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: 2 / 2 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 01:43 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.
2 / 2 with data · 0 missing or unmatched. Percentages use all 2 selected publications.
2 / 2 with data · 0 missing or unmatched. Percentages use all 2 selected publications.
2 / 2 with data · 0 missing or unmatched. Percentages use all 2 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 |
|---|---|---|---|---|---|
| 2024 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2026 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2024 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2026 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2024 | All selected publications | 5 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2026 | 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 2 / 2 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): 2026-10-21T07:22:57.140706+00:00 — 2026-10-22T01:43:12.750758+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 | 2 | 2/2 |
| 2018 | All selected publications | 0 | 2 | 2/2 |
| 2019 | All selected publications | 0 | 2 | 2/2 |
| 2020 | All selected publications | 0 | 2 | 2/2 |
| 2021 | All selected publications | 0 | 2 | 2/2 |
| 2022 | All selected publications | 0 | 2 | 2/2 |
| 2023 | All selected publications | 0 | 2 | 2/2 |
| 2024 | All selected publications | 1 | 2 | 2/2 |
| 2025 | All selected publications | 3 | 2 | 2/2 |
| 2026 | All selected publications | 2 | 2 | 2/2 |
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 |
|---|---|
| 2024 | 1 |
| 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 |
|---|---|---|---|---|---|---|---|---|
| International Journal of Business Ecosystem & Strategy Bussecon International Academy | 1 | 1 | 1 | 1/1 | 1.0 | 1 | 100.0 | 1/1 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 1 | 2 | 5 | 1/1 | 2.0 | 2 | 100.0 | 1/1 · 100.0% |
* Percentage among publications with a known citation count.
This study investigated the causal interactions between macroprudential policy, measured by the Macroprudential Policy Index (MPI), and financial cycles represented by the Aggregate Financial Cycle (AFC) and the Credit and Asset Prices Financial Cycle (CAFC) in South Africa from 1970q1 to 2023q2. Additionally, the study explored the effects of macroprudential policy during different phases of financial cycles. Using the time-varying Granger-causality model, the study found that the MPI Granger-caused financial cycles during the 2003/07 credit boom in South Africa, while the AFC and CAFC Granger-caused macroprudential policy during the Covid-19-induced collapse of financial markets. The results suggest that macroprudential policy is employed more proactively during financial booms and more reactively during financial busts in South Africa. The Markov switching dynamic regression model used to assess the MPI's effects revealed that macroprudential policy's effectiveness is stronger during financial busts and weaker during financial booms in South Africa. This is because financial institutions in South Africa tend to resist stricter regulations during boom phases due to heightened optimism about future prospects. Conversely, they are more receptive to stimulatory interventions during bust phases. Based on these findings, it is recommended that the South African Reserve Bank and the Prudential Authority use macroprudential policy more assertively during financial booms to enhance its effectiveness. This could involve setting more stringent parameters upon activating macroprudential policy tools or complementing macroprudential policy with monetary policy during financial booms.
The COVID-19 pandemic triggered a global economic crisis that significantly impacted the South African banking sector, already weakened by a pre-existing recession. While bank profitability and credit quality declined, stock market reactions appeared less severe, indicating a potential disconnect between fundamentals and investor behaviour. This study explores the influence of investor sentiment on credit risk, lending, and performance (ROA) of selected JSE-listed banks during the pandemic, incorporating behavioural finance perspectives. Panel regression analysis reveals that COVID-19 had a significant negative effect on stock, ROA and ROE, driven by heightened uncertainty and rising non-performing loans. Liquidity and capital positively influenced returns and ROE, reflecting investor confidence in resilient banks. Investor sentiment had a weakly positive impact on returns, but a significant negative effect on both ROA and ROE, suggesting that excessive optimism may have led to riskier lending practices and reduced profitability. Larger banks underperformed in terms of returns, likely due to operational complexity during the crisis. These findings highlight the critical role of behavioural factors in shaping financial outcomes during systemic shocks and provide new insights into emerging market banking dynamics under crisis conditions.
The COVID-19 pandemic triggered a global economic crisis that significantly impacted the South African banking sector, already weakened by a pre-existing recession. While bank profitability and credit quality declined, stock market reactions appeared less severe, indicating a potential disconnect between fundamentals and investor behaviour. This study explores the influence of investor sentiment on credit risk, lending, and performance (ROA) of selected JSE-listed banks during the pandemic, incorporating behavioural finance perspectives. Panel regression analysis reveals that COVID-19 had a significant negative effect on stock, ROA and ROE, driven by heightened uncertainty and rising non-performing loans. Liquidity and capital positively influenced returns and ROE, reflecting investor confidence in resilient banks. Investor sentiment had a weakly positive impact on returns, but a significant negative effect on both ROA and ROE, suggesting that excessive optimism may have led to riskier lending practices and reduced profitability. Larger banks underperformed in terms of returns, likely due to operational complexity during the crisis. These findings highlight the critical role of behavioural factors in shaping financial outcomes during systemic shocks and provide new insights into emerging market banking dynamics under crisis conditions.
This study investigated the causal interactions between macroprudential policy, measured by the Macroprudential Policy Index (MPI), and financial cycles represented by the Aggregate Financial Cycle (AFC) and the Credit and Asset Prices Financial Cycle (CAFC) in South Africa from 1970q1 to 2023q2. Additionally, the study explored the effects of macroprudential policy during different phases of financial cycles. Using the time-varying Granger-causality model, the study found that the MPI Granger-caused financial cycles during the 2003/07 credit boom in South Africa, while the AFC and CAFC Granger-caused macroprudential policy during the Covid-19-induced collapse of financial markets. The results suggest that macroprudential policy is employed more proactively during financial booms and more reactively during financial busts in South Africa. The Markov switching dynamic regression model used to assess the MPI's effects revealed that macroprudential policy's effectiveness is stronger during financial busts and weaker during financial booms in South Africa. This is because financial institutions in South Africa tend to resist stricter regulations during boom phases due to heightened optimism about future prospects. Conversely, they are more receptive to stimulatory interventions during bust phases. Based on these findings, it is recommended that the South African Reserve Bank and the Prudential Authority use macroprudential policy more assertively during financial booms to enhance its effectiveness. This could involve setting more stringent parameters upon activating macroprudential policy tools or complementing macroprudential policy with monetary policy during financial booms.
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 |