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: 11 / 11 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 01:10 UTC — 23 Oct 2026 07:30 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.
11 / 11 with data · 0 missing or unmatched. Percentages use all 11 selected publications.
11 / 11 with data · 0 missing or unmatched. Percentages use all 11 selected publications.
11 / 11 with data · 0 missing or unmatched. Percentages use all 11 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 | 2 | 2 | 2/2 | 2/2 |
| 2021 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2023 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 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 | 2 | 2/2 | 2/2 |
| 2021 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 0 | 3 | 3/3 | 3/3 |
| 2023 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 0 | 3 | 3/3 | 3/3 |
| 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 | 1 | 2 | 2/2 | 2/2 |
| 2021 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 6 | 3 | 3/3 | 3/3 |
| 2023 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 0 | 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 11 / 11 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): 2026-10-22T01:10:34.580557+00:00 — 2026-10-23T07:30:08.960889+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 | 11 | 11/11 |
| 2018 | All selected publications | 0 | 11 | 11/11 |
| 2019 | All selected publications | 0 | 11 | 11/11 |
| 2020 | All selected publications | 0 | 11 | 11/11 |
| 2021 | All selected publications | 0 | 11 | 11/11 |
| 2022 | All selected publications | 1 | 11 | 11/11 |
| 2023 | All selected publications | 1 | 11 | 11/11 |
| 2024 | All selected publications | 5 | 11 | 11/11 |
| 2025 | All selected publications | 1 | 11 | 11/11 |
| 2026 | All selected publications | 0 | 11 | 11/11 |
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 |
| 2020 | 2 |
| 2021 | 1 |
| 2022 | 3 |
| 2023 | 1 |
| 2025 | 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 Business Ecosystem & Strategy Bussecon International Academy | 1 | 0 | 0 | 1/1 | 0.0 | 0 | 0.0 | 1/1 · 100.0% |
| International Journal of Finance & Banking Studies SSBFNET | 8 | 0 | 6 | 8/8 | 0.0 | 0.0 | 0.0 | 8/8 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 2 | 0 | 2 | 2/2 | 0.0 | 0.0 | 0.0 | 2/2 · 100.0% |
* Percentage among publications with a known citation count.
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.
Stock returns reflect the investment returns investors receive from owning company shares, encompassing both dividends and stock price increases. High stock returns indicate strong company performance and are attractive to investors, often reflecting market expectations of the company's future profitability and contributing to increased company value. Conversely, low stock returns can reduce market confidence and negatively impact company value. This study examines factors influencing stock returns in cyclical consumer sector companies listed on the Indonesia Stock Exchange from 2019 to 2023, with a sample of 63 companies out of 124. The research utilizes principal component analysis (PCA) with regression mediation, analyzing variables such as liquidity, solvency, activity, profitability ratios, financial performance, good corporate governance, and macroeconomic factors. SPSS 25 is used for data analysis. Key findings reveal that Market Value Added (MVA) significantly impacts both stock returns and firm value, while exchange rates and GDP do not significantly affect stock returns or firm value in this sector. The money supply (M2) significantly influences stock returns but not firm value. The Sobel mediation test shows that stock returns significantly mediate the relationship between MVA and M2 with firm value, whereas they do not mediate the effects of exchange rates and GDP on firm value. These results offer insights into the economic and financial factors impacting stock performance and firm value in Indonesia's cyclical consumer sector.
The purpose of this study is to determine the impact of assets value added and assets liquidity to stock liquidity in the context and evidence in Indonesia and in relatively different situations and conditions. The novelty of this research is in the assets value added using net value-added measurements, the research was conducted in the context and evidence in Indonesia, this research was conducted in two different situations, namely 2018-2019 (before the corona virus occurred) and 2020 (the corona virus occurred). The contribution of this research fills the narrow research gap in the field of stock liquidity from the perspective of assets value added in the context and evidence in Indonesia. The population in this research are entities listed on the Indonesia Stock Exchange with a research sample of entities categorized as manufacturing entities, research year 2018-2020, data analysis methods in this research used multiple regression, moderate regression (MRA). Main Findings: (i) Assets Value Added, Assets Liquidity and Information Asymmetry simultaneously effect Stock Liquidity. (ii) Stock Liquidity is positively impacted by Assets Value Added. (iii) Stock Liquidity is negatively impacted by Assets Liquidity. (iv) Information asymmetry weakens the impact of Assets Value Added to Stock Liquidity. (v) Information asymmetry strengthens the impact of Assets Liquidity to Stock Liquidity. The results of this study provide evidence in favor of information asymmetry theory and signal theory.
This study aims to analyze the development and soundness of banks from financial performance ratios in banking companies listed on the IDX in 2016-2021 using the RGEC approach; analyze the effect of the ratios used in RGEC on stock returns in banking companies listed on the IDX in 2016-2019 or before Covid19; analyzed the effect of the ratios used in RGEC on stock returns in banking companies listed on the IDX in 2020-2021 or during the Covid-19 pandemic. The sample consisted of the banking sub-sector listed on the IDX in the period 2016 to 2021. The data analyzed using the RGEC method. Based on the calculation of bank rating (bank soundness level), it can be seen that the key figures for the risk profile, GCG, earning and equity (RGEC) in the banking sub-sector listed on the IDX developed well in 2016-2021 in the banking sector on average from 39 banks studied. There are 11 Banks that are in Composite Rating 1 (Very Healthy), 25 Banks are on Composite Rating 2 (Healthy) and 3 Banks are on Composite Rating 3 (Sufficiently Healthy). Based on the results of panel regression tests in 2016-2019 (before Covid-19), bank rating (bank soundness level) which include risk profile (NPL and LDR), GCG, earnings (ROA and NIM) and equity (CAR) together have an effect of 28.89% of bank stock returns and 71.11% of stock returns are influenced by other factors outside the variables studied. However, based on the p-value results, the variables NPL, GCG, NIM and CAR have no significant effect on bank stock returns, while the LDR and ROA variables have a significant effect on stock returns. In general, the soundness of banks listed on the IDX is in a healthy condition. Whereas the results of panel regression tests in 2020-2021 (after Covid-19), bank rating (bank soundness level) which include risk profile (NPL and LDR), GCG, earnings (ROA and NIM) and equity (CAR) together have an effect of 4.93% of bank stock returns and 95.07% of stock returns are influenced by other factors outside the variables studied. However, based on the results of the p-value variables, NPL, LDR, GCG, ROA, NIM and CAR have no significant effect on bank stock returns. However, in general, the health level of banks listed on the IDX during the Covid-19 pandemic is in a healthy condition.
The objective of this study is to understand the influence of capital structure, sales growth and firm size on firm value in the automotive and component sub-sectors that are registered on the Indonesia Stock Exchange during 2016-2020 period. The research method used in this research is descriptive and verification methods. The findings of the study state that capital structure significantly and positively influences firm value, while there is no effect of sales growth and firm size on firm value in the automotive and component industry sub-sector companies that are registered on the Indonesia Stock Exchange during 2016-2020 period.
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.
The valuation of redeemable semi-annual and quarterly bonds is a specialised and contentious subject in finance. While instructing on this subject, students frequently pose the subsequent four enquiries: Which semi-annual discount rate do we use to discount the bond’s cash flows to arrive at its present value? What time frame is utilised to compute the present value elements for discounting the bond's cash flows? Do we obtain the identical bond value when assessing it from the views of the corporation and the investors? What is the appropriate discount rate for scenarios involving numerous discount rates from the present to the bond's maturity date? This study illustrates that the semi-annual and annual percentage rate (APR) yield to maturity (YTM) rates are the most appropriate discount rates for valuing redeemable semi-annual and quarterly bonds. The present value factors are calculated using the time interval period, not the actual years. Assessing bonds from a company's standpoint by employing post-tax cash flows and discount rates results in an undervaluation of the bonds. The arbitrage-free valuation method is the most appropriate technique for assessing redeemable semi-annual and quarterly bonds. This method use various annual APR YTM rates derived from the bond's benchmark spot rate curve, valuing the bond as a collection of cash flows, with each cash flow treated as a zero-coupon bond. This allows each cash flow to be discounted at its specific discount rate
Stock returns reflect the investment returns investors receive from owning company shares, encompassing both dividends and stock price increases. High stock returns indicate strong company performance and are attractive to investors, often reflecting market expectations of the company's future profitability and contributing to increased company value. Conversely, low stock returns can reduce market confidence and negatively impact company value. This study examines factors influencing stock returns in cyclical consumer sector companies listed on the Indonesia Stock Exchange from 2019 to 2023, with a sample of 63 companies out of 124. The research utilizes principal component analysis (PCA) with regression mediation, analyzing variables such as liquidity, solvency, activity, profitability ratios, financial performance, good corporate governance, and macroeconomic factors. SPSS 25 is used for data analysis. Key findings reveal that Market Value Added (MVA) significantly impacts both stock returns and firm value, while exchange rates and GDP do not significantly affect stock returns or firm value in this sector. The money supply (M2) significantly influences stock returns but not firm value. The Sobel mediation test shows that stock returns significantly mediate the relationship between MVA and M2 with firm value, whereas they do not mediate the effects of exchange rates and GDP on firm value. These results offer insights into the economic and financial factors impacting stock performance and firm value in Indonesia's cyclical consumer sector.
The purpose of this study is to determine the impact of assets value added and assets liquidity to stock liquidity in the context and evidence in Indonesia and in relatively different situations and conditions. The novelty of this research is in the assets value added using net value-added measurements, the research was conducted in the context and evidence in Indonesia, this research was conducted in two different situations, namely 2018-2019 (before the corona virus occurred) and 2020 (the corona virus occurred). The contribution of this research fills the narrow research gap in the field of stock liquidity from the perspective of assets value added in the context and evidence in Indonesia. The population in this research are entities listed on the Indonesia Stock Exchange with a research sample of entities categorized as manufacturing entities, research year 2018-2020, data analysis methods in this research used multiple regression, moderate regression (MRA). Main Findings: (i) Assets Value Added, Assets Liquidity and Information Asymmetry simultaneously effect Stock Liquidity. (ii) Stock Liquidity is positively impacted by Assets Value Added. (iii) Stock Liquidity is negatively impacted by Assets Liquidity. (iv) Information asymmetry weakens the impact of Assets Value Added to Stock Liquidity. (v) Information asymmetry strengthens the impact of Assets Liquidity to Stock Liquidity. The results of this study provide evidence in favor of information asymmetry theory and signal theory.
This study aims to analyze the development and soundness of banks from financial performance ratios in banking companies listed on the IDX in 2016-2021 using the RGEC approach; analyze the effect of the ratios used in RGEC on stock returns in banking companies listed on the IDX in 2016-2019 or before Covid19; analyzed the effect of the ratios used in RGEC on stock returns in banking companies listed on the IDX in 2020-2021 or during the Covid-19 pandemic. The sample consisted of the banking sub-sector listed on the IDX in the period 2016 to 2021. The data analyzed using the RGEC method. Based on the calculation of bank rating (bank soundness level), it can be seen that the key figures for the risk profile, GCG, earning and equity (RGEC) in the banking sub-sector listed on the IDX developed well in 2016-2021 in the banking sector on average from 39 banks studied. There are 11 Banks that are in Composite Rating 1 (Very Healthy), 25 Banks are on Composite Rating 2 (Healthy) and 3 Banks are on Composite Rating 3 (Sufficiently Healthy). Based on the results of panel regression tests in 2016-2019 (before Covid-19), bank rating (bank soundness level) which include risk profile (NPL and LDR), GCG, earnings (ROA and NIM) and equity (CAR) together have an effect of 28.89% of bank stock returns and 71.11% of stock returns are influenced by other factors outside the variables studied. However, based on the p-value results, the variables NPL, GCG, NIM and CAR have no significant effect on bank stock returns, while the LDR and ROA variables have a significant effect on stock returns. In general, the soundness of banks listed on the IDX is in a healthy condition. Whereas the results of panel regression tests in 2020-2021 (after Covid-19), bank rating (bank soundness level) which include risk profile (NPL and LDR), GCG, earnings (ROA and NIM) and equity (CAR) together have an effect of 4.93% of bank stock returns and 95.07% of stock returns are influenced by other factors outside the variables studied. However, based on the results of the p-value variables, NPL, LDR, GCG, ROA, NIM and CAR have no significant effect on bank stock returns. However, in general, the health level of banks listed on the IDX during the Covid-19 pandemic is in a healthy condition.
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 |