Ethnic communities: a factor of industrial clustering. Issue 10 (9th October 2017)

• Record Type: Journal Article
• Authors: Kamran, Shah Muhammad; Fan, Hongzhong; Matiullah, Butt; Ali, Gulzar; Hali, Shafei Moiz
• Is Part Of: International journal of social economics. Volume 44:Issue 10(2017)
• Page Start: 1290
• Page End: 1306
• Publication Date: 2017-10-09
• Subjects: Confirmatory factor analysis -- Clustering factors -- Ethnic community -- Industrial/business clusters -- RMSEA; Economics -- Periodicals; Social sciences -- Periodicals; 330.05
• DOI: 10.1108/IJSE-01-2016-0038
• Languages: English
• ISSNs: 0306-8293

Abstract

Abstract : Purpose: This paper not only draws conclusions from the available literature but also offers some new factors as well, which are not included in the existing literature. To be more precise, the purpose of this paper is to ascertain factors behind the clustering of the motorcycle industry, a low-tech and low investment industry. This paper weighs the government's policies, role of factors of production, infrastructure, geography and other drivers for the subject industry and associated industries in the geographic location of Hyderabad. Design/methodology/approach: For collection of data, a questionnaire was designed to survey the cluster ( n =250) after reviewing the literature and conducting interviews of experts of the motorcycle manufacturing industry, i.e. owners, managers, auditors, suppliers, etc.; a component matrix was developed to reduce the dimension of factors and measure the correlation, which helped to weigh the influence of factors. A confirmatory factor analysis proposed four factors as the best fit. Findings: The study conjectured a new viable factor for industrial clustering: "ethnic community, " as it acts as a catalyst to diffuse knowledge, experience and skills within the industrial cluster. Research limitations/implications: This research does not find the weightage of the factors for industrial clustering, i.e. it does not calculate the influence of factors behind the industrial clustering. Practical implications: The above findings aim to stimulate policy makers and researchers alike to further pursue the line of inquiry developed in this paper. Originality/value: A first-time confirmatory factor analysis is used to find the reasons of industrial clustering. Root mean square error of approximation is used to test the model fit. Most importantly, it is the research about an emerging industrial cluster.