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: 9 / 9 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 — 22 Oct 2026 12:56 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.
9 / 9 with data · 0 missing or unmatched. Percentages use all 9 selected publications.
9 / 9 with data · 0 missing or unmatched. Percentages use all 9 selected publications.
9 / 9 with data · 0 missing or unmatched. Percentages use all 9 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 |
|---|---|---|---|---|---|
| 2019 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2022 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2023 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 9 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 13 | 2 | 2/2 | 2/2 |
| 2022 | All selected publications | 6 | 3 | 3/3 | 3/3 |
| 2023 | All selected publications | 3 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 30 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 23 | 2 | 2/2 | 2/2 |
| 2022 | All selected publications | 13 | 3 | 3/3 | 3/3 |
| 2023 | All selected publications | 3 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 1 | 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 9 / 9 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): 2026-10-21T07:22:45.986618+00:00 — 2026-10-22T12:56:30.842375+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 | 9 | 9/9 |
| 2018 | All selected publications | 0 | 9 | 9/9 |
| 2019 | All selected publications | 0 | 9 | 9/9 |
| 2020 | All selected publications | 2 | 9 | 9/9 |
| 2021 | All selected publications | 6 | 9 | 9/9 |
| 2022 | All selected publications | 8 | 9 | 9/9 |
| 2023 | All selected publications | 19 | 9 | 9/9 |
| 2024 | All selected publications | 16 | 9 | 9/9 |
| 2025 | All selected publications | 11 | 9 | 9/9 |
| 2026 | All selected publications | 8 | 9 | 9/9 |
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 |
|---|---|
| 2019 | 1 |
| 2020 | 1 |
| 2021 | 2 |
| 2022 | 3 |
| 2023 | 1 |
| 2024 | 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 Research in Business and Social Science SSBFNET | 9 | 32 | 70 | 9/9 | 3.56 | 3 | 88.9 | 9/9 · 100.0% |
* Percentage among publications with a known citation count.
E-commerce is one of the industries that are able to grow in the middle of the crisis because of the COVID-19 pandemic. This research aims to investigate the factors that influence the continuance intention of online shopping services in the scope of e-commerce. This research uses perceived ease of use as an independent variable that affects the intention with perceived usefulness and trust as mediating variables. This research is categorized as explanatory research. Respondents in this study are Klikindomaret users and the determination of the number of samples in this study using purposive sampling technique with a total sample of 170 respondents. The data collection method used a questionnaire and the data was analyzed using PLS-SEM. The findings of this study indicate that perceived ease of use, perceived usefulness, and trust have a significant effect on continuance intention. Perceived usefulness and trust also play a role in mediating the relationship between perceived ease of use and continuance intention.
This study aims to examine and prove empirically the effect of perceived usefulness, perceived ease of use, social influence, facilitating conditions on tax compliance. The sample selection technique uses convenience sampling obtained 100 samples taxpayers in Directorate General of Taxation III East Java. Data analyzed using partial least square. The result shows that (1) perceived ease of use does not affect tax compliance. (2) Perceived usefulness has a positive effect on tax compliance. (3) Social influence has a positive effect on tax compliance. (4) Facilitating conditions does not affect tax compliance.
Public service offices are currently required to provide services effectively and efficiently. Therefore, technology plays a role in helping employees to be able to finish the job. The use of technology makes employees able to achieve high satisfaction and productivity. To be able to take advantage of existing technology, a collaboration between employees is needed through knowledge sharing. This study focuses on the context of the role of task-technology fit in mediating the relationship of knowledge sharing intention to employee satisfaction and performance. This is explanatory research was conducted through a survey of 412 employees of the State Treasury Service Office (KPPN) in the East Java Province. The structural Equation Model (SEM) approach was used and 214 questionnaires were processed with WarpPLS. The results of this study indicate that knowledge sharing, intention affects task-technology fit (TTF) and task-technology fit (TTF) also has a significant effect on employee satisfaction and performance.
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 examine the factors that affect customers repurchase intention in the e-commerce marketplace during the COVID-19 pandemic on Java Island in Indonesia, using the extended Technology Acceptance Model (TAM) theory and UI/UX Concept. Perceived ease of use, shopping experience, e-trust, and age are variables that are predicted to affect e-commerce marketplace repurchase intention behavior during the COVID-19 pandemic. The data were collected through an online survey from e-commerce marketplace customers in Java who have had at least two experiences of purchasing goods or services through e-commerce in Java. In total, 250 valid responses were analyzed to test hypotheses using structural equation modeling and multi-group analysis. The results show that perceived ease of use, shopping experience, and e-trust are found to significantly affect the repurchase intention in e-commerce marketplace. The study also show that e-trust has a role in mediating the relationship between both perceived ease of use towards repurchase intention and shopping experience towards repurchase intention. However, the multi-group analysis did not find any significant differences between shopping experience and repurchase intention across age groups. Finally, this paper ended with some discussions, implications, limitations, and conclusions.
The number of digital banks in Indonesia is very large, resulting in intense competition between each digital bank. The tighter competition for digital banks, makes the interest in reusing digital bank users low and makes users tend to switch brands or providers in using digital banks. This study aims to determine the factors that influence e-servicescape on continuance intention mediated by brand image and performance expectancy. This research is categorized as explanatory research. Respondents in this study were users of the Blu by BCA Digital application on the island of Java, determining the number of samples in this study using purposive sampling technique with a total sample size of 249 respondents. The data collection method used a questionnaire, the data was analyzed using PLS-SEM. The results showed that e-servicescape had no significant effect on continuance intention. E-servicescape has a significant effect on brand image, brand image has a significant effect on continuance intention. E-servicescape has a significant effect on performance expectancy, performance expectancy has a significant effect on continuance intention. Brand image and performance expectancy can play a fully mediating role in the relationship between e-servicescape and continuance intention. Although e-servicescape does not have a significant influence directly, it is still part of the customer experience in using the Blu by BCA Digital app. Therefore, Blu by BCA Digital should continue to invest in technology and innovation to improve their e-servicescape. This includes the use of the latest technologies such as artificial intelligence (AI), data analytics, and personalized user experience. The better the e-servicescape, the better the brand image, performance expectancy and continuance intention of customers to keep using the service.
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 paper examines the factors that influence the intention to use FMIS in Indonesia’s local governments, especially in East Java region. The study model used the Technology Acceptance Model (TAM) that focuses on two specific salient beliefs-perceived ease of use and perceived usefulness. However, this has not discouraged researchers from incorporating additional constructs to the original model in their quest for increased predictive power. Here, an attempt is made to explain local governments' intention to use FMIS. Besides perceived ease of use and perceived usefulness, compatibility, external technical support, and management support are included in an extended TAM. The research data was obtained through questionnaires to 180 respondents of planning and financial managers at 9 local governments of the East Java region. The data analysis method used SEM-PLS with the SmartPLS 3.0 application. The results of this study showed that perceived usefulness and perceived ease of use are influenced by compatibility, external technical support, and management support. Compatibility also influenced intentions to use FMIS, and perceived ease of use influenced perceived usefulness. Intention to use FMIS was also affected by perceived usefulness and perceived ease of use. Lastly, perceived usefulness and perceived ease of use did not create a mediating effect between external variables and intention to use FMIS. This study concludes that TAM and external variables affect interest in using FMIS.
This study examines the effect of perceived ease of use and perceived security on repurchase intention mediated by the trust of e-commerce marketplace users in Samarinda City. This research was conducted on 150 users of the e-commerce marketplace. Data analysis uses partial least square (SEM-PLS) structural equation modeling with the Smart-PLS 3.0. The results showed that perceived ease of use significantly affected repurchase intention, but perceived security had no significant effect. This study also shows that trust successfully mediates the effect of perceived ease of use and perceived security on repurchase intention. From this research, it is suggested that e-commerce marketplace companies continue to innovate so that the trust felt by users increases. According to the findings of this study, e-commerce marketplace companies should continue to innovate to increase user trust.
This study aims to examine the factors that affect customers repurchase intention in the e-commerce marketplace during the COVID-19 pandemic on Java Island in Indonesia, using the extended Technology Acceptance Model (TAM) theory and UI/UX Concept. Perceived ease of use, shopping experience, e-trust, and age are variables that are predicted to affect e-commerce marketplace repurchase intention behavior during the COVID-19 pandemic. The data were collected through an online survey from e-commerce marketplace customers in Java who have had at least two experiences of purchasing goods or services through e-commerce in Java. In total, 250 valid responses were analyzed to test hypotheses using structural equation modeling and multi-group analysis. The results show that perceived ease of use, shopping experience, and e-trust are found to significantly affect the repurchase intention in e-commerce marketplace. The study also show that e-trust has a role in mediating the relationship between both perceived ease of use towards repurchase intention and shopping experience towards repurchase intention. However, the multi-group analysis did not find any significant differences between shopping experience and repurchase intention across age groups. Finally, this paper ended with some discussions, implications, limitations, and conclusions.
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 19:55 UTC · ijrbs | complete · ijrbs · 20 Oct 2026 19:50 UTC |
| openalex-enrichment | 21 Oct 2026 07:23 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 07:22 UTC |