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: 3 / 3 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 01:23 UTC — 22 Oct 2026 02:21 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.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 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 |
|---|---|---|---|---|---|
| 2021 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 9 | 3 | 3/3 | 3/3 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 17 | 3 | 3/3 | 3/3 |
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 3 / 3 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): 2026-10-22T01:23:48.169527+00:00 — 2026-10-22T02:21:59.177435+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 | 3 | 3/3 |
| 2018 | All selected publications | 0 | 3 | 3/3 |
| 2019 | All selected publications | 0 | 3 | 3/3 |
| 2020 | All selected publications | 0 | 3 | 3/3 |
| 2021 | All selected publications | 1 | 3 | 3/3 |
| 2022 | All selected publications | 4 | 3 | 3/3 |
| 2023 | All selected publications | 4 | 3 | 3/3 |
| 2024 | All selected publications | 4 | 3 | 3/3 |
| 2025 | All selected publications | 4 | 3 | 3/3 |
| 2026 | All selected publications | 0 | 3 | 3/3 |
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 |
|---|---|
| 2021 | 3 |
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 Research in Business and Social Science SSBFNET | 3 | 9 | 17 | 3/3 | 3.0 | 2 | 66.7 | 3/3 · 100.0% |
* Percentage among publications with a known citation count.
There are many forms of academic cheating that occur among students, especially the practice of cheating. There are many factors that encourage students' academic cheating practices, especially with the aim of getting a targeted high score. Therefore, the researcher tries to find out the factors that influence the actions of academic cheating among students by referring to the fraud triangle theory which is relevant to the theme of this research. The variables observed in this study were: pressure (X1), opportunity (X2), rationalization (X3), misuse of information technology (X4), and student integrity (X5) as independent variables (independent), while the dependent variable (dependent) used behavior academic cheating on students (Y). This study used an instrument in the form of an open questionnaire with a Likert scale. The population that took as many as 500 accounting students, UM, and samples collected as many as 30 samples that have been filtered according to the criteria (purposive sampling). Analysis of the data used is multiple linear regression analysis. The results show that simultaneously, the variables of pressure, opportunity, rationalization, misuse of information technology, and student integrity have an influence on student academic fraud. The partial test shows that only the misuse of information technology has an effect on academic cheating of accounting students, ME, while the other variables have no significant effect. This is because when students learn online during the Covid-19 pandemic, they are able to take advantage of technological sophistication in this globalization era, especially in the current industry 4.0 era to carry out academic cheating actions that are oriented towards targeted values (GPA).
Precise and clear Business Process Modeling (BPM) can be used to implement the all of processes according to the user’s needs. On the other hand, cloud computing has become a solution for data storage and more effective and efficient computing processes. State Polytechnic of Malang (Polinema) is one of the vocational higher education Institutions in Indonesia that requires business process modeling to facilitate an integrated understanding of process flow. This study aims to describe the business processes of the financial realization system and the structure of cloud computing that can be used. This research used Bizagi for designing Business Process Model and Notation (BPMN) in modeling business processes of financial system realization. This paper describes the business model of the financial realization system at Polinema. This study proposes the use of an Infrastructure as a Service IaaS-based cloud computing service model using a hybrid cloud approach to support the work of the system.
The purpose of this study was to see the effect of the establishment of village-owned enterprises (BUM Desa) and the support of the local government in increasing the village's original income. This research has a research sample using the purposive sampling method, namely BUM Desa Dukuh Dempok, and BUM Desa Kemiri. Data was obtained by conducting interviews, observations, and documentation. Interviews were conducted with in-depth interviews with the chairman and management of BUM Desa in Jember. Analysis was carried out on the data and information obtained to obtain research results. Tourism BUM Desa Gumuk Watu and Tourism BUM Desa Kemiri have a role in increasing the village's original income can be seen from the function of facilitator and mediator. The two BUM Desa include the role of the community in building and developing tourism. Local government support for BUM Desa tourism is very important. BUM Desa Wisata Gumuk Watu and BUM Desa Wisata Kemiri received full support from the government in the form of capital and socialization of BUM Desa to the community. The difference in income between the BUM Desa Gumuk Watu and the BUM Desa Kemiri. Tourism BUM Desa of Kemiri experienced a decrease in income by more than 50% but Tourism BUM Desa of Gumuk Watu experienced an increase due to the entertainment needs of the community. These results indicate that BUM Desa which is engaged in tourist areas needs the role of the government in helping to increase their income in the pandemic era.
The purpose of this study was to see the effect of the establishment of village-owned enterprises (BUM Desa) and the support of the local government in increasing the village's original income. This research has a research sample using the purposive sampling method, namely BUM Desa Dukuh Dempok, and BUM Desa Kemiri. Data was obtained by conducting interviews, observations, and documentation. Interviews were conducted with in-depth interviews with the chairman and management of BUM Desa in Jember. Analysis was carried out on the data and information obtained to obtain research results. Tourism BUM Desa Gumuk Watu and Tourism BUM Desa Kemiri have a role in increasing the village's original income can be seen from the function of facilitator and mediator. The two BUM Desa include the role of the community in building and developing tourism. Local government support for BUM Desa tourism is very important. BUM Desa Wisata Gumuk Watu and BUM Desa Wisata Kemiri received full support from the government in the form of capital and socialization of BUM Desa to the community. The difference in income between the BUM Desa Gumuk Watu and the BUM Desa Kemiri. Tourism BUM Desa of Kemiri experienced a decrease in income by more than 50% but Tourism BUM Desa of Gumuk Watu experienced an increase due to the entertainment needs of the community. These results indicate that BUM Desa which is engaged in tourist areas needs the role of the government in helping to increase their income in the pandemic era.
There are many forms of academic cheating that occur among students, especially the practice of cheating. There are many factors that encourage students' academic cheating practices, especially with the aim of getting a targeted high score. Therefore, the researcher tries to find out the factors that influence the actions of academic cheating among students by referring to the fraud triangle theory which is relevant to the theme of this research. The variables observed in this study were: pressure (X1), opportunity (X2), rationalization (X3), misuse of information technology (X4), and student integrity (X5) as independent variables (independent), while the dependent variable (dependent) used behavior academic cheating on students (Y). This study used an instrument in the form of an open questionnaire with a Likert scale. The population that took as many as 500 accounting students, UM, and samples collected as many as 30 samples that have been filtered according to the criteria (purposive sampling). Analysis of the data used is multiple linear regression analysis. The results show that simultaneously, the variables of pressure, opportunity, rationalization, misuse of information technology, and student integrity have an influence on student academic fraud. The partial test shows that only the misuse of information technology has an effect on academic cheating of accounting students, ME, while the other variables have no significant effect. This is because when students learn online during the Covid-19 pandemic, they are able to take advantage of technological sophistication in this globalization era, especially in the current industry 4.0 era to carry out academic cheating actions that are oriented towards targeted values (GPA).
Precise and clear Business Process Modeling (BPM) can be used to implement the all of processes according to the user’s needs. On the other hand, cloud computing has become a solution for data storage and more effective and efficient computing processes. State Polytechnic of Malang (Polinema) is one of the vocational higher education Institutions in Indonesia that requires business process modeling to facilitate an integrated understanding of process flow. This study aims to describe the business processes of the financial realization system and the structure of cloud computing that can be used. This research used Bizagi for designing Business Process Model and Notation (BPMN) in modeling business processes of financial system realization. This paper describes the business model of the financial realization system at Polinema. This study proposes the use of an Infrastructure as a Service IaaS-based cloud computing service model using a hybrid cloud approach to support the work of the system.
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 19:55 UTC · ijrbs | complete · ijrbs · 20 Oct 2026 19:50 UTC |
| openalex-enrichment | 21 Oct 2026 07:23 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 07:22 UTC |