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: 13 / 13 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 03:13 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.
13 / 13 with data · 0 missing or unmatched. Percentages use all 13 selected publications.
13 / 13 with data · 0 missing or unmatched. Percentages use all 13 selected publications.
13 / 13 with data · 0 missing or unmatched. Percentages use all 13 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 |
|---|---|---|---|---|---|
| 2023 | All selected publications | 13 | 13 | 13/13 | 13/13 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2023 | All selected publications | 28 | 13 | 13/13 | 13/13 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2023 | All selected publications | 37 | 13 | 13/13 | 13/13 |
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 13 / 13 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): 2026-10-21T07:22:47.409375+00:00 — 2026-10-22T03:13:13.680369+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 | 13 | 13/13 |
| 2018 | All selected publications | 0 | 13 | 13/13 |
| 2019 | All selected publications | 0 | 13 | 13/13 |
| 2020 | All selected publications | 0 | 13 | 13/13 |
| 2021 | All selected publications | 0 | 13 | 13/13 |
| 2022 | All selected publications | 0 | 13 | 13/13 |
| 2023 | All selected publications | 3 | 13 | 13/13 |
| 2024 | All selected publications | 11 | 13 | 13/13 |
| 2025 | All selected publications | 18 | 13 | 13/13 |
| 2026 | All selected publications | 5 | 13 | 13/13 |
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 |
|---|---|
| 2023 | 13 |
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 | 10 | 15 | 1/1 | 10.0 | 10 | 100.0 | 1/1 · 100.0% |
| International Journal of Finance & Banking Studies SSBFNET | 7 | 10 | 10 | 7/7 | 1.43 | 1 | 71.4 | 7/7 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 5 | 8 | 12 | 5/5 | 1.6 | 1 | 60.0 | 5/5 · 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.
The banking system’s role as an intermediary between depositors and borrowers makes its stability a crucial element of the economy. Individual bank stability impacts the stability of the financial sector and, consequently, the real sector and economic growth. This study is a detailed investigation of bank stability scores for the Turkish banking sector. Turkey experienced a major banking crisis from 2000 to 2001. However, with an improved regulatory environment and structural reforms, its resilience to shocks increased significantly in subsequent years. The present study contributes to the literature in three areas. First, it applies eight variations of the Z-score to the Turkish banking sector to test the importance of time and risk variation. Second, it investigates the impact on stability scores of several factors–the implementation of new capital regulations and Basel III liquidity rules, the Turkish lira crisis and the effects of COVID-19). Finally, it measures the contribution to systemic risk of each Turkish bank using the leave-one-out (LOO) Z-score method; this is the first study to use this method to analyse the contribution of different bank types to the overall stability of the sector.
The financial performance of banks relies heavily on properly utilizing their capital. However, bank ownership can have varying effects on the relationship between financial performance and capital. This study delves into this relationship by examining the impact of ownership on financial performance and bank capital. The study analyzes data from 44 commercial banks in Bangladesh and uses a two-step system generalized method of moments to address heteroscedasticity and autocorrelation issues. Unlike previous studies, this study confirms the significant effect of ownership on the relationship between bank financial performance and bank capital. The study's main findings are: (1) an inverted U-shaped relationship exists between bank capital and financial performance, implying that increasing capital can improve and reduce financial performance. (2) Private and Islamic commercial banks perform better than state-owned and conventional banks. (3) private-owned and Islamic commercial banks with higher capital are more likely to achieve higher profitability and financial success, while state-owned and conventional commercial banks with higher capital show lower profitability and weaker financial performance. Overall, this study offers significant practical implications for academics, researchers, and regulators interested in leveraging these findings.
The main objective of this study is to assess the impact of non-performing loans on bank lending behavior before and amid the COVID-19 pandemic. To do this research we have chosen fifteen private commercial banks in Bangladesh and data were taken from the year 2012 to the year 2021. Credit growth as a component of bank lending behavior was selected as the dependent variable and independent variables are non-performing loans to total loans, provision for loan losses to total loans, gross domestic product (GDP) rate, inflation rate, unemployment rate, total loans to total customer deposit, total equity to total asset, tier 1 ratio, growth of customer deposits, the dummy variables are used for incorporating the effect of covid-19. The paper suggests that NPL to total loan, provision for loan losses to total loan, total equity to total asset, and dummy variables (effect of covid-19 on NPL) are found statistically significant and inversely related to credit growth. Another three variables namely total loan to total deposit, deposit growth, and inflation variables are statistically significant and positively related to credit growth. All of the significant variables are consistent with general economic theory except the case for total equity to the total asset. To reduce non-performing loans, the banks may concentrate on improving corporate governance, maintaining strong loan review, thoroughly reviewing the KYC form, must ensure safety principles before approving a loan, collecting information from the CIB, accepting adequate collateral, examining the five C’s, name lending and connected party lending should be strictly prohibited which are quite related with behavioral and judgmental issues, would be a matter of further research.
The paper examines four main sources of financial risk exposure for four banks in the United Arab Emirates during the years 2003 to 2009. It begins by measuring stability levels for different types of financial risks using the coefficient of variation measure. The research then proceeds to investigate the nature of the relationship between financial risk and the actual reported financial performance of each company, namely the return on equity and return on assets. The findings show that all four sources of financial risk showed significant stability while both profitability indicators exhibited significant instability over the same period. Moreover, performance on the return on assets indicator was strongly and positively tied to a company’s return on equity indicator across the four banks but exhibited little or no interconnection with the four types of risks that banks are commonly exposed.
The Covid-19 pandemic has hit the world since the end of 2019 and has damaged the world economy, including Indonesia. Banking as a financial institution that provides funding to companies is also affected. Because the pandemic has reduced the company's performance, and this decline in performance has had an impact on banking. The aim of this research is to test whether the Covid-19 pandemic has an impact on the relationship between risk factors and bank performance. Bank performance is measured by profitability which consists of return on assets (ROA) and return on equity (ROE), while bank risk in this study consists of credit risk, liquidity risk, capital, operational risk and the Covid-19 pandemic. The population in this research is the Regional Development Banks (BPD) in Indonesia totaling 34 banks with a sample of 30 banks. The observation period was 4 years with quarterly data so that 320 observation data were collected. Hypothesis testing uses panel data regression, and to select the best regression model, the model will be tested using the Chow-test, Hausman-test, and Lagrange Multiplier test. After the model test was carried out, the fixed effect model was selected as the best model. The research results show that credit risk and operational risk have a significant and negative influence on all profitability, both ROA and ROE. Meanwhile, liquidity risk (LDR) has no effect on both types of profitability, while capital has an effect on ROE but has no effect on ROA. Another interesting result is that the Covid-19 pandemic has no impact on the influence of risk on bank performance.
The financial performance of banks relies heavily on properly utilizing their capital. However, bank ownership can have varying effects on the relationship between financial performance and capital. This study delves into this relationship by examining the impact of ownership on financial performance and bank capital. The study analyzes data from 44 commercial banks in Bangladesh and uses a two-step system generalized method of moments to address heteroscedasticity and autocorrelation issues. Unlike previous studies, this study confirms the significant effect of ownership on the relationship between bank financial performance and bank capital. The study's main findings are: (1) an inverted U-shaped relationship exists between bank capital and financial performance, implying that increasing capital can improve and reduce financial performance. (2) Private and Islamic commercial banks perform better than state-owned and conventional banks. (3) private-owned and Islamic commercial banks with higher capital are more likely to achieve higher profitability and financial success, while state-owned and conventional commercial banks with higher capital show lower profitability and weaker financial performance. Overall, this study offers significant practical implications for academics, researchers, and regulators interested in leveraging these findings.
Stokvel is a term used in South Africa to denote an informal saving club. A large population of South Africa are members of one stokvel or the other. A large amount of stokvel savings go unaccounted for in the banking sector resulting in distortions in the flow of funds in the banking sector. Thus, the objective of this paper was to establish the presence of a long-run relationship and causality between stokvel savings, money supply, gross domestic product growth rate and banking sector liquidity in South Africa. Applying the Autoregressive Distributed Lag (ARDL) and Error Correction Model (ECM) techniques on quarterly time series data for the period from 1987Q3 to 2020Q1, the study reveals that in the long run, stokvel savings and money supply were found to have a negative relationship with banking sector liquidity albeit insignificant, however, gross domestic product growth rate exhibited a negative and statistically significant relationship at 1%. The coefficient of the error correction model (ECM(-1)) was, as expected, negative and statistically significant thus providing evidence of a short-run relationship. This study concludes that informal savings must be included in the formal banking services to harness the cash that is held by the informal sector and thus improve banking sector liquidity while minimizing the prevalence of financial disintermediation.
In the modern world banks are unique. Formally, they are commercial enterprises, but in fact they have a government role, controlling all of the financial activity of the population and organizations. This article examines the roles of banks in the surveillance and pressuring of people that are not usually publicized. There is no political subtext in this article, as the situation described is typical for all countries. The fact that the majority of the examples given are from Russia and the USA simply reflects the experience of the authors. The other important thing to note is that the article describes the ongoing war of “banks against the people,” and the cascade of events is accelerating with nearly every month. And the winner in these intermittent skirmishes is always the same.
Banks are a high-risk industry, which is regulated by the government through the Financial Services Authority (FSA), so bank management must be very careful in managing banks so that risks can be controlled. The purpose of this study was to examine the effect of bank risk and third party funds on bank performance. Bank performance is measured by return on assets (ROA), while bank risk consists of operating risk which is measured by operating expenses to operating income ratio (EIR), liquidity risk is measured by loan to deposit ratio (LDR), capital risk is measured by capital adequacy ratio (CAR), and credit risk is measured by non-performing loans (NPL), and third party funds (TPF) are measured by the natural log of total credit. The research population consisted of 46 banks listed on the Indonesia Stock Exchange with a sample of 24 banks taken using a purposive sampling technique. The data collection period is four years with quarterly data (2019-2022). To test the hypothesis used panel data regression. After testing the model with the Chow-test and Hausman-test, the best regression model is the fixed effect model. The results of research using the fixed effect model show that operating risk (EIR) has a significant and negative effect on bank performance, while liquidity risk (LDR) has a significant positive effect on bank performance. Meanwhile, the capital adequacy ratio (CAR), non-performing loans (NPL), and third party funds (TPF) have no effect on bank 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 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 19:56 UTC · ijfbs | complete · ijfbs · 20 Oct 2026 19:55 UTC |
| openalex-enrichment | 21 Oct 2026 07:23 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 07:22 UTC |