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, 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 07:22 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 | 1 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2024 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 4 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2024 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2025 | 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 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): 2026-10-21T00:14:31.476573+00:00 — 2026-10-21T07:22:59.958589+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 | 2 | 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 | 1 |
| 2025 | 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 Business Ecosystem & Strategy Bussecon International Academy | 1 | 2 | 1 | 1/1 | 2.0 | 2 | 100.0 | 1/1 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 2 | 3 | 2 | 2/2 | 1.5 | 1.5 | 100.0 | 2/2 · 100.0% |
* Percentage among publications with a known citation count.
The study aimed to investigate factors influencing the intention to adopt e-health tools for accessing digital mental health services among university staff at a selected South African higher education institution. A quantitative case study was conducted at a single university in Cape Town, South Africa. Data was collected from 348 respondents using online questionnaires and analysed using descriptive statistics and multiple regression. The results revealed that stable internet access at home did not significantly influence the intention to adopt e-health tools (p =0.698, ? = -0.020), but concerns about internet costs were a barrier (p = 0.011, ? = 0.131). University staff who used multiple devices (p = 0.014, ? = 0.136) and preferred e-health tools over traditional face-to-face services were more likely to adopt them (p < 0.001, ? = 0.319). Moreover, social media usage (p < 0.766, ? = 0.016), searching for digital mental health services (p <0.107, ? = 0.085), and awareness of free university services (p < 0.307, ? = -0.058), DMHS providers (p < 0.529, ? = 0.038), and fourth industrial revolution technologies (p < 0.106, ? = -0.091) did not significantly influence adoption intentions. The study contributes to sustainable digital mental health support for university staff in South Africa. The results could potentially be applicable to similar contexts globally. Thus, helping to achieve the Sustainable Development Goal 3 of the United Nations of ensuring good health and wellbeing.
This study aims to explore the current state of the adoption of e-health tools for digital mental health services (DMHS) at a selected South African higher education institution. A quantitative case study was conducted at a single university in Cape Town, South Africa, surveying 348 voluntary participants. Data analysis involved descriptive statistics and exploratory factor analysis through Statistical Package for Social Sciences (SPSS) version 28. The results of this study indicate that awareness of freely accessible DMHS, service providers, and Fourth Industrial Revolution (4IR) technologies are significant factors driving the adoption of e-health tools. However, internet accessibility, internet cost concerns, multiple device use, social media use, searching for services, and preferences for digital over in-person services showed no significant correlation with the current state of adoption factor. The study concludes that awareness of DMHS, service providers, and 4IR technologies are key drivers for the sustainable adoption of e-health tools post-COVID-19. The results inform practical strategies for the sustainable adoption of e-health tools in SA HEIs and may be relevant to similar institutions in global contexts. The findings are potentially applicable to similar contexts globally. Thus, helping to achieve the Sustainable Development Goal 3 of the United Nations of ensuring good health and wellbeing.
The recent COVID-19 pandemic significantly exacerbated mental health issues worldwide, with South African university staff facing unique stressors that led to the adoption of e-health tools for digital mental health services. This study explores the university staff experiences gained during COVID-19 pandemic in the provision of digital mental health services through e-health tools. We conducted a case study at a public university in Cape Town, South Africa complemented by a survey using quantitative methods. Data from 348 respondents were collected via voluntary response sampling and analysed with Statistical Software Package for Social Sciences (SPSS) version 28, employing exploratory factor analysis. High factor loadings above 0.4 confirmed significant contributions from seven items: perceived usefulness of e-health tools, perceived usefulness of social media, user experience and satisfaction, university commitment, variety of digital mental health services, post-COVID-19 delivery mode preference and ethical considerations. These seven items revealed that during COVID-19, e-health tools provided significant benefits to university staff including increased access to digital mental services. However, the study findings revealed that university staff were concerned about the lack of a legal framework during COVID-19, and the role of AI-powered mental health applications did not significantly influence their experiences during the pandemic. Our study provides theoretical insights into the effectiveness and perceived usefulness of e-health tools for digital mental health services during COVID-19, reinforcing their significance in reducing anxiety, stress, and depression while enhancing emotional resilience. Practically, our study findings suggest that universities should integrate e-health tools into the provision of digital mental health services given that these tools proved effective during COVID-19.
This study aims to explore the current state of the adoption of e-health tools for digital mental health services (DMHS) at a selected South African higher education institution. A quantitative case study was conducted at a single university in Cape Town, South Africa, surveying 348 voluntary participants. Data analysis involved descriptive statistics and exploratory factor analysis through Statistical Package for Social Sciences (SPSS) version 28. The results of this study indicate that awareness of freely accessible DMHS, service providers, and Fourth Industrial Revolution (4IR) technologies are significant factors driving the adoption of e-health tools. However, internet accessibility, internet cost concerns, multiple device use, social media use, searching for services, and preferences for digital over in-person services showed no significant correlation with the current state of adoption factor. The study concludes that awareness of DMHS, service providers, and 4IR technologies are key drivers for the sustainable adoption of e-health tools post-COVID-19. The results inform practical strategies for the sustainable adoption of e-health tools in SA HEIs and may be relevant to similar institutions in global contexts. The findings are potentially applicable to similar contexts globally. Thus, helping to achieve the Sustainable Development Goal 3 of the United Nations of ensuring good health and wellbeing.
The study aimed to investigate factors influencing the intention to adopt e-health tools for accessing digital mental health services among university staff at a selected South African higher education institution. A quantitative case study was conducted at a single university in Cape Town, South Africa. Data was collected from 348 respondents using online questionnaires and analysed using descriptive statistics and multiple regression. The results revealed that stable internet access at home did not significantly influence the intention to adopt e-health tools (p =0.698, ? = -0.020), but concerns about internet costs were a barrier (p = 0.011, ? = 0.131). University staff who used multiple devices (p = 0.014, ? = 0.136) and preferred e-health tools over traditional face-to-face services were more likely to adopt them (p < 0.001, ? = 0.319). Moreover, social media usage (p < 0.766, ? = 0.016), searching for digital mental health services (p <0.107, ? = 0.085), and awareness of free university services (p < 0.307, ? = -0.058), DMHS providers (p < 0.529, ? = 0.038), and fourth industrial revolution technologies (p < 0.106, ? = -0.091) did not significantly influence adoption intentions. The study contributes to sustainable digital mental health support for university staff in South Africa. The results could potentially be applicable to similar contexts globally. Thus, helping to achieve the Sustainable Development Goal 3 of the United Nations of ensuring good health and wellbeing.
The recent COVID-19 pandemic significantly exacerbated mental health issues worldwide, with South African university staff facing unique stressors that led to the adoption of e-health tools for digital mental health services. This study explores the university staff experiences gained during COVID-19 pandemic in the provision of digital mental health services through e-health tools. We conducted a case study at a public university in Cape Town, South Africa complemented by a survey using quantitative methods. Data from 348 respondents were collected via voluntary response sampling and analysed with Statistical Software Package for Social Sciences (SPSS) version 28, employing exploratory factor analysis. High factor loadings above 0.4 confirmed significant contributions from seven items: perceived usefulness of e-health tools, perceived usefulness of social media, user experience and satisfaction, university commitment, variety of digital mental health services, post-COVID-19 delivery mode preference and ethical considerations. These seven items revealed that during COVID-19, e-health tools provided significant benefits to university staff including increased access to digital mental services. However, the study findings revealed that university staff were concerned about the lack of a legal framework during COVID-19, and the role of AI-powered mental health applications did not significantly influence their experiences during the pandemic. Our study provides theoretical insights into the effectiveness and perceived usefulness of e-health tools for digital mental health services during COVID-19, reinforcing their significance in reducing anxiety, stress, and depression while enhancing emotional resilience. Practically, our study findings suggest that universities should integrate e-health tools into the provision of digital mental health services given that these tools proved effective during COVID-19.
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 |