Determinants of construction companies' use of web‐based interorganizational information systems
Bibliographic record
Abstract
Purpose The purpose of this paper is to measure the influence of two categories of determinants – those tied to the characteristics of the organization and those tied to the characteristics of its supply chain relationships – on construction firms' use of web‐based interorganizational information systems (IOISs) to support interorganizational processes with their key suppliers. Design/methodology/approach Empirical evidence is gathered through an electronic survey conducted with 67 senior managers of Canadian construction companies. Findings Findings indicate that both categories of determinants strongly influence each of the facets of construction firms' use of web‐based IOISs to support interorganizational processes with their key suppliers. Findings also indicate that the two facets of the use of web‐based IOISs to support collaborative processes are influenced by the same characteristics of supply chain relationships while the use of both types of web‐based IOISs are not influenced by the same characteristics of the supply chain relationships. Practical implications This study shows construction managers that a supply chain approach may facilitate firms' adoption of IT to support their interorganizational processes with their suppliers. This research also exposes practitioners to the different ways web‐based IOISs can be used by construction firms to support their relationship with suppliers and emphasizes the need for managers to consider the characteristics of their supply chain relationships and not only technological factors while developing and implementing their web‐based IOISs strategy. Originality/value This study is amongst the few empirical studies on the use of information technology to support supply chain processes between construction companies and their suppliers. By proposing an approach to web‐based IOISs measurement comprising five facets, this research also makes a theoretical contribution to the field of IOISs.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".