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: 21 Oct 2026 07:22 UTC — 22 Oct 2026 03:12 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 |
|---|---|---|---|---|---|
| 2024 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2024 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2024 | All selected publications | 3 | 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): 2026-10-21T07:22:57.145020+00:00 — 2026-10-22T03:12:31.290264+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 | 0 | 2 | 2/2 |
| 2024 | All selected publications | 0 | 2 | 2/2 |
| 2025 | All selected publications | 1 | 2 | 2/2 |
| 2026 | All selected publications | 2 | 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 |
|---|---|
| 2024 | 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 Finance & Banking Studies SSBFNET | 1 | 2 | 3 | 1/1 | 2.0 | 2 | 100.0 | 1/1 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 1 | 0 | 0 | 1/1 | 0.0 | 0 | 0.0 | 1/1 · 100.0% |
* Percentage among publications with a known citation count.
One of the Indonesian government's policies since 2015 has been to increase development through its developed infrastructure, by increasing its budget for this sector from year to year. However, companies listed in the engineering and construction subsector on the Indonesian Stock Exchange, both state-owned enterprises and private companies, experienced losses so it could be said that their financial performance was poor. This study intends to evaluate the financial performance engineering and construction sub-sector companies using the Economic Value Added (EVA), Market Value Added (MVA), Refined Economic Value Added (REVA), Financial Value Added (FVA), and Shareholder Value Added (SVA) in 2018 – 2023 and determine their influence on stock prices. This type of research is quantitative descriptive research using E-Views 12 with a population 26 companies and a sample size 16 companies. Based on the results the calculations and analysis, it stated that EVA, MVA, REVA, FVA, SVA and stock prices engineering and construction companies, both state-owned and private, during the 2018 - 2023 period obtained an average of negative amounts. The research results show that the financial performance variables using value added have an influence on stock prices with influence of 74.57% while the rest is influenced by other variables not examined in this research. Partially only MVA and SVA have an influence on stock prices because based on the probability test using E-Views 12 it appears that only MVA and SVA produce results below the alpha. The suggestion for this research is that company management should pay attention to the company's financial performance, especially the MVA and SVA variables and further researcher can make comparative research between all value-added variables.
This paper seeks to evaluate proficient perceptions within business assessments in South Africa, where such perceptions play a significant role in the evaluation process, encompassing the selection of standards and the establishment of evaluation frameworks. The evolving economy of South Africa is marked by fluctuating economic circumstances and potential growth, compounded by inadequate benchmarks and a scarcity of relevant market information. Forecasting growth rates are further complicated by macroeconomic uncertainties, leading to a reliance on proficient perceptions when industry-specific guidelines and easily comprehensible alternatives to the Capital Asset Pricing Model (CAPM) are lacking. A mixed-methods strategy is employed in this study, combining quantitative and qualitative data, along with surveys set benchmarks for business assessments. The outcomes reveal that modifications based on judgment are necessary for variables like organizational size due to the absence of CAPM alternatives. This study stresses the importance of translating scholarly discoveries into practical applications, emphasizing the challenges and uncertainties that call for proficient perceptions in business evaluations. It advances the comprehension of these obstacles and proposes that further exploring macroeconomic variables and industry-specific guidelines could offer valuable insights. The study suggests that biases in evaluations, influenced by client interests, present a promising area for future inquiry.
One of the Indonesian government's policies since 2015 has been to increase development through its developed infrastructure, by increasing its budget for this sector from year to year. However, companies listed in the engineering and construction subsector on the Indonesian Stock Exchange, both state-owned enterprises and private companies, experienced losses so it could be said that their financial performance was poor. This study intends to evaluate the financial performance engineering and construction sub-sector companies using the Economic Value Added (EVA), Market Value Added (MVA), Refined Economic Value Added (REVA), Financial Value Added (FVA), and Shareholder Value Added (SVA) in 2018 – 2023 and determine their influence on stock prices. This type of research is quantitative descriptive research using E-Views 12 with a population 26 companies and a sample size 16 companies. Based on the results the calculations and analysis, it stated that EVA, MVA, REVA, FVA, SVA and stock prices engineering and construction companies, both state-owned and private, during the 2018 - 2023 period obtained an average of negative amounts. The research results show that the financial performance variables using value added have an influence on stock prices with influence of 74.57% while the rest is influenced by other variables not examined in this research. Partially only MVA and SVA have an influence on stock prices because based on the probability test using E-Views 12 it appears that only MVA and SVA produce results below the alpha. The suggestion for this research is that company management should pay attention to the company's financial performance, especially the MVA and SVA variables and further researcher can make comparative research between all value-added variables.
This paper seeks to evaluate proficient perceptions within business assessments in South Africa, where such perceptions play a significant role in the evaluation process, encompassing the selection of standards and the establishment of evaluation frameworks. The evolving economy of South Africa is marked by fluctuating economic circumstances and potential growth, compounded by inadequate benchmarks and a scarcity of relevant market information. Forecasting growth rates are further complicated by macroeconomic uncertainties, leading to a reliance on proficient perceptions when industry-specific guidelines and easily comprehensible alternatives to the Capital Asset Pricing Model (CAPM) are lacking. A mixed-methods strategy is employed in this study, combining quantitative and qualitative data, along with surveys set benchmarks for business assessments. The outcomes reveal that modifications based on judgment are necessary for variables like organizational size due to the absence of CAPM alternatives. This study stresses the importance of translating scholarly discoveries into practical applications, emphasizing the challenges and uncertainties that call for proficient perceptions in business evaluations. It advances the comprehension of these obstacles and proposes that further exploring macroeconomic variables and industry-specific guidelines could offer valuable insights. The study suggests that biases in evaluations, influenced by client interests, present a promising area for future inquiry.
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 |