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: 3 / 3 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: 22 Oct 2026 01:30 UTC — 22 Oct 2026 04:23 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.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 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 | 3 | 3 | 3/3 | 3/3 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 5 | 3 | 3/3 | 3/3 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 8 | 3 | 3/3 | 3/3 |
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 3 / 3 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): 2026-10-22T01:30:07.682350+00:00 — 2026-10-22T04:23:42.561386+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 | 3 | 3/3 |
| 2018 | All selected publications | 0 | 3 | 3/3 |
| 2019 | All selected publications | 0 | 3 | 3/3 |
| 2020 | All selected publications | 0 | 3 | 3/3 |
| 2021 | All selected publications | 0 | 3 | 3/3 |
| 2022 | All selected publications | 0 | 3 | 3/3 |
| 2023 | All selected publications | 2 | 3 | 3/3 |
| 2024 | All selected publications | 5 | 3 | 3/3 |
| 2025 | All selected publications | 0 | 3 | 3/3 |
| 2026 | All selected publications | 1 | 3 | 3/3 |
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 | 3 |
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 Research in Business and Social Science SSBFNET | 3 | 5 | 8 | 3/3 | 1.67 | 2 | 66.7 | 3/3 · 100.0% |
* Percentage among publications with a known citation count.
The study sought to explore the impact of generic strategies on competitive advantage in the medical training colleges in Kenya. The research was guided by the Dynamic capabilities theory, Resource-Based View theory and Knowledge-based view theories. The target population was 183 members of staff in the 4 medical training colleges in Nyeri County, Kenya. Data on 42 participants from the population was gathered from the top-level management, middle-level management and low-level management in these colleges using questionnaires. The research adopted a descriptive research design. Quantitative data analysis was conducted by use of descriptive statistics. Additionally, inferential statistical analysis on the observations was carried out. Qualitative data were analyzed by use of content analysis. Tables, bar graphs, and pie charts were employed for data presentation. The analyzed data revealed that cost leadership, differentiation and focus strategies positively contributed to the colleges’ competitive advantage with cost advantage having the greatest effect. The study recommends that colleges and other institutions employ the application of porter’s generic strategies since they have proved to be effective in contributing to competitive advantage.
This study purposed to investigate the effect of strategic responses on performance. The study adopted a descriptive research design. The target population for the study was 3834 employees drawn from four telecommunication firms in Kenya. Proportionate stratified random sampling was used to select the sample size of 385 respondents. The study findings indicated that (i) research and development, technology advancement, and strategic training all had a significant positive effect on firm performance; (ii) technology advancement was found to be a major determinant in the variation of performance of the telecommunication firms. Operational restructuring strategy, however, was found to have a negative effect on performance. The study finally concluded that technological advancement, research and development, and employee strategic training are strategic responses that telecommunication firms should invest into better performance.
The purpose of this study is to obtain a model to improve the performance of MSMEs through the application of competitiveness to enhance their corporate governance, motivation, and innovative behavior. A descriptive verification analysis was applied using 5 main variables which include corporate governance, motivation, innovative behavior, competitiveness, and performance of MSMEs. The population includes 106,038 MSMEs owners or employees in the Riau Islands Province while a total of 398 were selected as the subjects. Data were analyzed using Structural Equation Modeling (SEM) based on the variance which involves the Partial Least Square (PLS) data processing in SmartPLS Ver.3.0 software. The results showed that the level of governance, motivation, innovative behavior, and competitiveness in MSMEs can affect their level of performance. It was also discovered that competitiveness has a mediation role concerning the influence of corporate governance, motivation, and innovative behavior on performance. The novelty of this study is for it to serve as a strategic model to enhance the performance of MSMEs through their competitiveness by improving the corporate governance, motivation, and innovative behavior.
The purpose of this study is to obtain a model to improve the performance of MSMEs through the application of competitiveness to enhance their corporate governance, motivation, and innovative behavior. A descriptive verification analysis was applied using 5 main variables which include corporate governance, motivation, innovative behavior, competitiveness, and performance of MSMEs. The population includes 106,038 MSMEs owners or employees in the Riau Islands Province while a total of 398 were selected as the subjects. Data were analyzed using Structural Equation Modeling (SEM) based on the variance which involves the Partial Least Square (PLS) data processing in SmartPLS Ver.3.0 software. The results showed that the level of governance, motivation, innovative behavior, and competitiveness in MSMEs can affect their level of performance. It was also discovered that competitiveness has a mediation role concerning the influence of corporate governance, motivation, and innovative behavior on performance. The novelty of this study is for it to serve as a strategic model to enhance the performance of MSMEs through their competitiveness by improving the corporate governance, motivation, and innovative behavior.
This study purposed to investigate the effect of strategic responses on performance. The study adopted a descriptive research design. The target population for the study was 3834 employees drawn from four telecommunication firms in Kenya. Proportionate stratified random sampling was used to select the sample size of 385 respondents. The study findings indicated that (i) research and development, technology advancement, and strategic training all had a significant positive effect on firm performance; (ii) technology advancement was found to be a major determinant in the variation of performance of the telecommunication firms. Operational restructuring strategy, however, was found to have a negative effect on performance. The study finally concluded that technological advancement, research and development, and employee strategic training are strategic responses that telecommunication firms should invest into better performance.
The study sought to explore the impact of generic strategies on competitive advantage in the medical training colleges in Kenya. The research was guided by the Dynamic capabilities theory, Resource-Based View theory and Knowledge-based view theories. The target population was 183 members of staff in the 4 medical training colleges in Nyeri County, Kenya. Data on 42 participants from the population was gathered from the top-level management, middle-level management and low-level management in these colleges using questionnaires. The research adopted a descriptive research design. Quantitative data analysis was conducted by use of descriptive statistics. Additionally, inferential statistical analysis on the observations was carried out. Qualitative data were analyzed by use of content analysis. Tables, bar graphs, and pie charts were employed for data presentation. The analyzed data revealed that cost leadership, differentiation and focus strategies positively contributed to the colleges’ competitive advantage with cost advantage having the greatest effect. The study recommends that colleges and other institutions employ the application of porter’s generic strategies since they have proved to be effective in contributing to competitive advantage.
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 |