Beyond the ba: managing enabling contexts in knowledge organizations
Bibliographic record
Abstract
Purpose Looking at the practical experience of organizations pursuing knowledge management, it is found that their efforts are primarily focused on creating the conditions and the context that will enable knowledge creation. This need for developing enabling conditions and contexts was identified more than a decade ago when Nonaka and associates introduced the concept of “ba.” This paper aims to map the development of the concept of “ba” in a number of disciplines in order to understand its theoretical evolution and practical application. Design/methodology/approach A comprehensive search and evaluation of the literature resulted in a database of 135 papers, four dissertations and four books. Using content analysis, citation analysis, and concept mapping, four categories of research findings are identified that in turn suggest four groups of conditions for enabling knowledge creation. Findings The paper discusses each of these conditions (the social/behavioral, cognitive/epistemic, information systems/management, and strategy/structural), and introduces a framework that relates these conditions to the type of knowledge process and the level of interaction that characterize a knowledge management activity in the organization. Originality/value It is concluded that managing knowledge in organizations is fundamentally about creating an environment in the organization that is conducive to and encourages knowledge creation, sharing and use. Organizations interested in pursuing knowledge management and innovation may wish to be guided by the enabling conditions presented here that have been discovered over ten years of research. These conditions and the frameworks of which they are part can help managers to analyze, discuss, and introduce specific combinations of enabling factors that are tailored according to the type of knowledge process and level of interaction needed to address a particular knowledge problem or vision.
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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.018 | 0.029 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".