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Article

What Drive Residents to Adopt the Concept of Green Housing in Nanjing, China

by
Yuxiao Liu
1,
Xiaobin Li
2,
Hao Feng
2 and
Rong Zhu
2,*
1
College of Art & Design, Design and Architecture, Nanjing Tech University, Nanjing 211800, China
2
School of Design, Jiangnan University, Wuxi 214122, China
*
Author to whom correspondence should be addressed.
Buildings 2026, 16(2), 335; https://doi.org/10.3390/buildings16020335
Submission received: 12 December 2025 / Revised: 5 January 2026 / Accepted: 9 January 2026 / Published: 13 January 2026
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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Abstract

Although green housing is widely regarded as an effective solution to energy and environmental challenges, its actual rate of adoption remains lower than expected. In the context of increasingly prominent sustainable development goals, promoting residents’ adoption of green housing has become a key issue in advancing sustainable transformation within the housing sector. Consequently, enhancing residents’ willingness to adopt green housing is critical to its broader diffusion. Drawing on diffusion of innovation theory, attitude theory, and perceived value theory, this study develops a multidimensional integrated model to identify factors influencing the adoption of green housing. The model examines how the innovation attributes of green housing and residents’ psychological evaluations jointly shape adoption intention. A questionnaire survey was conducted among 387 residents in Nanjing, China, and the data were analysed using partial least squares modelling. The results indicate that the five attributes derived from diffusion of innovation theory are significant antecedents of residents’ attitudes. Relative advantage, compatibility, trialability, and observability exert significant positive effects on residents’ attitudes toward adopting green housing, with relative advantage emerging as the most influential factor. Complexity has a negative, though comparatively weaker, effect on residents’ attitudes toward green housing adoption. Residents’ attitudes and perceived value are identified as significant predictors of green housing adoption intention. These findings contribute to a clearer understanding of residents’ green housing adoption intentions for both researchers and practitioners. More importantly, the study offers general policy and managerial implications for governments and developers seeking to enhance the uptake of green housing.

1. Introduction

Against the backdrop of escalating climate change, energy crises, and environmental pressures, green buildings are regarded as vital vehicles for achieving energy conservation, emission reduction, enhanced residential health, and sustainable development [1]. They also represent one of the key pathways toward achieving the global “dual carbon” goals [2]. International green building certification systems, including the Leadership in Energy and Environmental Design in the United States, the Building Research Establishment Environmental Assessment Method in the United Kingdom, and the German Sustainable Building Council in Germany, have continuously promoted the global diffusion of green building principles, establishing both technical standards and institutional frameworks for sustainable built environments [3].
Green buildings are generally regarded as a broad concept encompassing multiple building uses, including green residential buildings, green commercial buildings, and other functional building types [4]. Green housing is an emerging concept within the construction sector, referring to residential buildings designed to promote harmonious coexistence between humans and nature while maximising resource efficiency [5]. In recent years, the concept of green housing has gained increasing prominence in China and has become a dominant trend in new residential development [6]. The definition of green housing in China is primarily grounded in national green building policies and certification systems. Since 2008, the Ministry of Housing and Urban–Rural Development of China has formally implemented the Green Building Evaluation and Labeling System. Under the Green Building Evaluation Standard (GB/T 50378-2019) [7], buildings are comprehensively assessed across multiple dimensions, including land use and outdoor environment, energy efficiency, water conservation, material efficiency, indoor environmental quality, and operational management. Based on the evaluation scores, projects are classified into one-star, two-star, and three-star levels, with the three-star rating representing the highest level of green performance under the current standard [8]. At present, green building technologies have become increasingly mature, achieving significant progress in energy-efficient construction [9,10,11], renewable energy utilization [12,13], and the development of evaluation standards and assessment systems [14,15,16]. Building on this foundation, scholarly attention has gradually shifted from purely technical considerations to issues of green housing promotion and adoption, examined from multidisciplinary perspectives including architecture, management, and psychology. Empirical studies across different countries and regions indicate that green housing faces a set of common constraints under varying institutional contexts and stages of development, and propose corresponding pathways for improvement. For example, Williams and Dair [17], drawing on five recently completed projects in the United Kingdom, identified twelve key categories of barriers that hinder the implementation of sustainable buildings during the development process. Using Tanzania as a case study, Ikingura et al. [18] identified major obstacles to the diffusion of green building technologies and proposed corresponding priority strategies for promotion. From a policy research perspective, Azhgaliyeva and Rahut [19] systematically reviewed the barriers to green building promotion, along with corresponding solutions and policy instruments. Moreover, studies adopting a multi-stakeholder perspective have further revealed structural differences among participating actors in the promotion of green housing. For instance, Cohen et al. [20], using Israel as a case, systematically analysed key factors constraining the development and upgrading of green buildings from the perspectives of homebuyers, developers, government agencies, local authorities, and standard-setting bodies. Focusing on China, Hu et al. [21] demonstrated that the decision-making behaviours and influencing factors of central governments, local governments, and developers differ substantially in the promotion of green buildings. In recent years, research has increasingly incorporated perspectives from environmental psychology, shifting attention toward residents’ adoption intentions. For example, Tunji-Olayeni et al. [22] applied the theory of planned behaviour to evaluate behavioural factors influencing South African professionals’ intentions to adopt green buildings. Cai et al. [23] investigated residents’ intentions to purchase green housing in three emerging first-tier cities in China, offering new insights into how local cultural and psychological factors shape green housing decisions. Using Tanzania as a representative developing economy, Mushi et al. [24] explored the conceptual factors influencing intentions to promote and adopt green buildings. Although existing studies have identified multiple determinants of green housing adoption across countries and stakeholder groups, the literature remains largely dominated by single-factor or single-perspective analyses, with relatively few studies systematically integrating multiple housing attributes into a comprehensive framework. In particular, how different attributes of green housing jointly influence adoption intentions through individuals’ psychological evaluation processes remains insufficiently examined through systematic empirical analysis. For cities in which green housing has already entered the occupancy stage, this internal mechanism has yet to be clearly elucidated and therefore warrants further in-depth investigation.
The diffusion of innovations theory provides a well-established framework for understanding how individuals accept or reject new technologies, products, and lifestyles. It posits that relative advantage, compatibility, complexity, trialability, and observability are the key determinants of individual attitudes and adoption behaviors [25]. As an emerging residential model, green housing embodies distinct innovative attributes, differing from conventional housing in material systems, energy models, environmental technologies, and health concepts [26]. Its adoption process is influenced by factors such as relative advantage, compatibility, complexity, trialability, and observability, thereby offering a coherent theoretical lens for explaining why residents choose to adopt or reject green housing. The diffusion of innovations theory has been extensively applied in studies of user behavior across fields such as renewable energy technologies [27,28], autonomous vehicles [29,30], and artificial intelligence [31,32]; however, its systematic application to green housing adoption remains limited. Building on this foundation, this study adopts the diffusion of innovation theory as its core analytical framework and takes Nanjing, China, as a case study to construct a mechanism model of residents’ green housing adoption. Using partial least squares structural equation modeling, the study systematically examines the effects of relative advantage, compatibility, complexity, trialability, and observability on residents’ attitudes, perceived value, and adoption intention. Unlike prior studies that predominantly analyze green housing adoption intention from a single-factor perspective, this research adopts a holistic approach to clarify how multiple innovation attributes jointly shape residents’ adoption intentions. The results not only confirm the established relationships between innovation attributes and adoption intention reported in the literature, but also, through comparative path analysis, reveal differences in the magnitude and relative importance of various innovation attributes in shaping attitudes, perceived value, and adoption intention. The contribution of this study extends beyond the regional case itself by offering a systematic model-based expansion of green housing adoption mechanisms. It provides a novel analytical framework for applying diffusion of innovation theory in the context of sustainable buildings and offers transferable insights for research on other forms of sustainable housing and green technology adoption.

2. Theoretical Background and Hypotheses Development

2.1. Research Theories

This study integrates the diffusion of innovations theory, attitude theory, and perceived value theory to construct a conceptual model of residents’ intention to adopt green housing, thereby identifying the key factors and pathways influencing their adoption behavior (Figure 1).
The diffusion of innovations theory was first proposed by Everett Rogers [33]. The theory describes the process by which new ideas, services, or technologies spread among the general population, explaining how target groups adopt these innovations over time. The theory posits that the adoption of new technologies is influenced by five key attributes: relative advantage, compatibility, complexity, trialability, and observability. This framework provides a comprehensive theoretical foundation for explaining user adoption processes and for understanding the factors that shape individual choices [34]. Previous studies have further identified additional factors influencing adoption behavior, including consumer attitudes and perceived value [35,36]. The attitude theory, proposed by Bagozzi [37], defines attitude as the degree to which an individual holds a favorable or unfavorable evaluation of a particular behavior. It plays a crucial role in describing people’s acceptance of new technologies, as innovation attributes directly influence attitudes and behavioral intentions [38,39]. Attitude has also been demonstrated to be one of the most important predictors of behavioral intention and actual behavior. Perceived value refers to consumers’ subjective assessment and overall evaluation of the utility of a product or service. A positive attitude exerts a favorable influence on perceived value. Moreover, perceived value serves as an important antecedent of behavioral intention, as consumers tend to adopt and continue using products or services they perceive as offering higher value [40].

2.2. Hypotheses Development

Drawing on the diffusion of innovations theory, this study integrates five key innovation attributes (relative advantage, compatibility, complexity, trialability, and observability) together with residents’ attitudes, perceived value, and adoption intentions to systematically reveal the mechanisms that shape green housing adoption behavior. This study aims to clarify how the innovation characteristics of green housing shape residents’ attitudes and perceived value, which in turn influence their intentions to adopt such housing. Based on the above theoretical analysis, several research hypotheses are proposed as follows.

2.2.1. Relative Advantage and Attitude Toward Green Housing Adoption

Relative advantage refers to the degree to which an innovation is perceived as superior to existing alternatives [41]. Previous studies have identified relative advantage as one of the most influential predictors of technology adoption [42]. In the context of green housing, relative advantage represents the perceived benefits of green homes compared with conventional ones. Empirical evidence suggests that when green housing demonstrates clear superiority in energy efficiency, residential health, environmental quality, or long-term economic benefits, residents are more likely to develop positive attitudes and stronger intentions to adopt it [3]. Based on these findings, this study proposes the following hypothesis:
H1. 
Relative advantage has a positive effect on residents’ attitudes toward adopting green housing.

2.2.2. Compatibility and Attitude Toward Green Housing Adoption

Compatibility refers to the degree to which an innovation aligns with an individual’s existing beliefs, values, and prior experiences [33,43]. Numerous studies have indicated that when individuals perceive a technology as consistent with their values and beliefs, they are more likely to adopt and use it [44]. The study by Khan et al. [45] confirmed that compatibility plays a crucial role in encouraging users to adopt new technologies and products. Accordingly, when green housing aligns with residents’ existing preferences in spatial design, lifestyle convenience, cultural values, or environmental consciousness, their attitudes toward green housing are likely to improve significantly. Therefore, this study proposes the following hypothesis:
H2. 
Compatibility has a significant positive effect on residents’ attitudes toward adopting green housing.

2.2.3. Complexity and Attitude Toward Green Housing Adoption

Complexity is commonly defined as the degree of difficulty perceived by users when engaging with an innovation or technology, encompassing their overall assessment of technological maturity, usage burden, and associated costs [33]. Prior studies emphasize that when an innovation is perceived as excessively complex, users are more likely to experience anxiety or avoidance tendencies, thereby reducing their willingness to adopt it [46]. A substantial body of empirical research consistently identifies technological complexity as a major barrier to diffusion, as it increases users’ difficulty in understanding and effectively using the technology [47,48]. Green housing incorporates multiple innovations, including energy-efficient technologies, intelligent environmental control systems, novel materials, and supporting equipment. The installation, commissioning, and subsequent maintenance of these systems may increase residents’ cognitive demands, financial burdens, and operational difficulty, thereby shaping their adoption attitudes. Accordingly, this study conceptualizes complexity as a unidimensional perceptual construct, referring to residents’ overall subjective perception of the difficulty associated with understanding, using, maintaining, and bearing the related costs of green housing. On this basis, the following hypothesis is proposed:
H3. 
Complexity has a significant negative effect on residents’ attitudes toward adopting green housing.

2.2.4. Trialability and Attitude Toward Green Housing Adoption

Trialability refers to the extent to which an innovation can be tested on a limited basis, allowing users to experience it firsthand before full adoption, thereby reducing uncertainty [33]. Previous studies suggest that trialability helps potential users reduce doubts and strengthen their direct understanding and attitudes toward the advantages of innovative technologies [49,50]. When residents gain real-world experience through model home visits, smart system demonstrations, environmental monitoring displays, or immersive showcase environments, they are better able to perceive the advantages of green housing in terms of energy efficiency, comfort, and health-related performance, thereby enhancing their attitudes toward adoption and purchase intentions [51]. Accordingly, the following hypothesis is proposed:
H4. 
Trialability has a significant positive effect on residents’ attitudes toward adopting green housing.

2.2.5. Observability and Attitude Toward Green Housing Adoption

Observability refers to the extent to which individuals can perceive the tangible outcomes or benefits of an innovation [33]. When the advantages, performance improvements, or benefits of an innovation are clearly observable, potential users are more likely to form favorable evaluations and make confident adoption decisions [52]. Karahanna et al. [43] demonstrated that observability is a key determinant influencing user attitudes. Previous studies on innovative technologies, such as financial technology [53] and ecological sanitation systems [54], have shown that observing others’ successful use of an innovation significantly enhances users’ adoption intentions. Similarly, in the context of green housing, when residents observe the operational performance of nearby projects, receive positive feedback from other occupants, or witness visible improvements in the surrounding environment, their attitudes toward green housing become more favorable. Therefore, the following hypothesis is proposed:
H5. 
Observability has a significant positive effect on residents’ attitudes toward adopting green housing.

2.2.6. Attitude, Perceived Value, and Adoption Intention of Green Housing

Previous studies have shown that attitude exerts a positive influence on perceived value [55]. Similar findings have been reported across other domains, such as augmented reality shopping [56], retail consumption [57], and luxury brand purchasing [58]. Multiple empirical studies consistently indicate that a positive attitude is a key determinant of the likelihood of adopting green technologies. Masukujjaman et al. [59] found that attitude has a significant positive influence on the intention to purchase green housing. Similarly, Chan et al. [60] revealed that consumer attitudes significantly affect the adoption of green building technologies. Moreover, numerous studies confirm that when consumers develop positive attitudes toward new technologies, products, or lifestyles, adoption behavior becomes more likely, whereas negative attitudes strongly inhibit it [61,62]. Therefore, the following hypotheses are proposed:
H6. 
Residents’ attitudes toward green housing have a significant positive effect on their perceived value.
H7. 
Residents’ attitudes toward green housing have a significant positive effect on their adoption intention.

2.2.7. Perceived Value and Intention to Adopt Green Housing

Perceived value emphasizes that individuals make decisions by weighing the benefits gained against the costs incurred, which subsequently shapes their behavioral intentions. Existing studies consistently indicate that consumers’ perceived value has a significant positive influence on their intention to use or adopt a product or service [63]. This relationship has been empirically validated across various fields [56,64]. When residents clearly perceive that green housing offers higher living quality, lower energy costs, and broader social benefits, they tend to develop more favorable attitudes and stronger intentions to adopt such housing. Based on these insights, the following hypothesis is proposed:
H8. 
Residents’ perceived value of green housing has a significant positive effect on their adoption intention.

3. Data and Methodology

3.1. Study Area

This study takes Nanjing, China, as the empirical setting for examining residents’ intentions to adopt green housing. As a major provincial capital in eastern China, Nanjing has in recent years continuously advanced green building policies, technological implementation, and the renewal of existing communities, thereby establishing a relatively systematic and stable environment for green housing development. In Nanjing, green housing has entered residents’ everyday living context through well-established technical standards, certification systems, and practical project implementations [65]. As a result, green housing is no longer merely an abstract concept or policy term, but a concrete housing category that residents can recognize, understand, and form relatively stable perceptions of. This provides a critical prerequisite for conducting questionnaire-based adoption intention research and helps reduce measurement bias arising from heterogeneous interpretations of the research object among respondents. Moreover, green housing in Nanjing has not yet achieved highly homogeneous coverage within the overall housing market and remains clearly distinguishable from conventional housing in terms of attribute configuration, technological applications, and residential experience. Under these conditions, residents’ evaluations of green housing are more likely to be grounded in assessments of specific housing attributes rather than being primarily driven by policy mechanisms or market inertia. This context therefore provides a clearer empirical setting for identifying how housing attributes influence residents’ attitudes, perceived value, and adoption intention.

3.2. Questionnaire Design

This study employs a quantitative research approach to systematically analyze the factors influencing residents’ adoption of green housing. To this end, a structured questionnaire was designed and administered, consisting of three main sections. The first section served as an introduction, informing respondents about the research context and providing assurances of confidentiality and anonymity to ensure informed consent and the protection of privacy [66]. The second section collected demographic information, including respondents’ gender, age, education level, and length of residence in green housing. The third section focused on questions related to respondents’ intentions to adopt or continue residing in green housing. As shown in Table 1, all questionnaire items were adapted from prior studies based on the diffusion of innovations theory and research on consumers’ intentions to adopt green housing, with minor modifications made to fit the present context. Respondents rated each item on a seven-point Likert scale, ranging from 1 (“strongly disagree”) to 7 (“strongly agree”).

3.3. Data Collection

Data were collected in Nanjing, China. The selection of sample communities followed three criteria: clearly defined green attributes, actual occupancy, and the formation of relatively stable resident perceptions, ensuring that respondents could evaluate green housing based on lived experience. Based on these criteria, three representative green residential communities in Nanjing were selected: Langshi Green District, Chengwang Lidu, and Chengkai Yuyuan. All selected communities had been awarded the three-star Green Building Label by the Ministry of Housing and Urban–Rural Development of China, ensuring consistency in certification level and minimising potential confounding effects arising from differences in green building standards. In addition, the three communities were completed between 2011 and 2014 and had entered a stage of long-term stable occupancy, rather than functioning as short-term showcase or policy demonstration projects. Under these conditions, residents’ perceptions of green housing were shaped less by policy messaging or initial impressions and more by sustained evaluations of housing performance, user experience, and operational outcomes during long-term residence. The study sample therefore reasonably represents residents who have already occupied green housing and developed stable perceptions, and the findings provide reference value for green residential communities at similar stages of development.
With the assistance of community staff, the research team collected data using a random door-to-door survey approach. Participant recruitment followed the principles of actual residency and voluntary participation; only adult residents who were currently living in the selected green residential communities were invited to participate, ensuring that respondents had genuine and sustained residential experience. Specifically, residential units within each community were randomly selected, and questionnaires were distributed to residents who met the residency criteria and consented to participate. No additional screening criteria based on gender, age, or occupation were applied, in order to minimise subjective selection bias. To enhance data quality, the research team provided one-to-one guidance and supervision during questionnaire completion, promptly addressing any comprehension issues to reduce missing or incorrectly completed responses. Upon completion of the questionnaire, each valid participant received RMB 10 as a token of appreciation, intended to encourage participation and facilitate the smooth conduct of the survey. In total, 420 questionnaires were distributed and 387 were returned, yielding a response rate of 92.1%. The collected questionnaires were deemed valid and reliable for subsequent data analysis. Descriptive statistics of the respondents are presented in Table 2.

3.4. Research Methods

This study employs partial least squares structural equation modeling (PLS-SEM) to analyze and validate both the measurement model and the structural model. PLS-SEM is a highly exploratory statistical technique that is particularly suitable for analyzing complex path models involving multiple exogenous variables and mediating pathways, while demonstrating strong robustness and flexibility in small-sample contexts and with non-normally distributed data [78]. Compared with conventional covariance-based structural equation modeling, PLS-SEM places greater emphasis on predictive accuracy and explanatory power rather than on strict goodness-of-fit testing of established theoretical models. The primary objective of this study is to use path analysis to elucidate the dynamic relationships among innovation attributes, attitudes, perceived value, and adoption intention, rather than merely to test predefined theoretical hypotheses, which aligns closely with the study’s research goals. Accordingly, this study focuses on the path structure through which multiple innovation attributes jointly influence adoption intention via attitudes and perceived value, with particular attention to the predictive relationships among variables and their relative effect strengths. This approach enables a nuanced examination of inter-variable relationships, facilitates the identification of key determinants of green housing adoption intention, and supports the derivation of actionable implications.

4. Results and Analysis

4.1. Common Method Bias Assessment

This study employs Harman’s single-factor analysis to test for common method bias in the questionnaire data. The analysis results show that the percentage of variance explained by the first principal component is 40.648%, which is below the critical value of 50%, indicating that the common method bias in this study is within an acceptable range. Additionally, we use the variance inflation factor (VIF) to test for multicollinearity in the structural model. Hair et al. [79] indicate that multicollinearity exists when VIF ≥ 5. In this study, all VIF values range from 1.000 to 1.922, suggesting that multicollinearity does not affect the structural model.

4.2. Sample Characteristics

To evaluate the reliability and validity of the constructs, we examined the measurement model. As shown in Table 3, the Cronbach’s alpha (CA) values for all constructs ranged from 0.787 to 0.881, and the composite reliability (CR) values ranged from 0.876 to 0.922, all exceeding the threshold of 0.70 [80], indicating satisfactory reliability. Convergent validity was assessed by examining the average variance extracted (AVE) and the factor loadings of all items associated with their respective latent variables. Furthermore, the item loadings for each construct ranged from 0.784 to 0.905, exceeding the acceptable threshold of 0.60 [81], and the AVE values ranged from 0.673 to 0.798, both above the recommended cutoff of 0.50 [80], demonstrating strong convergent validity.
This study assessed discriminant validity using both the Fornell–Larcker criterion and the heterotrait–monotrait (HTMT) ratio of correlations [82]. Table 4 presents the correlation matrix among the constructs, with the bold diagonal values representing the square root of the AVE for each construct. According to the Fornell–Larcker criterion, discriminant validity is established when the square root of a construct’s AVE exceeds its correlations with other latent variables. The results indicate that all diagonal values are greater than the corresponding correlation coefficients in their respective rows and columns, confirming clear distinction among the latent constructs and adequate discriminant validity. Furthermore, discriminant validity issues may arise when the HTMT ratio exceeds the threshold of 0.90 [83]. As shown in Table 5, all HTMT values fall below the threshold. Both methods consistently confirm that the collected data exhibit satisfactory discriminant validity.

4.3. Structural Model Evaluation

To test the hypothesized relationships among the constructs, a structural model evaluation was conducted. The results show that all eight proposed hypotheses were supported. Specifically, relative advantage (β = 0.340, t = 5.772, p < 0.05), compatibility (β = 0.104, t = 3.054, p < 0.05), trialability (β = 0.226, t = 4.791, p < 0.05), and observability (β = 0.295, t = 10.056, p < 0.05) exerted significant positive effects on attitude, supporting hypotheses H1, H2, H4, and H5. Complexity negatively affected attitude (β = −0.102, t = 2.452, p < 0.05), thereby supporting H3. In addition, attitude had a direct positive influence on both perceived value (β = 0.647, t = 15.600, p < 0.05) and adoption intention (β = 0.516, t = 14.278, p < 0.05). Perceived value also had a direct positive effect on adoption intention (β = 0.278, t = 6.612, p < 0.05), supporting hypotheses H6, H7, and H8 (Table 6 and Figure 2).
The coefficients of determination (R2), cross-validation redundancy index (Q2), and effect size (F2) were tested. R2 indicates the extent to which the variation in endogenous variables is explained. The R2 values for attitude, perceived value, and adoption intention are 0.683, 0.418, and 0.529, respectively, indicating that the model has good explanatory power. The predictive ability of the model can be assessed using the Q2 of the endogenous variables. A Q2 value greater than 0 indicates predictive relevance of the structural model for the endogenous variables. In this study, the Q2 values for attitude, perceived value, and adoption intention are 0.516, 0.276, and 0.385, respectively, indicating that the model demonstrates good predictive ability. F2 is an important indicator used to evaluate the relative impact of exogenous variables on endogenous latent variables. An effect size of approximately 0.35 indicates a relatively significant impact of the exogenous variable on the endogenous latent variable; an effect size of approximately 0.15 indicates a moderate impact; and an effect size of approximately 0.02 suggests a small effect. In this study, the effects of relative advantage, compatibility, complexity, trialability, and observability on attitude are 0.234, 0.020, 0.021, 0.084, and 0.175, respectively. The effect of attitude on perceived value is 0.718, and the effects of perceived value on adoption intention are 0.329 and 0.096. The F2 values between all variables are greater than or equal to 0.02. Therefore, the model in this study meets the testing criteria.

4.4. Mediation Effect Analysis

This study employs a bias-corrected bootstrap method with 5000 samples for the mediation effect test. Specifically, when the 95% bootstrap confidence interval (95% CI) includes 0, the effect is considered non-significant; when it does not include 0, the effect is considered significant. The results are presented in Table 7.
The results of the mediation effect test show that all mediation paths have positive effect values that are statistically significant (p < 0.05), and the 95% bootstrap confidence intervals do not include 0. This indicates that attitude and perceived value play significant mediating roles between the antecedent variables and adoption intention. Attitude and perceived value not only directly influence adoption intention but also enhance the impact of various innovation attributes on adoption intention through their mediating effects.

5. Discussion

5.1. Key Findings

Drawing upon the diffusion of innovations theory, attitude theory, and perceived value theory, this study investigates residents’ intentions to adopt green housing and reveals several insightful findings worthy of further exploration.
A substantial body of literature indicates that green housing outperforms conventional housing in terms of energy efficiency, air quality improvement, and residential comfort, making it an important vehicle for promoting healthy lifestyles and enhancing living experiences [1,26]. Our findings demonstrate that relative advantage exerts a significant positive influence on residents’ attitudes toward green housing, thereby supporting H1. This finding aligns with the central premise of the diffusion of innovations theory: individuals are more likely to adopt an innovation when they perceive it as providing superior benefits compared with the status quo [84]. Compared with other factors, relative advantage was identified as the most critical determinant influencing residents’ decisions to adopt green housing. This result is consistent with the findings of Ahmad et al. [85] and Agag and El-Masry [86]. Similarly, Zuo et al. [3] and Olubunmi et al. [87] emphasized that the advantages of green housing, including superior energy efficiency, improved health performance, and long-term economic returns, are key factors shaping residents’ attitudes.
The research results show that the compatibility of green housing positively influences residents’ attitudes, with H2 being supported. This suggests that when the concepts and usage of green housing align with residents’ ecological values and lifestyle habits, they are more likely to develop positive perceptions [88]. This finding is consistent with the research results of Ren and Wang [89], and Wu et al. [90], indicating that compatibility is a crucial factor in the adoption attitudes toward green buildings. Existing studies further suggest that residents’ attitudes toward adopting green housing also depend on the degree to which it fits the social and environmental atmosphere of their communities [91]. When green housing development fosters residents’ sense of belonging to a sustainable community, this identity perception further reinforces their positive attitudes toward adoption [92].
The findings indicate that the multiple complexities associated with green housing during its design, construction, and use stages significantly inhibit residents’ formation of positive attitudes toward adoption, thereby supporting H3 [93]. Wang et al. [94] similarly supported this conclusion, finding that the technological complexity of green housing hinders its promotion and weakens residents’ adoption intentions. Green housing technologies differ from conventional systems and are often more complex [95], incorporating specialized components such as passive energy-saving systems and intelligent ventilation devices [96]. On one hand, residents face knowledge barriers in understanding their operation [97]. On the other, they may be concerned about technological immaturity [71]. Some residents perceive green housing as involving higher upfront investment, maintenance costs, equipment replacement expenses, and system upgrade demands compared with conventional housing, leading to concerns about uncertain long-term costs and reduced enthusiasm for adoption [98,99]. Bal et al. [100] similarly found in the field of sustainable energy use that high perceived installation and maintenance costs significantly reduce users’ positive attitudes. Putra et al. [101] also observed that insufficient economic incentives impede the diffusion of new technologies.
In recent years, green housing has attracted increasing attention in urban development and the real estate market, becoming a prominent topic of public interest. Providing residents with opportunities for direct experience of green housing can play a particularly significant role in stimulating adoption demand [8]. This is consistent with prior findings that trialability allows users to reduce uncertainty through direct experience, thereby facilitating adoption [33,102]. The results show that when residents experience tangible advantages of green housing, such as improved ventilation, insulation, lighting, and energy performance, they tend to form more favorable attitudes toward green housing [65]. The empirical evidence of this study further supports this perspective, confirming H4. Previous research has indicated that the visibility of green technologies and lifestyles positively influences public attitudes toward green housing [74]. Consistently, our survey results show that observability has a significant positive effect on residents’ attitudes toward adopting green housing. Many respondents reported observing the tangible benefits of green housing, including energy savings, improved air quality, and enhanced comfort, through friends’ experiences, social media posts, and open-house demonstrations. Such visible benefits enhanced residents’ trust in and interest toward green housing. Moreover, as operational outcomes such as energy-saving metrics, air quality improvements, and health feedback become more visually accessible, residents are more likely to develop positive attitudes [103]. According to the diffusion of innovations theory, the more observable the benefits of an innovation, the more likely it is to generate positive evaluations among potential users [102]. The empirical findings of this study further support this notion, thereby confirming H5.
Attitude has a significant positive effect on perceived value. This finding is consistent with conclusions drawn from previous studies [104,105]. Attitude is generally defined as an individual’s positive or negative evaluation of a behavior or object. When residents develop positive emotional or cognitive evaluations toward green housing, they are more likely to perceive its value in terms of energy efficiency, environmental sustainability, health and comfort, and long-term economic returns, thereby enhancing their overall recognition and value assessment of green housing. Attitude also exerts a positive influence on adoption intention. This indicates that the more favorable users’ attitudes toward adopting green housing, the stronger their behavioral intentions are likely to be. This result is also consistent with prior findings in other contexts, such as smart energy technologies [106], brand purchasing decisions [107], and low-carbon travel behaviors [108]. Moreover, perceived value has a significant positive effect on adoption intention. When residents perceive green housing as offering higher value, their willingness to purchase or reside in such housing increases significantly. This finding aligns with Zhao and Chen [109], who identified perceived value as a key predictor of purchase intention for green housing. Liu and Zhao [110] also confirmed this relationship.
In summary, attitude plays a central role in the adoption decision of green housing. The direct impact of attitude on adoption intention is significantly stronger than that of perceived value, indicating that the decision to adopt green housing primarily depends on residents’ overall attitude judgment, rather than a simple value trade-off. Additionally, attitude has a significant positive impact on perceived value, suggesting that perceived value is more of an evaluative outcome that is further reinforced after the formation of attitude. At the level of innovation attributes, there are clear differences in how different attributes influence attitude. Among these, the influence of relative advantage is the most prominent, followed by observability and trialability, indicating that perceived and experiential advantages play a key role in the formation of attitude. In contrast, the influence of compatibility is relatively limited, while complexity exerts a suppressive effect on attitude, serving as an important constraint on residents’ attitudes. Although the empirical results of this study generally support the fundamental conclusions of innovation diffusion theory and related research in terms of path direction, the contribution of this study lies in further distinguishing the roles of different innovation attributes in adoption decisions, advancing the explanation of the formation mechanism of green housing adoption decisions. The results indicate that green housing adoption is not simply driven by a single advantage or barrier factor, but rather by multiple attributes that jointly influence adoption intention through different psychological evaluation pathways, such as attitude and perceived value, providing a more refined explanation for understanding residents’ adoption behavior.

5.2. Theoretical Implications

First, by adopting the diffusion of innovations theory as the core analytical framework and integrating the specific characteristics of green housing, this study provides a comprehensive theoretical model for examining residents’ adoption intentions, thereby extending the theoretical understanding of green housing adoption behavior. Second, the study identifies and elucidates the key factors influencing residents’ adoption of green housing, addressing gaps in existing research concerning the explanatory mechanisms underlying adoption behavior. Finally, in contrast to previous studies that treat innovation attributes, attitude, or adoption intention as relatively independent variables, this study integrates multiple innovation attributes with attitude, perceived value, and adoption intention within the same model framework. From a process perspective, it reveals the mechanism through which residents’ adoption behavior transforms from the recognition of housing attributes to the formation of adoption intention. This analytical perspective enables the study to move beyond the explanation of single variables or partial relationships, uncovering the structural characteristics of green housing adoption decisions, thereby providing new empirical insights based on existing research. The findings suggest that enhancing the user experience of green housing and strengthening residents’ positive attitudes and emotional identification may serve as critical pathways for promoting its large-scale adoption.

5.3. Practical Implications

The practical significance of this study lies in demonstrating that the adoption of green housing is not solely dependent on fully meeting multiple technological or functional attributes, but rather on the way different attributes influence residents’ decision-making processes. The empirical results show that attributes with higher perceived and experiential advantages are more likely to influence adoption intention through attitude mechanisms, while technological complexity, to some extent, constrains residents from forming positive judgments. This finding suggests that in the promotion of green housing, it is crucial to understand the differentiated roles of various attributes in the decision-making structure of residents. Based on these findings, this study offers the following practical recommendations:
First, with regard to relative advantage, real estate developers and policymakers should emphasize the superior performance of green housing in energy efficiency, health benefits, and indoor environmental quality, enabling residents to recognize its comprehensive advantages over conventional housing. Quantifying energy savings, disclosing air quality improvement data, and demonstrating long-term cost reduction from green technologies can effectively enhance residents’ recognition of green housing value.
Second, concerning compatibility and complexity, developers should highlight the alignment of green housing with residents’ everyday lifestyles in both design and communication. For example, the ease of use of energy-saving devices, the intuitiveness of smart control systems, and the comfort and safety of eco-friendly materials should be clearly demonstrated. Clear user manuals, pre-occupancy guidance, and responsive after-sales services can help lower operational barriers and mitigate negative perceptions arising from complexity. Developers may also utilize community engagement, media promotion, and public education to strengthen residents’ identification with sustainable lifestyles, positioning green housing not only as a residential option but also as a symbol of sustainable living. Moreover, the diffusion of green housing generates a social demonstration effect. As an increasing number of residences adopt green technologies or obtain green certifications, potential users are influenced by social recognition, conformity preferences, and reduced perceived risks, thereby increasing their likelihood of adoption.
Finally, with respect to trialability and observability, both enterprises and government agencies should promote open experiential initiatives such as model home visits, VR-based green living simulations, and resident experience sharing. These approaches allow potential buyers to directly observe the real-world performance of green technologies. Enhancing the social visibility of green housing can not only strengthen residents’ trust but also expand societal recognition through positive word-of-mouth dissemination.

5.4. Limitations and Future Research

Although this study collected basic demographic information, such as gender, age, and education level, the diffusion of innovations theory suggests that adoption behavior is shaped by a broader range of individual factors, including personality traits, socioeconomic characteristics, and levels of knowledge [111]. However, the analytical focus of this study is not on individual differences per se, but on how the objective attributes of green housing, as a complex housing innovation, are collectively perceived, interpreted, and translated into adoption judgements by residents. By contrast, individual characteristics primarily shape heterogeneity in adoption decisions. Accordingly, individual-level variables are treated as background information and are not incorporated into the structural model. Future research could build on the present model by incorporating individual characteristics as grouping or moderating variables, thereby examining variations in green housing adoption mechanisms across different population groups.
Secondly, this study focuses solely on Nanjing, China, as the study area. The results are expected to apply to cities where green housing has entered the practical residential application stage, and residents possess basic awareness of it, but where it has not yet achieved widespread homogeneity across the housing market. The findings may provide useful insights for other cities at a similar stage of development, as well as for cities in emerging economies, in understanding green housing adoption behavior. However, the applicability of these findings in cities where green housing is highly widespread, or where residents’ decisions are primarily influenced by market structure factors, requires further examination. Furthermore, although Nanjing provides a valuable empirical case for the study, the results may be influenced by the regional and socio-cultural context of the city. It is important to emphasise that this study does not aim to compare differences across regions or cultural contexts; rather, it focuses on the cognitive and psychological evaluation mechanisms associated with innovation attributes, with the objective of identifying the core pathways underlying green housing adoption decisions within a relatively controlled research setting. On this basis, contextual factors such as regional development level, institutional environment, and sociocultural characteristics are not included in the current model. Future research could build on the present framework by introducing these contextual variables as moderating factors, thereby systematically testing their effects on the relationships between innovation attributes, attitudes, and adoption intentions across different regional and sociocultural contexts, and enhancing the model’s explanatory power and applicability in cross-regional and cross-cultural studies.
Lastly, the model proposed in this study still has certain conceptual limitations. The model centers on attitude and perceived value as the core psychological mediators, excluding external factors such as social norms, institutional trust, or market constraints. Therefore, it is more suited for analyzing cognitive evaluation processes at the individual level rather than the complete market adoption mechanism.

6. Conclusions

This study aims to systematically examine the factors influencing residents’ adoption of green housing. It proposes that integrating diffusion of innovations theory, attitude theory, and perceived value theory offers a more comprehensive explanation of residents’ adoption intentions. All eight proposed hypotheses were empirically supported. The results indicate that relative advantage, compatibility, complexity, trialability, and observability are significant determinants of residents’ intentions to adopt green housing. Among these factors, relative advantage emerged as the most influential in shaping residents’ attitudes toward green housing adoption, while complexity exerted a weaker but negative effect on residents’ attitudes. Furthermore, the findings confirm that residents’ attitudes and perceived value play crucial mediating roles between innovation attributes and adoption intention. Accordingly, to promote green housing adoption, policymakers should develop strategies aimed at enhancing residents’ attitudes toward green housing and strengthening their perceived value.

Author Contributions

Y.L. drafted the main content of the paper. X.L. was responsible for data analysis and provided methodological guidance for the manuscript. H.F. was responsible for the literature review and part of the data processing. R.Z. provided overall guidance and detailed revisions throughout the manuscript, and was responsible for determining the research content and methodology. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Jiangnan University 2025 Undergraduate Education and Teaching Reform Project: “Integration of Industry-Education for Rural Revitalization and Environmental Design Curriculum Construction,” Grant No. JGZX20250702.

Institutional Review Board Statement

All subjects gave their informed consent for inclusion before they participated in the study. The study complied with the Declaration of Helsinki and received approval from the Medical Ethics Committee of Jiangnan University (JUN202506RB067; approval date: June 2025).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no competing interests.

Abbreviations

The following abbreviations are used in this manuscript:
PLS-SEMPartial least squares structural equation modeling
CACronbach’s alpha
CRComposite reliability
AVEAverage variance extracted
HTMTHeterotrait–monotrait
VIFVariance inflation factor

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Buildings 16 00335 g001
Figure 1. Research Model.
Figure 1. Research Model.
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Buildings 16 00335 g002
Figure 2. Results of the PLS-SEM model.
Figure 2. Results of the PLS-SEM model.
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Table 1. Measurement Items and Sources.
Table 1. Measurement Items and Sources.
VariableMeasurement ItemQuestionnaire ItemSource
Relative advantageQ1Compared with conventional housing, I believe green housing offers a significantly higher overall quality of living.[1,3,67,68]
Q2I believe green housing promotes a more energy-efficient and environmentally friendly lifestyle.
Q3The health and comfort performance of green housing is superior to that of conventional housing.
Q4Choosing green housing can bring me greater long-term benefits, such as energy savings and reduced costs.
CompatibilityQ5The environmental philosophy of green housing aligns with my personal values.[33,69]
Q6The living mode of green housing matches my expectations for an ideal residential environment.
Q7I believe green housing can harmonize with the lifestyle and atmosphere of the surrounding community.
ComplexityQ8I think the installation and maintenance costs of green housing are too high.[70,71]
Q9I believe the technologies used in green housing are not yet fully mature.
Q10Compared with conventional housing, I find the functions of green housing more complex.
TrialabilityQ11I would like to experience some features of green housing before making a purchase or moving in.[33,72]
Q12If I could try living in or learn about the real usage conditions of green housing, I would feel more confident choosing it.
Q13Having the opportunity to experience green housing in advance would help me better assess whether it suits me.
ObservabilityQ14I can clearly observe the tangible effects of green housing in areas such as energy savings, air quality, and noise reduction.[22,33,73]
Q15Communities or residents around me visibly demonstrate the positive changes brought by green housing.
Q16When others share their experiences with green housing, I can clearly recognize its value.
AttitudeQ17I hold an overall positive attitude toward green housing.[22,74,75]
Q18I believe living in a green home is an enjoyable experience.
Q19Compared with other housing options, adopting green housing represents a better behavioral choice.
Perceived valueQ20I believe green housing provides a lifestyle more aligned with future residential trends.[73,76,77]
Q21Choosing green housing makes me feel that I am contributing to the sustainable development of the city.
Q22I believe green housing will continue to enhance the overall sense of housing value in the future.
Q23Green housing gives me a sense of residential identity associated with quality and sophistication.
Adoption IntentionQ24If conditions allow, I am willing to choose green housing as my future living space.[22,75]
Q25I am willing to learn more about green housing projects and consider living in one.
Q26Given the same price, I would prefer to choose green housing over conventional housing.
Q27I am willing to recommend green housing to others around me.
Table 2. Demographic Characteristics of Respondents (n = 387).
Table 2. Demographic Characteristics of Respondents (n = 387).
CharacteristicCategoryN%
GenderMale21254.8
Female17545.2
Age20 years old or below7018.1
21–30 years old11830.5
31–40 years old14637.7
Above 40 years old5313.7
Education LevelHigh school or below/Vocational school6115.8
Junior college12331.8
Bachelor’s degree14537.4
Master’s degree or above5815.0
Duration of Residence in Green HousingLess than 1 year205.2
1–2 years8923.0
2–3 years17645.5
More than 3 years10226.3
Table 3. Coefficients of variables in the measurement model.
Table 3. Coefficients of variables in the measurement model.
VariableItemFactor LoadingCronbach’s AlphaCRAVE
Relative advantageQ10.8490.8630.9070.709
Q20.835
Q30.818
Q40.866
CompatibilityQ50.8780.7870.8760.702
Q60.828
Q70.806
ComplexityQ80.8890.8130.8890.728
Q90.833
Q100.836
TrialabilityQ110.8790.8210.8930.737
Q120.854
Q130.842
ObservabilityQ140.9050.8730.9220.798
Q150.875
Q160.900
AttitudeQ170.8910.8540.9110.774
Q180.861
Q190.886
Perceived valueQ200.8550.8380.8910.673
Q210.832
Q220.784
Q230.808
Adoption IntentionQ240.8770.8810.9180.738
Q250.839
Q260.835
Q270.884
Table 4. Fornell–Larcker criterion results.
Table 4. Fornell–Larcker criterion results.
Relative AdvantageCompatibilityComplexityTrialabilityObservabilityAttitudePerceived ValueAdoption Intention
Relative advantage0.842
Compatibility0.4970.838
Complexity−0.343−0.4450.853
Trialability0.5220.516−0.5510.858
Observability0.4360.502−0.4210.5030.893
Attitude0.6730.582−0.5130.6610.6510.880
Perceived value0.4340.307−0.2470.3790.4200.6470.820
Adoption Intention0.4840.385−0.2880.4090.4970.6960.6120.859
Note: The bolded diagonal values represent the square root of the AVE.
Table 5. HTMT.
Table 5. HTMT.
Relative AdvantageCompatibilityComplexityTrialabilityObservabilityAttitudePerceived ValueAdoption Intention
Relative advantage
Compatibility0.600
Complexity0.4080.559
Trialability0.6190.6410.674
Observability0.5000.6030.4990.595
Attitude0.7820.7090.6150.7890.755
Perceived value0.5080.3720.2970.4550.4890.763
Adoption Intention0.5540.4560.3370.4800.5640.8000.708
Table 6. Results of PLS-SEM path relationship testing.
Table 6. Results of PLS-SEM path relationship testing.
PathβSTDEVtpHypothesis
Relative advantage → Attitude0.3400.0595.7720.000Supported
Compatibility → Attitude0.1040.0343.0540.002Supported
Complexity → Attitude−0.1020.0422.4520.014Supported
Trialability → Attitude0.2260.0474.7910.000Supported
Observability → Attitude0.2950.02910.0560.000Supported
Attitude → Perceived value0.6470.04115.6000.000Supported
Attitude → Adoption Intention0.5160.03614.2780.000Supported
Perceived value → Adoption Intention0.2780.0426.6120.000Supported
Table 7. Mediation Effect Test.
Table 7. Mediation Effect Test.
PathEffectStandard DeviationtpBT = 5000 95% CI
LowerUpper
Relative advantage → Attitude → Perceived value → Adoption intention0.0610.0153.9650.0000.0340.094
Compatibility → Attitude → Perceived value → Adoption intention0.0190.0072.8130.0050.0070.033
Complexity → Attitude → Perceived value → Adoption intention−0.0180.0082.2970.022−0.034−0.003
Trialability → Attitude → Perceived value → Adoption intention0.0410.0113.7840.0000.0220.064
Observability → Attitude → Perceived value → Adoption intention0.0530.0115.0260.0000.0330.074
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Liu, Y.; Li, X.; Feng, H.; Zhu, R. What Drive Residents to Adopt the Concept of Green Housing in Nanjing, China. Buildings 2026, 16, 335. https://doi.org/10.3390/buildings16020335

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Liu Y, Li X, Feng H, Zhu R. What Drive Residents to Adopt the Concept of Green Housing in Nanjing, China. Buildings. 2026; 16(2):335. https://doi.org/10.3390/buildings16020335

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Liu, Yuxiao, Xiaobin Li, Hao Feng, and Rong Zhu. 2026. "What Drive Residents to Adopt the Concept of Green Housing in Nanjing, China" Buildings 16, no. 2: 335. https://doi.org/10.3390/buildings16020335

APA Style

Liu, Y., Li, X., Feng, H., & Zhu, R. (2026). What Drive Residents to Adopt the Concept of Green Housing in Nanjing, China. Buildings, 16(2), 335. https://doi.org/10.3390/buildings16020335

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