Development of a crisis negotiation database in The Netherlands insights for implementing intelligence-led policing systems

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Journal of Policing, Intelligence and Counter Terrorism
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Development of a crisis negotiation database
in The Netherlands: insights for implementing
intelligence-led policing systems
Jedidjah G. Schaaij, Miriam S. D. Oostinga & Ellen Giebels
To cite this article: Jedidjah G. Schaaij, Miriam S. D. Oostinga & Ellen Giebels (18 Jan 2026):
Development of a crisis negotiation database in The Netherlands: insights for implementing
intelligence-led policing systems, Journal of Policing, Intelligence and Counter Terrorism, DOI:
10.1080/18335330.2026.2614339
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Development of a crisis negotiation database in The 
Netherlands: insights for implementing intelligence-led 
policing systems
Jedidjah G. Schaaij , Miriam S. D. Oostinga and Ellen Giebels 
Faculty of Behavioural, Management, and Social Sciences, Psychology of Conflict, Risk, and Safety section, 
University of Twente, Enschede, The Netherlands
ABSTRACT 
Advancing the rapidly growing field of intelligence-led policing, 
this article describes the development of the Negotiator Database 
– Netherlands (NDB-NL), dedicated to the field of crisis 
negotiation. Obtaining crisis negotiation data that is 
representative in scale and comprehensive in (psychological) 
information is challenging but essential for both practical insights 
and scientific advancements. Established through close 
collaboration between Dutch practitioners and researchers, the 
NDB-NL addresses this need. This paper outlines the Database 
Development Model, describing its four phases: Preparation, 
Development, Pilot Testing, and Implementation. The NDB-NL 
workgroup, including the authors, prioritised system usability and 
practical and scientific value to enable practitioners to document 
information across 70 data fields following an incident 
deployment. Beyond its operational function, the NDB-NL 
enhances knowledge management by structuring, preserving, 
and facilitating the sharing of critical negotiation insights, 
ensuring long-term accessibility for analysis, training, and policy 
development. Through analysis, the NDB-NL has the potential to 
further professionalise the field by providing data-driven 
strategies that align with the principles of modern law 
enforcement. This paper highlights the importance of cross- 
disciplinary collaboration for policing and offers concrete 
guidelines for implementing or improving similar systems. 
Through structured data collection and analysis, the NDB-NL 
facilitates advancements in police crisis communication practice.
ARTICLE HISTORY
Received 30 September 2025 
Accepted 6 January 2026 
KEYWORDS 
Hostage negotiations; crisis 
communication; technology 
implementation; police 
system; intelligence-led 
policing
The world is increasingly adopting data-driven approaches across various sectors, including 
military, law enforcement, and government, to enhance decision making and operational 
efficiency. However, to effectively leverage data, organisations must ensure access to 
high-quality, relevant information and systems capable of supporting comprehensive 
and structured data collection for analysis. This paper presents a case study on the 
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cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the 
author(s) or with their consent. 
CONTACT Jedidjah G. Schaaij j.g.schaaij@utwente.nl Jedidjah Schaaij, Section of Psychology of Conflict, Risk, and 
Safety, University of Twente, Enschede 7500AE, The Netherlands
JOURNAL OF POLICING, INTELLIGENCE AND COUNTER TERRORISM 
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development of the Negotiator Database – Netherlands (NDB-NL)1, a structured information 
system designed to address these challenges specifically in the field of crisis negotiations.
Crisis negotiators play a critical role in guiding subjects towards peaceful resolution in 
high-stakes situations, including hostage scenarios, kidnappings, terrorism, and suicide 
interventions. They strive to influence behaviour, minimise harm, and ultimately aim for 
a voluntary surrender (Vecchi, Wong, Wong, & Markey, 2019). It is essential to understand 
the factors that influence these encounters to adopt evidence-based approaches and 
improve negotiations. To address this need, Dutch practitioners and researchers collabo-
rated to create the NDB-NL, a centralised knowledge repository designed to collect inci-
dent data and enable in-depth analysis that informs crisis communication strategies.
The NDB-NL addresses a significant gap in existing crisis negotiation research, which 
has traditionally relied on limited datasets (Grubb, 2020), including case studies, incident 
recordings (Donohue & Roberto, 1993; Giebels & Taylor, 2009; Sikveland, Kevoe-Feldman, 
& Stokoe, 2020; Stokoe & Sikveland, 2020; Taylor & Donald, 2006; Taylor & Thomas, 2008), 
interview studies (Grubb, Brown, & Hall, 2018; Nieboer-Martini, Dolnik, & Giebels, 2012), 
surveys (Almond & Budden, 2012; Johnson, Thompson, Hall, & Meyer, 2018), and exper-
iments (Giebels, Oostinga, Taylor, & Curtis, 2017; van der Klok, Oostinga, Russel, & 
Yansick, 2024). While the FBI developed a crisis incident database focusing on demo-
graphics (Neller, Healy, Dao, Meyer, & Barefoot, 2021), the NDB-NL goes further by incor-
porating important psychological factors. The aim is to improve negotiations strategies 
and inform de-escalation training, all through a system that aligns with intelligence-led 
policing (ILP) by enabling structured data collection and analysis. In this context, technol-
ogy plays a key role in knowledge management by preserving, structuring, and sharing 
knowledge (Chong & Chong, 2009; Coyne & Bell, 2011; Mc Evoy, Ragab, & Arisha, 2019), 
fostering data-driven decision making. Thus, the NDB-NL not only has the potential to 
further professionalise the field but also supports more effective crisis resolution 
through data-driven strategies.
The success of thewith 
explanations
Incorporating hover-over information symbols for brief explanations, balancing 
helpfulness with a clean interface.
(Continued ) 
JOURNAL OF POLICING, INTELLIGENCE AND COUNTER TERRORISM 19
https://doi.org/10.1111/j.1750-4716.2008.00016.x
https://crestresearch.ac.uk/comment/acceleratinginfluence-challenging-the-linear-paradigm-of-suicide-negotiation/
https://crestresearch.ac.uk/comment/acceleratinginfluence-challenging-the-linear-paradigm-of-suicide-negotiation/
https://www.politieenwetenschap.nl/publicatie/overzichtsstudies/2012/de-professionaliteit-van-de-politie-25
https://www.politieenwetenschap.nl/publicatie/overzichtsstudies/2012/de-professionaliteit-van-de-politie-25
https://doi.org/10.1016/j.avb.2019.08.002
https://www.tandfonline.com/doi/abs/10.1080/18335330.2024.2448343
https://www.politieacademie.nl/kennisenonderzoek/kennis/mediatheek/PDF/99025.PDF
https://www.politieacademie.nl/kennisenonderzoek/kennis/mediatheek/PDF/99025.PDF
https://doi.org/10.1080/10439460701718583
Table A2. Continued.
What Explanation
Shortened input option Offering a shorter version for aborted deployments, withNDB-NL, like any ILP system, depends on effective implementation 
and use. Despite the growing field of ILP, little is known about development lifecycle, 
including the implementation and use of such policing systems (Ernst, ter Veen, & Kop, 
2021; Kadry, 2021). This paper offers an in-depth account of the NDB-NL’s design, 
implementation, and operational integration, focusing on the collaboration between 
researchers and practitioners. We outline the Database Development Model, a four- 
phase framework incorporating Preparation, Development, Pilot Testing, and Implemen-
tation, which serves as a blueprint for developing policing databases. By detailing key 
design choices, lessons learned, and recommendations for future research, this paper pro-
vides a scalable method to construct information systems for law enforcement agencies.
The four phases of developing the Negotiation Database Netherlands.
Developing a law enforcement system requires a systematic approach to ensure its 
success. Given the limited literature on ILP system design, we turned to other disciplines 
where practical and scientific objectives are central. In this case, we drew inspiration from 
Educational Design Research (EDR), as its key features align closely with the objectives of 
the NDB-NL: (1) theoretically oriented and improve understanding; (2) create productive 
change; (3) collaborative; (4) informed by participant expertise, literature, and field 
2 J. G. SCHAAIJ ET AL.
testing; (5) iterative, with repeated cycles of development, testing, and revision (McKen-
ney & Reeves, 2021). As a result, our Database Development Model is based on the 
‘generic model for conducting EDR’ (McKenney & Reeves, 2021). Although EDR focuses 
on instructional design and curriculum development, and therefore does not thematise 
ILP, we adapted its principles for policing technology by modifying the terminology 
and content of the four phases.
The development of the NDB-NL through four stages is depicted in the Database 
Development Model (see Figure 1). The first phase, Preparation, involved exploring the 
context, forming a workgroup, and defining goals and functions. In the Development 
phase, programmers designed the system based on workgroup input. The Pilot Testing 
phase optimised the system with iterative improvements before the National Implemen-
tation. This phased approach mirrors McKenney and Reeves’ model (2019, 2021) with 
increasing adoption at each stage. The process, initiated in November 2022, culminated 
with the implementation in November 2024. In the following sections, we detail each 
phase and address the faced challenges.
Phase 1: preparation
The first phase establishes the foundation before involving the technical team. During this 
stage, the workgroup defined the framework, contextualised its use within the Netherlands, 
and examined existing international negotiator systems. A key aspect of preparation was 
outlining the scope, clarifying functional specifications, and identifying content require-
ments (Garrett, 2011). The following sections will detail these areas, emphasising the con-
textual considerations and system architecture guiding NDB-NL’s development.
Intelligence-led policing framework
Intelligence-led policing is a strategic framework that leverages data analysis in the 
decision making processes within law enforcement. This evidence-based approach aims 
to mitigate criminal activities and associated risks through empirically informed strategies 
Figure 1. Database development model.
Note: Figure 1 is inspired by the ‘generic model for conducting design research in education’ in ‘Conducting Educational 
Design Research’, by McKenney S. and Reeves T. C., London. Copyright 2019 by Routledge.
JOURNAL OF POLICING, INTELLIGENCE AND COUNTER TERRORISM 3
(Ratcliffe, 2003, 2014). Unlike traditional reactive policing, ILP advocates a proactive and 
problem-oriented approach (Burcher & Whelan, 2019; Chan, Brereton, Legosz, & Doran, 
2001; Dencik, Hintz, & Carey, 2017), yielding actionable insights from data. Terms like 
‘intelligence-led,’ ‘data-driven,’ and ‘evidence-based’ all converge on the overarching 
concept of informed, knowledge-driven policing (van der Vijver, 2012). Here, the distinc-
tion between ‘intelligence’ and ‘information’ is crucial, as intelligence represents infor-
mation refined through collection, analysis, and interpretation (Innes, Fielding, & Cope, 
2005). Cope (2004, p. 190) adds that ‘intelligence can be understood as information devel-
oped to direct police action’. This paper adopts the term ILP, as the database is designed 
to collect incident data to generate actionable intelligence for future crisis negotiations.
Exploration of the context
Before defining the content and functionalities, it is essential to establish a clear contex-
tual scope. In the Netherlands, crisis negotiators (CNs) work within their regional police 
unit teams, each varying in size. Every unit is overseen by a CN coordinator, who is also 
part of a Dutch negotiation network. Currently, approximately 100 active CNs operate 
in the Netherlands, most of whom undertake this role as a secondary responsibility along-
side their primary duties. Dutch CNs are typically deployed across a broad spectrum of 
crisis incidents, with roughly 60% involving expressive cases, such as suicidal threats, 
domestic cases, and barricaded individuals, and about 40% involving more instrumental 
cases, such as kidnappings and extortions (Giebels & Noelanders, 2004).
Current incident administration
As in many other countries, the Dutch CNs’ current incident administration lacks align-
ment with ILP principles. At present, deployments are digitally recorded with minimal 
details – only a title, date, and a single open text field. Due to the absence of structured 
documentation, recorded information is often incomplete and unsuitable for systematic 
analysis. Moreover, despite every incident being logged, there is no statistical or inferen-
tial data available on the number and type of deployments.
Internationally existing negotiator systems
Internationally, negotiation teams keep records of their deployments, but ‘the challenge 
has been to build and maintain a centralised, nationwide database that is accurate and 
reliable.’ (Herdon, 2009, p. 269). Access to CN data remains limited, with only four data-
bases coming to the authors’ attention. First, the Hostage Barricade Database System 
(HOBAS), developed by the FBI’s Crisis Negotiation Unit. Despite acknowledged limit-
ations and inherent biases (Lipetsker, 2004), it is utilised for research purposes (Neller 
et al., 2021). Second, the Queensland Police Service Negotiator Deployment Database 
(QPS-NDD) which captures incident specifics, demographic and health-related details, 
outcomes, and includes a narrative report (Steele et al., 2024). Third is a database that 
records descriptives of Scottish CN deployments (Alexander, 2011). Lastly, the National 
Negotiator Deployment Database (NNDD) was set up in 2015 in the United Kingdom 
(UK).2 It was designed with HOBAS’ limitations in mind by mandating administration of 
all deployments within fields such as socio-demographic subject information, situational 
and behavioural characteristics, and outcomes (Grubb, 2020).
4 J. G. SCHAAIJ ET AL.
Building on these systems, the NDB-NL goes a step further by incorporating additional 
psychological components, making it a unique and valuable initiative. Recognising the 
international similarities in crisis negotiation practices, the NDB-NL’s pursuit of science- 
driven insights holds significant potential for a broader, global audience.
Workgroup
The development of the database involved three core stakeholder groups. First, the coor-
dinators of the pilot units, who maintain contact through the national network, ensuring 
standardisation and overseeing (long-term) implementation. Second, researchers (the 
authors) helped align the database with scientific constructsand prior research, while 
encouraging data quality. Third, the Dutch Police Academy will incorporate the database 
and the upcoming insights into future training sessions. Apart from the three groups, a 
technical team from the police innovation platform managed database programming, 
system interface design, automated query mechanisms, and business analysis 
workflows. An overview of tasks and expertise per group is provided in Table 1.
The integration of these diverse groups had two primary reasons. First, researchers and 
practitioners were already well connected through previous projects and would both 
benefit from the resulting knowledge. Second, research highlights the value of involving 
external experts, such as academics, in police innovations to facilitate ILP (Gemke, Den 
Hengst, Rosmalen, & Boer, 2021; Kadry, 2021; Wood, Fleming, & Marks, 2008). The multi-
disciplinary NDB-NL workgroup ensured diverse perspectives were incorporated, aligning 
decisions with the database’s objective.
System specification
The workgroup aimed to create an easy-to-use system to log comprehensive CN incident 
information for practical insights and scientific analysis. This dual focus shaped the data-
base schema, data structuring principles, and feature requirements. Identifying the 
Table 1. Overview of the NDB-NL workgroup members and their tasks.
N̊ Stakeholder group Tasks and type of expertise
3 Crisis negotiators: 
Two of which are coordinators of regional 
units.
- Ensure relevance and applicability.
- Guide pilot testing.
- Oversee system governance and long-term use.
3 Researchers - Enhance scientific analysability (e.g. through item and scale 
formulations).
- Address data quality (e.g. reliability, validity, integrity).
- Train negotiators (to improve data quality).
1 Police academy teachers (section CN) - Introduce system to new CNs.
- Plan educational sessions to share insights.
- Integration within curriculum.
Police innovation platform: 
Business analyst and lead programmer
- Design and programme the system.
- Assure technological functioning.
- Contribute to risk assessment and privacy assurance.
Note: The table indicates the core stakeholders for the NDB-NL.
JOURNAL OF POLICING, INTELLIGENCE AND COUNTER TERRORISM 5
requirements (see Table A1) before technical development has two important benefits. 
First, establishing a clear vision early ensures stakeholder alignment, prevents scope 
creep (Garrett, 2011), and provides a focused roadmap for efficient resource allocation. 
Second, anticipating challenges early minimised delays and unforeseen issues. By clarify-
ing what not to build, the process reduced rework and improved scalability (Garrett, 
2011). For instance, while some databases include internal communication tools, a chat 
function was deliberately excluded as it added no value to structured data collection 
and would have diverted resources. This targeted functional analysis streamlined devel-
opment, focusing solely on enabling data-driven insights.
Practitioners played a central role in identifying essential features as they are the most 
productive source (Garrett, 2011) for ensuring practical alignment (Laufs & Borrion, 2022). 
Additionally, with attention paid to the human and cultural factors of CNs, the system 
aligns with organisational activities and goals (Castillo & Cazarini, 2014). For example, 
CNs preferred a reading function for quick incident overviews and a subject search func-
tion to track repeat individuals. A practitioner-led, user-centred approach ensured the 
database was seen as a practical tool rather than a data entry burden, increasing adoption 
and engagement.
Content specifications
Defining content proved more challenging than system functionalities, requiring itera-
tive refinement. Researchers collaborated with practitioners to draft components and 
data fields, using the British NNDD as a reference but adapting it to Dutch practices. 
For example, specific fields on victim interaction were added, addressing a frequent 
oversight in crisis negotiations (Giebels, Noelanders, & Vervaeke, 2005). Additionally, 
negotiators not only select the influencing strategies used, modelled after the Table 
of Ten (Euwema & Giebels, 2024), but also explain their application, providing practical 
insights. The revised Behavioural Influence Stairway Model (Vecchi et al., 2019) was 
integrated based on recent findings (Oostinga et al., under review). Rather than indi-
cating negotiation progress, they assess individual components, enhancing both prac-
tical applicability and research value. Throughout the process, the content fields 
underwent refinements to ensure real-life applicability (Garrett, 2011). The challenge 
was minimising entry time while preserving essential details. Through iterative 
testing and feedback loops, the workgroup optimised the reporting task to limit 
data entry time to 15 minutes.
Phase 2: development
The second phase entailed integrating preparation aspects by constructing the functional 
system. Development began February 2023 and concluded February 2024, with weekly 
iterations, continuous stakeholder feedback, and issue resolution. During meetings, the 
developer showcased the implemented changes, and everyone could propose new 
ideas, suggest changes, or address errors. To ensure efficiency, some points were 
addressed immediately, while others were shelved for later discussion or pilot testing, 
allowing hands-on user experience to inform refinements. Keeping a log of discussed 
points provided a clear overview of the made decisions.
6 J. G. SCHAAIJ ET AL.
Decision making: a balancing act
All decisions were led by a few consideration points and three criteria: system usability, 
practical value, and scientific value. First, usability focused on the user experience and 
the data entry’s efficiency (see Table A2). We questioned whether proposed changes 
would complicate or slow NDB-NL usage, and whether such changes were justifiable. 
Second, practical value emphasised content relevance: data entry fields must be intuitive, 
familiar to negotiators, and cover all contexts to generate useful practitioner insights. This 
requires flexible drop-down lists that allow manual input when predefined options are 
not applicable. Therefore, the fields’ relevance and the type of information collected 
should align with practical needs. Here, the wording regarding incident outcome 
acknowledged that ‘success’ is not solely defined by a perpetrator’s surrender (Royce, 
2009). Third, scientific value emphasised reliability and validity through careful item for-
mulation, coherent scales, and alignment with previous research to improve analysability. 
To support consistent data collection, uniform registration was essential, including stan-
dardised answer options and mandatory fields for each incident.
Beside these criteria, two other considerations shaped decision making. One was future 
proofness to reduce complexity for system maintenance and field adaptations, particularly 
as the technical team was limitedly available. The other consideration was security and 
accessibility to safeguard sensitive police data. Access to the system was restricted to auth-
orised users on official police devices, with coordinators holding special permissions to 
export anonymised data for external use.
Balancing criteria and justifying decisions required compromises, as practitioners 
prioritised operational efficiency while researchers focused on analysability. Despite the 
differences, the database was developed through collaboration and open dialogue to 
meet both parties’ needs. For example, conflicting views were resolved by referring to 
the system’s goal and purpose defined in the preparation phase (Garrett, 2011). One 
such conflict involved data entry methods. While drop-down menus require minimal 
space (Garrett, 2011), sliders or buttons reduce clicks and add variety but require external 
programming for future updates.After discussion, we chose drop-down menus, prioritis-
ing long-term adaptability and maintainability, i.e. future proofness over system usability.
Moreover, some early decisions were reconsidered as the NDB-NL evolved (see Table A3
for an overview). An example concerns time-related fields, which initially included ten time- 
specific fields from the British system. These were reduced to two, as negotiators struggled 
to recall precise times (usability). However, we later added the time of first contact, as it was 
deemed relevant and likely known by CNs (value). This iterative process underscores the 
importance of defining the information we can have, want, and need. The workgroup’s 
input was crucial in weighing pros and cons, exploring alternatives from multiple perspec-
tives, and understanding decision implications (Garrett, 2011). This emphasises the value of 
interdisciplinary collaboration in developing ILP systems.
Actual system and data fields
The final version of the NDB-NL consists of approximately 70 fields organised into eight 
logical components (see Table 2). The components were strategically arranged, often fol-
lowing a chronological structure to improve usability, information flow, and 
JOURNAL OF POLICING, INTELLIGENCE AND COUNTER TERRORISM 7
understanding (Garrett, 2011). The database consists of three distinct types of fields: (1) 
factual fields, which capture quantifiable, objective information essential for recording 
unambiguous data that forms the foundation for analysis. Examples include incident 
start and end times, location coordinates, and subject details. (2) Categorical fields, pre-
sented as drop-down menus, standardise data entry while allowing flexibility. These 
fields cover areas like incident location, weapon use, and crisis type, facilitating structured 
comparisons across cases. (3) Interpretative fields require negotiators to provide qualitative 
assessments based on their perceptions and situational factors. These fields enhance the 
database with insights into psychological dynamics, behavioural patterns, and nego-
tiation strategies, such as de-escalation techniques. To further enhance the data richness, 
open-ended fields were included, allowing negotiators to add narrative context, expla-
nations, and additional details. These fields provide the flexibility to capture unique or 
unforeseen situations, offering insights into the complexities of real-world crisis nego-
tiations (Gundhus, Talberg, & Wathne, 2022). Open fields are particularly useful for captur-
ing qualitative data that cannot be reduced to predefined categories or numerical values, 
ensuring the database remains adaptable to diverse situations. To improve usability, the 
system includes conditional (‘if-then’) fields that appear only when relevant. For instance, 
victim interaction fields are displayed only if victims are listed under involved persons. 
This multifaceted field design ensures that the system can handle both objective data 
for statistical analysis and subjective assessments for contextual and qualitative insights, 
making the NDB-NL a tool for both research and operational practice.
Phase 3: pilot testing
The final stage of development involved pilot testing in two CN units from October 2023 
to March 2024 to ensure system functionality, user satisfaction, and practical feasibility. 
This phase enabled refinements based on practitioner feedback from hands-on user 
experience.
Training
Training is a critical component during implementation to ensure effective utilisation and 
it can determine the extent to which users will benefit from the system (Lambri, Jackson, & 
Table 2. The NDB-NL components and topic of the fields.
Components Topic of the fields
Incident 
information
Date and times, negotiators and their roles, police unit, context of the situation.
Location and 
advice
Address, type of location, involved partners, intermediaries.
Type of incident Type, impact factors, threats.
Info subject(s) Names, ages, (contact with) victims, motives, physical state.
Communication Language, type of contact, obtained information, use of influential strategies, apologies, 
relationship with subjects.
Outcome Weapon use, extent of influenced behaviour, outcome, satisfaction with outcome and process.
Evaluation question Collaboration, lessons learned, points to consider.
Documents Place to upload all files connected to the incident.
Note: This is an indication of the themes and not a complete list of the 70 + items.
8 J. G. SCHAAIJ ET AL.
Cooke, 2011). Therefore, a comprehensive 3+ hours training was arranged per CN team to 
explain the NDB-NL and its practical application. This also included the familiarisation with 
the items and understanding how to fill everything in as intended. The one-session train-
ing consisted of four parts: (1) NDB-NL system introduction and procedure, (2) explanation 
and discussion of fields, (3) a practice case, and (4) a survey to gather user input. During 
each session a researcher and practitioner from the workgroup was present.
Introduction and procedure
The training began with an introduction to the NDB-NL, emphasising its purpose and the 
importance of data quality. Research shows that ILP system users often underestimate the 
impact of valid data or fail to recognise personal benefits (O’Connor, Ng, Hill, & Frederick, 
2022; Sanders, Weston, & Schott, 2015). While logging every incident is mandatory, 
reliable insights depend on both compliance and high-quality data. To address this, the 
training emphasised how the NDB-NL can improve CN skills, advance the professionalisa-
tion of their field, and enable ILP.
Additionally, since the NDB-NL replaces previous incident administration methods, 
clear guidelines were provided on when and how to use the system. Research suggests 
that seamless integration into existing workflows significantly improves user compliance 
(Castillo & Cazarini, 2014; Vermeulen, 2009). Considering negotiators’ existing post- 
deployment debriefing sessions, incident reporting was incorporated into this evaluation 
process. Ideally, deployed negotiators discuss and report case details on-site with the 
team, with the option to add information later. This approach allows for immediate 
input on interpretative fields and improves data quality through timely retention. To 
support CNs, they received a detailed user guide containing field descriptions and 
examples, along with a quick-reference handout highlighting fields that require team 
input.
Discussion of fields
During the second part of the training, each field and its answer options were discussed. 
An interactive approach engages attendees and acknowledges that standardised data 
entry, like drop-down menus, would not fully eliminate subjective categorisation 
(Sanders & Henderson, 2012) or misinterpretations (O’Connor et al., 2022).
Negotiators’ input helped clarify classifications and terminology, ensuring consistent 
interpretation across all items. As a result, discussions focused mainly on interpretative 
questions, while factual fields required less explanation. Attendees’ critical questions 
and experiences sparked in-depth conversations, improving items to better align with 
their perspectives (Garrett, 2011). Thus, the second part contained explanations of each 
field, its inclusion rationale, and clarified definitions and interpretations.
Practice
The third part consisted of a practice case3 to familiarise attendees with the system and 
practice data entry, addressing a common gap in Dutch ILP system users (Kort & Terpstra, 
2015). This approach aimed to enhance data reliability and consistency between different 
negotiators. By receiving data entries on the same case, the discussion of fields could be 
adjusted. For example, explanations of influencing strategies were shortened, while the 
distinction between satisfaction with the outcome and process needed further 
JOURNAL OF POLICING, INTELLIGENCE AND COUNTER TERRORISM 9clarification. This exercise allowed participants to practice using the system while also pro-
viding valuable feedback to refine its usability and effectiveness.
Practice case
Participants watched bodycam footage of a simulated domestic crisis involving a man, 
Frits, violating a restraining order to see his children, confronting his ex-partner, Alice, 
and holding her at knifepoint. The video covered key fields, such as victim dynamics 
and influencing strategies, and facilitated in-depth discussions. The 20-minute video, 
with a clear beginning and resolution, was ideal for training.
Input from the users
The last part of the training was obtaining information from the attendees. This also 
included a questionnaire about expectations, suggestions, and overall acceptance of 
the system to further optimise the system.
Feedback during pilot testing
After training, 25 CNs from two regional units tested the system. Coordinators knowledge-
able about the NDB-NL were available to assist and acknowledge bugs or suggest 
improvements. Users provided valuable feedback, such as unclear field intentions, 
missing selection options, and confusing wording. This input helped to ensure that 
users correctly interpret descriptions, labels, and terminology within the system, and 
that each step is logical and user-friendly (Garrett, 2011). Incorporating these changes 
based on feedback led to significantly fewer user inquiries during subsequent training 
sessions.
Phase 4: national implementation
After receiving positive feedback from pilot testers on the updated NDB-NL, national 
implementation began. The remaining eight units were trained to officially use the 
NDB-NL between April 2024 and November 2024.4 With the system fully operational, prac-
titioners and researchers could foster a forward-looking approach by considering govern-
ance and long-term sustainability.
Governance
To ensure optimal implementation and stimulate high data quality, research suggests 
having a dedicated database person per team for consistent reinforcement (Darroch & 
Mazerolle, 2012) as these systems require major management efforts (Chong & Chong, 
2009). Therefore, CN team coordinators with supervisory responsibilities address user 
questions and hold additional system rights to oversee effective utilisation.
Long-term maintenance and use
Discussing strategies to enhance data quality and ensure continuous usage is crucial for 
the NDB-NL’s long-term success. This approach protects the investment beyond its initial 
10 J. G. SCHAAIJ ET AL.
implementation and ensures that the use aligns with the system’s purpose. Regular pre-
sentations of NDB-NL updates and findings can encourage deeper user commitment and 
motivation, which facilitates ongoing dialogue, feedback, and knowledge transfer (Chong 
& Chong, 2009). A key, but often overlooked aspect to enable reliable ILP, is (maintaining) 
high-quality data (Burcher & Whelan, 2019; O’Connor et al., 2022; Sanders et al., 2015).
The quality of knowledge impacts the system’s usability and adoption (Haug, 2024), 
emphasising the importance of continuous knowledge validation (Mc Evoy et al., 2019). 
One effective way to check data quality is by comparing entries from the same incident 
to check for consistency and identify discrepancies (i.e. inter-rater reliability). Additionally, 
subjective evaluations in the NDB-NL could be verified with more objective measures, 
such as audio or video recordings of incidents. With the increasing use of police body- 
worn cameras, these recordings provide valuable verbal, non-verbal, and contextual 
insights that can validate NDB-NL incident reports. Ultimately, regular cross-validation 
checks can be beneficial to data integrity.
To further improve usability and data accuracy, the system will be continuously 
updated based on user feedback and technological advancements (Haug, 2024). 
Planned integrations with other police systems via APIs will automate data entry in objec-
tive fields, such as incident types and locations, minimising manual input errors and 
improving efficiency. This interoperability will decrease inefficient overlapping content 
(Haug, 2024). By combining technological enhancements with systematic validation pro-
cesses, the NDB-NL can remain a reliable and evolving tool for both practitioners and 
researchers.
Future directions of the NDB-NL
The success of the NDB-NL is not only dependent on the implementation and usage; its 
true value lies in the analysis of the inputted data. In the future, CNs will collect a wide 
range of data by recording every deployment in the NDB-NL. Through data analysis intel-
ligence can be generated, and operational effectiveness can be enhanced. The focus 
could be the relationship between negotiator and person in crisis, exploring locations 
and victim characteristics, identifying temporal trends, and assessing how the applied 
influencing strategies impact the interaction and outcome. Importantly, the analysis 
should be guided by the societal need to find peaceful resolutions and minimise risk 
(Velthuizen, 2025).
Another promising prospect is internationalisation of a NDB-NL-like system. If inter-
national CN teams collect (psychological) data in equivalent negotiator databases, it 
enables more comprehensive global analysis. Beyond operational variations, assessing 
cultural differences can offer valuable insights, especially since research indicates that 
negotiations can be culturally dependent (see Giebels et al., 2017; Giebels & Noelanders, 
2004; Giebels & Taylor, 2009). International cooperation and comparison would further 
advance the field of crisis negotiations.
Potential to integrate artificial intelligence
While not an immediate priority, Artificial Intelligence (AI) and Natural Language Proces-
sing (NLP) could enhance the NDB-NL by optimising data processing and analysis. AI- 
powered text recognition and NLP could standardise open field text entries, improving 
JOURNAL OF POLICING, INTELLIGENCE AND COUNTER TERRORISM 11
consistency by detecting contextual meanings and minimising ambiguity (Cook, 2024). 
Additionally, AI models capable of processing large-scale unstructured text could 
extract actionable insights, enhancing intelligence for training and operational decision 
making (European Union Agency for Law Enforcement Cooperation, 2024).
Beyond data entry, AI-driven analytics could identify patterns and trends in negotiation 
cases. For instance, analysis of NDB-NL data may reveal relationships between response 
time and location type, and influence strategies associated with resolution success 
rates. Predictive modelling could refine these insights by assessing resolution probabil-
ities based on key factors such as time to first contact, crisis duration, and negotiation 
strategies. In turn, interactive dashboards could visualise these findings, facilitating 
data-driven decision making.
Despite its potential, AI implementation is not a current priority for the NDB-NL due to 
resource constraints and ethical, legal, and operational challenges. A primary concern 
is data privacy, particularly given the sensitive nature of crisis negotiation records. 
Additionally, AI models trained on historically skewed data risk reinforcing biases, 
resulting in unfair or unreliable recommendations. Ensuring transparency and account-
ability is essential but challenging, as AI-driven decisions must remain explainable and 
trustworthy (European Union Agency for Law Enforcement Cooperation, 2024). Moreover, 
crisis negotiation depends heavily on context, emotional intelligence, and situational 
awareness which are elements AI cannot replicate. Over-reliance on AI-generated 
recommendations may cause negotiators to overlook critical nuances essential for 
effective resolution. Therefore, if integrated, the system should function as an assistive 
tool to ease human tasks (Velthuizen, 2025) and enhance human expertise rather than 
replace it.
Discussion
The increasingreliance on data-driven approaches, particularly within ILP, highlights the 
necessity of systems capable of collecting information effectively for analysis. ILP depends 
on comprehensive, representative, and structured datasets to generate actionable intelli-
gence, guide strategic decisions, and improve operational effectiveness. Despite its 
growing importance, the literature on the development and implementation of ILP 
systems and comprehensive crisis negotiation datasets remain limited (Grubb, 2020). 
To address this gap, this study introduced the Negotiator Database – Netherlands 
(NDB-NL) as a practical case study and a transferable blueprint for developing similar 
systems. The NDB-NL was developed collaboratively by practitioners and researchers 
over two years, resulting in a centralised system tailored to systematically record all 
Dutch crisis negotiator deployments. Its design encompasses 70 + fields, capturing 
both factual and psychological information critical to understanding and improving out-
comes in high-stakes situations. The development of the NDB-NL followed a structured, 
iterative process, divided into four phases: Preparation, Development, Pilot Testing, and 
Implementation (see Figure 1).
This paper has two main implications: the usefulness of the proposed model and the 
database system itself. First, the Database Development Model fills a gap in law enforce-
ment literature by providing a structured, adaptable framework for developing and imple-
menting an ILP system. The model’s four phases emphasise iterative refinement based on 
12 J. G. SCHAAIJ ET AL.
user feedback, ensuring its relevance to real-world applications. Additionally, the collab-
oration between practitioners and researchers highlights the value of integrating diverse 
perspectives, making the model a promising framework for future police initiatives balan-
cing usability, practical value, and scientific utility.
Second, the NDB-NL has significant implications for crisis negotiations. The centralised 
data collection enables systematic analysis of real incident data, offering insights into 
trends, patterns, and influencing strategies in crisis situations. Furthermore, by including 
psychological factors, e.g. about the dynamics between negotiators, subjects, and victims, 
the database goes beyond traditional demographic-focused databases to deepen under-
standing of high-stake interactions. For practitioners, the NDB-NL enhances situational 
understanding, improving deployment effectiveness and supporting professionalisation 
through insights that can inform training. Completing entries may also encourage reflec-
tion, helping negotiators adopt a more conscious approach. For researchers, the NDB-NL 
provides previously unavailable data that integrates psychological, communicative, and 
strategic elements, enabling advanced empirical studies on crisis negotiations. Overall, 
the NDB-NL advances professionalisation and evidence-based practices in crisis com-
munication, demonstrating the potential of collaborative, data-driven approaches to 
enhance both theory and practice.
Effective practices and recommendations
Based on our experience with the NDB-NL, we recommend the four-phased approach for 
similar police innovations. Lacking a comparable model, we drew inspiration from the 
generic Educational Design Research model (McKenney & Reeves, 2021). Existing frame-
works, like the Quality Implementation Framework (Meyers, Durlak, & Wandersman, 2012) 
or those focused on e-governance and policing (Ernst et al., 2021; Meijer, 2015), mainly 
address implementation challenges. While these are important, the literature lacks guide-
lines covering the entire development process. Our four-phase model provides a compre-
hensive, adaptable framework for police system innovations, addressing specific needs for 
scientific or practical utility.
In the preparation stage, we recommend involving proactive stakeholders to estab-
lish a clear vision for the system. During development, engaging in discussions on 
opposing views is crucial. While this process takes time, it helps achieve the system’s 
objectives (Garrett, 2011). To streamline this, we suggest using a shared document 
accessible to all parties, rather than relying on individual logs. In the pilot testing 
phase, we propose providing sufficient guidance (Spivak, McEwan, Luebbers, & 
Ogloff, 2021) and training, and promptly implementing user feedback. This ensures 
engagement and that the system meets practical needs. In the final phase, it is impor-
tant to look beyond implementation. For example, regular sessions to discuss insights 
from the system can highlight its benefits to users. Lastly, we recommend workgroup 
evaluations at all stages to identify and address underlying issues. Strong group 
dynamics, as seen in the NDB-NL’s small group size, foster critical discussions and 
active participation.
A key aspect in this model is collaboration between researchers and practitioners, an 
approach supported by existing literature on police innovation (Gemke et al., 2021; Wood 
et al., 2008). The early and active involvement of researchers, supported by their strong 
JOURNAL OF POLICING, INTELLIGENCE AND COUNTER TERRORISM 13
ties to Dutch negotiators, help practitioners adopt evidence-based approaches. While 
transdisciplinary collaboration presents challenges, it leads to solutions by integrating 
diverse perspectives. Allowing opposing views to be expressed and debated ensured 
that decisions aligned with the system’s objectives.
Challenges
Developing the NDB-NL involved unanticipated challenges, particularly in addressing 
resistance among CNs to adopt the system. Some users expressed doubts and pessimism 
regarding the required resources and benefits. To address these concerns, we highlighted 
the system’s added value during the training. However, arranging these sessions proved 
difficult as well due to personnel constraints, delaying the implementation phase. Despite 
this, majority participation allowed trained users to share knowledge and motivate hesi-
tant colleagues, helping to reduce resistance.
Another challenge that arose during the transition to the police maintenance unit after 
development was securing additional funding and ensuring prioritisation of the system 
for the maintenance unit. This caused delays in implementing necessary updates, 
which risked reducing user motivation. While coordinators were allowed to make some 
adaptations, certain changes required external programmers, further complicating the 
process. Consistent communication and a shared vision helped manage expectations 
and ensured timely decisions.
Limitations
Beyond the challenges in development, the model itself has several limitations. First, 
while the importance of data quality was emphasised during training and long-term 
system use, the model lacks a comprehensive mechanism for assessing data quality 
throughout its process. Although existing literature discusses data quality issues and con-
sequences (Burcher & Whelan, 2019; O’Connor et al., 2022; Sanders et al., 2015), empirical 
research remains scarce (which the authors aim to address in future studies). Thus, while 
the model pays some attention to data quality, it should be a priority throughout all 
stages of the model.
Second, the model does not adequately account for the risk of function creep, where 
system use expands beyond its original purpose without proper oversight, potentially 
leading to ethical and privacy concerns (Koops, 2021). Although designed as a practitioner 
support tool, the NDB-NL risks being used for broader monitoring or other purposes. 
Moreover, premature use of the NDB-NL for deployment decisions, without the necessary 
research, could result in misguided actions. To mitigate these risks active stakeholder 
engagement is essential to maintain transparency and accountability, thereby safeguard-
ing against the unintended consequences of function creep (Dahl& Sætnan, 2009). Thus, 
while any system can potentially be expanded in its use, it is essential to remain vigilant to 
prevent such expansions.
Third, the model emphasises extensive stakeholder collaboration and iterative cycles of 
preparation, development, testing and revision, all of which are time-consuming. While 
the teamwork between researchers and practitioners is beneficial, it can increase com-
plexity. For instance, ensuring that all stakeholder perspectives are adequately 
14 J. G. SCHAAIJ ET AL.
represented requires time and effort to discuss and reach consensus. The iterative 
process, involving frequent meetings and adaptations, also demands significant 
resources. Nonetheless, these aspects of the Database Development Model are crucial 
to achieve an effective and high-quality product that supports the objectives of the 
involved parties long-term.
Fourth, the NDB-NL’s development and implementation were tailored to the Nether-
lands, where the CN team is relatively small, with approximately 100 members. This facili-
tated small training groups and individualised discussions, as well as greater practitioner 
involvement through an established network. In other countries, logistical challenges, 
larger team sizes, and resource constraints could complicate similar initiatives. Literature 
indicates that scaling local innovations to broader organisational levels can be difficult 
(Darroch & Mazerolle, 2012). However, proper preparation and deliberate progress 
through the model’s stages can streamline the overall process of developing and imple-
menting new police innovations.
Conclusion
The Negotiation Database Netherlands (NDB-NL) was successfully developed through the 
collaborative efforts of practitioners and researchers, following the four stages of the 
Database Development Model: Preparation, Development, Pilot Testing, and Implemen-
tation. Achieving a balance between system usability and the practical and scientific 
value was crucial in creating a centralised system for collecting crisis negotiation data. 
This model serves as a blueprint for developing new and improving existing database 
systems to support ILP. Future research should focus on analysing data quality, potential 
function creep, and user input to refine the system and obtain the insights into crisis com-
munications that prompted the development of the NDB-NL.
Notes
1. The authors of this paper were active members of the NDB-NL workgroup throughout the 
development and implementation phases.
2. The project in the UK was initiated by Lou Provart, Superintendent in District Commander 
Breckland and West Norfolk, UK. A presentation of the NNDD sparked interest in establishing 
a similar system in the Netherlands.
3. Apart from the pilot units, we incorporated practice in the training. This allowed us to directly 
discuss the fields and assure everyone could have a guided practice. Given the limited time 
resources of CNs, we opted to have pilot-users complete the exercise beforehand.
4. Roughly 76 of the 100 CN in the Netherlands attended training.
Acknowledgements
We thank Lou Provart for the inspirational initiative in the UK and for sharing his experiences and 
insights. We are also grateful to the Dutch police innovation lab, the police academy, the Dutch 
negotiators network, and the participating negotiators for their contributions to the NDB-NL. 
Special thanks go to Peter van Os, Jan de Ridder, Harold van Opzeeland, and Eline Oskam 
for their enduring dedication, Annemiek Fokkens for assisting with the initial fields list, and 
Naomi Woestenenk for her efforts in obtaining funding. Lastly, we appreciate all the crisis 
negotiators within the (pilot) units for their practical feedback and active participation in refining 
the NDB-NL.
JOURNAL OF POLICING, INTELLIGENCE AND COUNTER TERRORISM 15
Disclosure statement
No potential conflict of interest was reported by the author(s).
Funding
The development of the NDB-NL was funded by the Dutch National Police’s ‘innovation budget’ 
(Dutch: Innovatiebudget Nationale Politie).
Statements and declarations
We used ‘GPT-4o mini’ to trim sections within the text to reduce the word count.
ORCID
Jedidjah G. Schaaij http://orcid.org/0000-0003-2178-5399
Miriam S. D. Oostinga http://orcid.org/0000-0002-8189-2690
Ellen Giebels http://orcid.org/0000-0002-3366-5872
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Appendices
Table A1. Required system functionalities and features for the NDB-NL.
What Explanation
Different authorization levels For safety and privacy, the coordinators should have additional rights.
Easy fill-in process Registering an incident should be quick, ideally under 15 min.
Export function Export feature should allow selection of unit, period, and exclusion of personal data 
(for anonymized files).
Live data New incidents/data should be immediately visible on other devices.
Mobile version In addition to desktop/laptop versions, users should be able to input data via mobile 
devices.
Reading option for an overview of 
cases
Provide a simple overview with basic information of the latest incidents for 
negotiators.
Search function Enable search functionalities on incident and subject level.
Secure environment System access should be restricted to devices within the police network.
Short report/ notifications Include a tab with notifications of the latest insights and simple descriptive graphs.
Upload file function Allow attaching additional files, such as reports, bodycam videos, and audio 
recordings, to incidents.
Table A2. Usability requirements for the development of the NDB-NL.
What Explanation
Intuitive navigation Implementing a banner-like table of contents for easy section navigation, with the 
current section highlighted for better orientation.
Minimalist design Minimizing cognitive load by using the organization’s blue-and-white colour 
scheme, creating a simple layout that reduces distractions and helps users focus.
If-then fields Using conditional logic to display only relevant fields based on the case’s needs, 
showing follow-up fields when applicable to streamline data entry.
Drop-down menus including 
filter-by-typing
Including filter-by-typing functionality to quickly find options, while drop-down 
menus reduce clutter and simplify field changes, despite requiring more clicks, the 
benefits outweighed the drawbacks.
Standardized entries Automating the filling of certain fields (e.g. language, end-date, negotiator), with 
exceptions manually editable to ensure consistency and save time.
Information symbol