ISSN 2663-0583 (Online)
ISSN 2663-0575 (Print)
  • Home
  • Big Data-Driven Predictive Policing: Global Practices and Lessons for ASEAN Countries
"

Big Data-Driven Predictive Policing: Global Practices and Lessons for ASEAN Countries

1. Introduction. – 2. Methodology. – 3. An Overview of Big Data-Driven Predictive Policing. – 3.1. Definition and Conceptual Foundations. – 3.2. Classification of BDPP Models. – 4. Global Practice of Big Data-Driven Predictive Policing. – 4.1. Implementation of Big Data-Driven Predictive Policing in Practice. – 4.2. Problems and Limitations. – 5. Recommendations for ASEAN countries. – 5.1. ASEAN’s Institutional and Regulatory Landscape. – 5.2. Policy Framework for ASEAN BDPP Adoption. – 6. Conclusions.

Full Text:

DOI

HTML Abstract About Author(s) References Reviews Українською

Background: The use of big data and algorithmic tools has become increasingly common in law enforcement. One notable development is Big Data-Driven Predictive Policing (BDPP), which seeks to predict where crime may occur or who may be involved by analysing large datasets. Although prediction has long been part of policing, BDPP differs in scale and automation, relying on advanced analytics and machine learning. While many police agencies view BDPP as a way to improve efficiency and move towards preventive policing, its use has raised important legal and social concerns. Issues such as transparency, data bias, accountability, and the protection of fundamental rights remain unresolved, particularly in ASEAN countries, where legal frameworks for algorithmic policing are still developing.

Method: This article is based on a qualitative literature review of academic studies, criminological research, and policy documents on predictive policing. It examines how BDPP operates in practice, with attention to both place-based and person-based models. Selected experiences from the United States, the Netherlands, and the United Kingdom are reviewed to assess the reported effectiveness of predictive policing, as well as its practical limitations and associated problems. Drawing on these findings, the article identifies lessons relevant to ASEAN countries considering adopting similar approaches.

Results and Conclusions: The article shows that while BDPP offers potential to enhance law enforcement efficiency and proactive resource allocation, global evidence from the US and Europe indicates mixed effectiveness and substantial risks related to algorithmic bias, transparency, and fundamental rights. For ASEAN countries, where institutional capacity and data protection regimes differ significantly, cautious and phased adoption is advisable. Priority should be given to controlled, place-based pilot projects, supported by clear legal mandates, data governance standards, independent review, and sustained human oversight.

Abstract

Background: The use of big data and algorithmic tools has become increasingly common in law enforcement. One notable development is Big Data-Driven Predictive Policing (BDPP), which seeks to predict where crime may occur or who may be involved by analysing large datasets. Although prediction has long been part of policing, BDPP differs in scale and automation, relying on advanced analytics and machine learning. While many police agencies view BDPP as a way to improve efficiency and move towards preventive policing, its use has raised important legal and social concerns. Issues such as transparency, data bias, accountability, and the protection of fundamental rights remain unresolved, particularly in ASEAN countries, where legal frameworks for algorithmic policing are still developing.

Method: This article is based on a qualitative literature review of academic studies, criminological research, and policy documents on predictive policing. It examines how BDPP operates in practice, with attention to both place-based and person-based models. Selected experiences from the United States, the Netherlands, and the United Kingdom are reviewed to assess the reported effectiveness of predictive policing, as well as its practical limitations and associated problems. Drawing on these findings, the article identifies lessons relevant to ASEAN countries considering adopting similar approaches.

Results and Conclusions: The article shows that while BDPP offers potential to enhance law enforcement efficiency and proactive resource allocation, global evidence from the US and Europe indicates mixed effectiveness and substantial risks related to algorithmic bias, transparency, and fundamental rights. For ASEAN countries, where institutional capacity and data protection regimes differ significantly, cautious and phased adoption is advisable. Priority should be given to controlled, place-based pilot projects, supported by clear legal mandates, data governance standards, independent review, and sustained human oversight.

About Authors

Thu Huong Vu*

LL.M., Lecturer, Faculty of Criminal Law, Hanoi Law University, Vietnam

huongvt@hlu.edu.vn

https://orcid.org/0009-0000-3653-2664

Corresponding author, responsible for writing – original draft; conceptualisation;

resources; project administration; methodology

 

Thi Phuong Do

PhD, Associate Professor, Vice Dean, Faculty of Criminal Law, Hanoi Law University, Vietnam

phuong@hlu.edu.vn

https://orcid.org/0009-0000-7197-8872

Co-author, responsible for writing, review & editing; methodology; conceptualisation;

 

Viet Khanh Hoa Nguyen

PhD, Lecturer, Faculty of Law, Hanoi Law University, Vietnam

Faculty of Criminal Law, Hanoi Law University, Vietnam

hoanvk@hlu.edu.vn

https://orcid.org/0009-0004-5888-3237

Co-author, responsible for conceptualisation, writing – original draft; resources

 

Competing interests: No competing interests were disclosed.

 

Disclaimer: The authors declare that their opinions and views expressed in this manuscript are free from any impact of any organisations.

Rights and Permissions

Copyright: © 2026 Thu Huong Vu, Thi Phuong Do and Viet Khanh Hoa Nguyen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Editors

Managing Editor – Prof. Iryna Izarova. English Editor – Robert Reddin. Ukrainian language Editor – Mag. Liliia Hartman.

References

1.      Ainsworth PB, Offender Profiling and Crime Analysis (Willan 2001) doi:10.4324/9781843924630

2.      Al Shamsi HRS and Safei S, ‘Artificial Intelligence Adoption in Predictive Policing to Predict Crime Mitigation Performance’ (2023) 14(3) International Journal of Sustainable Construction Engineering and Technology 289

3.      Amoore L and Piotukh V, ‘Life beyond Big Data: Governing with Little Analytics’ (2015) 44(3) Economy and Society 341, doi:10.1080/03085147.2015.1043793

4.      Angwin J and others, ‘Machine Bias’ (ProPublica, 23 May 2016) https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing accessed 13 October 2025

5.      Asor J and others, ‘Building Model for Crime Pattern Analysis Through Machine Learning Using Predictive Analytics’ (2022) 2(1) International Journal of Science, Technology, Engineering and Mathematics 61

6.      Bachner J, ‘Predictive Policing: Preventing Crime with Data and Analytics’ (Improving Performance Series, IBM Center for The Business of Government 2013)

7.      Baraniuk C, ‘Exclusive: UK Police Wants AI to Stop Violent Crime before It Happens’ (New Scientist, 26 November 2018) https://www.newscientist.com/article/2186512-exclusive-uk-police-wants-ai-to-stop-violent-crime-before-it-happens/ accessed 11 October 2025

8.      Braga AA and Bond BJ, ‘Policing Crime and Disorder Hot Spots: A Randomized Controlled Trial’ (2008) 46(3) Criminology 577, doi:10.1111/j.1745-9125.2008.00124.x

9.      Brayne S, ‘Big Data Surveillance: The Case of Policing’ (2017) 82(5) American Sociological Review 977, doi:10.1177/0003122417725865

10.   Carter JG and others, ‘The Indianapolis Harmspot Policing Experiment’ (2021) 74 Journal of Criminal Justice 101814, doi:10.1016/j.jcrimjus.2021.101814

11.   Chainey S and Ratcliffe J, GIS and Crime Mapping (John Wiley & Sons, 2013)

12.   Chase J and others, ‘GRAND-VISION: An Intelligent System for Optimized Deployment Scheduling of Law Enforcement Agents’ (Proceedings of the 31st International Conference on Automated Planning and Scheduling ICAPS 2021, June 7-12) 459

13.   Cheng S, ‘Predictive Policing in Practice: A Case Study of Chicago’s Strategic Subject List’ (Senior Thesis, Claremont McKenna College 2025)

14.   Couchman H, ‘Policing by Machine: Predictive Policing and the Threats to Our Rights Liberty’ (Liberty, 1 February 2019) https://www.libertyhumanrights.org.uk/issue/policing-by-machine/ accessed 10 October 2025

15.   De Graauw JS, ‘Tijdruimtelijk Voorspellen van Criminele Incidenten’ (Master thesis, Vrije Universiteit Amsterdam 2014)

16.   De Mauro A, Greco M and Grimaldi M, ‘A Formal Definition of Big Data Based on Its Essential Features’ (2016) 65(3) Library Review 122, doi:10.1108/LR-06-2015-0061 

17.   Ferguson AG, ‘Policing Predictive Policing’ (2017) 94(5) Washington University Law Review 1109

18.   Ferguson AG, ‘Predictive Policing Theory’ in Tamara Rice Lave and Eric J Miller (eds), The Cambridge Handbook of Policing in the United States (CUP 2019) 491, doi:10.1017/9781108354721.025

19.   Ferguson JM, Advisory Concerning the Chicago Police Department’s Predictive Risk Models: Report of the Public Safety Section of the Office of Inspector General (City of Chicago, Office of Inspector General 2020)

20.   Fitzpatrick DJ, Gorr WL and Neill DB, ‘Keeping Score: Predictive Analytics in Policing’ (2019) 2 Annual Review of Criminology 473, doi:10.1146/annurev-criminol-011518-024534

21.   Florence C et al., ‘Effectiveness of Anonymised Information Sharing and Use in Health Service, Police, and Local Government Partnership for Preventing Violence Related Injury: Experimental Study and Time Series Analysis’ (2011) 342 BMJ d3313, doi:10.1136/bmj.d3313

22.   Fosso Wamba S et al., ‘How “Big Data” Can Make Big Impact: Findings from a Systematic Review and a Longitudinal Case Study’ (2015) 165 International Journal of Production Economics 234, doi:10.1016/j.ijpe.2014.12.031

23.   Gurney K, An Introduction to Neural Networks (CRC Press 2018) doi:10.1201/9781315273570

24.   Hardyns W and Rummens A, ‘Predictive Policing as a New Tool for Law Enforcement? Recent Developments and Challenges’ (2018) 24 European Journal on Criminal Policy and Research 201, doi:10.1007/s10610-017-9361-2

25.   Hassija V et al., ‘Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence’ (2024) 16 Cognitive Computation 45, doi:10.1007/s12559-023-10179-8

26.   Haykin S, Neural Networks and Learning Machines (3rd edn, Prentice-Hall 2009)

27.   Hunt P, Saunders J and Hollywood JS, Evaluation of the Shreveport Predictive Policing Experiment (Safety and Justice Program, RAND Corporation, 2014)

28.   Jansen F, Data Driven Policing in the Context of Europe (ERC-funded project Data Justice Working Paper, Data Justice Lab 2018)

29.   Joh EE, ‘Policing by Numbers: Big Data and the Fourth Amendment’ (2014) 89(1) Washington Law Review 35

30.   Kaufmann M, Egbert S and Leese M, ‘Predictive Policing and the Politics of Patterns’ (2019) 59(3) The British Journal of Criminology 674, doi:10.1093/bjc/azy060

31.   Kelleher JD, Namee BM and D’Arcy A, Fundamentals of Machine Learning for Predictive Data Analytics, Second Edition: Algorithms, Worked Examples, and Case Studies (2nd edn, MIT Press 2020)

32.   Kim W et al., ‘A Taxonomy of Dirty Data’ (2003) 7 Data Mining and Knowledge Discovery 81, doi:10.1023/A:1021564703268

33.   Koss K, ‘Leveraging Predictive Policing Algorithms to Restore Fourth Amendment Protections in High-Crime Areas in a Post-Wardlow World’ (2015) 90(1) Chic-Kent Law Review 301

34.   Kow J, ‘On Legal Harmonisation Within ASEAN’ [2012] The Singapore Law Review http://www.singaporelawreview.com/juris-illuminae-entries/2015/on-legal-harmonisation-within-asean accessed 5 February 2026

35.   Lee Y, Bradford B, and Posch K, ‘The Effectiveness of Big Data-Driven Predictive Policing: Systematic Review’ (2024) 7(2) Justice Evaluation Journal 127, doi:10.1080/24751979.2024.2371781

36.   Lum K and Isaac W, ‘To Predict and Serve?’ (2016) 13(5) Significance 14, doi:10.1111/j.1740-9713.2016.00960.x

37.   Mangkhalasiri P and Poothakool K, ‘Data Science in Policing in Thailand: Challenges and Future Directions – A Review from International Perspectives’ (2025) 12(2) Rangsit Journal of Social Sciences and Humanities 18, doi:10.59796/jcsh.v12i2.18-34

38.   Marciniak D, ‘Algorithmic Policing: An Exploratory Study of the Algorithmically Mediated Construction of Individual Risk in a UK Police Force’ (2023) 33(4) Policing and Society 449, doi:10.1080/10439463.2022.2144305

39.   Martín S and others, Garbage In, Gospel Out: How Data-Driven Policing Technologies Entrench Historic Racism and “Tech-Wash” Bias in the Criminal Legal System (National Association of Criminal Defense Lawyers 2021)

40.   McCue C, Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis (2nd edn, Elsevier 2015) doi:10.1016/C2013-0-00434-3

41.   McGuire MR, ‘The Laughing Policebot: Automation and the End of Policing’ (2021) 31(1) Policing and Society 20, doi:10.1080/10439463.2020.1810249

42.   Meijer A and Wessels M, ‘Predictive Policing: Review of Benefits and Drawbacks’ (2019) 42(12) International Journal of Public Administration 1031, https://doi.org/10.1080/01900692.2019.1575664

43.   Miller S, ‘Predictive Policing’ in Edmonds D (ed), Future Morality (OUP 2021) 73, doi:10.1093/oso/9780198862086.003.0007

44.   Mohler GO and others, ‘Randomized Controlled Field Trials of Predictive Policing’ (2015) 110(512) Journal of the American Statistical Association 1399, doi:10.1080/01621459.2015.1077710

45.   Mohler GO et al., ‘Self-Exciting Point Process Modeling of Crime’ (2011) 106(493) Journal of the American Statistical Association 100, doi:10.1198/jasa.2011.ap09546

46.   Moses LB and Chan J, ‘Algorithmic Prediction in Policing: Assumptions, Evaluation, and Accountability’ (2018) 28(7) Policing and Society 806, doi:10.1080/10439463.2016.1253695

47.   Odering J, ‘Southeast Asian Region Countries Law: Regional and Comparative Resources’ (University of Melbourne, Library, 2020) https://unimelb.libguides.com/c.php?g=930183&p=6721948 accessed 5 February 2026

48.   Oosterloo S and van Schie G, ‘The Politics and Biases of the “Crime Anticipation System” of the Dutch Police’ (BIAS 2018 Bias in Information, Algorithms, and Systems, Sheffield, UK, 25 March 2018) vol 2103, 30

49.   Oswald M et al., ‘Algorithmic Risk Assessment Policing Models: Lessons from the Durham HART Model and “Experimental” Proportionality’ (2018) 27(2) Information & Communications Technology Law 223, doi:10.1080/13600834.2018.1458455

50.   Pamesti RCI and Hsi HM, ‘Applicability of Predictive Policing in Indonesia Police Agency’ (2024) 27(1) Jurnal Litbang Polri 1, doi:10.46976/litbangpolri.v27i1.234

51.   Perry WL and others, Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations (Rand Corporation 2013)

52.   Ratcliffe J, ‘Crime Mapping: Spatial and Temporal Challenges’ in Piquero AR and Weisburd D (eds), Handbook of Quantitative Criminology (Springer 2010) 5, doi:10.1007/978-0-387-77650-7_2

53.   Ratcliffe JH et al., ‘The Philadelphia Predictive Policing Experiment’ (2021) 17 Journal of Experimental Criminology 15, doi:10.1007/s11292-019-09400-2

54.   Richardson R, Schultz JM and Crawford K, ‘Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice’ (2019) 94 New York University Law Review Online 15 https://nyulawreview.org/online-features/dirty-data-bad-predictions-how-civil-rights-violations-impact-police-data-predictive-policing-systems-and-justice/ accessed 12 October 2025

55.   Ridgeway G, ‘Policing in the Era of Big Data’ (2018) 1 Annual Reviews of Criminology 401, doi:10.1146/annurev-criminol-062217-114209

56.   Rudin C, ‘Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead’ (2019) 1(5) Nature Machine Intelligence 206, doi:10.1038/s42256-019-0048-x

57.   Sachoulidou A, ‘Going beyond the “Common Suspects”: To Be Presumed Innocent in the Era of Algorithms, Big Data and Artificial Intelligence’ [2023] Artificial Intelligence and Law, doi:10.1007/s10506-023-09347-w

58.   Sagiroglu S and Sinanc D, ‘Big Data: A Review’ (2013 International Conference on Collaboration Technologies and Systems (CTS), 20-24 May 2013) doi:10.1109/CTS.2013.6567202

59.   Santos RB, Crime Analysis with Crime Mapping (SAGE Publications 2016)

60.   Sarzaeim P and others, ‘A Systematic Review of Using Machine Learning and Natural Language Processing in Smart Policing’ (2023) 12 Computers 255, doi:10.3390/computers12120255

61.   Saucier PB, Report Concerning the ShotSpotter and ResourceRouter Programs (City of Worcester Police Department 2024)

62.   Saunders J, Hunt P, and Hollywood JS, ‘Predictions Put into Practice: A Quasi-Experimental Evaluation of Chicago’s Predictive Policing Pilot’ (2016) 12 Journal of Experimental Criminology 347, doi:10.1007/s11292-016-9272-0

63.   Schlehahn E et al., ‘Benefits and Pitfalls of Predictive Policing’ 2015 European Intelligence and Security Informatics Conference (2015 European Intelligence and Security Informatics Conference, 7-9 September 2015)

64.   Selbst AD, ‘Disparate Impact in Big Data Policing’ (2018) 52 Georgia Law Review 109, doi:10.2139/ssrn.2819182

65.   Severino RC, ‘The ASEAN Developmental Divide and the Initiative for ASEAN Integration’ (2007) 24(1) ASEAN Economic Bulletin 35

66.   Shukla A and others, ‘Criminal Combat: Crime Analysis and Prediction Using Machine Learning’ (2021 International Conference on Intelligent Technologies (CONIT), 25-27 June 2021) doi:10.1109/CONIT51480.2021.9498397

67.   Smith MP, ‘Review of Selected Los Angeles Police Department Data-Driven Policing Strategies’ (Los Angeles Police Commission, Office of the Inspector General 2019) https://a27e0481-a3d0-44b8-8142-1376cfbb6e32.filesusr.com/ugd/b2dd23_21f6fe20f1b84c179abf440d4c049219.pdf accessed 12 October 2025

68.   Söderholm S, ‘Fundamental Rights Control When Implementing Predictive Policing – a European Perspective’ (2023) 11(1) Peking University Law Journal 91, doi:10.1080/20517483.2023.2223850

69.   Strikwerda L, ‘Predictive Policing: The Risks Associated with Risk Assessment’ (2021) 94(3) The Police Journal: Theory, Practice and Principles 422, doi:10.1177/0032258X20947749

70.   Su J, ‘The Use of Big Data in Criminal Justice and Its Challenges’ (2023) 11(1) Peking University Law Journal 105, doi:10.1080/20517483.2023.2223851

71.   Sucita N, Nita S, and Muriman C, ‘Predictive Policing by Bhabinkamtibmas as a Solution to Address Criminality in Cilempung Village, Pasirjaya, Karawang’ (2024) 4(10) International Journal of Social Service and Research 1, doi:10.46799/ijssr.v4i10.1048

72.   Surden H, ‘Machine Learning and Law’ (2014) 89(1) Washington Law Review 88

73.   Taylor L, ‘On the Presumption of Innocence in Data-Driven Government: Are We Asking the Right Question?’ in Emre Bayamlioglu and others (eds), BEING PROFILED: COGITAS ERGO SUM: 10 Years of Profiling the European Citizen (Amsterdam UP 2019) doi:10.1515/9789048550180-014

74.   Thanh H, ‘Research on the Application of AI in Preventing and Responding to Non-Traditional Security Threats’ Báo điện tử Tiền Phong (Hà Nội, 19 September 2025) [in Vietnamese] https://tienphong.vn/nghien-cuu-ung-dung-ai-trong-phong-ngua-ung-pho-voi-cac-de-doa-an-ninh-phi-truyen-thong-post1779329.tpo accessed 16 October 2025

75.   Turvey BE, Criminal Profiling: An Introduction to Behavioral Evidence Analysis (Academic Press, 2011)

76.   Uchida CD, ‘A National Discussion on Predictive Policing: Defining Our Terms and Mapping Successful Implementation Strategies’ (National Institute of Justice, 1 November 2009) NCJ 230404

77.

Reviews for article

Add a Review

Reviews are moderated and will be published after verification!
All comments not related to reviews of the article will be deleted!

АНОТАЦІЯ УКРАЇНСЬКОЮ МОВОЮ

 

Дослідницька стаття

 

ПРОГНОЗНА ПОЛІЦЕЙСЬКА ДІЯЛЬНІСТЬ НА ОСНОВІ ВЕЛИКИХ ДАНИХ: ГЛОБАЛЬНА ПРАКТИКА ТА УРОКИ ДЛЯ КРАЇН АСЕАН

 

Т. Хьонг Ву*, Т. Фуонг До, В. К. Хоа Нгуєн

АНОТАЦІЯ

Вступ.  Використання великих даних та алгоритмічних інструментів стає все більш поширеним у правоохоронних органах. Одним із помітних нововведень є прогнозна поліцейська діяльність на основі великих даних (BDPP), яка прагне передбачити, де може статися злочин або хто може бути причетний, за допомогою аналізу великих наборів даних. Хоча прогнозування вже давно є частиною поліцейської діяльності, BDPP відрізняється масштабом та автоматизацією, спираючись на передову аналітику та машинне навчання. Хоча багато поліцейських органів розглядають BDPP як спосіб підвищення ефективності та переходу до превентивної поліцейської діяльності, його використання спричинило важливі правові та соціальні проблеми. Такі питання, як прозорість, упередженість даних, підзвітність та захист основних прав, залишаються невирішеними, особливо в країнах АСЕАН, де правові межі для алгоритмічної поліцейської діяльності все ще розробляються.

Методи. Ця стаття базується на якісному огляді літератури академічних та кримінологічних досліджень, політичних документів щодо прогнозної поліцейської діяльності. У ній розглядається, як BDPP працює на практиці, з наголосом як на місцевих, так і на особистих моделях. У статті розглянуто вибраний досвід Сполучених Штатів, Нідерландів та Великої Британії для оцінки ефективності прогнозної поліцейської діяльності, а також її практичних обмежень та пов'язаних з цим проблем. З огляду на ці висновки, у статті визначено уроки, які можуть бути актуальними для країн АСЕАН, що розглядають можливість впровадження подібних підходів.

Результати та висновки. У статті показано, що хоча BDPP пропонує потенціал для підвищення ефективності правоохоронних органів та проактивного розподілу ресурсів, глобальні дані зі США та Європи вказують на неоднозначну ефективність та суттєві ризики щодо алгоритмічної упередженості, прозорості та основних прав. Для країн АСЕАН, де інституційний потенціал та режими захисту даних суттєво відрізняються, доцільним є обережне та поетапне впровадження. Пріоритет слід надавати контрольованим пілотним проєктам, які базуються на місцях, що підтримуються чіткими правовими мандатами, стандартами управління даними, незалежним оглядом та постійним людським наглядом.

Ключові слова. Великі дані, прогнозне поліцейське управління, алгоритмічний аналіз, аналітика даних.

Keywords

  • big data, predictive policing, algorithmic, data analytics

Publication history

  • DETAILS FOR PUBLICATION

    Date of submission: 5 Jan 2026

    Date of acceptance: 7 Feb 2026

    Online First Publication: 10 March 2026

    Last Publication: May 2026

    Was the manuscript fast-tracked? - No

    Number of reviewer reports submitted in the first round: 2 reports

    Number of revision rounds: 2 rounds with major and minor revisions

     

    Technical tools were used in the editorial process:

    Plagiarism checks - Turnitin from iThenticate https://www.turnitin.com/products/ithenticate/

    Scholastica for Peer Review https://scholasticahq.com/law-reviews

     

    AI Disclosure Statement:

    This article was prepared with the assistance of AI tools. Specifically, Grammarly was employed for proofreading during the drafting process (version reviewed: December 2025). The authors confirm that all content, arguments, and conclusions were generated independently and remain their sole responsibility. No AI tool was used for generating original research findings or analysis.

How to cite it?