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: 9 / 9 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 07:38 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.
9 / 9 with data · 0 missing or unmatched. Percentages use all 9 selected publications.
9 / 9 with data · 0 missing or unmatched. Percentages use all 9 selected publications.
9 / 9 with data · 0 missing or unmatched. Percentages use all 9 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 |
|---|---|---|---|---|---|
| 2023 | All selected publications | 9 | 9 | 9/9 | 9/9 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2023 | All selected publications | 65 | 9 | 9/9 | 9/9 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2023 | All selected publications | 93 | 9 | 9/9 | 9/9 |
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 9 / 9 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): 2026-10-21T07:22:47.405860+00:00 — 2026-10-22T07:38:28.206771+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 | 9 | 9/9 |
| 2018 | All selected publications | 0 | 9 | 9/9 |
| 2019 | All selected publications | 0 | 9 | 9/9 |
| 2020 | All selected publications | 0 | 9 | 9/9 |
| 2021 | All selected publications | 0 | 9 | 9/9 |
| 2022 | All selected publications | 0 | 9 | 9/9 |
| 2023 | All selected publications | 8 | 9 | 9/9 |
| 2024 | All selected publications | 29 | 9 | 9/9 |
| 2025 | All selected publications | 43 | 9 | 9/9 |
| 2026 | All selected publications | 13 | 9 | 9/9 |
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 |
|---|---|
| 2023 | 9 |
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 | 7 | 11 | 4/4 | 1.75 | 1.5 | 100.0 | 4/4 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 5 | 58 | 82 | 5/5 | 11.6 | 2 | 60.0 | 5/5 · 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.
Although numerous studies have been conducted on the impact of influencers on purchase intention. Researchers are divided into two factions, each yielding distinct outcomes. The initial group of researchers discovered that influencers exert a substantial impact on the desire to make a purchase. Conversely, the second group of researchers discovered that influencers do not have a substantial impact on purchase intention. According to the idea of planned behavior, a person's subjective opinions about their preferences or aversions have an impact on their decision to take action. This study focuses on Instagram users that follow Muslim fashion accounts such as Heaven Lights, Vanilla Hijab, Wearing Klambi, and Heylocal. These accounts identify the research objects as the products they offer. The sample technique employed is non-probability, specifically utilizing a purposive sampling approach. A total of 170 respondents were surveyed. The employed methodology for data collection involves the dissemination of questionnaires. The data analysis technique employed was Structural Equation Modeling-Partial Least Squares (SEM-PLS) utilizing smartPLS 3.0 software. The findings indicated that influencers did not have a substantial impact on the intention to make a purchase. The factors of attitude towards advertising and brand attitude completely mediate the influence of influencers on purchase intention. These findings corroborate the findings of the research team that concluded that influencers do not have a substantial impact on purchase intention. The findings of this study also demonstrate the crucial significance of the mediating factors' attitude towards advertising and brand attitude in the impact of influencers on purchase intention.
This study aims to examine and analyze the influence of social media communications created by companies (Firm-created Communication – FCC) and users (User-generated Communication – UGC) on the user's perspective on brand equity and brand attitudes that have an impact on the purchase intention of Samsung products. The population of this study is users who do not use Samsung products to describe their interest in buying Samsung products. The object of this research is social media communications made by companies and users about Samsung products. The research sample consisted of 218 respondents who were not Samsung users and who at the time of the study were domiciled in Java or outside Java. The research data were then analyzed using the Structural Equation Model (AMOS v 23). Based on the research results, it is known that social media communications made by companies positively influence brand attitudes. User-generated social media communications positively influence brand equity and brand attitudes. Brand attitude positively affects brand equity. Brand equity and brand attitude positively influence purchase intention. However, social media communications made by the company have no effect on brand equity. This study reveals that communication made by users has more influence on purchase intention through brand equity and attitudes. Where equity and brand attitude are important in buying interest in a product.
This study examines the impact of perceived influence on word-of-mouth and repurchase intentions, with brand trust as a moderator. This is an explanatory study using a quantitative approach. The study population consisted of Indonesians who had purchased a Yamaha brand motorcycle and followed automotive influencers on YouTube social media. Purposive sampling was utilized for the study. There were 206 samples in the study sample. This study collected data through surveys and documentation, and the instrument was tested using Likert scales. The data were analyzed using structural equation model partial least squares (SEM-PLS) modeling. The results of this study showed that Perceived Influence can increase attitude loyalty and behavioral loyalty of Yamaha motorcycle consumers. In Addition, Brand trust can increase behavioral loyalty and consumer attitudes influenced by automotive influencers on YouTube and social media. Brand Trust plays a partial mediating role in the influence of Perceived Influence on attitude and behavioral loyalty. The findings of this study suggest that Yamaha must pay attention to building attitudinal and behavioral consumer loyalty.
The tourism industry is a sector that continues to be developed by the Indonesian government. Especially during the current Covid 19 pandemic, tourism must be given a touch of differentiation to adapt to government regulations regarding the safety of tourism objects. The development of social media is essential in the world of tourism, where it can change the behaviour of tourists to enjoy tourism services. This study aims to understand the social media preference in tourist destinations so that tourism destinations can analyze social media marketing competitive strategy according to the target market. This study uses the MDS method and cluster analysis. This study collects data from 240 respondents. Respondents were asked to rate ten social media pairs where the social media observed were Instagram, YouTube, Facebook, Twitter, and Tik Tok. This research found that a perceptual map is essential to define consumer perceptions and preferences on social media.
In recent years, the aviation industry has transformed significantly, driven by the increasing importance of digital technologies and social media for communication and marketing. The COVID-19 pandemic accelerated this transformation, posing unprecedented challenges to airlines. The present study investigates how Turkish Airlines, Qatar Airways, and Emirates adapted their social media strategies before, during, and after the pandemic. The research has a threefold focus: (i) assessing pre-pandemic social media strategies, (ii) examining strategy evolution during the pandemic, and (iii) comparing the effectiveness of these strategies before and after COVID-19. By exploring how these airlines leveraged social media to navigate pandemic-related disruptions and maintain vital connections with passengers, this study provides valuable insights. These findings hold significant implications for aviation stakeholders, marketing professionals, and researchers, shedding light on the evolving role of social media in crisis communication and marketing. The study uncovered interesting changes in how airlines use Twitter. After the pandemic, they started using more emojis, and their messages shifted from promoting services to expressing hope during the pandemic and back to promotions afterward. They played with visuals and hashtags. Pricing-wise, they offered discounts for students, different ticket options, and bundled services. In promotions, they focused on being environmentally and socially responsible, user convenience, and specific events. They mainly shared updates on flights, achievements, and travel advice. To engage people, they used quizzes, replied directly to customers, and targeted specific groups. For marketing managers, the study suggests being more customer-focused, emphasizing environmental and social responsibility, offering diverse pricing, celebrating milestones, and creating engaging content. Overall, the findings provide practical insights for adjusting marketing strategies in the ever-changing world of airlines, especially considering the challenges brought by COVID-19.
Hotel accommodation is one of the most essential things in the tourism industry because it functions as a place to rest after a day of activities. The large number of hotel choices requires marketers in their fields to have a strategy that can make the best choice by consumers, especially in today's digital era. One of the measuring tools that can be used as the most effective promotional media is positive e-WOM. This helps marketers to determine what strategies can be maximized. This is self-congruity by matching perceptions between consumer self-concept and consumer image and positive emotional experience, which can also help consumers form positive perceptions of a place to create a satisfying experience or consumer satisfaction to 4 Star Hotels in Malang City. This study uses a quantitative approach. The population in this study are consumers from Hotel Bintang 4 yang ada di kota Malang. Sampling was carried out by using the non-probability sampling method. In this study, the sample was determined, namely the research item, so the number of respondents was 110. To get the desired result, as many as 190 respondents. Data collection techniques in this study were carried out by distributing questionnaires. Data analysis techniques were carried out by SEM-PLS using software 3.0. The results of this study indicate that there is an influence between self-congruity, positive emotional experience, and customer satisfaction to positive e-WOM Hotel in Malang City.
Although numerous studies have been conducted on the impact of influencers on purchase intention. Researchers are divided into two factions, each yielding distinct outcomes. The initial group of researchers discovered that influencers exert a substantial impact on the desire to make a purchase. Conversely, the second group of researchers discovered that influencers do not have a substantial impact on purchase intention. According to the idea of planned behavior, a person's subjective opinions about their preferences or aversions have an impact on their decision to take action. This study focuses on Instagram users that follow Muslim fashion accounts such as Heaven Lights, Vanilla Hijab, Wearing Klambi, and Heylocal. These accounts identify the research objects as the products they offer. The sample technique employed is non-probability, specifically utilizing a purposive sampling approach. A total of 170 respondents were surveyed. The employed methodology for data collection involves the dissemination of questionnaires. The data analysis technique employed was Structural Equation Modeling-Partial Least Squares (SEM-PLS) utilizing smartPLS 3.0 software. The findings indicated that influencers did not have a substantial impact on the intention to make a purchase. The factors of attitude towards advertising and brand attitude completely mediate the influence of influencers on purchase intention. These findings corroborate the findings of the research team that concluded that influencers do not have a substantial impact on purchase intention. The findings of this study also demonstrate the crucial significance of the mediating factors' attitude towards advertising and brand attitude in the impact of influencers on purchase intention.
This study examines the impact of perceived influence on word-of-mouth and repurchase intentions, with brand trust as a moderator. This is an explanatory study using a quantitative approach. The study population consisted of Indonesians who had purchased a Yamaha brand motorcycle and followed automotive influencers on YouTube social media. Purposive sampling was utilized for the study. There were 206 samples in the study sample. This study collected data through surveys and documentation, and the instrument was tested using Likert scales. The data were analyzed using structural equation model partial least squares (SEM-PLS) modeling. The results of this study showed that Perceived Influence can increase attitude loyalty and behavioral loyalty of Yamaha motorcycle consumers. In Addition, Brand trust can increase behavioral loyalty and consumer attitudes influenced by automotive influencers on YouTube and social media. Brand Trust plays a partial mediating role in the influence of Perceived Influence on attitude and behavioral loyalty. The findings of this study suggest that Yamaha must pay attention to building attitudinal and behavioral consumer loyalty.
This study aims to examine and analyze the influence of social media communications created by companies (Firm-created Communication – FCC) and users (User-generated Communication – UGC) on the user's perspective on brand equity and brand attitudes that have an impact on the purchase intention of Samsung products. The population of this study is users who do not use Samsung products to describe their interest in buying Samsung products. The object of this research is social media communications made by companies and users about Samsung products. The research sample consisted of 218 respondents who were not Samsung users and who at the time of the study were domiciled in Java or outside Java. The research data were then analyzed using the Structural Equation Model (AMOS v 23). Based on the research results, it is known that social media communications made by companies positively influence brand attitudes. User-generated social media communications positively influence brand equity and brand attitudes. Brand attitude positively affects brand equity. Brand equity and brand attitude positively influence purchase intention. However, social media communications made by the company have no effect on brand equity. This study reveals that communication made by users has more influence on purchase intention through brand equity and attitudes. Where equity and brand attitude are important in buying interest in a product.
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 |