Towards advancing African management scholarship
Bibliographic record
Abstract
Purpose This paper aims to address the main arguments put forward in Grietjie Verhoef’s article and contribute to a wider debate among management scholars on the role of indigenous theories. It challenges the view of African management as illusory and points to the rising support for indigenous theories as indicative of the weakening of the unquestioned dominance of universal theories. Design/methodology/approach This paper takes a conceptual and critically reflective approach, underpinned by a 360-degree evaluation of pertinent literature and theoretical arguments. Findings This paper reveals an underlying symmetry and interconnectedness, anchored on a shared communal ethos, among Afrocentric management concepts, specifically Ubuntu, Ekpe and Igbo apprenticeship systems. This symmetry points to an underlying indigenous management theory that begs to be further conceptualised, evidenced and advanced. Research limitations/implications This paper affirms Verhoef’s demand for Ubuntu, Ekpe, Igbo apprenticeship system to be more rigorously developed and theoretically coherent and urges scholars to intensify effort towards advancing the conceptual and empirical foundations of African management. Echoing Mahatma Gandhi’s timeless counsel, this paper calls on critics of African management to join the effort to bring about the change they wish to see in African management theorising. Social implications This paper disavows the alleged effort to impose a single “African management” model or perpetuate the “colonial/indigenous” binary divide but equally cautions against an effort to veto scholarly striving for a common identity, to learn from history or not embrace collective amnesia. As examples from the USA and Europe show, diversity, even heterogeneity, needs not to preclude the forging of a commonly shared identity complemented with appropriate sub-identities. Originality/value This paper links the African management-centred themes addressed by Verhoef to the wider debate among management scholars about lessening the dominance of universal theories and allowing space for context-resonant indigenous theories. It calls on African management scholars to invest the premium and intensified effort towards building a more robust and coherent body of indigenous theory that will have the capacity and efficacy to inform, explain and advance organisational practice and outcomes across Africa.
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".