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: 31 / 31 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 — 22 Oct 2026 02:38 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.
29 / 31 with data · 2 missing or unmatched. Percentages use all 31 selected publications.
28 / 31 with data · 3 missing or unmatched. Percentages use all 31 selected publications.
31 / 31 with data · 0 missing or unmatched. Percentages use all 31 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 | 2 | 2 | 2/2 | 2/2 |
| 2020 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 6 | 6 | 6/6 | 6/6 |
| 2023 | All selected publications | 10 | 10 | 10/10 | 10/10 |
| 2024 | All selected publications | 7 | 7 | 7/7 | 7/7 |
| 2025 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2026 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 3 | 2 | 2/2 | 2/2 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 1 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 26 | 6 | 6/6 | 6/6 |
| 2023 | All selected publications | 39 | 10 | 10/10 | 10/10 |
| 2024 | All selected publications | 4 | 7 | 7/7 | 7/7 |
| 2025 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2026 | All selected publications | 0 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 10 | 2 | 2/2 | 2/2 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 11 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 43 | 6 | 6/6 | 6/6 |
| 2023 | All selected publications | 68 | 10 | 10/10 | 10/10 |
| 2024 | All selected publications | 8 | 7 | 7/7 | 7/7 |
| 2025 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2026 | All selected publications | 0 | 2 | 2/2 | 2/2 |
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 31 / 31 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): 2026-10-21T07:22:43.110897+00:00 — 2026-10-22T02:38:04.929391+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 | 31 | 31/31 |
| 2018 | All selected publications | 0 | 31 | 31/31 |
| 2019 | All selected publications | 0 | 31 | 31/31 |
| 2020 | All selected publications | 1 | 31 | 31/31 |
| 2021 | All selected publications | 1 | 31 | 31/31 |
| 2022 | All selected publications | 4 | 31 | 31/31 |
| 2023 | All selected publications | 40 | 31 | 31/31 |
| 2024 | All selected publications | 37 | 31 | 31/31 |
| 2025 | All selected publications | 35 | 31 | 31/31 |
| 2026 | All selected publications | 22 | 31 | 31/31 |
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 | 2 |
| 2021 | 3 |
| 2022 | 6 |
| 2023 | 10 |
| 2024 | 7 |
| 2025 | 1 |
| 2026 | 2 |
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 | 31 | 73 | 140 | 31/31 | 2.35 | 1 | 61.3 | 31/31 · 100.0% |
* Percentage among publications with a known citation count.
This study aims to explain the effect of product knowledge, brand image, e-WOM on purchase intentions for edtech products in Indonesia. Data were obtained using a closed questionnaire involving 230 respondents, from Indonesians who know about edtech startup products and are between 18 and 35 years old. The sampling technique used was convenience sampling. The analysis technique used is Structural Equation Modeling (SEM) and uses the help of the AMOS version 24 program. The results show that 1) product knowledge has a positive but not significant effect on purchase intention, 2) product knowledge and brand image have a positive and significant effect on e-commerce WOM, 3) brand image and e-WOM have a positive and significant effect on purchase intention. It was also found that e-WOM functions as a mediator variable from the effect of product knowledge and brand image on purchase intention. The effect of the e-WOM mediator is stronger than the direct effect of brand image on purchase intention.
Nowadays online shopping industry is growing rapidly, and social media platforms are a major part of promotional activities for many companies in Indonesia. The research aims to examine the relationship between price perception, brand image and digital marketing variables and purchase intention and brand loyalty. Design research is a quantitative research using a survey method with a questionnaire; the sampling technique is purposive random sampling. Using a structural equation modeling approach with AMOS software and 303 respondents, results show that price perception corporate image, and digital marketing promotion activities have significant and positive effects on purchase intention, while brand image does not directly have a significant effect on purchase intention. Furthermore, purchase intention has a significant effect on brand loyalty.
Along with the increasing demand for qualified skincare products these days, there are many companies in Indonesia that produce facial beauty products that emphasize a good image in the minds of the consumers and intensive promotion. This study was conducted to determine whether the promotion, product quality, and brand image affect the purchase decisions. The research was conducted using a survey method using a questionnaire to a sample of 200 respondents who used Wardah Cosmetics beauty products. The sample was obtained by doing a purposive random sampling technique. The data analysis technique used in this research was using Structural Equation Modeling (SEM) using the AMOS program. The results of this study indicated that the promotion and the product quality had a significant effect on the brand image and brand image has a significant effect on purchasing decisions.
The purpose of this study was to examine the effect of the source credibility of celebrity endorsers such as trustworthiness, expertise, and attractiveness) on brand attitudes and purchase intentions for endorsed brands. The type of sampling used is purposive sampling with a total of 300 respondents. The analytical method used is the Structural Equation Modeling (SEM) method using Smart PLS (Partial Least Square) software. This study finds that the trustworthiness, expertise, and attractiveness of a celebrity endorser have a positive and significant effect on brand attitude and purchase intention. This shows that the better the trustworthiness, expertise, and attractiveness of a celebrity endorser, the better the consumer's positive attitude towards the brand (brand credibility). Furthermore, the better the consumer's positive attitude towards the brand (brand credibility), the higher the purchase intention that arises from a consumer.
Our brain has nearly 100 billion nerve cells, responsible for sensory perception and serving as a memory for all the information processed by the body. The brain is, therefore, the basis of cognition and emotions. This article aims to present the origins and the development timeline of neuromarketing from the first studies of the unconscious mind to the use of neurosciences techniques in the consumer behavior field. Therefore, the research method used in this paper is a systematic literature review based on a multidisciplinary theoretical exploration of events concerning the development of neuromarketing from 2010 to 2018. The paper theoretically proves that neuromarketing is the only field of research that induces the existence of unconscious, implicit, subliminal, and emotional processes that cannot be approached without the contribution of neurosciences. Still, its ethical nature limits its use and requires researchers to comply with specific regulations to protect consumers from possible risks of illegal exploitation. The results of this systematic literature review have shown that using neurosciences in marketing is crucial since it allows researchers to consider the unconscious and automatic elements in consumer decision-making.
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 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.
In the ever-changing realm of retail banking, it is essential for banks to grasp the elements that affect customer decisions to stay ahead in the competition. This study delves into customer preferences when selecting a bank, focusing specifically on Absa Bank in South Africa. By utilizing a case study approach, the research delves into the factors that influence customer behavior in the retail banking industry. Referencing the consumer decision model, the study investigates various stages such as problem recognition, information search, evaluation of options, purchase decision, and post-purchase assessment. Factors such as security, convenience, assurance, accessibility, and responsiveness are pinpointed as crucial aspects in bank selection. The study employs quantitative research techniques, conducting surveys with Absa Bank customers to dissect their preferences. Findings suggest that transaction fees, corporate image, bank coverage, and size play a significant role in influencing customer decisions. The study emphasizes the importance of addressing these customer-identified factors to deter customer attrition. Recommendations include conducting regular surveys to keep abreast of changing customer preferences and maintaining a competitive edge. The research offered valuable insights that can guide strategic decision-making and marketing efforts in the retail banking sector.
In an incisive analysis of the marketing mix's impact on small retail businesses in Durban's Central Business District (CBD), this study scrutinizes the extended 7Ps framework: product, price, place, promotion, process, people and physical evidence. Utilizing a quantitative methodology, data from 217 owner-managers, captured via a 5-point Likert scale, were analysed through ANOVA and Tukey's HSD tests, unearthing deep insights. The findings disrupt conventional marketing paradigms, revealing that an intense focus on product attributes does not correlate with enhanced performance. Small businesses that maintained competitive, yet balanced pricing mechanisms outstripped are those that engages in fierce price wars, underscoring the dangers of excessive price reductions. Strategic promotional activities and well-considered choice locations were pivotal, whereas overemphasis on physical evidence and internal processes proved detrimental. The study results advocate for a balanced, strategic approach to the marketing mix, urging small businesses to rethink traditional priorities. This study offers crucial guidance for business owners seeking to optimize performance and calls for targeted policy measures to support the retail sector’s growth and sustainability.
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 |