Exploring the adoption of photovoltaic cleaning robots: An institutional management approach
Bibliographic record
Abstract
There is increased adoption of renewable energy, particularly photovoltaic (PV), as a major source of clean and environmentally friendly energy. With the wide application of PV technologies, commonly called solar panels, their appropriate maintenance and cleaning are vital for optimum PV energy generation. The research focuses on exploring the various factors attributed to influence the want, intention and need to use PV panel cleaning robots in Thailand from an institutional management perspective. The study adopted the extended C-TAM-TPB by adding three more variables - Trust in technology, awareness of renewable energy, and environmental concern. A quantitative approach was used, where primary data was collected from PV institutional users. A population sample of 411 respondents was used. The confirmatory factor analysis (CFA) came in handy in evaluating the performance model, with the study's hypotheses being evaluated by the structural equation modeling (SEM). Results indicated that most of the subjective behaviors, perceived behavioral controls, and trust in technology played a key role in determining the intention and need to use solar panel cleaning robots. Perceived usefulness was found to have significant influence towards PV panel adoption. The influence of attitude was mediated by several factors – environmental concern, renewable energy awareness, trust in technology. The study recommended that the institutional users of solar panels should consider investing in knowledge regarding perceived behavioral control, developing confidence in the use of renewable and sustainable energy, and developing trust of the renewable and sustainable energy technologies. This could be achieved through programs such as public awareness, sharing accurate information regarding solar panel cleaning robots, and providing support and after sales support. Educational initiatives to change users' attitudes toward renewable energy technologies were recommended.
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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 | 0.000 | 0.001 |
| 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".