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 / 11 publications (90.9%). 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 02:42 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.
11 / 11 with data · 0 missing or unmatched. Percentages use all 11 selected publications.
11 / 11 with data · 0 missing or unmatched. Percentages use all 11 selected publications.
11 / 11 with data · 0 missing or unmatched. Percentages use all 11 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 |
|---|---|---|---|---|---|
| 2025 | All selected publications | 11 | 11 | 10/11 | 11/11 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2025 | All selected publications | 9 | 11 | 10/11 | 11/11 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2025 | All selected publications | 9 | 11 | 10/11 | 11/11 |
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 11 / 11 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): 2026-10-21T07:22:59.979221+00:00 — 2026-10-22T02:42:09.962135+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 | 11 | 11/11 |
| 2018 | All selected publications | 0 | 11 | 11/11 |
| 2019 | All selected publications | 0 | 11 | 11/11 |
| 2020 | All selected publications | 0 | 11 | 11/11 |
| 2021 | All selected publications | 0 | 11 | 11/11 |
| 2022 | All selected publications | 0 | 11 | 11/11 |
| 2023 | All selected publications | 0 | 11 | 11/11 |
| 2024 | All selected publications | 0 | 11 | 11/11 |
| 2025 | All selected publications | 1 | 11 | 11/11 |
| 2026 | All selected publications | 8 | 11 | 11/11 |
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 |
|---|---|
| 2025 | 11 |
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 | 3 | 3 | 3 | 3/3 | 1.0 | 1 | 66.7 | 3/3 · 100.0% |
| International Journal of Finance & Banking Studies SSBFNET | 1 | — | 0 | 1/1 | — | — | — | 0/1 · 0.0% |
| International Journal of Research in Business and Social Science SSBFNET | 7 | 6 | 6 | 7/7 | 0.86 | 1 | 57.1 | 7/7 · 100.0% |
* Percentage among publications with a known citation count.
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.
Industry 4.0 technologies have optimized efficiency and effectiveness through the manufacture of sophisticated products and services, which result in enhancement of operations, improvement in safety, time, and cost-saving, and have revolutionized operations in almost all industries and have tremendously transformed the aviation sector. This study aims to examine the passenger travel experience with the implementation of e-services before, during, and after trips, from ticket bookings, self-check-in, smart boarding, in-flight services to baggage tracking after flight. Furthermore, the study seeks to identify the modern technologies with which air passengers are familiar or actively use, and to analyze the factors that influence their acceptance of these e-services. A research model was developed based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) and modified to suit the objectives of this study. The study objectives were achieved through a survey, a self-administered online questionnaire from 253 air passengers was obtained, and data was analyzed using SPSS 25 software. The results reveal that performance expectancy, price-value, hedonic motivations, perceived benefits and habit have a positive significant influence on behavioral intention, while social influence, perceived challenges, facilitating conditions and effort expectancy were not supported. The results of the study give insights into how different passengers, different age groups, different geographical locations, and different technological maturity perceive such implementations. This will provide policymakers ideas on how to better manage and enhance e-services.
This exploratory analysis investigated the use of cloud accounting within the SME sector in Zimbabwe. This study addresses a significant gap in the existing literature, as no prior research has specifically explored the utilization of cloud accounting within the SME sector in Zimbabwe. The study employed a qualitative research approach using purposive sampling. Data was collected from semi-structured interviews and was thematically analyzed. The study revealed a low utilization of cloud accounting among SMEs in Zimbabwe. The lack of awareness is particularly pronounced, hindering SMEs from recognizing the benefits and functionalities of cloud accounting systems. The study found that SMEs implementing cloud accounting experience many benefits that significantly enhance their financial management processes, operational efficiency, and overall business performance. The study emphasized the need for targeted awareness campaigns, comprehensive training programs, and robust policy support to bridge the knowledge gap and facilitate the widespread adoption of cloud accounting. The findings offer valuable insights for policymakers, educators, and technology vendors aiming to promote the digital transformation of the SME sector in Zimbabwe. Value This study provides valuable insights into the barriers and opportunities for adopting cloud accounting in Zimbabwe's SME sector, highlighting the need for awareness, training, and policy support to drive digital transformation and improve financial management, operational efficiency, and overall business performance.
This study examines the impact of customer, supplier, and competitor pressures on the adoption of mobile commerce (m-commerce) by small and medium-sized enterprises (SMEs) in Polokwane, South Africa. The research follows a quantitative approach, with data collected from 146 randomly selected SMEs out of a total population of 261. A structured questionnaire was used to measure the pressures from customers, suppliers, and competitors, all assessed using a five-point Likert scale. Reliability of the constructs was confirmed with Cronbach's alpha coefficients exceeding 0.7, and the normality of the data was verified through the Kolmogorov-Smirnov test. To analyse the data, ANOVA and regression analysis were applied. The findings reveal that customer, supplier, and competitor pressures significantly influence SMEs’ decisions to adopt m-commerce solutions. These results contribute to the understanding of external factors that shape technology adoption in SMEs, offering both theoretical insights and practical implications. SMEs that respond to these pressures may improve their competitive position by integrating mobile technologies into their operations, leading to better market responsiveness and enhanced business processes. This research provides a foundation for future studies on mobile commerce adoption, focusing on underexplored external influences.
This paper evaluates how the perceptions of consumers from different demographic backgrounds influence their adoption and use of digital personal banking, and suggests marketing activities required to increase digital banking by these different demographic target groups. The method involved an online survey of a sample of 400 respondents with bank accounts in the iLembe district of KwaZulu-Natal, South Africa, focusing on the demographic dimensions of age, gender, education and income. The study found that high digital banking users were older (24+), with higher education and a higher income, while low digital banking users were younger (18-23), with a lower level of education and a lower income. No differences according to gender were found. The convenience of digital banking and a good online banking experience were the factors that most encouraged digital banking use by high users, while perceptions of high cost and having low control over their online banking activities were factors that discouraged low users from more digital banking use. These findings confirmed the previous research about the influence of age and education on digital banking use, but contributed new knowledge about the influence of income, and the lack of influence of gender, on digital banking adoption and use. Comprehensive recommendations for banks, policy makers and other stakeholders are provided.
This study investigates wearable activity tracker (WAT) adoption intention among South African Generation Y university students by extending the Theory of Reasoned Action (TRA) to include brand name as a predictor. A cross-sectional survey of 462 Generation Y students from three South African institutions was analyzed using exploratory and confirmatory factor analyses and structural equation modeling to test relationships between attitude, subjective norm, brand name, and adoption intention. Results indicate that attitude, subjective norm, and brand name significantly influence WAT adoption intention, with attitude as the strongest predictor, followed by brand name. The extended TRA model demonstrated acceptable fit, reliability, and validity, highlighting brand reputation's critical role in reducing perceived risk in emerging markets. The study is limited by its non-probability sample from three campuses in one province, suggesting future research should employ longitudinal designs, expand geographic scope, and incorporate additional constructs. Findings provide practical guidance for WAT manufacturers to prioritize building trustworthy brands and emphasizing health benefits, while marketing strategies leveraging peer influence and affordable pricing can enhance adoption among cost-conscious students. Promoting WAT adoption among youth can advance health awareness and public health goals in developing countries where proactive health monitoring technologies remain underutilized. This study offers one of the first empirical examinations of WAT adoption intention among South African Generation Y students using an extended TRA model. Unlike prior research focused on medical settings or limited samples, this study incorporates brand name as a novel construct, demonstrating its pivotal role in wearable tech adoption. This research contributes original empirical evidence from a developing market context, advancing theoretical and practical understanding of wearable technology adoption.
This research explores the technological factors that influence the adoption of mobile commerce by small and medium enterprises (SMEs) in the Polokwane Municipality. Using a quantitative approach, data were gathered from 146 SME owners, randomly selected from a total population of 261. The study applied descriptive statistics, analysis of variance (ANOVA), and regression techniques to examine the relationships between variables. ANOVA was used to evaluate the sub-hypotheses, while regression analysis tested the primary hypotheses. The instruments used in the study demonstrated strong internal consistency, with all Cronbach’s alpha values exceeding 0.7. Additionally, the Kolmogorov-Smirnov test confirmed that the data met the assumption of normality. The results showed that perceived cost, perceived benefit, and perceived compatibility each play a significant role in influencing m-commerce adoption. These findings offer valuable insights into the internal technological considerations that impact digital uptake in SMEs. The study provides both academic value by contributing to the theoretical understanding of technology adoption, and practical guidance for enhancing m-commerce use within the SME sector, particularly in similar socio-economic environments.
The research investigates how goal contagion impacts the acceptance of digital technologies and also explores the moderating effect of COVID-19 and survival instinct within the interconnection between goal contagion and the acceptance of digital technologies. The research has undertaken partial least squares structural equation modeling to identify the direct as well as moderating effects based on a sample of 250 individuals across diverse firms in Bangladesh. The research also involved the Importance-Performance Map Analysis approach (IPMA) to explore key areas of importance and performance, thus providing a holistic understanding of key factors impacting the acceptance of digital technologies. Then, an ANN model analysis was used to determine how important the components determined by the PLS-SEM study were in terms of relative influence. The study revealed that others’ behavior and situational context have a significant impact on goal contagion. The outcome also showed that goal contagion has a significant impact on the acceptance of digital technologies; besides, survival instinct and COVID-19 have a statistically significant moderating effect between goal contagion and the acceptance of digital technologies. The IPMA findings showed that COVID-19 and goal contagion are the most important factors for the acceptance of digital technologies. The ANN findings revealed that situational context is the most contributing construct for goal contagion. The findings also demonstrated that COVID-19 and goal contagion are the most influential constructs for the acceptance of digital technologies. The findings of this research contribute significantly to the relevant knowledge by promoting a theoretical framework for comprehending the factors that influence the acceptance of digital technologies. Furthermore, this research illustrates valuable insights for companies that are planning to make strategic decisions regarding their digital technologies.
This exploratory analysis investigated the use of cloud accounting within the SME sector in Zimbabwe. This study addresses a significant gap in the existing literature, as no prior research has specifically explored the utilization of cloud accounting within the SME sector in Zimbabwe. The study employed a qualitative research approach using purposive sampling. Data was collected from semi-structured interviews and was thematically analyzed. The study revealed a low utilization of cloud accounting among SMEs in Zimbabwe. The lack of awareness is particularly pronounced, hindering SMEs from recognizing the benefits and functionalities of cloud accounting systems. The study found that SMEs implementing cloud accounting experience many benefits that significantly enhance their financial management processes, operational efficiency, and overall business performance. The study emphasized the need for targeted awareness campaigns, comprehensive training programs, and robust policy support to bridge the knowledge gap and facilitate the widespread adoption of cloud accounting. The findings offer valuable insights for policymakers, educators, and technology vendors aiming to promote the digital transformation of the SME sector in Zimbabwe. Value This study provides valuable insights into the barriers and opportunities for adopting cloud accounting in Zimbabwe's SME sector, highlighting the need for awareness, training, and policy support to drive digital transformation and improve financial management, operational efficiency, and overall business performance.
The retail industry has seen a significant transformation since augmented reality (AR) technology was introduced into online shopping. However, several barriers continue to hinder the global adoption of online AR shopping, particularly for high-involvement products. This study investigates these barriers in the South African context, adopting a quantitative research approach. Data were collected through online self-administered questionnaires from a sample of 664 South African consumers. Multiple regression analysis was conducted to achieve the research objectives. The findings revealed that image barrier strongly inhibits AR adoption. Although insecurity and tradition barrier were not statistically significant, their negative effect suggests they may act as barriers to adoption. Surprisingly, discomfort and risk barrier yielded positive but non-significant results, leading to the rejection of these hypotheses. This research provides insights that are vital for retailers, technology developers, and policymakers striving to leverage AR technology effectively in online retail. The study offers practical strategies to address these barriers and facilitate wider adoption of AR technology. Additionally, it contributes to the broader discourse on the intersection of technology adoption, consumer behavior, and retail marketing strategies, particularly within the socio-economic and cultural complexities of South Africa.
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 |