Structural Equation Modeling for Analyzing Innovation Adoption in Residential Condominium Projects

Abstract

The aim of this study was to analyze innovation acceptance in condominiums using structural equation modeling (SEM) based on latent variables derived from a second-order confirmatory factor analysis (CFA). The authors focused on four groups of latent variables, namely, the characteristics of innovation adopters (CHARACTER), desired innovation categories (CATEGORY), trust in developers (TRUST), and innovation adoption (ADOPTION), collected from an intensive literature review. Data were gathered from 400 residents of high-rise condominiums across twenty-one central districts of Bangkok via purposive sampling. The analysis revealed that only the desired innovation categories had a direct effect on innovation adoption. In contrast, the characteristics of buyers and trust in developers did not have direct effects on innovation adoption but exerted indirect effects through the desired innovation categories. The findings illustrate how SEM can be applied to study the adoption of innovation by real estate buyers. In practical applications, project developers and designers should study which innovations are desired by buyers. This involves considering the buyers’ characteristics and level of trust in the developers. Such an analysis would enable them to design projects that maximize their responsiveness to buyers’ needs and would not impose excessive cost burdens as well as enhancing income opportunities and achieving sustainable competitive advantages.

1. Introduction

Given the current situation in Thailand, the real estate sector accounts for approximately three percent of the country’s gross domestic product (GDP). Among its segments, the residential real estate business plays a significant role in driving the economy, stimulating employment, and fostering the growth of various related industries [1]. The growth of the real estate sector has led to a highly competitive environment where developers can no longer rely solely on traditional competitive advantages such as location or pricing. Instead, project developers must turn their attention to offering innovations that meet the evolving needs of consumers. These changes include smaller family sizes, an aging population, advancements in communication technology, and shifting urban lifestyles [2]. As a result, many developers have integrated innovation as a key selling point into condominium projects [3]. It is anticipated that projects incorporating innovation will be able to command higher prices by better addressing customer demands [4]. This is consistent with a study conducted by Thailand’s Real Estate Information Center on the future trends of real estate development over the next 20 years, which indicated that innovation strategies will become the standard in property development. Innovation culture will become a key criterion in buyers’ decision-making processes, and artificial intelligence (AI) will emerge as a pivotal technology in the real estate sector. In addition, sustainability-related innovations related to emerging technologies will continue to develop. The impact of the COVID-19 pandemic has further accelerated the emergence of innovations. Technological and innovative transformations will directly influence customer behaviors and purchasing decisions, especially in housing choices. Innovation has, thus, become a significant factor in driving customer demand [5].

Moreover, a review of the literature on the current significance of innovation revealed that many organizations are attempting to develop strategies based on innovation adoption to better reach consumers. As reviewed by Siniak et al. [6], developers are actively incorporating various types of innovations into condominium projects. This is consistent with a substantial body of the literature that focuses on customers’ innovation adoption. For example, Pham, Dau, and Nguyen [7] suggested that understanding customer behaviors regarding innovation adoption within organizations can aid managerial decision-making and improve organizational effectiveness. Similarly, Sitek [8] found that studying innovation adoption in the real estate market can help identify potential risks for developers and investors. This aligns with the findings of Ahmed et al. [5], who emphasized that delivering innovations that meet customer needs, solve problems, and ensure safety are crucial factors in product success. An, Eck, and Yim [9] also found that innovations affecting customer perceptions—particularly regarding usefulness and convenience—can lead to meaningful business success.

However, although innovation currently plays a significant role in the residential real estate sector, a review of the literature revealed a notable gap: no prior research has specifically focused on innovation adoption in residential condominium projects. While some studies have indicated the challenges that condominium residents face in Thailand with building innovations that do not fully meet their needs [10], the condominium sector continues to show relatively strong growth, accounting for 60.50% of the country’s residential market share [11]. Nevertheless, no research has yet been conducted to systematically address these issues. This gap is largely due to limitations faced by developers and stakeholders in data collection for project development—particularly constraints related to time, economic fluctuations, and the diversity of contemporary innovations. As a result, there remains a lack of studies offering concrete recommendations or solutions to directly resolve these problems. This has led to the initiation of research on this issue to support project developers and real estate professionals in formulating more effective strategies [1]. To address the existing gap in the academic literature, the authors designed this study to explore the issue and propose solutions to the identified problems.

In addressing the research gap, a literature review was conducted to explore relevant approaches and tools for application, as the study of innovation adoption among residents in condominium projects involves the analysis of a large amount of data. The literature review revealed that structural equation modeling (SEM) is a suitable and powerful technique for studying causal relationships among theoretical latent variables (constructs) [12], which can have both direct and indirect effects. It is well suited for identifying complex factors in research related to residents’ attitudes and behaviors toward innovation. Based on the literature review, it was found that structural equation modeling (SEM) has been used in the analysis of resident satisfaction with condominium management [13]. However, it was also found that no studies have applied SEM to analyze residents’ acceptance of innovations in condominium projects. Therefore, in this study, a structural equation model was developed to analyze innovation adoption in condominium projects. It was used to examine the factors related to customers’ perceived innovation needs and their inherent innovation orientation, both of which influence attitudes and purchase intentions—particularly among target customer groups. The objectives of this research were to identify the innovation-related factors desired by condominium residents and to examine the characteristics of innovation adopters and the level of trust present in real estate developers. Furthermore, the aim of this study was to develop a model that can be applied by stakeholders in the actual development of condominium housing projects and serve as a foundation for further academic research. This will ultimately contribute to enhancing the industry’s ability to adapt effectively to changes in the modern era, with an emphasis on prioritizing technologies and innovations that are cost-efficient and not overly burdensome. Ultimately, this approach presents an opportunity to enhance revenue generation and create a sustainable competitive advantage. This study was structured according to the five-step procedure outlined by Hair et al. [14]: (1) model specification; (2) model identification; (3) model estimation; (4) model evaluation; and (5) model re-specification. This systematic methodology ensured the development of a structural equation model representative of the factors influencing innovation adoption in residential condominiums.

2. Literature Review

As the aim of this study was to examine innovation adoption in residential condominiums, the literature review focused on theories related to consumer adoption and the use of innovation. At present, various forms of technology are being adopted to achieve business objectives, such as risk assessment [15] and to enhance the understanding of customer innovation adoption in order to improve convenience [16]. This review included the components discussed below.

2.1. Innovation in the Real Estate Industry

At present, innovation has become a key market mechanism that significantly influences purchasing decisions [17]. It serves as an essential tool for creating competitive advantage [18,19], increasing sales [20,21], and promoting business growth [22,23]. Product innovation, as an outcome of organizational or business activity, may take the form of either goods or services [24,25,26]. It may involve improving existing products and services [27,28] or introducing entirely new ones. Innovation also encompasses improvements in work processes [29,30] and management processes to increase operational efficiency [31,32].

Real estate is a core industry where competition is driven by both product and service innovation as a means of differentiation. Innovation lies at the heart of business processes, driving changes in thinking, workflows, organizations, and value creation [33]. Emerging trends in innovation development are expected to add significant value to the real estate sector, such as through modeling, virtual environments, robotics systems, and other building components [34].

2.2. Concepts Related to Innovation Adoption

Based on the literature review, it was found that several theories address innovation adoption in terms of user perception and the acceptance of innovation for practical use. These theories are outlined below.

2.2.1. Characteristics of Innovations and Types of Innovation Adopters

Rogers [35] proposed five key attributes of innovations that influence individuals’ decisions to adopt them: (1) relative advantage; (2) compatibility; (3) complexity; (4) trialability; and (5) observability. As innovations diffuse throughout society, individuals—with various characteristics—adapt and respond to them differently. In addition, Rogers’ research also categorized innovation adopters into five groups, ranking them from the highest to the lowest level of innovation adoption as follows: (1) innovators; (2) early adopters; (3) early majority; (4) late majority; and (5) laggards [35].

This theory has been interpreted in the literature to formulate hypotheses and serve as a framework for comparing factors related to residential project studies. For example, a study by Sanders and van Bortel [36] applied the concept of innovation diffusion as a criterion for collecting data on the use of shared innovative spaces within housing estate projects. This aligns with the research conducted by Van Oorschot et al. [3], in which Rogers’ theory was employed to categorize the types of individuals likely to adopt innovations in residential settings, as well as to identify factors that influence the decision-making mechanisms for adoption. It is evident that Rogers’ framework directly informs analytical principles concerning both innovation and consumer behavior in practice. In this study, Rogers’ five key attributes of innovation and five groups of innovation adopters were used to form the latent variables “CHARACTER” and “ADOPTION” and their related observed variables, as explained in Table 1 of Section 4: Research Variable Framework.

2.2.2. Technology Readiness (TR)

Technology readiness refers to an individual’s propensity to embrace and use new technologies to achieve goals in both personal and professional environments [37]. The dimensions of technology readiness are the following: (1) optimism—a positive belief and attitude toward the benefits of technology; (2) innovativeness—a tendency to be an early adopter and to seek out and experiment with new technologies; (3) discomfort—a perceived lack of control or confidence when using technology, which may act as a barrier to adoption; and (4) insecurity—distrust or skepticism toward technology, particularly regarding its reliability and safety [38,39].

Ling and Moi [40] and Pesch, Endres, and Bouncken [41] further suggested that individuals are more likely to adopt new technologies when they help them achieve personal goals. Similarly, Lam [42] and Eppinger [43] found that behavior regarding readiness and the adoption of technology is influenced by changing environmental contexts. Lee [44] also studied how technologies that align with consumer behavior can enhance the likelihood of purchase decisions, noting that modern technology can stimulate buying intentions.

For this reason, innovation readiness theory is often applied in the context of innovation adoption in residential projects. This is evident in the work of Kaartinen [45], which found that the innovation readiness theory can effectively help identify consumer needs in the real estate market. As a result, developers are better able to anticipate market conditions and produce solutions that genuinely meet users’ needs, thereby influencing the overall potential of urban development. Similarly, a study by Yusof and Shafiei [46] demonstrated that the theory of innovation adoption enhances an understanding of residents’ needs. This directly influences the strategic direction of project developers and contributes to long-term urban improvement. In this study, the dimensions of technology readiness were used to form the latent variables “CHARACTER”, “TRUST”, and “ADOPTION” and their related observed variables, as explained in Table 1 of Section 4: Research Variable Framework.

2.2.3. Technology Acceptance Model (TAM)

The technology acceptance model explains how external variables influence two primary beliefs: perceived usefulness and perceived ease of use. These beliefs shape an individual’s attitude toward using technology, which, in turn, affects their behavioral intention to use it, ultimately leading to the actual use of systems, as illustrated in Figure 1. According to Davis [47], the key factors that primarily explain user behavior are (1) behavioral intention to use it, (2) perceived usefulness, and (3) perceived ease of use. These three elements serve as the core components of the model and are essential for understanding users’ acceptance and usage behavior regarding new technologies.

2.2.4. Unified Theory of Acceptance and Use of Technology (UTAUT)

The unified theory of acceptance and use of technology (UTAUT) has served as a foundational model widely applied in the study of various technologies, both within and outside organizational contexts [48,49]. The UTAUT has been adapted in three primary application contexts: (1) in emerging contexts, such as health information systems [50,51] and the formation of new cultural practices [52,53]; (2) in model extensions through new constructs to broaden the theoretical scope of UTAUT mechanisms, such as in autonomous mass transportation systems [54,55,56]; and (3) in the integration of external factors into the UTAUT framework [48,57,58]. Originally introduced by Venkatesh [59], the UTAUT model has four core constructs: (1) performance expectancy; (2) effort expectancy; (3) social influence; and (4) facilitating conditions. Subsequent research expanded the model to include seven constructs, with the addition of (1) hedonic motivation, (2) price value, and (3) habit. This led to the development of the more comprehensive UTAUT2 model, which also incorporates three moderate variables, namely, gender, age, and experience, as illustrated in Figure 2. In this study, the key elements of the UTAUT2 model were used to form the latent variables “CHARACTER”, “TRUST”, and “ADOPTION” and their related observed variables, as explained in Table 1 of Section 4: Research Variable Framework.

3. Research Variable Framework

In this study, a conceptual model was developed to define the observed variables of the specified latent variables. This framework was constructed by integrating preliminary survey results with relevant theoretical concepts from the literature. In this study, second-order confirmatory factor analysis (CFA) was employed to examine four main latent variables. The dependent latent variable (endogenous latent variable) of the model is innovation acceptance (ADOPTION) [35,47,60]. The determinants (exogenous latent variables) of the model comprise three latent variables: (1) desired innovation categories [61,62]; (2) the characteristics of innovation adopters [63,64]; and (3) trust in real estate developers [61,65,66]. These served as the core latent variables in the process of developing the structural model. For each latent variable, relevant observed variables were compiled to construct a corresponding measurement model supported by prior research. The details of each latent variable and their observed variables are presented in Table 1.

Table 1. List of variables in the structural equation model.

Construct (Latent Variable)			Observed Variable
Variable	Description	Supporting Research	Variable	Description	Supporting Research
CATEGORY (Desired Innovation Attributes)	The demand for products that create new value or that offer improvements over traditional methods to better meet contemporary living needs enhances the quality of life in various aspects such as convenience, economic efficiency, safety, and health care, as well as energy management and environmental sustainability.	[61,62]	SAFETY	Occupational Health and Safety	[67]
			DIGITAL	Digital and Intelligent Systems	[68]
			ENERGY	Energy Management	[69]
			MATERIAL	Materials and Construction	[70]
CHARACTER (Characteristics of Innovation Adopters)	Attitudes toward new innovations in terms of intended usage, readiness to adapt, and opportunities for application in work and daily life, as well as the perceived value of the innovation in alignment with personal experience and user needs—reflected through the awareness of usage procedures and perceived ease or difficulty of implementation.	[63,64]	ATTITUDE	Attitude Toward New Innovation	[71]
			KNOW	Perceived Ease of Use	[72]
			FACTOR	Other Factors Influencing the Adoption Decision: Advantage, Effectiveness, Freedom to Select, Problem Solving, Experience, Previous Desire to Use, and Security	[73]
TRUST (Trust in Developers)	Attitudes toward the image of leaders and personnel as real estate developers reflect the organization’s brand and its past performance resulting from management practices, as well as the consistent presentation of products that convey the organization’s culture of innovation to consumers.	[61,65,66]	PRODUCT	Organization’s Products	[5]
			OPERATE	Organizational Operations	[74]
			BRAND	Corporate Brand	[75,76]
ADOPTION (Innovation Adoption)	The process begins with the recognition of new ideas and approaches that lead to innovation, followed by an interest in innovation through information seeking, and it continues with an evaluation of its value and appropriateness for specific contexts. This process ultimately leads to trial use and the decision to adopt the innovation.	[35,47,60] (Dependent variable)	INTEREST	Stages of Expressing Interest and Information Gathering	[72]
			EVALUATE	Analysis and Evaluation Process	[40]
			TRIAL	Experimentation and Decision-Making Process	[35,71]

Table 1. List of variables in the structural equation model.

Construct (Latent Variable)			Observed Variable
Variable	Description	Supporting Research	Variable	Description	Supporting Research
CATEGORY (Desired Innovation Attributes)	The demand for products that create new value or that offer improvements over traditional methods to better meet contemporary living needs enhances the quality of life in various aspects such as convenience, economic efficiency, safety, and health care, as well as energy management and environmental sustainability.	[61,62]	SAFETY	Occupational Health and Safety	[67]
			DIGITAL	Digital and Intelligent Systems	[68]
			ENERGY	Energy Management	[69]
			MATERIAL	Materials and Construction	[70]
CHARACTER (Characteristics of Innovation Adopters)	Attitudes toward new innovations in terms of intended usage, readiness to adapt, and opportunities for application in work and daily life, as well as the perceived value of the innovation in alignment with personal experience and user needs—reflected through the awareness of usage procedures and perceived ease or difficulty of implementation.	[63,64]	ATTITUDE	Attitude Toward New Innovation	[71]
			KNOW	Perceived Ease of Use	[72]
			FACTOR	Other Factors Influencing the Adoption Decision: Advantage, Effectiveness, Freedom to Select, Problem Solving, Experience, Previous Desire to Use, and Security	[73]
TRUST (Trust in Developers)	Attitudes toward the image of leaders and personnel as real estate developers reflect the organization’s brand and its past performance resulting from management practices, as well as the consistent presentation of products that convey the organization’s culture of innovation to consumers.	[61,65,66]	PRODUCT	Organization’s Products	[5]
			OPERATE	Organizational Operations	[74]
			BRAND	Corporate Brand	[75,76]
ADOPTION (Innovation Adoption)	The process begins with the recognition of new ideas and approaches that lead to innovation, followed by an interest in innovation through information seeking, and it continues with an evaluation of its value and appropriateness for specific contexts. This process ultimately leads to trial use and the decision to adopt the innovation.	[35,47,60] (Dependent variable)	INTEREST	Stages of Expressing Interest and Information Gathering	[72]
			EVALUATE	Analysis and Evaluation Process	[40]
			TRIAL	Experimentation and Decision-Making Process	[35,71]

4. Research Methodology

4.1. Research Process

In this study, a quantitative investigation was conducted with the aim of developing a structural equation model (SEM) to analyze innovation adoption in residential condominiums. Furthermore, a survey was conducted to assess the demand for innovation, which analyzed the components of innovation in condominiums. This was complemented by an examination of behavioral variables and residents’ perceptions regarding innovation adoption. A two-tiered approach was employed: exploratory factor analysis (EFA) to identify the underlying structure of observed variables, followed by second-order confirmatory factor analysis (CFA) to validate the measurement model. This process led to the development of a structural equation model (SEM) encompassing four latent variables: (1) the characteristics of innovation adopters (CHARACTER); (2) desired innovation categories (CATEGORY); (3) trust in developers (TRUST); and (4) innovation adoption (ADOPTION).

Figure 3 illustrates the research process. Initially, a conceptual model was established to define the observed variables corresponding to the primary latent variables. This model was developed using information from the relevant literature and preliminary survey results, and it was refined using feedback from three experts. The preliminary survey facilitated the identification of variables through the EFA, revealing the underlying structure of the data. Subsequently, a second-order CFA was conducted to confirm the measurement model. The SEM development followed the five-step procedure of Hair et al. [14] from model specification to model re-specification. Model evaluation was based on several fit indices: chi-square, p-value (≥0.05), the root mean square error of approximation (RMSEA < 0.05), the goodness-of-fit index (GFI > 0.95), the adjusted goodness-of-fit index (AGFI > 0.95), the root mean square residual (RMR < 0.05), the comparative fit index (CFI > 0.95), and the non-normed fit index (NNFI > 0.95) or Tucker–Lewis index (TLI > 0.95), which are suggested by Hooper, Coughlan, and Mullen [77] to be the most informative indices available to researchers for determining model fit.

4.2. Study Population and Sample

The study population consisted of residents of high-rise residential condominium projects developed by companies listed on the Stock Exchange of Thailand (SET) and registered as condominiums between 1 January 2018 and 1 January 2023, located within twenty-one central districts of Bangkok (the capital city of Thailand). The estimated population is 144,000 people, which was calculated from 80,000 units of condominiums [78] × 90% occupancy rate [78] × 2 people per unit [79]. Questionnaires were collected from 400 residents across 24 projects, selected using the purposive sampling method from January 2023 to March 2023.

The sample size was determined based on the principles of structural equation modeling (SEM), which recommends a minimum of 200 sample units [80,81]. Furthermore, Schumacher and Lomax [82] and Ahmmed, Saha, and Tamal [83] suggested that the sample size in an SEM analysis can be determined based on the number of observed variables for at least 10 samples per observed variable. In this study, there were 13 observed variables, giving a calculated sample size of 10 × 13 = 130 samples. However, the researchers collected questionnaires from 400 residents, which is higher than the aforementioned criterion.

4.3. Research Tool

Because the unit of analysis in this study was at the individual level, questionnaires were used as tools to collect data on the innovation adoption and behaviors of the respondents to analyze the relationships between factors in the structural equation model. The questionnaires contained 91 questions, divided into two main parts. The first part contained 14 multiple-choice questions asking about the general information and residential requirements of the respondents. In addition, the second part contained 77 Likert-scale questions concerning the observed variables of each latent variable: 16 questions for innovator characteristics (CHARACTER), 25 questions for desired innovation categories (CATEGORY), 16 questions for trust in the developer (TRUST), and 20 questions for innovation adoption (ADOPTION).

To address the issue of variation in innovation adoption factors across different regions, the questions were adapted to align with the Thai context, and the content validity of each question was assessed by three experts using the index of item-objective congruence (IOC), which must be higher than 0.5 [84]. In addition, the reliability of the questionnaire was assessed using Cronbach’s alpha coefficient, and the test showed a coefficient of 0.957 for the whole questionnaire and a coefficient in the range of 0.951–0.996 for each latent variable, demonstrating that the research tool has high reliability compared to the value of 0.7 suggested by Tavakol and Dennick [85].

5. Research Findings

5.1. Demographic Data of the Respondents

Table 2. Demographic data of the respondents.

Criteria	Details	Number	Percentage
Gender	Male	226	56.50
	Female	174	43.50
Age	>25	24	6.00
	25–30	143	35.75
	31–35	125	31.25
	36–40	56	14.00
	41–50	44	11.00
	<50	8	0.02
Status	Single	212	53.00
	Married	176	44.00
	Divorce	12	3.00
Educational qualification	>Bachelor’s degree	8	0.02
	Bachelor’s degree	220	55.00
	Master’s degree	160	40.00
	Doctoral degree	12	3.00
Occupation	Student	24	6.00
	Company employee	311	77.75
	Owner	33	8.25
	Government officer	32	8.00
Income	<25,000	32	8.00
	25,001–50,000	124	31.00
	50,001–75,000	164	41.00
	75,001–100,000	56	14.00
	100,001–200,000	12	3.00
	>200,000	12	3.00
Number of residents per unit	1 person	76	19.00
	2 people	192	48.00
	3–4 people	128	32.00
	<5 people	4	1.00
Residential room type	Studio	36	9.00
	1 bedroom	248	62.00
	2 bedrooms	92	23.00
	3 bedrooms	16	4.00
	Penthouse	8	2.00

shows that the sample group consisted of a nearly equal proportion of males and females, with 56.50% identifying as male and 43.50% identifying as female. The majority of the respondents were aged between 25 and 30 years, accounting for 35.75%, followed by 31 and 35 years, accounting for 31.25%. Most respondents had a bachelor’s degree, comprising 55.00% of the sample, while 40.00% held a master’s degree. Regarding marital status, 53.00% were single individuals. The majority were employed as company employees, making up 77.75% of the sample. Regarding monthly income, the largest group earned between THB 50,001 and 75,000, accounting for 41.00% of the sample, with the next largest group earning between THB 25,001 and 50,000, comprising 31% of the sample. Finally, most of the respondents lived with one other person in a one-bedroom condominium unit.

The table shows that demographic data were utilized in the data analysis process. When constructing the model, it was anticipated that the characteristics of innovation adopters would influence adoption and usage within condominium projects. Consequently, such demographic information was collected to examine the roles, statuses, and readiness of innovation adopters, as well as their specific attributes.

5.1.1. Specification of Observed Variables

By reviewing the related literature and performing a second-order confirmatory factor analysis (CFA), the observed variables could be defined for innovator characteristics (CHARACTER), desired innovation categories (CATEGORY), trust in the developer (TRUST), and innovation adoption (ADOPTION). These variables were used as questionnaire items, resulting in 20, 25, 16, and 16 items, respectively, as shown in

Table A1. The observed variables of the measurement model.

Component Group	Questionnaire Items/Observed Variables
1. Attitude toward new innovations Code (ATTITUDE) MEAN (3.793) SD (0.676)	(BEHA1) You are willing to accept the risks associated with the adoption of new innovations data (BEHA2) You are proficient in using technology and confident in its application. (BEHA3) You are not afraid of change and consistently maintain a positive attitude toward new innovations. (BEHA4) The innovation you choose must be modern and appropriate for the current circumstances. (BEHA5) You work in a profession related to the use of technology and innovation. (BEHA6) You feel proud when using innovative products. (BEHA7) You believe that a good innovation should be accessible to people of diverse genders and ages. (BEHA8) The innovation you choose must be compatible with other innovations. (BEHA9) You prioritize convenience in daily life.
2. Perceived ease of use Code (KNOW) MEAN (4.209) SD (0.532)	(BEHA10) You accept innovations that do not involve complicated devices. (BEHA11) You accept innovations that do not require advanced skills to use. (BEHA12) You accept new innovations that are easier to use compared to previous innovations. (BEHA13) You choose innovations based on a thorough understanding of how they function.
3. Factors in the selection of use Code (FACTOR) MEAN (4.036) SD (0.581)	(BEHA14) You prioritize the benefits of an innovation in your consideration. (BEHA15) You base your consideration on the effectiveness of the innovation. (BEHA16) You have the freedom to decide on the selection of innovative products. (BEHA17) You believe that new innovations should be able to solve the problems associated with using previous innovations. (BEHA18) If you have previously used or experimented with similar innovations, you will find it easier to decide to adopt them. (BEHA19) You decide to adopt the innovation based on your previous desire to use it. (BEHA20) The sense of security in using the innovation influences your decision-making.

,

Table A2. The observed variables of the desired innovation variable set.

Component Group	Questionnaire Items/Observed Variables
1. Innovation in Safety and Health Code (SAFETY) MEAN (4.406) SD (0.526)	(TYPE1) Fire Detection and Alarm System (TYPE2) Closed-Circuit Television (CCTV) (TYPE3) Personal Screening Systems, e.g., Face Scan (TYPE4) Digital Smart Lock System (TYPE5) Motion Detection System for Theft Prevention (TYPE6) Touchless Devices for Disease Prevention (TYPE7) Modern Life-Saving Devices for Emergency First Aid (TYPE8) Air Circulation and Dust Prevention System (TYPE9) Devices with Gripping Mechanisms and Shock-Absorbent Flooring (TYPE10) Universal Design Innovation (TYPE11) Innovations to Reduce Pollution
2. Digital Innovation and Smart Systems Code (DIGITAL) MEAN (3.644) SD (0.758)	(TYPE12) IoT Innovation (Internet of Things) (TYPE13) Artificial Intelligence (AI) Innovation (TYPE14) Smart Home Innovation (Home Automation) (TYPE15) Automated Parking Services (Auto Parking) (TYPE16) Smart Locker with Smartphone Integration (TYPE17) Electric Vehicle Charging Station (EV Charger) (TYPE18) Virtual Reality Technology
3. Innovation in Energy Management Code (ENERGY) MEAN (3.793) SD (0.762)	(TYPE19) Water Recycling System in Projects (TYPE20) Energy-Saving Devices to Reduce Electricity Costs (TYPE21) Innovations to Reduce Heat Gain in Buildings, e.g., Walls, Glass (TYPE22) Solar Power System (Solar Cell)
4. Innovation in Materials and Construction Code (MATERIAL), MEAN (3.890), SD (0.736)	(TYPE23) Construction Materials Mimicking Nature (TYPE24) Prefabricated Construction System (Prefabs) (TYPE25) Robotic Construction Innovation

,

Table A3. The observed variables for the trust in entrepreneurs’ variable set.

Component Group	Questionnaire Items/Observed Variables
1. Organization’s Products Code (PRODUCT) MEAN (3.899) SD (0.650)	(CORP1) The organization’s products excel in research and innovation. (CORP2) The organization’s previous products have consistently gained good customer acceptance. (CORP3) The organization adds value to its new products. (CORP4) The organization’s products are designed to continuously address customer problems. (CORP5) The organization’s products provide a unique alternative that differentiates them from competitors. (CORP6) Most of the organization’s products stand out from competitors in the industry.
2. Organization’s Operations Code (OPERATE) MEAN (4.114) SD (0.570)	(CORP7) The organization has efficient marketing and management practices. (CORP8) The organization has consistently performed well in terms of business outcomes. (CORP9) The effectiveness of the organization’s management. (CORP10) The effectiveness of the organization’s employees. (CORP11) Innovation policies that reflect the organization’s operational methods. (CORP12) The organizational culture reflects a focus on innovation learning. (CORP13) Collaboration with other organizations, such as foreign entities.
3. Corporate Brand Code (BRAND) MEAN (4.019) SD (0.636)	(CORP14) The brand’s credibility. (CORP15) The organization’s social image. (CORP16) The organization’s environmental image.

and

Table A4. The observed variables of the innovation acceptance variable set.

Component Group	Questionnaire Items/Observed Variables
1. Interest and Information Search Code (INTEREST) MEAN (3.559) SD (0.740)	(PROC1) You enjoy trying new things. (PROC2) You are interested in thinking methods that lead to innovation. (PROC3) You are always interested in keeping up with news related to innovation. (PROC4) When an innovation emerges, you know where to search for information about it. (PROC5) Before deciding to choose an innovation, you always research and understand it first. (PROC6) You are interested in discussing with creative innovators or people who think of innovations. (PROC7) You always study the information of the company that produces the innovative product first.
2. Analysis and Evaluation Stage Code (EVALUATE) MEAN (4.147) SD (0.559)	(PROC8) You analyze the pros and cons of the innovation before deciding to use it. (PROC9) You always make decisions on innovations by considering their value for money. (PROC10) You choose innovations when you clearly see their benefits and tangible outcomes. (PROC11) You use an innovation when you see more people adopting it. (PROC12) You choose innovations that are modern and suitable for the current situation.
3 Experimental Stage for Decision-Making Code (TRIAL) MEAN (4.046) SD (0.615)	(PROC13) You choose innovations based on results from personally testing them. (PROC14) You choose innovations that can be physically touched and experienced. (PROC15) You choose innovations where you can clearly see how they work in practice. (PROC16) You choose innovations that you have had the opportunity to see used before or have had their benefits demonstrated to you.

in the Appendix A. The resulting measurement model for the latent variables of innovator characteristics (CHARACTER), desired innovation categories (CATEGORY), trust in the developer (TRUST), and innovation adoption (ADOPTION) is depicted in Figure 4.

5.1.2. Model Fit Assessment of the Measurement Model with Empirical Data

When the measurement models, analyzed by conducting a second-order confirmatory factor analysis (CFA) on the variables of innovator characteristics (CHARACTER), desired innovation categories (CATEGORY), trust in the developer (TRUST), and innovation adoption (ADOPTION), were assessed for model fit with empirical data using the chi-square, p-value, root mean square error of approximation (RMSEA), goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), root mean square residual (RMR), comparative fit index (CFI), and Tucker–Lewis index (TLI), it was found that all four of them exhibited the index values shown in

Table 3. The assessment results of the measurement model fit.

Model	Chi-Square	Df (df > 0)	p-Value (p-Value ≥ 0.05)	RMSEA (RMSEA < 0.05)	GFI (GFI > 0.95)	AGFI (AGFI > 0.95)	RMR (RMR < 0.05)	CFI (CFI > 0.95)	NNFI (NNFI > 0.95)
CHARACTER	117.29	121	0.578	0.000	0.972	0.951	0.017	1.000	1.000
CATEGORY	162.35	156	0.347	0.010	0.969	0.956	0.023	0.999	0.998
TRUST	83.96	69	0.106	0.023	0.975	0.951	0.016	0.996	0.993
ADOPTION	85.42	73	0.151	0.021	0.974	0.951	0.024	0.995	0.993

.

A review of the literature revealed that these index indicators can reflect the reliability and validity of the model, thereby enhancing its credibility in terms of accuracy and potential application. This is supported by the research of Zheng and Bentler [86], which states that these indices provide confidence in the model’s applicability and are considered suitable fit indices for studies in the fields of social and behavioral sciences.

As shown in Table 3, all four measurement models exhibited a good fit with the empirical data and could be used to develop a structural equation model in the next step.

5.2. Results of the Structural Model Analysis

The analysis presented in this section was conducted to examine the basic data of the thirteen observed variables, which served as indicators for the four latent variables previously analyzed: innovator characteristics, desired innovation categories, trust in the developer, and adoption of innovation. The results of this analysis are presented below.

5.2.1. Descriptive Statistics of the Observed Variables

Table 4. Descriptive data of the observed variables.

Variables	Code	Mean (Scale 1–5)	Mean (%)	S.D.	Skewness	Kurtosis
Safety and Health	SAFETY	4.41	88.11	0.53	−1.08	1.09
Digital and Intelligent Systems	DIGITAL	3.64	72.87	0.74	−0.60	0.12
Energy Management	ENERGY	3.79	75.85	0.76	−0.74	0.44
Materials and Construction	MATERIAL	3.89	77.80	0.74	−0.15	−0.87
Organization’s Products	PRODUCT	3.90	77.98	0.65	−0.43	−0.22
Organization’s Operations	OPERATE	4.11	82.28	0.57	−1.07	1.79
Organization’s Brand	BRAND	4.02	80.38	0.64	−0.48	0.19
Attitude Toward New Innovation	ATTITUDE	3.79	75.85	0.68	−0.55	−0.11
Perceived Ease of Use	KNOW	4.21	84.18	0.53	−0.56	0.77
Factors in Usage Selection	FACTOR	4.04	80.72	0.58	−0.58	0.29
The Stage of Attention and Information Search	INTEREST	3.56	71.18	0.74	−0.17	−0.70
The Stage of Analysis and Evaluation	EVALUATE	4.15	82.94	0.56	−0.59	0.49
The Stage of Experimentation and Decision-Making	TRAIL	4.05	80.91	0.62	−0.62	0.34

presents descriptive statistical results of the 13 observed variables, including the mean, percentage of the mean, standard deviation, skewness, and kurtosis. The majority of the data fell within the high-importance range (with mean values between 3.41 and 4.20, according to Best [87]), with standard deviations ranging from 0.526 to 0.762. The skewness values were negative, ranging from −0.51 to −1.00, indicating that the data were moderately left-skewed (mean < median < mode). The kurtosis values were mostly positive, ranging up to 1.00, suggesting that the distribution was relatively peaked in shape, resembling a normal curve.

The research findings shown in the table indicate that safety and health are the most significant factors when compared to the other factors, based on the interpretation criteria proposed by Best [87]. Other factors are also considered highly important, as reflected by the mean scores. Perceived ease of use was identified as the next most influential factor affecting innovation adoption. These results are consistent with the findings of Ahmed et al. [5], which indicate that responding to user needs by addressing problems and ensuring safety is a key factor that contributes to the successful implementation of innovation in business. These findings can provide guidance for real estate developers and designers by identifying the types of innovations and key considerations needed when implementing these innovations in condominium projects to enhance buyer acceptance.

5.2.2. Analysis of the Relationship Between the Structural Equation Model and the Influence of Variables on Innovation Adoption in Residential Condominium Buildings

When conducting the structural model analysis to examine innovation adoption in residential condominium buildings, the authors were able to summarize the factor loadings of the 13 observed variables, as well as the model performance indices, as shown in

Table 5. Structural model analysis results.

Variables	Factor Loadings of the Structural Model for Innovation Adoption in Residential Condominium Buildings
	Subcomponent			Subcomponent			Subcomponent			Subcomponent			R2
	CATEGORY			TRUST			CHARACTER			ADOPTION
	Coefficient	SE	t	Coefficient	SE	t	Coefficient	SE	t	Coefficient.	SE	t
SAFETY	0.74												0.55
DIGITAL	0.83	0.09	14.38										0.69
ENERGY	0.74	0.09	13.09										0.56
MATERIAL	0.55	0.07	12.16										0.30
PRODUCT				0.78	0.03	17.94							0.61
OPERATE				0.92	0.02	21.87							0.84
BRAND				0.64	0.03	13.79							0.41
ATTITUDE							0.91	0.03	19.72				0.83
KNOW							0.77	0.04	11.39				0.59
FACTOR							0.84	0.03	17.36				0.70
INTEREST										0.84			0.70
EVALUATE										0.76	0.04	14.62	0.58
TRIAL										0.58	0.04	11.18	0.33
	CATEGORY
	Coefficient	SE	t
TRUST	0.63	0.03	10.42	Chi-square = 47.51; df = 38; p-value = 0.138; RMSEA = 0.032
CHARACTER	0.34	0.03	6.61	GFI = 0.982; AGFI = 0.957; RMR = 0.0104; CFI = 0.997; TLI = 0.994
ADOPTION	0.90	0.01	13.22

.

The results of the structural model analysis for innovation adoption in residential condominium buildings, based on 13 observed variables, yielded the following fit indices: chi-square = 47.51; df = 38 (df > 0); p-value = 0.138 (p-value ≥ 0.05); RMSEA = 0.032 (RMSEA < 0.05); GFI = 0.982 (GFI > 0.95); AGFI = 0.957 (AGFI > 0.95); RMR = 0.010 (RMR < 0.05); CFI = 0.997 (CFI > 0.95); and NNFI (or TLI) = 0.994 (NNFI > 0.95). These results indicate that the model has a good fit with the empirical data. Regarding the R² of the model, the structural model yielded satisfactory explanatory power, with R² values of 0.869 for innovation adoption, 0.852 for trust in the developer, and 0.841 for the characteristics of the customers. However, the R² of desired innovation categories was relatively low (0.348), suggesting that the included predictors explained only a small portion of the variance in this construct. Despite the low R², the path from the characteristics of the customers to the desired innovation categories remained statistically significant, supporting the theoretical relevance of this relationship. The factor loadings of the four latent variables, CATEGORY, TRUST, CHARACTER, and ADOPTION, ranged from 0.58 to 0.92 and were all statistically significant at the 0.01 level. In addition, the t-values of all paths in the structural model are shown in

Table 6. The t-values of the paths between the latent variables in the structural model.

Path	Path Coefficient (β)	t-Value	p-Value
CHARACTER ➔ CATEGORY	0.34	2.957	0.052
TRUST ➔ CATEGORY	0.63	5.412	0.001
CATEGORY ➔ ADOPTION	0.90	10.271	0.001

. These findings suggest that each latent variable can marginally and significantly explain its corresponding observed variables.

The analysis results led to the development of a structural equation model for innovation adoption in condominium buildings, as shown in Figure 5. An analysis of the structural model revealed that all hypothesized paths between the latent variables were statistically significant, as indicated by their t-values. The path from the characteristics of the customers to innovation adoption demonstrated a high level of significance (β = 0.63, t = 5.412), supporting the assumption that individuals’ perceptions regarding innovation attributes play a central role in driving adoption behavior. Additionally, trust in the developer had a very strong and highly significant effect on innovation adoption (β = 0.90, t = 10.271), highlighting the critical influence of credibility, reliability, and brand trust in the decision-making process. While the path from the characteristics of the customers to desired innovation categories also reached statistical significance (β = 0.34, t = 2.957), the effect size was relatively modest. This suggests that, although the characteristics of the customers help shape how users categorize innovations, other contextual or psychological factors may also be at play and warrant further investigation in future studies. Overall, the results confirm the theoretical assumptions of the model and emphasize the importance of both perception- and trust-related constructs in influencing innovation-related behaviors.

6. Discussion and Conclusions

In conclusion, the objectives of this study were fulfilled through the development of a model that identifies the types of innovations adopted by residents in condominium projects, as well as the characteristics of the users. These findings are represented by four key interrelated latent factors, revealing that only the factor of desired innovation categories (CATEGORY) had a direct effect on innovation adoption (ADOPTION). In contrast, the characteristics of the adopter (CHARACTER) and trust in the developer (TRUST) did not exhibit direct effects but influenced adoption indirectly through CATEGORY. In other words, consumers or residents in condominium projects tend to place direct importance on the types of innovations that offer practical benefits, convenience, safety, and solutions to everyday problems rather than on personal attitudes or brand image. However, although the latter factors did not have a direct impact, they still exerted an indirect influence through the desired innovation categories. Therefore, in the context of innovation-driven competition, developers and project operators should continue to prioritize raising customer awareness by creating motivation and fostering positive attitudes toward their use while simultaneously building trust in their organizations among potential customers. Additionally, establishing credibility through high-quality products and demonstrating professionalism in operations can help reinforce the brand image and enhance consumer trust.

Regarding the details of each variable group previously mentioned, the structural equation model indicates that the central decision-making factor for residents in selecting condominiums with innovative features is the type of innovation incorporated into the project. The innovations are prioritized as follows: (1) digital and smart systems; (2) energy management; (3) safety and health features; and (4) materials and construction methods. This aligns with contemporary living trends emphasizing smart living, driven by advancements in digital communication technologies and a growing focus on health, environmental conservation, and energy efficiency. It should be highlighted that the relationship between the categories of innovations and innovation adoption is a factor that has not been addressed in previous innovation adoption theories.

Regarding adopter characteristics, three key components influence decision-making: (1) customer attitudes; (2) perceived ease of use; and (3) the factors influencing usage decisions. These findings are in accordance with the following former theories: (1) The attitude of customers supports innovation adoption. This aligns with the concept of explaining attitudes toward using innovations in the technology acceptance model (TAM), the concept of effort expectancy in the unified theory of acceptance and use of technology 2 (UTAUT2), the characteristics of innovation proposed by Rogers, and the concept of discomfort in the technology readiness (TR) theory. (2) The ease of use supports innovation adoption. This corresponds to Rogers’ concept of complexity in innovation and the TAM’s perceived ease of use. (3) Several other factors support innovation adoption, such as its advantages, effectiveness, security of use, and individual freedom to select innovation. This corresponds to Rogers’ concepts of relative advantages, compatibility, and trialability; TR’s concepts of optimism, innovativeness, and insecurity; the TAM’s perceived usefulness; and the UTAUT2’s performance expectancy concepts.

In addition, trust in the developer’s organization also plays a crucial role in adoption decisions, encompassing (1) organizational products, (2) organizational operations, and (3) organizational branding. This study found that organizational performance had the most substantial influence, indicating that developers with consistently positive performances build trust and confidence among residents, thereby facilitating innovation adoption. These research findings are consistent with the concept of discomfort in the technology readiness index (TR) and the concept of performance expectancy in the unified theory of acceptance and use of technology 2 (UTAUT2).

In this study, three stages were identified in the innovation adoption process: (1) attention and information gathering; (2) evaluation and assessment; and (3) trials and decision-making. The first stage of attention and information gathering was found to be the most influential, highlighting the importance of marketing strategies in capturing consumer interest and providing information about innovative features. Given that most condominium units are purchased before construction is complete, prospective buyers often do not have the opportunity to experience innovations firsthand. Therefore, effective marketing and advertising are essential to demonstrate the benefits and appeal of these innovations. When compared to Rogers’ [35] five-stage innovation adoption model, the three-stage model developed in this study aligns with the original framework. These stages correspond to the awareness, evaluation, and adoption phases, encompassing the entire decision-making process from initial interest to actual usage.

Based on all the findings of this study, benefits were identified for the following four key stakeholders: (1) For real estate developers, this study provides insights into the key factors influencing the adoption of innovation, enabling developers to identify and emphasize unique selling points related to innovation. This can enhance marketing strategies and reduce the time and scope of development processes. (2) For manufacturers and distributors of innovative products, our findings offer valuable information on the factors affecting purchasing decisions and adoption behavior, assisting in product development and marketing efforts. Additionally, this study highlights the importance of developer reputation, suggesting that both product quality and the developer’s performance and brand image significantly influence consumer trust and adoption. (3) For policymakers and urban planners, the findings of this study illustrate the behavior of innovation adopters. These insights can serve as a framework for setting strategic directions and forecasting future innovation trends for stakeholders across both the production and user sectors. Policymakers can leverage innovation to reduce real estate development costs and implement urban planning strategies that accommodate and support innovative lifestyles and technologies, both currently and in the long term. (4) For academia, the findings of this study support the applicability of existing innovation adoption theories, particularly those focusing on adopter-related factors, in explaining the adoption of innovation in residential condominium projects.

In conclusion, this study underscores the importance of understanding adopter characteristics, innovation types, and developer credibility in fostering innovation adoption in condominium settings. By aligning project offerings with customer expectations and building trust, developers can enhance the adoption of innovative features, contributing to the advancement of smart living environments.

However, the application of this model is subject to a few academic limitations. Firstly, this study was conducted using data collected solely from the Bangkok metropolitan area, which is characterized by an urban context. Therefore, when applying the model and findings derived from this study to other countries or regions, various factors influencing innovation adoption decisions should be considered. These include demographic characteristics, the behavioral traits of the population, living conditions, levels of technological advancement, government policies, and socioeconomic conditions. This study may serve as a prototype for re-examining relevant variables within the local context to obtain results and develop models that are more aligned with the specific conditions of the target area. Secondly, most of the factors used in this study were derived from the international literature. Consequently, there may be some discrepancies between these factors and those that are contextually relevant to the study area. To address this limitation, the authors considered the local context when formulating the questionnaire items and sought expert input to evaluate content validity and provide recommendations for refining the questions used in data collection. Thirdly, the R² of the desired innovation categories factor was relatively low, suggesting that additional factors may be required to explain its variance. Future studies may consider incorporating additional variables, such as new categories of innovation, to enhance the explanatory power of this construct.