The Effect of Employee Involvement in Strategic Change on the Performance of Insurance Companies in Zimbabwe
Bibliographic record
Abstract
Due to rapid technological advancements and intense competition, organizations must find new ways to do business. As a result, changes in an organization’s structures, systems, and strategies are now a pre-requisite to survive the competition. Involving employees in strategic change programmes will harness ideas that enhance competitive advantage and organizational performance. The purpose of this study is to inform industry executives, especially in insurance companies, that employees are crucial resources that must be valued for their contribution to the survival of the organization. A total of 115 respondents were surveyed using a 5-point Likert scale questionnaire in a quantitative research approach. This study employed the multiple regression method to test the effect of five employee involvement constructs on organizational performance using IBM SPSS V28 software. All five constructs, that is, participation in decision-making, teamwork, communication, creativity, and innovation, significantly affected the performance of insurance companies in Zimbabwe. This study’s findings will convince top managerial leaders of the insurance industry to acknowledge and appreciate the importance of involving employees in strategic change programmes. Furthermore, industry regulatory authorities can promote policies and practices that involve employees in decision-making.
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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 | 0.003 |
| 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 | 0.001 |
| 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".