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, 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 00:26 UTC — 22 Oct 2026 02:07 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 | 4 | 4 | 4/4 | 4/4 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 1 | 4 | 4/4 | 4/4 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 4 | 4 | 4/4 | 4/4 |
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): 2026-10-22T00:26:17.357230+00:00 — 2026-10-22T02:07:24.456811+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 | 1 | 4 | 4/4 |
| 2021 | All selected publications | 0 | 4 | 4/4 |
| 2022 | All selected publications | 1 | 4 | 4/4 |
| 2023 | All selected publications | 1 | 4 | 4/4 |
| 2024 | All selected publications | 1 | 4 | 4/4 |
| 2025 | All selected publications | 0 | 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 | 4 |
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 | 2 | 1 | 2 | 2/2 | 0.5 | 0.5 | 50.0 | 2/2 · 100.0% |
| International Journal of Finance & Banking Studies SSBFNET | 1 | 0 | 1 | 1/1 | 0.0 | 0 | 0.0 | 1/1 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 1 | 0 | 1 | 1/1 | 0.0 | 0 | 0.0 | 1/1 · 100.0% |
* Percentage among publications with a known citation count.
The practice of mock bank is an institutional arrangement to expose potential bankers to basic rudiments of banking operations with the ultimate aim of increasing the profitability of the organization through employee performance. This study assessed the impact of mock bank practices on employee performance in the banking sector as a panacea for repositioning the Nigerian economy with a specific focus on Union Bank Plc. The objective is to examine the contributions of operational experience to staff, ascertain the impact of professional ethics on commercial banks in Nigeria and identify the contributions of knowledge of organizational structure on the operations of commercial banks. T-statistics was used to test the three hypotheses. Results showed that there is a significant relationship between experienced, knowledge of job ethics, and organizational structure by employees before recruitment and performance at the workplace when compared to those recruited before training. Further results revealed that employees that had mock bank experiences perform better with less supervision. It was recommended that reinforcement of mock bank practices in tertiary institutions in Nigeria and allotment of higher credit units to mock bank practice should be encouraged.
The demonetisation footstep by the Government of India twisted complicated influences in the economy. Complete sectors of the economy had faced and produced mixed sensation results over the decision of demonetisation. India’s financial services struggled with demonetisation; on the other hand demonetisation affects utmost over the banking sector because it is substantial influenced services to transform money circulation in an Indian economy. Eradicating components of currency notes from circulation in an economy is demonetisation. It is as the processes of components of money are denied the status of legal tender. Consequently, ceased currency notes will not be account as valid currency in an economy. The term ‘demonetization’ is an instrument to shrink Inflation, Black Money, Corruption and terror funding, this step discourages a cash dependent economy in India. Government of India drive towards demonetisation has given a strong push to the popularity of digital banking and made helps with the alternative arrangements of e-banking and e –wallet to trade and commerce. Exploring the demonetisation emergence in an economy and impact on banking services ecosystem dynamics, this study take an abductive approach anchored in over 4 years of case study data regarding. The present study foremost intention is to be analysing the demonetisation impact over banking loans and advances. In this regard the present study is to be examining the pre demonetisation and post demonetisation period.
Clients’ non-performing loan is one of the problems faced by a Bank. The differences in character, culture, and people’s socio-economy influence a Bank, through its collectors, to solve the problem of this non-performing loan. The objective of this research was to find out social interaction done by Bank X in solving the non-performing loans of non-performing clients. The research used a qualitative method. The data were gathered by conducting interviews with nine informants – five clients, one collector, and three managers of Bank X in the area of Medan. The result of the research showed that in solving non-performing loan, Bank X did social interaction approach by conducting cooperation in achieving Agreement between collectors and clients, accommodation by understanding clients’ socio-cultural character in order to adjust to clients and to decrease the tension, and contravention by disgracing clients in front of their neighbors.
The introduction of the New Economic Policy (NEP) in 1991 by the Government of India (GoI) made it very clear that the viability of commercial banks is conditioned on their ability to make profits. In a globalized world, the whims and fancies of business cycles govern the fate of banks and a recessionary phase hampers the bank’s ability to pile on the profits thereby increasing their stock of non-performing assets. India, recently, is placed in a very bizarre situation, — some of the renowned banks are reporting huge losses but there is a very limited impact on stock markets. Strategically, the government, however, has taken corrective measures to arrest the growth of non-performing assets (NPAs) of banks. This paper builds upon a time series model capturing the position of NPAs of banks in India (excluding foreign banks operating in India). Empirically, there is a uni-directional causal relationship running from NPAs of banks to growth. Theoretically, what we can conclude is that imposing strict lending norms on banks actually manifests itself into a deeper recession, lowers growth and in turn aggravates the NPA problem further.
Clients’ non-performing loan is one of the problems faced by a Bank. The differences in character, culture, and people’s socio-economy influence a Bank, through its collectors, to solve the problem of this non-performing loan. The objective of this research was to find out social interaction done by Bank X in solving the non-performing loans of non-performing clients. The research used a qualitative method. The data were gathered by conducting interviews with nine informants – five clients, one collector, and three managers of Bank X in the area of Medan. The result of the research showed that in solving non-performing loan, Bank X did social interaction approach by conducting cooperation in achieving Agreement between collectors and clients, accommodation by understanding clients’ socio-cultural character in order to adjust to clients and to decrease the tension, and contravention by disgracing clients in front of their neighbors.
The practice of mock bank is an institutional arrangement to expose potential bankers to basic rudiments of banking operations with the ultimate aim of increasing the profitability of the organization through employee performance. This study assessed the impact of mock bank practices on employee performance in the banking sector as a panacea for repositioning the Nigerian economy with a specific focus on Union Bank Plc. The objective is to examine the contributions of operational experience to staff, ascertain the impact of professional ethics on commercial banks in Nigeria and identify the contributions of knowledge of organizational structure on the operations of commercial banks. T-statistics was used to test the three hypotheses. Results showed that there is a significant relationship between experienced, knowledge of job ethics, and organizational structure by employees before recruitment and performance at the workplace when compared to those recruited before training. Further results revealed that employees that had mock bank experiences perform better with less supervision. It was recommended that reinforcement of mock bank practices in tertiary institutions in Nigeria and allotment of higher credit units to mock bank practice should be encouraged.
The introduction of the New Economic Policy (NEP) in 1991 by the Government of India (GoI) made it very clear that the viability of commercial banks is conditioned on their ability to make profits. In a globalized world, the whims and fancies of business cycles govern the fate of banks and a recessionary phase hampers the bank’s ability to pile on the profits thereby increasing their stock of non-performing assets. India, recently, is placed in a very bizarre situation, — some of the renowned banks are reporting huge losses but there is a very limited impact on stock markets. Strategically, the government, however, has taken corrective measures to arrest the growth of non-performing assets (NPAs) of banks. This paper builds upon a time series model capturing the position of NPAs of banks in India (excluding foreign banks operating in India). Empirically, there is a uni-directional causal relationship running from NPAs of banks to growth. Theoretically, what we can conclude is that imposing strict lending norms on banks actually manifests itself into a deeper recession, lowers growth and in turn aggravates the NPA problem further.
The demonetisation footstep by the Government of India twisted complicated influences in the economy. Complete sectors of the economy had faced and produced mixed sensation results over the decision of demonetisation. India’s financial services struggled with demonetisation; on the other hand demonetisation affects utmost over the banking sector because it is substantial influenced services to transform money circulation in an Indian economy. Eradicating components of currency notes from circulation in an economy is demonetisation. It is as the processes of components of money are denied the status of legal tender. Consequently, ceased currency notes will not be account as valid currency in an economy. The term ‘demonetization’ is an instrument to shrink Inflation, Black Money, Corruption and terror funding, this step discourages a cash dependent economy in India. Government of India drive towards demonetisation has given a strong push to the popularity of digital banking and made helps with the alternative arrangements of e-banking and e –wallet to trade and commerce. Exploring the demonetisation emergence in an economy and impact on banking services ecosystem dynamics, this study take an abductive approach anchored in over 4 years of case study data regarding. The present study foremost intention is to be analysing the demonetisation impact over banking loans and advances. In this regard the present study is to be examining the pre demonetisation and post demonetisation period.
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 |