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: 17 / 17 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 18:22 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.
17 / 17 with data · 0 missing or unmatched. Percentages use all 17 selected publications.
17 / 17 with data · 0 missing or unmatched. Percentages use all 17 selected publications.
17 / 17 with data · 0 missing or unmatched. Percentages use all 17 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 |
|---|---|---|---|---|---|
| 2014 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2015 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2017 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2020 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 5 | 5 | 5/5 | 5/5 |
| 2023 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| 2024 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2026 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2014 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2015 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2017 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2020 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 12 | 5 | 5/5 | 5/5 |
| 2023 | All selected publications | 22 | 4 | 4/4 | 4/4 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2026 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2014 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2015 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 7 | 1 | 1/1 | 1/1 |
| 2017 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2020 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 22 | 5 | 5/5 | 5/5 |
| 2023 | All selected publications | 32 | 4 | 4/4 | 4/4 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 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 17 / 17 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 — 2026-10-22T18:22:15.929785+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 | 17 | 17/17 |
| 2018 | All selected publications | 1 | 17 | 17/17 |
| 2019 | All selected publications | 2 | 17 | 17/17 |
| 2020 | All selected publications | 1 | 17 | 17/17 |
| 2021 | All selected publications | 1 | 17 | 17/17 |
| 2022 | All selected publications | 1 | 17 | 17/17 |
| 2023 | All selected publications | 21 | 17 | 17/17 |
| 2024 | All selected publications | 19 | 17 | 17/17 |
| 2025 | All selected publications | 15 | 17 | 17/17 |
| 2026 | All selected publications | 6 | 17 | 17/17 |
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 |
|---|---|
| 2014 | 1 |
| 2016 | 1 |
| 2019 | 2 |
| 2020 | 1 |
| 2021 | 1 |
| 2022 | 5 |
| 2023 | 4 |
| 2025 | 1 |
| 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 Finance & Banking Studies SSBFNET | 1 | 1 | 1 | 1/1 | 1.0 | 1 | 100.0 | 1/1 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 16 | 40 | 67 | 16/16 | 2.5 | 1.0 | 68.8 | 16/16 · 100.0% |
* Percentage among publications with a known citation count.
The principal aim of this article, among other crucial determinants, is to elucidate that folklore systemically can be utilised for the sustainable development of tourism in South Africa and in the global sphere. The sociological perspective, through argumentation, is deployed to debate and demonstrate how and why folklore should be gauged and appreciated as an instrument of promoting South Africa’s socioeconomic and development plan. Through the discussions and findings, it becomes evident, therein, that folklore has not been accorded the opportunity to partake in tourism in most African countries, and in particular, South Africa. Given that, it becomes part of the article’s sociological debates to conclusively demonstrate, by appointing three folklore types, how folklore can address the problematised phenomenon. The concluding remarks challenge folklore and tourism scholars to collaboratively open and continue the discourse to serve the country's just and legitimate course. The significance of the article rests on the veracity that folklore and tourism have not been scholarly positioned as complementing or corroborating components in South Africa and elsewhere in the global sphere.
In the context of cittaslow (slow city), this research attempts to examine the effects of memorable tourism experience (MTE) on tourist satisfaction and revisiting intention. The research used a structured questionnaire and collected 605 valid responses from tourists that visited Sanyi Cittaslow. Analyses of the measurement model and structural model by AMOS 22.0 were used to find the associations between each MTE dimension and satisfaction, and revisit intention. Test results revealed that all MTE dimensions are predictors of tourist satisfaction, among which knowledge and involvement are the strongest predictors; satisfaction is the strongest predictor for revisiting and is the mediator for the associations of most MTE dimensions and revisiting. Since the cittaslow movement was closely related to sustainable tourism, research finding from this research provide a good reference with evidence for the promotion of cittaslow through MTE creation.
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.
Small, Medium, and Micro Enterprises (SMMEs) are the key players in the township tourism sector, which contributes substantially to the growth of the economy in South Africa. Township tourism SMMEs are critical to uplifting the rural and township economies. However, they remain marginalized and continue to struggle to be sustainable. The following paper explains how much township tourism SMMEs use to stay sustainable. The paper aims to identify SMMEs involved in township tourism. The paper also aims to establish factors influencing the sustainability of township tourism SMMEs. In addition, the paper also aims to determine the competitive strategy for the sustainability of township tourism SMMEs. The study adopted a positivist research paradigm through a quantitative approach and a survey research design. As a result of having no reliable available sampling frame, a non-probability sampling strategy was adopted, and relevant data was generated through questionnaires. The data was gathered from 61 owners or managers of township tourism SMMEs, in uMlazi, Durban. This paper’s findings revealed that the most influential factors affecting the sustainability of township tourism SMMEs were a lack of funding, strong business competition, and poor business location. The study's findings revealed that only a few entrepreneurs applied a competitive strategy to their businesses. From the data analyzed, relevant recommendations and conclusions were also drawn
This study seeks to measure the effectiveness of traditional media in promoting tourism in this digital age in KwaZulu-Natal, South Africa. This study employed a descriptive research design and a questionnaire was used to collect the data. The findings revealed that traditional and online media are more or less equally competitive in promoting tourism. Traditional media is still effective in promoting tourism in this digital age, especially in the domestic market. The study concluded and recommended that tourism marketers and authorities must come to terms with coexistence with traditional media and online media. There is no need to rely on one media to do it all. Where traditional media is lacking, online media must be there to make up for the difference and enhance the tourist experience.
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.
In the wake of the pandemic, countries worldwide have strived to revive their tourism sectors, with Thailand’s industry playing a crucial economic role. Grounded in Protection Motivation Theory (PMT), this study examines key psychological determinants of domestic travel intentions, including threat severity, response efficacy, and psychological resilience. Data from 509 Thai participants were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results reveal that heightened threat perceptions significantly weaken travel intentions, while confidence in protective measures enhances willingness to travel. Psychological resilience mitigates perceived threats and strengthens the effect of response efficacy on travel intention. Surprisingly, anxiety did not significantly impact travel intentions. This study offers a holistic perspective on travelers' cognitive and emotional responses to health crises. The findings provide valuable insights for policymakers and stakeholders to strengthen public resilience, foster trust in safety measures, and support sustainable recovery in the tourism sector.
The tourism industry is a substantial contributor to the South African economy. The main aim of this study is to analyse the tourism industry’s performance during the Covid-19 pandemic, with specific reference to tourism accommodation income, and to shed light on the recovery prospects of the tourism industry and the overall South African economy. The study followed a descriptive research design and quantitative method using secondary data obtained from Statistics South Africa. The data covers a span of over 16 years, from January 2007 to May 2023. Results indicated that the economic impact of the COVID-19 pandemic on the tourism industry was marked by a drastic drop in total income at the height of the pandemic, which coincided with similar performance of the overall economy. The tourism industry as well as the overall economy showed a V-shape recovery with a negative trendline suggesting their recovery potential and the need for sustained efforts by industry players and policymakers for full recovery in the medium term. Among the strategies for full recovery are diverse offerings by tourist accommodation providers, intensified digital marketing campaigns and providing exceptional service experiences.
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 principal aim of this article, among other crucial determinants, is to elucidate that folklore systemically can be utilised for the sustainable development of tourism in South Africa and in the global sphere. The sociological perspective, through argumentation, is deployed to debate and demonstrate how and why folklore should be gauged and appreciated as an instrument of promoting South Africa’s socioeconomic and development plan. Through the discussions and findings, it becomes evident, therein, that folklore has not been accorded the opportunity to partake in tourism in most African countries, and in particular, South Africa. Given that, it becomes part of the article’s sociological debates to conclusively demonstrate, by appointing three folklore types, how folklore can address the problematised phenomenon. The concluding remarks challenge folklore and tourism scholars to collaboratively open and continue the discourse to serve the country's just and legitimate course. The significance of the article rests on the veracity that folklore and tourism have not been scholarly positioned as complementing or corroborating components in South Africa and elsewhere in the global sphere.
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 19:56 UTC · ijfbs | complete · ijfbs · 20 Oct 2026 19:55 UTC |
| openalex-enrichment | 21 Oct 2026 07:23 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 07:22 UTC |