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Record W1553614654 · doi:10.5430/ijba.v6n3p72

Consumer Behavior towards Green Building: A Study in Abu Dhabi

2015· article· en· W1553614654 on OpenAlexvenueno aff
Emadeldin Abuamer, Mehraz Boolaky

Bibliographic record

VenueInternational Journal of Business Administration · 2015
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsAbu dhabiBusinessReal estateCompetition (biology)MarketingOrder (exchange)Green marketingGreen consumptionSample (material)Architectural engineeringGreen buildingEnvironmental economicsEconomicsEngineeringGeographyFinance

Abstract

fetched live from OpenAlex

Green energy is a concept that has developed dramatically in recent years, for both professionals and regular consumers. One of the most important applications of green energy is ‘Green Buildings’. This paper discusses Green Buildings from the perspective of the local consumers of Abu Dhabi city. Various elements of consumer preferences towards green buildings are evaluated in order to gauge their marketability in Abu Dhabi real estate market and the level of competition in the area. One of the research main themes is to evaluate existing Green Building support and recommending future anticipated support to boost the growth of the green construction industry. Primary data was collected through face to face interviews using a convenience sample of sixty respondents. This study has shown that Green Buildings are marketable products with their own marketable features; marketing tools sometimes are different from the tools used to market conventional buildings. Taken together, the study recommends stakeholders to invest more in green construction that will ultimately lead to solid growth in the industry of Green Buildings.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.040
GPT teacher head0.326
Teacher spread0.286 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations12
Published2015
Admission routes1
Has abstractyes

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