Evaluating the impact of 'overt' and 'covert' profiles in internet intelligence and investigation deployments on police legitimacy and compliance with National Police Chiefs' Council (NPCC) guidance.
| Lead institution | |
|---|---|
| Principal researcher(s) |
Liam Cahill
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| Collaboration and partnership |
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| Level of research |
PhD
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| Project start date |
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| Date due for completion |
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Research context
Internet intelligence and investigation (i3) is a constantly developing arena for policing. Balancing investigative opportunities with a need to respect the public’s privacy and maintain legitimacy is complex. To assist i3 practitioners, the NPCC issued guidance in 2018 (updated in 2020) around the use of 'overt' and 'covert' profiles in online deployments.
Research questions
Through a thorough literature review and evaluation of the national policy, research questions evolved to evaluate the strategy and how 'overt' profiles ensure police legitimacy.
- How are i3 practitioners employing 'overt' profiles as an operational tactic to conduct internet intelligence and investigation deployments?
- What are the operational circumstances where an 'overt' profile offers a tactical advantage over a 'covert’ profile?
- What impact does the NPCC's internet intelligence and investigation governance on ‘overt’ and ‘covert’ profiles have on 'internal' and 'external’ police legitimacy?
Aims
The research questions form the foundation of the aims and objectives of this research. The aims are to:
- understand the effectiveness of current policy and tactical advice in the deployment of 'overt' profiles
- establish whether they are being used by (i3) practitioners across the country
The goal is to comprehend the broader context in which 'overt' techniques affect police legitimacy – both externally with the public and internally in the context of police governance, transparency, and accountability.
The evidence will form recommendations for the NPCC to consider in their evaluation of national i3 guidance.
Research methodology
A 'pragmatic', methodological framework that supports the use of a mixed-methods approach and complements the research objectives serves as the foundation for this study.
This study will use a design called 'convergent triangulation'. This includes focus groups, an online survey and semi-structured interviews.
Focus groups
A purposeful selection criterion will make sure that people with different levels of experience and knowledge contribute to the discussion. Four focus groups will consist of ten people each. These are anticipated for early 2024.
Online survey
The sample size is about 12,000 operational i3 practitioners. The survey will allow practitioners to anonymously provide valuable insight into their operational practices and reasoning. Survey deployment is anticipated for early to mid-2024.
Semi-structured interviews
A purposeful selection process will facilitate 10 interviews with department heads across the country. A final interview will be conducted with the NPCC lead to understand the national strategy. Interviews will take place in mid-2024.
Analytical overview
The results will use a 'thematical' approach to interpret the qualitative and quantitative data together.
A descriptive statistical analysis will be used for the quantitative data, which will comprise categorical statistics (nominal and ordinal data types) captured in the survey.
Inferential statistics will be used to infer the sample data against the national population of i3 practitioners, providing generalisations about the community.
Using the confidence interval for the quantitative survey data – together with the qualitative data from the open survey questions, focus groups, and interviews – a 'thematical' analysis will be conducted to identify important trends in the data.
These will provide depth of meaning and assist in contextualising the varying paradigms that surround the subject. The results will be presented in a concurrent format, equally weighted, with identified themes from the research used to interpret the combined qualitative and quantitative data.