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: 3 / 3 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: 22 Oct 2026 01:09 UTC — 22 Oct 2026 01:17 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.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 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 |
|---|---|---|---|---|---|
| 2022 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 17 | 3 | 3/3 | 3/3 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 25 | 3 | 3/3 | 3/3 |
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 3 / 3 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): 2026-10-22T01:09:56.425585+00:00 — 2026-10-22T01:17:09.285177+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 | 3 | 3/3 |
| 2018 | All selected publications | 0 | 3 | 3/3 |
| 2019 | All selected publications | 0 | 3 | 3/3 |
| 2020 | All selected publications | 0 | 3 | 3/3 |
| 2021 | All selected publications | 0 | 3 | 3/3 |
| 2022 | All selected publications | 0 | 3 | 3/3 |
| 2023 | All selected publications | 6 | 3 | 3/3 |
| 2024 | All selected publications | 6 | 3 | 3/3 |
| 2025 | All selected publications | 8 | 3 | 3/3 |
| 2026 | All selected publications | 5 | 3 | 3/3 |
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 |
|---|---|
| 2022 | 3 |
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 | 3 | 17 | 25 | 3/3 | 5.67 | 5 | 66.7 | 3/3 · 100.0% |
* Percentage among publications with a known citation count.
Service delivery protests in the recent past demonstrate the dire state of local government in South Africa. The absence of public sector innovation can be linked to service delivery backlogs hence embracing digital innovations (Internet of Things (IoT), Big data and analytics, cloud, Biometrics, computers, mobile applications, 3D machines) in the Fourth Industrial Revolution (4IR) is strategic for enhancing municipal service provision and uplifting the living standards of citizens in communities. The study utilised an explorative qualitative case study design premised on an extensive literature and document review analysis to examine the barriers facing the City of Tshwane from adopting digital innovations in the 4IR. The analysis of documents revealed digital hesitancy, leadership void, lack of innovative research culture, weak municipal preparedness, and digital divide in adopting digital innovations are barriers to digital innovation adoption to enhance service delivery. The paper observes further the lack of systematic and evaluative studies to inform public sector innovation, or growth in digital innovations often led to implementation challenges in the City of Tshwane. Conclusions drawn for the article revealed that adopting digital innovations can be the panacea to accelerating urban service delivery in the City of Tshwane; hence institutional readiness, improved revenue streams, including a stable regulatory and policy environment, are imperatives in achieving a digitalised local government in the 4IR.
This study aims to investigate users’ satisfaction with e-Government services of Myanmar through a study of a specific e-Government service, the Myanmar Companies Online registration (MyCO) system. The MyCO system was chosen for this study because it is the initiative well-developed e-Government service in Myanmar. On the other hand, the company registration authority of Myanmar, the Directorate of Investment and Company Administration (DICA), has continuously made every endeavor to optimize responsive its e-Government services, after almost four years from the launching date of the MyCO system, systematically analyzing for optimizing the system still remains to research. In order to track public perception of the system, an online survey was conducted. The questionnaires were designed based on the Technology Acceptance Model in the fields of customer satisfaction consisting mainly of three main factors, perceived ease of use, perceived usefulness and perceived risk. Questionnaires were collected and analyzed with the respondents identifying themselves as having used the MyCO system. Notwithstanding the overall 79% of respondents were very satisfied and 20% were satisfied to some extent, payment transaction issues and some technical challenges are still remained in the system to be addressed to become a better user-centric public service. This study will support a number of contributions to find out the gap between e-Government services implementation and how publics’ expectations of e-Government services. The result of this study will provide awareness to DICA to improve more efficiency and effectiveness in implementing best practices in the provision of user-centric public services based on the public’s needs and demands.
This paper aims to examine the impact of IS Implementation on User Performance in the county Government of Machakos, Kenya. This paper made use of descriptive designs as well explanatory study designs. In addition, the research made use of primary data that was gathered using open and closed-ended questions in a research questionnaire. Data collected were analyzed using the SPSS software. It was established that IS implementation had a positive effect on user performance which in return impacts the organizational performance. The study revealed that IS's ability is to provide high-quality information, which reduces errors and resolves performance problems when they occur. The study concluded that IS is a major factor influencing user performance in Machakos County Government, Kenya and that there was a positive relationship between independent variables and dependent variables. Therefore, this study recommends that County Governments should invest heavily in IS without fear as this will improve their employee’s performance and in return improve the performance of the County Governments in terms of service delivery to the citizens.
This study aims to investigate users’ satisfaction with e-Government services of Myanmar through a study of a specific e-Government service, the Myanmar Companies Online registration (MyCO) system. The MyCO system was chosen for this study because it is the initiative well-developed e-Government service in Myanmar. On the other hand, the company registration authority of Myanmar, the Directorate of Investment and Company Administration (DICA), has continuously made every endeavor to optimize responsive its e-Government services, after almost four years from the launching date of the MyCO system, systematically analyzing for optimizing the system still remains to research. In order to track public perception of the system, an online survey was conducted. The questionnaires were designed based on the Technology Acceptance Model in the fields of customer satisfaction consisting mainly of three main factors, perceived ease of use, perceived usefulness and perceived risk. Questionnaires were collected and analyzed with the respondents identifying themselves as having used the MyCO system. Notwithstanding the overall 79% of respondents were very satisfied and 20% were satisfied to some extent, payment transaction issues and some technical challenges are still remained in the system to be addressed to become a better user-centric public service. This study will support a number of contributions to find out the gap between e-Government services implementation and how publics’ expectations of e-Government services. The result of this study will provide awareness to DICA to improve more efficiency and effectiveness in implementing best practices in the provision of user-centric public services based on the public’s needs and demands.
This paper aims to examine the impact of IS Implementation on User Performance in the county Government of Machakos, Kenya. This paper made use of descriptive designs as well explanatory study designs. In addition, the research made use of primary data that was gathered using open and closed-ended questions in a research questionnaire. Data collected were analyzed using the SPSS software. It was established that IS implementation had a positive effect on user performance which in return impacts the organizational performance. The study revealed that IS's ability is to provide high-quality information, which reduces errors and resolves performance problems when they occur. The study concluded that IS is a major factor influencing user performance in Machakos County Government, Kenya and that there was a positive relationship between independent variables and dependent variables. Therefore, this study recommends that County Governments should invest heavily in IS without fear as this will improve their employee’s performance and in return improve the performance of the County Governments in terms of service delivery to the citizens.
Service delivery protests in the recent past demonstrate the dire state of local government in South Africa. The absence of public sector innovation can be linked to service delivery backlogs hence embracing digital innovations (Internet of Things (IoT), Big data and analytics, cloud, Biometrics, computers, mobile applications, 3D machines) in the Fourth Industrial Revolution (4IR) is strategic for enhancing municipal service provision and uplifting the living standards of citizens in communities. The study utilised an explorative qualitative case study design premised on an extensive literature and document review analysis to examine the barriers facing the City of Tshwane from adopting digital innovations in the 4IR. The analysis of documents revealed digital hesitancy, leadership void, lack of innovative research culture, weak municipal preparedness, and digital divide in adopting digital innovations are barriers to digital innovation adoption to enhance service delivery. The paper observes further the lack of systematic and evaluative studies to inform public sector innovation, or growth in digital innovations often led to implementation challenges in the City of Tshwane. Conclusions drawn for the article revealed that adopting digital innovations can be the panacea to accelerating urban service delivery in the City of Tshwane; hence institutional readiness, improved revenue streams, including a stable regulatory and policy environment, are imperatives in achieving a digitalised local government in the 4IR.
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 |