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: 21 Oct 2026 00:14 UTC — 22 Oct 2026 01:24 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 |
|---|---|---|---|---|---|
| 2019 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 0 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 4 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2025 | 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 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): 2026-10-21T00:14:26.913075+00:00 — 2026-10-22T01:24:00.104578+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 | 2 | 4 | 4/4 |
| 2022 | All selected publications | 1 | 4 | 4/4 |
| 2023 | All selected publications | 1 | 4 | 4/4 |
| 2024 | All selected publications | 0 | 4 | 4/4 |
| 2025 | All selected publications | 0 | 4 | 4/4 |
| 2026 | All selected publications | 0 | 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 |
|---|---|
| 2019 | 1 |
| 2024 | 1 |
| 2025 | 2 |
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 | 2 | 4 | 4/4 | 0.5 | 0.0 | 25.0 | 4/4 · 100.0% |
* Percentage among publications with a known citation count.
Expressing the digitalization of industry in today's world which is rapidly developing day by day, Industry 4.0 is accepted as the most important technology revolution of our time. This shows that a new era in production has started and the industry has been digitized. In order to adapt to this change, companies should actively follow the innovations and shape the organizational structure accordingly. However, if this is the case, we can respond to the expectations of the current period. That is why it is necessary to activate Industry 4.0 after learning all aspects of it and to develop marketing strategies that can adapt to this digital age. In today's competitive environment, it is inevitable to act integrated into this new situation. This study is designed to emphasize how important it is to identify Industry 4.0 and the most heavily affected marketing strategies from it. It is also a review of the conceptual explanation of Industry 4.0 and the importance of advances in new technologies.
Automated flow line manufacturing systems are becoming more relevant in the automotive sector. Improving productivity in the manufacturing system is the core objective of all companies, measured by overall equipment effectiveness (OEE). OEE is a measure of how well a manufacturing operation is utilised compared to its full potential during the periods when it is scheduled to run. Organisations have thus made technological changes in their processes that have led to opportunities for greater flexibilities. This sentiment underpins the concept of automation. Hence, this study examines the impact of automation for productivity improvement in the selected automotive assembly organisation in South Africa. Automation is the creation and application of technology to monitor and control the production and delivery of products and services. The study was quantitative in design and examined production and the related experiences of the automotive assembly organisation that has adopted automation system in its automobile roof process in weld plant for productivity improvement. The Ordinary Least Squares (OLS) model, using the Statistical Package for Social Sciences (SPSS) was used to analyse data. The company operates in the eThekwini Municipality in KwaZulu-Natal province of South Africa. The study was achieved by collecting pre- and post-quarterly data for labour productivity, process efficiency and downtime. The results establish that labour productivity, process efficiency and downtime have a relationship and are statistically significant to company productivity after automation was implemented. It has the potential to reduce errors, increase productivity and augment human capacity. Hence, the original value of this study is its approach in uncovering strengths and weaknesses of automation for productivity improvement in South Africa.
Technological advancements are becoming increasingly significant due to the poor production levels of enterprises in South Africa. Consequently, businesses use technology modifications in their operations that create chances for enhanced flexibility and productivity enhancement. This emotion supports a transformative concept of digitisation. Digitalisation involves the separation of information and technologies designed to transform operational dynamics for enhanced corporate performance. It serves as both an enabler and a catalyst for enhancing productivity. This study investigates the impact of digitisation on productivity enhancement within a specific distribution sector of a food manufacturing company in South Africa. The study employed a quantitative approach to investigate production and the associated experiences inside the distribution division of a food manufacturing enterprise that implemented digitalisation to enhance productivity. The Ordinary Least Squares (OLS) model was employed to analyse data using the Statistical Package for Social Sciences (SPSS). The corporation functions within the eThekwini Municipality in KwaZulu-Natal, South Africa. The study was conducted by gathering pre- and post-quarterly data on product picking accuracy, hourly product loading, product replenishment rate, and product release rate. The results demonstrate a statistically significant association between the product release pace and the company’s production in the post-digitalization period. Nonetheless, it fails to demonstrate the correlation between product picking accuracy, product loading per hour, or product replenishment rate and productivity. A targeted organisational strategy on digitisation is expected to enhance the organization's performance. Digitising records enables the tracking of each phase of the production process, so maintaining transparency and instilling confidence in consumers. Therefore, the primary significance of this study lies in its examination of the strengths and shortcomings of digitalisation for enhancing productivity in South Africa.
The global business environment is undergoing significant changes due to the impact of COVID-19 and shifts in workforce demographics, making organizational change an inevitable trend. In this context, the smooth adoption of smart manufacturing in the manufacturing industry is crucial for enhancing organizational value. Through a literature review, this study consolidated key factors related to organizational innovation and the implementation of smart manufacturing. The analytic hierarchy process (AHP) was employed to identify the prioritized sequence of key strategic actions for innovative operations. The results indicated that among 27 assessment indicators, motivation is the most crucial factor for organizational innovation/change. Subsequently, organizations must also prioritize strategic human resource deployment to accelerate the development of long-term organizational goals. This study further examined solutions to ensure both talent and organizational growth during the transformation of innovation strategies.
Automated flow line manufacturing systems are becoming more relevant in the automotive sector. Improving productivity in the manufacturing system is the core objective of all companies, measured by overall equipment effectiveness (OEE). OEE is a measure of how well a manufacturing operation is utilised compared to its full potential during the periods when it is scheduled to run. Organisations have thus made technological changes in their processes that have led to opportunities for greater flexibilities. This sentiment underpins the concept of automation. Hence, this study examines the impact of automation for productivity improvement in the selected automotive assembly organisation in South Africa. Automation is the creation and application of technology to monitor and control the production and delivery of products and services. The study was quantitative in design and examined production and the related experiences of the automotive assembly organisation that has adopted automation system in its automobile roof process in weld plant for productivity improvement. The Ordinary Least Squares (OLS) model, using the Statistical Package for Social Sciences (SPSS) was used to analyse data. The company operates in the eThekwini Municipality in KwaZulu-Natal province of South Africa. The study was achieved by collecting pre- and post-quarterly data for labour productivity, process efficiency and downtime. The results establish that labour productivity, process efficiency and downtime have a relationship and are statistically significant to company productivity after automation was implemented. It has the potential to reduce errors, increase productivity and augment human capacity. Hence, the original value of this study is its approach in uncovering strengths and weaknesses of automation for productivity improvement in South Africa.
Technological advancements are becoming increasingly significant due to the poor production levels of enterprises in South Africa. Consequently, businesses use technology modifications in their operations that create chances for enhanced flexibility and productivity enhancement. This emotion supports a transformative concept of digitisation. Digitalisation involves the separation of information and technologies designed to transform operational dynamics for enhanced corporate performance. It serves as both an enabler and a catalyst for enhancing productivity. This study investigates the impact of digitisation on productivity enhancement within a specific distribution sector of a food manufacturing company in South Africa. The study employed a quantitative approach to investigate production and the associated experiences inside the distribution division of a food manufacturing enterprise that implemented digitalisation to enhance productivity. The Ordinary Least Squares (OLS) model was employed to analyse data using the Statistical Package for Social Sciences (SPSS). The corporation functions within the eThekwini Municipality in KwaZulu-Natal, South Africa. The study was conducted by gathering pre- and post-quarterly data on product picking accuracy, hourly product loading, product replenishment rate, and product release rate. The results demonstrate a statistically significant association between the product release pace and the company’s production in the post-digitalization period. Nonetheless, it fails to demonstrate the correlation between product picking accuracy, product loading per hour, or product replenishment rate and productivity. A targeted organisational strategy on digitisation is expected to enhance the organization's performance. Digitising records enables the tracking of each phase of the production process, so maintaining transparency and instilling confidence in consumers. Therefore, the primary significance of this study lies in its examination of the strengths and shortcomings of digitalisation for enhancing productivity in South Africa.
The global business environment is undergoing significant changes due to the impact of COVID-19 and shifts in workforce demographics, making organizational change an inevitable trend. In this context, the smooth adoption of smart manufacturing in the manufacturing industry is crucial for enhancing organizational value. Through a literature review, this study consolidated key factors related to organizational innovation and the implementation of smart manufacturing. The analytic hierarchy process (AHP) was employed to identify the prioritized sequence of key strategic actions for innovative operations. The results indicated that among 27 assessment indicators, motivation is the most crucial factor for organizational innovation/change. Subsequently, organizations must also prioritize strategic human resource deployment to accelerate the development of long-term organizational goals. This study further examined solutions to ensure both talent and organizational growth during the transformation of innovation strategies.
Expressing the digitalization of industry in today's world which is rapidly developing day by day, Industry 4.0 is accepted as the most important technology revolution of our time. This shows that a new era in production has started and the industry has been digitized. In order to adapt to this change, companies should actively follow the innovations and shape the organizational structure accordingly. However, if this is the case, we can respond to the expectations of the current period. That is why it is necessary to activate Industry 4.0 after learning all aspects of it and to develop marketing strategies that can adapt to this digital age. In today's competitive environment, it is inevitable to act integrated into this new situation. This study is designed to emphasize how important it is to identify Industry 4.0 and the most heavily affected marketing strategies from it. It is also a review of the conceptual explanation of Industry 4.0 and the importance of advances in new technologies.
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 |