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 01:04 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.
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 |
|---|---|---|---|---|---|
| 2020 | All selected publications | 11 | 11 | 11/11 | 11/11 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2020 | All selected publications | 76 | 11 | 11/11 | 11/11 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2020 | All selected publications | 168 | 11 | 11/11 | 11/11 |
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): 2026-10-22T01:04:51.961944+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 | 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 | 20 | 11 | 11/11 |
| 2022 | All selected publications | 30 | 11 | 11/11 |
| 2023 | All selected publications | 43 | 11 | 11/11 |
| 2024 | All selected publications | 37 | 11 | 11/11 |
| 2025 | All selected publications | 28 | 11 | 11/11 |
| 2026 | All selected publications | 8 | 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 |
|---|---|
| 2020 | 11 |
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 | 6 | 67 | 151 | 6/6 | 11.17 | 4.5 | 83.3 | 6/6 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 5 | 9 | 17 | 5/5 | 1.8 | 1 | 60.0 | 5/5 · 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.
This study aims to determine the effect of six-factor (market return, firm size, value, profitability, investment, human capital) on excess return in companies with shares listed in LQ45 for the 2015-2019 period. The population of this study consists of 45 LQ45 companies listed on the Indonesia Stock Exchange (IDX). The purposive sampling method is the sample selection method that is used in this study. As a result, it shows that there are 26 companies that fit the criteria and taken as samples. Furthermore, the data analysis method that is used in this study is the panel data regression model. Based on the results of this study, it is showed that partially, market return (X1), firm size (X2), profitability (X4), and investment (X5) have a significant positive effect on the excess return of companies listed in LQ45 for the 2015-2019 period. Whereas, value (X3) and human capital (X6) do not have a significant effect on the excess return of companies listed in LQ45 for the 2015-2019 period partially. Another result also shows that simultaneously or as a whole, six-factors (market return, firm size, value, profitability, investment, human capital) have a significant effect on the excess return of companies listed in LQ45 for the 2015-2019 period.
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.
This study aims to determine the effect of six-factor (market return, firm size, value, profitability, investment, human capital) on excess return in companies with shares listed in LQ45 for the 2015-2019 period. The population of this study consists of 45 LQ45 companies listed on the Indonesia Stock Exchange (IDX). The purposive sampling method is the sample selection method that is used in this study. As a result, it shows that there are 26 companies that fit the criteria and taken as samples. Furthermore, the data analysis method that is used in this study is the panel data regression model. Based on the results of this study, it is showed that partially, market return (X1), firm size (X2), profitability (X4), and investment (X5) have a significant positive effect on the excess return of companies listed in LQ45 for the 2015-2019 period. Whereas, value (X3) and human capital (X6) do not have a significant effect on the excess return of companies listed in LQ45 for the 2015-2019 period partially. Another result also shows that simultaneously or as a whole, six-factors (market return, firm size, value, profitability, investment, human capital) have a significant effect on the excess return of companies listed in LQ45 for the 2015-2019 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.
Human resources in a company will always communicate with each other. In this business world, nothing can be accomplished without communicating effectively with employers, employees, clients, suppliers, and customers. The purpose of this study is to analyze the influence of communication climate on motivation, to analyze the influence of motivation on employee performance, and the influence of communication climate on employee performance mediated by motivation. This study used descriptive analysis research methods and path analysis using SPSS version 25 software. The unit of analysis in this study were all employees of PT Lazada Express Bandung, so the population in this study were all employees totaling 52 people. The sampling method using a saturated sampling method so that the number of respondents as large as the population of 52 employees. The results showed that the climate of communication has an influence on employee motivation. The magnitude of the influence of the communication climate on employee performance is 79.60%. and there is an influence between work motivation on employee performance and motivation does not mediate the effect of communication climate on employee performance.
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 specifically aims to evaluate the influence of directly and indirectly internal business environments on the marketing strategy and marketing performance of the retail industry in the city of Bandung. The population in this study amounted to 442 minimarkets with the formula Slovin to determine the sample, obtained 82 samples to be researched. The methods in this study use descriptive and vermicular methods. The results in a partial internal environment have an influence on the marketing strategy of retail industry minimarket Bandung and marketing strategy also has an influence on the marketing performance of the retail industry minimarket Bandung City. Simultaneous testing found that the internal environment and marketing strategy had a significant influence on the marketing performance of retail industry minimarket Bandung with great influence 58.6% The remaining 41.4% influenced By other factors beyond this research. This research also finds indirectly internal environment also has a significant influence on the marketing strategy of the retail industry minimarket Bandung in a positive direction. So the ability to understand the internal environmental conditions well will help the accuracy in decision-making strategies and impact on the marketing performance improvement of the retail industry minimarket Bandung.
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 |