Next Article in Journal
Experimental and Numerical Study on the Seismic Performance of Base-Suspended Pendulum Isolation Structure
Previous Article in Journal
Intelligent ESG Evaluation for Construction Enterprises in China: An LLM-Based Model
Previous Article in Special Issue
A Bibliometric Analysis of Research on Human Settlements Improvement Based on CNKI and Web of Science
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Buildings
Volume 15
Issue 15
10.3390/buildings15152703
buildings-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Analyzing Influencing Factors of Low-Carbon Technology Adoption in Hospital Construction Projects Based on TAM-TOE Framework

by
Lei Jin
1,
Dezhi Li
2,3,*,
Yubin Zhang
1,4 and
Yi Zhao
5
1
The First Affiliated Hospital with Nanjing Medical University (Jiangsu Province Hospital), Nanjing 210029, China
2
School of Civil Engineering, Southeast University, Nanjing 211189, China
3
Engineering Research Center of Building Equipment, Energy, and Environment, Ministry of Education, Nanjing 211189, China
4
School of Energy and Environment, Southeast University, Nanjing 211189, China
5
School of Civil Engineering, Southeast University Chengxian College, Nanjing 210088, China
*
Author to whom correspondence should be addressed.
Buildings 2025, 15(15), 2703; https://doi.org/10.3390/buildings15152703 (registering DOI)
Submission received: 12 June 2025 / Revised: 22 July 2025 / Accepted: 29 July 2025 / Published: 31 July 2025
(This article belongs to the Special Issue Urban Infrastructure and Resilient, Sustainable Buildings)
Browse Figures
Versions Notes

Abstract

Hospitals rank among the most energy-intensive public building typologies and offer substantial potential for carbon mitigation. However, their construction phase has received limited scholarly attention within China’s ‘dual carbon’ agenda. To address this research gap, this study develops and empirically validates an integrated Technology Acceptance Model and Technology-Organization-Environment framework tailored for hospital construction projects. The study not only identifies 12 critical adoption factors but also offers recommendations and discusses the relevance to multiple Sustainable Development Goals. This research provides both theoretical and practical insights for promoting sustainable hospital construction practices.

1. Introduction

Nowadays, addressing climate change and energy shortages has emerged as an urgent global issue. As the primary driver of global climate change, carbon emissions have prompted an international consensus on implementing effective energy-saving and carbon reduction measures to mitigate rising global temperatures. Against this backdrop, world leaders adopted the Paris Agreement in 2015, establishing a global framework for achieving carbon neutrality and emphasizing the importance of international cooperation in addressing climate change. This initiative aligns closely with the United Nations Sustainable Development Goals (SDGs). In response to the commitments outlined in the Paris Agreement, China has proposed its ‘dual carbon’ strategic objective: to achieve ‘carbon peaking’ by 2030 and ‘carbon neutrality’ by 2060. The construction industry is a significant contributor to carbon emissions, accounting for 37% of global carbon emissions [1] and 47.1% of national carbon emissions in China. Furthermore, emissions from the construction industry continue to escalate [2]. Hospital buildings, as large-scale public facilities with complex mechanical and electrical systems, high occupancy rates, and extended operating hours, consume substantial amounts of energy resources. According to statistics, hospitals exhibit a carbon emission intensity that is 1.6–2 times higher than that of general public buildings [3], while contributing to approximately 4% of total emissions [4]. This places them among the highest emitters in this category, with significant potential for reducing their carbon emissions [5]. According to the China Health Statistics Yearbook, the total number of hospitals in China has steadily increased. The early stage construction phase, which encompasses hospital project design and implementation activities, not only impacts its own phase but also has a significant impact on the carbon emissions during the later operational phase after the hospital is completed. Therefore, it becomes imperative to embrace low-carbon technology in hospital construction projects.
Currently, an increasing number of countries are focusing on the adoption of low-carbon technology in hospitals. Most scholars concentrate on developing low-carbon technology during the operational phase of hospitals, including waste emission management, water resource management, and the utilization of higher-quality chemicals. Their aim is to reduce carbon emissions during hospital operations in countries such as the United States [6], Germany [7,8,9], Greece [10], Poland [11], and China [12]. However, some scholars also pay attention to low-carbon technology development during the construction phase of hospitals. For instance, countries like the UK [13] and Turkey [14] strive to decrease carbon emissions during hospital construction by enhancing ventilation conditions, researching appropriate material choices, selecting suitable building orientations and window-to-wall ratios, and adopting photovoltaic power generation schemes. Furthermore, certain scholars dedicate their efforts to studying carbon emissions throughout all lifecycle stages of hospitals. Countries like Canada [15], Australia [16,17,18,19] and China [20,21] calculate and evaluate carbon emissions from hospital projects’ lifecycles, with the aim of formulating plans to reduce them.
Existing research on low-carbon technology in hospitals has predominantly focused on qualitative analyses of specific technologies. However, limited studies have explored the managerial and behavioral factors influencing low-carbon technology adoption. Staff in the Infrastructure Construction Department of Hospitals (ICDH) are directly responsible for managing design and implementation processes, selecting technologies, and ensuring compliance with relevant low-carbon policies and standards. Given their central role in construction-related decision-making, they are an appropriate and relevant group for evaluating factors influencing low-carbon technology adoption in hospital construction projects. To address this gap, a staff perspective within ICDH is adopted, and an integrated model combining the Technology Acceptance Model (TAM) and the Technology-Organization-Environment (TOE) framework is proposed to examine factors influencing low-carbon technology adoption in hospital construction projects. Furthermore, integrating the TAM-TOE framework with legal, regulatory, and environmental sustainability frameworks is crucial for ensuring its practical applicability in hospital construction projects [22].
In China, policies such as the ‘Assessment standard for green building’ (GB/T 50378-2019) [23] and the ‘dual-carbon’ strategy have provided institutional and regulatory foundations that shape technology adoption behavior. Internationally, the Paris Agreement and the Sustainable Development Goals (SDGs) also offer strategic direction and standardized benchmarks for low-carbon practices in the construction sector. Therefore, embedding the TAM-TOE model within such regulatory and normative contexts enhances its explanatory power and policy relevance in driving sustainable transitions in the hospital construction industry. Although this study was conducted exclusively in economically developed provinces in China, its findings may serve as a reference for regions at comparable stages of development. However, broader applicability across diverse regions requires further empirical verification.
Following this analysis, the study identifies key factors and provides concrete recommendations. The paper is structured as follows: Section 2 proposes the TAM-TOE theoretical framework and develops the research hypotheses. Section 3 details the survey design and data collection methodology. Section 4 validates the hypothesized paths using structural equation modeling. Finally, Section 5 proposes recommendations and implementation strategies for enhancing low-carbon technology adoption in hospital construction projects, based on the validated model.

2. Theoretical Framework and Research Hypothesis

2.1. TAM-TOE Framework

2.1.1. TAM

The Technology Acceptance Model (TAM), proposed by Davis [24], is widely used to investigate individual-level technology adoption. TAM posits that adoption behavior is primarily determined by behavioral intention, encompassing key variables such as perceived ease of use (PEOU), perceived usefulness (PU), and attitude towards usage. TAM has been extensively applied across various domains, including higher education to examine artificial intelligence adoption [25,26], the business sector to explore factors influencing accounting information systems [27], agriculture to study the impact of digital extension services on productivity and resource efficiency [28], and architecture to investigate adoption factors for building information modeling [29] and analyze the impact mechanisms of virtual reality in design [30]. Furthermore, within healthcare, TAM has been employed to study nurses’ adoption of internet-based care services [31]. Based on TAM’s established applicability and the supporting literature, this study adopts the model to investigate technology adoption by staff in the ICDH within hospital construction projects. Consequently, four core TAM indicators—PEOU, PU, attitude toward usage, and behavioral intention—are utilized for this investigation.

2.1.2. TOE

The Technology-Organization-Environment (TOE) framework, proposed by Tornatzky and Fleischer [32], is widely adopted in academic research as an effective theory for analyzing organizational-level adoption of new technologies. The TOE framework posits that technology diffusion is influenced not only by the characteristics of the technology itself but also by organizational and environmental factors. This framework has been applied across diverse sectors: within business, to examine impacts of successful digital transformation initiatives [33]; in government, to explore factors influencing local governments’ e-participation adoption decisions [34]; in education, to investigate factors affecting student adoption of electronic platforms [35]; and in the construction industry, to identify factors influencing employee adoption of smart building technologies [36]. Consequently, this study adopts the three core dimensions of the TOE framework—technological, organizational, and environmental factors—as key analytical factors for understanding low-carbon technology adoption in hospital construction projects.

2.1.3. TAM-TOE

The Technology Acceptance Model (TAM) has undergone ongoing expansion, with research on technology adoption increasingly investigating its integration with complementary models or frameworks. In TAM, external variables that influence user perception are not explicitly constrained. However, TOE framework addresses this limitation by incorporating higher-level external factors, thereby aligning more closely with behavioral adoption patterns in the complex context of low-carbon technology adoption [37]. Compared with models such as UTAUT and DOI, TAM features a simpler structure and clearer variable definitions. It is particularly suitable for studying the adoption behavior of non-standardized and non-platform-based technologies, such as low-carbon technology, and can be more effectively integrated with the TOE framework. The integration of TAM and TOE frameworks enables simultaneous consideration of individual attitudes, organizational factors, and external environmental influences, thereby forming a more comprehensive and explanatory theoretical framework [38].
Building upon the TOE framework, Songer [39] proposed incorporating economic factors as an additional dimension. This recommendation arises from the recognition that economic considerations, particularly high initial investment requirements, exert a direct influence on decision-making behavior. Consequently, this study extends the TOE framework by introducing economic factors as a fourth category. This modification aims to enable a more comprehensive analysis of the factors influencing low-carbon technology adoption specifically within hospital construction projects.
The integrated theoretical framework (TAM-TOE) is illustrated in Figure 1.

2.2. Research Hypotheses

2.2.1. Identification of Influencing Factors

A literature review method was employed in this study to identify and synthesize relevant research related to ‘hospital, low-carbon technology, TAM and the TOE framework’. Based on the TOE framework presented in the previous chapter, the factors influencing the adoption of low-carbon technology by staff in the ICDH in construction projects were categorized into technological, organizational, environmental, and economic dimensions. The classification results will be employed in the subsequent construction of hypotheses and the conduct of survey design.

2.2.2. Hypotheses Based on the TAM-TOE

Low-carbon technology exhibits significant growth potential in hospital construction projects. PU reflects users’ belief that a new technology will enhance task performance, increase efficiency, and facilitate organizational goal attainment. In this study, PU represents the extent to which low-carbon technology aids ICDH staff in the following: (1) understanding carbon emissions generated during project construction, (2) reducing those emissions, and (3) improving project design and construction processes. PEOU denotes the user’s subjective assessment of the effortlessness involved in adopting a new technology. Here, PEOU encompasses ICDH staff’s following: (1) comprehension of low-carbon technology concepts and content, (2) perceived ease of learning the technology itself, and (3) understanding of carbon emission calculation principles and processes. TAM proposes that ICDH staff’s evaluations of low-carbon technology—based on PU and PEOU—significantly influence their attitude toward usage, thereby positively affecting their behavioral intention [40,41,42]. Furthermore, TAM posits that PEOU positively influences PU [43,44,45].
Based on these premises, the following hypotheses were proposed:
H1. 
Attitude towards usage exerts a significant positive impact on behavioral intention;
H2. 
PU exerts a significant positive impact on behavioral intention;
H3. 
PU exerts a significant positive impact on attitude towards usage;
H4. 
PEOU exerts a significant positive impact on PU;
H5. 
PEOU exerts a significant positive impact on attitude towards usage.
The TOE framework proposes that attitudes toward usage and behavioral intention are influenced by technological, organizational, environmental, and economic factors [46]. These external variables impact the PEOU and PU. Prior to constructing a low-carbon technology adoption model for hospital construction projects, it is imperative to analyze how these external variables affect the PEOU and PU.
Technological factors encompass the maturity level of carbon emission calculation software, compatibility with other architectural software, and operational stability. These factors contribute to a sense of ease in utilizing low-carbon technology among staff in the ICDH and serve as critical factors influencing its convenient implementation in hospital construction projects. Consequently, they can positively impact PEOU [41,47].
Organizational factors include policies and financial support from hospital senior managers, as well as the professional capabilities and project experience of low-carbon technology consulting teams in hospital construction. These factors play a critical role in facilitating project implementation, instilling confidence among staff in the ICDH to adopt low-carbon technology, and consequently positively influencing perceived usefulness PU and PEOU [48].
Environmental factors encompass policy requirements and incentives provided by government departments, as well as the adoption of low-carbon technology by other hospitals. Staff in ICDH are often influenced by external environmental factors and the choices made by their counterparts in neighboring institutions. The adoption of low-carbon technology by nearby hospitals enhances the perceived importance of such technology among staff in ICDH, thereby influencing PU [49,50].
Economic factors encompass costs associated with low-carbon technology consulting teams, costs related to the implementation of these technologies, and reduced operational maintenance costs following the completion of hospital construction projects. Low-cost and high-return technologies can mitigate adoption risks, thereby influencing staff’s assessment of the practical applicability of low-carbon technology. Furthermore, economic factors can also impact PU [48,51].
Therefore, the following hypotheses were proposed:
H6. 
Technological factors exert a significant positive impact on PEOU;
H7. 
Organizational factors exert a significant positive impact on PU;
H8. 
Organizational factors exert a significant positive impact on PEOU;
H9. 
Environmental factors exert a significant positive impact on PU;
H10. 
Economic factors exert a significant positive impact on PU.
In summary, this study incorporates the attributes of low-carbon technology to introduce technological, organizational, environmental, and economic factors from the TOE model. These factors enhance the explanatory power of the hypothetical model, which is grounded in TAM variables such as PU, PEOU, attitude toward usage, and behavioral intention. Furthermore, based on presumed relationships among these influencing factors, we construct a hypothetical model for the adoption of low-carbon technology in hospital construction projects, as illustrated in Figure 2.

3. Survey Design and Data Collection

3.1. Survey Design

In order to validate the hypothesis path, it is imperative to conduct a questionnaire survey among relevant personnel. The questionnaire comprises two sections. The first section encompasses fundamental information about the respondents, primarily encompassing gender, age, educational attainment, professional designation, years of work experience, understanding of low-carbon policies, etc. The second section employs a 5-level Likert scale to gauge the PU, PEOU, technological factors, organizational factors, environmental factors, economic factors, attitude towards usage, and behavioral intention of the respondents. The scale ranges from ‘strongly disagree’ to ‘strongly agree’. To measure the eight factors in our model accurately, we designed 23 specific items as observation variables based on an extensive review of pertinent literature (refer to Table 1). These items aim at assessing the relationship between influencing factors in low-carbon technology adoption within hospital construction projects. To ensure comprehensiveness and effectiveness of our survey questionnaire, it underwent rigorous scrutiny and revision by experts in hospital construction, low-carbon technology consulting teams, and university researchers. After multiple rounds of discussions and modifications, the clarity and precision of questions have been significantly enhanced.

3.2. Data Collection

To ensure the representativeness and relevance of the sample, purposive sampling (also known as judgmental sampling) was adopted. This method was chosen because the study aims to target a specific group of professionals—staff in the ICDH.
Jiangsu Province was selected as the sampling region because it is located in the Yangtze River Delta, one of the most economically developed and urbanized regions in China. Tertiary A-grade hospitals in this province typically have well-established infrastructure departments and are more likely to understand advanced low-carbon technology due to stronger financial and policy support.
Before administering the formal survey, a preliminary list of target hospitals was created based on public records of Tertiary A-grade hospitals. Staff in the ICDH were then contacted through hospital administrative offices and professional engineering associations. The questionnaires were distributed via an online platform from August to September 2024, and participants were invited through internal communication channels, receiving informed consent prior to participation. The flowchart to illustrate the full workflow of the research study is shown in Figure 3.
This purposive approach ensures that the sample consists of informed, relevant, and decision-involved professionals, which enhances the validity of the results. A total of 320 responses were obtained; following rigorous screening, 309 valid datasets were retained, resulting in a valid response rate of 96.6%. The demographic characteristics of the respondents (as shown in Table 2) demonstrate broad coverage in terms of gender, age, education level, professional titles, and years of experience, suggesting that the sample is reasonably diverse and representative of the hospital construction projects.

4. Results

The study utilizes the structural equation modeling (SEM) approach and employs AMOS 21.0 software (IBM Corp, Armonk, NY, USA) to estimate the hypothetical model developed in Section 2 and analyze the data collected in Section 3. The two-step approach recommended by Anderson [60] was adopted in this study. The first step is to test the validity and reliability of the measurement model using confirmatory factor analysis (CFA). The second step is to test whether the hypotheses fit the theoretical model using the structural model.

4.1. Measured Model

Reliability is a statistical method employed to assess the consistency and stability of questionnaire results, typically measured by Cronbach’s alpha. This coefficient ranges from 0 to 1, with values above 0.7 indicating favorable outcomes [61]. In this study, SPSS 25.0 software (IBM Corp, Armonk, NY, USA) was used to compute the Cronbach’s alpha coefficients for each variable and dimension, as presented in Table 3. The obtained coefficients for each latent variable ranged from 0.84 to 0.923, signifying satisfactory reliability for this measurement model.
Validity is conducted to evaluate the stability and structure of the measurement variables for each latent variable, encompassing both convergent validity and discriminant validity [62]. Convergent validity refers to the extent of correlation between the measurement variables and their corresponding latent variables. As presented in Table 3, all composite reliabilities (CR) for the latent variables in the measurement model surpass 0.7, while the average variance extracted (AVE) meets the recommended threshold of 0.5. Moreover, standardized factor loadings (FL) for each measurement variable range from 0.783 to 0.912, satisfying the requirement of being greater than 0.5 and achieving statistical significance at a level of p < 0.001. It indicates that the convergent validity of the measurement model meets the requirements [63].
Discriminant validity pertains to non-correlation among different latent variables’ measurement variables. As demonstrated in Table 4, all correlation coefficients between latent variables vary from 0.272 to 0.571, with each square root AVE value exceeding its respective correlation coefficient with other latent variables. It indicates that the discrimination validity of the measurement model meets the requirements. These results validate that the measurement model has sufficient discriminant validity, ensuring that each latent variable captures distinct constructs. This provides a robust foundation for subsequent structural analysis, fulfilling a key methodological requirement of the study.

4.2. Structural Model

4.2.1. Model Fitness Test

The structural model is used to estimate the relationships between latent variables in the research framework. In order to ensure accurate estimation of these relationships, a goodness-of-fit test needs to be conducted. The results of the test are shown in Table 5, and all fit indices meet the recommended values, indicating a good fit for the hypothetical model. Minor improvements could be made by addressing any residual covariance or ensuring even larger and more diverse samples in future studies.

4.2.2. Hypotheses Test

Utilizing SPSS 25.0 (IBM Corp, Armonk, NY, USA) for constructing a structural equation model, Table 6 presents the standardized coefficient table, while Figure 4 illustrates the standardized model.
The results reveal that H1 (β = 0.261, p < 0.001), H2 (β = 0.389, p < 0.001), H3 (β = 0.359, p < 0.001), H6 (β = 0.472, p < 0.001), H8 (β = 0.257, p < 0.001), and H9 (β = 0.397, p < 0.001) are validated at the significant level of p < 0.001. Also, H4 (β = 0.159, p < 0.05), H5 (β = 0.192, p < 0.05), and H10 (β = 0.174, p < 0.05) are accepted at the significant level of p < 0.05. However, H7 (β = 0.035, p = 0.669) is found to be negligible and does not reach statistical significance (p > 0.05). Therefore, the effect size is not only small (β = 0.035) but also statistically unsupported, indicating that organizational factors do not exert a meaningful influence on PU in this context. Overall, 9 out of the 10 hypotheses (H1–H10) received empirical support, except for H7. Notably, the strongest effects are observed between technological factors and PEOU (β = 0.472, p < 0.001), and between PU and behavioral intention (β = 0.389, p < 0.001), aligning with the theoretical expectations of the TAM-TOE framework. Although economic factors show a smaller standardized path coefficient (β = 0.174), the effect remains statistically significant (p < 0.05), suggesting its practical relevance should not be disregarded in policy considerations.

4.2.3. Mediating Effect Test

A total of 5000 Bootstrap tests were conducted on the potential mediating paths in the hypothetical model by employing the bias-corrected bootstrap method. The results (Table 7) indicate that 9 of the 11 tested paths were statistically significant. However, for the two paths involving the influence of organizational factors on PU, the 95% confidence intervals were [−0.041, 0.082] and [−0.01, 0.021]. These findings suggest that organizational factors do not significantly moderate the effects of PU on either attitude toward usage or behavioral intention. Thus, H7 is not supported. The mediating effect test further confirms the central role of PU and PEOU as intermediaries. Organizational factors, while not significantly influencing PU directly (as indicated by the non-significant H7), do exert indirect influence via PEOU.

5. Discussions and Implications

5.1. Discussions

By conducting a comprehensive analysis and rigorous testing of the TAM-TOE hypothetical model in this study, the following key conclusions can be derived:
(1) Technological factors exhibited both direct and indirect positive effects on PEOU, whereas environmental and economic factors have similar impacts on PU. Organizational factors do not significantly affect PU directly (H7 not supported) but have an indirect impact via PEOU. This may be attributed to ICDH staff perceiving support from senior administration and the exceptional expertise and project experience of low-carbon technology consulting team as key factors that enhance their perception of the ease of use of low-carbon technology, compared to the perceived usefulness alone [64,65]. The factor loadings of TF1, TF2, and TF3 all exceed 0.8, suggesting that, in terms of technological factors, the development maturity, operational stability, and compatibility with other software are all critically important features of carbon emission calculation software. The factor loadings of OF1, OF2, and OF3 all exceed 0.8, suggesting that within organizational factors, the support from hospital senior administration staff, as well as the exceptional expertise and project experience of the low-carbon technology consulting team, are of considerable significance. The factor loadings of ENF1, ENF2, and ENF3 all exceed 0.8, suggesting that laws, regulations, and policy support; incentive measures by government agencies for technology adoption; and the implementation of low-carbon technology by other hospitals effectively capture the underlying dimensions of environmental factors. The factor loadings of EOF1, EOF2, and EOF3 all exceed 0.8, indicating a strong association between the costs of the low-carbon technology consulting teams, the cost of the technology itself, and the operational maintenance costs following the completion of hospital construction projects with economic factors.
(2) PEOU and PU have a direct positive impact on the attitude towards usage. Specifically, PEOU serves as a mediating variable between technological and organizational factors, and it positively influences the attitude towards usage. Similarly, PU functions as a mediating variable between environmental and economic factors, also exerting a positive influence on the attitude towards usage. Therefore, both variables play significant roles in the model. On the other hand, the relatively low scores for PEOU1 and PEOU2 in the PEOU suggest that staff in ICDH currently lack a sufficient understanding of low-carbon technology. Similarly, the score for ATU3 is relatively low, suggesting that staff in ICDH exhibit limited willingness to invest time in learning low-carbon technology. This implies the need to enhance staff awareness and adoption of low-carbon technology through more accessible and intuitive software tools and effective dissemination strategies.
(3) The total influence coefficients of technological factors, organizational factors, environmental factors, and economic factors on behavioral intention are 0.06, 0.049, 0.191, and 0.084, respectively. This indicates that environmental factors exert the strongest influence on promoting the adoption of low-carbon technology in hospital construction projects. Therefore, enhancing the legal, regulatory, and policy frameworks for low-carbon technology, as well as improving the application environment, are crucial measures for promoting its implementation in hospital construction. This finding aligns with conclusions from previous studies [66].
(4) This study utilized SEM to test complex latent constructs. However, several limitations should be acknowledged. First, SEM assumes linear relationships among variables, which may oversimplify real-world interactions in behavioral adoption processes. Second, SEM requires an adequate sample size to ensure model stability and parameter reliability. While sample size (N = 309) meets general SEM criteria, future studies with larger and more diverse samples are encouraged to improve generalizability and robustness.

5.2. Implications

The implications are as follows:(1) Based on the results of the validated TAM-TOE model discussed in Section 5.1, this section proposes practical strategies to enhance the adoption of low-carbon technologies in hospital construction projects. These recommendations are structured around the significant paths identified through SEM analysis and are further categorized by key stakeholder groups to facilitate targeted implementation.
In regards to strengthening environmental incentives (H9), governments should prioritize unified low-carbon regulatory frameworks and provide financial support (e.g., performance-based subsidies) to foster a policy environment that enhances PU [67]. In regards to enhancing technological tool usability (H6), low-carbon consulting teams should improve compatibility, stability, and automation of carbon emission calculation software to improve PEOU and encourage broader use [68]. In regards to ensuring economic feasibility (H10), although the effect size is moderate, cost remains a key barrier. Governments and institutions should develop return on investment (ROI) models and co-funding mechanisms to support adoption [69]. In regards to promoting organizational readiness via PEOU (H8), while organizational factors do not directly affect PU, they shape behavioral intention through PEOU. Staff training, managerial support, and interdepartmental collaboration are essential.
(2) Based on the validated TAM-TOE framework, policy recommendations can be tailored to different stakeholders:
Government departments should establish unified carbon emission policies and implement incentive measures targeting low-carbon technology [70]. Currently, there are significant disparities in carbon emission-related standards and regulations among provinces and cities, which urgently require gradual integration and the establishment of a unified national standard system. Additionally, it is suggested to incorporate carbon emission assessments into the preliminary review process of construction projects, such as integrating them into the environmental impact assessment framework. Furthermore, the government could provide effective incentives to construction entities through a combination of base subsidies and performance-based subsidies.
Academic institutions and industry alliances can bridge the gap between policy and practice through empirical research, case studies, and technical guidance specific to the healthcare construction sector [71,72]. Academic institutions and industry alliances are recommended to regularly organize academic conferences related to low-carbon technology, such as inviting experts to deliver lectures or reports on low-carbon technology at academic conferences like the annual conference on hospital construction. Additionally, the industry is encouraged to actively collaborate with government departments in the formulation of carbon emission policies and standards specific to the hospital sector [73,74].
Hospital senior managers must foster an innovation-oriented culture, allocate resources for technical training, and encourage internal collaboration to support low-carbon adoption [75].
Staff in the ICDH should closely monitor national policy developments and trends in the construction industry while continuously enhancing their understanding of low-carbon technology. They should proactively report the current status and advantages of low-carbon technology to hospital senior managers outside the construction field, specifically highlighting that the application of low-carbon technology during the construction phase can significantly reduce operational and maintenance costs after the completion of hospital facilities.
Low-carbon consulting teams can collaborate with government departments, universities, and project owner to jointly conduct research and establish a low-carbon technology database and inventory database, thereby providing practical low-carbon implementation pathways for hospital construction projects. Additionally, these consulting teams should strengthen the maintenance and updates of carbon emission calculation software to continuously enhance its stability and compatibility with other building-related software [76]. Furthermore, low-carbon technology consulting teams should engage in regular technical exchanges with design institutes and construction companies to assist hospital infrastructure management personnel in selecting cost-effective carbon emission control technologies.
(3) As concluded in Section 5.1, environmental factors have the greatest influence on driving the adoption of low-carbon technology in hospital construction projects. Additionally, the recommendations proposed based on the TAM-TOE framework are highly aligned with certain aspects of the SDGs. Emphasis on software innovation and technical integration supports SDG 9 (Industry, Innovation, and Infrastructure), which promotes sustainable and resilient infrastructure through innovation. The prioritization of cost-effective low-carbon technologies aligns with SDG 12 (Responsible Consumption and Production), encouraging efficient resource use in the construction phase. Strengthening carbon standards, policy frameworks, and government incentives reflects SDG 13 (Climate Action), which advocates urgent actions to combat climate change. Moreover, these strategies contribute to SDG 3 (Good Health and Well-being) by promoting the development of low-carbon hospitals that offer healthier built environments. Therefore, this study not only identifies the key factors affecting the adoption of low-carbon technology but also proposes a series of comprehensive strategies to promote the achievement of multiple sustainable development goals related to hospital development.

6. Conclusions

The implementation of carbon emission reduction has become a global imperative, and hospital buildings, as critical public infrastructure, have emerged as a key focus in achieving the national ‘dual carbon’ goals. Given China’s large population and extensive number of hospitals, promoting the application of low-carbon technologies in hospital construction projects holds significant practical importance.
This study set out to investigate the key factors influencing the adoption of low-carbon technology in hospital construction projects by staff in the ICDH. Through integrating the TAM and the TOE framework, a comprehensive TAM-TOE model was constructed and validated using SEM based on 309 valid responses from Tertiary A-grade hospitals in Jiangsu Province, China. The model confirms that environmental and economic factors significantly influence PU, while technological and organizational factors shape perceived ease of use PEOU. PEOU and PU, in turn, influence users’ attitudes and behavioral intention toward adopting low-carbon technology. Among these, environmental factors exerted the strongest total effect on behavioral intention, highlighting the importance of external policy and institutional context. Notably, hypothesis H7 was not supported, suggesting that organizational support enhances adoption primarily through improving PEOU rather than PU directly. These findings successfully fulfill the study’s original objective of identifying and empirically validating the behavioral, technological, organizational, environmental, and economic drivers of low-carbon technology adoption in hospital construction.
Several limitations should be acknowledged. First, SEM assumes linear relationships among variables, which may oversimplify real-world interactions in behavioral adoption processes. Future research could explore non-linear or interaction-based models to better capture these dynamics. Second, SEM requires an adequate sample size to ensure model stability and parameter reliability. While sample size (N = 309) meets general SEM criteria, future studies with larger and more diverse samples are encouraged to improve generalizability and robustness. Third, the sample is geographically concentrated in Jiangsu Province and limited to ICDH staff. Broader validation in different regions, and involving other stakeholders such as consultants, designers, or contractors, would enhance the model’s generalizability [77].
Nonetheless, this work provides a valuable foundation for future extensions. The TAM-TOE framework can be expanded to explore the full lifecycle carbon performance of hospital buildings, including embodied carbon, greenhouse gas (GHG) accounting, and carbon lifecycle assessment (LCA). This can help align construction-phase technology decisions with operation and maintenance (O&M) strategies, strengthening the hospital’s decarburization pathway across its entire lifecycle. Moreover, the framework can contribute to global sustainability agendas, such as SDG 9, SDG 12 and SDG 13, by enabling data-driven and behaviorally informed policies.
In conclusion, this study provides theoretical and empirical insights into hospital construction decarburization, bridges behavioral models with sustainability science, and offers pathways to integrate low-carbon decision-making across construction and operations phases.

Author Contributions

Conceptualization, L.J. and D.L.; methodology, L.J.; software, L.J. and Y.Z. (Yi Zhao); validation, L.J., D.L. and Y.Z. (Yubin Zhang); formal analysis, L.J.; investigation, L.J. and Y.Z. (Yubin Zhang); resources, D.L.; data curation, L.J. and Y.Z. (Yi Zhao); writing—original draft preparation, L.J.; writing—review and editing, D.L.; supervision, D.L.; funding acquisition, D.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

All methods were carried out in accordance with relevant guidelines and regulations, and all experimental protocols were approved by Jiangsu Province Hospital (jsph20240715) on 15 July 2024.

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 on reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. 2023 Global Status Report for Buildings and Construction. Available online: https://unfccc.int/ttclear/misc_/StaticFiles/gnwoerk_static/tn_meetings/00cf22a4049c4ece9f414e190def4202/8dff87ea3e1e4e7ba7d349a83ed04cbd.pdf (accessed on 20 June 2024).
  2. Huang, G.; Li, D.; Ng, S.T.; Wang, L.; Zhang, Y. Vision-Based Personal Thermal Comfort Modeling under Facial Occlusion Scenarios. Energy Build. 2025, 335, 115566. [Google Scholar] [CrossRef]
  3. Zhang, J.; Yan, Z.; Bi, W.; Ni, P.; Lei, F.; Yao, S.; Lang, J. Prediction and Scenario Simulation of the Carbon Emissions of Public Buildings in the Operation Stage Based on an Energy Audit in Xi’an, China. Energy Policy 2023, 173, 113396. [Google Scholar] [CrossRef]
  4. Karliner, J.; Slotterback, S.; Boyd, R.; Ashby, B.; Steele, K.; Wang, J. Health Care’s Climate Footprint: The Health Sector Contribution and Opportunities for Action. Eur. J. Public Health 2020, 30, ckaa165.843. [Google Scholar] [CrossRef]
  5. Li, H.; Wu, Y.; Xin, B.; Xu, M.; Wu, S. Optimal Carbon–Neutral Strategies in the Healthcare System: A Three-Stage Stackelberg Game Model. Transp. Res. Part E: Logist. Transp. Rev. 2025, 198, 104128. [Google Scholar] [CrossRef]
  6. Alshqaqeeq, F.; Amin Esmaeili, M.; Overcash, M.; Twomey, J. Quantifying Hospital Services by Carbon Footprint: A Systematic Literature Review of Patient Care Alternatives. Resour. Conserv. Recycl. 2020, 154, 104560. [Google Scholar] [CrossRef]
  7. Quitmann, C.; Sauerborn, R.; Herrmann, A. Gaps in Reporting Greenhouse Gas Emissions by German Hospitals—A Systematic Grey Literature Review. Sustainability 2021, 13, 1430. [Google Scholar] [CrossRef]
  8. Schmidt, L.; Bohnet-Joschko, S. Planetary Health and Hospitals’ Contribution—A Scoping Review. Int. J. Environ. Res. Public Health 2022, 19, 13536. [Google Scholar] [CrossRef] [PubMed]
  9. Quitmann, C.; Sauerborn, R.; Danquah, I.; Herrmann, A. Reducing the Carbon Footprint of a German University Hospital: Perspectives from Hospital Stakeholders. J. Clim. Chang. Health 2023, 12, 100247. [Google Scholar] [CrossRef]
  10. Bozoudis, V.; Sebos, I. The Carbon Footprint of Transport Activities of the 401 Military General Hospital of Athens. Environ. Model. Assess. 2021, 26, 155–162. [Google Scholar] [CrossRef]
  11. Tomiak, W.; Kupis, R.; Kowalik, J.; Skrobisz, K.; Milewska-Augusto, M. Green hospitals: A remedy for the climate change burden on the polish healthcare system. Prospect. Pharm. Sci. 2024, 22, 45–49. [Google Scholar] [CrossRef]
  12. Guo, S.; Wang, W.; Zhou, Y. Research on Energy Saving and Economy of Old Buildings Based on Parametric Design: A Case Study of a Hospital in Linyi City, Shandong Province. Sustainability 2022, 14, 16681. [Google Scholar] [CrossRef]
  13. Townsend, K.; Dougherty, W.; Housley, R.; Walker, J.; Cruttenden-Wood, D. Exploring the Carbon Impact of Virtual Wards in a Large Acute Hospital. Computers 2025, 14, 230. [Google Scholar] [CrossRef]
  14. As, M.; Bilir, T. Enhancing Energy Efficiency and Cost-Effectiveness While Reducing CO2 Emissions in a Hospital Building. J. Build. Eng. 2023, 78, 107792. [Google Scholar] [CrossRef]
  15. Cimprich, A.; Young, S.B. Environmental Footprinting of Hospitals Organizational Life Cycle Assessment of a Canadian Hospital. J. Ind. Ecol. 2023, 27, 1335–1353. [Google Scholar] [CrossRef]
  16. Hensher, M. Health Technology Assessment and Healthcare Environmental Sustainability: Prioritizing Effort and Maximizing Impact. Int. J. Technol. Assess. Health Care 2024, 40, e25. [Google Scholar] [CrossRef]
  17. Bi, P.; Hansen, A. Carbon Emissions and Public Health: An Inverse Association? Lancet Planet. Health 2018, 2, e8–e9. [Google Scholar] [CrossRef] [PubMed]
  18. Verlis, K.; Haddock, R.; Barratt, A. The Current State of Sustainable Healthcare in Australia. Aust. Health Rev. 2024, 48, 489–496. [Google Scholar] [CrossRef] [PubMed]
  19. Malik, A.; Lenzen, M.; McAlister, S.; McGain, F. The Carbon Footprint of Australian Health Care. Lancet Planet. Health 2018, 2, e27–e35. [Google Scholar] [CrossRef]
  20. Yue, J.; Li, W.; Cheng, J.; Xiong, H.; Xue, Y.; Deng, X.; Zheng, T. A Dynamic Calculation Model of the Carbon Footprint in the Life Cycle of Hospital Building: A Case Study in China. Eng. Constr. Archit. Manag. 2023, 30, 4735–4761. [Google Scholar] [CrossRef]
  21. Yu, D.; Tan, X.; Liu, Z.; Li, D.; Wang, Z.; Yan, P.; Ni, J. Energy Saving and Carbon Reduction Schemes for Hospital with Photovoltaic Power Generation and System Upgrading Technology. Heliyon 2023, 9, e21447. [Google Scholar] [CrossRef]
  22. Motsi, L.; Chimbo, B. Success Factors for Evidence-Based Healthcare Practice Adoption. S. Afr. J. Inf. Manag. 2023, 25, a1622. [Google Scholar] [CrossRef]
  23. GB/T50378-2019; Assessment Standard for Green Building. China Building Industry Press: Beijing, China, 2019.
  24. Davis, F.D.; Bagozzi, R.P.; Warshaw, P.R. User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Manag. Sci. 1989, 35, 982–1003. [Google Scholar] [CrossRef]
  25. Nagy, A.S.; Tumiwa, J.R.; Arie, F.V.; Erdey, L. An Exploratory Study of Artificial Intelligence Adoption in Higher Education. Cogent Educ. 2024, 11, 2386892. [Google Scholar] [CrossRef]
  26. Helmiatin; Hidayat, A.; Kahar, M.R. Investigating the Adoption of AI in Higher Education: A Study of Public Universities in Indonesia. Cogent Educ. 2024, 11, 2380175. [Google Scholar] [CrossRef]
  27. Magboul, I.; Jebreel, M.; Shawtri, F.; Qabajeh, M.; Shanti, A.; Alqudah, M.; Abu Huson, Y. Factors Influencing Accounting Information System Usage by Oil Companies & Performance Outcomes. Cogent Bus. Manag. 2024, 11, 2369211. [Google Scholar] [CrossRef]
  28. Thi Hoa Sen, L.; Chou, P.; Dacuyan, F.B.; Nyberg, Y.; Wetterlind, J. Barriers and Enablers of Digital Extension Services’ Adoption among Smallholder Farmers: The Case of Cambodia, the Philippines and Vietnam. Int. J. Agric. Sustain. 2024, 22, 2368351. [Google Scholar] [CrossRef]
  29. Ozcan-Deniz, G.; Ozorhon, B.; Kaya, O.C. Building Information Modeling (BIM) Integration in Developing Countries: An in-Depth Examination of Adoption Factors from Public Clients’ Perspectives. Int. J. Archit. Comput. 2024, 23, 481–491. [Google Scholar] [CrossRef]
  30. Liu, Y.; Konduri, S.; Park, C. A Study on the Factors Influencing Willingness to Use Virtual Reality Systems for External Evaluation of Buildings. Buildings 2024, 14, 3714. [Google Scholar] [CrossRef]
  31. Lee, S.-Y.; Chang, C.-Y. Factors Influencing Nurses’ Satisfaction to Online Learning Approach: A Cross ICU Analysis. BMC Nurs. 2024, 23, 548. [Google Scholar] [CrossRef] [PubMed]
  32. Drazin, R. The Processes of Technological Innovation. Technol. Transf. Winter 1990, 16, 45–46. [Google Scholar] [CrossRef]
  33. N’Dri, A.B.; Su, Z. Successful Configurations of Technology–Organization–Environment Factors in Digital Transformation: Evidence from Exporting Small and Medium-Sized Enterprises in the Manufacturing Industry. Inf. Manag. 2024, 61, 104030. [Google Scholar] [CrossRef]
  34. Ninčević Pašalić, I.; Ćukušić, M. Understanding E-Participation Adoption: Exploring Technological, Organizational, and Environmental Factors. Technol. Forecast. Soc. Chang. 2024, 207, 123633. [Google Scholar] [CrossRef]
  35. Haripin; Warsono, S. Analysis of the Acceptance of E-Learning Platform (SIDEK-Edu) among High School Students with UTAUT2-TOE Approach: Implications for Digital Learning. Int. J. Inf. Learn. Technol. 2024, 41–45, 585–600. [Google Scholar] [CrossRef]
  36. Xue, H.; Zhang, S.; Wu, Z.; Zhang, L. How to Improve the Smart Construction Technology Usage Behavior of Construction Enterprise Employees?—TOE Framework Based on Configuration Study. Eng. Constr. Archit. Manag. 2023, 32, 785–804. [Google Scholar] [CrossRef]
  37. Sulaiman, T.T.; Mahomed, A.S.B.; Rahman, A.A.; Hassan, M. Understanding Antecedents of Learning Management System Usage among University Lecturers Using an Integrated TAM-TOE Model. Sustainability 2023, 15, 1885. [Google Scholar] [CrossRef]
  38. Bach, M.P.; Meško, M.; Stjepić, A.M.; Khawaja, S.; Quershi, F.H. Understanding Determinants of Management Simulation Games Adoption in Higher Educational Institutions Using an Integrated Technology Acceptance Model/Technology–Organisation–Environment Model: Educator Perspective. Information 2025, 16, 45. [Google Scholar] [CrossRef]
  39. Songera, D.; Young, R.; Davis, K.A. Social Architecture for Sustainable It Implementation in AEC/EPC. 2001. Available online: http://www.irbnet.de/daten/iconda/CIB20255.pdf (accessed on 1 July 2024).
  40. Kimiagari, S.; Baei, F. Promoting E-Banking Actual Usage: Mix of Technology Acceptance Model and Technology-Organisation-Environment Framework. Enterp. Inf. Syst. 2022, 16, 8–9. [Google Scholar] [CrossRef]
  41. Na, S.; Heo, S.; Han, S.; Shin, Y.; Roh, Y. Acceptance Model of Artificial Intelligence (AI)-Based Technologies in Construction Firms: Applying the Technology Acceptance Model (TAM) in Combination with the Technology–Organisation–Environment (TOE) Framework. Buildings 2022, 12, 90. [Google Scholar] [CrossRef]
  42. Yu, X.; Sheng, G.; Sun, D.; He, R. Effect of Digital Multimedia on the Adoption of Agricultural Green Production Technology among Farmers in Liaoning Province, China. Sci. Rep. 2024, 14, 13092. [Google Scholar] [CrossRef]
  43. Sanchís-Pedregosa, C.; Vizcarra-Aparicio, J.-M.; Leal-Rodríguez, A.L. BIM: A Technology Acceptance Model in Peru. J. Inf. Technol. Constr. 2020, 25, 99–108. [Google Scholar] [CrossRef]
  44. Desmaryani, S.; Soleh, A.; Irmanelly; Wiarta, I. Integration of Technology Acceptance Models and Government Support to Improve Digital Literacy. Heliyon 2024, 10, e34086. [Google Scholar] [CrossRef] [PubMed]
  45. Deng, M.; Guo, Y. A Study of Safety Acceptance and Behavioral Interventions for Autonomous Driving Technologies. Sci. Rep. 2022, 12, 17891. [Google Scholar] [CrossRef] [PubMed]
  46. Kittipanya-ngam, P.; Tan, K.H.; Cavite, H.J. Future-Ready AI: A Framework for Ethical and Sustainable Adoption. Technol. Soc. 2025, 83, 102993. [Google Scholar] [CrossRef]
  47. Jo, H.; Bang, Y. Understanding Continuance Intention of Enterprise Resource Planning (ERP): TOE, TAM, and IS Success Model. Heliyon 2023, 9, e21019. [Google Scholar] [CrossRef]
  48. Katebi, A.; Homami, P.; Najmeddin, M. Acceptance Model of Precast Concrete Components in Building Construction Based on Technology Acceptance Model (TAM) and Technology, Organization, and Environment (TOE) Framework. J. Build. Eng. 2022, 45, 103518. [Google Scholar] [CrossRef]
  49. Sampa, M.B.; Abdul Aziz, N.H.; Rahman, M.S.; Hasan, M.; Ab. Aziz, N.A. Factors Influencing the Adoption and Acceptance of eHealth in Malaysia: A Systematic Review. Crit. Public Health 2025, 35, 2519780. [Google Scholar] [CrossRef]
  50. Zhao, Y.; Sun, Y.; Zhou, Q.; Cui, C.; Liu, Y. How A/E/C Professionals Accept BIM Technologies in China: A Technology Acceptance Model Perspective. Eng. Constr. Archit. Manag. 2023, 30, 4569–4589. [Google Scholar] [CrossRef]
  51. Lee, M.; Chai, C.; Xiong, Y.; Gui, H. Technology Acceptance Model for Building Information Modelling Based Virtual Reality (BIM-VR) in Cost Estimation. J. Inf. Technol. Constr. 2022, 27, 914–925. [Google Scholar] [CrossRef]
  52. Xiang, W.; Gao, J. Do Not Be Anticlimactic: Farmers’ Behavior in the Sustainable Application of Green Agricultural Technology—A Perceived Value and Government Support Perspective. Agriculture 2023, 13, 247. [Google Scholar] [CrossRef]
  53. Dai, Q.; Cheng, K. What Drives the Adoption of Agricultural Green Production Technologies? An Extension of TAM in Agriculture. Sustainability 2022, 14, 14457. [Google Scholar] [CrossRef]
  54. Mousa, A.H.; Aljshamee, M.; Mohammed, A.H.; Mousa, S.H.; Obaid, H.A. A Systematic Literature Review: Measuring User Acceptance Models in e-Learning Adopted in Higher Education Institutes. The 8th International Conference on Applied Science and Technology (ICAST 2020). AIP Conf. Proc. 2020, 2290, 040021. [Google Scholar] [CrossRef]
  55. Jo, H.; Bang, Y. Analyzing ChatGPT Adoption Drivers with the TOEK Framework. Sci. Rep. 2023, 13, 22606. [Google Scholar] [CrossRef]
  56. Li, L.; Min, X.; Guo, J.; Wu, F. The Influence Mechanism Analysis on the Farmers’ Intention to Adopt Internet of Things Based on UTAUT-TOE Model. Sci. Rep. 2024, 14, 15016. [Google Scholar] [CrossRef]
  57. Davila Laguna, R.F.; Lizarzaburu Aguinaga, D.; Caceres Torres, D.E.; Larroche Cueto, B.A. Soft Skills and the Use of Industry 4.0 as Determinants of Professional Development in Engineering Graduates: A SEM Approach. Sustain. Futures 2025, 10, 100742. [Google Scholar] [CrossRef]
  58. Wong, L.-W.; Leong, L.-Y.; Hew, J.-J.; Tan, G.W.-H.; Ooi, K.-B. Time to Seize the Digital Evolution: Adoption of Blockchain in Operations and Supply Chain Management among Malaysian SMEs. Int. J. Inf. Manag. 2020, 52, 101997. [Google Scholar] [CrossRef]
  59. Ren, Z.; Zhong, K. Driving Mechanism of Subjective Cognition on Farmers’ Adoption Behavior of Straw Returning Technology: Evidence from Rice and Wheat Producing Provinces in China. Front. Psychol. 2022, 13, 922889. [Google Scholar] [CrossRef]
  60. Anderson, J.C.; Gerbing, D.W. Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychol. Bull. 1988, 103, 411–423. [Google Scholar] [CrossRef]
  61. Zhou, Q.; Deng, X.; Hwang, B.-G.; Ji, W. Integrated Framework of Horizontal and Vertical Cross-Project Knowledge Transfer Mechanism within Project-Based Organizations. J. Manag. Eng. 2020, 36, 04020062. [Google Scholar] [CrossRef]
  62. Huang, G.; Li, D.; Yu, L.; Yang, D.; Wang, Y. Factors Affecting Sustainability of Smart City Services in China: From the Perspective of Citizens’ Sense of Gain. Habitat Int. 2022, 128, 102645. [Google Scholar] [CrossRef]
  63. Abulail, R.N.; Badran, O.N.; Shkoukani, M.A.; Omeish, F. Exploring the Factors Influencing AI Adoption Intentions in Higher Education: An Integrated Model of DOI, TOE, and TAM. Computers 2025, 14, 230. [Google Scholar] [CrossRef]
  64. Henao-Ramírez, A.M.; López-Zapata, E. Analysis of the Factors Influencing Adoption of 3D Design Digital Technologies in Colombian Firms. J. Enterp. Inf. Manag. 2022, 35, 429–454. [Google Scholar] [CrossRef]
  65. Luo, G.; Serrão, C.; Liang, D.; Zhou, Y. A Relevance-Based Technology–Organisation–Environment Model of Critical Success Factors for Digital Procurement Adoption in Chinese Construction Companies. Sustainability 2023, 15, 12260. [Google Scholar] [CrossRef]
  66. Terenzi, M.; Ogheri, C.; Locatelli, G. Understanding Homeowners’ Behavioural Determinants: A Study on House Retrofitting and PV Adoption. Appl. Energy 2025, 397, 126319. [Google Scholar] [CrossRef]
  67. Tang, X.; Li, J. Study of the Mechanism of Digitalization Boosting Urban Low-Carbon Transformation. Front. Environ. Sci. 2022, 10, 982864. [Google Scholar] [CrossRef]
  68. Zhao, L.; Zhang, W.; Wang, W. BIM-Based Multi-Objective Optimization of Low-Carbon and Energy-Saving Buildings. Sustainability 2022, 14, 13064. [Google Scholar] [CrossRef]
  69. Oguntuase, O.J.; Adu, O.B.; Obayori, O.S. How Do Individual-Level Factors Influence the Adoption of Low-Carbon Technology? Proposing and Validating the Bioeconomy Technology Acceptance Model in the Context of Africa. Front. Sustain. 2023, 4, 1148001. [Google Scholar] [CrossRef]
  70. Do, M.-H.; Huang, Y.-F.; Hoang, T.-T. Blockchain Adoption in Green Supply Chains: Analyzing Key Drivers, Green Innovation, and Expected Benefits. J. Theor. Appl. Electron. Commer. Res. 2025, 20, 39. [Google Scholar] [CrossRef]
  71. Sun, T.; Di, K.; Hu, J.; Shi, Q.; Irfan, M. Digitally Empowered Green Public Services in Environmentally Vulnerable Areas: Insights from SEM-ANN Analysis. J. Retail. Consum. Serv. 2025, 84, 104216. [Google Scholar] [CrossRef]
  72. Huang, G.; Li, D.; Zhou, S.; Ng, S.T.; Wang, W.; Wang, L. Public Opinion on Smart Infrastructure in China: Evidence from Social Media. Util. Policy 2025, 93, 101886. [Google Scholar] [CrossRef]
  73. Du, Q.; Pang, Q.; Bao, T.; Guo, X.; Deng, Y. Critical Factors Influencing Carbon Emissions of Prefabricated Building Supply Chains in China. J. Clean. Prod. 2021, 280, 124398. [Google Scholar] [CrossRef]
  74. Ahankoob, A.; Abbasnejad, B.; Aranda-Mena, G. Building Information Modelling (BIM) Acceptance and Learning Experiences in Undergraduate Construction Education: A Technology Acceptance Model (TAM) Perspective—An Australian Case Study. Buildings 2025, 15, 1804. [Google Scholar] [CrossRef]
  75. Rotimi, F.E.; Silva, C.; Ramanayaka, C.E.D.; Rotimi, J.O.B. Digital Technology Knowledge Transfer Enablers Amongst End-Users in Architecture, Engineering, and Construction Organisations: New Zealand Insights. Buildings 2025, 15, 772. [Google Scholar] [CrossRef]
  76. Cudjoe, D.; Zhu, B.; Wang, H. The Role of Incentive Policies and Personal Innovativeness in Consumers’ Carbon Footprint Tracking Apps Adoption in China. J. Retail. Consum. Serv. 2024, 79, 103861. [Google Scholar] [CrossRef]
  77. Zhou, S.; Thomas Ng, S.; Huang, G.; Dao, J.; Li, D. Extracting Interrelated Information from Road-Related Social Media Data. Adv. Eng. Inform. 2022, 54, 101780. [Google Scholar] [CrossRef]
Buildings 15 02703 g001
Figure 1. TAM-TOE theoretical framework.
Figure 1. TAM-TOE theoretical framework.
Buildings 15 02703 g001
Buildings 15 02703 g002
Figure 2. Hypothetical model based on TAM-TOE framework.
Figure 2. Hypothetical model based on TAM-TOE framework.
Buildings 15 02703 g002
Buildings 15 02703 g003
Figure 3. The flowchart for the full workflow.
Figure 3. The flowchart for the full workflow.
Buildings 15 02703 g003
Buildings 15 02703 g004
Figure 4. Result of SEM analysis.
Figure 4. Result of SEM analysis.
Buildings 15 02703 g004
Table 1. Latent variables and observed variables in the hypothetical model.
Table 1. Latent variables and observed variables in the hypothetical model.
Latent VariableObserved VariableSource
PUPU1: Through the application of low-carbon technology, staff in the ICDH can gain a comprehensive understanding of carbon emissions across various stages of hospital construction projects.[24,52]
PUPU2: The application of low-carbon technology can effectively mitigate carbon emissions in hospital construction projects.
PUPU3: The application of low-carbon technology contributes to optimizing the design and construction processes of hospital construction projects, thereby enhancing their overall implementation quality.
PEOUPEOU1: Low-carbon technology and the primary technologies it encompasses are characterized by clarity and precision.[24,53]
PEOUPEOU2: Low-carbon technology demonstrates high acceptability and comprehensibility in terms of conceptual understanding and content mastery.
PEOUPEOU3: The principles and processes of carbon emission calculation offer a relatively high level of accessibility in terms of learning and understanding.
Technological FactorsTF1: Currently, the development of carbon emission calculation software has reached a relatively mature stage.[40,54,55,56,57]
Technological FactorsTF2: Carbon emission calculation software possesses excellent compatibility and can be effectively integrated with building-related software such as CAD, BIM, and engineering quantity list preparation.
Technological FactorsTF3: Carbon emission calculation software exhibits excellent stability and high reliability during practical applications.
Organizational FactorsOF1: Hospital senior managers demonstrate strong support for the application of low-carbon technologies in terms of economic resource allocation and policy guidance.[51,55]
Organizational FactorsOF2: The members of the low-carbon technology consulting team possess strong professional expertise and comprehensive overall competence.
Organizational FactorsOF3: Members of the low-carbon technology consulting team possess extensive engineering practice experience similar to hospital construction projects.
Environmental FactorsENF1: Government departments issue relevant policies mandatorily requiring the implementation of low-carbon technology during the construction project implementation process.[40,44,45,56]
Environmental FactorsENF2: Government departments grant corresponding rewards to construction entities that actively apply low-carbon technology in construction projects.
Environmental FactorsENF3: Other hospitals have actively incorporated low-carbon technology into their construction projects.
Economic FactorsEOF1: The cost associated with introducing a low-carbon technology consulting team into a hospital construction project is acceptable. [48,58]
Economic FactorsEOF2: The cost of incorporating low-carbon technology into hospital construction projects is deemed acceptable.
Economic FactorsEOF3: The utilization of low-carbon technology can lead to reduced operational maintenance costs following the completion of hospital construction projects.
Attitude Towards UsageATU1: The low-carbon technology holds great promise and exhibits significant potential for development.[24,59]
Attitude Towards UsageATU2: I advocate the prudent adoption of low-carbon technology in hospital construction projects.
Attitude Towards UsageATU3: I am committed to dedicating time towards comprehending and acquiring knowledge in the field of low-carbon technology.
Behavioral IntentionBI1: In the event of new hospital construction projects, I will employ low-carbon technology to ensure adherence to sustainable practices.[24]
Behavioral IntentionBI2: I will advocate for the adoption of low-carbon technology in hospital construction projects to other hospitals.
Table 2. Respondent demographic information.
Table 2. Respondent demographic information.
CharacteristicsCategoryFrequencyProportion (%)
GenderMale21268.6
Female9731.4
AgeUnder 305417.5
31–4010032.4
41–509831.7
Over 505718.4
Education levelAssociate degree or less175.5
Undergraduate degree19161.8
Postgraduate degree or more10132.7
Professional titleNone6320.4
Junior professional designation216.8
Intermediate professional designation10233
Senior professional designation12339.8
Years of work experience0–55618.1
6–103310.7
11–209932
Over 20 12139.2
Do you concur with the comprehension of the ‘dual carbon‘ policy?Disagree175.5
Neutral16653.7
Agree9330.1
Strongly agree3310.7
Do you concur with the comprehension of low-carbon technology?Disagree258.1
Neutral17556.6
Agree8928.8
Strongly agree206.5
Table 3. Reliability and validity test of measure model.
Table 3. Reliability and validity test of measure model.
Latent VariableItemsFLMeanSDAlphaCRAVE
PUPU10.8773.411.0610.8910.8880.726
PU20.8683.471.037
PU30.8103.531.049
PEOUPEOU10.8703.210.9920.8780.8790.707
PEOU20.8473.270.978
PEOU30.8053.611.009
Technological FactorsTF10.9023.591.0040.9230.9230.8
TF20.9123.591.008
TF30.8693.681.101
Organizational FactorsOF10.8763.731.0200.9040.9040.759
OF20.8613.621.114
OF30.8763.751.087
Environmental FactorsENF10.8553.661.0620.8740.8750.7
ENF20.8533.781.095
ENF30.8013.541.024
Economic FactorsEOF10.8413.460.9880.8960.8950.74
EOF20.8493.541.040
EOF30.8903.581.025
Attitude towards UsageATU10.7833.490.9760.8720.8760.702
ATU20.8733.481.031
ATU30.8553.250.964
Behavioral IntentionBI10.8463.551.1520.840.8130.684
BI20.8083.351.004
Table 4. Correlations among latent variables.
Table 4. Correlations among latent variables.
Latent VariablePUPEOUTechnological FactorsOrganizational FactorsEnvironmental FactorsEconomic FactorsAttitude Towards UsageBehavioral Intention
PU0.852
PEOU0.3820.841
Technological Factors0.4330.5080.895
Organizational Factors0.3490.3960.3870.871
Environmental Factors0.4830.4660.4430.4510.837
Economic Factors0.3720.3980.3640.5710.4030.860
Attitude towards Usage0.3830.2930.2720.4170.3310.3960.838
Behavioral Intention0.4200.4040.4200.4010.3810.3850.3690.827
Table 5. The recommended and actual value of fit indices.
Table 5. The recommended and actual value of fit indices.
IndexRecommended ValueActual Value
X2/df<31.515
GFI>0.90.921
AGFI>0.80.898
NFI>0.90.936
IFI>0.90.977
CFI>0.90.977
TLI>0.90.973
RMSEA>0.080.041
Table 6. Hypotheses test of the theoretical model.
Table 6. Hypotheses test of the theoretical model.
HypothesisPathStandardized Path CoefficientSEtpResult
H1Attitude towards Usage → Behavioral Intention0.2610.0733.822***Supported
H2PU → Behavioral Intention0.3890.0625.534***Supported
H3PU → Attitude towards Usage0.3590.0555.324***Supported
H4PEOU → PU0.1590.0712.4990.012 *Supported
H5PEOU → Attitude towards Usage0.1920.0612.9190.004 **Supported
H6Technological Factors → PEOU0.4720.0537.466***Supported
H7Organizational Factors → PU0.0350.0770.4280.669Not supported
H8Organizational Factors → PEOU0.2570.0514.249***Supported
H9Environmental Factors → PU0.3970.0775.660***Supported
H10Economic Factors → PU0.1740.0752.3330.02 *Supported
Note: *** p < 0.001, ** p < 0.01, * p < 0.05.
Table 7. Mediating effect of the theoretical model.
Table 7. Mediating effect of the theoretical model.
PathIndirect EffectSEBias-Corrected
95% Confidence Interval		p
LowerUpper
Technological Factors → PEOU → Attitude Towards Usage → Behavioral Intention0.0240.0140.0050.0590.007
Technological Factors → PEOU → PU → Behavioral Intention0.0290.0170.0030.0730.029
Technological Factors → PEOU → PU → Attitude Towards Usage → Behavioral Intention0.0070.0040.0010.0180.016
Organizational Factors → PU → Behavioral Intention0.0130.031−0.0410.0820.612
Organizational Factors → PU → Attitude Towards Usage → Behavioral Intention0.0030.008−0.010.0210.569
Organizational Factors → PEOU → Attitude Towards Usage → Behavioral Intention0.0130.0090.0020.0410.006
Organizational Factors → PEOU → PU → Behavioral Intention0.0160.0100.0020.0430.020
Organizational Factors → PEOU → PU → Attitude Towards Usage → Behavioral Intention0.0040.0020.0010.0110.013
Environmental Factors → PU → Behavioral Intention0.1540.0400.0850.2410.000
Environmental Factors → PU → Attitude Towards Usage → Behavioral Intention0.0370.0140.0160.0750.000
Economic Factors → PU → Behavioral Intention0.0680.030.0150.1330.011
Economic Factors → PU → Attitude Towards Usage → Behavioral Intention0.0160.0090.0040.0410.007
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Jin, L.; Li, D.; Zhang, Y.; Zhao, Y. Analyzing Influencing Factors of Low-Carbon Technology Adoption in Hospital Construction Projects Based on TAM-TOE Framework. Buildings 2025, 15, 2703. https://doi.org/10.3390/buildings15152703

AMA Style

Jin L, Li D, Zhang Y, Zhao Y. Analyzing Influencing Factors of Low-Carbon Technology Adoption in Hospital Construction Projects Based on TAM-TOE Framework. Buildings. 2025; 15(15):2703. https://doi.org/10.3390/buildings15152703

Chicago/Turabian Style

Jin, Lei, Dezhi Li, Yubin Zhang, and Yi Zhao. 2025. "Analyzing Influencing Factors of Low-Carbon Technology Adoption in Hospital Construction Projects Based on TAM-TOE Framework" Buildings 15, no. 15: 2703. https://doi.org/10.3390/buildings15152703

APA Style

Jin, L., Li, D., Zhang, Y., & Zhao, Y. (2025). Analyzing Influencing Factors of Low-Carbon Technology Adoption in Hospital Construction Projects Based on TAM-TOE Framework. Buildings, 15(15), 2703. https://doi.org/10.3390/buildings15152703

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop