Analysis of adoption rates for Needs Driven versus Value Driven innovation water technologies
Bibliographic record
Abstract
This paper analyzes six case studies of new water technology innovations in the last three decades and investigates the differences in timelines for moving through the various stages of water technology commercialization. The concept of two different types of innovation was explored: Crisis/Needs Driven and Value Driven. It was found that the case studies that mapped to the Crisis/Needs Driven innovation moved relatively quickly compared to Value Driven innovations and in most cases involved new entrants. New entrants refer to new companies or start-ups that have recently entered the water technology market. The case studies, which could be mapped to Value Driven innovation, had a slower rate of technology diffusion, and they involved a combination of existing companies as well as new entrants. PRACTITIONER POINTS: The paper identifies two key types of innovation: Crisis/Needs Driven and Value Driven. Legislation was observed to be a key driver for the adoption of new technology innovation in the water sector. The Crisis/Needs driven innovations studied were observed to diffuse through the Water Technology Diffusion model at up to twice the pace of Value driven innovation. Crisis/Needs driven innovation typically involves disruptive innovation offered by new entrants, whereas with Value driven innovation, the solutions are provided by both existing companies as well as new entrants. It is also observed that in most cases a technology that is adopted in order to meet a crisis or need in the market is more expensive at the outset compared with incumbent solutions. While value driven adoption has a slower cycle for adoption, it presents a lower risk as it is less dependent on external factors and timing of implementation of regulations or the occurrence of some public health related or environmental crisis.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".