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: 46 / 46 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 — 24 Oct 2026 06:46 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.
46 / 46 with data · 0 missing or unmatched. Percentages use all 46 selected publications.
46 / 46 with data · 0 missing or unmatched. Percentages use all 46 selected publications.
46 / 46 with data · 0 missing or unmatched. Percentages use all 46 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 |
|---|---|---|---|---|---|
| 2013 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2014 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2015 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2017 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2019 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| 2021 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| 2023 | All selected publications | 6 | 6 | 6/6 | 6/6 |
| 2024 | All selected publications | 8 | 8 | 8/8 | 8/8 |
| 2025 | All selected publications | 8 | 8 | 8/8 | 8/8 |
| 2026 | All selected publications | 10 | 10 | 10/10 | 10/10 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2013 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2014 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2015 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2017 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2019 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| 2021 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 12 | 4 | 4/4 | 4/4 |
| 2023 | All selected publications | 11 | 6 | 6/6 | 6/6 |
| 2024 | All selected publications | 11 | 8 | 8/8 | 8/8 |
| 2025 | All selected publications | 4 | 8 | 8/8 | 8/8 |
| 2026 | All selected publications | 0 | 10 | 10/10 | 10/10 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2013 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2014 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2015 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2017 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2019 | All selected publications | 4 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 6 | 4 | 4/4 | 4/4 |
| 2021 | All selected publications | 8 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 15 | 4 | 4/4 | 4/4 |
| 2023 | All selected publications | 14 | 6 | 6/6 | 6/6 |
| 2024 | All selected publications | 14 | 8 | 8/8 | 8/8 |
| 2025 | All selected publications | 4 | 8 | 8/8 | 8/8 |
| 2026 | All selected publications | 0 | 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 46 / 46 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): 2026-10-21T07:22:43.970618+00:00 — 2026-10-24T06:46:43.133192+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 | 46 | 46/46 |
| 2018 | All selected publications | 0 | 46 | 46/46 |
| 2019 | All selected publications | 1 | 46 | 46/46 |
| 2020 | All selected publications | 0 | 46 | 46/46 |
| 2021 | All selected publications | 1 | 46 | 46/46 |
| 2022 | All selected publications | 7 | 46 | 46/46 |
| 2023 | All selected publications | 7 | 46 | 46/46 |
| 2024 | All selected publications | 13 | 46 | 46/46 |
| 2025 | All selected publications | 21 | 46 | 46/46 |
| 2026 | All selected publications | 16 | 46 | 46/46 |
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 |
|---|---|
| 2013 | 1 |
| 2018 | 1 |
| 2019 | 1 |
| 2020 | 4 |
| 2021 | 3 |
| 2022 | 4 |
| 2023 | 6 |
| 2024 | 8 |
| 2025 | 8 |
| 2026 | 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 Research in Business and Social Science SSBFNET | 46 | 48 | 66 | 46/46 | 1.04 | 0.0 | 39.1 | 46/46 · 100.0% |
* Percentage among publications with a known citation count.
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.
Partial Least Squares Structural Equation Modeling has gained considerable attention across diverse academic fields, including business, social sciences, marketing, and management. A key challenge in utilizing PLS-SEM is balancing explanatory and predictive power when selecting the most suitable model from competing alternatives. This paper explores the effectiveness of various quality criteria for evaluating causal-predictive models, with a focus on resistance to change in Vietnamese SMEs. The study emphasizes the importance of both in-sample and out-of-sample predictions, using metrics such as R², BIC, AIC, Q², RMSE, MAE, and CVPAT. The findings reveal that traditional criteria like R² may not be sufficient for identifying the best model, while PLSpredict, CVPAT, BIC, and AIC offer superior performance in determining the optimal balance between explanatory and predictive capabilities. These insights provide practical implications for researchers and practitioners, highlighting the need to tailor model selection to specific objectives, such as theory development or real-world forecasting. For practitioners, the study underscores the benefits of leveraging simpler, more generalizable models for robust decision-making in dynamic or resource-constrained environments.
The Cultural and Creative Industries (CCI) thrive on innovation and creativity, necessitating robust cross-domain collaboration among diverse stakeholders. However, the current state of CCI co-creation mechanisms is marked by inadequacies. The primary objective is to provide practical and effective strategic guidance for co-creation endeavors within the dynamic CCI landscape. This study employs the Root Cause Analysis (RCA) method to identify and analyze critical elements for industrial co-creation, offering a comprehensive framework for understanding the intricacies of co-creation processes. The Cultural and Creative Industries Digital Business Ecosystem (CCI-DBE) model is then developed to encompass four foundational pillars: participation of multiple stakeholders, mechanism of co-creation, uniqueness of consumer experience, and output of co-created value. Participation of multiple stakeholders recognizes the importance of integrating perspectives from content creators, other companies, suppliers, and customers. The mechanism of co-creation delves into the necessity of a robust platform, supported by technological infrastructure, to facilitate seamless resource and knowledge exchange. The uniqueness of consumer experience emphasizes the need for tailoring offerings to meet consumers' unique experiential and meaningful needs. Output of co-created value focuses on enhancing product novelty, resource sharing, communication efficiency, information interaction, and cross-domain integration. This CCI-DBE model not only contributes to academic discourse but also provides actionable insights for practitioners seeking to navigate and enhance the co-creation process within the evolving CCI landscape. It stands as a valuable guide for fostering innovation, collaboration, and strategic alignment in the CCI sector.
In this study, we developed a tribal learning model based on indigenous knowledge to expand Taiwan's indigenous higher education system. The model adopts a culturally responsive teaching strategy that conforms to the cultural background and satisfies the learning needs of indigenous learners. The instructional design of the model is based on the outcomes of a literature review and comprises five stages: preparation, communication, execution, reflection, and feedback. The proposed model allows educators to develop culturally appropriate teaching plans that include indigenous knowledge. Applying the model allows educators to hone their self-study skills while improving the learning effectiveness of their learners.
Against the backdrop of rapid advancements in generative artificial intelligence (GenAI), ChatGPT has emerged as a representative application, drawing significant research interest in user behavior patterns. This study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) Model to investigate the key factors influencing the adoption of ChatGPT among users in Taiwan. Using a cross-sectional survey approach, we recruited a sample of 454 Taiwanese ChatGPT users through social media and online forums. The study analyzed the impact of four key constructs—performance expectancy, effort expectancy, social influence, and facilitating conditions—on behavioral intention and actual usage behavior. The results indicated that performance expectancy and effort expectancy play pivotal roles in shaping behavioral intention, while facilitating conditions significantly affect actual usage behavior. Furthermore, cultural background exerts a critical moderating effect on the influence of social factors. These findings highlight the unique characteristics of user behavior in Taiwan, provide practical insights for optimizing the design and promotion of GenAI tools, and offer valuable implications for technology adoption policies.
With the increasing awareness of health and the widespread adoption of social media advertising, health food advertising has become an important source of information influencing consumer decision-making. However, existing studies have primarily focused on the effects of attitudes or behaviors, with limited attention to the underlying psychological mechanisms. Therefore, this study applies optimal arousal theory to examine how expertise and trustworthiness in health food advertising influence consumers’ purchase intention through perceived information relevance. The findings indicate that both expertise and trustworthiness positively affect perceived information relevance, which in turn significantly enhances purchase intention. These results suggest that individuals tend to pursue an optimal level of stimulation, and when advertising information evokes an appropriate level of psychological arousal and aligns with personal needs, it is more likely to facilitate positive behavioral responses. In addition to confirming the importance of perceived information relevance, this study extends optimal arousal theory from the level of sensory stimulation to the cognitive level, thereby advancing the development of optimal arousal theory research.
This study aims to examine the effects of utilitarian and hedonic browsing on impulse buying tendency and the urge to buy impulsively in online shopping contexts. It further evaluates the mediating role of impulse buying tendency in this relationship. Drawing on Uses and Gratifications (U&G) theory and the Stimulus-Organism-Response (S-O-R) framework, a conceptual model is developed. A questionnaire survey was conducted, and 393 valid responses from online consumers were collected. Structural Equation Modeling (SEM) was employed to analyze the data and test the proposed hypotheses. The findings demonstrate that (i) hedonic browsing has a significant positive effect on impulse buying tendency, which subsequently increases the urge to buy impulsively; (ii) utilitarian browsing has a significant negative effect on impulse buying tendency and does not directly influence impulse buying urges; and (iii) impulse buying tendency plays a critical mediating role in transforming browsing motivations into immediate purchase impulses. These results suggest that impulse buying is not purely an immediate reaction but is shaped through underlying psychological tendencies. This study contributes to consumer behavior literature by extending the application of U&G theory in digital commerce contexts and provides practical implications for online platform design and marketing strategies.
This paper aims to examine the effects of leisure crafting on leisure satisfaction from a self-determination theory perspective. In this paper, it is also aimed to evaluate the mediating role of leisure benefits and the moderating role of work–leisure conflict within an integrated theoretical framework. We employed a quantitative analytical approach using structural equation modeling to test the proposed hypotheses and assess both direct and indirect relationships among the variables. A dataset of 325 hospitality industry newcomers in northern Taiwan was collected through a structured questionnaire and analyzed to examine the relationships among leisure crafting, leisure benefits, leisure satisfaction, and work–leisure conflict. Main findings of the study demonstrate that (i) leisure crafting has a significant positive effect on leisure benefits, indicating that proactive adjustment of leisure activities enhances psychological and social gains; (ii) leisure benefits significantly mediate the relationship between leisure crafting and leisure satisfaction, suggesting that psychological need satisfaction functions as a key explanatory mechanism; and finally (iii) work–leisure conflict negatively moderates the relationship between leisure crafting and leisure benefits, such that higher levels of conflict weaken the positive influence of leisure crafting on perceived benefits. Overall, the results extend the application of self-determination theory to the leisure domain and provide empirical evidence for the motivational mechanisms underlying leisure satisfaction in high work-demand contexts.
The widespread adoption of streaming services and social media has enhanced online spectators’ interactivity and immersion, yet it may also weaken fans’ willingness to attend games in person. Drawing on event system theory, this study examines how social media marketing activities and team identification influence intention to attend professional basketball games, with social presence serving as a mediating mechanism. Survey data were collected from 564 fans who had never attended a professional basketball game, and the proposed model was tested using structural equation modeling. The results show that social media marketing activities influence attendance intention only indirectly through social presence. In contrast, team identification not only directly predicts attendance intention but also exerts an indirect effect via social presence. Overall, the findings underscore social presence as a pivotal mechanism that converts online stimuli into intentions to attend live events and provide practical guidance for teams’ social community management and fan relationship strategies.
Green bonds are typically used for environmental protection projects. This paper explores the differences between general bonds and green bonds in Taiwan, as well as the impact of inflation. The findings are as follows: First, because green bonds support environmentally sustainable development projects, which require a longer time to realize their environmental benefits, they require longer repayment periods. Second, most green bonds have lower coupon rates than general bonds, indicating that investors are willing to accept lower interest income for the sake of environmental sustainability. Third, because green bonds invest in environmental protection projects with lower returns, investors also demand higher risk premiums due to relatively higher recovery risks. Finally, after inflation occurs, the increase in green bond coupon rates is less than that of general bonds. Inflation may have different effects on the dividend yields of individual companies, but there is no significant difference for the overall industry.
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 |