Factors for Implementation of Circular Economy in Firms in COVID-19 Pandemic Times: The Case of Peru
Bibliographic record
Abstract
The circular economy can contribute to the eco-efficient use of resources. Firms can obtain relevant benefits if they implement a circular economy. In Peru, the circular economy would create benefits, but it is not fully clear what factors explain the acceptance of firms of implementing a circular economy. Following the theory of planned behavior, the current research assesses the influence of attitudes, subjective norms, perceived behavioral norms, intentions, and pressures on behaviors towards the circular economy. A total of 71 medium-size firms based in Peru participated in an online survey. Six questions were focused on general information, and forty-seven questions evaluated the circular economy behavior of firms. A partial least square structural equation modeling technical analysis was used. It was found that attitudes (0.144), subjective norms (0.133), and perceived behavioral control (0.578) had a positive influence on intentions; also, perceived behavioral control (0.461) had a positive influence on behaviors towards the circular economy. Finally, pressures had a positive influence (0.162) on behaviors towards the circular economy. The model explained 64.3% of the behaviors towards the circular economy. The outcomes of the bootstrapping test were used to evaluate if the path coefficients are significant. This study showed that attitudes, subjective norms, perceived behavioral norms, intentions, and pressures explained circular economy behaviors. This information can help firms develop strategies to move forward a circular economy and provide governments information about the current situation of circular economy implementation to generate new norms and strategies for more implementation of circular economy measures in enterprises. The novelty is based on using the PLS-SEM technique.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".