Driving SMEs Through Nepotism and Individualism: A Cross Cultural Analysis & Implications for Enterprise Success in Sub-Sahara Africa
Bibliographic record
Abstract
As we gravitate deeper into the 21st century, work patterns that drive productivity tend towards teamwork, group specialization, hi-tech and hi-touch processes. This study in acknowledging this new paradigm advocates the adoption of the twin practices of nepotism and individualism for African SMEs. The authors argue that given the peculiar cultural inclinations of Africans where socio-economic activities are woven around family subsistence and individualism in optimizing skills and competences, coupled with readily available labour in most African families; it will be economically wise to drive SMEs set-up and start-ups through family. The authors contend that given the weak capacities of African entrepreneurs competitively, they can become effective if they make a strategic retreat and gradually develop their enterprises via deploying family resources cost effectively to increment capacities for productivity. The paper in examining extant literature evidenced the application of nepotism and individual acumen in the growth of enterprises across notable cultures in the world. Theories of entrepreneurship lending credence to arguments canvassed include Cantillon’s theory, the Knightian theory, the individual-opportunity nexus theory and the Mill’s theory of individualism among others. These along with empirical studies outcome cited reveal the immense benefits and successes recoverable in the creation/administration of SMEs along these dimensions. In the light of these benefits, the authors suggest among others that policies of government in sub-Saharan Africa should tilt towards incentives for family-patterned SMEs. It also advocates for a platform were innovative SMEs can receive recognition and sponsorship from government and trade/industrial associations. Finally, the paper suggests that SMEs in Africa link up via the Internet with SMEs abroad with a history of family business to learn success and survival strategies and gradually become global players themselves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".