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: 155 / 155 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 — 29 Oct 2026 16:49 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.
155 / 155 with data · 0 missing or unmatched. Percentages use all 155 selected publications.
155 / 155 with data · 0 missing or unmatched. Percentages use all 155 selected publications.
155 / 155 with data · 0 missing or unmatched. Percentages use all 155 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 | 3 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 15 | 15 | 15/15 | 15/15 |
| 2023 | All selected publications | 15 | 15 | 15/15 | 15/15 |
| 2024 | All selected publications | 40 | 40 | 40/40 | 40/40 |
| 2025 | All selected publications | 51 | 51 | 51/51 | 51/51 |
| 2026 | All selected publications | 31 | 31 | 31/31 | 31/31 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 6 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 37 | 15 | 15/15 | 15/15 |
| 2023 | All selected publications | 60 | 15 | 15/15 | 15/15 |
| 2024 | All selected publications | 70 | 40 | 40/40 | 40/40 |
| 2025 | All selected publications | 27 | 51 | 51/51 | 51/51 |
| 2026 | All selected publications | 4 | 31 | 31/31 | 31/31 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 8 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 40 | 15 | 15/15 | 15/15 |
| 2023 | All selected publications | 79 | 15 | 15/15 | 15/15 |
| 2024 | All selected publications | 81 | 40 | 40/40 | 40/40 |
| 2025 | All selected publications | 25 | 51 | 51/51 | 51/51 |
| 2026 | All selected publications | 3 | 31 | 31/31 | 31/31 |
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 155 / 155 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): 2026-10-21T07:22:43.910218+00:00 — 2026-10-29T16:49:13.181695+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 | 155 | 155/155 |
| 2018 | All selected publications | 0 | 155 | 155/155 |
| 2019 | All selected publications | 0 | 155 | 155/155 |
| 2020 | All selected publications | 0 | 155 | 155/155 |
| 2021 | All selected publications | 0 | 155 | 155/155 |
| 2022 | All selected publications | 2 | 155 | 155/155 |
| 2023 | All selected publications | 14 | 155 | 155/155 |
| 2024 | All selected publications | 54 | 155 | 155/155 |
| 2025 | All selected publications | 89 | 155 | 155/155 |
| 2026 | All selected publications | 77 | 155 | 155/155 |
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 |
|---|---|
| 2021 | 3 |
| 2022 | 15 |
| 2023 | 15 |
| 2024 | 40 |
| 2025 | 51 |
| 2026 | 31 |
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 | 155 | 204 | 236 | 155/155 | 1.32 | 1 | 50.3 | 155/155 · 100.0% |
* Percentage among publications with a known citation count.
The study looked at the role and contribution of entrepreneurship industry to foster social and economic development through small, micro, and medium-sized businesses in South Africa. The conceptual research looked at the influence of small, micro, and medium-sized businesses (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, policies and practices of big business and government that exacerbate social issues in communities and impede socioeconomic development, as well as the specific roles that SMMEs play in achieving socioeconomic development goals. Findings from research indicate that, among other development outcomes, SMMEs are at the vanguard of creating jobs and fostering social and political justice in the nation. Future empirical studies that account for and evaluate the importance of SMME contributions to the construction of economic, social, and political justice in the nation are advised because this work lacks the empirical support necessary to substantiate its conclusions and observations.
The present study undertakes an analysis of monitoring and evaluation as a pragmatic methodology for enhancing the efficacy of municipalities in the context of South Africa. Research findings indicate that a significant obstacle faced by South African municipalities is the issue of substandard performance. These aspects can be linked to several reasons, such as inadequate allocation of resources, limitations in capacity, and deficiencies in monitoring and assessment processes. This article posits that the use of monitoring and evaluation (M&E) is crucial in addressing instances of suboptimal performance within municipalities. There exists a necessity to enhance the monitoring and evaluation (M&E) systems in order to attain efficient performance management and ensure the provision of high-quality service delivery. The research was conducted among three distinct municipalities, namely Amajuba, uThukela, and uMgungundlovu, located in the province of KwaZulu-Natal (KZN). The sample population comprised of municipal officials belonging to the Performance Management System (PMS) and Corporate Services units. The study employed a qualitative methodology and deliberately selected important participants who possessed knowledge and a comprehensive understanding of the subject matter under investigation. Furthermore, it is worth noting that the sample size consisted of 20 people. The researchers organized focus group conversations in order to gather data, which was subsequently controlled through the utilization of the NVivo software application. The findings of the study indicate that monitoring and evaluation (M&E) is a crucial component in enhancing the performance of local government. Its primary purpose is to establish the essential circumstances for achieving greater levels of performance that are sustainable over an extended period. Municipalities are required to demonstrate a firm dedication to the prioritization and adoption of municipal monitoring and evaluation (M&E) as an essential tool for facilitating progressive local governance. This commitment involves the institutionalization of M&E practices, the enforcement of mandatory adherence to established systems and procedures, the integration of information technology systems to enhance and fortify the implementation of M&E, and the utilization of participatory M&E approaches to assess the effectiveness of municipal functions and development endeavors.
This study advances research and practice related to adopting artificial intelligence (AI) in the context of South Africa (SA). The study evaluated AI adoption by South African manufacturing Small and Medium Enterprises (SMEs); established the challenges faced by manufacturing SMEs in adopting AI; and developed a framework for adopting AI for manufacturing SMEs’ growth and survival. The study adopted a systematic literature review approach. Articles from Scopus and Google scholar databases, ranging from the years 2018 to 2024, were used. Of the 206 articles found, 54 were shortlisted. The systematic review analysis was performed using the PRISMA framework. The results identified AI adoption by South African manufacturing SMEs is low, limiting their innovation and productivity. The results also show, despite the numerous benefits AI adoption can offer manufacturing SMEs in the country, a major constraint is the lack of a framework to enhance adoption and implementation. Hence, this study was conducted to develop a framework to improve AI adoption by South African manufacturing SMEs. The findings contribute to the body of knowledge and provide new insights to manufacturing SME owners/managers, policymakers and practitioners into AI adoption to enhance manufacturing SMEs’ ability to compete on the global stage.
The proliferation of technology has innovated new ways of saving and investing money: online savings and investment platforms [OSIPs]. Predicting the intention to adopt Fintech has triggered the interest of many researchers in academia and industries. The primary objective of this study was to examine factors influencing the intention to adopt OSIPs in Nigeria by using data gathered from 640 digitally active citizens from selected cities in Nigeria. Reliability and validity tests were conducted through construct reliability and discriminant validity, respectively, through WarpPLS. Data were computed with the Statistical Package for the Social Sciences (version) 24 and analyzed with structural equation modeling via WarpPLS. The findings of this study showed that performance expectancy, effort expectancy, and social influence significantly affect attitude while facilitating conditions predict intention to adopt. It was also found that attitude significantly influences the intention to adopt OSIPs. The paper also discusses the study’s research limitations and presents implications for theory and practice.
Small and Medium Enterprises (SMEs) in South Africa previously faced challenges due to limited resources, restricted access to technology, and the need to constantly adapt to a dynamic business environment. The introduction of Generative Artificial Intelligence (AI) emerged as a potential solution to these issues, promising to enhance operational efficiency and strategic decision-making. As a representative of developing economies, South Africa experienced a growing interest in AI technologies. This study was conducted to explore the impact of generative AI on SME productivity in South Africa, an area which had been underexplored. Employing a qualitative methodology, the study evaluated the current state and implications of generative AI in South African SMEs. It involved in-depth interviews to gather perceptions, experiences, challenges, and benefits from SME owners and managers regarding the adoption of generative AI technologies. The findings analysed via R Statistical Software revealed significant insights into the specific areas where generative AI substantially impacted SME productivity. It also identified the challenges and opportunities associated with the adoption of generative AI by SMEs, as well as the potential long-term implications. Key findings included notable improvements in data-driven decision-making, operational efficiencies, and market expansion strategies. However, the study also highlighted barriers such as the lack of technical expertise, initial setup costs, and concerns over data security. Overall, the impact of generative AI on SMEs in South Africa was found to be predominantly positive, paving the way for further technological advancements and adoption in the sector.
This article employs a structured, theory-guided comparative policy analysis to examine the persistent deficits in community trust that undermine primary health care (PHC) service delivery in Nigeria. Despite numerous reforms targeting financing and administrative structures, PHC utilization remains low and citizen confidence fragile. Drawing on policy learning theory, this study argues that Nigeria’s PHC reforms have predominantly involved first-order, instrumental adjustments, failing to foster deeper shifts in governance philosophy or state-citizen relations essential for building trust. Using Rwanda as a contrasting case, the analysis illustrates how its post-genocide PHC reconstruction leveraged second- and third-order policy learning to embed community trust within governance through co-production, robust accountability mechanisms, and deliberate institutional design. The paper identifies context-sensitive policy lessons for Nigeria, emphasizing that trust must be recognized and actively governed as a strategic resource for legitimate, responsive, and sustainable PHC systems, rather than merely a residual outcome of service delivery.
The current economic crisis which is a consequence of fuel subsidy removal and devaluation of Nigerian currency through foreign exchange unification is a reality as nearly every household is feeling the heat. The evidence is plethora including high inflation, loss of jobs, malnutrition, imbalance diets among others. These relatively impactful factors have heightened psychological and economic stress in the country in recent time. Hence, citizens result to relieving pain via humorous interjections thereby creating a nexus between economic severity and humour. This paper interrogates “humour” as a coping mechanism and instrument of political critique in the ocean of economic hardship popularised by humourists, comedians and entertainment industrialists in an organised event and social media platforms. This study relies on secondary materials and memes from social media platforms and online newspapers between May 2023 till date and thereby subjected to qualitative content analysis. The selection criteria of the memes were based on the period when those policies were implemented. Coping theory was deployed as the theoretical foundation. The study found out that humour helps to strengthen social bonds, reduction of stress, change of perception, as a means of livelihood and political critique.
Artificial Intelligence (AI) is transforming higher education, offering both pedagogical advantages and potential academic risks for teaching and learning. This systematic review consolidates existing research to examine whether AI enhances or hinders pedagogical practices in universities. Results show that AI boosts efficiency, supports personalization, and promotes inclusivity through adaptive learning systems, intelligent tutoring platforms, and automated assessment tools. The findings, however, also indicate risks such as academic integrity issues, algorithmic bias, unequal access, and the potential deskilling of educators. Furthermore, policy gaps exacerbate these risks, subjecting institutions to ethical dilemmas and inconsistent practices. The review concludes that responsible integration, supported by strong governance frameworks, drives the impact of AI in academia. Recommendations for policymakers include introducing clear institutional policies for the use of AI in higher education, promoting equitable access to digital infrastructure, and mandating transparency and accountability to ensure trust and fairness in high-stakes decision-making. For higher education institutions, establishing comprehensive guidelines for AI use, investing in faculty training, and redesigning assessments can help ensure inclusivity, preserving creativity, and emphasizing originality and problem-solving skills less easily replicated by AI.
Customer service quality is still a major problem in Zimbabwe’s telecommunications sector. This study aimed to develop a framework to improve customer service in the sector. It focused on how network reliability, how quickly complaints are resolved, staff skills and empathy, and customer involvement in service improvement affect customer satisfaction. Customers often face network failures, slow responses to complaints, and poor communication with service providers. These problems reduce customer satisfaction.: The study used data collected in Manicaland Province, Zimbabwe. A convergent parallel mixed-methods approach was used. Quantitative data were collected from a random sample of 220 customers, while qualitative data were collected from 13 purposively selected participants, including managers and frontline customer service staff. Data analysis was conducted using SPSS AMOS version 23 and thematic analysis. The results showed that network reliability had a strong and positive effect on customer satisfaction. Staff competence and empathy also had a significant positive effect on customer satisfaction, showing that skilled, respectful, and caring staff improve customers’ experiences. In contrast, complaint-resolution efficiency did not have a significant effect on customer satisfaction. The analysis further showed that value co-creation partially mediated the relationship between network reliability and customer satisfaction, as well as between staff competence and customer satisfaction. The study recommended that regulators such as Postal and Telecommunications Regulatory Authority of Zimbabwe (POTRAZ) enforce service quality standards, and that policymakers support digital inclusion and innovation. The study contributes that customer satisfaction can be improved by upgrading network infrastructure, providing ongoing staff training, empowering frontline employees, and creating formal ways to engage customers.
Employee retention remains a critical concern across corporate sectors due to its far-reaching implications for operational efficiency, institutional knowledge preservation, and sustained organisational performance. This systematic literature review examines contemporary approaches to addressing employee retention challenges, with a specific focus on the manufacturing, healthcare, and tech industries. Drawing on peer-reviewed publications from 1998 to 2025, the study identifies and synthesises the primary drivers of employee turnover, while evaluating evidence-based strategies employed to mitigate it. The findings reveal that while financial incentives continue to play a foundational role in employee retention, they are increasingly insufficient in isolation. Sector-specific dynamics, such as labour inflexibility in manufacturing, emotional burnout in healthcare, and innovation fatigue in technology-intensive environments, necessitate tailored and context-sensitive retention strategies. Emerging themes identified include the growing influence of employee engagement initiatives, adaptive leadership practices, flexible work arrangements, and the strategic integration of technology in supporting retention goals. Notably, the review highlights a significant gap in the literature concerning psychological and behavioural dimensions of retention. Despite their demonstrated importance, factors such as intrinsic motivation, emotional well-being, and workplace stress remain underexplored. Addressing these elements is essential to formulating holistic, human-centred retention models. The study concludes by advocating for industry-aligned, psychologically informed, and adaptable frameworks that can respond to the evolving dynamics of modern organisational environments.
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 |