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: 6 / 6 publications (100.0%). Latest observation: 21 Oct 2026, 00:21 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 00:22 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 / 6 with data · 1 missing or unmatched. Percentages use all 6 selected publications.
5 / 6 with data · 1 missing or unmatched. Percentages use all 6 selected publications.
6 / 6 with data · 0 missing or unmatched. Percentages use all 6 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 | 2 | 2 | 2/2 | 2/2 |
| 2015 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2017 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2014 | All selected publications | 1 | 2 | 2/2 | 2/2 |
| 2015 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2017 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2014 | All selected publications | 5 | 2 | 2/2 | 2/2 |
| 2015 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2017 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | 3 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | 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 6 / 6 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): 2026-10-21T00:21:59.436931+00:00 — 2026-10-21T00:22:05.398068+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 | 6 | 6/6 |
| 2018 | All selected publications | 0 | 6 | 6/6 |
| 2019 | All selected publications | 1 | 6 | 6/6 |
| 2020 | All selected publications | 1 | 6 | 6/6 |
| 2021 | All selected publications | 0 | 6 | 6/6 |
| 2022 | All selected publications | 0 | 6 | 6/6 |
| 2023 | All selected publications | 4 | 6 | 6/6 |
| 2024 | All selected publications | 2 | 6 | 6/6 |
| 2025 | All selected publications | 2 | 6 | 6/6 |
| 2026 | All selected publications | 0 | 6 | 6/6 |
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 | 2 |
| 2016 | 1 |
| 2022 | 1 |
| 2023 | 1 |
| 2025 | 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 | 6 | 3 | 10 | 6/6 | 0.5 | 0.0 | 33.3 | 6/6 · 100.0% |
* Percentage among publications with a known citation count.
This research aims to contribute to solving the problem of tax evasion, by examine the impact of shareholders, intellectual capital, auditor reputation, and level of liabilities on tax evasion. The population is taken from manufacturing companies operating in consumer goods from 2017 to 2021 at IDX (Indonesia). The sample was selected using purposive sampling method. The dependent variable of this research is tax evasion. The independent variables are: type of shareholders, intellectual capital, auditor reputation and level of liabilities. The data analysis technique uses multiple linear regression, and it is processed using statistical software. The results found that domestic private shareholder, foreign private, the government, and individuals’ share holder have a significant negative impact on the Earnings Tax Ratio (ETR), which is a proxy for tax evasion. This means that all types of shareholders seek to avoid taxes, even in state-owned enterprises. Tax avoidance in Indonesia is difficult to eradicate because it is carried out by various parties, specifically tax officers, tax consultants, and taxpayers. The discussion of this research is limited to various factors that influence tax evasion. Subsequent research can identify how much the state loses due to tax evasion, which is carried out by tax officers, consultants, or by taxpayers. This research found that all types of owners avoid taxes. This can be prevented if the Indonesian government can 1) enforce the law, 2) increase the trust of taxpayers, 3) avoid showing off the luxurious life of tax officials and their families. 4) uphold honesty and avoid corrupt behaviour.
Taxation play an essential role both in a country and in a corporation. To minimize the tax payment, corporation conduct a corporate tax management. According to some of previous research, there is a correlation between corporate governance and corporate tax management. In this research we are focusing on three: number of board, number of independent board and board compensation. We measure corporate tax management by using effective tax rate (GAAP ETR and current ETR are used in this research). By using several other control variables, we found that corporate governance have a significant correlation to corporate tax management.
This study examines the relationship between audit quality and tax avoidance practices among listed banking companies in Bangladesh. The investigation utilized secondary data from the published annual reports of 36 listed banks on the Dhaka Stock Exchange, covering the period from 2018 to 2022. Therefore, the final dataset consists of 170 firm-year observations. In this research, audit quality was measured using four different variables: audit fees, audit tenure, the presence of a Big4 audit firm, and the disclosure of Key Audit Matters (KAM). The findings indicate that both the audit fees and KAM disclosure have a statistically significant positive relationship with tax avoidance. Conversely, while the presence of a Big4 auditor and audit tenure also demonstrate a positive relationship with tax avoidance, but this relation is not statistically significant. These findings suggest that rigorous auditing practices are essential for maintaining tax compliance within financial systems. Conducting audits with integrity, thoroughness, and adherence to professional standards can significantly impact tax compliance. Furthermore, the results highlight the need for tax authorities to identify and close the tax loopholes that companies exploit for tax avoidance. Promoting collaboration between regulatory bodies, tax authorities, and auditing firms is necessary to reduce tax avoidance activities by the firms.
Differences in interests related to taxation between the government and companies are still a current issue. The purpose of this study is to examine the impact of managerial and financial competencies on tax avoidance in Indonesian manufacturing companies. 149 financial statements of companies listed on the Indonesia Stock Exchange were used to compile the data. Panel data regression with a random-effects model was used to test and analyze the data. The findings revealed that financial challenges and managerial ability had a detrimental impact on tax avoidance, but there was no influence between the audit committee on tax avoidance. As for the control variable, there is a positive influence between profitability on tax avoidance, and there is no influence between leverage on tax avoidance.
Tax policy is one of the most important policy in consideration of investment development in certain industry. Research by Newlon (1987), Swenson (1994) and Hines (1996) concluded that tax rate is one of the most important thing considered by investors in a foreign direct investment. One of tax policy could be used to attract foreign direct investment is income tax incentives. The attractiveness of income tax incentives to a foreign direct investment is as much as the attractiveness to a domestic investment (Anwar and Mulyadi, 2012).
 In this paper, we have conducted a study of income tax incentives in food and agriculture industry; where we conduct a thorough study of income tax incentives and corporate performance in Indonesian and Australian food and agriculture industry. Our research show that there is a significant influence of income tax incentives to corporate performance. Based on our study, we conclude that the significant influence of income tax incentives to Indonesian corporate performance somewhat in a higher degree than the Australian peers. We have also concluded that Indonesian government provide a relatively more interesting income tax incentives compare to Australian government. However, an average method of net income –a method applied in Australia– could be considered by Indonesian government to avoid a market price fluctuation in this industry.
This study examines the relationship between audit quality and tax avoidance practices among listed banking companies in Bangladesh. The investigation utilized secondary data from the published annual reports of 36 listed banks on the Dhaka Stock Exchange, covering the period from 2018 to 2022. Therefore, the final dataset consists of 170 firm-year observations. In this research, audit quality was measured using four different variables: audit fees, audit tenure, the presence of a Big4 audit firm, and the disclosure of Key Audit Matters (KAM). The findings indicate that both the audit fees and KAM disclosure have a statistically significant positive relationship with tax avoidance. Conversely, while the presence of a Big4 auditor and audit tenure also demonstrate a positive relationship with tax avoidance, but this relation is not statistically significant. These findings suggest that rigorous auditing practices are essential for maintaining tax compliance within financial systems. Conducting audits with integrity, thoroughness, and adherence to professional standards can significantly impact tax compliance. Furthermore, the results highlight the need for tax authorities to identify and close the tax loopholes that companies exploit for tax avoidance. Promoting collaboration between regulatory bodies, tax authorities, and auditing firms is necessary to reduce tax avoidance activities by the firms.
This research aims to contribute to solving the problem of tax evasion, by examine the impact of shareholders, intellectual capital, auditor reputation, and level of liabilities on tax evasion. The population is taken from manufacturing companies operating in consumer goods from 2017 to 2021 at IDX (Indonesia). The sample was selected using purposive sampling method. The dependent variable of this research is tax evasion. The independent variables are: type of shareholders, intellectual capital, auditor reputation and level of liabilities. The data analysis technique uses multiple linear regression, and it is processed using statistical software. The results found that domestic private shareholder, foreign private, the government, and individuals’ share holder have a significant negative impact on the Earnings Tax Ratio (ETR), which is a proxy for tax evasion. This means that all types of shareholders seek to avoid taxes, even in state-owned enterprises. Tax avoidance in Indonesia is difficult to eradicate because it is carried out by various parties, specifically tax officers, tax consultants, and taxpayers. The discussion of this research is limited to various factors that influence tax evasion. Subsequent research can identify how much the state loses due to tax evasion, which is carried out by tax officers, consultants, or by taxpayers. This research found that all types of owners avoid taxes. This can be prevented if the Indonesian government can 1) enforce the law, 2) increase the trust of taxpayers, 3) avoid showing off the luxurious life of tax officials and their families. 4) uphold honesty and avoid corrupt behaviour.
Differences in interests related to taxation between the government and companies are still a current issue. The purpose of this study is to examine the impact of managerial and financial competencies on tax avoidance in Indonesian manufacturing companies. 149 financial statements of companies listed on the Indonesia Stock Exchange were used to compile the data. Panel data regression with a random-effects model was used to test and analyze the data. The findings revealed that financial challenges and managerial ability had a detrimental impact on tax avoidance, but there was no influence between the audit committee on tax avoidance. As for the control variable, there is a positive influence between profitability on tax avoidance, and there is no influence between leverage on tax avoidance.
<p>Tax policy is one of the most important policy in consideration of investment development in certain industry. Research by Newlon (1987), Swenson (1994) and Hines (1996) concluded that tax rate is one of the most important thing considered by investors in a foreign direct investment. One of tax policy could be used to attract foreign direct investment is income tax incentives. The attractiveness of income tax incentives to a foreign direct investment is as much as the attractiveness to a domestic investment (Anwar and Mulyadi, 2012). In this paper, we have conducted a study of income tax incentives in food and agriculture industry; where we conduct a thorough study of income tax incentives and corporate performance in Indonesian and Australian food and agriculture industry. Our research show that there is a significant influence of income tax incentives to corporate performance. Based on our study, we conclude that the significant influence of income tax incentives to Indonesian corporate performance somewhat in a higher degree than the Australian peers. We have also concluded that Indonesian government provide a relatively more interesting income tax incentives compare to Australian government. However, an average method of net income –a method applied in Australia– could be considered by Indonesian government to avoid a market price fluctuation in this industry.</p>
Taxation play an essential role both in a country and in a corporation. To minimize the tax payment, corporation conduct a corporate tax management. According to some of previous research, there is a correlation between corporate governance and corporate tax management. In this research we are focusing on three: number of board, number of independent board and board compensation. We measure corporate tax management by using effective tax rate (GAAP ETR and current ETR are used in this research). By using several other control variables, we found that corporate governance have a significant correlation to corporate tax management.
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 00:21 UTC · ijfbs | complete · ijfbs · 21 Oct 2026 00:21 UTC |
| ojs-rest | 21 Oct 2026 00:21 UTC · ijfbs | complete · ijfbs · 21 Oct 2026 00:14 UTC |
| oai-pmh | 20 Oct 2026 19:56 UTC · ijfbs | complete · ijfbs · 20 Oct 2026 19:55 UTC |
| openalex-enrichment | 21 Oct 2026 00:22 UTC · ijfbs | complete · ijfbs · 21 Oct 2026 00:21 UTC |