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: 43 / 43 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: 22 Oct 2026 01:04 UTC — 22 Oct 2026 07:38 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.
43 / 43 with data · 0 missing or unmatched. Percentages use all 43 selected publications.
43 / 43 with data · 0 missing or unmatched. Percentages use all 43 selected publications.
43 / 43 with data · 0 missing or unmatched. Percentages use all 43 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 |
|---|---|---|---|---|---|
| 2015 | All selected publications | 5 | 5 | 5/5 | 5/5 |
| 2016 | All selected publications | 12 | 12 | 12/12 | 12/12 |
| 2017 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| 2018 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2019 | All selected publications | 10 | 10 | 10/10 | 10/10 |
| 2020 | All selected publications | 8 | 8 | 8/8 | 8/8 |
| 2021 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2015 | All selected publications | 44 | 5 | 5/5 | 5/5 |
| 2016 | All selected publications | 16 | 12 | 12/12 | 12/12 |
| 2017 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| 2018 | All selected publications | 5 | 2 | 2/2 | 2/2 |
| 2019 | All selected publications | 55 | 10 | 10/10 | 10/10 |
| 2020 | All selected publications | 41 | 8 | 8/8 | 8/8 |
| 2021 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2015 | All selected publications | 95 | 5 | 5/5 | 5/5 |
| 2016 | All selected publications | 33 | 12 | 12/12 | 12/12 |
| 2017 | All selected publications | 18 | 4 | 4/4 | 4/4 |
| 2018 | All selected publications | 17 | 2 | 2/2 | 2/2 |
| 2019 | All selected publications | 79 | 10 | 10/10 | 10/10 |
| 2020 | All selected publications | 54 | 8 | 8/8 | 8/8 |
| 2021 | All selected publications | 3 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 1 | 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 43 / 43 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): 2026-10-22T01:04:38.938722+00:00 — 2026-10-22T07:38:18.273817+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 | 12 | 43 | 43/43 |
| 2018 | All selected publications | 14 | 43 | 43/43 |
| 2019 | All selected publications | 37 | 43 | 43/43 |
| 2020 | All selected publications | 31 | 43 | 43/43 |
| 2021 | All selected publications | 46 | 43 | 43/43 |
| 2022 | All selected publications | 37 | 43 | 43/43 |
| 2023 | All selected publications | 38 | 43 | 43/43 |
| 2024 | All selected publications | 28 | 43 | 43/43 |
| 2025 | All selected publications | 37 | 43 | 43/43 |
| 2026 | All selected publications | 12 | 43 | 43/43 |
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 |
|---|---|
| 2015 | 5 |
| 2016 | 12 |
| 2017 | 4 |
| 2018 | 2 |
| 2019 | 10 |
| 2020 | 8 |
| 2021 | 1 |
| 2025 | 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 |
|---|---|---|---|---|---|---|---|---|
| Bussecon Review of Social Sciences Bussecon International Academy | 4 | 34 | 42 | 4/4 | 8.5 | 7.0 | 75.0 | 4/4 · 100.0% |
| International Journal of Business Ecosystem & Strategy Bussecon International Academy | 12 | 52 | 80 | 12/12 | 4.33 | 2.0 | 75.0 | 12/12 · 100.0% |
| International Journal of Finance & Banking Studies SSBFNET | 7 | 38 | 94 | 7/7 | 5.43 | 2 | 57.1 | 7/7 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 20 | 44 | 84 | 20/20 | 2.2 | 2.0 | 70.0 | 20/20 · 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
This paper demonstrates the relationship between CAMELS ratios and credit ratings of deposit banks in Turkey. Annual data was used for the period between 2004 and 2014 in this study. Moreover, 20 deposit banks of Turkey were analyzed and 21 different ratios of CAMELS components were used. In addition to that, credit ratings of these banks were provided from Moody’s corporation or annual activity reports of the banks. After that, we created multi nominal logistic regression analysis in order to illustrate the relationship. The major finding in this study is that three components (Asset Quality, Management Quality, and Sensitivity to Market Risk) of CAMELS have effects on credit ratings whereas the ratios related to Capital Adequacy and Earnings are not effective. As a result, it was recommended that Turkish deposit banks should concentrate on the percentage of fixed assets and interest income to have a better rating. Moreover, having high market share with respect to total assets and lower interest expense are also other important points for this purpose. On the other hand, Turkish deposit banks should control the proportion of financial assets and increase the amount of FX liquid assets to prevent credit ratings to decrease. Additionally, market share of banks for loans should not reach at high level for this objective.
The recruitment process is more of an issue for many businesses. The process of determining the appropriate candidate to hire is often a costly, time-consuming process. Besides, due to incorrect decision-making or lack of objectivity in hiring processes, recruitment processes may not proceed effectively. Businesses are trying to use technology in their recruitment processes to avoid these problems. Currently, many businesses use internet and software technologies to receive applications and evaluate candidates. But despite these technologies, it takes time and additional personnel costs for people to coordinate all processes. Due to these and similar situations, there has been an increase in the use of artificial intelligence technologies in recruitment processes in the world recently. The use of artificial intelligence in recruitment processes has the effect of reducing costs and decision-making errors and appears to be beneficial in saving time. In this study, the use of artificial intelligence in the recruitment processes of businesses in Turkey was examined. In this context, interviews were conducted with the human resources managers of 22 businesses. According to research results, it was understood that artificial intelligence was benefited only as an auxiliary element in recruitment processes. It has been found that businesses do not rely much on artificial intelligence in their recruitment processes, so they do not use it or partially use it.
Spiritual well-being is the affirmation of one’s life in a relation to external world, one’s ownself and a transcendental power like God. It reflects positive feelings, attitudes, and perceptions leading to a sense of identity, wholeness, peace, harmony, and purpose in life. In this study, it is predicted that individuals with high spiritual well-being will be more spiritually resistant during the COVID-19 pandemic, which is one of the most noteworthy periods when people need to be psychologically strong and resilient. In this study, resilience has been defined as the individual endurance against difficulties and the ability to bounce back and even get better. Hence we hypothesized that spiritual well-being of individuals will be effective on their resilience. We conducted our study in Turkey among adults older than 20 years old. We obtained 384 usable data and we analysed our date by using SPSS 20.0 program. Results confirmed the assumption of this study, regarding spiritual well-being/ resilience relationship.
In modern age, positive psychological capital is as important as financial capital for organizational development and sustainability. Efficacy, hope, optimism, and resilience are the dimensions of positive psychological capital. Emotional labor is defined by Hochschild as control of self emotions at work by workers who have a high degree of contact with coworkers within the organization or with external clients, so as to create an expression, voice or body gesture which is acceptible to the clients. Emotional labor is analysized under three components: surface acting, deep acting and naturally felt emotions. This study is aimed to investigate the impact of positive psychological capital on emotional labor. So a research is conducted with the aim of exploring the relationship between the sub-dimensions of psychological capital and emotional labour.
There has been an increase in the elderly population worldwide in recent years. This situation creates some needs both at individual and social level. In particular, elderly individuals need support in carrying out their daily activities. Therefore, elderly care centers are seen as an important supporter in this respect. This study aims to identify the criteria affecting the selection of elderly care centers and prioritize them with SWARA method. The SWARA method was used to weight the criteria affecting the selection of elderly care centers in order of priority. Eight criteria identified through literature review were evaluated by five experts experienced in the relevant field. The experts evaluated the criteria by ranking them according to their importance and the final prioritization was carried out by taking the geometric mean of the evaluations. According to the results of the analysis, the most important factor affecting the selection of an elderly care center was the Quality-of-Life Comfort and Service with a weight of 22%. This was followed by Financial Status with 20%, Health Services with 16%, and Professional Staff with 13%. Location and Transportation criterion was determined as the least important factor with a weight of 0.7%. The fact that life comfort and service quality have the highest weight indicates that elderly individuals expect a quality and comfortable living environment. The results obtained from the study show that policies for elderly care homes should be organized in a way that aims to increase the comfort of living, improve service quality, ensure economic accessibility and ensure the sustainability of health services.
Cryptocurrencies are attracting considerable attention around the world because of the various advantages that they offer. On the other hand, they also carry some inherent risks. Although monetary authorities broadly agree that cryptocurrencies do not engender an immediate threat to national and global financial systems, the future is full of unknowns. In this regard, drawing a framework based on the current drivers of demand for cryptocurrencies would help visualize the prospects for these assets and create a roadmap to avoid or manage any disruptive risks. This discussion paper aims to contribute to the literature by examining the key factors that will determine the future performance of cryptocurrencies. The main conclusion derived from the discussion is that national regulations will potentially affect the direction of cryptocurrencies, as well as the need for any special efforts in the domain of monetary policy
Spiritual well-being is the affirmation of one’s life in a relation to external world, one’s ownself and a transcendental power like God. It reflects positive feelings, attitudes, and perceptions leading to a sense of identity, wholeness, peace, harmony, and purpose in life. In this study, it is predicted that individuals with high spiritual well-being will be more spiritually resistant during the COVID-19 pandemic, which is one of the most noteworthy periods when people need to be psychologically strong and resilient. In this study, resilience has been defined as the individual endurance against difficulties and the ability to bounce back and even get better. Hence we hypothesized that spiritual well-being of individuals will be effective on their resilience. We conducted our study in Turkey among adults older than 20 years old. We obtained 384 usable data and we analysed our date by using SPSS 20.0 program. Results confirmed the assumption of this study, regarding spiritual well-being/ resilience relationship.
The recruitment process is more of an issue for many businesses. The process of determining the appropriate candidate to hire is often a costly, time-consuming process. Besides, due to incorrect decision-making or lack of objectivity in hiring processes, recruitment processes may not proceed effectively. Businesses are trying to use technology in their recruitment processes to avoid these problems. Currently, many businesses use internet and software technologies to receive applications and evaluate candidates. But despite these technologies, it takes time and additional personnel costs for people to coordinate all processes. Due to these and similar situations, there has been an increase in the use of artificial intelligence technologies in recruitment processes in the world recently. The use of artificial intelligence in recruitment processes has the effect of reducing costs and decision-making errors and appears to be beneficial in saving time. In this study, the use of artificial intelligence in the recruitment processes of businesses in Turkey was examined. In this context, interviews were conducted with the human resources managers of 22 businesses. According to research results, it was understood that artificial intelligence was benefited only as an auxiliary element in recruitment processes. It has been found that businesses do not rely much on artificial intelligence in their recruitment processes, so they do not use it or partially use it.
Employer branding is an important issue that businesses cannot neglect, as it is the process of creating a brand for employees and potential candidates. Especially with the development of technology, it has become easier to reach many resources. However, it has always been more difficult to reach talented human resources. At this point, discovering, attracting, and working with talented employees for a long time has attributed a key role to the employer brand. In this direction, the aim of the study is to investigate the roles of employer branding practices in attracting talents to the company in job applications. Content analysis, which is a qualitative analysis method, was used in the study. Maxqda 20 software program was used to access the findings of this analysis. In the research findings, it was seen that concepts such as reputation, communication, awareness, and the concept of Employer Brand were used together intensively. As a result of the research; It has been understood that businesses that want to attract talented employees to the company use the employer brand and communicate effectively with the external environment. It has been seen that this interaction supports the corporate reputation and this has played an important role in increasing the awareness of the company and in the preference and reliability
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 19:56 UTC · ijfbs | complete · ijfbs · 20 Oct 2026 19:55 UTC |
| openalex-enrichment | 21 Oct 2026 07:23 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 07:22 UTC |