Development of a structural model of environmentally conscious manufacturing drivers

• Published in: Journal of manufacturing technology management Vol. 25; no. 8; pp. 1195 - 1208
• Main Authors: Kumar Mittal, Varinder, Singh Sangwan, Kuldip
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Group Publishing Limited 30.09.2014
• Subjects: Advanced manufacturing technologies; Collaboration; Competition; Competitive advantage; Consumption; Cost control; Data acquisition; Data analysis; Economic models; Economics; Electrochemical machining; Employees; Energy consumption; Environmental management; Factor analysis; Greenhouse effect; Greenhouse gases; Industrial development; ISO standards; Landfill; Legislation; Manufacturing; Mathematical models; Methodology; Model testing; Natural resources; Operations management; Policies; R&D; Research & development; Research centers; Skill development; Social problems; Social responsibility; Statistical analysis; Stockholders; Studies; Suppliers; Supply chain management; Supply chains; Sustainable development; Waste disposal; China
• ISSN: 1741-038X; 1758-7786
• DOI: 10.1108/JMTM-02-2013-0012

Purpose – Manufacturing firms consume energy and natural resources in highly unsustainable manner and release huge amounts of green house gases leading to many economic, environmental and social problems; from local waste disposal to climate change. Consciousness about these issues has lead to a new manufacturing paradigm of environmentally conscious manufacturing (ECM). There exist many social, legislative, policy, economic, internal, and environmental factors which can motivate and/or force industry to adopt ECM. The purpose of this paper is to identify the drivers for ECM, developing a model of these drivers using statistical analysis and testing the model using structural equation modeling (SEM) technique. Design/methodology/approach – The basic steps of methodology are ECM driver development, survey instrument development, data collection, model proposition, and model validation. The main data analysis approaches are exploratory factor analysis, confirmatory factor analysis, and SEM to develop a model of drivers and validating the same based on the data collected from the manufacturing industry. Findings – The reliable, valid, and tested model has three types of drivers – internal, policy, and economic. It has been found through hypothesis testing that internal drivers for the implementation of ECM are positively related to policy and economic drivers; and policy drivers are positively related to economy drivers. This research is expected to help government and industry in developing policies and strategies for the successful implementation of ECM. Practical implications – The novelty of this study is that it provides the relationship among the drivers which can be leveraged by the managers to focus on the root drivers for smooth and effective implementation of ECM. Originality/value – This paper provides new theoretical insight into the factors motivating the industry to implement ECM systems in the industry with special focus on manufacturing sector of emerging economies.