CiteNScope — Scholarly Index & Analytics
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CiteNScope / Analytics

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Research across participating journals · All insights follow your collection filters.

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OpenAlex coverage 100.0% · 2/2 recordsLast OpenAlex observation 21 Oct 2026, 00:22 UTC
PUBLICATIONS2In the selected collection
CROSSREF CITATIONS52/2 with known counts
OPENALEX CITATIONS122/2 with known counts
OPENALEX MEAN6.0Per article with a known count
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Output and reach

Publication activity alongside incoming citations.

Publication activity

Articles grouped by publication year

Publications

Hover over a year or focus the chart and use arrow keys to inspect values.

2026 is year to date. 0 undated publications excluded.

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Citations received

OpenAlex · year of the citing work

OpenAlex

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.

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Research landscape

Leading affiliations and themes in this collection.

Full distributions & methodology ↓

Institutions

University of Stavanger22/2 records with data

Countries

Norway22/2 records with data

Primary topics

Monetary Policy and Economic Impact22/2 records with data
Two perspectives on citations

Follow the research impact

Independent sources. Clear time scales.

Lifetime citations by publication year

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.

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Citations received each year

OpenAlex · incoming citation links grouped by citing-work year

OpenAlex

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.

View data & coverage
Annual history: 2/2 articles. Latest history observation: 2026-10-21T00:22:03.857213+00:00. Source totals are never added together.
Research side by side

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Select journals to compare the same collection filters. Source totals stay separate. These measures describe research activity, not journal quality.

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Display range
International Journal of Finance & Banking Studies

Publications

Articles by publication year

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Crossref citations

Latest lifetime totals · grouped by publication year

View data & coverage

OpenAlex citations

Latest lifetime totals · grouped by publication year

View data & coverage

OpenAlex · Citations received

By year of the citing work · 2017–2026

View data & coverage

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.

Full analytics report All distributions, publications, comparisons & data provenance
2 MATCHING PUBLICATIONSExplore results →Export CSV ↓
INDEXED PUBLICATIONS2
CROSSREF CITATIONS5
CROSSREF · CITED PUBLICATIONS50.0%
CROSSREF · MEAN CITATIONS2.5

Crossref citation coverage: 2 / 2 publications (100.0%). Latest observation: 21 Oct 2026, 00:21 UTC. Means and percentages use records with known counts.

OpenAlex / Separate citation source

A broader view of this research

DOI-matched OpenAlex observations for the same selected publications. These counts are separate from Crossref and are never added together.

OPENALEX CITATIONS12
DOI MATCH COVERAGE100.0%2 / 2 publications
MEAN · KNOWN COUNTS ONLY6.02 known · 0 unknown
MEDIAN · KNOWN COUNTS ONLY6.0

Observation range: 21 Oct 2026 00:22 UTC — 21 Oct 2026 00:22 UTC. Missing matches are not zero citations. Previous observations may remain if a refresh fails or a DOI no longer matches.

OpenAlex citation distributionNo citations01–516–20121+0Unknown0

Institutions, countries & topics

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.

Institutions

2 / 2 with data · 0 missing or unmatched. Percentages use all 2 selected publications.

Countries

2 / 2 with data · 0 missing or unmatched. Percentages use all 2 selected publications.

Primary topics

2 / 2 with data · 0 missing or unmatched. Percentages use all 2 selected publications.

Citation year / OpenAlex

Citations received over time

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): 2026-10-21T00:22:03.855911+00:00 — 2026-10-21T00:22:03.857213+00:00. Older observations may be retained after failed or incomplete refreshes.

OpenAlex · Citations received by year

52.50 2017 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 2/22018 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 2/22019 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 2/22020 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 2/22021 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 2/22022 · All selected publications · OpenAlex · Citations received by year: 5 · Annual history coverage 2/22023 · All selected publications · OpenAlex · Citations received by year: 3 · Annual history coverage 2/22024 · All selected publications · OpenAlex · Citations received by year: 2 · Annual history coverage 2/22025 · All selected publications · OpenAlex · Citations received by year: 1 · Annual history coverage 2/22026 · All selected publications · OpenAlex · Citations received by year: 1 · Annual history coverage 2/2 2017201820192020202120222023202420252026
View openalex · citations received by year data & coverage
Citation yearJournal / scopeOpenAlex · Citations received by yearPublicationsAnnual history known
2017All selected publications022/2
2018All selected publications022/2
2019All selected publications022/2
2020All selected publications022/2
2021All selected publications022/2
2022All selected publications522/2
2023All selected publications322/2
2024All selected publications222/2
2025All selected publications122/2
2026All selected publications122/2

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 ↗

Crossref citation distribution

Latest Crossref totals for the selected publications; not citations received in the selected years.

Mean2.5
Median2.5
No citations11–516–20021+0Unknown0

Unknown counts are excluded from the mean, median and cited-publication rate.

Journal comparison

Same filters, source and counting rules across journals. Descriptive statistics, not a quality ranking.

Journal / publisherPublicationsCrossref citationsOpenAlex citationsOpenAlex coverageCrossref meanCrossref medianCrossref cited %*Crossref coverage
International Journal of Finance & Banking Studies
SSBFNET
25122/22.52.550.02/2 · 100.0%

* Percentage among publications with a known citation count.

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View all 2 →

Most cited · Crossref

JOURNAL ARTICLE

A Two-Period Decision Model for Central Bank Digital Currencies and Households

Guizhou Wang · Kjell Hausken

International Journal of Finance & Banking Studies · 2022

Read abstract

Central bank digital currencies (CBDCs) give rise to many possibilities including those of negative interest rates. A two-period decision model is presented between one central bank and one representative household. The central bank applies the Taylor (1993) rule to choose its interest rate. The household allocates its resources strategically to production, consumption, CBDC holding, and non-CBDC holding. The results are determined analytically and illustrated numerically by varying 19 parameter values. Interesting novelties of the article are that the central bank may choose negative CBDC interest rates when the household holds far more CBDC than non-CBDC, for low inflation rates, low real interest rates, low household’s potential production, low weight assigned to inflation in the Taylor (1993) rule, high target inflation rate, and high household’s production parameter. That usually causes the household to decrease its CBDC holding and increase its non-CBDC holding, production and consumption. The central bank may increase its CBDC interest rate to compete with an increasing non-CBDC interest rate if the household’s transaction efficiencies for CBDC and non-CBDC increase, or the household’s transaction efficiency for consumption decreases. Shocks to production, inflation and interest rates are analyzed.

DOI 10.20525/ijfbs.v11i2.17895 Crossref citations11 OpenAlex citations
JOURNAL ARTICLE

Comparative Analysis of Households and Digital Currencies for the US, China and Russia

Guizhou Wang · Kjell Hausken

International Journal of Finance & Banking Studies · 2022

Read abstract

In a two-period decision model, a central bank chooses a CBDC (central bank digital currency) interest rate and a representative household allocates resources into production, consumption, CBDC holding, and non-CBDC holding. The model’s analytical results and a plausible benchmark are compared with the empirics for the US, China and Russia. Interesting novelties of the article are that the model predicts that the US in 2021/2022 should choose  rather than 0.125% CBDC interest to combat its high October 2021 empirical inflation of 6.2%. That would induce households to hold more CBDC, hold less non-CBDC, and produce and consume less. In contrast, the model predicts that China should choose a low  rather than  CBDC interest rate. That would decrease each household’s CBDC holding and increase the low inflation. The model predicts that Russia should choose  rather than  CBDC interest rate. Russia’s strategy is remarkably consistent with the model’s predictions. The model predicts that the central bank should choose negative CBDC interest rate when the inflation and real interest rate are low, and the inflation target is high. The article shows how extremely high inflation, which increases the CBDC interest rate, makes production and consumption nearly impossible, unless the real interest rate is extremely negative.

DOI 10.20525/ijfbs.v11i2.17900 Crossref citations1 OpenAlex citations

Selected results

JOURNAL ARTICLE

A Two-Period Decision Model for Central Bank Digital Currencies and Households

Guizhou Wang · Kjell Hausken

International Journal of Finance & Banking Studies · 2022

Read abstract

Central bank digital currencies (CBDCs) give rise to many possibilities including those of negative interest rates. A two-period decision model is presented between one central bank and one representative household. The central bank applies the Taylor (1993) rule to choose its interest rate. The household allocates its resources strategically to production, consumption, CBDC holding, and non-CBDC holding. The results are determined analytically and illustrated numerically by varying 19 parameter values. Interesting novelties of the article are that the central bank may choose negative CBDC interest rates when the household holds far more CBDC than non-CBDC, for low inflation rates, low real interest rates, low household’s potential production, low weight assigned to inflation in the Taylor (1993) rule, high target inflation rate, and high household’s production parameter. That usually causes the household to decrease its CBDC holding and increase its non-CBDC holding, production and consumption. The central bank may increase its CBDC interest rate to compete with an increasing non-CBDC interest rate if the household’s transaction efficiencies for CBDC and non-CBDC increase, or the household’s transaction efficiency for consumption decreases. Shocks to production, inflation and interest rates are analyzed.

DOI 10.20525/ijfbs.v11i2.17895 Crossref citations11 OpenAlex citations
JOURNAL ARTICLE

Comparative Analysis of Households and Digital Currencies for the US, China and Russia

Guizhou Wang · Kjell Hausken

International Journal of Finance & Banking Studies · 2022

Read abstract

In a two-period decision model, a central bank chooses a CBDC (central bank digital currency) interest rate and a representative household allocates resources into production, consumption, CBDC holding, and non-CBDC holding. The model’s analytical results and a plausible benchmark are compared with the empirics for the US, China and Russia. Interesting novelties of the article are that the model predicts that the US in 2021/2022 should choose  rather than 0.125% CBDC interest to combat its high October 2021 empirical inflation of 6.2%. That would induce households to hold more CBDC, hold less non-CBDC, and produce and consume less. In contrast, the model predicts that China should choose a low  rather than  CBDC interest rate. That would decrease each household’s CBDC holding and increase the low inflation. The model predicts that Russia should choose  rather than  CBDC interest rate. Russia’s strategy is remarkably consistent with the model’s predictions. The model predicts that the central bank should choose negative CBDC interest rate when the inflation and real interest rate are low, and the inflation target is high. The article shows how extremely high inflation, which increases the CBDC interest rate, makes production and consumption nearly impossible, unless the real interest rate is extremely negative.

DOI 10.20525/ijfbs.v11i2.17900 Crossref citations1 OpenAlex citations

Data coverage & freshness

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

SourceMost recent successful journal syncLatest attempt
crossref21 Oct 2026 00:21 UTC · ijfbscomplete · ijfbs · 21 Oct 2026 00:21 UTC
ojs-rest21 Oct 2026 00:21 UTC · ijfbscomplete · ijfbs · 21 Oct 2026 00:14 UTC
oai-pmh20 Oct 2026 19:56 UTC · ijfbscomplete · ijfbs · 20 Oct 2026 19:55 UTC
openalex-enrichment21 Oct 2026 00:22 UTC · ijfbscomplete · ijfbs · 21 Oct 2026 00:21 UTC
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