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: 5 / 5 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 01:44 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.
5 / 5 with data · 0 missing or unmatched. Percentages use all 5 selected publications.
5 / 5 with data · 0 missing or unmatched. Percentages use all 5 selected publications.
5 / 5 with data · 0 missing or unmatched. Percentages use all 5 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 |
|---|---|---|---|---|---|
| 2024 | All selected publications | 5 | 5 | 5/5 | 5/5 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2024 | All selected publications | 15 | 5 | 5/5 | 5/5 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2024 | All selected publications | 16 | 5 | 5/5 | 5/5 |
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 5 / 5 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): 2026-10-21T07:22:54.952089+00:00 — 2026-10-22T01:44:56.244943+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 | 5 | 5/5 |
| 2018 | All selected publications | 0 | 5 | 5/5 |
| 2019 | All selected publications | 0 | 5 | 5/5 |
| 2020 | All selected publications | 0 | 5 | 5/5 |
| 2021 | All selected publications | 0 | 5 | 5/5 |
| 2022 | All selected publications | 0 | 5 | 5/5 |
| 2023 | All selected publications | 0 | 5 | 5/5 |
| 2024 | All selected publications | 5 | 5 | 5/5 |
| 2025 | All selected publications | 8 | 5 | 5/5 |
| 2026 | All selected publications | 3 | 5 | 5/5 |
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 |
|---|---|
| 2024 | 5 |
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 | 3 | 12 | 12 | 3/3 | 4.0 | 1 | 100.0 | 3/3 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 2 | 3 | 4 | 2/2 | 1.5 | 1.5 | 100.0 | 2/2 · 100.0% |
* Percentage among publications with a known citation count.
This paper aims to examine the financial performance of Bangladeshi listed commercial banks. The study analyzed the financial performances of banks categorized into three generations—first, second, and third—based on their year of commencement of operations in the country. The data of a total of 24 banks, 8 from each generation, listed in the Dhaka Stock Exchange (DSE), has been collected from audited financial statements for the time period of 13 years, from 2010 to 2022. Data has been analyzed using different ratios of profitability, liquidity, capital adequacy, operational efficiency, and credit risk. The ANOVA test also has been used to make a comparison of profitability—the return on assets (ROA), and return on equity (ROE) of the banks of different generations. According to the results, the average profitability ratios in terms of ROA, and ROE of the second-generation banks showed their credibility than banks of other generations; while first generation banks kept higher liquidity, and capital adequacy with higher credit risk than the banks of other generations. The first-generation banks also showed a lower cost-expense ratio, and a higher efficiency in terms of operation, than the banks in the second and third generations. The research also discovered that the profitability of banks of different generations significantly varies from one another.
The main goal of this study is to evaluate customer perceptions of the competitiveness of Absa Bank in South Africa. To achieve this objective, the study is divided into two sections, which were designed as the main research questions. The first question aimed to determine customer perceptions of the level of bank competition in South Africa, while the second question focused on customer perceptions of Absa's competitiveness within the South African banking sector. It was revealed through an Absa Bank communication that Barclays Bank (UK), the major shareholder, was withdrawing from Barclays Africa Group aka Absa Bank due to ongoing uncertainties in the African market. This departure has resulted in mixed reactions from the market, with customers expressing skepticism about the potential impact on the bank's performance. The study was conducted at the Durban Central branch, which serves approximately 150,000 customers. A sample of 400 walk-in customers at the branch was selected using a non-probability convenience sampling technique. Out of the 400 questionnaires distributed, 13 were discarded due to incomplete responses. This resulted in 387 questionnaires being returned. The collected data was analysed using the latest computerized Statistical Package for Social Sciences (SPSS) version 24.0. The findings of the study indicate that competition does exist within the banking sector, with banks employing pricing strategies to compete for customers. Most customers perceive Absa Bank as being competitive in the market. As a recommendation, it is suggested that branch management regularly conduct customer surveys to engage customers and identify areas for improvement that can be incorporated into the branch operations.
The research was conducted to see how cost of funds (COC), and non-performing loans (NPL) on profitability is measured by return on assets (ROA). The research method used is descriptive and verification methods. Secondary data is sourced from Regional Development Banks of West Java and Banten Provinces in Indonesia quarterly financial reports for 2014 – 2022, while the data analysis technique uses multiple linear regression analysis. The results of the research are that COF and NPL has a negative and significant effect on ROA. Simultaneously, COF and NPL have a significant effect on ROA. The results of this research are useful for bank management in determining funding strategies with low cost of funds, and credit management strategies so that non-performing remains healthy.
This study examines the correlation between industry-specific loan disbursement and profitability in the banking sector of Bangladesh, with a particular focus on the influence of targeted lending on profitability. The study examines the impact of different variables such as total assets, total revenue, non-performing loans, and sector-specific loans (agricultural, transportation, construction, textile, pharmaceuticals, and food procurement), on profit after tax (PAT) by analyzing data from 41 commercial banks between 2009 and 2022. By employing various techniques such as Pooled Ordinary Least Squares (POLS), Two-Stage Least Squares (2SLS), and the Generalized Method of Moments (GMM), we have determined that total assets have a noteworthy adverse effect on PAT in both POLS and 2SLS models. Conversely, construction loans have a positive influence on PAT in these models. The total revenue consistently has a favorable effect on the profit after tax (PAT) in all models. In the GMM model, transportation loans and textile loans have a notable adverse effect on PAT, but pharmaceutical loans have a slight favorable effect. The presence of non-performing loans, total loans, agricultural loans, and food procurement loans does not have a substantial impact on the profitability after tax (PAT) in any of the models. So overall total revenue, construction loan and pharmaceutical loan have a positive impact on PAT and total assets, transportation loan and textile loan have a negative impact on PAT. This study emphasizes the significance of strategic lending in underdeveloped industries and provides practical implications for banking professionals and regulators to identify high-risk lending sectors and devise measures to improve long-term profitability.
Commercial banks in Kenya operate in quite dynamic financial market environment and continue to experience stiff competition in the provision of financial services. The liberation of the financial markets has resulted into an upsurge in the number of institutions providing financial products and services, threatening the future of Kenyan commercial banks. This study aimed at determining the effect of strategic alliances on competitive advantage of commercial banks in Kenya. The objectives were to determine effect of joint ventures, equity alliances, and joint research and development on the competitiveness of Kenyan banks. The following theories informed this research; Resource Based View theory, competitive theory, and resource dependence theory. Causal research design was adopted where all the 42 Kenyan commercial banks formed the target population. Census survey sampling design was applied and semi-structured questionnaire used. Reliability and validity results were used to improve the research instrument prior to the actual data collection. Data was analyzed using descriptive and inferential statistics. Study found that joint venture (?=0.435, p=0.000) equity alliances (?=0.227, p=0.018), and joint research and development (?=0.0.612, p=0.000) had positive significant effects on competitive advantage. With a coefficient of determination (R-square) of 68.9% and overall p-value of 0.000, the study concluded strategic alliances have a statistical significant effect on competitive advantage. The study recommends that managements of commercial banks should fast-truck implementation of such alliances to for improved competitiveness.
The research was conducted to see how cost of funds (COC), and non-performing loans (NPL) on profitability is measured by return on assets (ROA). The research method used is descriptive and verification methods. Secondary data is sourced from Regional Development Banks of West Java and Banten Provinces in Indonesia quarterly financial reports for 2014 – 2022, while the data analysis technique uses multiple linear regression analysis. The results of the research are that COF and NPL has a negative and significant effect on ROA. Simultaneously, COF and NPL have a significant effect on ROA. The results of this research are useful for bank management in determining funding strategies with low cost of funds, and credit management strategies so that non-performing remains healthy.
Commercial banks in Kenya operate in quite dynamic financial market environment and continue to experience stiff competition in the provision of financial services. The liberation of the financial markets has resulted into an upsurge in the number of institutions providing financial products and services, threatening the future of Kenyan commercial banks. This study aimed at determining the effect of strategic alliances on competitive advantage of commercial banks in Kenya. The objectives were to determine effect of joint ventures, equity alliances, and joint research and development on the competitiveness of Kenyan banks. The following theories informed this research; Resource Based View theory, competitive theory, and resource dependence theory. Causal research design was adopted where all the 42 Kenyan commercial banks formed the target population. Census survey sampling design was applied and semi-structured questionnaire used. Reliability and validity results were used to improve the research instrument prior to the actual data collection. Data was analyzed using descriptive and inferential statistics. Study found that joint venture (?=0.435, p=0.000) equity alliances (?=0.227, p=0.018), and joint research and development (?=0.0.612, p=0.000) had positive significant effects on competitive advantage. With a coefficient of determination (R-square) of 68.9% and overall p-value of 0.000, the study concluded strategic alliances have a statistical significant effect on competitive advantage. The study recommends that managements of commercial banks should fast-truck implementation of such alliances to for improved competitiveness.
This study examines the correlation between industry-specific loan disbursement and profitability in the banking sector of Bangladesh, with a particular focus on the influence of targeted lending on profitability. The study examines the impact of different variables such as total assets, total revenue, non-performing loans, and sector-specific loans (agricultural, transportation, construction, textile, pharmaceuticals, and food procurement), on profit after tax (PAT) by analyzing data from 41 commercial banks between 2009 and 2022. By employing various techniques such as Pooled Ordinary Least Squares (POLS), Two-Stage Least Squares (2SLS), and the Generalized Method of Moments (GMM), we have determined that total assets have a noteworthy adverse effect on PAT in both POLS and 2SLS models. Conversely, construction loans have a positive influence on PAT in these models. The total revenue consistently has a favorable effect on the profit after tax (PAT) in all models. In the GMM model, transportation loans and textile loans have a notable adverse effect on PAT, but pharmaceutical loans have a slight favorable effect. The presence of non-performing loans, total loans, agricultural loans, and food procurement loans does not have a substantial impact on the profitability after tax (PAT) in any of the models. So overall total revenue, construction loan and pharmaceutical loan have a positive impact on PAT and total assets, transportation loan and textile loan have a negative impact on PAT. This study emphasizes the significance of strategic lending in underdeveloped industries and provides practical implications for banking professionals and regulators to identify high-risk lending sectors and devise measures to improve long-term profitability.
The main goal of this study is to evaluate customer perceptions of the competitiveness of Absa Bank in South Africa. To achieve this objective, the study is divided into two sections, which were designed as the main research questions. The first question aimed to determine customer perceptions of the level of bank competition in South Africa, while the second question focused on customer perceptions of Absa's competitiveness within the South African banking sector. It was revealed through an Absa Bank communication that Barclays Bank (UK), the major shareholder, was withdrawing from Barclays Africa Group aka Absa Bank due to ongoing uncertainties in the African market. This departure has resulted in mixed reactions from the market, with customers expressing skepticism about the potential impact on the bank's performance. The study was conducted at the Durban Central branch, which serves approximately 150,000 customers. A sample of 400 walk-in customers at the branch was selected using a non-probability convenience sampling technique. Out of the 400 questionnaires distributed, 13 were discarded due to incomplete responses. This resulted in 387 questionnaires being returned. The collected data was analysed using the latest computerized Statistical Package for Social Sciences (SPSS) version 24.0. The findings of the study indicate that competition does exist within the banking sector, with banks employing pricing strategies to compete for customers. Most customers perceive Absa Bank as being competitive in the market. As a recommendation, it is suggested that branch management regularly conduct customer surveys to engage customers and identify areas for improvement that can be incorporated into the branch operations.
This paper aims to examine the financial performance of Bangladeshi listed commercial banks. The study analyzed the financial performances of banks categorized into three generations—first, second, and third—based on their year of commencement of operations in the country. The data of a total of 24 banks, 8 from each generation, listed in the Dhaka Stock Exchange (DSE), has been collected from audited financial statements for the time period of 13 years, from 2010 to 2022. Data has been analyzed using different ratios of profitability, liquidity, capital adequacy, operational efficiency, and credit risk. The ANOVA test also has been used to make a comparison of profitability—the return on assets (ROA), and return on equity (ROE) of the banks of different generations. According to the results, the average profitability ratios in terms of ROA, and ROE of the second-generation banks showed their credibility than banks of other generations; while first generation banks kept higher liquidity, and capital adequacy with higher credit risk than the banks of other generations. The first-generation banks also showed a lower cost-expense ratio, and a higher efficiency in terms of operation, than the banks in the second and third generations. The research also discovered that the profitability of banks of different generations significantly varies from one another.
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 |