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: 7 / 7 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 02:09 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.
6 / 7 with data · 1 missing or unmatched. Percentages use all 7 selected publications.
6 / 7 with data · 1 missing or unmatched. Percentages use all 7 selected publications.
7 / 7 with data · 0 missing or unmatched. Percentages use all 7 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 |
|---|---|---|---|---|---|
| 2013 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2014 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2015 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2017 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2013 | All selected publications | 5 | 2 | 2/2 | 2/2 |
| 2014 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2015 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 1 | 2 | 2/2 | 2/2 |
| 2017 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 7 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2013 | All selected publications | 13 | 2 | 2/2 | 2/2 |
| 2014 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2015 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 6 | 2 | 2/2 | 2/2 |
| 2017 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 7 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 1 | 1 | 1/1 | 1/1 |
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 7 / 7 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): 2026-10-21T07:22:45.988916+00:00 — 2026-10-22T02:09:34.529854+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 | 1 | 7 | 7/7 |
| 2018 | All selected publications | 3 | 7 | 7/7 |
| 2019 | All selected publications | 0 | 7 | 7/7 |
| 2020 | All selected publications | 4 | 7 | 7/7 |
| 2021 | All selected publications | 2 | 7 | 7/7 |
| 2022 | All selected publications | 1 | 7 | 7/7 |
| 2023 | All selected publications | 3 | 7 | 7/7 |
| 2024 | All selected publications | 1 | 7 | 7/7 |
| 2025 | All selected publications | 5 | 7 | 7/7 |
| 2026 | All selected publications | 2 | 7 | 7/7 |
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 |
|---|---|
| 2013 | 2 |
| 2016 | 2 |
| 2021 | 1 |
| 2022 | 1 |
| 2024 | 1 |
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 | 4 | 7 | 19 | 4/4 | 1.75 | 1.5 | 100.0 | 4/4 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 3 | 7 | 9 | 3/3 | 2.33 | 0 | 33.3 | 3/3 · 100.0% |
* Percentage among publications with a known citation count.
The main aim of this study was to investigate the impact of foreign portfolio investment on economic growth in Africa. The motivation to explore the effects of portfolio investment and growth stemmed from the fact that Africa has recorded large capital inflows since the global financial crisis. These capital flows can have positive or negative consequences. The effects of foreign portfolio investment on the African economy is an area of interest because very little has been done on the subject area. The study used quarterly panel from 1995 to 2014. The study used only five African countries; South Africa, Botswana, Kenya, Mauritius and Nigeria. These countries were chosen because they are in the top five of the African financial markets index. A GMM model was adopted to test the effects of portfolio flows on economic growth in the selected African states. Results showed that there is a weak relationship between portfolio investment and economic growth and that there is a positive relationship between INT and GDP. Results also show a positive relationship between EXCH and GDP in the long run. A depreciation in the rand exchange rate is seen to be depressing economic growth. The study recommended that the Central Banks in African countries develop better strategies to enhance capital flows' benefits. This can be done by establishing and improving financial institutions which are still developing.
This study is about foreign exchange reserves of Bangladesh. The main purpose of this study is to the influence of exchange rateson foreign exchange reserves to the Bangladesh context. Both the primary and secondary data has been used in this study. The primary data has been collected through a structured questionnaire from 50 respondents. The secondary data, namely Bangladesh foreign exchange reserves (FER), Bangladesh current account balance (CAB), Bangladesh capital and financial account balance (CFAB), and BDT/USD exchange rates (ER). This study covers yearly data from July 01, 1996 to June 30, 2005 and quarterly data from July 01, 2005 to June 30, 2012. Findings of this study shows that out of the selected 16 factors affecting foreign exchange reserves, exchange rates occupy the first position, weighted average score (WAS) being 4.56. Foreign exchange reserves (FER) and current account balance (CAB) have increased by 502.9087% and 1451.218%, whereas capital and financial account (CFAB) has decreased by -649.024% on June 30, 2012 compared to June 30, 1997. The influence of other factors held constant, as ER changes by 285.6894 units due to one unit change in FER, on average in the same direction which represents that ER has positive effect on the FER and this relationship is statistically significant. 62.1526 percent of the variation in FER is explained by ER. The outcomes of Breusch-Godfrey test (LM test), ARCH test, and the Normality test are that there is a serial correlation among residuals, the variance of residuals is not constant, and the residuals are not normally distributed.
The International financial crisis caused the recession of the industrial sector in most developed countries. The effect of the crisis in the developing countries was different. This research aimed at investigating the effect of international financial crisis on industrial sector in Jordan. Different financial ratios were estimated using the financial statements of the industrial enterprises for the period 2000-2008. The financial data were classified to two periods. The first period 2000-2005 and 2006-2008 for this purpose. The results show very slight effect on the industrial sector in Jordan results of the limitations of most companies on their activities as a part of risk management policy.
This paper provides historical backgrounds of dollarization, the introduction of the Khmer riel and macroeconomic performance in the context of high dollarization after the Khmer Rouge regime which ended in year 1979. The high level of dollarization was caused by both economic and political factors. The history of large exchange rate depreciation and high inflation, trust in new local currency (which was abolished during the Khmer Rouge), political unrests, spending in U.S. dollars by international organizations for running elections, are among those factors. Macroeconomic environment was favourable as low inflation, stable exchange rate against U.S. dollar and high rate of GDP growth were achieved recently. Policy to gradually de-dollarize the economy is in place. However, dollarization cannot cushion Cambodian economy against recent global economic shocks such as global financial crisis in 2008 and Covid-19. A more active dedollarization policy shall be considered.
The main objective of this paper is to predict the consequences of China's impending economic crisis on global economy – with reference to Sub-Saharan Africa (SSA) in particular. The specific objective of this paper is to investigate and explore the increasing dominance of economic practice of China in SSA. China is a critical principal player in the economy of SSA. China's influence and dominance of the SSA economy might have negative effect on SSA in case of any implosion of the Chinese economy. Data were collected from print and electronic sources extracted from the vast body of empirical scholarship of different disciplines on China in SSA. The results of this paper revealed that China is indeed dominating the economy in SSA. Pointers are that China's economic implosion would have consequences for SSA in the same way as the 2008-2009 global economic recession had around the world. This paper positively predicts that China's economic and financial implosion remains a possibility, and would impact on SSA.
Financial cycles are widely regarded as key drivers of global financial conditions and major contributors to international financial crises. Understanding the extent to which these cycles are interconnected is essential for developing early warning systems to predict financial crises and for informing the design of macroprudential and monetary policies. In this context, a dynamic factor model and time-varying Granger causality (TVGC) are employed to assess the degree of financial cycle synchronization between advanced economies and systemic middle-income countries (SMICs). Our data spans the period from 1980Q1 to 2023Q4. The analysis reveals that a common factor—shared by both advanced and systemic middle-income economies—plays a significant and persistent role in driving financial cycles across these regions. Notably, the influence of this common factor intensified following the 2007–2009 global financial crisis, highlighting the growing financial interconnectedness between these economies. These results strongly support the argument for closer coordination of macroprudential policies between advanced economies and SMICs. The findings from the TVGC analysis further reinforce the conclusions drawn from the dynamic factor model. Specifically, the TVGC results indicate a bidirectional causality between the financial cycles of advanced economies and SMICs, suggesting mutual influence. This bidirectional relationship underscores the importance of enhanced policy coordination across these economies to mitigate systemic risks.
The main aim of this study was to investigate the impact of foreign portfolio investment on economic growth in Africa. The motivation to explore the effects of portfolio investment and growth stemmed from the fact that Africa has recorded large capital inflows since the global financial crisis. These capital flows can have positive or negative consequences. The effects of foreign portfolio investment on the African economy is an area of interest because very little has been done on the subject area. The study used quarterly panel from 1995 to 2014. The study used only five African countries; South Africa, Botswana, Kenya, Mauritius and Nigeria. These countries were chosen because they are in the top five of the African financial markets index. A GMM model was adopted to test the effects of portfolio flows on economic growth in the selected African states. Results showed that there is a weak relationship between portfolio investment and economic growth and that there is a positive relationship between INT and GDP. Results also show a positive relationship between EXCH and GDP in the long run. A depreciation in the rand exchange rate is seen to be depressing economic growth. The study recommended that the Central Banks in African countries develop better strategies to enhance capital flows' benefits. This can be done by establishing and improving financial institutions which are still developing.
This paper provides historical backgrounds of dollarization, the introduction of the Khmer riel and macroeconomic performance in the context of high dollarization after the Khmer Rouge regime which ended in year 1979. The high level of dollarization was caused by both economic and political factors. The history of large exchange rate depreciation and high inflation, trust in new local currency (which was abolished during the Khmer Rouge), political unrests, spending in U.S. dollars by international organizations for running elections, are among those factors. Macroeconomic environment was favourable as low inflation, stable exchange rate against U.S. dollar and high rate of GDP growth were achieved recently. Policy to gradually de-dollarize the economy is in place. However, dollarization cannot cushion Cambodian economy against recent global economic shocks such as global financial crisis in 2008 and Covid-19. A more active dedollarization policy shall be considered.
For more than a decade, China has a policy of managing its currency exchange rate (RMB) to limit its appreciation against other currencies like U.S dollar. This policy has been subjected to many criticisms from United States’ lawmakers as currency manipulation. The U.S. argues that China is gaining an advantage of export and attracting direct foreign investment at the expense of other countries including the United State. The claim also has included that China’s manipulation causes U.S. trade deficit as well as high rate of unemployment. Meanwhile, China’s policy makers argue that its policy of exchange rate is a mechanism tool to enhance the development of the country and attaining market growth to make China rich and powerful. This research paper underlines the root of this argument and how china’s currency policy has affected both economics of U.S. and China. Many economists have emphasized on the appreciation of RMB as an important factor to attain the trade balance. However, this research argues that the appreciation is not going to matter. Pressure has been put on Obama’s Administration to push China to appreciate its currency and to designate China as a "currency manipulator". Several Bills have been introduced to discuss this issue.From a legal perspective, two entities could tackle this issue. They are the World Trade Organization (WTO) and the International Monetary Fund (IMF). However, IMF lack legitimacy and leverage and WTO has no jurisdiction over the exchange rate. So, none of these entities could handle the currency issue. Therefore, this paper analyzes some possible solutions such as Omnibus Act, tariffs, import quotas and forming new legislation. Where, it concludes that the best solution could be via forming a new international agency.
The main objective of this paper is to predict the consequences of China's impending economic crisis on global economy – with reference to Sub-Saharan Africa (SSA) in particular. The specific objective of this paper is to investigate and explore the increasing dominance of economic practice of China in SSA. China is a critical principal player in the economy of SSA. China's influence and dominance of the SSA economy might have negative effect on SSA in case of any implosion of the Chinese economy. Data were collected from print and electronic sources extracted from the vast body of empirical scholarship of different disciplines on China in SSA. The results of this paper revealed that China is indeed dominating the economy in SSA. Pointers are that China's economic implosion would have consequences for SSA in the same way as the 2008-2009 global economic recession had around the world. This paper positively predicts that China's economic and financial implosion remains a possibility, and would impact on SSA.
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 |