Factors Influencing Sustainable Eco-Friendly Housing Purchase Intention in Thailand: A Structural Equation Model Analysis

Abstract

This study develops and validates a structural equation model (SEM) to investigate the determinants of sustainable eco-friendly housing purchase intention in Thailand. Based on survey data from 320 potential homebuyers, the integrated model exhibits strong predictive power. The analysis identifies Service Quality as the most influential direct driver of purchase intention, with Marketing Communication and Environmental Awareness also being significant direct predictors. Critically, Marketing Communication plays a central role by exerting substantial indirect effects through the other constructs. These findings provide a strategic framework for developers, highlighting that market success requires an integrated strategy that leverages persuasive communication, superior service quality, and aligned environmental values.

1. Introduction

In an era of increasing global environmental discourse, there is a growing collective emphasis on living in greater harmony with the natural world. This shift in awareness emphasizes practical stewardship, minimizing pollution, promoting waste recycling, and utilizing resources more efficiently to create a cleaner, healthier living environment for both current and future generations [1]. This philosophy has catalyzed a significant trend across industries, characterized by eco-innovation in the business and construction sectors [2]. This approach focuses on creating products, services, and processes that support sustainable development, create competitive advantages, and reduce negative environmental impacts [3]. Concurrently, environmental management strategies have gained prominence, emphasizing material efficiency and accelerating technologies that drive innovation and stimulate responsible investment [4].

The construction industry holds particular significance in this transition. As a major consumer of resources, its choices, from the selection of building materials to project execution, have a direct impact on waste generation and energy consumption. Thus, the industry is uniquely positioned to champion practices that support material efficiency and sustainable substitutes, contributing to a more circular economy [3,5].

However, Thailand’s business, tourism, and construction industries are currently facing a slowdown in growth [6]. This is driven by rising raw material costs and a labor shortage in quantity and quality [7]. The industry’s reliance on unskilled labor often results in output value below wage costs. Furthermore, raw materials and labor costs constitute approximately 80% of the total production cost for construction contractors. This situation is further compounded by a slowdown in the real estate sector due to stricter loan-to-value (LTV) regulations [8], economic and political uncertainties, and the aftermath of the COVID-19 pandemic [9], resulting in a noticeable deceleration in both industries. Household debt remains one of the biggest economic hurdles, with household debt to gross domestic product (GDP) at 86.8% as of June 2025, one of the highest in Asia [10].

The construction materials industry, encompassing cement, steel, concrete, and glass, is a cornerstone of the Thai economy [11]. The residential sector accounted for 35.7% of the total real estate market value in 2024 [12]. Moreover, according to the Office of the National Economic and Social Development Council (NESDC), the construction and real estate sector collectively contributed approximately 6.5% to Thailand’s GDP in 2023, a significant share of the national economic output. The growth of this sector remains intrinsically linked to the performance of the real estate market. To better align with global sustainability trends and enhance competitiveness, Thai construction material entrepreneurs have increasingly invested in research and development, adopting advanced technologies such as industrial IoT [13] and process automation to improve efficiency and production, while reducing costs and minimizing environmental impact [14]. A key outcome of this innovation drive is the development of ‘eco-friendly construction materials’ [15], including low-carbon cement and recycled aggregates, positioning the industry as a critical enabler of sustainable development [16].

Modern buyers place greater importance on the location, security, and connectivity of a property, as well as its sustainability [17]. Features such as energy efficiency and the use of eco-friendly materials are becoming increasingly important when purchasing a home [15,18]. Moreover, Shen et al. [19] reported that developers are under pressure to implement sustainable green building practices while reducing carbon emissions and complying with Thailand’s Taxonomy Phase 2 environmental, social, and governance (ESG) framework. In such an environment, marketing and branding efforts will be crucial for establishing a reliable image and reassuring customers of a commitment, thereby inspiring consumer interest [20].

Despite rebounding from the post-pandemic slump, the real estate sector in Bangkok faces its first-ever structural crisis in 2025 [7]. Home loan growth is expected to turn negative for the first time due to a massive oversupply, poor consumer purchasing power, and stricter bank lending policies. The downturn, which experts say is worse than the pandemic, began in the low-end housing market but is now spreading to mid- and high-end housing. Experts also say price wars could ensue as property’s long-term value is destroyed, eroding homeowners’ wealth and threatening to undermine the broader economy. As such, the Bank of Thailand (BOT) [21] has relaxed its LTV rule on new mortgage loans for a limited period from May 2025 to 1 July 2026, extending a lifeline to ailing parts of the Thai economy.

In the context of a developing country progressing towards future growth, Thailand has an increasingly strong and sustained demand for housing, with its main urban center being Bangkok. Thus, this research aims to examine the factors influencing the decision to purchase green housing [22], particularly the impact of marketing communication (MC), environmental awareness (EA), and service quality (SQY) on home purchase intention (PI) [23]. The results of this study will help businesses refine their marketing strategies, gain a deeper understanding of consumer needs, and ultimately establish a competitive edge in the growing market for green housing.

However, despite the growing global demand for sustainable housing, a significant research gap persists in the Thai context. While previous studies have often examined factors such as marketing communication, service quality, and environmental awareness in isolation or within different cultural contexts, there is a lack of integrated models that simultaneously examine how these factors interact to drive purchase intention. This study aims to fill this gap by developing and validating a comprehensive structural equation model (SEM) that elucidates the complex interrelationships between these constructs.

The primary objective of this research is to identify and validate the determinants of sustainable eco-friendly housing purchase intention (PI) among consumers in Bangkok, Thailand. Specifically, the study pursues the following research objectives (ROs):

RO1: To examine the direct effect (DE) of Marketing Communication (MC) on PI.

RO2: To investigate the direct effect (DE) of Service Quality (SQY) on PI.

RO3: To analyze the direct effect (DE) of Environmental Awareness (EA) on PI.

RO4: To develop and validate an SEM that predicts sustainable eco-friendly housing PI based on these three key constructs.

These objectives are operationalized through the following research questions (RQs):

RQ1: What is the DE of MC on the sustainable eco-friendly housing PI in Thailand?

RQ2: What is the DE of SQY on the sustainable eco-friendly housing PI in Thailand?

RQ3: What is the DE of EA on the sustainable eco-friendly housing PI in Thailand?

RQ4: To what extent can the integrated MC, SQY, and EA model explain the variance in sustainable eco-friendly housing PI in Thailand?

By addressing these questions, this study provides much-needed empirical evidence and a strategic framework for understanding the Thai sustainable housing market.

2. Literature Review and Hypotheses Development

2.1. Theoretical Foundation and Research Gap

Existing research on green real estate has often focused on either macroeconomic factors or general environmental attitudes [24,25], leaving a gap in understanding how targeted marketing efforts and perceived service quality interact with EA to shape specific purchase intentions in emerging economies like Thailand. This study addresses this gap by developing and testing a strong model that integrates MC, SQY, and EA to predict the PI [26] of sustainable eco-friendly housing [27].

Sustainable consumer behavior (SCB) adoption is a complex process influenced by cognitive, social, and contextual factors, particularly in high-investment sectors like real estate [28]. While established technology adoption models provide a foundation, the specific context of eco-friendly housing necessitates an integrated framework that considers both the practical aspects of the purchase and the underlying psychological drivers of sustainable behavior.

2.2. Marketing Communication (MC) and Purchase Intention (PI)

A growing body of research confirms that MC is a decisive driver of eco-friendly housing PI [29]. Similar studies have also shown that targeted sustainability messaging significantly enhances buyer interest in high-investment real estate sectors. This finding is consistent with Ho et al. [30], who demonstrated that integrated advertising, promotions, and public relations efforts reduce perceived risk and stimulate intention in residential green building markets.

Marketing communication is also an essential strategic tool for informing, persuading, and reminding consumers about products and services [31]. Effective communication is paramount in the context of eco-friendly housing, which represents benefits that are not immediately observable. To achieve this, modern digital marketing strategies, including social media platforms, now enable targeted messaging [32] that educates potential buyers on the long-term economic and environmental benefits of sustainable features [33], thereby reducing perceived uncertainty [34].

The Hierarchy of Effects Model suggests that communication moves consumers from awareness to knowledge to liking, preference, conviction, and finally, PI [35]. Considering a purchase like a house involves a multi-channel approach, including advertising, which builds brand awareness and educates on green features. It also involves sales promotions (e.g., green rebates, free smart home devices), which can provide immediate incentives and enhance public relations, thereby increasing credibility through third-party endorsements and certifications. Personal selling is critical, as it allows for detailed and customized explanations of complex sustainable technologies [32]. Finally, direct marketing facilitates timely and personalized follow-up [31]. When consumers are consistently exposed to clear and compelling messages about the unique value proposition of eco-friendly homes, their PI is likely to be strengthened [36]. We therefore hypothesize:

H1: 

Marketing communication (MC) has a positive DE on sustainable eco-friendly housing purchase intention (PI).

H2: 

Marketing communication (MC) has a positive DE on sustainable eco-friendly housing environmental awareness (EA).

H3: 

Marketing communication (MC) has a positive DE on sustainable eco-friendly housing service quality (SQY).

2.3. Service Quality (SQY) and Purchase Intention (PI)

Service quality (SQY) likewise strongly influences purchase decisions [37]. Additionally, perceived quality of low-carbon building features, especially assurance and reliability, enhances housing PI [19,38]. Shen et al. [19] further document that the higher environmental, social, and governance (ESG)—related service performance of property management firms translates into premium housing prices, underscoring the trust-building role of service excellence [36].

Moreover, SQY determines customer satisfaction and loyalty, particularly in service-intensive industries like real estate development [39]. It reflects the gap between customer expectations and perceptions of the service delivered [40]. In the context of purchasing a house, which is a high-involvement decision, SQY extends beyond the point of sale to encompass the entire customer journey.

The SERVQUAL model dimensions are highly relevant to PI [41]. These include tangibles (the aesthetic and functional quality of the show unit and sales office) [42]; assurance (the knowledge, courtesy, and trustworthiness of sales and project staff) [43]; reliability (the developer’s ability to deliver the promised project on time and with the advertised green specifications) [42]; responsiveness (the promptness and efficiency in addressing client inquiries and concerns); and empathy (the provision of individualized attention and understanding of the customer’s specific needs).

The assurance dimension is essential for a sustainable home, where consumers may have technical questions about energy efficiency or the materials used. High service quality also reduces perceived risk and builds developer trust, a critical precursor to forming a PI for a high-value asset. Thus, we propose:

H4: 

Service quality (SQY) has a positive DE on sustainable eco-friendly housing purchase intention (PI).

2.4. Environmental Awareness (EA) and Purchase Intention (PI)

Environmental Awareness (EA) remains a core antecedent of sustainable consumption [44] and has been identified as a significant predictor of sustainable housing PI in Germany, aligning with value–belief–norm theory. Moreover, Camilleri et al. [45] have reported that pro-environmental habits and concerns positively shape consumers’ willingness to adopt green products, including eco-friendly homes.

As a multi-dimensional construct, EA reflects an individual’s awareness of ecological issues, concern for the environment, and willingness to act pro-environmentally [46]. It is also grounded in value–belief–norm theory, which posits that individuals who hold strong biospheric values are more likely to engage in acts that reduce environmental harm [47].

This construct can also be broken down into cognitive (knowledge about environmental issues and the impact of one’s choices), affective (concern and attitude towards environmental protection), and behavioral (readiness to act) components [46,48]. In the housing market, environmentally aware consumers are not just buying a physical structure but investing in a lifestyle and a set of values [49]. They derive satisfaction from knowing their home reduces their carbon footprint, conserves resources, and contributes to a healthier ecosystem. This alignment between personal values and product attributes is a powerful motivator, with studies in Malaysia confirming that EA significantly influences the purchase decision for green residential properties [50,51]. Therefore, we hypothesize:

H5: 

Environmental awareness (EA) has a positive DE on sustainable eco-friendly housing purchase intention (PI).

2.5. The Integrated Research Model and Hypotheses

Collectively, recent studies advocate for an integrated model in which MC activates EA, while high SQY reinforces the message’s credibility, jointly amplifying purchase intention [52]. This parsimonious framework addresses the identified gap by linking communication, service excellence, and environmental values to sustainable housing adoption in emerging markets such as Thailand.

The integrated research model posits that MC, SQY, and EA are three pivotal antecedents that explain a significant portion of the variance in sustainable eco-friendly housing PI. The model suggests that while environmental values are a key driver, they are activated and channeled by effective market communications and reinforced by the confidence instilled through high-quality service interactions.

3. Materials and Methods

The present study adopted a quantitative, cross-sectional research approach to create a Structural Equation Model (SEM) explaining the sustainable eco-friendly housing PI among consumers in Thailand. A structured questionnaire and a sample of potential homebuyers were utilized in the study. Bangkok, Thailand, was selected as the study area because it is home to a dynamic real estate market and urban sustainability initiatives. While cross-sectional data provide valuable insights into the formation of intentions, causal relationships cannot be inferred from them. Therefore, future longitudinal research is recommended.

Further, the questionnaire included a consumer preferences section, which elicited information on the individuals involved in the decision-making process, the time of purchase, location and price preferences, and attitudes toward the importance of various eco-friendly features (rated on a 5-point Likert scale).

3.1. Conceptual Framework

This conceptual framework (Figure 1) postulates that three latent constructs, including marketing communication (MC), environmental awareness (EA), and service quality (SQY), have a direct and positive influence on consumers’ purchase intention (PI) in the sustainable housing market in Bangkok. The framework was developed based on an integrative literature review and verified by three academic experts in consumer behavior and green marketing to ensure that the constructs aligned with theory and fit within the contextual setting.

3.2. Population and Sampling

The target population consisted of potential homebuyers who had registered interest in or inquired about purchasing sustainable, eco-friendly homes in the Bangkok Metropolitan Region (BMR). This population segment was chosen because it reflects latent demand and awareness relevant to green housing adoption [22,53]. Bangkok was selected due to its high urban density and environmental initiatives. However, it is acknowledged that this approach limits the generalizability of the findings to the broader population of mainstream buyers who lack pre-existing interest.

3.3. Data Collection and Ethical Considerations

Data were collected in April and May 2025 using a mixed-mode approach, which combined structured face-to-face interviews and online surveys to enhance coverage and reduce sampling bias. Respondents were screened for inclusion criteria and provided informed consent prior to participation.

For in-person interviews, written consent was obtained under the authority of the Faculty of Hospitality Industry, Kasetsart University. For online participation, electronic consent was secured via Google Forms (“Yes, I agree to participate”). Respondents declining consent were automatically excluded.

All participants were informed of the study’s purpose, assured of anonymity, and reminded of their rights to voluntary participation. No identifying information was collected. Ethical approval was granted by the Kasetsart University Research Ethics Committee (KUREC) (Exemption No. KUREC-KPS68/109, 18 August 2025). The study complied with the Declaration of Helsinki (2013 revision) and the NRCT Guidelines for Behavioral and Social Science Research [54,55].

3.4. Sample Size Determination

The sample size was determined according to SEM guidelines. According to Legate et al. [56], an adequate SEM sample should comprise between 10 and 20 respondents per observed variable. With 16 observed indicators, a minimum of 320 respondents was required. The study achieved this target (n = 320), ensuring statistical power for model estimation and stable covariance structures. A priori power analysis (G*Power 3.1) confirmed sufficient sensitivity to detect medium effect sizes (f² = 0.15, α = 0.05, power = 0.95).

3.5. Sampling Technique

A multi-stage sampling strategy was applied to enhance representativeness and control for district-level variation (

Table 1. Sample distribution across selected Bangkok districts.

District	Population	Sample Size	%
1. Phra Nakhon	43,062	14	4.22%
2. Dusit	81,494	26	8.28%
3. Nong Chok	178,856	59	18.59%
4. Bang Rak	45,015	14	4.37%
5. Bang Khen	186,200	62	19.37%
6. Lat Krabang	178,424	59	18.59%
7. Yan Nawa	75,076	25	7.65%
8. Samphanthawong	20,777	6	1.89%
9. Phaya Thai	66,212	22	6.72%
10. Thon Buri	101,217	33	10.32%
Total	976,333	320	100%

Source: The authors.

).

Stage 1: Random Selection

All 50 Bangkok districts were assigned equal selection probability. Ten districts were randomly selected using a lottery method, with proportional allocation based on population size.

Stage 2: Purposive Screening

Within selected districts, potential respondents were screened using the qualifying question:

“Have you previously registered interest in purchasing an eco-friendly home?” (☐ Yes ☐ No)

Only “Yes” responses were included. This ensured that all participants were genuine potential buyers, not general residents.

3.6. Research Instrument and Measurement

Data were collected via a six-section structured questionnaire. Constructs were measured using five-point Likert scales (1 = strongly disagree to 5 = strongly agree). The original English questionnaire was translated into Thai using the DeepSeek V-3 AI model. This initial translation was then thoroughly reviewed and refined by a native English-speaking academic editor to ensure conceptual equivalence, linguistic accuracy, and contextual appropriateness. The instrument underwent pilot testing with 30 respondents for clarity, item comprehension, and scale reliability prior to full deployment (Cronbach’s α > 0.80 for all constructs).

The instrument underwent pilot testing with 30 respondents for clarity, item comprehension, and scale reliability prior to full deployment (Cronbach’s α > 0.80 for all constructs).

Section 1: Demographic variables (gender, age, marital status, education, income, occupation, housing type, living arrangement).

Section 2: Marketing Communication (MC)—5 items adapted from Mocanu and Szkal [34] and Armstrong et al. [31]; reliability α = 0.86.

Section 3: Service Quality (SQY)—5 items (tangibles, reliability, responsiveness, assurance, empathy); α = 0.87 [53].

Section 4: Environmental Awareness (EA)—3 items reflecting cognitive, affective, and behavioral dimensions [46,48]; α = 0.89.

Section 5: Purchase Intention (PI)—3 items (satisfaction, trust, word-of-mouth intention) [50]; α = 0.88.

3.7. Data Analysis Strategy

The normality of the data was assessed prior to SEM analysis. The skewness and kurtosis values for all observed variables fell within the acceptable range of ±2, indicating no severe departure from univariate normality. Data analysis followed a two-step SEM approach using LISREL 9.10.

3.7.1. Step 1: Measurement Model Validation

Confirmatory factor analysis (CFA) was used to assess construct reliability, convergent validity, and discriminant validity. Composite reliability (CR > 0.7), average variance extracted (AVE > 0.5), and Fornell–Larcker criteria were used to verify validity [56]. Harman’s single-factor test and variance inflation factors (VIF < 3.0) confirmed the absence of significant common method bias.

3.7.2. Step 2: SEM Assessment

The hypothesized model was tested for the DEs of MC, SQY, and EA on PI. Model fit was evaluated using χ²/df (<3.0), comparative fit index (CFI > 0.90), Tucker–Lewis Index (TLI > 0.90), and root mean square error of approximation (RMSEA < 0.08). Bootstrapping with 5000 resamples was employed to estimate standard errors and path stability, thereby enhancing the robustness of the results.

4. Results

4.1. The Sample’s Descriptive Statistics

Three hundred twenty prospective or current homeowners participated in a survey regarding sustainable, eco-friendly housing PI in Thailand (

Table 2. Demographic characteristics of the sample (n = 320).

Characteristic	Category	n	%
Gender	Male	156	48.75%
	Female	164	51.25%
Age	18–20 years	37	11.56%
	21–40 years	125	39.06%
	41–56 years	78	24.38%
	57–75 years	57	17.82%
	Over 75 years	23	7.18%
Relationship Status	Single	143	44.68%
	Married	153	47.82%
	Divorced	24	7.50%
Education	Below Bachelor’s Degree	23	7.18%
	Bachelor’s Degree	174	54.38%
	Above Bachelor’s Degree	123	38.44%
Monthly income (THB)	<20,000 THB ≈ $556	5	1.56%
	20,001 THB–40,000 THB ≈ $556–$1100	56	17.50%
	40,001 THB–60,000 THB ≈ $1100–$1667	124	38.75%
	60,001 THB–80,000 THB ≈ $1667–$2222	68	21.25%
	80,001 THB–100,000 THB ≈ $2222–$2778	45	14.07%
	>100,000 THB ≈ $2778+	22	6.87%
Occupation	Freelance/Self-employed	58	18.12%
	Government/State Enterprise	72	22.51%
	Private Company Employee	96	30.00%
	Business Owner	94	29.37%
Current Housing Type	Single-detached House	136	42.50%
	Townhouse	76	23.75%
	Twin House	42	13.13%
	Condominium	66	20.62%
Home Ownership Status	Owner (Self/Family)	184	57.50%
	Non-owner (Renting, etc.)	136	42.50%
Living Arrangements	Alone	45	14.06%
	With Family	255	79.68%
	With Friends	20	6.26%

Note. During the survey, the Thai baht to USD conversion rate was approximately 1 USD ≈ = 36 THB. Source: The authors.

). The participants in the sample were almost equally divided by gender, with 51.25% female and 48.75% male. Moreover, the largest age group was 21–40 (39.06%). The majority of respondents (92.82%) held a bachelor’s degree or higher, were married (47.82%), and worked as employees of private companies (30.00%) or business owners (29.37%). A large portion, 38.75%, had an income of 40,001–60,000 THB per month, equivalent to $1100–$1667. Most respondents lived in a single-detached house (42.50%), which they or their family owned (57.50%), and the vast majority lived with their family (79.68%).

4.2. Consumer Preferences for Eco-Friendly Homes

Participants were asked about their decision-making process, buying timeframe, location, price sensitivity, and which specific environmentally friendly forms were important to them. The results are presented in

Table A1. Consumer opinions on purchasing eco-friendly homes (n = 320).

Opinions	n	%
Q2.1: If you decided to purchase an eco-friendly home, who would be involved in the decision-making process with you?
Only myself	155	48.43
Family	157	49.06
Friends	5	1.57
Partner	3	0.94
Totals	320	100.00
Q2.2. When would you consider purchasing an eco-friendly home?
Within the next year	120	37.50
In 1–3 years	78	24.37
In 4–5 years	54	16.87
In more than 5 years	32	10.00
Not sure/Undecided	36	11.26
Totals	320	100.00
Q2.3. In which location would you prefer to buy an eco-friendly home?
Other provinces (outside the Bangkok metropolitan area)	54	16.87
Suburban area of Bangkok (Outer Bangkok)	120	37.51
The Bangkok area is connected to the city center	84	26.25
Central Bangkok area	62	19.37
Totals	320	100.00
Q2.4. What is a suitable price point for an eco-friendly home compared to a standard single house?
A similar price point	106	33.13
10% higher	135	42.14
20% higher	23	7.19
30% higher	14	4.38
40% higher	17	5.32
50% higher	12	3.75
Price is not a primary concern	13	4.07
Totals	320	100.00

Source: The authors.

and

Table A2. Level of importance of eco-friendly home features (most to least).

Environmentally Friendly Housing Components	M (Mean)	SD
Building materials that help reduce heat	4.35	0.80
Ventilation structure	4.33	0.81
Use of environmentally friendly materials	4.32	0.81
Water-saving toilets and plumbing systems	4.32	0.83
Roof insulation	4.28	0.82
Sufficient green space	4.27	0.82
Solar roof panels	4.24	0.81
Features that absorb indoor pollution	4.24	0.84

Note: 5-point scale where 5 = Strongly Agree (Most Important) and 1 = Strongly Disagree (Least Important). Source: The authors.

of Appendix A. The majority of those who made purchase decisions for an eco-friendly home were family members (49.06%) and buyers who made decisions independently (48.44%) (Table A1). Immediate interest in this market was demonstrated, with 37.50% indicating they would purchase an eco-friendly home within one year. The most preferred location was the suburban area of Bangkok (37.50%). Regarding price sensitivity, the largest group of respondents (42.19%) was willing to pay a premium of 10% for an eco-friendly home compared to a standard house.

The importance ratings of various eco-friendly features are presented in Table A2. The overall mean (M) score was 4.29 (SD = 0.65), indicating that respondents, on average, rated all features as being of “Highest” importance. The top three most important features were ‘building materials that help reduce heat’ (M = 4.35), ‘ventilation structure’ (M = 4.33), and ‘the use of environmentally friendly materials’ (M = 4.32).

4.3. Descriptive Statistics and Measurement Model Validation

Before testing the structural model, the measurement model was validated. As shown in

Table 3. Descriptive statistics of variables.

Latent & Observed Variables	M	SD
Marketing communication (MC)	4.08	0.70
Advertising (MC1)	4.10	0.70
Sales promotion (MC2)	4.04	0.75
Public relations (MC3)	4.07	0.71
Personal selling (MC4)	4.07	0.73
Direct marketing (MC5)	4.11	0.71
Service quality (SQY)	4.12	0.68
Tangibles (SQY1)	4.13	0.73
Reliability (SQY2)	4.19	0.75
Responsiveness (SQY3)	4.09	0.72
Assurance (SQY4)	4.04	0.76
Empathy (SQY5)	4.14	0.74
Environmental awareness (EA)	4.07	0.74
Environmental knowledge (EA1)	4.04	0.75
Environmental attitude (EA2)	4.12	0.74
Environmental behavior (EA3)	4.06	0.75
Purchase intention (PI)	4.18	0.72
Purchase satisfaction (PI1)	4.14	0.74
Trust (PI2)	4.19	0.74
Word-of-Mouth (PI3)	4.21	0.70

Note. All mean values were evaluated as ‘highest’. Source: The authors.

, all latent constructs demonstrated high mean scores for their indicator items, ranging from 4.07 to 4.18 on a 5-point scale. This suggests respondents generally agreed with all measured variables. The constructs of MC, SQY, EA, and PI were all perceived at a high level. The “word-of-mouth intention” indicator of PI had the highest average score (M = 4.21, SD = 0.70), indicating that satisfied potential buyers are highly willing to recommend eco-friendly homes to others.

4.4. Assessment of Reliability and Validity

To verify the robustness of the measurement model, convergent and discriminant validity were assessed. As shown in

Table 4. Confirmatory factor analysis (CFA), reliability, and convergent validity results.

Latent Variable	OV	λ	SE	t-Value	R2	CR	AVE
MC	MC1	0.82 **	0.05	14.60	0.60	0.92	0.69
	MC2	0.86 **	0.06	14.74	0.63
	MC3	0.81 **	0.06	13.42	0.59
	MC4	0.83 **	0.06	13.57	0.61
	MC5	0.84 **	0.05	13.63	0.61
SQY	SQY1	0.88 **	-	-	0.65	0.93	0.72
	SQY2	0.80 **	0.07	13.02	0.59
	SQY3	0.85 **	0.08	13.74	0.62
	SQY4	0.84 **	0.08	13.65	0.62
	SQY5	0.87 **	0.07	14.82	0.63
EA	EA1	0.79 **	-	-	0.57	0.83	0.62
	EA2	0.82 **	0.07	14.62	0.61
	EA3	0.76 **	0.07	11.72	0.54
PI	PI1	0.92 **	-	-	0.67	0.96	0.90
	PI2	0.95 **	0.07	15.62	0.68
	PI3	0.97 **	0.08	15.78	0.70

Note: ** p < 0.01, ov = observed variable, λ indicates standardized component weights. <-> Indicates mandatory parameters; therefore, SE and t values are not reported. CR = Composite Reliability, AVE = Average Variance Extracted. Source: The authors.

, all constructs demonstrated strong convergent validity. The Average Variance Extracted (AVE) for each construct exceeded the recommended threshold of 0.50, and the Composite Reliability (CR) values were all above 0.80, confirming the internal consistency and reliability of the constructs [56,57].

Discriminant validity was established using the Fornell-Larcker criterion [58]. As presented in

Table 5. Discriminant validity assessment using the Fornell-Larcker criterion.

Constructs	MC	SQY	EA	PI
MC	0.83
SQY	0.68 **	0.85
EA	0.62 **	0.59 **	0.79
PI	0.64 **	0.43 **	0.57 **	0.95

Note: ** p < 0.01; Diagonal elements (in bold) are the square roots of the AVE. Off-diagonal elements are the correlations between constructs. Discriminant validity is established as the square root of AVE for each construct is greater than its correlation with other constructs.

, the square root of the AVE for each construct (shown on the diagonal) was greater than its highest correlation with any other construct (off-diagonal values), confirming that the constructs are distinct from one another.

The overall fit of the measurement model was excellent across all standard indices (χ²/df = 0.73, RMSEA = 0.00, CFI = 0.99, GFI = 0.99), surpassing established benchmarks for excellent fit [59,60,61]. The Cronbach’s Alpha values for all constructs ranged from 0.86 to 0.89, indicating high internal consistency reliability [62]. This confirms that the measured variables reliably represent their underlying theoretical constructs.

4.5. Correlation Matrix Between Latent Variables Testing

The correlation matrix between latent variables (

Table 6. Correlation matrix between latent variables.

Constructs	Mean	SD	Skewness	Kurtosis	MC	SQY	EA	PI
MC	4.08	0.70	−0.22	−4.49	1
SQY	4.12	0.68	−0.35	−4.75	0.68 **	1
EA	4.07	0.74	−0.38	−5.17	0.62 **	0.59 **	1
PI	4.18	0.72	−1.09	−4.89	0.64 **	0.43 **	0.57 **	1

Note: ** p < 0.01, Source: The authors.

) showed significant positive relationships among all constructs, with correlation coefficients ranging from 0.43 to 0.68. These correlations provide preliminary evidence for the proposed relationships in the structural model. The results also confirmed discriminant validity, as the square root of the AVE for each construct was greater than its correlations with other constructs.

4.6. Assessment of the Structural Model and Hypothesis Testing

The structural equation model (SEM) was examined to test the hypothesized relationships. The model perfectly fit the data (χ²/df = 0.73, RMSEA = 0.00, CFI = 0.99, GFI = 0.99). The model’s explanatory power was strong, explaining 75% (R² = 0.75) of the variance in PI, as shown in

Table 7. Standardized direct (DE), indirect (IE), and total (TE) effects among constructs.

Dependent Variable	R2	Effect	Causal Variable
			SQY	EA	MC
EA	0.52	DE	-	-	0.46 **
		IE	-	-	-
		TE	-	-	0.46 **
SQY	0.68	DE	-	0.48 **	0.45 **
		IE	-	-	-
		TE	-	0.48 **	0.45 **
PI	0.75	DE	0.56 **	0.48 **	0.52 **
		IE	-		0.32 **
		TE	0.56 **	0.48 **	0.84 **

Note: ** p < 0.01. According to the research hypothesis, the symbol <-> means no parameter line. Source: The authors.

.

Path analysis provided strong support for all five direct-effect hypotheses.

H1, H2, H3: Marketing communication (MC) demonstrated a strong DE on Purchase Intention (PI) (β = 0.52, p < 0.01), supporting H1. It also significantly directly affected SQY (β = 0.45, p < 0.01; H2) and EA (β = 0.46, p < 0.01; H3).

H4 and H5: Both SQY (β = 0.56, p < 0.01; H4) and EA (β = 0.48, p < 0.01; H5) had significant positive DEs on PI.

4.7. Mediation Analysis (Indirect Effects)

A bootstrapped mediation analysis was conducted to explore the indirect (IE) pathways through which MC influences PI. The results, presented in

Table 8. Bootstrapped indirect effects (IEs).

Mediation Path	IE (β)	95% CI (Lower–Upper)	p-Value	Results
MC → SQY → PI	0.25	0.18–0.34	<0.001	Significant
MC → EA → PI	0.22	0.15–0.31	<0.001	Significant
MC → SQY → EA→ PI	0.10	0.05–0.17	0.001	Significant

Note: Bootstrapped with 5000 samples; All effects standardized.

, confirm the presence of significant mediation effects.

The analysis reveals that MC directly fosters purchase intention and sequentially enhances perceptions of SQY and EA. Specifically, the significant IEs demonstrate that:

SQY is a significant mediator between MC and PI (MC → SQY → PI);

EA is a significant mediator between MC and PI (MC → EA → PI);

There is a significant serial mediation path where MC improves SQY, thereby strengthening EA, which ultimately leads to higher PI (MC → SQY → EA → PI).

This underscores that the effect of MC is partially channeled through building positive service perceptions and elevating EA.

4.8. Theoretical Implications of the Final Model

The final model (Figure 2) integrates all findings. The results align with the Theory of Planned Behavior (TPB), where external information (MC) shapes attitudes (EA) and perceived behavioral control (linked to SQY as a proxy for developer reliability), which in turn drives intention (PI) (

Table 9. Hypothesis testing.

Hypotheses Statements	Coef.	t-Test	Supported
H1: Marketing Communication (MC) has a positive DE on sustainable eco-friendly housing Purchase Intention (PI).	0.52	10.42 **	confirmed
H2: Marketing Communication (MC) has a positive DE on Sustainable Eco-Friendly Housing Environmental Awareness (EC).	0.46	9.77 **	confirmed
H3: Marketing Communication (MC) has a positive DE on sustainable eco-friendly housing Service Quality (SQY).	0.45	9.36 **	confirmed
H4: Service Quality (SQY) has a positive DE on sustainable eco-friendly housing Purchase Intention (PI).	0.56	10.85 **	confirmed
H5: Environmental Awareness (EC) has a positive DE on sustainable eco-friendly housing Purchase Intention (PI).	0.48	10.14 **	confirmed

Note: ** p < 0.01, Source: The authors.

). The strong TE of MC (TE = 0.84) highlights its role not just as a direct persuader but as a fundamental antecedent that activates and strengthens other key drivers, namely service perceptions and environmental values. The fact that SQ had the strongest DE on PI underscores that tangible reliability remains paramount for high-investment decisions, even for environmentally motivated consumers.

5. Discussion

This study developed and validated a structural equation model (SEM) to explain purchase intention (PI) for sustainable eco-friendly housing in Thailand. The integrated model, incorporating Marketing Communication (MC), Service Quality (SQY), and Environmental Awareness (EA), demonstrated strong predictive power. The discussion is organized to interpret the key findings, relate them to established theory, and elucidate their broader implications.

5.1. Interpretation of Key Findings and Theoretical Integration

5.1.1. The Pivotal Role of Service Quality (SQY)

The finding that Service Quality (SQY) was the strongest direct predictor of purchase intention (β = 0.56) underscores a critical insight for the sustainable housing market: even for environmentally motivated consumers, pragmatic trust in the developer is paramount. This aligns strongly with the SERVQUAL framework [40,41]. Purchasing a house is a high-involvement decision fraught with perceived risk. High SQY—manifested through reliability (delivering on promises), assurance (knowledgeable staff), and responsiveness—signals developer competence and integrity. This reduces the perceived risk associated with a major financial commitment, giving buyers the confidence to proceed and effectively acting as a form of perceived behavioral control within the Theory of Planned Behavior (TPB). Our results suggest that for Thai consumers, excellent service is not just a value-add but a fundamental prerequisite that validates the entire purchase decision.

5.1.2. The Direct and Catalytic Power of Marketing Communication (MC)

Marketing Communication (MC) demonstrated a significant DE on PI (β = 0.52), confirming its role as a primary driver. In the context of eco-friendly housing, where benefits are often latent (e.g., energy efficiency, indoor air quality), effective MC is essential to educate consumers and build a compelling value proposition [31,34]. This finding aligns with the Hierarchy of Effects Model [35], which posits that communication progresses consumers from awareness to conviction. Furthermore, MC’s significant direct effects on both SQY (β = 0.45) and EA (β = 0.46) reveal its catalytic function. It shapes perceptions of service quality before a sale is even made (e.g., through professional marketing materials and responsive sales agents) and plays an educational role in elevating EA. Thus, MC operates not only as a direct persuader but also as an antecedent that cultivates the other two key drivers in the model.

5.1.3. The Influence of Environmental Awareness (EA)

The significant direct effect of Environmental Awareness (EA) on PI (β = 0.48) confirms that value-expressive motives drive a segment of the Thai housing market. This finding strongly supports the Value–Belief–Norm (VBN) theory [63], which posits that individuals holding strong biospheric values are more likely to undertake pro-environmental actions [47]. For these consumers, purchasing a sustainable home is not merely a transaction but an act of aligning their consumption with their personal values concerning environmental stewardship [49]. This transforms the decision from a purely economic calculus to one involving normative beliefs and self-identity, providing a powerful motivational force that complements the more pragmatic drivers of SQY and MC.

5.2. Synthesis: The Integrated Model and Its Theoretical Contribution

The integrated model’s high explanatory power (R² = 0.75) robustly demonstrates that isolated factors do not drive purchase intention, but by a synergistic interplay of cognitive (MC), pragmatic (SQY), and affective (EA) drivers. The most profound insight from this synthesis is the central, multifaceted role of Marketing Communication. Its total effect (TE = 0.84) was the strongest in the model, achieved through its significant direct influence on PI and its substantial indirect effects mediated through both SQY and EA. This indicates that strategic communication is the linchpin of the model: it directly persuades, simultaneously builds the perception of service reliability, and actively cultivates the environmental values that make the product appealing in the first place.

While individual constructs are established in the literature, this study’s theoretical contribution lies in empirically validating a specific configuration of relationships within the unique context of Thailand’s emerging eco-housing market. It demonstrates that in a high-involvement, collectivist culture, successful market penetration requires an approach where communication strategies are designed not just to inform, but to build trust and resonate with deeper environmental values simultaneously.

5.3. Practical Implications

The findings offer a clear and actionable strategic framework for developers and policymakers.

5.3.1. For Developers: Communicate Value Comprehensively

Move beyond technical specifications. Craft multi-channel campaigns that articulate both the tangible benefits (e.g., cost savings from energy efficiency) and the intangible, value-expressive benefits (e.g., a healthier lifestyle, ethical consumption) of eco-friendly homes.

5.3.2. Operationalize Service Excellence

Prioritize training for sales and project staff on the reliability and assurance dimensions of SERVQUAL. Ensure flawless execution from initial contact through to post-purchase service, building the critical trust that directly translates to purchase intention.

Authentically Engage Environmental Values: Use “green storytelling” in marketing materials and show units. Pursue credible third-party certifications (e.g., GREEN MARK, LEED) to validate environmental claims and connect with the segment of buyers motivated by Environmental Awareness.

5.3.3. For Policymakers

Support the development and promotion of clear, national standards and certification for sustainable housing. This reduces information asymmetry, increases consumer trust, and helps accelerate the maturation of the green building market.

6. Conclusions

This study successfully constructed and validated a comprehensive model that explains the purchase intention for sustainable, eco-friendly housing in Thailand. The findings clearly demonstrate that purchase intention is not the result of a single factor, but rather emerges from the synergistic effect of three key drivers: strategic marketing communication (SMC), high service quality (HSQ), and a deeply held environmental awareness (DEA).

The primary theoretical insight of this research is the elucidation of the central, multifaceted role of MC. It acts not only as a direct persuader but also as a fundamental catalyst that enhances perceptions of service quality and elevates environmental awareness, thereby exerting the strongest total influence on purchase intention.

While the individual constructs are established in the literature, this study’s main contribution lies in empirically validating this specific integrated model within the understudied context of Thailand’s emerging eco-housing market. The results provide a robust empirical framework for understanding the decision-making of a crucial segment of Thai homebuyers. For practitioners, the findings offer a clear mandate: successful market penetration requires an integrated strategy that simultaneously leverages persuasive communication, demonstrates unwavering service reliability, and authentically connects with the environmental values of modern consumers.

7. Theoretical and Practical Implications

From a practical standpoint, our findings offer a clear and actionable strategic roadmap for industry stakeholders:

For Developers: Making successful homes does not end at building “green” houses. This customer segment requires a new strategic orientation that:

Communicates value comprehensively: Craft multi-channel campaigns that articulate both the tangible benefits (e.g., “building materials that reduce heat by X%, leading to Y% savings on air conditioning”) and the intangible lifestyle value (e.g., “live in harmony with nature for a healthier family”) of eco-friendly features.

Operationalizes Service Excellence: Focus on the ‘Reliability’ (delivering projects on time with promised green specifications) and ‘Assurance’ (training sales staff to be knowledge experts on sustainable technologies) dimensions of SERVQUAL to build trust and reduce perceived risk.

Authentically Engages Environmental Values: Utilize “green storytelling” in show units, pursue credible third-party certifications, and provide transparent data on environmental impact to align with buyers’ values.

For policymakers, this result highlights the need to support public awareness campaigns and help develop comprehensive certification standards for environmentally friendly housing projects, thereby increasing consumer trust, reducing information asymmetry, and accelerating the market adoption of sustainable building practices.

8. Future Research

Future research using a random sample split can be performed to test the model. Secondly, the data are drawn from Thailand. Cross-cultural studies would be beneficial to test the model in other emerging economies. Thirdly, a longitudinal study can be undertaken to examine how the intention is executed and how prospective buyers make decisions regarding sustainable real estate, thereby depicting their decision-making process more accurately.