Developing a hybrid evaluation approach for the low carbon performance on sustainable manufacturing environment

dc.contributor.author Ali, Sadia Samar
dc.contributor.author Kaur, Rajbir
dc.contributor.author Persis, D. Jinil
dc.contributor.author Saha, Raiswa
dc.contributor.author Pattusamy, Murugan
dc.contributor.author Sreedharan, V. Raja
dc.date.accessioned 2022-03-27T02:12:32Z
dc.date.available 2022-03-27T02:12:32Z
dc.date.issued 2020-01-01
dc.description.abstract Societal emergence and sustainability are results of human actions and practices which can either imbalance it with maximum exploitation or retain it through responsible utilization of resources. Based on theories on institutional and resource-based views, the study explores the enablers of green sustainable practices of procurement, logistics, product and process design and regulatory frameworks for low carbon performance.The study employs a Hybrid approach of step-by-step empirical process to examine the impact of sustainable practices on low carbon performance which further affects the sustainable manufacturing and societies. A theoretical model developed based on hypothesis is tested first using modified Dillman’s approach. Then it is tested in in the PLS-SEM package using 380 data responses collected from the various manufacturers. Further robustness of proposed model is validated using different ML (machine learning) followed by post hoc analysis using Item Response Theory to validate the scale and efficacy of the measurement model. The study validates the positive relationships of sustainable practices on the low carbon performance which eventually is responsible for sustainable societies. The area of sustainable manufacturing is found relatively lacking and requires further attention of leadership for better societal establishments. The study hopes to further enrich the literature with its unique Hybrid approach of SEM/PLS Machine Learning and IRT which is used to presents carbon performance as a central entity deriving from green practices and driving sustainable manufacturing and societies.
dc.identifier.citation Annals of Operations Research
dc.identifier.issn 02545330
dc.identifier.uri 10.1007/s10479-020-03877-1
dc.identifier.uri http://link.springer.com/10.1007/s10479-020-03877-1
dc.identifier.uri https://dspace.uohyd.ac.in/handle/1/5037
dc.subject Item response theory
dc.subject Low carbon performance
dc.subject Machine learning
dc.subject Regulatory framework
dc.subject Sustainable manufacturing
dc.subject Sustainable society
dc.title Developing a hybrid evaluation approach for the low carbon performance on sustainable manufacturing environment
dc.type Journal. Article
dspace.entity.type
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