Critical Smart Functions for Smart Living Based on User Perspectives

Abstract

Smart living is strongly promoted to enhance the quality of life via the application of innovative solutions, and this is driven by domain specialists and policymakers, including designers, urban planners, computer engineers, and property developers. Nonetheless, the actual user, whose views ought to be considered during the design and development of smart living systems, has received little attention. Thus, this study aims to identify and examine the critical smart functions to achieve smart living in smart buildings based on occupants’ perceptions. The aim is achieved using a sequential quantitative research method involving a literature review and 221 valid survey data gathered from a case of a smart student residence in Hong Kong. The method is further integrated with descriptive statistics, the Kruskal–Walli’s test, and the criticality test. The results were validated via a post-survey with related experts. Twenty-six critical smart functions for smart living were revealed, with the top three including the ability to protect personal data and information privacy, provide real-time safety and security, and the ability to be responsive to users’ needs. A need was discovered to consider the context of buildings during the design of smart living systems, and the recommendation is for professionals to understand the kind of digital technology to be integrated into a building by strongly considering the context of the building and how smart living will be achieved within it based on users’ perceptions. The study provides valuable insights into the occupants’ perceptions of critical smart features/functions for policymakers and practitioners to consider in the construction of smart living systems, specifically students’ smart buildings. This study contributes to knowledge by identifying the critical smart functions to achieve smart living based on occupants’ perceptions of smart living by considering the specific context of a smart student building facility constructed in Hong Kong.

1. Introduction

The global smart home market size is projected to reach USD 622.59 billion by 2026, and this exhibits a compound annual growth rate (CAGR) of 29.3% during the forecast period [1]. The market was previously valued at USD 79.90 billion in 2018, and it has gained momentum from the rising mergers and acquisitions between prominent companies for developing novel solutions for residences [2]. With such a swift drift toward smart homes, smart living has become a critical ingredient to be considered in improving the quality of life of residents. Smart living, as defined by Intille [3], constitutes the idea of integrating technology into humanity to promote smartness in buildings. The European Commission [EC] [4] defined smart living as involving original and innovative solutions to make life in buildings more efficient, controllable, economical, productive, integrated, and sustainable. This relates to the idea of a smart building, where digital technologies such as the Internet of Things [IoT], artificial intelligence [AI], big data, cloud computing, etc., are implemented in buildings to allow buildings to become people- and environment-friendly [5]. This study, therefore, follows the definition by EC [4], as it embraces the concept of functionalities and technologies. In addition, “smart home” and “smart building” may be used interchangeably in this study, as they portray similar concepts. With that said, the global advancement of smart living in a specific context enables the development of smart buildings that are adapted to the needs of their occupants during operations and maintenance (O&M) [6]. This has then necessitated the construction professionals to upscale their capabilities to satisfy the needs of the occupants/users.

The architecture, engineering, construction, operations, and facility management (AECO-FM) industry and academia have been pushing together to promote the idea of smart living in buildings that suit the occupant’s needs. The AECO-FM has provided a collaborative environment involving multiple disciplines and activities throughout the building lifecycle process to ensure that client needs are effectively factored in right from the planning stage [7,8]. This collaboration requires the iterative and cooperative exchange of information, and it improves the building designs over multiple lifecycle stages toward the occupants’ requirements [9,10]. The users’/occupants’ requirement in this study refers to the smart functions expected of the smart building for smart living by the occupants. While the AECO-FM industry executes its role by setting up practical policies to integrate the concept of smart living in buildings toward occupants’ satisfaction, academia reports on the industry works and innovative proposals through laboratory work, experiments, case studies, etc. An effective policy can be achieved depending on the collaboration between the AECO-FM industry and academia. This may result from a great effort by academia to create awareness, knowledge, understanding, and experience in smart living across various perspectives.

Regardless of the progress made, domain specialists continue to work toward designing and implementing smart living from the top down with no or minimal involvement of users’/occupants’ requirements [6]. While the top-down approach plays a significant role in designing and developing smart homes, attention needs to be given to the occupants’ perspectives from the bottom up. Professionals could find direct data and information to understand the occupants’ requirements for smart living by involving the occupants in the loop of decision-making during the design and development stages of the building [11]. To develop an interactive building system that promotes smart living, the International Organization for Standardization (ISO) [12] mandated that the building environment and users’ perceptions must be considered. This displays the user-centered theory, which implies that users and their relevant activities and requirements in smart buildings must be prioritized during the design of smart living [13,14,15,16]. Although it is required that designers understand occupants’ demands when creating an interactive building, it is nonetheless challenging. Existing studies have considered the integration of occupants’ perceptions of smart living from different perspectives. For instance, from a green perspective, Leaman and Bordass [17] and Leaman et al. [18] considered the perception of occupants of green buildings. However, smart living goes beyond the green building, hence their findings cannot be applied in the smart building domain. Sultan and Yusuf [19] explored the occupants’ perception of smart homes and their interactions with the technologies. Nevertheless, the authors did not receive actual viewpoints from the occupants on their smart function choices. Ghansah et al. [6] filled in the existing gap by developing a user perception model for smart living. However, the critical smart functions were not explored to impact experts’ decisions in developing smart buildings. These smart functions could serve as critical features that could satisfy occupants to achieve smart living in smart buildings.

This study, therefore, investigates the critical smart functions to achieve smart living based on occupants’ perceptions of smart living. The specific objective is to identify and examine the critical smart functions for smart living based on the users’ perspectives. This considers the specific context of a smart student building facility yet to be constructed in Hong Kong. The study provides valuable insights into the occupants’ perceptions of critical smart features/functions for policymakers and practitioners to consider in the construction of smart living systems, specifically, students’ smart buildings. Practically, this study could aid designers and developers in decision-making to fulfill students’ (users’) expectations of smart student residences, though experts’ judgement may be required. This also illustrates the growing demand for smart building professionals to work with users on the design and development of smart buildings for smart living.

2. Literature Review

2.1. Smart Living

Smart living has been a buzzword in recent years, and many countries are promoting the idea as one of the six parts of smart cities; the others include smart environment, smart economy, smart government, smart people, and smart mobility [20]. Intille [3] linked the concept of smart living to the integration of technology into humans, whereas the EC [4] described smart living as original and inventive solutions that make life in buildings more efficient, controllable, affordable, productive, integrated, and sustainable. The smart living concept incorporates technology into human activities to improve human efficiency and building performance across the building lifecycle stages. This common belief enhances standards in everyday life, including the household, workplaces, building sites, and how people commute within cities [21]. This relates to the concept of smart buildings, which use digital technologies to make buildings more human- and environment-friendly [5]. This study conforms to the definition provided by EC [4], as it includes the functionality and technology used. As a result, smart living in the context of buildings during the operations and maintenance (O&M) phase can be defined as innovative, faster, cost-effective, and sustainable ways in buildings that strive to make life more efficient, controlled, cheaper, productive, integrated, and sustainable.

The genesis of smart living is technology-driven, which is recognized during the O&M phase based on smart building technology, functionality, and purpose [22,23,24], as illustrated in Figure 1. King and Perry [25] highlighted smart building technologies such as automated controls, networked sensors and meters, sophisticated building automation, data analytics software, energy management and information systems, and monitoring-based commissioning. These innovations were found to increase building performance, with an emphasis on system configuration. Hoy et al. [26] discussed how smart technologies combine energy efficiency, networked sensors, and data recording in buildings in new ways. They claimed that modern buildings could alter light, heating, and cooling output to maximize efficiency, give improved physical security, and increase occupant wayfinding capabilities. Other studies have examined incorporating smart technologies into buildings to increase performance [27,28,29,30] as part of the smart living idea. As a result, unless otherwise stated, the terms “building” and “smart building” will be used interchangeably throughout this study.

Smart living promotion in AECO-FM has caused buildings to undergo a digital metamorphosis, from traditional to automated to smart buildings, resulting in a significant digital revolution [25,31,32]. This portrays users and facility managers as being smartly connected to buildings, potentially increasing the precision of data-driven decision-making [33]. As a result, buildings must be adaptive and responsive to the shifting needs of their users.

Figure 1. A typical high-rise smart building with available component systems [34,35].

Figure 1. A typical high-rise smart building with available component systems [34,35].

2.2. Smart Functions

Smart functions, in this study’s context, refer to the users’ requirements and expectations of smart buildings for smart living by the users [36,37]. There is an evolutionary change from a purely expert-based form of smart living in buildings to a more complicated one that incorporates user perceptions into building design. Yi Fu Tuan investigated environmental perceptions and attitudes in the 1970s; therefore, user perception dates back to that time [38]. Taking a broader view, user impressions of buildings have been linked to various perspectives. For example, from a green building standpoint, Leaman and Bordass [17] investigated how people view green buildings as superior. The author’s results pointed to gains in some areas, such as how the users’ demands are satisfied, but the green buildings were found in danger of repeating previous mistakes, particularly if the users find it difficult to maintain. Baird and Field [39] investigated consumers’ perceptions of thermal comfort conditions, such as temperature and air quality, in sustainable buildings. The findings revealed a high level of user satisfaction with internal thermal comfort conditions when design and operations are carefully considered, taking into account user perceptions. However, EC [4], Chen et al. [40], and Bolchini et al. [5] argue that smart living in smart buildings differs from other views, as do its users’ perceptions.

It is important to recognize the user-centered theory since the smart building supports the activities it shelters, including the user [14]. Donald Norman, a cognitive science researcher, invented the term “user-centered theory” to describe a design decision based on user needs and desires [15,28]. The theory employs support for users’ wants and activities as a measure of effective smart living; therefore, it implies that insufficient support for users is a bad scenario with an undesirable influence on user pleasure and comfort [13]. Thus, the user-centered theory underpins this study, implying that users and their relevant activities and requirements in smart buildings should be prioritized during the design of smart living.

Buildings may offer comfort along with effectively responding to occupants’ requirements through smart living systems [41], if user perceptions are considered. Prior research has focused on users’ impressions of smart living in smart buildings [6,16,18,28,29,36,37,42]. However, critical functions that are taken into account to understand how consumers define and desire smart living in buildings remain a challenge. As a result, this study examined relevant literature to explore potential key variables of smart living functions across different domains (see

Table 1. A desk literature review of potential key variables for smart living.

No.	Key Variable	Brief Description	Reference Code
1	Real-time automated monitoring and prediction of building occupancy behavior	The real-time function is vital, based on the demands of the sensed elements, the capacity of the system, or time and event constraints.	[45,46]
2	Component tracking and control	The building should be capable of tracking components and control in real time in the operations and maintenance phase. This records the current state of building components to help facilitate effective maintenance.	[47,48,49]
3	Energy efficiency monitoring	Real-time monitoring of energy usage is an effective component of the building. Systems capable of visualizing real-time energy usage should be implanted into the building. Artificial intelligence (AI) could also be applied to predict real-time energy usage in buildings.	[47,48,49]
4	Real-time defect detection and protection	Focusing on the geometric model of the physical building, the DT component of the cognitive architecture provides a visual and efficient way for inspection and detection. This is done by processing forms of data, such as point clouds, digital images, thermal images, and sensor data from laser scanners, cameras, thermal imaging devices, sensors, and other devices.	[50]
5	A comprehensive view of the building’s state and improved information utilization efficiency	The visualization feature of such buildings endows systems that can allow for viewing the state of buildings and retrieving and analyzing improved information utilization efficiency. This process is automated due to the system’s autonomous nature of the building.	[51]
6	Effective structural health monitoring of facilities	System functions ensuring real-time structural monitoring should be implanted into the building. This can leverage the capability of the cognitive system to predict the structural behavior of facilities.	[52]
7	Human-environment-facility social interaction	Smart living in buildings needs to consider human and environmental interaction in the cyber-physical system (CPS) loop. This is because (1) human actors and environmental factors are considered important components that need to be represented digitally on the cyber side, and (2) there is interaction among the CPS components, environment, and humans. Therefore, technologies need to be implanted into the building to address needs such as tracking, monitoring, and predicting human behavior and interaction with the environment and the CPS elements.	[47,53,54]
8	Rapid collection, transmission, and exchange of building services data	One of the essential features of achieving smartness in the building is bi-directional data exchange between the physical world and its cyber counterparts. This is because sensors perceive and transfer data to cyberspace, and once the data is processed, the retrieved information is sent back to the physical world through actuators.	[55,56]
9	Ability to interact with all elements in the system—processor, devices, cloud services, user, and environment (safe and interoperable smart networked systems)	Systems implanted into the building must interact with each other while creating a heterogeneous and massive amount of shared information. Interoperability platforms in the interactive building remain one of the biggest challenges to the cognitive application for user well-being and productivity at the facility management (FM) phase.	[57,58]
10	Real-time adaptability of the facility	This serves as a decisive factor for the implementation of a cognitive building. This is the capability of the building to be real-time reconfigurable and has multiple alternatives to enable adoption to dynamic environments such as room temperature, thermal conditions, etc.	[45,59]
11	Building security enhancement	Protection of personal data and information privacy in buildings should be addressed at the root of data security issues, having a system that provides transparency and secures users’ information.	[48,49,60,61]
12	Assurance of reliability and convenience	The systems must interact and work flawlessly to achieve the intended goal. Reliability and convenience need to be ascertained through various approaches, including certification, regulations, and compliance with standard requirements.	[60,61]
13	The flexibility of the automation systems in the use of the facility	Flexibility should be obtained in the functional nature of the building. It must be embedded with systems that will ensure an automated system in its operation without complexity.	[62]
14	Effective building document management	This is an important feature of the building that ensures that documents are properly managed, especially in cloud technology. Documents are protected and can be retrieved anytime and anywhere.	[48,49]
15	Cost-effectiveness provision	The building must be integrated with components that can work to achieve user well-being and productivity with minimal cost.	[48,49]
16	Efficient logistics and material tracking	The building possesses the features to ensure efficient tracking of materials usage. This optimizes materials usage towards productivity.	[48,49]
17	Maintenance management efficiency via predictive systems	The capability of a building to predict systems ensures proper maintenance of building components as preventive measures are taken to manage systems.	[54,63,64,65]
18	Accurate detection of building occupancy pattern	Systems ensuring accurate detection and analysis of user behavior and patterns must be needed to ensure building productivity. This is facilitated with AI applications and other cognitive application tools.	[66]
19	Effective measurement of environmental conditions of the facility	The building achieves productivity by effectively measuring the environmental conditions that affect the building and user conditions. This becomes possible due to its effective interactions with the environment and the real-time collection of data in such a domain.	[63]
20	Remember previous interactions in a process and return suitable information	This constitutes a knowledge database in cyberspace that is informed by cognitive computing. This knowledge consists of historical data from previous interactions, and they are dependent on making future predictions on user well-being and productivity.	[67]
21	Autonomous decision-making using reinforced learning	The capability of the building to autonomously process raw data and provide valuable insight to humans. At this stage, a higher level of automation, such as automatic updates of building and user information, is needed.	[45]
22	Well-defined services to support various activities such as monitoring, maintenance, management, optimization, and safety	The building must be tailored to the specific needs of the problem sought to be solved. This can be achieved by ensuring well-defined services by including a network of nodes that need processing, storage, and communication.	[45,62]
23	Reliable decision-making workflow	The knowledge base implanted in the building must be reliable for decision-making towards user well-being and productivity. Cognitive computing must provide a result capable of providing highly probabilistic solutions.	[54]
24	Increasing use of spaces	Systems in buildings must interact to provide feedback that can be used to optimize spaces in buildings. Spaces in facilities can be monitored and visualized when there are efficient interactions among systems.	[54]
25	Easier problem identification in facilities	The building must be capable of visualizing and identifying the problems in a physical facility. This is done by transferring data using sensors from the physical location to cyberspace. The transferred data are processed and analyzed, and insights are drawn using cognitive computing.	[54]
26	Automated work order management/planning	This is attained in building due to the interactive building nature of the systems. The building needs to apply and utilize all accessible information and understand how it impacts user well-being and productivity.	[54]
27	Enhancement of occupant’s comfort and satisfaction	The cognitive computing feature integrated into the building’s function analyzes data to produce insight applicable to enhance user well-being and productivity.	[54,60,68]
28	Responsiveness to occupants’ needs in the facility	The cognitive computing capability implanted into the building must provide insight to enhance the value of FM that ensures user well-being and productivity. This is facilitated by proper interaction of the IoT data, which ingests data and loads the data into the database for further analysis.	[60]
29	Automatic facility identity recognition	The automated system in the building must be used to identify and check an individual’s identity in just a few seconds based on collected historical data. This is facilitated by the digital shadow of the physical entity incorporated with cognitive computing.	[46]
30	Occupant behavior-centered building design	The design of the interactive building is tailored to monitor user behavior. The system augments the processes in facilities and its interactions with users and environments. It monitors the behavior patterns of users and makes future predictions based on the data available.	[47]
31	Mimic the ability of the human brain to learn and provide valuable insight on asset usage and current performance through learning	Systems are implanted into the building to mimic human intelligence, which helps produce results on asset usage and performance. This becomes possible due to cognitive technology integration.	[54,67]
32	Identify contextual elements such as meaning, syntax, time, location, user’s profile, etc.	Contextual identification is a critical feature of the building. The system should be designed to recognize user profile, location, time, and other elements that contribute to the users’ well-being and productivity. It also relates significantly to physical occurrences at specific locations and times.	[46,67]

For reference code details, check Supplementary Information S1.

). The knowledge gap demonstrated that while smart living conditions were expected to occur in buildings, they do not always ensure the users’ comfort and satisfaction. As a result, occupants will be presented with a potential list of identified smart functions to assist them in choosing smart living in buildings. This will aid in understanding the users’ perspectives, which may then be included in the design and construction of smart living environments. The study adopted smart student residences due to the consideration of the specific, unique context of a smart living environment [43]. Specific smart student residences in Hong Kong were considered due to the economy’s well-known uniqueness in adopting a smart city living environment [44]. The prospective smart functions were identified as relating to user comfort and the processes in a building environment that assure user satisfaction.

3. Research Method

The study’s method is based on ISO 9241-210 [12], which addresses the ergonomics of interactive human-centered design, including smart dwelling systems. It gives specifications and recommendations on how human-centered design should be implemented in computer-based interactive systems at every stage of their development. This is designed for use by individuals in charge of design processes and focuses on how smart system hardware and software might enhance human–machine interaction. This study supports the idea that explicit awareness of the user’s needs, tasks, and surroundings is the foundation for designing a smart system that enables smart living, as stated in ISO 9241-210. The study then followed three main stages: a literature review, a survey, and data analysis, as illustrated in Figure 2.

3.1. Smart Function Identification by Literature Review

Thirty-two potential key variables of smart functions in buildings were identified via literature review, as indicated in Table 1, using keywords such as: “smart living” OR “smart functions” OR “smart features” OR “intelligent features” AND “user satisfaction” OR “user comfort” OR “occupant comfort” OR “occupant satisfaction” OR “smart living functions” OR “smart living features” AND “built environment” OR “construction industry.” In a pilot study, an academician (X1), a facilities manager (X2), and a designer (X3) screened and confirmed the potential list, and their profiles are provided in

Table 2. Profile of experts engaged in piloting and the result validation.

Activity	Experts	Designation	Organization	Years of Experience
Pilot study	X1	Professor/Academician	Education	12
	X2	Facility manager	Consultant	8
	X3	Designer	Consultant	10
Post-survey study	A	Facility manager	Consultant	12
	B	Facility manager	Consultant	8
	C	Facility manager	Consultant	10
	D	Professor/Academician	Education	12
	E	Designer	Consultant	10

. The experts had over eight years of experience. The experts were requested to view the smart functions to see how they can obtain smart living if users are to rank the functions. After combining some of the potential key variables of smart functions and others being eliminated owing to recurrence, a total of 26 potential smart functions were determined to be suitable for the survey to continue (

Table 3. Potential smart functions for smart living after piloting.

Code	Potential Smart Functions for Smart Living	References
SF1	Ability to interact with user and environment effectively.	[47,53,54]
SF2	Ability to effectively collect, transmit, and exchange building services data.	[55]
SF3	Capacity to effectively interact with all elements in the system (processor, devices, and cloud services).	[57]
SF4	Capability to adapt in real time to changing environment interaction.	[45,59]
SF5	Ability to be responsive to users’ needs.	[54,60]
SF6	Detect and analyze users’ behavior patterns for space utilization.	[63]
SF7	Ability to identify and check users’ identities in just a few seconds based on collected historical data.	[46,66]
SF8	Apply and utilize all accessible information and understand how it impacts user wellbeing and productivity.	[45,54,60,64,65]
SF9	Ability to identify contextual elements such as meaning, syntax, time, location, and user’s profile.	[46,66]
SF10	Ability to detect falls and accidents and send emergency rescue signals.	[54,66]
SF11	Track, predict, monitor, and control the behavior of users, state of building components, and defects in buildings in real time.	[45,46]
SF12	Visualize and monitor in real time the energy and water usage.	[48,69]
SF13	Track and minimize energy usage and save operation cost in real time.	[47,69]
SF14	Track and optimize material usage in real time.	[48]
SF15	Provide real-time safety and security.	[45,59,60]
SF16	Ability to ensure smart door opening and locking and active emergency response service.	[45,62]
SF17	Effectively manage building service documents.	[48]
SF18	Ability to protect personal data and information privacy.	[50]
SF19	Ability to provide a convenient and reliable system to achieve users’ goals of wellbeing and productivity.	[60]
SF20	Capability to provide flexible automation system in its operation without complexity.	[62]
SF21	Predict to achieve proper maintenance of heating, ventilation, and air conditioning (HVAC) systems.	[54,64,70]
SF22	Effectively measure environmental conditions such as temperature, humidity, and indoor air quality.	[54,60]
SF23	Remember previous interactions in a process and return suitable information.	[63]
SF24	Make effective autonomous decisions for the user.	[45]
SF25	Ability to provide efficient feedback to ensure effective space optimization.	[54]
SF26	Ability to ensure effective schedule management and provide daily life information, such as weather.	[54]

) based on experts’ opinions. Hence, the 26 potential smart functions informed the final questionnaire (Supplementary Information S2: Informed consent form; Supplementary Information S3: Questionnaire).

3.2. Data Collection via Survey

A survey can be used to gather user requirements, as permitted by ISO 9241:210, because of its scalable advantage, reaching a large number of respondents efficiently [71]. However, it might have some limitations, and this includes non-responses skewing the data and respondents interpreting the answer options differently (e.g., “slightly important” can be vague) [71]. Given that this study requires a large response, a survey is a suitable way to gather opinions from a wide range of students who are yet to settle in a smart facility.

The University of Hong Kong (HKU) students were contacted to participate in an online survey using “Qualtrics XM” [72] to rate the importance of the possible smart functions in smart students’ residences through a Likert scale: “11 = not important all,” “12 = slightly important,” 13 = “moderately important,” 14 = “very important,” and “15 = extremely important.” It additionally urged respondents to provide contributions other than the variables specified in the questionnaire. A statement was added to the survey to brief the students on what smart living in smart student residences means to aid them in responding to the survey. The online questionnaire link was distributed to HKU’s massive student population [73] via the “university’s mass email delivery system.” Using simple random sampling, each student had an equal chance of responding to the online survey, which adhered to Kock and Hadaya [74], who specified a minimum sample size of 100. The data-gathering period lasted nearly a semester (1 October 2021 to 31 December 2021). This cleared the way for the study to collect as much data as possible. Students were also issued reminders to answer the questionnaire.

3.3. Data Analysis and Results Validation Method

First, data cleansing was performed to eliminate duplicate/repeated responses and incomplete surveys. Duplicates were found because the data had duplicate email addresses. To deal with the duplicate responses, the study accepted the older response while removing the most recent one. Cronbach’s Alpha was conducted to measure the internal consistency of the data, followed by descriptive statistics, including mean scores, which were produced for the constructs at a 95% confidence level. Kurtosis and skewness were used to assess data normalcy.

Second, the Kruskal–Wallis test (KWt) was also used to check whether the respondents’ educational level (undergraduate, taught postgraduate, and research postgraduate) altered how the students rated the variables with 95% confidence, hence, checking the degree of association of the students’ responses. Criticality analysis was then performed to identify the most critical smart functions for smart living in students’ smart residences. Finally, due to the non-parametric nature of the data, Spearman’s correlation was adopted to understand how the critical smart functions relate among themselves toward achieving smart living in smart buildings.

After analyzing the data from the survey, a post-survey study, which is usually used in the built environment to validate findings from questionnaire surveys [75], was conducted with five experts in the built environment to validate the results and enrich the explanation of the results. Three of the experts were facility managers at Hong Kong University (experts A, B, and C), one is a professor in the built environment (D) with research expertise in smart building, and the other is a designer in the built environment (E). All experts have more than 8 years of experience in the adoption of smart buildings. The profiles of the experts are provided in Table 2. The identified list of the critical smart functions was presented to the experts to rank their level of agreement with them in working alongside to provide smart living for the students in smart residences (Supplementary Information S4: Informed consent form; Supplementary Information S5: Questionnaire).

4. Findings

4.1. Demographic Data

Two hundred and twenty-one (221) students from Hong Kong responded to the survey. Generally, a response of 100 or higher provides an acceptable margin of error [74,76]; hence, the 221 students are regarded as appropriate for this study. Figure 3 illustrates the distribution of respondents. The gender, being male or female, influences the data by distinguishing the safety needs, the technology usage patterns in a smart environment, and the level of inclusivity expected to be in smart living design. The education level has an impact on the data, as it affects the digital literacy, environmental awareness, and readiness to experience smart living in energy-saving or app-based systems. Finally, the type of student residences assists in determining the extent of adopting smart technologies, such as keyless entry, energy management, etc. Overall, smart living is influenced by gender (social), education (cognitive), and residence type (physical) factors that impact how individuals interact with and benefit from smart living environments. This adds to the reliability and validity of the main data, showing strong and inclusive demographic data.

4.2. Chronbach’s Alpha (CA) Test

The reliability test was conducted using CA to measure the internal consistency of the data by juxtaposing the results to the rule of thumb: a CA value < 6.00 connotes low reliability and is unacceptable; 0.60–0.80 means moderate and acceptable; and 0.80–1.00 connotes very good, depicting higher internal consistency and validity of the data [77]. The CA for this study’s measuring scale was calculated as 0.923. This demonstrates a very good internal consistency in the data. As a result, further analysis can be carried out.

4.3. Descriptive Statistics

Table 4. Descriptive statistics result.

Smart Functions	Mean	95% Confidence Interval		Skewness	Kurtosis	KWc (p-Value)	Ns
		Lower Bound	Upper Bound
SF1	13.86	13.76	13.96	−0.294	−0.017	0.606	0.620
SF2	13.62	13.50	13.74	−0.377	−0.278	0.960	0.655
SF3	13.63	13.52	13.75	−0.167	−0.241	0.718	0.658
SF4	13.95	13.84	14.06	−0.442	−0.346	0.707	0.650
SF5	14.41	14.31	14.51	−1.220	0.922	0.310	0.803 c
SF6	13.69	13.56	13.82	−0.670	0.396	0.613	0.673
SF7	13.41	13.25	13.56	−0.593	−0.312	0.060	0.603
SF8	13.71	13.59	13.83	−0.702	0.746	0.699	0.678
SL9	13.37	13.22	13.51	−0.351	−0.218	0.414	0.593
SF10	14.21	14.09	14.33	−0.892	0.058	0.740	0.803
SF11	13.63	13.50	13.75	−0.554	0.179	0.136	0.658
SF12	13.76	13.64	13.88	−0.756	0.390	0.258	0.690
SF13	13.99	13.88	14.10	−0.518	−0.215	0.155	0.663
SF14	13.72	13.59	13.85	−0.555	−0.221	0.972	0.680
SF15	14.26	14.15	14.37	−0.867	0.225	0.921	0.815 b
SF16	13.86	13.74	13.99	−0.596	−0.203	0.612	0.715
SF17	13.38	13.25	13.52	−0.475	0.127	0.031 ***	0.595
SF18	14.42	14.32	14.52	−1.415	2.338	0.741	0.855 a
SF19	14.00	13.88	14.12	−1.096	1.305	0.802	0.750
SF20	13.94	13.84	14.04	−0.408	−0.044	0.914	0.647
SF21	14.10	13.99	14.21	−1.055	1.700	0.453	0.775
SF22	13.99	13.89	14.09	−0.419	−0.136	0.972	0.663
SF23	13.56	13.44	13.68	−0.387	−0.013	0.898	0.640
SF24	13.39	13.25	13.53	−0.373	−0.216	0.329	0.598
SF25	13.54	13.41	13.67	−0.562	0.133	0.949	0.635
SF26	13.61	13.49	13.73	−0.446	0.030	0.259	0.653

Note: Ns = normalization score = (actual mean–minimum mean)/(maximum mean-minimum mean); only normalization scores ≥ 0.5 are deemed critical by the respondents; *** significant at a 95% confidence level (p-value (Asymp. Sig. [2-tailed] ≤ 0.05); ^{a, b, c} = top 3 critical smart functions; with the same Ns from two variables, the variable with the highest mean value is selected.

displays the absolute values of skewness and kurtosis, indicating that the data do not deviate significantly from the normal distribution; hence, the data are non-parametric. The mean for the variables ranged from 13.37 to 14.41 on the Likert scale. Table 4 also included 95% confidence intervals.

The Kruskal–Wallis test (KWt) was adopted to determine if students’ educational levels influenced their assessments of potential smart functions in smart student housing. The educational qualifications were utilized as “grouping variables,” whereas the smart functions were employed as “testing variables.” The Kruskal–Wallis coefficient (KWc) in Table 4 indicates that respondents viewed only one of the smart functions (SF17) differently (p < 0.05). Other smart functions have p-values greater than 0.05. This implies that combining all the respondents’ responses had no significant effect on the findings’ overall reliability, with the exception of “effectively manage building service documents” (SF17), which had a p-value = 0.031. Hence, SF17 was taken out of the list to continue the analysis.

4.4. Criticality Test and Spearman’s Correlation Test

Based on the mean score, the normalization score (Ns) was computed to determine the level of criticality of the smart functions based on the responses. Hence, a Ns ≥ 0.50 is deemed more critical [78]. Table 4 also depicts the critical smart factions based on the Ns. Lastly, Spearman’s correlation test was conducted to measure how the critical smart functions in this context correlate among themselves based on the rankings of the responses. With this, the rule of thumb states that the coefficient should be between a value of +1 and −1, where −1 indicates a perfect negative correlation and +1 indicates a perfect positive correlation [79]. This is situated at a statistical significance of p-value ≤ 0.05.

Table 5. Result of Spearman’s correlation test.

	SF1	SF2	SF3	SF4	SF5	SF6	SF7	SF8	SF9	SF10	SF11	SF12	SF13	SF14	SF15	SF16	SF18	SF19	SF20	SF21	SF22	SF23	SF24	SF25	SF26
SF1	1.000
SF2	0.521 **	1.000
SF3	0.523 **	0.538 **	1.000
SF4	0.506 **	0.562 **	0.529 **	1.000
SF5	0.456 **	0.211 **	0.231 **	0.397 **	1.000
SF6	0.568 **	0.533 **	0.415 **	0.579 **	0.422 **	1.000
SF7	0.360 **	0.399 **	0.457 **	0.462 **	0.279 **	0.491 **	1.000
SF8	0.546 **	0.537 **	0.487 **	0.462 **	0.225 **	0.495 **	0.523 **	1.000
SF9	0.439 **	0.472 **	0.457 **	0.372 **	0.108	0.476 **	0.570 **	0.557 **	1.000
SF10	0.050	(0.033)	0.045	0.008	0.272 **	0.101	0.225 **	0.146 *	0.136 *	1.000
SF11	0.390 **	0.397 **	0.370 **	0.279 **	0.312 **	0.307 **	0.447 **	0.399 **	0.418 **	0.361 **	1.000
SF12	0.239 **	0.123	0.212 **	0.098	0.167 *	0.103	0.094	0.200 **	0.054	0.346 **	0.320 **	1.000
SF13	0.297 **	0.104	0.303 **	0.207 **	0.251 **	0.075	0.083	0.117	0.057	0.254 **	0.228 **	0.674 **	1.000
SF14	0.377 **	0.124	0.425 **	0.205 **	0.226 **	0.120	0.224 **	0.245 **	0.253 **	0.194 **	0.236 **	0.525 **	0.596 **	1.000
SF15	0.159 *	−0.021	0.160 *	0.163 *	0.141 *	0.168 *	0.136 *	0.023	0.112	0.398 **	0.089	0.284 **	0.437 **	0.301 **	1.000
SF16	0.490 **	0.366 **	0.440 **	0.358 **	0.120	0.398 **	0.432 **	0.584 **	0.498 **	0.201 **	0.347 **	0.206 **	0.207 **	0.347 **	0.379 **	1.000
SF18	0.139 *	(0.101)	(0.001)	0.123	0.179 **	0.143 *	0.038	(0.009)	(0.043)	0.365 **	0.056	0.270 **	0.297 **	0.196 **	0.507 **	0.231 **	1.000
SF19	0.386 **	0.228 **	0.291 **	0.277 **	0.173 **	0.256 **	0.296 **	0.301 **	0.251 **	0.240 **	0.269 **	0.181 **	0.185 **	0.229 **	0.372 **	0.436 **	0.304 **	1.000
SF20	0.512 **	0.303 **	0.460 **	0.438 **	0.249 **	0.350 **	0.347 **	0.396 **	0.253 **	0.142 *	0.254 **	0.170 *	0.217 **	0.281 **	0.266 **	0.426 **	0.273 **	0.536 **	1.000
SF21	0.323 **	0.157 *	0.324 **	0.290 **	0.111	0.228 **	0.185 **	0.257 **	0.144 *	0.167 *	0.062	0.326 **	0.336 **	0.369 **	0.491 **	0.359 **	0.445 **	0.215 **	0.376 **	1.000
SF22	0.395 **	0.385 **	0.222 **	0.350 **	0.358 **	0.410 **	0.224 **	0.291 **	0.265 **	0.250 **	0.269 **	0.295 **	0.395 **	0.364 **	0.428 **	0.370 **	0.313 **	0.318 **	0.315 **	0.496 **	1.000
SF23	0.388 **	0.469 **	0.418 **	0.420 **	0.284 **	0.411 **	0.423 **	0.472 **	0.361 **	0.074	0.455 **	0.269 **	0.224 **	0.159 *	0.044	0.338 **	(0.004)	0.222 **	0.342 **	0.323 **	0.432 **	1.000
SF24	0.394 **	0.498 **	0.435 **	0.449 **	0.306 **	0.522 **	0.513 **	0.411 **	0.391 **	(0.050)	0.441 **	0.019	0.066	0.064	(0.051)	0.290 **	(0.118)	0.215 **	0.314 **	0.048	0.294 **	0.634 **	1.000
SF25	0.564 **	0.385 **	0.470 **	0.417 **	0.294 **	0.427 **	0.388 **	0.311 **	0.380 **	0.087	0.479 **	0.320 **	0.323 **	0.425 **	0.159 *	0.354 **	0.219 **	0.271 **	0.379 **	0.319 **	0.429 **	0.512 **	0.554 **	1.000
SF26	0.341 **	0.291 **	0.341 **	0.447 **	0.271 **	0.340 **	0.435 **	0.373 **	0.352 **	0.362 **	0.472 **	0.340 **	0.305 **	0.297 **	0.235 **	0.404 **	0.244 **	0.290 **	0.317 **	0.321 **	0.489 **	0.535 **	0.362 **	0.516 **	1.000

Note: (…) represents negative correlation; p-values [Sig. (two-tailed)]: ** correlation is significant at the 0.01 level (2-tailed); * correlation is significant at the 0.05 level (two-tailed).

summarizes the results of Spearman’s correlation test for this study.

4.5. Result Validation

A post-survey study engaging experts was conducted to validate the critical smart functions, and the data were analyzed using the one-sample Wilcoxon signed-rank test to determine whether the sample median differs significantly from the hypothesis median value. This was also informed by the non-parametric nature of the post-survey data. In this context, the hypothesis median value on the post-survey data on the critical sample test was set to be 4 due to the adoption of the five-point Likert Scale: “1 = Strongly disagree; 2 = Disagree; 3 = Neutral; 4 = Agree; 15 = Strongly agree.” If the calculated value was > 4 and the p-value was < 0.05, we could conclude that the median value was significantly > 4, indicating that the experts agree with the critical smart functions and could help inform decisions to design and develop smart student residences to help students achieve smart living. The result is illustrated in

Table 6. Results of the post-survey data analysis toward validation.

Smart Functions	Hypothetical Median	Observed Median	(Asymptotic Sig. (2-Sided Test) [p-Value]
SF1			0.025
SF2			0.034
SF3			0.038
SF4			0.038
SF5			0.025
SF6			0.038
SF7			0.034
SF8			0.034
SL9			0.034
SF10			0.034
SF11			0.038
SF12	4	5	0.034
SF13			0.038
SF14			0.038
SF15			0.025
SF16			0.034
SF18			0.038
SF19			0.034
SF20			0.038
SF21			0.034
SF22			0.034
SF23			0.034
SF24			0.034
SF25			0.034
SF26			0.034

.

5. Discussion

5.1. Disparity Among the Smart Functions

With a p-value of 0.031 using KWt (p ≥ 0.05), the smart function “effectively manage building service documents (SF17)” is the only function that varied according to the users’ perception. This implies that SF17 is not a top concern for users (students) when they are thinking about smart living. As a result, practitioners and professionals must understand that the kind of digital technology to be integrated into a building will rely on the context of the building and how smart living is achieved within it. However, this does not negate the significance of SF17 as a smart living function, as it is clear that SF17 is a necessary component of smart living in a building, particularly in an office environment. It guarantees efficient document management [62], particularly in the context of cloud computing, where documents can be safely stored and accessed from any location at any time [80]. Thus, users’ expectations for smart living vary depending on the context (e.g., a student’s home differs from a workplace or other location), and this may require the building to meet the basic critical smart functions for smart living.

5.2. Criticality of the Smart Functions Towards Smart Living

The study revealed all the potential smart functions to be critical for smart living according to the study’s context with the Ns exceeding 0.50. However, the top three critical functions for smart living from the perspective users’ perspective include the ability to protect personal data and information privacy (SF18) [0.855], the ability to provide real-time safety and security (SF15) [0.815], and the ability to be responsive to users’ needs (SF5) [0.803]. The critical smart functions are logged as the important perceptions provided by this study required from students’ smart residences to experience smart living, hence, being a real and original contribution to knowledge. This is heavily dependent on Vischer’s [13] explanation of the user-centered theory, which emphasizes the use of user activities as a gauge of successful smart living, including sufficient support for users’ activities in a favorable environment that enhances comfort and a sense of satisfaction. The set of critical smart functions as discovered by this study could be defined as important requirements from occupants’ viewpoints, and this could direct designers and developers of smart spaces to guarantee user comfort and satisfaction. Regarding ISO 9241-210 in the AECO-FM industry, these critical smart functions lay the path for recognizing the needs of students in terms of comfort and satisfaction when developing student residences toward smart living [12].

To realize smart living in this study’s context, it is sensible to understand the top three critical smart functions along with the others for smart living in smart student residences, recognizing the essence of user-centered theory. This solidifies the fact that users and their relevant activities and requirements must be prioritized during the design of smart living in student smart residences.

The topmost critical smart function for students, according to this study, to be considered in impacting experts’ decisions for smart living in smart student residences is the ability to protect personal data and information privacy (SF18) regarding users. This buttressed the prior studies, which posited the need for protecting user’s privacy and data in smart buildings due to the vulnerable nature of smart systems because it is a relatively new concept [81]. For instance, Ahmed [82] revealed the need to protect users’ data in a smart system, and this may rely on the efficiency and effectiveness of the IoT system deployed. This requires the protection of a smart building user’s personal data from those who should not have access to it and the ability of individuals to determine who can access their personal information [83]. As such, SF18 then explains the system’s ability to protect the privacy of students and ensure their safety with regard to stopping unauthorized external individuals from accessing their information. To achieve SF18, smart student residences can be designed and developed to protect personal data and information privacy [83,84] by making sure that any personal data within data is either encrypted or anonymized. This study, therefore, recommends smart student residences to be designed and developed by paying attention to how students can protect their data and information while experiencing comfort and satisfaction. This can help prevent security breaches or any possible dangers of real-time data collection and monitoring in smart student residences. With this, professionals must consider the ethical issues with data collection, privacy, and user permission in the implementation of the smart functionalities in a smart living environment.

The next top critical smart function for students that needs to be considered in experts’ decision-making for smart living in smart buildings is the ability to provide real-time safety and security (SF15). SF15 differs from SF18, as SF18 relates more to the protection of users’ data. This finding is parallel to Böke et al. [59] and Intecho [85] by stating that the smart buildings’ need the capability to detect and deter criminal activity in real-time, monitor building access in real-time, and provide real-time alerts to security personnel. Implementing SF15 in smart student residences protects students’ properties with intelligent security devices, online access to monitor and control smart security systems, and smart notifications sent to emergency services when an incident is detected. This could be integrated with advanced analytics and machine learning/artificial intelligence to enhance the system’s ability to detect and respond to potential security threats, reducing the likelihood of security breaches and other risks [45,86]. To achieve SF15 in smart student residences, experts need to incorporate the safety and security of students to ensure a safe smart environment [87]. This study therefore attests to the fact that safety and security of students in a smart environment is a key consideration for smart living; hence, experts need to consider that during the design and construction of smart student residences.

Lastly, the next top smart function to be considered critical in achieving smart living, according to the study’s context, is the ability to be responsive to users’ needs (SF5). This aligns with Almeida et al. [88] and Carlucci [89] by explaining that smart buildings’ responsiveness is vital for smart living by providing significant improvements in energy efficiency and internal comfort. SF5 requires smart buildings to be designed and constructed to adapt to the changing needs of the user as well as the changing environmental conditions to allow smart living [90,91]. Hence, SF5 places the users at the center of the design process of smart buildings, creating spaces that adapt to the specific needs of the users and their preferences. For instance, intelligent lighting systems can be incorporated into smart buildings for smart living by adjusting the lighting levels based on the user presence and natural light availability, creating optimal lighting conditions while reducing energy waste [92,93]. In addition, temperature and air quality systems in smart buildings can be responsive to the users’ levels and preferences, ensuring comfort and well-being [94]. To achieve SF5 in smart student residences, smart living needs to be tailored to the students’ requirements and be responsive to enhance their overall experiences and satisfaction. This study therefore reveals the needs to the world for responsive architecture in smart student residences to create dynamic and adaptable spaces capable of responding to the changing needs and environment to achieve smart living.

Using the Spearman correlation test, the correlation matrix among the critical smart functions denoted some significant relationships among themselves, as illustrated in Table 5. For instance, the strongest significant positive correlation (ρ = 0.674, p = 0.00) was found between “visualize and monitor in real-time the energy and water usage (SF12)” and “track and minimize energy usage and save operation cost in real time (SF13).” This denotes that users in one way or the other may want to visualize and monitor their energy and water usage to help them save some cost in real time. With this, students may also be able to learn about their environmental impact and make informed decisions to conserve energy [95,96,97]. This empowers future generations to become more conscious of their energy usage and contribute to a greener world. The study also revealed some negative correlations among the smart functions. However, these correlations were noted to be insignificant (p > 0.05), denoting that to be of no influence among the smart functions. Overall, there is a fairly positive correlation among all the critical smart functions, depicting that pragmatic strategy involving these critical smart functions to inform experts’ decisions in the design and construction of smart buildings could help users, especially students, to attain smart living in smart student residences. This was eventually confirmed by the experts (A, B, C, D, and E) as they validated the critical smart functions to significantly (p-value < 0.05) inform their decisions in designing and developing smart student residences to help students achieve smart living, hence, displaying the core and practicality of the user-centered theory.

5.3. Practical/Managerial Implications

First, identifying the top three critical smart functions based on students’ (users’) perceptions could inform experts’ decisions in designing and developing smart student residences for students to achieve smart living. The study made it clear that the purpose of a smart building is realized if the users’ expectations of comfort and satisfaction are met. The study recommends the need to incorporate the users’ perception of smart living into the design and development process of smart buildings, considering the specific use context. It is critical for experts to engage other stakeholders in decision-making on achieving smart living in smart buildings, not only using the professionals’ expertise, but also evaluating the feasibility of users’ perceptions of smart living. In doing this, errors and reworks relating to a poor understanding of smart living design would be prevented. The findings of this study offer valuable insights for experts in designing and developing smart buildings to promote smart living by highlighting the critical smart functions with a focus on user perception. Moreover, the study recommends that practitioners and professionals understand that the kind of digital technology to be integrated into a building will rely on the context of the building and how smart living is achieved within it. In addition, considering the strong significant correlation between “provide real-time safety and security (SF15)” and “ability to protect personal data and information privacy (SF18),” the study admonishes engaging computer scientists, designers, psychologists, and policymakers to address privacy and security challenges holistically.

Second, the findings of this study could aid designers and developers in decision-making to fulfill students’ (users’) expectations of smart student residences, though experts’ judgement may be required. This is clearly shown in this study, as experts were contacted to validate the critical smart functions by showing their level of agreement and the feasibility of the critical smart functions as denoted by the students/users. The findings make the list of requirements available to understand the students’ expectations of smart living in smart student residences. These critical smart functions may influence professionals’ choices in developing smart buildings for students’ comfort and satisfaction. Professionals, such as designers, could rely on the study’s findings to forecast and understand user expectations of what is needed in student smart residences for smart living, including comfortable room-space temperature to suit the body temperature of students via automatic and operable sensor-based smart air-conditioning systems, as well as effective real-time monitoring, tracking, and prediction for the benefits of the students without compromising the users’ security, safety, and privacy. This could be achieved by professionals understanding the students’ requirements. Consequently, the findings of this study could inform decisions when designing and developing smart student residences in different locations, hence, confirming the significance of ISO 9241-210 in the AECO-FM industry. It also advocates for the regulations mandating user involvement in smart building designs, ensuring compliance with ISO 9241-210.

Lastly, the study also illustrates the growing demand for smart building professionals to work with users on the design and development of smart buildings for smart living. Because of poor collaboration, the objective of smartness in buildings is not met, as users become uncomfortable and dissatisfied. This leads to reworks on smart buildings to meet the growing demands of users. Instead of relying on experts’ opinions, it is critical to team up with facility users about their perceptions and expectancies in smart living based on specific contexts.

5.4. Theoretical Implications

First, this study contributed to the existing body of knowledge by identifying the critical smart functions for smart living in the context of smart student residences by considering the perspective of students (users). The findings suggest that all the smart functions (SFs) were critical, with the top three critical SFs consisting of the ability to protect personal data and information privacy, the ability to provide real-time safety and security, and the ability to be responsive to users’ needs. Overall, there is a fairly positive correlation among all the critical smart functions, demonstrating that a pragmatic strategy involving these critical smart functions to inform experts’ decisions in the design and construction of smart buildings could help users, especially students, achieve smart living in smart student residences. This demonstrated the relevance of user-centered theory and smart building theory for smart living, advocating for a building to be designed to continuously interact with its four basic elements, namely users, places (fabric, structure, facility), management (performance and maintenance), and processes (automation, control, and system). Theoretically, the study implies that designing and developing smart buildings needs to consider users and their specific contexts to help achieve users’ comfort and satisfaction for smart living, thus contributing to the user-centered theory of smart buildings in the built environment.

Second, the findings aligned with previous studies on demonstrating the importance of achieving smart living in smart buildings [45,54,55,60,63]. Unlike the previous studies that have considered the need to collaborate with the users in developing smart buildings for smart living [6,18,35,36,39], this study investigated the critical smart functions to achieve smart living based on users’ perceptions of smart living by considering the specific context of a smart student building facility yet to be constructed in Hong Kong. Furthermore, several differences were observed in this study in the perceived criticality of the smart functions and the relationships among them, contributing to the knowledge body in this field.

6. Conclusions and Recommendations

An adequate understanding of the users’ perception of smart living is critical for designing and developing smart buildings. Professional recommendations and experts’ opinions have dominated the design of smart buildings, yet these individuals are not typically the ones who utilize such facilities. As the demand for smart buildings rises due to the associated benefits, it is critical to consider how users perceive and anticipate smart living in buildings. The dynamic nature of users’ requirements may be difficult to comprehend, as they differ from one user to another. Nevertheless, it is crucial to take into consideration clear critical smart functions representing users’ requirements to influence design based on users’ perceptions and the context use of the building. This study adopted a sequential quantitative research method by focusing on students’ perception of smart living in a proposed smart student residence in Hong Kong. Following the collection of quantitative data, analysis was conducted using descriptive statistics, disparity analysis, and criticality analysis. A post-survey study was finally carried out to validate the findings of the analysis.

The study revealed the critical smart functions for smart living, with the top three consisting of the ability to protect personal data and information privacy, the ability to provide real-time safety and security, and the ability to be responsive to users’ needs, which represent a unique contribution to knowledge body in this field. The critical smart functions represent a set of important requirements from occupants’ (students’) viewpoints, and this could direct designers and developers of smart spaces to guarantee student comfort and satisfaction. Regarding ISO 9241-210 in the AECO-FM industry, the findings lay the path for recognizing the needs of students in terms of comfort and satisfaction when developing student residences toward smart living. The study again discovered the need to consider the context of buildings during the design and recommends that practitioners and professionals understand the kind of digital technology to be integrated into a building by considering the context of the building and how smart living will be achieved within it based on users’ perceptions. For instance, smart living conditions in office buildings are likely to be different from smart living in residential buildings, especially for student residences. As such, the study revealed that “effectively managing building service documents” is not a priority in the context of achieving smart living in smart student residences, though it is important and maybe a priority for workers in office buildings. Therefore, efficient smart living functions rely on the context of usage of the building, and professionals are recommended to acknowledge when planning, designing, and developing smart living systems along with experts’ judgment, considering ISO 9241-210. Overall, a fairly positive correlation was discovered among all the critical smart functions, depicting that a pragmatic strategy involving these critical smart functions to inform experts’ decisions in the design and development of smart buildings could help users, especially students, to attain smart living in smart student residences.

The study illustrates practical and theoretical implications for the proactive design and development of smart buildings to fulfill users’ expectations in smart living systems, considering the study context. Moreover, it sheds light on the importance of the user-centered theory and smart building theory in the built environment by offering insight from users’ perspectives toward smart living. As the user perception is a top priority in smart living, it is important to consider the practicality of adopting the critical smart functions in terms of cost and technology and how they will work with the existing infrastructure. With this, attention must be paid to how the smart functionalities may be sustained or improved over time.

Despite achieving the objective of this study, there are still limitations that need to be acknowledged. The study did not consider how the users’ judgement of the smart functions may evolve over time as they get used to the smart system; hence, it could be an area for future research to probe using longitudinal data. This could capture the evolving user needs as smart technologies advance. IoT sensors can also be used to track how user preferences evolve with the technology adoption. The study was carried out in Hong Kong by considering the context of the students’ smart residential environment. Further studies could also compare the results of this study with data from other economies (Europe or Africa) in different contexts and cultural backgrounds to see if the results are generalizable to other residential contexts (e.g., workplaces, elderly houses, hospitals, etc.). This will aid in comprehending the regional, contextual, and cultural differences in how users perceive smart living in buildings, using this study as a lesson. Although the study has mentioned the links between the critical smart functions, the potential trade-offs have not been completely addressed; for instance, improving security may compromise user privacy. Hence, future studies can consider investigating such trade-offs as well as conducting cost-benefit analyses on them. The maturity level of the necessary smart technologies and difficulties in integrating them into new or existing buildings were not discussed in this study, despite the fact that the critical smart functions were identified; thus, it serves as an area for future research to probe. This could ensure that smart systems integrate seamlessly with new or existing building infrastructure. In addition, future research could consider the wider environmental effects of implementing widely used smart functionalities, though this study discussed the functionality of energy monitoring.