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Improving data quality in construction engineering projects: an action design research approach
University of Agder.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0002-5506-9633
2014 (English)In: Journal of Management in Engineering, ISSN 0742-597X, E-ISSN 1943-5479, Vol. 30, no 3, article id 5014003Article in journal (Refereed) Published
Abstract [en]

The topic of data and information quality (DQ/IQ) is a longstanding issue of interest in both academia and practice in the construction engineering field. Poor DQ/IQ has led to poor engineering drawings that, in turn, have led to delays and eventuality to cost overruns. In this paper, we report a study that took an Action Design Research (ADR) approach to develop and evaluate a DQ/IQ assessment tool, which we call Information Quality System (IQS), in a large global engineering and construction company. The evaluation was performed by comparing the level of DQ/IQ in a project that used IQS with two projects that did not use the tool. The result is encouraging: the DQ/IQ in the project using IQS was significantly higher overall than in the two other projects. The implication is that a tool based on the design principles on which IQS was built is likely to help improve DQ/IQ in engineering systems and, hence, in engineering drawings. Consequently, it will decrease project delays and cost overruns. More generally, our paper adds to the discourse in the literature on the use of information and communication technologies (ICT) in the construction context. Our paper illustrates another successful application of action-oriented research that can solve practical problems while generating academic knowledge. In taking a design approach, we augment the literature on the use of action research in construction engineering and management.

Place, publisher, year, edition, pages
2014. Vol. 30, no 3, article id 5014003
National Category
Information Systems, Social aspects
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:ltu:diva-9039DOI: 10.1061/(ASCE)ME.1943-5479.0000202ISI: 000334604700003Local ID: 799cf8f3-bd81-4f26-bd92-0ac94781af72OAI: oai:DiVA.org:ltu-9039DiVA, id: diva2:981977
Note
Validerad; 2014; 20130722 (mausei)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved

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