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: 7 / 7 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 02:36 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.
7 / 7 with data · 0 missing or unmatched. Percentages use all 7 selected publications.
7 / 7 with data · 0 missing or unmatched. Percentages use all 7 selected publications.
7 / 7 with data · 0 missing or unmatched. Percentages use all 7 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 |
|---|---|---|---|---|---|
| 2019 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2024 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2026 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 11 | 3 | 3/3 | 3/3 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2026 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 19 | 3 | 3/3 | 3/3 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2026 | All selected publications | 1 | 1 | 1/1 | 1/1 |
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 7 / 7 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): 2026-10-21T07:22:43.102851+00:00 — 2026-10-22T02:36:17.085100+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 | 7 | 7/7 |
| 2018 | All selected publications | 0 | 7 | 7/7 |
| 2019 | All selected publications | 0 | 7 | 7/7 |
| 2020 | All selected publications | 0 | 7 | 7/7 |
| 2021 | All selected publications | 0 | 7 | 7/7 |
| 2022 | All selected publications | 0 | 7 | 7/7 |
| 2023 | All selected publications | 4 | 7 | 7/7 |
| 2024 | All selected publications | 7 | 7 | 7/7 |
| 2025 | All selected publications | 9 | 7 | 7/7 |
| 2026 | All selected publications | 5 | 7 | 7/7 |
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 |
|---|---|
| 2019 | 1 |
| 2021 | 1 |
| 2022 | 1 |
| 2023 | 3 |
| 2026 | 1 |
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 | 2 | 6 | 11 | 2/2 | 3.0 | 3.0 | 50.0 | 2/2 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 5 | 10 | 14 | 5/5 | 2.0 | 1 | 100.0 | 5/5 · 100.0% |
* Percentage among publications with a known citation count.
This research explores the effectiveness of destination awareness, destination image, perceived quality, and brand engagement on visit intention among TikTok users toward destinations in Yogyakarta. The sample in this research is TikTok users who have seen tourism video content in Yogyakarta. Data was obtained through an online survey constructed with Google Forms which 225 respondents filled out. The data used in this study were analyzed using the PLS-SEM method by SPSS and SmartPLS. According to the research findings, destination awareness and destination image positively impact the perceived quality of TikTok users. Furthermore, perceived quality positively affects the intention to visit and brand engagement for TikTok users. These findings provide implications and empirical studies related to encouraging marketing potential on social media TikTok for visiting the tourism industry in Yogyakarta.
This study examines the effect of authenticity, social media, and destination image on revisit intention. This study uses explanatory research with a quantitative approach. The population in this study were domestic tourists or foreign tourists who had visited tourist destinations in Gianyar at least once. The sampling technique used in the study was purposive sampling. The sample in the study was 250 respondents. Data collection was used through surveys, documentation, and Likert scales to test instruments. The data were analyzed using structural equation model partial least square (SEM-PLS) modeling. The results of this study indicate that authenticity and social media can increase revisit intention. In addition, authenticity and social media can also improve destination image. The destination can also increase revisit intention. Overall destination image) has the most significant influence on destination selection. Destination image can mediate between authenticity and social media on revisit intention. The better the authenticity and utilization of social media in Gianyar tourist destinations, the better the destination image will increase revisit intention. The results of this study suggest that the management of Gianyar tourism can maintain the authenticity of Gianyar tourist destinations and increase the use of social media to attract tourists to revisit Gianyar tourism.
The tourism sector is currently developing rapidly, thereby aiding in the significant boost of Indonesia's economy. Tourist villages are among the natural tourism product portfolios provided to tourists, which the government adequately develops to become a new and attractive alternative tourist destination. For the short term, this research examines the loyalty of tourist village destinations as a perspective destination quality, satisfaction, and intentions of practice in Kerinci Regency, Jambi Province. In the long term, it is expected to increase tourist village destinations' loyalty in Jambi Province. This research was carried out in three selected villages, namely Lempur, Pendung Talang Genting, and Jernih Jaya, Kerinci Regency, Jambi Province. The findings and discussion concluded that tourist satisfaction influences tourist destination loyalty and behavioral intention. Meanwhile, the perceptions of the quality of tourism destinations influence tourists' loyalty and urge to return to the village. Tourism satisfaction also affects the loyalty of tourist village destinations. However, its perception in terms of quality can influence tourist loyalty.
The rapid development of immersive technologies, including Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), presents novel opportunities to enhance tourist experiences and promote sustainable village tourism. This study is particularly significant as Generation Z, as digitally savvy travellers, plays a strategic role in shaping sustainable tourism practices, especially within rural destinations. The research aims to examine the influence of Generation Z’s awareness of immersive technologies on their positive attitudes toward village tourism, visit intentions, and perceptions of the role of immersive technologies in supporting sustainable tourism principles. A quantitative approach was employed using Partial Least Squares Structural Equation Modelling (PLS-SEM). Data were collected through structured questionnaires from 150 Generation Z respondents across various regions who have previously interacted with immersive technologies and plan to visit village tourism destinations. The findings indicate that awareness of immersive technologies significantly enhances positive attitudes toward village tourism, which in turn influences visit intentions and perceptions of immersive technologies’ role in fostering community-oriented sustainable tourism. These results underscore that integrating immersive technologies not only enriches tourist experiences but also supports cultural preservation, local economic empowerment, and sustainable tourism practices. Practically, the findings provide guidance for village tourism managers, policymakers, and stakeholders in designing effective digital strategies to develop community-based sustainable tourism initiatives.
The aim of this study is to determine smart governance, smart tourism, smart mobility, and smart sustainability are used by stakeholders in Lake Toba tourism objects to prepare smart tourism destinations (STD) for the effects of covid-19. This research is in line with the strategic plan of the Medan State University research which focuses on the development of tourism areas in North Sumatra. This study uses a Research and Development type with a basic-based research approach through needs analysis, model confirmation, and evaluation. Tourists that visited Lake Toba attractions in April 2022 were given questionnaires, and data were analyzed using pls-sem. The study shows that smart governance influences smart travel, smart tourism, and smart sustainability. On the other hand, smart mobility does not have a positive effect on smart sustainability, smart tourism has a positive and significant effect on smart sustainability. Overall, further research are needed to use other variables.
The rapid development of immersive technologies, including Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), presents novel opportunities to enhance tourist experiences and promote sustainable village tourism. This study is particularly significant as Generation Z, as digitally savvy travellers, plays a strategic role in shaping sustainable tourism practices, especially within rural destinations. The research aims to examine the influence of Generation Z’s awareness of immersive technologies on their positive attitudes toward village tourism, visit intentions, and perceptions of the role of immersive technologies in supporting sustainable tourism principles. A quantitative approach was employed using Partial Least Squares Structural Equation Modelling (PLS-SEM). Data were collected through structured questionnaires from 150 Generation Z respondents across various regions who have previously interacted with immersive technologies and plan to visit village tourism destinations. The findings indicate that awareness of immersive technologies significantly enhances positive attitudes toward village tourism, which in turn influences visit intentions and perceptions of immersive technologies’ role in fostering community-oriented sustainable tourism. These results underscore that integrating immersive technologies not only enriches tourist experiences but also supports cultural preservation, local economic empowerment, and sustainable tourism practices. Practically, the findings provide guidance for village tourism managers, policymakers, and stakeholders in designing effective digital strategies to develop community-based sustainable tourism initiatives.
Batu is the most popular destination in East Java and even in Indonesia. Batu continues to develop its tourism potential by developing tourist villages in the Batu area. Tourism villages have become something new in village development by attracting the flow of tourists to rural areas This study will analyze how the quality of service at Brakseng Tourism in Batu, East Java, Indonesia. In knowing the perceptions of consumers (visitors) of Brakseng tourism, researchers used SERQUAL analysis. Based on the calculation of the customer satisfaction index, the visitors to Brakseng have a high level of satisfaction. Safety is a dimension that needs to get the most attention because it is in quadrant A. Attributes in quadrant A are considered important attributes and are a priority and affect user satisfaction. The management is obliged to allocate its resources to improve the performance of the attributes that fall into this quadrant.
This study examines the effect of authenticity, social media, and destination image on revisit intention. This study uses explanatory research with a quantitative approach. The population in this study were domestic tourists or foreign tourists who had visited tourist destinations in Gianyar at least once. The sampling technique used in the study was purposive sampling. The sample in the study was 250 respondents. Data collection was used through surveys, documentation, and Likert scales to test instruments. The data were analyzed using structural equation model partial least square (SEM-PLS) modeling. The results of this study indicate that authenticity and social media can increase revisit intention. In addition, authenticity and social media can also improve destination image. The destination can also increase revisit intention. Overall destination image) has the most significant influence on destination selection. Destination image can mediate between authenticity and social media on revisit intention. The better the authenticity and utilization of social media in Gianyar tourist destinations, the better the destination image will increase revisit intention. The results of this study suggest that the management of Gianyar tourism can maintain the authenticity of Gianyar tourist destinations and increase the use of social media to attract tourists to revisit Gianyar tourism.
This research explores the effectiveness of destination awareness, destination image, perceived quality, and brand engagement on visit intention among TikTok users toward destinations in Yogyakarta. The sample in this research is TikTok users who have seen tourism video content in Yogyakarta. Data was obtained through an online survey constructed with Google Forms which 225 respondents filled out. The data used in this study were analyzed using the PLS-SEM method by SPSS and SmartPLS. According to the research findings, destination awareness and destination image positively impact the perceived quality of TikTok users. Furthermore, perceived quality positively affects the intention to visit and brand engagement for TikTok users. These findings provide implications and empirical studies related to encouraging marketing potential on social media TikTok for visiting the tourism industry in Yogyakarta.
The aim of this study is to determine smart governance, smart tourism, smart mobility, and smart sustainability are used by stakeholders in Lake Toba tourism objects to prepare smart tourism destinations (STD) for the effects of covid-19. This research is in line with the strategic plan of the Medan State University research which focuses on the development of tourism areas in North Sumatra. This study uses a Research and Development type with a basic-based research approach through needs analysis, model confirmation, and evaluation. Tourists that visited Lake Toba attractions in April 2022 were given questionnaires, and data were analyzed using pls-sem. The study shows that smart governance influences smart travel, smart tourism, and smart sustainability. On the other hand, smart mobility does not have a positive effect on smart sustainability, smart tourism has a positive and significant effect on smart sustainability. Overall, further research are needed to use other variables.
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 |