Publication activity
Articles grouped by publication year
Hover over a year or focus the chart and use arrow keys to inspect values.
2026 is year to date. 0 undated publications excluded.
Research across participating journals · All insights follow your collection filters.
Publication activity alongside incoming citations.
Articles grouped by publication year
Hover over a year or focus the chart and use arrow keys to inspect values.
2026 is year to date. 0 undated publications excluded.
OpenAlex · year of the citing work
Hover over a year or focus the chart and use arrow keys to inspect values.
2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.
Leading affiliations and themes in this collection.
Full distributions & methodology ↓Independent sources. Clear time scales.
Crossref and OpenAlex · separate totals for the same publication cohorts
Hover over a year or focus the chart and use arrow keys to inspect values.
Latest lifetime totals, grouped by the cited article’s year. These are not citations received in that year.
OpenAlex · incoming citation links grouped by citing-work year
Hover over a year or focus the chart and use arrow keys to inspect values.
2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.
Select journals to compare the same collection filters. Source totals stay separate. These measures describe research activity, not journal quality.
Articles by publication year
Latest lifetime totals · grouped by publication year
Latest lifetime totals · grouped by publication year
By year of the citing work · 2017–2026
2026 is year to date. Crossref and OpenAlex lifetime charts share a citation scale. Unknown observations appear as gaps; known zero stays zero. Annual receipts use available OpenAlex histories and may not sum to lifetime totals. 0 undated publications excluded from publication-year charts.
Crossref citation coverage: 4 / 4 publications (100.0%). Latest observation: 21 Oct 2026, 07:22 UTC. Means and percentages use records with known counts.
DOI-matched OpenAlex observations for the same selected publications. These counts are separate from Crossref and are never added together.
Observation range: 21 Oct 2026 07:22 UTC — 22 Oct 2026 02:20 UTC. Missing matches are not zero citations. Previous observations may remain if a refresh fails or a DOI no longer matches.
OpenAlex affiliations and primary-topic classification. Each publication counts once per institution or country; multi-affiliated publications appear in multiple groups, so percentages can sum above 100%. Countries describe author affiliations, not nationality. Topics use one primary topic per publication.
2 / 4 with data · 2 missing or unmatched. Percentages use all 4 selected publications.
2 / 4 with data · 2 missing or unmatched. Percentages use all 4 selected publications.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 selected publications.
Citations are the latest recorded totals for articles published in each year, not citations received during that year. Crossref and OpenAlex remain separate and share the same citation scale. 2026 is incomplete; newer articles have had less time to receive citations.
0 undated publications excluded. Unknown citation counts are shown as —, never as zero. Partial coverage can understate totals; expand each table for known / total records.
| Publication year | Journal / scope | Publications | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2024 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2024 | All selected publications | 6 | 4 | 4/4 | 4/4 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2024 | All selected publications | 8 | 4 | 4/4 | 4/4 |
Incoming citations to the selected publications, grouped by the year of the citing work. This is separate from lifetime citation totals grouped by the cited article’s publication year.
The publication filters above define which articles receive the citations. The citation-year range below only controls this chart and the incoming-citation comparison. Removing publication-year filters includes older articles’ incoming citations too.
Annual history available for 4 / 4 selected publications. Supported display window: 2017–2026; 2026 is incomplete. Within a successfully fetched ten-year window, omitted years mean zero. Missing histories and years beyond a saved observation’s window remain unknown (—); partial coverage can understate totals.
Annual-history observations (UTC): 2026-10-21T07:22:54.958006+00:00 — 2026-10-22T02:20:21.241400+00:00. Older observations may be retained after failed or incomplete refreshes.
| Citation year | Journal / scope | OpenAlex · Citations received by year | Publications | Annual history known |
|---|---|---|---|---|
| 2017 | All selected publications | 0 | 4 | 4/4 |
| 2018 | All selected publications | 0 | 4 | 4/4 |
| 2019 | All selected publications | 0 | 4 | 4/4 |
| 2020 | All selected publications | 0 | 4 | 4/4 |
| 2021 | All selected publications | 0 | 4 | 4/4 |
| 2022 | All selected publications | 0 | 4 | 4/4 |
| 2023 | All selected publications | 0 | 4 | 4/4 |
| 2024 | All selected publications | 2 | 4 | 4/4 |
| 2025 | All selected publications | 1 | 4 | 4/4 |
| 2026 | All selected publications | 5 | 4 | 4/4 |
Counts represent citation links, not unique citing papers across a journal: one paper citing two selected articles contributes two links. Older citations outside this window are excluded, so annual sums need not equal lifetime totals. OpenAlex methodology ↗
Publications by publication year. 2026 is an incomplete year.
| Period | Publications |
|---|---|
| 2024 | 4 |
Latest Crossref totals for the selected publications; not citations received in the selected years.
Unknown counts are excluded from the mean, median and cited-publication rate.
Same filters, source and counting rules across journals. Descriptive statistics, not a quality ranking.
| Journal / publisher | Publications | Crossref citations | OpenAlex citations | OpenAlex coverage | Crossref mean | Crossref median | Crossref cited %* | Crossref coverage |
|---|---|---|---|---|---|---|---|---|
| International Journal of Business Ecosystem & Strategy Bussecon International Academy | 1 | 2 | 2 | 1/1 | 2.0 | 2 | 100.0 | 1/1 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 3 | 4 | 6 | 3/3 | 1.33 | 2 | 66.7 | 3/3 · 100.0% |
* Percentage among publications with a known citation count.
The integration of technology has been identified as a major factor that can improve the performance of community police, reduce crime rates, and enhance community security. The purpose of this study was therefore to identify the technologies employed by the community police unit of the Ghana Police Service and the challenges that impede the full integration of technology into community policing. By engaging 27 participants from the Ghana Police Service in the Greater-Accra, Ashanti and Northern Regions, the research used a qualitative descriptive approach to analyse the use of communication technologies, surveillance tools, and data management systems in community policing. Key findings revealed that technologies such as mobile phones, CCTV cameras, and data mapping tools are instrumental in enhancing real-time communication, monitoring, and crime prevention. However, significant obstacles, including financial constraints, low digital literacy among officers, and community trust issues, hinder full integration. The study recommended that the Ghana Police Service seeks partnerships with private organizations to secure funding for technological resources, expand digital literacy programs for officers, and engage communities to build trust around technology use in policing. These findings contribute to the discourse on modernising law enforcement and underscore the importance of technology for effective community engagement and public safety in Ghana. By addressing these challenges, law enforcement can achieve a balanced integration of technology to support community policing objectives.
This paper aims to investigate the incorporation of the ILP approach in the research area. It assesses If this approach has been incorporated into crime fighting in the police station’s areas which form part of the research. The research was based in the province of KwaZulu-Natal. The aim was to establish the incorporation of ILP in the day-to-day operation of the police, and 8 participants were drawn between the CIO and OPO stationed in four districts (Mkhanyakude, Zululand, uThukela, and Amajuba). The participants included two former police officers (one former Commander of CI and a former Station Commander) who were used to validate information by active police officers. One District Commander was also interviewed. Qualitative research methodology was followed to conduct the research and a purposive method of data gathering was used targeting police officers who work in crime intelligence units and those who have authority over the operational members of the SAPS. They provided information that was used to determine the incorporation of the ILP in the day-to-day operation of the SAPS in the research area. The paper found that this policing approach has not been incorporated into some components of policing in the research area. The major identified stumbling in the incorporation is the structural arrangement of the Crime Intelligence Unit which does not sync with crime combating at the station level.
This article examines the necessity for implementing a more intelligent, inventive, and disruptive policing paradigm, such as intelligence-driven community policing, to address both traditional and technology-facilitated crimes. Intelligence-driven community policing is a comprehensive policing paradigm that emphasises the identification and prevention of habitual criminals, concentrating on groups that present a threat and risk to the community. It employs community intelligence alongside other intelligence-driven policing strategies and methodologies to address crime and criminal activity. The research employed a mixed-methods approach, integrating qualitative and quantitative data collection techniques. The data for this research was gathered in three countries: Denmark, the two South African metropolitan provinces of Gauteng and Western Cape, and three Nigerian cities: Lagos, Abuja, and Port Harcourt. The findings demonstrate that community policing has been embraced and executed in the majority of jurisdictions; yet, numerous issues hinder its efficient implementation. This encompasses a deficiency in comprehending the model's conceptual framework, misinterpretations and misconceptions by those responsible for its implementation, and an incapacity among the police, stakeholders, and the general public to distinguish community policing from state and regional policing. Innovative policing has significant consequences for international law enforcement in an increasingly digitised and complicated environment. Technological advancements have obscured the distinctions between the physical and digital domains, rendering the criminal scene increasingly global and complex. National and international law enforcement authorities must use innovative approaches and remain abreast of swift digitalisation and technical progress. This study recommends shifting to intelligence-driven community policing by integrating intelligence-led police strategies, techniques, and approaches into the community policing implementation framework. Understanding the conceptual framework of community policing and contemporary implementation methods tailored to the specific demands of each jurisdiction, police agency, culture, and circumstances is essential. A comprehensive recommendation is proposed to enhance the capacity and capability development of criminal justice, law enforcement, and intelligence personnel regarding the execution of intelligence-driven community policing.
Integrating technology into policing has the potential to greatly improve the effectiveness, transparency, and community participation of law enforcement agencies. This study examines the profound influence of technology on the Ghana Police Service, with a specific emphasis on the Greater Accra Region. The main aim of this research is to examine how technological advancements might enhance police results and tackle existing difficulties in the context of law enforcement in Ghana. The study adopts a qualitative research approach and collects data from police officers and community members through the use of semi-structured interviews and focus group discussions. The investigation involved the participation of a combined total of twenty-six police officials from the Tema and Accra Regional Headquarters, as well as twenty-two community members. The data gathering procedure sought to gather a wide range of viewpoints on the present condition of technology in law enforcement, its advantages, difficulties, and prospects for future advancement. The results demonstrate that the utilisation of technology, such as mobile data terminals and social media platforms, can greatly improve the effectiveness of police operations, promote community involvement, and increase transparency. Nevertheless, the presence of obstacles such as insufficient infrastructure, cultural opposition, and the necessity for extensive training were also acknowledged. The research enhances the current knowledge by presenting empirical evidence on the benefits and constraints of technology in law enforcement, particularly in the setting of Ghana. This study emphasises the significance of making strategic investments in technology infrastructure and training, as well as the necessity of establishing legal frameworks to tackle ethical and privacy concerns. Additionally, it proposes that future studies should prioritise examining the enduring viability of technological advancements in law enforcement and investigating their effects in various geographical areas. The study suggests that politicians and law enforcement organisations should promote the deployment of technology in a way that is successful and build public trust in the Ghana Police Service.
The integration of technology has been identified as a major factor that can improve the performance of community police, reduce crime rates, and enhance community security. The purpose of this study was therefore to identify the technologies employed by the community police unit of the Ghana Police Service and the challenges that impede the full integration of technology into community policing. By engaging 27 participants from the Ghana Police Service in the Greater-Accra, Ashanti and Northern Regions, the research used a qualitative descriptive approach to analyse the use of communication technologies, surveillance tools, and data management systems in community policing. Key findings revealed that technologies such as mobile phones, CCTV cameras, and data mapping tools are instrumental in enhancing real-time communication, monitoring, and crime prevention. However, significant obstacles, including financial constraints, low digital literacy among officers, and community trust issues, hinder full integration. The study recommended that the Ghana Police Service seeks partnerships with private organizations to secure funding for technological resources, expand digital literacy programs for officers, and engage communities to build trust around technology use in policing. These findings contribute to the discourse on modernising law enforcement and underscore the importance of technology for effective community engagement and public safety in Ghana. By addressing these challenges, law enforcement can achieve a balanced integration of technology to support community policing objectives.
This article examines the necessity for implementing a more intelligent, inventive, and disruptive policing paradigm, such as intelligence-driven community policing, to address both traditional and technology-facilitated crimes. Intelligence-driven community policing is a comprehensive policing paradigm that emphasises the identification and prevention of habitual criminals, concentrating on groups that present a threat and risk to the community. It employs community intelligence alongside other intelligence-driven policing strategies and methodologies to address crime and criminal activity. The research employed a mixed-methods approach, integrating qualitative and quantitative data collection techniques. The data for this research was gathered in three countries: Denmark, the two South African metropolitan provinces of Gauteng and Western Cape, and three Nigerian cities: Lagos, Abuja, and Port Harcourt. The findings demonstrate that community policing has been embraced and executed in the majority of jurisdictions; yet, numerous issues hinder its efficient implementation. This encompasses a deficiency in comprehending the model's conceptual framework, misinterpretations and misconceptions by those responsible for its implementation, and an incapacity among the police, stakeholders, and the general public to distinguish community policing from state and regional policing. Innovative policing has significant consequences for international law enforcement in an increasingly digitised and complicated environment. Technological advancements have obscured the distinctions between the physical and digital domains, rendering the criminal scene increasingly global and complex. National and international law enforcement authorities must use innovative approaches and remain abreast of swift digitalisation and technical progress. This study recommends shifting to intelligence-driven community policing by integrating intelligence-led police strategies, techniques, and approaches into the community policing implementation framework. Understanding the conceptual framework of community policing and contemporary implementation methods tailored to the specific demands of each jurisdiction, police agency, culture, and circumstances is essential. A comprehensive recommendation is proposed to enhance the capacity and capability development of criminal justice, law enforcement, and intelligence personnel regarding the execution of intelligence-driven community policing.
Integrating technology into policing has the potential to greatly improve the effectiveness, transparency, and community participation of law enforcement agencies. This study examines the profound influence of technology on the Ghana Police Service, with a specific emphasis on the Greater Accra Region. The main aim of this research is to examine how technological advancements might enhance police results and tackle existing difficulties in the context of law enforcement in Ghana. The study adopts a qualitative research approach and collects data from police officers and community members through the use of semi-structured interviews and focus group discussions. The investigation involved the participation of a combined total of twenty-six police officials from the Tema and Accra Regional Headquarters, as well as twenty-two community members. The data gathering procedure sought to gather a wide range of viewpoints on the present condition of technology in law enforcement, its advantages, difficulties, and prospects for future advancement. The results demonstrate that the utilisation of technology, such as mobile data terminals and social media platforms, can greatly improve the effectiveness of police operations, promote community involvement, and increase transparency. Nevertheless, the presence of obstacles such as insufficient infrastructure, cultural opposition, and the necessity for extensive training were also acknowledged. The research enhances the current knowledge by presenting empirical evidence on the benefits and constraints of technology in law enforcement, particularly in the setting of Ghana. This study emphasises the significance of making strategic investments in technology infrastructure and training, as well as the necessity of establishing legal frameworks to tackle ethical and privacy concerns. Additionally, it proposes that future studies should prioritise examining the enduring viability of technological advancements in law enforcement and investigating their effects in various geographical areas. The study suggests that politicians and law enforcement organisations should promote the deployment of technology in a way that is successful and build public trust in the Ghana Police Service.
This paper aims to investigate the incorporation of the ILP approach in the research area. It assesses If this approach has been incorporated into crime fighting in the police station’s areas which form part of the research. The research was based in the province of KwaZulu-Natal. The aim was to establish the incorporation of ILP in the day-to-day operation of the police, and 8 participants were drawn between the CIO and OPO stationed in four districts (Mkhanyakude, Zululand, uThukela, and Amajuba). The participants included two former police officers (one former Commander of CI and a former Station Commander) who were used to validate information by active police officers. One District Commander was also interviewed. Qualitative research methodology was followed to conduct the research and a purposive method of data gathering was used targeting police officers who work in crime intelligence units and those who have authority over the operational members of the SAPS. They provided information that was used to determine the incorporation of the ILP in the day-to-day operation of the SAPS in the research area. The paper found that this policing approach has not been incorporated into some components of policing in the research area. The major identified stumbling in the incorporation is the structural arrangement of the Crime Intelligence Unit which does not sync with crime combating at the station level.
Source coverage refers to this result set; a publication may occur in several sources. Sync times refer to contributing journals.
Missing abstracts: 0 · Missing authors: 0 · Missing publication dates: 0 · Dates without day precision: 0
| Source | Most recent successful journal sync | Latest attempt |
|---|---|---|
| crossref | 21 Oct 2026 07:22 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 07:22 UTC |
| ojs-rest | 21 Oct 2026 07:22 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 06:46 UTC |
| oai-pmh | 20 Oct 2026 19:55 UTC · ijrbs | complete · ijrbs · 20 Oct 2026 19:50 UTC |
| openalex-enrichment | 21 Oct 2026 07:23 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 07:22 UTC |