Using big data to make better decisions in the digital economy
The question this special issue would like to address is how to harvest big data to help decision-makers to deliver better fact-based decisions aimed at improving performance or to create better strategy? This special issue focuses on the big data applications in supporting operations decisions, inc...
| Main Authors: | , , , |
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| Format: | Article |
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Taylor & Francis
2017
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| Online Access: | https://eprints.nottingham.ac.uk/43577/ |
| _version_ | 1848796718861123584 |
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| author | Tan, Kim Hua Ji, Guojun Lim, Chee Peng Tseng, Ming-Lang |
| author_facet | Tan, Kim Hua Ji, Guojun Lim, Chee Peng Tseng, Ming-Lang |
| author_sort | Tan, Kim Hua |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The question this special issue would like to address is how to harvest big data to help decision-makers to deliver better fact-based decisions aimed at improving performance or to create better strategy? This special issue focuses on the big data applications in supporting operations decisions, including advanced research on decision models and tools for the digital economy. Responds to this special issue was great and we have included many high-quality papers. We are pleased to present 13 of the best papers. The techniques presented include data mining, simulation and expert system with applications span across online reviews, food retail chain to e-health. |
| first_indexed | 2025-11-14T19:52:26Z |
| format | Article |
| id | nottingham-43577 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:52:26Z |
| publishDate | 2017 |
| publisher | Taylor & Francis |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-435772020-05-04T18:46:46Z https://eprints.nottingham.ac.uk/43577/ Using big data to make better decisions in the digital economy Tan, Kim Hua Ji, Guojun Lim, Chee Peng Tseng, Ming-Lang The question this special issue would like to address is how to harvest big data to help decision-makers to deliver better fact-based decisions aimed at improving performance or to create better strategy? This special issue focuses on the big data applications in supporting operations decisions, including advanced research on decision models and tools for the digital economy. Responds to this special issue was great and we have included many high-quality papers. We are pleased to present 13 of the best papers. The techniques presented include data mining, simulation and expert system with applications span across online reviews, food retail chain to e-health. Taylor & Francis 2017-05-24 Article PeerReviewed Tan, Kim Hua, Ji, Guojun, Lim, Chee Peng and Tseng, Ming-Lang (2017) Using big data to make better decisions in the digital economy. International Journal of Production Research, 55 (17). pp. 4998-5000. ISSN 1366-588X big data business analytics decision support systems data mining digital economy http://www.tandfonline.com/doi/abs/10.1080/00207543.2017.1331051 doi:10.1080/00207543.2017.1331051 doi:10.1080/00207543.2017.1331051 |
| spellingShingle | big data business analytics decision support systems data mining digital economy Tan, Kim Hua Ji, Guojun Lim, Chee Peng Tseng, Ming-Lang Using big data to make better decisions in the digital economy |
| title | Using big data to make better decisions in the digital economy |
| title_full | Using big data to make better decisions in the digital economy |
| title_fullStr | Using big data to make better decisions in the digital economy |
| title_full_unstemmed | Using big data to make better decisions in the digital economy |
| title_short | Using big data to make better decisions in the digital economy |
| title_sort | using big data to make better decisions in the digital economy |
| topic | big data business analytics decision support systems data mining digital economy |
| url | https://eprints.nottingham.ac.uk/43577/ https://eprints.nottingham.ac.uk/43577/ https://eprints.nottingham.ac.uk/43577/ |