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: 19 / 19 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 — 23 Oct 2026 01:20 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.
19 / 19 with data · 0 missing or unmatched. Percentages use all 19 selected publications.
18 / 19 with data · 1 missing or unmatched. Percentages use all 19 selected publications.
19 / 19 with data · 0 missing or unmatched. Percentages use all 19 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 |
|---|---|---|---|---|---|
| 2019 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2022 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2023 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| 2024 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| 2025 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2026 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 0 | 2 | 2/2 | 2/2 |
| 2022 | All selected publications | 0 | 2 | 2/2 | 2/2 |
| 2023 | All selected publications | 0 | 4 | 4/4 | 4/4 |
| 2024 | All selected publications | 0 | 4 | 4/4 | 4/4 |
| 2025 | All selected publications | 0 | 2 | 2/2 | 2/2 |
| 2026 | All selected publications | 0 | 4 | 4/4 | 4/4 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 6 | 2 | 2/2 | 2/2 |
| 2022 | All selected publications | 5 | 2 | 2/2 | 2/2 |
| 2023 | All selected publications | 3 | 4 | 4/4 | 4/4 |
| 2024 | All selected publications | 3 | 4 | 4/4 | 4/4 |
| 2025 | All selected publications | 0 | 2 | 2/2 | 2/2 |
| 2026 | All selected publications | 0 | 4 | 4/4 | 4/4 |
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 19 / 19 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): 2026-10-21T07:22:48.942099+00:00 — 2026-10-23T01:20:12.444091+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 | 19 | 19/19 |
| 2018 | All selected publications | 0 | 19 | 19/19 |
| 2019 | All selected publications | 0 | 19 | 19/19 |
| 2020 | All selected publications | 0 | 19 | 19/19 |
| 2021 | All selected publications | 0 | 19 | 19/19 |
| 2022 | All selected publications | 3 | 19 | 19/19 |
| 2023 | All selected publications | 5 | 19 | 19/19 |
| 2024 | All selected publications | 5 | 19 | 19/19 |
| 2025 | All selected publications | 4 | 19 | 19/19 |
| 2026 | All selected publications | 0 | 19 | 19/19 |
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 |
|---|---|
| 2019 | 1 |
| 2021 | 2 |
| 2022 | 2 |
| 2023 | 4 |
| 2024 | 4 |
| 2025 | 2 |
| 2026 | 4 |
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 | 4 | 0 | 3 | 4/4 | 0.0 | 0.0 | 0.0 | 4/4 · 100.0% |
| International Journal of Finance & Banking Studies SSBFNET | 3 | 0 | 2 | 3/3 | 0.0 | 0 | 0.0 | 3/3 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 12 | 0 | 12 | 12/12 | 0.0 | 0.0 | 0.0 | 12/12 · 100.0% |
* Percentage among publications with a known citation count.
This research aims to see to how far extent the user interface and user experience an impact on consumer have purchasing decisions through E-Impulse Buying. The correspondents of this study were IKIP PGRI Samarinda students who were active in online purchases through the Shopee application with a usage time of 0-6 years and between 18-24 years of age. The total sample is 131 respondents using purposive sampling method. Research data comes from questionnaires distributed using Google Forms or Manually. The data analysis technique uses path analysis through the SPSS 22 program. The results found show that the user interface and user experience have an impact on purchasing decisions directly, the user interface has an impact on purchasing decisions through E-Impulse Buying, and User Experience has no Impact on decisions purchase through E-Impulse Buying.
The objective of this article is to identify the determinants of saving decisions on fixed deposits. The effect of customers' prior experience with Non-Bank Finance & Leasing Institutions (NBFLI) has been investigated as a mediation factor. Structural Equation Modelling (SEM) in AMOS has been used to analyze the results and confirmed the results by using the bootstrap technique of SEM. The results show that the stability of the NBFLIs, security for the fund, interest rate of the fixed deposits, and quality and reliability of the service directly affect the decision to save fixed deposits. The institution's geographical location has not directly affected the decision on fixed deposits. The factors of the security, interest rate, quality, and reliability of the service have an indirect effect on the decision of saving fixed deposits with prior experience, except for the factor of stability of the institutions.
In Indonesia, motorbikes dominated public transit, making motorcycle sales extremely competitive for a number of brands, particularly Honda and Yamaha. The goal of this research is to identify and assess the effects of pricing perspective qualities, brand advantages, media branding, promotions, after-sales guarantee, products, colors, and designs, as well as fuel consumption as motor product items on consumer behavior. Purposive sampling was used in this investigation, with a total sample size of 194 participants. Using SPSS software, descriptive analysis and multiple linear regression analysis were used to analyze the data. The findings show that Honda's products play a larger role in the execution of its marketing plans, accounting for 87.75 percent of the total, with Yamaha's quality accounting for the remaining 79.40 percent influenced by other factors. This shows that respondents choose motorcycles with the following criteria: Engine, Media Branding, Price, Advertising and Fuel Consumption affect consumer behavior to buy both Honda and Yamaha products.
With the increasing awareness of health and the widespread adoption of social media advertising, health food advertising has become an important source of information influencing consumer decision-making. However, existing studies have primarily focused on the effects of attitudes or behaviors, with limited attention to the underlying psychological mechanisms. Therefore, this study applies optimal arousal theory to examine how expertise and trustworthiness in health food advertising influence consumers’ purchase intention through perceived information relevance. The findings indicate that both expertise and trustworthiness positively affect perceived information relevance, which in turn significantly enhances purchase intention. These results suggest that individuals tend to pursue an optimal level of stimulation, and when advertising information evokes an appropriate level of psychological arousal and aligns with personal needs, it is more likely to facilitate positive behavioral responses. In addition to confirming the importance of perceived information relevance, this study extends optimal arousal theory from the level of sensory stimulation to the cognitive level, thereby advancing the development of optimal arousal theory research.
This study mainly aimed to examine the impact of university students' purchasing tendency from e-commerce sites and demographic characteristics on student satisfaction through the example of a leading foundation research university in Türkiye. In the study, it was investigated whether there was a significant difference between students' purchasing tendencies from e-commerce sites and student satisfaction. It was also investigated whether there was a significant difference between demographic characteristics and student satisfaction. Methodologically, a descriptive online survey method including a questionnaire was applied to collect data. The survey was responded by 240 randomly selected students. The data were analyzed with SPSS-26. The study concluded that there was no significant difference between the average daily time spent on the internet and the type of device used to connect to e-commerce sites, which are among the criteria reflecting the tendency to purchase from e-commerce sites, and student satisfaction whereas there was a significant difference between the number of products purchased through e-commerce sites and student satisfaction. Furthermore, it was revealed that there was no significant difference between demographic characteristics such as gender, marital status, education and monthly income and student satisfaction, whereas there was a significant difference between age and student satisfaction. Accordingly, it was indicated that the difference in terms of age was especially in favor of students in the 22-25 age group. It is thought that the study will contribute to the literature by examining the impact of university students' purchasing tendency from e-commerce sites and demographic characteristics on student satisfaction, together with the example of a leading foundation research university. The study is limited by the selected sample foundation research university, the number of students responded, survey questions-answers, the applied statistical analysis methods and tests. Therefore, in order to reach more general results, it is suggested to expand the studies with more diverse and larger numbers of foundation research universities, students, survey questions and statistical methods.
This study investigated the factors influencing consumer repurchase intentions of beauty and haircare products on e-commerce platforms in South Africa. With the growing trend of online shopping, understanding the determinants of repurchase intention became essential for businesses looking to retain customers. The purpose of the study was to explore how various factors such as brand trust, security risk perception, ease of use, customer readiness, and loyalty impact consumer repurchase behaviour. A quantitative research design was employed, using an online questionnaire to collect data from 164 respondents who had previously purchased beauty and haircare products on an online shopping site for cosmetics in South Africa. The study utilised multiple regression analysis to assess the relationships among the identified factors and their influence on repurchase intentions. The findings revealed that brand trust, ease of use, and customer loyalty were significant positive predictors of repurchase intentions, while security risk perception had a negative impact. Readiness did not show a significant relationship with repurchase intentions. Based on these results, the study recommends that e-commerce businesses in the beauty sector focus on building trust, enhancing user-friendly interfaces, and fostering customer loyalty programs to encourage repeat purchases.
With the increasing awareness of health and the widespread adoption of social media advertising, health food advertising has become an important source of information influencing consumer decision-making. However, existing studies have primarily focused on the effects of attitudes or behaviors, with limited attention to the underlying psychological mechanisms. Therefore, this study applies optimal arousal theory to examine how expertise and trustworthiness in health food advertising influence consumers’ purchase intention through perceived information relevance. The findings indicate that both expertise and trustworthiness positively affect perceived information relevance, which in turn significantly enhances purchase intention. These results suggest that individuals tend to pursue an optimal level of stimulation, and when advertising information evokes an appropriate level of psychological arousal and aligns with personal needs, it is more likely to facilitate positive behavioral responses. In addition to confirming the importance of perceived information relevance, this study extends optimal arousal theory from the level of sensory stimulation to the cognitive level, thereby advancing the development of optimal arousal theory research.
This study mainly aimed to examine the impact of university students' purchasing tendency from e-commerce sites and demographic characteristics on student satisfaction through the example of a leading foundation research university in Türkiye. In the study, it was investigated whether there was a significant difference between students' purchasing tendencies from e-commerce sites and student satisfaction. It was also investigated whether there was a significant difference between demographic characteristics and student satisfaction. Methodologically, a descriptive online survey method including a questionnaire was applied to collect data. The survey was responded by 240 randomly selected students. The data were analyzed with SPSS-26. The study concluded that there was no significant difference between the average daily time spent on the internet and the type of device used to connect to e-commerce sites, which are among the criteria reflecting the tendency to purchase from e-commerce sites, and student satisfaction whereas there was a significant difference between the number of products purchased through e-commerce sites and student satisfaction. Furthermore, it was revealed that there was no significant difference between demographic characteristics such as gender, marital status, education and monthly income and student satisfaction, whereas there was a significant difference between age and student satisfaction. Accordingly, it was indicated that the difference in terms of age was especially in favor of students in the 22-25 age group. It is thought that the study will contribute to the literature by examining the impact of university students' purchasing tendency from e-commerce sites and demographic characteristics on student satisfaction, together with the example of a leading foundation research university. The study is limited by the selected sample foundation research university, the number of students responded, survey questions-answers, the applied statistical analysis methods and tests. Therefore, in order to reach more general results, it is suggested to expand the studies with more diverse and larger numbers of foundation research universities, students, survey questions and statistical methods.
This study provides a comprehensive analysis of the marketing mix strategies that influence customer acquisition at Spar in Durban. Utilizing a quantitative methodology, data were collected through structured questionnaires completed by 278 respondents. The findings indicate that Spar effectively employs digital marketing tools—including social media, WhatsApp, and loyalty programs—as key communication channels to engage customers. Reliability analysis confirmed the validity of the measurement instrument (Cronbach’s ? = 0.902; KMO = 0.899). The results demonstrate that younger consumers are more responsive to digital platforms, particularly social media and loyalty programs, while traditional methods such as printed flyers are less effective. No significant differences were observed in the perceived effectiveness of specific digital channels across different respondent groups, suggesting a consistent perception of Spar’s digital engagement. The study emphasizes the importance of integrating digital strategies with factors such as pricing, convenience, and product assortment to enhance customer acquisition in Durban’s competitive retail environment.
This study aims to determine the influence of differentiation strategies, product quality, and price on purchasing decisions in Mataram coffee shops. The type of research used in this research is quantitative research. Meanwhile, the data used in this study is primary data obtained from respondents' answers to the questionnaire to be shared. The sampling method uses the accidental sampling method. The population in this study is visitors to Mataram coffee shops every day. The sample in this study was 75 respondents. The data analysis in this study used multiple linear regression using validity test, reliability test, multiple linear regression analysis test and t hypothesis test and determination coefficient test with the SPSS 26 For Windows statistical application program. The results of this study show that differentiation strategies, product quality, and price have a positive and significant effect on consumer purchasing decisions. Mataram coffee shop management should improve marketing strategies in the form of more competitive prices, good product quality, and innovative differentiation strategies in an effort to improve purchase decisions. By implementing the right differentiation strategy in mataram coffee shops. By implementing the right differentiation strategy, Mataram coffee shops can increase the attractiveness of their products, attract new customers, and build long-term loyalty. Therefore, differentiation remains an element that needs to be strengthened to strengthen the business position in the midst of fierce competition.
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 |