An Investigation on the Adoption of Electronic Commerce Systems for the Operators of Construction Companies
Bibliographic record
Abstract
E-commerce is emerging and growing in all kinds of businesses and industries over the world especially in the construction industry. This research focuses on the adoption of business-to- business kind of e-commerce in construction industry. This research aims to identify the motivational and barrier factors of the construction workers in influencing the adoption of e- commerce specifically in the construction industry world. As part of the research, a multinational company, which produces tools for the construction industry, will be used as a case study to look into how the customers of this company are adopting e-commerce. This research uses the Diffusion of Innovation model as the based research framework to understand the factors that explain the rate of adoption and how these factors are influencing the adoption in the construction industry. Other internal and external factors of companies such as the organizational factor, technological factor digital readiness will also be looked into to understand their influence as well when it comes to adoption of e-commerce. Data collection is done by distributing questionnaires to the related participants in the construction industry. Findings from this study provides insights on the factors of adoption by applying the diffusion of innovation model and recommendation of strategies for this industry to address the problem of low adoption of e-commerce among construction workers in the construction industry. This research showed that relative advantage, compatibility, complexity, Trialability, organizational readiness and trust are significant factors leading to the adoption of e-commerce systems. Culture as well as technological and digital readiness were found to be insignificant. Overall, the study’s findings enrich the discourse related to the adoption of e-commerce systems by construction companies in Asia and other parts of the world. The findings will be relevant for construction companies around the world planning to introduce or improve the e-commerce adoption of the customers. The study’s findings could also be relevant for future analysis of e-commerce adoption.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".