Organization studies Based Appraisal of Institutional Propositions in the Nigeria Data Protection Regulation

Abstract

The central notion in the organization studies is that the formation, sustenance and demise of organizations are directly connected to the socio-cultural contexts where they are situated. So, the extent organizations are able to handle the challenges to personal data management can be linked to the socio-cultural contexts where they are situated. This justifies the reasons why regulations such as the Nigeria Data Protection Regulation (NDPR) are produced to initiate institutional propositions that shape socio-cultural contexts of organizations into forms that promote efficient personal data management. This study, a part of a larger study, appraises the NDPR in order to assess how the institutional propositions in the regulation can help shape the socio-cultural contexts of organizations in Nigeria, and then, promote personal data management practices. The study uses the narrative literature method to select the publications that were used to reach its objectives. The study addresses the dearth of scientific research that describes how institutional propositions in the NDPR can impact on the socio-cultural contexts of organizations in Nigeria. It also ameliorates gaps in scientific knowledge about how institutional propositions in the NDPR can be legitimized and help restructure socio-cultural contexts of organizations in Nigeria, and in effect, promote efficient personal data management practices. Key words: Personal Data Management, Data Management Regulations, Nigeria Data Protection Regulation, Institutional Theory, Organizations, Nigeria Proceedings Citation Format Sabo, S.B. & Utulu, S.C.A. (2023): Organization studies Based Appraisal of Institutional Propositions in the Nigeria Data Protection Regulation. Proceedings of the Cyber Secure Nigeria Conference. Nigerian Army Resource Centre (NARC) Abuja, Nigeria. 11-12th July, 2023. Pp 115-124. https://cybersecurenigeria.org/conference-proceedings/volume-2-2023/. dx.doi.org/10.22624/AIMS/CSEAN-SMART2023P14