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: 3 / 3 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 04:14 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.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 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 |
|---|---|---|---|---|---|
| 2022 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 2 | 3 | 3/3 | 3/3 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 4 | 3 | 3/3 | 3/3 |
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 3 / 3 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): 2026-10-21T07:22:43.923249+00:00 — 2026-10-22T04:14:15.109814+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 | 3 | 3/3 |
| 2018 | All selected publications | 0 | 3 | 3/3 |
| 2019 | All selected publications | 0 | 3 | 3/3 |
| 2020 | All selected publications | 0 | 3 | 3/3 |
| 2021 | All selected publications | 0 | 3 | 3/3 |
| 2022 | All selected publications | 0 | 3 | 3/3 |
| 2023 | All selected publications | 4 | 3 | 3/3 |
| 2024 | All selected publications | 0 | 3 | 3/3 |
| 2025 | All selected publications | 0 | 3 | 3/3 |
| 2026 | All selected publications | 0 | 3 | 3/3 |
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 |
|---|---|
| 2022 | 3 |
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 Research in Business and Social Science SSBFNET | 3 | 2 | 4 | 3/3 | 0.67 | 0 | 33.3 | 3/3 · 100.0% |
* Percentage among publications with a known citation count.
This study investigates the impact of COVID-19 induced global panic on crude oil and natural gas volatility. The author uses the Structural Vector Auto Regression (SVAR) to examine the magnitude of shocks in global oil and gas prices caused by COVID-19 induced panic between 3rd January 2020 and 30th June 2021. The results show that shocks in oil and gas prices were negative and more severe in the first five (5) months of 2020 when the pandemic was spreading across the globe forcing countries' prices into lockdowns. The negative shocks gradually diminished in the following periods as the prices recovered courtesy of global economic recovery and vaccine rollouts. Furthermore, the panic was more pronounced in causing oil prices shocks as gas prices were already suffering amid mild temperatures during the 2020 winter season. The author stresses the need for swift actions during the early days of the crisis to adjust oil and gas supply to match demand shrinkage so as to stabilize their prices given their enormity to the global economy. The Russia-Saudi Arabia delays in agreeing on oil supply restrictions may have amplified the magnitude of negative shocks in oil prices. Existing studies have examined the country-level impacts of COVID-19 on energy prices focusing mainly on oil. However, oil and gas are among the most traded commodities in the world thus the stability of their prices is of global concern. This study examines this phenomenon on a global scale by utilizing the novel global coronavirus panic index.
In the past 20 years, commodity markets have been examined under the hypothesis of whether the prices exhibit recurrent patterns or follow the stochastic processes. In this study, we analyze both global energy markets and food markets to detect the presence of multiple equilibria, which may lead to higher spikes in prices and to the occurrence of intermittency. This study focuses on daily prices in Brent petrol and Natural gas markets from 23 May 2017 to 23 May 2022. This time interval covers the period where extraordinary economic and financial policies have been preferred in countries worldwide amid the Covid-19 pandemic crisis. On the other hand, the study investigates global Food index monthly data from 2007M11 to 2022M4. Similarly, the time interval under examination points out the effects of the 2008 global crisis where expansionary monetary policies have been implemented both by the Federal Reserve of the U.S and the European Central Bank. We employed the phase-space reconstruction method for the Crude oil Brent Europe and spot Natural gas prices series, and global Food index data respectively. The main findings of the study demonstrate that commodity markets do not converge to a unique equilibrium level, instead multiple equilibria persist and chaotic behavior occurs. The presence of multiple equilibria leads to an increase in complexity and recurrently causes volatility in commodity markets, which may have spillover effects on other financial markets. Our results suggest that these effects simultaneously increase global inflationary pressures. From the perspective of policy making, it is crucial to establish a strategy to eliminate multiple equilibria and prevent high price spikes. Our findings have important implications for Central bank policies in emerging markets and portfolio and risk management.
This paper examines the impact of the September 11th attacks on the United States financial and non-financial sectors. The empirical analysis includes 5 financial and 28 non-financial sub-sector indices using the event study method, structural break test, and volatility analyses. The results of the estimation show that except for the short-lived impact on the real estate sector, the attacks had no significant impact on financial sector indices. Different reactions among non-financial sectors are observed. Travel and leisure and leisure goods sectors were the two industries that were mostly affected by the attacks due to the nature of their business. The outcomes of the analyses provide several diversification strategies and speculation opportunities for both portfolio managers and investors.
In the past 20 years, commodity markets have been examined under the hypothesis of whether the prices exhibit recurrent patterns or follow the stochastic processes. In this study, we analyze both global energy markets and food markets to detect the presence of multiple equilibria, which may lead to higher spikes in prices and to the occurrence of intermittency. This study focuses on daily prices in Brent petrol and Natural gas markets from 23 May 2017 to 23 May 2022. This time interval covers the period where extraordinary economic and financial policies have been preferred in countries worldwide amid the Covid-19 pandemic crisis. On the other hand, the study investigates global Food index monthly data from 2007M11 to 2022M4. Similarly, the time interval under examination points out the effects of the 2008 global crisis where expansionary monetary policies have been implemented both by the Federal Reserve of the U.S and the European Central Bank. We employed the phase-space reconstruction method for the Crude oil Brent Europe and spot Natural gas prices series, and global Food index data respectively. The main findings of the study demonstrate that commodity markets do not converge to a unique equilibrium level, instead multiple equilibria persist and chaotic behavior occurs. The presence of multiple equilibria leads to an increase in complexity and recurrently causes volatility in commodity markets, which may have spillover effects on other financial markets. Our results suggest that these effects simultaneously increase global inflationary pressures. From the perspective of policy making, it is crucial to establish a strategy to eliminate multiple equilibria and prevent high price spikes. Our findings have important implications for Central bank policies in emerging markets and portfolio and risk management.
This study investigates the impact of COVID-19 induced global panic on crude oil and natural gas volatility. The author uses the Structural Vector Auto Regression (SVAR) to examine the magnitude of shocks in global oil and gas prices caused by COVID-19 induced panic between 3rd January 2020 and 30th June 2021. The results show that shocks in oil and gas prices were negative and more severe in the first five (5) months of 2020 when the pandemic was spreading across the globe forcing countries' prices into lockdowns. The negative shocks gradually diminished in the following periods as the prices recovered courtesy of global economic recovery and vaccine rollouts. Furthermore, the panic was more pronounced in causing oil prices shocks as gas prices were already suffering amid mild temperatures during the 2020 winter season. The author stresses the need for swift actions during the early days of the crisis to adjust oil and gas supply to match demand shrinkage so as to stabilize their prices given their enormity to the global economy. The Russia-Saudi Arabia delays in agreeing on oil supply restrictions may have amplified the magnitude of negative shocks in oil prices. Existing studies have examined the country-level impacts of COVID-19 on energy prices focusing mainly on oil. However, oil and gas are among the most traded commodities in the world thus the stability of their prices is of global concern. This study examines this phenomenon on a global scale by utilizing the novel global coronavirus panic index.
This paper examines the impact of the September 11th attacks on the United States financial and non-financial sectors. The empirical analysis includes 5 financial and 28 non-financial sub-sector indices using the event study method, structural break test, and volatility analyses. The results of the estimation show that except for the short-lived impact on the real estate sector, the attacks had no significant impact on financial sector indices. Different reactions among non-financial sectors are observed. Travel and leisure and leisure goods sectors were the two industries that were mostly affected by the attacks due to the nature of their business. The outcomes of the analyses provide several diversification strategies and speculation opportunities for both portfolio managers and investors.
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 19:55 UTC · ijrbs | complete · ijrbs · 20 Oct 2026 19:50 UTC |
| openalex-enrichment | 21 Oct 2026 07:23 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 07:22 UTC |