The Role of Public Relations on Reputation Management: A Case Study of Orange Mobile Company Sierra Leone Limited
Bibliographic record
Abstract
A case study of Orange Mobile Company in Sierra Leone was used in the study to assess the impact of public relations on reputation management. The study's goals were to understand the roles that public relations play in managing Orange Sierra Leone's reputation, identify the problems with public relations that arise when managing Orange Sierra Leone's reputation, identify the best solutions to these problems, and look into the relationship between public relations and Orange Sierra Leone's reputation management. The study used an informal research design to reflect various aspects of study respondents' perceptions, feelings, experiences, facts, and emotional reactions to the study issue. This was a study of a problem or subject that looked at how one factor or variable affected another. This was due to the fact that the resulting research questions need explanatory, descriptive, and analytical components of the research to be observed. The study used a 60-person sample that was taken from a 600-person population. The top administrators of Orange Sierra Leone participated in key informant interviews, which the researcher coordinated and which enriched the study's findings. Data was as well gathered through self-administered questionnaires, which were given to representatives of the Public Relations department so they may respond. The majority of respondents generally felt that representing the company or individual to the media is one of the most well-known functions, which is proof that the concept of public relations is very important to the brand image of the organization. The study comes to the conclusion that creating written and oral documentation is another role of public relations. The study suggests that public relations training should be established in order to provide managers and practitioners with the necessary information and abilities. It is important to arrange seminars and conferences so that staff employees and practitioners with less experience can pick the brains of more knowledgeable individuals. This tactic should reduce dial errors brought on by limited experience.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".