Managing innovation in a knowledge intensive technology organisation (KITO)
Bibliographic record
Abstract
This study aims to add to the existing knowledge of how innovation works in organisations. By understanding how to assess/evaluate processes that support and enable innovation, managers can better manage innovation as a business process. This paper addresses elements of organisational behaviour that relate to people management where innovation and technology management is concerned. Perception plays a crucial role in driving behaviour and therefore the widely accepted business scorecard methodology has been used to measure innovation practices in the organisation. The research was done in a knowledge intensive technology organisation (KITO) in South Africa. Interviews with managers of R&D were conducted. These interviews were used to adapt an existing audit instrument to suit the technology–based organisation. Thereafter, a comprehensive audit of innovation was conducted at three different management levels using the adapted instrument. Over 100, mostly R&D managers, were asked to complete a scorecard–based questionnaire and to draw a visual representation (VR) of innovation. The results of the interviews, audit and VRs were used to produce a management framework that is not only applicable to a KITO, but can also be used widely to improve innovation through enhanced visual understanding of any technology–based organisation. The results of the study indicate that measuring innovation through a validated instrument is highly valuable. The Holistic System Framework for innovation and the measurement instrument facilitated (1) management of, and (2) organisational learning about innovation. The comprehensive audit indicated, on a strategic level, the strengths and weaknesses of the innovation process as practised in the organisation. The instrument is valuable at a strategic management level as it indicates where in the organisation the gaps exist regarding the management of the process of innovation with the aim to create a competitive advantage.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".