TECHNOLOGY AND INTELLIGENCE LED POLICING IN NAIROBI CITY COUNTY, KENYA

GRACE ZAWADI MANGI; JOHN KANDIRI

doi:10.61426/rjssh.v5i1.219

GRACE ZAWADI MANGI Kenyatta University, Kenya
JOHN KANDIRI, PhD Kenyatta University, Kenya

DOI: https://doi.org/10.61426/rjssh.v5i1.219

Keywords: Expectancy, Credibility, Technology, Intelligence Policing, Crime Investigation

Abstract

This study evaluated the determinants of use of technology in intelligence policing in Nairobi City County, Kenya. The Unified Theory of Acceptance and Use of Technology and Ratcliffe Model were relied on in this study. The research design used was descriptive, with the populace being the DCI department in Nairobi City County comprising of 175 staff from 13 sub departments. The sample was 91 respondents drawn from the target population through stratified sampling. Data was sourced utilizing questionnaires. Additionally, questionnaire was tested to ascertain the validity and reliability. Reliability was done based on the Cronbach’s alpha whose threshold was 0.70 and from the results all variables were reliable. Analysis was done using SPSS version 24. Based on the regression output in performance expectancy has a positive and significant effect on intelligence policing and crime investigations in Nairobi City County. The regression coefficient is 0.807 while the p value is .003 which indicate significance. Based on the regression output perceived credibility has a positive and significant effect on intelligence policing and crime investigations in Nairobi City County. The regression coefficient is 1.025 while the p value is .000 which indicate significance. Based on the regression output, effort expectancy has a positive and significant effect on intelligence policing and crime investigations in Nairobi City County. The regression coefficient is 0.318 while the p value is .043 which indicates significance. Based on the regression output facilitating conditions has a positive and significant effect on intelligence policing and crime investigations in Nairobi City County. The regression coefficient is 0.616 while the p value is .020 which indicates significance. The research concluded that technology has a significant effect on intelligence led policing in Kenya specifically in crime investigations. Specifically the study concludes that performance expectancy, perceived credibility, effort expectancy and facilitating conditions of technology have significant and positive effect on the intelligence led policing and crime investigation process. Based on the findings, the study recommended that there should be adequate security features in the technology used in intelligence policing and crime investigations. Secondly the study recommends that DCIO should invest more in the upgrading of its investigative systems and technologies to simplify investigation processes. The study also recommended the improvement of computer crime mapping systems to help in intelligence policing. Finally the study recommended that the investigative agencies should recruit more confidential informants as they are very vital in intelligence policing.

Author Biographies

GRACE ZAWADI MANGI, Kenyatta University, Kenya

Student, Masters of Arts in Security Management, School of Humanities and Social Sciences and Police Studies, Kenyatta University, Kenya

JOHN KANDIRI, PhD, Kenyatta University, Kenya

Senior Lecturer, Department of Computing & Information Technology, Kenyatta University, Kenya

References

Boscolegal. (2020, September 30). Social Media Case Law. Retrieved from www.boscolegal.org: https://www.boscolegal.org/court-resources/social-media-case-law/

Canaday, J. (2017). How the democratization of technology enhances intelligence-led policing and serves the community. (MA Dissertation, Naval Postgraduate School, Monterey, California).

Carter, J.G., Phillips, S.W., & Gayadeen, S.M. (2014). Implementing intelligence led policing: An application of loose coupling theory. Journal of Criminal Justice. 42(6),433-442.

Connor Reporting(2019)https:www.connorreporting.com/photo-video-evidence-always-admissible-court.html

Denley, J., & Ariel, B. (2019). Whom should we target to prevent? Analysis of organized crime in England using intelligence records. European Journal of Crime, Criminal Law and Criminal Justice. 27(1), 13-44.

Ezeji, C., & Olutola, A. (2018). The use of intelligence led policing in combating technology based crimes in South Africa. Journal of African Foreign Affairs. 5(2), 167-188.

Faggela, D. (2019). AI for crime prevention and detection-5 current applications.

Field Manual (FM) (2006) 3–19.50 – Police Intelligence Operations . rdl.train.army.mil.

Fortin, F., Donne, J.D., & Knop, J. (2021). The use of social media in intelligence and its impact on police work. ResearchGate. 13(2), 213-231.

Gerell, M., Kardell, J., & Kindgren, J. (2020). Minor Covid-19 association with crime in Sweden. Crime Science.

Gill, P., & Phythian, M. (2013). From intelligence cycle to web of intelligence: Complexity and the conceptualization of Intelligence.

https://emerj.com/ai-sector-overviews/ai-crime-prevention-5-current-applications/

https://privacyinternational.org/explainer/55/social-media-intelligence

https://repository.iiitd.edu.in/xmlui/handle/123456789/509

https://www.college.police.uk/What-we-do/Standards/Guidelines/Neighbourhood

Kenya Police Service. (2018). Annual Crime Report.

Kietzmann, J.H., & Kristopher, H (2011). "Social media? Get serious! Understanding the functional building blocks of social media". Business Horizons (Submitted manuscript). 54 (3): 241–251. doi:10.1016/j.bushor.2011.01.005

Mashiloane, N.P. (2014). The use of intelligence led policing in crime prevention by the South African Police service. (PHD Dissertation, University of South Africa).

Metscher, R., & Gilbride, B. (2005). Intelligence as an investigative function. International Foundation for Protection Officers (IFPO).

Numbeo.com. (2020, September 30). Crime in Nairobi Kenya. Retrieved from numbeo.com: https://www.numbeo.com/crime/in/Nairobi

O'Hara C.E., & Gregory L. O. (1994) Fundamentals of Criminal Investigation (Sixth Edition). ; ISBN 0-398-05889-X

Privacy International. (2020, September 14). Social Media International. Retrieved from www.privacyinternational.org:

Ratcliffe, J. (2016). Intelligence led policing. New York: Routledge.

Sachdeva, N., & Kumaraguru, P. (2017). Social media and policing: computational approaches to enhancing collaborative action between residents and law enforcement. (PHD Dissertation, Indraprastha Institute of Information Technology, Delhi).

Sanders, C., & Condon, C. (2017). Crime analysis and cognitive effects: the practice of policing through flows of data. Global Crime. 18(3), 237-255, DOI: 10.1080/17440572.2017.1323637

Santos, R.B. (2014). The effectiveness of crime analysis for crime reduction: Cure or diagnosis? Journal of Contemporary Criminal Justice. 30(2), 147-168.

Sigilai, A. (2018). Assessing the impact of information technology on the gathering of crime intelligence by DCI investigators. (MA Dissertation, University of Nairobi).

Stention, A.E. (2009). Crime analysis: An examination of crime prevention and reduction strategies. (MA Dissertation, University of Ottawa, Canada).

Stott, C., West, O., & Harrison, M. (2020). A Turning Point, Securitization, and Policing in the Context of Covid-19: Building a New Social Contract Between State and Nation?. Policing: A Journal of Policy and Practice, paaa021. https://doi.org/10.1093/police/paaa021

Strom, M. (2017). Research on the impact of technology on policing strategy in the 21st century. Office of Justice Programs’ National Criminal Justice Reference. Mashiloane, P.N. (2014). The use of intelligence led policing in crime prevention by the South African police service. Phd dissertation, University of South Africa.

Turck, L. (2016). An investigation into the utilisation of social media by the SAPs in solving Crime. University of South Africa.

Warner, M. (2013). The past and future of the intelligence cycle: Understanding the intelligence cycle.