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: 6 / 7 publications (85.7%). Latest observation: 21 Oct 2026, 00:21 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: 22 Oct 2026 01:25 UTC — 22 Oct 2026 02:23 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.
6 / 7 with data · 1 missing or unmatched. Percentages use all 7 selected publications.
6 / 7 with data · 1 missing or unmatched. Percentages use all 7 selected publications.
7 / 7 with data · 0 missing or unmatched. Percentages use all 7 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 | 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 | 0 | 0 | 0/0 | 0/0 |
| 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 | 2 | 2 | 2/2 | 2/2 |
| 2024 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 1 | 1 | 0/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2013 | All selected publications | 4 | 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 | 1 | 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 | — | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | 3 | 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 | 10 | 2 | 2/2 | 2/2 |
| 2024 | All selected publications | 4 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | — | 1 | 0/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2013 | All selected publications | 6 | 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 | 4 | 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 | — | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | 14 | 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 | 11 | 2 | 2/2 | 2/2 |
| 2024 | All selected publications | 3 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 0 | 1 | 0/1 | 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 7 / 7 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): 2026-10-22T01:25:24.995440+00:00 — 2026-10-22T02:23:51.466642+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 | 7 | 7/7 |
| 2018 | All selected publications | 0 | 7 | 7/7 |
| 2019 | All selected publications | 3 | 7 | 7/7 |
| 2020 | All selected publications | 1 | 7 | 7/7 |
| 2021 | All selected publications | 3 | 7 | 7/7 |
| 2022 | All selected publications | 7 | 7 | 7/7 |
| 2023 | All selected publications | 4 | 7 | 7/7 |
| 2024 | All selected publications | 8 | 7 | 7/7 |
| 2025 | All selected publications | 6 | 7 | 7/7 |
| 2026 | All selected publications | 3 | 7 | 7/7 |
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 |
| 2016 | 1 |
| 2020 | 1 |
| 2023 | 2 |
| 2024 | 1 |
| 2025 | 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 | 7 | 22 | 38 | 7/7 | 3.67 | 4.0 | 100.0 | 6/7 · 85.7% |
* Percentage among publications with a known citation count.
E-commerce is gaining popularity due to its importance for both merchants and consumers, particularly in light of the COVID-19 pandemic, which restricts mass movement and gathering, as well as the high costs associated with traveling to different markets. Despite mounting evidence demonstrating the importance of internet in retails business, little is known about the potential mechanisms that increase consumer purchasing intentions when using online shopping platforms. In today's high customer sensitivity to service quality, successful entrepreneurs have recently thrived under competitive pressures. Online transactions are more difficult because buyers and sellers do not know each other, yet they must provide financial information to intermediaries. Thus, it is now clear that increasing customer trust is a viable strategy for driving e-business success today. Considering this, a new model demonstrating a potential mechanism that increases customers' trust in intermediaries and thus leads to their online purchasing intentions was developed. This innovative model connects website quality to the online purchasing intentions of the customers, using trust in intermediaries as a mediator and moderating the effect of perceived usefulness. To prove or disprove the assumptions highlighted in this study, a cross-sectional research design was used with data from 568 clients of nine domestic e-commerce platforms operating in Rwanda. To investigate the relationship between the variables hypothesized in this study, a variety of methods and tools were used. To assess the potential effect of each variable in this model, we used CFA in SEM-Amos, mean and standard deviation in SPSS, and Hayes macro process. The overall results obtained using the various techniques specified supported the current hypothesized model. We believe that the current study provided an innovative and useful mechanism as one of the potential solutions for today's merging online businesses.
In Indonesia, a QR code payment is referred to as QRIS (Quick Respond Code Indonesian Standard). QR code technology is employed by QRIS, a contemporary payment system, to establish connections between a variety of financial transactions. QRIS offers numerous advantages to businesses, including the ability to obtain numerous promotions, the automated recording of transactions, the ability to track financial income and expenses, and the elimination of the need to search for change. Regrettably, the low level of digital literacy in Indonesia has prevented certain individuals from adopting digital transactions. Consequently, policymakers and regulators will be able to make more informed decisions regarding the expansion of QRIS in Indonesia by gaining a more comprehensive understanding of the perceptions of utility and perceived ease of use among Indonesian users. This research involves individuals who have implemented QRIS. The sample size in this study was sufficient due to the use of PLS-SEM, a powerful multivariate analysis technique that is recognized for its statistical efficacy in small sample sizes. The study explores the behavioral intention to use QRIS among Indonesian users, emphasizing perceived usefulness and perceived ease of use while incorporating control variables such as age, experience, gender, income, and education. The analysis results indicate that perceived usefulness has a substantial impact on the behavior intention of QRIS, and perceived ease of use also has an impact on the behavior intention of using QRIS. Education and income were found to have no significant difference in relation to behavior intention of using QRIS, whereas age, experience, gender, and age were associated with a significant difference.
Examining the reasons why Albanians use internet banking services is the focus of this paper. Due to recent technological developments, Albania's banking industry is utilizing technology for an increasing number of banking procedures. We started by presenting some of the key elements that have been identified as having a significant impact on the literature regarding the adoption of online banking, including demographic, socioeconomic, and other elements based on the technology adoption model (TAM). For our empirical research, we used primary data that was collected through surveys that various bank customers in Albania completed. The decreased desire to utilize cash appears to be a key element influencing the rise in the usage of online banking applications and services. Other important factors are the application technical issues and reduction of banking operations time as well as the application security level. The findings of this study will help banks better understand their customers' needs and will provide them with suggestions on how to make technology development plans easier.
This paper investigated the influence of factors on the intention to adopt Internet banking services among students of higher education in Pakistan. Theoretical framework used for this study has been adopted from Technology Acceptance Model (TAM) with four independent variables. Convenience sampling method was used with a total of valid 220 respondents, which included students of Khadim Ali Shah Bukhari Institute of Technology (KASBIT), Karachi, Pakistan. Data was collected through self administered questionnaire of two parts: Demographic and Likert scale multi-item scale for variables under study. Results indicatedthat convenience, perceived credibility, and perceived usefulness had significant positive influence among students on the intention to adopt Internet banking. The findings from this research would be useful for banks in the subject area, particularly for students in Pakistan.
The rapid development of internet technology can provide various facilities to access various activities from the internet. One of the activities that can be done from the internet is to make payments on a non-cash basis or commonly called an e-wallet. Linkaja is one of the e-wallets established by the merger of five State-Owned Enterprises in one platform, namely PT Telekomunikasi Seluler, Bank Mandiri, Bank Rakyat Indonesia, Bank Negara Indonesia, Bank Tabungan Negara, PT Pertamina and also PT Asuransi Jiwasraya. This study aims to determine the influence of promotion mix and benefits of perception in increasing re-use of linkaja applications for consumers in the city of bandung. The method of collecting data in this study is to use a questionnaire given to 115 respondents Linkaja users in the city of Bandung. The data analysis technique used is linear regression model. The results of the questionnaire were processed using the spss for windows version 25 program. The results of the study showed that the influence of the promotion mix variable on Linkaja e-wallet repurchase intention was 68.4% and for the influence of perceived usefulness variables on Linkaja e-wallet repurchase intention was 71.17%. Whereas for the simultaneous effect it was obtained the result of 51.4% and the remaining 49.6% was influenced by other factors.
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.
In Indonesia, a QR code payment is referred to as QRIS (Quick Respond Code Indonesian Standard). QR code technology is employed by QRIS, a contemporary payment system, to establish connections between a variety of financial transactions. QRIS offers numerous advantages to businesses, including the ability to obtain numerous promotions, the automated recording of transactions, the ability to track financial income and expenses, and the elimination of the need to search for change. Regrettably, the low level of digital literacy in Indonesia has prevented certain individuals from adopting digital transactions. Consequently, policymakers and regulators will be able to make more informed decisions regarding the expansion of QRIS in Indonesia by gaining a more comprehensive understanding of the perceptions of utility and perceived ease of use among Indonesian users. This research involves individuals who have implemented QRIS. The sample size in this study was sufficient due to the use of PLS-SEM, a powerful multivariate analysis technique that is recognized for its statistical efficacy in small sample sizes. The study explores the behavioral intention to use QRIS among Indonesian users, emphasizing perceived usefulness and perceived ease of use while incorporating control variables such as age, experience, gender, income, and education. The analysis results indicate that perceived usefulness has a substantial impact on the behavior intention of QRIS, and perceived ease of use also has an impact on the behavior intention of using QRIS. Education and income were found to have no significant difference in relation to behavior intention of using QRIS, whereas age, experience, gender, and age were associated with a significant difference.
Examining the reasons why Albanians use internet banking services is the focus of this paper. Due to recent technological developments, Albania's banking industry is utilizing technology for an increasing number of banking procedures. We started by presenting some of the key elements that have been identified as having a significant impact on the literature regarding the adoption of online banking, including demographic, socioeconomic, and other elements based on the technology adoption model (TAM). For our empirical research, we used primary data that was collected through surveys that various bank customers in Albania completed. The decreased desire to utilize cash appears to be a key element influencing the rise in the usage of online banking applications and services. Other important factors are the application technical issues and reduction of banking operations time as well as the application security level. The findings of this study will help banks better understand their customers' needs and will provide them with suggestions on how to make technology development plans easier.
E-commerce is gaining popularity due to its importance for both merchants and consumers, particularly in light of the COVID-19 pandemic, which restricts mass movement and gathering, as well as the high costs associated with traveling to different markets. Despite mounting evidence demonstrating the importance of internet in retails business, little is known about the potential mechanisms that increase consumer purchasing intentions when using online shopping platforms. In today's high customer sensitivity to service quality, successful entrepreneurs have recently thrived under competitive pressures. Online transactions are more difficult because buyers and sellers do not know each other, yet they must provide financial information to intermediaries. Thus, it is now clear that increasing customer trust is a viable strategy for driving e-business success today. Considering this, a new model demonstrating a potential mechanism that increases customers' trust in intermediaries and thus leads to their online purchasing intentions was developed. This innovative model connects website quality to the online purchasing intentions of the customers, using trust in intermediaries as a mediator and moderating the effect of perceived usefulness. To prove or disprove the assumptions highlighted in this study, a cross-sectional research design was used with data from 568 clients of nine domestic e-commerce platforms operating in Rwanda. To investigate the relationship between the variables hypothesized in this study, a variety of methods and tools were used. To assess the potential effect of each variable in this model, we used CFA in SEM-Amos, mean and standard deviation in SPSS, and Hayes macro process. The overall results obtained using the various techniques specified supported the current hypothesized model. We believe that the current study provided an innovative and useful mechanism as one of the potential solutions for today's merging online businesses.
The rapid development of internet technology can provide various facilities to access various activities from the internet. One of the activities that can be done from the internet is to make payments on a non-cash basis or commonly called an e-wallet. Linkaja is one of the e-wallets established by the merger of five State-Owned Enterprises in one platform, namely PT Telekomunikasi Seluler, Bank Mandiri, Bank Rakyat Indonesia, Bank Negara Indonesia, Bank Tabungan Negara, PT Pertamina and also PT Asuransi Jiwasraya. This study aims to determine the influence of promotion mix and benefits of perception in increasing re-use of linkaja applications for consumers in the city of bandung. The method of collecting data in this study is to use a questionnaire given to 115 respondents Linkaja users in the city of Bandung. The data analysis technique used is linear regression model. The results of the questionnaire were processed using the spss for windows version 25 program. The results of the study showed that the influence of the promotion mix variable on Linkaja e-wallet repurchase intention was 68.4% and for the influence of perceived usefulness variables on Linkaja e-wallet repurchase intention was 71.17%. Whereas for the simultaneous effect it was obtained the result of 51.4% and the remaining 49.6% was influenced by other factors.
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 00:21 UTC · ijfbs | complete · ijfbs · 21 Oct 2026 00:21 UTC |
| ojs-rest | 21 Oct 2026 00:21 UTC · ijfbs | complete · ijfbs · 21 Oct 2026 00:14 UTC |
| oai-pmh | 20 Oct 2026 19:56 UTC · ijfbs | complete · ijfbs · 20 Oct 2026 19:55 UTC |
| openalex-enrichment | 21 Oct 2026 00:22 UTC · ijfbs | complete · ijfbs · 21 Oct 2026 00:21 UTC |