Assessment of the challenges influencing the adoption of smart building technologies

Abstract

Introduction: Over the past few decades, there has been an increasing focus on Smart Building Projects (SBP) and the technologies associated with them. Numerous studies have been conducted globally to define smart building technologies (SBT), identify challenges, and explore areas for improvement. This study aims to examine the concept and terminology of SBT and the expertise and experience of participants in SBP in the Arab Gulf countries, specifically Saudi Arabia. The study also investigates the challenges faced by SBT throughout its life cycle.

Methods: To identify and assess the challenges affecting the adoption of smart building technologies. This approach included a literature review, pilot-testing, and a questionnaire survey. The survey targeted a sample of 90 architects/engineers, managers, and contractors.

Results: A total of 55 challenges were identified and categorized into four groups, corresponding to the key phases of the project life cycle. These phases include the programming and feasibility analysis phase, design phase, installation and commissioning phase, and operation and maintenance phase. The findings of this research expand the body of knowledge by providing architects/engineers, managers, and contractors in the architecture, engineering, construction, and facility management (AEC/FM) industry with insights into the influential challenges related to the adoption of SBT. In conclusion, this study sheds light on the concept and terminology of smart building technologies and explores the challenges faced by SBT during its life cycle. By identifying and categorizing these challenges, the study provides valuable information to AEC/FM practitioners, enabling them to overcome obstacles and improve the adoption of SBT.

1 Introduction

The purpose of buildings is to fulfill the requirements and preferences of their inhabitants, with an increasing focus on enhancing comfort within the living environment (Hamida et al., 2022). The planning of building systems holds a pivotal position in elevating user contentment and the overall standard of living (Hamida et al., 2022). Smart buildings, in particular, have surfaced as a method to enhance the efficiency, safety, and comfort of users’ lives (Hamida et al., 2022). The phrase “smart building” made its debut in 1981 when the United Technology Building Systems (UTBS) Corporation in the United States first introduced the concept. Their emphasis was on employing building automated systems (BMS) for the regulation of security and HVAC systems. A notable milestone occurred in 1983 with the construction of the City Place building in the United States, which was proudly marketed as the world’s inaugural intelligent building (Fabi et al., 2017a; Pramanik et al., 2019). Essentially, a smart building integrates various systems such as HVAC, power management, lighting, security, safety, and shared networks to efficiently manage resources and enhance building performance (Hamida et al., 2022). It combines the best available technologies, designs, materials, and systems to improve occupants’ lives and provide cost-effective environments (Hamida et al., 2022). Smart buildings leverage technology to enhance building services and operations for the benefit of users (Ghansah et al., 2020). They aim to provide a dynamic infrastructure that optimizes energy efficiency, flexibility, cost, and comfort (Hamida et al., 2022). The concept of smartness in buildings encompasses characteristics such as green energy, zero emissions, space flexibility, population health, and working efficiency (Hamida et al., 2022). Smart buildings are described as equipped buildings with interconnected sensors, communication networks, and controllable devices (Fabi et al., 2017a). They are also defined as flexible buildings that interact and connect with the ecosystem while generating and storing energy (Fabi et al., 2017a). The aim of smart building technologies is to reduce energy consumption and improve comfort conditions and human welfare (Fabi et al., 2017a; Pramanik et al., 2019).

Recent definitions of smart buildings focus on the adoption of structure connectivity solutions (SCS) to address various building systems, including security, data networking, and environmental control (Sathesh and Hamdan, 2021). Designers of smart buildings should consider a range of smart building technologies and user characteristics, adopting a “respondents-oriented” approach (Pramanik et al., 2019). However, the definition of intelligent buildings remains ambiguous, with diverse definitions across different countries (Sathesh and Hamdan, 2021). Initially, early definitions primarily focused on automated functionalities, but subsequently, newer definitions have broadened their scope to encompass additional aspects. During the 1990s, the concept of smart buildings evolved to emphasize the integration of building occupants, intelligent systems, and environmental considerations to elevate the overall quality of life. Within the body of literature dedicated to smart buildings, there is a general consensus on three key defining characteristics: the incorporation of technology, the delivery of services, and the capacity to fulfill user requirements (Da Xu et al., 2014; Jacobsson, 2016; Pašek and Sojková, 2018). The core technologies of a smart building encompass hardware and software components, including sensors and home appliances. Sensors, whether physical devices or technological tools, detect changes in human behavior and environmental challenges (Belani et al., 2014; Ghaffarianhoseini et al., 2016; El-Rashidy et al., 2021; Saad et al., 2022). By integrating sensors into household equipment and connecting them through wired or wireless systems, residents’ habits during activities such as watching television, cooking, sleeping, and cleaning can be observed and tracked (Fabi et al., 2017b). The system, consisting of a variety of appliance and sensor configurations, facilitates a wide range of tailored tasks and services to meet the needs of the inhabitants (Pramanik et al., 2019; Alsolami, 2022; Alanne and Sierla, 2022). A smart building can be defined as a dwelling equipped with sensors and connected domestic devices that form a communication network, providing lifestyle support (Pašek and Sojková, 2018; Sánchez-Corcuera et al., 2019). In essence, a smart building integrates smart devices and sensors into an intelligent system that provides management, surveillance, assistance, and responsive services. This convergence yields a multitude of advantages spanning diverse areas, including the economy, society, health, emotional wellbeing, sustainability, and security, among others (Jacobsson, 2016; Gadakari et al., 2014).

In the last 30 years, numerous institutions around the world have undertaken comprehensive research to define and gain insight into smart building projects (SBPs) along with their fundamental principles and objectives (Sathesh and Hamdan, 2021; Iwuagwu et al., 2014). Thus, this study aims to enhance the knowledge regarding the challenges and barriers that SBTs may face, providing valuable insights for stakeholders to promote effective SBT implementation in the big cities of the Arab Gulf (Marigo et al., 2023).

Sustainable and smart building is a growing trend that is catching on in public opinion and making its way into the agendas of researchers and city authorities worldwide. Indeed, 5G technology can significantly impact building construction, operation, and management by providing high-quality services and efficient functionalities. Singapore is one of the top smart cities in the world and among the first countries to adopt 5G technology in various sectors, including smart buildings. This article discusses the international trends in 5G applications for smart buildings, R&D, and test-bedding works conducted in 5G labs. It also reviews 5G technology development, use cases, applications, and future projects supported by the Singapore government. Lastly, the study discusses 5G use cases for smart buildings and building environment improvement applications. This research can serve as a benchmark for researchers and industries for future progress and development of smart cities in the context of big data (Huseien and Shah, 2022).

Large amounts of data are generated daily by sub-meters and smart sensors in residential buildings. Properly leveraging this data could help end users, energy producers, and utility companies in identifying unusual power consumption and understanding the causes of each anomaly (Himeur et al., 2021).

Currently, analyzing, detecting, and visualizing unusual power consumption patterns of households are some of the main challenges in finding ways to decrease power usage (Himeur et al., 2020).

Smart cities strive to achieve their net-zero emissions targets by minimizing wasted energy, enhancing grid stability, and fulfilling service demand. This is achievable by adopting advanced energy systems that utilize artificial intelligence, the Internet of Things (IoT), and communication technologies to gather and analyze large amounts of data in real time, thus efficiently managing city services. However, training machine learning algorithms to perform various energy-related tasks in sustainable smart cities presents a difficult data science challenge.

2 Research methodology

FIGURE 1

Through the execution of this extensive research methodology, the study sought to collect valuable data concerning the obstacles related to the adoption of smart building technologies and to evaluate their level of severity as perceived by AE/C/FM practitioners in Riyadh, Saudi Arabia. Table 1 furnishes details about the demographic characteristics of the respondents, specifically architects/engineers (A/Es), contractors, and facilities managers. The data encompass the frequency and percentage of respondents in each category, stratified by their years of experience and their engagement in the design or construction of smart building systems (Jayasinghe et al., 2023; Kaklauskas et al., 2019).

Figure 2 illustrates the dispersion of participants within each group categorized by their years of experience. It provides data on the count and proportion of respondents falling into various experience brackets, including 1–5 years, 6–10 years, 11–15 years, and over 15 years. Furthermore, the table also displays the percentage of respondents who indicated their involvement in the design or construction of smart building systems, which is notably high across all three groups (El-Rashidy et al., 2021).

FIGURE 2

Alsolami, 2022).

3 The literature review

The purpose of this article is to examine and evaluate the challenges that impact smart building technologies (SBTs) in the Arab Gulf States across their entire life cycle, spanning four crucial phases: the conceptual planning and feasibility study phase, the design and engineering phase, the construction phase, and the operation and maintenance phase (Pašek and Sojková, 2018). The following is a review of the existing literature and pilot testing; a total of 55 challenges that influence the adoption of smart buildings throughout their life cycle were identified. These challenges were categorized within the aforementioned four key phases, and their descriptions are presented in the following overview (Ahmed et al., 2021).

3.1 Challenges pertaining to the programming and feasibility analysis phase

3.2 Challenges pertaining to the design phase

3.3 Challenges pertaining to the installation and commissioning phase

3.4 Challenges pertaining to the operation and maintenance phase

4 Assessment of the challenges affecting the adoption of smart building technologies

4.1 Data collection

A questionnaire survey was created employing a five-point Likert scale to evaluate the significance of the 55 challenges, as detailed in Table 2. The participants, comprising AE/C/FM practitioners located in Riyadh City, Saudi Arabia, were tasked with assigning importance ratings to each of the factors. The rating options included “Extremely challenging” (4), “Very challenging” (3), “challenging” (2), “Somewhat challenging” (1), and “Not challenging” (0). The ratings were determined according to the perceived influence of each challenge on the capacity to adhere to project schedules, financial limitations, and investor anticipations related to work excellence. It was presumed that client requisites had already been addressed and integrated during the architectural programming phase, with an emphasis on the long-term robustness and excellence of the construction. The survey garnered responses from a total of 90 professionals, with equal representation from architects/engineers (A/Es), contractors, and facilities managers, with each group encompassing 30 respondents.

4.2 Data analysis

The obtained data from the four groups of respondents were tabulated and analyzed, separately and collectively. The relative challenge index (RCI) was calculated for each factor, using the following equation (1):

Where:

i: Response category index.

a_i: Weight given to i response.

x_i: Variable expressing the frequency of (i).

To calculate the RCI, the appropriate values for the weights (a_i) and frequencies (x_i) need to be substituted into the equation. The weights (a_i) represent the importance or significance assigned to each response category, and the frequencies (x_i) represent the number of occurrences or observations for each category, as listed in Table 3 (Figure 3) (Sathesh and Hamdan, 2021; Jayasinghe et al., 2023; Attoue et al., 2018; So and Wong, 2002; Adelgren et al., 2012).

FIGURE 3

Alsolami, 2022).

Based on the ranking of the challenges, the correlation among the three groups of respondents was analyzed to determine their level of agreement on the importance of the challenges. Spearman’s coefficient of rank correlation was used to determine the level of agreement on the importance of the challenges. The analysis of the agreement was performed through three-paired comparisons, namely, A/Es and contractors, A/Es and facilities managers, and contractors and facilities managers (Hamida et al., 2022). The following equation was used to calculate the Spearman’s coefficient of rank correlation: as shown in Table 4

Where:

Spearman’s coefficient of rank correlation.

Sum of the squared differences in ranks of the paired comparison.

Number of parameters for which the ranking is made (Zhao et al., 2021).

A summary of Figures 4–7 showcasing the relative challenge index (RCI) as a percentage pertaining to different phases of smart building projects is provided. These phases encompass the programming and feasibility analysis phase comprising 22 challenges, the design phase comprising 11 challenges, the installation and commissioning phase comprising 9 challenges, and the operation and maintenance phase comprising 13 challenges. Over recent decades, smart building projects have garnered substantial attention, leading to a multitude of studies aimed at establishing global definitions for smart building technologies (SBTs), recognizing the challenges encountered, and identifying areas for enhancement. The study highlighted here centered on the examination of the concept and terminology associated with SBTs, in addition to the proficiency and hands-on experience of individuals engaged in smart building projects within Saudi Arabia. Moreover, the study sought to determine the features, challenges, and hindrances encountered by SBTs throughout their entire life cycle. While the specific challenges within the Figures are not specified, it can be inferred that the RCI reflects the relative level of complexity or challenge associated with each phase.

FIGURE 4

Alsolami, 2022).

FIGURE 5

Alsolami, 2022).

FIGURE 6

Saad et al., 2022; Alsolami, 2022).

FIGURE 7

Saad et al., 2022; Alsolami, 2022).

5 Findings and discussion

Table 5 illustrates the findings of the assessment. It presents the RCI, challenge rating, challenge group ranking, and overall ranking of each challenge.

5.1 Programming and feasibility analysis phase

This category encompasses 22 challenges. Among these, the respondents identified “The total cost of ownership of smart buildings is unclear or relatively forecasted as a high investment” as the most critical factor and assigned it a rating of “Very Challenging.” The authors concur with this evaluation as many respondents tended to perceive a smart building as being at the forefront of technology. However, in reality, a smart building is characterized by its capacity to seamlessly and efficiently integrate various categories and types of diverse technologies. This discovery aligns with the findings of other studies as well (Ibrhem et al., 2020; Bibri, 2021). “The lack of consumers” (e.g., designers, owners, and users) awareness of the technical specifications of smartness” was rated “Very Challenging” and was ranked first by A/Es and second by facility managers. This finding is also in agreement with the outcomes of different studies (Belani et al., 2014; Ahmed et al., 2021; Zhao et al., 2021).

This is a reasonable assertion as awareness levels are also contingent on several challenges, including the accessibility of information. The findings reveal that A/Es ranked “The lack of historical databases of existing smart buildings’ performance” as the second most crucial challenge in this phase, attributing it an “Extremely Important” importance rating. The authors concur with this evaluation as comprehending the concept of smart buildings is a fundamental step in gaining a better grasp of smart buildings, and the historical databases related to this concept remain ambiguous, lacking a well-defined framework.

5.2 Design phase

This category encompasses 11 challenges, as outlined in Table 3. A/Es, contractors, and facilities managers all identified “The lack of measures to maintain the privacy and data security of users” as the most critical challenge during this phase, assigning it a rating of “Very Challenging” in terms of importance. The authors concur with these assessments as a substantial number of respondents were driven to transition to smart technologies due to their notable advantages in enhancing efficiency, curbing energy expenses, and furnishing analytics that underpin environmentally sustainable initiatives. Nevertheless, the more technology a business integrates, the more exposed it becomes to potential data breaches (Gadakari et al., 2014; Sathesh and Hamdan, 2021). “The lack of descriptive guidelines for the design of different systems in smart buildings” was rated second by facilities managers and third by A/Es, with an importance rating of “Very Challenging.” This is justified, since acquiring users with the needed information of smart features is a vital aspect of persuading them to adopt smart building technologies (Saad et al., 2022).

5.3 Installation and commissioning phase

This group consists of 9 challenges. As shown in Table 2, most of the challenges that face implementing smart building technologies in Saudi Arabia during installation and commissioning phase are due to the high costs of devices and installations. This finding is also in agreement with the outcomes of different studies (El-Motasem et al., 2021; AlMuharraqi et al., 2022). The secondary source of challenges encountered during the installation and commissioning phase relates to “scope variations in smart building projects that lead to significant budget overruns.” This can be rationalized by the absence of a facility management team at this stage and the absence of an all-encompassing framework capable of meeting the objectives related to the life cycle costs of smart building technologies.

5.4 Operation and maintenance phase

This category comprises 13 challenges. The respondents ranked “The operations and maintenance costs (OPEX) are relatively high compared to regular buildings” as the most significant factor in this group, assigning it a “Very Challenging” rating. The authors concur with this evaluation, given that many of the challenges tend to surface during the operation and maintenance phase, often resulting from unresolved and cumulative issues. Contractors and facilities managers identified “The social implications of the accessibility of smart technologies (e.g., elderly use of technologies, gender requirements)" as the second most crucial challenge during this phase, giving it a “Very Challenging” importance rating. This factor was rated third by A/Es, with identical importance ratings. These findings are consistent with previous research (El-Motasem et al., 2021; AlMuharraqi et al., 2022; Ejidike and Mewomo, 2023).

6 Conclusion and recommendations

This study explored the challenges that impact the adoption of smart building technologies throughout their life cycle in Saudi Arabia. Through a combination of a literature review and pilot testing, a total of 55 challenges were identified and subsequently categorized based on their alignment with the life cycle phases, which include the programming and feasibility analysis phase, design phase, installation and commissioning phase, and operation and maintenance phase. To assess the significance of these challenges, a five-point Likert-scale questionnaire survey was developed and pilot tested. This questionnaire served as a valuable tool for quantifying the qualitative research variables that were identified. As a result, it streamlined the process of prioritizing and rating the importance of these challenges with regard to their influence on the execution of smart building technologies in Saudi Arabia. This assessment was primarily focused on understanding the significance of these challenges in terms of their impact on the successful implementation of smart building projects while adhering to specified budget constraints, targeted project durations, and the mandated quality criteria that align with stakeholder expectations.

The evaluation process involved the engagement of three distinct practitioner groups, namely, A/Es, contractors, and facilities managers. Each group provided 30 responses to the questionnaire survey, and these responses were carefully tabulated and analyzed to compute the relative challenge index (RCI) value, rate index percentage, group ranking of challenges, and overall rankings for each factor. Furthermore, an extensive analysis encompassing all 90 responses from all three groups was conducted to ascertain the overall assessment of the challenges. This process also entailed an examination of the consensus among the three groups of respondents.

These challenges are evidently linked to different stages of the project’s life cycle. The results substantiate the interrelation and direct influence of these challenges on the successful execution of these projects, especially within the context of a life cycle. Furthermore, the outcomes from both the comprehensive evaluation and individual assessments align with the discussions and conclusions presented in the existing literature. Remarkably, a high level of consensus regarding the significance of these challenges was evident among all three respondent groups. This underscores the critical importance of all the identified challenges in realizing smart building projects while adhering to the specified time, quality, and budgetary constraints.

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