Bibliographic record
Abstract
This chapter emphasizes the importance of formally promoting close social interaction and open knowledge sharing to achieve superior innovation capability. It does so by discussing the advantages of developing Communities of Innovation and citing a case study that exemplifies these concepts. This chapter addresses the challenges and opportunities faced by businesses in today’s complex and often unpredictable business environments. For success, an organization must be able to combine and recombine their resources in novel ways, eliminating or reconfiguring resources that are no longer relevant, and acquiring new resources. An organization’s capability to change by manipulating resources continuously and rapidly—to innovate—is a competitive advantage that is not readily imitated by competitors. Innovation is critical to an organization’s viability since it enables the development and introduction of new products and services and thus enables an organization to maintain, or improve, its current business position. The chapter reviews the numerous theories of change and change management in the literature based on practice and precept. However, research shows that competitive advantage is increasingly located by authorities in an organization’s intellectual resources including the skill base, business systems and intellectual property of its employees: its Human Capital. Organizational innovation depends on the individual and collective know-how of employees, and innovation is characterised by an iterative process of people working together, sharing insights, and building on the creative ideas of one another. The chapter emphasizes that an organization’s intellectual resources have significant potential to realize innovation and change capabilities, but that the impact of these capabilities largely depends on the means of an organization to foster close community social interaction and open knowledge sharing, and to leverage its informal leadership as a precursor to and part of any related Knowledge Management (KM) initiative.Request access from your librarian to read this chapter's full text.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".