| Title | Linking big data analytics and operational sustainability practices for sustainable business management |
|---|---|
| ID_Doc | 34492 |
| Authors | Raut, RD; Mangla, SK; Narwane, VS; Gardas, BB; Priyadarshinee, P; Narkhede, BE |
| Title | Linking big data analytics and operational sustainability practices for sustainable business management |
| Year | 2019 |
| Published | |
| Abstract | Big data analytics is becoming very popular concept in academia as well as in industry. It has come up with new decision tools to design data-driven supply chains. The manufacturing industry is under huge pressure to integrate sustainable practices into their overall business for sustainbale operations management. The purpose of this study is to analyse the predictors of sustainable business performance through big data analytics in the context of developing countries. Data was collected from manufacturing firms those have adopted sustainable practices. A hybrid Structural Equation Modelling - Artificial Neural Network model is used to analyse 316 responses of Indian professional experts. Factor analysis results shows that management and leadership style, state and central-government policy, supplier integration, internal business process, and customer integration have a significant influence on big data analytics and sustainability practices. Furthermore, the results obtained from structural equation modelling were feed as input to the artificial neural network model. The study findings shows that management and leadership style, state and central-government policy as the two most important predictors of big data analytics and sustainability practices. The results provide unique insights into manufacturing firms to improve their sustainable business performance from an operations management viewpoint. The study provides theoretical and practical insights into big data implementation issues in accomplishing sustainability practices in business organisations of emerging economies. (C) 2019 Elsevier Ltd. All rights reserved. |
| https://hdl.handle.net/10026.1/14932 |
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