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: 4 / 4 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 01:51 UTC — 29 Oct 2026 10:32 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.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 selected publications.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 selected publications.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 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 |
|---|---|---|---|---|---|
| 2020 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2020 | All selected publications | 3 | 4 | 4/4 | 4/4 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2020 | All selected publications | 8 | 4 | 4/4 | 4/4 |
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 4 / 4 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): 2026-10-22T01:51:13.032470+00:00 — 2026-10-29T10:32:47.648270+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 | 4 | 4/4 |
| 2018 | All selected publications | 0 | 4 | 4/4 |
| 2019 | All selected publications | 0 | 4 | 4/4 |
| 2020 | All selected publications | 0 | 4 | 4/4 |
| 2021 | All selected publications | 2 | 4 | 4/4 |
| 2022 | All selected publications | 0 | 4 | 4/4 |
| 2023 | All selected publications | 1 | 4 | 4/4 |
| 2024 | All selected publications | 2 | 4 | 4/4 |
| 2025 | All selected publications | 3 | 4 | 4/4 |
| 2026 | All selected publications | 0 | 4 | 4/4 |
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 |
|---|---|
| 2020 | 4 |
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 | 4 | 3 | 8 | 4/4 | 0.75 | 0.5 | 50.0 | 4/4 · 100.0% |
* Percentage among publications with a known citation count.
Previous studies have established that the telecommunications industry in Kenya is characterized by decreasing voice revenues, many regulations, technological progress and varying consumer needs. This phenomenon has resulted in industry companies developing sound innovation strategies to make sure they form a competitive advantage over their rivals with the aim of ensuring their survival and growth in the evermore cut-throat marketplace. Nevertheless, in spite of the essential part played by these innovation strategies to safeguard competitive advantage, several companies have failed to translate the plans into reality. Therefore, the major objective of this study was to investigate the influence of technological innovation on the competitive advantage of telecommunication companies in Kenya. Technological innovation was measured by expansion of Global System for Mobile Communications (GSM) networks; new equipment and software; and partnerships and synergies. To satisfy the objective of this paper, a positivist philosophical stance was adopted and research design using cross-sectional survey data used. Data was collected using proportionate sampling from 247 managers. The results indicate that technological innovation improves the competitive advantage of telecommunication companies in Kenya. However, with government regulation as a moderating variable, only the new equipment, and software constructs had a positive and significant influence on the dependent variable. Ultimately, the findings suggest that government regulation moderates the influence of technological innovation on the competitive advantage of telecommunication companies in Kenya.
Proper management of finances in private primary schools is very imperative to their operations. There are, however, serious financial challenges in these private schools in Kenya as characterized by unprecedented high fees charged on students. The objective of this study was to assess the role of financial control in the growth of private primary schools in Kenya. The study was guided by the Cash Management Theory that gives emphasis to reasonable ways to deal with organizational finance management and efficient utilization as well as the Endogenous Growth Theory which stipulates that, in the long-run growth rate depends on a stable business environment. The study employed both quantitative and qualitative study design, which targeted 7,418 private primary schools in Kenya. Accessible population constituted of 3,431 heads of schools in four regions of Kenya namely: Nairobi, Central Kenya, Northeastern, and the Coastal regions. A random sampling method was used to draw a sample of 320 respondents who were either the principals’/Head teachers or deputy principal of the schools. A structured questionnaire was used to collect data. Structural Equation modeling using Analysis of Moment Structures was used to analyze the data. The fitness of the hypothesized structural and measurement models was tested using the Normed Fit Index and the Root Mean Squared Error. The overall path coefficients obtained were positive and significant at a 0.05 level of significance. The study established that financial control positively and significantly influenced the growth of private primary schools. The study recommended that private primary schools should have effective budget management mechanisms and strong financial controls.
The research and development (R&D) process is critical to a firm’s competitive advantage and often requires external funding. Yet, we know little about how different types of investors respond to the cash needs of established R&D intensive firms nor about how external financial analysts influence those decisions. We address these gaps by examining how a firm’s patenting activity affects its ability to raise cash. We distinguish the motivations of two investor groups: open-market and alliance partners. We focus on how patents based on emergent technologies impact two types of investors and their willingness to fund the R&D process. We develop theory and test our hypotheses using data from publicly traded biopharmaceutical firms by drawing upon knowledge-based view, alliance, and investment theories. We find evidence that patents built upon emergent technologies are viewed differently by the two types of investors. We find open market investors were less likely to invest in emergent technologies and invested less when they did. Conversely, alliance partner investors would be more appreciative of the opportunities new technology inputs present, thus, more likely to invest in firms using emergent technologies and invest more.
AS5[v.Dec:2017] issued by the Public Company Accounting Oversight Board [PCAOB] requires the use of Analytical Procedures [AP] at the Planning and Substantive Phases of Assurance Audits for firms traded on active exchanges. We argue that one aspect of AP, relative to risk-setting, should be vetting the information that is produced/published by the audit client pertaining to Regulation G [v.SEC:2003] called: Non-GAAP information. In our research, we intend to leverage the longstanding Reg[G] requirements to extend the Non-GAAP information to firm performance profiles reported for the Environment, Social, and Governance[ESG]Platform on BloombergÒ. There are two research foci: (1) Offer an AP-Model that uses GAAP & ESG variables to contribute audit evidence useful in making the decision to launch an AP-Extended Procedures examination of the firm’s Enterprise Resource Planning & Control [ERP&C] protocols, and (2) Profile a random accrual-set of firms indexed on Bloomberg so as to offer population parameter estimates for refining the AP-Model. The AP-Model is based upon correlational associations for the ESG- & GAAP-variables from the: Income, Balance Sheet & Cash Flow Statements. If there seems to be a disconnect between the nature of these associations for the ESG-variables and those of the GAAP-variables, the auditor may use this as audit evidence in making the decision to conduct an Extended Procedures Examination of the firm’s [ERP&C] protocols. As for the other focus, we found that for the accrual of firms tested there is no inferential evidence that the ERP&C-protocols are consistent drivers for both the ESG- and the GAAP variable sets.
Proper management of finances in private primary schools is very imperative to their operations. There are, however, serious financial challenges in these private schools in Kenya as characterized by unprecedented high fees charged on students. The objective of this study was to assess the role of financial control in the growth of private primary schools in Kenya. The study was guided by the Cash Management Theory that gives emphasis to reasonable ways to deal with organizational finance management and efficient utilization as well as the Endogenous Growth Theory which stipulates that, in the long-run growth rate depends on a stable business environment. The study employed both quantitative and qualitative study design, which targeted 7,418 private primary schools in Kenya. Accessible population constituted of 3,431 heads of schools in four regions of Kenya namely: Nairobi, Central Kenya, Northeastern, and the Coastal regions. A random sampling method was used to draw a sample of 320 respondents who were either the principals’/Head teachers or deputy principal of the schools. A structured questionnaire was used to collect data. Structural Equation modeling using Analysis of Moment Structures was used to analyze the data. The fitness of the hypothesized structural and measurement models was tested using the Normed Fit Index and the Root Mean Squared Error. The overall path coefficients obtained were positive and significant at a 0.05 level of significance. The study established that financial control positively and significantly influenced the growth of private primary schools. The study recommended that private primary schools should have effective budget management mechanisms and strong financial controls.
Previous studies have established that the telecommunications industry in Kenya is characterized by decreasing voice revenues, many regulations, technological progress and varying consumer needs. This phenomenon has resulted in industry companies developing sound innovation strategies to make sure they form a competitive advantage over their rivals with the aim of ensuring their survival and growth in the evermore cut-throat marketplace. Nevertheless, in spite of the essential part played by these innovation strategies to safeguard competitive advantage, several companies have failed to translate the plans into reality. Therefore, the major objective of this study was to investigate the influence of technological innovation on the competitive advantage of telecommunication companies in Kenya. Technological innovation was measured by expansion of Global System for Mobile Communications (GSM) networks; new equipment and software; and partnerships and synergies. To satisfy the objective of this paper, a positivist philosophical stance was adopted and research design using cross-sectional survey data used. Data was collected using proportionate sampling from 247 managers. The results indicate that technological innovation improves the competitive advantage of telecommunication companies in Kenya. However, with government regulation as a moderating variable, only the new equipment, and software constructs had a positive and significant influence on the dependent variable. Ultimately, the findings suggest that government regulation moderates the influence of technological innovation on the competitive advantage of telecommunication companies in Kenya.
The research and development (R&D) process is critical to a firm’s competitive advantage and often requires external funding. Yet, we know little about how different types of investors respond to the cash needs of established R&D intensive firms nor about how external financial analysts influence those decisions. We address these gaps by examining how a firm’s patenting activity affects its ability to raise cash. We distinguish the motivations of two investor groups: open-market and alliance partners. We focus on how patents based on emergent technologies impact two types of investors and their willingness to fund the R&D process. We develop theory and test our hypotheses using data from publicly traded biopharmaceutical firms by drawing upon knowledge-based view, alliance, and investment theories. We find evidence that patents built upon emergent technologies are viewed differently by the two types of investors. We find open market investors were less likely to invest in emergent technologies and invested less when they did. Conversely, alliance partner investors would be more appreciative of the opportunities new technology inputs present, thus, more likely to invest in firms using emergent technologies and invest more.
AS5[v.Dec:2017] issued by the Public Company Accounting Oversight Board [PCAOB] requires the use of Analytical Procedures [AP] at the Planning and Substantive Phases of Assurance Audits for firms traded on active exchanges. We argue that one aspect of AP, relative to risk-setting, should be vetting the information that is produced/published by the audit client pertaining to Regulation G [v.SEC:2003] called: Non-GAAP information. In our research, we intend to leverage the longstanding Reg[G] requirements to extend the Non-GAAP information to firm performance profiles reported for the Environment, Social, and Governance[ESG]Platform on BloombergÒ. There are two research foci: (1) Offer an AP-Model that uses GAAP & ESG variables to contribute audit evidence useful in making the decision to launch an AP-Extended Procedures examination of the firm’s Enterprise Resource Planning & Control [ERP&C] protocols, and (2) Profile a random accrual-set of firms indexed on Bloomberg so as to offer population parameter estimates for refining the AP-Model. The AP-Model is based upon correlational associations for the ESG- & GAAP-variables from the: Income, Balance Sheet & Cash Flow Statements. If there seems to be a disconnect between the nature of these associations for the ESG-variables and those of the GAAP-variables, the auditor may use this as audit evidence in making the decision to conduct an Extended Procedures Examination of the firm’s [ERP&C] protocols. As for the other focus, we found that for the accrual of firms tested there is no inferential evidence that the ERP&C-protocols are consistent drivers for both the ESG- and the GAAP variable sets.
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 |