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: 13 / 13 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 — 29 Oct 2026 17:36 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.
13 / 13 with data · 0 missing or unmatched. Percentages use all 13 selected publications.
13 / 13 with data · 0 missing or unmatched. Percentages use all 13 selected publications.
13 / 13 with data · 0 missing or unmatched. Percentages use all 13 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 | 13 | 13 | 13/13 | 13/13 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 407 | 13 | 13/13 | 13/13 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 633 | 13 | 13/13 | 13/13 |
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 13 / 13 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): 2026-10-21T07:22:43.122435+00:00 — 2026-10-29T17:36:17.961976+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 | 13 | 13/13 |
| 2018 | All selected publications | 2 | 13 | 13/13 |
| 2019 | All selected publications | 2 | 13 | 13/13 |
| 2020 | All selected publications | 1 | 13 | 13/13 |
| 2021 | All selected publications | 2 | 13 | 13/13 |
| 2022 | All selected publications | 25 | 13 | 13/13 |
| 2023 | All selected publications | 101 | 13 | 13/13 |
| 2024 | All selected publications | 208 | 13 | 13/13 |
| 2025 | All selected publications | 177 | 13 | 13/13 |
| 2026 | All selected publications | 103 | 13 | 13/13 |
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 | 13 |
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 | 2 | 28 | 48 | 2/2 | 14.0 | 14.0 | 100.0 | 2/2 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 11 | 379 | 585 | 11/11 | 34.45 | 18 | 100.0 | 11/11 · 100.0% |
* Percentage among publications with a known citation count.
This study aims to evaluate the factors that influence data saturation in qualitative studies. The study adopted a systematic literature review of 24 journal articles published between 2018 and 2022. The articles were obtained from a Google scholar database. The findings point to five factors that affect data saturation namely, pre-determined codes and themes, sample size, relevancy of research subjects (respondents), number of research methods, and length of data collection sessions. It is argued in this paper, therefore, that in order to attain data saturation, researchers should consider multiple factors that may affect data saturation in order to increase the validity of qualitative studies.
This study examines the strengths and limitations of 22 published journal articles by conducting a systematic literature review. The review revealed that the qualitative approach was flexible, offered in-depth and detailed information, allowed the use of multiple data collection methods and minimised the chance of having missing data. The approach was found (i) to integrate human touch, was cost-effective and was indeed the only option in some cases of research problems; (ii) to be prone to researchers’ subjectivity, involves complex data analysis, makes anonymity difficult and has limited scope in its generalizability, and (iii) to make replication of findings challenging, and the findings may be influenced by the researcher’s bias. It is concluded, therefore, that researchers should take necessary precautions when using the approach to ensure that the weaknesses of qualitative research do not bar them from achieving research objectives.
The Covid-19 pandemic, a catalyst in embracing the online mode of learning, is one of the key motivating factors for a paradigm shift in curriculum development due to the increased focus and reliance on technology. For the effective education of tertiary students globally, a revised curriculum development integrated with the Learning Management Systems and other eLearning platforms or tools that support asynchronous learning play a key role. This study seeks to determine the influence of user experience and user interface design of e-learning platforms on learning experience in higher education. A systematic review of studies regarding usability of e-learning platforms at tertiary institutions was conducted to determine the common issues and successes. A snapshot of research over the last five years was used in a systematic review to determine the influence of UX/UI on eLearning concerning higher education. Using the ERIC and Google scholar databases of journals, and specific criteria for selection of publications, 25 articles were examined in this study. Findings indicate that focused attention on interface design and user experience of eLearning systems for effective learning is crucial.
Gen Z controls almost the entire population in the world with a total number of almost two billion people worldwide. This generation will be the largest consumer group in the world. In Indonesia itself, In 2020, Gen Z occupied the highest percentage of the population. Therefore, Gen Z is a potential target market that has a considerable influence on purchasing decisions. Based on the characteristics of Gen Z, who are digital natives, the use of social media is the right marketing strategy for consumers who are counted into Gen Z category. Promotional activities, as part of a marketing strategy, especially through social media require influencers as endorsers. This study aims to investigate the factors that influence the credibility attributes of social media influencers on the purchase intentions of Gen Z consumers. This study uses influencer credibility attributes (attractiveness, expertise, and trustworthiness) as independent variables that influence the purchase intention of Gen Z consumers with brand image as a mediation. This research is categorized as explanatory research. The respondents in this study were Gen Z who used Korean cosmetic products throughout Indonesia, the determination of the number of samples in this study used a purposive sampling technique with a sample of 210 respondents. The method of collecting data is using a questionnaire, the data is analyzed using PLS-SEM. The results showed that the three attributes of influencer credibility had a significant effect on the purchase intention of Gen Z. The three attributes of credibility had a significant effect on brand image, brand image had a significant effect on purchase intention. Brand image can provide a full mediating role in the relationship of influencer credibility to the purchase intention of Gen Z consumers. Local cosmetic industry players are expected to improve marketing strategies through online channels, especially social media by utilizing the right influencers as their endorsers, because it is proven in this study, credibility of social media influencers have a significant effect on the brand image of a product and the purchase intention of Gen Z consumers, so that the local cosmetic industry in Indonesia can compete with foreign cosmetic industries.
Novice and experienced researchers may encounter numerous methodological challenges when it comes to data collection. Data have to be collected from the right sources using appropriate data collection tools to make research studies impactful. There are numerous data collection methods that researchers may use but not all methods are appropriate for all research studies. Choosing inappropriate research methods may result in invalid research findings and lead to erroneous conclusions. This means researchers have to carefully choose data collection methods to make their studies achieve their respective goals. This study intended to identify factors that researchers have to consider in choosing data collection methods. A systematic literature review was used to analyze 28 journal articles published between 2018 and 2022. From the analysis the study identified ten (10) factors that researchers have to consider in choosing data collection methods. These factors are; the research goal, the scope of the study, sample size, type of data, time and user-friendliness to the subjects. Other factors are research approach, safety and security of a researcher, the need for triangulation and theoretical framework. Before choosing methods for data collection, it is important to put into consideration these factors to increase the chances of achieving research objectives and answering research questions of research projects.
The Covid-19 pandemic, a catalyst in embracing the online mode of learning, is one of the key motivating factors for a paradigm shift in curriculum development due to the increased focus and reliance on technology. For the effective education of tertiary students globally, a revised curriculum development integrated with the Learning Management Systems and other eLearning platforms or tools that support asynchronous learning play a key role. This study seeks to determine the influence of user experience and user interface design of e-learning platforms on learning experience in higher education. A systematic review of studies regarding usability of e-learning platforms at tertiary institutions was conducted to determine the common issues and successes. A snapshot of research over the last five years was used in a systematic review to determine the influence of UX/UI on eLearning concerning higher education. Using the ERIC and Google scholar databases of journals, and specific criteria for selection of publications, 25 articles were examined in this study. Findings indicate that focused attention on interface design and user experience of eLearning systems for effective learning is crucial.
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.
This study examines the strengths and limitations of 22 published journal articles by conducting a systematic literature review. The review revealed that the qualitative approach was flexible, offered in-depth and detailed information, allowed the use of multiple data collection methods and minimised the chance of having missing data. The approach was found (i) to integrate human touch, was cost-effective and was indeed the only option in some cases of research problems; (ii) to be prone to researchers’ subjectivity, involves complex data analysis, makes anonymity difficult and has limited scope in its generalizability, and (iii) to make replication of findings challenging, and the findings may be influenced by the researcher’s bias. It is concluded, therefore, that researchers should take necessary precautions when using the approach to ensure that the weaknesses of qualitative research do not bar them from achieving research objectives.
This empirical study seeks to examine the effect of purchase intention within the scope of corporate social responsibility. Via the housing sector in Garden City in Sulaymaniyah City, Kurdistan Region, Iraq. For this study, the data was collected through primary sources using a survey questionnaire to collect data from 181 customers. The obtained data were analyzed using statistical tools like SPSS V.24. The results showed a relationship between corporate social responsibility and purchase intention. Following the recommendation, The Halabja Group should seek to increase corporate social responsibility by using the appropriate strategic direction towards achieving their goals, and that is by setting short-, medium, and long-term plans to achieve them.
Mining sector companies are closely related to pollution, waste, and strong interactions with the environment. If the pollution and waste are ignored, it will cause environmental damage that consequently impacts environmental sustainability and the community around the company. Therefore, companies are requested to address environmental and social issues through corporate social responsibility activities. Determinants of corporate social responsibility disclosure include company size, liquidity, leverage, and profitability. The purpose of this study is to examine the effect of the determinants of corporate social responsibility disclosure on corporate social responsibility in mining companies listed on the Indonesia Stock Exchange (IDX) from 2016 to 2020. The number of sample companies studied was seven mining sector companies, and the number of data tested was 35 (for five years). The data used in this research were derived from financial and sustainability reports. The sampling technique used in this study was the saturated sample. Data were analyzed using regression analysis processed using the SPSS application. This study revealed that company size, liquidity, leverage, and profitability affect corporate social responsibility disclosure.
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 |