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Record W4390943404 · doi:10.11159/ijepr.2024.001

Tap and Bottled Water Consumption in a Higher Education Institution: Applying the Theory of Planned Behaviour

2024· article· en· W4390943404 on OpenAlexvenueno aff
Sara Sousa, Elisabete Correia, Manuela Larguinho, Clara Viseu

Bibliographic record

VenueInternational Journal of Environmental Pollution and Remediation · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsBottled waterTap waterTheory of planned behaviorConsumption (sociology)InstitutionWater consumptionBusinessEnvironmental scienceEconomicsEnvironmental engineeringSociologySocial scienceManagement

Abstract

fetched live from OpenAlex

This research study explores tap and bottled water consumption in a Portuguese public Higher Education Institution (HEI). Based on a sample of 413 valid responses,collected in an online survey that took place during the first quarter of 2022, and applying the Theory of Planned Behaviour (TPB) framework, the present study achieved relevant results.It is observed a positive and significant influence of individuals` attitudes, subjective norms, and perceived behavioural control in their intention to consume tap water, which has a positive and significant impact on tap water consumption behaviour.Nevertheless, it is identified the existence of an intentionbehaviour gap, revealing that individuals still consume bottled water, despite their willingness to drink more tap water.Increasing the scientific information on the individuals` behaviour regarding tap and bottled water consumption, allowing policy makers and educational institutions to adopt more effective measures and policies to change behaviours and promote more tap water consumption, thus avoiding the adverse environmental effects associated with the consumption of water in single use plastic bottles.

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)0.001
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 score0.208
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.258
Teacher spread0.246 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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