Expanding the concept of Ba: managing enabling contexts in knowledge organizations
Bibliographic record
Abstract
This paper investigates and analyses the concept of ba - or enabling context - in the fields of information science, management/business and information systems literature in order to understand its conceptual evolution, discussions, applications and expansion since its introduction in 1998 by Nonaka et al. (Nonaka and Konno, 1998; Nonaka et al., 2000; Nonaka and Toyama, 2002; Nonaka et al., 2006). 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 (conceptual 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, inter-organizational. 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. Concerning the management of enabling contexts in knowledge organizations, the study revealed that the main arising challenges rely on the implementation and development of the issues comprised on the four groups of enabling conditions identified, most especially social/behavioral and business/managerial.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.012 | 0.026 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".