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: 3 / 3 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 01:32 UTC — 22 Oct 2026 02:37 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.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 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 |
|---|---|---|---|---|---|
| 2021 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 2 | 3 | 3/3 | 3/3 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 6 | 3 | 3/3 | 3/3 |
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 3 / 3 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): 2026-10-22T01:32:20.785059+00:00 — 2026-10-22T02:37:04.082805+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 | 3 | 3/3 |
| 2018 | All selected publications | 0 | 3 | 3/3 |
| 2019 | All selected publications | 0 | 3 | 3/3 |
| 2020 | All selected publications | 0 | 3 | 3/3 |
| 2021 | All selected publications | 0 | 3 | 3/3 |
| 2022 | All selected publications | 0 | 3 | 3/3 |
| 2023 | All selected publications | 2 | 3 | 3/3 |
| 2024 | All selected publications | 3 | 3 | 3/3 |
| 2025 | All selected publications | 0 | 3 | 3/3 |
| 2026 | All selected publications | 1 | 3 | 3/3 |
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 |
|---|---|
| 2021 | 3 |
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 Finance & Banking Studies SSBFNET | 1 | 0 | 0 | 1/1 | 0.0 | 0 | 0.0 | 1/1 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 2 | 2 | 6 | 2/2 | 1.0 | 1.0 | 100.0 | 2/2 · 100.0% |
* Percentage among publications with a known citation count.
This study aims to examine and analyze the effect of financial distress with the Altman and Springate Models on stock returns either directly or indirectly by involving earnings management as a mediation. This study uses secondary data from Textile and Garment Companies listed on the Indonesia Stock Exchange from 2015-2019, with a sample of 20 companies using sampling so that 100 observations are obtained. The data is obtained from the annual financial statements. The data analysis technique used SEM-PLS with the help of WarpPLS 6.0 software. The results of the study provide empirical evidence that financial distress has a positive effect on earnings management, while financial distress and earnings management has a negative effect on stock returns. Earnings management is able to mediate the effect of financial distress on stock returns.
The widespread failure of DT SACCOs in Kenya is likely to lead to a loss of confidence among the current and potential members of the DT SACCOs in Kenya and eventually threatens to kill the sector. The study examined the effect of related party transactions on financial distress in DT-SACCOs in Kenya; the study adopted the following theories as to the basis of analyzing the collected data; wrecker’s financial distress theories and Agency theory. The target population was 176 Deposit-Taking SACCOs. Secondary data was obtained from SACCOs records as published by SASRA. The study used a systematic sampling technique based on the sampling formula by Taro Yamane (1967) to obtain the appropriate sample size of 68 DT SACCOs. Data were analyzed using STATA computer software. Data collection covered seven (7) year period from 2008 -2014, this period of 7 years was selected for the study because SASRA has enacted in 2010 therefore the justification for the choice of the study period, that is 3 years before and 4 years after SASRA enactment. The study established that related party transactions had a significant effect on financial distress in DT SACCOs in Kenya. The implications of the findings are that if the DT-SACCOs do not manage related party transactions then they will continue experiencing financial distress. The government through SASRA therefore should enact strict regulations in directors borrowing from SACCOs. The study has expounded on the body of knowledge on measures of financial distress in finance by introducing new relationships established. In terms of the contribution of the current study to innovation, the study used panel data analysis while other studies used OLS.
Related party transactions are a key factor to the sustainability of Savings and Credit Cooperatives (SACCOs). In view of this fact, loans to directors and staff are viewed as a factor which can greatly influence savings and credit cooperatives into either falling into financial distress or helping them to remain a float and become financially stable entities. The stakeholder theory holds that entities should be managed a manner that will satisfy every stakeholder. The stewardship theory reiterates that managers have the organisation at heart and can it to profitability as if it were their own. However approval of loans and other transactions to managers and staff, may not positively impact the entity. This study was designed to establish the effect of related party transactions on the relationship between board characteristics and financial distress of deposit taking SACCOs in Nairobi County. We applied Descriptive research design on Deposit taking SACCOs in Nairobi County which was identified purposively while a census was conceded for all deposit taking SACCOs in the county. We obtained secondary data from SASRA using a data collection sheet after which we performed panel data analysis by use of STATA software. Findings were presented using tables. The study concluded that related party transactions influenced the relationship between board characteristics and financial distress of Deposit Taking SACCOs in Nairobi County. Related party transactions can be an avenue of causing financial distress and should be kept as low as possible. The regulator should come up with a tool based on Altman’s Z score models to predict financial distress in SACCOs in order to offer timely advice to alleviate more distress and consequent bankruptcy which may lead to closure of SACCOs. Another research may be carried out to establish other factors causing financial distress and how to turn around the SACCOs already in distress.
This study aims to examine and analyze the effect of financial distress with the Altman and Springate Models on stock returns either directly or indirectly by involving earnings management as a mediation. This study uses secondary data from Textile and Garment Companies listed on the Indonesia Stock Exchange from 2015-2019, with a sample of 20 companies using sampling so that 100 observations are obtained. The data is obtained from the annual financial statements. The data analysis technique used SEM-PLS with the help of WarpPLS 6.0 software. The results of the study provide empirical evidence that financial distress has a positive effect on earnings management, while financial distress and earnings management has a negative effect on stock returns. Earnings management is able to mediate the effect of financial distress on stock returns.
Related party transactions are a key factor to the sustainability of Savings and Credit Cooperatives (SACCOs). In view of this fact, loans to directors and staff are viewed as a factor which can greatly influence savings and credit cooperatives into either falling into financial distress or helping them to remain a float and become financially stable entities. The stakeholder theory holds that entities should be managed a manner that will satisfy every stakeholder. The stewardship theory reiterates that managers have the organisation at heart and can it to profitability as if it were their own. However approval of loans and other transactions to managers and staff, may not positively impact the entity. This study was designed to establish the effect of related party transactions on the relationship between board characteristics and financial distress of deposit taking SACCOs in Nairobi County. We applied Descriptive research design on Deposit taking SACCOs in Nairobi County which was identified purposively while a census was conceded for all deposit taking SACCOs in the county. We obtained secondary data from SASRA using a data collection sheet after which we performed panel data analysis by use of STATA software. Findings were presented using tables. The study concluded that related party transactions influenced the relationship between board characteristics and financial distress of Deposit Taking SACCOs in Nairobi County. Related party transactions can be an avenue of causing financial distress and should be kept as low as possible. The regulator should come up with a tool based on Altman’s Z score models to predict financial distress in SACCOs in order to offer timely advice to alleviate more distress and consequent bankruptcy which may lead to closure of SACCOs. Another research may be carried out to establish other factors causing financial distress and how to turn around the SACCOs already in distress.
The widespread failure of DT SACCOs in Kenya is likely to lead to a loss of confidence among the current and potential members of the DT SACCOs in Kenya and eventually threatens to kill the sector. The study examined the effect of related party transactions on financial distress in DT-SACCOs in Kenya; the study adopted the following theories as to the basis of analyzing the collected data; wrecker’s financial distress theories and Agency theory. The target population was 176 Deposit-Taking SACCOs. Secondary data was obtained from SACCOs records as published by SASRA. The study used a systematic sampling technique based on the sampling formula by Taro Yamane (1967) to obtain the appropriate sample size of 68 DT SACCOs. Data were analyzed using STATA computer software. Data collection covered seven (7) year period from 2008 -2014, this period of 7 years was selected for the study because SASRA has enacted in 2010 therefore the justification for the choice of the study period, that is 3 years before and 4 years after SASRA enactment. The study established that related party transactions had a significant effect on financial distress in DT SACCOs in Kenya. The implications of the findings are that if the DT-SACCOs do not manage related party transactions then they will continue experiencing financial distress. The government through SASRA therefore should enact strict regulations in directors borrowing from SACCOs. The study has expounded on the body of knowledge on measures of financial distress in finance by introducing new relationships established. In terms of the contribution of the current study to innovation, the study used panel data analysis while other studies used OLS.
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 |