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, 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: 01 Nov 2026 03:00 UTC — 01 Nov 2026 12:26 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 |
|---|---|---|---|---|---|
| 2018 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2018 | All selected publications | 0 | 3 | 3/3 | 3/3 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2018 | All selected publications | 11 | 3 | 3/3 | 3/3 |
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): 2026-11-01T03:00:34.022320+00:00 — 2026-11-01T12:26:25.680738+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 | 1 | 3 | 3/3 |
| 2021 | All selected publications | 3 | 3 | 3/3 |
| 2022 | All selected publications | 0 | 3 | 3/3 |
| 2023 | All selected publications | 6 | 3 | 3/3 |
| 2024 | All selected publications | 1 | 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 |
|---|---|
| 2018 | 3 |
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 | 3 | 0 | 11 | 3/3 | 0.0 | 0 | 0.0 | 3/3 · 100.0% |
* Percentage among publications with a known citation count.
The success of SMEs is determined by the business capital and able to give value added to the business more productively. Various financing pattern schemes for SMEs have been provided in various regions and all economic sectors. However, the development of SMEs credit is still concentrated in several regions in Indonesia this can lead to inequality in the growth of SMEs. This study aims to analyze the convergence of absolute beta and conditional beta in the Provinces in Indonesia and calculate the half-time or time required to achieve steady-state conditions of the SME credit convergence process. Convergence test used is beta conditional convergence and absolute convergence with the monthly time period during 2017.1 until 2017.7. The determinant of SMEs convergence of credit in Indonesia is a number of third-party funds. The model used is dynamic panel data regression and the estimation method used is Arellano-Bond Generalized Method of Moment (AB-GMM). The results of the study show that during the study period, there has been a convergence credit process in Indonesia for both absolute and conditional convergence models. The conditional beta-convergence model provides a smaller beta value than the absolute model. The speed of credit growth convergence between provinces in Indonesia with half-time of 6.94 months for the absolute model and the half-time of 6.96 months for the conditional model
Application of IAS 41-Agriculture in developed countries contrast with agricultural entities conditions in developing countries especially for Indonesia. The adoption of IAS 41 makes DSAK-IAI issue PSAK 69 Agriculture intended for agricultural entities so that they can measure and record related biological assets of a company. However, the application of PSAK 69 to agricultural companies also facing problems such as changes in accounting treatment especially for accounting method which are changed form historical value to fair value. The purpose of this study is to provide an overview of accounting treatment of biological asset in Jember plantation companies in state-owned plantation companies in Jember and Bondowoso regencies, namely PTPN 12 and PTPN 11 (PG Pradjekan). The methode used was descriptive qualitative. The researchs showed that differences in biological assets of each company can cause differences in the recording of biological assets. The differences between the two companies are in terms of plant type, plant age and costs incurred in each type of plant. However, in other cases, the accounting treatment of biological assets in PTPN 11 (PG Pradjekan) and PTPN 12 has similarity on recording biological assets at acquisition cost method. This study shows that currently both companies are still used accounting records with a historical value approach that accumulates all costs incurred from the nursery process until the plants are transformed into yielding crops.
In the globalization and ASEAN Economic Community era, the competitiveness of Indonesian industries to sell goods and services is facing more severe challenges. It is partly due to the low performance of Indonesian logistics sector, and one of the contributing factors to the unsatisfactory logistics is poor logistics infrastructure. In this regard, this paper has several research objectives. The first is to analyze the performance of Indonesia logistics infrastructure in the past decade. The second is to analyze Indonesian logistics infrastructure development with its relevant fiscal policy support and evaluate its short-term progress. The research method adopted in this study is descriptive analytical method. In comparison with other countries, especially ASEAN region, the performance of Indonesian logistics infrastructure over the past decade tends to be steadily incompetitive, below the average of ASEAN countries. It is found to be one of the prime determinants of the high logistics costs in Indonesia. The government serious attention and the consistency of its policy towards improving logistics infrastructure during the 2014-2019 period has been partially seen, but the program sustainability is required in subsequent periods. In the short term, a temporary evaluation until 2017 of the sea toll program integrated with the construction of logistics infrastructure shows a positive impact. The positive impact is indicated from the decreasing prices of basic necessities in the eastern region of Indonesia by 20%-40%, and the economic revival of Eastern Indonesia region due to currently cheaper transportation costs.
Application of IAS 41-Agriculture in developed countries contrast with agricultural entities conditions in developing countries especially for Indonesia. The adoption of IAS 41 makes DSAK-IAI issue PSAK 69 Agriculture intended for agricultural entities so that they can measure and record related biological assets of a company. However, the application of PSAK 69 to agricultural companies also facing problems such as changes in accounting treatment especially for accounting method which are changed form historical value to fair value. The purpose of this study is to provide an overview of accounting treatment of biological asset in Jember plantation companies in state-owned plantation companies in Jember and Bondowoso regencies, namely PTPN 12 and PTPN 11 (PG Pradjekan). The methode used was descriptive qualitative. The researchs showed that differences in biological assets of each company can cause differences in the recording of biological assets. The differences between the two companies are in terms of plant type, plant age and costs incurred in each type of plant. However, in other cases, the accounting treatment of biological assets in PTPN 11 (PG Pradjekan) and PTPN 12 has similarity on recording biological assets at acquisition cost method. This study shows that currently both companies are still used accounting records with a historical value approach that accumulates all costs incurred from the nursery process until the plants are transformed into yielding crops.
In the globalization and ASEAN Economic Community era, the competitiveness of Indonesian industries to sell goods and services is facing more severe challenges. It is partly due to the low performance of Indonesian logistics sector, and one of the contributing factors to the unsatisfactory logistics is poor logistics infrastructure. In this regard, this paper has several research objectives. The first is to analyze the performance of Indonesia logistics infrastructure in the past decade. The second is to analyze Indonesian logistics infrastructure development with its relevant fiscal policy support and evaluate its short-term progress. The research method adopted in this study is descriptive analytical method. In comparison with other countries, especially ASEAN region, the performance of Indonesian logistics infrastructure over the past decade tends to be steadily incompetitive, below the average of ASEAN countries. It is found to be one of the prime determinants of the high logistics costs in Indonesia. The government serious attention and the consistency of its policy towards improving logistics infrastructure during the 2014-2019 period has been partially seen, but the program sustainability is required in subsequent periods. In the short term, a temporary evaluation until 2017 of the sea toll program integrated with the construction of logistics infrastructure shows a positive impact. The positive impact is indicated from the decreasing prices of basic necessities in the eastern region of Indonesia by 20%-40%, and the economic revival of Eastern Indonesia region due to currently cheaper transportation costs.
The success of SMEs is determined by the business capital and able to give value added to the business more productively. Various financing pattern schemes for SMEs have been provided in various regions and all economic sectors. However, the development of SMEs credit is still concentrated in several regions in Indonesia this can lead to inequality in the growth of SMEs. This study aims to analyze the convergence of absolute beta and conditional beta in the Provinces in Indonesia and calculate the half-time or time required to achieve steady-state conditions of the SME credit convergence process. Convergence test used is beta conditional convergence and absolute convergence with the monthly time period during 2017.1 until 2017.7. The determinant of SMEs convergence of credit in Indonesia is a number of third-party funds. The model used is dynamic panel data regression and the estimation method used is Arellano-Bond Generalized Method of Moment (AB-GMM). The results of the study show that during the study period, there has been a convergence credit process in Indonesia for both absolute and conditional convergence models. The conditional beta-convergence model provides a smaller beta value than the absolute model. The speed of credit growth convergence between provinces in Indonesia with half-time of 6.94 months for the absolute model and the half-time of 6.96 months for the conditional model
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 |