Performance Of Industrial Clusters Research Articles

E-Commerce Performance of Industrial Clusters in India

The Ministry of MSME administers the Micro, Small & Medium Enterprises (MSME) Mart, while the Ministry of Commerce and Industry administers the Government E-Market (GeM). A study was conducted to understand the performance of industrial clusters in India on these portals with the objective of studying the physical and financial performance of MSME Mart and GeM for MSMEs. The methodology of this study consists of the collection of secondary data from the MSME Mart website using input variables like No. of MSMEs, Micro (Mn), Small (Sn), Medium (Dn), Total Uydam Registration (Ut), No. of MSME Clusters in India (Cn), No. of Working Enterprises in India (Un), Employment in India (En), Original Value of P & M in India (INR crores; Pc), Market Value of Fixed Assets in India (INR crores; Mc), Award of Contract in India (Ac) and output variables like Gross Output in India (INR crores; Gc), which were analysed using descriptive analysis. It was found that there is an increase in the CAGR. The Award of Contract given to MSMEs should be increased as Karnataka and West Bengal are performing well in MSME Mart. The input variables collected from GeM like No. of Orders Placed (Op), No. of Unique Products Sold (Up), No. of Total Products Sold (Ps), No. of Organizations Registered (Cumulative) on GeM (Gn), No. of Sellers/Service Providers (Cumulative; Sn), Order Value (INR crores) by State/UT’s (Os), Order Value (INR crores) by Central Organizations (Co.) and No. of MSE Sellers (Cumulative; Mn) and output variables like Order Value (INR crores; Ove) were analysed using descriptive analysis; it was found that the CAGR is increasing. A correlation analysis was conducted; the findings reveal a strong relationship between the input and output variables. The trend and regression analyses reveal increases in output and input variables. The Government of India and the concerned state governments implement the MSME Mart and GeM for MSME clusters in India to reduce the costs of production and marketing and increase profits, which allows MSMEs to compete in the domestic and international markets through ecommerce.

Разработка методологического инструментария оценки эффективности деятельности кластеров в Российской Федерации

The article suggests the author’s version of the approach to assessing the effectiveness of clusters existing in the territory of the Russian Federation. The methodological tools are focused on assessing the effectiveness of industrial clusters, included in the register of the Ministry of Industry and Trade of the Russian Federation, and sectoral territorial and industrial clusters, supervised by the subjects of the Russian Federation. They are differentiated and include three evaluation criteria for each type of cluster. The evaluation system was tested on the example of 191 clusters. Information was collected through GISP and EMISS systems, sites-aggregators of data on companies, telephone interviews with public authorities responsible for the implementation of cluster policy in the regions, official response letters from ministries and cluster development centers in Russia. The work also proposed a system for assessing the performance of industrial clusters, supervised by the Ministry of Industry and Trade of the Russian Federation. The results made it possible to identify clusters with high and low levels of efficiency and effectiveness. This approach can be used by state authorities as part of the process of monitoring the activities of clusters and the implementation of cluster policy in order to optimize funding and improve the effectiveness of their support activities.

Prioritizing factors affecting regional competitiveness in industrial clusters

This study aimed to rank the factors affecting regional competitiveness in industrial clusters of equipment for oil, gas, and petrochemicals in Iran. The research was descriptive-analytical in terms of methodology. In addition, data were collected using questionnaires, whose reliability and validity was confirmed. Moreover, data analysis was performed in SPSS and LISREL. According to the results, the factors affecting regional competitiveness increase were classified into four categories of spatial-geographical relations, institutional-organizational relations, economic relations, and social relations. Therefore, improving the industrial cluster's performance will directly help the oil industry achieve its localization and self-efficiency goals, especially in terms of the supply of equipment. On the other hand, this issue had a significant effect on the development of various economic, social, educational, and employment aspects in oil, gas, and petrochemical regionals.

Development of Eco-industrial Parks in China

Over the past 20 years, China has made outstanding progress in the development of eco-industrial parks. Their number exceeds 110 parks, which is more than in any other country. They play an important role in improving the environmental performance of industrial clusters and transport hubs in many areas of the PRC. Their environmental performance indicators are increasing year by year. Their contribution to the economic and environmental performance of the territories is the subject of many studies. However, most papers study individual types of effects or the effectiveness of individual eco-industrial parks. This article systematically explores China's experience in the development of eco-industrial parks in terms of the dynamics of their total number, distribution across all territories of the stratum and all groups of effects. The study is structured as a comprehensive literature review using qualitative and quantitative tools to synthesize existing findings and official documents. As part of the study of temporal dynamics, the average duration of the creation of an eco-industrial park and its tendency to increase to 10 in 2022 were identified. In 2015-2022 a trend was found to limit park approvals and to focus on accelerating the certification of already approved parks. Eco-industrial parks are located in the most ecologically unfavorable provinces, which indicates a country-wide planned effort to improve the environmental situation with the help of eco-industrial parks. Over the past 5 years, a trend has been revealed to expand the geography of parks. The synthesis of studies on the effectiveness of parks indicate the high potential of parks to create positive economic, production and environmental effects. At the same time, the absence of documented social effects was revealed. The analysis of the eco-industrial park program in China made it possible to formulate recommendations for managing similar programs in Russia.

Industrial clusters development and organisation model

Industrial clusters have been a prevalent element of several national competitiveness policies for the last 15 years The author of this paper has followed the birth, organisation and performance of industrial clusters in Slovenia and Austria for the period of three years. Based on several in-depth case studies in Slovenia and Austria I have built a cluster development and organisation model applicable to smaller (transitional) countries. I have identified factors that have an impact on cluster development and organisation at the level of general business environment. At the same time I have identified a government role in fostering clusters. But external factors are not the only factors influencing clusters. There are also internal factors that are in the hands of the cluster actors. These are factors that directly influence cluster development and organisation process. I have classified them in four areas and they will be also presented in this paper. I have also identified four stages of cluster birth, organisation and growth. The model is highly applicable as it combines research results with best practices based on several case studies.

Survey factors affecting performance of industrial clusters by using Panel-VAR model

In every country, the efficiency and probability of small and medium firms will cause to economic growth. In this regard, present study aimed to investigate the effective factors on the performance of active industrial clusters in large and industrial provinces by Panel-VAR model during 2006-2015. Results indicated that the access to loan, production rate, cluster size, marketing sector in cluster, closeness of cluster to the market, and the increase of manager’s experience have a positive effect while bank facility interest can negatively influence on the performance of industrial clusters.

RETRACTED ARTICLE: Investigation of the evaluation system of SMEs’ industrial cluster management performance based on wireless network development

Today, with the rapid development of mobile Internet technology, the operation of enterprises is basically based on the mobile network platform. Therefore, the study of the evaluation system of SMEs’ industrial cluster management performance based on wireless network development is proposed. After briefly describing the relevant research of industrial cluster performance evaluation, the knowledge innovation of wireless network era is the core of SME industrial cluster management, and a set of industrial cluster management performance evaluation index system has been constructed. Based on this, a comprehensive evaluation method based on neural network algorithm is designed. In a subsequent experiment, it is demonstrated that this method can evaluate the level of cluster management performance.

Using data envelopment analysis to investigate the performance of industrial clusters to utilise resources

Industrial clusters use different resources to realise their desired performance. Managers and decision makers tend to know how clusters use resources to create outputs and products and which of them efficiently/non-efficiently utilise input resources. In this study some of the industrial clusters in different business sections ('handicrafts', 'food', 'construction' and 'manufacturing') have been considered. For each cluster, 'technology', 'human resources skill' and 'work environment' are supposed as input resources. In addition, 'production capability' and 'products quality' are considered as output factors. Basic CCR DEA model has been applied to calculate the efficiency factors for two approaches. First, all input resources have been considered and the efficiency factors of the industrial clusters have been attained to show which of them efficiently or non-efficiently utilise inputs. Afterwards, input resources have been investigated individually to indicate which cluster efficiently or non-efficiently use each input resources ('technology', 'human resources skill' and 'work environment').

Measuring the social capabilities and the implication on innovation

Purpose– The purpose of this paper is to seek to develop a rigorous analysis of social capabilities (SC) and measuring them in a special industrial cluster, considering the key role of knowledge and SC in innovation.Design/methodology/approach– The paper sets off from the definition of SC and the elements that characterize them in an industrial cluster. The focus also examines how it is possible to measure SC within a specific area, using the scoreboard approach. The empirical aspect remains based upon the original results of surveys conducted on a sample of entrepreneurs specializing in a special industrial cluster utilizing a multi-method approach and the paper presents the empirical analysis estimating an innovation equation.Findings– The results of the case study show that the relationship between SC and innovation does not only involve large enterprises but small- and medium-sized businesses as well.Originality/value– Measuring SC is very complex since not all the highlighted determinants can be subject to empirical verification and given that they are intangible elements, residual and often incorporated into tacit knowledge and a literature that can help us in defining and measuring them accurately is not always available for all the indicators. In this research the author proposes a measure of SC using a composite indicator and investigates the effective role of these inputs in stimulating innovative activity in an industrial cluster performance.

The Relationship between the Performance of Industrial Clusters and Renovation of Small Industries

The purpose of the present study is investigating the relationship between the performance of industrial clusters and renovation of small industries in East Azerbaijan, Iran. In this study, the relationship between the performance of the industrial clusters and its dimensions including the supporting institutions, communicative sectors and production-business sectors has been investigated using the renovation indices of small industries. The participants of the study include all 243 industrial and production units in auto-parts manufacturing cluster of the province. Due to the limited number of subjects, census-taking was used. The data collection instrument for the study was questionnaire. The validity of questionnaire was confirmed through face value of content and its reliability was confirmed using Cronbach’s Alpha, with 0.838 for the questions on industrial clusters’ performance and 0.726 for the questions on small industries development. To test the research hypotheses, Pearson and Friedman tests were used. The results indicate that there is a relationship between the performance of industrial clusters and its dimensions, with renovation of small industries in the province.

High‐tech industry clustering in less favoured areas

PurposeThe aim of this paper is to demonstrate that synergy between firms and local institutions may lead to success even in an economic environment which cannot offer competitive advantages to high tech industrial clustering. However, the condition for such a complex result is convergence between cluster‐based regional development policy, the related industry national strategy and the central government preferential policy for less developed areas.Design/methodology/approachThe experiences selected as case studies are the Xi'an National Civil Aerospace Base in Shaanxi Province (China) and CampaniAerospace in Campania region (Italy). As information was gathered both through policy documents (China and Italy) and direct interviews to cluster's stakeholders (Italy), the outcome is, according to the criteria suggested by Eisenhardt and Yin, a qualitative research. Comparing economic data provided by the respective countries' Statistical Offices, the authors assessed that Shaanxi Province and the Campania region share a “peripheral” position within their respective national context. Starting from this, the authors analyzed the formal documents which reported the experience of the two clusters.FindingsThe finding is that the outcome (the status of cluster which is assumed as “a value in itself”) of the convergence between firms' will to gain agglomeration advantages and the policymakers' plan to redress interregional economic disparities is definitely not an efficiency‐driven process. In the high tech sectors which are very sensitive to the increasing competition for technology on the international market, the pursuit of efficiency may, on the contrary, be dissipated.Originality/valueAs attested by the comparative literature, aims and performance of industrial clusters differ in accordance with the stage of economic development as well as the institutional and regulatory framework. The paper demonstrates, however, that in the take‐off stage, it is the position occupied by the host region within the national context which determines the behaviour of the actors concerned as well as the outcome of their commitment.

Environment, network interactions and innovation performance of industrial clusters

PurposeThe purpose of this paper is to categorize industrial clusters, and then compare three industrial clusters of three countries from the perspectives of hard environment, soft environment, factors from supply and demand sides, and the network mechanism.Design/methodology/approachData were collected through interview with cluster coordinators. Qualitative case studies were conducted.FindingsThe center of excellence behaves well in nearly all aspects, while the spatially narrowly distributed specific center of innovation mainly exploits benefits from its concentrated sector. For the Chinese comprehensive technology incubator, relatively limited geographical space and broad sectorial distribution endow it with unclear strengths, implying the inadequacy of interconnectedness and industry relatedness mentioned by Porter.Research limitations/implicationsData were collected mainly from cluster coordinators, implying further data collecting and more comprehensive analysis.Practical implicationsIt only makes sense to compare industrial clusters that are comparable with each other. Elements must be matched to facilitate the network interactions, and hence the innovation performance of clusters.Originality/valueThis paper contributes to the theoretical basis through it analyzing and clarifying the scales to measure industrial clusters, and answers the question: what is the situation of industrial clusters behaving in several aspects including hard environment, soft environment, supply, demand, network interactions and innovation performance?

Driving industrial clusters to be nationally competitive

Industrial clusters have received considerable attention from economists and industrial analysts. The main purpose of this paper is to contribute to the understanding of the driving forces behind the growth of industrial clusters. Another core viewpoint anchored in this paper is that national competitive advantages can be achieved by industrial clusters. We examine the impacts of and determine the relationships among different driving forces. Taiwan's Hsinchu Science Park, because of its connection with innovative participators, is a prime example for this paper. This research adopts the Decision Making Trial and Evaluation Laboratory (DEMATEL) as the main analytical tool. Our research results show that local demand conditions and factor conditions (which are specialised factors of production such as skilled labour, capital and information infrastructure) are the main causal driving forces for advancing Taiwan's industrial cluster performance. This paper concludes by discussing managerial implications for industry and government.

The evaluation of cluster policy by fuzzy MCDM: Empirical evidence from HsinChu Science Park

In the recent years, industrial clusters have received considerable attention from economists and industrial analysts, because they are seen as the main reason for economic growth and success of certain economic region. This study systematically reviews past researches of industrial cluster. The purpose of this paper is to contribute to the understanding of this issue regarding the driving forces for the growth of industrial cluster and find out the priority among these cluster policies. Taiwan HsinChu Science Park is a prime example for this paper, and its connection with the innovative participators. We begin with an examination of the literature on cluster about its driving forces and policies upon which we propose a conceptual framework. In doing so, we explore the cluster-based industrial system. Then this research adopts the Fuzzy Analytic Hierarchy Process as the analytical tool. The Fuzzy Analytic Hierarchy Process method is used to determine the weightings for evaluation dimension among decision makers. From our research results, the Factor Conditions is the most important driving force for advancing the industrial cluster performance. Moreover, the promotion of international linkages policy and broader framework policies rank the first two priorities for cluster policy. Overall, this paper concludes with some simulations of cluster policy alternatives confronting the industry and the Taiwanese government.

A measurement system for managing performance of industrial clusters

PurposeThe purpose of this article is to present and discuss a conceptual model for performance measurement and management of a cluster based on the concepts of the Balanced Scorecard and other models.Design/methodology/approachThis paper was based on a case research as the field research methodology for theory testing and refinement.FindingsThe proposed model adequately captures the perspectives of performance management of a cluster, emphasizing the importance of measuring leading and lagging dimensions of performance such as collective efficiency and economic/social results.Research limitations/implicationsResearch is still at an early stage and applications of the model need to be carry on further so as to review and validate it.Practical limitationsAbsence of a culture of decision making based on analysis of information and lack of resources may create some difficulty in using metrics for a cluster of SMEs.Originality/valueThe paper proposes applying the concepts and techniques of business performance management and measurement to manage performance of clusters of companies, offering a new approach on how to improve the collective efficiency of a cluster.