Using the TOE theoretical framework to study the adoption of BIM-AR in a developing country: the case of Ghana
Bibliographic record
Abstract
Purpose Building information modelling (BIM) and augmented reality (AR) are unique technologies in the digitalized construction industry. In spite of the numerous benefits of BIM-AR, its adoption has been at a relatively slow pace. The purpose of this study is to investigate how the factors within technology–organization–environment (TOE) framework influence the adoption of BIM-AR in the context of construction companies in a developing country. Design/methodology/approach By using a mainly deductive quantitative design, survey data were collected from senior management of built environment companies in Ghana using questionnaires. The study adopted a mixture of both purposive and snowball sampling approaches. Partial least squares structural equation modelling was used to analyse how the factors within the TOE framework explain BIM-AR adoption in Ghana. Findings Findings from the study show that the top three factors within the TOE framework that facilitate the adoption of BIM-AR include ICT infrastructure within construction firms; the size of the construction firm, which may influence the financial capacity to accommodate BIM-AR; and competitive pressure. The inhibitors of BIM-AR at the company level included external support and trading partners’ readiness. Research limitations/implications Implicit is that the significant factors will be useful to policymakers and companies in developing programs that appeal to non-adopters to aid in mitigating their challenges and further enhance BIM-AR adoption. Originality/value The value of this paper has been the use of the theoretical framework TOE to explain the adoption factors of BIM-AR in the Ghanaian construction industry. The originality of the paper is further anchored in consideration of BIM-AR, which is quite nascent in emerging countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".