Sustainable development early warning and financing risk management of resource-based industrial clusters using optimization algorithms

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yawen Wang ◽  
Weixian Xue
Keyword(s):  
Risk Assessment ◽  
Early Warning ◽  
Industrial Cluster ◽  
Industrial Clusters ◽  
Shaanxi Province ◽  
Support Vector ◽  
Content Type ◽  
Financing Risk ◽  
Yulin City ◽  
Warning Model

PurposeThe purpose is to analyze and discuss the sustainable development (SD) and financing risk assessment (FRA) of resource-based industrial clusters under the Internet of Things (IoT) economy and promote the application of Machine Learning methods and intelligent optimization algorithms in FRA.Design/methodology/approachThis study used the Support Vector Machine (SVM) algorithm that is analyzed together with the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithm. First, Yulin City in Shaanxi Province is selected for case analysis. Then, resource-based industrial clusters are studied, and an SD early-warning model is implemented. Then, the financing Risk Assessment Index System is established from the perspective of construction-operation-transfer. Finally, the risk assessment results of Support Vector Regression (SVR) and ACO-based SVR (ACO-SVR) are analyzed.FindingsThe results show that the overall sustainability of resource-based industrial clusters and IoT industrial clusters is good in the Yulin City of Shaanxi Province, and the early warning model of GA-based SVR (GA-SVR) has been achieved good results. Yulin City shows an excellent SD momentum in the resource-based industrial cluster, but there are still some risks. Therefore, it is necessary to promote the industrial structure of SD and improve the stability of the resource-based industrial cluster for Yulin City.Originality/valueThe results can provide a direction for the research on the early warning and evaluation of the SD-oriented resource-based industrial clusters and the IoT industrial clusters, promoting the application of SVM technology in the engineering field.

A research on the cluster competitiveness evaluation of the Chinese automobile industry based on cuckoo-AHP

2016 ◽  
Vol 10 (4) ◽  
pp. 746-769 ◽  
Author(s):  
Xiujie Wang ◽  
Jian Liu ◽  
Can Ma
Keyword(s):  
Automobile Industry ◽  
Evaluation Method ◽  
Industrial Cluster ◽  
Cuckoo Search ◽  
Evaluation Methods ◽  
Industrial Clusters ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Content Type ◽  
Cluster Development

Purpose The purpose of this study is that on the basis of the competitive edge theory, source mechanism and evaluation approaches of industrial cluster competitiveness, combined with international trends in the automobile industry and the features of Chinese automobile industrial cluster development, an evaluation index system about cluster competitiveness of auto industry is built with comprehensive consideration of factors such as cluster development environment, external scale effect and internal competitiveness from the perspective of value chain of automobile industry. Design/methodology/approach An evaluation index system for automobile industrial cluster competitiveness was realized by integrating current strengths and future growth capacities with multidimensional, dynamic and comprehensive characteristics, which included 3 second-level, 10 third-level and 16 fourth-level indices. In the light of evaluation methods, a group intelligence optimization algorithm – (cuckoo search) – and traditional methods of complex decision-making system – analytic hierarchy process (AHP) – were combined to propose the cuckoo-AHP evaluation method. It was applied for the calculation and optimization of weight values in an automobile industrial cluster competitiveness evaluation index for the purpose of obtaining better scientific and more reliable results. Findings The research might further enrich the evaluation theory of automobile industrial cluster competitiveness and also can be useful for showing how traditional evaluation methods can be combined with intelligent algorithms to carry out better automobile industrial cluster competitiveness evaluations. In addition, studies of channels for kick-starting Chinese auto industrial cluster competitiveness are expected to provide references for how to enhance the cluster competitiveness of the Chinese automobile industry. Practical implications Changsha and Liuzhou, the Guangxi automobile industrial clusters as the two empirical analysis objects selected for this paper, are geographically adjacent to each other. The automobile industries of the two cities are local pillar industries with the strong support of the local government. Both clusters have their own advantages and weak points with different characteristics of cluster development, and they enjoy a representative significance amongst China’s numerous auto industrial clusters that are taking shape. Comparative analysis of both clusters serves as a good reference for the objective evaluation of the competitiveness of Chinese automobile clusters in terms of their real and practical developments and in respect of the success of reasonable scientific and industrial cluster policies. Originality/value Multidimensional, dynamic, integrated evaluation index systems are constructed around automobile industrial cluster competitiveness, which has taken into account developments in current strengths and future growth capacity. The cuckoo-AHP evaluation method has been formed by combining the traditional decision-making method known as AHP with a new meta-heuristic optimization algorithm called “cuckoo search”. Both have been used in evaluations of automobile industrial cluster competitiveness in Liuzhou and Changsha, which will be beneficial for enriching automobile industrial cluster competitiveness evaluation theory and new evaluation methods that will enable better evaluations of automobile industrial cluster competitiveness.

Value creation in industrial clusters: the strategic nature of relationships with stakeholders and the policy environment

2020 ◽  
Vol 13 (4) ◽  
pp. 535-550 ◽  
Author(s):  
Subrata Chakrabarty
Keyword(s):  
Conceptual Framework ◽  
Stakeholder Theory ◽  
Value Creation ◽  
Industrial Cluster ◽  
Industrial Clusters ◽  
Policy Environment ◽  
High Concentration ◽  
Relational View ◽  
Content Type ◽  
Stakeholder Relationships

PurposeGiven that an industrial cluster contains a high concentration of numerous stakeholders, a firm in an industrial cluster often ends up forming relationships with many of the stakeholders. The research questions are as follows: Does stakeholder-based management always lead to greater value creation? What are the moderators in this association? This paper proposes that although relationships with stakeholders can act as a “catalyst” for value-creation, they can also act as a “retardant.” A combination of (1) the strategic nature of the relationships and (2) the policy environment determines whether the relationships with stakeholders act as catalysts or retardants.Design/methodology/approachUsing relationship-focused theory, a conceptual framework that adopts a relational view of stakeholder theory is developed. Given the high concentration of stakeholders in industrial clusters, the conceptual framework uses stakeholders in industrial clusters as a setting. A firm can form relationships with a variety of stakeholders in an industrial cluster. The strategic nature of a relationship with a stakeholder is assessed in terms of variations in strategic intent and intellectual spillover.FindingsThe key argument is the following: whether a relationship with a stakeholder becomes a catalyst or a retardant for value creation is contingent on the fit between the strategic nature of the relationship and the policy environment. For instance, in a probusiness policy environment, relying on relationships with stakeholders that maximize intellectual spillover can act as a catalyst for value creation. In contrast, in an antibusiness environment, not having to rely on intellectual spillover is a safer option.Originality/valueWhereas the literature implicitly assumes that stakeholder theory has relational essence, the conceptual framework developed in this paper adopts a relational view of stakeholder theory in a very explicit way. This paper applies relationship-focused theory by making explicit the different forms of stakeholder relationships. Such an explicitly relational approach in theorizing can help in more in-depth research on the link between stakeholder relationships and value creation. The conceptual framework will allow future research to analyze value creation in an industrial cluster, especially in terms of how stakeholder relationships can act as either catalysts or retardants.

Evaluate the sustainable reuse strategy of the corporate financial management based on the big data model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Weige Yang ◽  
Yuqin Zhou ◽  
Wenhai Xu ◽  
Kunzhi Tang
Keyword(s):  
Big Data ◽  
Development Strategy ◽  
Early Warning ◽  
Financial Management ◽  
Prediction Models ◽  
Sustainable Growth ◽  
Management Model ◽  
Content Type ◽  
Early Warning Model ◽  
Warning Model

PurposeThe purposes are to explore corporate financial management optimization in the context of big data and provide a sustainable financial strategy for corporate development.Design/methodology/approachFirst, the shortcomings of the traditional financial management model are analyzed under the background of big data analysis. The big data analytic technology is employed to extract financial big data information and establish an efficient corporate financial management model. Second, the deep learning (DL) algorithm is applied to implement a corporate financial early-warning model to predict the potential risks in corporate finance, considering the predictability of corporate financial risks. Finally, a corporate value-centered development strategy based on sustainable growth is proposed for long-term development.FindingsThe experimental results demonstrate that the financial early-warning model based on DL has an accuracy of 90.7 and 88.9% for the two-year financial alert, which is far superior to the prediction effect of the traditional financial risk prediction models.Originality/valueThe obtained results can provide a reference for establishing a sustainable development pattern of corporate financial management under the background of big data.

The impact of industrial clusters on human resource and firms performance

2014 ◽  
Vol 9 (2) ◽  
pp. 141-159 ◽  
Author(s):  
Maw-Shin Hsu ◽  
Yung-Lung Lai ◽  
Feng-Jhy Lin
Keyword(s):  
Human Resources ◽  
Positive Impact ◽  
Industrial Cluster ◽  
Industrial Clusters ◽  
Global Market ◽  
Content Type ◽  
Key Factor ◽  
The Face ◽  
Processing Zone ◽  
The Impact

Purpose – The purpose of this study was to explore the impact of the formation of industrial clusters on the obtainment of professional human resources, to verify the impact of human resources on clustering relationships and firm’s performance and to understand whether the formation of clusters can contribute to the obtainment of professional human resources and the improvement of competitiveness of enterprises. It was expected that solutions could be found to make new contributions through the verification of special economic zones (SEZs). Design/methodology/approach – Using manufacturers in Taiwan’s SEZs as the subjects, this study explored the impact on the obtainment of professional human resources after the formation of industrial clusters in SEZs, through conducting and empirical study with a questionnaire survey. Findings – The professional human resources are the essential factor for the formation of industrial clusters and the improvement of competitiveness. This study also confirmed that industries can have professional human resources by industrial clustering and that this will produce a positive impact on the enterprise clustering relationships, which can also have a positive impact on firm’s performance and can enhance the enterprise’s competitive advantage. Practical implications – Industrial clustering is the key factor to attract professional human resources; industrial clusters can enhance firm’s performance; and professional human resources affect firm’s performance of enterprises. Originality/value – No study has discussed the topic of clusters from the perspective of SEZs also including six export processing zone (EPZ) parks in Taiwan. This study discussed the topic using theories relating to clustering and human resources. The formation of industrial clusters can result in higher competitiveness in the face of the global market. The EPZ industrial cluster provides an excellent investment environment. Coupled with one-stop express services and geographic advantage, the land-use rate is up to 97 per cent and the per hectare output value amounts to NTD 3.2 billion, setting a successful example of an industrial cluster.

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