| Title | Big data analytics for sustainable cities: An information triangulation study of hazardous materials transportation |
| Authors | Ye, Lisha Pan, Shan L. Wang, Jingyuan Wu, Junjie Dong, Xiaoying |
| Affiliation | China Mobile Res Inst, Dept Strategy & Ind, Beijing, Peoples R China UNSW Business Sch UNSW, Sch Informat Syst Technol & Management, Sydney, NSW, Australia Beihang Univ, Sch Comp Sci & Engn, Beijing, Peoples R China Beihang Univ, Sch Econ & Management, Beijing, Peoples R China Peking Univ, Guanghua Sch Management, Beijing, Peoples R China Beihang Univ, State Key Lab Software Dev Environm, Beijing, Peoples R China |
| Issue Date | May-2021 |
| Publisher | JOURNAL OF BUSINESS RESEARCH |
| Abstract | Big data analytics (BDA) is regarded as an advanced tool for achieving sustainable development as part of the grand challenges (GCs). However, it is not clear how BDA can be used by data scientists to solve the GCs with multisource data in a cross-disciplinary approach. Based on a case study of city-based dangerous goods transportation (DGT), this paper explores how data scientists use BDA to triangulate data, methods, knowledge and solutions for solving GCs. The contribution of this study is threefold: (1) it contributes to research on GCs and discusses how BDA can be used in problem solving for multidomain GCs from a management perspective; (2) it enriches the theory of information triangulation and proposes several steps for information triangulation in BDA to solve GCs; and (3) it contributes some practical implications for the management of organizations when solving social problems and pursuing sustainable development. |
| URI | http://hdl.handle.net/20.500.11897/612390 |
| ISSN | 0148-2963 |
| DOI | 10.1016/j.jbusres.2021.01.057 |
| Indexed | SSCI |
| Appears in Collections: | 光华管理学院 |
