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: 6 / 6 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 — 22 Oct 2026 21:14 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.
6 / 6 with data · 0 missing or unmatched. Percentages use all 6 selected publications.
6 / 6 with data · 0 missing or unmatched. Percentages use all 6 selected publications.
6 / 6 with data · 0 missing or unmatched. Percentages use all 6 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 | 6 | 6 | 6/6 | 6/6 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 4 | 6 | 6/6 | 6/6 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 11 | 6 | 6/6 | 6/6 |
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 6 / 6 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): 2026-10-21T07:22:44.494232+00:00 — 2026-10-22T21:14:58.508287+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 | 6 | 6/6 |
| 2018 | All selected publications | 0 | 6 | 6/6 |
| 2019 | All selected publications | 0 | 6 | 6/6 |
| 2020 | All selected publications | 0 | 6 | 6/6 |
| 2021 | All selected publications | 0 | 6 | 6/6 |
| 2022 | All selected publications | 0 | 6 | 6/6 |
| 2023 | All selected publications | 3 | 6 | 6/6 |
| 2024 | All selected publications | 4 | 6 | 6/6 |
| 2025 | All selected publications | 3 | 6 | 6/6 |
| 2026 | All selected publications | 1 | 6 | 6/6 |
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 | 6 |
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 Research in Business and Social Science SSBFNET | 6 | 4 | 11 | 6/6 | 0.67 | 0.5 | 50.0 | 6/6 · 100.0% |
* Percentage among publications with a known citation count.
Agricultural growth is closely associated with sustainable economic development. This is especially true from the perspective of developing countries, such as Indonesia and Pakistan, where significant portions of the labour force are dependent on agriculture for their livelihood. This paper aims to examine the effects of macroeconomic policy (i.e. monetary policy) on employment, food inflation, and agricultural growth by analysing to what extent monetary policy is effective in controlling food price inflation, the effect of contractionary monetary policy on the agricultural sector’s employment and productivity, and the extent of monetary policy transmission to money market rates and 10-year interest rates. We employed a factor-augmented vector autoregressive model proposed by Bernanke et al. (2005) to agricultural data from 1995 and 1996 to 2016 for Indonesia and Pakistan, respectively. Main findings of the study demonstrate that tight monetary policy significantly reduced food inflation and agricultural production while increasing the rural unemployment rate. Short-term and 10-year interest rates increased owing to the contractionary monetary policies pursued by both countries. An inclusive monetary policy whereby policymakers work alongside governments to achieve price stabilisation and reasonable employment rates is recommended.
This study aims to identify problems in the implementation of bureaucratic reform in the field of employment, especially civil servants’ promotion. This study is carried out using a qualitative approach with the case study method. The data collection technique is done using observation, documentation study, and interviews with two key informants. The data is then analyzed in several stages, namely data reduction, data display, and conclusion drawing. The findings indicate that there is an injustice and non-transparent phenomenon in civil servants’ promotion, especially in the initial promotion, promotion by extraordinary work performance, and promotion by graduation certificate. This condition has the potential to cause corruption, collusion, and nepotism, and might reduce employee performance in providing public service.
Labour, entrepreneurship, and technology are important components in the success of GDP Per capita, the existing workforce is not only fixated by numbers but also must be focused on quality to have a significant impact, in addition to the ability and desire of a nation to grow interested in becoming entrepreneurs. This research base on Solow and Swan's Theory said that economic growth was influenced by the growth of production factors such as population, labor, and capital accumulation, as well as the level of technological progress described in the development of knowledge. There is an element of technological progress in the Solow-Swan model which is the main differentiator from the Harrod-Domar growth model. This study used 42 observers consisting of 7 ASEAN countries, namely; Singapore, Thailand, Malaysia, Indonesia, Philippines, Vietnam, and Cambodia, using multiple linear regression and using classical assumption test. The research method in this study uses multiple linear regression of panel data, using the classical assumption test. To get the best model, the data will be tested for Pool Effect, Fix Effect and Random Effects Models, while the determination of the best model is based on the results of the Chow and Hausman tests. The results are shown that labor does not have a significant influence on GDP Per Capita revenue, while the entrepreneurship Index (GEI) and Technology Index (GII) variables have a positive and significant impact on GDP Per Capita revenue. The workforce hasn't affected, which is broadly described in this study because the absorption of labor has not been optimal or the provision of jobs whose proportions are not balanced with the number of the workforce.
This paper aims to examine the effect of institutional distance on the level of equity ownership. The sampling technique in this research used a purposive sampling technique. The sample of this research is cross-border acquisition transactions between Chinese multinational companies and target countries in the Southeast Asia region in the 2015 – 2019 period; thus, the total sample of this research is 32 transactions. The analysis technique in this study was Multiple Linear Regression with the Ordinary Least Square (OLS) model. The main finding of this study demonstrates that economic and geographical distance partially affect the level of equity ownership significantly. Meanwhile, administrative distance does not partially affect the level of equity ownership of the target company by Chinese multinational companies.
This research is in the form of quantitative research that aims to determine differences in stock prices, abnormal returns, and stock trading volumes before and after the stock split. The sample selection in this study was carried out using a purposive sampling method and obtained a sample of 58 companies from companies that listed on the Indonesia Stock Exchange and carried out stock split corporate actions during 2016 until 2020 period. Hypothesis testing in this study used the SPSS version 28 program using the Paired Sample T-test method for data that is normally distributed and the Wilcoxon Signed-Rank Test for data that is not normally distributed. The result of the research is that there is no difference between stock prices before and after the stock split, while there is a difference between abnormal returns before and after the stock split, and there is a difference between the stock trading volume before and after the stock split.
Agricultural growth is closely associated with sustainable economic development. This is especially true from the perspective of developing countries, such as Indonesia and Pakistan, where significant portions of the labour force are dependent on agriculture for their livelihood. This paper aims to examine the effects of macroeconomic policy (i.e. monetary policy) on employment, food inflation, and agricultural growth by analysing to what extent monetary policy is effective in controlling food price inflation, the effect of contractionary monetary policy on the agricultural sector’s employment and productivity, and the extent of monetary policy transmission to money market rates and 10-year interest rates. We employed a factor-augmented vector autoregressive model proposed by Bernanke et al. (2005) to agricultural data from 1995 and 1996 to 2016 for Indonesia and Pakistan, respectively. Main findings of the study demonstrate that tight monetary policy significantly reduced food inflation and agricultural production while increasing the rural unemployment rate. Short-term and 10-year interest rates increased owing to the contractionary monetary policies pursued by both countries. An inclusive monetary policy whereby policymakers work alongside governments to achieve price stabilisation and reasonable employment rates is recommended.
This paper aims to examine the effect of institutional distance on the level of equity ownership. The sampling technique in this research used a purposive sampling technique. The sample of this research is cross-border acquisition transactions between Chinese multinational companies and target countries in the Southeast Asia region in the 2015 – 2019 period; thus, the total sample of this research is 32 transactions. The analysis technique in this study was Multiple Linear Regression with the Ordinary Least Square (OLS) model. The main finding of this study demonstrates that economic and geographical distance partially affect the level of equity ownership significantly. Meanwhile, administrative distance does not partially affect the level of equity ownership of the target company by Chinese multinational companies.
This study aims to identify problems in the implementation of bureaucratic reform in the field of employment, especially civil servants’ promotion. This study is carried out using a qualitative approach with the case study method. The data collection technique is done using observation, documentation study, and interviews with two key informants. The data is then analyzed in several stages, namely data reduction, data display, and conclusion drawing. The findings indicate that there is an injustice and non-transparent phenomenon in civil servants’ promotion, especially in the initial promotion, promotion by extraordinary work performance, and promotion by graduation certificate. This condition has the potential to cause corruption, collusion, and nepotism, and might reduce employee performance in providing public service.
This research is in the form of quantitative research that aims to determine differences in stock prices, abnormal returns, and stock trading volumes before and after the stock split. The sample selection in this study was carried out using a purposive sampling method and obtained a sample of 58 companies from companies that listed on the Indonesia Stock Exchange and carried out stock split corporate actions during 2016 until 2020 period. Hypothesis testing in this study used the SPSS version 28 program using the Paired Sample T-test method for data that is normally distributed and the Wilcoxon Signed-Rank Test for data that is not normally distributed. The result of the research is that there is no difference between stock prices before and after the stock split, while there is a difference between abnormal returns before and after the stock split, and there is a difference between the stock trading volume before and after the stock split.
Labour, entrepreneurship, and technology are important components in the success of GDP Per capita, the existing workforce is not only fixated by numbers but also must be focused on quality to have a significant impact, in addition to the ability and desire of a nation to grow interested in becoming entrepreneurs. This research base on Solow and Swan's Theory said that economic growth was influenced by the growth of production factors such as population, labor, and capital accumulation, as well as the level of technological progress described in the development of knowledge. There is an element of technological progress in the Solow-Swan model which is the main differentiator from the Harrod-Domar growth model. This study used 42 observers consisting of 7 ASEAN countries, namely; Singapore, Thailand, Malaysia, Indonesia, Philippines, Vietnam, and Cambodia, using multiple linear regression and using classical assumption test. The research method in this study uses multiple linear regression of panel data, using the classical assumption test. To get the best model, the data will be tested for Pool Effect, Fix Effect and Random Effects Models, while the determination of the best model is based on the results of the Chow and Hausman tests. The results are shown that labor does not have a significant influence on GDP Per Capita revenue, while the entrepreneurship Index (GEI) and Technology Index (GII) variables have a positive and significant impact on GDP Per Capita revenue. The workforce hasn't affected, which is broadly described in this study because the absorption of labor has not been optimal or the provision of jobs whose proportions are not balanced with the number of the workforce.
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 |