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: 12 / 12 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 02:50 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.
11 / 12 with data · 1 missing or unmatched. Percentages use all 12 selected publications.
11 / 12 with data · 1 missing or unmatched. Percentages use all 12 selected publications.
12 / 12 with data · 0 missing or unmatched. Percentages use all 12 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 |
|---|---|---|---|---|---|
| 2022 | All selected publications | 12 | 12 | 12/12 | 12/12 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 49 | 12 | 12/12 | 12/12 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 74 | 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 12 / 12 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): 2026-10-21T07:22:43.125988+00:00 — 2026-10-22T02:50:47.089852+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 | 12 | 12/12 |
| 2018 | All selected publications | 0 | 12 | 12/12 |
| 2019 | All selected publications | 0 | 12 | 12/12 |
| 2020 | All selected publications | 0 | 12 | 12/12 |
| 2021 | All selected publications | 0 | 12 | 12/12 |
| 2022 | All selected publications | 1 | 12 | 12/12 |
| 2023 | All selected publications | 17 | 12 | 12/12 |
| 2024 | All selected publications | 26 | 12 | 12/12 |
| 2025 | All selected publications | 18 | 12 | 12/12 |
| 2026 | All selected publications | 12 | 12 | 12/12 |
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 |
|---|---|
| 2022 | 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 | 1 | 0 | 1 | 1/1 | 0.0 | 0 | 0.0 | 1/1 · 100.0% |
| International Journal of Finance & Banking Studies SSBFNET | 1 | 2 | 4 | 1/1 | 2.0 | 2 | 100.0 | 1/1 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 10 | 47 | 69 | 10/10 | 4.7 | 1.5 | 70.0 | 10/10 · 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.
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.
This research aims to know the effect of financial knowledge on firm performance; the role of financial behavior and access to finance as mediation and the role of financial risk attitude as moderation. The object of this research is the owner of small-medium enterprises, sector food and beverage in Malang. Data collection was carried out using a questionnaire of 150 respondents. Data analysis of this research uses partial least square (PLS). The finding indicates that financial knowledge has a positive and significant effect on firm performance; financial behavior mediates the relationship of financial knowledge dan firm performance; while access to finance doesn’t mediate the relationship of financial knowledge with firm performance. This research also found that financial risk attitude weakens the relationship of financial knowledge with financial behavior.
The purpose of the study is to prove that social media has an effect on the behavior of millennial investors mediated by financial risk tolerance. The data collected through the questionnaire was processed and tested for the validity and reliability of the construct. This study uses Structure Equational Modelling (SEM) and passes the criteria for model fit and quality indices to determine the strength of the structural model of the study before testing the hypothesis. This study found that social media and financial risk tolerance variables directly affect investor behavior, social media affects financial risk tolerance and is able to mediate social media relations with investor behavior. The mediating effect of risk tolerance is very strong on social media relations as a provider of market information with investor’s stock trading behavior, both buying and selling. This study also found that investors tend to make biased decisions when faced with bear market conditions, namely showing overreaction behavior.
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.
Apart from many studies related to Social Capital, Financial Literacy, Financial Access, and Financial Performance, there are still few studies that are associated between social capital and financial literacy with business performance that uses financial access as a mediation. This research was guided by the Theory of Behavioral Finance (TBF) and Dual-Process Theory (DPT). This study aimed to examine and analyze the effect of social capital and financial literacy on business performance through financial access for Batik SMEs in Pamekasan Regency. This study uses the Pamekasan Regency Batik SMEs as the object of research. This study uses a quantitative approach. The population in this study is Batik SMEs in Pameksan Regency. The sampling technique used is proportional sampling. The sample in this study is calculated using the Slovin formula to produce 135 respondents of SMEs Batik Pamekasan Regency. The data collection technique in this study was carried out by distributing questionnaires. The data analysis technique was carried out by SEM-PLS using 3.0 software. The results show an influence of social capital, financial literacy, and financial access on the business performance of Batik SMEs in Pamekasan Regency. Access to finance can mediate the influence between social capital and financial literacy on the business performance of Pamekasan Batik SMEs.
The purpose of the study is to prove that social media has an effect on the behavior of millennial investors mediated by financial risk tolerance. The data collected through the questionnaire was processed and tested for the validity and reliability of the construct. This study uses Structure Equational Modelling (SEM) and passes the criteria for model fit and quality indices to determine the strength of the structural model of the study before testing the hypothesis. This study found that social media and financial risk tolerance variables directly affect investor behavior, social media affects financial risk tolerance and is able to mediate social media relations with investor behavior. The mediating effect of risk tolerance is very strong on social media relations as a provider of market information with investor’s stock trading behavior, both buying and selling. This study also found that investors tend to make biased decisions when faced with bear market conditions, namely showing overreaction behavior.
This study aims to determine the effect of financial literacy and information technology knowledge on the motivation of the millennial generation to invest in shares on the Indonesian Stock Exchange. The population in this study are millennial investors who have joined two securities companies on the Indonesian Stock Exchange, namely Mirae Aset Sekuritas and Phintraco Sekuritas. Determination of the number of samples using purposive sampling method, namely only those willing to fill out a questionnaire distributed online, and the results obtained are 44 samples. The analytical tool used in this research is Multiple Linear Regression analysis. The study results show that: 1) Financial literacy significantly affects millennial motivation to invest in shares on the Indonesian Stock Exchange. The higher their financial literacy, the more motivated they are to invest in stocks; 2) Knowledge of information technology has no significant effect on millennial motivation to invest in shares on the Indonesian Stock Exchange, and 3) Simultaneously, the investment motivation variable can be explained by the financial literacy and information technology knowledge variables by 58%. In contrast, the remaining 42% can be explained by other variables not included in this study.
This study aims to examine the factors that influence investment intention, namely investment motivation, investment knowledge and information technology in the millennial generation in banyumas with perceived risk as a moderation variable. The samples used in this study were 220 samples. The sampling method is non-probability sampling and the sampling technique is the convenience sampling method. Structural model analysis is carried out using PLS through several stages of analysis, namely outer model analysis, inner model analysis and hypothesis testing. The results of this study show that (i) Investment Motivation has a significant effect on Investment Intention, (ii) Investment Knowledge has a significant effect on Investment Intention, (iii) Information Technology has a significant effect on Investment Intention, (iv) Perceived Risk has a significant effect on Investment Intention, (v) Perceived Risk has been proven to be able to moderate the relationship between Investment Motivation and Investment Intention, (vi) Perceived Risk is proven to be able to moderate the relationship between Information Technology Investment Intention and (vii) Perceived Risk is not proven to be able to moderate the relationship between Investment Knowledge and Investment Intention.
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 |