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: 2 / 2 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: 21 Oct 2026 07:22 UTC — 22 Oct 2026 01:46 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.
2 / 2 with data · 0 missing or unmatched. Percentages use all 2 selected publications.
2 / 2 with data · 0 missing or unmatched. Percentages use all 2 selected publications.
2 / 2 with data · 0 missing or unmatched. Percentages use all 2 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 |
|---|---|---|---|---|---|
| 2024 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2024 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 3 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2024 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 4 | 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 2 / 2 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): 2026-10-21T07:22:56.428337+00:00 — 2026-10-22T01:46:06.617978+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 | 2 | 2/2 |
| 2018 | All selected publications | 0 | 2 | 2/2 |
| 2019 | All selected publications | 0 | 2 | 2/2 |
| 2020 | All selected publications | 0 | 2 | 2/2 |
| 2021 | All selected publications | 0 | 2 | 2/2 |
| 2022 | All selected publications | 0 | 2 | 2/2 |
| 2023 | All selected publications | 0 | 2 | 2/2 |
| 2024 | All selected publications | 0 | 2 | 2/2 |
| 2025 | All selected publications | 1 | 2 | 2/2 |
| 2026 | All selected publications | 3 | 2 | 2/2 |
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 |
|---|---|
| 2024 | 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 |
|---|---|---|---|---|---|---|---|---|
| International Journal of Finance & Banking Studies SSBFNET | 1 | 3 | 4 | 1/1 | 3.0 | 3 | 100.0 | 1/1 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 1 | 0 | 0 | 1/1 | 0.0 | 0 | 0.0 | 1/1 · 100.0% |
* Percentage among publications with a known citation count.
In the quest to achieve SDG 1 on eradicating poverty, improving savings rates in East Africa is crucial, as it lays the foundation for reducing financial vulnerability and achieving economic resilience. This paper investigated the determinants of individual savings in selected East African countries. Specifically, internet access, financial inclusion, and borrowing were the covariates while individual savings in East Africa were the predicted. This study utilized data from the Global Findex 2021 World Bank database. This dataset was selected because it is one of the most comprehensive databases on financial inclusion, providing a comprehensive view of adults' savings, borrowing, payments, and risk management across global and regional contexts. This study used a basic framework to explore the factors influencing individual savings by examining production, income distribution, and accumulation. A panel data logistic regression model was utilized, and Instrumental Variables (2SLS) regression was employed to test endogeneity. The Sargan and Basmann tests assessed overidentification restrictions to validate the instruments. Shea’s partial R-squared measured the unique contribution of each instrument in explaining the variation in endogenous variables, accounting for the influence of other instruments and endogenous variables. The Hausman test which model was suitable for panel data modeling. The 2SLS regression showed no evidence of endogeneity, as internet access was insignificant with a high p-value (> 0.05). Overidentification tests confirmed instrument validity with p-values of 0.1018 and 0.1016. However, Shea’s partial R-squared revealed weak instruments, especially for borrowing, with an adjusted value of 0.0328. Results revealed that borrowing is strongly and positively associated with saving. The higher the borrowing, the more the savings. In East Africa, most borrowers are savers. These use the borrowed funds to create an injection of income flow through capital accumulation. Internet access significantly predicts savings in East Africa today. In this digital era, most businesses are run online. Therefore, it is almost impossible to save with no internet access. Similarly, those who use digital payment systems are most likely to enhance their savings. Age and gender do not influence the saving of an individual in East Africa. Likely, owning a financial account does not translate to more savings. Most East Africans have non-functional bank accounts. Education levels significantly influence the savings of individuals in East Africa. Highly educated people utilize financial literacy skills to save more. Being out of the workforce reduces individual savings since most of these are out of the working age.
This paper explores how expatriates deal with host country informal business practices that differ from their home country institutional frameworks. We note that expatriate business people working in emerging markets of Africa often contend with corrupt business practices rooted in local informal institutions and the lack of strong formal institutions regulating business practices. We also observe that while local people take informal institutions such as magendo, as a way of life and adjust to them, expatriates being ‘outsiders’ respond in different ways. The study employed a qualitative phenomenological design that involved in-depth interview of a small sample of expatriates operating in Uganda. For data analysis, NVivo 11 was used to search for and retrieve chunks of labelled data. The key findings indicate that expatriates from countries with strong formal institutions experience the most frustrations and often reject informal business practices. On the other hand, expatriates from countries with weak institutions are more pragmatic and easily adapt to existing practices in the new environments. Multinationals however, often reject informal business practices due to their market power and strict codes of conduct.The study recommends that small-medium category expatriates targeting Africa need to align with influential local people so as to navigate business informalities that characterise informal business environments.
In the quest to achieve SDG 1 on eradicating poverty, improving savings rates in East Africa is crucial, as it lays the foundation for reducing financial vulnerability and achieving economic resilience. This paper investigated the determinants of individual savings in selected East African countries. Specifically, internet access, financial inclusion, and borrowing were the covariates while individual savings in East Africa were the predicted. This study utilized data from the Global Findex 2021 World Bank database. This dataset was selected because it is one of the most comprehensive databases on financial inclusion, providing a comprehensive view of adults' savings, borrowing, payments, and risk management across global and regional contexts. This study used a basic framework to explore the factors influencing individual savings by examining production, income distribution, and accumulation. A panel data logistic regression model was utilized, and Instrumental Variables (2SLS) regression was employed to test endogeneity. The Sargan and Basmann tests assessed overidentification restrictions to validate the instruments. Shea’s partial R-squared measured the unique contribution of each instrument in explaining the variation in endogenous variables, accounting for the influence of other instruments and endogenous variables. The Hausman test which model was suitable for panel data modeling. The 2SLS regression showed no evidence of endogeneity, as internet access was insignificant with a high p-value (> 0.05). Overidentification tests confirmed instrument validity with p-values of 0.1018 and 0.1016. However, Shea’s partial R-squared revealed weak instruments, especially for borrowing, with an adjusted value of 0.0328. Results revealed that borrowing is strongly and positively associated with saving. The higher the borrowing, the more the savings. In East Africa, most borrowers are savers. These use the borrowed funds to create an injection of income flow through capital accumulation. Internet access significantly predicts savings in East Africa today. In this digital era, most businesses are run online. Therefore, it is almost impossible to save with no internet access. Similarly, those who use digital payment systems are most likely to enhance their savings. Age and gender do not influence the saving of an individual in East Africa. Likely, owning a financial account does not translate to more savings. Most East Africans have non-functional bank accounts. Education levels significantly influence the savings of individuals in East Africa. Highly educated people utilize financial literacy skills to save more. Being out of the workforce reduces individual savings since most of these are out of the working age.
This paper explores how expatriates deal with host country informal business practices that differ from their home country institutional frameworks. We note that expatriate business people working in emerging markets of Africa often contend with corrupt business practices rooted in local informal institutions and the lack of strong formal institutions regulating business practices. We also observe that while local people take informal institutions such as magendo, as a way of life and adjust to them, expatriates being ‘outsiders’ respond in different ways. The study employed a qualitative phenomenological design that involved in-depth interview of a small sample of expatriates operating in Uganda. For data analysis, NVivo 11 was used to search for and retrieve chunks of labelled data. The key findings indicate that expatriates from countries with strong formal institutions experience the most frustrations and often reject informal business practices. On the other hand, expatriates from countries with weak institutions are more pragmatic and easily adapt to existing practices in the new environments. Multinationals however, often reject informal business practices due to their market power and strict codes of conduct.The study recommends that small-medium category expatriates targeting Africa need to align with influential local people so as to navigate business informalities that characterise informal business environments.
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 |