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: 4 / 4 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 02:59 UTC — 23 Oct 2026 12:55 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.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 selected publications.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 selected publications.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 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 |
| 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 | 6 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 3 | 2 | 2/2 | 2/2 |
| 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 | 6 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 3 | 2 | 2/2 | 2/2 |
| 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 4 / 4 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): 2026-10-22T02:59:13.348461+00:00 — 2026-10-23T12:55:35.123683+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 | 4 | 4/4 |
| 2018 | All selected publications | 0 | 4 | 4/4 |
| 2019 | All selected publications | 0 | 4 | 4/4 |
| 2020 | All selected publications | 0 | 4 | 4/4 |
| 2021 | All selected publications | 0 | 4 | 4/4 |
| 2022 | All selected publications | 0 | 4 | 4/4 |
| 2023 | All selected publications | 0 | 4 | 4/4 |
| 2024 | All selected publications | 0 | 4 | 4/4 |
| 2025 | All selected publications | 0 | 4 | 4/4 |
| 2026 | All selected publications | 9 | 4 | 4/4 |
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 |
| 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 | 4 | 9 | 9 | 4/4 | 2.25 | 1.5 | 50.0 | 4/4 · 100.0% |
* Percentage among publications with a known citation count.
Competitive intelligence strategies are employed by higher education institutions to assist with student recruitment. These competitive intelligence (CI) strategies help maintain or increase market share in education. Utilising competitive intelligence is crucial for firms in competitive markets, assisting in strategic planning and execution. Higher education organisations can benefit from CI to make informed decisions and develop strategic goals. The competition between selected public universities that reside in the same town has resulted in devising competitive strategies to attract grade 12 students with effective and innovative student recruitment strategies. The study sought to examine how competitive intelligence is used as a strategy to promote student recruitment at selected public universities. The study adopted a quantitative research approach where 234 first-time entering university students were used as key informants. To collect data, a questionnaire was used and is attached. It was discovered that both institutions utilised similar strategies to recruit students. Results reveal that the selected public universities employ traditional and technology-based strategies for student recruitment, relying on past success rather than a standardised approach based on competitive intelligence. Further research is needed to evaluate the effectiveness of competitive intelligence strategies in student recruitment, aiming to promote diverse communication needs among current and potential students.
The Covid-19 pandemic has profoundly transformed the competitive intelligence (CI) landscape across various businesses. The research examines the swift trend of competitive intelligence by bibliometric analysis. A total of 356 documents were picked from the Scopus database, covering the years 2020 to 2024, to delineate the domain of competitive intelligence and its research horizons. Findings indicate a substantial shift towards digital transformation, improved data analytics, and an increased focus on agility and resilience in competitive intelligence initiatives. These findings illustrate the dynamic influence of CI and delineate the developmental trends, themes, and patterns for academic practitioners and policymakers, who must adopt these emerging trends to enhance, promote, and sustain CI longevity, operational efficiency, and capability within the swiftly evolving business ecosystem. Prevalent topics encompass digital transformation, agility, resilience, collaboration, networking, ethics, and compliance. Geographically, Asia, North America, and Europe dominate research production in CI during and after Covid-19, although emerging economies exhibit interest. Citation analysis identified prominent authors, providing direction for subsequent research. Nonetheless, research deficiencies and obstacles remain, particularly the necessity for additional empirical investigations, the amalgamation of competitive intelligence with big data, comprehensive competitive intelligence frameworks for small enterprises, innovation, and the Internet of Things (IoT). This bibliometric literature study seeks to analyse and synthesise the fast developments in CI during and subsequent to the COVID-19 pandemic. This study analyses major topics, approaches, and changes in CI practices through the examination of several academic articles. The study offers significant insights and underscores the transformative potential of CI techniques.
External environmental changes manifest in erratic economic situations that adversely impact human lives, economies, and SMEs globally. This constitutes a significant global danger to small and medium-sized enterprises (SMEs). Small and medium-sized enterprises significantly contribute to the national gross domestic product. Nevertheless, the majority of SMEs have limitations, particularly in terms of resources. SMEs must embrace CI to forecast and make educated decisions regarding the efficient management of existing, often limited resources. Government assistance, in multiple capacities, is equally essential. Moreover, the implementation of Continuous Improvement (CI) by Small and Medium Enterprises (SMEs) may facilitate nations in attaining the United Nations Sustainable Development Goal 8, which aims for reduced unemployment through sustainable economic growth, employment, and decent work for all. Nonetheless, there exists a paucity of literature regarding CI implementation frameworks that assist SMEs in implementing CI techniques for resource management and the advancement of a sustainable economy. This study seeks to establish a framework for Continuous Improvement (CI) implementation to assist SMEs in adopting CI technologies for resource management and enhancing operational efficiency. A systematic literature review (SRL) methodology is utilised to discern essential elements vital for the implementation of continuous improvement (CI) in small and medium-sized enterprises (SMEs). This study enhances the sparse information on CI in SME contexts by synthesising best practices and significant insights from global literature. It enables SMEs to manoeuvre through economic volatility and assists policymakers in formulating specific actions. The framework ultimately promotes sustainable economic development and enhances the capacity of SMEs to endure external shocks, including IoT and AI analytics, as well as their consequences for business ecosystems.
This study examines the influence of strategic foresight on the financial performance of small and medium-sized firms (SMEs) within the mining sector of South Africa's Eastern Limb. It examines the roles of scenario planning, external environmental assessment, and organisational culture in fostering financial resilience and competitive advantage. The research utilises a quantitative methodology, employing survey data gathered from 310 participants, comprising firm owners, managers, and employees within mining SMEs. The study employs linear regression analysis to evaluate the correlations between strategic foresight activities and financial performance metrics, including revenue growth, profitability, and return on investment (ROI). Reliability was established via Cronbach’s alpha (exceeding 0.9 for all constructs), while validity was assessed using Pearson correlation coefficients. The findings demonstrate that all three elements of strategic foresight—scenario planning, external environmental scanning, and organisational culture—exhibit a substantial and favourable correlation with financial success in mining SMEs. The investigation indicates that SMEs with strong strategic foresight systems are more adept at risk mitigation, regulatory adaptation, and maintaining financial growth. The research highlights the essential function of strategic foresight in improving financial resilience and stability in mining-focused SMEs. Companies that actively participate in scenario planning and environmental monitoring while fostering a robust organisational culture are more likely to adeptly manage market uncertainty. The findings provide guidance to SME leaders, policymakers, and researchers on incorporating foresight-driven strategies for enduring sustainability and financial prosperity.
External environmental changes manifest in erratic economic situations that adversely impact human lives, economies, and SMEs globally. This constitutes a significant global danger to small and medium-sized enterprises (SMEs). Small and medium-sized enterprises significantly contribute to the national gross domestic product. Nevertheless, the majority of SMEs have limitations, particularly in terms of resources. SMEs must embrace CI to forecast and make educated decisions regarding the efficient management of existing, often limited resources. Government assistance, in multiple capacities, is equally essential. Moreover, the implementation of Continuous Improvement (CI) by Small and Medium Enterprises (SMEs) may facilitate nations in attaining the United Nations Sustainable Development Goal 8, which aims for reduced unemployment through sustainable economic growth, employment, and decent work for all. Nonetheless, there exists a paucity of literature regarding CI implementation frameworks that assist SMEs in implementing CI techniques for resource management and the advancement of a sustainable economy. This study seeks to establish a framework for Continuous Improvement (CI) implementation to assist SMEs in adopting CI technologies for resource management and enhancing operational efficiency. A systematic literature review (SRL) methodology is utilised to discern essential elements vital for the implementation of continuous improvement (CI) in small and medium-sized enterprises (SMEs). This study enhances the sparse information on CI in SME contexts by synthesising best practices and significant insights from global literature. It enables SMEs to manoeuvre through economic volatility and assists policymakers in formulating specific actions. The framework ultimately promotes sustainable economic development and enhances the capacity of SMEs to endure external shocks, including IoT and AI analytics, as well as their consequences for business ecosystems.
The Covid-19 pandemic has profoundly transformed the competitive intelligence (CI) landscape across various businesses. The research examines the swift trend of competitive intelligence by bibliometric analysis. A total of 356 documents were picked from the Scopus database, covering the years 2020 to 2024, to delineate the domain of competitive intelligence and its research horizons. Findings indicate a substantial shift towards digital transformation, improved data analytics, and an increased focus on agility and resilience in competitive intelligence initiatives. These findings illustrate the dynamic influence of CI and delineate the developmental trends, themes, and patterns for academic practitioners and policymakers, who must adopt these emerging trends to enhance, promote, and sustain CI longevity, operational efficiency, and capability within the swiftly evolving business ecosystem. Prevalent topics encompass digital transformation, agility, resilience, collaboration, networking, ethics, and compliance. Geographically, Asia, North America, and Europe dominate research production in CI during and after Covid-19, although emerging economies exhibit interest. Citation analysis identified prominent authors, providing direction for subsequent research. Nonetheless, research deficiencies and obstacles remain, particularly the necessity for additional empirical investigations, the amalgamation of competitive intelligence with big data, comprehensive competitive intelligence frameworks for small enterprises, innovation, and the Internet of Things (IoT). This bibliometric literature study seeks to analyse and synthesise the fast developments in CI during and subsequent to the COVID-19 pandemic. This study analyses major topics, approaches, and changes in CI practices through the examination of several academic articles. The study offers significant insights and underscores the transformative potential of CI techniques.
This study examines the influence of strategic foresight on the financial performance of small and medium-sized firms (SMEs) within the mining sector of South Africa's Eastern Limb. It examines the roles of scenario planning, external environmental assessment, and organisational culture in fostering financial resilience and competitive advantage. The research utilises a quantitative methodology, employing survey data gathered from 310 participants, comprising firm owners, managers, and employees within mining SMEs. The study employs linear regression analysis to evaluate the correlations between strategic foresight activities and financial performance metrics, including revenue growth, profitability, and return on investment (ROI). Reliability was established via Cronbach’s alpha (exceeding 0.9 for all constructs), while validity was assessed using Pearson correlation coefficients. The findings demonstrate that all three elements of strategic foresight—scenario planning, external environmental scanning, and organisational culture—exhibit a substantial and favourable correlation with financial success in mining SMEs. The investigation indicates that SMEs with strong strategic foresight systems are more adept at risk mitigation, regulatory adaptation, and maintaining financial growth. The research highlights the essential function of strategic foresight in improving financial resilience and stability in mining-focused SMEs. Companies that actively participate in scenario planning and environmental monitoring while fostering a robust organisational culture are more likely to adeptly manage market uncertainty. The findings provide guidance to SME leaders, policymakers, and researchers on incorporating foresight-driven strategies for enduring sustainability and financial prosperity.
Competitive intelligence strategies are employed by higher education institutions to assist with student recruitment. These competitive intelligence (CI) strategies help maintain or increase market share in education. Utilising competitive intelligence is crucial for firms in competitive markets, assisting in strategic planning and execution. Higher education organisations can benefit from CI to make informed decisions and develop strategic goals. The competition between selected public universities that reside in the same town has resulted in devising competitive strategies to attract grade 12 students with effective and innovative student recruitment strategies. The study sought to examine how competitive intelligence is used as a strategy to promote student recruitment at selected public universities. The study adopted a quantitative research approach where 234 first-time entering university students were used as key informants. To collect data, a questionnaire was used and is attached. It was discovered that both institutions utilised similar strategies to recruit students. Results reveal that the selected public universities employ traditional and technology-based strategies for student recruitment, relying on past success rather than a standardised approach based on competitive intelligence. Further research is needed to evaluate the effectiveness of competitive intelligence strategies in student recruitment, aiming to promote diverse communication needs among current and potential students.
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 |