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: 40 / 40 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 00:49 UTC — 22 Oct 2026 01:46 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.
38 / 40 with data · 2 missing or unmatched. Percentages use all 40 selected publications.
38 / 40 with data · 2 missing or unmatched. Percentages use all 40 selected publications.
40 / 40 with data · 0 missing or unmatched. Percentages use all 40 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 |
|---|---|---|---|---|---|
| 2014 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2015 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2017 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| 2018 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 9 | 9 | 9/9 | 9/9 |
| 2022 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| 2023 | All selected publications | 5 | 5 | 5/5 | 5/5 |
| 2024 | All selected publications | 9 | 9 | 9/9 | 9/9 |
| 2025 | All selected publications | 5 | 5 | 5/5 | 5/5 |
| 2026 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2014 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2015 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2017 | All selected publications | 9 | 4 | 4/4 | 4/4 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 37 | 9 | 9/9 | 9/9 |
| 2022 | All selected publications | 56 | 4 | 4/4 | 4/4 |
| 2023 | All selected publications | 58 | 5 | 5/5 | 5/5 |
| 2024 | All selected publications | 10 | 9 | 9/9 | 9/9 |
| 2025 | All selected publications | 3 | 5 | 5/5 | 5/5 |
| 2026 | All selected publications | 0 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2014 | All selected publications | 5 | 1 | 1/1 | 1/1 |
| 2015 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2017 | All selected publications | 15 | 4 | 4/4 | 4/4 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 76 | 9 | 9/9 | 9/9 |
| 2022 | All selected publications | 74 | 4 | 4/4 | 4/4 |
| 2023 | All selected publications | 82 | 5 | 5/5 | 5/5 |
| 2024 | All selected publications | 12 | 9 | 9/9 | 9/9 |
| 2025 | All selected publications | 3 | 5 | 5/5 | 5/5 |
| 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 40 / 40 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): 2026-10-21T00:49:28.560557+00:00 — 2026-10-22T01:46:19.160669+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 | 1 | 40 | 40/40 |
| 2018 | All selected publications | 0 | 40 | 40/40 |
| 2019 | All selected publications | 2 | 40 | 40/40 |
| 2020 | All selected publications | 0 | 40 | 40/40 |
| 2021 | All selected publications | 11 | 40 | 40/40 |
| 2022 | All selected publications | 26 | 40 | 40/40 |
| 2023 | All selected publications | 39 | 40 | 40/40 |
| 2024 | All selected publications | 72 | 40 | 40/40 |
| 2025 | All selected publications | 76 | 40 | 40/40 |
| 2026 | All selected publications | 39 | 40 | 40/40 |
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 |
|---|---|
| 2014 | 1 |
| 2017 | 4 |
| 2020 | 1 |
| 2021 | 9 |
| 2022 | 4 |
| 2023 | 5 |
| 2024 | 9 |
| 2025 | 5 |
| 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 | 40 | 175 | 269 | 40/40 | 4.38 | 1.5 | 70.0 | 40/40 · 100.0% |
* Percentage among publications with a known citation count.
Today is the era of digitalization. Nowadays, people prefer to attach their lives to social media presence on Facebook, Instagram, Twitter, YouTube, and LinkedIn. Hence, manufacturers and producers use social media tremendously in this digital era to reach customers with their products and services. Thus, social media marketing has become famous for businesses to promote their products or services, engage with their customers, and build brand awareness. The present study focuses on reviewing the mediating role played by several social media platforms to gain the interest of consumers. This paper also aims to analyze the necessity of social media marketing compared to other marketing communication and promotion forms. This paper helps the reader decide whether to depend on social media only for marketing purposes or whether there can be a choice of another means. Without analyzing the pros and cons, relying heavily on social media platforms for marketing is not always a good idea. The author employed a qualitative and descriptive method for constructing the paper. This paper will review the literature regarding social media marketing compared to other means, try to recommend, and shed light on selecting the right marketing platform by different marketers. The study's main findings demonstrate that measures should be taken to enhance responsible use and limit some of its harmful effects. Furthermore, in providing insights into whether social media marketing is a necessity or a choice, the author suggests that productive utilization should be the main priority for all, whether social media or other platforms.
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.
The aim of the paper is to investigate the impact of SEO on the business performance of a private university in Sarajevo. Thus, the main research question provides the finding on how does the implementation of SEO influence the performance of the business. Moreover, the tested hypothesis presents whether SEO positively influences the business performance of International Burch University (IBU). The research strategy is to analyze primary data derived from a case study, which is generated following a conversation with the Head of the IBU Marketing and PR team. The data sample is derived from Google Analytics (focusing on the number of visits and sessions, average engagement time, keywords and SERP positioning). Seobility tools are employed in data analysis. Business performance is calculated through the IBU CRM system, focusing on student enrolment. Findings indicate that increasing a site's rankings on search engine results pages (SERPs) led to a variety of positive outcomes for companies including an increase in the number of visitors to the site, an increase in the average amount of time users spent on the site, increased user engagement, and an increase in student enrollment, which resulted in IBU increased annual sales revenue. It will benefit many different groups, including the government, which will benefit in both microeconomic and macroeconomic senses, digital marketing enthusiasts and SEO experts, and the academic world, which will benefit as a framework for future studies and research in the field of SEO recognition and implementation in business queries.
The study was motivated by the need to highlight the roles played by social media marketing on consumers' purchase intention during the COVID-19 pandemic. Since it was a quantitative study, hypotheses were tested to generate findings. We used IBM SPSS 25.0 software to analyze using reliability statistics, descriptive statistics, and IBM SPSS Amos 26.0 software was used to do confirmatory factor analysis for the purpose of developing hypothesis, and SEM used for trying to fit the model. The primary data was mustered by personal interview in an online survey method through a structured questionnaire with five-point Likert scales from 240 Bangladeshi online consumers. These findings publicized that social media marketing (Facebook, Instagram, and YouTube) a significant stimulus on consumers purchase intention during COVID-19, and all factors except discount or coupon offer and brand awareness had an epoch-making and constructive connection with consumers’ purchase intention during the coronavirus malady (COVID-19) epidemic in Bangladesh.
This study aims to analyze the influence of elements of social media marketing activity on consumer engagement and brand knowledge including brand awareness and image. This study used a quantitative method and purposive sampling technique on 250 respondents. The research was conducted on all Indonesian citizens who use social media WhatsApp, Facebook, Instagram, youtube, and TikTok regarding the culinary business of celebrities and YouTubers in Indonesia. The method used is the Structural Equation Model with AMOS 24.0 Software. The results of this study indicate that the interaction variables, trends, and EWOM have a positive and significant effect on consumer engagement, and consumer engagement has a positive and significant effect on brand awareness and brand image. While the entertainment and customization variables have no significant effect on consumer engagement.
This study investigates the relationship between exposure to online promotional content and changes in business image evaluations and purchase intention toward informal clothing enterprises in Bangladesh. Based on the O-S-O-R framework, the study conceptualizes online promotional content as the stimulus, post-exposure business image as the post-orientation construct, and purchase intention as the response, considering age group and frequency of social media usage as pre-orientation factors. Using a nonprobability purposive sampling approach with self-selected participation, the study implemented a one-group pretest-posttest design through an online questionnaire administered to 391 respondents in Bangladesh. Analyses included descriptive statistics, reliability tests, exploratory factor analysis, paired-samples t tests, Spearman correlations, and linear regression. Results indicated statistically significant increases between pre-exposure and post-exposure for business image (mean difference = −1.297, t = −50.945, p < .001) and purchase intention (mean difference = −1.254, t = −39.308, p < .001). Age group was negatively associated with post-exposure business image, whereas frequency of social media use was positively associated with it. Post-exposure business image was also a strong predictor of purchase intention (β = .486, p < .001, R² = .236). This study contributes to the literature by extending extant research on digital consumer response in informal small-business clothing retail in Bangladesh and suggests that well-designed promotional content is positively associated with business evaluations and purchase-related intentions. Since this study used a one-group pretest-posttest design, the results should be interpreted as evidence of association rather than causation.
The study seeks to ascertain the factors of electronic word of mouth (e-WOM) influencing consumer buying behaviour. This research aims to contribute to the ongoing debate about the factors of e-WOM that influence consumer buying decisions while making an online purchase. Structural equation modelling (SEM) was used to measure and analyse the relationships between observed and latent variables. The results reveal that e-WOM valence is the most influential e-WOM factor during online purchase decisions. Marketers must pay close attention to negative e-WOMs, monitor unfavourable evaluations and use negative feedback to improve product and service quality.
Instagram has emerged not only as a social networking platform but also as a vital digital marketing tool for fashion brands. However, missed marketing opportunities persist due to factors such as limited computer literacy, unfamiliarity with digital tools, and resistance to new media. The aim of the study is to explore the opportunities for leveraging Instagram as an effective marketing tool within the fashion industry in Durban. Specifically, it seeks to identify how fashion businesses can utilise Instagram to enhance brand visibility, engage with their target audience, and maximize promotional efforts to drive success in the digital marketplace. This quantitative study employed a purposive sampling method and focused on descriptive analysis through a structured online questionnaire distributed to 130 fashion businesses in Durban. The findings underscore Instagram's efficacy in enabling fashion businesses to effectively showcase products, engage visually with their target audience, and amplify brand awareness. The platform's visual-centric nature inherently supports robust brand marketing and promotion, particularly in the fashion sector. Recommendations highlight the imperative for fashion brands to strategically leverage Instagram to enhance visibility and augment brand perception. Success on Instagram necessitates a well-coordinated marketing approach to ensure impactful brand promotion and engagement with followers.
This paper aims to evaluate the effects of demographic targeting, platform selection, and engagement metrics on the effectiveness of social media advertising campaigns. In this paper, it is also aimed to provide a comprehensive understanding of how these factors influence conversion outcomes. We employed a hybrid analytics method combining ANOVA, correlation, and regression analysis to examine patterns of user engagement and ad performance. A dataset of 200,235 ad campaign records conducted in the United States was analyzed to assess variations across gender, age groups, and platforms. The main findings of the study demonstrate that (i) women consistently exhibited higher conversion rates compared to men, highlighting the potential of gender-specific content strategies; (ii) users aged 35–44 achieved the highest engagement, suggesting that middle-aged audiences may be more responsive to targeted campaigns; and finally, (iii) among platforms, Instagram led in conversion performance, while Facebook showed comparable engagement rates, and both significantly outperformed other platforms in terms of overall impact. Click-through rate was found to be moderately correlated with conversions, whereas impressions showed weak returns, indicating diminishing value at higher exposure levels. These results underscore the importance of optimizing both message and delivery channel to specific audience segments.This study contributes to digital marketing literature by integrating demographic variables, engagement indicators, and platform performance into a unified model. The insights derived offer practical implications for businesses and advertisers aiming to enhance return on investment and optimize campaign strategies in a fast-paced, digitally driven advertising landscape.
This study investigates the strategic effectiveness of social media advertising by analyzing the relationship between platform selection, audience demographics, and campaign performance metrics. Using a dataset encompassing Instagram, Facebook, Pinterest, and Twitter, the research evaluates key indicators such as engagement, conversion rates, and return on investment (ROI). By employing statistical methods like ANOVA and regression analysis in SPSS, the study aims to identify optimal platform-audience combinations and assess how different campaign goals influence outcomes. The findings will provide valuable insights for advertisers and businesses seeking to enhance their social media strategies and maximize advertising effectiveness.
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 |