Learning Orientation and Competitive Advantage of Insurance Companies in Kenya: The Moderating Role of Senior Executive Team Integration
Bibliographic record
Abstract
Studies on learning orientation have gained increasing momentum over time; and the proliferation which; shows no indication of abating. This study aimed to advance knowledge and was based on the premise that learning orientation affected competitive advantage through the moderating effect of senior executive team integration. The study was anchored on the dynamic capabilities’ theory. The overall objective of the study was to examine the effect of senior executive team integration on the relationship between learning orientation and competitive advantage of insurance companies in Kenya. The study employed a positivist research philosophy and a descriptive cross-sectional survey design. The population of study comprised all the 56 insurance firms registered and licensed by Insurance Regulatory Authority. Descriptive statistics, correlation analysis and regression analysis were used for analysis of data. Regression analysis was carried out to understand the relationships among the variables. The findings established that learning orientation had a statistically significant effect on competitive advantage of insurance firms in Kenya. However, the moderating effect of senior executive team integration on the relationship between learning orientation and competitive advantage was not statistically significant. The study concludes that for insurance firms to create and sustain competitive advantage, they must embrace a learning-oriented culture whilst recognizing that managing companies require collaborative interaction. The findings of the study validated some key theoretical frameworks in strategic management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".