A Dual-Factor Structural Model for Assessing the Determinants of Prefabrication and Modular Construction Uptake in India
Bibliographic record
Abstract
Rapid urbanization demands improved project delivery in terms of fast, affordable, and sustainable building solutions.Despite the prefabrication and modular solutions offering a wide range of benefits, the Indian construction industry is well behind in adapting to a changeover.Even though several research works have been carried out in the domain of technology adoption challenges, the results are broad and encompass global trends.This study was conducted under industry-specific conditions to measure and analyse the influence of various latent variables on adopting prefabrication methods in the Indian scenario.This study aims to explore and analyse those retarding factors along with enablers that affect the adoption of various prefabrication methods with a dual-factor perspective.The primary cause factors were extracted from an in-depth literature review and converted into measurable indicators within a structured questionnaire.The survey was conducted among industry professionals actively involved in prefabricated construction projects.Collected data was analysed using the Partial Least Squares Path Modeling (PLS-PM) method, keeping the technology implementer and receiver as the primary decision-makers.The results reveal how the indicators associated with the negative customer perceptions (technology receiver group) have a greater impact on adopting prefabrication and modular methods.Also, internal organization barriers like huge initial investment requirement and skill shortage retard the transformation process.The study projects the need for skill upliftment in the prefab sector, an increased customer awareness, and improved building performance (post construction), which play a significant role in the shift.Furthermore, the study provides practical insights to industry professionals seeking to modernize their construction practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".