Big data analytics for sustainable cities: An information triangulation study of hazardous materials transportation

Lisha Ye, Shan L Pan, Jingyuan Wang, Junjie Wu and Xiaoying Dong

Journal of Business Research, 2021, vol. 128, issue C, 381-390

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.

Keywords: Big data analytics; Grand challenges; Information triangulation; Case study (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0148296321000606
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:128:y:2021:i:c:p:381-390

DOI: 10.1016/j.jbusres.2021.01.057

Access Statistics for this article

Journal of Business Research is currently edited by A. G. Woodside

More articles in Journal of Business Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().