Top Executive Characteristics, Turnaround Strategies, and Firm Performance: Insights From Kenya’s Manufacturing Industry
Bibliographic record
Abstract
In today’s turbulent business environment, manufacturing firms face persistent challenges ranging from regulatory pressures and global competition to supply-chain fragility and post-pandemic disruptions. These dynamics often result in organizational decline, manifested in reduced profitability, weakened competitiveness, and operational inefficiencies. Corporate turnaround strategies have therefore become critical in reversing such decline and restoring firm performance. While much of the existing literature explores turnaround strategies, limited attention has been paid to the mediating role of leadership particularly CEO characteristics within emerging economy contexts such as Kenya. This study undertakes a systematic review of theoretical and empirical literature to examine how CEO attributes shape the effectiveness of turnaround strategies in manufacturing firms. Drawing on the Resource-Based View and Upper Echelons Theory, the paper conceptualizes CEO characteristics as tenure, educational background, age, gender, and nationality as pivotal mediators influencing the link between strategic interventions and firm outcomes. Turnaround strategies are operationalized through four dimensions: financial restructuring, strategic repositioning, market refocusing, and organizational reconfiguration. Firm performance is assessed across financial, operational, and innovation-based indicators such as return on investment, product quality, market growth, and competitiveness. Findings highlight that while turnaround strategies provide a pathway for recovery, their success is significantly contingent upon the strategic vision, decision-making capacity, and adaptability of CEOs. Case illustrations from the Kenyan manufacturing sector, such as Mumias Sugar, Eveready East Africa, and Bidco Africa, demonstrate how leadership stability, experience, and foresight can either hinder or accelerate organizational recovery. By proposing a theoretical framework that positions CEO characteristics as mediating variables, this study advances understanding of the interplay between leadership and strategy in turbulent environments. The paper contributes to strategic management scholarship and offers practical insights for policymakers and industry leaders seeking to revitalize Kenya’s manufacturing sector in line with the Bottom-Up Economic Transformation Agenda (BETA). Future empirical research is recommended to validate and refine the proposed model across diverse contexts.
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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 | 0.000 | 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 | 0.001 | 0.002 |
| 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".