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Record W7057097137

Human-in-the-Circular-Loop: A consumer attributions-based approach for investigating the effect of enterprise greenwashing on wishcycling.

2024· other· en· W7057097137 on OpenAlexfundno aff

Bibliographic record

VenueBournemouth University Research Online (Bournemouth University) · 2024
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersCanada Excellence Research Chairs, Government of CanadaBournemouth University
KeywordsGreenwashingCircular economyContext (archaeology)PerceptionCorporate social responsibilityCore (optical fiber)Work (physics)Competitive advantage
DOInot available

Abstract

fetched live from OpenAlex

With increasing consumers awareness of environmental problems, corporate greenwashing practices have become common for companies to gain, sustain, and improve a competitive advantage without bearing the costs of moving to more sustainable practices. However, although there is extensive research on greenwashing, there is limited work studying the degree of consumer attribution on corporate greenwashing practices and their consequences on wishcycling. This thesis presents a novel approach to investigating the human aspects of Circular Economy (CE) ecosystems, introducing Human-in-the-circular-loop (HITCL). The framework integrates established theories from different disciplines, such as psychology, human resource management and marketing, to provide an understanding of the human factors influencing the adoption of circular practices. Acknowledging the important part that humans play as both consumers and employees in shifting to a CE, the HITCL framework provides a lens through which to study how individuals embrace the circular economy concept and how this influences their behaviors and decision-making regarding circular practices can be studied. The theoretical contribution of this thesis is the introduction of the HITCL framework, which builds upon mature theories from diverse academic fields and incorporates them into circular studies, thereby advancing the social aspects of circular economy research. This thesis addresses the issue of corporate greenwashing and its impact on consumer behaviour, specifically in the context of circular food and beverage packaging. A survey was completed by 537 participants, and Structural Equation Modelling (SEM) was utilised to analyse the relationships between perceived company motives, consumer attributions, perceptions of greenwashing, and wishcycling behaviour. Additionally, the moderating effect of core self-evaluation on the relationship between circular packaging and greenwashing techniques was explored. The findings highlight the mediating role of consumer perceptions of company motives in the relationship between corporate greenwashing and wishcycling. Specifically, consumers are more inclined to engage in wishcycling when they attribute greenwashing practices to societal reasons thereby rather than business motives, despite their ability to recognise greenwashing techniques in both scenarios. It was also observed that consumer personality traits, particularly core self-evaluation, moderate the relationship between circular packaging and perceptions of greenwashing. A confident consumer will purchase products packaged in what they perceive as circular packaging, when they are confident that they are not being subjected to greenwashing tactics. These results underscore the importance of understanding consumer behaviour and perceptions in circular environments and policy domains. The findings offer valuable insights for policymakers, businesses, and researchers aiming to promote circular consumption and mitigate environmental harm in the transition towards a more circular economy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.174
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.051
GPT teacher head0.323
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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