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: 32 / 32 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 00:14 UTC — 21 Oct 2026 07:22 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.
32 / 32 with data · 0 missing or unmatched. Percentages use all 32 selected publications.
32 / 32 with data · 0 missing or unmatched. Percentages use all 32 selected publications.
32 / 32 with data · 0 missing or unmatched. Percentages use all 32 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 |
|---|---|---|---|---|---|
| 2021 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 11 | 11 | 11/11 | 11/11 |
| 2023 | All selected publications | 18 | 18 | 18/18 | 18/18 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 6 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 6 | 11 | 11/11 | 11/11 |
| 2023 | All selected publications | 32 | 18 | 18/18 | 18/18 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 11 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 19 | 11 | 11/11 | 11/11 |
| 2023 | All selected publications | 70 | 18 | 18/18 | 18/18 |
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 32 / 32 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): 2026-10-21T00:14:27.773828+00:00 — 2026-10-21T07:22:50.541217+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 | 32 | 32/32 |
| 2018 | All selected publications | 0 | 32 | 32/32 |
| 2019 | All selected publications | 0 | 32 | 32/32 |
| 2020 | All selected publications | 0 | 32 | 32/32 |
| 2021 | All selected publications | 0 | 32 | 32/32 |
| 2022 | All selected publications | 1 | 32 | 32/32 |
| 2023 | All selected publications | 20 | 32 | 32/32 |
| 2024 | All selected publications | 37 | 32 | 32/32 |
| 2025 | All selected publications | 27 | 32 | 32/32 |
| 2026 | All selected publications | 15 | 32 | 32/32 |
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 |
|---|---|
| 2021 | 3 |
| 2022 | 11 |
| 2023 | 18 |
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 | 6 | 8 | 12 | 6/6 | 1.33 | 0.5 | 50.0 | 6/6 · 100.0% |
| International Journal of Finance & Banking Studies SSBFNET | 2 | 1 | 5 | 2/2 | 0.5 | 0.5 | 50.0 | 2/2 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 24 | 35 | 83 | 24/24 | 1.46 | 1.0 | 79.2 | 24/24 · 100.0% |
* Percentage among publications with a known citation count.
This study was conducted to determine the effect of self-efficacy on employee performance and work motivation, and work involvement in mediating the effect of self-efficacy on employee performance of manufacturing companies in DKI Jakarta. The sample in this study amounted to 100 respondents. This study uses quantitative descriptive analysis techniques. The analysis method in this study uses Path Analysis using SmartPLS. The results of the analysis in this study are: (1) self-efficacy has a positive and significant effect on employee performance, (2) work motivation mediates positively and significantly the relationship between self-efficacy and employee performance, and (3) work involvement mediates positively and significantly the relationship between self-efficacy and employee performance. This research is expected to contribute to the development of Human Resource Management, especially in research on improving employee performance.
This research was conducted to identify the determinants of SME's performance as well as look at the underlying conditional factors. Researchers examine the direct effect of product innovation on SME's performance and consider the moderating role of market orientation and organizational learning. Using the quantitative method, the researchers conducted a survey of 170 SME owners in DKI Jakarta Province who were selected using a purposive sampling method. This survey was conducted online by distributing questionnaires through an online platform. The data is then analyzed using Structural Equation Model with AMOS and Process by Hayes. The results of hypothesis testing show that product innovation has a positive effect on the performance of SMEs, and this effect will be stronger when SMEs have high market orientation and high organizational learning. This research can be used as a reference by owners or managers of SME to advance their businesses. SME need to innovate products and form a conducive climate in market-oriented organizations, as well as a climate that allows them to carry out organizational learning.
Within these dynamics and disruptive eras, it is crucial for all kinds of organization to build and ensure their competencies in increasing their performance and maintaining their position, including for SMEs. This study aims to identify the role of exploitative and organizational learning in influencing SMEs' performance. It also considers the moderating role of environmental dynamism as the conditional factor that underlies the relationship. We carried out the study on 150 SMEs in Jakarta, Indonesia, which was chosen using purposive sampling. The data is collected by distributing an online questionnaire to the respondents, which is then processed and analyzed using Structural Equation Modeling with AMOS. The results of this study indicate that exploitative learning and organizational learning lead to higher SME performance. Furthermore, this influence is more potent when SMEs exist within a dynamic environment, as they will feel the necessity to learn more to face the challenges and seize the opportunities.
This study aims to examine does independence of the Board of Commissioner (BOC) is still relevant as a corporate governance mechanism regarding monitoring managers’ activity, such as aggressive in financial reporting and tax avoidance during financial distress condition. The focus of this study is listed companies on the Indonesia Stock Exchange (IDX), particularly on Mining and Consumer Good Industry sector for period 2016 until 2018. Using multiple linear regression analysis, this study documents that Independency of board commissioner has significant effect on manager’s activity in avoiding tax, however this study failed to document that independency moderates the effect of financial distress and financial reporting aggressiveness on tax avoidance. In addition, this study find that financial distress and financial re-porting aggressiveness positively affect tax avoidance. This study contributes on two ways, first, it adds empirical evidence regarding the relevancy of board of commissioner’s independency as a measure of corporate governance mechanism to monitor managers’ activities in avoiding tax. Second, it also adds evidence that independence is unable to moderates the effect of financial distress and financial reporting aggressiveness on tax avoidance activities performed by managers. In brief, this study implies that the independence of BoC, solely as a measure of corporate governance mechanism is less relevant in a current situation especially when the company facing financial distress conditions and managers’ aggressiveness in financial reporting. Managers should more pay attention to the discretion of tax avoidance activities particularly when facing financial distress condition. The results also imply that regulatory bodies, for instance, Stock Exchange Supervisory Board under the Indonesian Financial Services Authority should reconsider or reformation the concept of independence of Board Commissioners.
This paper aims to analyze cryptocurrency volatility by examining the effect of Gold, Dollar Index, and Composite Stock Price Index (IHSG) as independent variables and on Bitcoin and Ethereum as dependent variables. The cryptocurrency objects in this study are Bitcoin and Ethereum, which have the largest market capitalization. The data in this study used the period January 1, 2018, to December 31, 2021. This study used GARCH analysis. This study's results indicate that Bitcoin's volatility is influenced by the price of Bitcoin itself, gold, and the stock exchange index, and Ethereum and the stock exchange index influence Ethereum. This shows that the cryptocurrency market is inefficient as the prices are also affected by past prices.
This research aims to find out and analyze the effect of the bonus mechanism, tunneling incentive, and exchange rate on company decisions to perform transfer pricing practices in 35 manufacturing and mining sector companies listed on the Indonesia Stock Exchange for the 2018–2020 period. The population of this research is used in the manufacturing and mining sectors listed on the Indonesia Stock Exchange (IDX). This research is a type of quantitative research using secondary data in the form of the company’s annual report and financial statement. The Random Effect Model (REM) with panel data regression was used in this study using STATA version 16. Based on the test results and analysis, it can be concluded that the bonus mechanism has a significant positive effect on transfer pricing, the exchange rate has a significant negative effect on transfer pricing, and the tunneling incentive has no effect on transfer pricing.
Gojek has become a widely-known online transportation service in Indonesia, including East Bekasi, a major city that encompasses one of the biggest population numbers, right behind Jakarta. This results in the occurrences of traffic jams and, consecutively, the public’s need for comfortable, fast, and safe transportation. The purpose of this research is to identify, prove and analyze the loyalty of East Bekasi Gojek customers. In this paper, the researched variables include a brand image, promotion, service quality, customer satisfaction and customer loyalty. This research uses a quantitative method, namely the purposive sampling method, using samples from 96 respondents. The data used are gathered from the questionnaire spread, and data analysis is done using SmartPLS 3.0. The result of this research finds that for the direct influence of brand image, promotion, and service quality on customer loyalty, all variables show positive and significant effects, with brand awareness showing an influence degree of 0,350, promotion with 0,364, and service quality with 0,289. As for the indirect influence of brand image, promotion, and service quality towards customer loyalty, brand awareness shows positive and significant effects on the degree of 0,319 while on the other hand, promotion, and service quality show positive yet insignificant effects with promotion being on the degree of 0,222 and service quality on 0,179. As for the indirect influence, the influence towards loyalty through customer satisfaction shows positive and significant effects with brand image on the degree of 0,098, promotion on 0,102, and service quality on 0,081. Meanwhile, the direct joint contribution of brand image, promotion, and service quality for customer satisfaction shows a degree of 81,70%, and indirectly it shows a degree of 82,40%, while the rest of the figure is caused by other factors not included within the model of this research.
This research was conducted to identify the determinants of SME's performance as well as look at the underlying conditional factors. Researchers examine the direct effect of product innovation on SME's performance and consider the moderating role of market orientation and organizational learning. Using the quantitative method, the researchers conducted a survey of 170 SME owners in DKI Jakarta Province who were selected using a purposive sampling method. This survey was conducted online by distributing questionnaires through an online platform. The data is then analyzed using Structural Equation Model with AMOS and Process by Hayes. The results of hypothesis testing show that product innovation has a positive effect on the performance of SMEs, and this effect will be stronger when SMEs have high market orientation and high organizational learning. This research can be used as a reference by owners or managers of SME to advance their businesses. SME need to innovate products and form a conducive climate in market-oriented organizations, as well as a climate that allows them to carry out organizational learning.
This study examines the influence of global stock indices and macroeconomic conditions in Indonesia on the Jakarta Composite Index (JCI). The global stock indices analyzed in this study are the Dow Jones Industrial Average (DJIA), Nikkei 225 (N225), Shanghai Stock Exchange Composite (SSE), Strait Singapore index. The macroeconomic indicators that analyzed in this study are the inflation rate, the US dollar exchange rate against the Indonesian rupiah, and the BI rate. This study was conducted using secondary data. The research period was 3 years for 36 months from January 2018 to December 2020. The data used in this study are secondary data with a sample collection method using Purposive Sampling against JCI. Data analysis in this study was carried out using Microsoft Office Excel 2016, as well as hypothesis testing using panel data regression analysis with the Eviews program version 10.0 with a significance level of 5%. Research Results The results showed that in the short term, Index DJIA, Nikkei Index 225, Interest rates positively affect the movement of JCI. This suggests that an increase in the DJIA index, Nikkei 225 Index, and interest rates will result in an increase in the JCI, While Shanghai Index, STI, Inflation does not affect the JCI.
This paper aims to analyze cryptocurrency volatility by examining the effect of Gold, Dollar Index, and Composite Stock Price Index (IHSG) as independent variables and on Bitcoin and Ethereum as dependent variables. The cryptocurrency objects in this study are Bitcoin and Ethereum, which have the largest market capitalization. The data in this study used the period January 1, 2018, to December 31, 2021. This study used GARCH analysis. This study's results indicate that Bitcoin's volatility is influenced by the price of Bitcoin itself, gold, and the stock exchange index, and Ethereum and the stock exchange index influence Ethereum. This shows that the cryptocurrency market is inefficient as the prices are also affected by past prices.
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 |