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: 39 / 39 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 01:12 UTC — 29 Oct 2026 04:31 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.
39 / 39 with data · 0 missing or unmatched. Percentages use all 39 selected publications.
39 / 39 with data · 0 missing or unmatched. Percentages use all 39 selected publications.
39 / 39 with data · 0 missing or unmatched. Percentages use all 39 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 | 39 | 39 | 39/39 | 39/39 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2020 | All selected publications | 165 | 39 | 39/39 | 39/39 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2020 | All selected publications | 258 | 39 | 39/39 | 39/39 |
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 39 / 39 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): 2026-10-22T01:12:33.648490+00:00 — 2026-10-29T04:31:36.180699+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 | 39 | 39/39 |
| 2018 | All selected publications | 0 | 39 | 39/39 |
| 2019 | All selected publications | 0 | 39 | 39/39 |
| 2020 | All selected publications | 10 | 39 | 39/39 |
| 2021 | All selected publications | 18 | 39 | 39/39 |
| 2022 | All selected publications | 42 | 39 | 39/39 |
| 2023 | All selected publications | 59 | 39 | 39/39 |
| 2024 | All selected publications | 52 | 39 | 39/39 |
| 2025 | All selected publications | 53 | 39 | 39/39 |
| 2026 | All selected publications | 24 | 39 | 39/39 |
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 |
|---|---|
| 2020 | 39 |
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 | 8 | 51 | 67 | 8/8 | 6.38 | 4.5 | 75.0 | 8/8 · 100.0% |
| International Journal of Business Ecosystem & Strategy Bussecon International Academy | 13 | 40 | 60 | 13/13 | 3.08 | 2 | 84.6 | 13/13 · 100.0% |
| International Journal of Finance & Banking Studies SSBFNET | 2 | 4 | 8 | 2/2 | 2.0 | 2.0 | 100.0 | 2/2 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 16 | 70 | 123 | 16/16 | 4.38 | 4.0 | 75.0 | 16/16 · 100.0% |
* Percentage among publications with a known citation count.
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.
In this study, 396 university students in Turkey are surveyed online regarding their attitudes towards financial issues, their level of financial knowledge, and their financial behavior. The latest version of OECD/INFE Financial Literacy Survey (2018) is used in the study. Using SPSS and LISREL. Factor analysis, Cronbach's Alpha, and Structural Equation Modeling (SEM) are used to analyze the relationship between financial knowledge of the participants with their financial attitude and behavior as well as the mediation effect of financial attitude in this relationship. In line with the common wisdom that the relationship between knowledge and behavior is higher if knowledge is better reflected in the attitude of the individual, the mediation effect of financial attitude strengthens the financial knowledge and financial behavior relationship.
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.
This study aims to examine the concept of stress, which has become a part of our daily lives, in an organizational context, and to determine whether there are a relationship and interaction between stress management and job performance in organizations. In this article, which is an exploratory study, the sources behind organizational stress are investigated, and the individual and organizational results created by organizational stress management on business performance are discussed. Within the scope of the study, the studies in the literature conducted between 2005 and 2019 in different sectors and their results were examined. In the obtained results, significant negative relationships were found between stress management and job performance. Furthermore, in these results, the mediating role of stress management was also detected.
The business transition process from conventional to digital forms causes companies to think about strategic steps to make the new business processes that are in it become sustainable. One way is to take advantage of technological developments in the era of industry 4.0 that change the way work is done from the conventional way towards digital technology by utilizing e-commerce. This research uses descriptive research method with a qualitative analysis approach. Data collection techniques in this study used primary data and secondary data approaches. Data obtained from research are empirical data. The methodology used in this study is a conceptual review, which is a method carried out by collecting data and information related to market potential, changes in consumer behavior and how to use e-commerce technology as an alternative solution in innovating marketing strategies with the aim to capture potential market potential. greater than. The results of the research resulted nine e-commerce strategies in the industry 4.0 era to achieve the goals of a business with challenges and high competitiveness in the e-commerce market, business people in industry 4.0 era can use 9 strategies to successful in this technological era, including; framing the market opportunity; formulating the marketing strategy; designing the customer experience; crafting the customer interface; designing the marketing program; leveraging customer information through technology; character and competent delegation; systemized market customers; and evaluating the marketing program.
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.
With the negative effects of global financial crisis in 2008-2009 and recent Covid 19 pandemic, the need for stress management for individuals and business organizations is significantly increasing. However, the latest advances in digitalization and digital transformation of service companies are reshaping and redesigning the working conditions. This study aimed to explain how stress and burnout syndrome affects the individuals and business organizations. It is also aimed to maintain a theoretical perspective on burnout syndrome models. Main contribution of the paper to literature is its theoretical perspective with fundamental theories and models of burnout syndrome.
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
The purpose of this paper is to compare three contemporary accident causation models, namely the Swiss Cheese, HFACS, and Fu (2018) Model-based on two accidents existing in the literature. The accidents reviewed are a mine explosion accident and an electrical plant accident. In this way, the validity of the models can be evaluated and weaknesses and strengths revealed. This study discussed the advantages and possible limitations of these models, and according to this discussion, all these models include human and organizational factors and have been found scientific and systematic. According to the results, Fu (2018) and HFACS are more modern, since they were developed based on Swiss Cheese. The product of this research will be a recommendation for safety investigators and accident inspectors which way to turn when choosing the most applicable accident analysis method
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 |