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: 3 / 3 publications (100.0%). Latest observation: 21 Oct 2026, 00:14 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 — 22 Oct 2026 02:03 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.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 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 |
| 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 |
|---|---|---|---|---|---|
| 2024 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 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 |
|---|---|---|---|---|---|
| 2024 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 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 3 / 3 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): 2026-10-21T00:14:30.102280+00:00 — 2026-10-22T02:03:39.184284+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 | 3 | 3/3 |
| 2018 | All selected publications | 0 | 3 | 3/3 |
| 2019 | All selected publications | 0 | 3 | 3/3 |
| 2020 | All selected publications | 0 | 3 | 3/3 |
| 2021 | All selected publications | 0 | 3 | 3/3 |
| 2022 | All selected publications | 0 | 3 | 3/3 |
| 2023 | All selected publications | 0 | 3 | 3/3 |
| 2024 | All selected publications | 0 | 3 | 3/3 |
| 2025 | All selected publications | 1 | 3 | 3/3 |
| 2026 | All selected publications | 1 | 3 | 3/3 |
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 |
| 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 | 3 | 2 | 2 | 3/3 | 0.67 | 1 | 66.7 | 3/3 · 100.0% |
* Percentage among publications with a known citation count.
Penetration and adoption of YouTube is growing internationally and locally. YouTube access has resulted in the emergence of vlogging. Vlogging resultantly has caught the attention of researchers, yet its role in determining consumers' buying behaviour, in general, has been less explored. There is little research on the factors that encourage Generation Z (Gen Z) purchasing behaviour based on YouTube beauty makeup vlogging. To close this gap this study adopted qualitative research. Convenience non-probability sampling was conducted using semi-structured interviews in the collection of data amongst seven Gen Z (female) vlog make-up users between the ages of 18 and 27. Thematic analysis technique was used to analyse the collected data. The main findings of the study revealed that beauty makeup vlogging affects the decision to purchase branded makeup products based on the beauty makeup awareness, vlogger product knowledge, makeup brand preference, attitude toward the brand, beauty makeup application learning techniques, brand affordability and vlogging channel usage. Furthermore, South African Gen Z consumers’ preference for international beauty makeup brands stems from the greater exposure of those brands, in comparison to local brands. The study concluded with contributions and future research directions.
The absence of a digital platform that facilitates customised and personalised, accurately fitted apparel has created a vacuum for those looking for tailored apparel as a form of individualism that mirrors their perceived personality, attitudes, and behaviour. This research study investigated the need to purchase customised and personalised apparel online in South Africa against the backdrop of evolving disruptive technologies driving a growing global economy and marketplace. This has dramatically influenced the textile and fashion industry to integrate with the digital world. A sample of 100 participants was divided into 4 age categories. A total of 27 responses were collected utilising an online survey questionnaire and analysed using descriptive statistics on Microsoft Excel. The demand for customised, personalised apparel online has been investigated by analysing who purchases apparel from a dressmaker. The results, while indicating that less than half of the respondents purchase customised, personalised apparel, also highlight a significantly high percentage that does, substantiating the potential of this market. Furthermore, the findings reveal a strong willingness among respondents to use 3D mobile scanning, purchase more if it is more accessible, and participate in the design process online. The study is essential to understanding motivations for purchasing customised and personalised apparel, market demand and consumer preferences to boost local economic growth, and customising and personalising clothing as an individual expression within a unique social context. The outcome will enable customers to partake in online design and add their body measurements to purchase apparel tailored to their specific needs.
This study examined media advertisement, self-image, and self-esteem as predictors of skin bleaching behavior among Nigerian undergraduate students, grounded in Social Learning Theory. A cross-sectional survey of 350 undergraduates (56.3% female) from five on-campus hostels used the Rosenberg Self-Esteem Scale, an adapted Body Self-Image Questionnaire, and media influence assessments. Multiple regression analysis examined predictive relationships. The findings reveal media advertisements were the strongest predictor of skin bleaching behavior , followed by self-image and self-esteem. The model explained 62.4% of variance. Currently, 25.4% of participants used skin bleaching products, 77.1% had family history of the practice, and only 8.1% demonstrated adequate knowledge of health risks. The findings highlight media influence as the primary behavioral driver, supporting Social Learning Theory's emphasis on environmental factors and observational learning. Interventions must address media messaging, regulatory frameworks, and health literacy rather than relying solely on individual psychological interventions.
This study examined media advertisement, self-image, and self-esteem as predictors of skin bleaching behavior among Nigerian undergraduate students, grounded in Social Learning Theory. A cross-sectional survey of 350 undergraduates (56.3% female) from five on-campus hostels used the Rosenberg Self-Esteem Scale, an adapted Body Self-Image Questionnaire, and media influence assessments. Multiple regression analysis examined predictive relationships. The findings reveal media advertisements were the strongest predictor of skin bleaching behavior , followed by self-image and self-esteem. The model explained 62.4% of variance. Currently, 25.4% of participants used skin bleaching products, 77.1% had family history of the practice, and only 8.1% demonstrated adequate knowledge of health risks. The findings highlight media influence as the primary behavioral driver, supporting Social Learning Theory's emphasis on environmental factors and observational learning. Interventions must address media messaging, regulatory frameworks, and health literacy rather than relying solely on individual psychological interventions.
Penetration and adoption of YouTube is growing internationally and locally. YouTube access has resulted in the emergence of vlogging. Vlogging resultantly has caught the attention of researchers, yet its role in determining consumers' buying behaviour, in general, has been less explored. There is little research on the factors that encourage Generation Z (Gen Z) purchasing behaviour based on YouTube beauty makeup vlogging. To close this gap this study adopted qualitative research. Convenience non-probability sampling was conducted using semi-structured interviews in the collection of data amongst seven Gen Z (female) vlog make-up users between the ages of 18 and 27. Thematic analysis technique was used to analyse the collected data. The main findings of the study revealed that beauty makeup vlogging affects the decision to purchase branded makeup products based on the beauty makeup awareness, vlogger product knowledge, makeup brand preference, attitude toward the brand, beauty makeup application learning techniques, brand affordability and vlogging channel usage. Furthermore, South African Gen Z consumers’ preference for international beauty makeup brands stems from the greater exposure of those brands, in comparison to local brands. The study concluded with contributions and future research directions.
The absence of a digital platform that facilitates customised and personalised, accurately fitted apparel has created a vacuum for those looking for tailored apparel as a form of individualism that mirrors their perceived personality, attitudes, and behaviour. This research study investigated the need to purchase customised and personalised apparel online in South Africa against the backdrop of evolving disruptive technologies driving a growing global economy and marketplace. This has dramatically influenced the textile and fashion industry to integrate with the digital world. A sample of 100 participants was divided into 4 age categories. A total of 27 responses were collected utilising an online survey questionnaire and analysed using descriptive statistics on Microsoft Excel. The demand for customised, personalised apparel online has been investigated by analysing who purchases apparel from a dressmaker. The results, while indicating that less than half of the respondents purchase customised, personalised apparel, also highlight a significantly high percentage that does, substantiating the potential of this market. Furthermore, the findings reveal a strong willingness among respondents to use 3D mobile scanning, purchase more if it is more accessible, and participate in the design process online. The study is essential to understanding motivations for purchasing customised and personalised apparel, market demand and consumer preferences to boost local economic growth, and customising and personalising clothing as an individual expression within a unique social context. The outcome will enable customers to partake in online design and add their body measurements to purchase apparel tailored to their specific needs.
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 00:14 UTC · ijbes | complete · ijbes · 21 Oct 2026 00:14 UTC |
| ojs-rest | 21 Oct 2026 00:14 UTC · ijbes | complete · ijbes · 21 Oct 2026 00:02 UTC |
| oai-pmh | 20 Oct 2026 19:49 UTC · ijbes | complete · ijbes · 20 Oct 2026 19:48 UTC |
| openalex-enrichment | 21 Oct 2026 00:14 UTC · ijbes | complete · ijbes · 21 Oct 2026 00:14 UTC |