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 07:22 UTC — 22 Oct 2026 01:49 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 |
|---|---|---|---|---|---|
| 2022 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 5 | 3 | 3/3 | 3/3 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | 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): 2026-10-21T07:22:43.123654+00:00 — 2026-10-22T01:49:17.181338+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 | 3 | 3 | 3/3 |
| 2024 | All selected publications | 3 | 3 | 3/3 |
| 2025 | All selected publications | 5 | 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 |
|---|---|
| 2022 | 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 Research in Business and Social Science SSBFNET | 3 | 5 | 11 | 3/3 | 1.67 | 1 | 100.0 | 3/3 · 100.0% |
* Percentage among publications with a known citation count.
This study analyzes the bankruptcies of companies in the plastic and packaging industry listed on the Indonesian stock exchange. This analysis is essential to know the condition of the company's performance and in what position. The hope is to assist stakeholders in making the best decisions for sustainability. The positive contribution of the plastics and packaging industry in supporting economic growth in Indonesia, as well as the challenges of global issues regarding the negative impact of use, requires companies to avoid financial distress or bankruptcy. However, recent research argues that very few company managers try hard to avoid the risk of bankruptcy. This quantitative descriptive research takes a sample of 10 companies selected purposively with predetermined criteria. Following the arguments of Altman (1983), the research analysis uses the Altman Z-Score for a sample of firms in one homogeneous industry. The results showed that, in general, the plastic and packaging industry was potentially vulnerable and had financial distress or went bankrupt, and there was only one healthy company. The results of this study also have an essential contribution for stakeholders to detect early on the company's financial condition and help make sound managerial decisions.
The purpose of this study is to examine the differences in the value of the financial distress of manufacturing companies before and after the Covid-19 pandemic, as well as to examine the determinants that affect the financial distress of manufacturing companies before and after the Covid-19 pandemic. This study is a quantitative study with the population in this study namely manufacturing companies in Indonesia. The sample is 1005 Firm Years in manufacturing companies in Indonesia. These Manufacturing Companies were taken from 2016-2021, but we divided the sample into 2 categories, namely 2016-2019, namely in the pre-pandemic period as many as 653 firm years, and during the pandemic as many as 352 firm years. The dependent variable used in this study is financial distress, measured by the O-Score Model. Furthermore, the independent variables are profitability using ROA, liquidity using the current ratio, and leverage using the Debt to Assets Ratio. The test results show no difference between the financial distress of manufacturing companies in Indonesia before and during the pandemic. During the pandemic, financial ratios became more considered in predicting financial distress than before the pandemic. The role of profitability is a factor that impacts financial distress is more impactful in the pre-pandemic period. The role of liquidity being a factor that impacts financial distress is more impactful during the pandemic. Leverage can also be a good indicator in predicting financial distress both in the pre-pandemic and during the pandemic.
Beginning in the mid-1970s, financial distress in local governments became big news when New York City nearly collapsed due to the energy crisis and high inflation. When local governments experience financial distress, the responsibility to provide services to the community both now and in the future cannot be fulfilled. Local governments can only maintain services to the community if they are able to avoid financial distress. If the financial distress condition is maintained, there will be a stagnation in the level of community welfare which will have an impact on regional economic growth because it is not supported by adequate public services. Some local governments like Pennsylvania, have implemented a measure to indicate financial distress as stipulated in the 1987 Financially Distressed City Act (UU 47). However, the local government in Indonesia does not yet have an absolute measure for this financial distress condition. For this reason, financial distress in this study will use a relative measure indicated by the probability value measured using a defined approach. The results of the study provide an overview of how the relevant values in local government financial statements are able to predict financial distress in local governments. The findings show that the variables intergovernmental revenue (IGR), financial independence (FIND), employee expenditure (EMPEX), capital expenditure (CAPEX), and operating position (OPPOS) have a significant effect and can be used as an alternative predictor of financial distress with more comprehensive suitability with the financial characteristics of local government in East Java Province.
The purpose of this study is to examine the differences in the value of the financial distress of manufacturing companies before and after the Covid-19 pandemic, as well as to examine the determinants that affect the financial distress of manufacturing companies before and after the Covid-19 pandemic. This study is a quantitative study with the population in this study namely manufacturing companies in Indonesia. The sample is 1005 Firm Years in manufacturing companies in Indonesia. These Manufacturing Companies were taken from 2016-2021, but we divided the sample into 2 categories, namely 2016-2019, namely in the pre-pandemic period as many as 653 firm years, and during the pandemic as many as 352 firm years. The dependent variable used in this study is financial distress, measured by the O-Score Model. Furthermore, the independent variables are profitability using ROA, liquidity using the current ratio, and leverage using the Debt to Assets Ratio. The test results show no difference between the financial distress of manufacturing companies in Indonesia before and during the pandemic. During the pandemic, financial ratios became more considered in predicting financial distress than before the pandemic. The role of profitability is a factor that impacts financial distress is more impactful in the pre-pandemic period. The role of liquidity being a factor that impacts financial distress is more impactful during the pandemic. Leverage can also be a good indicator in predicting financial distress both in the pre-pandemic and during the pandemic.
This study analyzes the bankruptcies of companies in the plastic and packaging industry listed on the Indonesian stock exchange. This analysis is essential to know the condition of the company's performance and in what position. The hope is to assist stakeholders in making the best decisions for sustainability. The positive contribution of the plastics and packaging industry in supporting economic growth in Indonesia, as well as the challenges of global issues regarding the negative impact of use, requires companies to avoid financial distress or bankruptcy. However, recent research argues that very few company managers try hard to avoid the risk of bankruptcy. This quantitative descriptive research takes a sample of 10 companies selected purposively with predetermined criteria. Following the arguments of Altman (1983), the research analysis uses the Altman Z-Score for a sample of firms in one homogeneous industry. The results showed that, in general, the plastic and packaging industry was potentially vulnerable and had financial distress or went bankrupt, and there was only one healthy company. The results of this study also have an essential contribution for stakeholders to detect early on the company's financial condition and help make sound managerial decisions.
Beginning in the mid-1970s, financial distress in local governments became big news when New York City nearly collapsed due to the energy crisis and high inflation. When local governments experience financial distress, the responsibility to provide services to the community both now and in the future cannot be fulfilled. Local governments can only maintain services to the community if they are able to avoid financial distress. If the financial distress condition is maintained, there will be a stagnation in the level of community welfare which will have an impact on regional economic growth because it is not supported by adequate public services. Some local governments like Pennsylvania, have implemented a measure to indicate financial distress as stipulated in the 1987 Financially Distressed City Act (UU 47). However, the local government in Indonesia does not yet have an absolute measure for this financial distress condition. For this reason, financial distress in this study will use a relative measure indicated by the probability value measured using a defined approach. The results of the study provide an overview of how the relevant values in local government financial statements are able to predict financial distress in local governments. The findings show that the variables intergovernmental revenue (IGR), financial independence (FIND), employee expenditure (EMPEX), capital expenditure (CAPEX), and operating position (OPPOS) have a significant effect and can be used as an alternative predictor of financial distress with more comprehensive suitability with the financial characteristics of local government in East Java Province.
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 19:55 UTC · ijrbs | complete · ijrbs · 20 Oct 2026 19:50 UTC |
| openalex-enrichment | 21 Oct 2026 07:23 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 07:22 UTC |