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: 11 / 11 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 00:01 UTC — 21 Oct 2026 07:23 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.
11 / 11 with data · 0 missing or unmatched. Percentages use all 11 selected publications.
11 / 11 with data · 0 missing or unmatched. Percentages use all 11 selected publications.
11 / 11 with data · 0 missing or unmatched. Percentages use all 11 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 |
|---|---|---|---|---|---|
| 2014 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2015 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2017 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2019 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| 2024 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2014 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2015 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2017 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2019 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | 20 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 12 | 4 | 4/4 | 4/4 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2014 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2015 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2017 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2019 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | 32 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 18 | 4 | 4/4 | 4/4 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | 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 11 / 11 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): 2026-10-21T00:01:59.957867+00:00 — 2026-10-21T07:23:00.720931+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 | 11 | 11/11 |
| 2018 | All selected publications | 0 | 11 | 11/11 |
| 2019 | All selected publications | 0 | 11 | 11/11 |
| 2020 | All selected publications | 0 | 11 | 11/11 |
| 2021 | All selected publications | 3 | 11 | 11/11 |
| 2022 | All selected publications | 2 | 11 | 11/11 |
| 2023 | All selected publications | 11 | 11 | 11/11 |
| 2024 | All selected publications | 13 | 11 | 11/11 |
| 2025 | All selected publications | 19 | 11 | 11/11 |
| 2026 | All selected publications | 7 | 11 | 11/11 |
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 ↗
Records with full publication dates only. 2026 is an incomplete year.
| Period | Publications |
|---|---|
| 2014-07 | 1 |
| 2018-02 | 1 |
| 2020-03 | 1 |
| 2021-12 | 1 |
| 2022-12 | 1 |
| 2023-01 | 1 |
| 2023-05 | 1 |
| 2023-07 | 1 |
| 2023-10 | 1 |
| 2025-05 | 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 |
|---|---|---|---|---|---|---|---|---|
| Bussecon Review of Social Sciences Bussecon International Academy | 2 | 3 | 3 | 2/2 | 1.5 | 1.5 | 100.0 | 2/2 · 100.0% |
| International Journal of Business Ecosystem & Strategy Bussecon International Academy | 2 | 11 | 17 | 2/2 | 5.5 | 5.5 | 100.0 | 2/2 · 100.0% |
| International Journal of Finance & Banking Studies SSBFNET | 3 | 20 | 32 | 3/3 | 6.67 | 0 | 33.3 | 3/3 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 4 | 3 | 3 | 4/4 | 0.75 | 1.0 | 75.0 | 4/4 · 100.0% |
* Percentage among publications with a known citation count.
Increase in losses borne by banks as a result of inadequate operational risk management practices and the adverse impact on banks’ financial performance has been a major concern to bank management and regulators. This study analysed the impact of operational risk management practices on the financial performance of commercial banks in Nigeria. 10-years (2008 - 2017) secondary data extracted from audited financial statements of selected commercial banks in Nigeria was used for the study. The data was analysed using the Linear Multiple Regression Model. The results showed that there is a positive relationship between operational risk management and the financial performance of banks. The findings revealed that sound operational risk management practices impact positively on the financial performance of banks. We, therefore, recommend that banks’ management should deploy adequate resources towards understanding operational risk to ensure sound operational risk management and improved financial performance of banks.
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.
Despite the institutionalization of operational risk management in banks and the strict supervision of bank regulators, operational risk events are still on the increase. It is becoming evident to banks that there is a need to identify the drivers of this risk and nib it at the root to reduce the probability of recurrence. Hence, this study examined the drivers of operational risks in Nigerian commercial banks and the extent to which each driver contributes to operational risk. To achieve the study’s objectives, primary data were collected from the Operational Risk Management Desks of six (6) sampled commercial banks and analyzed using SPSS and Microsoft Excel. The result showed that Internal processes, IT systems and Quality of Risk Officers are determinants of operational losses in banks. The internal process was however indicated as having the most impact. The study concluded that Internal Process is the major driver of operational risk in Nigerian Commercial banks. The researcher, therefore, recommends that bank management must have defined procedures for core activities and prioritize regular review of their critical processes to reduce operational risk events and the associated costs.
This study examines the impacts of credit risk management on the growth of Nigerian banks. Specifically, the study focused mainly on the impacts of credit risk management among Deposit Money Banks in Nigeria. The study employed an ex-post facto research design, utilising secondary data from the financial information of 14 selected listed Deposit Money Banks from 2007 to 2022. The study population was defined as the total number of listed Deposit Money Banks, from which a sample of six banks was selected using a simple random sampling technique. The operationalisation of variables included (1) the use of natural logarithm for bank size, (2) a three-level scale for credit risk, and (3) alternative measurements for collateral management and credit scores. Data analysis involved the calculation of descriptive statistics, correlation analysis, variance inflation factors, and regression analysis. The research hypotheses are also validated to assess the significance of various factors in credit risk management. The findings revealed that credit scores significantly enhance the effectiveness of credit risk assessment, and collateral management did not significantly contribute to mitigating credit risk. The findings also revealed that default risk management significantly reduces the credit risk of Deposit Money Banks in Nigeria. These results highlight the intricate nature of credit risk management in the banking sector and emphasize the need for a multifaceted approach.
Petroleum is a gift of nature deposited under the earth’s surface. The ‘black gold’ as it is generally called, is viewed as a treasure that has the capabilities of transforming the social and economic fortunes of any country. This study explores risk management in Nigeria oil and gas sector with reference to management of government equities in the Nigerian oil and gas sector. A survey design was used for the study. By means of questionnaire, primary data was obtained from randomly selected members of the sampled organisations. The research hypotheses were subjected to chi-square analysis. Analysis was based on fifty-three copies of questionnaire filled and retrieved out of seventy-five questionnaires administered to employees of NNPC, NLNG and Shell companies in Nigeria. Findings from the study revealed some deficiencies in the way government equities in the Nigerian oil and gas sector are being managed, such lapses include government undue interference, lack of transparency, absence of established positive culture and too much bureaucracy. The study concludes that the Nigerian government equities in the oil and gas sector are not effectively managed; the fiscal policy in Nigeria’s oil and gas sector is inadequate; NNPC as presently constituted is not managing government equities invested in oil and gas JV agreements. Among the recommendations of the study include: the government should monitor returns on equities in the oil and gas sector in Nigeria; the government and stakeholders need the establishment a good culture and practice to ensure profitable ownership and collaboration.
This study examines the impacts of credit risk management on the growth of Nigerian banks. Specifically, the study focused mainly on the impacts of credit risk management among Deposit Money Banks in Nigeria. The study employed an ex-post facto research design, utilising secondary data from the financial information of 14 selected listed Deposit Money Banks from 2007 to 2022. The study population was defined as the total number of listed Deposit Money Banks, from which a sample of six banks was selected using a simple random sampling technique. The operationalisation of variables included (1) the use of natural logarithm for bank size, (2) a three-level scale for credit risk, and (3) alternative measurements for collateral management and credit scores. Data analysis involved the calculation of descriptive statistics, correlation analysis, variance inflation factors, and regression analysis. The research hypotheses are also validated to assess the significance of various factors in credit risk management. The findings revealed that credit scores significantly enhance the effectiveness of credit risk assessment, and collateral management did not significantly contribute to mitigating credit risk. The findings also revealed that default risk management significantly reduces the credit risk of Deposit Money Banks in Nigeria. These results highlight the intricate nature of credit risk management in the banking sector and emphasize the need for a multifaceted approach.
Petroleum is a gift of nature deposited under the earth’s surface. The ‘black gold’ as it is generally called, is viewed as a treasure that has the capabilities of transforming the social and economic fortunes of any country. This study explores risk management in Nigeria oil and gas sector with reference to management of government equities in the Nigerian oil and gas sector. A survey design was used for the study. By means of questionnaire, primary data was obtained from randomly selected members of the sampled organisations. The research hypotheses were subjected to chi-square analysis. Analysis was based on fifty-three copies of questionnaire filled and retrieved out of seventy-five questionnaires administered to employees of NNPC, NLNG and Shell companies in Nigeria. Findings from the study revealed some deficiencies in the way government equities in the Nigerian oil and gas sector are being managed, such lapses include government undue interference, lack of transparency, absence of established positive culture and too much bureaucracy. The study concludes that the Nigerian government equities in the oil and gas sector are not effectively managed; the fiscal policy in Nigeria’s oil and gas sector is inadequate; NNPC as presently constituted is not managing government equities invested in oil and gas JV agreements. Among the recommendations of the study include: the government should monitor returns on equities in the oil and gas sector in Nigeria; the government and stakeholders need the establishment a good culture and practice to ensure profitable ownership and collaboration.
This paper focuses on employee engagement in a sector that transitioned from a fully government-owned to a privatized entity while retaining the majority of the legacy staff and bringing in new employees. The electricity sector has always piqued the interest of investors due to its strategic importance to the growth and development of other sectors, particularly in developing countries. This paper investigates some of the factors that influence employee engagement in the Nigerian power and electricity subsector. The study includes 824 employees from Nigeria's downstream electricity sector. Descriptive and ordinal regression analyses were used to determine the relationships between the variables. The study reveals that parameters such as office ambiance, clear communication, tool availability, health, and safety have relationships with or impact employee engagement. As a result, organizations' management must pay attention to and improve on them in order to retain their employees and provide their best discretionary effort. This research expands on theories of engagement in the post-privatization era and investigates the influence of key drivers on employee engagement in Nigeria's downstream power sector.
Insurance plays an essential role in stimulating economic growth. Insurance is an intangible product, and prompt claim settlement proves that insurers fulfill their promises to the insureds. This paper analyses the impacts of insurance claims settlement on economic growth. It examines the effect of insurance claims' settlements on a nation's economic growth, using Nigeria as a case study. The research utilised an ex-post facto design, using 28-year time series data (1992 – 2019). Gross Domestic Product (GDP) and Nigeria's insurance companies' claims settlement are the dependent and independent variables used for the study, respectively. The Long-run co-integration result revealed that INCLM (Insurance claims) has an insignificant negative effect on GDP. The coefficient shows that a percentage increment in INCLM (Insurance claims) would result in a 1.22 decrease in GDP. The results indicate that insurance claims settlement has an insignificant negative effect on economic growth. This implies a negative relationship between insurance claims settled by insurance companies and economic growth in Nigeria. The finding is surprising as one expects that settlement of claims by insurers should positively impact economic growth. The implication is that the relationship between insurance claims settlement and economic growth varies depending on several factors, including country-specific factors and the performance of the country's insurance industry.
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.
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 |