Cooling off innovation hotspots: smaller businesses need to look wide and deep
Bibliographic record
Abstract
Purpose This paper aims to provide evidence-based managerial advice for identifying, developing and managing a broad-based innovation system for organizations to reap optimal performance outcomes. Design/methodology/approach The general review is based upon large-scale peer-reviewed academic studies of innovation practices in organizations and their performance outcomes and synthesized here through two illustrative case studies. The first case deals with Brightsquid Secure Communications as they expanded their product-focused innovation practices to include broad-based organizational improvements, while the second case focuses on Trimet Building Products and their use of broad-based innovation to turn around declining revenue. Findings Using the SME context, this study outlines an innovation management model that highlights the importance of leadership attention on organization-wide innovation and the interdependence of investments across functions.[AQ2] To enhance their performance, SMEs must implement broad learning programs across the organization that provides formal and informal cross-training and takes a cross-functional approach to innovation and problem-solving. Originality/value Reviewing and integrating the literature on different innovation types and outcomes, this study proposes a novel broad-based innovation model that guides firms that overemphasize improvements within a single function. Further, drawing on the learning literature, this paper recommends an organizational learning and collaboration model to achieve organization-wide innovation for optimal outcomes.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".