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: 4 / 4 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: 22 Oct 2026 01:43 UTC — 22 Oct 2026 05:57 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.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 selected publications.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 selected publications.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 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 |
|---|---|---|---|---|---|
| 2019 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 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 |
|---|---|---|---|---|---|
| 2019 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 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 |
|---|---|---|---|---|---|
| 2019 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 4 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 3 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 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 4 / 4 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): 2026-10-22T01:43:25.841945+00:00 — 2026-10-22T05:57:21.888101+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 | 4 | 4/4 |
| 2018 | All selected publications | 0 | 4 | 4/4 |
| 2019 | All selected publications | 0 | 4 | 4/4 |
| 2020 | All selected publications | 0 | 4 | 4/4 |
| 2021 | All selected publications | 0 | 4 | 4/4 |
| 2022 | All selected publications | 2 | 4 | 4/4 |
| 2023 | All selected publications | 2 | 4 | 4/4 |
| 2024 | All selected publications | 1 | 4 | 4/4 |
| 2025 | All selected publications | 2 | 4 | 4/4 |
| 2026 | All selected publications | 0 | 4 | 4/4 |
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 |
|---|---|
| 2019 | 1 |
| 2021 | 1 |
| 2022 | 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 | 1 | 0 | 0 | 1/1 | 0.0 | 0 | 0.0 | 1/1 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 3 | 2 | 7 | 3/3 | 0.67 | 1 | 66.7 | 3/3 · 100.0% |
* Percentage among publications with a known citation count.
Sustainable Development Goals (SDGs) are a set of goals for sustainable development which are consist of 17 goals that are expected to be achieved by 2030. 91% of Indonesia's territory is a rural area, consisting of more than 17,000 islands, almost 7500 villages. The villages contribute about 74% to the achievement of the national SDGs. Indonesia implements The Village SDGs which have 18 goals. The 18th goal is to achieve dynamic village institutions and adaptive village culture designed to accommodate various local cultures that characterize village management and are still applied by the community. Almost all district in Indonesia do not yet have a mapping of the status of achieving the Village SDGs. It will be easier for the district government to prioritize village development programs according to the needs of each village. In this study, research variables that measure 17 village goals will be used and grouped into 4 aspects, namely economic aspects, environmental aspects, health and education aspects and welfare aspects. Because village SDGs data is a mixture of numeric and categorical, ROCK ensemble analysis is used which is a grouping method for mixed data. In this study, it was found that the majority of villages in Sidoarjo Regency were still lagging behind in the fields of economy, health, education, and community welfare. This can be seen in the number of villages that are included in the low group based on the results of the analysis.
The Covid-19 pandemic has occurred in Indonesia since March 2020. The pandemic period required adjustments in all sectors of life, including changes in people's behavior, namely spending more time at home. Changes in people’s behavior are needed to break the chain of Covid-19 transmissions, one of which is the implementation of Health protocols. Research on the behavior of the Indonesian people in dealing with the Covid-19 pandemic includes public knowledge and understanding, compliance in implementing the 3M health protocols, and the important factors that influence changes in people's behavior to comply with the health protocol. Data was collected by distributing online questionnaires, surveys to crowd locations and FGDs. National online and offline surveys are divided into 3 regions, namely Java, West Indonesia and East Indonesia. The analysis was carried out using statistical methods, namely dependency analysis, factor analysis, and multinomial regression. The conclusion is that people who have implemented the 3M protocol and have confidence in the effectiveness of 3M tend to believe that the 3M protocol is important to continue to be implemented. In addition, it is known that social media, TV, and online media almost always ranked in the top three of the most media respondents to get information about the importance of implementing health protocols.
Capital financing plays a crucial role in sustaining the growth potential of pharmaceutical companies, enabling them to expand operations and compete effectively in the market. In the face of a slowing global economy, maintaining solid financial performance across existing business lines becomes increasingly important. This study aims to examine the relationship between capital structure and the financial performance of pharmaceutical companies in Indonesia. The research sample consists of nine publicly listed pharmaceutical firms over the period 2013–2023. The empirical analysis employs three panel data estimation approaches—Pooled Ordinary Least Squares (Pooled OLS), Fixed Effects Model (FEM), and Random Effects Model (REM)—to account for different assumptions about unobserved firm-specific characteristics. Based on the Hausman test, FEM is determined as the most suitable model for the dataset. The estimation results reveal that the Long-term Debt to Total Assets (LDTA) ratio exerts a statistically significant negative effect on Return on Assets (ROA), indicating that a higher proportion of long-term debt tends to reduce profitability. Conversely, the Short-term Debt to Total Assets (SDTA) and Total Debt to Total Equity (TDTE) ratios have positive but statistically insignificant impacts on ROA, suggesting that neither short-term liabilities nor overall leverage significantly influence profitability in the observed period. These results imply that excessive reliance on long-term borrowing can impose interest burdens and financial constraints, thereby weakening performance. Consequently, financial managers in the pharmaceutical sector are encouraged to optimize their financing structure, prioritizing internal funds or short-term financing sources that align with operational needs and market conditions to sustain profitability and ensure long-term competitiveness.
This research aims to investigate the factors influencing the households in East Nusa Tenggara (NTT) Indonesia to use seasonal forecast issued by the Meteorological Office (BMKG) Indonesia. NTT is a highly vulnerable region to drought, and hence, understanding the weather and climate condition is a crucial issue. Household survey was conducted to interview 300 households in NTT asking about their intention to utilize the forecast information in supporting their livelihood. Furthermore, this research investigates whether the households characteristics influencing the intention. Using the Classification and Regression Tree (CART), we found that the intention to use forecast is dominantly influenced by age of the household head, ownership of household assets, main source of income, crop and livestock activities and water source for livestock.
Capital financing plays a crucial role in sustaining the growth potential of pharmaceutical companies, enabling them to expand operations and compete effectively in the market. In the face of a slowing global economy, maintaining solid financial performance across existing business lines becomes increasingly important. This study aims to examine the relationship between capital structure and the financial performance of pharmaceutical companies in Indonesia. The research sample consists of nine publicly listed pharmaceutical firms over the period 2013–2023. The empirical analysis employs three panel data estimation approaches—Pooled Ordinary Least Squares (Pooled OLS), Fixed Effects Model (FEM), and Random Effects Model (REM)—to account for different assumptions about unobserved firm-specific characteristics. Based on the Hausman test, FEM is determined as the most suitable model for the dataset. The estimation results reveal that the Long-term Debt to Total Assets (LDTA) ratio exerts a statistically significant negative effect on Return on Assets (ROA), indicating that a higher proportion of long-term debt tends to reduce profitability. Conversely, the Short-term Debt to Total Assets (SDTA) and Total Debt to Total Equity (TDTE) ratios have positive but statistically insignificant impacts on ROA, suggesting that neither short-term liabilities nor overall leverage significantly influence profitability in the observed period. These results imply that excessive reliance on long-term borrowing can impose interest burdens and financial constraints, thereby weakening performance. Consequently, financial managers in the pharmaceutical sector are encouraged to optimize their financing structure, prioritizing internal funds or short-term financing sources that align with operational needs and market conditions to sustain profitability and ensure long-term competitiveness.
Sustainable Development Goals (SDGs) are a set of goals for sustainable development which are consist of 17 goals that are expected to be achieved by 2030. 91% of Indonesia's territory is a rural area, consisting of more than 17,000 islands, almost 7500 villages. The villages contribute about 74% to the achievement of the national SDGs. Indonesia implements The Village SDGs which have 18 goals. The 18th goal is to achieve dynamic village institutions and adaptive village culture designed to accommodate various local cultures that characterize village management and are still applied by the community. Almost all district in Indonesia do not yet have a mapping of the status of achieving the Village SDGs. It will be easier for the district government to prioritize village development programs according to the needs of each village. In this study, research variables that measure 17 village goals will be used and grouped into 4 aspects, namely economic aspects, environmental aspects, health and education aspects and welfare aspects. Because village SDGs data is a mixture of numeric and categorical, ROCK ensemble analysis is used which is a grouping method for mixed data. In this study, it was found that the majority of villages in Sidoarjo Regency were still lagging behind in the fields of economy, health, education, and community welfare. This can be seen in the number of villages that are included in the low group based on the results of the analysis.
The Covid-19 pandemic has occurred in Indonesia since March 2020. The pandemic period required adjustments in all sectors of life, including changes in people's behavior, namely spending more time at home. Changes in people’s behavior are needed to break the chain of Covid-19 transmissions, one of which is the implementation of Health protocols. Research on the behavior of the Indonesian people in dealing with the Covid-19 pandemic includes public knowledge and understanding, compliance in implementing the 3M health protocols, and the important factors that influence changes in people's behavior to comply with the health protocol. Data was collected by distributing online questionnaires, surveys to crowd locations and FGDs. National online and offline surveys are divided into 3 regions, namely Java, West Indonesia and East Indonesia. The analysis was carried out using statistical methods, namely dependency analysis, factor analysis, and multinomial regression. The conclusion is that people who have implemented the 3M protocol and have confidence in the effectiveness of 3M tend to believe that the 3M protocol is important to continue to be implemented. In addition, it is known that social media, TV, and online media almost always ranked in the top three of the most media respondents to get information about the importance of implementing health protocols.
This research aims to investigate the factors influencing the households in East Nusa Tenggara (NTT) Indonesia to use seasonal forecast issued by the Meteorological Office (BMKG) Indonesia. NTT is a highly vulnerable region to drought, and hence, understanding the weather and climate condition is a crucial issue. Household survey was conducted to interview 300 households in NTT asking about their intention to utilize the forecast information in supporting their livelihood. Furthermore, this research investigates whether the households characteristics influencing the intention. Using the Classification and Regression Tree (CART), we found that the intention to use forecast is dominantly influenced by age of the household head, ownership of household assets, main source of income, crop and livestock activities and water source for livestock.
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 |