Research on police motivation is rare within the Arab world, a region distinct for its economic, cultural, religious, and social landscape, compared to the commonly studied Western settings. Addressing this gap, we integrated frameworks of motivational theory into a comprehensive survey completed by over 350 officers from a single police department in the United Arab Emirates. Our study examined factors derived from expectancy, goal-setting, and public service motivation theories, coupled with the job characteristic model. Furthermore, we investigated the potential impacts of hygiene factors on police motivation. Employing logistic regression analysis, we discerned factors associated with decreased motivation. Notably, gender, performance feedback, work-life balance and quality, and social support emerged as significant. We discuss these in the context of existing literature, examining if police motivation can be considered universal or if it is swayed by region-specific influences Our research design offers replicability, granting police agencies the blueprint to gauge both organizational and individual officer motivation and implement tailored interventions if deemed necessary.

Whether a missing person is considered to meet preconceived norms and societal expectations heavily influences the response of the police, public, and media. Nils Christie's 'ideal victim' theory therefore lends itself well to the study of missing people. This article examines this using police data from the United Kingdom. A comparison of solved versus long-term unresolved cases are used as a case study to explore the hypothesis that perceptions of weakness, vulnerability, and blameworthiness may be reflected in the outcome of missing persons cases. To test this, a multivariable logistic regression model is used to compare cases and confirms individuals with characteristics of ideal missing persons, such as being a girl, being suicidal or being bullied, are less likely to remain missing long-term. In contrast, individuals perceived as 'non-ideal', such as being of Asian ethnicity, those who prepared to go missing, or are recorded in the Child Protection Registry are more likely that their cases remain unresolved long-term. The implications potentially highlight the influence of biases and stereotypes in police missing persons risk assessments, underscoring the need for greater awareness and objectivity in managing missing person cases.

This scoping review examines the current landscape of police risk and threat assessment in England and Wales, identifying origin, breadth and scope of application, as well as empirical efficacy. The analysis highlights a strong adherence to core principles of risk and threat assessment, with structured professional judgement (SPJ) approaches being favoured. The most rigorous and empirically validated assessments are found at the individual or typological level, and relate to sexual and violent offenders, domestic abuse, threats to public figures, and indecent image offenders. In contrast, issues are identified in application of dynamic assessments related to decision-making and vulnerability related risk, as well as typological assessments regarding missing persons, police custody, honour-based abuse, anti-social behaviour, and mental health, where frameworks are either absent, new and untested, lack empirical foundations, or are inconsistently applied. At the strategic level, risk and threat governance instruments remain largely unevaluated despite their central role in setting priorities at local and national levels. The discussion highlights the challenge in the trade-off between breadth and depth. Dynamic models seek to raise standards and consistency, while sacrificing the precision often provided by specialist tools, which remain narrowly applied. Finally, recurring issues of repurposing tools across domains and inconsistent human application highlight that the limitations of assessment are not solely technical but also institutional and primarily relate to culture, resources and training.

Capacity and capability building are essential for ensuring UK police services can effectively meet evolving demands. This chapter explores how these concepts are integrated into the Force Management Statement (FMS) process, enabling forces to identify workforce challenges and implement strategic planning. Capacity building focuses on resource allocation and operational resilience, while capability building enhances individual skills and expertise. Examining Cumbria Constabulary and Humberside Police—both recognized for excellence—this chapter highlights key enablers, including leadership, innovation, human resource management, technology, and knowledge management. By adopting evidence-based strategies, police services can enhance workforce resilience, retention, and operational effectiveness.

Clear, Hold, Build (CHB) has been promoted by the United Kingdom Home Office as a place-based framework for tackling serious and organised crime (SOC), with official reporting suggesting an almost 25% reduction in acquisitive offences. This article assesses whether CHB represents substantive innovation beyond established crime-prevention and reduction approaches and evaluates the robustness of the claimed impacts. The analysis examines CHB doctrine and compares its operational tactics with existing SOC literature, and evidence synthesised in systematic reviews of crime prevention and reduction, providing an independent methodological reappraisal of the Home Office impact evaluation, including re-examination of the accompanying community survey findings.The findings indicate that CHB largely repackages existing disruption, prevention and enforcement methods rather than introducing a distinct intervention logic. Further, the reported acquisitive-crime reductions are not supported robustly when set against identified evaluation limitations and potential confounding from concurrent initiatives, including hotspot policing. Survey results show little corresponding improvement in community outcomes and instead point to less favourable perceptions of safety, cohesion, and police effectiveness in pilot areas. The article concludes with recommendations for clearer phase-specific success metrics, stronger evaluation transparency, and greater alignment of CHB delivery with evidence-based approaches that balance enforcement with legitimacy and durable guardianship.

This article critically examines the United Kingdom's Prevent strategy, arguing that its assessment mechanisms are fundamentally flawed. Current reliance on a risk assessment focused on vulnerability and susceptibility to radicalisation is ineffective at identifying individuals on escalating trajectories toward targeted violence, leading to significant false negatives. A scoping review of 15 counterterrorism frameworks identified the Pathway to Violence Model (PTVM) as a potential augmentation tool. The discussion outlines how PTVM's six-stage evidence-based framework enables a shift toward a contextualized, threat-based approach, thereby improving triage and resource prioritization. The 2024 Southport attack is analysed to illustrate how it can aid the prevention of the identified systemic issues and demonstrates the PTVM’s utility in preventing future failures.

Purpose The purpose of the paper is to conceptualise and develop a new framework and screening tool modelled on the adverse childhood experiences (ACEs) study. It identifies and categorises the most common operational, investigative and organisational adversities police officers face to enable measurement of cumulative career trauma and its association with both health and occupational outcomes. Design/methodology/approach The study uses a scoping review methodology following Arksey and O'Malley's (2005) five-stage framework, guided by the Population-Exposure-Outcome (PEO) model. Literature was systematically reviewed across policing domains and thematic analysis (Braun and Clarke, 2006) was applied to identify recurring adverse experiences. These were synthesised into 13 police adverse career experiences (PACEs) and formulated into a preliminary binary self-report survey tool modelled on the ACE framework. Findings The study identified 13 recurrent PACEs across three overarching domains: Operational, Investigative and Organisational. Operational PACEs included exposures such as witnessing death, serious assault, fatal collisions, handling human remains, responding to suicides and loss of colleagues are all associated with PTSD, anxiety, prolonged grief and sleep disturbance. Investigative PACEs encompassed experiences like investigating child deaths and sexual abuse cases, linked to burnout, vicarious trauma and depression. Organisational PACEs involved procedural injustice, moral injury, misconduct inquiries, bullying and negative leadership, which were tied to depression, anxiety, PTSD, presenteeism, turnover intentions and suicide risk. Practical implications The tool created provides a comprehensive, police-specific model for capturing exposure to adversity across operational, investigative and organisational domains, something not currently offered by existing stress or trauma assessment tools. Unlike symptom-based instruments, PACEs focuses on exposure rather than diagnosis, enabling early identification of officers at risk of negative outcomes such as PTSD, burnout, misconduct or ill-health retirement. Originality/value This lies in its introduction of the PACEs framework, which is the first empirical attempt to systematically identify, categorise and operationalise adverse experiences unique to policing into a survey tool.

At present, there is no research available that has explored how the coronavirus pandemic affected intelligence work. Understanding this is vital as any factor that may increase the likelihood of intelligence gaps is worthy of examination because they are frequently identified as a major causal factor of the more harmful issue of intelligence failures within law enforcement. Recent research (Marani, et al, 2021) states that despite the pandemic abating, the risk of further global incidents remains. Therefore, lessons need to be identified to reduce potential gaps and failures occurring during future pandemics. We seek to achieve this by asking how Covid-19 affected intelligence work within UK policing by interviewing fifteen intelligence personnel from one police service. Using a framework from the practice of knowledge management (KM) we analyze how the pandemic affected the processes, technology, individual and organisational willingness to share intelligence, workloads, location, and structure of intelligence delivery (Abrahamson and Goodman-Delaunty, 2014). Findings indicate that all were negatively impacted by changes in working priorities, increased demand on analysts, and the ability of the police to gather intelligence from covert human intelligence sources and partner agencies. Such implications are discussed in the context of wider intelligence literature and future preparedness.

This article provides two outputs. First, it introduces the Transformer Led Policing (TLP) model, which is a structured framework for integrating Generative Artificial Intelligence (GenAI) in policing. The purpose of developing the framework is to provide a model for police practitioners and researchers considering implementing or studying GenAI to conduct policing functions. Doing so provides consistency and adherence to the European Union's AI Act and the United Kingdom's (UK) Covenant for Using Artificial Intelligence in Policing. The TLP framework outlines a three-tier model: Devise, Discuss, Deploy. Each strand includes four components including development of police use cases, prompts, and application programming interfaces, through to implementation and evaluation. To ensure police services adopting GenAI can retain public confidence, the model incorporates components related to stakeholder management regarding legality, security, ethics, and legitimacy. A brief test case is provided to demonstrate the TLPs applicability. Second, the article provides a risk analysis of use cases for academics and practitioners considering the application of GenAI within policing graded against the European Union's AI Act risk definitions. The use cases presented provide opportunities to apply the TLP and include responding to the public, criminal investigation, intelligence analysis, safeguarding, workforce management, learning and development and administration. The overall intention of the article is to stir debate and discussion about applying GenAI within policing and provide an implementation framework for those considering doing so.

This study uses automation probability scores from the Office for National Statistics (ONS) to establish police functions at risk of automation due to technological advancements, including generative artificial intelligence (GenAI). Simulations explore how improving AI and technology acceptance affect probability and scale of impact. Findings indicate large numbers of employees are at high risk of full or partial automation or augmentation, dependent on the scale of adoption. 'Back office' roles comprising civilian staff, are most affected. While frontline policing is susceptible, augmentation is the most likely outcome. Senior command and clinical roles are the least affected. The results suggest potential inequality across employee groups, raising concerns regarding fairness of organisational responses including redundancy, retraining, and redeployment, particularly for lower-skilled roles or those in non-leadership ranks. Findings carry implications for strategic workforce planning, underscoring necessity for reskilling/upskilling initiatives, well-being support, and measures to safeguard morale and public confidence as policing transitions.

This study examines the offence behaviours, spatial patterns, and target preferences of foraging burglars, an emerging offender typology inspired by Optimal Foraging Theory (OFT). Foraging burglars carefully balance effort, risk, and reward in target selection, much like foraging animals in the natural world. Using data from 400 crimes identified by the police as committed by foraging burglars, I use a mix of multidimensional scaling and cluster analysis to identify nine initial subtypes of foraging burglars. Using professional judgement, these are subsequently consolidated into three typologies. This approach enables the study to consider the full range of potential profiles produced by various methodological approaches before reaching a refined classification. In doing so, I identify three subtypes: Generalised Foraging Opportunists, Organised Weekend Foragers, and Specialist Foragers. These three typologies highlight diversity within foraging burglars, and differ in their prioritisation of effort, risk, and reward. Generalised Foraging Opportunists focus on fast, low-effort gains, while Organised Weekend Foragers emphasise reward, alongside careful avoidance of detection. Specialist Foragers, in contrast, invest significant effort to target high-value items such as vehicles and jewellery. These distinctions have implications for the police, strongly indicating the need for tailored interventions based on the specific typology to optimise crime prevention, reduction, and offender apprehension. Applying OFT in this way supports a “whole system” response that is especially valuable given the high potential for crime displacement among foraging burglars, who quickly modify their strategies in response to police pressure. Through this study, I advance burglary offender profiling by demonstrating how OFT can generate practically relevant typologies and, by offering evidence-based insights, directly inform how the police can counter offending through crime reduction and prevention strategies.

Traditional crime linkage methods face challenges with complex datasets, arguably necessitating more sophisticated analytical tools. This research investigates this issue by exploring the application of machine learning, specifically the Random Forest algorithm, as a method to enhance crime linkage analysis of residential burglary cases.Using a dataset of 200 pairs of linked residential burglaries from the United Kingdom, this study employs the Random Forest technique to examine 67 identified crime features, including those within categories related to inter-crime distance, temporal patterns, such as time and day of the week, target selection, entry behaviour, crime scene conduct, and property stolen.The key objective is to identify and reduce predictive characteristics that reliably link burglaries, whilst potentially overcoming the limitations of conventional approaches. Findings generally support existing literature but provide increased nuance by indicating that certain factors specifically related to shorter inter-crime distances, the time and date of the offences, and the target's dwelling type, significantly contribute to accurately linking crimes. We discuss these findings in the context of existing research on the subject.Finally, we consider the benefits of using this novel methodology as a tool for crime linking. We argue that the improved accuracy, interpretability, and provision of multiple decision trees offers significant advantages for refining crime linkage practices, both operationally and in criminological research.

THRIVE (Threat, Harm, Risk, Investigation, Vulnerability, and Engagement) represents a decision-making framework introduced by the United Kingdom's (UK) National Police Chiefs' Council in 2017, with a particular focus on vulnerability. Alongside THRIVE other intelligence-led policing models such as the National Intelligence Model (NIM), have become integral to policing practices. While THRIVE is widely adopted as a primary analysis and decision-making framework in UK police services, its examination remains limited, including its impact on the NIM and its use by intelligence personnel. Interviews with 15 police personnel from operation intelligence units within a specific English service were conducted to ascertain its level of adoption. A series of Freedom of Information (FOI) requests to all 43 UK police services in England and Wales were then initiated, to understand if the THRIVE model is adopted and, if so, where within their respective units. The findings indicate widespread acceptance and integration of THRIVE among intelligence practitioners, without immediate adverse effects on the application of the NIM. The use of heuristic naturalistic decision-making processes in THRIVE assessments, suggests a need for further research. Though, there is a risk of reduced decision-making capacity among frontline intelligence workers using THRIVE within the constraints of the NIM.

Using a case study approach, this article examines how organizational trauma emerges and manifests within UK police services. A scoping review methodology is used to identify 21 high-profile policing cases that fall within a taxonomy of organizational trauma (Isik, 2017). Cases were classified across three typologies: events resulting from internal organizational processes, trauma-prone occupations, and catastrophic events. Using thematic analysis to examine documents from government inquiries and independent investigations reveals several findings. Police organizational trauma in the cases examined is often internally controllable, caused through endemic failures in professional standards and unpreparedness, compounded by reactive leadership and insufficient employee support. Collective impacts on employees' manifests in over corrective bureaucracy, operational paralysis, and symptoms of post-traumatic stress disorder and burnout. External consequences include diminished public confidence, trust, and legitimacy. To mitigate, recommendations include reframing collective trauma as an institutional risk, adopting trauma-informed interventions, and investing in leadership development and ethical vigilance.

This article introduces the Decision-Making Framework for Policing (DMFP), a comprehensive tool designed to enhance the decision-making understanding of police officers. The DMFP considers the principles of heuristic, naturalistic, and rational decision-making along a fluid cognitive continuum to create a framework that addresses the limitations of the existing police National Decision Model (NDM). It achieves this by including 10 proposed typologies of police decisionmaking including: Routine, Tactical, Operational, Crisis, Investigative, Ethical, Interpersonal, Administrative, Managerial, and Strategic. These are integrated alongside existing and adapted decision-making models which are presented using a mnemonic letter strategy. Although the DMFP is theoretical, and its utility is presently untested in comparison to the existing NDM, it is presented to provide a tool to help improve officers' tacit knowledge, pattern recognition, and experiential learning through provision of easily recallable mnemonic decision-models. Thereby fostering a deeper understanding of cognitive processes and the factors influencing police decisions, potentially increasing consistency in reasoning, reducing decision errors, and enhancing policing outcomes.

The coronavirus pandemic affected policing in a number of both anticipated, and unexpected ways. However, the impact on police intelligence remains an unexplored area. Understanding how the pandemic affected the volume of police intelligence is important as it underpins the intelligence-led policing model, which is as a key system that helps drive police activity. In this study, data from 20 police services over a 4-year period that outlines the annual volume of intelligence reports retained by services is analysed using inferential statistics to establish that during 2020 there was a significant rise in intelligence held by the police. In this study, several hypothesis are considered as causal factors that contributed to the rises and conclude that the pandemic is the most likely reason, which is caused by a rise in public order intelligence related to breaches of coronavirus legislation. The impact on the division of labour that arises from tasking such police intelligence is discussed, and the article calls upon similar research on the issuance of coronavirus fixed penalties and stop and search activity during the pandemic, to suggest that the rises have the potential to contribute to the disproportionate targeting of black and minority ethnic communities. We call for further research to explore this further.

General purpose artificial intelligence (GPAI) is a form of advanced AI system that includes the recently introduced ChatGPT. GPAI is known for its capacity to understand and emulate human responses, and potentially offers an opportunity to reduce human error when conducting tasks that involve analysis, judgement, and reasoning. To support officers to do this, the police presently use a range of decision-making support tools, one of which is called THRIVE (Threat, Harm, Risk, Investigation, Vulnerability, and Engagement). THRIVE is designed to provide police practitioners with a model to improve their identification and response to vulnerability. Despite the existence of such decision models, a 2020 meta-analysis of police cases resulting in death or serious injury identified contributory failures that included poor risk identification, risk management, failure to adhere to evidentiary processes, poor criminal investigations, and inadequate police engagement with victims, including the level of care and assistance provided (Allnock, et al, 2020). Importantly, this report outlined human error as being a major underpinning factor of the failures. Although GPAI offers an opportunity to improve analysis, judgement, and reasoning, such systems have not yet been tested in policing, a field where any reduction in human error, particularly in the assessment of threat, harm, risk, and vulnerability can potentially save lives. This study is the first attempt to do this by using the chain-of-thought prompt methodology to test the GPAI ChatGPT (3.5 vs 4) in a controlled environment using 30 life-like police scenarios, crafted, and analyzed by expert practitioners. In doing so, we identify that ChatGPT 4 significantly outperforms its 3.5 predecessor, indicating that GPAI presents considerable opportunity in policing. However, systems that use this technology require extensive directional prompting to ensure outputs that can be considered accurate, and therefore, potentially safe to utilize in an operational setting. The article concludes by discussing how practitioners and researchers can further refine police related chain-of-thought prompts or use application programming interfaces (APIs) to improve responses provided by such GPAI.

Optimal forager theory (OFT) initially emerged from ecological studies, elucidating how foraging organisms seek resources. In recent decades, this ecological theory has migrated to the realm of criminology, where it is used to identify burglary offenders and inform crime analysis. Several police services employ optimal forager theory-based analysis to guide hotspot patrol interventions aimed at reducing domestic burglary. Crime displacement resulting from hotspot interventions has been a subject of debate, with approximately a quarter of cases experiencing some form of displacement, the underlying reasons for which remain unclear. This study postulates that the presence of the optimal forager typology of offender may be one contributing factor. To test this hypothesis, we analyze the cumulative crime diffusion and displacement effects of ten optimal forager theory-inspired hotspot interventions employing the weighted displacement quotient (WDQ) technique (Bowers and Johnson, 2003) and the Cambridge harm index (CHI) (Sherman et al., 2016). The findings reveal the interventions' marked efficacy in reducing domestic burglary within response areas. However, this reduction is overshadowed by the substantial spatial and offense displacement they induce, encompassing both crime count and harm. These results provide insights into the proportion of hotspot interventions that trigger crime displacement, and policy implications for the choice and selection of crime reduction strategies. Supported by ecological studies of optimal foragers, we argue that this phenomenon stems from the exceptional motivation of foraging offenders and their inclination toward anti-detection behavior, specifically, relocating to alternative crime areas.

This study examines the utility of a virtual reality (VR) arson crime scene investigation simulation developed by the Abu Dhabi Police service. Utilizing qualitative interviews with participants from the Saif Bin Zayed Academy for Security and Policing Sciences, the study captures views of the VR training experience with an emphasis on learning effectiveness, engagement, skill acquisition, cost and time efficiency, and inclusivity and accessibility. The findings are discussed in the context of a theoretical framework provided by the technology acceptance model (TAM) and indicate high levels of engagement and immersion among the participants. Many expressed a preference for VR training over classroom training. Thus, the 'perceived usefulness' of the technology was high. The interviewees also reported significant perceived benefits in terms of acquiring procedural knowledge and skills. The immersive nature of the VR was identified as a key factor in its utility. The cost and time efficiencies driven by the capability to train multiple officers simultaneously without the need for physical resources and with fewer of the risks commonly associated with live training are also outlined. The study also identified limitations regarding the inclusivity and accessibility of the technology, including among individuals with disabilities. Nevertheless, the overall reception of the simulation was positive. The findings indicate that VR is widely accepted within the police service and has great potential for wider use to enhance training in other areas if it serves to deliver content focused on policies and practice.