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Record W2878474064 · doi:10.1002/bse.2185

Identifying consumers' engagement with renewable energy

2018· article· en· W2878474064 on OpenAlexafffundabout
Aparna Venugopal, Dhirendra Shukla

Bibliographic record

VenueBusiness Strategy and the Environment · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of New Brunswick
FundersMitacs
KeywordsRenewable energyIncentiveWillingness to payMoral disengagementBusinessSustainabilityMarketingEnergy (signal processing)Environmental economicsEconomicsPublic economicsMicroeconomicsPsychologySocial psychology

Abstract

fetched live from OpenAlex

Abstract A moral engagement with sustainability enhances consumers' willingness to pay an increased rate for renewable energy when utility firms offer a variety of financial incentives to buy, rent and lease energy solutions to promote the uptake of energy‐efficient technologies. Using structural equation modelling, with data collected from 140 residential energy consumers in Canada, this paper tests and finds evidence that consumers' moral disengagement with environmental concerns negatively influences their willingness to pay more for renewable energy. Furthermore, it is observed that a consumer's perceived sense of control when evaluating energy solutions further accentuates the effects of moral disengagement on willingness to pay. These findings also illuminate the socio‐economic factors that encourage moral engagement concerning renewables. These findings underscore and build upon the claims of the theory of moral self‐regulation. The results and implications guide energy suppliers in potential niche business models to promote the uptake of energy efficient technologies. Similarly, the findings can guide policy‐makers on the cognitive and psychological factors that shape consumers' moral engagement with environmental concerns.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.014
GPT teacher head0.224
Teacher spread0.210 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations26
Published2018
Admission routes3
Has abstractyes

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