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 — 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.
19 / 19 with data · 0 missing or unmatched. Percentages use all 19 selected publications.
19 / 19 with data · 0 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 |
|---|---|---|---|---|---|
| 2021 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| 2022 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2023 | All selected publications | 7 | 7 | 7/7 | 7/7 |
| 2024 | All selected publications | 5 | 5 | 5/5 | 5/5 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 13 | 4 | 4/4 | 4/4 |
| 2022 | All selected publications | 40 | 3 | 3/3 | 3/3 |
| 2023 | All selected publications | 12 | 7 | 7/7 | 7/7 |
| 2024 | All selected publications | 9 | 5 | 5/5 | 5/5 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 33 | 4 | 4/4 | 4/4 |
| 2022 | All selected publications | 55 | 3 | 3/3 | 3/3 |
| 2023 | All selected publications | 17 | 7 | 7/7 | 7/7 |
| 2024 | All selected publications | 9 | 5 | 5/5 | 5/5 |
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): 2026-10-21T07:22:48.955489+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 | 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 | 2 | 19 | 19/19 |
| 2022 | All selected publications | 8 | 19 | 19/19 |
| 2023 | All selected publications | 23 | 19 | 19/19 |
| 2024 | All selected publications | 33 | 19 | 19/19 |
| 2025 | All selected publications | 28 | 19 | 19/19 |
| 2026 | All selected publications | 20 | 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 |
|---|---|
| 2021 | 4 |
| 2022 | 3 |
| 2023 | 7 |
| 2024 | 5 |
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 | 13 | 4/4 | 1.75 | 1.5 | 100.0 | 4/4 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 15 | 67 | 101 | 15/15 | 4.47 | 2 | 80.0 | 15/15 · 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.
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.
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.
This study aims to analyze the role of psychological engagement, encompassing cognitive, emotional, and physical dimensions, as well as member behavioral engagement, including participation and word of mouth, and its influence on brand loyalty, which includes cognitive, affective, conative, and action loyalty in the National Cooking Club (NCC) in Indonesia. Utilizing a quantitative approach with survey techniques, an online questionnaire was distributed to 500 respondents, yielding 162 valid responses. Data analysis was conducted using multiple linear regression with SPSS version 16. The results indicate that action loyalty does not significantly affect physical engagement, whereas emotional engagement does. Additionally, both emotional and physical engagement significantly influence participation, whereas cognitive engagement does not. Furthermore, emotional engagement has a significant effect on word of mouth, contrary to cognitive and physical engagement, which do not significantly influence word of mouth among NCC members.
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.
This research examines the influence of Social Media Marketing Activities (SMMA) on consumer behaviour towards local fashion brands in Indonesia. The study focuses on five key SMMA dimensions: interactivity, informativeness, personalization, trendiness, and word-of-mouth (WOM). It extends previous research by investigating the moderating effect of customer engagement levels on the relationships between SMMA dimensions, consumer attitudes, brand experiences, and purchase intentions. A quantitative approach was employed through an online survey distributed to 878 social media users who actively follow and purchase from local fashion brands. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to analyse the data and identify the influence of SMMA dimensions on consumer attitudes, experiences, purchase intentions, and the moderating effect of customer engagement levels. The findings aim to identify the influence of each SMMA dimension on consumer attitudes, experiences, and purchase intentions, quantify the relative impact of each SMMA dimension on these consumer behaviour factors, determine the SMMA dimension with the strongest influence on consumer response, and investigate the moderating effect of customer engagement levels on the relationships between SMMA and the other variables. These findings provide valuable guidance for local fashion brands in developing effective social media marketing strategies to enhance customer engagement and drive purchase intentions.
The customer journey towards purchase has received substantial attention from businesses and academics, especially in relationship marketing and customer engagement. This interest has intensified due to the rise of social media, enabling customer-to-customer interactions alongside conventional marketing strategies. Customers increasingly seek information independently in the life insurance sector, and comprehending the decision-making process is critical. In Indonesia, low insurance literacy and inclusion add to the challenges of the traditional, face-to-face sales process. This research empirically investigates the impact of social media and traditional marketing communications on customer-to-customer interactions and subsequent customer decision-making. Brand preference is examined as a mediating variable. Quantitative methods, including online questionnaires and expert interviews, were employed in Greater Jakarta, Indonesia, targeting non-life insurance customers. Structural equation modeling (SEM) via LISREL was utilized to analyze data from 310 respondents. Results indicate that while social media communications have an insignificant impact on customer-to-customer interactions (t-value 1.15), traditional marketing communications positively influence customer-to-customer interactions (t-value 11.87). Customer-to-customer interactions also found to positively influence brand preference (t-value 10.14) while brand preference positively influences customer decision-making (t-value 3.86) Additionally, customer-to-customer interactions positively affect customer decision-making (t-value 3.54), with brand preference mediating this relationship (t-value 3.85).
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.
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 |