Roles of Managers and Stakeholders Perception on Solar Technology Adoption Intention: A Case of Micro, Small and Medium Enterprises (MSMEs) in Lagos State, Nigeria
Bibliographic record
Abstract
Powers shortages is rampant in Africa of which Nigeria is not an exception and solar technology as a viable alternative source of electricity which would mitigate this problem has meted slow adoption. This study aimed to explore the impact of mindset/attitude from Theory of planned behavior (TPB), Disruptive Innovation Theory (DIA), awareness-knowledge, opportunity and barrier over managers (owners) of MSMEs intention to adopt solar technology for their businesses. A questionnaire was administrated to collect data from a sample of 400 managers (owners) of MSMEs respondents’ in Lagos State, Nigeria. A multivariate technique was applied to test the hypotheses using Structural Equation Modeling (AMOS-23). The findings showed that mindset/attitude, (DIA) and opportunity have a significant impact on solar technology intention, however, awareness-knowledge and barrier were not significant. These independent variables explained 71% variance of the dependent variable intention. In addition, DIA was found to have a significant impact on opportunity, barrier and mindset/attitude however, barrier on mindset/attitude was not significant. These findings not only provide evidence for MSMEs strategic planning to ensure sustainable business growth for their businesses but also provide new knowledge to policy and decision makers, the manufacturing & installation (suppliers) companies and other stakeholders for renewable energy as a part of long term sustainable development.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".