Enterprises In Industrial Clusters Research Articles

Research on distributed energy sharing strategies of industrial clusters under consumers’ low-carbon preferences

Distributed energy sharing is an effective means for enterprises to improve energy utilization efficiency and reduce carbon emissions. With the continuous improvement of consumers' low-carbon awareness, low-carbon preference has an important impact on distributed energy sharing strategies. In this context, we introduce the energy saving benefit sharing contract and use the backward induction method to analyze the optimal equilibrium strategies of distributed energy sharing in industrial clusters. Then we conduct a comparative analysis of the equilibrium results. Finally, the above results are analyzed and verified by numerical simulation. The results show that when the ratio of energy low-carbon cost coefficient is within a certain range, energy sharing with integrated energy service providers is the optimal choice for enterprises in industrial clusters, and the centralized decision is better than the decentralized decision; The higher the consumer's low-carbon preference, the higher the product's low-carbon level and total system profitability; As the energy saving benefit sharing ratio increases, the low carbon level of the product and the profit of the integrated energy service provider also increases, and the total profit of the enterprises first increases and then decreases.

Will technological innovation uncertainty affect the distribution of benefits from low‐carbon innovation activities in industrial clusters?—A study based on gray Shapley values

AbstractTechnological innovation of industrial cluster is an important measure to realize low‐carbon development. However, the uncertainty associated with new technologies can have a significant impact on the earnings of enterprises. This paper examines the benefit distribution of collaborative technological innovation in industrial clusters through the gray Shapley value, which makes up for the problem that existing research ignores the impact of the uncertainty of technological innovation on the distribution of benefits and provides a new research perspective. In addition, this paper also provides some countermeasures for the Chinese government to encourage enterprises in industrial clusters carry out low‐carbon innovation activities.

Low-carbon technology collaborative innovation in industrial cluster with social exclusion: An evolutionary game theory perspective.

As governments implement low-carbon economy widely, boosting low-carbon transformation in industrial clusters has become a challenge. This study establishes an evolutionary game model of low-carbon technology collaborative innovation based on spatial public goods game to solve the free-riding problem effectively in research and development. By introducing a social exclusion mechanism, we explore the requirements for the emergence of cooperation between enterprises, and we consider the heterogeneity and scale-free characteristics of industrial clusters comprehensively. Simulation results confirm that social exclusion can significantly promote cooperation as a form of cooperation with additional cost. When exclusion cost decreases and probability increases, an excluder can survive in a lower enhancement factor, which guarantees a stable exclusion mechanism. Furthermore, this mechanism is key to forming and maintaining cooperative behavior. When a cluster follows a scale-free distribution, the sparse network structure can avoid cooperation collapse. Moreover, heterogeneous investment is a robust alternative in the face of invading defectors. This study provides a new understanding to promote the collaborative innovation of enterprises in industrial clusters.

The Relationship between Ambidextrous Knowledge Sharing and Innovation within Industrial Clusters: Evidence from China

Aim/Purpose: This study examines the influence of ambidextrous knowledge sharing in industrial clusters on innovation performance from the perspective of knowledge-based dynamic capabilities. Background: The key factor to improving innovation performance in an enterprise is to share knowledge with other enterprises in the same cluster and use dynamic capabilities to absorb, integrate, and create knowledge. However, the relationships among these concepts remain unclear. Based on the dynamic capability theory, this study empirically reveals how enterprises drive innovation performance through knowledge sharing. Methodology: Survey data from 238 cluster enterprises were used in this study. The sample was collected from industrial clusters in China’s Fujian province that belong to the automobile, optoelectronic, and microwave communications industries. Through structural equation modeling, this study assessed the relationships among ambidextrous knowledge sharing, dynamic capabilities, and innovation performance. Contribution: This study contributes to the burgeoning literature on knowledge management in China, an important emerging economy. It also enriches the exploration of innovation performance in the cluster context and expands research on the dynamic mechanism from a knowledge perspective. Findings: Significant relationships are found between ambidextrous knowledge sharing and innovation performance. First, ambidextrous knowledge sharing positively influences the innovation performance of cluster enterprises. Further, knowledge absorption and knowledge generation capabilities play a mediating role in this relationship, which confirms that dynamic capabilities are a partial mediator in the relationship between ambidextrous knowledge sharing and innovation performance. Recommendations for Practitioners: The results highlight the crucial role of knowledge management in contributing to cluster innovation and management practices. They indicate that cluster enterprises should consider the importance of knowledge sharing and dynamic capabilities for improving innovation performance and establish a multi-agent knowledge sharing platform. Recommendation for Researchers: Researchers could further explore the role of other mediating variables (e.g., organizational agility, industry growth) as well as moderating variables (e.g., environmental uncertainty, learning orientation). Impact on Society: This study provides a reference for enterprises in industrial clusters to use knowledge-based capabilities to enhance their competitive advantage. Future Research: Future research could collect data from various countries and regions to test the research model and conduct a comparative analysis of industrial clusters.

Effects of Informal Contracts on Innovative Cooperation among Enterprises in Industrial Clusters: An Evolutionary Game Analysis

Industrial cluster theory has important guiding significance for regional industrial development and industrial agglomeration advantages. Cooperation among enterprises is the corner stone of industrial clusters. The purpose of the paper is to explore the effects of cluster informal contracts on cluster enterprises and the behavior of external partners. Based on the dynamic evolutionary game theory, this paper constructs a model, which incorporates several main factors influencing the innovative cooperation among local and external cluster enterprises. By calculating the replicator dynamics equations and analyzing the evolutionary stable strategies, this paper discusses the evolution process of cooperation strategies of enterprises in different situation. Furthermore, by using MATLAB software to simulate the model, this paper verifies the accuracy and reliability of the game model. Results show that, in addition to the formal market contract, effective implementations of cluster informal contracts can reduce opportunistic behavior in innovative cooperation among internal and external enterprises. Meanwhile, we should pay attention to strengthen the external innovative cooperation, increase severity of penalties, enhance the credit network externality, and avoid the relevant risks. The paper enriches our understanding about how informal contracts can help promote and cultivate good cooperative order in innovative cooperation of clusters.

Sustainability indicators for small and medium enterprises in industrial clusters

The high competitivity in the market and the theme of environmental conservation that has emerged in the last 30 years has led companies, governments and many other groups to introduce solutions that meet the market needs. Indicators have been created as tools of action and control both for internal and external issues, aiming to meet the new demands of economic, social and environmental sustainability. Within these concepts, the companies organised in clusters have real possibilities to be in evidence because of their potential in knowledge use, experience exchange, flexibility and social impact. This article aimed to understand how sustainability indicators can be used for small and medium enterprises (SMEs) belonging to clusters, and what is the approach of academic articles. Through a bibliographic search, we understood the importance of the concepts of sustainability for the creation of competitiveness and social structuring. However, the low number of articles related to the proposed theme shows that, although sustainability is widely disseminated, its use by SMEs is not yet common, offering opportunity for deeper academic research.

Sustainability indicators for small and medium enterprises in industrial clusters

Sustainability indicators for small and medium enterprises in industrial clusters

A multi-echelon multi-product stochastic model to supply chain of small-and-medium enterprises in industrial clusters

Abstract An Industrial Cluster (IC) is a set of similar and interrelated firms in a specific field situated in a geographic concentration to share joint resources. To date, the interrelations between ICs and Supply Chain Management (SCM) have been improperly mathematically studied, despite their inherent relationship. In this paper, a Supply-Demand Hub in Industrial Clusters (SDHIC) as a specific common provider of warehousing and logistics activities managed by a third-party logistics provider (3PL) is proposed to minimize the total cost of the considered supply chain. Activities of businesses in IC is modeled thorough a two-stage stochastic programming model followed by an acceleration techniques for the Benders decomposition. The numerical experiments comprising sensitivity analysis are conducted over a case study to show the attractiveness of the proposed model. Some managerial insights are presented based on the obtained results.

Multi-level two-way cross-chain inventory coordination model that combines the same level with different levels in multi-supply chains

A cross-chain inventory emergency replenishment model that comprises the enterprises of manufacturers, wholesalers, and retailers at the same level and at different levels based on a co-opetition relationship in cluster supply chains is constructed in this work. The model is built by studying two three-level supply chains that each comprises manufacturers, wholesalers, and retailers using the system dynamics method. Subsequently, the proposed cross-chain inventory collaboration model is compared with currently available models and then simulated using the system dynamics simulation software Vensim. Simulation results show that the proposed model further improves existing theoretical research achievements in the field both locally and abroad, thereby advancing theoretical research on cross-chain inventory collaboration. This study also provides decision-making references for the practice of inventory collaboration among enterprises in industrial clusters.

The Collective Learning Model and Competitiveness of Industrial Clusters

There are few enterprises in economy scale among the majority of enterprises in industrial clusters, and they cannot carry out independent research-and-development of new products. Therefore, they have to rely on innovating resources of the clusters to enhance their comprehensive strength in competition. In this study, we analyzed the driving forces for collective learning in industry clusters, and try to establish a model for collective learning and competitiveness of industry clusters, and provided a new approach for upgrading industrial clusters.

Differences in Management Practices and Productivity in Micro and Small Enterprises in Industrial Clusters

In recent years, several randomized controlled experiments as well as experiments that are not randomized have been conducted to assess the impacts of management training intervention on the productivity and other aspects of business performance of firms. Yet the role played by management improvement vis-à-vis that of technology transfer or borrowing in industrial development remains unclear. This paper attempts to narrow this gap by developing a heuristic model and reviewing experimental studies of management training and case studies of Micro and Small Enterprises (MSEs) in cluster-based industrial development. The paper argues among other things that the improvement of management provides the basis for successful technology transfer.