The Role of Employee Behaviour and Organizational Structure in the Relationship Between Strategic Planning and Competitive Advantage of Large Manufacturing Firms in Kenya
Bibliographic record
Abstract
Several studies have been carried out in the past to find out how strategic planning and competitive advantage are connected and the causes of differences in competitive advantage among firms. Scholars have argued that competitive advantage can emanate from either internal or external sources and is usually in several forms which include; valuable resources, the position held within the industry, position within the marketplace, operating at lower costs than rival firms, differentiation, capabilities and dynamic capabilities. This study sought to advance knowledge and was based on the premise that strategic planning influences competitive advantage both directly and also indirectly by way of the mediating influence of employee behaviour and the moderating effect of organizational structure. The study was underpinned by the competitive advantage typology/theory, the resource-based theory, dynamic capabilities theory, goal-setting theory and contingency theory. The study used a positivist research paradigm and a cross-sectional survey design. Data collected from 122 large manufacturing firms was analyzed using both descriptive and inferential statistics. Hypotheses were tested using both simple and multivariate regression analysis as well as hierarchical analysis for mediating and moderating effects. The findings indicate that overall strategic planning has a statistically significant influence on competitive advantage and that employee behaviour completely mediates the relationship between strategic planning and competitive advantage while organizational structure has a partial moderating effect between strategic planning and competitive advantage. In addition, the joint influence of employee behaviour and organizational structure is different from the influence of individual variables on the relationship between strategic planning and competitive advantage. The outcomes from this research lend support to previous enquiries and support all the theories used to underpin the study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".