The Innovation Management Modelling in the Water Sector in the United Arab Emirates: A Mixed-Methods Study
Bibliographic record
Abstract
The United Arab Emirates (UAE) is encountering a scarcity of water resources. It is counting on innovation management to alleviate the situation. In that context, there is a need for a managerial framework for this subject. Therefore, the aim of the current study is to build up an innovative managerial model. To establish this model, we applied a convergent, parallel, mixed-methods design. The study participants (n = 42) consisted mostly of leaders and experts working for the main water institutions. We analysed the quantitative method via partial least squares structural equation modelling (PLS-SEM), a SmartPLS software. Qualitative method procedures were conducted starting from coding, categorising, obtaining themes, and lastly, the establishment of grounded theory. We obtained two rigid inputs (quantitative and qualitative models) for the last phase (mixed-methods analysis). The quantitative findings revealed a significant and robust relationship (t value = 26.6, p = 0.000, coefficient = 0.888, R2 = 0.788). The qualitative findings also produced a steady grounded theory. Both quantitative and qualitative models were crossed according to the ‘convergence coding matrix’ and ‘triangulation analysis protocol’. Ultimately, we built a holistic framework named ‘the UAE water innovation model’, consisting of 12 components (meta-themes). This model should be adopted as the main guide for innovation management and strategy in water public sector institutions. Globally, this model could be a significant contribution, and it would be applicable to any country in the world with the same arid environment as the UAE.
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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 | 0.031 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".