Determinants of the propensity to use of digital technologies in the Brazilian construction Industry

Rocha, Larissa de Moraes; Cândido, Luis Felipe; Barros Neto, José de Paula

doi:10.1590/s1678-86212025000100900

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Special issue: the best papers of ENTAC 2024 • Ambiente Construído 25 • 2025 • https://doi.org/10.1590/s1678-86212025000100900 linkcopy

Determinants of the propensity to use of digital technologies in the Brazilian construction Industry

Determinantes da propensão ao uso de tecnologias digitais na indústria da construção civil brasileira

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

This study analyzes the factors that influence the use of Digital Technologies (DTs) in the Brazilian construction industry. Based on the literature, a theoretical model with 10 factors (30 variables) was initially proposed. Through Exploratory Factor Analysis and Confirmatory Factor Analysis using Structural Equation Modelling, this structure was refined to 7 factors (25 variables). Data were collected via a survey of professionals in the Brazilian construction industry, yielding a final sample of 144 valid responses. Key findings reveal that BIM (Building Information Modeling), Cloud Computing, Unmanned Aerial Vehicles, and Virtual Reality are the most widely used technologies. These technologies are primarily applied in Design/Feasibility, Construction Management, and Execution phases. Moreover, the study confirms the importance of Perceived Usefulness, Organizational Aspects, Necessary Resources, and Current Capacity to use DTs. On the other hand, Ease of Use, Business Environment, and Lean Construction were not found to be significant factors influencing DT adoption. The results provide a comprehensive overview of the current use of DTs in Brazil. Additionally, they offer insights that can aid policymakers and industry managers in promoting digital transformation.

Keywords
Digital transformation; Construction management; Digital technologies; Construction Industry

DT	Brief description
Big Data	The process entails collecting, processing, and analyzing large datasets to address key questions and support decision-making.
Blockchain	A technology that records all events and transactions of validated, encrypted digital data, executed and shared among participants, ensuring the security of the involved data.
Cloud Computing	Cloud computing is the capacity to store, process, and retrieve data in the cloud
Unmanned Aerial Vehicles (UAV)	A remotely controlled aerial vehicle, equipped or not with sensors, to supervise, assess, and inspect ongoing operations.
Digital Twin	A digital or simulated representation of a real-world physical process or system, updated with specific frequency and accuracy, and employed for simulate, monitor, and optimize performance
3D Printing	Also known as additive manufacturing, 3D Printing is a technology used to produce 3D objects by layering materials such as concrete, polymers, or other materials
Artificial Intelligence	The ability of machines and technology to make decisions and solve complex problems using algorithms based on human intelligence
Internet of Things (IoT)	IoT is the interconnected network of physical objects with sensors, software, and network connectivity
Building Information Modeling (BIM)	BIM can be defined as a technology and methodology for managing the design and data of a building in digital format throughout its life cycle, using an intelligent information model
Augmented Reality (AR)	Technologies that incorporate virtual elements and computer-generated objects into a physical or real environment through visualization devices.
Virtual Reality (VR)	The use of a computer-generated environment or an immersive simulation in which users are placed within virtual scenarios that can enhance comprehension among construction project stakeholders and elevate their ability to complete projects successfully
Robotics	The use of programmed machines to autonomously interact with objects and perform tasks with greater safety and efficiency.
Sensors	The use of sensory devices to capture real-time information, which is then processed by algorithms and analytical tools to execute actions and make decisions in real time.
Wearables	Smart electronic devices incorporated into clothing and/or worn by the user, detecting behavior and movement while processing this data

Size of Companies	Age of Companies
Size of Companies	up to 1	1 to 5	6 to 10	11 to 20	Above 20	Total by size	% by size
Sole proprietor	6	15	4	4	2	31	21.5
Micro-enterprise	2	16	5	3	0	26	18.1
Small company	5	14	5	6	2	32	22.2
Medium company	3	5	12	7	10	37	25.7
Large company	3	4	1	3	7	18	12.5
Total by age	19	54	27	23	21	144	100
% by age	13.19	37.5	18.7	16	14.6	100

Type of projects	Size of projects
Type of projects	Micro	Small	Medium	Large	Mega	Total	% by type
High-income residential buildings	10	19	11	17	8	65	15.8
Middle-income residential buildings	20	23	16	13	6	78	18.9
Low-income residential buildings	13	14	4	10	0	41	9.9
Commercial buildings	17	21	13	14	7	72	17.5
Industrial buildings	3	6	9	14	7	39	9.5
Public buildings (government facilities)	3	6	16	13	1	39	9.5
Public buildings (infrastructure)	4	4	11	19	8	46	11.2
Public buildings (social housing)	1	2	4	12	0	19	4.6
Other	0	2	3	2	6	13	3.2
Total	26	33	30	37	18	144	100
% by size	18.06	22.92	20.83	25.7	12,5	100	-

Indices/Factors	PU	AT	OA		NR
Number of Rotations	5	2	2	2	1
p-value	0.067	0.267	0.776	0.585	0.431
χ²	17.373	1.230	0.081	0.298	1.681
χ²/df (<5)	1.737	1.230	0.081	0.298	0.840
GFI (>0.90)	0.966	0.996	1.000	0.999	0.994
CFI (>0.95)	0.994	1.000	1.000	1.000	1.000
RMSEA	0.072	0.040	0.000	0.000	0.000
Lower Factor Loading	0.768 (UP5)	0.706 (AT3)	0.806 (AO4)	0.888 (CE4)	0.810 (RN4)
Lower Communality	0.590 (UP5)	0.550 (AT3)	0.649 (AO4)	0.788 (CE4)	0.656 (RN4)
Lower Critical Ratio	13.124 (UP5)	12.388 (AT1)	13.200 (AO1)	17.097 (CE1)	11.791 (RN3)
Explained Variance (%)	80.735	81.503	81.865	86.765	74.606
Cronbach's alpha (α>0.8)	0.966	0.915	0.946	0.962	0.920
McDonald's omega (ω>0.7)	0.968	0.929	0.947	0.963	0.921

Path	SE	p-value	Hypothesis	Test
PU→ AT	0.797	***	H1b	Not rejected
AT→ IU	0.378	***	H1	Not rejected
OA→ IU	0.183	0.016	H3	Not rejected
LC→ IU	0.141	0.059	H4	Rejected
NR→ IU	0.399	***	H5	Not rejected
NR→ AC	0.436	***	H6	Not rejected
AC→ AU	0.579	***	H7	Not rejected
IU→ AU	0.177	0.011	H8	Not rejected

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[1] Source: based on Shawhney, Riley and Irizarry (2020).