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: 48 / 48 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 00:02 UTC — 22 Oct 2026 00:37 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.
41 / 48 with data · 7 missing or unmatched. Percentages use all 48 selected publications.
41 / 48 with data · 7 missing or unmatched. Percentages use all 48 selected publications.
48 / 48 with data · 0 missing or unmatched. Percentages use all 48 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 | 1 | 1 | 1/1 | 1/1 |
| 2014 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| 2015 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| 2016 | All selected publications | 6 | 6 | 6/6 | 6/6 |
| 2017 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2018 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2019 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2021 | All selected publications | 5 | 5 | 5/5 | 5/5 |
| 2022 | All selected publications | 6 | 6 | 6/6 | 6/6 |
| 2023 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2024 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| 2025 | All selected publications | 6 | 6 | 6/6 | 6/6 |
| 2026 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2013 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2014 | All selected publications | 11 | 4 | 4/4 | 4/4 |
| 2015 | All selected publications | 6 | 4 | 4/4 | 4/4 |
| 2016 | All selected publications | 9 | 6 | 6/6 | 6/6 |
| 2017 | All selected publications | 12 | 3 | 3/3 | 3/3 |
| 2018 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2019 | All selected publications | 32 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 20 | 3 | 3/3 | 3/3 |
| 2021 | All selected publications | 12 | 5 | 5/5 | 5/5 |
| 2022 | All selected publications | 9 | 6 | 6/6 | 6/6 |
| 2023 | All selected publications | 7 | 3 | 3/3 | 3/3 |
| 2024 | All selected publications | 1 | 4 | 4/4 | 4/4 |
| 2025 | All selected publications | 2 | 6 | 6/6 | 6/6 |
| 2026 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2013 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2014 | All selected publications | 18 | 4 | 4/4 | 4/4 |
| 2015 | All selected publications | 32 | 4 | 4/4 | 4/4 |
| 2016 | All selected publications | 16 | 6 | 6/6 | 6/6 |
| 2017 | All selected publications | 23 | 3 | 3/3 | 3/3 |
| 2018 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2019 | All selected publications | 54 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 37 | 3 | 3/3 | 3/3 |
| 2021 | All selected publications | 18 | 5 | 5/5 | 5/5 |
| 2022 | All selected publications | 24 | 6 | 6/6 | 6/6 |
| 2023 | All selected publications | 9 | 3 | 3/3 | 3/3 |
| 2024 | All selected publications | 2 | 4 | 4/4 | 4/4 |
| 2025 | All selected publications | 2 | 6 | 6/6 | 6/6 |
| 2026 | 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 48 / 48 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): 2026-10-21T00:02:00.475715+00:00 — 2026-10-22T00:37:34.033213+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 | 2 | 48 | 48/48 |
| 2018 | All selected publications | 7 | 48 | 48/48 |
| 2019 | All selected publications | 8 | 48 | 48/48 |
| 2020 | All selected publications | 20 | 48 | 48/48 |
| 2021 | All selected publications | 25 | 48 | 48/48 |
| 2022 | All selected publications | 25 | 48 | 48/48 |
| 2023 | All selected publications | 34 | 48 | 48/48 |
| 2024 | All selected publications | 43 | 48 | 48/48 |
| 2025 | All selected publications | 32 | 48 | 48/48 |
| 2026 | All selected publications | 19 | 48 | 48/48 |
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 | 1 |
| 2014 | 4 |
| 2015 | 4 |
| 2016 | 6 |
| 2017 | 3 |
| 2018 | 1 |
| 2019 | 1 |
| 2020 | 3 |
| 2021 | 5 |
| 2022 | 6 |
| 2023 | 3 |
| 2024 | 4 |
| 2025 | 6 |
| 2026 | 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 |
|---|---|---|---|---|---|---|---|---|
| Bussecon Review of Social Sciences Bussecon International Academy | 1 | 1 | 1 | 1/1 | 1.0 | 1 | 100.0 | 1/1 · 100.0% |
| International Journal of Finance & Banking Studies SSBFNET | 29 | 88 | 179 | 29/29 | 3.03 | 1 | 65.5 | 29/29 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 18 | 33 | 56 | 18/18 | 1.83 | 1.0 | 50.0 | 18/18 · 100.0% |
* Percentage among publications with a known citation count.
The main purpose of this study is to investigate inflation rate and its impact on the growth rate or to GDP growth for Eurozone countries, using panel data for the period 1997-2017, on an annual basis with a total of 257 observations. For conducting the study, and achieving results, a multiple linear regression model with the least squares regression is used. Moreover, multiple linear regression analysis has been applied in order to investigate whether Inflation rate, as an independent variable, has any significant impact on economic growth. Consequently, in order to test the data used in the model we have applied diagnostic tests, such as Durbin-Watson test to analyze the correlation of serial correlation, as well as the Breusch-Pagan test for heteroskedasticity. The tests’ results give us strong indications that the model has no relation between of serial correlation and there is no heteroskedasticity either. The study conducted shows results generated from the model, and according to the econometric results indicate that Inflation rate has positive impact on the economic growth rate for euro area.
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).
<p>There is a long discussion among academics and central bankers about the theories of money supply. According to the exogenous view, central banks have the full control over money supply via policy actions including the adjustments of interest rates and reserve ratios, both of which alter commercial banks’ lending decisions. However, the theory of endogenous money supply emphasizes the role of demand for bank loans in money creation. More specifically, banks create money by meeting the demand of economic agents. In this study, we investigate which of the money supply theories holds in Turkish economy for the period 2006-2015 by employing cointegration and causality tests. Our findings show that the causality runs from bank loans to money supply both in the short and long terms, which supports the endogenous view in a sense that central bank and the banks fully meet the total demand for money in Turkish economy.</p>
The main purpose of this study is to investigate if determinants that we selected in our analysis have any effects on inflation rate in Western Balkans Countries[1] by using panel data for the period of 2001-2017, in yearly basis in total of 102 observation. The study used quantitative analysis approach and secondary data by applying the multivariate time series, respectively vector error correction model [VECM]. Multivariate time series was applied to investigate whether the budget deficit and other explanatory variable have any significant impact on inflation rate. The results from our analysis shows that three of four determinates that we used are significant on inflation rate. The model summaries statistics for inflation rate which shows that inflation rate has a moderate correlation with explanatory variables that we used in our model, that explanatory variables explain 45.5 percent of dependent variable and we can conclude that a model is a proper and fit. The results suggest that one percent point increase in budget deficit to GDP ratio is associated with about a 9.34 percent point increase in inflation rate. The overall inference is that the ratios that we selected has a significant influence on the inflation rate in Western Balkans Countries. [1] Western Balkans Countries: Albania, Bosnia & Hercegovina, Kosovo, Montenegro, North Macedonia and Serbia.
The link between stock market development and economic activity has always been the subject of considerable debate in the field of economics and it raises empirical question whether stock market development influences economic activity or whether it is a consequence of increased economic activity. This study attempts to investigate the direction of causality between stock market development and economic growth in the Indian context. Using the cointegration and causality tests for the period June 1991 to June 2013, the study confirms a well defined long-run equilibrium relationship between the stock market development indicators and economic growth in India. The empirical results show bidirectional causality between market capitalisation and economic growth and unidirectional causality from turnover ratio to economic growth in the long-run and short-run. By and large, it can be inferred that the stock market development indicators viz. market capitalisation and turnover ratio have a positive influence on economic growth in India.
The Marshall-Lerner condition states that a trade balance improves after currency depreciation if trade elasticities exceed one. Using the Vector Error Correction Method (VECM), this study tests the condition for South Africa, focusing on how exchange rate changes affect the trade balance under a floating exchange rate regime. The analysis examines short- and long-run impacts on exports and imports to assess whether exchange rate adjustments can improve the trade balance. Findings reveal that global income growth and exchange rate appreciation positively influence exports, while imports increase with both appreciation and higher domestic income. Although exports and imports respond strongly to exchange rate changes, the Marshall-Lerner condition does not hold for South Africa, suggesting that exchange rate adjustments alone may not improve the trade balance.
The present study aims to examine the relationship between exchange rate undervaluation and economic growth in South Africa, focusing on the various transmission mechanisms through which this relationship occurs. Through the adoption of secondary quarterly data spanning the period 1980 to 2018 and a two-step Generalised Method of Moments approach. The study estimated a Behavioural Equilibrium Exchange Rate of South Africa and used it to construct a measure of Exchange rate undervaluation. Further, the study examined the effects of Exchange rate undervaluation on the tradable sector, the savings/capital accumulation sector, and the total factor productivity sector. The study found evidence of the tradable sector and total factor productivity (TFP) channels in South Africa. Therefore, we conclude that there exist valuable lessons to be learnt through the analysis of this relationship on a micro level compared to a macro level. Macro-level studies overlook the individual effects, hence why results have been inconclusive. This, therefore, means that South Africa has an incentive to keep the Rand at a competitive level since the industrial and agricultural sectors benefit from a competitive Exchange rate.
This study investigates the dynamic relationship between credit supply and demand shocks and household consumption in South Africa using a quantile autoregressive distributed lag (QARDL) model. Motivated by the increasing reliance of South African households on debt amid stagnant income growth, rising living costs, and persistent unemployment, the research aims to understand how credit market conditions influence consumption across different segments of the income distribution. Drawing on data spanning from 1970 to 2024, the study incorporates key macroeconomic variables including real GDP per capita, household income per capita, household debt as a percentage of GDP, the credit-to-GDP ratio, inflation, interest rates, and the unemployment rate. The QARDL framework allows for the estimation of both short-run and long-run relationships while accounting for heterogeneity across consumption quantiles. The results indicate that household consumption is more responsive to income shocks and credit availability in the lower and middle quantiles, suggesting heightened sensitivity among poorer households. Credit supply, proxied by the credit-to-GDP ratio, exhibits weak but positive short-run effects, while household debt consistently exerts a negative influence on consumption across all quantiles and time horizons, with stronger effects in lower quantiles. These findings highlight the consumption-depressing effects of excessive debt, especially for financially vulnerable households. Moreover, macroeconomic stability indicators such as inflation and interest rates are found to negatively affect consumption, particularly in lower quantiles, underscoring the uneven transmission of monetary policy. The empirical results support the hypothesis that household debt contributes to financial fragility and undermines consumption over time, while credit supply shocks play a more modest role. The study concludes that policies aimed at improving income stability, managing household debt levels, and enhancing financial inclusion—particularly for low-income groups—are essential for sustaining household consumption and promoting inclusive economic growth in South Africa.
Several official dating institutions viz: SARB, NBER, OECD, CEPR, and others, provide Business Cycle (BC) chronologies with lags ranging from three months to several years. This is problematic for the various oppressed as it poses limits to the usefulness of BC indicators in terms of forecasting and policy implementation at large. In this article, we propose the construction of composite indicators as accurate measures of business cycles in South Africa. We use this composite index to facilitate comparison of both a non-parametric Bry and Boschan dating algorithm and a parametric Markov Switching dating method, in terms of performance and accuracy of dating business cycle turning points in South Africa. Utilising data spanning the period 2000M1 – 2018M12, empirical evidence obtained is such that, composite indices are better than single indicators due to information rich. Further, while the parametric and non-parametric methods’ performances are matched in terms of the number of turning points identified, however, the non-parametric method is more accurate in identifying these turning points. Due to its accuracy the non-parametric method proved to be a promising method of dating business cycle turning points in South Africa and alleviate the problems which are currently facing the South African Reserve Bank, the private sector and the economy at large.
The article reports on the complementarity and substitutability of Macroprudential and Monetary policies in South Africa, based on the interactions of both Business and Financial cycles. To this end, a Dynamic Conditional Correlations and the Asymmetric Dynamic Conditional Correlations MGARCH models together with the Granger Causality model, the Artificial Neural Network VAR model, and the Structural VAR model, were adopted for the analyses of synchronization, causality, and the analysis of shocks to cycles, respectively. Empirical evidence obtained is such that, under conditions of financial and real economic stress in South Africa, there exists high synchronicity and bidirectional causality between CBCI and CFCI. Hence, Macroprudential and Monetary policies become complements, there exists interdependence between the two policies and actions of one policy contributes to the improvement of the other. However, under normal times there exist no synchronicity and a unidirectional causality relationship running from CBCI to CFCI, was observed. Under such conditions, Macroprudential and Monetary policies become substitutes, therefore, the two policies become independent of each other, hence, only one policy can achieve the desired outcome. Overall, a shock to one cycle is a major determinant of the fluctuations of the other cycle.
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 |