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 — 23 Oct 2026 07:28 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 |
|---|---|---|---|---|---|
| 2020 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2023 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2020 | All selected publications | 11 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 6 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 3 | 2 | 2/2 | 2/2 |
| 2023 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2020 | All selected publications | 23 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 5 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 7 | 2 | 2/2 | 2/2 |
| 2023 | All selected publications | 0 | 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 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): 2026-10-21T07:22:45.238963+00:00 — 2026-10-23T07:28:27.884377+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 | 3 | 5 | 5/5 |
| 2021 | All selected publications | 2 | 5 | 5/5 |
| 2022 | All selected publications | 2 | 5 | 5/5 |
| 2023 | All selected publications | 8 | 5 | 5/5 |
| 2024 | All selected publications | 9 | 5 | 5/5 |
| 2025 | All selected publications | 7 | 5 | 5/5 |
| 2026 | All selected publications | 4 | 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 |
|---|---|
| 2020 | 1 |
| 2021 | 1 |
| 2022 | 2 |
| 2023 | 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 | 2 | 12 | 24 | 2/2 | 6.0 | 6.0 | 100.0 | 2/2 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 3 | 8 | 11 | 3/3 | 2.67 | 2 | 66.7 | 3/3 · 100.0% |
* Percentage among publications with a known citation count.
An increase in MSMEs in large numbers is very instrumental in increasing economic growth in developing countries, including Indonesia. The research objective is based on identifying the effect of investment credit, interest rates, and labor on economic growth through the role of MSMEs. The design of this research is descriptive-verification to present a structured, factual and accurate picture and test hypotheses empirically through the MRA model. Empirical analysis proves that investment credit and interest rates through the role of MSMEs can influence positively and significantly on Indonesia's economic growth. In other results, it appears that the role of MSMEs is less able to mediate the effect of labor on Indonesia's economic growth. This fact is based on a negative and insignificant relationship. In a sentence or two, enter the implications and limitations of your research. In Indonesia, increasing bank lending to MSMEs in practice is undermined by lending policies by banks and by macroeconomic factors (economic growth, interest rates, investment credit, and labor).
This study aims to identify the convergence of financial sector development and the effect of macroeconomic variables on each financial sector development indicator in Asia. The sample used consists of 24 countries in Asia during the period 2010-2018. Identification of convergence using ?-convergence absolute and conditional. Indicators are used to represent the development of the financial sector namely private credit, liquid liabilities, stock market capitalization, and stock market turnover. Empirical evidence was based on the Generalized Method of Moment (GMM) estimation technique. The results showed that there was convergence in Asia and that macroeconomic variables had a significant effect on the development of the financial sector.
Agricultural growth is closely associated with sustainable economic development. This is especially true from the perspective of developing countries, such as Indonesia and Pakistan, where significant portions of the labour force are dependent on agriculture for their livelihood. This paper aims to examine the effects of macroeconomic policy (i.e. monetary policy) on employment, food inflation, and agricultural growth by analysing to what extent monetary policy is effective in controlling food price inflation, the effect of contractionary monetary policy on the agricultural sector’s employment and productivity, and the extent of monetary policy transmission to money market rates and 10-year interest rates. We employed a factor-augmented vector autoregressive model proposed by Bernanke et al. (2005) to agricultural data from 1995 and 1996 to 2016 for Indonesia and Pakistan, respectively. Main findings of the study demonstrate that tight monetary policy significantly reduced food inflation and agricultural production while increasing the rural unemployment rate. Short-term and 10-year interest rates increased owing to the contractionary monetary policies pursued by both countries. An inclusive monetary policy whereby policymakers work alongside governments to achieve price stabilisation and reasonable employment rates is recommended.
Almost 2 years, the world has been facing the COVID-19 attack, there is no exception including Indonesia, which incidentally is a developing country. During the pandemic, various industries in all sectors experienced a decline in sales so that they need an injection of funds from investment where with the presence of investors, industry movements, include of small, medium, and large industries could increase and have an influence on national economic growth and development. As a country that has many islands, of course investment as a very important economic sector. Many factors influence developments in an area, some of which are inflation, interest rates, and the economy of each region. To see the development of investment in Indonesia during the COVID-19 pandemic (2020-2021Q3), this study uses panel data to be more specific and detail. The result of the research is that interest rates affect investment developments, while inflation and economic growth have no effect on investment developments in Indonesia.
Despite the many studies on inflation, stock prices, and foreign exchange reserves, there is still little research linking these three with world oil prices to the rupiah exchange rate, especially comparing them to conditions before and after the pandemic. This study aimed to test and analyze the effect of inflation, stock prices, foreign exchange reserves, and world oil prices on the rupiah exchange rate against the dollar. The object of this research is Bank Indonesia and the Stock Exchange by producing a sample in this study which is 62 months. The data is divided into 2 categories 31 months before the co-19 pandemic and 31 months during the co-19 pandemic. Data collection techniques in this study used secondary data.
Despite the many studies on inflation, stock prices, and foreign exchange reserves, there is still little research linking these three with world oil prices to the rupiah exchange rate, especially comparing them to conditions before and after the pandemic. This study aimed to test and analyze the effect of inflation, stock prices, foreign exchange reserves, and world oil prices on the rupiah exchange rate against the dollar. The object of this research is Bank Indonesia and the Stock Exchange by producing a sample in this study which is 62 months. The data is divided into 2 categories 31 months before the co-19 pandemic and 31 months during the co-19 pandemic. Data collection techniques in this study used secondary data.
Agricultural growth is closely associated with sustainable economic development. This is especially true from the perspective of developing countries, such as Indonesia and Pakistan, where significant portions of the labour force are dependent on agriculture for their livelihood. This paper aims to examine the effects of macroeconomic policy (i.e. monetary policy) on employment, food inflation, and agricultural growth by analysing to what extent monetary policy is effective in controlling food price inflation, the effect of contractionary monetary policy on the agricultural sector’s employment and productivity, and the extent of monetary policy transmission to money market rates and 10-year interest rates. We employed a factor-augmented vector autoregressive model proposed by Bernanke et al. (2005) to agricultural data from 1995 and 1996 to 2016 for Indonesia and Pakistan, respectively. Main findings of the study demonstrate that tight monetary policy significantly reduced food inflation and agricultural production while increasing the rural unemployment rate. Short-term and 10-year interest rates increased owing to the contractionary monetary policies pursued by both countries. An inclusive monetary policy whereby policymakers work alongside governments to achieve price stabilisation and reasonable employment rates is recommended.
Almost 2 years, the world has been facing the COVID-19 attack, there is no exception including Indonesia, which incidentally is a developing country. During the pandemic, various industries in all sectors experienced a decline in sales so that they need an injection of funds from investment where with the presence of investors, industry movements, include of small, medium, and large industries could increase and have an influence on national economic growth and development. As a country that has many islands, of course investment as a very important economic sector. Many factors influence developments in an area, some of which are inflation, interest rates, and the economy of each region. To see the development of investment in Indonesia during the COVID-19 pandemic (2020-2021Q3), this study uses panel data to be more specific and detail. The result of the research is that interest rates affect investment developments, while inflation and economic growth have no effect on investment developments in Indonesia.
This study aims to identify the convergence of financial sector development and the effect of macroeconomic variables on each financial sector development indicator in Asia. The sample used consists of 24 countries in Asia during the period 2010-2018. Identification of convergence using ?-convergence absolute and conditional. Indicators are used to represent the development of the financial sector namely private credit, liquid liabilities, stock market capitalization, and stock market turnover. Empirical evidence was based on the Generalized Method of Moment (GMM) estimation technique. The results showed that there was convergence in Asia and that macroeconomic variables had a significant effect on the development of the financial sector.
An increase in MSMEs in large numbers is very instrumental in increasing economic growth in developing countries, including Indonesia. The research objective is based on identifying the effect of investment credit, interest rates, and labor on economic growth through the role of MSMEs. The design of this research is descriptive-verification to present a structured, factual and accurate picture and test hypotheses empirically through the MRA model. Empirical analysis proves that investment credit and interest rates through the role of MSMEs can influence positively and significantly on Indonesia's economic growth. In other results, it appears that the role of MSMEs is less able to mediate the effect of labor on Indonesia's economic growth. This fact is based on a negative and insignificant relationship. In a sentence or two, enter the implications and limitations of your research. In Indonesia, increasing bank lending to MSMEs in practice is undermined by lending policies by banks and by macroeconomic factors (economic growth, interest rates, investment credit, and labor).
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 |