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: 10 / 10 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 11:41 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 / 10 with data · 1 missing or unmatched. Percentages use all 10 selected publications.
9 / 10 with data · 1 missing or unmatched. Percentages use all 10 selected publications.
10 / 10 with data · 0 missing or unmatched. Percentages use all 10 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 |
|---|---|---|---|---|---|
| 2024 | All selected publications | 10 | 10 | 10/10 | 10/10 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2024 | All selected publications | 10 | 10 | 10/10 | 10/10 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2024 | All selected publications | 12 | 10 | 10/10 | 10/10 |
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 10 / 10 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): 2026-10-21T07:22:53.441830+00:00 — 2026-10-22T11:41:10.843180+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 | 10 | 10/10 |
| 2018 | All selected publications | 0 | 10 | 10/10 |
| 2019 | All selected publications | 0 | 10 | 10/10 |
| 2020 | All selected publications | 0 | 10 | 10/10 |
| 2021 | All selected publications | 0 | 10 | 10/10 |
| 2022 | All selected publications | 0 | 10 | 10/10 |
| 2023 | All selected publications | 0 | 10 | 10/10 |
| 2024 | All selected publications | 0 | 10 | 10/10 |
| 2025 | All selected publications | 5 | 10 | 10/10 |
| 2026 | All selected publications | 7 | 10 | 10/10 |
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 |
|---|---|
| 2024 | 10 |
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 | 1 | 0 | 0 | 1/1 | 0.0 | 0 | 0.0 | 1/1 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 9 | 10 | 12 | 9/9 | 1.11 | 1 | 55.6 | 9/9 · 100.0% |
* Percentage among publications with a known citation count.
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.
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).
This study aims to examine the importance of feature selection in machine learning, specifically in predicting user engagement with social media post photographs on university Facebook pages. The paper uses a thorough analysis to demonstrate the crucial significance of choosing suitable features and their corresponding algorithms. The research intends to demonstrate how this strategic approach affects the accuracy of prediction findings in social media interaction. The research presents a compelling case study involving 24 leading universities from Australia, the United Kingdom, and the United States. The results underscore the efficacy of the method, stressing that the meticulous selection of characteristics and the use of appropriate algorithms are crucial elements for attaining the best results in social media forecasts. Implications: The study's results have important consequences, particularly within the changing environment of machine learning and its use in social media. Feature selection and algorithm choice are vital for optimizing social media initiatives for institutions.
The increased digital acumen among students has necessitated institutions to adopt digital marketing platforms such as websites to enhance brand awareness for competitive advantage purposes. The study therefore investigated the influence of website use on brand awareness and its sub-factors in the Zimbabwean polytechnic context. The study adopted the positivist paradigm, a descriptive research design and is quantitative in nature. A self-administered online questionnaire was used to collect primary data and through convenience sampling, 492 valid and reliable responses were collected. The collected data was analysed using descriptive analysis and inferential statistics. The empirical results indicated a statistically significant relationship between website use and brand awareness, specifically when looking at the brand awareness sub-factors of top-of-mind and brand recall in the context of Zimbabwean polytechnics. However, a statistically insignificant relationship was noted between website use and brand recognition. The study proposed contributions to polytechnic marketers on website use to enhance brand awareness for their institutions to ensure competitive advantage. The empirically tested model provides a basis for future research in the marketing of polytechnics.
This study investigates the influence of Corporate Social Responsibility (CSR) communication strategies via social media on corporate e-reputation in Nigeria. In an era where online platforms shape brand perception and consumer behavior, CSR communications have emerged as a vital tool for companies to engage with stakeholders, enhance transparency, and foster trust. The research employs an abductive methodology and systematic literature review (SLR), exploring CSR awareness, consumer responses, and behavioral shifts driven by CSR initiatives communicated on social media. Using online polls and Q&A sessions for data collection, the study analyzes the effectiveness of CSR strategies and their impact on consumer trust, loyalty, and purchasing decisions. Findings reveal that CSR communication through social media significantly enhances e-reputation, positively influencing brand loyalty and consumer perception. The study underscores the importance of transparent and engaging CSR communication in building a strong online reputation, and highlights how visual content, storytelling, and real-time updates can amplify CSR efforts. This research contributes to the understanding of the role of digital communication in shaping corporate e-reputation in Nigeria.
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.
South Africa is one of the most popular tourist destinations on the African continent, with timeshares—vacation properties shared by multiple owners—being an important feature of the tourism industry as it contributes significant growth and sustainability of tourist destinations. After its introduction in the 1960s, timeshare gained popularity through its capability to provide luxury holiday affordability and security in South Africa. Since then, timeshare businesses have used traditional public relations, marketing, advertising strategies and tactics to increase brand awareness and reach sales targets. During the difficult COVID-19 period, global tourism declined drastically due to travel restrictions, extreme lockdowns and tight regulations, forcing businesses to adapt their marketing strategies to cope with unprecedented challenges and the loss of customers. Before the pandemic’s culmination, travel agencies and timeshares thus utilized digital marketing and social media to effectively strategise and publicly engage with their audiences online. Drawing upon McGuire’s (1969) theory of information processing, and Echtner and Ritchie’s (1993) destination image branding, a content and thematic analysis of two South African timeshare companies and their Facebook page content was studied to investigate the public relations tactics and marketing strategies implemented during the COVID-19 pandemic. Key findings reveal that timeshares engaged publicly with customers through posts which stimulated online participation, which included the sharing of images, information, updates, competitions and discounts, thus maximising the potential of social media to engage with stakeholders on a short to long term basis.
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 |