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 07:23 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.
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 |
|---|---|---|---|---|---|
| 2023 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2023 | All selected publications | 6 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 3 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2023 | All selected publications | 6 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 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): 2026-10-21T00:14:31.451019+00:00 — 2026-10-21T07:23:03.236979+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 | 2 | 3 | 3/3 |
| 2025 | All selected publications | 3 | 3 | 3/3 |
| 2026 | All selected publications | 4 | 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 |
|---|---|
| 2023 | 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 | 1 | 1 | 1/1 | 1.0 | 1 | 100.0 | 1/1 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 2 | 8 | 8 | 2/2 | 4.0 | 4.0 | 100.0 | 2/2 · 100.0% |
* Percentage among publications with a known citation count.
The primary aim of this study was to assess the demand for internet services before and during the Covid-19 pandemic, considering the challenges and opportunities brought about by the global health crisis. While the pandemic has had numerous negative impacts on people's lives, it has also facilitated advancements in technology, particularly the adoption of the 4th industrial revolution. To explore the positive impacts of these technological advancements, the study focused on analysing changes in household internet usage using the 2019 and 2021 General Household Survey data obtained from STATS SA. The study examined the shifts in internet usage between the two data sets and found a modest increase in internet usage over time. To further investigate the determinants of household internet usage, the study employed descriptive analysis, cross-tabulations, and a binary logistic regression model. Income, age, household size, and gender were used as independent variables, while internet usage served as the dependent variable. The results revealed that all the independent variables were statistically significant factors influencing the probability of internet usage. Income and household size demonstrated a positive relationship with internet usage, indicating that higher levels of income and larger household sizes were associated with increased demand for internet services. Conversely, the age of the household head showed a negative effect on internet usage, suggesting that as individuals grew older, their likelihood of using the internet decreased. Additionally, the study found that male-headed households exhibited higher levels of internet usage compared to their female counterparts. To ensure that digital inclusion is prioritized, it is crucial for authorities to ensure that internet access is accessible to low-income households. Addressing the disparity in internet usage between higher and lower-income households is essential. Government regulators can encourage broadband providers to expand affordable internet access, while reducing administrative burdens to facilitate network deployment, thereby supporting the current levels of internet usage, and promoting further growth. By considering these findings, policymakers and stakeholders can develop strategies to bridge the digital divide and ensure equal access to internet services for all segments of society. This will contribute to a more inclusive and equitable digital landscape, fostering social and economic development in the medium to long term.
This article examines the load-shedding crisis in South Africa, primarily emphasising its disruption of digital development and its hindrance to the nation's ability to fulfil the requirements of digitalisation and the Fourth Industrial Revolution (4IR). The report aimed to evaluate the impact of digitalisation on community development following COVID-19. The South African government has implemented strategies aimed at addressing the crises of unequal access to digital resources in communities; however, the ongoing load-shedding crisis continues to hinder progress by imposing daily power cuts, thereby exacerbating the digital divide. It is essential to acknowledge that COVID-19 has compelled the government and private sector organisations to transition into the digital era, where operations are conducted without physical presence, relying on technical innovations. The research concluded that the government has insufficiently addressed the issue of load-shedding, now referred to as load-reduction, and has demonstrated an incapacity to implement effective systems to respond to the crises. The study contends that the issue of load-shedding in South Africa can be mitigated if the government invests in nations with sufficient energy connectivity to support digital advancement. The study asserts that the nation will lag in benefiting from digitalisation unless the obstacles impeding it are promptly resolved. The study employed a qualitative methodology, enhanced by a critical evaluation and analysis of secondary data obtained from electronic documents. The study substantially identifies deficiencies related to the load-shedding issues and proposes ways to aid the government in effectively addressing these crises, ensuring equitable access to technology, and bridging the South African digital divide.
The COVID-19 pandemic has highlighted the critical role of access to information in determining socioeconomic outcomes, especially in the context of poverty alleviation. This study explores the nexus between access to information and poverty in South Africa, emphasizing the role of digital connectivity in socio-economic development using data from the General Household Survey (GHS) 2023 collected by STATSSA. Through logistic regression analysis, the study examines the impact of internet access and other information channels on poverty levels, with a particular focus on marginalized groups, including female-headed households. Using regression analysis and descriptive statistics, the findings reveal that access to internet services significantly influences household poverty levels. Households with access to mobile internet, Wi-Fi, and other forms of digital connectivity are less likely to experience poverty, highlighting the critical role of technology in improving access to education, employment, and government services. However, significant disparities persist, with approximately 29% of households lacking mobile internet and over 93% without access to public Wi-Fi, exacerbating the digital divide, particularly in rural and low-income communities. The results further indicate that socio-demographic factors, including age, gender, race, and household size, intersect with digital access to shape poverty outcomes. Female-headed and black households are disproportionately affected by poverty, reflecting broader structural inequalities. Limited access to technology among these groups amplifies their vulnerability, emphasizing the urgent need for targeted interventions. The results underscore the importance of addressing information disparities to promote financial inclusion and drive sustainable development in the post-pandemic era. This paper provides evidence-based insights for policymakers to develop targeted interventions that bridge the digital divide and support vulnerable populations in South Africa. The study concludes that improving digital inclusion is essential for addressing poverty, fostering economic mobility, and reducing inequality in the post-COVID-19 era. It calls for policy efforts to enhance internet accessibility and bridge the digital divide, particularly for marginalized populations.
This article examines the load-shedding crisis in South Africa, primarily emphasising its disruption of digital development and its hindrance to the nation's ability to fulfil the requirements of digitalisation and the Fourth Industrial Revolution (4IR). The report aimed to evaluate the impact of digitalisation on community development following COVID-19. The South African government has implemented strategies aimed at addressing the crises of unequal access to digital resources in communities; however, the ongoing load-shedding crisis continues to hinder progress by imposing daily power cuts, thereby exacerbating the digital divide. It is essential to acknowledge that COVID-19 has compelled the government and private sector organisations to transition into the digital era, where operations are conducted without physical presence, relying on technical innovations. The research concluded that the government has insufficiently addressed the issue of load-shedding, now referred to as load-reduction, and has demonstrated an incapacity to implement effective systems to respond to the crises. The study contends that the issue of load-shedding in South Africa can be mitigated if the government invests in nations with sufficient energy connectivity to support digital advancement. The study asserts that the nation will lag in benefiting from digitalisation unless the obstacles impeding it are promptly resolved. The study employed a qualitative methodology, enhanced by a critical evaluation and analysis of secondary data obtained from electronic documents. The study substantially identifies deficiencies related to the load-shedding issues and proposes ways to aid the government in effectively addressing these crises, ensuring equitable access to technology, and bridging the South African digital divide.
The COVID-19 pandemic has highlighted the critical role of access to information in determining socioeconomic outcomes, especially in the context of poverty alleviation. This study explores the nexus between access to information and poverty in South Africa, emphasizing the role of digital connectivity in socio-economic development using data from the General Household Survey (GHS) 2023 collected by STATSSA. Through logistic regression analysis, the study examines the impact of internet access and other information channels on poverty levels, with a particular focus on marginalized groups, including female-headed households. Using regression analysis and descriptive statistics, the findings reveal that access to internet services significantly influences household poverty levels. Households with access to mobile internet, Wi-Fi, and other forms of digital connectivity are less likely to experience poverty, highlighting the critical role of technology in improving access to education, employment, and government services. However, significant disparities persist, with approximately 29% of households lacking mobile internet and over 93% without access to public Wi-Fi, exacerbating the digital divide, particularly in rural and low-income communities. The results further indicate that socio-demographic factors, including age, gender, race, and household size, intersect with digital access to shape poverty outcomes. Female-headed and black households are disproportionately affected by poverty, reflecting broader structural inequalities. Limited access to technology among these groups amplifies their vulnerability, emphasizing the urgent need for targeted interventions. The results underscore the importance of addressing information disparities to promote financial inclusion and drive sustainable development in the post-pandemic era. This paper provides evidence-based insights for policymakers to develop targeted interventions that bridge the digital divide and support vulnerable populations in South Africa. The study concludes that improving digital inclusion is essential for addressing poverty, fostering economic mobility, and reducing inequality in the post-COVID-19 era. It calls for policy efforts to enhance internet accessibility and bridge the digital divide, particularly for marginalized populations.
The primary aim of this study was to assess the demand for internet services before and during the Covid-19 pandemic, considering the challenges and opportunities brought about by the global health crisis. While the pandemic has had numerous negative impacts on people's lives, it has also facilitated advancements in technology, particularly the adoption of the 4th industrial revolution. To explore the positive impacts of these technological advancements, the study focused on analysing changes in household internet usage using the 2019 and 2021 General Household Survey data obtained from STATS SA. The study examined the shifts in internet usage between the two data sets and found a modest increase in internet usage over time. To further investigate the determinants of household internet usage, the study employed descriptive analysis, cross-tabulations, and a binary logistic regression model. Income, age, household size, and gender were used as independent variables, while internet usage served as the dependent variable. The results revealed that all the independent variables were statistically significant factors influencing the probability of internet usage. Income and household size demonstrated a positive relationship with internet usage, indicating that higher levels of income and larger household sizes were associated with increased demand for internet services. Conversely, the age of the household head showed a negative effect on internet usage, suggesting that as individuals grew older, their likelihood of using the internet decreased. Additionally, the study found that male-headed households exhibited higher levels of internet usage compared to their female counterparts. To ensure that digital inclusion is prioritized, it is crucial for authorities to ensure that internet access is accessible to low-income households. Addressing the disparity in internet usage between higher and lower-income households is essential. Government regulators can encourage broadband providers to expand affordable internet access, while reducing administrative burdens to facilitate network deployment, thereby supporting the current levels of internet usage, and promoting further growth. By considering these findings, policymakers and stakeholders can develop strategies to bridge the digital divide and ensure equal access to internet services for all segments of society. This will contribute to a more inclusive and equitable digital landscape, fostering social and economic development in the medium to long term.
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 |