An ethical framework for big data and smart cities


This paper presents an ethical framework for Big Data and Smart cities, focusing on contemporary ethical and non-ethical issues in big data analytics applications in smart cities and public transportation systems. The framework provides reviews and analysis of ethical and emerging issues and provides a summary of recommendations and discussions for four emerging areas. By reviewing recent studies on both the technological development and emerging ethical problems in the emerging industries, this paper seeks to find and raise public awareness of ethical issues lying in urban big data analytics and public transportation systems. In order to deal with emerging issues, four recommendations have been explained and subsequently, two areas of discussion have been described in detail to support the ethical framework. This paper addresses emerging issues and their ethical concerns for big data and smart cities. Possible recommendations and solutions have been demonstrated to promote the competency of companies and organizations in this big data era. How the ethical framework can be used by six smart cities have been described. Our findings and analysis for big data for high growth, innovation and core competencies and validity of the ethical framework have been justified.

Publication DOI:
Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Funding Information: This research is supported by VC Research ( VCR 0000003 ).
Uncontrolled Keywords: Big data,Ethical framework,Ethics for big data,Smart cities,Business and International Management,Applied Psychology,Management of Technology and Innovation
Publication ISSN: 1873-5509
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.sci ... 3858?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2021-04-01
Published Online Date: 2021-01-15
Accepted Date: 2020-12-21
Authors: Chang, Victor (ORCID Profile 0000-0002-8012-5852)

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