measuring information dependency for construction engineering projects

Clicks: 304
ID: 160317
2013
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #2 of 15 articles by views in public organization review

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
Information dependency may be the most important key for managing information exchange to reduce project risks. Studies to date have not successfully discovered objective and quantitative surrogate to measure information dependency. This paper suggests an approach to measure information dependency with the productivity relationships among various disciplines for heavy industrial engineering projects. As part of a Construction Industry Institute (CII) study, the authors identified the information exchange pattern of engineering disciplines. Based on the patterns, the authors discovered the information dependency that various engineering disciplines had with their productivity relationships and conducted a survey afterwards for validation. Both results show significant and consistent evidence suggesting that: 1) information of equipment and piping disciplines is statistically dependent rather than the other paired disciplines; and 2) productivity relationship can be a legitimate surrogate to measure information dependency between equipment and piping disciplines. As such, this study enlightens a research trajectory for improvement of engineering productivity.
Reference Key
liao2013journalmeasuring Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Pin-Chao Liao;Stephen R. Thomas;William J. O’Brien
Journal public organization review
Year 2013
DOI
10.3846/13923730.2012.743924
URL
Keywords

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.