Measuring innovation culture in organizations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Academic and practitioner interest has focused on innovation as a method of competitive differentiation and as a way to create customer value. However, less attention has been devoted to developing a measure of innovation culture. The purpose of this paper is to develop an empirically‐based comprehensive instrument for measuring an organization's innovation culture. Design/methodology This paper describes a procedure which explicates the innovation culture construct, and proposes a multi‐item measure of innovation culture predicated on exploratory factor analysis. These descriptors were derived from extant literature, key informant interviews, and a survey of over 282 employees from the financial services industry. Findings Findings suggest that an innovation culture scale may best be represented through a structure that consists of seven factors identified as innovation propensity, organizational constituency, organizational learning, creativity and empowerment, market orientation, value orientation, and implementation context. Practical implications The seven‐factor model can be used both descriptively and diagnostically. Among other things, it presents a practical way to measure an organization's innovation culture, and could initially be used to establish a baseline level of innovation culture. From there, it could be used as a metric to chart the organization's efforts as it moves to engender innovation. Originality/value More effort should be devoted to developing measures to assess innovation culture specifically. This model presents an innovation culture construct that is complimentary to work that has preceded it. The findings combined with the suggestions provide an alternative perspective as a measure of innovation and extends a basic framework for further investigation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.015 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it