| Title | Modelling the importance of social responsibility standards (ISO 26000:2010) practices adoption in freight transport industry |
|---|---|
| ID_Doc | 73210 |
| Authors | Kumar, A |
| Title | Modelling the importance of social responsibility standards (ISO 26000:2010) practices adoption in freight transport industry |
| Year | 2022 |
| Published | |
| Abstract | The United Nations agenda for sustainable development attracts governments, supply chain actors, researchers, and societies to promote sustainability practices in their businesses. Most of the scholarly work focusing on the economic and environmental sustainability of supply chain and logistics processes. Most developed countries have incorporated the social responsibility of their businesses to improve the nation's social wellbeing. There is less exploration of the social responsibility of logistics operations in developing nations. In the above connection, the International Organization for Standardization (ISO) proposes a global standard, i.e., ISO26000, in 2010 (ISO26000:2010). Even minimal freight logistics firms are adopting the ISO26000 standard for improving sus-tainable performance. To bridge the above research gap, this research analyses the importance of ISO 26000 practices in the freight transport industry. The significance of the practices is assessed with a multi-criteria decision-making method under a fuzzy environment, i.e., the fuzzy best-worst method (FBWM). The advan-tage of using fuzzy set theory is to include the uncertainty and ambiguity of the decision-makers. The ISO26000 practices are ranked to make appropriate managerial decisions for improving the social responsibility of the Indian freight transport industry. Fair operating practices are obtained top rank, followed by labour and workforce practices. The overall analysis shows that health and safety at the workplace is a top practice to improve sustainability performance. The rank variation of the ISO26000 practices are tested with sensitivity analysis. The obtained results are less sensitive to weight variations, which is claimed as the model robustness. |
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