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 / 10 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 11:28 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.
10 / 10 with data · 0 missing or unmatched. Percentages use all 10 selected publications.
10 / 10 with data · 0 missing or unmatched. Percentages use all 10 selected publications.
10 / 10 with data · 0 missing or unmatched. Percentages use all 10 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 |
|---|---|---|---|---|---|
| 2023 | All selected publications | 10 | 10 | 10/10 | 10/10 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2023 | All selected publications | 22 | 10 | 10/10 | 10/10 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2023 | All selected publications | 37 | 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 10 / 10 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): 2026-10-21T07:22:46.044879+00:00 — 2026-10-23T11:28:07.052936+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 | 10 | 10/10 |
| 2018 | All selected publications | 0 | 10 | 10/10 |
| 2019 | All selected publications | 0 | 10 | 10/10 |
| 2020 | All selected publications | 0 | 10 | 10/10 |
| 2021 | All selected publications | 0 | 10 | 10/10 |
| 2022 | All selected publications | 0 | 10 | 10/10 |
| 2023 | All selected publications | 3 | 10 | 10/10 |
| 2024 | All selected publications | 19 | 10 | 10/10 |
| 2025 | All selected publications | 9 | 10 | 10/10 |
| 2026 | All selected publications | 5 | 10 | 10/10 |
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 |
|---|---|
| 2023 | 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 Finance & Banking Studies SSBFNET | 2 | 10 | 11 | 2/2 | 5.0 | 5.0 | 100.0 | 2/2 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 8 | 12 | 26 | 8/8 | 1.5 | 1.0 | 62.5 | 8/8 · 100.0% |
* 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.
This study aims to analyze the perceived ease of use, perceived usefulness, and perceived benefits on interest in using Bank Syariah Indonesia mobile banking. The research method used is a quantitative approach. The sample size for this study was 250 respondents. Data were analyzed by structural equation modeling (SEM) and processed with AMOS 24.0. The results of the SEM analysis show that perceived usefulness has a significant positive impact on attitudes toward mobile banking use. Perceived Ease of Use has a positive and significant effect on the Perceived Usefulness of mobile banking. Perceived ease of use has no significant effect on attitudes toward using mobile banking. Perceived Usefulness has a positive and significant effect on Behavioral Intention to Use mobile banking. Therefore, attitudes toward using mobile banking have a significant positive impact on behavioral intentions to use mobile banking.
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 study seeks to explain the intent of SMEs business owners to implement omnichannel retail in their operations. This study employs the UTAUT model and adds innovativeness as a variable that can influence the intention to adopt an omnichannel. The sample for this research consisted of 90 SMEs proprietors in Malang City. This study's data were gathered via a questionnaire and analyzed using PLS-SEM. This study found that personal innovativeness and performance expectations significantly influence the intention to adopt omnichannel. In this study, effort expectations have no significant effect on the intention to implement omnichannel. It has been demonstrated that personal innovativeness can moderate the relationship between effort and performance expectations in the UTAUT model when it comes to the intention of MSME proprietors to implement omnichannel. In addition to focusing on consumers as end users, this study concludes that omnichannel service providers must also consider enterprises as omnichannel users.
This study aims to explore how subjective norms and perceptions of food safety influence people's behavioral intention to use and continue using delivery apps during the coronavirus pandemic. According to the TPB model, subjective norms were anticipated to be positively linked to both intention and continued behavior. The study's objective also was to ascertain the advantageous consequences of perceived food safety and intention on continued usage. The study used a quantitative approach, with a sample of 300 mobile food ordering app users in Yogyakarta selected using non-probability sampling. The data collection method was a validated and reliable questionnaire, and data analysis used SEM AMOS. The results showed that subjective norms and perceived food safety had a significant positive influence on behavioral intention and that behavioral intention and perceived food safety affected continued use. This study contributes to the existing literature on the benefits of subjective norms and perceived food safety on mobile app usage, and its findings have both theoretical and practical implications.
While studying the adoption of mobile banking, its antecedents must be considered to have a better understanding and complete picture of the factors affecting the adoption of mobile banking. There is a scarcity of studies about the effect of the (Rogers, 1995, 2003) five attributes of innovation on the adoption of mobile banking. So, this paper examined the effect of relative advantage, compatibility, complexity, trialability, and observability on the adoption of mobile banking in Egypt. The study hypotheses were tested using primary data collected online. The results show that relative advantage, compatibility, and observability have a significant positive impact on the adoption of mobile banking. On the other hand, the effect of complexity and trialability on the adoption of mobile banking was negative but nonsignificant.
The aim of this study in general is to examine the influence of perceived ease of use, perceived usefulness, and perceived risk on intention to use. The subject of this study is the account officer of the big data user of a state-owned company in Malang, East Java, Indonesia. Specifically, this research has the purpose of finding out both the simultaneous and partial influence of perceived use, perceived usefulness, and perceived risk on the intention to use the big data of State-Owned Bank customers. This study is a survey employing both quantitative and qualitative data analysis. The samples of the subjects used were 95 account officers of a state-owned bank in East Java who used customer big data. The analysis conducted in this study included multiple linear regression testing and hypothesis testing for both simultaneous and partial The result of the F testing indicates that collectively, the perceived ease of use, perceived usefulness, and perceived risk variables have a simultaneous and positive influence on the intention to use the customer's big data. However, based on the T-test, it was observed that perceived usefulness is the only variable that has a partial influence on the intention to use the big data of the customers. While the other two variables, perceived ease of use and perceived risk, were found to have no significant influence on the intention to use the bank’s customers' big data.
This study aims to analyze the perceived ease of use, perceived usefulness, and perceived benefits on interest in using Bank Syariah Indonesia mobile banking. The research method used is a quantitative approach. The sample size for this study was 250 respondents. Data were analyzed by structural equation modeling (SEM) and processed with AMOS 24.0. The results of the SEM analysis show that perceived usefulness has a significant positive impact on attitudes toward mobile banking use. Perceived Ease of Use has a positive and significant effect on the Perceived Usefulness of mobile banking. Perceived ease of use has no significant effect on attitudes toward using mobile banking. Perceived Usefulness has a positive and significant effect on Behavioral Intention to Use mobile banking. Therefore, attitudes toward using mobile banking have a significant positive impact on behavioral intentions to use mobile banking.
The Kenya Airport Police Unit has started using smartphones to deter crime, but there are still reservations about their potential impact on policing. The study sought to identify the factors that influence police officers' adoption and use of smart phone applications for crime prevention. The police officers assigned to the Kenya Airport Police Unit (1382) were the study's target group. For this study, a sample of 274 police officers was selected using a simple random sampling approach. The study was guided by the Mobile Technology Acceptance Model (M-TAM), which discovered that smartphones were useful instruments for crime prevention. Police officers discovered that programs such as Facebook, WhatsApp, Twitter, emails, and short text messages helped to prevent crime. The study suggests that tailored police smart phone applications be developed, as well as officer capacity training, to improve their utilization and help to crime prevention efforts.
This study examined the influence of customer experience on the preference for online bus booking services in Dar es Salaam, Tanzania. Specifically, the study aims to: i) Determine the influence of familiarity with online booking services on the customer preference for online bus booking services, ii) Examine the influence of perception on the customer preference for online booking services, iii) Examine the influence of customer satisfaction on the customer preference for online bus booking services. The study employed a convergent parallel research design whereas both qualitative and quantitative data were collected. A sample of 384 users of buses for their transport was involved using a questionnaire that was administered through Google form as one of the web-based tools. Qualitative data were analyzed using content analysis whereas quantitative data were analyzed using descriptive statistics whereas mean scores, standard deviations, frequency, and percent were computed. Further, a binary logistic regression model was performed to analyze quantitative data. The findings show that customer perception and satisfaction were statistically significant in influencing customers’ preference for online bus booking. The study recommends to all key players in the bus transport sector make use of the findings to enrich policies and strategies in the adoption of online bus booking.
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 |