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 01:41 UTC — 22 Oct 2026 02:22 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.
3 / 4 with data · 1 missing or unmatched. Percentages use all 4 selected publications.
3 / 4 with data · 1 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 |
|---|---|---|---|---|---|
| 2018 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2018 | All selected publications | 5 | 4 | 4/4 | 4/4 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2018 | All selected publications | 12 | 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): 2026-10-22T01:41:59.145135+00:00 — 2026-10-22T02:22:59.479824+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 | 3 | 4 | 4/4 |
| 2020 | All selected publications | 0 | 4 | 4/4 |
| 2021 | All selected publications | 3 | 4 | 4/4 |
| 2022 | All selected publications | 0 | 4 | 4/4 |
| 2023 | All selected publications | 3 | 4 | 4/4 |
| 2024 | All selected publications | 2 | 4 | 4/4 |
| 2025 | All selected publications | 0 | 4 | 4/4 |
| 2026 | All selected publications | 1 | 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 |
|---|---|
| 2018 | 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 | 5 | 12 | 4/4 | 1.25 | 1.0 | 75.0 | 4/4 · 100.0% |
* Percentage among publications with a known citation count.
The previous research reveals the significant influence of entrepreneurial competencies on entrepreneur's performance. This finding indicates that there should be further exploration of entrepreneurial competencies. This study aims to determine the entrepreneurial competencies regarding the process. Moreover, this research uses the quantitative approach with non-parametric structural equation modeling. The population of this study is the creative industry in Bandung. The research samples are 119 entrepreneurs in the creative industry. This study finds that some competencies are the components that create the entrepreneurial competencies of creative industries entrepreneurs in Bandung. The components are Relationship Competencies, Opportunity Competencies, Strategic Competencies, Human Competencies and Personal Strength Competencies. The research implication recommends increasing the value of the creator's components of entrepreneurial competencies. Hence, they have implications on the performance of MSMEs in the sector of creative industry in Bandung.
Globally, countries are strategically positioning themselves for market leadership due to dynamic business environment. Entrepreneurial spirit is seen as the strategy that will deliver this agenda. This spirit, is believed to be behind the innovative business that revolutionizes the business world. In a dynamic and complex environment, the success of any business is pegged on the entrepreneurial operations of a firm. Entrepreneurial-oriented firms have been proven to be ahead of competition because they are always introduced new products and services and in turn improve their financial results. Scholars believe that learning and development can occur amongst people who actively engage in a common enterprise. This makes learning empowering and productive and thus sustains entrepreneurial orientation. This in turn produces communities of entrepreneurial practice. The role of the entrepreneurial manager is to nurture communities of growth-oriented firms where entrepreneurial learning takes place. The purpose of this study is to assess the relationship between entrepreneurial management and performance of incubator centres in Kenya. The study used a correlation design because it focused on a causal-effect relationship. The study population was 41 incubator managers in Kenya. After missing data analysis two respondents were expunged leaving 39 respondents. Secondary data was obtained from published sources such as company reports, manuals and research done by other scholars. Structural Equation Modelling (SEM) approach was used to analyze the measurement model and test the hypothesized relationship in this study. Simple linear regression model was used to measure the strength of the relationship between entrepreneurial management and performance incubator centre in Kenya. The joint effect model results indicated that entrepreneurial management had a significant relationship with performance of incubator centre.
Business incubators are regarded as entrepreneurial hubs, unleashing entrepreneur’s ideas and businesses into the market, in turn, jobs are created and the economy of the area is improved. Due to the stiff competition for placement into an incubator program it is imperative for incubator management come-up with strategies of being more efficient and effective in utilizing resources to achieve superior performance. Hence the need to critically select clients, whose ideas fit the incubator’s mission and upon graduation create high growth businesses, with a higher survival rate of 90%. The study is anchored on Resource Based Theory. The study used a correlation design that focused on causal relationship of client selection criteria and incubator centre performance (ICP). The study population was 41 incubator managers in Kenya. After missing data analysis two respondents were expunged leaving 39 respondents. Secondary data was obtained from published sources such as company reports, manuals and research done by other scholars. Structural Equation Modelling (SEM) approach was used to analyze the measurement model and test the hypothesized relationship in this study. Simple linear regression model was used to measure the strength of the relationship between client selection criteria and performance incubator centre in Kenya. The results of the combined effect model indicated that client selection criteria had a significant relationship with performance of incubator centres.
Manufacturing small and medium enterprises (SMEs) are the breeding ground for human capital competencies, creativity and innovation, which are important inputs for manufacturing competitiveness. In Kenya, manufacturing SMEs contribute 14% of gross domestic product (GDP), and train and employ 30% of the workforce. However, their growth and competitiveness are undermined by challenges in the firms’ operations management. Consequently, the firms struggle to survive as competitive enterprises, both domestically and globally. The purpose of this study was to establish how entrepreneurial orientation (EO) enhances the relationship between operations management and firm performance. Quantitative primary data were collected from managers of 83 firms registered by the Kenya Association of Manufacturers in the food and beverage sub-sector using a self-administered questionnaire. Structural equation modelling was used to analyse the data for relationships between the study variables. The study found a positive relationship between operations management and EO, and between EO and firm performance. The study also found that EO is a mediator of the relationship between operations management and performance of manufacturing SMEs in Kenya. The study recommends that for manufacturing SMEs to effectively deploy operations management competencies and gain global competitiveness, they must engage EO as a strategy to foment organisational experimentation and exploration and commercialize the resultant innovations. At the macroeconomic level, the government should support manufacturing SMEs through enactment and promotion of policies that enable operations managers to exploit their firm’s EO stock.
The previous research reveals the significant influence of entrepreneurial competencies on entrepreneur's performance. This finding indicates that there should be further exploration of entrepreneurial competencies. This study aims to determine the entrepreneurial competencies regarding the process. Moreover, this research uses the quantitative approach with non-parametric structural equation modeling. The population of this study is the creative industry in Bandung. The research samples are 119 entrepreneurs in the creative industry. This study finds that some competencies are the components that create the entrepreneurial competencies of creative industries entrepreneurs in Bandung. The components are Relationship Competencies, Opportunity Competencies, Strategic Competencies, Human Competencies and Personal Strength Competencies. The research implication recommends increasing the value of the creator's components of entrepreneurial competencies. Hence, they have implications on the performance of MSMEs in the sector of creative industry in Bandung.
Manufacturing small and medium enterprises (SMEs) are the breeding ground for human capital competencies, creativity and innovation, which are important inputs for manufacturing competitiveness. In Kenya, manufacturing SMEs contribute 14% of gross domestic product (GDP), and train and employ 30% of the workforce. However, their growth and competitiveness are undermined by challenges in the firms’ operations management. Consequently, the firms struggle to survive as competitive enterprises, both domestically and globally. The purpose of this study was to establish how entrepreneurial orientation (EO) enhances the relationship between operations management and firm performance. Quantitative primary data were collected from managers of 83 firms registered by the Kenya Association of Manufacturers in the food and beverage sub-sector using a self-administered questionnaire. Structural equation modelling was used to analyse the data for relationships between the study variables. The study found a positive relationship between operations management and EO, and between EO and firm performance. The study also found that EO is a mediator of the relationship between operations management and performance of manufacturing SMEs in Kenya. The study recommends that for manufacturing SMEs to effectively deploy operations management competencies and gain global competitiveness, they must engage EO as a strategy to foment organisational experimentation and exploration and commercialize the resultant innovations. At the macroeconomic level, the government should support manufacturing SMEs through enactment and promotion of policies that enable operations managers to exploit their firm’s EO stock.
Globally, countries are strategically positioning themselves for market leadership due to dynamic business environment. Entrepreneurial spirit is seen as the strategy that will deliver this agenda. This spirit, is believed to be behind the innovative business that revolutionizes the business world. In a dynamic and complex environment, the success of any business is pegged on the entrepreneurial operations of a firm. Entrepreneurial-oriented firms have been proven to be ahead of competition because they are always introduced new products and services and in turn improve their financial results. Scholars believe that learning and development can occur amongst people who actively engage in a common enterprise. This makes learning empowering and productive and thus sustains entrepreneurial orientation. This in turn produces communities of entrepreneurial practice. The role of the entrepreneurial manager is to nurture communities of growth-oriented firms where entrepreneurial learning takes place. The purpose of this study is to assess the relationship between entrepreneurial management and performance of incubator centres in Kenya. The study used a correlation design because it focused on a causal-effect relationship. The study population was 41 incubator managers in Kenya. After missing data analysis two respondents were expunged leaving 39 respondents. Secondary data was obtained from published sources such as company reports, manuals and research done by other scholars. Structural Equation Modelling (SEM) approach was used to analyze the measurement model and test the hypothesized relationship in this study. Simple linear regression model was used to measure the strength of the relationship between entrepreneurial management and performance incubator centre in Kenya. The joint effect model results indicated that entrepreneurial management had a significant relationship with performance of incubator centre.
Business incubators are regarded as entrepreneurial hubs, unleashing entrepreneur’s ideas and businesses into the market, in turn, jobs are created and the economy of the area is improved. Due to the stiff competition for placement into an incubator program it is imperative for incubator management come-up with strategies of being more efficient and effective in utilizing resources to achieve superior performance. Hence the need to critically select clients, whose ideas fit the incubator’s mission and upon graduation create high growth businesses, with a higher survival rate of 90%. The study is anchored on Resource Based Theory. The study used a correlation design that focused on causal relationship of client selection criteria and incubator centre performance (ICP). The study population was 41 incubator managers in Kenya. After missing data analysis two respondents were expunged leaving 39 respondents. Secondary data was obtained from published sources such as company reports, manuals and research done by other scholars. Structural Equation Modelling (SEM) approach was used to analyze the measurement model and test the hypothesized relationship in this study. Simple linear regression model was used to measure the strength of the relationship between client selection criteria and performance incubator centre in Kenya. The results of the combined effect model indicated that client selection criteria had a significant relationship with performance of incubator centres.
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 |