Rebuilding Stakeholder Confidence in Health-Relevant Big Data Applications: A Social Representations Perspective
Big data applications are at the epicentre of recent breakthroughs in digital health. However, controversies over privacy, security, ethics, accountability, and data governance have tarnished stakeholder trust, leaving health-relevant big data projects under threat, delayed, or abandoned. Taking the...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-09-01
|
| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/13/9/441 |
| _version_ | 1797486890809556992 |
|---|---|
| author | Anthony M. Maina Upasana G. Singh |
| author_facet | Anthony M. Maina Upasana G. Singh |
| author_sort | Anthony M. Maina |
| collection | DOAJ |
| description | Big data applications are at the epicentre of recent breakthroughs in digital health. However, controversies over privacy, security, ethics, accountability, and data governance have tarnished stakeholder trust, leaving health-relevant big data projects under threat, delayed, or abandoned. Taking the notion of big data as social construction, this work explores the social representations of the big data concept from the perspective of stakeholders in Kenya’s digital health environment. Through analysing the similarities and differences in the way health professionals and information technology (IT) practitioners comprehend the idea of big data, we draw strategic implications for restoring confidence in big data initiatives. Respondents associated big data with a multiplicity of concepts and were conflicted in how they represented big data’s benefits and challenges. On this point, we argue that peculiarities and nuances in how diverse players view big data contribute to the erosion of trust and the need to revamp stakeholder engagement practices. Specifically, decision makers should complement generalised informational campaigns with targeted, differentiated messages designed to address data responsibility, access, control, security, or other issues relevant to a specialised but influential community. |
| first_indexed | 2024-03-09T23:39:47Z |
| format | Article |
| id | doaj.art-3ff521fcd71043dd9fd81e328f75aad1 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-09T23:39:47Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-3ff521fcd71043dd9fd81e328f75aad12023-11-23T16:53:35ZengMDPI AGInformation2078-24892022-09-0113944110.3390/info13090441Rebuilding Stakeholder Confidence in Health-Relevant Big Data Applications: A Social Representations PerspectiveAnthony M. Maina0Upasana G. Singh1School of Computer Science and IT, Dedan Kimathi University of Technology, Private Bag, Nyeri 10143, KenyaSchool of Management, IT, and Governance, University of KwaZulu-Natal, Durban 4000, South AfricaBig data applications are at the epicentre of recent breakthroughs in digital health. However, controversies over privacy, security, ethics, accountability, and data governance have tarnished stakeholder trust, leaving health-relevant big data projects under threat, delayed, or abandoned. Taking the notion of big data as social construction, this work explores the social representations of the big data concept from the perspective of stakeholders in Kenya’s digital health environment. Through analysing the similarities and differences in the way health professionals and information technology (IT) practitioners comprehend the idea of big data, we draw strategic implications for restoring confidence in big data initiatives. Respondents associated big data with a multiplicity of concepts and were conflicted in how they represented big data’s benefits and challenges. On this point, we argue that peculiarities and nuances in how diverse players view big data contribute to the erosion of trust and the need to revamp stakeholder engagement practices. Specifically, decision makers should complement generalised informational campaigns with targeted, differentiated messages designed to address data responsibility, access, control, security, or other issues relevant to a specialised but influential community.https://www.mdpi.com/2078-2489/13/9/441big databig data technologiesdigital healthhealth policysocial representations theory |
| spellingShingle | Anthony M. Maina Upasana G. Singh Rebuilding Stakeholder Confidence in Health-Relevant Big Data Applications: A Social Representations Perspective Information big data big data technologies digital health health policy social representations theory |
| title | Rebuilding Stakeholder Confidence in Health-Relevant Big Data Applications: A Social Representations Perspective |
| title_full | Rebuilding Stakeholder Confidence in Health-Relevant Big Data Applications: A Social Representations Perspective |
| title_fullStr | Rebuilding Stakeholder Confidence in Health-Relevant Big Data Applications: A Social Representations Perspective |
| title_full_unstemmed | Rebuilding Stakeholder Confidence in Health-Relevant Big Data Applications: A Social Representations Perspective |
| title_short | Rebuilding Stakeholder Confidence in Health-Relevant Big Data Applications: A Social Representations Perspective |
| title_sort | rebuilding stakeholder confidence in health relevant big data applications a social representations perspective |
| topic | big data big data technologies digital health health policy social representations theory |
| url | https://www.mdpi.com/2078-2489/13/9/441 |
| work_keys_str_mv | AT anthonymmaina rebuildingstakeholderconfidenceinhealthrelevantbigdataapplicationsasocialrepresentationsperspective AT upasanagsingh rebuildingstakeholderconfidenceinhealthrelevantbigdataapplicationsasocialrepresentationsperspective |