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: 8 / 8 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 12:22 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.
8 / 8 with data · 0 missing or unmatched. Percentages use all 8 selected publications.
8 / 8 with data · 0 missing or unmatched. Percentages use all 8 selected publications.
8 / 8 with data · 0 missing or unmatched. Percentages use all 8 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 |
|---|---|---|---|---|---|
| 2021 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2023 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2024 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 5 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 38 | 2 | 2/2 | 2/2 |
| 2023 | All selected publications | 6 | 3 | 3/3 | 3/3 |
| 2024 | All selected publications | 5 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 14 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 52 | 2 | 2/2 | 2/2 |
| 2023 | All selected publications | 6 | 3 | 3/3 | 3/3 |
| 2024 | All selected publications | 4 | 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 8 / 8 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): 2026-10-21T07:22:50.553040+00:00 — 2026-10-23T12:22:15.262708+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 | 8 | 8/8 |
| 2018 | All selected publications | 0 | 8 | 8/8 |
| 2019 | All selected publications | 0 | 8 | 8/8 |
| 2020 | All selected publications | 0 | 8 | 8/8 |
| 2021 | All selected publications | 2 | 8 | 8/8 |
| 2022 | All selected publications | 5 | 8 | 8/8 |
| 2023 | All selected publications | 12 | 8 | 8/8 |
| 2024 | All selected publications | 24 | 8 | 8/8 |
| 2025 | All selected publications | 18 | 8 | 8/8 |
| 2026 | All selected publications | 15 | 8 | 8/8 |
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 |
|---|---|
| 2021 | 1 |
| 2022 | 2 |
| 2023 | 3 |
| 2024 | 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 | 8 | 54 | 76 | 8/8 | 6.75 | 3.5 | 87.5 | 8/8 · 100.0% |
* Percentage among publications with a known citation count.
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.
Donations through social media or any online platforms are becoming a new trend these days, thanks to the use of emotional marketing through narrations and visual depictions showing the real condition of people who need supports. Organizations are led to raise people’s emotions to increase their intention to make donations. This study aims to examine the effect of emotional marketing on donation intention through social media platforms and people’s willingness to use technology (UTAUT). This is explanatory research was conducted through a survey on 365 respondents of Malang city who had seen a crowdfunding commercial of Kitabisa.com. The structural equation analysis has led to findings that emotional marketing significantly influences people’s donation intention, implying that the commercial is able to affect people’s emotion into empathy and willingness to make donations through the charity campaign. Furthermore, this study also finds that UTAUT has a significant effect on the intention. The findings are useful for Kitabisa.com in their effort to increase people’s donation intention through the use of emotional marketing.
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.
Despite many studies on online advertising and its effects, there are still few studies that try to examine the avoidance of advertising by a person. However, the number of advertisements that appears will make someone feel annoyed and trigger avoidance behavior towards the advertisement they see. Referring to psychological reactance theory, advertising can become a nuisance when it is considered unrelated and interferes with a person's freedom to access media which in turn can trigger avoidance behavior in an effort to regain his freedom. Using psychological reactance theory, the aim of this research was to further investigate the relationship between advertising linkage and advertising avoidance behavior mediated by advertising distraction and advertising engagement variables. This research uses Instagram as the object of research. Instagram was chosen because it is a social media whose number of users is growing every year and this is what triggers many marketers to place ads on Instagram. Respondents in this research were Instagram users who had avoided advertisements that appeared on their accounts, determining the number of samples in this research using a purposive sampling technique with a sample of 160 respondents. The method of collecting data is using a questionnaire, the data is analyzed using PLS-SEM.
The study investigates the impact of user-generated content (UGC) on the purchase intention (PI) of ChatGPT Premium among undergraduate students, focusing on the mediating roles of brand attitude (BA) and brand trust (BT) by using SOR and HOE models. Using quantitative research methods and analyzing data from 105 undergraduate students who using TikTok in Malang City, Indonesia. Data analysis using SEM-PLS, and the results show that user-generated content has no direct effect on purchase intention. Meanwhile, user-generated content has a direct positive and significant effect on brand attitude and brand trust. Each brand attitude and brand trust also have a significant positive effect on purchase intention. Positive UGC can increase brand attitude and brand trust as well as increase interest in purchasing premium ChatGPT. Furthermore, based on the results of the indirect effect, it is known that brand attitude and brand trust can mediate the influence of user-generated content on purchase intention, where both act as full mediators in this research. This paper offers valuable insights into how UGC on platforms like TikTok influences consumer behavior in the context of freemium to premium transitions.
This study aims to analyse the influence of perceived advertising values which include informativeness, entertainment, and irritation on purchase intention through attitudes towards Instagram advertising. This study was conducted on Generation Z who followed Emina's Instagram account in Malang City. Using a quantitative approach, data was obtained through a questionnaire using the purposive sampling method and analyzed using the PLS-SEM model. The results showed that informativeness and entertainment had a positive influence on purchase intention, while irritation had no significant effect. In addition, attitudes towards Instagram advertising have been proven to mediate the relationship between the three elements of perceived advertising values and purchase intention. These findings reinforce the importance of creating informative and entertaining Instagram ads, while minimizing irritating elements to increase positive attitudes towards ads and consumer purchase intent. This research makes a theoretical contribution by supporting the theory of Planned Behavior and offers practical insights for marketers in designing advertising strategies on social media.
The study investigates the impact of user-generated content (UGC) on the purchase intention (PI) of ChatGPT Premium among undergraduate students, focusing on the mediating roles of brand attitude (BA) and brand trust (BT) by using SOR and HOE models. Using quantitative research methods and analyzing data from 105 undergraduate students who using TikTok in Malang City, Indonesia. Data analysis using SEM-PLS, and the results show that user-generated content has no direct effect on purchase intention. Meanwhile, user-generated content has a direct positive and significant effect on brand attitude and brand trust. Each brand attitude and brand trust also have a significant positive effect on purchase intention. Positive UGC can increase brand attitude and brand trust as well as increase interest in purchasing premium ChatGPT. Furthermore, based on the results of the indirect effect, it is known that brand attitude and brand trust can mediate the influence of user-generated content on purchase intention, where both act as full mediators in this research. This paper offers valuable insights into how UGC on platforms like TikTok influences consumer behavior in the context of freemium to premium transitions.
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.
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 |