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: 18 / 18 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 — 23 Oct 2026 11:34 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.
18 / 18 with data · 0 missing or unmatched. Percentages use all 18 selected publications.
18 / 18 with data · 0 missing or unmatched. Percentages use all 18 selected publications.
18 / 18 with data · 0 missing or unmatched. Percentages use all 18 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 |
|---|---|---|---|---|---|
| 2018 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2019 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2020 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2021 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2023 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2024 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2025 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2018 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2019 | All selected publications | 3 | 2 | 2/2 | 2/2 |
| 2020 | All selected publications | 4 | 3 | 3/3 | 3/3 |
| 2021 | All selected publications | 4 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 5 | 2 | 2/2 | 2/2 |
| 2023 | All selected publications | 5 | 2 | 2/2 | 2/2 |
| 2024 | All selected publications | 6 | 3 | 3/3 | 3/3 |
| 2025 | All selected publications | 4 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2018 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2019 | All selected publications | 5 | 2 | 2/2 | 2/2 |
| 2020 | All selected publications | 6 | 3 | 3/3 | 3/3 |
| 2021 | All selected publications | 8 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 5 | 2 | 2/2 | 2/2 |
| 2023 | All selected publications | 5 | 2 | 2/2 | 2/2 |
| 2024 | All selected publications | 6 | 3 | 3/3 | 3/3 |
| 2025 | All selected publications | 4 | 2 | 2/2 | 2/2 |
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 18 / 18 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): 2026-10-21T07:22:50.520943+00:00 — 2026-10-23T11:34:21.077063+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 | 18 | 18/18 |
| 2018 | All selected publications | 0 | 18 | 18/18 |
| 2019 | All selected publications | 1 | 18 | 18/18 |
| 2020 | All selected publications | 0 | 18 | 18/18 |
| 2021 | All selected publications | 1 | 18 | 18/18 |
| 2022 | All selected publications | 6 | 18 | 18/18 |
| 2023 | All selected publications | 3 | 18 | 18/18 |
| 2024 | All selected publications | 11 | 18 | 18/18 |
| 2025 | All selected publications | 7 | 18 | 18/18 |
| 2026 | All selected publications | 11 | 18 | 18/18 |
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 |
|---|---|
| 2018 | 1 |
| 2019 | 2 |
| 2020 | 3 |
| 2021 | 3 |
| 2022 | 2 |
| 2023 | 2 |
| 2024 | 3 |
| 2025 | 2 |
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 | 1 | 1 | 1/1 | 1.0 | 1 | 100.0 | 1/1 · 100.0% |
| International Journal of Finance & Banking Studies SSBFNET | 2 | 2 | 3 | 2/2 | 1.0 | 1.0 | 100.0 | 2/2 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 15 | 29 | 36 | 15/15 | 1.93 | 2 | 100.0 | 15/15 · 100.0% |
* Percentage among publications with a known citation count.
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.
Hand-shaken beverages represent a unique business model in Taiwan. This study explores the impact of four dimensions of brand equity on consumers' word-of-mouth and purchase intention regarding hand-shaken beverages. A survey was conducted among hand-shaken beverage consumers. A total of 352 valid questionnaires were recovered, and the data were analyzed using structural equation modeling. The results showed that consumers' perceptions of brand awareness, quality, and brand association affected word-of-mouth. These dimensions had an indirect impact on purchase intention through word-of-mouth, suggesting that when consumers develop an attachment to a brand, they engage in positive behaviors concerning the brand. However, due to the high homogeneity and lack of distinctiveness of Taiwanese hand-shaken beverage products, they fail to attract consumer loyalty.
This article investigates the impact of social influence on online food delivery (OFD) purchase behavior in Vietnam, specifically examining the moderating role of trust and physical obstacles during the COVID-19 pandemic. A total of 378 respondents participated in the research, and the findings highlight the significance of social networks and interpersonal relationships in the OFD market. Regarding the role of trust as a moderator, the findings highlighting the importance of building trustworthiness and credibility in the OFD industry. The findings suggest that physical obstacles, such as the closure of non-essential stores and social distancing measures, weaken the influence of social factors on consumers' behavior. These changes in individuals' lifestyles have introduced physical barriers to traditional dining experiences, consequently increasing reliance on online food delivery (OFD) services. Overall, this study contributes to the existing literature on consumer behavior and provides insights for industry practitioners and policymakers to develop effective marketing and communication strategies and support mechanisms to facilitate the growth and sustainability of the online food delivery industry in Vietnam.
Impulse purchases play a vital role in the sales revenue of shopping malls. This study utilized the Stimulus-Organism-Response framework to explore how shopping mall atmosphere stimulates consumer emotions, thereby impacting impulse purchase behavior. Structural equation modeling was employed for data analysis. The findings revealed that both positive and negative emotions can act as mediators in the process of forming impulse purchase behavior, albeit with distinct and sometimes antagonistic effects. This phenomenon is attributed to the differential impact of the service atmosphere on stimulating consumer emotions. Additionally, the study identified that only positive emotions can serve as mediators between spatial atmosphere and impulse purchases. In essence, stimuli in the shopping environment can influence consumer emotional responses, thereby prompting impulse purchase behavior.
This study probes how mobile health monitoring contributes to health behavior change, with a particular focus on the roles of self-quantification and health consciousness. The findings indicate that mobile health monitoring effectively facilitates positive changes in health behaviors. Moreover, there is a significant interplay between self-quantification, mobile health monitoring, and behavior change—individuals are more likely to adopt healthier lifestyles when they can independently monitor and interpret their health data. Health consciousness further reinforces this relationship, as individuals with higher health awareness are more likely to transform data-driven insights into actionable and sustained health behaviors.
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.
This study examines the relationship between technical readiness (TR) and the Unified Theory of Acceptance and Use of Technology (UTAUT) within the framework of Over-the-Top (OTT) media services, particularly Netflix. The study employs the UTAUT model and integrates the TR model's external factors of "optimism," "innovativeness," "inadaptability," and "insecurity" to examine their moderating influences on customer utilisation of Netflix. In 2024, 475 questionnaires were disseminated in this study, of which 453 were considered valid. The study revealed that customer trust in relationships significantly influences user involvement in 'Performance Expectancy' (PE), 'Effort Expectancy' (EE), 'Social Influence' (SI), and 'Facilitating Conditions' (FC). Furthermore, consumers' intention to interact with Netflix's 'Performance Expectancy' (PE), 'Effort Expectancy' (EE), and 'Social Influence' (SI) considerably and positively affects their behaviour. The research employed hierarchical regression analysis to assess the impact of customer willingness to "Intention to Use" on the UTAUT regarding Netflix consumption. The findings indicate that the Trustworthiness Rating of consumers substantially affects the UTAUT dimensions of Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions. Moreover, consumers' inclination to use Netflix is positively influenced by the constructs of Performance Expectancy, Effort Expectancy, and Social Influence. The results demonstrate that consumers' Trustworthiness Rating substantially influences the UTAUT aspects of Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions. Moreover, the constructs of performance expectancy, effort expectancy, and social influence positively affect customers' inclination to utilise Netflix. The results of this study may inform strategic approaches to improve user engagement and satisfaction in the competitive OTT market.
Hand-shaken beverages represent a unique business model in Taiwan. This study explores the impact of four dimensions of brand equity on consumers' word-of-mouth and purchase intention regarding hand-shaken beverages. A survey was conducted among hand-shaken beverage consumers. A total of 352 valid questionnaires were recovered, and the data were analyzed using structural equation modeling. The results showed that consumers' perceptions of brand awareness, quality, and brand association affected word-of-mouth. These dimensions had an indirect impact on purchase intention through word-of-mouth, suggesting that when consumers develop an attachment to a brand, they engage in positive behaviors concerning the brand. However, due to the high homogeneity and lack of distinctiveness of Taiwanese hand-shaken beverage products, they fail to attract consumer loyalty.
Impulse purchases play a vital role in the sales revenue of shopping malls. This study utilized the Stimulus-Organism-Response framework to explore how shopping mall atmosphere stimulates consumer emotions, thereby impacting impulse purchase behavior. Structural equation modeling was employed for data analysis. The findings revealed that both positive and negative emotions can act as mediators in the process of forming impulse purchase behavior, albeit with distinct and sometimes antagonistic effects. This phenomenon is attributed to the differential impact of the service atmosphere on stimulating consumer emotions. Additionally, the study identified that only positive emotions can serve as mediators between spatial atmosphere and impulse purchases. In essence, stimuli in the shopping environment can influence consumer emotional responses, thereby prompting impulse purchase behavior.
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 |