Bibliographic record
Abstract
Product innovation is alive and well in Australia. There was overwhelming support for and interest in the topic, as evidenced by the large crowds we had at the various events on our recent tour; attendees were bubbling with enthusiasm when they spoke about their companies, their innovation activities, and their new product successes. Australia has the good fortune of being a Western-style, English-speaking economy, and has many international firms operating there; thus, Australia is well plugged into the methods, practices and thinking in terms of innovation management that we are familiar with in North America. But the country is also sufficiently geographically isolated that it has developed a certain degree of independence from other Western countries, and so it has many home-grown products that are not simply copies or imported designs or concepts from a sister or parent company (for example, GM and Ford both develop unique cars locally in Australia, and Australian winemakers have been particularly innovative in recent years, both in terms of product development and marketing). In many respects, Australia resembles Canada more than the United States when it comes to product development. It is a large country with a relatively small population, concentrated largely in a handful of coastal cities. It is a resource-rich and hence largely resource-based economy, and thus, not surprisingly, spends relatively less on RD minerals and steel; forestry products; and the like. And like Canada, Australia tends to lag the U.S. in terms of adoption of new management techniques. Many companies had only been recently introduced to new-product portfolio management, and even idea-to-launch processes (such as my Stage-Gate system) were a novelty to many. Five Major Themes Perhaps the greatest insights of the tour came from the intense breakout sessions we ran as part of our events. Their purpose was to identify the major problems, challenges and issues faced by Australian companies in product innovation. Five major themes emerged from these sessions, which were attended by almost 200 directors and managers--in R&D, new product development and marketing--from over 80 companies: 1. A lack of understanding and commitment from the top.--Machoism is flourishing in Australia. A very common theme across almost all companies was that there are too many executive pet projects--projects driven by some senior executive, no matter how bad the project is! Senior management does not really understand its role in leading the NPD effort. Additionally, in too many Australian firms, senior management is not really committed to NPD, has failed to lead the development of an innovation strategy for the business, and suffers from a short-term and immediate-profits focus. …
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.033 |
| Scholarly communication | 0.029 | 0.031 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.020 | 0.028 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".