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: 3 / 3 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 10:28 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.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 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 |
|---|---|---|---|---|---|
| 2022 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 5 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 5 | 1 | 1/1 | 1/1 |
| 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 3 / 3 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): 2026-10-21T07:22:51.913975+00:00 — 2026-10-23T10:28:17.937920+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 | 3 | 3/3 |
| 2018 | All selected publications | 0 | 3 | 3/3 |
| 2019 | All selected publications | 0 | 3 | 3/3 |
| 2020 | All selected publications | 0 | 3 | 3/3 |
| 2021 | All selected publications | 0 | 3 | 3/3 |
| 2022 | All selected publications | 0 | 3 | 3/3 |
| 2023 | All selected publications | 1 | 3 | 3/3 |
| 2024 | All selected publications | 0 | 3 | 3/3 |
| 2025 | All selected publications | 5 | 3 | 3/3 |
| 2026 | All selected publications | 3 | 3 | 3/3 |
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 |
|---|---|
| 2022 | 1 |
| 2023 | 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 Business Ecosystem & Strategy Bussecon International Academy | 1 | 0 | 1 | 1/1 | 0.0 | 0 | 0.0 | 1/1 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 2 | 7 | 8 | 2/2 | 3.5 | 3.5 | 100.0 | 2/2 · 100.0% |
* Percentage among publications with a known citation count.
The growth of capital market investors and financial literacy has increased every year and investors in making an investment decision However, financial theory argues that there are psychological biases that can influence investors in making inappropriate investment decisions. This study aims to examine the influence and analyze Overconfidence, Loss Aversion and Herding Bias on investment decisions and Financial Literacy as a mediating variable. Financial Literacy is used as mediating variable in the relationship of Overconfidence, Loss Aversion. However Herding Bias variables on Investment Decisions. This study uses non-probability sampling techniques to as many as 163 investors stock in Yogyakarta as respondents. Findings/Results: the study shows that overconfidence, loss aversion and herding bias have a significant effect on investment decisions and overconfidence, loss aversion and herding bias are not mediated by financial literacy in making investment decisions.
The implementation of Financial Accounting Standards for Micro, Small, and Medium Enterprises is very important as a basis for presenting reliable financial statements to gain public trust because the financial statements are written based on applicable accounting standards. The research population consisted of MSME players in Indonesia. The sample in this study was selected using purposive sampling technique. The research hypothesis was tested using SmartPLS 3.0 software with the Structural Equation Modeling Partial Least Squares (SEM-PLS) method. The results of the research showed that the use of information technology (IT), level of education, socialization of financial accounting standards for Micro, Small, and Medium Entity (SAK EMKM) and business age had no effect on the implementation of SAM EMKM, while accounting knowledge, the level of readiness of market participants, and taxpayer compliance were proven to have a positive effect on the implementation of SAK EMKM.
Micro, Small and Medium Enterprises (MSMEs) are economic actors that are more able to stand firmly compared to large companies when an economic crisis occurs. SAK–EMKM is the Financial Accounting Standard for Micro, Small and Medium Enterprises. This study aims to determine the preparedness of MSMEs to implement SAK-EMKM. The qualitative method was used by collecting the data using observation, interview, and direct documentation of the MSME actors as the object of the research. The sampling technique used was the purposive sampling method. The research sample was MSMEs that had been operating for more than one year and were located in Sungai Bangkong Village of Pontianak City. The results of the study show that the MSMEs in Sungai Bangkong Village of Pontianak City was not aware of SAK-EMKM. Their method of accounting recording was not based on the accrual basis, but they have implemented the concept of business entity management and had adequately capable human resources.
The implementation of Financial Accounting Standards for Micro, Small, and Medium Enterprises is very important as a basis for presenting reliable financial statements to gain public trust because the financial statements are written based on applicable accounting standards. The research population consisted of MSME players in Indonesia. The sample in this study was selected using purposive sampling technique. The research hypothesis was tested using SmartPLS 3.0 software with the Structural Equation Modeling Partial Least Squares (SEM-PLS) method. The results of the research showed that the use of information technology (IT), level of education, socialization of financial accounting standards for Micro, Small, and Medium Entity (SAK EMKM) and business age had no effect on the implementation of SAM EMKM, while accounting knowledge, the level of readiness of market participants, and taxpayer compliance were proven to have a positive effect on the implementation of SAK EMKM.
The growth of capital market investors and financial literacy has increased every year and investors in making an investment decision However, financial theory argues that there are psychological biases that can influence investors in making inappropriate investment decisions. This study aims to examine the influence and analyze Overconfidence, Loss Aversion and Herding Bias on investment decisions and Financial Literacy as a mediating variable. Financial Literacy is used as mediating variable in the relationship of Overconfidence, Loss Aversion. However Herding Bias variables on Investment Decisions. This study uses non-probability sampling techniques to as many as 163 investors stock in Yogyakarta as respondents. Findings/Results: the study shows that overconfidence, loss aversion and herding bias have a significant effect on investment decisions and overconfidence, loss aversion and herding bias are not mediated by financial literacy in making investment decisions.
Micro, Small and Medium Enterprises (MSMEs) are economic actors that are more able to stand firmly compared to large companies when an economic crisis occurs. SAK–EMKM is the Financial Accounting Standard for Micro, Small and Medium Enterprises. This study aims to determine the preparedness of MSMEs to implement SAK-EMKM. The qualitative method was used by collecting the data using observation, interview, and direct documentation of the MSME actors as the object of the research. The sampling technique used was the purposive sampling method. The research sample was MSMEs that had been operating for more than one year and were located in Sungai Bangkong Village of Pontianak City. The results of the study show that the MSMEs in Sungai Bangkong Village of Pontianak City was not aware of SAK-EMKM. Their method of accounting recording was not based on the accrual basis, but they have implemented the concept of business entity management and had adequately capable human resources.
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 |