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, 00:21 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 00:49 UTC — 22 Oct 2026 18:37 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.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 selected publications.
4 / 4 with data · 0 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 |
|---|---|---|---|---|---|
| 2019 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 5 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 6 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 6 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 9 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 10 | 2 | 2/2 | 2/2 |
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): 2026-10-22T00:49:24.531355+00:00 — 2026-10-22T18:37:25.688651+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 | 1 | 4 | 4/4 |
| 2020 | All selected publications | 1 | 4 | 4/4 |
| 2021 | All selected publications | 0 | 4 | 4/4 |
| 2022 | All selected publications | 4 | 4 | 4/4 |
| 2023 | All selected publications | 13 | 4 | 4/4 |
| 2024 | All selected publications | 4 | 4 | 4/4 |
| 2025 | All selected publications | 2 | 4 | 4/4 |
| 2026 | All selected publications | 0 | 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 |
|---|---|
| 2019 | 1 |
| 2020 | 1 |
| 2021 | 2 |
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 Finance & Banking Studies SSBFNET | 4 | 12 | 25 | 4/4 | 3.0 | 3.0 | 75.0 | 4/4 · 100.0% |
* Percentage among publications with a known citation count.
The theory of dynamic resource orchestration explains the differentiated response of homogeneous ecosystem organizations to systemic disruptive shocks. The Covid-19 precautionary measures in Kenya have exempted some essential service providers and government agencies, resulting in a differentiated Covid-19 impact across the national SMEs landscape. This article adopted an extractive thematic analysis technique to draw insights from in-depth interviews and discussions with owners and managers of 6 broad-range SMEs considered non-essential services providers excluded from the Covid-19 cessation of movement waiver. The article advanced insights on SMEs' resilience through dynamic resource orchestration capability. It sought to establish whether the possession and orchestration of dynamic resources capabilities differentiated highly resilient SMEs from non-resilient ones. The article identified business diversification, slack finance, intra-stakeholder collaboration, self-reinvention, positive psychology, technology leverage, and cost management as precursory resilience agents within Kenyan SMEs. Research gaps were identified, and recommendations for future research were proposed.
Short-term debt is regarded as an important source of financing for Small and Medium-sized enterprises (SMEs). This is because it can be easily accessed and useful during times of emergent working capital shortage. However, short-term debt is the least researched among the components of capital structure, which explains why its contribution to the financial performance of small and medium-sized businesses still lacks empirical validation especially in the Ugandan context. This paper sought to determine the effect of short-term debt on financial performance of Small and Medium Enterprises in Uganda. The study adopted a descriptive cross-sectional research design to collect and analyse the data. Stratified random sampling technique was used to select SMEs while purposive sampling technique was used to select one key respondent from each of the sampled 453 SMEs in Uganda. Primary data was collected using survey questionnaire. Data was analysed using descriptive statistics and simple linear regression analysis. The findings indicted that short-term debt had a negative and significant effect on financial performance of SMEs as measured by return on assets. The study provides empirical evidence to support the propositions in the extant literature that short-term debt significantly hampers financial performance of SMEs. The study recommends that SMEs should adopt low cost operation procedures to improve profitability. This would lead to accumulated profits that can be used for investment purposes as a means of driving growth among the SMEs without resorting to borrowing. This paper suggests that further research should be conducted to establish the justification for the negative and significant effect of short-term debt on financial performance using qualitative approaches.
Financial performance of a commercial banks driven by a number of capabilities drawn from either the internal or the external environment. The Kenyan Government and commercial banks in particular have invested in e-commerce solution. Despite these invetments over the years, the impact is yet to be felt. The empirical literure reveals that there is a link between commercial banks e-commerce custiomization capability and financial peroformance.The study empirically analyzed the effect of e- commerce customization capability on financial performance of commercial banks in Kenya. E-commerce customization capability measured using online registration,online recommendation and realtime support while financial performance was measured using Return on Assets (ROA). The study was anchored on the Resource-Based View Theory. The study used explanatory design. A census of 43 commercial banks was taken; data for performance was extracted from audited banks statements for financial year 2016/2017. Data for e-commerce customization capability was collected from commercial banks websites. Data for financial performance was extracted from audited financial ststements of commercial banks. Data analysis was done using descriptive and inferential statistics. The study findings indicated that e-commerce customization capability had a significant effect on financial performance of commercial banks in Kenya. The study concluded that e-commerce customization capability significantly affected financial performance of commercial banks in Kneya. The study recommends that the management of commercial banks in Kenya should invest more in customization capability to improve their performance.
The collective investment schemes in Kenya have witnessed increased volatility in their earnings, resulting in irregular growth in the industry. This necessitates the need to understand the factors contributing to poor financial returns from collective investment schemes. Hence this study sought to investigate the effect of equity investments and bond investments on Kenyan CIS’s performance. The specific objectives were: To assess the effect of equity investments, bond investments on financial performance of collective investment schemes in Kenya. The study was anchored on: modern portfolio theory and the efficient market hypothesis. The positivism philosophy was applied, with the firms adopting an explanatory research design. The target population was 17 Collective Investment Schemes registered by the Capital Markets Authority and were operational in the period 2010 to 2018. Secondary data was sought from the Capital Markets Authority Annual reports and from the respective websites of the CIS’. Data was analyzed using descriptive statistics, correlational analysis and panel regression analysis. Hypotheses were tested at a significance level of 0.05. Findings indicate that equity investment, bond investments have an insignificant effect on CIS’ return on assets. Further, equity investments had a positive and significant effect on liquidity whereas bond investments had an insignificant effect on liquidity. The study recommends that CISs actively revise their equity investments and bond investments to stimulate financial returns.
The theory of dynamic resource orchestration explains the differentiated response of homogeneous ecosystem organizations to systemic disruptive shocks. The Covid-19 precautionary measures in Kenya have exempted some essential service providers and government agencies, resulting in a differentiated Covid-19 impact across the national SMEs landscape. This article adopted an extractive thematic analysis technique to draw insights from in-depth interviews and discussions with owners and managers of 6 broad-range SMEs considered non-essential services providers excluded from the Covid-19 cessation of movement waiver. The article advanced insights on SMEs' resilience through dynamic resource orchestration capability. It sought to establish whether the possession and orchestration of dynamic resources capabilities differentiated highly resilient SMEs from non-resilient ones. The article identified business diversification, slack finance, intra-stakeholder collaboration, self-reinvention, positive psychology, technology leverage, and cost management as precursory resilience agents within Kenyan SMEs. Research gaps were identified, and recommendations for future research were proposed.
The collective investment schemes in Kenya have witnessed increased volatility in their earnings, resulting in irregular growth in the industry. This necessitates the need to understand the factors contributing to poor financial returns from collective investment schemes. Hence this study sought to investigate the effect of equity investments and bond investments on Kenyan CIS’s performance. The specific objectives were: To assess the effect of equity investments, bond investments on financial performance of collective investment schemes in Kenya. The study was anchored on: modern portfolio theory and the efficient market hypothesis. The positivism philosophy was applied, with the firms adopting an explanatory research design. The target population was 17 Collective Investment Schemes registered by the Capital Markets Authority and were operational in the period 2010 to 2018. Secondary data was sought from the Capital Markets Authority Annual reports and from the respective websites of the CIS’. Data was analyzed using descriptive statistics, correlational analysis and panel regression analysis. Hypotheses were tested at a significance level of 0.05. Findings indicate that equity investment, bond investments have an insignificant effect on CIS’ return on assets. Further, equity investments had a positive and significant effect on liquidity whereas bond investments had an insignificant effect on liquidity. The study recommends that CISs actively revise their equity investments and bond investments to stimulate financial returns.
Short-term debt is regarded as an important source of financing for Small and Medium-sized enterprises (SMEs). This is because it can be easily accessed and useful during times of emergent working capital shortage. However, short-term debt is the least researched among the components of capital structure, which explains why its contribution to the financial performance of small and medium-sized businesses still lacks empirical validation especially in the Ugandan context. This paper sought to determine the effect of short-term debt on financial performance of Small and Medium Enterprises in Uganda. The study adopted a descriptive cross-sectional research design to collect and analyse the data. Stratified random sampling technique was used to select SMEs while purposive sampling technique was used to select one key respondent from each of the sampled 453 SMEs in Uganda. Primary data was collected using survey questionnaire. Data was analysed using descriptive statistics and simple linear regression analysis. The findings indicted that short-term debt had a negative and significant effect on financial performance of SMEs as measured by return on assets. The study provides empirical evidence to support the propositions in the extant literature that short-term debt significantly hampers financial performance of SMEs. The study recommends that SMEs should adopt low cost operation procedures to improve profitability. This would lead to accumulated profits that can be used for investment purposes as a means of driving growth among the SMEs without resorting to borrowing. This paper suggests that further research should be conducted to establish the justification for the negative and significant effect of short-term debt on financial performance using qualitative approaches.
Financial performance of a commercial banks driven by a number of capabilities drawn from either the internal or the external environment. The Kenyan Government and commercial banks in particular have invested in e-commerce solution. Despite these invetments over the years, the impact is yet to be felt. The empirical literure reveals that there is a link between commercial banks e-commerce custiomization capability and financial peroformance.The study empirically analyzed the effect of e- commerce customization capability on financial performance of commercial banks in Kenya. E-commerce customization capability measured using online registration,online recommendation and realtime support while financial performance was measured using Return on Assets (ROA). The study was anchored on the Resource-Based View Theory. The study used explanatory design. A census of 43 commercial banks was taken; data for performance was extracted from audited banks statements for financial year 2016/2017. Data for e-commerce customization capability was collected from commercial banks websites. Data for financial performance was extracted from audited financial ststements of commercial banks. Data analysis was done using descriptive and inferential statistics. The study findings indicated that e-commerce customization capability had a significant effect on financial performance of commercial banks in Kenya. The study concluded that e-commerce customization capability significantly affected financial performance of commercial banks in Kneya. The study recommends that the management of commercial banks in Kenya should invest more in customization capability to improve their performance.
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 00:21 UTC · ijfbs | complete · ijfbs · 21 Oct 2026 00:21 UTC |
| ojs-rest | 21 Oct 2026 00:21 UTC · ijfbs | complete · ijfbs · 21 Oct 2026 00:14 UTC |
| oai-pmh | 20 Oct 2026 19:56 UTC · ijfbs | complete · ijfbs · 20 Oct 2026 19:55 UTC |
| openalex-enrichment | 21 Oct 2026 00:22 UTC · ijfbs | complete · ijfbs · 21 Oct 2026 00:21 UTC |