How do high‐performing organizations define their mission in Ghana?
Bibliographic record
Abstract
Purpose High performing firms have been associated with “quality” mission statements defined by the choice of components. In an attempt to extend our knowledge in order to give more legitimacy to these claims and also provide more local and relevant reference for Ghana‐based firms, the purpose of this paper is to investigate, through a component analysis, how high‐performing Ghana‐based firms define their mission. Design/methodology/approach Mission statements of 50 of the Ghana Club 100 firms, primarily extracted from the official web sites, Initial Public Offer prospectuses and annual reports of the firms, were subjected to content analysis which evaluated and scored the mission statements based on the occurrence of 20 specific components. Findings The paper found that high‐performing Ghana‐based firms define missions to include components that the literature uses to measure quality; and these are similar to those of the UK, Canada and Ireland. Based on the ranking of the components, three categories were identified: the imperatives, the highlights, and the adjuncts. Research limitations/implications The paper lumped together all firms irrespective of industry or sector. There is, therefore, the need to conduct further research to identify possible industry or sectoral differences, for better insight and relevance. Practical implications Ideas generated in this paper provide a guide to practitioners and firms regarding how they can develop mission statements, drawing on experiences of high‐performing Ghana‐based firms. Originality/value This is the first attempt to study how high‐performing Ghana‐based firms define their mission and hence is a major contribution to the scarce if not non‐existent Africa‐specific studies. It also provides a more prescriptive approach to crafting mission statements by proposing hierarchies of the components of mission statements.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".