Information and communication technologies (ICT)-enabled severe moral communities and how the (Covid19) pandemic might bring new ones

Carlos M. Parra, Manjul Gupta and Patrick Mikalef

International Journal of Information Management, 2021, vol. 57, issue C

Abstract: In this study we present an autopoietic social systems model based on Collectively Prevalent Interpretants (CPIs). We adapt this model to represent and exemplify how Information and Communication Technologies (ICTs) may enable the emergence of severe moral communities. In particular, we argue that ICTs may help severe moral communities promote increasingly polarized, radicalized and even extremist viewpoints. We also discuss our propositions in the context of the current pandemic (caused by Covid19) and warn about the possible emergence of new and related severe moral communities. In an effort to help understand and manage the enabling role of ICTs, the study also presents recommendations for theory and practice, which may prove useful in advancing digital resiliency (by empowering individuals and communities to recognize when this may be occurring).

Keywords: Autopoietic social systems; Collectively prevalent interpretants; Severe moral communities; Pandemic; (Covid19) (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0268401220314705
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:ininma:v:57:y:2021:i:c:s0268401220314705

DOI: 10.1016/j.ijinfomgt.2020.102271

Access Statistics for this article

International Journal of Information Management is currently edited by Yogesh K. Dwivedi

More articles in International Journal of Information Management from Elsevier
Bibliographic data for series maintained by Catherine Liu ().