Influence of Product Quality on Organizational Performance of Seed Maize Companies in Kenya
Bibliographic record
Abstract
A number of new seed entrepreneurs were established in Kenya, however, the majority of them fail to achieve the required business growth and competiveness. As a result, they remain small and producing less quantities of seed compared to the few large seed companies in the same market. This study evaluated the influence of product quality on organizational performance of seed maize companies in Kenya. The study adopted a cross-sectional survey research design to collect data from the target population which comprised of seed maize companies in Kenya. The sampling frame of the study was the registered seed maize companies at the Seed Trade Association of Kenya which was the unit of analysis while the respondents were the managerial employees within the seed companies and key seed experts in Kenya. Primary data was obtained by administering questionnaires to four employees within each seed company. The four employees were randomly selected from the production, marketing, finance and warehousing departments. The key seed experts were selected through snow balling and judgment technique. Interviews were conducted with the selected seed experts. The collected data was analyzed using SPSS software. Factor analysis was done to establish the appropriateness of the questionnaire constructs. Both descriptive and inferential statistics were used. Inferential statistics included the use of bivariate analysis and the study used the Pearson correlation coefficient. The study also ran a multiple regression model in order to establish the effect of product quality on organizational performance of seed maize companies. Results indicated that the original source of seed can affect product credibility and sales, seed certification standards influenced product credibility and sales, characteristics of seed varieties affect product performance and use of hotlines to report seed failure influences the credibility of the seed and the distributor. The study concludes that managers can increase profitability by putting in place appropriate quality management systems (QMS) and product quality standardization of seeds produced to ensure high quality seed. The study recommends that the management of seed companies should ensure they embark on improving the product quality of seeds produced so as to meet customer requirements and enhance the firm’s performance. This can be achieved by implementing appropriate QMS, securing contracts with large farmers who have irrigation facilities to guarantee adequate seed fields isolation, high productivity and quality seed production.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".