CiteNScope — Scholarly Index & Analytics
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Research across participating journals · All insights follow your collection filters.

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OpenAlex coverage 100.0% · 6/6 recordsLast OpenAlex observation 23 Oct 2026, 10:30 UTC
PUBLICATIONS6In the selected collection
CROSSREF CITATIONS16/6 with known counts
OPENALEX CITATIONS16/6 with known counts
OPENALEX MEAN0.17Per article with a known count
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Output and reach

Publication activity alongside incoming citations.

Publication activity

Articles grouped by publication year

Publications

Hover over a year or focus the chart and use arrow keys to inspect values.

2026 is year to date. 0 undated publications excluded.

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Citations received

OpenAlex · year of the citing work

OpenAlex

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.

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Research landscape

Leading affiliations and themes in this collection.

Full distributions & methodology ↓

Institutions

Durban University of Technology2Vaal University of Technology1Tshwane University of Technology16/6 records with data

Countries

South Africa5Lesotho16/6 records with data

Primary topics

AI and HR Technologies66/6 records with data
Two perspectives on citations

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Independent sources. Clear time scales.

Lifetime citations by publication year

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.

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Citations received each year

OpenAlex · incoming citation links grouped by citing-work year

OpenAlex

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.

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Annual history: 6/6 articles. Latest history observation: 2026-10-23T10:30:01.804334+00:00. Source totals are never added together.
Research side by side

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Select journals to compare the same collection filters. Source totals stay separate. These measures describe research activity, not journal quality.

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Display range
International Journal of Business Ecosystem & StrategyInternational Journal of Research in Business and Social Science

Publications

Articles by publication year

View data & coverage

Crossref citations

Latest lifetime totals · grouped by publication year

View data & coverage

OpenAlex citations

Latest lifetime totals · grouped by publication year

View data & coverage

OpenAlex · Citations received

By year of the citing work · 2017–2026

View data & coverage

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.

Full analytics report All distributions, publications, comparisons & data provenance
6 MATCHING PUBLICATIONSExplore results →Export CSV ↓
INDEXED PUBLICATIONS6
CROSSREF CITATIONS1
CROSSREF · CITED PUBLICATIONS16.7%
CROSSREF · MEAN CITATIONS0.17

Crossref citation coverage: 6 / 6 publications (100.0%). Latest observation: 21 Oct 2026, 07:22 UTC. Means and percentages use records with known counts.

OpenAlex / Separate citation source

A broader view of this research

DOI-matched OpenAlex observations for the same selected publications. These counts are separate from Crossref and are never added together.

OPENALEX CITATIONS1
DOI MATCH COVERAGE100.0%6 / 6 publications
MEAN · KNOWN COUNTS ONLY0.176 known · 0 unknown
MEDIAN · KNOWN COUNTS ONLY0.0

Observation range: 21 Oct 2026 07:23 UTC — 23 Oct 2026 10:30 UTC. Missing matches are not zero citations. Previous observations may remain if a refresh fails or a DOI no longer matches.

OpenAlex citation distributionNo citations51–516–20021+0Unknown0

Institutions, countries & topics

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.

Institutions

6 / 6 with data · 0 missing or unmatched. Percentages use all 6 selected publications.

Countries

6 / 6 with data · 0 missing or unmatched. Percentages use all 6 selected publications.

Primary topics

6 / 6 with data · 0 missing or unmatched. Percentages use all 6 selected publications.

Citation year / OpenAlex

Citations received over time

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 6 / 6 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): 2026-10-21T07:23:01.321712+00:00 — 2026-10-23T10:30:01.804334+00:00. Older observations may be retained after failed or incomplete refreshes.

OpenAlex · Citations received by year

10.50 2017 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 6/62018 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 6/62019 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 6/62020 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 6/62021 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 6/62022 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 6/62023 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 6/62024 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 6/62025 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 6/62026 · All selected publications · OpenAlex · Citations received by year: 1 · Annual history coverage 6/6 2017201820192020202120222023202420252026
View openalex · citations received by year data & coverage
Citation yearJournal / scopeOpenAlex · Citations received by yearPublicationsAnnual history known
2017All selected publications066/6
2018All selected publications066/6
2019All selected publications066/6
2020All selected publications066/6
2021All selected publications066/6
2022All selected publications066/6
2023All selected publications066/6
2024All selected publications066/6
2025All selected publications066/6
2026All selected publications166/6

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 ↗

Crossref citation distribution

Latest Crossref totals for the selected publications; not citations received in the selected years.

Mean0.17
Median0.0
No citations51–516–20021+0Unknown0

Unknown counts are excluded from the mean, median and cited-publication rate.

Journal comparison

Same filters, source and counting rules across journals. Descriptive statistics, not a quality ranking.

Journal / publisherPublicationsCrossref citationsOpenAlex citationsOpenAlex coverageCrossref meanCrossref medianCrossref cited %*Crossref coverage
International Journal of Business Ecosystem & Strategy
Bussecon International Academy
3113/30.33033.33/3 · 100.0%
International Journal of Research in Business and Social Science
SSBFNET
3003/30.000.03/3 · 100.0%

* Percentage among publications with a known citation count.

Explore publications

View all 6 →

Most cited · Crossref

JOURNAL ARTICLE

Perceptions of artificial intelligence on the future of employees' job security in Africa: secondary data analysis

Godfrey Maake · Esabel Mathekgane

International Journal of Business Ecosystem & Strategy · 2025

Read abstract

In the rapidly evolving digital era, introducing AI has become a pivotal factor across all organisational levels. However, the global proliferation of artificial intelligence has sparked immediate apprehension among the currently employed and those seeking employment. As we navigate the societal transformations of the twenty-first century, the dynamic interplay between AI and job security has risen as a crucial area of investigation within Human Resources Management research. This investigation delves into the global perceptions of artificial intelligence on the future of employees' job security in the workplace, a question of paramount importance in the current landscape. This study is a systematic review, with the data comprising previously published research articles, reports, and studies collected and analysed to gain comprehensive insights. The systematic literature review was conducted in five steps: developing explicit questions for critique, collecting and categorising data, evaluating, summarising evidence, and discussing. The process necessitated retrieving data from various databases, including Scopus, Sabinet, Science Direct and Elicit. The research adhered to the PRISMA guidelines, a widely accepted framework for conducting systematic reviews, to ensure methodological rigour and transparency in the research process. The findings offer mixed perceptions of artificial intelligence on future employees' job security in Africa. The study provides themes in relation to how employees in the African affiliation view the integration of AI in the workplace: Future uncertainties, Role of AI in HR Practices, Employee well-being, training and development, Positive attitudes towards AI and job opportunities. The investigation highlighted AI challenges and opportunities in Africa, making sense of why employees do not trust the introduction of AI, as it is perceived as a threat to their jobs. It offers valuable guidelines for decision-makers in Africa to ensure that AI is utilised legally and ethically and that steps are in place to mitigate employment losses.

DOI 10.36096/ijbes.v7i2.7861 Crossref citations1 OpenAlex citations
JOURNAL ARTICLE

Transitioning from traditional performance management to electronic performance management in the workplace: A critical analysis and lessons for Lesotho

Mildred Mahapa

International Journal of Research in Business and Social Science · 2025

Read abstract

Modern businesses require flexible, agile systems that can track progress in real time, provide continuous feedback and adapt to dynamic workplace environment. As the workplace is evolving rapidly, so are the expectations around employee performance. Using qualitative approach, this paper unpacks the opportunities and challenges of EPMS as well as provide recommendations on how organizations in Lesotho can make use of it. Despite the vast literature on digitalization, there has been a lot of hesitation on the total adoption of EPMS in most organization mostly in developing countries like Lesotho.  The methodology involved a comprehensive review of previous studies, primarily relying on secondary data to explore the concept of digital HRM and its various dimensions. Thematic analysis was conducted to identify patterns and insights related to EPMS. The findings revealed that EPMS can provide opportunities for orgnaisations in Lesotho which include real-time feedback, objective continuous performance monitoring, data-driven insights, reduction in operational costs and improved communication. However, despite EPMS’s benefits it comes with challenges, such as data overload, costs, algorithmic bias, data privacy concerns and loss of personal interaction. Based on the findings, organisations must invest in robust data security measures, such as encryption and secure storage protocols, to mitigate these risks and having clear policies that relates to EPMS.

DOI 10.20525/ijrbs.v14i7.44990 Crossref citations0 OpenAlex citations
JOURNAL ARTICLE

Leveraging the power of human resource analytics for enhanced decision making: opportunities and challenges

Joelle Danielle Ngo Ndjama

International Journal of Research in Business and Social Science · 2025

Read abstract

The fast-changing technological environment compels organisations to make swift and adaptive strategic choices, necessitating the use of tools and systems that can effectively manage complexities, foresee challenges, and capitalise on opportunities; as a result, there is a growing trend towards the adoption of human resource analytics to improve decision-making processes in the ever-evolving field of human resources. Despite the increasing use of HR metrics and analytics in human resource management, HR professionals have been relatively slow to embrace a data-driven approach, and existing research on data-driven decision-making in HRM often lacks the comprehensive frameworks necessary to provide practical guidance for the effective integration of human resource analytics. Hence, this study aims to explore the utilisation of human resource analytics to improve decision-making processes within organisations, highlighting both the potential benefits and the challenges that may arise. A systematic literature review revealed that the adoption of Human Resource Analytics offers organisations significant benefits, such as enhanced decision-making, better alignment with strategic objectives, improved employee experiences, a stronger competitive advantage, and reduced time expenditures. However, challenges persist, including concerns related to data privacy and security, the quality, integrity, and accuracy of data, a lack of data literacy and skills among employees, and inadequate technological infrastructure. To address data security concerns, HR technology providers must adopt robust security measures, including encryption, firewalls, and intrusion detection systems. Additionally, they should develop user-friendly data management systems and analytical tools that enable HR professionals to derive valuable insights from the data effectively.

DOI 10.20525/ijrbs.v14i6.42760 Crossref citations0 OpenAlex citations
JOURNAL ARTICLE

Mitigating recruitment and selection challenges through the utilization of AI

Simangele Mkhize · Melanie Lourens

International Journal of Research in Business and Social Science · 2025

Read abstract

Human resource management (HRM) is a crucial component of an organization’s management and aims to enhance employee efficiency and an organization’s competitiveness. As organizations advance and grow, recruitment and selection decisions become more critical. The thinking of human resources practitioners and experts must be transformed to accommodate the current workplace. Practitioners and experts must ensure that qualified candidates are attracted to the organization at the right time to fill open positions. Traditional HRM methods are insufficient for the progressively more complicated HRM challenges. Recruitment and selection, as one HRM component, is a process with numerous challenges. This study examines how the use of AI technologies can assist in mitigating recruitment and selection challenges. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and soliciting data from databases such as Web of Science, Google Scholar, and Science Direct, a systematic literature review analysis was conducted to investigate the human resources challenges in recruitment and selection and explore how AI can moderate these challenges. The analysis indicated several challenges, including high costs, bribery and corruption, political interference, inadequate job descriptions, nepotism and favoritism, and lengthy recruitment and selection processes. Recommendations of the study suggest that the accuracy and efficiency of recruitment and selection can be enhanced by involving AI technologies, which can assist in lowering the risks and expenses associated with recruitment and selection.

DOI 10.20525/ijrbs.v14i4.39730 Crossref citations0 OpenAlex citations
JOURNAL ARTICLE

The effect of emerging technologies on the performance and engagement of human resources (HR) professionals: evidence of existing studies

Anos Chitamba · L. Z. Ngidi · S. Bangura

International Journal of Business Ecosystem & Strategy · 2025

Read abstract

The swift advancement of emerging technologies has profoundly changed the workplace, especially within human resource management (HRM) practices and applications. Innovations such as data analytics, artificial intelligence (AI), and employee self-service (ESS) systems are pivotal to this transformation. While these technologies offer considerable advantages, they also present challenges that organizations need to address for effective integration into their operations. This study aims to examine the impact of emerging technologies on the performance and engagement of HR professionals, drawing on existing research. To achieve this, a comprehensive literature review was conducted. The findings indicate that emerging technologies have significantly transformed the workplace and human resource management, enhancing engagement within these areas. Furthermore, their implementation has notably advanced the modern workplace and the HR profession. Despite these benefits, challenges such as job security, potential skill displacement, and concerns about skill replacement exist. However, the research suggests that these challenges can be mitigated through the implementation of training programs, leadership endorsement initiatives, and technical support. The theoretical implications of these findings contribute valuable insights regarding the role of emerging technologies in HRM practices. Practically, the findings provide a framework for managers and organizational stakeholders to effectively leverage emerging technologies in human resource management applications.

DOI 10.36096/ijbes.v7i3.8230 Crossref citations0 OpenAlex citations

Selected results

JOURNAL ARTICLE

Transitioning from traditional performance management to electronic performance management in the workplace: A critical analysis and lessons for Lesotho

Mildred Mahapa

International Journal of Research in Business and Social Science · 2025

Read abstract

Modern businesses require flexible, agile systems that can track progress in real time, provide continuous feedback and adapt to dynamic workplace environment. As the workplace is evolving rapidly, so are the expectations around employee performance. Using qualitative approach, this paper unpacks the opportunities and challenges of EPMS as well as provide recommendations on how organizations in Lesotho can make use of it. Despite the vast literature on digitalization, there has been a lot of hesitation on the total adoption of EPMS in most organization mostly in developing countries like Lesotho.  The methodology involved a comprehensive review of previous studies, primarily relying on secondary data to explore the concept of digital HRM and its various dimensions. Thematic analysis was conducted to identify patterns and insights related to EPMS. The findings revealed that EPMS can provide opportunities for orgnaisations in Lesotho which include real-time feedback, objective continuous performance monitoring, data-driven insights, reduction in operational costs and improved communication. However, despite EPMS’s benefits it comes with challenges, such as data overload, costs, algorithmic bias, data privacy concerns and loss of personal interaction. Based on the findings, organisations must invest in robust data security measures, such as encryption and secure storage protocols, to mitigate these risks and having clear policies that relates to EPMS.

DOI 10.20525/ijrbs.v14i7.44990 Crossref citations0 OpenAlex citations
JOURNAL ARTICLE

Leveraging the power of human resource analytics for enhanced decision making: opportunities and challenges

Joelle Danielle Ngo Ndjama

International Journal of Research in Business and Social Science · 2025

Read abstract

The fast-changing technological environment compels organisations to make swift and adaptive strategic choices, necessitating the use of tools and systems that can effectively manage complexities, foresee challenges, and capitalise on opportunities; as a result, there is a growing trend towards the adoption of human resource analytics to improve decision-making processes in the ever-evolving field of human resources. Despite the increasing use of HR metrics and analytics in human resource management, HR professionals have been relatively slow to embrace a data-driven approach, and existing research on data-driven decision-making in HRM often lacks the comprehensive frameworks necessary to provide practical guidance for the effective integration of human resource analytics. Hence, this study aims to explore the utilisation of human resource analytics to improve decision-making processes within organisations, highlighting both the potential benefits and the challenges that may arise. A systematic literature review revealed that the adoption of Human Resource Analytics offers organisations significant benefits, such as enhanced decision-making, better alignment with strategic objectives, improved employee experiences, a stronger competitive advantage, and reduced time expenditures. However, challenges persist, including concerns related to data privacy and security, the quality, integrity, and accuracy of data, a lack of data literacy and skills among employees, and inadequate technological infrastructure. To address data security concerns, HR technology providers must adopt robust security measures, including encryption, firewalls, and intrusion detection systems. Additionally, they should develop user-friendly data management systems and analytical tools that enable HR professionals to derive valuable insights from the data effectively.

DOI 10.20525/ijrbs.v14i6.42760 Crossref citations0 OpenAlex citations
JOURNAL ARTICLE

Mitigating recruitment and selection challenges through the utilization of AI

Simangele Mkhize · Melanie Lourens

International Journal of Research in Business and Social Science · 2025

Read abstract

Human resource management (HRM) is a crucial component of an organization’s management and aims to enhance employee efficiency and an organization’s competitiveness. As organizations advance and grow, recruitment and selection decisions become more critical. The thinking of human resources practitioners and experts must be transformed to accommodate the current workplace. Practitioners and experts must ensure that qualified candidates are attracted to the organization at the right time to fill open positions. Traditional HRM methods are insufficient for the progressively more complicated HRM challenges. Recruitment and selection, as one HRM component, is a process with numerous challenges. This study examines how the use of AI technologies can assist in mitigating recruitment and selection challenges. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and soliciting data from databases such as Web of Science, Google Scholar, and Science Direct, a systematic literature review analysis was conducted to investigate the human resources challenges in recruitment and selection and explore how AI can moderate these challenges. The analysis indicated several challenges, including high costs, bribery and corruption, political interference, inadequate job descriptions, nepotism and favoritism, and lengthy recruitment and selection processes. Recommendations of the study suggest that the accuracy and efficiency of recruitment and selection can be enhanced by involving AI technologies, which can assist in lowering the risks and expenses associated with recruitment and selection.

DOI 10.20525/ijrbs.v14i4.39730 Crossref citations0 OpenAlex citations
JOURNAL ARTICLE

The effect of emerging technologies on the performance and engagement of human resources (HR) professionals: evidence of existing studies

Anos Chitamba · L. Z. Ngidi · S. Bangura

International Journal of Business Ecosystem & Strategy · 2025

Read abstract

The swift advancement of emerging technologies has profoundly changed the workplace, especially within human resource management (HRM) practices and applications. Innovations such as data analytics, artificial intelligence (AI), and employee self-service (ESS) systems are pivotal to this transformation. While these technologies offer considerable advantages, they also present challenges that organizations need to address for effective integration into their operations. This study aims to examine the impact of emerging technologies on the performance and engagement of HR professionals, drawing on existing research. To achieve this, a comprehensive literature review was conducted. The findings indicate that emerging technologies have significantly transformed the workplace and human resource management, enhancing engagement within these areas. Furthermore, their implementation has notably advanced the modern workplace and the HR profession. Despite these benefits, challenges such as job security, potential skill displacement, and concerns about skill replacement exist. However, the research suggests that these challenges can be mitigated through the implementation of training programs, leadership endorsement initiatives, and technical support. The theoretical implications of these findings contribute valuable insights regarding the role of emerging technologies in HRM practices. Practically, the findings provide a framework for managers and organizational stakeholders to effectively leverage emerging technologies in human resource management applications.

DOI 10.36096/ijbes.v7i3.8230 Crossref citations0 OpenAlex citations
JOURNAL ARTICLE

Leveraging HR analytics for data-driven business decisions: a strategic approach for SMMEs

Reginald M. Ndwamai · Mutshidza K. Muenda · Khathutshelo Khashane

International Journal of Business Ecosystem & Strategy · 2025

Read abstract

In the rapidly evolving landscape of contemporary business, Small, Medium and Micro enterprises (SMMEs) must adopt intelligent and strategic approaches to maintain their competitive edge. A significant method for achieving this is using Human Resource (HR) analytics, which enables organisations to make informed decisions based on data insights. By examining workforce patterns, companies can enhance recruitment processes, retain high-performing employees, and increase productivity. Nevertheless, numerous SMMEs face challenges in fully integrating HR analytics due to limited resources, insufficient expertise, and resistance to change, which complicates effective workforce management. This paper investigates how HR analytics can empower SMMEs to make informed, data-driven choices that fortify their operations. It aligns with Sustainable Development Goal (SDG) 8, which advocates for sustainable economic growth, productive employment, and decent work for all individuals. Through a systematic review of existing literature, this study assesses the advantages, obstacles, and best practices associated with implementing HR analytics in smaller enterprises. The results indicate that HR analytics can revolutionise personnel management within SMMEs, facilitating processes from selecting suitable candidates to forecasting employee turnover and enhancing overall workplace productivity. It offers actionable recommendations for SMMEs to adopt HR analytics, including using cost-effective digital tools, fostering a data-centric organisational culture, and investing in employee development.

DOI 10.36096/ijbes.v7i3.8510 Crossref citations0 OpenAlex citations

Data coverage & freshness

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

SourceMost recent successful journal syncLatest attempt
crossref21 Oct 2026 07:22 UTC · ijrbscomplete · ijrbs · 21 Oct 2026 07:22 UTC
ojs-rest21 Oct 2026 07:22 UTC · ijrbscomplete · ijrbs · 21 Oct 2026 06:46 UTC
oai-pmh20 Oct 2026 19:55 UTC · ijrbscomplete · ijrbs · 20 Oct 2026 19:50 UTC
openalex-enrichment21 Oct 2026 07:23 UTC · ijrbscomplete · ijrbs · 21 Oct 2026 07:22 UTC
Full import history & methodology →