Innovation in the knowing organization: a case study of an e‐commerce initiative
Bibliographic record
Abstract
This paper explores the dynamics of information‐ and knowledge‐based activities in one of the world’s leading foreign exchange banks and its development of an innovative online trading system. These activities are analyzed using the framework of “the knowing organization,” which postulates that learning and innovation in organizations result from managing holistically the activities of sensemaking, knowledge creation, and decision‐making (Choo, 1998, 2002). In sensemaking, project members at the bank were driven by their shared beliefs about the competition, customers and technology to enact the challenge of building an online dealing system. Knowledge creation focused on filling perceived gaps, and involved both expanding non‐traditional capabilities within the group and acquiring expertise from outside the group. Decision making at the enterprise level to approve the project was formal and procedural, while decision making at the operational level was open and entrepreneurial. As predicted by the model, the interactions between these activities were vital. The outcome of sensemaking provided the context for knowledge creation and decision making, while the results of knowledge creation provided expanded resources for decision making. The three sets of activities were integrated through strong leadership, group norms of trust and openness, and a set of shared vision and values.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| 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".