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: 25 / 25 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 02:08 UTC — 01 Nov 2026 16:49 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.
25 / 25 with data · 0 missing or unmatched. Percentages use all 25 selected publications.
25 / 25 with data · 0 missing or unmatched. Percentages use all 25 selected publications.
25 / 25 with data · 0 missing or unmatched. Percentages use all 25 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 |
|---|---|---|---|---|---|
| 2013 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2014 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2015 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2017 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2018 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2020 | All selected publications | 8 | 8 | 8/8 | 8/8 |
| 2021 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2023 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2026 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2013 | All selected publications | 6 | 2 | 2/2 | 2/2 |
| 2014 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2015 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 5 | 2 | 2/2 | 2/2 |
| 2017 | All selected publications | 38 | 1 | 1/1 | 1/1 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 8 | 2 | 2/2 | 2/2 |
| 2020 | All selected publications | 46 | 8 | 8/8 | 8/8 |
| 2021 | All selected publications | 7 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 9 | 3 | 3/3 | 3/3 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2026 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2013 | All selected publications | 10 | 2 | 2/2 | 2/2 |
| 2014 | All selected publications | 5 | 2 | 2/2 | 2/2 |
| 2015 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 13 | 2 | 2/2 | 2/2 |
| 2017 | All selected publications | 86 | 1 | 1/1 | 1/1 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 16 | 2 | 2/2 | 2/2 |
| 2020 | All selected publications | 92 | 8 | 8/8 | 8/8 |
| 2021 | All selected publications | 13 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 14 | 3 | 3/3 | 3/3 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2026 | All selected publications | 0 | 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 25 / 25 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): 2026-10-22T02:08:41.219913+00:00 — 2026-11-01T16:49:56.834227+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 | 3 | 25 | 25/25 |
| 2018 | All selected publications | 6 | 25 | 25/25 |
| 2019 | All selected publications | 7 | 25 | 25/25 |
| 2020 | All selected publications | 25 | 25 | 25/25 |
| 2021 | All selected publications | 33 | 25 | 25/25 |
| 2022 | All selected publications | 41 | 25 | 25/25 |
| 2023 | All selected publications | 43 | 25 | 25/25 |
| 2024 | All selected publications | 49 | 25 | 25/25 |
| 2025 | All selected publications | 30 | 25 | 25/25 |
| 2026 | All selected publications | 10 | 25 | 25/25 |
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 |
|---|---|
| 2013 | 2 |
| 2014 | 2 |
| 2016 | 2 |
| 2017 | 1 |
| 2019 | 2 |
| 2020 | 8 |
| 2021 | 3 |
| 2022 | 3 |
| 2024 | 1 |
| 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 | 9 | 61 | 133 | 9/9 | 6.78 | 2 | 88.9 | 9/9 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 16 | 62 | 118 | 16/16 | 3.88 | 2.5 | 87.5 | 16/16 · 100.0% |
* Percentage among publications with a known citation count.
The study examines the bank-specific and macroeconomic determinants of banks profitability in Nigeria analyzing audited financial reports of selected sixteen (16) commercial banks over the period of 2010 to 2015 making up to 96 observations. The study identified that existing studies are sketchy in developing economies even though many studies have emerge in developed economies. The bank profitability is measured by return on assets and return on equity as function of bank-specific and macroeconomic determinants. Using the balanced panel data set, the empirical results of the study shows that capital adequacy and liquidity have a positive and significant effect on bank profitability. However, efficiency ratio have a negative and significant effect on bank profitability. With regards to macroeconomic variable, GDP growth also have a positive and significant impact on banks profitability. The empirical results of the study suggested that banks can improve their profitability through increasing capital and liquidity, decreasing operating cost with conscious effort to maintain transparency in their operations. In addition, a good economic environment for financial institutions foster increase in bank profitability. Hence, the study recommends that further studies can expand the scope while extending to other industries as well.
SME restaurants have reported declined earnings and faced challenges to remain open during the Movement Control Order (MCO) period imposed by the Malaysian government due to the Coronavirus (COVID-19) outbreak. SME decision-makers were observed to be making changes regarding their day-to-day operations and management strategies to mitigate MCO restrictions. This paper aims to review the significant adaptations made by SME restaurants in Malaysia throughout the MCO period. Three (3) prominent areas of adaptations made by decision-makers have been identified based upon ceaseless news reports and media contents. The adaptations made commonly depict actions to (i) Nurture Creativity, (ii) Sustain Reputation, and (iii) Maintain Profitability. The outcomes of this paper provide essential survival guides for SME restauranteurs to embrace the COVID-19 outbreak. These can be helpful to related policy and decision-makers to implement the crucial traits of SME restaurants to reduce the impact on SME restaurants for the foreseeable future.
This paper attempts to explore the issue of enhancing pedagogical skills among the college teachers of Bangladesh through CPD. This is a literature review paper that divided the topic into different themes and sub-themes in order to analyze the issue properly. After analyzing 60 studies including journal articles, conference proceedings, and research reports of different authors and organizations this study found several positive impacts of CPD on the teachers regarding uplifting their professional skills. It has also found some challenges of implementing CPD for college teachers identifying the scarcity of resources at the heart of all the challenges. Finally, the paper ends up with some recommendations to enhance the teachers’ professional skills through CPD in Bangladeshi colleges
An increase in MSMEs in large numbers is very instrumental in increasing economic growth in developing countries, including Indonesia. The research objective is based on identifying the effect of investment credit, interest rates, and labor on economic growth through the role of MSMEs. The design of this research is descriptive-verification to present a structured, factual and accurate picture and test hypotheses empirically through the MRA model. Empirical analysis proves that investment credit and interest rates through the role of MSMEs can influence positively and significantly on Indonesia's economic growth. In other results, it appears that the role of MSMEs is less able to mediate the effect of labor on Indonesia's economic growth. This fact is based on a negative and insignificant relationship. In a sentence or two, enter the implications and limitations of your research. In Indonesia, increasing bank lending to MSMEs in practice is undermined by lending policies by banks and by macroeconomic factors (economic growth, interest rates, investment credit, and labor).
The increase of scandals involving non-governmental organizations (NGOs) has provoked concerns from the public for their accountability. In the case of Yemen, the NGOs receive financial support from both local and international donors. These organisations, however, are not obliged to disclose how they manage the donation funds, although the donors usually outline specific requirements on how the funds should be utilised. At the same time, the success of projects undertaken by the NGOs funded by the donors can help to enhance the organisation’s reputation and legitimize its activities to secure more funds. This study investigated the level of accountability practices in the Yemeni local NGOs. A questionnaire survey was sent to 75 active local NGOs. The findings show that the level of accountability practices among local NGOs is moderate. Most of these organisations evaluated the impact of their projects at their completion. However, they tend to avoid an independent assessment of any third party. They also restricted the local communities’ involvement in determining where and how the money of the project should be spent. This suggests that the local NGOs are more likely to be “seen” as being accountable rather than being accountable. A further improvement perhaps through policymaking is thus necessary to ensure effective accountability practices in funds management of the NGOs.
Marriage is one of the most important social institutions, as it greatly influences the stability of families, population, and socio-economic well-being. The changing trends of marriages and divorces in South Africa confirm the importance of predictive analysis on the stability of marriages. Marriage duration prediction is a challenging task, as it is affected by a number of factors. Recent developments in machine learning techniques provide new opportunities to model this problem. This experiment used the following seven different classifications: Random Forest, Gradient Boosting, XGBoost, Logistic Regression, Support Vector Machine, K Nearest Neighbours, and Decision Tree, which aimed to forecast the categories of marriage length (short, medium, and long). The models used the following metrics: accuracy, precision, recall, F1-score, macro ROC-AUC, and the ROC curve. The results showed that ensemble-based approaches performed better than conventional classifiers. Gradient Boosting had the best performance in macro ROC AUC and was best at discriminating classes, especially for long and medium-length marriages. Random Forest had the best overall accuracy and was best at balancing classes. XGBoost had a similar performance to that of Random Forest in predicting. SVM had a high precision value but a lower recall value. Decision Trees and KNN had poor performance. Gradient Boosting was found to be the best-performing method for multi-class prediction of duration of marriage and achieved a balance between sensitivity and specificity. The results obtained from these studies prove that ensemble learning methods can be effectively utilized for modelling complex societal events.
This study systematically reviews literature to explore the Moderated Mediation Model of Customer Intention in Omnichannel Technology, particularly under the transformative influence of technologies like AI, AR, and big data analytics. Amidst evolving consumer expectations and the drastic shifts induced by the COVID-19 pandemic, the retail industry's shift from multichannel to omnichannel strategies has become crucial. This review analyzes 59 studies published between 2018 and April 2023 to discern the dynamics influencing customer intention within the omnichannel framework. Our research highlights the sparse but critical discourse on the mediating roles of perceived value and trust and the moderating effects of factors like age and product involvement. Although extensive, the literature reveals gaps, particularly in unified frameworks integrating these diverse insights. We propose a novel conceptual framework centered on these mediators and moderators to better understand and harness customer intentions toward omnichannel technology adoption. Key findings indicate that seamless integration across online and offline channels, responsive to the nuanced needs and behaviors of consumers, significantly enhances customer engagement and loyalty. This synthesis not only offers a consolidated view of the omnichannel retail landscape but also provides strategic pathways for retailers aiming to optimize customer interactions and satisfaction in a digitally dominated era. Our review contributes to the omnichannel retail literature by offering a structured overview of the factors driving customer intentions and suggesting directions for future research to bridge identified gaps, particularly through longitudinal studies and expanded demographic analyses.
Entrepreneurial orientation (ENO), social network ties (SOT) and market intelligence generation (MIT) are independently modeled as distinct antecedents of entrepreneurial intention (EIN). However, the intervening variables for such models are typically under-explored in their applicability to university students in sub-Saharan Africa. Accordingly, drawing on the theory of planned behavior (TPB), the present study argues that ENO impacts EIN, primarily explained by SOT and MIT. Using cross-sectional data gathered from university students in Ghana, a sub-Saharan African country, our findings reveal that ENO has both direct and indirect relationships with EIN. Thus, this relationship is mainly mediated when students build and develop social network ties (SOT) instead of gathering marketing intelligence (MIT).
The increase of scandals involving non-governmental organizations (NGOs) has provoked concerns from the public for their accountability. In the case of Yemen, the NGOs receive financial support from both local and international donors. These organisations, however, are not obliged to disclose how they manage the donation funds, although the donors usually outline specific requirements on how the funds should be utilised. At the same time, the success of projects undertaken by the NGOs funded by the donors can help to enhance the organisation’s reputation and legitimize its activities to secure more funds. This study investigated the level of accountability practices in the Yemeni local NGOs. A questionnaire survey was sent to 75 active local NGOs. The findings show that the level of accountability practices among local NGOs is moderate. Most of these organisations evaluated the impact of their projects at their completion. However, they tend to avoid an independent assessment of any third party. They also restricted the local communities’ involvement in determining where and how the money of the project should be spent. This suggests that the local NGOs are more likely to be “seen” as being accountable rather than being accountable. A further improvement perhaps through policymaking is thus necessary to ensure effective accountability practices in funds management of the NGOs.
Despite official crime figures showing a continuous decline in the rate of crime, the perception of the high level of crime by many Malaysians suggests other factors are affecting their perception of safety from crime. The objective of this study is to identify and understand these factors through a qualitative study that employs online focus group discussions. This study argues that the perception of safety from crime is the result of interactions of a number of factors such as the experience of victimisation, crime information, neighbourhood conditions as well as the perception of law enforcement and government crime reduction policy. The sense of safety from crime is also contextual as it may fluctuate according to time, place, activities and situations. Analysis of the data from the focus group discussions found that participants generally have a high perception of safety from crime. However, participants felt less safe when they were in public places and when interacting with online services. In most circumstances, participants' sense of safety is affected by the pressure to be vigilant and to avoid criminal victimisation. This pressure is exacerbated by low-quality information on crime that is often received through social media. Crime information on social media has a significant impact on participants’ perceptions of crime. The overall analysis of the findings suggests that the most important factor which affects the perception of safety from crime among these selected Malaysians is their sense of safety in their homes and their neighbourhood. Hence, efforts to increase the sense of safety among Malaysians should focus on this context.
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 |