Exploring the influence of information and communication technology (ICT) on construction supply chain management: Empirical evidence from a construction project’s perspective
Bibliographic record
Abstract
The purpose of this paper is to explore the influence of ICT on construction supply chain management (CSCM). Furthermore, this research proposes a model for ICT that can be used to test and improve CSCM within the Nigerian construction industry. The relationship between ICT and CSCM was highlighted in an integrated model. Structural equation modelling (SEM) was used to validate the hypothesised relationship by evaluating the responses of practitioners working in construction project management related firms using a sample of 214 respondents. The findings of this study show a link between ICT and CSCM on five (5) of the constructs on targeted construction firms engaged in supply chain (SC)-related construction activities in Port-Harcourt, Rivers State, Nigeria. This research is supported by the Technology Organisation and Environment (TOE) Model as a key theory of technology adoption. With an average R2 value, the SEM model is 76%, which is commendable (high). The current study offers an empirical and theoretical explanation of various aspects of ICT and CSCM, particularly in the Nigerian construction industry. As a result, this study adds to the body of knowledge by providing crucial insight into the influence of ICT on CSCM throughout the entire construction supply chain. The knowledge gained will aid industry stakeholders and the government in developing policies that will increase ICT adoption in current practice.
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 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".