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: 50 / 50 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 00:14 UTC — 21 Oct 2026 07:23 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.
48 / 50 with data · 2 missing or unmatched. Percentages use all 50 selected publications.
48 / 50 with data · 2 missing or unmatched. Percentages use all 50 selected publications.
50 / 50 with data · 0 missing or unmatched. Percentages use all 50 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 |
|---|---|---|---|---|---|
| 2019 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2021 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2022 | All selected publications | 12 | 12 | 12/12 | 12/12 |
| 2023 | All selected publications | 11 | 11 | 11/11 | 11/11 |
| 2024 | All selected publications | 9 | 9 | 9/9 | 9/9 |
| 2025 | All selected publications | 12 | 12 | 12/12 | 12/12 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 2 | 3 | 3/3 | 3/3 |
| 2021 | All selected publications | 3 | 2 | 2/2 | 2/2 |
| 2022 | All selected publications | 49 | 12 | 12/12 | 12/12 |
| 2023 | All selected publications | 29 | 11 | 11/11 | 11/11 |
| 2024 | All selected publications | 9 | 9 | 9/9 | 9/9 |
| 2025 | All selected publications | 14 | 12 | 12/12 | 12/12 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 7 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 9 | 3 | 3/3 | 3/3 |
| 2021 | All selected publications | 9 | 2 | 2/2 | 2/2 |
| 2022 | All selected publications | 74 | 12 | 12/12 | 12/12 |
| 2023 | All selected publications | 47 | 11 | 11/11 | 11/11 |
| 2024 | All selected publications | 12 | 9 | 9/9 | 9/9 |
| 2025 | All selected publications | 12 | 12 | 12/12 | 12/12 |
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 50 / 50 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): 2026-10-21T00:14:27.796455+00:00 — 2026-10-21T07:23:04.590554+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 | 50 | 50/50 |
| 2018 | All selected publications | 0 | 50 | 50/50 |
| 2019 | All selected publications | 0 | 50 | 50/50 |
| 2020 | All selected publications | 0 | 50 | 50/50 |
| 2021 | All selected publications | 1 | 50 | 50/50 |
| 2022 | All selected publications | 8 | 50 | 50/50 |
| 2023 | All selected publications | 33 | 50 | 50/50 |
| 2024 | All selected publications | 46 | 50 | 50/50 |
| 2025 | All selected publications | 42 | 50 | 50/50 |
| 2026 | All selected publications | 40 | 50 | 50/50 |
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 |
|---|---|
| 2019 | 1 |
| 2020 | 3 |
| 2021 | 2 |
| 2022 | 12 |
| 2023 | 11 |
| 2024 | 9 |
| 2025 | 12 |
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 | 4 | 2 | 3 | 4/4 | 0.5 | 0.0 | 25.0 | 4/4 · 100.0% |
| International Journal of Finance & Banking Studies SSBFNET | 6 | 7 | 16 | 6/6 | 1.17 | 1.0 | 66.7 | 6/6 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 40 | 97 | 151 | 40/40 | 2.42 | 1.0 | 60.0 | 40/40 · 100.0% |
* Percentage among publications with a known citation count.
Financial management for young entrepreneurs needs to be done because disciplined financial arrangements will be able to minimize the risk of uncertainty. Young entrepreneurs have problems with financial management behavior, that is knowledge and skills in managing finances which have an impact on financial decision-making. This study aims to see the effect of financial knowledge, financial attitudes, and personality on financial management behavior in young entrepreneurs in Malang. The approach used in this research is quantitative with an explanatory approach. This study's population is young entrepreneurs in the Brawijaya University environment. The sampling technique used purposive sampling with a sample size of 150 samples. The results of this study prove that partially financial knowledge has no significant effect on Financial Management Behavior. Financial attitudes have a significant effect on Financial Management Behavior, and personality has a significant effect on Financial Management Behavior in Young Entrepreneurs in Malang City.
Investment intention in millennials and Gen Z in Indonesia grows yearly. However, the cause of the growing enthusiasm for investment among millennials and Gen Z is recently fear of being vulnerable to Fear of Missing Out (FOMO) behaviour alone, not based on financial factors such as financial knowledge, financial well-being and financial attitude. This study aims to determine the role of financial knowledge and financial well-being on investment intention mediated by financial attitude. This research belongs to the type of explanatory research. Respondents in this study are Millennials and Gen Z generations who invest and live in Malang City. Determination of the number of samples in this study using the purposive sampling technique with a total sample of 400 respondents. Methods of collecting data using a questionnaire, then analyzed using PLS-SEM. The study results confirm that financial knowledge positively affects investment intention. Financial well-being does not affect investment intention. Financial attitude has a positive influence on investment intention. Financial knowledge and financial well-being have a positive influence on financial attitude. Financial attitude can partially mediate the relationship between financial knowledge and investment intention. In addition, financial attitude can also mediate the relationship between financial well-being and investment intentions fully. Millennials and Gen Z are expected to improve financial knowledge and attitudes to increase interest in more targeted investments. This advice will also positively impact the Financial Well-being of millennials and Gen Z in the future.
This study investigates the impact of financial literacy on small and medium-sized enterprises (SMEs), focusing on its role in enhancing financial decision-making, access to finance, fintech adoption, and risk management. Grounded in Resource-Based Theory (RBT) and Human Capital Theory (HCT), financial literacy is positioned as both a strategic capability and a form of human capital that strengthens firm resilience and competitiveness. A systematic literature review was conducted, synthesising evidence from 18 empirical studies published between 2016 and 2024. Findings reveal that financial literacy significantly improves SMEs’ ability to budget, manage debt, and evaluate investment decisions. It also enhances creditworthiness through improved financial record-keeping, thereby facilitating access to financing. Moreover, digital financial literacy is identified as a catalyst for fintech adoption, which contributes to cost reduction, operational efficiency, and financial transparency. The study concludes that targeted financial literacy interventions are essential, especially in developing economies where SMEs face structural constraints. It calls for integrated financial education strategies involving policymakers, financial institutions, and SME development agencies. Further empirical research is recommended to examine the long-term and digital dimensions of financial literacy as a driver of sustainable SME performance.
Behavioral finance elucidates how investors rationally commit errors in the process of making investment decisions as a result of cognitive and emotional biases, ultimately resulting in unfavorable investment choices. People are unable to avoid a variety of behavioral biases that have an impact on investors' investment decision-making processes. The human mind employs shortcuts and emotional filters to interpret information, leading to errors in the decision-making process. The objective of this study is to investigate and assess the impact of behavioral biases such as disposition effects, herding, and overconfidence on investment decision-making. Additionally, we will examine how financial literacy moderates these biases. The research employed a purposive sample strategy to select generation Z investors in Kediri City. The sample size of 400 respondents was determined using the Krejcie & Morgan table. This research employs the Partial Least Squares (PLS) Structural Equation Model (SEM) analysis method using the SmartPLS version 3 data processing application. The research findings indicate that the disposition effect and overconfidence exert a substantial impact on investment decision-making; however, herding does not exhibit a significant influence on investment choice-making. Financial knowledge enhances the impact of the disposition effect on investment decision-making in a positive way. Financial literacy has a negative moderating effect on the influence of herding and overconfidence on investment decision-making among Generation Z investors in Kediri City.
The rise in the cryptocurrency market has led many individuals to start their investment. However, individuals are affected by several factors in their decision-making process to invest. The objective of this study is to examine the factors that can predict the decision to invest in the cryptocurrency market, by utilizing the variables of overconfidence, financial literacy, and attitude. This study also explores the role of attitude in mediating the influence of overconfidence and financial literacy on investor decisions. The population of this study is individual student investors in three top universities in Indonesia. This study uses a non-probability sampling technique, with purposive sampling that includes several criteria to determine the number of samples. The data is collected by distributing questionnaires to the students using Google Forms. The total number of respondents in this study, who returned the questionnaire that has been filled is 297 students. The data obtained from respondents is analyzed using Structural Equation Modeling with Partial Least Square (SEM-PLS). Finally, the results of this study have proved that: (1) Overconfidence has a positive influence on attitude; (2) Financial literacy has a positive influence on attitude; (3) Overconfidence has a positive influence on investor decision; (4) Financial literacy has a positive influence on investor decision; (5) Attitude has a positive influence on investor decision; (6) Attitude mediates the influence of overconfidence on investor decision; and (7) Attitude mediates the influence of overconfidence on investor decision. The implications for scholars and crypto investors are also provided in this study.
In the context of rapid technological advancement and evolving financial ecosystems, this study investigates the interplay between financial literacy, digital finance, and financial behaviour among high school teachers in KwaZulu-Natal (KZN), South Africa. Structural equation modelling (SEM) approach was used to describe intricate relationships between latent constructs, namely, financial literacy, financial Behaviour and, digital finance Knowledge as well as observed variables (B7 to B22, C23 to C44, D45 to D56, demographics). Demographic variables include Gender, Age Category, Qualification Type, Teaching Duration, Financial Literacy Course and Field Employed. The model defined each construct as a linear combination of its observed variables as well as demographic variables under its measurement component, and hypothetical relationships among three latent variables under its structural component. We used Python software and machine learning code to create and estimate the model using observed data. The missing values in each observed variable were imputed with averages of the variables’ data. The tests of normality assumption for SEM were performed using histogram and q-q plots, Shapiro-Wilk test D'Agostino and Pearson's test. Most data vectors violated the normality assumption. Diagonally Weighted Least Squares (DWLS) was used for parameter estimation as a suitable method that can provide better estimation for more complex models with many parameters. This method has an additional advantage of being less sensitive to outliers. The DWLS parameter estimation results, based on p-values, suggest that financial literacy and financial behaviour have a negative association which means that lacking financial behaviour results in financial literacy. A positive association between has been identified between financial behaviour and digital finance knowledge. Financial literacy and digital finance knowledge have a weak or no relationship. Thus, having or not having digital finance knowledge does not necessarily determine the possibility of experiencing financial literacy. Furthermore, among demographic variables, the financial literacy Course is the only demographic variable that has a significant impact, and only on Financial Behaviour and Financial literacy. The rest of the variables do not significantly influence the latent constructs. This study uniquely applies SEM using the DWLS estimator to explore personal finance dynamics in an emerging economy, offering concise insights into key relationships.
The literature reveals that have poor spending habits arising from poor budgeting skills and lack financial knowledge, among others. However, it is unclear which individual lifestyles and attitudes influence the students' spending habits. This study, therefore, fills this gap with empirical evidence from accounting students in South Africa. Specifically, the study aims to investigate the existence of relationships between socio-demographic, attitudinal and lifestyle factors on the one hand and students' spending behaviour, on the other. Using an online structured questionnaire, we collected data from 290 accounting students at the University of KwaZulu-Natal in South Africa, which was analysed using descriptive and inferential statistical techniques. The study finds that socio-demographic factors such as students' age and parental income have the greatest influence on students’ spending habits. Students' spending habits were also found to be influenced by attitudinal factors such as confidence in investment decision-making and shopping for necessities. Finally, it was observed that living an extravagant lifestyle, which includes purchasing branded clothing, compulsive shopping, the latest cell phones, and excessive alcohol purchases, were the primary cause of students' poor spending habits. These findings primarily support the social learning theory, implying that parents and/or guardians play an important role in shaping their children's spending habits.
This study investigates the relationship between financial literacy and financial well-being among youth entrepreneurs in Tanzania, focusing on the mediating role of financial behaviour. Drawing on human capital theory and behavioural economics, this study employed a cross-sectional design and collected data from a sample of youth entrepreneurs using structured questionnaires in selected urban areas in Tanzania. Data were analysed using Structural Equation Modelling (SEM) to assess both direct and indirect relationships among the variables. The findings reveal that financial literacy significantly and positively affects financial well-being, underscoring the foundational role of financial knowledge in enhancing economic decision making. Furthermore, financial literacy significantly influenced financial behaviour, and financial behaviour partially mediated the relationship between financial literacy and financial well-being. These results suggest that while financial knowledge is critical, it is the consistent application of this knowledge through daily financial practices, such as budgeting, saving, and debt management that ultimately leads to improved financial outcomes. This study contributes to the growing literature on financial capability and entrepreneurial sustainability in developing economies and supports the advancement of Sustainable Development Goals, particularly SDG 1, SDG 4.4, and SDG 8. These findings have important implications for policymakers, educators, and development partners aiming to enhance youth financial empowerment through integrated financial education and behaviour-focused intervention.
Small, Medium, and Micro Enterprises (SMMEs) are the foundation of South Africa's economy, driving job creation, innovation, and local economic growth. However, these businesses frequently face challenges in critical areas such as bookkeeping and financial literacy, which have a direct impact on operational performance and sustainability. Limited financial knowledge and inadequate record-keeping practices have long been issues for Alice's SMMEs, posing significant challenges to effective financial management. These gaps in financial literacy and bookkeeping make it difficult for SMMEs to manage cash flow, make informed decisions, and plan for growth, limiting their ability to fully contribute to the local economy. Addressing these challenges has become critical for SMMEs, as improved financial management practices are required for success and resilience in a competitive market. The study's exploring the impact of bookkeeping and financial literacy on the performance of SMMEs in the context of Alice. Data was collected qualitatively through in-depth interviews with 15 SMME operators and analysed thematically to gain a thorough understanding of their experiences and practices. The findings show that both accurate bookkeeping and financial literacy improve business performance, allowing owners to make data-driven decisions, avoid cash flow issues, and lay the groundwork for future growth. Those who fall short in these areas suffer from financial mismanagement, which harms their businesses. Despite the challenges, the study identified strategies implemented by SMMEs owners to improve both bookkeeping and financial literacy such as digital bookkeeping tools, external financial advisory support, and collaborative financial management across teams. Finding from this study are crucial for enhancing SMMEs’ operational performance and fostering economic resilience within South Africa's competitive market.
This study aims to examine and analyze the effect of income on financial well-being, with financial literacy serving as a mediating variable. A quantitative research approach was employed, with the object of study being the community of Poncokusumo Tourism Village. The sample size was determined using a saturated sampling technique, involving 154 respondents from the village community. Data were collected through questionnaires and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that: (i) income has a significant effect on financial well-being, (ii) financial literacy significantly influences financial well-being, (iii) income positively affects financial literacy, and (iv) financial literacy mediates the relationship between income and financial well-being. Income plays a crucial role as it significantly enhances individuals' quality of life by enabling them to meet daily needs, improve living standards, and plan for future financial security. Furthermore, stable income contributes to greater economic resilience among families and fosters community development. Respondents reported a general satisfaction with their monthly income, which in turn facilitates better financial management and supports the achievement of both short-term and long-term financial goals. This study contributes to the literature by suggesting that village government institutions, non-governmental organizations, and other stakeholders should take initiatives to provide financial literacy training. Future research is encouraged to incorporate additional variables to deepen the understanding of factors influencing financial well-being.
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 |