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.
New articles are checked daily. Citation sources are refreshed monthly. All times below are UTC. A successful check may find no new publications.
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: 10 / 10 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: 31 Oct 2026 20:56 UTC — 03 Nov 2026 23:54 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.
10 / 10 with data · 0 missing or unmatched. Percentages use all 10 selected publications.
10 / 10 with data · 0 missing or unmatched. Percentages use all 10 selected publications.
10 / 10 with data · 0 missing or unmatched. Percentages use all 10 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 | 10 | 10 | 10/10 | 10/10 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2015 | All selected publications | 25 | 10 | 10/10 | 10/10 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2015 | All selected publications | 51 | 10 | 10/10 | 10/10 |
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 10 / 10 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): 2026-10-31T20:56:26.710299+00:00 — 2026-11-03T23:54:59.308971+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 | 5 | 10 | 10/10 |
| 2018 | All selected publications | 6 | 10 | 10/10 |
| 2019 | All selected publications | 8 | 10 | 10/10 |
| 2020 | All selected publications | 7 | 10 | 10/10 |
| 2021 | All selected publications | 3 | 10 | 10/10 |
| 2022 | All selected publications | 8 | 10 | 10/10 |
| 2023 | All selected publications | 4 | 10 | 10/10 |
| 2024 | All selected publications | 3 | 10 | 10/10 |
| 2025 | All selected publications | 3 | 10 | 10/10 |
| 2026 | All selected publications | 0 | 10 | 10/10 |
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 | 10 |
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 Research in Business and Social Science SSBFNET | 10 | 25 | 51 | 10/10 | 2.5 | 1.0 | 70.0 | 10/10 · 100.0% |
* Percentage among publications with a known citation count.
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.
The aim of this study is to investigate the relationship between teamwork and organizational trust. In the implementation section the data from the survey of 250 workers is employed in call centers in Erzurum by using relevant statistical methods. Consequently, it is found that there is a positive and significant relationship between teamwork and organizational trust. Thus, the hypothesis of the study is supported as it was expected. Besides, it is found that there are positive and significant relationships between communication, openness to innovation, participation-trust in teamwork and organizational trust, trust in management, trust in co-workers, and trust in workplace.
In a data set, an outlier refers to a data point that is considerably different from the others. Detecting outliers provides useful application-specific insights and leads to choosing right prediction models. Outlier detection (also known as anomaly detection or novelty detection) has been studied in statistics and machine learning for a long time. It is an essential preprocessing step of data mining process. In this study, outlier detection step in the data mining process is applied for identifying the top 20 outlier firms. Three outlier detection algorithms are utilized using fundamental analysis variables of firms listed in Borsa Istanbul for the 2011-2014 period. The results of each algorithm are presented and compared. Findings show that 15 different firms are identified by three different outlier detection methods. KCHOL and SAHOL have the greatest number of appearances with 12 observations among these firms. By investigating the results, it is concluded that each of three algorithms makes different outlier firm lists due to differences in their approaches for outlier detection.
Innovation is the only way of solution to achieve sustainable growth, social welfare and employment in a country. This study principally focuses on the relationship between growth and innovation in the light of information derived from a general literature review about definition, sources and risks of innovation, and measurement of innovation performance. Then, the contributions of innovative capability on economic growth and employment as well as innovation systems on a country basis have been discussed, and accordingly, actions to be taken, including a shift in paradigm, for a growth-innovation-national innovation system and its sustainability have been addressed. In addition, current innovation performance indicators of Turkey have been discussed in the light of a scope which is outlined in the initial sections of the study, and the change in such performance indicators between 1998 and 2009 has been examined. After analysis of the above mentioned criteria and comparisons against practices in developed countries and communities, suggestions have been made about the activities to be carried out in order to make Turkey’s current innovation system “sustainable”, to support and improve innovation. In the study, a research application has been conducted using the content analysis method on the “President’s Message” letters of 158 state and foundation universities that are located in Turkey and that have a website, and the importance accorded to innovation by the universities has been determined.
This research paper examines the corporate dividend payout behaviors of non-financial firms from Istanbul Stock Exchange (Borsa Istanbul). Survey method is conducted to investigate managerial views on corporate dividend policy. The study investigates whether the evidence in Turkish stock market on dividend policy is similar to the European and the U.S. firms’ results which are reported earlier by other studies, and moreover in what extent Lintner’s (1956) findings on dividends is supported by today’s listed firms in an emerging market. The financial managers from 38 firms out of 216 non-financial companies responded the survey. The results show that there is a significant positive relationship between cash dividends and earnings. Earnings are viewed as the most important factor in dividend decision like in European and the U.S. firms. Sustainable change in earnings, stability and level of future earnings, and the desire to distribute a proportion of earnings to shareholders are the common determinants of dividend policy. The majority of the respondents reports that they target dividends. Dividend yield is the most common measure for dividend targeting. Share repurchases are not viewed as alternative to dividend payouts unlike the U.S. firms. The study finds supporting evidences for bird-in-the-hand and signaling hypotheses, and Lintner’s model.
Innovation is the only way of solution to achieve sustainable growth, social welfare and employment in a country. This study principally focuses on the relationship between growth and innovation in the light of information derived from a general literature review about definition, sources and risks of innovation, and measurement of innovation performance. Then, the contributions of innovative capability on economic growth and employment as well as innovation systems on a country basis have been discussed, and accordingly, actions to be taken, including a shift in paradigm, for a growth-innovation-national innovation system and its sustainability have been addressed. In addition, current innovation performance indicators of Turkey have been discussed in the light of a scope which is outlined in the initial sections of the study, and the change in such performance indicators between 1998 and 2009 has been examined. After analysis of the above mentioned criteria and comparisons against practices in developed countries and communities, suggestions have been made about the activities to be carried out in order to make Turkey’s current innovation system “sustainable”, to support and improve innovation. In the study, a research application has been conducted using the content analysis method on the “President’s Message” letters of 158 state and foundation universities that are located in Turkey and that have a website, and the importance accorded to innovation by the universities has been determined.
The employment policy is one of the most disputable areas between Turkey and the EU. The EU declared two opening conditions, one about union rights and the other one about preparing an action plan displaying step by step the transposing of EU laws. Although Turkey claims that she met those criteria, the EU insists on keeping the accession negotiations in this area closed. However, there is serious gap between Turkish and European employment policies and in order to comply with the EU’s, there is a strong need of enthusiasm in Turkey for proper implementation of changes made in legal and institutional structures. Simultaneously, the EU has to offer a considerable incentive, e.g. membership, for Turkey to encourage her to adapt EU rules in employment policy. Unfortunately, it is argued in this paper that the current situation is the opposite, i.e. there is no substantial incentive from the EU to encourage Turkey and thus, no enthusiasm from Turkey to comply with the EU policy. In order to analyze the impact of the EU on Turkey’s employment policy the main question designed in this paper is: ‘To what extent does the EU have influence on policy convergence in a candidate country that does not have a clear membership perspective? ‘. It is argued in this study that “without a concrete incentive, European Union’s impact on a candidate country would be limited”. Thus, the patterns of policy change at the national level are the dependent variable to be observed. The European Union’s conditionality is the independent variable that is expected to alter the national patterns.
This paper examines the relationship between the lobbying power of different interest groups and public education spending in a panel data estimation during the period 1996-2009 for 132 countries. The resource rents, manufacture exports, and agriculture value added are used as proxy variables for the lobbying power of the natural resource owners, manufacturers, and landowners, respectively, in order to substantiate the definition of the lobbying power of the interest groups more with economic fundamentals. As lobbying power is mediated through political institutions, different governance indicators are used individually and in interaction terms with the proxy variables in the estimations. It is found that when the country is more politically stable and the more the rule of law applies, the negative (positive) effect of the lobbying power of natural resource owners (manufacturers) on public education spending intensifies. The negative effect of landowners’ lobbying power diminishes as institutional quality as measured by governance indicators improves.
This research paper examines the corporate dividend payout behaviors of non-financial firms from Istanbul Stock Exchange (Borsa Istanbul). Survey method is conducted to investigate managerial views on corporate dividend policy. The study investigates whether the evidence in Turkish stock market on dividend policy is similar to the European and the U.S. firms’ results which are reported earlier by other studies, and moreover in what extent Lintner’s (1956) findings on dividends is supported by today’s listed firms in an emerging market. The financial managers from 38 firms out of 216 non-financial companies responded the survey. The results show that there is a significant positive relationship between cash dividends and earnings. Earnings are viewed as the most important factor in dividend decision like in European and the U.S. firms. Sustainable change in earnings, stability and level of future earnings, and the desire to distribute a proportion of earnings to shareholders are the common determinants of dividend policy. The majority of the respondents reports that they target dividends. Dividend yield is the most common measure for dividend targeting. Share repurchases are not viewed as alternative to dividend payouts unlike the U.S. firms. The study finds supporting evidences for bird-in-the-hand and signaling hypotheses, and Lintner’s model.
In a data set, an outlier refers to a data point that is considerably different from the others. Detecting outliers provides useful application-specific insights and leads to choosing right prediction models. Outlier detection (also known as anomaly detection or novelty detection) has been studied in statistics and machine learning for a long time. It is an essential preprocessing step of data mining process. In this study, outlier detection step in the data mining process is applied for identifying the top 20 outlier firms. Three outlier detection algorithms are utilized using fundamental analysis variables of firms listed in Borsa Istanbul for the 2011-2014 period. The results of each algorithm are presented and compared. Findings show that 15 different firms are identified by three different outlier detection methods. KCHOL and SAHOL have the greatest number of appearances with 12 observations among these firms. By investigating the results, it is concluded that each of three algorithms makes different outlier firm lists due to differences in their approaches for outlier detection.
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 |
|---|---|---|
| Not available · | Not run |