Investigating Residents' Adoption of a Recycling Application and Acceptance of Corporate Sponsorship: A Case Study of New Jersey
Bibliographic record
Abstract
Information Communication Technologies (ICTs) have created new opportunities to deliver recycling education. This study uses the Unified Theory of Acceptance and Use of Technology-2 (UTAUT-2)modelto inspect primary factorsimpactingU.S. residents'intentionto usearecycling application called "Recycle Coach." The data from an online survey of 1,215 app users located in New Jersey is analyzed using Partial Least Squares-Structural Equation Modelling (PLS-SEM). Results demonstrate that performance expectancy, facilitating conditions, hedonic motivation, and habit have a positive and significant effect on the intention to use recycling apps. The intention to use apps also has a positive and significant effect on the intention to recycle. The results support the use of ICT as a tool for building recycling habits. The study also inspected the funding mechanisms of Recycle Coach and various aspects of integrating corporate sponsorship in their business model. The results indicate that a majority of respondents accept app sponsorship. However, according to the results of the one-way analysis of variance (ANOVA), levels of sponsorship differ significantly depending on the sponsor's method of communication. Users negatively respond to seeing video ads within the app interface, whereas text notes and logos/banners representing the sponsor are more tolerable. Users also prefer that the sponsor is congruent with the sustainable mission and vision of Recycle Coach and are likely to have a more positive attitude towards the company sponsoring the app. Recommendations for solid waste management practitioners, app developers and marketers are discussed.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".