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: 13 / 13 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 07:23 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.
12 / 13 with data · 1 missing or unmatched. Percentages use all 13 selected publications.
11 / 13 with data · 2 missing or unmatched. Percentages use all 13 selected publications.
13 / 13 with data · 0 missing or unmatched. Percentages use all 13 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 |
|---|---|---|---|---|---|
| 2019 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| 2020 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2021 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2023 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2025 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2026 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 5 | 4 | 4/4 | 4/4 |
| 2020 | All selected publications | 7 | 2 | 2/2 | 2/2 |
| 2021 | All selected publications | 7 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2025 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2026 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 12 | 4 | 4/4 | 4/4 |
| 2020 | All selected publications | 17 | 2 | 2/2 | 2/2 |
| 2021 | All selected publications | 20 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 4 | 2 | 2/2 | 2/2 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2025 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2026 | All selected publications | 0 | 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 13 / 13 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): 2026-10-21T00:14:30.169046+00:00 — 2026-10-21T07:23:02.057603+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 | 13 | 13/13 |
| 2018 | All selected publications | 0 | 13 | 13/13 |
| 2019 | All selected publications | 0 | 13 | 13/13 |
| 2020 | All selected publications | 3 | 13 | 13/13 |
| 2021 | All selected publications | 7 | 13 | 13/13 |
| 2022 | All selected publications | 11 | 13 | 13/13 |
| 2023 | All selected publications | 12 | 13 | 13/13 |
| 2024 | All selected publications | 12 | 13 | 13/13 |
| 2025 | All selected publications | 8 | 13 | 13/13 |
| 2026 | All selected publications | 2 | 13 | 13/13 |
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 |
|---|---|
| 2019 | 4 |
| 2020 | 2 |
| 2021 | 1 |
| 2022 | 2 |
| 2024 | 2 |
| 2025 | 1 |
| 2026 | 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 | 2 | 2 | 2 | 2/2 | 1.0 | 1.0 | 50.0 | 2/2 · 100.0% |
| International Journal of Finance & Banking Studies SSBFNET | 3 | 7 | 14 | 3/3 | 2.33 | 3 | 66.7 | 3/3 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 8 | 14 | 39 | 8/8 | 1.75 | 1.0 | 75.0 | 8/8 · 100.0% |
* Percentage among publications with a known citation count.
The purpose of this paper is to investigate and analyze the demographic bonus from the perspective of human capital in Indonesia. This research methodology uses qualitative methods and uses literature and documentation study techniques. This research data comes from secondary data sources, based on literature studies, and is analyzed descriptively qualitatively which emphasizes the use of scientific logic. The results of the study found that Indonesia is in a very good demographic bonus position, and is expected to be able to take advantage of the age structure, especially the portion of the productive age which has the potential to be the main driver of economic growth. Therefore, the government must have a supporting policy to encourage economic growth by having a strategy and implementing competency-based training (CBT) through the implementation of Deming's (1986) plan-do-check-action (PDCA) as a human resource development program that includes material for integrating technical skills and competencies in the form of skills, competencies, and the right point of view or character to make students become professionals. On the other hand, the government is expected to be able to create jobs and open up a wide work environment for young people, the workforce skilled as human capital.
The purpose of this paper is to find out if selected determinants have any effect on the economic growth rate using the strong balanced panel data for the Western Balkan countries for the period 2001-2017, and the data used are on an annual basis, which in total there are 102 observation periods. For the realization of the paper, secondary data and an advanced dynamic approach were used, such as pooled OLS methods, fixed and random effects model, to test economic growth rate as dependent variable, and explanatory variables such as working remittances to GDP, exports to GDP, imports to GDP, foreign direct investment to GDP and inflation rate. From the generated outputs, it is true to say that working remittances to GDP, exports to GDP, and imports to GDP have an effect that influences economic growth, respectively GDP growth. Even though foreign direct investment to GDP and inflation rate does not have a significant effect on economic growth, respectively GDP growth. Keywords: Economic growth; macroeconomic determinants; panel data. JEL code: O47, O11, C23
This study investigates how the development of human capital is related to economic growth in Nigeria from 1980 to 2015. Data were sourced from the Central Bank of Nigeria and the National Bureau of Statistics. The study employed Augmented Dickey-Fuller (ADF) to estimate the relationship among the variables used in this study which revealed that there is a positive long-run relationship among public expenditure on education and health, total school enrolment, gross capital formation, employment rate, life expectancy rate and economic growth. The study recommended that the government should put in place the required education and training policy that would guarantee quality schooling for different level of education. Government should also commit more funds to health sector to enhance human capital formation. It was also recommended there should be more pragmatic means to develop human capabilities.
The Human Development Index (HDI), which measures a country’s human development level, considering the health, education and income indicators of countries has been published in the Human Development Report each year since 1990 by the United Nations Development Programme. Norway, which is a highly developed country, was at the top of the Human Development Index. Therefore, the aim of this study is to evaluate Norway’s human development performance. In this context, the relationship between human development and economic growth has been examined at empirical level for Norway for the period between 1990-2017. In the study, firstly, ADF and PP unit root tests were performed. Then, Granger causality analysis was applied. According to the results of Granger causality analysis there is a one-way causality relationship from human development to economic growth.
This research examines the correlation between human capital and economic growth in South Africa. Human capital is represented by Government Expenditure on Health (GEH), Government Expenditure on Education (GEE), and Secondary School Enrolment (SER). Notwithstanding substantial investments in these domains, South Africa has enduring economic difficulties, prompting apprehensions regarding the efficacy of existing human capital policies. This research aims to evaluate the influence of differences in GEH, GEE, and SER on economic productivity, consequently guiding policy decisions. We utilised a quantitative methodology employing annual time series data obtained from the World Bank and the South African Reserve Bank. The analysis incorporated unit root tests (ADF) to determine data stationarity and Johansen cointegration tests to examine long-term correlations among the variables. The data indicate that GEH positively influences economic productivity, suggesting a correlation between increased health spending and elevated GDP. Conversely, SER exhibits a negative correlation with economic growth, indicating that merely augmenting enrolment numbers does not yield enhanced economic results. While Foreign Direct Investment (FDI) has a favourable correlation with economic growth, it is statistically insignificant. The report advocates for South Africa to prioritise educational quality over enrolment figures and to ensure continuous investment in healthcare services. Furthermore, subsequent research ought to investigate the ramifications of human capital within particular sectors, such as tourism, to yield focused policy recommendations for fostering economic growth.
This study examines whether movement out of agriculture accelerates productivity convergence in West Africa. We assemble an annual country panel for 16 West African economies over 2000–2023 from the World Development Indicators, Penn World Table 11.0 and Worldwide Governance Indicators. The estimable dynamic specification contains 237 country-year observations for 11 economies because the Penn World Table human-capital series determines the common complete-case sample. A pre-specified two-step System GMM model is rejected because second-order serial correlation is significant and the Hansen test is uninformative. Inference therefore rests on a dynamic country-and-year fixed-effects model with Driscoll–Kraay standard errors, supplemented by a first-order Nickell-bias assessment and a three-sector shift-share decomposition. Labour productivity is conditionally convergent: the lag coefficient is 0.841, implying a four-year half-life, or about 9.7 years under the conservative bias approximation. Capital deepening is positive and statistically precise, while manufacturing, services and agricultural-employment shares are not separately significant. This absence of statistical precision does not imply that structural transformation is irrelevant: employment reallocation contributes positively in all 16 economies. Regionally, within-sector gains account for 75.2% of observed productivity change, reallocation for 29.3%, and the interaction term for −4.5%. Policy should combine labour mobility with capital formation and productivity upgrading inside agriculture, manufacturing and services rather than pursue sector-share targets alone.
Over the years, productivity performance has been the subject that generated enormous interest amongst economic scholars. This shows the value of productivity on business growth. Hence, the implementation of improved interventions for productivity improvement requires appropriate innovative practices for industries. This sentiment supports the importance of innovation for productivity improvement. Consequently, this study assesses the influence of innovation on labour productivity in an automotive assembly organisation in South Africa. It has been established that the manufacturing industry in South Africa has low labour productivity compared with its BRICS counterpart countries like Russia, India and China. As a result, this study centres its attention on innovation, considering low levels of labour productivity in the manufacturing sector of South Africa. It was quantitative in design and the investigation was achieved by collecting pre-and post-innovative quarterly data for spoilage and the achievement of production targets. The study analysed the production and related experiences of an automotive assembly organisation, operating in KwaZulu-Natal in the eThekwini Municipality that had adopted an innovative seat-handling manipulator in its assembly process. The Ordinary Least Squares (OLS) model, using the Statistical Package for Social Sciences (SPSS), was used to analyse data. The study indicates that spoilage rate and the achievement of production targets have the relationship with labour productivity after an innovative seat handling manipulator was implemented in the organisation.
This research examines the correlation between human capital and economic growth in South Africa. Human capital is represented by Government Expenditure on Health (GEH), Government Expenditure on Education (GEE), and Secondary School Enrolment (SER). Notwithstanding substantial investments in these domains, South Africa has enduring economic difficulties, prompting apprehensions regarding the efficacy of existing human capital policies. This research aims to evaluate the influence of differences in GEH, GEE, and SER on economic productivity, consequently guiding policy decisions. We utilised a quantitative methodology employing annual time series data obtained from the World Bank and the South African Reserve Bank. The analysis incorporated unit root tests (ADF) to determine data stationarity and Johansen cointegration tests to examine long-term correlations among the variables. The data indicate that GEH positively influences economic productivity, suggesting a correlation between increased health spending and elevated GDP. Conversely, SER exhibits a negative correlation with economic growth, indicating that merely augmenting enrolment numbers does not yield enhanced economic results. While Foreign Direct Investment (FDI) has a favourable correlation with economic growth, it is statistically insignificant. The report advocates for South Africa to prioritise educational quality over enrolment figures and to ensure continuous investment in healthcare services. Furthermore, subsequent research ought to investigate the ramifications of human capital within particular sectors, such as tourism, to yield focused policy recommendations for fostering economic growth.
This research work examines the impacts of foreign aids and human capital on economic growth of African oil and non-oil producing countries. Adopting the time series data from 2000 to 2021, the study employs Westerlund Error Correction Based panel cointegration test to investigate the impacts of Physical Capital (FA, GCF, FDI), Human Capital (EDU, HLT) and Policy Variables (REV, EXR) on Gross Domestic Product growth rate (GDPgr). Findings from results of the study affirms that there is a long-term co-movement between the GDPgr and the explanatory variables (FA, GCF, FDI, EDU, HLT, REV, EXR) of both African oil and non-oil producing countries. The study also reveals that foreign aids are better effectively put into use in the African non-oil producing countries than in the oil producing countries. Findings in the study equally confirm that financial resources in the African non-oil producing countries are better efficiently spent on human capital and domestic investment than in the African oil producing countries. The study further shows that revenue generation and foreign exchange earnings stimulate growth better in the African oil producing countries than the non-oil countries counterparts in Africa.
Education is generally considered to be the core source of individuals’ better life and countries' economic and social development. Consequently, attending university and getting high qualifications are perceived as tools that generate and enhance economic growth. The current study aimed to analyse the effect of various educational levels on South African economic growth. The study employed quarterly data from 1995Q1 to 2021Q4. The autoregressive distributed lag (ARDL) model was employed to assess both long-run and short-run relationships among variables. The long-run results revealed that people who completed their grade 12 (with matric certificate) and bachelor's degrees contribute positively to South African economic growth. However, honours and higher degrees (master's and PhD) were found to have an inverse relationship with economic growth in South Africa. On the other hand, the short-run relationship indicated that all education levels contribute to economic growth in the short term. Based on these findings, the author suggests that the South African higher education curriculum should be revised as the negative effect of high education towards economic growth should be resulting in growing unemployment and graduates waiting to be employed rather than creating jobs themselves. Additionally, increasing job creation for graduates with higher qualifications would reduce the number of graduates that leave the country looking for employment abroad.
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 |