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.
Top 5 in the selected collection · Ranked by Crossref lifetime citations. Only articles with at least one known citation are included; source totals are never combined.
2025 · Anggun Nabila Tri Auriellia · Tri Gunarsih
2023 · Surifah Surifah
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 / 6 publications (66.7%). 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: 22 Oct 2026 01:27 UTC — 29 Oct 2026 16:45 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.
6 / 6 with data · 0 missing or unmatched. Percentages use all 6 selected publications.
6 / 6 with data · 0 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 |
|---|---|---|---|---|---|
| 2022 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2024 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2026 | All selected publications | 2 | 2 | 0/2 | 2/2 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2026 | All selected publications | — | 2 | 0/2 | 2/2 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 6 | 2 | 2/2 | 2/2 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2026 | All selected publications | 0 | 2 | 0/2 | 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 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): 2026-10-22T01:27:02.126252+00:00 — 2026-10-29T16:45:34.091048+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 | 0 | 6 | 6/6 |
| 2020 | All selected publications | 0 | 6 | 6/6 |
| 2021 | All selected publications | 0 | 6 | 6/6 |
| 2022 | All selected publications | 0 | 6 | 6/6 |
| 2023 | All selected publications | 1 | 6 | 6/6 |
| 2024 | All selected publications | 4 | 6 | 6/6 |
| 2025 | All selected publications | 1 | 6 | 6/6 |
| 2026 | All selected publications | 2 | 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 |
|---|---|
| 2022 | 1 |
| 2023 | 2 |
| 2025 | 1 |
| 2026 | 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 | 6 | 4 | 8 | 6/6 | 1.0 | 1.0 | 50.0 | 4/6 · 66.7% |
* Percentage among publications with a known citation count.
This study analyzes the effect of sustainable investment awareness and risk perception on students' interest in green finance instruments, with financial literacy as a mediating variable. A quantitative approach was used, involving 180 student respondents in the Special Region of Yogyakarta, who were selected through a purposive sampling technique using an online questionnaire via Google Forms. Data analysis was conducted using the SEM-PLS method through SmartPLS version 4.1.1.2. The results showed that sustainable investment awareness, risk perception, and financial literacy positively and significantly affect student investment interest in green finance. In addition, financial literacy acts as a significant mediator in the relationship between sustainable investment awareness and risk perception on interest in investing in green finance. This study contributes to developing literature on sustainable investment behavior among the younger generation, especially university students, by integrating financial literacy variables as a mediating mechanism. The findings expand the understanding of how psychological factors and financial knowledge influence investment decisions in sustainability.
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.
Hedging as a derivatives instrument is one of the practices in risk management. The hedging decision, among other things, comprises forwards, futures, options, and swaps. This study analyzes managerial ownership, financial distress, leverage, liquidity, profitability, and company size on hedging decisions. This study was conducted in a mining sector company listed on the IDX in 2016-2020. The samples were 27 listed companies that were selected based on the purposive sampling method. The logistic regression model was implemented to test the hypothesis. The results of this study are as follows: managerial ownership, financial distress, and company size significantly affected hedging decisions; liquidity variables have a significant adverse impact on hedging decisions; other variables, namely leverage, and profitability, do not affect hedging decisions because they are insignificant. This study suggests that the management prefers implementing hedging to reduce future risks.
This study compares Corporate Governance (CG) between Hungary and Indonesia and analyzes the causality between CG and financial performance in both countries. CG proxy is the ownership structure. In the monitoring function, CG will improve the company's performance. The company's performance, on the other hand, can affect the ownership structure as one of the markets for corporate control in the CG mechanism. The causality test will inform whether the monitoring or market for corporate control is implemented as a CG mechanism. This study uses the Granger causality test to analyze the bi-causality between CG and financial performance. The sample consists of companies listed on the Budapest Stock Exchange (BSX) and the Indonesia Stock Exchange (IDX). The results showed that only two pairs of bi-causality relationships (out of six pairs) occurred in the BSE sample companies, and there were three pairs of bi-causality relationships (out of nine pairs) in the IDX sample companies. This research contributes to the relationship between ownership and firm performance based on agency problems from the perspective of the monitoring function and the market for firm control.
This article maps the knowledge structure and evolution of bankruptcy/default prediction research in financial institutions using a bibliometric co-citation approach, which identifies works that are often cited together due to the proximity of their theories, methods, or themes. Bibliographic data were collected from the Web of Science (WoS) for the period 1970-2025, then extracted in a complete bibliographic format and prepared for network analysis. The co-citation procedure is carried out by extracting the reference list, normalizing and unifying citation variations, and preparing a co-citation matrix, which is then visualized and classified using VOSviewer with minimum threshold parameters and normalized association strength. The results of the document ranking based on the number of citations and Total Link Strength (TLS) show the development trajectory of the field from the ratio and Z-Score approach to the logit probabilistic model, the market foundation based on the structural theory of credit risk, the governance perspective supported by agency theory, the dynamic panel methodology/GMM for longitudinal data, to computational waves (neural networks and machine learning) to capture nonlinear patterns of credit scoring. The co-citation map uncovers four main clusters: econometrics & banking risk, structural theory, ratios, and computational, with Merton and Altman acting as the cross-cluster binding nodes. These findings support the design of a more comprehensive early warning system (EWS) by integrating ratio signals, market indicators, governance/agency factors, and ML models to improve accuracy and adaptation.
This article maps the knowledge structure and evolution of bankruptcy/default prediction research in financial institutions using a bibliometric co-citation approach, which identifies works that are often cited together due to the proximity of their theories, methods, or themes. Bibliographic data were collected from the Web of Science (WoS) for the period 1970-2025, then extracted in a complete bibliographic format and prepared for network analysis. The co-citation procedure is carried out by extracting the reference list, normalizing and unifying citation variations, and preparing a co-citation matrix, which is then visualized and classified using VOSviewer with minimum threshold parameters and normalized association strength. The results of the document ranking based on the number of citations and Total Link Strength (TLS) show the development trajectory of the field from the ratio and Z-Score approach to the logit probabilistic model, the market foundation based on the structural theory of credit risk, the governance perspective supported by agency theory, the dynamic panel methodology/GMM for longitudinal data, to computational waves (neural networks and machine learning) to capture nonlinear patterns of credit scoring. The co-citation map uncovers four main clusters: econometrics & banking risk, structural theory, ratios, and computational, with Merton and Altman acting as the cross-cluster binding nodes. These findings support the design of a more comprehensive early warning system (EWS) by integrating ratio signals, market indicators, governance/agency factors, and ML models to improve accuracy and adaptation.
This study aims to assess the impact of exposure to Carbon Emission Disclosure (CED) and the level of Greenhouse Gas (GHG) emissions on the valuation of companies operating in the Indonesian banking sector. This in-depth study also evaluates the influence of capital structure and intellectual property assets. In addition, this study explicitly examines how financial performance moderates the relationship between these variables and the company's value. Using a quantitative research method, this analysis observed a sample consisting of banking entities listed on the Indonesia Stock Exchange (IDX) from 2020 to 2024. The theoretical framework underpinning this research comprises Legitimacy Theory, Stakeholder Theory, Signal Theory, Preference Order Theory, and the resource-based view (RBV). The results reveal that transparent disclosure of carbon emissions and the accumulation of intellectual capital positively influence the company's value, while GHG emissions and leverage have adverse effects. Crucially, financial performance (ROA) moderates these relationships by weakening the positive impact of carbon disclosure and buffering the negative impact of GHG emissions but does not significantly moderate the effects of capital structure or intellectual capital. These findings offer practical guidelines for banking management to align environmental sustainability practices with financial strategies to enhance corporate value.
This study analyzes the effect of sustainable investment awareness and risk perception on students' interest in green finance instruments, with financial literacy as a mediating variable. A quantitative approach was used, involving 180 student respondents in the Special Region of Yogyakarta, who were selected through a purposive sampling technique using an online questionnaire via Google Forms. Data analysis was conducted using the SEM-PLS method through SmartPLS version 4.1.1.2. The results showed that sustainable investment awareness, risk perception, and financial literacy positively and significantly affect student investment interest in green finance. In addition, financial literacy acts as a significant mediator in the relationship between sustainable investment awareness and risk perception on interest in investing in green finance. This study contributes to developing literature on sustainable investment behavior among the younger generation, especially university students, by integrating financial literacy variables as a mediating mechanism. The findings expand the understanding of how psychological factors and financial knowledge influence investment decisions in sustainability.
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.
Hedging as a derivatives instrument is one of the practices in risk management. The hedging decision, among other things, comprises forwards, futures, options, and swaps. This study analyzes managerial ownership, financial distress, leverage, liquidity, profitability, and company size on hedging decisions. This study was conducted in a mining sector company listed on the IDX in 2016-2020. The samples were 27 listed companies that were selected based on the purposive sampling method. The logistic regression model was implemented to test the hypothesis. The results of this study are as follows: managerial ownership, financial distress, and company size significantly affected hedging decisions; liquidity variables have a significant adverse impact on hedging decisions; other variables, namely leverage, and profitability, do not affect hedging decisions because they are insignificant. This study suggests that the management prefers implementing hedging to reduce future risks.
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 |