Campaigning by Numbers: The Role of Data-Driven Practices in Civil Society Organisations. / Macintyre, Amber.

2021.

Research output: ThesisDoctoral Thesis

Unpublished

Documents

Abstract

This research examines common claims about how personal data is used in political communication, focusing on civil society organisations (CSOs). Two ethnographic case studies are carried out to investigate the differences between a traditional membership-run CSO,Amnesty International, and a grant-funded CSO, Tactical Technology Collective. The findings are threefold. Firstly, new civil society organisations, such as Avaaz, 38 Degrees and Change.org, assert that data-driven technologies support their audience-led models. However,both organisations in this research engage in data-driven practices to persuade the audience to support the strategy set by organisational staff, corroborating the critical claims that data-driven practices reinforce expert-led models. Secondly, rhetoric around the uptake of new data-driven practices has been based on the assumption that distinct data-driven ways of working have become normalised. The findings show, however, that these two CSOs still rely on deliberation,personal judgement, and relationships to make strategic decisions. Finally, decision-making surrounding data-driven practices can be influenced by the opaque role of data scientists and data technologies. The findings show how placing these agents outside of strategic decision-making affects the organisation’s ability to manage personal data consistently across projects. The research is significant in understanding the complexity and nuance in the adoption, and rejection,of new data-driven practices. Further, the research makes a case for practitioners and researchers alike to be cautious about claims that data-driven practices support audience-led models, and to be open to the benefits of expert-led models.
Original languageEnglish
QualificationPh.D.
Awarding Institution
Supervisors/Advisors
Thesis sponsors
  • Leverhulme Trust
Award date1 Feb 2021
Publication statusUnpublished - Jan 2021
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 41174631