The purpose of this study is to address the impact of three issues: degree of skills, favorable working environment and R&D on manufacturing productivity of labor‐intensive industries.
Convenience sampling method has been used to conduct this study. Three categories of labor‐intensive manufacturing industries (A, B and C) have been chosen to perform this research. Industrial category A represents the manufacturing operations which are based on skill of labor. Category B is a group of industries which provides a favorable working environment to utilize the potential of skill in manufacturing process. Category C is a specialized group of industries and its manufacturing process is dependent on R&D. Input–output data for manufacturing operations of the sample industries have been analyzed by using standard statistical techniques to establish the relationship between dependent and independent variables.
The degree of skill and productivity is positively correlated, but not strongly (r<0.5). The study found that productivity is positively (r>0.5) associated with favorable working environment. However, a significantly positive correlation (r>0.7) is found between R&D expenditure and productivity. The study concludes that a higher degree of skills, favorable working environment and R&D are important inputs to a labor‐intensive manufacturing process, which is positively associated with productivity.
Manufacturing productivity is an important parameter of industrial growth, and this paper addresses this issue. The current work addresses the garments sector, i.e. a part of the labor‐intensive industries. Though this work is focused on only one part of this sector; the findings of this study have significant policy implications. The results would be useful for manufacturing business executives and policy makers.
This study addresses the important issue of productivity of labor‐intensive manufacturing industries, and generates quantitative evidence of the impact of degree of skills, favorable working environment and R&D on productivity. The generated information enriches the present knowledge stock of manufacturing systems. The findings could be the basis for further academic research on manufacturing systems.
