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 — 30 Oct 2026 06:54 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.
12 / 12 with data · 0 missing or unmatched. Percentages use all 12 selected publications.
12 / 12 with data · 0 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 |
|---|---|---|---|---|---|
| 2021 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| 2022 | All selected publications | 7 | 7 | 7/7 | 7/7 |
| 2023 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 9 | 4 | 4/4 | 4/4 |
| 2022 | All selected publications | 9 | 7 | 7/7 | 7/7 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 17 | 4 | 4/4 | 4/4 |
| 2022 | All selected publications | 14 | 7 | 7/7 | 7/7 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 0 | 1 | 1/1 | 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 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): 2026-10-21T07:22:45.263275+00:00 — 2026-10-30T06:54:24.358672+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 | 1 | 12 | 12/12 |
| 2022 | All selected publications | 5 | 12 | 12/12 |
| 2023 | All selected publications | 7 | 12 | 12/12 |
| 2024 | All selected publications | 9 | 12 | 12/12 |
| 2025 | All selected publications | 7 | 12 | 12/12 |
| 2026 | All selected publications | 2 | 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 |
|---|---|
| 2021 | 4 |
| 2022 | 7 |
| 2025 | 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 | 2 | 0 | 0 | 2/2 | 0.0 | 0.0 | 0.0 | 2/2 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 10 | 18 | 31 | 10/10 | 1.8 | 0.5 | 50.0 | 10/10 · 100.0% |
* Percentage among publications with a known citation count.
From various reviews about innovation, this article will discuss technology orientation and innovation capabilities through digital transformation 4.0 in small businesses. There is a need to examine the relationship between the two from a useful perspective about see toward using various studies related to technology orientation and innovation capabilities in the industrial era 4.0. This study aims to increase understanding of technology orientation and innovation capabilities in the context of small businesses through Digital Transformation 4.0 by reviewing the empirical literature. The articles presented can guide further studies related to technology orientation with innovation capabilities so that it can be easily understood by researchers and entrepreneurs among small businesses.
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.
Perception determines the way we behave towards an object or problem, how everything that affects a person's perception will later affect the chosen behavior h. Accounting students by understanding the negative consequences of tax evasion actions and penalties in the form of sanctions can know signs in decision-making and behavioral patterns of academic compliance in higher education. This study seeks to examine the effect of student perceptions of tax evasion and tax sanctions on the ethical behavior of accounting students' compliance. Results show that student perception as variable X1 on tax evasion does not have a significant effect to Ethical Behavior academic compliance of accounting students at the State University of Malang. Next on variable X2 result testing perception on penalty positive and significant effect on behavior ethical obedience academic accounting students at the State University of Malang
The purpose of this study is to examine the differences in the value of the financial distress of manufacturing companies before and after the Covid-19 pandemic, as well as to examine the determinants that affect the financial distress of manufacturing companies before and after the Covid-19 pandemic. This study is a quantitative study with the population in this study namely manufacturing companies in Indonesia. The sample is 1005 Firm Years in manufacturing companies in Indonesia. These Manufacturing Companies were taken from 2016-2021, but we divided the sample into 2 categories, namely 2016-2019, namely in the pre-pandemic period as many as 653 firm years, and during the pandemic as many as 352 firm years. The dependent variable used in this study is financial distress, measured by the O-Score Model. Furthermore, the independent variables are profitability using ROA, liquidity using the current ratio, and leverage using the Debt to Assets Ratio. The test results show no difference between the financial distress of manufacturing companies in Indonesia before and during the pandemic. During the pandemic, financial ratios became more considered in predicting financial distress than before the pandemic. The role of profitability is a factor that impacts financial distress is more impactful in the pre-pandemic period. The role of liquidity being a factor that impacts financial distress is more impactful during the pandemic. Leverage can also be a good indicator in predicting financial distress both in the pre-pandemic and during the pandemic.
Capital financing plays a crucial role in sustaining the growth potential of pharmaceutical companies, enabling them to expand operations and compete effectively in the market. In the face of a slowing global economy, maintaining solid financial performance across existing business lines becomes increasingly important. This study aims to examine the relationship between capital structure and the financial performance of pharmaceutical companies in Indonesia. The research sample consists of nine publicly listed pharmaceutical firms over the period 2013–2023. The empirical analysis employs three panel data estimation approaches—Pooled Ordinary Least Squares (Pooled OLS), Fixed Effects Model (FEM), and Random Effects Model (REM)—to account for different assumptions about unobserved firm-specific characteristics. Based on the Hausman test, FEM is determined as the most suitable model for the dataset. The estimation results reveal that the Long-term Debt to Total Assets (LDTA) ratio exerts a statistically significant negative effect on Return on Assets (ROA), indicating that a higher proportion of long-term debt tends to reduce profitability. Conversely, the Short-term Debt to Total Assets (SDTA) and Total Debt to Total Equity (TDTE) ratios have positive but statistically insignificant impacts on ROA, suggesting that neither short-term liabilities nor overall leverage significantly influence profitability in the observed period. These results imply that excessive reliance on long-term borrowing can impose interest burdens and financial constraints, thereby weakening performance. Consequently, financial managers in the pharmaceutical sector are encouraged to optimize their financing structure, prioritizing internal funds or short-term financing sources that align with operational needs and market conditions to sustain profitability and ensure long-term competitiveness.
Perception determines the way we behave towards an object or problem, how everything that affects a person's perception will later affect the chosen behavior h. Accounting students by understanding the negative consequences of tax evasion actions and penalties in the form of sanctions can know signs in decision-making and behavioral patterns of academic compliance in higher education. This study seeks to examine the effect of student perceptions of tax evasion and tax sanctions on the ethical behavior of accounting students' compliance. Results show that student perception as variable X1 on tax evasion does not have a significant effect to Ethical Behavior academic compliance of accounting students at the State University of Malang. Next on variable X2 result testing perception on penalty positive and significant effect on behavior ethical obedience academic accounting students at the State University of Malang
The purpose of this study is to examine the differences in the value of the financial distress of manufacturing companies before and after the Covid-19 pandemic, as well as to examine the determinants that affect the financial distress of manufacturing companies before and after the Covid-19 pandemic. This study is a quantitative study with the population in this study namely manufacturing companies in Indonesia. The sample is 1005 Firm Years in manufacturing companies in Indonesia. These Manufacturing Companies were taken from 2016-2021, but we divided the sample into 2 categories, namely 2016-2019, namely in the pre-pandemic period as many as 653 firm years, and during the pandemic as many as 352 firm years. The dependent variable used in this study is financial distress, measured by the O-Score Model. Furthermore, the independent variables are profitability using ROA, liquidity using the current ratio, and leverage using the Debt to Assets Ratio. The test results show no difference between the financial distress of manufacturing companies in Indonesia before and during the pandemic. During the pandemic, financial ratios became more considered in predicting financial distress than before the pandemic. The role of profitability is a factor that impacts financial distress is more impactful in the pre-pandemic period. The role of liquidity being a factor that impacts financial distress is more impactful during the pandemic. Leverage can also be a good indicator in predicting financial distress both in the pre-pandemic and during the pandemic.
Numerous government bodies continue to experience difficulties in accounting for historical assets. The purpose of this research is to ascertain the identification, quantification, presentation, and disclosure of historical assets, particularly the Situs Watu Gong in Malang, East Java. The purpose of this study is to contribute to the concept of identifying, measuring, presenting, and also exposing historical assets through the use of a qualitative technique and a case study at the Situs Watu Gong. The data collection approaches used were observation, interviewing, and documentation. As a result of this investigation, it was determined that the manager of the Situs Watu Gong did not properly account for previous assets. The manager of the Situs Watu Gong should have valued historical assets at cost and measured them using the cost technique. Historic assets are classed as fixed assets and are not included in PPE (Plant, Property, and Equipment) on the balance sheet.
The competition in the early childhood education industry is getting tougher with new preschools with various types and innovations on offer. This exploratory research reviews the situation and condition of preschools in Indonesia with a qualitative approach based on case studies. Secondary data collection and interviews with millennial mothers were carried out to support the existing data to analyze the gaps in the marketing strategies used by preschools with expectations in the community. The results of this study indicate that preschools must understand the current needs of their prospective customers, especially millennial mothers who currently have great potential to become their primary target market. The strategy used can depart from the marketing mix analysis, such as optimizing the school's website and social media as a reference communication medium for parents in choosing a preschool for their children.
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 |