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, 00:14 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 00:14 UTC — 21 Oct 2026 00:14 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 | 0 | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2026 | 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 | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | 10 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2026 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 3 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | 15 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2026 | All selected publications | 2 | 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): 2026-10-21T00:14:27.766849+00:00 — 2026-10-21T00:14:33.539647+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 | 2 | 4 | 4/4 |
| 2023 | All selected publications | 2 | 4 | 4/4 |
| 2024 | All selected publications | 5 | 4 | 4/4 |
| 2025 | All selected publications | 7 | 4 | 4/4 |
| 2026 | All selected publications | 4 | 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 |
| 2023 | 1 |
| 2026 | 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 Business Ecosystem & Strategy Bussecon International Academy | 4 | 13 | 20 | 4/4 | 3.25 | 1.0 | 100.0 | 4/4 · 100.0% |
* Percentage among publications with a known citation count.
Commercial banks accept deposits and lend money for investment and consumption. This study used five (5) first-tier banks in Nigeria as a case study to analyze the effects of credit risk management on the financial performance of commercial banks. The study examined fifteen (15) years' worth of panel data (from 2005 to 2019), taken from the audited financial reports of five first-tier listed banks. Deposit Money Banks (DMBs) are the only banks used, and they are all listed on the Nigerian Stock Exchange. Non-performing loans (NPL) and expected credit loss impairment provisions (ECL) were utilized in this study as indicators of credit risk management. At the same time, return on assets (ROA) was employed to measure financial performance.
This study explored the bank-specific and macroeconomic factors influencing capital adequacy in South African commercial banks. In this research, a balanced panel data set comprising 11 recapitalized banks was employed, spanning the period from 2008 to 2019. Employing panel regression techniques including OLS, Fixed Effects, Random Effects, and the Generalized Method of Moments (GMM), the analysis examines the influence of profitability (ROA), bank size, leverage, broad money supply, and the central bank’s monetary policy rate on the Capital Adequacy Ratio (CAR). The results indicate that profitability, bank size, leverage, and the broad money supply are critical determinants of capital sufficiency. Banks with increased profitability and increased exposure to the money supply typically demonstrate more robust capital positions. Larger bank size and increased leverage levels negatively affect capital adequacy ratios. The little impact of the delayed CAR signifies minimal persistence in capital buffer methods over time, implying that banks may not significantly depend on prior year capital conditions in their current capital planning. The study highlights the complex nature of capital sufficiency, influenced by microeconomic factors such as size and leverage, as well as macroeconomic indices like the money supply. The findings substantiate the Capital Buffer Theory and furnish critical insights for regulators, bank executives, and policymakers seeking to bolster the resilience of the banking industry in South Africa.
The COVID-19 pandemic triggered a global economic crisis that significantly impacted the South African banking sector, already weakened by a pre-existing recession. While bank profitability and credit quality declined, stock market reactions appeared less severe, indicating a potential disconnect between fundamentals and investor behaviour. This study explores the influence of investor sentiment on credit risk, lending, and performance (ROA) of selected JSE-listed banks during the pandemic, incorporating behavioural finance perspectives. Panel regression analysis reveals that COVID-19 had a significant negative effect on stock, ROA and ROE, driven by heightened uncertainty and rising non-performing loans. Liquidity and capital positively influenced returns and ROE, reflecting investor confidence in resilient banks. Investor sentiment had a weakly positive impact on returns, but a significant negative effect on both ROA and ROE, suggesting that excessive optimism may have led to riskier lending practices and reduced profitability. Larger banks underperformed in terms of returns, likely due to operational complexity during the crisis. These findings highlight the critical role of behavioural factors in shaping financial outcomes during systemic shocks and provide new insights into emerging market banking dynamics under crisis conditions.
The study examined the dynamic responses of profitability indexes to capital adequacy ratios of authorized internationalized deposit money banks in Nigeria. The data were sourced from the financial year books of the deposit money banks and analyzed with static and dynamic panel estimators. The static estimator shows that the banks have differences in managerial style, size and profitability. Also, it was revealed that return on asset and return on equity responded positively to asset size, efficiency of the use of asset and current ratio in the static models and they were highly significant. However, they were insignificant in the dynamic specifications except asset size that was significant in the return on asset model showing a weak dynamic response of profitability to capital adequacy ratios. Hence the study recommended that Banks should improve their share based as to increase the asset as this will improve profitability.
This study explored the bank-specific and macroeconomic factors influencing capital adequacy in South African commercial banks. In this research, a balanced panel data set comprising 11 recapitalized banks was employed, spanning the period from 2008 to 2019. Employing panel regression techniques including OLS, Fixed Effects, Random Effects, and the Generalized Method of Moments (GMM), the analysis examines the influence of profitability (ROA), bank size, leverage, broad money supply, and the central bank’s monetary policy rate on the Capital Adequacy Ratio (CAR). The results indicate that profitability, bank size, leverage, and the broad money supply are critical determinants of capital sufficiency. Banks with increased profitability and increased exposure to the money supply typically demonstrate more robust capital positions. Larger bank size and increased leverage levels negatively affect capital adequacy ratios. The little impact of the delayed CAR signifies minimal persistence in capital buffer methods over time, implying that banks may not significantly depend on prior year capital conditions in their current capital planning. The study highlights the complex nature of capital sufficiency, influenced by microeconomic factors such as size and leverage, as well as macroeconomic indices like the money supply. The findings substantiate the Capital Buffer Theory and furnish critical insights for regulators, bank executives, and policymakers seeking to bolster the resilience of the banking industry in South Africa.
The COVID-19 pandemic triggered a global economic crisis that significantly impacted the South African banking sector, already weakened by a pre-existing recession. While bank profitability and credit quality declined, stock market reactions appeared less severe, indicating a potential disconnect between fundamentals and investor behaviour. This study explores the influence of investor sentiment on credit risk, lending, and performance (ROA) of selected JSE-listed banks during the pandemic, incorporating behavioural finance perspectives. Panel regression analysis reveals that COVID-19 had a significant negative effect on stock, ROA and ROE, driven by heightened uncertainty and rising non-performing loans. Liquidity and capital positively influenced returns and ROE, reflecting investor confidence in resilient banks. Investor sentiment had a weakly positive impact on returns, but a significant negative effect on both ROA and ROE, suggesting that excessive optimism may have led to riskier lending practices and reduced profitability. Larger banks underperformed in terms of returns, likely due to operational complexity during the crisis. These findings highlight the critical role of behavioural factors in shaping financial outcomes during systemic shocks and provide new insights into emerging market banking dynamics under crisis conditions.
Commercial banks accept deposits and lend money for investment and consumption. This study used five (5) first-tier banks in Nigeria as a case study to analyze the effects of credit risk management on the financial performance of commercial banks. The study examined fifteen (15) years' worth of panel data (from 2005 to 2019), taken from the audited financial reports of five first-tier listed banks. Deposit Money Banks (DMBs) are the only banks used, and they are all listed on the Nigerian Stock Exchange. Non-performing loans (NPL) and expected credit loss impairment provisions (ECL) were utilized in this study as indicators of credit risk management. At the same time, return on assets (ROA) was employed to measure financial performance.
The study examined the dynamic responses of profitability indexes to capital adequacy ratios of authorized internationalized deposit money banks in Nigeria. The data were sourced from the financial year books of the deposit money banks and analyzed with static and dynamic panel estimators. The static estimator shows that the banks have differences in managerial style, size and profitability. Also, it was revealed that return on asset and return on equity responded positively to asset size, efficiency of the use of asset and current ratio in the static models and they were highly significant. However, they were insignificant in the dynamic specifications except asset size that was significant in the return on asset model showing a weak dynamic response of profitability to capital adequacy ratios. Hence the study recommended that Banks should improve their share based as to increase the asset as this will improve profitability.
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 00:14 UTC · ijbes | complete · ijbes · 21 Oct 2026 00:14 UTC |
| ojs-rest | 21 Oct 2026 00:14 UTC · ijbes | complete · ijbes · 21 Oct 2026 00:02 UTC |
| oai-pmh | 20 Oct 2026 19:49 UTC · ijbes | complete · ijbes · 20 Oct 2026 19:48 UTC |
| openalex-enrichment | 21 Oct 2026 00:14 UTC · ijbes | complete · ijbes · 21 Oct 2026 00:14 UTC |