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 00:21 UTC — 21 Oct 2026 00:49 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 |
|---|---|---|---|---|---|
| 2014 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 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 | 1 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2014 | All selected publications | 3 | 1 | 1/1 | 1/1 |
| 2015 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 3 | 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 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2014 | All selected publications | 3 | 1 | 1/1 | 1/1 |
| 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 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 3 | 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): 2026-10-21T00:21:59.465627+00:00 — 2026-10-21T00:49:29.359653+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 | 5 | 5/5 |
| 2018 | All selected publications | 0 | 5 | 5/5 |
| 2019 | All selected publications | 2 | 5 | 5/5 |
| 2020 | All selected publications | 2 | 5 | 5/5 |
| 2021 | All selected publications | 2 | 5 | 5/5 |
| 2022 | All selected publications | 1 | 5 | 5/5 |
| 2023 | All selected publications | 2 | 5 | 5/5 |
| 2024 | All selected publications | 1 | 5 | 5/5 |
| 2025 | All selected publications | 0 | 5 | 5/5 |
| 2026 | All selected publications | 1 | 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 |
|---|---|
| 2014 | 1 |
| 2016 | 2 |
| 2019 | 1 |
| 2020 | 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 | 5 | 7 | 4/4 | 1.25 | 1.0 | 50.0 | 4/4 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 1 | 3 | 5 | 1/1 | 3.0 | 3 | 100.0 | 1/1 · 100.0% |
* Percentage among publications with a known citation count.
This paper employs a quarterly time series to determine the timing of structural breaks for interest rates in USA over the last 60 years. The Chow test is used for investigating the non-stationary, where the date of the potential break is assumed to be known. Moreover, we empirically examined the deviation from an assumed interest rate as given in a standard Taylor rule and consequences on financial sectors. The empirical analysis is strengthened by analysing the rule from a historical perspective and look at the effect of setting the interest rate by the central bank on financial imbalances. The empirical evidence indicates that deviation in monetary policy has a potential causal factor in the build up of financial imbalances and the subsequent crisis where macro prudential intervention could have beneficial effect. Thus, our findings tend to support the view, which states that the probable existence of central banks has been one source of global financial crisis since the past decade.
The telecommunication industry is different from other industries in the basis of innovation rate. This industry continuously provides end users with new technological generations and disruptive technologies. Due to the complexity of the nature of the product served, telecommunication industry is an important case to study in order to explain the real reasons for the challenges facing telecom companies. Researching in this area will explain why some companies survive while others fail. The wide range served technology innovations is most likely to result in rapid mutation of consumers’ spending habits thus directly influencing the mobile phone companies. The aim of this is to test the reliability of innovations and its impact on companies’ profitability. For that purpose, a survey with 200 end users and 20 employees from two telecom providers (“Alfa, and “Touch”) was conducted to investigate various disruptive and conventional means and their impact on the financial performance of telecom industry. The results show that most of the revenue of telecom industry comes from usage of data while the industry is lacking for competition, and quality services while charging high tariffs for customers. Currently customers are migrating to data, therefore performing a pressure on the industry to improve its services. To conclude, network operators need to decide what to they want to sacrifice and how far they want to go to reach targeted results.
This research examined the impact of Basel III capital regulation (BCR) on credit risk (CR) using a sample of 25 commercial banks in Lebanon over the period 2012–2017. BCR is measured using the capital adequacy ratio (CAR) and the common equity tier one ratio (CET1 ratio), CR is measured using net provision for credit losses /total assets. To analyze the data, we constructed a hybrid model based on 3 statistical approaches. First, we modelled the dual impact of BCR and CR using probabilistic inference in the framework of Bayesian Belief Network formalism (BBN). Second, to highlight more about the correlation between BCR and CR, we used Spearman correlation test as a nonparametric approach. Third to study the simultaneous effect of CAR and CET1 ratio on CR we applied multivariate regression analysis. By analyzing the probabilistic inference for the first approach we concluded that there is an effect of BCR on CR especially for the high level of CET1 ratio, but when we investigated more if this effect is significant using the Spearman correlation test and the multivariate regression analysis, we concluded that there is no effect statistically significant of Basel III capital regulation (BCR) on credit risk (CR).
The contemporary economy has changed dramatically and moved from the industrial age to the information age, pushing businesses to embark into a predominantly intangible economy. Thus, and in addition to solid and liquid value, there is a gaseous value which is the intangible capital. This exploratory study of 14 Lebanese companies analyzed with the “Tropes” software focuses on the constituent elements of intangible capital, its place within these companies and its contribution to the creation of value. This work concludes a first proposal for the classification of the components of intangible capital.
<p><em>This paper employs a quarterly time series to determine the timing of structural breaks for interest rates in USA over the last 60 years. <strong>The Chow test</strong> is used for investigating the non-stationary, where the date of the potential break is assumed to be known. Moreover, we empirically examined the deviation from an assumed interest rate as given in a standard Taylor rule and consequences on financial sectors. The empirical analysis is strengthened by analysing the rule from a historical perspective and look at the effect of setting the interest rate by the central bank on financial imbalances. The empirical evidence indicates that deviation in monetary policy has a potential causal factor in the build up of financial imbalances and the subsequent crisis where macro prudential intervention could have beneficial effect. Thus, our findings tend to support the view, which states that the probable existence of central banks has been one source of global financial crisis since the past decade.</em></p>
This research examined the impact of Basel III capital regulation (BCR) on credit risk (CR) using a sample of 25 commercial banks in Lebanon over the period 2012–2017. BCR is measured using the capital adequacy ratio (CAR) and the common equity tier one ratio (CET1 ratio), CR is measured using net provision for credit losses /total assets. To analyze the data, we constructed a hybrid model based on 3 statistical approaches. First, we modelled the dual impact of BCR and CR using probabilistic inference in the framework of Bayesian Belief Network formalism (BBN). Second, to highlight more about the correlation between BCR and CR, we used Spearman correlation test as a nonparametric approach. Third to study the simultaneous effect of CAR and CET1 ratio on CR we applied multivariate regression analysis. By analyzing the probabilistic inference for the first approach we concluded that there is an effect of BCR on CR especially for the high level of CET1 ratio, but when we investigated more if this effect is significant using the Spearman correlation test and the multivariate regression analysis, we concluded that there is no effect statistically significant of Basel III capital regulation (BCR) on credit risk (CR).
The contemporary economy has changed dramatically and moved from the industrial age to the information age, pushing businesses to embark into a predominantly intangible economy. Thus, and in addition to solid and liquid value, there is a gaseous value which is the intangible capital. This exploratory study of 14 Lebanese companies analyzed with the “Tropes” software focuses on the constituent elements of intangible capital, its place within these companies and its contribution to the creation of value. This work concludes a first proposal for the classification of the components of intangible capital.
The telecommunication industry is different from other industries in the basis of innovation rate. This industry continuously provides end users with new technological generations and disruptive technologies. Due to the complexity of the nature of the product served, telecommunication industry is an important case to study in order to explain the real reasons for the challenges facing telecom companies. Researching in this area will explain why some companies survive while others fail. The wide range served technology innovations is most likely to result in rapid mutation of consumers’ spending habits thus directly influencing the mobile phone companies. The aim of this is to test the reliability of innovations and its impact on companies’ profitability. For that purpose, a survey with 200 end users and 20 employees from two telecom providers (“Alfa, and “Touch”) was conducted to investigate various disruptive and conventional means and their impact on the financial performance of telecom industry. The results show that most of the revenue of telecom industry comes from usage of data while the industry is lacking for competition, and quality services while charging high tariffs for customers. Currently customers are migrating to data, therefore performing a pressure on the industry to improve its services. To conclude, network operators need to decide what to they want to sacrifice and how far they want to go to reach targeted results.
<p><em>This paper employs a quarterly time series to determine the timing of structural breaks for interest rates in USA over the last 60 years. <strong>The Chow test</strong> is used for investigating the non-stationary, where the date of the potential break is assumed to be known. Moreover, we empirically examined the deviation from an assumed interest rate as given in a standard Taylor rule and consequences on financial sectors. The empirical analysis is strengthened by analysing the rule from a historical perspective and look at the effect of setting the interest rate by the central bank on financial imbalances. The empirical evidence indicates that deviation in monetary policy has a potential causal factor in the build up of financial imbalances and the subsequent crisis where macro prudential intervention could have beneficial effect. Thus, our findings tend to support the view, which states that the probable existence of central banks has been one source of global financial crisis since the past decade.</em></p>
This paper employs a quarterly time series to determine the timing of structural breaks for interest rates in USA over the last 60 years. The Chow test is used for investigating the non-stationary, where the date of the potential break is assumed to be known. Moreover, we empirically examined the deviation from an assumed interest rate as given in a standard Taylor rule and consequences on financial sectors. The empirical analysis is strengthened by analysing the rule from a historical perspective and look at the effect of setting the interest rate by the central bank on financial imbalances. The empirical evidence indicates that deviation in monetary policy has a potential causal factor in the build up of financial imbalances and the subsequent crisis where macro prudential intervention could have beneficial effect. Thus, our findings tend to support the view, which states that the probable existence of central banks has been one source of global financial crisis since the past decade.
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 |