Entrepreneurial Orientation and Performance of Small and Medium- Sized Enterprises in Nairobi City County, Kenya
Bibliographic record
Abstract
Kenya’s economic growth is estimated to have decelerated to 4.4 per cent in the third quarter of 2017 compared to 5.6 per cent in a similar period of 2016. During the quarter, the macroeconomic fundamentals remained largely stable and supportive of growth. However, uncertainty associated with political environment coupled with effects of adverse weather conditions slowed down the performance of the economy. As a result, most sectors of the economy posted poor performance during the quarter under review compared to the same quarter of 2016. (Kenya Economic Outlook, 2017).The Micro and Small Enterprises sector in Kenya is regarded as the driving force to spur economic growth, innovation and job creation. The general objective of the study is to investigate the effect of entrepreneurial orientation on performance of SMES in Nairobi County, Kenya. The specific objectives of the study are; to determine the effect of innovation, analyze the effect of risk taking and establish the effect of pro-activeness on performance of SMEs in Nairobi County. This study used descriptive research design. The study population is 2300 SMEs registered to operate in Nairobi County. Stratified sampling was used to obtain a sample size of 230 respondents. Data was collected using semi-structured questionnaires. Descriptive and inferential statistics was used in the analysis of data using SPSS and Ms Excel. Data was then be presented using tables, graphs and figures. Based on the findings, the study concluded that entrepreneurial orientation is useful as a Predictor of performance of SMEs. All the Entrepreneurial orientation dimensions: innovativeness, proactiveness and risk-taking had Positive significant effect on performance of SMEs. This implies that behaviors associated with innovativeness, proactiveness and risk-taking when taken as an overall strategic may indeed help SMEs in Kenya to grow. Further, the results suggests that EO-oriented activities within an organization not only results in better performance but also assist owners of SMEs to make better decision regarding the choice of strategic resources acquired. The findings of this study add to our understanding on the relationship between EO and performance of SMEs and represent an important contribution to the body of knowledge in the field of entrepreneurship. Based on the research findings and conclusion this study recommends that: SMEs need to embrace the entrepreneurial orientation dimensions, innovativeness, risk taking and proactiveness to increase business performance. Entrepreneurs need to consider risk-taking to effectively and successfully respond to the dynamic environments that require organizations to increase decision-making speed. Entrepreneurs should be innovative and develop new products ahead of their competitors. They should also be proactive by carrying out strategic environmental scans for new opportunities in the market. Finally, there is need for the Department of Micro and Small-Enterprise Development (DMSED) to consider in its blue print, facilitation of workshops and seminars for small and medium entrepreneurs to sensitize them on the significance of these dimensions in business performance. This study recommends that future researchers should carry out research on the Factors that play a mediating role in the influence of entrepreneurial orientation on performance like munificence, dynamism and hostility should be in future studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".