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: 21 Oct 2026 07:22 UTC — 23 Oct 2026 10:55 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 | 2 | 2 | 2/2 | 2/2 |
| 2020 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2023 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 3 | 2 | 2/2 | 2/2 |
| 2020 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 1 | 2 | 2/2 | 2/2 |
| 2023 | All selected publications | 6 | 3 | 3/3 | 3/3 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 13 | 2 | 2/2 | 2/2 |
| 2020 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 9 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 5 | 2 | 2/2 | 2/2 |
| 2023 | All selected publications | 8 | 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): 2026-10-21T07:22:47.410602+00:00 — 2026-10-23T10:55:39.207385+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 | 2 | 11 | 11/11 |
| 2021 | All selected publications | 1 | 11 | 11/11 |
| 2022 | All selected publications | 6 | 11 | 11/11 |
| 2023 | All selected publications | 7 | 11 | 11/11 |
| 2024 | All selected publications | 12 | 11 | 11/11 |
| 2025 | All selected publications | 6 | 11 | 11/11 |
| 2026 | All selected publications | 2 | 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 | 2 |
| 2020 | 1 |
| 2021 | 3 |
| 2022 | 2 |
| 2023 | 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 | 11 | 14 | 36 | 11/11 | 1.27 | 1 | 81.8 | 11/11 · 100.0% |
* Percentage among publications with a known citation count.
This study examines the effect of inflation on firm value in Indonesia and uses earnings quality to moderate inflation’s effect on firm value. The aim of this study is to discuss whether earnings quality can weaken the effect of inflation on firm value. The sample of this study is consumer firm listed on the Indonesia Stock Exchange (IDX) from 2016-2021 with a total of 696 firm-years. Using EViews 9 as a statistical software to test the hypothesis, the results show that inflation harms the firm value, and also proved that earnings quality can weaken the effect of inflation’s negative effect on firm value. The findings provide insight that earnings quality can be a solution to reduce the effect of inflation on firm value. Overall, the findings support the previous studies that suggest inflation is an external factor that affects the firm value and can be solved by maintaining the quality of earnings.
This paper investigates the relationship between macroeconomic factors, firm financial position, and firm value in Indonesia's property and real estate sector from 2017 to 2022. The property and real estate sectors significantly influence Indonesia's economic growth. Nevertheless, the real estate sector has encountered significant obstacles in recent years, notably during the COVID-19 pandemic, due to volatility in interest rates, high construction costs, and the sluggish property market. This study aims to address the existing research gap by investigating the relationship between macroeconomic indicators, including GDP growth, inflation rates, and interest rates, as well as financial factors, such as profitability and leverage, on the industry’s firm value over a specified timeframe. Using panel data regressions, the study analyzes 162 observations from 27 listed property and real estate companies. The empirical findings demonstrate that property and real estate firms have the potential to enhance their overall performance and increase shareholder value by considering the interconnectedness of economic conditions and the financial position of the firm. Profitability emerges as a significant predictor of firm value, while the selected macroeconomic factors have significant and insignificant effects on the valuation of firms. Meanwhile, since this industry has substantial leverage, this variable does not affect firm value. This study also highlights that the critical component that drives the evaluation of a firm's value is heavily influenced by the specifics of the industry and the regulatory framework in which the business operates.
This study purposes to determine the impact of risks of investment on stock returns of the agricultural sector in wetlands listed on the IDX for the period 2016-2019. This study uses secondary data while the analysis tool uses multiple linear regression (SPSS 21). The research population is 21 companies and the sample is 17 companies. The research sampling technique uses purposive sampling. The object of research is the agricultural sector which is listed on the Indonesia Stock Exchange for the period 2016 to 2019. This study also uses some classical assumptions. Testing the research hypothesis was done by t-test (partial test) and the coefficient of determination test. The results of the study are that 2 independent variables have a significant effect, namely interest rate risk (X1) in a positive direction and exchange risk in a negative direction, while liquidity risk and inflation risk have no significant effect with negative and positive directions on stock returns of the agricultural sector listed on the IDX for the period 2016-2019. The result of the research determination coefficient is 15.1%.
Price-Earning Ratio or P/E Multiple is a widely used, straightforward investment assessment tool in developed countries. However, the method has not been utilized as much in stock market performance analysis in developing countries such as the Philippines. Using the top ten universal banks in the country, this paper utilized Price-Earnings Ratio [PER] as valuation tool and dependent variable, and sought to determine its value drivers. Used as independent variables are macroeconomic variables gross domestic product [GDP] growth rate, inflation rate, annual interest rate; stock market index Philippine Stock Exchange [PSEi]; and firm-specific variables return on equity [ROE], growth rate of ROE, growth rate of earnings per share, dividend payout ratio [DPO, growth rate of income, and price-book value [PBV] ratio. Results showed that among the independent variables, ROE, PBV ratio, and PSE index are statistically significant. The model’s (R2) is 63.7%, which is a fairly good fit.
This research was conducted to determine the effect of Gross Domestic Product, company value, systematic risk on stock return of the kompas100 listed company. The population of this study were companies listed on the Kompas100 index for the 2017-2020 period, with a total sample of 53 companies based on a purposive sampling technique. This study uses panel data regression analysis with the Fixed Effect Model (FEM) regression model. Classical Assumption Test was adopted to test data validation. The results showed that Gross Domestic Product and systematic risk had a negative effect on stock returns, while firm value had no effect on stock returns, with a coefficient of determination of 74 percent. The findings in this study explain that Gross Domestic Product and Systematic Risk had a negative effect on stock return explained by the investors’ behavior in terms of decision-making. They tend to avoid market risk and purchase blue chip stock when it is reached its lowest. While firm value had no effect on stock return due to investors’ confidence and the good credibility of Kompas100 Index in sorting 100 stocks.
This paper investigates the relationship between macroeconomic factors, firm financial position, and firm value in Indonesia's property and real estate sector from 2017 to 2022. The property and real estate sectors significantly influence Indonesia's economic growth. Nevertheless, the real estate sector has encountered significant obstacles in recent years, notably during the COVID-19 pandemic, due to volatility in interest rates, high construction costs, and the sluggish property market. This study aims to address the existing research gap by investigating the relationship between macroeconomic indicators, including GDP growth, inflation rates, and interest rates, as well as financial factors, such as profitability and leverage, on the industry’s firm value over a specified timeframe. Using panel data regressions, the study analyzes 162 observations from 27 listed property and real estate companies. The empirical findings demonstrate that property and real estate firms have the potential to enhance their overall performance and increase shareholder value by considering the interconnectedness of economic conditions and the financial position of the firm. Profitability emerges as a significant predictor of firm value, while the selected macroeconomic factors have significant and insignificant effects on the valuation of firms. Meanwhile, since this industry has substantial leverage, this variable does not affect firm value. This study also highlights that the critical component that drives the evaluation of a firm's value is heavily influenced by the specifics of the industry and the regulatory framework in which the business operates.
This study examines the effect of inflation on firm value in Indonesia and uses earnings quality to moderate inflation’s effect on firm value. The aim of this study is to discuss whether earnings quality can weaken the effect of inflation on firm value. The sample of this study is consumer firm listed on the Indonesia Stock Exchange (IDX) from 2016-2021 with a total of 696 firm-years. Using EViews 9 as a statistical software to test the hypothesis, the results show that inflation harms the firm value, and also proved that earnings quality can weaken the effect of inflation’s negative effect on firm value. The findings provide insight that earnings quality can be a solution to reduce the effect of inflation on firm value. Overall, the findings support the previous studies that suggest inflation is an external factor that affects the firm value and can be solved by maintaining the quality of earnings.
This research was conducted to determine the effect of Gross Domestic Product, company value, systematic risk on stock return of the kompas100 listed company. The population of this study were companies listed on the Kompas100 index for the 2017-2020 period, with a total sample of 53 companies based on a purposive sampling technique. This study uses panel data regression analysis with the Fixed Effect Model (FEM) regression model. Classical Assumption Test was adopted to test data validation. The results showed that Gross Domestic Product and systematic risk had a negative effect on stock returns, while firm value had no effect on stock returns, with a coefficient of determination of 74 percent. The findings in this study explain that Gross Domestic Product and Systematic Risk had a negative effect on stock return explained by the investors’ behavior in terms of decision-making. They tend to avoid market risk and purchase blue chip stock when it is reached its lowest. While firm value had no effect on stock return due to investors’ confidence and the good credibility of Kompas100 Index in sorting 100 stocks.
The purpose of this study is to empirically analyze the performance of liquidity, leverage, profitability, and activities of firms on the Indonesia Stock Exchange during the Covid-19 pandemic based on a quantitative approach. This study uses secondary data from financial reports of firms listed on the Indonesia Stock Exchange that are included in the LQ45 index, which represents the movement of the capital market based on specific criteria. The results reveal that the Covid-19 pandemic has hurt the Indonesian firm’s financial performance. The worst condition has increased the leverage ratio, and the profitability and activity ratio declined, as experienced in several emerging countries. The findings of this research can be a bunch of information for investors in making decisions that these financial ratios are sensitive to economic volatility. In addition, this finding also adds insight to the company's management, especially LQ45 companies, that the company is expected to be able to maintain its financial fundamentals of these ratios. Further research needs to be carried out on how the effects of the Covid-19 pandemic influence financial performance according to the sector type of the firm and conducted in more time series periods.
This research is in the form of quantitative research that aims to determine differences in stock prices, abnormal returns, and stock trading volumes before and after the stock split. The sample selection in this study was carried out using a purposive sampling method and obtained a sample of 58 companies from companies that listed on the Indonesia Stock Exchange and carried out stock split corporate actions during 2016 until 2020 period. Hypothesis testing in this study used the SPSS version 28 program using the Paired Sample T-test method for data that is normally distributed and the Wilcoxon Signed-Rank Test for data that is not normally distributed. The result of the research is that there is no difference between stock prices before and after the stock split, while there is a difference between abnormal returns before and after the stock split, and there is a difference between the stock trading volume before and after the stock split.
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 |