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.
ISSN 2687-2293 • Journal website ↗
Top 5 in the selected collection · Ranked by Crossref lifetime citations. Only articles with at least one known citation are included; source totals are never combined.
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: 9 / 9 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: 22 Oct 2026 02:27 UTC — 27 Oct 2026 19:03 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.
9 / 9 with data · 0 missing or unmatched. Percentages use all 9 selected publications.
9 / 9 with data · 0 missing or unmatched. Percentages use all 9 selected publications.
9 / 9 with data · 0 missing or unmatched. Percentages use all 9 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 |
|---|---|---|---|---|---|
| 2025 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| 2026 | All selected publications | 5 | 5 | 5/5 | 5/5 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2025 | All selected publications | 0 | 4 | 4/4 | 4/4 |
| 2026 | All selected publications | 0 | 5 | 5/5 | 5/5 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2025 | All selected publications | 0 | 4 | 4/4 | 4/4 |
| 2026 | All selected publications | 0 | 5 | 5/5 | 5/5 |
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 9 / 9 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): 2026-10-22T02:27:50.349300+00:00 — 2026-10-27T19:03:47.823183+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 | 9 | 9/9 |
| 2018 | All selected publications | 0 | 9 | 9/9 |
| 2019 | All selected publications | 0 | 9 | 9/9 |
| 2020 | All selected publications | 0 | 9 | 9/9 |
| 2021 | All selected publications | 0 | 9 | 9/9 |
| 2022 | All selected publications | 0 | 9 | 9/9 |
| 2023 | All selected publications | 0 | 9 | 9/9 |
| 2024 | All selected publications | 0 | 9 | 9/9 |
| 2025 | All selected publications | 0 | 9 | 9/9 |
| 2026 | All selected publications | 0 | 9 | 9/9 |
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 |
|---|---|
| 2025 | 4 |
| 2026 | 5 |
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 | 9 | 0 | 0 | 9/9 | 0.0 | 0 | 0.0 | 9/9 · 100.0% |
* Percentage among publications with a known citation count.
The rapid integration of Artificial Intelligence (AI) into higher education is fundamentally reshaping how students access and present knowledge. Within legal education particularly, AI tools increasingly enable students to generate summaries, conduct research, draft legal content and retrieve information with minimal engagement in traditional legal reasoning processes. At the same time, South African law faculties remain tasked with cultivating industry ready candidate legal practitioners who possess critical thinking abilities, ethical judgment, persuasive communication skills as well as strong legal research competencies. This creates an important pedagogical tension. While AI may enhance efficiency and technological fluency, its uncritical use may simultaneously undermine the intellectual rigour required for competent legal practice. This paper critically examines whether the growing integration of AI in legal education supports or compromises the cultivation of graduate attributes required within the South African legal profession. Using a doctrinal and conceptual methodology, the study analyses the graduate competency frameworks advanced by the Council on Higher Education and the South African Qualifications Authority. It further interrogates whether AI tools meaningfully contribute toward the development of legal reasoning, ethical sensitivity, drafting proficiency and contextual interpretation, all of which remain central to professional legal readiness. The paper argues that although AI presents significant opportunities for enhancing legal research efficiency and technological competence, overreliance on these tools risks producing graduates who are technologically capable yet jurisprudentially underdeveloped. It contends that AI cannot replace the human processes of critical engagement, ethical reflection and contextual legal interpretation that define legal education within a constitutional democracy. The study concludes that responsible AI integration in legal education requires careful pedagogical design and ethical oversight to ensure that innovation does not come at the expense of professional competence and intellectual integrity.
The study sought to relationship between enterprise risk management practices (ERM) and perceived sustainability of emerging and junior mining sector in South Africa as it creates employments and mineral diversification. This sector remains vulnerable to various risks factors including financial and regulatory among others. The objective of the study was to examine the relationship between enterprise risk management practices and perceived sustainability of emerging and junior mining sector in South Africa. The study was anchored upon the governance theory. The study adopted the use of a quantitative methodology with a cross-sectional design using a secondary data panel downloaded from Kaggle data repository. The sample size of the study was 500 which included 262 males and 238 females who work in different mining organisational sizes. Regression analysis and structural equation modeling were tested and run to test the hypothesised statements. The results of OLS regression model 1 show that ERM_Governance has a positive and significant effect on sustainability and ERM_Value shows a negative and significant association, and other ERM dimensions are positive but not statistically significant. OLS regression Model 2 for perceived socio-economic importance shows that ERM_learning is a positive and significant predictor of socio-economic importance, and other ERM dimensions are not significant in this model. On the SEM front, ERM practices exhibited strong positive effects on both perceived sector sustainability and socio-economic importance (p < 0.001). Perceived sector sustainability significantly mediated the relationship between ERM practices and socio-economic importance (p < 0.001). Additionally, organisations size and mining experience were found to significantly moderate the ERM-sustainability relationship (p < 0.05), indicating the presence of contextual effects within the emerging and junior mining sector. The theoretical implication suggests that ERM should be taken as a monolithic construct but rather as a set of complementary dimensions with distinct outcome pathways. Practically, the results imply that policymakers and mining executives should prioritise strong governance and accountability systems to enhance sustainability perceptions while simultaneously investing in learning and collaborative capabilities to maximise socio-economic impact.
Cross-sector fraud between South Africa’s financial and telecommunications sectors has intensified, driven by sophisticated criminal networks that exploit operational disparities between these industries. Traditional fraud detection systems, which depend on centralised data sharing and siloed organisational structures, encounter substantial legal and practical obstacles under the Protection of Personal Information Act (POPIA). These include restrictions on cross-entity data transfer, data minimisation requirements, and transparency obligations for automated decision-making. This study developed and evaluated a privacy-preserving framework that integrates Federated Learning (FL) and Explainable Artificial Intelligence (XAI) to enable collaborative fraud detection while safeguarding customer privacy and ensuring regulatory compliance. A mixed-method approach was employed. Quantitative experiments were conducted on a dataset of 35,675 credit card transactions linked to telecommunications parameters, training a federated global model across simulated financial and telecommunications providers. A qualitative systematic literature review identified key barriers to cross-sector collaboration and distilled lessons from prior Federated Learning and Explainable Artificial Intelligence implementations. The federated model demonstrated strong detection performance, achieving a ROC-AUC of 0.98, near-perfect precision and recall for legitimate transactions, and an F1-score of 0.89 for the minority fraud class, all without exchanging raw customer data between institutions. SHAP analysis identified TransactionAmt and card1 as the most influential features, thereby satisfying POPIA's transparency mandates. While federated approaches did not consistently surpass siloed models in raw accuracy, this marginal trade-off reflects the essential privacy-utility equilibrium required for legal compliance. The framework effectively reconciled collective security with individual privacy, confirming that POPIA adherence need not diminish detection efficacy. This study provides a validated blueprint for financial and telecommunications providers in South Africa to collaboratively counter cross-sector fraud within regulatory constraints. The framework further supports audit trail generation and decision rationales, addressing compliance obligations. Drawing on systematic literature review insights, particularly regarding collaboration barriers and prior Federated Learning and Explainable Artificial Intelligence implementations, the findings offer regulators and investors practical guidance for meeting statutory requirements without compromising privacy rights.
The study examined the critical approach adopted by entrepreneurship and policy development in promoting socio-economic development. In this review. It also looked at the difficulties small, micro, and medium-sized businesses in South Africa face. The conceptual research looked at the influence of small, micro, and medium-sized companies (SMMEs) on South African development. The study's objective was to evaluate how small, medium-sized businesses (SMMEs) are now driving economic growth and solving social issues in a setting where big businesses and governments appear to be doing very little. As a result, the study examines how SMMEs might help the nation solve persistent societal evils, including poverty, inequality, and social isolation. A desktop literature review research design was adopted to collect data on the role of entrepreneurship in South Africa. The study further looks at policies and practices of big business and government that exacerbate social issues in communities and impede socio-economic development, as well as the specific roles that SMMEs play in achieving socio-economic development goals. The findings reveal that, among various developmental outcomes, SMMEs are at the forefront of generating employment and promoting social and political justice within the nation. Future empirical studies should account for and evaluate the importance of SMME contributions to constructing economic, social, and political justice in the nation.
This study explored the relationship between consumerism, corporate social responsibility (CSR), and ethical performance in Zimbabwe through a survey analysis. The research explored the differences in consumerism between developed and less developed countries and identified various influence consumerism, including economic situation, level of consumer education and awareness, and cultural considerations. The concept of consumerism was familiar to Zimbabwean consumers, but they lacked a comprehensive understanding of how it could be used to hold businesses accountable. Consumerism is in its infancy in Zimbabwe, and citizens lack awareness of their rights and obligations. This research relied on the understanding and perceptions of consumers to examine the relationship between consumerism, CSR, and ethical performance in Zimbabwe, as well as the effects of consumer activism on corporate behaviour. The data for this exploratory study was collected and analysed using qualitative methodologies from a representative sample of 200 Zimbabwean consumers chosen at random. The collected questionnaires were carefully analysed and structured to identify themes and key differences, providing a holistic understanding of the study. The findings indicated that consumers in Zimbabwe prioritise price, brand, quality, and country of origin over CSR and the ethical performance of organisations. This suggests that businesses in Zimbabwe have little incentive to prioritise CSR and ethical behaviour unless it aligns with the demands of consumers. The research highlighted the need for more education and awareness-raising efforts around CSR and ethical business practices in Zimbabwe. Overall, the study underscored the importance of consumerism and its impact on corporate social responsibility and ethical performance in Zimbabwe.
The rapid integration of Artificial Intelligence (AI) into higher education is fundamentally reshaping how students access and present knowledge. Within legal education particularly, AI tools increasingly enable students to generate summaries, conduct research, draft legal content and retrieve information with minimal engagement in traditional legal reasoning processes. At the same time, South African law faculties remain tasked with cultivating industry ready candidate legal practitioners who possess critical thinking abilities, ethical judgment, persuasive communication skills as well as strong legal research competencies. This creates an important pedagogical tension. While AI may enhance efficiency and technological fluency, its uncritical use may simultaneously undermine the intellectual rigour required for competent legal practice. This paper critically examines whether the growing integration of AI in legal education supports or compromises the cultivation of graduate attributes required within the South African legal profession. Using a doctrinal and conceptual methodology, the study analyses the graduate competency frameworks advanced by the Council on Higher Education and the South African Qualifications Authority. It further interrogates whether AI tools meaningfully contribute toward the development of legal reasoning, ethical sensitivity, drafting proficiency and contextual interpretation, all of which remain central to professional legal readiness. The paper argues that although AI presents significant opportunities for enhancing legal research efficiency and technological competence, overreliance on these tools risks producing graduates who are technologically capable yet jurisprudentially underdeveloped. It contends that AI cannot replace the human processes of critical engagement, ethical reflection and contextual legal interpretation that define legal education within a constitutional democracy. The study concludes that responsible AI integration in legal education requires careful pedagogical design and ethical oversight to ensure that innovation does not come at the expense of professional competence and intellectual integrity.
The study sought to relationship between enterprise risk management practices (ERM) and perceived sustainability of emerging and junior mining sector in South Africa as it creates employments and mineral diversification. This sector remains vulnerable to various risks factors including financial and regulatory among others. The objective of the study was to examine the relationship between enterprise risk management practices and perceived sustainability of emerging and junior mining sector in South Africa. The study was anchored upon the governance theory. The study adopted the use of a quantitative methodology with a cross-sectional design using a secondary data panel downloaded from Kaggle data repository. The sample size of the study was 500 which included 262 males and 238 females who work in different mining organisational sizes. Regression analysis and structural equation modeling were tested and run to test the hypothesised statements. The results of OLS regression model 1 show that ERM_Governance has a positive and significant effect on sustainability and ERM_Value shows a negative and significant association, and other ERM dimensions are positive but not statistically significant. OLS regression Model 2 for perceived socio-economic importance shows that ERM_learning is a positive and significant predictor of socio-economic importance, and other ERM dimensions are not significant in this model. On the SEM front, ERM practices exhibited strong positive effects on both perceived sector sustainability and socio-economic importance (p < 0.001). Perceived sector sustainability significantly mediated the relationship between ERM practices and socio-economic importance (p < 0.001). Additionally, organisations size and mining experience were found to significantly moderate the ERM-sustainability relationship (p < 0.05), indicating the presence of contextual effects within the emerging and junior mining sector. The theoretical implication suggests that ERM should be taken as a monolithic construct but rather as a set of complementary dimensions with distinct outcome pathways. Practically, the results imply that policymakers and mining executives should prioritise strong governance and accountability systems to enhance sustainability perceptions while simultaneously investing in learning and collaborative capabilities to maximise socio-economic impact.
Cross-sector fraud between South Africa’s financial and telecommunications sectors has intensified, driven by sophisticated criminal networks that exploit operational disparities between these industries. Traditional fraud detection systems, which depend on centralised data sharing and siloed organisational structures, encounter substantial legal and practical obstacles under the Protection of Personal Information Act (POPIA). These include restrictions on cross-entity data transfer, data minimisation requirements, and transparency obligations for automated decision-making. This study developed and evaluated a privacy-preserving framework that integrates Federated Learning (FL) and Explainable Artificial Intelligence (XAI) to enable collaborative fraud detection while safeguarding customer privacy and ensuring regulatory compliance. A mixed-method approach was employed. Quantitative experiments were conducted on a dataset of 35,675 credit card transactions linked to telecommunications parameters, training a federated global model across simulated financial and telecommunications providers. A qualitative systematic literature review identified key barriers to cross-sector collaboration and distilled lessons from prior Federated Learning and Explainable Artificial Intelligence implementations. The federated model demonstrated strong detection performance, achieving a ROC-AUC of 0.98, near-perfect precision and recall for legitimate transactions, and an F1-score of 0.89 for the minority fraud class, all without exchanging raw customer data between institutions. SHAP analysis identified TransactionAmt and card1 as the most influential features, thereby satisfying POPIA's transparency mandates. While federated approaches did not consistently surpass siloed models in raw accuracy, this marginal trade-off reflects the essential privacy-utility equilibrium required for legal compliance. The framework effectively reconciled collective security with individual privacy, confirming that POPIA adherence need not diminish detection efficacy. This study provides a validated blueprint for financial and telecommunications providers in South Africa to collaboratively counter cross-sector fraud within regulatory constraints. The framework further supports audit trail generation and decision rationales, addressing compliance obligations. Drawing on systematic literature review insights, particularly regarding collaboration barriers and prior Federated Learning and Explainable Artificial Intelligence implementations, the findings offer regulators and investors practical guidance for meeting statutory requirements without compromising privacy rights.
The study examined the critical approach adopted by entrepreneurship and policy development in promoting socio-economic development. In this review. It also looked at the difficulties small, micro, and medium-sized businesses in South Africa face. The conceptual research looked at the influence of small, micro, and medium-sized companies (SMMEs) on South African development. The study's objective was to evaluate how small, medium-sized businesses (SMMEs) are now driving economic growth and solving social issues in a setting where big businesses and governments appear to be doing very little. As a result, the study examines how SMMEs might help the nation solve persistent societal evils, including poverty, inequality, and social isolation. A desktop literature review research design was adopted to collect data on the role of entrepreneurship in South Africa. The study further looks at policies and practices of big business and government that exacerbate social issues in communities and impede socio-economic development, as well as the specific roles that SMMEs play in achieving socio-economic development goals. The findings reveal that, among various developmental outcomes, SMMEs are at the forefront of generating employment and promoting social and political justice within the nation. Future empirical studies should account for and evaluate the importance of SMME contributions to constructing economic, social, and political justice in the nation.
This study explored the relationship between consumerism, corporate social responsibility (CSR), and ethical performance in Zimbabwe through a survey analysis. The research explored the differences in consumerism between developed and less developed countries and identified various influence consumerism, including economic situation, level of consumer education and awareness, and cultural considerations. The concept of consumerism was familiar to Zimbabwean consumers, but they lacked a comprehensive understanding of how it could be used to hold businesses accountable. Consumerism is in its infancy in Zimbabwe, and citizens lack awareness of their rights and obligations. This research relied on the understanding and perceptions of consumers to examine the relationship between consumerism, CSR, and ethical performance in Zimbabwe, as well as the effects of consumer activism on corporate behaviour. The data for this exploratory study was collected and analysed using qualitative methodologies from a representative sample of 200 Zimbabwean consumers chosen at random. The collected questionnaires were carefully analysed and structured to identify themes and key differences, providing a holistic understanding of the study. The findings indicated that consumers in Zimbabwe prioritise price, brand, quality, and country of origin over CSR and the ethical performance of organisations. This suggests that businesses in Zimbabwe have little incentive to prioritise CSR and ethical behaviour unless it aligns with the demands of consumers. The research highlighted the need for more education and awareness-raising efforts around CSR and ethical business practices in Zimbabwe. Overall, the study underscored the importance of consumerism and its impact on corporate social responsibility and ethical performance in Zimbabwe.
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 |