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: 8 / 8 publications (100.0%). Latest observation: 21 Oct 2026, 07:22 UTC. Means and percentages use records with known counts.
DOI-matched OpenAlex observations for the same selected publications. These counts are separate from Crossref and are never added together.
Observation range: 21 Oct 2026 00:14 UTC — 21 Oct 2026 07:23 UTC. Missing matches are not zero citations. Previous observations may remain if a refresh fails or a DOI no longer matches.
OpenAlex affiliations and primary-topic classification. Each publication counts once per institution or country; multi-affiliated publications appear in multiple groups, so percentages can sum above 100%. Countries describe author affiliations, not nationality. Topics use one primary topic per publication.
8 / 8 with data · 0 missing or unmatched. Percentages use all 8 selected publications.
8 / 8 with data · 0 missing or unmatched. Percentages use all 8 selected publications.
8 / 8 with data · 0 missing or unmatched. Percentages use all 8 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 |
|---|---|---|---|---|---|
| 2020 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2024 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2026 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2020 | All selected publications | 3 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 9 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 12 | 2 | 2/2 | 2/2 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2026 | All selected publications | 0 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2020 | All selected publications | 6 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 10 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 4 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 16 | 2 | 2/2 | 2/2 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2026 | All selected publications | 0 | 2 | 2/2 | 2/2 |
Incoming citations to the selected publications, grouped by the year of the citing work. This is separate from lifetime citation totals grouped by the cited article’s publication year.
The publication filters above define which articles receive the citations. The citation-year range below only controls this chart and the incoming-citation comparison. Removing publication-year filters includes older articles’ incoming citations too.
Annual history available for 8 / 8 selected publications. Supported display window: 2017–2026; 2026 is incomplete. Within a successfully fetched ten-year window, omitted years mean zero. Missing histories and years beyond a saved observation’s window remain unknown (—); partial coverage can understate totals.
Annual-history observations (UTC): 2026-10-21T00:14:26.918902+00:00 — 2026-10-21T07:23:08.957409+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 | 8 | 8/8 |
| 2018 | All selected publications | 0 | 8 | 8/8 |
| 2019 | All selected publications | 0 | 8 | 8/8 |
| 2020 | All selected publications | 0 | 8 | 8/8 |
| 2021 | All selected publications | 1 | 8 | 8/8 |
| 2022 | All selected publications | 1 | 8 | 8/8 |
| 2023 | All selected publications | 6 | 8 | 8/8 |
| 2024 | All selected publications | 6 | 8 | 8/8 |
| 2025 | All selected publications | 11 | 8 | 8/8 |
| 2026 | All selected publications | 12 | 8 | 8/8 |
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 ↗
Records with full publication dates only. 2026 is an incomplete year.
| Period | Publications |
|---|---|
| 2020-02 | 1 |
| 2021-12 | 1 |
| 2022-06 | 1 |
| 2023-02 | 1 |
| 2023-10 | 1 |
| 2025-07 | 1 |
| 2026-05 | 1 |
| 2026-07 | 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 | 3 | 12 | 17 | 3/3 | 4.0 | 3 | 66.7 | 3/3 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 5 | 14 | 20 | 5/5 | 2.8 | 1 | 60.0 | 5/5 · 100.0% |
* Percentage among publications with a known citation count.
Assessing the readiness and maturity of small and medium enterprises (SMEs) is a foundation for implementing emerging technologies like big data analytics and artificial intelligence to drive their digital transformation endeavours. This study emphasises that readiness and maturity dimensions offer descriptive and prescriptive guidelines for gauging the current and desired levels of preparedness and maturity required to achieve desired digital transformation outcomes. However, prevailing readiness and maturity models overlook the diverse stages of advancement in big data analytics and artificial intelligence. This research explores the dimensions essential for assessing SMEs' readiness to adopt big data analytics and artificial intelligence. This paper identifies the key dimensions for evaluating SMEs' readiness and maturity across different categories of big data analytics and artificial intelligence by conducting a systematic literature review and employing cluster analysis. The study's principal findings underscore that SMEs' readiness for maturity is influenced prominently by strategic leadership and organisational culture, closely trailed by information technology, security, and business model transformation. Additionally, three pivotal dimensions encompass data analytics and governance, cost-benefit and risk management, and environmental factors. Consequently, proposing that evaluating digital readiness and maturity for SMEs should encompass these six dimensions, thoughtfully considering various prerequisites related to analytics and artificial intelligence.
The automotive industry is standing on the edge of the digital era. Digitization is the motivating force behind this change and is influencing every aspect of consumer behavior. Forces behind this digital transformation are the new technological possibilities which range from comprehensive connectivity, 3D printing, and the social network of virtual solutions as well. The impact of digitization can already be seen in today’s world and also in the automotive industry of India. Digitization will play an imperative role in the upcoming years such as artificial intelligence. This paper is primarily concerned with the recent trends of digitization and it also tries to measure the impact of digitization on the automobile supply chain between automobile manufacturers and suppliers. It is discussed the thrust on the potentialities of digitalization in the automobile industry of India and its impact on the overall performance of the industry. The study concluded that digitization has a significant impact on the automotive industry of India. Today, all the business practices become digitalized and foreign players use advanced technology in their products as compared to the national automotive players. The selling of automotive products is increased after the adoption of more advanced technology. It upgrades the quality of products and its service after sale which ultimately leads to the customer and brand loyalty used by the consumers.
Today, where competition among sectors is more intense and harder than ever, enterprises have started to implement innovations in their strategies. These new strategy moves are mainly on technology, informatics and software. The companies have focused on their investments in social network advertising in order to keep themselves at the forefront and increase their profit rates. In this article, it has been determined that businesses can develop their competitive advantage by creating new strategies in a digital environment.
The purpose of this research which is predicated on a qualitative case study design was to examine digital transformation in an insurance firm in Gauteng Province of South Africa with an extensive footprint throughout the country. The study aimed to gain a deeper understanding of the insurance organisation’s response to the implementation of digital transformation and how it affects the organisational strategy. Purposive sampling was adopted to select participants, who are managers and heads of departments. Data was collected through in-depth interviews. Thematic analysis was used to analyse data. The study found that the firm understands the significance of incorporating digital transformation into the strategy. The findings revealed that the firm is taking steps to incorporate digital transformation in the business strategy, however, they are not doing enough to fully address the problem of slow adoption. It was recommended that the company should fully embrace digital transformation and consider undertaking fundamental changes in the business strategy or to redefine the strategy.
The present study attempts to assess the effect of resource allocation on digital transformation in the Zimbabwean Life insurance industry. The study used a positivist research philosophy and a quantitative research approach. Data was collected using an email survey questionnaire from employees and management of four big Life insurance companies in Zimbabwe. The study findings revealed that resource allocation positively influenced digital transformation in the Zimbabwean Life insurance industry. The study recommends that managers in the Life insurance industry hire technologically proficient people to ensure digital transformation success and hire digital transformation consultants to help them with their digitalization process. Life insurance companies are urged to develop a digital transformation strategy. Future researchers are urged to look at other factors which affect digital transformation.
The study investigated the impact of digital transformation on non-financial performance of small and medium-sized enterprises (SMEs) in the City of Mbombela Local Municipality. The study is of quantitative nature and sampled 142 SMEs operating in the municipality. A self-administered, closed ended questionnaire was employed and tested for validity and reliability before collecting data from respondents. The study employed a simple linear regression analysis and performed Cronbach’s Alpha to test the reliability of the instrument. The data was analysed using inferential and descriptive statistics. The study found a statistically significant and positive relationship between operational efficiency and digital transformation of SMEs. Furthermore, there is a positive relationship between business growth and digital transformation of SMEs. However, there is an insignificant relationship between conversion rate and digital transformation of SMEs. The practical implications of the study inform the owners and managers of SMEs that adopting strategic innovative digital technologies improves non-financial performance of the business. Therefore, emphasises the significance of digital transformation aligned with the core business which promotes operational efficiency and growth. The results are expected to persuade SMEs to devote necessary attention to digital technologies implemented within the businesses.
Digital transformation (DT) is becoming essential for the competitiveness and sustainability of Small, Medium and Micro Enterprises (SMMEs), and yet many South African (SA) retailers have to overcome inhibitors that hinder them from digitally transforming. Therefore, this study explores these inhibitors to DT within the retail SMMEs sector in Eastern Cape and Western Cape provinces in South Africa using an exploratory multiple-case study design. Data from 23 in-depth semi-structured purposively sampled interviews were transcribed and thematically analysed on Atlas.ti 25 using the Braun and Clarke six-step framework to produce qualitative themes. The results reveal patterns of digital infrastructure constraints, a lack of digital literacy, financial and cost barriers, technical and operational challenges, psychological and attitudinal resistance, a lack of trust, safety concerns, and cyber risk, as well as business survival and strategic pressures, including the negative impact of the location where the business is located. Phased implementation, partnership with external stakeholders, and other strategic responses to these inhibitors requires targeted and strategic leadership and managerial interventions. This is the first study to combine Limits to Success Architype (LSA) and Adaptive Structuration (AST) in exploring the inhibitors to DT in retail SMMEs in South Africa.
Innovation and small and medium enterprises (SMEs) are associated with economic development and industrial revitalization. Innovation and digitalization have long been established as the primary contributors to improved business performance in SMEs, and digitalization is becoming increasingly regarded as enabling the transformation of business model and operational capabilities. However, South African SMEs face many challenges in adopting digitization and fostering innovation. This paper critically examines how South African SMEs can systematically leverage business innovation and digitalization to acquire sustainable competitive advantage. The study used a systematic literature review approach, focusing on literature published from 2019 through 2024. Through database analysis of academic literature and industry trends, the study aimed to discover the best practices, barriers, and the role of external partnerships in fostering innovation and digital transformation. The findings indicate that SMEs that emphasize innovation and digital technology improve their productivity and competitiveness. However, obstacles including a lack of funding, expertise, and restricted access to technology prevent many SMEs from reaching their full potential. To overcome these obstacles and promote a culture of ongoing innovation, cooperation with outside partners and emphasis on consumer interactions is crucial. This article contributes to innovation in the digital transformation landscape of SMEs by offering approaches and frameworks for South African SMEs to facilitate the adoption of innovation and digitization for sustainable competitive advantage. This addresses a core gap by guiding how to overcome barriers such as constraints of resources and focus on collaborative networks to drive digital and innovative growth.
Assessing the readiness and maturity of small and medium enterprises (SMEs) is a foundation for implementing emerging technologies like big data analytics and artificial intelligence to drive their digital transformation endeavours. This study emphasises that readiness and maturity dimensions offer descriptive and prescriptive guidelines for gauging the current and desired levels of preparedness and maturity required to achieve desired digital transformation outcomes. However, prevailing readiness and maturity models overlook the diverse stages of advancement in big data analytics and artificial intelligence. This research explores the dimensions essential for assessing SMEs' readiness to adopt big data analytics and artificial intelligence. This paper identifies the key dimensions for evaluating SMEs' readiness and maturity across different categories of big data analytics and artificial intelligence by conducting a systematic literature review and employing cluster analysis. The study's principal findings underscore that SMEs' readiness for maturity is influenced prominently by strategic leadership and organisational culture, closely trailed by information technology, security, and business model transformation. Additionally, three pivotal dimensions encompass data analytics and governance, cost-benefit and risk management, and environmental factors. Consequently, proposing that evaluating digital readiness and maturity for SMEs should encompass these six dimensions, thoughtfully considering various prerequisites related to analytics and artificial intelligence.
The present study attempts to assess the effect of resource allocation on digital transformation in the Zimbabwean Life insurance industry. The study used a positivist research philosophy and a quantitative research approach. Data was collected using an email survey questionnaire from employees and management of four big Life insurance companies in Zimbabwe. The study findings revealed that resource allocation positively influenced digital transformation in the Zimbabwean Life insurance industry. The study recommends that managers in the Life insurance industry hire technologically proficient people to ensure digital transformation success and hire digital transformation consultants to help them with their digitalization process. Life insurance companies are urged to develop a digital transformation strategy. Future researchers are urged to look at other factors which affect digital transformation.
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 |