Implications of orality for management practices in Iran: an application of Walter Ong’s theory
Bibliographic record
Abstract
Purpose This paper aims to analyze the implications of orality for management practices in a developing country such as Iran. Design/methodology/approach This paper relies on the seminal theory of Walter Ong (1982) and a leading line of anthropological research to analyze the implications of orality/literacy for management practices in Iran. The authors first define orality and literacy as distinct modes of communication and examine their conceptual properties. Then, the authors draw on the existing literature to analyze the five main management functions impacted by orality. Findings The analyses suggest that the predominance of orality in Iran is associated with a wide range of management practices, including short-term or unstructured planning, spontaneous decision-making, fluid organizational structure, the prevalence of interpersonal relations, authoritarian and traditional leadership and behavior-based controlling mechanisms. Originality/value While most studies have focused on the impacts of cultural dimensions and economic variables, this paper offers a novel approach to analyzing management practices. More specifically, the paper suggests that in addition to the implications of cultural dimensions and economic variables, the mode of communication, namely, orality/literacy, could have significant implications for management practices.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".