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: 4 / 5 publications (80.0%). 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 07:23 UTC — 22 Oct 2026 12:25 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.
5 / 5 with data · 0 missing or unmatched. Percentages use all 5 selected publications.
5 / 5 with data · 0 missing or unmatched. Percentages use all 5 selected publications.
5 / 5 with data · 0 missing or unmatched. Percentages use all 5 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 |
|---|---|---|---|---|---|
| 2020 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2025 | All selected publications | 1 | 1 | 0/1 | 1/1 |
| 2026 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2020 | All selected publications | 7 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 4 | 2 | 2/2 | 2/2 |
| 2025 | All selected publications | — | 1 | 0/1 | 1/1 |
| 2026 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2020 | All selected publications | 8 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 4 | 2 | 2/2 | 2/2 |
| 2025 | All selected publications | 0 | 1 | 0/1 | 1/1 |
| 2026 | All selected publications | 0 | 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 5 / 5 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): 2026-10-21T07:23:05.949774+00:00 — 2026-10-22T12:25:40.323648+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 | 5 | 5/5 |
| 2018 | All selected publications | 0 | 5 | 5/5 |
| 2019 | All selected publications | 0 | 5 | 5/5 |
| 2020 | All selected publications | 0 | 5 | 5/5 |
| 2021 | All selected publications | 2 | 5 | 5/5 |
| 2022 | All selected publications | 0 | 5 | 5/5 |
| 2023 | All selected publications | 1 | 5 | 5/5 |
| 2024 | All selected publications | 3 | 5 | 5/5 |
| 2025 | All selected publications | 3 | 5 | 5/5 |
| 2026 | All selected publications | 3 | 5 | 5/5 |
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 |
|---|---|
| 2020 | 1 |
| 2024 | 2 |
| 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 | — | 0 | 1/1 | — | — | — | 0/1 · 0.0% |
| International Journal of Research in Business and Social Science SSBFNET | 4 | 11 | 12 | 4/4 | 2.75 | 2.0 | 50.0 | 4/4 · 100.0% |
* Percentage among publications with a known citation count.
Among the various online services provided by the government, e-tax system is one of the commonly used e-government platforms, which offers significant benefits to government and citizens by reducing costs, eliminate human errors, and reduce the turnaround time for processing income tax returns. Despite these plausible benefits, some reservations have been articulated regarding the continuance usage intention of e-tax system. Thus, we investigate the most critical factors of e-tax continuance usage in this paper. We developed an integrated theoretical model based on the technology continuance theory (TCT) extended with social influence and perceived risk. The proposed conceptual model was empirically validated with data collected from a sample of 345 e-tax service users in Bangladesh. Partial least squares structural equation modeling (PLS-SEM) was employed to analyze the collected data. The study findings suggest that user satisfaction, perceived usefulness, and attitude have a direct impact on continuance usage intention where confirmation, ease of use and social influence have indirect influence on continuance usage intention of e-tax system. This paper discusses some contributions to theory and practice based on our findings
The novelty and advancement of technology have explored new avenues in the education sectors. Now e-learning has blended with face-to-face learning to increase its effectiveness. An integrated model is being proposed in this study to measure the continuance usage intention of students adopting e-learning services. With the constructs of ECM (confirmation, perceived usefulness), perceived ease of use and perceived self-efficacy proposed to measure their effect on satisfaction. Besides, to explore the impact of quality features on satisfaction and continuance intention, three major determinants of D&M’s ISS model such as system quality, service quality and information quality included in this study. 410 responses collected to do quantitative analysis. The PLS-SEM analysis showed that perceived ease of use has the strongest effect on perceived usefulness, followed by perceived self-efficacy on perceived ease of use and satisfaction with continuance intention.The study reveals that challenges in developing countries are unique and drives the learners differently than developed countries. The growing e-learning industry requires widespread research from national and institutional perspectives for holistic development in the Bangladesh education market.
This qualitative study explores the complex factors that affect the acceptance of digital technologies (ADT) in the setting of a least developed country (LDC). In-depth interviews using purposive and convenience sampling techniques were conducted with business professionals (n=40) in Bangladesh, acknowledging the distinct challenges and opportunities that these settings present. Qualitative research offers the chance to delve into professional experiences and obtain comprehensive insights into how they view digital technologies in the real world. In order to provide a theoretical framework for future research, this study employed grounded theory and thematic analysis to explain the factors of the acceptance of digital technologies (ADT). This study proposed five factors to re-conceptualize ADT based on three theories. The study offered a novel insight into factors for ADT from the viewpoints of varied professions, which provided a framework that may serve as the basis for explaining ADT.
This study aims to examine online shopping behaviour on social media platforms. This study formulates a research model integrating trust with the flow theory and some basic constructs of the UTAUT. To analyze the data from an online survey involving 305 participants actively making online purchases through social media platforms. This study applied the Structural Equation Modeling-Artificial Neural Networks (SEM-ANN) technique. Incorporating statistically significant SEM findings, the ANN model was used to analyze linear and nonlinear interactions among proposed variables. The research findings demonstrate that flow emerges as the most significant determinant, succeeded by effort expectancy, social influence, and performance expectancy, in defining the concept of trust. However, the sensitivity analysis using ANN indicates that effort expectancy is the most important factor in establishing trust, followed by flow, social influence, and performance expectancy. The predictive power of intention to use is noteworthy in determining actual use behaviour, with trust and flow emerging as influential factors favourably impacting this intention.
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 qualitative study explores the complex factors that affect the acceptance of digital technologies (ADT) in the setting of a least developed country (LDC). In-depth interviews using purposive and convenience sampling techniques were conducted with business professionals (n=40) in Bangladesh, acknowledging the distinct challenges and opportunities that these settings present. Qualitative research offers the chance to delve into professional experiences and obtain comprehensive insights into how they view digital technologies in the real world. In order to provide a theoretical framework for future research, this study employed grounded theory and thematic analysis to explain the factors of the acceptance of digital technologies (ADT). This study proposed five factors to re-conceptualize ADT based on three theories. The study offered a novel insight into factors for ADT from the viewpoints of varied professions, which provided a framework that may serve as the basis for explaining ADT.
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 study aims to examine online shopping behaviour on social media platforms. This study formulates a research model integrating trust with the flow theory and some basic constructs of the UTAUT. To analyze the data from an online survey involving 305 participants actively making online purchases through social media platforms. This study applied the Structural Equation Modeling-Artificial Neural Networks (SEM-ANN) technique. Incorporating statistically significant SEM findings, the ANN model was used to analyze linear and nonlinear interactions among proposed variables. The research findings demonstrate that flow emerges as the most significant determinant, succeeded by effort expectancy, social influence, and performance expectancy, in defining the concept of trust. However, the sensitivity analysis using ANN indicates that effort expectancy is the most important factor in establishing trust, followed by flow, social influence, and performance expectancy. The predictive power of intention to use is noteworthy in determining actual use behaviour, with trust and flow emerging as influential factors favourably impacting this intention.
The novelty and advancement of technology have explored new avenues in the education sectors. Now e-learning has blended with face-to-face learning to increase its effectiveness. An integrated model is being proposed in this study to measure the continuance usage intention of students adopting e-learning services. With the constructs of ECM (confirmation, perceived usefulness), perceived ease of use and perceived self-efficacy proposed to measure their effect on satisfaction. Besides, to explore the impact of quality features on satisfaction and continuance intention, three major determinants of D&M’s ISS model such as system quality, service quality and information quality included in this study. 410 responses collected to do quantitative analysis. The PLS-SEM analysis showed that perceived ease of use has the strongest effect on perceived usefulness, followed by perceived self-efficacy on perceived ease of use and satisfaction with continuance intention.The study reveals that challenges in developing countries are unique and drives the learners differently than developed countries. The growing e-learning industry requires widespread research from national and institutional perspectives for holistic development in the Bangladesh education market.
Among the various online services provided by the government, e-tax system is one of the commonly used e-government platforms, which offers significant benefits to government and citizens by reducing costs, eliminate human errors, and reduce the turnaround time for processing income tax returns. Despite these plausible benefits, some reservations have been articulated regarding the continuance usage intention of e-tax system. Thus, we investigate the most critical factors of e-tax continuance usage in this paper. We developed an integrated theoretical model based on the technology continuance theory (TCT) extended with social influence and perceived risk. The proposed conceptual model was empirically validated with data collected from a sample of 345 e-tax service users in Bangladesh. Partial least squares structural equation modeling (PLS-SEM) was employed to analyze the collected data. The study findings suggest that user satisfaction, perceived usefulness, and attitude have a direct impact on continuance usage intention where confirmation, ease of use and social influence have indirect influence on continuance usage intention of e-tax system. This paper discusses some contributions to theory and practice based on our findings
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 |