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Record W2127191903 · doi:10.1111/1467-9310.00272

Managing innovation in a knowledge intensive technology organisation (KITO)

2002· article· en· W2127191903 on OpenAlexaff
Audrey Verhaeghe, R. Kfir

Bibliographic record

VenueR and D Management · 2002
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsCanadian Society of Intestinal Research
Fundersnot available
KeywordsBalanced scorecardKnowledge managementAuditBusinessStrengths and weaknessesProcess (computing)Innovation managementProcess managementComputer sciencePsychology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0080.005
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.014
GPT teacher head0.210
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2002
Admission routes1
Has abstractyes

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