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 ↓No data available.
0/1 records with dataNo data available.
0/1 records with dataIndependent 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: 1 / 1 publications (100.0%). Latest observation: 21 Oct 2026, 00:01 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: 22 Oct 2026 01:50 UTC — 22 Oct 2026 01:50 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.
0 / 1 with data · 1 missing or unmatched. Percentages use all 1 selected publications.
No verified institutions data for this selection.
0 / 1 with data · 1 missing or unmatched. Percentages use all 1 selected publications.
No verified countries data for this selection.
1 / 1 with data · 0 missing or unmatched. Percentages use all 1 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 |
|---|---|---|---|---|---|
| 2022 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 3 | 1 | 1/1 | 1/1 |
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 1 / 1 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): 2026-10-22T01:50:43.700432+00:00 — 2026-10-22T01:50:43.700432+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 | 1 | 1/1 |
| 2018 | All selected publications | 0 | 1 | 1/1 |
| 2019 | All selected publications | 0 | 1 | 1/1 |
| 2020 | All selected publications | 0 | 1 | 1/1 |
| 2021 | All selected publications | 0 | 1 | 1/1 |
| 2022 | All selected publications | 0 | 1 | 1/1 |
| 2023 | All selected publications | 2 | 1 | 1/1 |
| 2024 | All selected publications | 1 | 1 | 1/1 |
| 2025 | All selected publications | 0 | 1 | 1/1 |
| 2026 | All selected publications | 0 | 1 | 1/1 |
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 |
|---|---|
| 2022 | 1 |
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 |
|---|---|---|---|---|---|---|---|---|
| Bussecon Review of Social Sciences Bussecon International Academy | 1 | 2 | 3 | 1/1 | 2.0 | 2 | 100.0 | 1/1 · 100.0% |
* Percentage among publications with a known citation count.
The study examined the role of training needs assessment on the training outcomes in the insurance sector in Kenya. A desktop methodology was used to carry out this study. This necessitated the use of secondary data obtained from publicly available sources such as published studies, reports, and statistics. This approach of data collection is used by many researchers since it saves time and lowers the cost of data collection. It is also quite reliable and has a wide variety of insights since it is compiled from well-known sources. The current study made use of online journals and libraries, both of which make secondary data readily available. The results revealed that there exist conceptual and geographical gaps relating to the study on the role of training needs assessment on the training outcomes in the insurance sector in Kenya. Most of the results from the empirical review revealed that the main role of training needs assessment on training outcomes is to ensure that the training programs implemented are effective, meet the employees’ needs and to improve the performance of the insurance sectors in Kenya. In addition, TNA enabled organizations to implement flexible, practical oriented training programs so as to keep the employees motivated. The results of the study will be of great significance to the management team of the insurance sector, since it will provide a good knowledge base on what training needs assessment involves and its role on training outcomes in the insurance sector in Kenya. Employees in the insurance sector will also benefit from the findings of this study since it will provide a well-detailed information on some of the employee needs that spearhead the implementation of training programs. The results of the study will also benefit the policymakers in the insurance sector by identifying the appropriate training needs assessment models that will improve the training outcomes of insurance firms in Kenya. Lastly, the findings of this study will serve as a foundation for literature review of researchers and academicians interested in carrying out a study related to these research topics.
The study examined the role of training needs assessment on the training outcomes in the insurance sector in Kenya. A desktop methodology was used to carry out this study. This necessitated the use of secondary data obtained from publicly available sources such as published studies, reports, and statistics. This approach of data collection is used by many researchers since it saves time and lowers the cost of data collection. It is also quite reliable and has a wide variety of insights since it is compiled from well-known sources. The current study made use of online journals and libraries, both of which make secondary data readily available. The results revealed that there exist conceptual and geographical gaps relating to the study on the role of training needs assessment on the training outcomes in the insurance sector in Kenya. Most of the results from the empirical review revealed that the main role of training needs assessment on training outcomes is to ensure that the training programs implemented are effective, meet the employees’ needs and to improve the performance of the insurance sectors in Kenya. In addition, TNA enabled organizations to implement flexible, practical oriented training programs so as to keep the employees motivated. The results of the study will be of great significance to the management team of the insurance sector, since it will provide a good knowledge base on what training needs assessment involves and its role on training outcomes in the insurance sector in Kenya. Employees in the insurance sector will also benefit from the findings of this study since it will provide a well-detailed information on some of the employee needs that spearhead the implementation of training programs. The results of the study will also benefit the policymakers in the insurance sector by identifying the appropriate training needs assessment models that will improve the training outcomes of insurance firms in Kenya. Lastly, the findings of this study will serve as a foundation for literature review of researchers and academicians interested in carrying out a study related to these research topics.
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 00:01 UTC · brss | complete · brss · 21 Oct 2026 00:01 UTC |
| ojs-rest | 21 Oct 2026 00:01 UTC · brss | complete · brss · 21 Oct 2026 00:00 UTC |
| oai-pmh | 20 Oct 2026 19:50 UTC · brss | complete · brss · 20 Oct 2026 19:49 UTC |
| openalex-enrichment | 21 Oct 2026 00:02 UTC · brss | complete · brss · 21 Oct 2026 00:01 UTC |