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.
Top 5 in the selected collection · Ranked by Crossref lifetime citations. Only articles with at least one known citation are included; source totals are never combined.
2020 · Talitha Nathaniela Nariswari · Nugi Mohammad Nugraha
2020 · Rommy Pramudya · Rommy Pramudya
2020 · Fariz Mohamad Iqmal · Ivan Gumilar Sambas Putra
2020 · Muhammad Fikri · Annisa Lisdayanti
2024 · Sakina Ichsani · Leni Nur Pratiwi · Denny Saputera
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: 8 / 8 publications (100.0%). Latest observation: 21 Oct 2026, 00:21 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 01:31 UTC — 29 Oct 2026 17:09 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.
8 / 8 with data · 0 missing or unmatched. Percentages use all 8 selected publications.
8 / 8 with data · 0 missing or unmatched. Percentages use all 8 selected publications.
8 / 8 with data · 0 missing or unmatched. Percentages use all 8 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 |
|---|---|---|---|---|---|
| 2020 | All selected publications | 6 | 6 | 6/6 | 6/6 |
| 2021 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2020 | All selected publications | 67 | 6 | 6/6 | 6/6 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2020 | All selected publications | 151 | 6 | 6/6 | 6/6 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 3 | 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 8 / 8 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): 2026-10-22T01:31:45.491164+00:00 — 2026-10-29T17:09:14.189163+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 | 8 | 8/8 |
| 2018 | All selected publications | 0 | 8 | 8/8 |
| 2019 | All selected publications | 0 | 8 | 8/8 |
| 2020 | All selected publications | 1 | 8 | 8/8 |
| 2021 | All selected publications | 16 | 8 | 8/8 |
| 2022 | All selected publications | 26 | 8 | 8/8 |
| 2023 | All selected publications | 42 | 8 | 8/8 |
| 2024 | All selected publications | 35 | 8 | 8/8 |
| 2025 | All selected publications | 26 | 8 | 8/8 |
| 2026 | All selected publications | 10 | 8 | 8/8 |
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 |
|---|---|
| 2020 | 6 |
| 2022 | 1 |
| 2024 | 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 | 8 | 69 | 156 | 8/8 | 8.62 | 2.5 | 75.0 | 8/8 · 100.0% |
* Percentage among publications with a known citation count.
As an industry that has an important role for other industrial sectors, the plastic and packaging industry is the supply chain for consumer products. Behind the high industrial growth and the potential is still large, there are problems with plastic waste, consumption power, and changes in people's lifestyles that can affect the growth of the plastic and packaging industry. This study aims to determine how much impact the NPM (Net Profit Margin), GPM (Gross Profit Margin), TAT (Total Assets Turnover) on Profit Growth in the plastic and packaging industry sub-sector companies listed in IDX (Indonesia Stock Exchange) for the period 2014 to 2018. The method used in this study is a quantitative descriptive. The sampling technique used was purposive sampling in order to obtain a sample of 11 companies from a population of 12 companies. In the study the method of data analysis used the multiple linear regression analysis techniques by processing data using Eviews 8, because the data is not normally distributed, it uses the log transformation method. The results showed that simultaneously NPM (Net Profit Margin), GPM (Gross Profit Margin), TAT (Total Assets Turnover) had a significant effect on profit growth. And partially NPM (Net Profit Margin) has a significant effect on profit growth, while GPM (Gross Profit Margin) and TAT (Total Assets Turnover) have no significant effect on profit growth.
The purpose of this study is to determine which indicators are more capable of showing more accurate sell and buy signals on the LQ45 index by using the oscillator indicator Moving Average Divergent Convergent (MACD), Bollinger Band, and Relative Strength Index (RSI). The results in this study indicate that the sell signal can be captured well by the Bollinger band and MACD indicators, but it cannot be captured properly by the RSI, the volume can be small or heading and are in the side ways, while the MACD plays a too slow role in capturing the signal buy compared to Bollinger bands and RSI. The use of a single indicator will never show a buy and sell signal that is really accurate, this is based on the results of research that shows the difference in timeliness in Bollinger, RSI and also MACD so that the combination of several types of indicators will be better compared to using single indicators. Although in statistics there are no significant differences, there are only differences in increment and improvement in the placement of existing values, but in this case, the order of this value is crucial for traders because it requires very high accuracy to determine the right decision in daily transactions.
This research aims to determine the influence of macroeconomic variables on stock return and its impact on corporate values. The population in this research is the sector of agriculture because based on the results of the sector's observation, the most severe decline in performance among others. Further sampling is done by the purposive sampling method, so from 22 companies that listings only 15 companies that meet the criteria. The analytical technique to be used in this study is to use a double linear regression analysis technique expanded with a pathway analysis method to obtain a comprehensive picture of the relationship between variables of one variable with other. We find is an inflation and interest rate negatively and significantly affecting the return of shares. The exchange rate positively and significantly affects the stock return and the influential stock return rate positively and significant to the corporate values.
The rapid development of internet technology can provide various facilities to access various activities from the internet. One of the activities that can be done from the internet is to make payments on a non-cash basis or commonly called an e-wallet. Linkaja is one of the e-wallets established by the merger of five State-Owned Enterprises in one platform, namely PT Telekomunikasi Seluler, Bank Mandiri, Bank Rakyat Indonesia, Bank Negara Indonesia, Bank Tabungan Negara, PT Pertamina and also PT Asuransi Jiwasraya. This study aims to determine the influence of promotion mix and benefits of perception in increasing re-use of linkaja applications for consumers in the city of bandung. The method of collecting data in this study is to use a questionnaire given to 115 respondents Linkaja users in the city of Bandung. The data analysis technique used is linear regression model. The results of the questionnaire were processed using the spss for windows version 25 program. The results of the study showed that the influence of the promotion mix variable on Linkaja e-wallet repurchase intention was 68.4% and for the influence of perceived usefulness variables on Linkaja e-wallet repurchase intention was 71.17%. Whereas for the simultaneous effect it was obtained the result of 51.4% and the remaining 49.6% was influenced by other factors.
One of the Indonesian government's policies since 2015 has been to increase development through its developed infrastructure, by increasing its budget for this sector from year to year. However, companies listed in the engineering and construction subsector on the Indonesian Stock Exchange, both state-owned enterprises and private companies, experienced losses so it could be said that their financial performance was poor. This study intends to evaluate the financial performance engineering and construction sub-sector companies using the Economic Value Added (EVA), Market Value Added (MVA), Refined Economic Value Added (REVA), Financial Value Added (FVA), and Shareholder Value Added (SVA) in 2018 – 2023 and determine their influence on stock prices. This type of research is quantitative descriptive research using E-Views 12 with a population 26 companies and a sample size 16 companies. Based on the results the calculations and analysis, it stated that EVA, MVA, REVA, FVA, SVA and stock prices engineering and construction companies, both state-owned and private, during the 2018 - 2023 period obtained an average of negative amounts. The research results show that the financial performance variables using value added have an influence on stock prices with influence of 74.57% while the rest is influenced by other variables not examined in this research. Partially only MVA and SVA have an influence on stock prices because based on the probability test using E-Views 12 it appears that only MVA and SVA produce results below the alpha. The suggestion for this research is that company management should pay attention to the company's financial performance, especially the MVA and SVA variables and further researcher can make comparative research between all value-added variables.
One of the Indonesian government's policies since 2015 has been to increase development through its developed infrastructure, by increasing its budget for this sector from year to year. However, companies listed in the engineering and construction subsector on the Indonesian Stock Exchange, both state-owned enterprises and private companies, experienced losses so it could be said that their financial performance was poor. This study intends to evaluate the financial performance engineering and construction sub-sector companies using the Economic Value Added (EVA), Market Value Added (MVA), Refined Economic Value Added (REVA), Financial Value Added (FVA), and Shareholder Value Added (SVA) in 2018 – 2023 and determine their influence on stock prices. This type of research is quantitative descriptive research using E-Views 12 with a population 26 companies and a sample size 16 companies. Based on the results the calculations and analysis, it stated that EVA, MVA, REVA, FVA, SVA and stock prices engineering and construction companies, both state-owned and private, during the 2018 - 2023 period obtained an average of negative amounts. The research results show that the financial performance variables using value added have an influence on stock prices with influence of 74.57% while the rest is influenced by other variables not examined in this research. Partially only MVA and SVA have an influence on stock prices because based on the probability test using E-Views 12 it appears that only MVA and SVA produce results below the alpha. The suggestion for this research is that company management should pay attention to the company's financial performance, especially the MVA and SVA variables and further researcher can make comparative research between all value-added variables.
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.
As an industry that has an important role for other industrial sectors, the plastic and packaging industry is the supply chain for consumer products. Behind the high industrial growth and the potential is still large, there are problems with plastic waste, consumption power, and changes in people's lifestyles that can affect the growth of the plastic and packaging industry. This study aims to determine how much impact the NPM (Net Profit Margin), GPM (Gross Profit Margin), TAT (Total Assets Turnover) on Profit Growth in the plastic and packaging industry sub-sector companies listed in IDX (Indonesia Stock Exchange) for the period 2014 to 2018. The method used in this study is a quantitative descriptive. The sampling technique used was purposive sampling in order to obtain a sample of 11 companies from a population of 12 companies. In the study the method of data analysis used the multiple linear regression analysis techniques by processing data using Eviews 8, because the data is not normally distributed, it uses the log transformation method. The results showed that simultaneously NPM (Net Profit Margin), GPM (Gross Profit Margin), TAT (Total Assets Turnover) had a significant effect on profit growth. And partially NPM (Net Profit Margin) has a significant effect on profit growth, while GPM (Gross Profit Margin) and TAT (Total Assets Turnover) have no significant effect on profit growth.
Instrument which conventionaly used to invest in a capital market is stock. Investor who invested in stocks wanted a certain level of profits from it, the profit from the result of the investment is called stock return. The increase or decreases of investors’ stock return acquisition were determined by monetary performance projected in the company monetary report. The purpose of this research is to discover the monetary performance of stock return on the category of consumer goods industry company in 2015-2018 periode using EPS (Earnings Per Share), ROE (Return on equity), and DER (Debt to equity ratio) as its measurement tools.
 The method used in this research is causal method with consumer goods industry company sector as its research object. Purposive sampling was used as research sample extraction and as a result, 14 companies were determined as data sample. The data were analyzed using classic assumption test (normality, multicollinearity dan heteroskedasticity). The samples were analyzed using data analysis panel or pooled data. 
 From the result of the research it was determined that monetary performance projected in return on equity was significantly affected the stock return. As for the monetary performance projected in earning per share and debt to equity ratio, was partially insignificant to its effect on stock return. Both monetary performance projected in earning per share, return on equity and debt to equity ratio was significantly affected the stock return of consumer goods industry company in 2015-2018.
This research aims to determine the influence of return on assets, return on equity, and net profit margin against stock valuation reflected with the price earning ratio and its impact on the price to book value of retail trade companies listed on the Indonesia Stock Exchange period 2009-2018. The method used in this research is a descriptive method of analysis with a quantitative approach. The type of data used in this study is secondary data. The population in this research is a trade retail company listed on the Indonesia Stock Exchange period 2009-2018, consisting of 31 retail trade companies. Sampling technique used in this research is purposive sampling technique, with certain predefined criteria, which obtained 11 trade retail companies that meet the criteria. The analytical technique that will be used in this study is a double linear regression analysis technique that expanded by a method of pathway analysis to obtain a thorough picture of the relationship between one variable and another variable. The results found that a partial return on asset variable, return on equity, and net profit margin had a significant influence on the price earning ratio, and the price earning ratio also had a significant influence on price to book value.
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 00:21 UTC · ijfbs | complete · ijfbs · 21 Oct 2026 00:21 UTC |
| ojs-rest | 21 Oct 2026 00:21 UTC · ijfbs | complete · ijfbs · 21 Oct 2026 00:14 UTC |
| oai-pmh | 20 Oct 2026 19:56 UTC · ijfbs | complete · ijfbs · 20 Oct 2026 19:55 UTC |
| openalex-enrichment | 21 Oct 2026 00:22 UTC · ijfbs | complete · ijfbs · 21 Oct 2026 00:21 UTC |