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: 2 / 2 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 01:34 UTC — 23 Oct 2026 13:11 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.
2 / 2 with data · 0 missing or unmatched. Percentages use all 2 selected publications.
2 / 2 with data · 0 missing or unmatched. Percentages use all 2 selected publications.
2 / 2 with data · 0 missing or unmatched. Percentages use all 2 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 |
|---|---|---|---|---|---|
| 2023 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2023 | All selected publications | 10 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2023 | All selected publications | 14 | 2 | 2/2 | 2/2 |
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 2 / 2 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): 2026-10-22T01:34:29.429161+00:00 — 2026-10-23T13:11:46.718678+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 | 2 | 2/2 |
| 2018 | All selected publications | 0 | 2 | 2/2 |
| 2019 | All selected publications | 0 | 2 | 2/2 |
| 2020 | All selected publications | 0 | 2 | 2/2 |
| 2021 | All selected publications | 0 | 2 | 2/2 |
| 2022 | All selected publications | 0 | 2 | 2/2 |
| 2023 | All selected publications | 3 | 2 | 2/2 |
| 2024 | All selected publications | 3 | 2 | 2/2 |
| 2025 | All selected publications | 2 | 2 | 2/2 |
| 2026 | All selected publications | 6 | 2 | 2/2 |
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 |
|---|---|
| 2023 | 2 |
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 | 6 | 6 | 1/1 | 6.0 | 6 | 100.0 | 1/1 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 1 | 4 | 8 | 1/1 | 4.0 | 4 | 100.0 | 1/1 · 100.0% |
* Percentage among publications with a known citation count.
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 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.
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 |