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Record W1988609174 · doi:10.5539/jms.v4n2p12

Sustainability Aspects of Facilities Management Companies

2014· article· en· W1988609174 on OpenAlexvenueno aff
Alan K. Millin

Bibliographic record

VenueJournal of Management and Sustainability · 2014
Typearticle
Languageen
FieldPsychology
TopicFacilities and Workplace Management
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityBusinessSustainability organizationsMaturity (psychological)SuiteSustainability reportingMarketingEnvironmental resource managementPublic relationsProcess managementPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Purpose: In a fiercely competitive post-recession business climate facilities management companies have to do more than deliver sustainable services to clients, they also have to stay in business and prosper. There is a need for relevant business sustainability aspects to be identified and documented. This paper meets that need. A literature review, followed by an online survey was used. FM practitioners within the Gulf Cooperation Council region were asked about their companies’ adoption of sustainability related practices. The research has enabled the identification of key sustainability aspects that should be considered by FM leadership teams. Primary research reveals that within the GCC FM community business leaders are failing to identify risk to their businesses while useful guidance in the form of published standards is largely ignored. Sustainability maturity among these companies is generally low. A taxonomy of sustainability aspects is introduced which may be used to guide and support FM companies towards sustainability maturity and also provide the foundation for year-on-year performance improvement. Primary research was only conducted within the Gulf Cooperation Council region. Future research should consider the wider FM community. A suite of performance indicators for the identified sustainability aspects should be developed.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.009
GPT teacher head0.262
Teacher spread0.253 · 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 designTheoretical or conceptual
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

Citations3
Published2014
Admission routes1
Has abstractyes

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