Factors Influencing the Adoption Intentions of Households Toward Solar Photovoltaic Technology in Jordan: A Structural Equation Modeling Analysis
Bibliographic record
Abstract
Most developing countries, including Jordan, have abundant renewable energy sources, such as solar energy. However, despite these enormous potentials, the adoption rate remains low. Several previous studies addressed the feasibility, potential, and policies supporting investment in this technology, but none have been addressed from the perspective of customers at the national level. Therefore, this study attempts to examine factors influencing the adoption intentions of households toward solar photovoltaic (PV), using the innovation diffusion theory (IDT).Four dimensions from the diffusion of innovation theory have been adopted to assess household purchase intentions toward solar PV. Questionnaires were distributed through social media, with a total of 202 respondents. Structural equation modeling (SEM) was used to examine the data and derive the causal relationships of the proposed hypotheses. Results confirmed that relative advantage and observability have an influence on consumer intentions toward adoption, while compatibility and complexity have no influence on adoption intentions. The successful approach to support solar PV adoption will be helpful in reducing household electricity bills and thus support sustainable development.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.046 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".