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: 21 Oct 2026 00:14 UTC — 21 Oct 2026 07:22 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.
2 / 3 with data · 1 missing or unmatched. Percentages use all 3 selected publications.
2 / 3 with data · 1 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 |
|---|---|---|---|---|---|
| 2021 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 3 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | 3 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 3 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | 9 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | 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 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): 2026-10-21T00:14:35.704574+00:00 — 2026-10-21T07:22:48.059817+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 | 2 | 3 | 3/3 |
| 2024 | All selected publications | 9 | 3 | 3/3 |
| 2025 | All selected publications | 1 | 3 | 3/3 |
| 2026 | All selected publications | 0 | 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 |
|---|---|
| 2021 | 1 |
| 2023 | 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 Business Ecosystem & Strategy Bussecon International Academy | 1 | 0 | 0 | 1/1 | 0.0 | 0 | 0.0 | 1/1 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 2 | 6 | 12 | 2/2 | 3.0 | 3.0 | 100.0 | 2/2 · 100.0% |
* Percentage among publications with a known citation count.
This study aims to empirically examine the differences of perception between generation (generation gap) in fraud prevention, which was analyzed by seven variables, namely tone of the top, anti-fraud training, code of conduct, whistle blowing system (WBS), segregation of duties, fraud risk assessment and background checking. This study used 398 questionnaires consisting of 149 gen Z respondents, 87 gen X respondents, 154 gen Y/Millennials respondents and 8 Boomers respondents. The analytical method used was Mann-Whitney test by SPSS v.26.0. The results show that there was generation gap between generations Z and X (in the tone of the top variable), between generations Z and Y (in the tone of the top variable and WBS) and between Generations X and Y (in the tone of the top variable). However, there were no generation gap between generation Z and boomers, X and boomers and Z and boomers in the overall fraud prevention variables studied. The limitation of this study was the limited number of respondents, especially from the boomer’s generation. This research is expected to provide benefits for determining anti-fraud prevention strategies that are in accordance with the character of each generation.
The aim of this study was to determine millennial influence on organization change in the hospitality industry Naivasha sub-county, Kenya. The study was guided by the following specific objectives: determine the influence of millennial innovativeness on organization change. This study used innovation theory. The study will adopt a descriptive survey research design. The current study targets rated hotels in Naivasha. This study targeted 180 respondents the respondents were: 10 Managers, 40 departmental heads from the four departments in the hotels, 10 Millennial and 120 supervisors. Employees of these hotels will be the respondents in the study. Data was collected via a self –administered questionnaires and an interview guide. Validity and reliability were ensured by conducting a pilot test in Narok County. Analysis of data comprised descriptive statistics and regression analysis with the help of SPSS. Results indicate that organizational change in the hospitality industry was positively significantly associated with millennial Innovation change in the hospitality industry. From the findings, the researcher concluded that millennials are innovative and easily come up with new ideas further they are proficient in technology and techno-savvy. .
There is inadequate literature on research comparing the motivations of Generations Y and Z, especially for non-academic university staff. Therefore, this research compared the motivation of the two generational cohorts amongst non-academic staff at the Zimbabwe Open University. Two sub-objectives guided the research: first, to compare the intrinsic motivation of the two generations and the extrinsic motivation of the two generations. The generational theory guided the research, elaborating that generational cohorts have different preferences. The supplementary theory was Maslow’s hierarchy of needs, which shows how employees are motivated by the provision of needs from basic to fulfillment needs. The study was quantitative, using descriptive statistics to compare the motivation of Generation Y and Z. The research utilised closed-ended questionnaires, which were distributed to 196 respondents. SPSS version 30.0 was used for data analysis. Reliability, validity, and data normality tests confirmed the data. The findings show that both generations are motivated intrinsically, but preferences on the intrinsic motivators diverge. The research found that Generation Y is more extrinsically motivated than Generation Z. The research recommends using intrinsic motivation for Generation Z employees and using both intrinsic and extrinsic motivation for Generation Y employees.
There is inadequate literature on research comparing the motivations of Generations Y and Z, especially for non-academic university staff. Therefore, this research compared the motivation of the two generational cohorts amongst non-academic staff at the Zimbabwe Open University. Two sub-objectives guided the research: first, to compare the intrinsic motivation of the two generations and the extrinsic motivation of the two generations. The generational theory guided the research, elaborating that generational cohorts have different preferences. The supplementary theory was Maslow’s hierarchy of needs, which shows how employees are motivated by the provision of needs from basic to fulfillment needs. The study was quantitative, using descriptive statistics to compare the motivation of Generation Y and Z. The research utilised closed-ended questionnaires, which were distributed to 196 respondents. SPSS version 30.0 was used for data analysis. Reliability, validity, and data normality tests confirmed the data. The findings show that both generations are motivated intrinsically, but preferences on the intrinsic motivators diverge. The research found that Generation Y is more extrinsically motivated than Generation Z. The research recommends using intrinsic motivation for Generation Z employees and using both intrinsic and extrinsic motivation for Generation Y employees.
This study aims to empirically examine the differences of perception between generation (generation gap) in fraud prevention, which was analyzed by seven variables, namely tone of the top, anti-fraud training, code of conduct, whistle blowing system (WBS), segregation of duties, fraud risk assessment and background checking. This study used 398 questionnaires consisting of 149 gen Z respondents, 87 gen X respondents, 154 gen Y/Millennials respondents and 8 Boomers respondents. The analytical method used was Mann-Whitney test by SPSS v.26.0. The results show that there was generation gap between generations Z and X (in the tone of the top variable), between generations Z and Y (in the tone of the top variable and WBS) and between Generations X and Y (in the tone of the top variable). However, there were no generation gap between generation Z and boomers, X and boomers and Z and boomers in the overall fraud prevention variables studied. The limitation of this study was the limited number of respondents, especially from the boomer’s generation. This research is expected to provide benefits for determining anti-fraud prevention strategies that are in accordance with the character of each generation.
The aim of this study was to determine millennial influence on organization change in the hospitality industry Naivasha sub-county, Kenya. The study was guided by the following specific objectives: determine the influence of millennial innovativeness on organization change. This study used innovation theory. The study will adopt a descriptive survey research design. The current study targets rated hotels in Naivasha. This study targeted 180 respondents the respondents were: 10 Managers, 40 departmental heads from the four departments in the hotels, 10 Millennial and 120 supervisors. Employees of these hotels will be the respondents in the study. Data was collected via a self –administered questionnaires and an interview guide. Validity and reliability were ensured by conducting a pilot test in Narok County. Analysis of data comprised descriptive statistics and regression analysis with the help of SPSS. Results indicate that organizational change in the hospitality industry was positively significantly associated with millennial Innovation change in the hospitality industry. From the findings, the researcher concluded that millennials are innovative and easily come up with new ideas further they are proficient in technology and techno-savvy. .
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 |