Importance of Design in Smart Digitalization: Smart Living Environments for the Aging Korean Elderly

Abstract

This study explores the key considerations in designing a smart environment for the elderly, aiming to enhance their effective use of such space environments within a structure such as a building. A survey was conducted among a limited sample of elderly recipients in South Korea to explore the relationship between the frequency and usefulness of smart digitalization usage and various factors, including design elements, age, and perceived necessity. By identifying the needs of modern elderly people regarding smart environments, the study aims to provide implications for the direction of smart environments for the elderly, thereby contributing to the creation of a sustainable society in various ways. The study results are as follows. First, in terms of design factors, eco-friendliness was identified as a major factor influencing both the frequency of use and the convenience of the elderly in smart environments. Second, the age group of the elderly was found to be a significant variable affecting the frequency of use and the convenience of smart environments. Third, variables such as an emergency environment, a comfortable environment, and a healthy environment with regard to environmental factors, as well as maintenance, aesthetics, and safety in design elements, were found to have no statistically significant impact. These findings suggest that simply considering environmental friendliness or aesthetics is insufficient in designing a smart residential environment for the elderly, and that design strategies prioritizing the actual user experience and convenience are necessary.

1. Introduction

Since 2020, with South Korea’s baby boomer generation becoming elderly, there has been an increase in the adoption of smart devices within living spaces [1]. The Korean baby boomer generation represents a transitional generation moving from a traditional era to a modern and globalized one, characterized by independence, autonomy, and a more active lifestyle compared to previous generations [2]. In educational and work environments, they have been more exposed to smart environments compared to elderly individuals born before 1950 [3]. Recently, South Korea has hosted events like the “Silver Generation-Centered Smart Expo”, where products for seniors vulnerable to digital environments are demonstrated and exhibited, reflecting the active trend in the industry of launching smart products designed for the elderly [4]. Specifically, the greater need for smart products among elderly individuals corresponds to a higher demand for smart digitalization within a structure where the elderly live. This relationship suggests that as older adults recognize the value of smart digitalization in meeting their space-related needs, their expectations and reliance on smart products increase.

Smart products for the elderly enable “Aging in Place” (AIP). AIP refers to the ability of elderly individuals to continue living independently in their current homes or communities for as long as possible [5]. Elderly individuals, even as they age, wish to live in familiar environments and maintain independent lifestyles for as long as they can. Technological support in smart and digitalization-equipped structures helps the elderly remain self-sufficient. For example, smart building technologies that allow doctors or caregivers to monitor the elderly 24 h a day, or telemedicine services, can protect seniors from dangerous situations. These technologies enable the elderly to live more safely and conveniently, fostering independent living [6]. In particular, a study by Cho and Kim [7] found that elderly individuals who receive support from digitalization-equipped experience an increase in quality of life. However, research on smart environments and digitalization-equipped structures for the elderly is still lacking. While there are existing studies on this subject, most focus on analyzing behaviors related to physical frailty in the elderly [8,9,10], or the development of smart devices tailored to the specific lifestyle habits of seniors [11,12]. For instance, research on smart living spaces applied to senior housing often focuses on managing the daily lives of elderly people through smart monitoring devices [13] or developing smart devices that manage the nutritional needs of seniors [14]. These studies contribute to improving the overall living environment of the elderly, making it more convenient.

However, compared to previous studies on the utility of smart environments, there is limited research focusing on vulnerable populations, particularly the elderly, who require healthcare services. Specifically, the needs of elderly individuals mean they are likely to place greater emphasis on details related to technology usage, such as design factors, reflecting their unique preferences and the challenges in adopting these technologies. On the condition that the needs of a smart environment that are necessary for the elderly to enjoy convenience in their living spaces are identified, environments that deeply satisfy their daily lives can be planned. By understanding the needs of the elderly in smart environments, design can help them maintain autonomy in their daily lives while improving their quality of life [15].

In this context, digitalization-equipped design within a structure plays a key role in effectively aligning the usability of products or environments with the needs and abilities of users. Designs that enhance the convenience and independence of the elderly contribute to boosting their self-esteem and improving their self-efficacy and well-being [16]. Particularly, design focuses on improving safety, comfort, accessibility, and quality of life in residential spaces [17,18]. For example, large fonts, vivid colors, and easy-to-operate touchscreens enhance the accessibility and ease of use in smart digitalization within a structure.

Additionally, design can maintain a sustainable environment through eco-friendly design [19]. Eco-friendly design is a strategy aimed at efficiently using resources, reducing environmental pollution, and minimizing negative impacts on the ecosystem [20]. To achieve this, products and spaces are created with goals such as energy efficiency, recyclability, and reducing environmental pollution. For example, in residential design for the elderly, buildings can be designed in a way that not only protects the health of seniors through energy-efficient structures, natural lighting, and facilities that block fine dust, but also saves energy [21]. In product design for the elderly, household appliances, furniture, and everyday items made from eco-friendly materials and that are easy to use can enhance convenience for seniors while minimizing the environmental impact [22].

Furthermore, digitalization-equipped design within a structure helps identify potential risk factors in smart environments and plans to mitigate them, providing a safer environment [23]. For example, it analyzes risk factors in the lives of the elderly and suggests the need for smart features such as fall detectors, smoke detectors, and emergency call buttons. The design offers convenience by enabling elderly individuals to access and utilize the latest technologies easily. For instance, integrating smart home technology ensures that seniors can control their environments easily through voice commands or simple touchscreen interfaces. The design also allows for personalization and customization. Since the needs and abilities of elderly individuals may vary, it is important to include personalized features in the design. For example, user analysis can help set personal settings such as adjusting voice volume or screen brightness.

This study aims to explore the key considerations in designing smart digitalization and the associated environment within a structure for the elderly to use more effectively. The research aims to assess the positive effects and limitations of smart digitalization and the associated environments, and it proposes design strategies within a building structure that improve the quality of life for the elderly. Therefore, the significance of this study lies in providing practical data and feedback necessary for developing tailored solutions in the development of smart residential environments for the elderly. The expected effect of this study is to identify the needs of modern seniors concerning smart environments and offer solutions for the direction of smart environments for the elderly.

The result of this study is expected to contribute to building a sustainable society in various ways to see how a building structure can be benefited by accepting the design with smart digitalization. For example, seniors can reduce their dependence on external services by leveraging smart digitalized environments, particularly those designed to assist with chronic disease management. These technologies support more effective self-care, enabling older adults to manage health conditions independently, which, in turn, alleviates the overall costs on society. As a result, the resources within a structure can be allocated more efficiently, ensuring that the system can focus on those with more acute needs [24,25,26]. Additionally, maintaining physical and mental health improves the quality of life for seniors, which enhances personal satisfaction and increases social participation [27]. In other words, by increasing resource efficiency, a sustainable environment can be promoted (Figure 1).

2. Conceptual Background

2.1. Characteristics of the Elderly

Generally, the elderly refer to socially vulnerable individuals aged 65 or older who are physically and mentally frailer compared to the general population [28]. They have gone through childhood, adolescence, and adulthood, and experienced marriage, parenting, children leaving home, and retirement from work. They have transitioned to a status where they are no longer part of the economically active population and are protected by society. This transition is often recognized through environmental changes, starting from physical, mental, and social aging, including visual, physical, and psychological changes.

Aging refers to the biological response of weakening as one gets older, which includes a decline in physical abilities, reduced adaptability, and changes in behavioral values [29]. As individuals age, the characteristics of the elderly can be distinguished from those of the general population in terms of physical, mental, and social differences (Figure 2). Physically, the elderly experience a decline in the musculoskeletal system, leading to reduced muscle strength, diminished support capability, decreased agility, significantly lower endurance, and impaired grip strength. The deterioration of bones and joints results in a hunched back and reduced height. As bones weaken, joint stiffness, arthritis, and osteoporosis occur frequently [30].

In terms of sensory systems, there is a noticeable decline in both vision and hearing [31,32]. Vision is affected by presbyopia, causing changes in the lens, which leads to blurred vision [31]. Common related diseases include cataracts and glaucoma [33]. Hearing loss is also a characteristic of aging, with symptoms such as tinnitus, and more than 40% of the elderly, after the age of 70, can only perceive about 50% of sounds. By age 80, hearing function declines significantly, making it difficult to hear sounds clearly [34]. Regarding internal organ function, digestive abilities decline, and symptoms such as respiratory diseases, arteriosclerosis, hypertension, and diabetes appear, with eating habits being strongly related to the diseases of the elderly.

Because of the physical changes, the psychological characteristics of the elderly include emotional issues such as forgetfulness, depression, anxiety, and paranoia, as well as attachment to familiar objects and active problem-solving abilities [35]. Furthermore, social characteristics involve the loss of status and roles, decreased economic capacity, and reduced opportunities to acquire information [36], and the life cycle also changes [37]. Additionally, the elderly may relocate due to a decrease in family members or health issues [38].

As such, these characteristics of the elderly can lead to dangerous situations in daily life. For example, the decline in musculoskeletal function often results in frequent falls [39], and sensory discomfort impairs the ability to distinguish objects, which can lead to accidents such as burns due to the inability to properly perceive the features of objects [40]. Internal diseases like respiratory ailments and hypertension can suddenly put elderly individuals at risk. Forgetfulness can lead to accidents such as leaving the gas stove on, or it can worsen into symptoms of dementia. An uncomfortable environment can exacerbate pre-existing health conditions in the elderly. Another issue related to the characteristics of the elderly is that they often struggle to manage themselves, making it easy to forget tasks like taking medication on time or attending appointments. Such environments can further deteriorate the condition of the elderly. Therefore, designing for the elderly must consider characteristics such as reduced physical abilities and diminished sensory functions.

2.2. Smart Digitalization Environment Within a Structure

The concept of a smart residential environment refers to a living space that maximizes convenience, safety, and efficiency for its residents by utilizing the latest information and communication technologies [41]. A smart residential environment is designed by integrating various technologies such as the Internet of Things (IoT), artificial intelligence (AI), big data, and cloud computing, allowing all systems and devices in the home to be interconnected, automatically controlled, and managed [42]. This environment helps people of all ages live more safely and conveniently. The key features of a smart residential environment include automation systems, safety and security, energy efficiency, convenience, and lifestyle management. According to Patel, Gevariya, and Kapadia [43], these features streamline daily tasks, enhance safety through real-time monitoring, optimize energy consumption, and support personalized lifestyle needs, thus improving overall quality of life in smart home settings.

First, examples of automation systems include smart lighting, smart heating and cooling, and smart appliances. Smart lighting automatically controls lights through motion detection, voice commands, or smartphone apps [44]. With the automatic brightness adjustment feature, it offers both energy savings and convenience. Smart heating and cooling systems automatically regulate the temperature and humidity of the home to maintain a comfortable indoor environment, and they manage energy efficiently according to the user’s daily patterns. Smart appliances, such as refrigerators, ovens, and washing machines, can be remotely controlled via IoT technology, and the operation times can be set automatically.

Second, in terms of safety and security, there are smart security systems and emergency response systems. Smart security systems, such as closed-circuit television (CCTV), smart door locks, and motion sensors, monitor the home’s safety and send real-time alerts when an abnormal situation occurs [45]. Emergency response systems can detect emergencies like fires or gas leaks and automatically notify the fire department or family members.

Third, energy efficiency includes energy management systems and the integration of renewable energy. An example of an energy management system is the real-time monitoring of electricity, gas, and water usage, which can be automatically adjusted to ensure efficient energy consumption. The integration of renewable energy, such as solar panels and geothermal systems, reduces energy costs and helps create an eco-friendly living environment [46].

Fourth, the convenience and lifestyle management of a smart residential environment include voice assistants and AI voice recognition technology, as well as healthcare integration. Voice assistants and AI control all home devices through voice recognition technology and offer personalized services by learning the user’s daily patterns [47]. Healthcare integration enables real-time health monitoring, connecting individuals with medical services when necessary. Smart residential environments are particularly important for the elderly, as they address problems encountered in daily life and significantly improve safety, independence, and quality of life through smart technology [48,49,50].

Despite the positive effects of smart digital environments on older adults, there are significant limitations in practical applications. Studies in Korea indicate that older adults with low digital literacy and limited experience with digital devices struggle to effectively utilize smart environments, which often results in lower life satisfaction [51]. This digital divide has a negative impact on the quality of life for older adults, highlighting the need to enhance accessibility and provide training to maximize the benefits of smart digital environments [52]. The limitations of smart environments for older adults have been similarly underscored. For instance, Kang, Mahoney, and Hoenig [53] report that while smart home technology offers various benefits, its complexity, privacy concerns, and maintenance challenges hinder technology adoption among older adults. Many feel financially burdened by these technologies and exhibit high levels of distrust toward their use. Zhang et al. [54] show the importance of developing user-friendly designs and educational programs tailored to older adults. These studies offer valuable insights for evaluating the feasibility of smart technologies for older adults and highlight the necessity for a deeper review of their limitations and support strategies.

3. Literature Review and Hypotheses

3.1. Design Elements Within a Structure for the Elderly

Evaluation factors such as safety, aesthetics, environmental protection, and maintenance play a critical role in enhancing intelligent environments for older adults, as they have a tangible impact on their quality of life. Kim et al. [51] identify safety and ease of use as key considerations when older adults access digital and smart environments, highlighting the importance of these factors in the overall effectiveness of technology. Additionally, the study by Kang, Mahoney, and Hoenig [53] emphasizes the need to consider privacy, physical safety, and psychological comfort in smart home design for older adults, indicating that these elements are essential for successful technology adoption.

Since the elderly spend more time indoors due to limited mobility or health issues, their living environments should be designed to be comfortable, safe, and capable of meeting their physical and emotional needs [55]. First, safety is the top priority. Falls among the elderly can lead to serious injuries, so safety must be the foremost consideration in environmental design [56]. Design elements to enhance safety include non-slip flooring, adequate lighting, and clear signage [57]. Additionally, as many elderly individuals have limited mobility, universal design is crucial. This involves installing ramps instead of stairs and providing facilities like automatic doors to help elderly individuals move around more easily. Safety-focused design improves the quality of life for the elderly and serves as an essential factor in supporting their independent living. These elements not only help maintain the independence of the elderly but also contribute to reducing societal costs [58].

The second element is eco-friendly design. Eco-friendly design has a direct impact on the health of the elderly. For example, designs that maximize natural light promote vitamin D synthesis, and proper ventilation systems improve indoor air quality, helping to prevent respiratory diseases [59]. Moreover, increasing energy efficiency in the living environments of the elderly not only reduces financial burdens but also positively affects the environment. Eco-friendly technologies like energy-efficient heating and cooling systems or the installation of solar panels support the economic independence of the elderly while minimizing environmental impacts [60]. An eco-friendly residential environment for the elderly also promotes social sustainability. For example, designing spaces that allow elderly individuals to live in harmony with nature within their communities strengthens social bonds and helps reduce feelings of loneliness.

The third element is aesthetics. The importance of aesthetics in environments for the elderly goes beyond mere visual satisfaction; it has a significant impact on their mental and emotional health [61]. A well-designed aesthetic environment provides psychological comfort to the elderly [62]. For example, windows with beautiful views of nature, warm-colored interiors, and quiet, serene spaces help reduce stress and promote emotional stability in the elderly. This also contributes to the prevention of depression in the elderly. Furthermore, aesthetics in design can add joy to their daily lives. For instance, living environments that include art, plants, and thoughtful lighting design provide enjoyment and satisfaction, helping elderly individuals actively engage in social activities and lead a positive life [63]. Aesthetics also play an important role in maintaining the elderly’s memories and identity. For example, environments or objects that the elderly have been familiar with for a long time offer emotional stability, and designs that consider these elements can help them retain their sense of identity [64]. Aesthetics not only contribute to visual beauty but also act as an essential element that supports the health and well-being of the elderly, fostering emotional stability and encouraging a positive lifestyle.

The fourth element is maintenance. Maintenance is a key factor in ensuring a safe and comfortable living environment. The spaces where elderly individuals live should be designed for easy maintenance. For instance, given that the elderly may have physical limitations, the space should feature flooring that is easy to clean and furniture that is simple to repair [65]. Additionally, efficient maintenance should consider both long-term cost savings and environmental sustainability. The cost of maintaining a residential environment for the elderly should be managed within a limited budget, and high-efficiency systems and durable materials should be used in this process. This reduces the financial burden on the elderly while minimizing the environmental impact [21]. Furthermore, regular maintenance and inspections are crucial for ensuring the safety of the elderly. For example, periodic checks of electrical installations, gas systems, and heating devices can prevent accidents such as fires or gas leaks [66].

As such, smart environments improve the safety, aesthetics, eco-friendliness, and maintenance aspects of living environments for the elderly, playing an important role in providing a comfortable living environment [67]. Therefore, in this study, these factors—safety, aesthetics, eco-friendliness, and maintenance—were used as variables influencing the use of smart devices.

3.2. Improving the Quality of Life for the Elderly via Smart Digitalization Design

Smart digitalization for the elderly aims to improve their quality of life by supporting safe and independent living [68], which ultimately increases the benefits from elderly healthcare. A design that takes the elderly into account provides an environment where they have more choices in daily life, thereby enhancing personal autonomy, promoting independence in daily activities, and boosting self-esteem [69,70]. Smart environment design plays a crucial role in making the elderly’s daily lives safer, more comfortable, and independent.

The key elements of smart environment design and their effects are as follows. First, smart environment design enhances safety. Safety-enhancing design focuses on preventing risks such as falls and other accidents, ensuring that the elderly can live more safely in their homes [71]. Examples include fall detection and prevention systems, emergency call devices, and automatic lighting systems. For instance, fall detection systems monitor the elderly’s movements through sensors placed throughout the home and send alerts immediately when a fall is detected. Automatic lighting systems detect movement at night and automatically turn on lights, helping to prevent falls in dark areas [72].

Second, smart environment design enhances convenience and independence. Such design enables elderly individuals to live independently without needing help from others, which boosts their self-esteem. Examples include smart appliances, automation systems, and personalized interfaces. Smart appliances are controlled easily through voice commands or smartphone apps, allowing the elderly to perform everyday household tasks with ease [47,73]. Automation systems automatically control lighting, heating, and curtains, allowing elderly individuals to easily adjust their home environment with simple operations. At the same time, a personalized interface is designed with large text and clear icons to make smart devices easy for the elderly to use through an intuitive interface [74,75].

Third, smart environment design manages the elderly’s health. Continuously managing health helps prevent diseases and detect them early, while regular medication reminders and exercise can help maintain health. Examples include real-time health monitoring, medication reminder systems, and exercise and activity tracking systems. In the case of the medication reminder system, it monitors vital signs such as heart rate, body temperature, and blood pressure, automatically sending alarms to healthcare professionals if any abnormalities are detected [76]. The medication reminder system sends notifications when it is time to take medication, helping the elderly to remember their doses. The exercise and activity tracking system monitors physical activity and provides feedback to encourage appropriate exercise.

Fourth, smart environment design provides psychological comfort and social connection for the elderly. This reduces social isolation and provides psychological comfort, helping to maintain emotional health and vitality in life. Examples include virtual communication platforms, psychological support systems, and support for hobbies and leisure activities. Virtual communication platforms allow the elderly to stay connected with family and friends through remote calls and online gatherings [77]. Psychological support systems offer emotional support and companionship through voice assistants and virtual companions [78]. Considering the above, the following hypotheses can be proposed.

H1. 

The frequency of use will vary according to design factors (maintenance, eco-friendliness, aesthetics, and safety).

H2. 

The convenience of use will vary according to design factors (maintenance, eco-friendliness, aesthetics, and safety).

3.3. Environmental Factors as Evaluation Items

Regarding a safe environment for the elderly, Rashidi and Mihailidis [79] highlighted that emergency call systems support independent living for the elderly, especially emphasizing the importance of features such as fall detection and location tracking. Similarly, Demiris and Hensel [80] emphasized that emergency call systems play a role in minimizing the risks that may arise in the daily lives of the elderly, helping them maintain independent living. Tamura, Yoshimura, Uchida, and Tanaka [81] also emphasized the importance of emergency call systems, stating that they can reduce the risk of injury and death by enabling immediate alerts in case of falls. Mihailidi, Carmichael, and Boger [82] pointed out that the elderly may struggle to detect and appropriately respond to gas leaks due to physical and cognitive limitations. This is because the elderly may have a weakened sense of smell due to physical decline, making it harder to detect gas, and accidents like leaving the gas stove on can frequently occur due to memory loss or dementia [79]. To prevent gas leak accidents for the elderly, installing gas detectors that sound alarms when leaks are detected or systems that automatically shut off the gas if it is left on for an extended period can enhance safety [83].

Another necessary function for a safe environment is monitoring. Liu, Stroulia, Nikolaidis, Miguel-Cruz, and Rincon [84] noted that intrusion detection and security systems can ensure the safety of the elderly by protecting them from accidents or crimes in the home and supporting independent living. Additionally, Maswadi, Ghani, and Hamid [85] emphasized the importance of health monitoring and environmental monitoring systems as part of smart home technology. Health monitoring systems track various vital signs such as body temperature, blood pressure, and heart rate, while environmental monitoring systems track factors like humidity and lighting, which are essential for the elderly.

A comfortable environment is another important consideration. Providing a comfortable environment is essential for maintaining the physical, mental, and emotional health of the elderly and improving their quality of life. Park and Kim [86] mentioned the importance of smart lighting, stating that it helps address vision problems associated with aging, supports daily activities, and improves sleep patterns. For instance, automatic lighting adjustment, which modifies brightness based on time of day and external lighting, reduces eye strain and provides a comfortable environment, making it essential for the elderly. Smart temperature and humidity control, which automatically detects environmental changes, is crucial for maintaining the health of the elderly [87]. Wang and Zhang [88] highlighted the importance of indoor air quality and a comfortable living environment, noting that indoor air quality directly affects the respiratory health of the elderly. Smart ventilation systems, which detect pollutants in the air and supply fresh air to maintain indoor air quality, are especially necessary for the elderly during winter or on days with high levels of fine dust, when ventilation is challenging.

As an example, Nakamura and Matsumoto [89] stated that elderly individuals may have difficulties using furniture or facilities at certain heights due to physical activity limitations. Therefore, height-adjustable furniture that can be customized to the user’s needs can provide elderly individuals with a more convenient and comfortable living environment. Energy conservation is also essential for maintaining a comfortable living environment and can help reduce costs. To alleviate the financial burden on the elderly, improving energy efficiency is crucial. A smart energy-saving system optimizes power usage and reduces unnecessary energy waste [90].

The final environmental factor necessary for the elderly is smart healthcare functions. Yousafzai and Yousaf [91] highlighted that elderly individuals are vulnerable to malnutrition due to reduced appetite and difficulties in food preparation, emphasizing the importance of meal management. Smart meal management systems monitor the elderly’s nutritional status and help plan balanced meals. Additionally, they provide reminders to ensure meals are consumed at the correct time, playing a vital role in maintaining elderly health. Similarly, Klimova and Maresova [92] discussed cognitive decline in the elderly and noted that smart memory aids for schedule management and memory improvement can emphasize independence and convenience in daily life. Medication adherence is crucial for maintaining the health of the elderly, and smart medication reminders, which provide alerts to take medications on time and track medication adherence (ref. [93]), are essential in elderly environments. In the context of smart healthcare features, Kim and Kim [94] emphasized the need for smart refrigerators that help elderly individuals easily prepare meals. Smart refrigerators track the expiration dates of food inside, notify users of missing items, and prevent the consumption of expired foods, thus preventing food poisoning and contributing to a healthier diet for the elderly.

Based on the above, the following hypotheses can be established:

H3. 

The frequency of use will vary according to necessity factors (Em, Ec, and Eh).

H4. 

The convenience of use will vary according to necessity factors (Em, Ec, and Eh).

3.4. Smart Usage Based on Elderly Age Groups

When classifying elderly individuals by age, the ‘young-old’ refers to those aged 50 to 54, the ‘middle-old’ refers to those aged 55 to 64, and the ‘old-old’ refers to those aged 64 and older [95]. Additionally, the elderly can also be categorized into early, middle, and late groups. The early elderly, aged 55 to 64, are considered pre-elderly and still part of the working population but are beginning to experience physical aging changes. They grew up in a different macroenvironment compared to the current elderly population. While they may feel discomfort, they remain physically independent and do not yet require assistance with tasks. Those aged 65 to 74 are considered physically semi-dependent. They can live independently but may need assistance with certain aspects of daily life [96]. Elderly individuals aged 75 and older in South Korea experienced the Korean War and are more accustomed to sitting on the floor than living in a standing (upright) environment [97]. Depending on the situation, they can live independently, but they require assistance from others in their daily lives.

Even among elderly individuals, the environments and lifestyles they have experienced can vary based on the times they lived through. Therefore, research on the elderly must consider the specific generational context to understand their characteristics. In South Korea, generational shifts can be divided into the “Silver Generation” and the “New Silver Generation”. The New Silver Generation, represented primarily by the Baby Boomers, typically refers to individuals born from the early 1950s to the early 1960s. This generation, which experienced a population boom after the Korean War, played a leading role in the country’s economic development and social and cultural change [98].

The Baby Boomer generation in Korea enjoyed a relatively prosperous life in terms of education and economics [97,99]. There are significant differences in values, mental and physical health, and lifestyles between the traditional Silver Generation and the New Silver Generation.

In Korea, individuals from this generation either experienced the Korean War during their childhood or were indirectly affected by it. Therefore, they comprise the group most acutely aware of the changes in Korea’s residential environment.

The New Silver Generation prefers a lifestyle distinct from the traditional Silver Generation, with individuals’ preferences being influenced by their education and financial status. Their sense of psychological security may also differ significantly from that of the previous generation. The New Silver Generation desires a comfortable, leisurely, independent, and active lifestyle. While the traditional Silver Generation is more prone to illness, displays more rigidity, and tends to be more conservative, the New Silver Generation is healthier, more positive, and exhibits greater flexibility [100,101].

The perception of aging differs between these two groups as well. The traditional Silver Generation often views aging as the final stage of life, while the New Silver Generation sees it as an opportunity for self-actualization, viewing aging as a “third phase” of life. In terms of retirement planning, the traditional elderly population tends to rely on their children, while the New Silver Generation plans their retirement independently and does not expect to depend on their children. Their approach to leisure is also different, viewing it as an opportunity to discover their senses and potential rather than simply a time for rest, and they actively participate in social activities. Though they grew up in an analog world, they are exposed to IT and live in a digital environment. Based on the above, the following hypotheses can be established:

H5. 

The frequency of use will vary by age.

H6. 

The convenience of use will vary by age.

This study aims to analyze the influence of various independent variables on two key aspects of the elderly’s use of smart environments, specifically, for the elderly individuals with healthcare needs: usage frequency and convenience. The empirical model of the study is composed of the following elements, with hypotheses established to examine the relationships between each variable.

The empirical model investigates the following relationships. First, it explores the relationship between the usage frequency of smart environments and factors such as design, necessity, and age. The hypotheses for this are as follows. H1: Design factors (maintenance, eco-friendliness, aesthetics, and safety) are assumed to influence the frequency of use in the elderly’s smart environments. H3: Necessity is expected to influence the frequency of use, depending on the level of necessity perceived by the elderly for using smart environments. H5: Age is considered an important factor influencing the usage frequency of smart environments among the elderly (Figure 3).

The second relationship is between the convenience of smart environments and factors such as design, necessity, and age. The hypotheses for this are as follows. H2: Design factors (maintenance, eco-friendliness, aesthetics, and safety) are expected to have a significant impact on the convenience of smart environments. H4: The convenience of using smart environments will vary according to the necessity perceived by the elderly. H6: The age of the elderly is considered a variable that influences the perception of convenience in smart environments.

Therefore, the convenience of use will vary by age. To test the above hypotheses, an empirical analysis will be conducted to assess the impact of each independent variable on the two dependent variables (usage frequency and convenience).

3.5. Comparison with Other Country

Japan is one of the most rapidly aging countries in the world, with approximately 29% of its population aged 65 and over as of 2023 [102]. In response to this aging society, the Japanese government and private companies are actively promoting the use of smart devices to improve the quality of life for the elderly. For example, various local governments in Japan offer smartphone training programs for older adults, and technologies such as smart healthcare devices are being employed to enhance health monitoring for the elderly [103]. Additionally, the spread of IoT-based smart home technology is helping create an environment where elderly individuals can live safely in their own homes [104].

Similar to other rapidly aging societies, China is witnessing an accelerated increase in its elderly population, with approximately 14–15% of its citizens aged 65 and older as of 2023 [105]. In response to this demographic shift, the Chinese government has implemented initiatives to leverage smart technologies to support the health and well-being of older adults. For instance, efforts from both governmental and private sectors are promoting the use of smart healthcare devices and wearables to aid in routine health monitoring for the elderly [106]. Additionally, programs designed to improve digital literacy and reduce digital exclusion among the elderly are increasingly common, providing training on smartphones and other digital devices [107]. Furthermore, IoT-based smart home technologies are being introduced to create safer, more independent living environments for older adults [108].

In line with South Korea’s advancements, Japan and China are also progressing in developing smart environments for the elderly, incorporating technologies such as IoT-based smart home systems, digital literacy programs, and smart healthcare devices to improve safety, independence, and health monitoring for aging populations.

3.6. Research Method

3.6.1. Analytic Process

This study examines how specific design factors—maintenance, eco-friendliness, aesthetics, and safety—influence two primary aspects of user interaction within smart digital environments: frequency of use and convenience of use.

Frequency of use represents how often users engage with a particular environment or product, providing insights into habitual engagement levels. The survey items used for this purpose include, for example, “How often do you use the product or environment each week, considering its maintenance requirements, eco-friendliness, aesthetic appeal, and safety features?” Meanwhile, convenience of use reflects the perceived ease of interaction, encompassing both usability and user satisfaction. The survey item used was, ‘How easy is it to use this product or environment, considering its maintenance needs, eco-friendly aspects, aesthetic design, and safety features?”. To substantiate the hypotheses, an ordinary least squares (OLS) regression analysis will be performed.

However, as the study targeted individuals registered nationally, the sample size was inevitably limited. To address this limitation, bootstrap resampling was employed to enhance the robustness of the findings.

3.6.2. Measurement Date

This study followed the process outlined in Figure 4. The survey was conducted targeting elderly individuals aged 55 and older. Considering the literacy and cognitive abilities of the elderly, all surveys were conducted through face-to-face interviews. Data were collected from Seoul, Gyeonggi, and Chungcheong provinces, areas where the distribution of smart environments for the elderly is actively promoted, and which have several pilot cases of elderly environment improvement. The research targeted residents aged 55 and older residing in low-income apartment complexes. The sampling method utilized a government-maintained list of elderly individuals eligible for basic livelihood security benefits. From this list, participants were randomly selected, and in-person visits were conducted to survey these individuals. Researchers conducted in-person visits to verify residency and age eligibility, after which participants were randomly selected for on-site interviews to ensure a representative sample of eligible elderly residents. A total of 130 individuals were invited to participate in the interviews; however, only 106 consented to participate.

In this study, efforts were made to enhance the validity and reliability of the survey questions by conducting in-depth interviews with five experts holding doctoral degrees or higher, which informed the development of the survey items. To further ensure response accuracy, the researcher personally met with each respondent to thoroughly explain the survey’s purpose and the background of each question before administering the survey. This procedure was designed to facilitate respondents’ full understanding of the questions and to encourage objective answers. The survey was also conducted in a comfortable, independent setting to minimize external influences, thereby strengthening the reliability of the data collected.

The sample consisted of elderly individuals receiving basic livelihood security in South Korea, which means that these individuals are recipients of healthcare benefits. Recipients of basic livelihood security are part of a socioeconomically vulnerable group selected based on criteria established by the government, and the proportion of this group within the overall elderly population is limited [109]. Therefore, their accessibility and the likelihood of them participating in the survey may be relatively lower compared to the general population. Furthermore, in the literature review, individuals aged 50 and older are classified as “young-old” and considered as part of the elderly population. However, in Korea, financial support programs such as housing pensions [110] and employment incentives designate individuals [111] as elderly from age 55 for eligibility. Therefore, this study focuses on those aged 55 and above.

The study focused on a specific population group, recipients of basic livelihood security, to provide detailed data on the frequency and convenience of their experiences with smart environments. The survey period was from 15 April to 30 June 2019, during which 120 samples were collected. However, four elderly individuals who did not complete the survey were excluded from the analysis. Therefore, the final sample size was 106 participants, all of whom either lived alone or with a spouse. Considering that there are relatively few basic livelihood security recipients in South Korea who receive healthcare benefits, a sample size of 106 individuals constitutes a significant sample for research purposes.

3.6.3. Variables

This study aims to analyze the influence of various independent variables on the two key dependent variables of smart environments (frequency of use of smart environments and convenience of smart environments), as shown in

Table 1. Variables and questionnaire.

Factor	Items	Questionnaire	Code
Dependent Variable (DV)	Convenience of Smart Environment Frequency of Use of Smart Environment
Independent Variable Necessity of Environmental Factors	Environment for emergency (Em)	Em1. Smart Emergency Call	Very Necessary = 1 Not Necessary = 4
		Em2. Smart Gas Leak Prevention	Very Necessary = 1 Not Necessary = 4
		Em3. Smart Monitoring	Very Necessary = 1 Not Necessary = 4
		Em4. Smart Ventilation	Very Necessary = 1 Not Necessary = 4
	Environment for comfort (Ec)	Ec1. Smart Lighting	Very Necessary = 1 Not Necessary = 4
		Ec2. Smart Height Adjustment	Very Necessary = 1 Not Necessary = 4
		Ec3. Smart Water Level Control	Very Necessary = 1 Not Necessary = 4
		Ec4. Smart Energy Saving	Very Necessary = 1 Not Necessary = 4
	Environment for health (Eh)	Eh1. Smart Medication Reminder	Very Necessary = 1 Not Necessary = 4
		Eh2. Smart Meal Management	Very Necessary = 1 Not Necessary = 4
		Eh3. Smart Memory Aid	Very Necessary = 1 Not Necessary = 4
		Eh4. Smart Refrigerator	Very Necessary = 1 Not Necessary = 4
Independent Variable Design Factors	Perception of design factors	Da1. Maintenance	Very Important = 1 Not Important = 4
		Da2. Eco-friendliness	Very Important = 1 Not Important = 4
		Da3. Aesthetics	Very Important = 1 Not Important = 4
		Da4. Safety	Very Important = 1 Not Important = 4
Independent Variable Age		A1. Under 60 years old	Under 60 years = 1 60–70 years = 2 Over 70 years = 3
		A2. 60 to 70 years old
		A3. Over 70 years old
Sex			Male = 1 Female= 2

. The variables and survey items used in the study are as follows. Dependent Variable 1 is the convenience of the smart environment. This variable measures the convenience of smart environments, evaluating how convenient elderly individuals find smart environments in their daily lives. Dependent Variable 2 is the frequency of use of the smart environment. This variable measures the frequency of use, evaluating how often elderly individuals use smart environments in their everyday lives.

There are three independent variables. Independent Variable 1 is the necessity of the smart environment (necessity). The first category is environment for emergency (Em), which measures the necessity of smart devices in emergency situations. This includes smart emergency calls, smart gas leak prevention, and smart monitoring. The second category is environment for comfort (Ec), which measures the necessity of smart devices for daily comfort. This includes smart lighting, smart height adjustment, smart water level control, and smart energy saving. The third category is environment for health (Eh), which measures the necessity of smart devices for health-related functions. The questions include the need for a smart medication reminder, smart meal management, smart memory aid, and smart refrigerator. The code for the necessity of smart environments is 1 = Very Necessary and 4 = Not Necessary.

Independent Variable 2 is design factors. To evaluate the impact of design elements of smart environments on the perception of the elderly, the following items were assessed: maintenance, eco-friendliness, aesthetics, and safety. The coding for these items is 1 = Very Important and 4 = Not Important. Independent Variable 3 is age, used to analyze differences in the frequency of use and perception of convenience based on age groups. Group 1 includes those aged 60 or younger, Group 2 includes those aged between 60 and 70, and Group 3 includes those aged 70 and older. The coding is 1 = Group 1, 2 = Group 2, and 3 = Group 3. Sex is also considered as a variable that may influence the frequency of use and perception of convenience of smart environments. Male is coded as 1 and female as 2.

In this study, the reliability and suitability of various independent variables, including the measurement of convenience and frequency of use in smart environments, were evaluated. As shown in

Table 2. Statistical summary of variables.

	Items	Range	Cron, Alpha	RMSEA(CFA)	Mean	SD	Frequency	%
Convenience	1	1–4			2.38	0.95	106	100
Frequency	1	1–4			2.31	1.06	106	100
Em	4	4–16	0.77	0.000
Lf	4	4–16	0.85	0.039
Hc	4	4–16	0.85	0.000
Maintenance	1	1–4			1.66	0.80
Eco-friendliness	1	1–4			1.90	0.84
Aesthetics	1	1–4			2.34	0.92
Safety	1	1–4			1.51	0.66
Sex	1				1.81	0.39
Age	1	1–3			2.66	1.54

, the descriptive statistics and reliability analysis are as follows. The mean for the dependent variable related to convenience is 2.38, with a standard deviation (SD) of 0.95. The mean for the dependent variable concerning frequency of use is 2.31, with a standard deviation (SD) of 1.06. For the independent variable ‘necessity of the smart environment’, the reliability (Cronbach’s Alpha) for the emergency environment was 0.77, and RMSEA (CFA) was 0.00. The CFI was 1.00, SRMR was 0.00, and CD was measured at 0.84. The reliability of the ‘comfortable environment’ was 0.85, with RMSEA (CFA) at 0.039, CFI at 1.00, SRMR at 0.02, and CD measured at 0.93. The reliability for ‘the healthy environment’ was 0.85, with RMSEA (CFA) at 0.00, CFI at 1.00, SRMR at 0.01, and CD calculated at 0.9.

Thus, the reliability values for the independent variables, evaluated by Cronbach’s Alpha, ranged between 0.77 and 0.85, indicating high reliability across all items [112]. Additionally, the confirmatory factor analysis (CFA) results showed RMSEA values between 0.000 and 0.039, indicating an excellent model fit. The CFI values, ranging from 0 to 1, demonstrated a good fit between the hypothesized model and the observed data, while SRMR values from 0.00 to 0.02 further supported the model’s fit. CD values were calculated between 0.84 and 0.93, indicating that the model explains approximately 90% of the variance in the outcome variables. These values all meet the criteria for variable validity [113], ensuring the validity of each variable.

3.6.4. Result

Figure 5 illustrates the age group composition of the study participants. The largest proportion of participants falls within the 55–59 age range, comprising 30.2% of the total sample. This is followed by the 60–64 age group, representing 22.6%, and the 65–69 age group, representing 17.0%. Smaller proportions are observed in the 70–74 and 75–79 age ranges, accounting for 14.2% and 11.3%, respectively. The 80+ age group represents the smallest portion of the sample, representing 4.7%. This distribution reflects the diverse age range of elderly participants included in the study, ensuring a representative analysis of varying age-related perspectives and behaviors.

In this study, OLS (ordinary least squares) was used as the primary model, as shown in Model (1):

$$Y\left(Frequency\right)=f(\sum {Environments}_i, \sum {Design}_j, Sex, Age)$$

(1)

where i indicates emergency environment, comfortable environment, and healthy environment; j is maintenance, eco-friendliness, aesthetics, and safety.

OLS is the most commonly used analytical method to estimate the relationship between dependent and independent variables. It assumes a linear relationship between the data and estimates the regression coefficients to minimize errors. However, due to the small sample size, the p-value threshold was set at 0.1, and bootstrapping was employed to estimate standard errors and confidence intervals to ensure reliability [114].

In the bootstrapping methodology, resampling techniques were employed to address the limitations imposed by the sample size, enabling a more robust estimation of population parameters [115]. Bootstrap minimizes the influence of outliers or anomalies in the sample and enhances reliability by estimating the sampling distribution through repeated and random sampling of the data for multiple regression analyses. In this study, bootstrap samples were generated through 500 iterations, and confidence intervals were calculated based on these samples [116]. The analysis was performed using Stata 18.0, a software widely used for statistical analysis and data management. Stata provides statistical results for various methods, including OLS and bootstrap, yielding more accurate OLS estimates and confidence intervals.

In this study, Y (frequency) was set as the dependent variable, and an analysis was conducted using environmental factors $\sum {Environments}_i, \sum {Design}_j, Sex, Age$ as the main independent variables to explain it. As shown in

Table 3. Results.

	Model (1)		Model (2)
	OLS	OLS with Bootstrap	OLS	OLS with Bootstrap
	Coef. (S.E.)	Coef. (S.E.)	Coef. (S.E.)	Coef. (S.E.)
Environmental Factors
Emergency Environment	−0.04 (0.04)	−0.04 (0.05)	0.02 (0.04)	0.02 (0.05)
Comfortable Environment	−0.03 (0.03)	−0.03 (0.03)	0.04 (0.03)	0.04 (0.02)
Healthy Environment	0.07 (0.04) *	0.07 (0.04) *	−0.05 (0.03)	−0.05 (0.05)
Design
Maintenance	−0.18 (0.20)	−0.18 (0.20)	−0.05 (0.18)	−0.05 (0.21)
Eco-friendliness	0.33 (0.17) *	0.33 (0.16) **	−0.32 (0.16) *	−0.32 (0.15) **
Aesthetics	0.25 (0.14) *	0.25 (0.14) *	−0.20 (0.13)	−0.20 (0.14)
Safety	0.06 (0.18)	0.06 (0.18)	0.19 (0.17)	0.19 (0.17)
Age
Group 2	−0.52 (0.23) **	−0.52 (0.24) **	0.36 (0.21) *	0.36 (0.20) *
Group 3	−0.65 (0.26) **	−0.65 (0.27) **	0.51 (0.24) **	0.51 (0.23) **
Sex	0.36 (0.26)	0.36 (0.28)	−0.02 (0.24)	−0.02 (0.24)
Constant	0.95 (0.72)	0.95 (0.73)	2.91 (0.67) ***	2.91 (0.58) ***
R2	0.33	0.33	0.28	0.28
F	4.65 ***	57.52 ***	3.71 ***	44.55 ***

Note. * p < 0.10; ** p < 0.05; *** p < 0.01.

, the analysis results indicated an F-test result of 4.65 (p < 0.01), suggesting that the model is statistically significant. The model’s R-squared value was 33%, indicating that the model sufficiently explains the variability in the dependent variable. Environmental factors $\sum {Environments}_i$ were found to partially have a significant effect on Y (frequency of use). Emergency environment, comfortable environment, and healthy environment did not show statistically significant impacts on the frequency of use, with p-values exceeding 0.10, indicating that these factors did not influence usage frequency. However, the healthy environment had a coefficient of 0.07 (p < 0.10), indicating a significant impact. Design factors $\sum {Design}_j$ also partially influenced Y (frequency of use). Maintenance and safety did not show a significant relationship with usage frequency, but eco-friendliness and safety were statistically significant at a relevant level for the dependent variable. In particular, eco-friendliness showed a positive correlation with Y (frequency of use), indicating that eco-friendly elements tend to increase the usage frequency of smart environments by about 33%. This suggests that elderly individuals view eco-friendly environments positively, making it a key factor in promoting the use of smart environments. Sex did not show a significant difference in its relationship with Y (frequency of use). This suggests that sex has relatively little influence on the frequency of smart environment use, or that the sample characteristics in this study do not reveal significant sex differences.

Age had a significant effect on Y (frequency of use). Specifically, for Group 3 (70 years and older), the coefficient was −0.65 (p < 0.05), indicating a statistically significant negative impact on smart environment usage frequency. This means that individuals in Group 3 are likely to use the smart environment 0.65 times less frequently than those in Group 1. This result suggests that individuals in the older age group may find it more challenging to utilize smart environments or are less familiar with them compared to younger age groups.

The results from 500 bootstrap iterations of the OLS model produced nearly identical findings. The fact that the R-squared value remained at 33% and the F-value was significant; combined with the consistency of the bootstrap results with the OLS results, it suggests that the findings are robust and reliable.

The following model sets Y (convenience) as the dependent variable, and an analysis was conducted using environmental factors $\sum {Environments}_i, \sum {Design}_j, Sex, Age$ as the main independent variables to explain it, as in Model (2):

$$Y\left(Convenience\right)=f(\sum {Environments}_i, \sum {Design}_j, Sex, Age)$$

(2)

The analysis results (Table 3) show an F-test result of 3.71 (p < 0.01), indicating that the model is statistically significant. The model’s R-squared value is 28%, suggesting that the proposed model has significant explanatory power regarding the perception of convenience.

Of the environmental factors $\sum {Environments}_i$, only eco-friendliness had a partially significant effect on Y (convenience). Maintenance, aesthetics, and safety were not significantly related to convenience. However, it is notable that eco-friendliness had a coefficient of −0.32 (p < 0.10), showing a statistically significant negative impact on the perceived convenience of smart environments. This suggests that while consumers may view eco-friendly environments positively, such elements could hinder convenience. In other words, environments with a strong emphasis on eco-friendly features might be perceived as less convenient. The $Sex$ variable had a coefficient of −0.02 (p > 0.10) for convenience, showing no statistically significant effect, indicating that gender does not influence the perception of convenience in smart environments. For the $Age$ variable, Group 2 had a coefficient of 0.36 (p < 0.10), indicating a positive effect on perceived convenience. The older Group 3 had a coefficient of 0.51 (p < 0.05), indicating a statistically significant positive effect, meaning that older individuals are more likely to perceive smart environments as convenient.

The results from 500 bootstrap iterations of the OLS model showed nearly identical findings. With an R-squared value of 29% and a significant F-value, along with the consistency of the bootstrap results with the OLS results, these findings are considered robust and reliable.

The negative association between age and smart environment usage frequency observed in the results suggests that older individuals may face unique barriers, such as technological unfamiliarity or physical limitations, that hinder their engagement with such environments.

4. Discussion, Implications, and Limitations

4.1. Discussion

The results of this study provide valuable insights into the factors influencing the frequency of use and convenience of smart environments among the elderly.

This study identifies eco-friendliness as a key factor influencing the frequency of use and perceived convenience in smart environments for elderly individuals. Eco-friendly features enhance usage frequency but can reduce perceived convenience due to potential discomfort, complexity, or difficulty in understanding new technologies. Elements like sensor-based lighting or automated energy-saving systems, while sustainable, may seem complex, highlighting the need to balance eco-friendly design with usability and accessibility to promote effective engagement.

Additionally, age impacts both frequency of use and perceived convenience. As age increases, the frequency of use decreases, while perceptions of convenience improve, potentially due to different levels of familiarity with technology. Younger seniors, more accustomed to modern tech, may have higher expectations and find usability issues more noticeable, while older seniors, with fewer expectations, maybe more content with basic features.

Lastly, factors like emergency readiness, comfort, health-focused design, maintenance, aesthetics, and safety did not significantly impact elderly interactions with smart environments. Older adults prioritize features that offer immediate daily convenience, and they may not view emergency or health features as essential, thus affecting their overall engagement.

Similarly, design elements like maintenance, aesthetics, and safety may not have an immediate or noticeable impact during everyday use. Aesthetic qualities, while contributing to the overall user experience, might not be a primary concern for elderly users when assessing smart technologies. Maintenance and safety features may also go unnoticed or be taken for granted, further reducing their perceived importance. These insights suggest that for smart environments to be more effective for the elderly, designs should focus on features that provide clear, immediate benefits in daily life, aligning closely with their practical needs and priorities.

In construction and space planning, the role of design elements, such as layout, materials, lighting, and accessibility, is essential. These factors not only serve functional purposes but also enhance usability, comfort, and aesthetic appeal, which are particularly important when constructing environments for specific populations, like the elderly. The results of this study highlight how eco-friendliness, safety, and ease of maintenance impact the frequency and convenience of space use, providing valuable insights for construction planning. By aligning these design elements with the study findings, this research offers a nuanced understanding of how well-considered environments can increase engagement and satisfaction among elderly users, supporting more effective construction strategies for age-friendly spaces.

Also, the study reveals that age significantly influences both the frequency of use and perceived convenience within smart environments for older adults. As individuals move from young-old to middle-old, their interactions with technology evolve, shaped by factors such as physical capabilities, cognitive adaptability, and past exposure to technology. Young-old and middle-old adults, with greater familiarity and higher expectations for digital devices, may be more sensitive to usability issues in smart environments. In contrast, old-old adults often prioritize simplicity and ease, finding satisfaction in basic functionality that meets essential needs. These distinctions emphasize the importance of considering different age groups within the elderly population to ensure that smart environments are accessible and engaging across a variety of abilities.

4.2. Implication

The results of this study highlight that, in the design of smart residential environments for the elderly, it is not sufficient to focus solely on eco-friendliness or aesthetics. Rather, design strategies must prioritize the actual user experience and convenience of the elderly. The following are detailed design strategies:

First, smart environment design should be user-centered. Considering the various factors that influence the frequency of use and convenience, it is essential to adopt a user-centered approach to designing smart environments for the elderly. This approach involves actively incorporating feedback from the elderly and continuously improving the design to minimize discomfort during actual use.

Second, environmental and design factors should be re-evaluated. The finding that environmental factors such as emergency environments, comfortable environments, and healthy environments, as well as design elements like maintenance, aesthetics, and safety, do not significantly influence the frequency of use or convenience suggests that these factors either do not have a substantial impact on the elderly or that the elderly are not particularly sensitive to them. This implies a need to focus on the elements that are truly important to the elderly when improving the design. For instance, practicality, intuitiveness, and clear information provision may be more critical design elements for elderly spaces than aesthetics.

Third, eco-friendly design should be reconsidered. While eco-friendliness had a positive effect on the frequency of use in smart environments for the elderly, it had a negative effect on the perceived convenience. This indicates that, although the elderly respond positively to eco-friendly design, they may experience discomfort during actual use. Therefore, when incorporating eco-friendly elements, it is crucial to consider the physical and cognitive limitations of the elderly in the design. For example, while using eco-friendly materials, the interface should also be easy to use and intuitive.

Fourth, the design should consider differences in user experience by age. The trend that frequency of use decreases with age, but the perception of convenience increases, suggests that user experiences may differ across age groups among the elderly. Hence, it is important to enhance elements in the design that allow elderly individuals to intuitively understand and easily access smart environments.

The findings of this study hold substantial implications for construction practices aimed at improving the well-being of elderly individuals. For industry professionals, incorporating design elements that align with the preferences and limitations of elderly users—such as eco-friendly materials that are also easy to maintain and safety features that promote accessibility—can lead to spaces that are both sustainable and user-friendly. This research encourages builders and planners to prioritize elderly-centered design features, ultimately supporting the development of environments that meet both aesthetic standards and practical needs, thus bridging humanitarian considerations with technical construction requirements.

Additionally, the findings suggest that tailored smart environment designs are necessary to meet the diverse needs of older adults across life stages. For practitioners and designers, this involves integrating flexible features that accommodate varying levels of technological comfort and physical ability. Age-specific design adaptations, such as simplified interfaces, improved accessibility, or advanced options for younger age groups, can enhance engagement and usability. Recognizing these distinctions enables construction and design professionals to create more inclusive smart environments that meet the varied needs of older adults, ultimately promoting a higher quality of life and sustained interaction with smart technologies.

4.3. Limitation

This study has two primary limitations:

First, the number of variables considered in the study was limited. Since the participants were elderly, the number of survey questions had to be restricted to minimize fatigue and ensure response accuracy. Elderly individuals may find it challenging to answer lengthy surveys or complex questions, so the variables in the study were selected with this in mind. As a result, various factors were not included in the analysis, which may limit the generalizability of the study’s findings.

Second, the sample size in this study was relatively small due to the specific focus on elderly individuals in Korea who are recipients of basic livelihood security. By targeting this unique demographic, the study’s population was inherently limited, resulting in a smaller sample that may have introduced potential biases and uncertainties into the findings. Such constraints can be challenging in statistical analysis, as a limited and unbalanced sample can affect the representativeness and generalizability of the results. To address these issues and improve the robustness of the study, the bootstrap technique was applied as a supplementary statistical approach. Through this method, the analysis was able to generate multiple resamples from the data, thereby reducing uncertainty related to sample size limitations and enhancing the overall reliability and validity of the study’s outcomes. This compensatory approach ultimately strengthened the conclusions drawn from the data, making the findings more robust despite the inherent sample size constraints. Future research will aim to address the limitations posed by the relatively small sample size to enhance the reliability and robustness of the findings.

Third, the data collected from 15 April to 30 June 2019 may not entirely represent the current advancements in smart technology, as substantial progress has been made over the past five years. This study acknowledges the potential limitations in data relevance due to rapid technological evolution, and future research should consider incorporating more recent data to better capture contemporary trends and innovations in smart environments.

Fourth, a limitation of this study is the inability to collect objective data, such as usage logs from smart devices, due to existing constraints. In future research, if it becomes feasible to incorporate objective data collection, we plan to include such data to further enhance the validity and reliability of our findings.

Despite these limitations, the study provides valuable insights into the use of smart environments by the elderly. Future research should aim to include a wider range of variables and a larger sample size to enhance the objectivity and generalizability of the findings.