how a firm’s structural position in its supply network affects its competitive actions, an important intermediate mechanism for competitive advantage that has been overlooked in the supply chain management literature.