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SWOT Analysis of Extended Reality in Architecture Engineering and Construction Organizations

2023· article· en· W4391307135 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicStrategic Planning and Analysis
Canadian institutionsOntario College of Art and DesignGeorge Brown College
FundersCanada Research Chairs
KeywordsSWOT analysisThematic analysisStrengths and weaknessesArchitectureWeaknessProcess (computing)Process managementConstruction industryComputer scienceEngineering managementKnowledge managementEngineeringBusinessConstruction engineeringMarketingQualitative researchSociologyGeographyMedicine

Abstract

fetched live from OpenAlex

This paper proposes SWOT (strength, weakness, opportunity, and threat) based criteria to evaluate the usefulness of Extended Reality (XR) in Architecture, engineering, and Construction (AEC) organizations, using XR literature in the AEC industry for the period 2018 to 2022, inclusive. A SWOT matrix is developed using thematic analysis to highlight the strengths, weaknesses, opportunities, and threats associated with XR in AEC organizations. A total of 105 articles were identified and analyzed from literature using a structured process in a spreadsheet matrix. A total of 22 criteria were developed and described 5 criteria for strength, 6 criteria for weakness, 6 criteria for opportunities, and 5 criteria for threats. The developed criteria are proposed to aid AEC organizations in achieving their strategic goal for the implementation of XR technologies. This paper identifies criteria that may be useful to XR researchers and industry practitioners.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
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 head0.012
GPT teacher head0.221
Teacher spread0.209 · 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

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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