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Article

Smart Retirement Villages as Sustainable Housing Solutions: A TAM-Based Study of Elderly Intention to Relocate

by
Booi Chen Tan
1,
Teck Chai Lau
2,
Clare D’Souza
3,
Nasreen Khan
1,
Wooi Haw Tan
4,
Chee Pun Ooi
4 and
Suk Min Pang
1,*
1
Faculty of Management, Multimedia University, Cyberjaya 63100, Selangor, Malaysia
2
Entrepreneurship and Enterprise Hub, Xi’an Jiaotong-Liverpool University, Suzhou 215000, China
3
Management and Marketing, La Trobe University, Bendigo, VIC 3552, Australia
4
Faculty of Engineering, Multimedia University, Cyberjaya 63100, Selangor, Malaysia
*
Author to whom correspondence should be addressed.
Buildings 2025, 15(15), 2768; https://doi.org/10.3390/buildings15152768
Submission received: 25 June 2025 / Revised: 30 July 2025 / Accepted: 3 August 2025 / Published: 6 August 2025
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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Versions Notes

Abstract

Globally, technologically integrated housing solutions are increasingly relevant in addressing the challenges of aging populations and sustainable urban development. Drawing on the Technology Acceptance Model (TAM), this research investigates how perceptions of usefulness, ease of use, and attitudes influence relocation intention to smart retirement villages (SRVs), while also examining any significant differences between the socio-demographic variables and such intention. A total of 305 individuals aged 55 and above participated in an online survey, with data analyzed using IBM SPSS Statistics version 27 and AMOS-SEM version 25. The findings reveal that elderly individuals of Chinese ethnicity, those who are married, and those aged between 66 and 70 are more inclined to relocate to SRVs. Attitude and perceived usefulness significantly predict relocation intention, while perceived ease of use exerts an indirect effect through usefulness. These results highlight the importance of integrating user-centered technological design with socio-cultural and demographic considerations in the development of age-friendly built environments. The study offers insights for urban planners, policymakers, and developers seeking to create inclusive and sustainable smart housing solutions for aging populations.

1. Introduction

Smart retirement villages are an emerging multi-billion-dollar industry that provides an alternative modern lifestyle for retirees. The aging population is growing faster than expected in Malaysia and worldwide [1]. The Malaysian Department of Statistics indicates that an aging population of 5.6 million seniors will comprise 15% of the total population by 2035 [1]. The substantial increase in the elderly population is predicted to continue to expand rapidly or even accelerate, given the falling fertility rates and the improvement in the overall life expectancy. Past researchers have conducted studies on the elderly to better understand the needs of the elderly [2,3,4]. This includes healthcare, home safety, and medical needs of the elderly. According to Tan et al. [5], the majority of the elderly have expressed a desire for a smart emergency contact system to be provided at SRVs. However, they do not prefer smart window blinds using smartphones or buttons. Joint efforts are required from both the public and private sectors to mitigate the impact of aging on healthcare and social costs. Old age often relates to the perception of illness, disability, and dependency. It is essential to develop a physical and social infrastructure that will support greater health and well-being of the elderly in their golden age. This is to ensure a prolonged period of excellent health, a sustained sense of well-being, an extended period of social engagement and productivity for the elderly.
There is a new option in the housing market that focuses on elderly healthcare, integrating lifestyle services, and facilities for the elderly in smart retirement homes, villages, and aged care homes. Despite numerous past studies on the elderly, there is a dearth of research that scrutinized the socio-demographic trend of the aging population to better anticipate their demand for housing services. This is critical in many countries with an emerging aging population. Against this backdrop, the current study aims to firstly examine whether any significant differences exist between socio-demographic variables and intention to relocate to a smart retirement village (SRV). Secondly, the study will investigate the predictors of such intention via the Technology Acceptance Model (TAM). The concept of SRV in this context is an extension of the existing retirement village, which is equipped with amenities of independent living, institutionalization, and an age-built environment [5]. These smart homes include a network of communication, sensors, electronic appliances, and other devices that users can control remotely at home. Various smart home technologies or services are essential, as they will enhance the quality of life of the elderly [6]. This study provides important insights to relevant parties such as practitioners, developers, and marketers to make informed policy decisions in developing housing services for the elderly.

2. Literature Review and Hypothesis Development

2.1. Socio-Demographic Factors of the Elderly

Socio-demographic factors are highly effective in providing variability to any research design and can be an important area for investigation [7]. Tzeng et al. [8] highlighted that the analysis of the socio-demographic characteristics of the elderly can provide insights for policy and decision-makers to develop necessary engagements. Many psychological factors surveyed in the past show that this is an important area for investigation. Interestingly, Meneguci et al. [9] stated that several socio-demographic factors are related to the high sitting time of the elderly from southeastern Brazil. It is essential to identify the significant differences between demographic type and intention to relocate to smart retirement villages. The following discussion leads to the hypothesis development.

2.2. Age-Related Demographic Factor

Past researchers have considered aging from a three-dimensional perspective: institution to institution, home to institution, and home to home [10]. When compared to relocation in old age, the ecological theory of aging proposes that when health declines as one ages, environmental pressures rise with regard to personal capacities, and this results in poor person-environment fit [11]. Studies that investigated younger individuals or individuals with greater age distance demonstrated that certain characteristics of housing are critical when considering relocation [12]. Purpose-built homes are now considered a favorable option within the relocation landscape for older adults [13]. Thus, we propose:
H1a: 
There is a significant difference between the age of the elderly and their intention to relocate to the smart retirement village.

2.3. Gender-Related Demographic Factor

Contextual factors such as gender should not be ignored when considering relocation transition. There are differing perceptions for men and women due to their work histories, employment opportunities, and general life experiences [14]. There are mixed results where, for instance, Sobieraj and Kramer [15] reported that men were more interested in accessing and using information and communication technology compared to women. In Canada, Xiong et al. [16] discovered that the female elderly group was more likely to have a better understanding of technology for caregiving, whereas the elderly male group was more prepared to pay a greater price for these technologies. Based on this discourse, we propose the following:
H1b: 
There is a significant difference between gender of the elderly and their intention to relocate to a smart retirement village.

2.4. Ethnicity-Related Demographic Factor

Smart retirement villages can be perceived as trying to facilitate a friendly environment that encourages a sense of community and solidarity with the aim of creating an atmosphere rich in social capital. Researchers within the broader aspect of social capital have also shown that there is a demarcation between ethnically segregated retirement villages serving the needs of different groups of ethnicity and those ethnic-specific villages in providing an enhanced quality of life [17]. Cultural ethnicity is dynamic and influences an individual’s self-concept and behavior, and can be expected to impact retirement village purchase decisions [18]. Given that Malaysia is a multicultural, diverse country with major representations of ethnic groups such as Malays, Chinese, Indians, Bumiputera, Eurasians, and others, there are bound to be differences in their thoughts and perceptions; hence, we propose:
H1c: 
There is a significant difference between the ethnicity of the elderly and their intention to relocate to a smart retirement village.

2.5. Marital Status-Related Demographic Factor

Apart from the type of housing required, community activities, support networks, and lifestyles can impact single or married couples in their decision to move to a smart retirement village. Since the 1990s, there has already been research in this research area. Potential friendships that will develop between residents in SRVs with the same marital status would be an important factor [19]. Although marital status was the prime factor in selecting friends, certain sub-groups were perceived as at risk of social isolation [20]. Since marital status and social networks have been perceived to relate to the well-being of older people, marital status can impact the intention to relocate. Thus, we propose:
H1d: 
There is a significant difference between the marital status of the elderly and their intention to relocate to a smart retirement village.

2.6. Education-Related Demographic Factor

According to Smith [21], younger, higher-income, and better-educated seniors utilize the internet and broadband at rates that are comparable to or even greater than the overall population. It is also reported that there is an increased percentage of the elderly who prefer to live independently or buy in a retirement village due to modernization and higher education [22]. Those who are accustomed to technology will embrace new devices more quickly since they can draw on their previous experiences. Smart and modern retirement villages that offer tech-enabled facilities, hi-tech communication platforms, and other internet services that are likely to attract elderly citizens who are educated and proactive in their adoption of technology. It is also more likely that the elderly with higher education will use technological equipment more willingly. On this note, we propose:
H1e: 
There is a significant difference between the educational level of the elderly and their intention to relocate to the smart retirement village.

2.7. Occupation-Related Demographic Factor

Occupation status, depending on the job performed, can impact financial ability, retirement savings, and lifestyle in general. Research on this topic has been ongoing since the 1990s, and there are significant differences in the type of retirement village based on pre-retirement occupation and sources of income [23]. For relocating to a retirement village, the most common rationales were downsizing, mitigating maintenance of the home, lesser stress-related lifestyle, and better-anticipated access to health care [24]. Thus, the recourse to smart living even in retirement age can influence an individual’s intention to relocate:
H1f: 
There is a significant difference between the occupation of the elderly and their intention to relocate to a smart retirement village.

2.8. Living Conditions Related to Demographic Factor

Living conditions are situations where the elderly live alone or with their children or spouses. In a smart retirement village, living conditions play a key role in the decision pertaining to companionship and privacy, as both are critical elements that need to be properly managed [25]. Similarly, the issue of loneliness when living alone poses other health-related issues, such as physical and mental health [26]. Some research indicates that men who are older and residing alone are more likely to experience social isolation [27]. Living in a smart retirement village could foster an environment that empowers the elderly to be independent and can mitigate loneliness by fostering social interactions. Thus, the following hypothesis is suggested:
H1g: 
There is a significant difference between the living conditions of the elderly and their intention to relocate to a smart retirement village.

2.9. Technology Acceptance Model (TAM)

Retirement communities are still an important part of elder care. There is an increase in the number of elderly people opting to move to retirement villages, which provide independent living while being accessible to amenities and support services [28]. There is considerable literature about the decision of the elderly to move or relocate to a retirement village. For instance, Ng [22] reported that intention of the elderly to buy or move into a retirement village was significantly influenced by their attitudes. He also described that the elderly’s attitude towards the design of the retirement community was not associated with the intention to relocate.
The Technology Acceptance Model (TAM) has been applied extensively over the years [29,30] in predicting the usage and acceptance of technology. TAM is a model that predicts how people will adopt and use technology based on two specific beliefs: perceived usefulness and perceived ease of use. Perceived usefulness refers to “the degree to which an individual believes that using a particular system would enhance his or her job performance,” and perceived ease of use refers to “the degree to which an individual believes that using a particular system would be free of physical and mental effort” [31]. The model posited that perceived ease of use (PEOU) influences perceived usefulness (PU) and attitude (ATT), perceived usefulness (PU) influences attitude (ATT) and intention (INT), and lastly, attitude (ATT) influences behavioral intention (INT). Table 1 presents empirical evidence from several countries on consumers’ intention to use smart home products or services using TAM. However, the application of TAM in studies related to SRV is still limited.
In conjunction with the above discussion on TAM, we propose the following hypotheses:
H2: 
Perceived ease of use (PEOU) is positively related to perceived usefulness (PU) of smart home technology.
H3: 
Perceived ease of use (PEOU) is positively related to attitude (ATT) towards smart retirement villages.
H4: 
Perceived usefulness (PU) is positively related to attitude (ATT) towards smart retirement villages.
H5: 
Perceived usefulness (PU) is positively related to intention to relocate (INT) to a smart retirement village.
H6: 
Attitude (ATT) is positively related to intention to relocate (INT) to a smart retirement village.
Figure 1 illustrates a research framework based on TAM, which consists of four constructs: perceived ease of use (PEOU), perceived usefulness (PU), attitude (ATT), and intention to relocate to a smart retirement village (INT).

3. Methods

This study applied the purposive sampling method to reach elderly individuals 55 years old and above residing in Selangor and Kuala Lumpur, Malaysia. To broaden our reach while adhering to this age requirement, this study implemented a snowball sampling method: participants younger than 55 were requested to share the survey with eligible contacts within their network. This entire procedure was transparently detailed on the survey’s initial page. The survey link was posted via Facebook community groups and WhatsApp messaging. WhatsApp is currently the most popular application used by people of all ages, including the elderly [36]. The online method was also deemed a safer way to protect the elderly during the COVID-19 pandemic compared to the face-to-face survey method. A pilot study with 30 respondents was first conducted to ascertain the validity of the instruments. Subsequently, the main research, with a larger sample of 305 respondents, was conducted. Tests of the demographic hypotheses were conducted via IBM SPSS Statistics version 27 using the independent sample T-test and one way ANOVA test. In addition, convergent, discriminant, and mediation analyses were applied via measurement and structural model assessment using AMOS-SEM version 25. All measurement scales (5-point Likert scale) were adopted and adapted from previous scales, and validated and checked by three academic experts. The 5-item attitude (ATT) scale was adapted from Ajzen and Fishbein [37], the 3-item perceived ease of use (PEOU) scale and 4-item perceived usefulness (PU) scale were adapted from Davis et al. [38], and the 4-item intention (INT) scale to measure the intention to relocate to a smart retirement village was adapted from Ha and Janda [39].

4. Results

4.1. Gender and Independent Sample T-Test

Table 2 reports the percentage of respondents based on gender, the average mean score of intention to relocate to a smart retirement village (SRV), and the result from the independent sample T-test by gender. From the descriptive analysis, 42% of participants in this study were female and 58% were male. The independent sample T-test is a statistical technique that is used to compare the mean values of two independent groups [40]. It was performed to identify whether there was a significant difference between gender and intention to relocate to an SRV (H1). From the result, H1b was not supported (p = 0.440, >0.05). It revealed no significant differences between females and males in their intention to relocate to an SRV.

4.2. Other Socio-Demographic Characteristics and Analysis of Variance (ANOVA)

Table 2 also presents the percentage of respondents according to their socio-demographic characteristics (except gender), the average mean score of intention based on each socio-demographic characteristic, and results from the AVONA test. Respondents’ background was as follows: 44.9% were from the age group of 55–60, 40.3% were ethnic Malay, 60.3% were married, 43.3% had obtained secondary education, 44.9% worked part-time, and 43% lived with their spouses. Analysis of Variance (ANOVA) was conducted to assess the significant differences in intention to relocate to an SRV across different socio-demographic characteristics among the elderly (H1a, H1c to H1g). The results show that H1a (age) (p = 0.045, <0.05), H1c (ethnicity) (p = 0.001, <0.01), and H1d (marital status) (p = 0.04, <0.05) were supported, while H1e (education) (p = 0.564, > 0.05), H1f (occupation) (p = 0.326, > 0.05), and H1g (living conditions) (p = 0.654, > 0.05) were not supported. Based on the results, age, ethnicity, and marital status affected their intention to relocate significantly. Elders from the age of 66 to 70 (mean = 4.12), those of Chinese ethnicity (mean = 4.07), and those who were married (mean = 4.08) generally had higher intentions to relocate to an SRV. On the other hand, no statistically significant differences were reported for the education, occupation, and living status of the elderly and their intention to relocate to an SRV.

4.3. Assessment of Measurement Model

The measurement model was evaluated before the hypotheses were tested on the structural model [41]. The original measurement model was revised, as the indices did not fulfill the desired range. After PU1, PEOU3, and ATT5 were removed, the revised measurement model indicated good model fit (CMIN/df = 2.833, GFI = 0.927, RMSEA = 0.078, NFI = 0.909, CFI = 0.939, and TLI = 0.919) and was subsequently used for further analysis (Figure 2).

4.4. Convergent Validity

Convergent validity was tested to assess whether the measurement items of the construct were correlated through several criteria indicated by Hair et al. [40]. First, standardized factor loading estimates should be 0.50 or above; second, average variance extracted (AVE) of 0.50 or higher suggests adequate convergence; and third, convergent validity is achieved if the composite reliability meets the cutoff criterion of 0.60. The standardized factor loading estimates for all measurement items ranged from 0.589 to 0.855 and were above the minimum point of 0.50. The average variance extracted values for PU = 0.67, PEOU = 0.68, ATT = 0.66, and INT = 0.64 exceeded the minimum threshold of 0.50. In addition, the composite reliability values for PU = 0.75, PEOU = 0.838, ATT = 0.839, and INT = 0.805 were above the recommended point of 0.60. Thus, the convergent validity of all the constructs was sufficient, as they met the three recommended criteria.

4.5. Discriminant Validity

Discriminant validity was assessed to confirm whether a construct was distinctive from other constructs and captured some meanings where other constructs did not. It is essential to ensure that each construct measures its meaning, as they differ from those of other constructs. Discriminant validity was commonly assessed in two ways [40]. First, the correlation values between constructs must not exceed 0.80 to indicate the distinctness of measures [42]. If two or more constructs were conceptually distinct, then the measures of those constructs must be weakly correlated. Next, the AVE should be higher than the squared multiple correlation (SMC) estimates [43]. The correlation values (<0.80), AVE, and SMC estimates (AVE > SMC) supported the discriminant validity in this study. All the constructs in this study were truly distinct from each other and did not measure the same concept. Otherwise, it would cause multicollinearity issues.

4.6. Assessment of Structural Model

The structural model aims to assess the hypothesized structural linkages among the constructs. Table 3 presents the goodness-of-fit indices of the original structural model based on the criteria suggested by Hair et al. [40]. No further modification or improvement was required because the goodness-of-fit indices of the structural model showed a good fit with the data. Therefore, the original structural model (Figure 3) was retained for further analysis.

4.7. Results of Hypothesis Testing (H2–H6)

Table 4 presents the results of the hypothesis testing. As indicated by the structural coefficients and associated significance level, perceived ease of use (β = 0.884, p < 0.001) was positively and significantly related to perceived usefulness (PEOU → PU). Hence, H2 was supported. The influence from both perception constructs (i.e., perceived ease of use and perceived usefulness) on attitude of the elderly was not significant (PEOU → ATT: p = 0.325 >0.05; PU → ATT: p = 0.094 >0.05), suggesting that H3 and H4 were not supported. In contrast, the effect of perceived usefulness on intention of the elderly to relocate to an SRV was significant and positive (β = 0.287, p < 0.001), and a similar result was reported for the relationship between attitude and intention (β = 0.573, p < 0.001). Therefore, H5 and H6 were supported. Further investigation revealed that attitude (β = 0.573) was the most important predictor of relocation intention, followed by perceived usefulness (β = 0.287). The mediated effect of perceived usefulness was demonstrated by significant paths from PEOU → PU and PU → INT, with coefficient values of 0.884 and 0.287 (p < 0.001), respectively. The mediated effect was tested based on the criteria suggested by MacKinnon et al. [44], where the independent variable (perceived ease of use) is related to the mediating variable (perceived usefulness) and the mediating variable (perceived usefulness) is also significantly related to the dependent variable (intention). In brief, the path model derived from this study showed that perceived ease of use influenced perceived usefulness, which in turn influenced relocation intention (PEOU → PU → INT).

5. Discussion

As the older population in Malaysia increases every year, the demand for alternative housing options such as villages, retirement homes, and aged care homes is on the rise. These alternatives enhance the quality of life for the elderly, encourage social interaction, and promote independence. For this reason, it is vital to understand the impact of demographic shifts, the housing demand of the elderly, and challenges they would encounter to ensure age-friendly retirement homes.
In terms of socio-demographic indicators, there is a significant difference between age (66 to 70), ethnicity (Chinese), and marital status (married) in the intention of the elderly to relocate to an SRV. Many housing developers have the misconception that the elderly comprise a single market that only needs a single common market strategy [45]. The current socio-demographic results proved otherwise. Within the elderly group, there is a need for further market segmentation. The four basic types of market segmentations are demographic, psychographic, behavioral, and geographic segmentations. Based on the current results, the elderly market could be further distinguished in terms of demographic profile. Segregating the aging market into different segments and developing business strategies by targeting each different segment will likely increase the market potential. Tailoring retirement homes to meet the needs of a particular elderly group will enhance the effectiveness of strategic effort and targeted outcomes. If a retirement home meets the needs of a specific elderly group, there is a higher possibility of uptake of these homes. Modifying marketing and promotion techniques to induce a particular elderly group could also be a more effective strategy. These methods will provide effectiveness in budgetary initiatives on implementing marketing efforts to the right segments.
The results indicated a significant positive effect of perceived ease of use towards perceived usefulness, perceived usefulness towards intention, and attitude towards intention. These are consistent with past studies [33,35,36]. Elderly citizens who perceived relocation to a smart retirement village to be hassle-free and easy to adapt to the new environment would more likely to alter their perception of the usefulness of such an endeavor; hence, it will encourage them to consider relocating to a smart retirement village. The general perception is that most elderly are not technology-savvy and are reluctant to learn or adopt new technologies. The current findings dispel the notion that the elderly hesitate to accept new technology. The important aspect is to ensure that the elderly can easily adapt to the new technology available at the smart retirement village without much hassle. Once they become familiar with the “smart” technologies, it would be convenient for them to improve or enhance their daily living. As a result, the elderly are likely to have a positive attitude towards the technology available at SRVs and thus be more inclined to relocating to an SRV.
Generally, older people have specific needs, such as the need for easy access to amenities, ease of upkeep of living spaces, operations of facilities, security, and the provision of medical services due to their diminished physical mobility [46]. Therefore, technology in areas such as healthcare (smart emergency contact systems, smart medication assistance), safety features such as safety and home security devices (smart gas detector), and lifestyle support (smart displays, smart lighting, smart alarms, smart kitchen appliances) could be fitted at the smart retirement village [5]. Assuring the elderly of these technologies ease of use and usefulness would change their attitude to being more open to relocating to a smart retirement village.
Companies in SRVs for the elderly can have campaigns to collaborate with famous elderly celebrities (such as sportspersons, singers, and actors) to demonstrate how easy it is to use technologies that are available in the SRV. These campaigns will create awareness and subsequently build confidence in the elderly to use such technologies. Furthermore, companies can organize tours to smart retirement villages so that the elderly can have real-life demonstrations of using these technologies.

6. Conclusions

In Malaysia, smart homes are becoming more prevalent due to the advancement of technology and widespread market demand. However, the concept of smart retirement villages (SRVs) that integrate these technologies remains novel, especially given the nation’s growing aging population. While housing developers are beginning to explore such designs, it is still uncertain whether elderly individuals are ready to embrace and relocate to these new types of communities. Therefore, this study consisted of two objectives. First, it aimed to examine whether there are any significant differences in the elderly’s socio-demographic characteristics and their intention to relocate to a smart retirement village. The second objective investigated the predictors of such intention based on the TAM model. Data were collected from 305 participants with knowledge of smart home technology and retirement villages via online methods. The results revealed that socio-demographic characteristics, such as age, ethnicity, and marital status, were found to differ in the intention of the elderly to relocate to SRVs. No statistically significant differences were found between education, occupation, and the living status of the elderly and intention to relocate. Among the tested constructs based on the TAM model, perceived usefulness and attitude were positively related to relocation intention, and perceived ease of use influenced intention through the perceived usefulness indirectly. This implies that if the elderly consider smart home technology useful and that it can enhance their quality of life, they will be more likely to relocate to a retirement village with integrated smart home technology. Advertising and promotion should also design messages to create a favorable attitude among the elderly towards staying at a smart retirement village to instill in them a future intention to relocate. When the elderly perceived that the smart home technology is simple and easy to use, it will enhance their perceived usefulness of the smart home technology, ultimately motivating them to relocate to an SRV. Lastly, this study concluded that both psychological factors and socio-demographic differences are crucial in determining relocation decisions of the elderly.

Author Contributions

Conceptualization, B.C.T., C.D., N.K., W.H.T., and C.P.O.; methodology, B.C.T. and W.H.T.; formal analysis, B.C.T., T.C.L., and N.K.; writing—original draft preparation, B.C.T., T.C.L., and S.M.P.; writing—review and editing, B.C.T., T.C.L., and S.M.P. All authors have read and agreed to the published version of the manuscript.

Funding

The authors acknowledge the grant (FRGS/1/2018/SS06/MMU/02/3) provided by the Malaysian Ministry of Higher Education to conduct the research.

Institutional Review Board Statement

This study received research ethical approval (code: EA3312021) from the ethical review board of Multimedia University, Malaysia.

Informed Consent Statement

Informed consent was obtained from all individual participants included in this study.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

The authors would like to thank Multimedia University and the respondents who have participated in this study, as well as the editors and anonymous reviewers who have provided their precious feedback and comments.

Conflicts of Interest

The authors declare no conflicts of interest.

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Buildings 15 02768 g001
Figure 1. Proposed research framework.
Figure 1. Proposed research framework.
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Figure 2. Final measurement model.
Figure 2. Final measurement model.
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Figure 3. Structural model.
Figure 3. Structural model.
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Table 1. Empirical evidence based on TAM in the context of smart homes.
Table 1. Empirical evidence based on TAM in the context of smart homes.
No.AuthorINTPEOUPUATTSampleCountry
1.Ferdhany and Aldianto (2020) [32]Intention to use smart home productsPEOU → PU *PU → INT *ATT → INT *General
(all ages)		Indonesia
2.Hubert et al. (2019) [33]Intention to use smart home applicationsPEOU → PU *PU → INT *N/AGeneral
(all ages)		United Kingdom
3.Park et al. (2018) [34]Intention to use smart home servicesPEOU → PU *
PEOU → ATT **		PU → INT *ATT → INT *General
(all ages)		Korea
4.Pal et al. (2017) [6]Intention to adopt smart home servicesPEOU → PU *
PEOU → ATT *		PU → ATT *ATT → INT *ElderlyThailand
5.Tsai (2020) [35]Intention to use smart clothing (general and with cardiovascular disease, CD)PEOU → PU *
PEOU → ATT *
(general)
PEOU → PU **
(with CD)
PEOU → ATT **
(with CD)		PU → ATT *
(general and with CD)
PU → INT **
(general and with CD)		ATT → INT *
(general)		ElderlyTaiwan
Note: * = significant; ** = not significant (legend: PEOU = perceived ease of use; PU = perceived usefulness; ATT = attitude; INT = intention; CD = cardiovascular disease).
Table 2. ANOVA results and average mean scores for other socio-demographic characteristics.
Table 2. ANOVA results and average mean scores for other socio-demographic characteristics.
ConstructSocio-DemographicFrequency (N)Percentage (%)Average Meanp-ValueFindingHypothesis
Intention to relocate to a smart retirement villageGenderFemale 12842.03.960.440Not
significant		H1b not
supported
Male17758.04.01
Age 55 to 60 13744.93.880.045 *SignificantH1a
supported
61 to 658026.24.08
66 to 70 6822.34.12
71 & above206.63.93
EthnicityMalay12340.33.950.001 **SignificantH1c
supported
Chinese12139.74.07
Indian55184.04
Others623.00
Marital statusSingle9230.23.890.040 *SignificantH1d
supported
Married18460.34.08
Others299.53.72
EducationTertiary 13042.64.020.564Not
significant
H1e not
supported
Secondary 13243.33.99
Primary 3812.53.93
Others51.63.65
OccupationRetired 11136.43.950.326Not
significant		H1f not
supported
Working Part-time 13744.93.98
Working full-time 5718.74.10
Living conditionsLiving with children9832.14.050.654Not
significant		H1g not
supported
Living with spouse13143.03.98
Living alone6922.63.94
Other arrangements72.33.82
Note: * p < 0.05; ** p < 0.01.
Table 3. Structure model fit index.
Table 3. Structure model fit index.
Goodness-of-Fit IndicesDesirable RangeOriginal Structural Model
CMIN/DF<52.939
CFI>0.900.934
GFI>0.900.923
NFI>0.900.934
TLI>0.900.914
RMSEA<0.080.079
Table 4. Results of the hypothesis testing.
Table 4. Results of the hypothesis testing.
HypothesisPathStandardized Coefficientp ValueResults
H2 PEOU → PU 0.884***Supported
H3PEOU → ATT0.2230.325Not supported
H4PU → ATT0.3820.094Not supported
H5PU → INT0.287***Supported
H6ATT → INT0.573***Supported
Note: *** p < 0.001.
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Tan, B.C.; Lau, T.C.; D’Souza, C.; Khan, N.; Tan, W.H.; Ooi, C.P.; Pang, S.M. Smart Retirement Villages as Sustainable Housing Solutions: A TAM-Based Study of Elderly Intention to Relocate. Buildings 2025, 15, 2768. https://doi.org/10.3390/buildings15152768

AMA Style

Tan BC, Lau TC, D’Souza C, Khan N, Tan WH, Ooi CP, Pang SM. Smart Retirement Villages as Sustainable Housing Solutions: A TAM-Based Study of Elderly Intention to Relocate. Buildings. 2025; 15(15):2768. https://doi.org/10.3390/buildings15152768

Chicago/Turabian Style

Tan, Booi Chen, Teck Chai Lau, Clare D’Souza, Nasreen Khan, Wooi Haw Tan, Chee Pun Ooi, and Suk Min Pang. 2025. "Smart Retirement Villages as Sustainable Housing Solutions: A TAM-Based Study of Elderly Intention to Relocate" Buildings 15, no. 15: 2768. https://doi.org/10.3390/buildings15152768

APA Style

Tan, B. C., Lau, T. C., D’Souza, C., Khan, N., Tan, W. H., Ooi, C. P., & Pang, S. M. (2025). Smart Retirement Villages as Sustainable Housing Solutions: A TAM-Based Study of Elderly Intention to Relocate. Buildings, 15(15), 2768. https://doi.org/10.3390/buildings15152768

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