The post Nonaka concept of ba: Eclectic roots, evolutionary paths and future advancements
Bibliographic record
Abstract
Abstract This paper investigates and analyses the concept of ba – or enabling context – in the fields of information science, information systems and management/business literature in order to understand its conceptual evolution, discussions, applications and expansion since its introduction in 1998 by Nonaka et al. The qualitative methodology is bibliographic and comprises – among others – the methods of citation analysis and content analysis. A resulting selection of 135 papers, 4 dissertations/theses and 4 books constituted the research's final database. Data analysis consisted of three flows of activities: data reduction, data displays (in the forms of both conceptual and mind maps) and conclusion drawing/verification. The results point out to the identification of four major groups of enabling conditions – social/behavioral, cognitive/epistemic, informational and business/managerial – which can be singly or freely combined into different knowledge processes – creation, sharing/transfer and use – occurring in different levels of interactions – individual, group, organizational and inter‐organizational. Based on these results, a decision cube is proposed in the form of a framework for designing enabling contexts in knowledge organizations. The conclusions suggest that the concept of ba and its underlying concepts are indeed sine qua non conditions for organizational knowledge creation and innovation processes, though ba is still both theoretically and empirically under‐explored. Organizations interested in pursuing knowledge management (KM), innovation and ba may wish to be guided by the enabling conditions presented in this paper.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".