Categories of Intellectual Capital Disclosed by Service-based Companies in Botswana
Bibliographic record
Abstract
While many service based companies globally have valued and utilised their intellectual capitals to gain competitive advantage, many service-oriented companies in African nations such as Botswana have not done the same. But with the rapid decline in mineral resources in Botswana, and the government’s economic diversification drive, service-oriented companies are being encouraged by the government to contribute more to the economy. Weak understanding of the intellectual capitals constrained service-based companies from capitalizing on their assets for competitive advantage or other benefits. Harnessing these assets is critical to business diversification. This study investigated the varieties of intellectual capital disclosed by five service-based companies operating in the Botswana context. Using an interpretive approach, with documents as data sources, we found all three varieties of intellectual capital disclosed: human, structural and relational. The motives for disclosing these assets were linked to factors inside and others outside the companies. But while intellectual capital was disclosed, the reporting was sporadic. The value of intellectual capital that managers articulated in their rhetoric was absent in practice. Various implications are discussed. The study is of benefit to corporate managers, investors, academics and policymakers who are keen about intellectual capital development.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".