Open AccessDesign documentation quality influential variables in the construction sector
Author(s) -
Peter Dodzi Kwasi Agbaxode,
Sitsabo Dlamini,
Ehsan Saghatforoush
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/654/1/012007
Subject(s) - documentation , quality (philosophy) , variables , computer science , internal documentation , empirical research , process management , knowledge management , engineering , statistics , mathematics , software construction , philosophy , epistemology , software , machine learning , software system , programming language
There are numerous studies on design documentation variables without efforts to evaluate their level of importance. Therefore, the aim of this study is to evaluate the significance of these variables towards improving design documentation quality. A questionnaire survey to identify the significance of key quality variables was carried out. A total of 139 variables on design documentation quality were used. The mean score and Standard deviation of each factor were used to determine the level of significance. Based on responses from 112 construction industry players, the significance of each variable is determined. Design documentation is fit for purpose was considered highly important as an attribute while Inadequate and Insufficient documentation was ranked highest for quality influential factors. The findings will provide valuable data to stakeholders, researchers, and academics and will help enhance project performance because professionals will be aware of key factors that can influence design documentation quality. It will also aid in providing solution to sustainable infrastructure design and delivery challenges in the industry. The study offers a pragmatic data and empirical evidence to expand knowledge on design documentation quality. It is the first of its kind that explored the significance of design documentation quality variables based on the outcome of a meta-synthesis.