The Main Barriers Limiting the Development of Smart Buildings

Abstract

Smart buildings play a key role in the complex ecosystem of cities and are often subject to barriers that limit their development. Although identifying these barriers is fundamental to creating an enabling environment for this segment’s expansion, few works aim to identify these challenges. This work has two main objectives: (1) to research the main barriers limiting the development of new smart building projects and (2) to prioritize these barriers from the perspective of professionals with experience in the field. We adopted an exploratory approach common in research that focuses on identifying and prioritizing variables related to a phenomenon, which is based on two main actions: obtaining information through a careful literature review and consulting professionals who work in the concerned field. The results showed that professionals assessed the 23 barriers identified through bibliographic research as important, with the most important being related to lack of qualified professionals, shortage of government policies, higher initial and construction costs, macroeconomic barriers and access to financing, high cost of intelligent systems and technologies, regulatory barriers, lack of knowledge about the current and potential benefits of smart buildings, and more complex design and construction.

1. Introduction

Improving the intelligence of buildings is a frequent topic on sustainability improvement agendas. Smart buildings (SBs) are more operationally efficient than traditional buildings, produce less environmental impact, and can better meet the expectations and needs of their managers and users.

According to Froufe et al. [1], although we do not have a fully accepted concept of “smart building”, its evolution is mainly due to the benefits provided by technological innovations, and its understanding usually occurs based on the functionalities provided by these benefits.

Authors such as Ghaffarianhoseini et al. [2], Dong et al. [3], Benavente-Peces [4], Senitkova [5], and Raveendran et al. [6] highlight the improvement in comfort, health, and well-being of users provided by the building’s ability to manage environmental variables such as temperature, humidity, lighting, level of occupancy of spaces, and user routine. Houran et al. [7], Lam et al. [8], and Alsafery et al. [9] point to improving the building’s energy efficiency by automated energy management. Kubicki et al. [10], Alohan et al. [11], Ejidike et al. [12], and Apanavičienė et al. [13] highlight improving the protection against cyber-attacks and the safety of life and security of occupants, including concerning emergency response, mainly through intelligent surveillance systems, access control, and monitoring of environmental variables. Gadakari et al. [14], Bandara et al. [15], and Lam et al. [8] highlight the improvement of building operational performance, mainly through the use of intelligent and interactive interfaces to automate daily routines and manage the performance of devices and systems, as well as creating a more productive and friendly environment for building users and employees. Smart buildings also reduce operational and maintenance costs and improve asset management and return on investment [12,14,16]. Smart buildings are also more sustainable, mainly because they contribute to reducing the consumption of natural resources, production of waste, and emission of pollutants and prioritize the use of renewable energy [17,18,19,20].

In short, buildings become more intelligent as integrating smart devices, systems, and technologies, such as big data and the Internet of Things, makes it possible to improve the meeting of stakeholders’ expectations and needs and their ability to be more sustainable. From this perspective, the intelligence degree of buildings may vary depending on the evaluation criteria used to measure the degree to which these improvement requirements are met. Indicators and instruments for assessing the intelligence degree of smart buildings have been the subject of several authors, such as Indrawat et al. [21], Wong et al. [22], Alwaer and Clements-Croome [23], Arditi et al. [24], Kolokotsa et al. [25], and Guanatilaka et al. [26]. As innovations have been incorporated into building systems, new indicators have been established.

Smart buildings have also received other approaches, mainly concerning understanding the concept of “smart buildings”, determining the contribution of technologies to improving a building’s intelligence, and identifying barriers and challenges to the development of the sector.

Regarding identifying barriers that limit the development of smart buildings, we did not identify works whose main objective was to identify these barriers and address them in an integrated way. However, we identified works that focus on specific aspects, such as the difficulties imposed on project management by the requirements and specificities of smart buildings [27,28]. We also identified works that, although they did not aim to identify barriers, cited some barriers or presented situations that could be identified as barriers.

This work has two objectives: (1) to identify barriers that limit the development of new smart building projects based on the interpretation of the work of researchers who publish on smart buildings and (2) to identify the importance of the barriers from the perspective of professionals working in the field.

By systematizing and expanding knowledge about the barriers that impact the development of smart buildings and providing a comprehensive view of the limitations faced by stakeholders, this work contributes to a critical reflection on the environment in which smart buildings are developed. It also helps investors, decision makers, and policymakers to identify opportunities to overcome the limitations imposed by barriers and establish strategies to leverage the sector’s sustainable growth.

This article is structured as follows: Section 2 presents the barriers identified in the literature. Section 3 presents the methodological procedures for the bibliographic research, barriers identification, expert opinion survey, and data analysis. Section 4 presents and discusses the research results. Conclusions are provided in Section 5.

2. The Barriers Limiting the Development of Smart Buildings

Through a literature review, we identified the following barriers limiting the development of smart buildings: building management systems’ functionality limitations; difficulties imposed by deficiencies in the city’s infrastructure; difficulties in adopting and coordinating energy-efficient systems; difficulty accessing smarter technologies, materials, and equipment; greater complexity of data protection and personal and property security systems; greater complexity of intelligent technologies; high cost of available intelligent systems and technologies; high level of risk compared to that for traditional buildings; higher initial and construction costs compared to those of conventional buildings; incompatibility between equipment, installations, devices, and computerized systems; increased expenses resulting from the lack of familiarity of the project team and contractors with methods required by smart buildings; increased project management complexity; lack of confidence in privacy protection instruments; lack of confidence in incorporating and managing different smart technologies; lack of knowledge about current and potential benefits of smart buildings; lack of qualified and specialized professionals; limited demand for smart building projects; macroeconomic barriers and access to financing; more complex design and construction; regulatory barriers; smart building project management demanding specific skills from the management team; shortage of government policies; and structure, organization and behavior of the construction industry.

Regarding the barrier “building management systems’ functionality limitations”, Eini et al. [29] highlight that a comprehensive development of building management systems (BMSs) is challenging due to many building components, the vast amount of data, the diversity in building dynamics, and the inevitable uncertainties. Rodrigues et al. [30] highlight that managing large volumes of data is a complex activity that makes developing BMSs difficult. Pašek and Sojková [31] and Marinakis [32] reached the same conclusion. For Alohan et al. [11], BMSs are usually complex systems due to their specificities and user interfaces. In agreement, Gobbo Jr. et al. [33] highlight the need for more intuitive and user-friendly interfaces. They also highlight that systems intelligence and behavioral inference are obstacles, as systems based on inference are never absolutely accurate. Aliero et al. [34] highlight the need to improve interoperability between the different systems and devices in a building.

Regarding “difficulties imposed by deficiencies in the city’s infrastructure”, integrating smart buildings with cities involves integrating building systems into the cities’ infrastructure. However, deficiencies and obsolescence in energy and internet infrastructure make integrating with advanced technologies such as communication networks, IoT, and renewable energy sources difficult. Deficiencies in the internet infrastructure can limit access to information and communication capacity, hindering the integration of smart buildings with urban services [11]. Deficiencies in electrical energy infrastructure, caused mainly by the obsolescence of distribution system assets, contribute to frequent interruptions [33,35] and hinder its integration with a building’s intelligent systems. Apanavičienė and Shahrabani [13] identified some challenges for this integration: integrating energy storage systems, investing in infrastructure, coordinating various energy resources, ensuring data privacy, and enabling system interoperability.

Regarding “difficulties in adopting and coordinating energy-efficient systems”, we identified approaches related to increased energy use due to the growing number of installed devices, the need for energy management improvements, and incentives for consumption reduction [11,30,36,37,38]. The difficulties in introducing renewable energy generation systems are pointed out by Apanavičienė and Shahrabani [13], and the need for an adequate energy microgeneration tax system is described by Gobbo Jr. et al. [33]. Additionally, for some authors, the development of energy efficiency in smart buildings [4,39,40] and the integration, production, and coordination of different energy sources [13,40] represent essential challenges to be overcome. Solving these problems is essential for reducing energy consumption [41,42].

Regarding the barrier “Difficulty accessing smarter technologies, materials, and equipment”, the unavailability of sustainable materials and smart equipment [15,30,43,44], the time required to approve new technologies [30,44], and the complexity of competition rules and processes are factors identified as inhibitors of the smart construction market [45,46].

The barrier “greater complexity of data protection and personal and property security systems” is related to the difficulties in deploying strong security procedures to secure sensitive data and guarantee a building and users’ security, mainly concerning cybersecurity, a lack of trust in technological services, and adequate communication protocols. Authors such as Benavente-Peces [4], Rafiq et al. [47], Shah et al. [39], Apanavičienė and Shahrabani [13], and Rodrigues et al. [28] highlight the need for adequate safeguards against cyber threats. Apanavičienė and Shahrabani [13] highlight concerns about the security aspects of surveillance and technologies. According to Alohan et al. [11], the lack of trust in technological services, the high rate of cybercrime, and the absence of adequate communication protocols contribute to insecurity in smart buildings. Authors such as Benavente-Peces [4], Merabet et al. [48], Opawole et al. [49], and Apanavičienė and Shahrabani [13] point out the need to integrate advanced security features, such as mutual authentication.

Regarding the “greater complexity of intelligent technologies”, complexity and intelligence are intrinsically related. The advancement of smart technology has brought a lot of technological complexity [30]. The degree of intelligence of smart buildings is strongly influenced by the performance of systems made up of devices and software. As these systems have evolved, they have also become more complex [50]. Furthermore, the specificities of smart buildings require more complex technologies, both in the design and construction phases [15]. The increase in complexity also extends to algorithms [4] and terminology, since smart building technologies frequently use complex terminology [2,51].

The barrier “high cost of available intelligent systems and technologies” is an important obstacle to adopting smart building technologies [2,28,43,44]. For Bagheri et al. [40], installing expensive automation systems and information and communication technology (ICT) infrastructure is costly. Tanko et al. [52] cite the extra cost of deploying IoT technologies.

The barrier “high level of risk compared to that for traditional buildings” is widely cited in the literature. Authors such as Belani et al. [53], Yang et al. [54], and Rodrigues et al. [28] highlight the risks related to more significant expenses for equipment and materials and the usage of new or unproven technologies. Säynäjoki et al. [55] and Bandara et al. [15] point out data and investment security risks. Radziejowska and Sobodka [16] point out the increased risks related to financial, regulatory, security, and design issues. Regarding the risks associated with project management, Rodrigues et al. [28] highlight the risks associated with defining the project scope and the need for in-depth knowledge of the project to be able to anticipate risks.

“Higher initial and construction costs compared to those of traditional buildings” is frequently addressed in the literature [33,56]. Using intelligent materials, equipment, and specialized technical personnel increases construction costs [30,54,57], which is mainly challenging for developing countries [44]. Higher costs are also associated with the initial cost of installations [11]; investment costs [40]; the cost of using renewable energy for solutions that improve sustainability [43] and are based on newer technologies, such as the Internet of Things [47], at the cost of procurement practices [15]; and additional expenses, such as consultant fees [49,58]. Practices and equipment required by design and project management are also associated with higher costs.

“Incompatibility between equipment, installations, devices, and computerized systems” refers to interoperability problems, that is, difficulty in connecting and making technologies from different manufacturers and models work in an integrated manner. According to Benavente-Peces [4], Gobbo Jr. et al. [33], and Holroyd et al. [56], interoperability, the ability of systems and devices to work together in an integrated manner, is fundamental for the adequate performance of smart buildings. Pritoni et al. [59] cite the lack of semantic interoperability between data in different systems as a significant obstacle that impacts the ability to understand and exchange information. The lack of a common language makes efficient communication between different devices difficult [34]. Pathmabandu et al. [60] point out difficulties with interoperability regarding the extraction of privacy policy statements from linked documents of different providers. For Apanavičienė and Shahrabani [13], data interoperability is another critical issue, as the ability of different systems to share data efficiently is fundamental for the integrated functioning of the smart building. Furthermore, the lack of flexibility regarding devices from different suppliers represents an important limitation [45,46,49].

The barrier “increased expenses resulting from the lack of familiarity of the project team and contractors with methods required by smart buildings” refers to the increase in expenses for the specific training of teams involved in the project and construction due to the lack of familiarity of the construction team project and contractors with smart building methods, hiring specialized professionals, and increasing consultancy fees [49,58].

Regarding “increased project management complexity”, the requirements and specificities of smart buildings influence project management and demand the coordination and integration of multidisciplinary teams, external consultancies, and the use of new technologies related to project management. For example, among the initial project management actions is defining the project scope, which is impacted by the need to identify and include the main requirements of smart buildings. Furthermore, the increasing complexity of projects demands integrated management [61], including managing more complex supply chains [16]. These challenges highlight the need for a specialized and integrated approach to project management to achieve success in smart building projects [27,28].

Regarding the “lack of confidence in privacy protection instruments”, concerns about using and sharing personal data collected by smart building technologies, especially those related to telecommunication technologies and the Internet of Things (IoT), generate fear and hesitation in their adoption. For Alohan et al. [11], there is a lack of trust in data protection, generating concerns about possible privacy violations. Another issue that has aroused researchers’ interest is the privacy and security of data obtained through IoT. The potential combination of data collected by multiple IoT devices and immature IoT solutions with inadequate privacy controls could expose sensitive information about building occupants [60]. Saputro and Akkaya [62] address the privacy of data obtained through energy smart meters since the exposure of this data can lead to several privacy issues. Another concern is the interference with privacy and individual freedom caused by surveillance systems that use continuous monitoring [33]. Furthermore, addressing social challenges related to privacy is also essential to overcome obstacles in the implementation effectiveness of smart building technologies [42].

The barrier “lack of confidence in incorporating and managing different smart technologies” concerns fear regarding the ability to understand and operate technologies considered complex. Authors such as Williams and Dair [45], Johnny and Heng [46], and Opawole et al. [49] point out the difficulty in adopting new processes and work methods to apply new technologies. The lack of qualified and specialized workers [33] causes fear that it will not be possible to count on specialized professionals promptly. There is also concern about the risks of using new and “untested” technologies [14] and the professional ability to introduce and manage smart technologies [28,53].

Regarding the “lack of knowledge about the current and potential benefits of SBs”, the lack of knowledge on the part of developers and owners represents a significant challenge for adopting smart buildings [14,15,28]. Some authors highlight as obstacles the difficulty in modifying consumer behavior [9], the need to raise public awareness of the socioeconomic and environmental benefits of SBs [28], low awareness of the positive impacts of SBs in the long term [2], and the lack of understanding of the opportunities and benefits of sustainable and smart construction in a significant portion of society [16]. Also cited in the literature is the belief that smart buildings only apply to large structures [16]; the perception that smart technology is divisive, exclusive, or irrelevant [33,63]; and the lack of empirical evidence that smart technologies offer the benefits attributed to them [2].

Regarding the “lack of qualified professionals”, some authors point out as limitations the lack of knowledge about the specificities and requirements of SBs [45,46,49]; the lack of workers with adequate technical qualifications [8,27,30,33,53,61], mainly for small commercial, educational, and residential buildings; and the lack of professional training in the field of SBs [49,64], as well as labor as a factor that hinders the execution of new smart constructions [65].

The “limited demand for SB projects” barrier is mainly related to the low interest of consumers and builders in investing in smart buildings due to factors such as higher initial cost, uncertainty about the return on investment, and the lack of incentives for taxes. Authors such as Radziejowska and Sobodka [16] and Ghaffarianhoseini et al. [2] point out the lack of understanding by a significant portion of society about the opportunities and benefits provided by smart buildings. Gobbo Jr. et al. [33] highlight the importance of more effective consumer communication. Radziejowska and Sobodka [16] state that offers are not easily accessible to society. According to Ma et al. [66], the fear of uncertainty in sharing information, losing money, and the lack of trust decrease the interest of construction companies in undertaking smart buildings.

Regarding “macroeconomic barriers and access to financing”, the literature has highlighted difficulties in accessing capital [11], mainly due to the lack of financial incentives and financing [33], notably in poorer countries with weaker economies [16], and the insufficiency of economic resources [2,28,53,67].

Regarding “more complex design and construction”, authors such as Rodrigues et al. [28], Yang et al. [54], Gobbo Jr. et al. [33], and Balta-Ozkan et al. [63] found that smart buildings, compared to traditional buildings, imply more complex design and construction procedures. These are factors that increase complexity, the need for more changes in the design [28,44,61], the incorporation of a large number of systems in the building [31], the need for systems optimization [36], the lack of understanding of the needs of smart building users [33,56,68], and the need to satisfy specific requirements of both the users and owners of these buildings [28,30,31,57]. Furthermore, the design of privacy solutions for IoT [60] and the absence of a common framework that efficiently integrates IoT aspects with operational-level construction practices increases complexity [39,49]; Apanavičienė and Shahrabani [13] highlight that the need for scalable and cost-effective solutions adds a challenging component to the overall complexity faced in creating smart buildings.

Regarding “regulatory barriers”, the literature mentions the slowness of the approval process for new smart building technologies due to the lack of adequate regulation [28,33,44], the direct and indirect influence of the lack or inadequacy of legislation on the design of SBs [5,33,69,70], the rigidity of regulatory guidance mechanisms [45,46,49], and the lack of regulations and wide variety of standards [4].

Concerning “SB project management demanding specific skills from the management team”, the specificities of smart buildings demand the development of new skills from the project management team, such as, for example, those related to technical project requirements and risk management. Obstacles such as the lack of experience in this type of project and the management of the installation specificities impact the quality of project management [28,61]. Behavioral issues within the project team can also impact management processes [30,44]. Furthermore, the lack of familiarity with smart building technologies is one of the most mentioned obstacles in project management [27,28]. Rodrigues et al. [30] state that “project managers should focus on nine competencies: technical competencies, leadership, communication, budgeting, attitudes toward risk, strategic management, organization, and specifying real requirements”.

Regarding “shortage of government policies”, authors such as Ghansah et al. [2], Belani et al. [53], Ehrenhard et al. [65], Rodrigues et al. [30], and Radziejowska and Sobodka [16] point out that the absence of legal, fiscal, and tax incentives; subsidies; and specific credit lines hinders the development of the smart building market and the participation of new players. Benavente-Peces [4] highlights the lack of political support. Ghaffarianhoseini et al. [2] found the incentive and support programs for using smart building systems ineffective and inadequate. According to Bandara et al. [15], these challenges reflect a lack of effective political guidance in this domain.

Regarding the barrier “structure, organization, and behavior of the construction industry”, researchers such as Ghansah et al. [27], Ghansah et al. [44], and Rodrigues et al. [5] found that the structure and organization of the construction sector hinder the smart construction market. There is a lack of institutional structures to encourage and drive the adoption of smart technologies and materials [14,43]. Additionally, the fragmentation of responsibility in the construction and real estate sectors [45,46,49] and the absence of more appropriate business models [28,47] affect companies’ ability to operate in a selective market, such as smart construction. Ghansah et al. [44] cite resistance to changing traditional practices as an obstacle to the smart building market, and Ma et al. [66] highlight the lack of concern of builders with the building monitoring and maintenance after the project is completed due to the lack of incentives.

Table 1. Selected barriers.

Barriers	Concepts Presented in the Questionnaire	References
Building management systems’ functionality limitations	The BMSs have limitations concerning meeting the specific demands of smart buildings.	[11,29,30,31,32,33,34]
Difficulties imposed by deficiencies in the city’s infrastructure	Deficiencies and obsolescence in a city’s infrastructure make integration with advanced building technologies difficult.	[11,13,33,35]
Difficulties in adopting and coordinating energy-efficient systems	Difficulty integrating and coordinating different technologies for energy management, such as sensors, automation, and control systems.	[4,11,30,36,37,38,39,40,41,42]
Difficulty accessing smarter technologies, materials, and equipment	Difficulties are mainly associated with the unavailability of innovative technologies, especially the most recent ones.	[30,43,44]
Greater complexity of data protection and personal and property security systems	Data protection and personal and property security systems require more robust security measures to protect sensitive data and guarantee the safety of the building and its users.	[4,8,13,28,39,47,48,49]
Greater complexity of intelligent technologies	The specificities of SBs require more complex technologies, including those related to their terminology.	[4,13,15,28,54]
High cost of intelligent systems and technologies	Significant initial investment for the acquisition and implementation of advanced technologies.	[2,27,28,40,43,44,52,71]
High level of risk compared to that for traditional buildings	Uncertainties regarding return on investment, possibility of technical failures, or early obsolescence of technologies.	[15,16,28,30,53,54,55,72,73,74]
Higher initial and construction costs compared to those for traditional buildings	Additional costs are demanded mainly for intelligent materials and equipment, technological infrastructure, and specialized technical personnel.	[11,15,16,28,30,33,40,43,44,47,56,61,75]
Incompatibility between equipment, installations, devices, and computerized systems	Difficulty in connecting technologies from different manufacturers and models and making them work in an integrated manner.	[4,13,33,34,42,45,46,49,56,59,60]
Increased expenses resulting from the lack of familiarity of the project team and contractors with topics related to SBs	Increased expenses resulting from the hiring of specialized professionals and consultancies and the specific training of design and construction teams	[49,58]
Increased project management complexity	The requirements and specificities of smart buildings demand the coordination and integration of multidisciplinary teams, external consultancies, and the use of new technologies related to project management.	[13,16,27,28,61]
Lack of confidence in privacy protection instruments	Concerns about using and sharing personal data collected by smart technologies generate fear and hesitation in adopting them.	[11,13,28,33,42,47,60,62]
Lack of confidence in incorporating and managing different smart technologies	Concerns related to the ability to understand and operate technologies that are considered complex.	[2,14,28,30,45,46,49,53]
Lack of knowledge about current and potential benefits of smart buildings	There is little understanding of the advantages of smart buildings, mainly in terms of efficiency, sustainability, comfort, and well-being.	[2,13,14,15,16,28,30,33,45,46,49,53,57,61,63]
Lack of qualified professionals	Lack of professionals with training and experience in designing, implementing, and managing smart building projects.	[2,11,16,30,33,43,45,46,49,53,61,64,65]
Limited demand for SB projects	Low interest from consumers and builders in investing in smart building solutions due to factors such as higher initial cost and uncertainty about return on investment.	[11,16,33,45,46,49]
Macroeconomic barriers and access to financing	Economic instability, high interest rates, and difficulty obtaining specialized financing for innovative projects such as smart buildings can make their viability difficult.	[2,14,15,16,28,30,33,43,53,67]
More complex design and construction	The design and construction of smart buildings are more complex, mainly due to the greater need to incorporate new practices and methods that make it possible to meet the specificities of these buildings.	[13,27,28,29,30,31,33,36,39,44,49,50,54,56,57,60,61,63,68]
Regulatory barriers	Outdated or absent laws and standards that do not consider the specificities of smart buildings can hinder their development.	[4,13,15,27,28,33,45,46,49,69,70]
SB project management demanding specific skills from the management team	The requirements and specificities of smart buildings require the project management team to develop new skills and abilities.	[28,30,44,61]
Shortage of government policies	The absence of tax incentive programs, subsidies, and specific credit lines hinders the development of the smart building market and the participation of new players.	[2,4,15,16,27,30,45,46,49,53,65]
Structure, organization, and behavior of the construction industry	Construction companies and suppliers may not be prepared or willing to enter the smart building market.	[14,15,16,27,30,43,44,45,46,49]

summarizes the barriers with their respective conceptualizations and the authors who referenced them.

3. Materials and Methods

This work has two main objectives: (1) to research the main barriers limiting the development of smart buildings and (2) to prioritize these barriers from the perspective of professionals working in the field. We adopted an exploratory approach common in research that focuses on identifying and prioritizing variables related to a phenomenon, which is based on two main actions: gathering knowledge on a given topic through a rigorous literature review and consulting professionals who work with the topic. We used this approach since our goal was not to draw statistical conclusions but to identify patterns and provide insights on the topic. We adopted an approach in three steps: identification of barriers limiting the development of smart buildings, survey, and data analysis.

3.1. Identification of Barriers Limiting the Smart Buildings Development

We used the keywords “smart building” and “intelligent building” combined with the keywords “barrier” and “challenge” to carry out detailed bibliographic research in the Web of Science, Scopus, and Scielo databases. We also consulted the references of the selected works. We adopted the four phases of the PRISMA (preferred reporting items for systematic reviews and meta-analyses) method, highlighting (1) the number of articles identified, (2) the number of articles included, (3) the number of articles excluded, and (4) the reason for removing the articles [76]. Initially, we quickly read titles and abstracts to select works with some evidence or information on the covered topics that were available in full and peer reviewed. Of the 1479 articles identified, 203 articles were pre-selected. We then read these 203 articles, and 95 were selected because they contained a complete description of the research procedures and results supported by the methodology. Of the 95 article, 66 were selected to support the work. Figure 1 summarizes the article selection process from the PRISMA flowchart.

To identify the barriers, we consulted the few works with this objective and works that, although they did not have this objective, at some point in the context or analysis referred to some barrier or, even without using the term “barrier”, used an approach characterizing some element as a barrier.

3.2. Survey

Using an online platform (Google Forms), we developed a questionnaire containing three sections: the first containing questions related to demographic data, the second containing the conceptualization of the 23 barriers, and the third containing a table in which the rows contained the barriers and the columns contained a five-point Likert scale, ranging from “very little limiting” to “extremely limiting”. Respondents were asked to evaluate the potential of each barrier to limit the development of smart buildings using a five-point scale. The barriers with their respective conceptualizations can be seen in Table 1.

To increase the quality of the data, we carried out the following actions:

(a) We conceptualized the 23 barriers identified in the literature to establish a single interpretation by the evaluators;

(b) We displayed the barriers concurrently to make the comparison between them more accessible and randomly to reduce the possibility of bias depending on the order in which they would appear;

(c) We used purposive sampling to ensure that respondents met specific criteria of holding at least a bachelor’s degree and having experience in the field. We used the authors’ relationship network to choose respondents recognized by at least one of the authors as capable of meeting the established criteria. As a result, 43 professionals working in Rio de Janeiro, Brazil, completed the entire questionnaire. Figure 2 synthesizes the sample profile.

3.3. Data Analysis

The main result of the survey provided by Google Forms is a spreadsheet in which the rows contain the respondents’ responses, the columns contain the 23 barriers, and each cell contains the psychometric item from the five-point Likert scale that the respondent selected as the most appropriate to represent their judgment regarding a given barrier. The psychometric items of each cell were converted into ordinal numbers so that quantitative analysis could be carried out. For each cell, the scale that varies from “very little limiting” to “very limiting” was converted into numbers in the range of an ordinal scale from 1 to 5. It is a procedure frequently adopted by researchers who work with psychometric scales.

Cronbach’s alpha was employed to assess data quality, and a value of 0.94 validated reliability. Ordinal data are better analyzed using position metrics such as the median. To prioritize barriers, we used the relative median, which takes into account the distance between the median and the nearest class [77], with the following formula:

$$\mathrm{R}\mathrm{m}=\left\{\begin{array}{c}1+\frac{\mathrm{P}\mathrm{r}}{{\mathrm{j}}_1} \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{m}=1 \\ \mathrm{m}+\frac{\mathrm{P}\mathrm{r}-{(\mathsf{\Sigma }}_{\mathrm{i}=1}^{\mathrm{m}-1}{\mathrm{j}}_{\mathrm{i}+1})}{{\mathrm{j}}_{\mathrm{i}}} \mathrm{f}\mathrm{o}\mathrm{r} 2\le \mathrm{m}<\mathrm{N} \mathrm{e} \mathrm{m}=\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{g}\mathrm{e}\mathrm{r} \\ \mathrm{m}+0.5 \mathrm{f}o\mathrm{r} 1\le \mathrm{m}<\mathrm{N} \mathrm{a}\mathrm{n}\mathrm{d} \mathrm{m}=\mathrm{f}\mathrm{r}\mathrm{a}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{a}\mathrm{l} \mathrm{n}\mathrm{u}\mathrm{m}\mathrm{b}\mathrm{e}\mathrm{r}\\ \mathrm{N} \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{m}=\mathrm{N} \\ \end{array}\right\}$$

where Rm is the relative median, m is the median, Pr is the median position, N is the number of respondents, and ji is the number of respondents assigned the semantic classification of “i”.

4. Results and Discussion

Figure 3 presents the barriers from Table 1 ranked by their relative median. Respondents rated all barriers as important since the relative medians were larger than 3.0, which supports the findings of scholars who have published on the subject.

Figure 3 shows that eight barriers were considered the most limiting for the development of smart buildings, among which only the “lack of qualified professionals” was assessed by respondents as being extremely limiting, and seven barriers were considered significantly limiting: “higher initial and construction costs compared to those of traditional buildings”, “macroeconomic barriers and access to financing,” “high cost of intelligent systems and technologies”, “regulatory barriers”, “lack of knowledge about current and potential benefits of the SBs”, and “more complex design and construction”.

Regarding the “lack of qualified professionals”, smart buildings demand a broad range of technologies and systems that are constantly evolving, demanding specialized services from a wide variety of professionals. However, professional training has not been able to keep up with the speed of technological innovations. Contributing to this scenario is the fact that there is a shortage of professional training programs in the area and that a significant portion of construction professionals are unaware of the specificities and opportunities related to smart buildings. Furthermore, according to Gobbo Junior et al. [33], the diversity of technologies together with the low standardization of their requirements and characteristics leads to the segmentation of professionals based on their knowledge of a given technology. This context affects services such as installing, operating, and maintaining devices, equipment, and software and providing technical assistance to building managers.

The vast majority of designers and builders are unfamiliar with the requirements and specificities of smart buildings, requiring the hiring of specialized consultants who are not always available locally. This increases dependence on information from technology suppliers, which is not always easy to understand and operate. The lack of qualified professionals also affects the management of smart building projects. In this sense, Rodrigues et al. [30] highlight the need for project managers to develop new skills related to leadership, communication, budgeting, attitudes toward risk, strategic management, organization, and specifying project requirements.

Authors in ref. [78] highlight that the willingness of construction professionals to adopt smart and sustainable construction technologies is influenced by the presence of facilitating conditions and the perception of ease of use and usefulness. Therefore, overcoming this barrier involves strengthening and expanding partnerships between companies and educational institutions aiming to use the wide range of educational techniques and tools currently available to develop education and training programs and professional certifications that meet market demands regarding the characteristics and needs of the workforce. Furthermore, companies and organizations in the sector can collaborate to develop standards and guidelines that facilitate integrating and operating systems in smart buildings, making the adoption technologies more accessible.

Regarding the shortage of government policies, those aimed at the built environment establish guidelines for developing city functions based on government initiatives and programs at the federal, state, and municipal levels. They are mainly formed by tax incentives that establish discounts on the payment of tax obligations, financial incentives that aim to attract interest in reducing costs, and increased market demand provided by these incentives and government actions, such as those encouraging education and training [79].

In Brazil, technological development has slowly evolved when compared to that in other countries. Many Brazilian municipalities, especially smaller ones and those far from large urban centers, have old infrastructure unattractive to technological innovations. Many public policies related to the built environment have aimed at solving structural problems, and the scope of visibility of possible results has influenced priorities. Regarding policies to improve the intelligence of the built environment, the priority has been to enhance the intelligence of city services, such as urban mobility. It is a reality faced by many cities, especially those in underdeveloped and developing countries. According to Gobbo Junior et al. [33], in these cities, smart buildings are built in areas without smart infrastructure, limiting their intelligence to internal management. Furthermore, as pointed out by Pramanik et al. [80], there is a lack of policies to encourage the development of knowledge and methods for smart construction aimed at designers and developers in Brazil.

Public policies are also important for developing an environment conducive to investment. Munhoz et al. [81] highlight their importance for regulating financing mechanisms and legal and financial guarantees. They also shape regulatory instruments, such as laws and technical standards. Gobbo Junior et al. [33] highlight the need to simplify the regulatory system to promote innovation and competitiveness.

Regarding the costs of smart buildings, the results highlight that they are perceived to be greater than those of traditional buildings and that smart systems and technologies are expensive. The finding that the initial and construction costs of smart buildings are higher is widely cited in the literature, although cost reduction throughout the life cycle is also mentioned. Ghansah et al. [44] highlight that the cost increase caused by using intelligent materials, equipment, and specialized technical personnel is a challenge, mainly for developing countries. The cost of technologies used in smart buildings is a crucial factor considered during design and construction [2], since the decision-making process of real estate developers must evaluate the return on investment provided by these technologies. However, this analysis depends on the ability to adequately assess the improvement in attractiveness that increasing a building’s intelligence can bring about in potential buyers, which is a complex task since the lack of adequate knowledge about the benefits provided throughout the building life cycle can undermine this analysis.

Thus, the lack of knowledge about the current and potential benefits of smart buildings, also cited by respondents as an important barrier, hinders the expansion of smart buildings since many owners, builders, and users are not yet fully aware of their advantages, such as greater energy efficiency, improved comfort, advanced safety, and greater operational productivity. Ghaffarianhoseini et al. [2] highlight a lack of empirical evidence that smart technologies offer the benefits attributed to them. There is also a lack of understanding about the positive impact of smart buildings on the quality of life and the environment [1] and the belief that improving building intelligence is only viable for large buildings [16].

It is essential to overcome this barrier; raise awareness through conferences, seminars, forums, and exhibitions that enable the sharing of success stories and practical demonstrations of the tangible benefits of smart buildings; and promote a culture of innovation in society. Investments in qualification and training programs for professionals in the construction industry and technology suppliers are also essential.

Regarding the macroeconomic barrier and access to financing, in recent years, the Brazilian macroeconomic scenario has been influenced by political instability, high public deficit, high unemployment, inflation above the target, and high interest rates, making access to credit difficult and creating uncertainty for investors and developers, making them reluctant to make long-term investments in smart building projects.

In this context, partnerships between the public and private sectors are essential to facilitate access to financial resources and promote the adoption of innovative technologies. Investments in education and awareness are also essential to increase understanding of the benefits of smart buildings and stimulate interest from investors and consumers.

Brazil has the opportunity to be inspired by some EU practices, such as the regulation of sustainable investment and the carbon credit market [82]. Additionally, Brazil can expand access to private financing by incorporating sustainability strategies into investment decisions.

Regarding regulatory barriers, in Brazil and other developing countries, legislation often does not keep up with the rapid advancement of technologies used in these buildings, resulting in regulatory gaps and legal uncertainties for investors and developers.

Issues related to data privacy, cybersecurity, and system interoperability are especially critical and require clear and comprehensive regulation to ensure the protection of users and the integrity of systems. Authors such as Mulholland [83] question the effectiveness of data protection and privacy policies, highlighting that personal data has value in terms of privacy and identity and its economic use, such as in the monetization of data by large technology companies.

The lack of specific standards and regulations for smart buildings can also make certification and compliance with technical and security requirements difficult. A coordinated effort to overcome these barriers is needed between the government, companies, and educational and research institutions to develop and implement regulations that promote innovation while ensuring the interests and safety of stakeholders.

Regarding the greater complexity of design and construction, the advancement of smart technology has brought a lot of technological complexity, especially for small and medium-sized companies that do not have the knowledge or skills to deal with these technologies. Integrating automation systems, IoT sensors, and energy management systems, among others, requires a multidisciplinary approach and intense coordination between architects, engineers, specialized consultants, and technology suppliers. The selection and specification of materials and equipment compatible with the technological solutions adopted also become more challenging. Project development requires a detailed understanding of stakeholders’ needs. In this sense, Baharetha et al. [68] highlight that distinguishing the intelligence requirements of owners, users, operators, and facility managers is complex. Furthermore, the characteristics and specificities of smart buildings and the increase in design changes [28] require using technologies such as BIM to develop the design process, which is not always accessible to small and medium-sized construction companies. Construction management is also more complex, demanding new skills from the management team, such as technical competencies and leadership [28].

This work contributes to the literature by identifying barriers that limit the development of smart buildings based on the interpretation of the work of researchers working in the concerned field and prioritizing them from the perspective of professionals working in the area.

Regarding practical implications, this research shows that some priority actions are necessary to develop the smart building sector. Financial and tax incentive policies are needed, coordinated at the federal, state, and municipal levels, to facilitate access to credit and make it more advantageous for construction companies and technology providers to invest in the smart buildings segment.

Policies encouraging professional qualification are also necessary, including encouraging partnerships between companies and educational institutions to develop education and training programs and professional certifications. Gobbo Junior et al. [33] highlighted that integrating technologies demanded by smart buildings requires specialized services from a wide range of professionals with a limited supply in Brazil. This is also a reality in developing countries.

The results also show the need to develop norms, standards, and specific legislation that guarantee the protection of users and the integrity of systems concerning data privacy and cyber security and to direct and facilitate the integration and operation of technologies demanded by smart buildings and the innovation environment. Although Brazil has a regulatory framework for innovation management, as De Alcantara et al. [77] highlighted, new technologies resulting from technical advancements are still being developed.

Creating an environment for sharing experiences and disseminating the benefits, advantages, and opportunities provided by smart buildings while considering different stakeholders is also essential.

5. Conclusions

In this work, we researched the main barriers that limit the development of smart buildings and prioritized them based on the evaluation of professionals who work in the field. After conducting an in-depth literature review, we identified 23 barriers, 8 of which were deemed the most significant by professionals in the field: a lack of qualified professionals, shortage of government policies, higher initial and construction costs, macroeconomic barriers and access to financing, high costs of intelligent systems and technologies, regulatory barriers, a lack of knowledge about current and potential benefits of the smart buildings, and more complex design and construction.

The findings confirmed the point of view of the authors whose work was used to support this research, since all barriers were considered important by the respondents.

Some aspects of the work must be considered concerning the scope of the results and limitations. The first is that the 23 barriers addressed in this work reflect the point of view of researchers who publish on the topic, as they were identified from detailed bibliographical research in important knowledge bases. However, there is always the possibility that a significant article was not considered. This is a typical limitation of works based on bibliographical research to support their analyses. Concerning the barriers assessed as most important, it must be taken into account that this was limited to assessments by Brazilian respondents, with the risk that the reality of Brazilian cities may have influenced the assessment. As a result, they must be understood in light of each country’s specific realities. As highlighted by Guedes et al. [84], the context in which cities develop influences how the built environment is perceived by society. However, it is important to highlight that the reality and features of many cities in developing countries are similar to those of Brazilian cities. It must also be taken into account that the evaluators’ understanding of what is being assessed gives studies based on opinion research a certain degree of subjectivity.

For further studies, we suggest: (1) searching for instruments that contribute to alleviating the damage produced by the barriers highlighted in this work, (2) examining in greater depth the repercussions of these barriers, (3) interviewing experts from different nations to compare variances and similarities in perceptions, and (4) identifying barriers related to improving the intelligence of existing buildings.