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: 4 / 4 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 01:24 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.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 selected publications.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 selected publications.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 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 |
|---|---|---|---|---|---|
| 2021 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2026 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 4 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2026 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 7 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | — | 0 | 0/0 | 0/0 |
| 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 4 / 4 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): 2026-10-21T07:22:56.439098+00:00 — 2026-10-22T01:24:51.971008+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 | 4 | 4/4 |
| 2018 | All selected publications | 0 | 4 | 4/4 |
| 2019 | All selected publications | 0 | 4 | 4/4 |
| 2020 | All selected publications | 0 | 4 | 4/4 |
| 2021 | All selected publications | 0 | 4 | 4/4 |
| 2022 | All selected publications | 1 | 4 | 4/4 |
| 2023 | All selected publications | 3 | 4 | 4/4 |
| 2024 | All selected publications | 0 | 4 | 4/4 |
| 2025 | All selected publications | 1 | 4 | 4/4 |
| 2026 | All selected publications | 2 | 4 | 4/4 |
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 ↗
Records with full publication dates only. 2026 is an incomplete year.
| Period | Publications |
|---|---|
| 2021-11 | 1 |
| 2022-01 | 1 |
| 2024-08 | 1 |
| 2026-06 | 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 Research in Business and Social Science SSBFNET | 4 | 4 | 7 | 4/4 | 1.0 | 0.0 | 25.0 | 4/4 · 100.0% |
* Percentage among publications with a known citation count.
The ready-made garments (RMG) sector of Bangladesh has developed immensely under the broad radar of the export market, and its size is beyond imagination. Unfortunately, there has been no study that explores the whole universe of the RMG sector of Bangladesh rather the story of the RMG sector often involves the export market only. The discussion of this paper intends to explore the methodology of unboxing the whole universe of the RMG sector of Bangladesh that includes small and medium-sized manufacturing and processing units that are often involved with subcontracting for other factories (producing for the export market as well as domestic market), are suppliers of the domestic market, are producers of accessories, and are providing backward linkages and associated services. The discussion addresses that along with export-oriented RMG factories there exists an unexplored and unboxed universe of manufacturing and processing units associated with the RMG sector of Bangladesh that contributes to investment, employment, trade, export, and the economy as a whole, all of which remains unaccounted for.
This study explores the integration of smart apparel textiles into South African industrial workwear, aiming to enhance worker safety, comfort, and productivity in modern labour environments. Smart textiles, fabrics embedded with functional materials and electronic components, are gaining attention globally for their applications in healthcare, sports, and occupational safety. However, South Africa has been slow to adopt these innovations within its industrial sectors. Using a qualitative research design, the study employed semi-structured interviews with professionals in apparel production and industrial supply to examine the opportunities, challenges, and market readiness for smart textile integration. Thematic analysis identified six key issues hindering adoption: high production costs, lack of innovation, concerns over material durability, limited government intervention, low awareness, and insufficient skills development. Findings indicate that while smart textiles have potential to revolutionize industrial workwear by offering real-time monitoring and enhanced protective functions, widespread adoption remains constrained by systemic barriers. Participants emphasized that technological unfamiliarity, conservative market behavior, and insufficient infrastructure contribute to a sluggish innovation ecosystem. Additionally, the study found a significant gap in local capacity for producing, maintaining, and certifying smart garments under demanding industrial conditions. Despite these challenges, the research highlights growing interest in collaborative solutions involving government, academia, and industry stakeholders. Recommendations include policy development, investment in skills training, increased awareness campaigns, and the implementation of cost-effective design strategies. The study concludes that South Africa’s industrial workwear sector can benefit from targeted interventions that promote innovation and address structural limitations. Integrating smart textiles offers not only enhanced safety and functionality but also alignment with global trends toward sustainable, tech-enabled work environments.
This paper aims to identify the factors influencing companies’ performance of Textiles and Apparel Industries in Ethiopia, particularly, in the Eastern Industrial Zone. The researchers employed Maximum Likelihood with Robust (MLR) in Confirmatory Factor Analysis Model (CFA) for survey data gathered from the employees of the Textiles and Apparel industries operating in the Eastern Industrial Zone in Ethiopia which took place from 2020-2021. Main findings of this study indicate that (i) one-unit enhancement in Companies’ Performance yields a favorable impact on raw materials & financial situation; (ii) human capital experiences a rise in the expected units followed, (iii) competitive environment displaying an increase of the units as well, (iv) policies environment demonstrates an improvement, finally (v) organizational development exhibits growth of the expected units as well. This study has identified that these five endogenous latent variables are positively influenced by exogenous latent variable, Companies’ Performance because all have shown the results above the expectation. This study brings insight to investors how they boost their companies’ performance and develops the best strategies for the success of their companies.
It has been widely acknowledged that female workers account for 80% of the Ready Made Garments (RMG) industry’s workforce in Bangladesh but a number of studies estimated different male to female workers’ ratios ranging from 35: 65 to 55:45. To contribute to such debate, this paper leverage the data of the ‘Mapped in Bangladesh’ (MiB) project. While the objective of the MiB project is to enable transparency and accountability in the RMG sector by providing the industry stakeholders accurate, updated and authentic factory data collected through the factory census method and published in a digital map; this paper aims to shed light on the male to female ratio of workers employed in the RMG factories of Bangladesh is not 20:80, but it is 42:58 according to the findings from MiB data. Presenting such data, the study seeks to discuss how factory issues can influence the gender composition of RMG Workers. These issues such as factory locations, factory type, factory size and production sections are important to understand the challenges of future research addressing the gender composition of RMG workers in Bangladesh.
This study explores the integration of smart apparel textiles into South African industrial workwear, aiming to enhance worker safety, comfort, and productivity in modern labour environments. Smart textiles, fabrics embedded with functional materials and electronic components, are gaining attention globally for their applications in healthcare, sports, and occupational safety. However, South Africa has been slow to adopt these innovations within its industrial sectors. Using a qualitative research design, the study employed semi-structured interviews with professionals in apparel production and industrial supply to examine the opportunities, challenges, and market readiness for smart textile integration. Thematic analysis identified six key issues hindering adoption: high production costs, lack of innovation, concerns over material durability, limited government intervention, low awareness, and insufficient skills development. Findings indicate that while smart textiles have potential to revolutionize industrial workwear by offering real-time monitoring and enhanced protective functions, widespread adoption remains constrained by systemic barriers. Participants emphasized that technological unfamiliarity, conservative market behavior, and insufficient infrastructure contribute to a sluggish innovation ecosystem. Additionally, the study found a significant gap in local capacity for producing, maintaining, and certifying smart garments under demanding industrial conditions. Despite these challenges, the research highlights growing interest in collaborative solutions involving government, academia, and industry stakeholders. Recommendations include policy development, investment in skills training, increased awareness campaigns, and the implementation of cost-effective design strategies. The study concludes that South Africa’s industrial workwear sector can benefit from targeted interventions that promote innovation and address structural limitations. Integrating smart textiles offers not only enhanced safety and functionality but also alignment with global trends toward sustainable, tech-enabled work environments.
This paper aims to identify the factors influencing companies’ performance of Textiles and Apparel Industries in Ethiopia, particularly, in the Eastern Industrial Zone. The researchers employed Maximum Likelihood with Robust (MLR) in Confirmatory Factor Analysis Model (CFA) for survey data gathered from the employees of the Textiles and Apparel industries operating in the Eastern Industrial Zone in Ethiopia which took place from 2020-2021. Main findings of this study indicate that (i) one-unit enhancement in Companies’ Performance yields a favorable impact on raw materials & financial situation; (ii) human capital experiences a rise in the expected units followed, (iii) competitive environment displaying an increase of the units as well, (iv) policies environment demonstrates an improvement, finally (v) organizational development exhibits growth of the expected units as well. This study has identified that these five endogenous latent variables are positively influenced by exogenous latent variable, Companies’ Performance because all have shown the results above the expectation. This study brings insight to investors how they boost their companies’ performance and develops the best strategies for the success of their companies.
The ready-made garments (RMG) sector of Bangladesh has developed immensely under the broad radar of the export market, and its size is beyond imagination. Unfortunately, there has been no study that explores the whole universe of the RMG sector of Bangladesh rather the story of the RMG sector often involves the export market only. The discussion of this paper intends to explore the methodology of unboxing the whole universe of the RMG sector of Bangladesh that includes small and medium-sized manufacturing and processing units that are often involved with subcontracting for other factories (producing for the export market as well as domestic market), are suppliers of the domestic market, are producers of accessories, and are providing backward linkages and associated services. The discussion addresses that along with export-oriented RMG factories there exists an unexplored and unboxed universe of manufacturing and processing units associated with the RMG sector of Bangladesh that contributes to investment, employment, trade, export, and the economy as a whole, all of which remains unaccounted for.
It has been widely acknowledged that female workers account for 80% of the Ready Made Garments (RMG) industry’s workforce in Bangladesh but a number of studies estimated different male to female workers’ ratios ranging from 35: 65 to 55:45. To contribute to such debate, this paper leverage the data of the ‘Mapped in Bangladesh’ (MiB) project. While the objective of the MiB project is to enable transparency and accountability in the RMG sector by providing the industry stakeholders accurate, updated and authentic factory data collected through the factory census method and published in a digital map; this paper aims to shed light on the male to female ratio of workers employed in the RMG factories of Bangladesh is not 20:80, but it is 42:58 according to the findings from MiB data. Presenting such data, the study seeks to discuss how factory issues can influence the gender composition of RMG Workers. These issues such as factory locations, factory type, factory size and production sections are important to understand the challenges of future research addressing the gender composition of RMG workers in Bangladesh.
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 |