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: 7 / 7 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 02:19 UTC — 28 Oct 2026 21:31 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.
7 / 7 with data · 0 missing or unmatched. Percentages use all 7 selected publications.
7 / 7 with data · 0 missing or unmatched. Percentages use all 7 selected publications.
7 / 7 with data · 0 missing or unmatched. Percentages use all 7 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 | 7 | 7 | 7/7 | 7/7 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 42 | 7 | 7/7 | 7/7 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 64 | 7 | 7/7 | 7/7 |
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 7 / 7 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): 2026-10-22T02:19:05.640122+00:00 — 2026-10-28T21:31:36.155719+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 | 7 | 7/7 |
| 2018 | All selected publications | 0 | 7 | 7/7 |
| 2019 | All selected publications | 1 | 7 | 7/7 |
| 2020 | All selected publications | 10 | 7 | 7/7 |
| 2021 | All selected publications | 16 | 7 | 7/7 |
| 2022 | All selected publications | 12 | 7 | 7/7 |
| 2023 | All selected publications | 11 | 7 | 7/7 |
| 2024 | All selected publications | 6 | 7 | 7/7 |
| 2025 | All selected publications | 8 | 7 | 7/7 |
| 2026 | All selected publications | 0 | 7 | 7/7 |
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 | 7 |
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 | 7 | 42 | 64 | 7/7 | 6.0 | 2 | 71.4 | 7/7 · 100.0% |
* Percentage among publications with a known citation count.
Nowadays, effective business performance depends on digital competitive factors and its ability to transform corporate capabilities in the light of digitalization. In this study, the evolving role of automated systems for global business operations in the era of Industry 4.0 has been assessed from a theoretical perspective with a practical approach. It is aimed to demonstrate the role of high technologies in an effective supply chain management system with new technological components and cyber-security issues of automated vehicles in SCM. This study highlights that developments in Artificial Neural Networks, Image Processing, Multi-Purpose Decision-Making, Blurred Linguistic Variables, and automated systems increase the performance of the supply chain management system and boost business performance. It is also shown that the evolving role of the automated system requires capital investments on Research and Development efforts
The term high-tech, covering the high-tech industry and the information-intensive service sector, is based on advanced scientific and technological expertise that requires science, technology, and innovation (STI), and is based on Research & Development expenditure. Sectoral, product and patent approaches are used for classification by OECD and European Union. Literature review on high-tech show that countries focusing on Research and Development Expenditures and new patents have succeeded in increasing their high-tech exports as well. Turkey is one of the countries where the levels of high-tech export is not at the desired levels yet therefore the government must give incentives for Research and Development expenditures and new patents for innovation, as high-tech export affects GDP growth positively.
The concept of big data is one of the important issues in business decision making in recent years. The expansion of social media platforms, the increase in data production devices and the evaluation and interpretation of the data produced by developing technology become crucial. Previous studies in the big data area have addressed the issue in limited contexts, and there are few studies in the field of marketing with a bibliometric approach. This study, which aims to examine how big data concept is evaluated in marketing literature, examines the publications on big data in indexed marketing journals using bibliometric methodology. This study starts with descriptive statistical information and then includes the top published journals, authors and corresponding author’s countries statistics. This study also includes most influential studies for big data concept in marketing literature, employs spectroscopy for detecting historical roots of studies and finally plots growth progress of keywords for predicting, future themes. This study contributes to current literature by providing a summarizing and instructive content for researchers interested in big data in marketing.
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.
With the information age, businesses have accepted the importance of intangible resources rather than material resources and have produced new approaches in this field. In order to investigate the relationship between intellectual capital, one of these approaches, and human resources practices and education, a literature review has been conducted. This study demonstrated that there is a strong connection between them.
The concept of big data is one of the important issues in business decision making in recent years. The expansion of social media platforms, the increase in data production devices and the evaluation and interpretation of the data produced by developing technology become crucial. Previous studies in the big data area have addressed the issue in limited contexts, and there are few studies in the field of marketing with a bibliometric approach. This study, which aims to examine how big data concept is evaluated in marketing literature, examines the publications on big data in indexed marketing journals using bibliometric methodology. This study starts with descriptive statistical information and then includes the top published journals, authors and corresponding author’s countries statistics. This study also includes most influential studies for big data concept in marketing literature, employs spectroscopy for detecting historical roots of studies and finally plots growth progress of keywords for predicting, future themes. This study contributes to current literature by providing a summarizing and instructive content for researchers interested in big data in marketing.
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.

 
 
 
 The crucial role of delivering health care services belongs to the health care providers. Among them, nurses have an important place both in regards to their numbers and the burden they take on. Therefore, the purpose of this research is to understand the relations between their work performances and their burnout level in conjunction with their internal& external motivation. The research is a cross-sectional observational study. Surveys were distributed to 600 nurses across the Asian part of ?stanbul among 4 hospitals. The study included the volunteer nurses who were actively working in the institutions, who were given permission to participate in the study, except for the nurses who were on leave/had a report during the data collection period. While collecting data, burnout, motivation and workers’ perceived performance scales were used. 458 nurses completed the surveys out of 600 distributed, of which 439 were deemed to be appropriate to be included in the analysis. All the data were analyzed at SPSS 22.0 version, with the mean, standard deviation, range and such descriptive statistics being used with p<0.05 significance level. Analyzes demonstrated a statistically significant negative correlation between burnout and perceived performance (p<0.01, r=-,422). On the other hand, the analyzes also demonstrated a positive but statistically significant and low-level correlation between motivation (internal and external) and perceived performance (p<0.01, r=,173; 226). Burnout caused by independent variables has a statistically significant and negative correlation between both internal and external motivation (p<0.01). Perceived work performance of nurses, one of the most important factors in delivering health care services, can be elevated by decreasing burnout level and by increasing their internal/external motivation. So, it is important for administrations to take appropriate measures to lower the burnout level and increase the motivation of nurses.
 
 
 
Multi-game simulations are the most widely used tools in international business and strategic management education. In Business Schools, all courses from Strategy to Finance are taught from case studies and books, using research results. But when managing a business, there is no way to learn the parts of the business from books by gaining experience in complete integrity. Company management competence requires practice in areas such as production, marketing, R & D, finance, operations, and strategy. The simplest way to practice this in the learning process is through business simulations and multi-game applications. These simulations help students to acquire workforce skills by practicing critical thinking, making better decisions and solving business problems. It has taken its place in education life as a powerful teaching tool for business simulations. The student teams compete with each other by running multinational enterprises over several game rounds, and the round represents a fiscal year. International business development games are decided and risk-free. During the simulation process, they have to decide on a product portfolio, pricing, advertising, production, R & D and financial structure in each round. International business aspects of the games include exchange rate fluctuations, trade barriers, different tax rates between countries, shipping points and international cash management. In the room, players are interested in the current national differences, including preferred ones, price sensitivity, production location, market growth, business environment, and economic growth. Instructors follow which decisions will be highlighted in the course's planned learning objectives and guide preferences for students. We will explore the decisions and knowledge of CAPSIM Global DNA in a multi-game design. This article differs from other studies in that it is a study that puts forward the theory and application of a computerized representation simultaneously.
Nowadays, effective business performance depends on digital competitive factors and its ability to transform corporate capabilities in the light of digitalization. In this study, the evolving role of automated systems for global business operations in the era of Industry 4.0 has been assessed from a theoretical perspective with a practical approach. It is aimed to demonstrate the role of high technologies in an effective supply chain management system with new technological components and cyber-security issues of automated vehicles in SCM. This study highlights that developments in Artificial Neural Networks, Image Processing, Multi-Purpose Decision-Making, Blurred Linguistic Variables, and automated systems increase the performance of the supply chain management system and boost business performance. It is also shown that the evolving role of the automated system requires capital investments on Research and Development efforts
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 |