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, 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 — 23 Oct 2026 13:53 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.
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 |
|---|---|---|---|---|---|
| 2017 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 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 | 1 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2023 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2017 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 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 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 0 | 2 | 2/2 | 2/2 |
| 2023 | All selected publications | 0 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2017 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 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 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 9 | 2 | 2/2 | 2/2 |
| 2023 | All selected publications | 4 | 2 | 2/2 | 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): 2026-10-21T07:22:49.775321+00:00 — 2026-10-23T13:53:58.731028+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 | 1 | 6 | 6/6 |
| 2023 | All selected publications | 3 | 6 | 6/6 |
| 2024 | All selected publications | 8 | 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 |
|---|---|
| 2017 | 1 |
| 2021 | 1 |
| 2022 | 2 |
| 2023 | 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 | 2 | 0 | 1 | 2/2 | 0.0 | 0.0 | 0.0 | 2/2 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 4 | 0 | 13 | 4/4 | 0.0 | 0.0 | 0.0 | 4/4 · 100.0% |
* Percentage among publications with a known citation count.
The unit of analysis in this study is local government data from Indonesia. The goal of this research is to identify the impact of fiscal stress and citizen trust on digital and green innovation strategies. This study also seeks to as certain the impact of digital and green innovation strategies on the impact of fiscal stress and citizen trust on sustainable financial performance. This research uses a descriptive verification method in conjunction with a quantitative approach. Path analysis is an analytical method used in the processing of statistical data. The research samples in this study are 148 Regional Apparatus Organizations (OPD) from East Java Province. The tests showed that fiscal pressure and citizen trust have a significant impact on digital and green innovation strategies. The test results also show that fiscal stress and digital and green innovation strategies have a significant effect on sustainable financial performance, whereas citizen trust has no significant effect on sustainable financial performance. Simultaneously, digital and green innovation strategy are critical for mitigating the impact of fiscal stress and increasing citizens' trust sustainable financial performance.
This study aimed to examine the influence of the financial statements on local revenue by using the results of the examination opinion as moderating variable. The study used Legitimacy Theory to explain and analyze the relationship between variables. The population in the study was all the districts and cities in Indonesia totaling 497 county and city governments. The sampling method used in the study was judgment sampling. Samples that met the criteria of the study were 116 local governments. The analytical tool used in this research was the analysis of MRA (Moderated Regression Analysis). The results showed that the effects of the partial test of the quality of financial reports to local revenues were not significant positive effects. While the partial test results stating the influence of audit opinion on local revenue having positive effects were not significant. Other results showed that the interaction between the results of the examination opinion to the quality of financial statements was insignificant on the quality of financial statements. In other words, the results of the examination opinions moderated (strengthened) the relationship between quality of financial report on local revenue of district and city governments in Indonesia.
This study aims to identify and analyze the dominant factors that influence regional tax revenues in Jambi Province, especially in 2015-2019. The variables used are taxpayers, per capita income, number of industries, banking credit, balancing funds, infrastructure, domestic investment (PMDN), and micro, small and medium enterprises (UMKM) as independent variables, while regional tax revenue is the dependent variable. This analysis tool uses quantitative analysis with multiple regression formulations of panel data, a combination of the time series in the form of a 2015-2019 time series and a cross-section, namely the latitude of the Regency / City in Jambi Province. The income per capita, taxpayers, bank credit, balancing funds, Micro, Small and Medium Enterprises (UMKM) and Domestic Investment (PMDN) significantly affect regional tax revenues. Meanwhile, the number of industries, infrastructure, and balancing funds do not significantly affect regional tax revenues.
Indonesia is a country ranked fourth as the most populous country in the world, so it's no wonder there are many socio-economic problems. Development has several indicators, namely social conditions and economic conditions related to SDGs 3, 4, 8, 9 and 11 goals, where the goal to be achieved is to see which areas have optimal economic growth values. To encourage the achievement of SDGs in Indonesia, social conditions and economic conditions have a role to play in supporting economic growth. Economic growth in this study is described by the GRDP, namely the GRDP describes the total gross added value of each economic sector. By using a sustainability analysis technique, namely Qualitative Comparative Analysis (QCA) using Tosmana software. Using the year of research in 2020 and research objects in 34 provinces. Based on the results of the study, it was found that the regions with optimal economic growth from social and economic conditions were North Sulawesi, Riau, Jambi, South Sumatra, Lampung, West Kalimantan, South Sulawesi, Bali, Kep. Riau, Central Java, East Java and West Java. The relationship between socio-economic conditions on economic growth is expected to provide an increase in economic growth and see what programs have been successfully implemented that can have a positive influence on social and economic conditions.
This study aims to analyze the management strategy of increasing revenue potential to improve the performance of the local government of Takalar Regency, South Sulawesi Province. The type of research is quantitative with a descriptive approach. The technique of data analysis is Klassen Typology analysis, a technique of grouping a sector by seeing the growth and contribution of certain sectors to the total Gross Regional Domestic Product (GRDP) from 2016 to 2020. Based on the results of the analysis, there will be a policy in determining management strategies if revenue potential to improve local government performance. The study found that the leading sectors are agriculture, forestry, fisheries, electricity and gas procurement, real estate, government administration, defense and social security. The management strategies of these sectors are intensification, partnership, and improvement of human resources. However, the developing sector does not exist. The potential sectors are mining and quarrying, water supply, waste management, waste and recycling, construction, providing accommodation for food and drink, and information and communication. The management strategies of these sectors are education, innovation and partnerships. While, the underdeveloped sectors are manufacturing, wholesale and retail trade sectors; car and motorcycle repair, transportation and warehousing, financial and insurance services, corporate services, education services, health services and social activities, and other services. The management strategies of these sectors are extensification or expansion.
This study aims to identify and analyze the dominant factors that influence regional tax revenues in Jambi Province, especially in 2015-2019. The variables used are taxpayers, per capita income, number of industries, banking credit, balancing funds, infrastructure, domestic investment (PMDN), and micro, small and medium enterprises (UMKM) as independent variables, while regional tax revenue is the dependent variable. This analysis tool uses quantitative analysis with multiple regression formulations of panel data, a combination of the time series in the form of a 2015-2019 time series and a cross-section, namely the latitude of the Regency / City in Jambi Province. The income per capita, taxpayers, bank credit, balancing funds, Micro, Small and Medium Enterprises (UMKM) and Domestic Investment (PMDN) significantly affect regional tax revenues. Meanwhile, the number of industries, infrastructure, and balancing funds do not significantly affect regional tax revenues.
Indonesia is a country ranked fourth as the most populous country in the world, so it's no wonder there are many socio-economic problems. Development has several indicators, namely social conditions and economic conditions related to SDGs 3, 4, 8, 9 and 11 goals, where the goal to be achieved is to see which areas have optimal economic growth values. To encourage the achievement of SDGs in Indonesia, social conditions and economic conditions have a role to play in supporting economic growth. Economic growth in this study is described by the GRDP, namely the GRDP describes the total gross added value of each economic sector. By using a sustainability analysis technique, namely Qualitative Comparative Analysis (QCA) using Tosmana software. Using the year of research in 2020 and research objects in 34 provinces. Based on the results of the study, it was found that the regions with optimal economic growth from social and economic conditions were North Sulawesi, Riau, Jambi, South Sumatra, Lampung, West Kalimantan, South Sulawesi, Bali, Kep. Riau, Central Java, East Java and West Java. The relationship between socio-economic conditions on economic growth is expected to provide an increase in economic growth and see what programs have been successfully implemented that can have a positive influence on social and economic conditions.
This study aims to analyze the management strategy of increasing revenue potential to improve the performance of the local government of Takalar Regency, South Sulawesi Province. The type of research is quantitative with a descriptive approach. The technique of data analysis is Klassen Typology analysis, a technique of grouping a sector by seeing the growth and contribution of certain sectors to the total Gross Regional Domestic Product (GRDP) from 2016 to 2020. Based on the results of the analysis, there will be a policy in determining management strategies if revenue potential to improve local government performance. The study found that the leading sectors are agriculture, forestry, fisheries, electricity and gas procurement, real estate, government administration, defense and social security. The management strategies of these sectors are intensification, partnership, and improvement of human resources. However, the developing sector does not exist. The potential sectors are mining and quarrying, water supply, waste management, waste and recycling, construction, providing accommodation for food and drink, and information and communication. The management strategies of these sectors are education, innovation and partnerships. While, the underdeveloped sectors are manufacturing, wholesale and retail trade sectors; car and motorcycle repair, transportation and warehousing, financial and insurance services, corporate services, education services, health services and social activities, and other services. The management strategies of these sectors are extensification or expansion.
Budgetary slack occurs because of the potential difference with the revenue budget target. the difference in potential revenue with the revenue budget target indicates the occurrence of individual behavior lowering the income target to facilitate the achievement of the government budget. The purpose of this study is to empirically prove the effect of budget participation, information asymmetry, and job insecurity that trigger budgetary slack. The population of this research is officials of the Indonesian: Regional Working Unit in the province of East Java, Indonesia. The sampling technique used is proportional sampling, the research respondents were 84 people. The results show that budget participation, information asymmetry, and job insecurity have a positive effect on budgetary slack. The high budget participation of public sector employees can trigger budgetary slack. Information asymmetry motivates budget implementers to take action to reduce revenue targets and increase government spending. High job insecurity in the work environment creates pressure on employees so that budgetary slack is created.
The unit of analysis in this study is local government data from Indonesia. The goal of this research is to identify the impact of fiscal stress and citizen trust on digital and green innovation strategies. This study also seeks to as certain the impact of digital and green innovation strategies on the impact of fiscal stress and citizen trust on sustainable financial performance. This research uses a descriptive verification method in conjunction with a quantitative approach. Path analysis is an analytical method used in the processing of statistical data. The research samples in this study are 148 Regional Apparatus Organizations (OPD) from East Java Province. The tests showed that fiscal pressure and citizen trust have a significant impact on digital and green innovation strategies. The test results also show that fiscal stress and digital and green innovation strategies have a significant effect on sustainable financial performance, whereas citizen trust has no significant effect on sustainable financial performance. Simultaneously, digital and green innovation strategy are critical for mitigating the impact of fiscal stress and increasing citizens' trust sustainable financial performance.
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 |