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: 84 / 84 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 06:09 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.
84 / 84 with data · 0 missing or unmatched. Percentages use all 84 selected publications.
84 / 84 with data · 0 missing or unmatched. Percentages use all 84 selected publications.
84 / 84 with data · 0 missing or unmatched. Percentages use all 84 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 |
|---|---|---|---|---|---|
| 2014 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2015 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2016 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2017 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | 23 | 23 | 23/23 | 23/23 |
| 2023 | All selected publications | 23 | 23 | 23/23 | 23/23 |
| 2024 | All selected publications | 15 | 15 | 15/15 | 15/15 |
| 2025 | All selected publications | 12 | 12 | 12/12 | 12/12 |
| 2026 | All selected publications | 9 | 9 | 9/9 | 9/9 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2014 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2015 | All selected publications | 7 | 1 | 1/1 | 1/1 |
| 2016 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2017 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | 274 | 23 | 23/23 | 23/23 |
| 2023 | All selected publications | 77 | 23 | 23/23 | 23/23 |
| 2024 | All selected publications | 19 | 15 | 15/15 | 15/15 |
| 2025 | All selected publications | 10 | 12 | 12/12 | 12/12 |
| 2026 | All selected publications | 22 | 9 | 9/9 | 9/9 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2014 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2015 | All selected publications | 11 | 1 | 1/1 | 1/1 |
| 2016 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2017 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | 403 | 23 | 23/23 | 23/23 |
| 2023 | All selected publications | 92 | 23 | 23/23 | 23/23 |
| 2024 | All selected publications | 24 | 15 | 15/15 | 15/15 |
| 2025 | All selected publications | 12 | 12 | 12/12 | 12/12 |
| 2026 | All selected publications | 22 | 9 | 9/9 | 9/9 |
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 84 / 84 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): 2026-10-21T07:22:43.921410+00:00 — 2026-10-22T06:09:45.285888+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 | 84 | 84/84 |
| 2018 | All selected publications | 1 | 84 | 84/84 |
| 2019 | All selected publications | 0 | 84 | 84/84 |
| 2020 | All selected publications | 2 | 84 | 84/84 |
| 2021 | All selected publications | 2 | 84 | 84/84 |
| 2022 | All selected publications | 15 | 84 | 84/84 |
| 2023 | All selected publications | 69 | 84 | 84/84 |
| 2024 | All selected publications | 168 | 84 | 84/84 |
| 2025 | All selected publications | 179 | 84 | 84/84 |
| 2026 | All selected publications | 128 | 84 | 84/84 |
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 |
|---|---|
| 2014 | 1 |
| 2015 | 1 |
| 2022 | 23 |
| 2023 | 23 |
| 2024 | 15 |
| 2025 | 12 |
| 2026 | 9 |
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 Business Ecosystem & Strategy Bussecon International Academy | 2 | 2 | 2 | 2/2 | 1.0 | 1.0 | 50.0 | 2/2 · 100.0% |
| International Journal of Finance & Banking Studies SSBFNET | 3 | 7 | 11 | 3/3 | 2.33 | 0 | 33.3 | 3/3 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 79 | 400 | 551 | 79/79 | 5.06 | 1 | 53.2 | 79/79 · 100.0% |
* Percentage among publications with a known citation count.
This study aims to evaluate the factors that influence data saturation in qualitative studies. The study adopted a systematic literature review of 24 journal articles published between 2018 and 2022. The articles were obtained from a Google scholar database. The findings point to five factors that affect data saturation namely, pre-determined codes and themes, sample size, relevancy of research subjects (respondents), number of research methods, and length of data collection sessions. It is argued in this paper, therefore, that in order to attain data saturation, researchers should consider multiple factors that may affect data saturation in order to increase the validity of qualitative studies.
This study examines the strengths and limitations of 22 published journal articles by conducting a systematic literature review. The review revealed that the qualitative approach was flexible, offered in-depth and detailed information, allowed the use of multiple data collection methods and minimised the chance of having missing data. The approach was found (i) to integrate human touch, was cost-effective and was indeed the only option in some cases of research problems; (ii) to be prone to researchers’ subjectivity, involves complex data analysis, makes anonymity difficult and has limited scope in its generalizability, and (iii) to make replication of findings challenging, and the findings may be influenced by the researcher’s bias. It is concluded, therefore, that researchers should take necessary precautions when using the approach to ensure that the weaknesses of qualitative research do not bar them from achieving research objectives.
This study aimed to explore how soft skills development is done in higher education institutions (HEIs) by considering the roles of HEIs and students. The study used a mixed research approach comprising qualitative and quantitative methods. Qualitative data was collected from 16 students using semi-structured interviews. In comparison, quantitative data was collected using a standard questionnaire from 342 students pursuing management and administrative undergraduate programmes in one of the universities in Tanzania. Through questionnaires, participants were asked to rank soft skills according to their relevance to their careers. The top five most relevant soft skills identified were communication, leadership, decision-making, teamwork/orientation and problem-solving. In addition, participants self-evaluated their levels of soft skills. Findings demonstrate that students perceived to have more customer orientation, communication, decision-making, problem-solving, and work-ethic skills. Through qualitative interviews, participants narrated the role of HEIs in developing soft skills and their personal initiatives to acquire the skills. It was found that joining clubs and associations, taking leadership roles, reading self-development books, watching videos and attending soft skills training are initiatives that students use for soft skills acquisition.
Novice and experienced researchers may encounter numerous methodological challenges when it comes to data collection. Data have to be collected from the right sources using appropriate data collection tools to make research studies impactful. There are numerous data collection methods that researchers may use but not all methods are appropriate for all research studies. Choosing inappropriate research methods may result in invalid research findings and lead to erroneous conclusions. This means researchers have to carefully choose data collection methods to make their studies achieve their respective goals. This study intended to identify factors that researchers have to consider in choosing data collection methods. A systematic literature review was used to analyze 28 journal articles published between 2018 and 2022. From the analysis the study identified ten (10) factors that researchers have to consider in choosing data collection methods. These factors are; the research goal, the scope of the study, sample size, type of data, time and user-friendliness to the subjects. Other factors are research approach, safety and security of a researcher, the need for triangulation and theoretical framework. Before choosing methods for data collection, it is important to put into consideration these factors to increase the chances of achieving research objectives and answering research questions of research projects.
This review provides a comprehensive, step-by-step guide to the application of partial least squares structural equation modeling (PLS-SEM) for novice researchers. This is a conceptual and literature-based review that focuses on best practices and PLS-SEM literature. It highlights the rationale for using PLS-SEM, sample size, software tools, and essential metrics in PLS-SEM analysis. Drawing on best practices and recent literature, the review offers a framework for conducting and reporting PLS-SEM analysis. The review presents essential such as outer loadings, Cronbach’s alpha coefficients, average variance extracted (AVE), composite reliability, cross-loadings, Heterotrait-Monotrait ratio of correlations (HTMT), the Fornell-Larcker criterion, variance inflation factor (VIF), and redundancy analysis. Moreover, for more consistent results, the paper emphasizes on researchers to employ 10,000 bootstrap subsamples and Bias-corrected and accelerated (BCa) bootstrap in assessing the structural model. Insights regarding path coefficients, p-values, R-square (R2), f-square (f2), and Q-square (Q2), are also presented. Furthermore, the review underscores the trade-off between predictive power and model fit when applying PLS-SEM. The presented practical insights alert novice researchers in avoiding common pitfalls and enhance the methodological rigor of empirical research that utilizes PLS-SEM. This step-by-step guide supports early-career researchers and contributes to the ongoing debates on improving methodological clarity and transparency.
This study investigates whether Strategic Communication Competence (SCC) predicts Organizational Performance (OP) among employees of Mbeya City Council (MCC), a Tanzanian local government authority, and whether Financial Resource Management (FRM) independently predicts OP and moderates the SCC–OP relationship. Grounded in Communication Accommodation Theory and the Resource-Based View, a structured questionnaire was administered to 501 MCC employees, of whom 471 provided valid and fully completed responses, yielding a 94.0% response rate. The data were analyzed using Partial Least Squares Structural Equation Modelling. The findings indicate that SCC emerged as the strongest predictor of OP, while FRM also significantly and independently predicted OP (β = 0.243, p < .001). However, FRM did not significantly moderate the SCC–OP relationship, suggesting that, within Mbeya City Council, the two capabilities function as parallel and independently associated predictors of organizational performance rather than as interacting capabilities. Collectively, SCC and FRM explained 58.3% of the variance in OP, while the measurement model demonstrated sound reliability and validity. Given the cross-sectional and single-site design, these findings should be interpreted as evidence of significant associations and predictive relationships rather than causal effects. Based on these findings, recommendations are offered primarily for MCC and, more cautiously, for similarly structured Tanzanian local government authorities pending multi-site replication. In particular, MCC should prioritize strategic communication capability-building through feedback systems, information-sharing routines, and communication training, given SCC’s comparatively greater contribution to the explained variance in organizational performance. At the same time, financial resource governance should be maintained as an independently justified budgetary priority rather than being contingent upon communication outcomes.
This study examines the effects of disaggregated corporate social responsibility (CSR) practices on the economic wellbeing of host communities in Tanzania’s mining sector. Drawing on stakeholder theory, the study adopts a quantitative cross-sectional design using primary survey data collected from 393 respondents in three gold-mining regions - Geita, Mara, and Kagera, with 332 valid responses analysed using SmartPLS 4. The analysis focuses on the effects of economic, legal, ethical, and philanthropic CSR practices on community-level economic outcomes. The findings indicate that ethical, legal, and philanthropic CSR practices have positive and statistically significant effects on economic wellbeing, while economic CSR practices are not statistically significant. These findings suggest that while legal CSR practices contribute positively to economic wellbeing, their influence remains relatively modest, indicating that compliance-based interventions alone may not generate substantial household-level improvements. In contrast, responsible business conduct and community-oriented investments appear to exert a stronger and more direct influence on local development outcomes. The study contributes to the literature by providing empirical support for disaggregating CSR into distinct dimensions when assessing development outcomes in extractive industries and highlights the need to move beyond aggregate CSR measures toward more differentiated and context-sensitive analyses. From a policy and practice perspective, the findings underscore the importance of aligning CSR initiatives with community priorities and strengthening implementation frameworks to enhance their developmental impact in resource-dependent economies.
This paper searches the impact of trade credit on firms’ performance in 15 sub-Saharan African (SSA) countries. In particular, we also examine whether trade credit can advance in an environment of higher financial inclusion rate. We achieve this by using a fixed effect estimation model and solving endogeneity by GMM and IV-2SLS methods by utilizing firm level data from the Bloomberg terminal from 2010 to 2023. Baseline results summarize that firm performance grows with the level of trade credit finance. We find a firm’s industry and SSA blocs heterogeneity the trade credit-firm performance nexus. Additionally, financial inclusion alone is positive and when interacted with trade credit it becomes significant. The major implication among others is that trade credit facilitates more firm performance in a situation of higher financial inclusion level.
This paper uses primary data from a large-scale cross-sectional study to provide evidence on how the concept of development is defined, conceptualised, configured and operationalised so as to achieve the intended IDF project outcomes. Theoretical constructs and measures were developed drawing from both Activity theory and Distributed Leadership Theory to help understand the concept of development from the interactions of multiple actors at local, national, regional and even global within integrated transboundary landscapes and seascape. Findings indicate that in addition to development to be perceived by global actors as international development, local community define development from its contribution towards achievement of other multiple outcomes and bring scalable impacts to people, nature and economy. National government they define development from its contribution towards increased Growth Domestic Product (GDP) and Per Capital Income. Regional platforms defined development from its role it plays within transboundary landscapes and seascapes as development corridors. We use GDP as benchmarking for growth and the square multiple correlations to assess the integrated transboundary approaches as predictors of IDF outcomes. Following statistical analysis with support of structural equation model (SEM), the review of the square multiple correlations indicated that the predictors of IDF explain 9.8 percent of its variance. In other words, the error variance of IDF is approximately 90.2 percent of the variance of IDF itself. The study policy and practical implications of the measures on equality, equity, fairness, inclusivity, inequities, rights and responsibility and limitations for future studies are discussed. Practically, it confirms that, when integrated transboundary approaches are used as specified distributed practices or collective practices, they are significant for framing and increasing the descriptive, discursive and explanatory power of the concept of development when defining, conceptualising, configuring and operationalising IDF activities within integrated transboundary landscapes and seascape.
The research investigated the degree to which client appraisal relate to financial sustainability among Microfinance Institutions in Tanzania. Grounded on Resource Based View Theory, the study employed an explanatory research design whereby data was collected from a sample of 291 microfinance institutions from Dodoma, Iringa and Shinyanga regions. Semi-structured questionnaire in form of 5-point Likert scale was used to collect data which were analysed through inferential method. Research findings revealed that credit terms negatively influenced financial sustainability, whereas integration capabilities significantly and positively affected financial sustainability. Research findings further revealed that introduction of dynamic capabilities into the structural model improved the coefficient of determination (R²) value from 0.150 to 0.218, confirming their moderating role. Specifically, integration capability amplified the positive impact of internal control (IIC → SI, β = 0.157, p = 0.006), while sensing and learning did not significantly alter base relationships. Moreover, the research findings indicated that R² of 0.218 (adjusted R² = 0.175) accounted for approximately 22 percent of the variance in financial sustainability, reflecting the combined influence of loan delinquency strategies and dynamic capabilities. The study concludes that financial sustainability of MFIs in Tanzania depended not only on internal management practices, but also on external environmental conditions and institutional maturity, thereby necessitating multi-level policy interventions. The study recommends that MFIs in Tanzania adopt context-sensitive credit policies, such as granting of grace periods or seasonal repayment structures, especially for clients in agriculture and informal sectors. Also, strengthening learning systems including provision of staff training, knowledge sharing, and performance reflection could complement integration capabilities to boost long-term sustainability. Finally, MFIs should adopt adaptive management frameworks that combine governance, data analytics, and collaborative networks to manage delinquency dynamics.
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 |