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: 4 / 5 publications (80.0%). 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 01:01 UTC — 22 Oct 2026 06:06 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.
5 / 5 with data · 0 missing or unmatched. Percentages use all 5 selected publications.
5 / 5 with data · 0 missing or unmatched. Percentages use all 5 selected publications.
5 / 5 with data · 0 missing or unmatched. Percentages use all 5 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 | 0 | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2017 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 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 | 0/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2014 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2015 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2017 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2026 | All selected publications | — | 1 | 0/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2014 | All selected publications | 9 | 1 | 1/1 | 1/1 |
| 2015 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2017 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | — | 0 | 0/0 | 0/0 |
| 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 | 0/1 | 1/1 |
Incoming citations to the selected publications, grouped by the year of the citing work. This is separate from lifetime citation totals grouped by the cited article’s publication year.
The publication filters above define which articles receive the citations. The citation-year range below only controls this chart and the incoming-citation comparison. Removing publication-year filters includes older articles’ incoming citations too.
Annual history available for 5 / 5 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): 2026-10-22T01:01:12.205363+00:00 — 2026-10-22T06:06:19.392845+00:00. Older observations may be retained after failed or incomplete refreshes.
| Citation year | Journal / scope | OpenAlex · Citations received by year | Publications | Annual history known |
|---|---|---|---|---|
| 2017 | All selected publications | 2 | 5 | 5/5 |
| 2018 | All selected publications | 0 | 5 | 5/5 |
| 2019 | All selected publications | 3 | 5 | 5/5 |
| 2020 | All selected publications | 0 | 5 | 5/5 |
| 2021 | All selected publications | 2 | 5 | 5/5 |
| 2022 | All selected publications | 0 | 5 | 5/5 |
| 2023 | All selected publications | 1 | 5 | 5/5 |
| 2024 | All selected publications | 0 | 5 | 5/5 |
| 2025 | All selected publications | 0 | 5 | 5/5 |
| 2026 | All selected publications | 0 | 5 | 5/5 |
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 |
| 2016 | 1 |
| 2019 | 1 |
| 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 | 4 | 0 | 4 | 4/4 | 0.0 | 0 | 0.0 | 3/4 · 75.0% |
| International Journal of Research in Business and Social Science SSBFNET | 1 | 2 | 9 | 1/1 | 2.0 | 2 | 100.0 | 1/1 · 100.0% |
* Percentage among publications with a known citation count.
An individual’s attitude toward financial risk tolerance (FRT) is an important preference that influences financial decision-making under uncertainty. FRT involves inter-temporal resource allocation. Accurate and reliable measures of FRT are essential for professionals advising consumers as well as researchers who are trying to predict, and understand consumer behavior. This study explores cross-cultural risk tolerance by comparing random samples from Turkey, and the United States. Similar and distinctive attitudes and patterns regarding FRT are identified. Three subjective measures of FRT are employed in this study: Grable and Lytton (1999), Hanna, Gutter and Fan’s (2001) improved version of Barsky, Juster, Kimball and Shapiro (1997), and the Survey of Consumer Finance’s item on risk tolerance. Data was collected via an online survey that used the above-mentioned FRT measures. There were two versions: the original American English version, and a culturally translated Turkish version. To explore the correlation of FRT among measures, we use bivariate analysis by individually employing Pearson Chi-square test of independence, and cross tabulations analysis to each sample. In addition, by pooling both samples, we conduct cumulative logistic regression. We delineate FRT differences and consistencies between countries across subjective financial risk tolerance measures.
The literature laments college students’ lack of financial literacy and their lackadaisical interest in learning. However, our research demonstrates that students can be enticed such that they get interested in acquiring financial skills. Generally, college students live hand-to-mouth and are happy to learn about money-making opportunities (not necessarily financial management). Students were informed that qualified faculty will conduct a series of workshops whereby they will learn financial tools and skills and will also get an opportunity to trade stocks and options first-hand. Literature on pedagogy confirms that experiential learning is the best way to learn. Students also prefer practical, hands-on learning more than listening to a lecture or reading a book. Moreover, if workshops (for which professionals charge exorbitant sums) are offered for free, students’ interest soars, and they enrol in hordes. This is confirmed by our workshops which proved successful in enhancing student interest and imparting financial skills. The workshops were innovative and unique as they were online, interactive, tutored, and free. They were open to students from all majors, faculty, and staff. Students are excited about stock trading but are generally apprehensive of and intimidated by financial options. This was true of our sample of students as well, but the faculty-led, friendly, and riskless environment of hands-on learning provided a great opportunity, and over 100 individuals enrolled in our workshops. A comparison of pre- and post-survey quizzes shows that participants’ interest in, and knowledge of options trading increased significantly due to the workshops.
This paper investigates whether false confidence, as characterized by a high level of personal mastery and a low level of intelligence (IQ), results in frequent investor trading and subsequent investor wealth erosion across time. Using the National Longitudinal Survey (NLSY79), change in wealth and asset allocation across time is modeled as a function of various behavioral, socio-economic and demographic variables drawn from prior literature. Findings of this research reveal that false confidence is indeed a predictor of trading activity in individual investment assets, and it also has a negative impact on individual wealth creation across time.
<p>This paper investigates whether false confidence, as characterized by a high level of personal mastery and a low level of intelligence (IQ), results in frequent investor trading and subsequent investor wealth erosion across time. Using the National Longitudinal Survey (NLSY79), change in wealth and asset allocation across time is modeled as a function of various behavioral, socio-economic and demographic variables drawn from prior literature. Findings of this research reveal that false confidence is indeed a predictor of trading activity in individual investment assets, and it also has a negative impact on individual wealth creation across time.</p>
This study examines how first-generation immigrants in the United States plan for retirement, report income, and engage with the Social Security system. The analysis combines an original anonymous survey of 120 first-generation immigrants with descriptive, correlational, and illustrative aggregate evidence constructed from Social Security Administration, Census Bureau, and related institutional data for 2013–2023. The survey results show three consistent patterns. First, retirement knowledge is limited: most respondents do not understand how Social Security benefits are calculated, yet a large majority would attend retirement-planning workshops. Second, financial behavior is shaped by legal status and labor-market formality. Tax filing rates are high overall, but underreporting incentives are stronger among respondents paid in cash and among groups facing legal or administrative uncertainty. Third, family obligations materially affect long-term planning. Many respondents prioritize supporting relatives over retirement savings, and women report lower earnings despite comparable English proficiency and, in this sample, higher educational attainment. Over 2013–2023, Social Security program income and program cost rose alongside the immigrant population, but this aggregate exercise is illustrative only, is not causal, and cannot identify immigrants’ net fiscal impact on the system. The paper’s main contribution is therefore behavioral: it shows how limited knowledge, payment formality, and family obligations shape retirement planning within a non-representative regional sample of first-generation immigrants.
This study examines how first-generation immigrants in the United States plan for retirement, report income, and engage with the Social Security system. The analysis combines an original anonymous survey of 120 first-generation immigrants with descriptive, correlational, and illustrative aggregate evidence constructed from Social Security Administration, Census Bureau, and related institutional data for 2013–2023. The survey results show three consistent patterns. First, retirement knowledge is limited: most respondents do not understand how Social Security benefits are calculated, yet a large majority would attend retirement-planning workshops. Second, financial behavior is shaped by legal status and labor-market formality. Tax filing rates are high overall, but underreporting incentives are stronger among respondents paid in cash and among groups facing legal or administrative uncertainty. Third, family obligations materially affect long-term planning. Many respondents prioritize supporting relatives over retirement savings, and women report lower earnings despite comparable English proficiency and, in this sample, higher educational attainment. Over 2013–2023, Social Security program income and program cost rose alongside the immigrant population, but this aggregate exercise is illustrative only, is not causal, and cannot identify immigrants’ net fiscal impact on the system. The paper’s main contribution is therefore behavioral: it shows how limited knowledge, payment formality, and family obligations shape retirement planning within a non-representative regional sample of first-generation immigrants.
The literature laments college students’ lack of financial literacy and their lackadaisical interest in learning. However, our research demonstrates that students can be enticed such that they get interested in acquiring financial skills. Generally, college students live hand-to-mouth and are happy to learn about money-making opportunities (not necessarily financial management). Students were informed that qualified faculty will conduct a series of workshops whereby they will learn financial tools and skills and will also get an opportunity to trade stocks and options first-hand. Literature on pedagogy confirms that experiential learning is the best way to learn. Students also prefer practical, hands-on learning more than listening to a lecture or reading a book. Moreover, if workshops (for which professionals charge exorbitant sums) are offered for free, students’ interest soars, and they enrol in hordes. This is confirmed by our workshops which proved successful in enhancing student interest and imparting financial skills. The workshops were innovative and unique as they were online, interactive, tutored, and free. They were open to students from all majors, faculty, and staff. Students are excited about stock trading but are generally apprehensive of and intimidated by financial options. This was true of our sample of students as well, but the faculty-led, friendly, and riskless environment of hands-on learning provided a great opportunity, and over 100 individuals enrolled in our workshops. A comparison of pre- and post-survey quizzes shows that participants’ interest in, and knowledge of options trading increased significantly due to the workshops.
This paper investigates whether false confidence, as characterized by a high level of personal mastery and a low level of intelligence (IQ), results in frequent investor trading and subsequent investor wealth erosion across time. Using the National Longitudinal Survey (NLSY79), change in wealth and asset allocation across time is modeled as a function of various behavioral, socio-economic and demographic variables drawn from prior literature. Findings of this research reveal that false confidence is indeed a predictor of trading activity in individual investment assets, and it also has a negative impact on individual wealth creation across time.
<p>This paper investigates whether false confidence, as characterized by a high level of personal mastery and a low level of intelligence (IQ), results in frequent investor trading and subsequent investor wealth erosion across time. Using the National Longitudinal Survey (NLSY79), change in wealth and asset allocation across time is modeled as a function of various behavioral, socio-economic and demographic variables drawn from prior literature. Findings of this research reveal that false confidence is indeed a predictor of trading activity in individual investment assets, and it also has a negative impact on individual wealth creation across time.</p>
An individual’s attitude toward financial risk tolerance (FRT) is an important preference that influences financial decision-making under uncertainty. FRT involves inter-temporal resource allocation. Accurate and reliable measures of FRT are essential for professionals advising consumers as well as researchers who are trying to predict, and understand consumer behavior. This study explores cross-cultural risk tolerance by comparing random samples from Turkey, and the United States. Similar and distinctive attitudes and patterns regarding FRT are identified. Three subjective measures of FRT are employed in this study: Grable and Lytton (1999), Hanna, Gutter and Fan’s (2001) improved version of Barsky, Juster, Kimball and Shapiro (1997), and the Survey of Consumer Finance’s item on risk tolerance. Data was collected via an online survey that used the above-mentioned FRT measures. There were two versions: the original American English version, and a culturally translated Turkish version. To explore the correlation of FRT among measures, we use bivariate analysis by individually employing Pearson Chi-square test of independence, and cross tabulations analysis to each sample. In addition, by pooling both samples, we conduct cumulative logistic regression. We delineate FRT differences and consistencies between countries across subjective financial risk tolerance measures.
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 |