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: 16 / 17 publications (94.1%). Latest observation: 21 Oct 2026, 00:21 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 00:22 UTC — 22 Oct 2026 02:33 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.
14 / 17 with data · 3 missing or unmatched. Percentages use all 17 selected publications.
14 / 17 with data · 3 missing or unmatched. Percentages use all 17 selected publications.
17 / 17 with data · 0 missing or unmatched. Percentages use all 17 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 |
|---|---|---|---|---|---|
| 2012 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2013 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2014 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2015 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2016 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2017 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2026 | All selected publications | 1 | 1 | 0/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2012 | All selected publications | 14 | 2 | 2/2 | 2/2 |
| 2013 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2014 | All selected publications | 1 | 2 | 2/2 | 2/2 |
| 2015 | All selected publications | 1 | 2 | 2/2 | 2/2 |
| 2016 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2017 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 7 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 4 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 8 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 11 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 3 | 2 | 2/2 | 2/2 |
| 2026 | All selected publications | — | 1 | 0/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2012 | All selected publications | 30 | 2 | 2/2 | 2/2 |
| 2013 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2014 | All selected publications | 3 | 2 | 2/2 | 2/2 |
| 2015 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2016 | All selected publications | 5 | 1 | 1/1 | 1/1 |
| 2017 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 3 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 14 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 4 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 8 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 12 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 4 | 2 | 2/2 | 2/2 |
| 2026 | All selected publications | 0 | 1 | 0/1 | 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 17 / 17 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): 2026-10-21T00:22:01.664398+00:00 — 2026-10-22T02:33:06.535491+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 | 2 | 17 | 17/17 |
| 2018 | All selected publications | 3 | 17 | 17/17 |
| 2019 | All selected publications | 5 | 17 | 17/17 |
| 2020 | All selected publications | 4 | 17 | 17/17 |
| 2021 | All selected publications | 8 | 17 | 17/17 |
| 2022 | All selected publications | 8 | 17 | 17/17 |
| 2023 | All selected publications | 3 | 17 | 17/17 |
| 2024 | All selected publications | 10 | 17 | 17/17 |
| 2025 | All selected publications | 19 | 17 | 17/17 |
| 2026 | All selected publications | 15 | 17 | 17/17 |
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 |
|---|---|
| 2012 | 2 |
| 2014 | 2 |
| 2015 | 2 |
| 2016 | 1 |
| 2019 | 1 |
| 2021 | 3 |
| 2022 | 1 |
| 2023 | 1 |
| 2024 | 1 |
| 2025 | 2 |
| 2026 | 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 | 17 | 49 | 85 | 17/17 | 3.06 | 1.5 | 62.5 | 16/17 · 94.1% |
* Percentage among publications with a known citation count.
Less attention has been given to well-being and other household characteristics that influence clients’ access to micro-credit among women households especially. The paper investigates the determinants of access to credit by 320 women entrepreneurs in the Mfantsiman Municipality in the Central Region of Ghana. Data for the study was collected in June-July 2010 from six communities including Mankessim, Saltpond, Anomabu, Biriwa and Yamoransa. Sturctured questionnaires were used to collect the data from women entrepreneurs. The results of the study indicate that clients’ well-being influences access to credit amount. Clients who have been in business for long time are likely to access larger loan amounts. Marital status and education do not influence access to credit. Among the recommendations are that microfinance should not only target clients with high well-being scores but those with low well-being scores since the original aim of microfinance is to lift the poor out of poverty. It is important to give credit to aged business owners instead of young ones. Giving credit to married women because their husbands could serve as guarantors does not matter much but rather women who do not have husbands also need to benefit from micro-credit.
Financial inclusion, especially the adoption of fintech, has emerged as a crucial avenue for removing the financial access divides in developing countries, including Bangladesh. Following the comprehensive analysis, this article intends to outline Bangladesh's recent experiences with fintech-based financial inclusion, identify the short-comings and hence make policy suggestions. Being a descriptive research, cross-sectional and observational research approaches are applied for the explanatory analysis of secondary data. Results suggest an urban-rural gap in the implementation of digital financial services, with urban areas consistently having higher rates of fully online branches and a greater number of financial machines. Despite this, the sharp rise in the issuance of active cards is a positive sign suggesting an expansion of consumer base and an adoption of electronic modes of payment. Moreover, inter-bank electronic fund transfers and online banking have experienced increasing uptake, revealing a progressing trend towards digital transactions within the banking industry. Additionally, the study identifies a speedy growth in the number of mobile financial services accounts in both urban and rural areas. This study adds to the exchange of ideas regarding financial inclusion by carrying out a detailed study of Bangladesh's digital financial transformation, exemplifying the accomplishments, obstacles, and future research directions. It highlights the need for targeted policies and ongoing research efforts to build inclusive financial systems to serve the entire society’s interest. Nevertheless, the observations will have both theoretical and practical significance, contributing towards building capability in harnessing technology for sustainability, community uplifting, and shaping a brighter future.
In the past decade, Southeast Asia has seen the development of financial inclusion, as evidenced by the growth of the penetration, availability, and usage dimensions of financial inclusion. This study analyses the impact financial inclusion has on economic growth, represented by GDP per capita growth, and unemployment using data from Indonesia, Malaysia, Thailand, the Philippines, and Cambodia. The authors employed panel data regression with fixed effect and random effect models to analyse the data. The results show that the number of commercial bank branches and outstanding loans statistically significantly affect GDP per capita growth, while the number of saving/deposit accounts does not. All three variables are shown to have a statistically significant impact on unemployment.
The purpose of this study is to examine the drivers of the ownership and use of mobile accounts as compared to those of financial institution accounts in Côte d’Ivoire. In this perspective, individual level data from the Global Findex 2017 database were used to carry out simple probit and Heckman probit estimations. The results revealed that the ownership of mobile accounts is associated with determinants related to individuals in the least vulnerable strata of society (wealthier and more educated individuals and individuals in the labour force), while their use is associated with determinants related to individuals in the most vulnerable strata of society (women and individuals out of the labour force). On the other hand, the ownership and use of financial institution accounts are associated with the determinants related to individuals in the least vulnerable strata of society. Therefore, the delay between the financial institution accounts’ penetration rate and that of mobile accounts in Côte d’Ivoire may be explained by the greater use of mobile accounts by individuals in the most vulnerable strata of society, which represent the large share of the unbanked.
The paper investigates whether financial literacy influences financial inclusion in Uganda on the premise that there are currently few to no studies that investigate this causality and the general lack of consensus on an appropriate measure for financial literacy. It uses data from the FinScope (2018) consumer survey on Uganda and applies Principal Component Analysis (PCA) to construct a composite financial literacy index of the adult bankable population (16 years and older). The index is then regressed - alongside other demand-side control variables, against a measure of financial inclusion using logistic models. Our measure of financial literacy significantly and positively affects financial inclusion in Uganda even in the presence of variables like age, gender, income, and education. Individuals who make financial ends meet, plan for their financial future welfare, seek financial advice, and are receptive towards technology, are 'ceteris paribus', more likely to be financially included than not. Technology and mobile money adoption enhance financial inclusion while more men are financially included than women. While the dataset is limited to demand-side variables of Uganda and cannot be generalised, comparative cross-country studies with robust datasets are needed to provide further insights. The paper advances a novel approach for measuring financial literacy for developing economies while contributing to efforts to standardize an international measure. It also provides empirical insights to support the notion that financial literacy should be addressed more holistically and recommends this approach for improving financial inclusion in Uganda and globally.
This study aims to assess and rank all thanas in Bangladesh according to their performance in financial inclusion. It employed a composite index for that purpose, using three proxies of financial inclusion: deposit per account, advance per account, and advance deposit ratio. Cross-sectional data obtained from the Scheduled Banks Statistics of Bangladesh Bank are utilized to rank 615 thanas in Bangladesh. It categorizes 615 thanas into three classifications: high, medium, and poor financial inclusion. There are large differences in the availability and utilization of financial services among thanas. Thanas have a low level of integration into the formal financial system, as indicated by the average financial inclusion index of 0.068 and a maximum of 0.628. The rate of financial inclusion is continuously lower in rural, remote, and hilly regions compared to economically active and urban areas. Kotwali in Chattogram, Dhaka, etc., is a higher-ranked thana that demonstrates stronger financial inclusion, with higher per-account values and greater reliance on credit. By comparison, the lower-ranked thanas such as Ramgati, Char Shasivuson, Madan etc. are characterized by a low level of financial integration, poor institutionalization as well as extensive use of informal finance. The paper concludes that the present policy actions have failed to create broad financial inclusion improvements. It proposes multi-dimensional interventions, which are focused to tackle the urban-rural divide. This study will provide the policy makers with useful information to determine the actual situation by determining the highly included and excluded thanas in Bangladesh. Eventually, it will help the interested parties to develop thana or area-based measures to secure financial inclusiveness of all that result to promoting comprehensive and sustainable economic development. The study is also unique because it is likely to be the first initiative that has attempted the study at micro level i.e. thana based analysis of financial inclusion in Bangladesh.
A country's ability to develop depends on the empowerment of its women. Evidence from several countries indicates that giving Self-Help Group (SHG) member’s microcredit can help empower rural women. The main objective of the study is impact of creation of SHGs on rural women in Assam. Also purpose of this research was to determine how well SHGs in Assam contribute to women's economic empowerment. The study's primary data came from interviews with SHG beneficiaries conducted in the Bongaigaon District of Assam. In all, sixty samples are gathered from two blocks of the Bongaigaon district. According to the survey, the majority of respondents are between the ages of 25 and 45, and they have completed elementary and middle level of school. After joining SHGs, participants engaged in a variety of income-generating activities. The outcome demonstrated a favorable shift in economic empowerment metrics, such as income, employment, and savings, following SHG membership. For the economic indicators, the estimated value of the Wilcoxon signed Ranks Test was significant. It was found that, in comparison to the pre-SHG scenario, the majority of the respondents' income, employment days, and savings amount rose in the post-SHG scenario. Therefore, the study came to the conclusion that SHGs were essential to rural women's empowerment in the study region.
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.
Financial inclusion, especially the adoption of fintech, has emerged as a crucial avenue for removing the financial access divides in developing countries, including Bangladesh. Following the comprehensive analysis, this article intends to outline Bangladesh's recent experiences with fintech-based financial inclusion, identify the short-comings and hence make policy suggestions. Being a descriptive research, cross-sectional and observational research approaches are applied for the explanatory analysis of secondary data. Results suggest an urban-rural gap in the implementation of digital financial services, with urban areas consistently having higher rates of fully online branches and a greater number of financial machines. Despite this, the sharp rise in the issuance of active cards is a positive sign suggesting an expansion of consumer base and an adoption of electronic modes of payment. Moreover, inter-bank electronic fund transfers and online banking have experienced increasing uptake, revealing a progressing trend towards digital transactions within the banking industry. Additionally, the study identifies a speedy growth in the number of mobile financial services accounts in both urban and rural areas. This study adds to the exchange of ideas regarding financial inclusion by carrying out a detailed study of Bangladesh's digital financial transformation, exemplifying the accomplishments, obstacles, and future research directions. It highlights the need for targeted policies and ongoing research efforts to build inclusive financial systems to serve the entire society’s interest. Nevertheless, the observations will have both theoretical and practical significance, contributing towards building capability in harnessing technology for sustainability, community uplifting, and shaping a brighter future.
In the past decade, Southeast Asia has seen the development of financial inclusion, as evidenced by the growth of the penetration, availability, and usage dimensions of financial inclusion. This study analyses the impact financial inclusion has on economic growth, represented by GDP per capita growth, and unemployment using data from Indonesia, Malaysia, Thailand, the Philippines, and Cambodia. The authors employed panel data regression with fixed effect and random effect models to analyse the data. The results show that the number of commercial bank branches and outstanding loans statistically significantly affect GDP per capita growth, while the number of saving/deposit accounts does not. All three variables are shown to have a statistically significant impact on unemployment.
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 00:21 UTC · ijfbs | complete · ijfbs · 21 Oct 2026 00:21 UTC |
| ojs-rest | 21 Oct 2026 00:21 UTC · ijfbs | complete · ijfbs · 21 Oct 2026 00:14 UTC |
| oai-pmh | 20 Oct 2026 19:56 UTC · ijfbs | complete · ijfbs · 20 Oct 2026 19:55 UTC |
| openalex-enrichment | 21 Oct 2026 00:22 UTC · ijfbs | complete · ijfbs · 21 Oct 2026 00:21 UTC |