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: 4 / 4 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: 22 Oct 2026 02:07 UTC — 22 Oct 2026 05:18 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.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 selected publications.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 selected publications.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 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 |
|---|---|---|---|---|---|
| 2019 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2020 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 0 | 2 | 2/2 | 2/2 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 17 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 1 | 2 | 2/2 | 2/2 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 21 | 2 | 2/2 | 2/2 |
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 4 / 4 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): 2026-10-22T02:07:24.456811+00:00 — 2026-10-22T05:18:13.825866+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 | 4 | 4/4 |
| 2018 | All selected publications | 0 | 4 | 4/4 |
| 2019 | All selected publications | 0 | 4 | 4/4 |
| 2020 | All selected publications | 1 | 4 | 4/4 |
| 2021 | All selected publications | 2 | 4 | 4/4 |
| 2022 | All selected publications | 1 | 4 | 4/4 |
| 2023 | All selected publications | 9 | 4 | 4/4 |
| 2024 | All selected publications | 3 | 4 | 4/4 |
| 2025 | All selected publications | 6 | 4 | 4/4 |
| 2026 | All selected publications | 0 | 4 | 4/4 |
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 |
|---|---|
| 2019 | 2 |
| 2021 | 2 |
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 | 0 | 1 | 2/2 | 0.0 | 0.0 | 0.0 | 2/2 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 2 | 17 | 21 | 2/2 | 8.5 | 8.5 | 100.0 | 2/2 · 100.0% |
* Percentage among publications with a known citation count.
In this paper, we investigate the definition of Fintech and measure the extent of the impact of Fintech variables on the Cybersecurity as the dependent variable. The hypotheses developed by the authors which are based on research through the statistical results of the research variables and as all correlation coefficients were a positive relationship and at the level of significance 0.01 which made the authors accept the correlation hypotheses, and for the results of the influence factor between the research variables it also had a positive effect when the level of 0.05 for all sections of the independent variable, as we have also noticed that the influence of the independent variable of financial technology when linked to all of its sections with Cybersecurity. We find the influence of the influence coefficient significantly increased to 0.908, which explains that there is a complementarity between the sections of the independent variable.
This research aims to investigate the effect of the social customer relationship management (CRM) process on achieving superior levels of costumers and financial performance. The object of this research is the Iraqi firms at the Iraqi environment market, while the subject is more than 200 respondents. Six variables from the research data were gathered through an instrument model invalid form that structured to be measured through reliable questionnaires. Statistical Analysis of the research data used partial least squares structural equation modeling with the significance in accordance with the output of SPSS 22.0. The findings indicate that the social CRM technologies of the firms improve the innovation activities on Both (services and products). This has a positive effect on the ability to achieve high performance through building customer-linking capabilities by adopting innovations, resulting in higher levels of efficiency. In exchange, higher levels of consumer contribution lead to having positive levels of customer and financial performance.
Non-tariff measures such as food safety and technical standards are used to achieve the non-trade objective of protecting consumers’ health and safety. On the other hand, they can also be deployed as a trade secure tool to drive a price wedge between foreign and domestic producers. This study investigates the protectionist tools of 34 developing countries food safety standards using a sample of developing countries food imported from developed and other developing countries with a specific focus on vegetables, Trunks, machinery, and tobacco. We employ theoretical framework of gravity equation by applying SGMM estimation. Our results indicate that vegetable and machinery variables are negative and statistically significant. Meaning that both variables have a negative impact on GDP, in other words, both variables are not support GDP and economic growth in the developing countries. Finally, Trunk variable is positive and statistically significant. It shows that, this variable leads to promote economic growth in the developing countries. While Tobacco is positive and statistically insignificant, meaning that this product is not play an important role in the trade sector in our sample countries.
The demonetisation footstep by the Government of India twisted complicated influences in the economy. Complete sectors of the economy had faced and produced mixed sensation results over the decision of demonetisation. India’s financial services struggled with demonetisation; on the other hand demonetisation affects utmost over the banking sector because it is substantial influenced services to transform money circulation in an Indian economy. Eradicating components of currency notes from circulation in an economy is demonetisation. It is as the processes of components of money are denied the status of legal tender. Consequently, ceased currency notes will not be account as valid currency in an economy. The term ‘demonetization’ is an instrument to shrink Inflation, Black Money, Corruption and terror funding, this step discourages a cash dependent economy in India. Government of India drive towards demonetisation has given a strong push to the popularity of digital banking and made helps with the alternative arrangements of e-banking and e –wallet to trade and commerce. Exploring the demonetisation emergence in an economy and impact on banking services ecosystem dynamics, this study take an abductive approach anchored in over 4 years of case study data regarding. The present study foremost intention is to be analysing the demonetisation impact over banking loans and advances. In this regard the present study is to be examining the pre demonetisation and post demonetisation period.
This research aims to investigate the effect of the social customer relationship management (CRM) process on achieving superior levels of costumers and financial performance. The object of this research is the Iraqi firms at the Iraqi environment market, while the subject is more than 200 respondents. Six variables from the research data were gathered through an instrument model invalid form that structured to be measured through reliable questionnaires. Statistical Analysis of the research data used partial least squares structural equation modeling with the significance in accordance with the output of SPSS 22.0. The findings indicate that the social CRM technologies of the firms improve the innovation activities on Both (services and products). This has a positive effect on the ability to achieve high performance through building customer-linking capabilities by adopting innovations, resulting in higher levels of efficiency. In exchange, higher levels of consumer contribution lead to having positive levels of customer and financial performance.
In this paper, we investigate the definition of Fintech and measure the extent of the impact of Fintech variables on the Cybersecurity as the dependent variable. The hypotheses developed by the authors which are based on research through the statistical results of the research variables and as all correlation coefficients were a positive relationship and at the level of significance 0.01 which made the authors accept the correlation hypotheses, and for the results of the influence factor between the research variables it also had a positive effect when the level of 0.05 for all sections of the independent variable, as we have also noticed that the influence of the independent variable of financial technology when linked to all of its sections with Cybersecurity. We find the influence of the influence coefficient significantly increased to 0.908, which explains that there is a complementarity between the sections of the independent variable.
Non-tariff measures such as food safety and technical standards are used to achieve the non-trade objective of protecting consumers’ health and safety. On the other hand, they can also be deployed as a trade secure tool to drive a price wedge between foreign and domestic producers. This study investigates the protectionist tools of 34 developing countries food safety standards using a sample of developing countries food imported from developed and other developing countries with a specific focus on vegetables, Trunks, machinery, and tobacco. We employ theoretical framework of gravity equation by applying SGMM estimation. Our results indicate that vegetable and machinery variables are negative and statistically significant. Meaning that both variables have a negative impact on GDP, in other words, both variables are not support GDP and economic growth in the developing countries. Finally, Trunk variable is positive and statistically significant. It shows that, this variable leads to promote economic growth in the developing countries. While Tobacco is positive and statistically insignificant, meaning that this product is not play an important role in the trade sector in our sample countries.
The demonetisation footstep by the Government of India twisted complicated influences in the economy. Complete sectors of the economy had faced and produced mixed sensation results over the decision of demonetisation. India’s financial services struggled with demonetisation; on the other hand demonetisation affects utmost over the banking sector because it is substantial influenced services to transform money circulation in an Indian economy. Eradicating components of currency notes from circulation in an economy is demonetisation. It is as the processes of components of money are denied the status of legal tender. Consequently, ceased currency notes will not be account as valid currency in an economy. The term ‘demonetization’ is an instrument to shrink Inflation, Black Money, Corruption and terror funding, this step discourages a cash dependent economy in India. Government of India drive towards demonetisation has given a strong push to the popularity of digital banking and made helps with the alternative arrangements of e-banking and e –wallet to trade and commerce. Exploring the demonetisation emergence in an economy and impact on banking services ecosystem dynamics, this study take an abductive approach anchored in over 4 years of case study data regarding. The present study foremost intention is to be analysing the demonetisation impact over banking loans and advances. In this regard the present study is to be examining the pre demonetisation and post demonetisation period.
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 |