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: 3 / 3 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 07:23 UTC — 22 Oct 2026 04:53 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.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 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 |
|---|---|---|---|---|---|
| 2025 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2026 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2025 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2026 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2025 | All selected publications | 0 | 2 | 2/2 | 2/2 |
| 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 3 / 3 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): 2026-10-21T07:23:09.634730+00:00 — 2026-10-22T04:53:15.361378+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 | 3 | 3/3 |
| 2018 | All selected publications | 0 | 3 | 3/3 |
| 2019 | All selected publications | 0 | 3 | 3/3 |
| 2020 | All selected publications | 0 | 3 | 3/3 |
| 2021 | All selected publications | 0 | 3 | 3/3 |
| 2022 | All selected publications | 0 | 3 | 3/3 |
| 2023 | All selected publications | 0 | 3 | 3/3 |
| 2024 | All selected publications | 0 | 3 | 3/3 |
| 2025 | All selected publications | 0 | 3 | 3/3 |
| 2026 | All selected publications | 0 | 3 | 3/3 |
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 |
|---|---|
| 2025 | 2 |
| 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 Finance & Banking Studies SSBFNET | 2 | 2 | 0 | 2/2 | 1.0 | 1.0 | 50.0 | 2/2 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 1 | 0 | 0 | 1/1 | 0.0 | 0 | 0.0 | 1/1 · 100.0% |
* Percentage among publications with a known citation count.
The current research aims to examine the accounting of digital assets by linking accounting measurement theory to financial disclosure requirements, in light of the challenges related to the absence of specialized international accounting standards for digital assets. The research was divided into two parts: theoretical and applied. The theoretical part included a review of the literature related to accounting measurement (historical cost, fair value, and impairment model) and financial disclosure requirements according to International Financial Reporting Standards (IAS/IFRS) and American standards (FASB, AICPA, and SEC). The applied part used a questionnaire and administered to a sample of financial report users, including academics and professionals, to obtain their opinions on the adequacy of current measurement models, financial disclosure requirements, and shortcomings in international standards. It also explored the possibility of formulating an integrated framework for measuring and disclosing digital assets. The results of the analysis of the questionnaire showed that fair value measurement is more appropriate for the nature of digital assets than traditional models, with the need to enhance disclosure of risks associated with price volatility, liquidity, and operational risks. It also showed that the absence of a specific accounting standard for digital assets leads to poor comparability and increased variation in accounting practices across economic entities. Based on the survey results, an integrated theoretical framework for accounting for digital assets was proposed. This framework includes principles of recognition, measurement, and disclosure, and contributes to achieving the objectives of financial reporting in accordance with the International Accounting Standards Board (IASB) conceptual framework, ensuring relevance, accurate representation, comparability, clarity, and timeliness. The study concluded that developing a specialized accounting framework for digital assets represents a strategic necessity to ensure the reliability and transparency of financial reporting and enhance user confidence, given the rapid growth in the adoption of digital assets within global economic activities.
This research aims to demonstrate the role of financial statements (balance sheet) in reporting the risks faced by government units and the extent of the risks within these units, in addition to demonstrating the steps through which risks can be managed in these units, whether they are governmental or private economic units. Financial risks are one of the problems facing economic units, whether private or governmental. In turn, decision-makers, both internal and external, work to measure the financial risks facing these units. Therefore, the research problem is as follows, Do financial position statement contribute to reporting financial risks in governmental units (Baghdad Municipality)? In order to achieve the research objectives, the financial position statement of the Baghdad Municipality Department (a government unit) was used, and a set of indicators was used to reach a judgment on the presence or absence of risks, as well as the level of risks facing this unit. It was found that there is a possibility of knowing the risks through the financial position statement, in addition to discovering that the level of risks facing this unit (Baghdad Municipality) is considered high.
The purpose of this research is to contribute to the development of the accounting process and disclosure in the context of the disruption that brings in artificial intelligence by providing an integrated framework, from the perspective of internal development of accounting processes to the external requirements of the disclosure. The framework is based on the following five pillars: digital transformation policies; influence of artificial intelligence on the quality of accounting information; basic principles of intelligent models; governance mechanisms; and related risks. This is a descriptive and analytical study. It discussed the theoretical background and accounting implications of artificial intelligence, proposed a disclosure framework, and then it utilized content and gap analysis to determine to what extent the considered annual report met the requirements of the frameworkThe above analysis was based on the 2024 annual report of Asiacell Communications Company-Private Joint Stock Company. The disclosures of artificial intelligence, digital transformation, applications in accounting and finance, information quality, governance, and technological risk were selected and analyzed together with 24 disclosure items based on the proposed framework. The findings show a large disparity between the technological sophistication of the company and its level of accounting disclosure on artificial intelligence. The fermentations described in the present disclosure examples were at 25%, but only 25% of the disclosures were reached, and disclosures of assumptions underlying predictive models and related risks were missing. The results also suggest that the introduced framework, if adopted, would cover all the 24 disclosure items with a maximum proposed disclosure score of 100%.Also, based on model assumptions, data sources, levels of confidence, responsibilities in governance, and procedures for verification, transparency would be increased, the credibility of accounting estimates would be strengthened, the level of information uncertainty would be decreased, and the level of investor confidence in financial reports produced in an artificial intelligence milieu would be enhanced
The purpose of this research is to contribute to the development of the accounting process and disclosure in the context of the disruption that brings in artificial intelligence by providing an integrated framework, from the perspective of internal development of accounting processes to the external requirements of the disclosure. The framework is based on the following five pillars: digital transformation policies; influence of artificial intelligence on the quality of accounting information; basic principles of intelligent models; governance mechanisms; and related risks. This is a descriptive and analytical study. It discussed the theoretical background and accounting implications of artificial intelligence, proposed a disclosure framework, and then it utilized content and gap analysis to determine to what extent the considered annual report met the requirements of the frameworkThe above analysis was based on the 2024 annual report of Asiacell Communications Company-Private Joint Stock Company. The disclosures of artificial intelligence, digital transformation, applications in accounting and finance, information quality, governance, and technological risk were selected and analyzed together with 24 disclosure items based on the proposed framework. The findings show a large disparity between the technological sophistication of the company and its level of accounting disclosure on artificial intelligence. The fermentations described in the present disclosure examples were at 25%, but only 25% of the disclosures were reached, and disclosures of assumptions underlying predictive models and related risks were missing. The results also suggest that the introduced framework, if adopted, would cover all the 24 disclosure items with a maximum proposed disclosure score of 100%.Also, based on model assumptions, data sources, levels of confidence, responsibilities in governance, and procedures for verification, transparency would be increased, the credibility of accounting estimates would be strengthened, the level of information uncertainty would be decreased, and the level of investor confidence in financial reports produced in an artificial intelligence milieu would be enhanced
The current research aims to examine the accounting of digital assets by linking accounting measurement theory to financial disclosure requirements, in light of the challenges related to the absence of specialized international accounting standards for digital assets. The research was divided into two parts: theoretical and applied. The theoretical part included a review of the literature related to accounting measurement (historical cost, fair value, and impairment model) and financial disclosure requirements according to International Financial Reporting Standards (IAS/IFRS) and American standards (FASB, AICPA, and SEC). The applied part used a questionnaire and administered to a sample of financial report users, including academics and professionals, to obtain their opinions on the adequacy of current measurement models, financial disclosure requirements, and shortcomings in international standards. It also explored the possibility of formulating an integrated framework for measuring and disclosing digital assets. The results of the analysis of the questionnaire showed that fair value measurement is more appropriate for the nature of digital assets than traditional models, with the need to enhance disclosure of risks associated with price volatility, liquidity, and operational risks. It also showed that the absence of a specific accounting standard for digital assets leads to poor comparability and increased variation in accounting practices across economic entities. Based on the survey results, an integrated theoretical framework for accounting for digital assets was proposed. This framework includes principles of recognition, measurement, and disclosure, and contributes to achieving the objectives of financial reporting in accordance with the International Accounting Standards Board (IASB) conceptual framework, ensuring relevance, accurate representation, comparability, clarity, and timeliness. The study concluded that developing a specialized accounting framework for digital assets represents a strategic necessity to ensure the reliability and transparency of financial reporting and enhance user confidence, given the rapid growth in the adoption of digital assets within global economic activities.
This research aims to demonstrate the role of financial statements (balance sheet) in reporting the risks faced by government units and the extent of the risks within these units, in addition to demonstrating the steps through which risks can be managed in these units, whether they are governmental or private economic units. Financial risks are one of the problems facing economic units, whether private or governmental. In turn, decision-makers, both internal and external, work to measure the financial risks facing these units. Therefore, the research problem is as follows, Do financial position statement contribute to reporting financial risks in governmental units (Baghdad Municipality)? In order to achieve the research objectives, the financial position statement of the Baghdad Municipality Department (a government unit) was used, and a set of indicators was used to reach a judgment on the presence or absence of risks, as well as the level of risks facing this unit. It was found that there is a possibility of knowing the risks through the financial position statement, in addition to discovering that the level of risks facing this unit (Baghdad Municipality) is considered high.
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 |