Awareness of Big Data concept in the Dominican Republic construction industry: an empirical study

Purpose The construction industry, being one of the main activities 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 such as health and retail has had positive impacts in aspects such as decision-making processes and forecasting trends that allow planning some future business movements. Hence, the question of whether these results can be imitated in the construction industry. Therefore, this paper aims to address the level of awareness identified as a first step towards implementation of the BD concept within the construction industry in the Dominican Republic (DR). Design/methodology/approach As little to no information exist on the subject; the selected approach to perform this research was qualitative methodology; 21 semi-structured interviews were studied using situational awareness. Four levels of awareness were developed based on the Endsley’s Situation Awareness model. Findings The results showed that nearly 95% of the interviewees had either no knowledge or very basic awareness of the BD requirements or intermediate awareness, but only 5% had applied BD concepts in the construction industry. Originality/value This study shows the gaps that exist in the understanding and implementation of BD concepts in the DR construction industry. This paper establishes the need to develop continuous professional development programmes for construction professionals and a need to update curriculum in construction-related education.

[1]  Masoud Mahdianpari,et al.  Google Earth Engine for geo-big data applications: A meta-analysis and systematic review , 2020 .

[2]  Mohammad Nazir Ahmad,et al.  Analyzing diffusion patterns of big open data as policy innovation in public sector , 2019, Comput. Electr. Eng..

[3]  Leon Michael Caesarius,et al.  Searching for big data , 2018, Scandinavian Journal of Management.

[4]  M. Crouch,et al.  The logic of small samples in interview-based qualitative research , 2006 .

[5]  Ahmed Ghenabzia,et al.  An intelligent system for energy management in smart cities based on big data and ontology , 2020 .

[6]  Philip J. Hills,et al.  International Journal of Information Management , 2006, Int. J. Inf. Manag..

[7]  Suresh Renukappa,et al.  BIM in the water industry: addressing challenges to improve the project delivery process , 2020 .

[8]  Lukumon O. Oyedele,et al.  Big Data in the construction industry: A review of present status, opportunities, and future trends , 2016, Adv. Eng. Informatics.

[9]  Bon-Gang Hwang,et al.  Factor-based big data and predictive analytics capability assessment tool for the construction industry , 2020 .

[10]  Pooya Tabesh,et al.  Implementing big data strategies: A managerial perspective , 2019, Business Horizons.

[11]  Sarah A. Mason,et al.  Practice makes better? Testing a model for training program evaluators in situation awareness. , 2020, Evaluation and program planning.

[12]  Bin Jiang,et al.  Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges , 2015, ArXiv.

[13]  Okba Kazar,et al.  Using a distributed deep learning algorithm for analyzing big data in smart cities , 2020 .

[14]  L. Vaughn,et al.  Semistructured interviewing in primary care research: a balance of relationship and rigour , 2019, Family Medicine and Community Health Journal.

[15]  S. Gronlund,et al.  Situation Awareness , 2006 .

[16]  Richard T. Watson,et al.  Digital Data Streams: Creating Value from the Real-Time Flow of Big Data , 2016 .

[17]  D. Boyd,et al.  CRITICAL QUESTIONS FOR BIG DATA , 2012 .

[18]  Indratmo,et al.  Systematic Review of the Literature on Big Data in the Transportation Domain: Concepts and Applications , 2019, Big Data Res..

[19]  Seungjun Ahn,et al.  VR-Based investigation of forklift operator situation awareness for preventing collision accidents. , 2020, Accident; analysis and prevention.

[20]  Manuel Alexander Silverio-Fernandez,et al.  Evaluating critical success factors for implementing smart devices in the construction industry , 2019, Engineering, Construction and Architectural Management.

[21]  Peng Liu,et al.  A survey of remote-sensing big data , 2015, Front. Environ. Sci..

[22]  Dejan Filipovic,et al.  GEOSPATIAL EVALUATION OF BELGRADE FOR THE PURPOSES OF DETERMINATION OF SUITABLE LOCATIONS FOR THE CONSTRUCTION OF PV PLANTS , 2020 .

[23]  Murtaza Haider,et al.  Beyond the hype: Big data concepts, methods, and analytics , 2015, Int. J. Inf. Manag..

[24]  Belaid Bouikhalene,et al.  Big Data Approach and its applications in Various Fields: Review , 2019, Procedia Computer Science.

[25]  Raza Ali Khan,et al.  Role of Construction Sector in Economic Growth: Empirical Evidence from Pakistan Economy. , 2008 .

[26]  Melanie C. Wright,et al.  Adaptation and Validation of the Situation Awareness Global Assessment Technique for Nurse Anesthesia Graduate Students , 2020 .

[27]  Feng Liu,et al.  Big data driven Hierarchical Digital Twin Predictive Remanufacturing paradigm: Architecture, control mechanism, application scenario and benefits , 2020 .

[28]  Mica R. Endsley,et al.  Toward a Theory of Situation Awareness in Dynamic Systems , 1995, Hum. Factors.

[29]  Laura Johnson,et al.  How Many Interviews Are Enough? , 2006 .

[30]  Davi Viana,et al.  Mental health ubiquitous monitoring supported by social situation awareness: A systematic review , 2020, J. Biomed. Informatics.