Awareness of big data concept in the Dominican Republic construction industry: an empirical study
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AbstractThe construction industry, being one of the main characters in the ever-demanding need for technology developments, sometimes falls short of other industries in terms of implementation. The adoption of Big Data (BD) in industries like health and retail has had positive impacts in aspects such as decision-making processes and forecasting trends that allow planning some future business movements in advance. Hence, the question of whether these results can be recreated in construction industry. Therefore, this paper addresses the level of awareness identified as the first step towards implementation of the BD Concept within the construction industry of Dominican Republic (DR). Since little to no information exist on the subject the selected approach to perform this research was qualitative, twenty-one semi-structured interviews were studied using content analysis. Four levels of awareness is developed based on the Endsley situation awareness model. The results showed that nearly ninety-five percent of the interviewees had either no knowledge or a very basic awareness of the BD requirements or intermediate awareness but only five percent had actually applied BD in the construction industry. This paper provides the level of awareness of BD in the DR construction industry and provides evidence for the need to provide continuous professional development programmes for construction professionals and a need for an update of curriculum in construction-related education.
CitationReyes-Vera, P.F., Renukappa, S. and Suresh, S. (2020) Awareness of big data concept in the Dominican Republic construction industry: an empirical study, in Dawood, N., Rahimian, F., Seyedzadeh, S. & Sheikhkhoshkar, M. (eds.) Proceedings of the 20th International Conference on Construction Applications of Virtual Reality, 30th September-2nd October, 2020, Teeside University, Middlesborough.
Description© 2020 The Authors. Published by Teeside University. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://convr2020.com/
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by/4.0/